Aberrant EZHIP expression drives tumorigenesis in osteosarcoma
Bibliographic record
Abstract
Osteosarcomas (OS) are aggressive bone tumors known for their extensive structural variations and rare recurrent oncogenic driver mutations. In this study, we identify ectopic expression of the oncohistone-mimic EZHIP in 20% of patients across two independent OS cohorts. We demonstrate that reduced deposition of the repressive H3K27me3 mark correlates with poor histological response to neoadjuvant therapy, serving as a predictor of patient outcomes. Through gain- and loss-of-function experiments, we provide functional evidence of the oncogenic activity of EZHIP in enhancing the aggressive characteristics of OS both in vitro and in vivo. EZHIP-induced epigenetic reprogramming reactivates developmental pathways and impedes the differentiation of mesenchymal progenitors, pushing them towards smooth muscle lineage commitment at the cost of other fates. Targeting residual H3K27me3 with EZH2 inhibitors may offer therapeutic benefit in EZHIP-expressing OS. Our findings highlight EZHIP expression as a prevalent driver in OS, offering insights into its pathogenic mechanisms and potential therapeutic strategies for this debilitating cancer. Osteosarcomas (OS) are aggressive bone tumors which have no actionable recurrent driver mutations. Here the authors identify aberrant expression of EZHIP in a subset of OS patients as an oncogenic driver, which exhibits vulnerability to epigenetic therapies.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".