Mechanisms and Antitumor Activity of a Binary EGFR/DNA–Targeting Strategy Overcomes Resistance of Glioblastoma Stem Cells to Temozolomide
Bibliographic record
Abstract
PURPOSE: Glioblastoma (GBM) is a fatal primary malignant brain tumor. GBM stem cells (GSC) contribute to resistance to the DNA-damaging chemotherapy, temozolomide. The epidermal growth factor receptor (EGFR) displays genomic alterations enabling DNA repair mechanisms in half of GBMs. We aimed to investigate EGFR/DNA combi-targeting in GBM. EXPERIMENTAL DESIGN: -derived GSC sublines, and U87/EGFR isogenic cell lines stably expressing EGFR/wild-type or variant III (EGFRvIII). We evaluated its antitumor activity in mice harboring orthotopic EGFRvIII or mesenchymal TMZ-resistant GSC tumors. RESULTS: ZR2002 induced submicromolar antiproliferative effects and inhibited neurosphere formation of all GSCs with marginal effects on normal human astrocytes. ZR2002 inhibited EGF-induced autophosphorylation of EGFR, downstream Erk1/2 phosphorylation, increased DNA strand breaks, and induced activation of wild-type p53; the latter was required for its cytotoxicity through p53-dependent mechanism. ZR2002 induced similar effects on U87/EGFR cell lines and its oral administration significantly increased survival in an orthotopic EGFRvIII mouse model. ZR2002 improved survival of mice harboring intracranial mesenchymal temozolomide-resistant GSC line, decreased EGFR, Erk1/2, and AKT phosphorylation and was detected in tumor brain tissue by MALDI imaging mass spectrometry. CONCLUSIONS: These findings provide the molecular basis of binary EGFR/DNA targeting and uncover the oral bioavailability, blood-brain barrier permeability, and antitumor activity of ZR2002 supporting potential evaluation of this first-in-class drug in recurrent GBM.
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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".