Stratification according to HGG-IMMUNO RPA model predicts outcome in a large group of patients with relapsed malignant glioma treated by adjuvant postoperative dendritic cell vaccination
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PURPOSE: Adult patients with relapsed high-grade glioma are a very heterogenous group with, however, an invariably dismal prognosis. We stratified patients with relapsed high-grade glioma treated with re-operation and postoperative dendritic cell (DC) vaccination according to a simple recursive partitioning analysis (RPA) model to predict outcome. PATIENTS AND METHODS: Based on age, pathology, Karnofsky performance score, and mental status, 117 adult patients with relapsed malignant glioma, undergoing re-operation, and postoperative adjuvant dendritic cell (DC) vaccination were stratified into 4 classes. Kaplan-Meier survival estimates were generated for each class of this HGG-IMMUNO RPA model. Extent of resection was documented but not included in the prognostic model. RESULTS: Kaplan-Meier overall survival estimates revealed significant (p < 0.0001) differences among the 4 HGG-IMMUNO RPA classes. Long-term survivors, surviving more than 24 months after the re-operation and vaccination, are seen in 54.5, 26.7, 11.5, and 0 % for the classes I, II, III, and IV respectively. CONCLUSION: This HGG-IMMUNO RPA classification is able to predict overall survival in a large group of adult patients with a relapsed malignant glioma, treated with re-operation and postoperative adjuvant DC vaccination in the HGG-IMMUNO-2003 cohort comparison trial. The model appears useful for prognostic patient counseling for patients participating in DC vaccination trials. A substantial number of long-term survivors after relapse are seen in class I to III, but not in class IV patients.
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