Blockade of Crk eliminates Yki/YAP-activated tumors via JNK-mediated apoptosis in Drosophila
Bibliographic record
Abstract
Selective elimination of cancer cells without causing deleterious effects on normal cells is an ideal anti-cancer strategy. Here, using Drosophila cancer model, we performed an in vivo RNAi screen for anti-cancer targets that selectively eliminate tumors without affecting normal tissue growth. In Drosophila imaginal epithelium, clones of cells expressing oncogenic Ras with simultaneous mutations in the cell polarity gene scribble (RasV12/scrib−/−) develop into malignant tumors. We found that knockdown of Crk, the Drosophila ortholog of human CRK (CT10 regulatory kinase) and CRKL (Crk-like) adapter proteins, significantly suppresses growth of RasV12/scrib−/− tumors by inducing c-Jun N-terminal kinase (JNK)-mediated apoptosis, while it does not affect growth of normal epithelium. Mechanistically, Crk inhibition blocks Yorkie (Yki)/YAP activity by impairing F-actin accumulation, an upstream event of Yki/YAP activation in tumors. Inhibition of Yki/YAP in tumors causes intracellular JNK signaling to be used for apoptosis induction. Given that molecules and signaling pathways identified in Drosophila are highly conserved and activated in human cancers, our findings would provide a novel, to the best of our knowledge, anti-cancer strategy against YAP-activated cancers. A genetic screen in Drosophila identifies a target gene for selective elimination of cancer cells without causing deleterious effects on normal cells.
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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) | 0.001 | 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) | 0.001 | 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".