A preclinical trial to evaluate therapies for <i>BRCA</i>-associated pancreatic cancer.
Bibliographic record
Abstract
332 Background: Pancreatic ductal adenocarcinoma (PDAC) associated with germline BRCA1 or BRCA2mutations may have selective sensitivity to agents that exploit homologous recombination DNA repair defects. We hypothesized that cisplatin, a DNA-crosslinking agent, and talazoparib, a poly(ADP-ribose) polymerase (PARP) inhibitor, monotherapies are efficacious in BRCA-associated PDAC, while combination therapies with these agents have enhanced efficacy. Methods: A preclinical trial was performed in subcutaneous patient-derived tumor xenograft (PDX) mouse models with (n = 5) and without (n = 2) germline BRCA1 or BRCA2mutations. Mice harboring PDX tumors were treated with vehicle, talazoparib, cisplatin, gemcitabine, talazoparib with cisplatin (TC), talazoparib with gemcitabine (TG), or cisplatin with gemcitabine (CG). Twelve tumors were randomized into each trial arm, treated for 28 days, and then monitored without further treatment to assess tumor regrowth. Results: Treatment with cisplatin and talazoparib monotherapies resulted in 89.8% and 88.2% tumor growth inhibition (GI) in BRCA-associated cases versus 34.0% and 34.7% GI in the cases without germline BRCAmutations, respectively. Gemcitabine-based combinations were more effective than monotherapies in BRCA-associated cases (p < 0.001), with CG and TG resulting in tumor regression in five and three BRCA-associated cases, respectively. Although we also observed the treatment effect with these combinations in non-BRCA cases, it paralleled the gemcitabine monotherapy effect in these cases (p = 0.322). Following cessation of treatment, we observed heterogeneity in tumor regrowth. CG and TG demonstrated sustained responses across all BRCA-associated cases, however, TG had greater toxicity than CG. Conclusions: Our preclinical data suggest that targeted therapy with CG is efficacious with a sustained response and less toxicity than TG in BRCA-associated 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".