Antonopoulos Markos, VAN Gool Stefaan W, Dionysiou Dimitra, Graf Norbert, Stamatakos Georgios
Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
Immuno-Oncologic Center Köln, Köln, Germany.
Anticancer Res. 2019 Apr;39(4):2043-2051. doi: 10.21873/anticanres.13315.
BACKGROUND/AIM: The need for more effective treatment modalities that can improve the clinical outcome of patients with glioblastoma multiforme remains imperative. Dendritic cell vaccination is a fast-developing treatment modality, currently under exploration. Functional immune cell subpopulations may play a role in the final outcome.
Data from 101 patients drawn from the HGG-2010 trial, including baseline patient characteristics and fluorescence-activated cell sorting of immune cell subpopulations, were analyzed by statistical and machine-learning methods.
The analysis revealed strong correlations between immune profiles and overall survival, when the extent of resection and the vaccination schedule were used as stratification variables.
A systematic, in silico workflow detecting strong and statistically significant correlations between overall survival and immune profile-derived quantities obtained at the start of dendritic cell vaccination was devised. The derived correlations could serve as a basis for the identification of prognostic markers discriminating between potential long- and short-term survivors of patients with glioblastoma multiforme.
背景/目的：对于多形性胶质母细胞瘤患者，仍迫切需要能改善临床结局的更有效治疗方式。树突状细胞疫苗接种是一种快速发展的治疗方式，目前正在探索中。功能性免疫细胞亚群可能在最终结局中发挥作用。
对来自HGG - 2010试验的101例患者的数据进行分析，包括患者基线特征以及免疫细胞亚群的荧光激活细胞分选，采用统计和机器学习方法。
当将切除范围和疫苗接种方案用作分层变量时，分析显示免疫特征与总生存期之间存在强相关性。
设计了一种系统性的计算机模拟工作流程，用于检测树突状细胞疫苗接种开始时获得的总生存期与免疫特征衍生量之间的强且具有统计学意义的相关性。所衍生的相关性可为识别多形性胶质母细胞瘤患者潜在长期和短期幸存者的预后标志物提供依据。
