The DRD2 Antagonist Haloperidol Mediates Autophagy-Induced Ferroptosis to Increase Temozolomide Sensitivity by Promoting Endoplasmic Reticulum Stress in Glioblastoma
Bibliographic record
Abstract
PURPOSE: Temozolomide resistance remains a major obstacle in the treatment of glioblastoma (GBM). The combination of temozolomide with another agent could offer an improved treatment option if it could overcome chemoresistance and prevent side effects. Here, we determined the critical drug that cause ferroptosis in GBM cells and elucidated the possible mechanism by which drug combination overcomes chemoresistance. EXPERIMENTAL DESIGN: Haloperidol/temozolomide synergism was assessed in GBM cell lines with different dopamine D2 receptor (DRD2) expression in vitro and in vivo. Inhibitors of ferroptosis, autophagy, endoplasmic reticulum (ER) stress and cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA) were used to validate the specific mechanisms by which haloperidol and temozolomide induce ferroptosis in GBM cells. RESULTS: In the present work, we demonstrate that the DRD2 level is increased by temozolomide in a time-dependent manner and is inversely correlated with temozolomide sensitivity in GBM. The DRD2 antagonist haloperidol, a butylbenzene antipsychotic, markedly induces ferroptosis and effectively enhances temozolomide efficacy in vivo and in vitro. Mechanistically, haloperidol suppressed the effect of temozolomide on cAMP by antagonizing DRD2 receptor activity, and the increases in cAMP/PKA triggered ER stress, which led to autophagy and ferroptosis. Furthermore, elevated autophagy mediates downregulation of FTH1 expression at the posttranslational level in an autophagy-dependent manner and ultimately leads to ferroptosis. CONCLUSIONS: Our results provide experimental evidence for repurposing haloperidol as an effective adjunct therapy to inhibit adaptive temozolomide resistance to enhance the efficacy of chemoradiotherapy in GBM, a strategy that may have broad prospects for clinical application.
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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".