Targeting reduced mitochondrial DNA quantity as a therapeutic approach in pediatric high-grade gliomas
Bibliographic record
Abstract
BACKGROUND: Despite increased understanding of the genetic events underlying pediatric high-grade gliomas (pHGGs), therapeutic progress is static, with poor understanding of nongenomic drivers. We therefore investigated the role of alterations in mitochondrial function and developed an effective combination therapy against pHGGs. METHODS: Mitochondrial DNA (mtDNA) copy number was measured in a cohort of 60 pHGGs. The implication of mtDNA alteration in pHGG tumorigenesis was studied and followed by an efficacy investigation using patient-derived cultures and orthotopic xenografts. RESULTS: Average mtDNA content was significantly lower in tumors versus normal brains. Decreasing mtDNA copy number in normal human astrocytes led to a markedly increased tumorigenicity in vivo. Depletion of mtDNA in pHGG cells promoted cell migration and invasion and therapeutic resistance. Shifting glucose metabolism from glycolysis to mitochondrial oxidation with the adenosine monophosphate-activated protein kinase activator AICAR (5-aminoimidazole-4-carboxamide ribonucleotide) or the pyruvate dehydrogenase kinase inhibitor dichloroacetate (DCA) significantly inhibited pHGG viability. Using DCA to shift glucose metabolism to mitochondrial oxidation and then metformin to simultaneously target mitochondrial function disrupted energy homeostasis of tumor cells, increasing DNA damage and apoptosis. The triple combination with radiation therapy, DCA and metformin led to a more potent therapeutic effect in vitro and in vivo. CONCLUSIONS: Our results suggest metabolic alterations as an onco-requisite factor of pHGG tumorigenesis. Targeting reduced mtDNA quantity represents a promising therapeutic strategy for pHGG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".