CNSC-29. INVESTIGATING THE INFLUENCE OF DOPAMINERGIC ACTIVITY ON THE GLIOBLASTOMA NICHE
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is an incurable disease of adults and children and the deadliest form of central nervous system (CNS) malignancy. Despite major advances in our understanding of GBM biology in recent years, the prognosis for patients who develop this disease has not improved. If we hope to find new treatments for GBM that are safe and effective, we desperately need to reform our thinking. The brain’s chemical milieu is rich with neurotransmitters, and our lab’s screen of 680 neuroactive compounds on patient-derived glioblastoma stem cells (GSCs) in vitro strongly implicates neurotransmitter pathways as critical regulators of the GSC niche. More specifically, disrupting dopamine signaling by inhibiting its receptor D4 on GSCs causes substantial GSC apoptosis in vitro and attenuates GBM growth in vivo (Dolma et al., Cancer Cell, 2016). Dopamine (DA) is a catecholamine neurotransmitter that is essential for reward learning and movement and has numerous roles in cognition. Consequentially, dysregulation of DA signaling is associated with a diversity of brain diseases ranging from drug addiction to schizophrenia to Parkinson’s. Our research aims to determine how DA signaling affects normal neural stem cell (NSC) and tumorigenic GSC populations, as we hypothesize that GSCs arising/residing in DA projection zones exploit dopaminergic (DAergic) activity for GBM growth. Toward these aims, we have developed in vivo model systems and harnessed them to study NSC and GSC niches in the context of either controlled activation or depletion of DAergic neurons. These manipulations of the brain's DAergic neurons are achieved using optogenetic stimulation, genetic depletion, and neurotoxin-mediated ablation in mice. Ultimately, unraveling the dopaminergic influence on GBM may contribute to a redeployment of existing treatments—that modulate DA signaling and are already approved to treat CNS disorders—to patients with this deadly brain cancer.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".