Systematic Screening Identifies Dual PI3K and mTOR Inhibition as a Conserved Therapeutic Vulnerability in Osteosarcoma
Bibliographic record
Abstract
PURPOSE: Osteosarcoma is the most common cancer of bone occurring mostly in teenagers. Despite rapid advances in our knowledge of the genetics and cell biology of osteosarcoma, significant improvements in patient survival have not been observed. The identification of effective therapeutics has been largely empirically based. The identification of new therapies and therapeutic targets are urgently needed to enable improved outcomes for osteosarcoma patients. EXPERIMENTAL DESIGN: We have used genetically engineered murine models of human osteosarcoma in a systematic, genome-wide screen to identify new candidate therapeutic targets. We performed a genome-wide siRNA screen, with or without doxorubicin. In parallel, a screen of therapeutically relevant small molecules was conducted on primary murine- and primary human osteosarcoma-derived cell cultures. All results were validated across independent cell cultures and across human and mouse osteosarcoma. RESULTS: The results from the genetic and chemical screens significantly overlapped, with a profound enrichment of pathways regulated by PI3K and mTOR pathways. Drugs that concurrently target both PI3K and mTOR were effective at inducing apoptosis in primary osteosarcoma cell cultures in vitro in both human and mouse osteosarcoma, whereas specific PI3K or mTOR inhibitors were not effective. The results were confirmed with siRNA and small molecule approaches. Rationale combinations of specific PI3K and mTOR inhibitors could recapitulate the effect on osteosarcoma cell cultures. CONCLUSIONS: The approaches described here have identified dual inhibition of the PI3K-mTOR pathway as a sensitive, druggable target in osteosarcoma, and provide rationale for translational studies with these agents.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".