ArticleESMO open2025
PARPi and myeloid neoplasms; the Italian MITO-MaNGO experience based on a multicentric survey.
Article in ESMO open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionThe introduction of poly (ADP-ribose) polymerase inhibitors (PARPi) in ovarian cancer has raised increasing concerns about PARPi-related myeloid neoplasms (PrMN). Therapy-related neoplasms, including myelodysplastic syndromes and acute myeloid leukemia, account for 10%-20% of cases. Expanding PARPi indications, longer survival, and aging populations may contribute to rising PrMN incidence. Randomized clinical trials and real-world analyses show a significant risk increase, but data on individual PARPi, treatment lines, and prior therapies are limited.
methodsWe evaluated PrMN incidence across 17 Italian centers using a 71-item survey distributed to MITO (Multicenter Italian Trials on Ovarian cancer) and MaNGO (Mario Negri Gynecologic Oncology) centers, focusing on patients treated with PARPi outside clinical trials.
resultsOf 2320 patients (1254 BRCA-mutated), 56 (2.55%) developed MN: 35 myelodysplastic syndromes and 21 acute myeloid leukemia (2 patients had both, counted once). Among them, 31 had BRCA mutations (2.5%). Incidence by drug was: olaparib 2.5%, niraparib 2%, and rucaparib 3.4%. An unclear correlation emerged between treatment duration and PrMN risk, with a median onset of 18.9 months. Risk increased with additional therapy lines: 0.52% (first), 4.2% (second), 1.8% (third), 10.8% (fourth), and 12.2% (>fourth lines). Among PrMN cases, 4 achieved remission, 4 had partial responses, 8 progressed, and 37 died.
conclusionsWhile this survey is meant as hypotheses-generating, PrMN represent a rare but clinically relevant complication, particularly uncommon when PARPi are administered as first-line therapy. Their occurrence does not appear to be associated with the specific PARPi used or with BRCA mutation status. Early detection, monitoring, and identification of predictive factors are crucial as ovarian cancer outcomes improve and treatment exposure increases.
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