Hyperactive mTOR and MNK1 phosphorylation of eIF4E confer tamoxifen resistance and estrogen independence through selective mRNA translation reprogramming
Bibliographic record
Abstract
The majority of breast cancers expresses the estrogen receptor (ER + ) and is treated with anti-estrogen therapies, particularly tamoxifen in premenopausal women. However, tamoxifen resistance is responsible for a large proportion of breast cancer deaths. Using small molecule inhibitors, phospho-mimetic proteins, tamoxifen-sensitive and tamoxifen-resistant breast cancer cells, a tamoxifen-resistant patient-derived xenograft model, patient tumor tissues, and genome-wide transcription and translation studies, we show that tamoxifen resistance involves selective mRNA translational reprogramming to an anti-estrogen state by Runx2 and other mRNAs. Tamoxifen-resistant translational reprogramming is shown to be mediated by increased expression of eIF4E and its increased availability by hyperactive mTOR and to require phosphorylation of eIF4E at Ser209 by increased MNK activity. Resensitization to tamoxifen is restored only by reducing eIF4E expression or mTOR activity and also blocking MNK1 phosphorylation of eIF4E. mRNAs specifically translationally up-regulated with tamoxifen resistance include Runx2 , which inhibits ER signaling and estrogen responses and promotes breast cancer metastasis. Silencing Runx2 significantly restores tamoxifen sensitivity. Tamoxifen-resistant but not tamoxifen-sensitive patient ER + breast cancer specimens also demonstrate strongly increased MNK phosphorylation of eIF4E. eIF4E levels, availability, and phosphorylation therefore promote tamoxifen resistance in ER + breast cancer through selective mRNA translational reprogramming
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".