Radiotherapy drives lung metastasis in PDAC via a RhoGTPase signaling shift towards MRCK-dependency
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with an extremely poor prognosis, partly owing to its highly metastatic nature. Although radiotherapy is an effective and potentially curative treatment modality, research over the last few decades has indicated that it may induce a more metastatic phenotype in surviving cancer cells. We demonstrate for the first time that clinically relevant doses of image-guided targeted radiotherapy induce metastasis in a genetically engineered KPC mouse model of PDAC. Furthermore, this induction is largely driven by an organotropic switch towards lung metastasis. Using an in vitro RNAi screening approach, we identified a key role for myotonic dystrophy-related Cdc42-binding kinase (MRCK) in driving this response. MRCK activity was spatially upregulated at the plasma membrane in response to radiotherapy in PDAC cell lines as well as at the invasive margins of PDAC tumors. This upregulation of activity was maintained in metastases, suggesting an important role for MRCK in not only triggering local invasion in response to radiotherapy but also promoting distant metastases. Importantly, inhibition of MRCK with a small-molecule inhibitor (BDP9066) specifically opposed radiation-driven MRCK upregulation and pro-metastatic response.
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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) | 0.004 | 0.001 |
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".