医学
养生
内科学
粒细胞巨噬细胞集落刺激因子
维持疗法
临床研究阶段
肿瘤科
粒细胞集落刺激因子
黑色素瘤
化疗
胃肠病学
外科
免疫学
细胞因子
癌症研究
作者
Steven O’Day,Peter D. Boasberg,Lawrence D. Piro,Timothy S. Kristedja,Hong Wang,Maureen A. Martin,Regina Deck,Patricia Ames,Kelly Shinn,Hannah Kim,Patricia Fournier,Guy Gammon
出处
期刊:PubMed
日期:2002-09-01
卷期号:8 (9): 2775-81
被引量:66
摘要
A prospective Phase II study of a novel maintenance biotherapy regimen after induction biochemotherapy was conducted in patients with metastatic melanoma in efforts to maintain responses and improve survival.Thirty-three patients with poor prognosis metastatic melanoma who achieved a partial response (PR) or stable disease (SD) to induction concurrent biochemotherapy were treated with chronic low-dose interleukin (IL)-2 and granulocyte macrophage-colony stimulating factor, and intermittent pulses of intermediate/high-dose decrescendo IL-2 over a 12-month period. The outcome of these patients was compared with a control group of patients at our institution who were treated recently with induction biochemotherapy and achieved a PR or SD.Five patients (15%) achieved a complete response, and 4 patients (12%) maintained SD for at least 6 months on maintenance biotherapy. The median progression-free survival (PFS) and overall survival (OS) were 8.1 months and 18.5 months, respectively, compared with historical controls, which were PFS 5.9 months (P = 0.0015) and OS 9.3 months (P = 0.0004). Administration of maintenance biotherapy was a significant predictor of PFS (P = 0.0008) and OS (P = 0.0001) in multivariate and matched-pair analyses (P = 0.002). The maintenance biotherapy regimen was well tolerated with no dose-limiting acute or cumulative toxicities.In this single institution study, maintenance biotherapy with IL-2 and granulocyte macrophage colony-stimulating factor in patients achieving PR or SD to induction biochemotherapy improved PFS and OS compared with historical controls. A larger multicenter Phase II trial has been initiated in an effort to confirm these results.
科研通智能强力驱动
Strongly Powered by AbleSci AI