Decision letter: Localization of KRAS downstream target ARL4C to invasive pseudopods accelerates pancreatic cancer cell invasion
Bibliographic record
Abstract
Most cases of pancreatic cancer are detected in the later stages when they are difficult to treat and, as a result, survival is low. Over 90% of pancreatic cancers contain genetic changes that increase the activity of a protein called KRAS. This hyperactive KRAS drives cancer growth and progression. Attempts to treat pancreatic cancer using drugs that reduce the activity of KRAS have so far failed. The KRAS protein can accelerate growth in healthy cells as well as in cancer and it does this by activating various other proteins. Drugs that target some of these other proteins could be more effective at treating pancreatic cancer than the drugs that target KRAS. One of these potential targets is called ARL4C. ARL4C is active during fetal development, but it is often not present in adult tissues. Harada et al. investigated whether the protein is important in pancreatic cancer, and what other roles it has in the body, to better understand if it is a good target for cancer treatment. First, Harada et al. used cells grown in the lab to show that ARL4C contributes to the aggressive spread of human pancreatic cancers. Using mice, Harada et al. also showed that blocking the activity of ARL4C in pancreatic cancers helped to slow their progression. Harada et al.’s results suggest that ARL4C could be a good target for new drugs treating pancreatic cancers. Given that this protein does not seem to have important roles in the cells of adults, targeting it is unlikely to have major side effects. Further investigation of ARL4C in more human-like animal models will help to confirm these results.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.104 | 0.059 |
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".