Oncostatin M Induces Bone Loss and Sensitizes Rat Osteosarcoma to the Antitumor Effect of Midostaurin <i>In vivo</i>
Bibliographic record
Abstract
PURPOSE: In cultures, the cytokine oncostatin M (OSM) reduces the growth and induces differentiation of osteoblasts and osteosarcoma cells into glial/osteocytic cells. Moreover, OSM sensitizes these cells to apoptosis driven by various death inducers such as the kinase inhibitor staurosporine. Here, we asked whether OSM would have similar effects in vivo. EXPERIMENTAL DESIGN: Adenoviral gene transfer of OSM (AdOSM) was done in naive and osteosarcoma-bearing rats, alone or in combination with Midostaurin (PKC412), a derivative of staurosporine currently used in cancer clinical trials. Bone variables were analyzed by micro-computed tomography scanner, by histology, and by the levels of various serum bone markers. Osteosarcoma progression was analyzed by the development of the primary bone tumor, evolution of pulmonary metastasis, histology (necrosis and fibrosis), and animal survival. RESULTS: In naive rats, AdOSM reduced serum osteoblastic and osteoclastic markers in correlation with a reduced trabecular bone volume. In an osteosarcoma rat model, the combination of AdOSM with PKC412 reduced the progression of the primary bone tumor, pulmonary metastatic dissemination, and increased overall survival, whereas these agents alone had no antitumor effect. Increased tumor necrosis and tissue repair (fibrosis) were observed with this combination. CONCLUSION: These in vivo experiments confirm that systemic OSM overexpression alters osteoblast/osteosarcoma activity. Because OSM sensitizes rat osteosarcoma to apoptosis/necrosis, the use of kinase inhibitors such as Midostaurin in association with OSM could represent new adjuvant treatments for this aggressive malignancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".