Inhibition of nicotinamide dinucleotide salvage pathway counters acquired and intrinsic poly(ADP-ribose) polymerase inhibitor resistance in high-grade serous ovarian cancer
Bibliographic record
Abstract
Epithelial ovarian cancer is the most lethal gynecological malignancy, owing notably to its high rate of therapy-resistant recurrence in spite of good initial response to chemotherapy. Although poly(ADP-ribose) polymerase inhibitors (PARPi) have shown promise for ovarian cancer treatment, extended therapy usually leads to acquired PARPi resistance. Here we explored a novel therapeutic option to counter this phenomenon, combining PARPi and inhibitors of nicotinamide phosphoribosyltransferase (NAMPT). Cell-based models of acquired PARPi resistance were created through an in vitro selection procedure. Using resistant cells, xenograft tumors were grown in immunodeficient mice, while organoid models were generated from primary patient tumor samples. Intrinsically PARPi-resistant cell lines were also selected for analysis. Our results show that treatment with NAMPT inhibitors effectively sensitized all in vitro models to PARPi. Adding nicotinamide mononucleotide, the resulting NAMPT metabolite, abrogated the therapy-induced cell growth inhibition, demonstrating the specificity of the synergy. Treatment with olaparib (PARPi) and daporinad (NAMPT inhibitor) depleted intracellular NAD+ , induced double-strand DNA breaks, and promoted apoptosis as monitored by caspase-3 cleavage. The two drugs were also synergistic in mouse xenograft models and clinically relevant patient-derived organoids. Therefore, in the context of PARPi resistance, NAMPT inhibition could offer a promising new option for ovarian cancer patients.
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) | 0.000 | 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".