SalvGlandDx – a comprehensive salivary gland neoplasm specific next generation sequencing panel to facilitate diagnosis and identify therapeutic targets
Bibliographic record
Abstract
Diagnosis of salivary gland neoplasms is often challenging due to their high morphological diversity and overlaps. Several recurrent molecular alterations have been described recently, which can serve as powerful diagnostic tools and potential therapeutic targets (e.g. NTRK or RET fusions). However, current sequential molecular testing can be expensive and time consuming. In order to facilitate the diagnosis of salivary gland neoplasms, we designed an all-in-one RNA-based next generation sequencing panel suitable for the detection of mutations, fusions and gene expression levels (including NR4A3) of 27 genes involved in salivary gland neoplasms. Here we present the validation of the "SalvGlandDx" panel on FFPE histological specimen including fine needle aspiration (FNA) cell block material, against the standard methods currently used at our institution. In a second part we describe selected unique cases in which the SalvGlandDx panel allowed proper diagnosis and new insights into special molecular characteristics of selected salivary gland tumors. We characterize a unique salivary gland adenocarcinoma harboring a ZCCHC7-NTRK2 fusion, a highly uncommon spindle cell and pseudoangiomatoid adenoid-cystic carcinoma with MYBL1-NFIB fusion, and a purely oncocytic mucoepidermoid carcinoma, whereas diagnosis could be made by detection of a CRTC3-MAML2 rearrangement on the cell block specimen of the FNA. Further, a rare case of a SS18-ZBTB7A rearranged low-grade adenocarcinoma previously described as potential spectrum of microsecretory adenocarcinoma, is reported. In addition, features of six cases within the spectrum of polymorphous adenocarcinoma / cribriform adenocarcinoma of salivary gland including PRKD1 p.E710D mutations and novel fusions involving PRKAR2A-PRKD1, SNX9-PRKD1 and ATL2-PRKD3, are described.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".