Thomas Urup1, Signe Regner Michaelsen2, Lars Rønn Olsen3, Anders Toft2, Ib Jarle Christensen4, Kirsten Grunnet2, Ole Winther5, Helle Broholm6, Michael Kosteljanetz7, Shohreh Issazadeh-Navikas8, Hans Skovgaard Poulsen9, Ulrik Lassen10. 1. Department of Radiation Biology, The Finsen Center, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen, Denmark. Electronic address: thomas.urup@regionh.dk. 2. Department of Radiation Biology, The Finsen Center, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen, Denmark. 3. Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet, Building 208, DK-2800 Lyngby, Denmark; Bioinformatics Centre, Department of Biology and Biotech Research and Innovation Centre (BRIC), University of Copenhagen, DK-2200, Denmark. 4. Department of Gastroenterology, Hvidovre Hospital, Kettegård Allé 30, DK-2650 Hvidovre, Denmark. 5. Bioinformatics Centre, Department of Biology and Biotech Research and Innovation Centre (BRIC), University of Copenhagen, DK-2200, Denmark. 6. Department of Neuropathology, Center of Diagnostic Investigation, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen, Denmark. 7. Department of Neurosurgery, The Neurocenter, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen, Denmark. 8. Neuroinflammation Unit, BRIC, University of Copenhagen, DK-2100 Copenhagen, Denmark. 9. Department of Radiation Biology, The Finsen Center, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen, Denmark; Department of Oncology, The Finsen Center, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen, Denmark. 10. Department of Radiation Biology, The Finsen Center, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen, Denmark; Department of Oncology, The Finsen Center, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen, Denmark; Phase I Unit, Finsencenter, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen, Denmark.
Abstract
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.
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 glioblastomapatients. METHODS: The study included a total of 82 recurrent glioblastomapatients 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.
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Authors: Thomas Urup; Line Mærsk Staunstrup; Signe Regner Michaelsen; Kristoffer Vitting-Seerup; Marc Bennedbæk; Anders Toft; Lars Rønn Olsen; Lars Jønson; Shohreh Issazadeh-Navikas; Helle Broholm; Petra Hamerlik; Hans Skovgaard Poulsen; Ulrik Lassen Journal: BMC Cancer Date: 2017-04-18 Impact factor: 4.430