eIF4E/4E-BP Ratio Predicts the Efficacy of mTOR Targeted Therapies
Bibliographic record
Abstract
Active-site mTOR inhibitors (asTORi) hold great promise for targeting dysregulated mTOR signaling in cancer. Because of the multifaceted nature of mTORC1 signaling, identification of reliable biomarkers for the sensitivity of tumors to asTORi is imperative for their clinical implementation. Here, we show that cancer cells acquire resistance to asTORi by downregulating eukaryotic translation initiation factor (eIF4E)-binding proteins (4E-BPs-EIF4EBP1, EIF4EBP2). Loss of 4E-BPs or overexpression of eIF4E renders neoplastic growth and translation of tumor-promoting mRNAs refractory to mTOR inhibition. Conversely, moderate depletion of eIF4E augments the anti-neoplastic effects of asTORi. The anti-proliferative effect of asTORi in vitro and in vivo is therefore significantly influenced by perturbations in eIF4E/4E-BP stoichiometry, whereby an increase in the eIF4E/4E-BP ratio dramatically limits the sensitivity of cancer cells to asTORi. We propose that the eIF4E/4E-BP ratio, rather than their individual protein levels or solely their phosphorylation status, should be considered as a paramount predictive marker for forecasting the clinical therapeutic response to mTOR inhibitors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".