An-An Yin1,2, Nan Lu1,3, Amandine Etcheverry4,5,6, Marc Aubry5,7, Jill Barnholtz-Sloan8, Lu-Hua Zhang9, Jean Mosser2,3,4,5, Wei Zhang2, Xiang Zhang2, Yu-He Liu1, Ya-Long He2. 1. Department of Neurosurgery, The 88th Hospital of the People's Liberation Army, Taian, Shandong Province, China. 2. Department of Neurosurgery, Xijing Institute of Clinical Neuroscience, Xijing Hospital, Air Force Military Medical University, Xi'an, Shaanxi Province, China. 3. Department of Neurosurgery, Changhai Hospital, Navy Military Medical University, Shanghai, China. 4. CNRS, UMR 6290, Institut de Génétique et Développement de Rennes (IGdR), Rennes, France. 5. UEB, UMS 3480 Biosit, Faculté de Médecine, Université Rennes1, Rennes, France. 6. CHU Rennes, Service de Génétique Moléculaire et Génomique, Rennes, France. 7. Plate-forme Génomique Santé Biosit, Université Rennes1, Rennes, France. 8. Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA. 9. Department of Neurosurgery, No. 425 Hospital of the People's Liberation Army, San Ya, Hainan Province, China.
Abstract
AIMS: We aimed to identify a clinically useful biomarker using DNA methylation-based information to optimize individual treatment of patients with glioblastoma (GBM). METHODS: A six-CpG panel was identified by incorporating genome-wide DNA methylation data and clinical information of three distinct discovery sets and was combined using a risk-score model. Different validation sets of GBMs and lower-grade gliomas and different statistical methods were implemented for prognostic evaluation. An integrative analysis of multidimensional TCGA data was performed to molecularly characterize different risk tumors. RESULTS: The six-CpG risk-score signature robustly predicted overall survival (OS) in all discovery and validation cohorts and in a treatment-independent manner. It also predicted progression-free survival (PFS) in available patients. The multimarker epigenetic signature was demonstrated as an independent prognosticator and had better performance than known molecular indicators such as glioma-CpG island methylator phenotype (G-CIMP) and proneural subtype. The defined risk subgroups were molecularly distinct; high-risk tumors were biologically more aggressive with concordant activation of proangiogenic signaling at multimolecular levels. Accordingly, we observed better OS benefits of bevacizumab-contained therapy to high-risk patients in independent sets, supporting its implication in guiding usage of antiangiogenic therapy. Finally, the six-CpG signature refined the risk classification based on G-CIMP and MGMT methylation status. CONCLUSIONS: The novel six-CpG signature is a robust and independent prognostic indicator for GBMs and is of promising value to improve personalized management.
AIMS: We aimed to identify a clinically useful biomarker using DNA methylation-based information to optimize individual treatment of patients with glioblastoma (GBM). METHODS: A six-CpG panel was identified by incorporating genome-wide DNA methylation data and clinical information of three distinct discovery sets and was combined using a risk-score model. Different validation sets of GBMs and lower-grade gliomas and different statistical methods were implemented for prognostic evaluation. An integrative analysis of multidimensional TCGA data was performed to molecularly characterize different risk tumors. RESULTS: The six-CpG risk-score signature robustly predicted overall survival (OS) in all discovery and validation cohorts and in a treatment-independent manner. It also predicted progression-free survival (PFS) in available patients. The multimarker epigenetic signature was demonstrated as an independent prognosticator and had better performance than known molecular indicators such as glioma-CpG island methylator phenotype (G-CIMP) and proneural subtype. The defined risk subgroups were molecularly distinct; high-risk tumors were biologically more aggressive with concordant activation of proangiogenic signaling at multimolecular levels. Accordingly, we observed better OS benefits of bevacizumab-contained therapy to high-risk patients in independent sets, supporting its implication in guiding usage of antiangiogenic therapy. Finally, the six-CpG signature refined the risk classification based on G-CIMP and MGMT methylation status. CONCLUSIONS: The novel six-CpG signature is a robust and independent prognostic indicator for GBMs and is of promising value to improve personalized management.
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