Abstract A6: Dissecting mechanisms of RANKL dependency in osteosarcoma
Bibliographic record
Abstract
Abstract Osteosarcoma is the most common bone tumor in adolescents, severely impacts quality of life, and ∼40% of patients die of metastases to lungs and liver. In an effort to study this poorly understood cancer, we have generated a novel transgenic mouse model of osteosarcoma (designated MOTO) through osteocalcin promoter-driven SV40 T-antigen expression. The MOTO model is 100% penetrant and recapitulates all critical features of the human disease including skeletal tumor distribution, radiology, histology, genomic instability and metastasis. Using genomic screens, we have identified the cytokine RANKL (Receptor Activator of Nuclear Factor kappa B Ligand), and its receptor RANK, to be aberrantly expressed in MOTO tumors and cell lines. Genetic studies using RANKL deficient MOTO mice reveal osteosarcoma to be broadly dependent on its expression. To further dissect the role of RANKL signaling in osteosarcoma, we have performed gain- and loss-of-function studies to determine if RANKL affects classic oncogenic cell parameters. Specifically, loss of RANKL in MOTO tumor cell lines inhibits invasion and soft agar colony formation, indicating its ability to contribute to tumorigenesis in a cell autonomous manner. To support this hypothesis, we are testing the effects of de novo RANK expression on the tumorigenic properties of osteoblastic cells. Together, these studies are dissecting the individual contributions of RANKL and RANK in controlling primary bone tumorigenesis. Citation Information: Cancer Res 2009;69(23 Suppl):A6.
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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) | 0.004 | 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".