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 in KRAS with variants in TP53 , CDKN2A and SMAD4 also prevalent. The presence of oncogenic fusions including NTRK fusions are rare but important to identify. Here we ascertain the prevalence of NTRK fusions 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 an NTRK fusion, two EML4-NTRK3 ( KRAS -WT) and a single novel KANK1-NTRK3 fusion. The latter occurring in the presence of a subclonal KRAS mutation. Typical PDAC drivers were present including mutations in TP53 and CDKN2A . Substitution base signatures and tumour mutational burden were similar to typical PDAC. The prevalence of NTRK fusions was 0.8% (3/400), while in KRAS wild-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 the KANK1-NTRK3 fusion but positive in a EML4-NTRK3 case, highlighting lower sensitivity of IHC. Conclusion NTRK fusions are rare; however, with emerging therapeutic options targeting these fusions, detection is vital. Reflex testing for KRAS mutations and subsequent RNA-based screening could help identify these cases in PDAC.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".