Loss of Wasl improves pancreatic cancer outcome
Bibliographic record
Abstract
Several studies have suggested an oncogenic role for the neural Wiskott-Aldrich syndrome protein (N-WASP, encoded by the Wasl gene), but thus far, little is known about its function in pancreatic ductal adenocarcinoma (PDAC). In this study, we performed in silico analysis of WASL expression in PDAC patients and found a correlation between low WASL expression and prolonged survival. To clarify the role of Wasl in pancreatic carcinogenesis, we used 2 oncogenic Kras-based PDAC mouse models with pancreas-specific Wasl deletion. In line with human data, both mouse models had an increased survival benefit due to either impaired tumor development in the presence of the tumor suppressor Trp53 or the delayed tumor progression and senescent phenotype upon genetic ablation of Trp53. Mechanistically, loss of Wasl resulted in cell-autonomous senescence through displacement of the N-WASP binding partners WASP-interacting protein (WIP) and p120ctn; vesicular accumulation of GSK3β, as well as YAP1 and phosphorylated β-catenin, which are components of the destruction complex; and upregulation of Cdkn1a(p21), a master regulator of senescence. Our findings, thus, indicate that Wasl functions in an oncogenic manner in PDAC by promoting the deregulation of the p120-catenin/β-catenin/p21 pathway. Therefore, strategies to reduce N-WASP activity might improve the survival outcomes of PDAC patients.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".