Phase 2 Study of Iparomlimab and Tuvonralimab with Paclitaxel and Cisplatin as Neoadjuvant Therapy for Locally Advanced and Fertility-Sparing Cervical Cancer

What this Trial Is About

Researchers are exploring new treatment options for locally advanced cervical cancer, a condition with a low survival rate despite standard radical chemoradiotherapy. This study focuses on evaluating the combination of Iparomlimab and Tuvonralimab, two engineered monoclonal antibodies targeting PD-1 and CTLA-4, together with paclitaxel and cisplatin as neoadjuvant therapy. The study includes patients with various stages of cervical cancer, including those desiring fertility-preserving treatment, aiming to improve tumor response and reduce recurrence risks. Participants will receive three cycles of neoadjuvant therapy with Iparomlimab and Tuvonralimab at 5 mgkg every three weeks, plus paclitaxel 135-175 mgm2 and cisplatin 75-80 mgm2 administered every three weeks. After completing therapy, patients who achieve complete or partial response will undergo surgery radical hysterectomy plus lymphadenectomy for locally advanced cases, or fertility-preserving surgery such as conization, simple or radical trachelectomy plus lymphadenectomy for those aiming to preserve fertility. This approach seeks to optimize treatment effectiveness while considering fertility outcomes. During the study, participants will be closely monitored through imaging, biomarker analyses, and pathological assessments to evaluate tumor response and safety. Primary outcomes include objective response rate and pathological complete response measured at 9 weeks and 3 months, respectively, alongside changes in tumor-related biomarkers. Secondary outcomes assess overall survival, disease-free survival, and adverse events over two years. Participants must comply with study procedures and follow-up visits for up to two years to ensure thorough evaluation of treatment effects and safety.

Iparomlimab and Tuvonralimab Combined With Paclitaxel and Cisplatin as Neoadjuvant Therapy for CC