Autophagy Inhibition via Hydroxychloroquine or 3-Methyladenine Enhances Chemotherapy-Induced Apoptosis in Neuro-blastoma and Glioblastoma
Bibliographic record
Abstract
Neuroblastoma is the most common tumour in children under 1 year old, accounting for approximately 50% of infant cancer cases. Although current treatments are relatively efficacious against this cancer, associated adverse effects could be detrimental to growth and development. In contrast, glioblastoma accounts for 52% of brain tumours and has an extremely poor prognosis. Current chemotherapeutics include temozolomide, which has numerous negative side-effects and a low-effective rate. Previous studies have shown the manipulation of autophagy to be a promising method for targeting cancers, including glioblastoma. We sought to determine the effects of autophagic alterations in combination with current chemotherapies in both neuroblastoma and glioblastoma. Supplementing cisplatin or temozolomide with autophagy activator rapamycin stabilized cancer cell mitochondria, despite having little effect on apoptosis or oxidative stress. Autophagy inhibition via 3-methyladenine or hydroxychloroquine alongside standard chemotherapies enhanced apoptosis and oxidative stress, with 3-methyladenine also disrupting mitochondrial health. Importantly, combining hydroxychloroquine with 0.5 µM cisplatin or 50 µg/mL temozolomide was as or more effective than 2 µM cisplatin or 100 µg/mL temozolomide alone. Analyzing these interesting results, a combined treatment of autophagy inhibitor with a standard chemotherapeutic agent could help to improve patient prognosis and reduce chemotherapy doses and their associated side-effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".