Inhibition of Poly ADP-Ribose Glycohydrolase Sensitizes Ovarian Cancer Cells to Poly ADP-Ribose Polymerase Inhibitors and Platinum Agents
Bibliographic record
Abstract
BACKGROUND: Poly ADP-ribose glycohydrolase (PARG) is responsible for the catabolism of PARP-synthesized PAR to free ADP-ribose. Inhibition of PARG leads to DNA repair interruption and consequently induces cell death. This study aims to evaluate the effect of a PARG inhibitor (PARGi) on epithelial ovarian cancer (OC) cell lines, alone and in combination with a PARP inhibitor (PARPi) and/or Cisplatin. METHODS: PARG mRNA levels were studied in three different OC datasets: TCGA, Hendrix, and Meyniel. PARG protein levels were assessed in 100 OC specimens from our bio-bank. The therapeutic efficacy of PARGi was assessed using cell migration and clonogenic formation assays. Flow cytometry was used to evaluate the cell apoptosis rate and the changes in the cell cycle. RESULTS: PARG protein was highly expressed in 34% of the OC tumors and low expression was found in another 9%. Similarly, Hendrix, Meyneil and TCGA databases showed a significant up-regulation in PARG mRNA expression in OC samples as compared to normal tissue (P=0.001, P=0.005, P=0.005, respectively). The use of PARGi leads to decreased cell migration. PARGi in combination with PARPi or Cisplatin induced decreased survival of cells as compared to each drug alone. In the presence of PARPi and Cisplatin, PARG knockdown cell lines showed significant G2/M cell cycle arrest and cell death induction. CONCLUSIONS: PARG inhibition appears as a complementary strategy to PARP inhibition in the treatment of ovarian cancer, especially in the presence of homologous recombination defects.
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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) | 0.001 | 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".