Mechanisms of Resistance to <scp>PARPi</scp> in Pancreatic Ductal Adenocarcinoma
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly fatal disease with limited treatment options. PARP inhibitors (PARPi) have shown promise in treating PDAC with homologous recombination deficiency (HRD), but rapid acquisition of resistance limits their efficacy. Our objective is to investigate mechanisms of resistance to PARPi in BRCA2-mutant PDAC cells and identify potential therapeutic targets to modulate this resistance. We developed olaparib- and talazoparib-resistant Capan-1 cell lines and characterised their resistance profiles using viability assays, RNA sequencing and metabolomic profiling. We also developed a cisplatin-resistant Capan-1 cell line to compare resistance mechanisms between PARPi and platinum agents. Both olaparib- and talazoparib-resistant cells showed cross-resistance to other PARPi and oxaliplatin, but not to gemcitabine or 5-FU. Talazoparib-resistant cells exhibited a similar resistance profile to cisplatin-resistant cells, including decreased PARP1 expression and altered metabolomic profiles. RNA sequencing and metabolomic profiling revealed significant enrichment of metabolic pathways, including oxidative phosphorylation and glycolysis, in resistant cells. Our study highlights the complexity of resistance mechanisms to PARPi in PDAC and identifies potential therapeutic targets in metabolism. The differences in the resistance profiles between olaparib and talazoparib suggest that PARP-trapping potency may play a role in resistance development. Further research is needed to validate these findings and explore novel therapeutic strategies to overcome resistance to PARPi in PDAC.
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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 | 0.001 | 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".