Gene Expression Profiling of Head and Neck Tumors Identifies FOXP1 and SOX10 Expression as Useful for Distinguishing Ameloblastoma From Basaloid Salivary Gland Tumors
Bibliographic record
Abstract
Odontogenic tumors show considerable morphologic heterogeneity and at times the diagnosis can be challenging. Ameloblastoma, the most common odontogenic tumor, can have morphologic similarity to some salivary gland tumors and therefore we sought to identify biomarkers that might aid in the diagnosis by performing transcriptome wide gene expression profiling of 80 odontogenic and salivary gland neoplasms. These data identified the FOXP1/SOX10 expression profile as characteristic of many odontogenic tumors including ameloblastoma but largely absent in salivary gland tumors. We then assessed 173 salivary gland tumors and 108 odontogenic tumors by immunohistochemistry for FOXP1 and SOX10 expression and found that 34/35 (97%) cases of ameloblastomas were diffusely positive for FOXP1 but completely negative for SOX10. None of the basaloid salivary neoplasms (basal cell adenoma, adenoid cystic carcinoma, polymorphous adenocarcinoma, and myoepitheloma) demonstrated FOXP1/SOX10 expression pattern. Taken together, the results of this study suggest that the FOXP1/SOX10 immunophenotype is common in odontogenic tumors including ameloblastoma and might be useful distinguishing these from similar appearing basaloid salivary gland tumors.
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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 | 0.001 | 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) | 0.000 | 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 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".