Mammalian Target of Rapamycin Inhibition Promotes Response to Epidermal Growth Factor Receptor Kinase Inhibitors in PTEN-Deficient and PTEN-Intact Glioblastoma Cells
Bibliographic record
Abstract
The epidermal growth factor receptor (EGFR) is commonly amplified, overexpressed, and mutated in glioblastoma, making it a compelling molecular target for therapy. We have recently shown that coexpression of EGFRvIII and PTEN protein by glioblastoma cells is strongly associated with clinical response to EGFR kinase inhibitor therapy. PTEN loss, by dissociating inhibition of the EGFR from downstream phosphatidylinositol 3-kinase (PI3K) pathway inhibition, seems to act as a resistance factor. Because 40% to 50% of glioblastomas are PTEN deficient, a critical challenge is to identify strategies that promote responsiveness to EGFR kinase inhibitors in patients whose tumors lack PTEN. Here, we show that the mammalian target of rapamycin (mTOR) inhibitor rapamycin enhances the sensitivity of PTEN-deficient tumor cells to the EGFR kinase inhibitor erlotinib. In two isogenic model systems (U87MG glioblastoma cells expressing EGFR, EGFRvIII, and PTEN in relevant combinations, and SF295 glioblastoma cells in which PTEN protein expression has been stably restored), we show that combined EGFR/mTOR kinase inhibition inhibits tumor cell growth and has an additive effect on inhibiting downstream PI3K pathway signaling. We also show that combination therapy provides added benefit in promoting cell death in PTEN-deficient tumor cells. These studies provide strong rationale for combined mTOR/EGFR kinase inhibitor therapy in glioblastoma patients, particularly those with PTEN-deficient tumors.
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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) | 0.001 | 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".