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

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.