Targeting KRAS mutations in pancreatic cancer: opportunities for future strategies
Bibliographic record
Abstract
Targeting the RAS pathway remains the holy grail of precision oncology. In the case of pancreatic ductal adenocarcinomas (PDAC), 90–92% harbor mutations in the oncogene KRAS, triggering canonical MAPK signaling. The smooth structure of the altered KRAS protein without a binding pocket and its affinity for GTP have, in the past, hampered drug development. The emergence of KRASG12C covalent inhibitors has provided renewed enthusiasm for targeting KRAS. The numerous pathways implicated in RAS activation do, however, lead to the development of early resistance. In addition, the dense stromal niche and immunosuppressive microenvironment dictated by oncogenic KRAS can influence treatment responses, highlighting the need for a combination-based approach. Given that mutations in KRAS occur early in PDAC tumorigenesis, an understanding of its pleiotropic effects is key to progress in this disease. Herein, we review current perspectives on targeting KRAS with a focus on 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".