Targeted <scp>RNA</scp> sequencing in the routine clinical detection of fusion genes in salivary gland tumors
Bibliographic record
Abstract
Salivary gland tumors represent a diverse group of neoplasms that occasionally pose a diagnostic challenge for pathologists, particularly with limited sampling. Gene fusions, which may reflect genetic drivers, are increasingly recognized in a subset of these neoplasms, and can be leveraged for diagnostic purposes. We performed a retrospective analysis on a cohort of 80 benign and malignant salivary gland tumors, enriched for subtypes known to harbor recurrent fusion events, to validate the diagnostic use of a targeted RNA sequencing assay to detect fusion transcripts. Testing identified fusion genes in 71% (24/34) of pleomorphic adenoma and carcinoma-ex-pleomorphic adenoma, with 56% of cases showing rearrangement of PLAG1 and 15% HMGA2. In addition to confirming known partners for these genes, novel PLAG1 fusion partners were identified, including DSTN, NTF3, and MEG3; CNOT2 was identified as a novel fusion partner for HMGA2. In adenoid cystic carcinoma, 95% of cases (19/20) were positive for a fusion event. MYB was rearranged in 60% (12/20), MYBL1 in 30% (6/20), and NFIB in 5% (1/20); two tumors exhibited novel fusion products, including NFIB-TBPL1 and MYBL1-VCPIP1. Fusion genes were identified in 64% (9/14) of cases of mucoepidermoid carcinoma; MAML2 was confirmed to partner with either CRTC1 (43%) or CRTC3 (21%). One salivary duct carcinoma was found to harbor a novel RAPGEF6-ACSL6 fusion gene. Finally, as anticipated, gene fusions were not detected in any of the five acinic cell carcinomas included in the cohort. In summary, targeted RNA sequencing represents a diagnostically useful ancillary technique for identifying a variety of existing, and novel, fusion transcripts in the classification of salivary gland neoplasms.
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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 | 0.000 | 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 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".