Modeling Primary Bone Tumors and Bone Metastasis with Solid Tumor Graft Implantation into Bone
Bibliographic record
Abstract
Primary bone tumors or bone metastasis from solid tumors result in painful osteolytic, osteoblastic, or mixed osteolytic/osteoblastic lesions. These lesions compromise bone structure, increase the risk of pathologic fracture, and leave patients with limited treatment options. Primary bone tumors metastasize to distant organs, with some types capable of spreading to other skeletal sites. However, recent evidence suggests that with many solid tumors, cancer cells that have spread to bone may be the primary source of cells that ultimately metastasize to other organ systems. Most syngeneic or xenograft mouse models of primary bone tumors involve intra-osseous (orthotopic) injection of tumor cell suspensions. Some animal models of skeletal metastasis from solid tumors also depend on direct bone injection, while others attempt to recapitulate additional steps of the bone metastatic cascade by injecting cells intravascularly or into the organ of the primary tumor. However, none of these models develop bone metastasis reliably or with an incidence of 100%. In addition, direct intra-osseous injection of tumor cells has been shown to be associated with potential tumor embolization of the lung. These embolic tumor cells engraft but do not recapitulate the metastatic cascade. We reported a mouse model of osteosarcoma in which fresh or cryopreserved tumor fragments (consisting of tumor cells plus stroma) are implanted directly into the proximal tibia using a minimally invasive surgical technique. These animals developed reproducible engraftment, growth, and, over time, osteolysis and lung metastasis. This technique has the versatility to be used to model solid tumor bone metastasis and can readily employ grafts consisting of one or multiple cell types, genetically-modified cells, patient-derived xenografts, and/or labeled cells that can be tracked by optical or advanced imaging. Here, we demonstrate this technique, modeling primary bone tumors and bone metastasis using solid tumor graft implantation into bone.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".