RANKL blockade prevents and treats aggressive osteosarcomas
Bibliographic record
Abstract
Osteosarcoma (OS) is the most common primary bone cancer, which occurs primarily in children and adolescents, severely affecting survivors' quality of life. Despite its chemosensitivity and treatment advances, long-term survival rates for OS patients have stagnated over the last 20 years. Thus, it is necessary to develop new molecularly targeted therapies for this metastatic bone cancer. Mutations in TP53 and RB are linked to OS predisposition and to the evolution of spontaneous OS. We established receptor activator of nuclear factor κB ligand (RANKL) as a therapeutic target for suppression and prevention of OS. Combined conditional osteoblast-specific deletions of Rb, p53, and the protein kinase A (PKA) regulatory subunit Prkar1α genes in genetically engineered mouse models (GEMMs) generate aggressive osteosarcomas, characterized by PKA, RANKL, and osteoclast hyperactivity. Whole-body Rankl deletion completely abrogates tumorigenesis. Although osteoblastic Rank deletion has little effect, osteoclastic Rank deletion delays tumorigenesis and prolongs life span. The latter is associated with inactivation of osteoclastogenesis and up-regulation of the tumor suppressor phosphatase and tensin homolog (PTEN). Further, we use these GEMMs as preclinical platforms to show that RANKL blockade with RANK-Fc arrests tumor progression and improves survival and also inhibits lung metastasis. Moreover, preemptive administration of RANK-Fc completely prevents tumorigenesis in mice highly predisposed to this aggressive cancer. Denosumab, a fully human monoclonal antibody against RANKL, is currently used to treat patients with osteoporosis or bone metastases. Our studies provide a strong rationale to consider RANKL blockade for the treatment and prevention of aggressive RANKL-overexpressing OS in humans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".