MYB-NFIB gene fusions identified in archival adenoid cystic carcinoma tissue employing NanoString analysis: an exploratory study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Adenoid cystic carcinoma (ACC) is a slow growing salivary gland malignancy that is molecularly characterized by t(6:9)(q22-23;p23-24) translocations which predominantly result in MYB-NFIB gene fusions in nearly half of tumours. Detection of MYB-NFIB transcripts is typically performed with fresh ACC tissue using conventional RT-PCR fragment analysis or FISH techniques, which are prone to failure when only archival formalin fixed paraffin embedded (FFPE) tissue is available. The purpose of this pilot study was to evaluate the utility of NanoString probe technology for the detection of MYB-NFIB transcripts in archival ACC tissue. METHODS: A NanoString probeset panel was designed targeting the junctions of three currently annotated MYB-NFIB fusion genes as well as 5'/3' MYB probesets designed to detect MYB gene expression imbalance. RNA isolated from twenty-five archival ACC specimens was profiled and analyzed. RT-qPCR and sequencing were performed to confirm NanoString results. MYB protein expression was analyzed by immunohistochemistry. RESULTS: Of the 25 samples analyzed, 11/25 (44%) expressed a high degree of MYB 5'/3' imbalance and five of these samples were positive for at least one specific MYB-NFIB variant in our panel. MYB-NFIB variant detection on NanoString analysis was confirmed by direct cDNA sequencing. No clinical correlations were found to be associated with MYB fusion status. CONCLUSION: We conclude that the application of NanoString digital probe counting technology is well suited for the detection and quantification of MYB-NFIB fusion transcripts in archival ACC specimens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it