SC-05 * DOPAMINE RECEPTOR ANTAGONISTS ARE SELECTIVE INHIBITORS OF GLIOBLASTOMA STEM CELLS THROUGH IMPAIRMENT OF AUTOPHAGY
Bibliographic record
Abstract
Glioblastoma is an incurable cancer. The tumorigenic potential of glioblastoma resides within cells with a stem cell phenotype called glioblastoma stem cells. In order to identify novel glioblastoma drugs, we interrogated a library of 680 neuromodulatory compounds for their activity on glioblastoma derived neural stem cells (GNS), normal human fetal neural stem (NS) cells and normal fibroblasts to identify selective inhibitors. Compounds modulating dopaminergics, serotonergics and cholinergics pathways are enriched in the hits. In particular, two dopamine receptor antagonists demonstrated great selectivity for GNS cells and were synergistic with the chemotherapeutic drug temozolomide. These compounds reduced the clonogenic potential of primary glioblastoma cells and the growth of glioblastoma xenografts. Primary glioblastoma tumor and GNS cells expressed dopamine receptor. Mechanistic studies by pharmalogical inhibition and genetic knockdown of dopamine receptor suppressed glioblastoma stem cells growth and the ERK1/2 pathway. Furthermore, genome wide expression array showed downregulation of genes involved in cell cycle progression and upregulation of genes involved in autophagy. Further investigation revealed accumulations of autophagic vacuole with increased p62 suggesting a block in autophagy leading to cell death. This study identified new candidates for glioblastoma therapy and suggests neurochemical modifications should be focus for further drug development.
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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".