Head and Neck Mesenchymal Neoplasms With GLI1 Gene Alterations
Bibliographic record
Abstract
Soft tissue tumors with GLI1 gene fusions or amplifications have been recently described as a unique pathologic entity with an established risk of malignancy. We herein expand these findings by investigating a cohort of 11 head and neck lesions with GLI1 alterations, including 8 from the tongue, for their clinicopathologic and molecular features. The tumors commonly affected males in their 30s (male:female ratio 2.7:1; range: 1 to 65). Tumors showed a multinodular growth pattern, nested architecture separated by a delicate, arborizing vascular network, monotonous round to ovoid nuclei, and clear cytoplasm. Tumor protrusion into vascular spaces was common. Genetic alterations were investigated by fluorescence in situ hybridization and/or targeted RNA sequencing. Seven tumors harbored GLI1 fusions with the following partners: ACTB (n=4), PTCH1 (n=2), or MALAT1 (n=1). The remaining 4 cases showed coamplifications of GLI1 with CDK4 and MDM2 genes. Tumors were commonly positive for S100 protein and CD56. CDK4, MDM2, and STAT6 were positive in GLI1-amplified tumors. Two of 6 patients with available follow-up (1 each with GLI1 amplification and PTCH1-GLI1 fusion) developed distant metastases. Both tumors showed a high mitotic index and tumor necrosis. The head and neck region, particularly tongue, is a common location for GLI1-related mesenchymal tumors. Although a morphologic overlap was noted with the previously reported "pericytoma with t(7,12) translocation," often occurring in the tongue, our findings expand the original findings, to include a more variable immunophenotype, propensity for late distant metastases, and alternative mechanisms of GLI1 oncogenic activation, such as various GLI1 fusion partners or GLI1 coamplifications with MDM2 and CDK4 genes.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".