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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".