Abstract 2417: eIF4E phosphorylation licenses Myc-driven metabolic and stress reprogramming
Bibliographic record
Abstract
Abstract eIF4E, Eukaryotic Translation Initiation Factor 4E, regulates the rate limiting cap-binding step in mRNA translation. Oncogenic pathways converge to increase eIF4E expression, as well as phosphorylation of eIF4E (S209) and its inhibitor 4EBPs, which are believed to facilitate the translation of “weak'' mRNAs including cell type-specific and oncogenic targets. eIF4E phosphorylation, but not eIF4E, is dispensable for normal development. Pharmacologic inhibition of either p-eIF4E or p-4EBP has limited antitumor efficacy. Our current study aims to better define the role of p-eIF4E in colon cancer. We find that p-eIF4E, not eIF4E transcript or protein is elevated in human colon tumors and precursors. Knockin (KI) of phosphorylation-defective eIF4E mutant allele (4ES209A) in human colon cancer cells strongly suppresses Myc translation and cell growth under stress or in mice, while minimally impacting Cap binding or global translation. Surprisingly, p-eIF4E is found to be required to maintain p-4EBP1 levels, dissociate eIF4E from 4E-BP1, and stimulate glutamine metabolism via integrated stress response (ISR). Further, p-eIF4E and p-4EBP1 are highly elevated in the polyps in APC min/+ mice compared to adjacent normal intestinal crypts, and 4ES209A KI significantly inhibits intestinal polyposis and glutamine metabolism gene signatures. Finally, p-eIF4E renders CRC cells addicted to glutamine and glutaminolysis by preventing Myc- and ISR-driven metabolic crisis and cell death. In summary, we demonstrate that eIF4E phosphorylation is central to oncogenic translation, and is required to license Myc-driven metabolic adaptation and addiction via ISR-dependent transcriptional programs in colon cancer. These novel findings help reconcile the long-standing and context-dependent roles of ISR in cancer cell growth, death, plasticity and heterogeneity, and provide new ways for its therapeutic exploitation. Citation Format: Hang Ruan, Xiangyun Li, Brian Leibowitz, Jingshan Tong, Xinghua Lu, Nahum Sonenberg, Xiang Xu, Lin Zhang, Jian Yu. eIF4E phosphorylation licenses Myc-driven metabolic and stress reprogramming [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2417.
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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) | 0.007 | 0.002 |
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".