Oncogenic KRAS signalling in pancreatic cancer
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is almost universally fatal. The annual number of deaths equals the number of newly diagnosed cases, despite maximal treatment. The overall 5-year survival rate of <5% has remained stubbornly unchanged over the last 30 years, despite tremendous efforts in preclinical and clinical science. There is unquestionably an urgent need to further improve our understanding of pancreatic cancer biology, treatment response and relapse, and to identify novel therapeutic targets. Rigorous research in the field has uncovered genetic aberrations that occur during PDAC development and progression. In most cases, PDAC is initiated by oncogenic mutant KRAS, which has been shown to drive pancreatic neoplasia. However, all attempts to target KRAS directly have failed in the clinic and KRAS is widely assumed to be undruggable. This has led to intense efforts to identify druggable critical downstream targets and nodes orchestrated by mutationally activated KRAS. This includes context-specific KRAS effector pathways, synthetic lethal interaction partners and KRAS-driven metabolic changes. Here, we review recent advances in oncogenic KRAS signalling and discuss how these might benefit PDAC treatment in the future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".