Characteristics of <i>KRAS</i> WT pancreatic adenocarcinomas: results of a large French multicentric cohort
Bibliographic record
Abstract
Background: Genome and transcriptome analysis has enhanced the characterisation of pancreatic ductal adenocarcinoma (PDAC), paving the way for targeted therapies. Tumours KRAS wild type (WT) represent a unique subgroup. Objectives: Characterise the population and molecular abnormalities present in KRAS WT PDAC. Design: Clinical and molecular data from a large retrospective cohort of KRAS WT PDAC were analysed. Methods: Next-generation sequencing (NGS) was used to analyse DNAs and RNAs, allowing molecular and transcriptomic characterisation. Results: We identified 93/1059 (9%) KRAS WT PDAC, among which eight had druggable fusions ( n = 8/30 contributive samples), six had BRAF mutations and 19 ( n = 19/47) had mutations in homologous recombination (HR) pathway genes. Potential molecular targets in this series may be underestimated due to many non-contributive results. Clinical characteristics and survival did not differ between patients with KRAS WT and KRAS- mutated tumours. Transcriptomic data were available for 350 samples. Their analysis shows a difference in phenotype between mutated and WT tumours, with a molecular profile that appears to be better prognostic for KRAS WT tumours. Conclusion: KRAS WT tumours are enriched with molecular abnormalities of therapeutic interest. These include oncogene driver alterations (gene fusions and mutations) and mutations in genes of the HR pathway. Targeted therapy strategies for PDAC rely on molecular testing beyond RAS , but further research is needed to identify new therapeutic approaches that improve outcomes 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".