Silencing of Epidermal Growth Factor Receptor Suppresses Hypoxia-Inducible Factor-2–Driven <i>VHL</i> −/− Renal Cancer
Bibliographic record
Abstract
Inactivating mutations in the von Hippel-Lindau (VHL) tumor suppressor gene are associated with clear cell renal cell carcinoma (VHL-/- RCC), the most frequent malignancy of the human kidney. The VHL protein targets the alpha subunits of hypoxia-inducible factor (HIF) transcription factor for ubiquitination and degradation. VHL-/- RCC cells fail to degrade HIF resulting in the constitutive activation of its target genes, a process that is required for tumorigenesis. We recently reported that HIF activates the transforming growth factor-alpha/epidermal growth factor receptor (TGF-alpha/EGFR) pathway in VHL-defective RCC cells. Here, we show that short hairpin RNA (shRNA)-mediated inhibition of EGFR is sufficient to abolish HIF-dependent tumorigenesis in multiple VHL-/- RCC cell lines. The 2alpha form of HIF (HIF-2alpha), but not HIF-1alpha, drives in vitro and in vivo tumorigenesis of VHL-/- RCC cells by specifically activating the TGF-alpha/EGFR pathway. Transient incubation of VHL-/- RCC cell lines with small interfering RNA directed against EGFR prevents autonomous growth in two-dimensional culture as well as the ability of these cells to form dense spheroids in a three-dimensional in vitro tumor assay. Stable expression of shRNA against EGFR does not alter characteristics associated with VHL loss including constitutive production of HIF targets and defects in fibronectin deposition. In spite of this, silencing of EGFR efficiently abolishes in vivo tumor growth of VHL loss RCC cells. These data identify EGFR as a critical determinant of HIF-2alpha-dependent tumorigenesis and show at the molecular level that EGFR remains a credible target for therapeutic strategies against VHL-/- renal carcinoma.
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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) | 0.001 | 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".