Eukaryotic Translation Initiation Factor 4E–Induced Progression of Primary Human Mammary Epithelial Cells along the Cancer Pathway Is Associated with Targeted Translational Deregulation of Oncogenic Drivers and Inhibitors
Bibliographic record
Abstract
Pathologic redirection of translational control by constitutive activation of eukaryotic translation initiation factor 4F (eIF4F), the cap-dependent translation initiation apparatus, is an obligatory step in oncogenesis; however, its mechanism remains undefined. Here, we simulate this pro-oncogenic state by overexpressing eIF4E, the rate-limiting component of eIF4F, in primary human mammary epithelial cells (HMECs) and examine the resultant changes in cell biology and gene expression profiles of total and polyribosome-bound mRNA genome wide. Overexpressed eIF4E rescues primary HMECs from telomere-independent growth arrest and disables checkpoints governing S-phase entry as well as apoptosis in HMECs immortalized by telomerase, imparting cells with proliferative and survival autonomy. Although the transcriptional response to increased eIF4E was modest, the translational response was large, selective, and bidirectional. In addition to translational activation of known and novel eIF4E-responsive oncogenic drivers regulating cell growth and survival, our data unveil previously unrecognized cellular defenses including translational activation of tumor suppressors, translational repression of transcripts enriched with miRNA target sites, and translational modulation of genes governing translation itself. These findings provide insight into the proneoplastic and compensatory mechanisms embedded in the oncogenic translational program. They support a model whereby deregulated eIF4E moves human epithelial cells along the cancer pathway by profoundly altering ribosomal recruitment to cancer-related transcripts, and eIF4E-modified cells counter these potentially oncogenic alterations with a compensatory translational mechanism that mitigates acquisition of malignancy.
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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) | 0.002 | 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".