Inhibition of eEF2K synergizes with glutaminase inhibitors or 4EBP1 depletion to suppress growth of triple-negative breast cancer cells
Bibliographic record
Abstract
The eukaryotic elongation factor-2 kinase, eEF2K, which restricts protein translation elongation, has been identified as a potential therapeutic target for diverse types of malignancies including triple negative breast cancer (TNBC). However, the contexts in which eEF2K inhibition is essential in TNBC and its consequences on the proteome are largely unknown. Here we show that genetic or pharmacological inhibition of eEF2K cooperated with glutamine (Gln) starvation, and synergized with glutaminase (GLS1) inhibitors to suppress growth of diverse TNBC cell lines. eEF2K inhibition also synergized with depletion of eukaryotic translation initiation factor 4E-binding protein 1 (eIF4EBP1; 4EBP1), a suppressor of eukaryotic protein translation initiation factor 4E (eIF4E), to induce c-MYC and Cyclin D1 expression, yet attenuate growth of TNBC cells. Proteomic analysis revealed that whereas eEF2K depletion alone uniquely induced Cyclin Dependent Kinase 1 (CDK1) and 6 (CDK6), combined depletion of eEF2K and 4EBP1 resulted in overlapping effects on the proteome, with the highest impact on the 'Collagen containing extracellular matrix' pathway (e.g. COL1A1), as well as the amino-acid transporter, SLC7A5/LAT1, suggesting a regulatory loop via mTORC1. In addition, combined depletion of eEF2K and 4EBP1 indirectly reduced the levels of IFN-dependent innate immune response-related factors. Thus, eEF2K inhibition triggers cell cycle arrest/death under unfavourable metabolic conditions such as Gln-starvation/GLS1 inhibition or 4EBP1 depletion, uncovering new therapeutic avenues for TNBC and underscoring a pressing need for clinically relevant eEF2K 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 | 0.000 | 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) | 0.001 | 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".