Pathophysiologic interactions in skeletal metastasis
Bibliographic record
Abstract
BACKGROUND: This review summarizes evidence that the formation of bone metastases is the result of multiple synergistic cellular and molecular interactions between metastatic cells and the unique microenvironment in bone. METHODS: Molecular technologies have been used to detect cancer cells in bone and to define their genotypic and phenotypic properties. Bone organ cultures have been employed to analyze the ability of tumor cells to modulate bone resorption and to study the effects of resorption products on the phenotypic properties of cancer cells. Experimental models of bone metastasis provide the ability to examine the effects of modulating specific host or tumor properties in vivo by quantifying their effects on the formation of bone tumors. RESULTS: By means of the blood stream, cells from many common neoplasms seed bone marrow as an early clinical event. The subsequent growth of these cells into clinically significant metastatic lesions is associated with their ability to stimulate bone resorption through osteoclasts and macrophages or through a direct action on bone. In turn, the products of bone resorption, which include matrix-derived growth factors, act on the tumor cells to stimulate the expression of properties that promote their metastatic competence. These include the induction of integrin adhesion molecules, the stimulation of cell motility and chemotaxis, the enhanced expression of matrix metalloproteinases, and the stimulation of tumor cell growth. CONCLUSIONS: The interdependency of tumor cells and bone was recognized by Steven Paget over 100 years ago, and it provides a rational basis for the development of current therapeutic strategies against 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".