Immunohistochemical study of histone protein 3 modification in pediatric osteosarcoma identifies reduced H3K27me3 as a marker of poor treatment response
Bibliographic record
Abstract
The most common pediatric primary malignant bone tumor, osteosarcoma, is often described as genetically non-recurrent and heterogeneous. Neoadjuvant chemotherapy is typically followed by resection and assessment of treatment response, which helps inform prognosis. Identifying biomarkers that may impact chemotherapy response and survival could aid in upfront risk stratification and identify patients in highest need of innovative therapies for future clinical trials. Relative to conventional genetics, little is known about osteosarcoma epigenetics. We aimed to characterize the methylation and phosphorylation status in osteosarcoma using histone markers found in primary diagnostic biopsies and their paired metastases. We constructed two tissue microarray sets from 58 primary diagnostic samples and 54 temporally-separated but related metastatic or recurrent samples, with tissue blocks available from 2002-2022. Clinical charts were reviewed for post-therapy necrosis response, presence of metastatic disease or recurrence, and overall survival. We evaluated 6 histone H3 residues using immunohistochemistry, including H3K4me3, H3K9me3, H3K27me2, H3K27me3, H3S10T11phos, and H3S28phos. Tumors were scored with low (<25%) or high (≥25%) nuclear staining of tumor cells. Diagnostic biopsies with low H3K27me3 nuclear staining were associated with poor treatment response (≤90% necrosis) at the time of definitive excision (P<0.05). We observed loss of H3S10T11phos expression in metastatic and recurrent resections specimens compared to the primary tumor (P<0.05). Expression patterns for the remaining histone markers did not show significant associations with disease parameters or survival. Although larger cohort studies are needed, these results support the expanded evaluation of histone markers, particularly H3K27me3 and H3S10T11phos, in osteosarcoma biology and risk stratification.
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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 | 0.000 | 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".