eIF4A inhibition circumvents uncontrolled DNA replication mediated by 4E-BP1 loss in pancreatic cancer
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) relies on hyperactivated protein synthesis. Consistently, human and mouse PDAC lose expression of the translational repressor and mTOR target 4E-BP1. Using genome-wide polysome profiling, we here explore mRNAs whose translational efficiencies depend on the mTOR/4E-BP1 axis in pancreatic cancer cells. We identified a functional enrichment for mRNAs encoding DNA replication and repair proteins, including RRM2 and CDC6. Consequently, 4E-BP1 depletion favors DNA repair and renders DNA replication insensitive to mTOR inhibitors, in correlation with a sustained protein expression of CDC6 and RRM2, which is inversely correlated with 4E-BP1 expression in PDAC patient samples. DNA damage and pancreatic lesions induced by an experimental pancreatitis model uncover that 4E-BP1/2-deleted mice display an increased acinar cell proliferation and a better recovery than WT animals. Targeting translation, independently of 4E-BP1 status, using eIF4A RNA helicase inhibitors (silvestrol derivatives) selectively modulates translation and limits CDC6 expression and DNA replication, leading to reduced PDAC tumor growth. In summary, 4E-BP1 expression loss during PDAC development induces selective changes in translation of mRNA encoding DNA replication and repair protein. Importantly, targeting protein synthesis by eIF4A inhibitors circumvents PDAC resistance to mTOR inhibition.
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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) | 0.001 | 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".