Poly-ADP-Ribose Polymerase as a Therapeutic Target in Pediatric Diffuse Intrinsic Pontine Glioma and Pediatric High-Grade Astrocytoma
Bibliographic record
Abstract
Pediatric high-grade astrocytomas (pHGA) and diffuse intrinsic pontine gliomas (DIPG) are devastating malignancies for which no effective therapies exist. We investigated the therapeutic potential of PARP1 inhibition in preclinical models of pHGA and DIPG. PARP1 levels were characterized in pHGA and DIPG patient samples and tumor-derived cell lines. The effects of PARP inhibitors veliparib, olaparib, and niraparib as monotherapy or as radiosensitizers on cell viability, DNA damage, and PARP1 activity were evaluated in a panel of pHGA and DIPG cell lines. Survival benefit of niraparib was examined in an orthotopic xenograft model of pHGA. About 85% of pHGAs and 76% of DIPG tissue microarray samples expressed PARP1. Six of 8 primary cell lines highly expressed PARP1. Interestingly, across multiple cell lines, some PARP1 protein expression was required for response to PARP inhibition; however, there was no correlation between protein level or PARP1 activity and sensitivity to PARP inhibitors. Niraparib was the most effective at reducing cell viability and proliferation (MTT and Ki67). Niraparib induced DNA damage (γH2AX foci) and induced growth arrest. Pretreatment of pHGA cells with a sublethal dose of niraparib (1 μmol/L) before 2 Gy of ionizing radiation (IR) decreased the rate of DNA damage repair, colony growth, and relative cell number. Niraparib (50 mg/kg) inhibited PARP1 activity in vivo and extended survival of mice with orthotopic pHGA xenografts, when administered before IR (20 Gy, fractionated), relative to control mice (40 vs. 25 days). Our data provide in vitro and in vivo evidence that niraparib may be an effective radiosensitizer for pHGA and DIPG.
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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) | 0.000 | 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".