Open AccessAngiotensinogen and HLA class II predict bevacizumab response in recurrent glioblastoma patients
Author(s) -
Urup Thomas,
Michaelsen Signe Regner,
Olsen Lars Rønn,
Toft Anders,
Christensen Ib Jarle,
Grunnet Kirsten,
Winther Ole,
Broholm Helle,
Kosteljanetz Michael,
Issazadeh-Navikas Shohreh,
Poulsen Hans Skovgaard,
Lassen Ulrik
Publication year - 2016
Publication title -
molecular oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.332
H-Index - 88
eISSN - 1878-0261
pISSN - 1574-7891
DOI - 10.1016/j.molonc.2016.05.005
Subject(s) - bevacizumab , medicine , oncology , proportional hazards model , logistic regression , glioblastoma , biomarker , human leukocyte antigen , multivariate analysis , chemotherapy , immunology , antigen , cancer research , biology , biochemistry
Background Bevacizumab combination therapy is among the most frequently used treatments in recurrent glioblastoma and patients who achieve response to bevacizumab have improved survival as well as quality of life. Accordingly, the aim of this study was to identify predictive biomarkers for bevacizumab response in recurrent glioblastoma patients. Methods The study included a total of 82 recurrent glioblastoma patients treated with bevacizumab combination therapy whom were both response and biomarker evaluable. Gene expression of tumor tissue was analyzed by using a customized NanoString platform covering 800 genes. Candidate gene predictors associated with response were analyzed by multivariate logistic and Cox regression analysis. Results Two genes were independently associated with response: Low expression of angiotensinogen (2‐fold decrease in AGT; OR = 2.44; 95% CI: 1.45–4.17; P = 0.0009) and high expression of a HLA class II gene (2‐fold increase in HLA‐DQA1; OR = 1.22; 95% CI: 1.01–1.47; P = 0.04). These two genes were included in a model that is able predict response to bevacizumab combination therapy in clinical practice. When stratified for a validated prognostic index, the predictive model for response was significantly associated with improved overall survival. Conclusion Two genes (low angiotensinogen and high HLA‐class II expression) were predictive for bevacizumab response and were included in a predictive model for response. This model can be used in clinical practice to identify patients who will benefit from bevacizumab combination therapy.