Molecular characterisation of pancreatic ductal adenocarcinoma with<i>NTRK</i>fusions and review of the literature
Bibliographic record
Abstract
Aims The majority of pancreatic ductal adenocarcinomas (PDACs) harbour oncogenic mutations inKRASwith variants inTP53,CDKN2AandSMAD4also prevalent. The presence of oncogenic fusions includingNTRKfusions are rare but important to identify. Here we ascertain the prevalence ofNTRKfusions and document their genomic characteristics in a large series of PDAC. Methods Whole genome sequencing and RNAseq were performed on a series of patients with resected or locally advanced/metastatic PDAC collected between 2008 and 2020 at a single institution. A subset of specimens underwent immunohistochemistry (IHC) analysis. Clinical and molecular characterisation and IHC sensitivity and specificity were evaluated. Results 400 patients were included (resected n=167; locally advanced/metastatic n=233). Three patients were identified as harbouring anNTRKfusion, twoEML4-NTRK3(KRAS-WT) and a single novelKANK1-NTRK3fusion. The latter occurring in the presence of a subclonalKRASmutation. Typical PDAC drivers were present including mutations inTP53andCDKN2A. Substitution base signatures and tumour mutational burden were similar to typical PDAC. The prevalence ofNTRKfusions was 0.8% (3/400), while inKRASwild-type tumours, it was 6.25% (2/32). DNA prediction alone documented six false-positive cases. RNA analysis correctly identified the in-frame fusion transcripts. IHC analysis was negative in theKANK1-NTRK3fusion but positive in aEML4-NTRK3case, highlighting lower sensitivity of IHC. Conclusion NTRKfusions are rare; however, with emerging therapeutic options targeting these fusions, detection is vital. Reflex testing forKRASmutations and subsequent RNA-based screening could help identify these cases in PDAC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".