Inhibition of the Translation Initiation Factor eIF4A Enhances Tumor Cell Radiosensitivity
Bibliographic record
Abstract
A fundamental component of cellular radioresponse is the translational control of gene expression. Because a critical regulator of translational control is the eukaryotic translation initiation factor 4F (eIF4F) cap binding complex, we investigated whether eIF4A, the RNA helicase component of eIF4F, can serve as a target for radiosensitization. Knockdown of eIF4A using siRNA reduced translational efficiency, as determined from polysome profiles, and enhanced tumor cell radiosensitivity as determined by clonogenic survival. The increased radiosensitivity was accompanied by a delayed dispersion of radiation-induced γH2AX foci, suggestive of an inhibition of DNA double-strand break repair. Studies were then extended to (-)-SDS-1-021, a pharmacologic inhibitor of eIF4A. Treatment of cells with the rocaglate (-)-SDS-1-021 resulted in a decrease in translational efficiency as well as protein synthesis. (-)-SDS-1-021 treatment also enhanced the radiosensitivity of tumor cell lines. This (-)-SDS-1-021-induced radiosensitization was accompanied by a delay in radiation-induced γH2AX foci dispersal, consistent with a causative role for the inhibition of double-strand break repair. In contrast, although (-)-SDS-1-021 inhibited translation and protein synthesis in a normal fibroblast cell line, it had no effect on radiosensitivity of normal cells. Subcutaneous xenografts were then used to evaluate the in vivo response to (-)-SDS-1-021 and radiation. Treatment of mice bearing subcutaneous xenografts with (-)-SDS-1-021 decreased tumor translational efficiency as determined by polysome profiles. Although (-)-SDS-1-021 treatment alone had no effect on tumor growth, it significantly enhanced the radiation-induced growth delay. These results suggest that eIF4A is a tumor-selective target for radiosensitization.
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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".