<i>H3F3A</i> K36M-mutant Epithelioid Neoplasm: A Report of Two Novel Cases of a Non-chondrogenic H3K36-altered Mesenchymal Tumor
Bibliographic record
Abstract
Background: Identifying the genetic signatures in bone and soft tissue tumors enhances our understanding of tumor biology and aids in the subclassification of tumors for personalized treatment. Histone H3.3 alterations play a pivotal role in H3F3A/B K36M-mutant chondroblastomas and H3F3A G34W/L-mutant giant cell tumors of the bone. Methods and Results: In this report, we describe 2 cases of a distinct epithelioid neoplasm with H3F3A K36M mutation but lacking features of chondroblastoma, which extends the spectrum of H3.3-mutant mesenchymal tumors. The 2 cases occurred in pediatric patients, had an aggressive clinical presentation, distinct epithelioid histomorphology with diffuse cytokeratin and TFE3 expression, and identical H3F3A K36M mutations. No gene fusions were identified. Methylation analysis using the DKFZ sarcoma classifier v12.3 pipeline did not classify these tumors with known entities, suggesting the existence of non-chondrogenic mesenchymal tumors within the H3.3-mutant tumor spectrum. Conclusions: The distinctive histological and molecular features of these 2 cases expand the spectrum of H3.3-mutant tumors and call for further investigation of the biological underpinnings of this group of tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".