Cancer Cells Exploit eIF4E2-Directed Synthesis of Hypoxia Response Proteins to Drive Tumor Progression
Bibliographic record
Abstract
Human tumors display considerable diversity in their genetic makeup but share common physiologic attributes such as a hypoxic microenvironment that contribute to the malignant phenotype. Hypoxic cells switch from eukaryotic initiation factor 4E (eIF4E) to eIF4E2 cap-dependent translation to synthesize a portion of their proteins. Here, we show that genetically distinct human cancer cells exploit eIF4E2-directed protein synthesis to form cellular masses larger than approximately 0.15 mm, the diffusion limit of oxygen. Cancer cells depleted of eIF4E2 are indistinguishable from control cells under normoxic conditions, but are unable to survive and proliferate in low oxygen conditions. Activation of eIF4E2-directed translation is essential for cancer cells to form a hypoxic tumor core in in vitro spheroids and to form detectable tumors in in vivo xenograft assays. In contrast, the eIF4E-directed protein synthesis pathway alone cannot sustain cellular adaptation to hypoxia in vitro or confer tumorigenic potential in xenograft assays. These data demonstrate that the phenotypic expression of the cancer genome requires translation by the eIF4E2-directed hypoxic protein synthesis machinery.
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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".