Bibliographic record
Abstract
Abstract Control of translation initiation plays an important role in cell growth, proliferation and cancer development and progression. mRNA translation is dysregulated in many cancers via a combination of protein overexpression and defects in the pathways that signal to the translation machinery. In support of the critical function of translational control in cancer is the discovery of mutations in translational components in genetic syndromes associated with cancer susceptibility. A key regulator of translation initiation is the mRNA 5'-cap-binding protein, eIF4E. eIF4E overexpression engenders transformation of rodent and human cells and causes cancer in mice. eIF4E levels and phosphorylation are elevated in many types of human cancers. Phosphorylation of eIF4E on Ser209 is required for efficient transformation by eIF4E. We generated eIF4E ‘knock-in’ mice in which Ser209 was mutated to alanine. These mice were resistant to prostate cancer induced by PTEN deletion, or cancers induced by other means. An important downstream component of the PTEN/Akt/mTOR signaling pathway, which is strongly implicated in cancer etiology, is the translation machinery. A well-characterized target of mTOR is 4E-BP1, which binds to eIF4E and inhibits cap-dependent translation. mTOR forms two distinct complexes, mTORC1 and mTORC2. mTORC1 integrates growth factor and nutrient signals to control cell proliferation (increase in cell number) and cell growth (increase in mass). mTORC1 controls these processes by stimulating mRNA translation via phosphorylation of its two major downstream targets: the 4E-binding proteins (4E-BP1, 2 and 3) and the S6 kinases (S6K1 and 2). 4E-BPs impair the assembly of the eIF4F pre-initiation complex by competing with eIF4G for binding to eIF4E, whereas S6Ks phosphorylate a number of targets including ribosomal protein S6 and eIF4B. We showed that the 4E-BPs do not affect cell size, but strongly inhibit cell proliferation, by selectively inhibiting the translation of mRNAs encoding for proliferation-promoting proteins, and proteins involved in cell cycle progression. The hyperactivation of the mechanistic/mammalian target of rapamycin (mTOR) occurs in the majority of cancers. Therefore, targeted inhibition of mTOR is an attractive anti-cancer strategy. We show that high expression of eIF4E confers increased resistance of cells to the anti-proliferative effects of asTORi. Conversely, depletion of eIF4E levels by RNAi potentiates the anti-neoplastic effects of asTORi in vitro and in vivo. Anti-sense therapy against eIF4E is presently evaluated in clinical trials against cancers. Our data indicate that combining this strategy with targeted mTOR inhibition could have increased therapeutic benefits. Citation Format: Nahum Sonenberg. Translational control of cancer via eIF4E/4E-BPs. [abstract]. In: Proceedings of the Third AACR International Conference on Frontiers in Basic Cancer Research; Sep 18-22, 2013; National Harbor, MD. Philadelphia (PA): AACR; Cancer Res 2013;73(19 Suppl):Abstract nr IA7.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.004 | 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".