Nuclear EGFRvIII‐STAT5b complex contributes to glioblastoma cell survival by direct activation of the Bcl‐XL promoter
Bibliographic record
Abstract
Aberrant EGFR signaling strongly promotes glioma malignancy and treatment resistance. The most prevalent mutation, ΔEGFR/EGFRvIII, is an in-frame deletion of the extracellular domain, which occurs in more than 25% of glioblastomas and enhances growth and survival of tumor cells. Paradoxically, the signaling of the potent oncogene ΔEGFR is of low intensity, raising the question of whether it exhibits preferential signaling to key downstream targets. We have observed levels of phosphorylation of STAT5 at position Y699 in cells expressing ΔEGFR that are similar or higher than in cells that overexpress EGFR and are acutely stimulated with EGF, prompting us to investigate the role of STAT5 activation in glioblastoma. Here, we show that in human glioblastoma samples, pSTAT5 levels correlated positively with EGFR expression and were associated with reduced survival. Interestingly, the activation of STAT5b downstream of ΔEGFR was dependent on SFKs, while the signal from acutely EGF-stimulated EGFR to STAT5b involved other kinases. Phosphorylated STAT5b and ΔEGFR associated in the nucleus, bound DNA and were found on promoters known to be regulated by STAT5 including that of the Aurora A gene. ΔEGFR cooperated with STAT5b to regulate the Bcl-XL promoter and knockdown of STAT5b suppressed anchorage independent growth, reduced the levels of Bcl-XL and sensitized glioblastoma cells to cisplatin. Together these results delineate a novel association of nuclear ΔEGFR with STAT5b, which promotes oncogenesis and treatment resistance in glioblastoma by direct regulation of anti-apoptotic gene, Bcl-XL.
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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".