PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 4, 2026Journal of Clinical Oncology0 citations

Real-world survival disparities and comorbidity predictors with Lu-177–PSMA-617 and PARP inhibitors in metastatic prostate cancer.

View Full Paper
COChiugo OkoyeCEChinemerem M EmeasobaUAUchenna Maureen Amaechi

Key Points

  • This analysis aims to evaluate survival outcomes and predictors of mortality in patients treated with Lu-177–PSMA-617 and PARP inhibitors.
  • Conducted two retrospective cohort analyses utilizing the TriNetX Research Network.
  • Patients were grouped by treatment: Lu-177–PSMA-617 versus PARP inhibitors.
  • Cox proportional hazards models assessed mortality associations, adjusting for various demographics and comorbid conditions.
  • Lu-177–PSMA-617 treatment linked to a 24% lower mortality risk compared to controls (HR 0.76).
  • PARP inhibitors showed a 21% higher mortality risk, reflecting later-line therapy usage (HR 1.21).
  • Age and specific comorbidities such as diabetes and hepatic disease significantly increased mortality risk.

Abstract

125 Background: Lu-177–PSMA-617 (Pluvicto) and PARP inhibitors represent distinct targeted treatment paradigms in metastatic prostate cancer (mPCa). Real-world comparative data describing their impact on survival and comorbidity-related outcomes remain limited. We utilized the TriNetX global federated database to evaluate mortality associations and clinical predictors among patients receiving these therapies. Methods: We conducted two retrospective cohort analyses using the TriNetX Research Network (110 healthcare organizations, 2010–2025). Patients with mPCa (ICD-10 C61 plus C77–C79) were grouped by receipt of Lu-177–PSMA-617 (vipivotide tetraxetan) or PARP inhibitors (olaparib, rucaparib, niraparib, talazoparib). Cox proportional hazards models assessed all-cause mortality, adjusting for age, race, ethnicity, and comorbidities (hypertension, diabetes, liver disease, dementia, thromboembolic events, cerebrovascular disease). Results: The Lu-177–PSMA-617 cohort included 2,009 patients vs 119,042 controls; the PARP inhibitor cohort included 1,830 vs 119,221. 1) Lu-177–PSMA-617 was associated with lower mortality risk (HR 0.76; 95% CI 0.70–0.82; p <0.001). 2) PARP inhibitors were linked to higher observed mortality (HR 1.21; 95% CI 1.14–1.30; p <0.001), likely reflecting later-line or biomarker-selected use. 3) Age was predictive in both models (HR 1.04 per year; p <0.001). Diabetes (HR ≈ 1.15), hepatic disease (HR ≈ 1.41), and thromboembolic events (HR ≈ 1.29) increased mortality risk, while Hispanic ethnicity was protective (HR ≈ 0.62; p <0.001). Conclusions: In this real-world analysis, Lu-177–PSMA-617 improved survival, whereas PARP inhibitor–treated patients had higher mortality, likely reflecting more advanced disease. Cardiometabolic and hepatic comorbidities independently predicted poorer outcomes, emphasizing the need to account for comorbidity burden in managing advanced mPCa. Cox proportional hazards model for mortality in metastatic prostate cancer (TriNetX 2010–2025). Variable Hazard Ratio (HR) 95% CI p-Value Interpretation Lu-177–PSMA-617 (vs no Lu-177–PSMA-617) 0.76 0.70–0.82 <0.001 24% lower mortality risk PARP inhibitor (vs no PARP) 1.21 1.14–1.30 <0.001 21% higher mortality risk* Age (per year increase) 1.04 1.03–1.04 <0.001 Increased risk with age Type 2 diabetes mellitus 1.15 1.12–1.17 <0.001 Adverse impact Hepatic disease (fibrosis/cirrhosis) 1.41 1.31–1.53 <0.001 Adverse impact Thromboembolic disease (DVT/PE) 1.29 1.23–1.35 <0.001 Adverse impact Cerebrovascular disease 1.16 1.11–1.22 <0.001 Adverse impact Hispanic ethnicity 0.62 0.59–0.66 <0.001 Protective association Male sex 0.88 0.74–1.06 0.18 NS Black race 1.08 1.04–1.13 <0.001 Modestly higher risk Asian race 1.24 1.18–1.31 <0.001 Higher risk vs White *Likely reflects later-line therapy and selection for biomarker-positive advanced disease.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Okoye et al. (2026) studied this question.

synapsesocial.com/papers/69a7cd3dd48f933b5eed96e5https://doi.org/10.1200/jco.2026.44.7_suppl.125
Ask AI
Helpful
Bookmark
Share
View Full Paper