Multiscale characterization of the mineral phase at skeletal sites of breast cancer metastasis
Bibliographic record
Abstract
Skeletal metastases, the leading cause of death in advanced breast cancer patients, depend on tumor cell interactions with the mineralized bone extracellular matrix. Bone mineral is largely composed of hydroxyapatite (HA) nanocrystals with physicochemical properties that vary significantly by anatomical location, age, and pathology. However, it remains unclear whether bone regions typically targeted by metastatic breast cancer feature distinct HA materials properties. Here we combined high-resolution X-ray scattering analysis with large-area Raman imaging, backscattered electron microscopy, histopathology, and microcomputed tomography to characterize HA in mouse models of advanced breast cancer in relevant skeletal locations. The proximal tibial metaphysis served as a common metastatic site in our studies; we identified that in disease-free bones this skeletal region contained smaller and less-oriented HA nanocrystals relative to ones that constitute the diaphysis. We further observed that osteolytic bone metastasis led to a decrease in HA nanocrystal size and perfection in remnant metaphyseal trabecular bone. Interestingly, in a model of localized breast cancer, metaphyseal HA nanocrystals were also smaller and less perfect than in corresponding bone in disease-free controls. Collectively, these results suggest that skeletal sites prone to tumor cell dissemination contain less-mature HA (i.e., smaller, less-perfect, and less-oriented crystals) and that primary tumors can further increase HA immaturity even before secondary tumor formation, mimicking alterations present during tibial metastasis. Engineered tumor models recapitulating these spatiotemporal dynamics will permit assessing the functional relevance of the detected changes to the progression and treatment of breast cancer 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".