Translational up-regulation of the EGFR by tumor hypoxia provides a nonmutational explanation for its overexpression in human cancer
Bibliographic record
Abstract
Overexpression of the EGF receptor (EGFR) is a recurrent theme in human cancer and is thought to cause aggressive phenotypes and resistance to standard therapy. There has, thus, been a concerted effort in identifying EGFR gene mutations to explain misregulation of EGFR expression as well as differential sensitivity to anti-EGFR drugs. However, such genetic alterations have proven to be rare occurrences in most types of cancer, suggesting the existence of a more general physiological trigger for aberrant EGFR expression. Here, we provide evidence that overexpression of wild-type EGFR can be induced by the hypoxic microenvironment and activation of hypoxia-inducible factor 2-alpha (HIF2alpha) in the core of solid tumors. Our data suggest that hypoxia/HIF2alpha activation represents a common mechanism for EGFR overexpression by increasing EGFR mRNA translation, thereby diminishing the necessity for gene mutations. This allows for the accumulation of elevated EGFR levels, increasing its availability for the autocrine signaling required for tumor cell growth autonomy. Taken together, our findings provide a nonmutational explanation for EGFR overexpression in human tumors and highlight a role for HIF2alpha activation in the regulation of EGFR protein synthesis.
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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".