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Integrative genomic analysis facilitates precision strategies for glioblastoma treatment

Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a dismal prognosis. Currently, the standard treatments for GBM rarely achieve satisfactory results, which means that current treatments are not individualized and precise enough. In this study, a multiomics-based GBM classification was established and three subclasses (GPA, GPB, and GPC) were identified, which have different molecular features both in bulk samples and at single-cell resolution. A robust GBM poor prognostic signature (GPS) score model was then developed using machine learning method, manifesting an excellent ability to predict the survival of GBM. NVP-BEZ235, GDC-0980, dasatinib and XL765 were ultimately identified to have subclass-specific efficacy targeting patients with a high risk of poor prognosis. Furthermore, the GBM classification and GPS score model could be considered as potential biomarkers for immunotherapy response. In summary, an integrative genomic analysis was conducted to advance individual-based therapies in GBM.

 

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The study established a multiomics-based classification of glioblastoma (GBM) into three subclasses (GPA, GPB, and GPC) with different molecular features and developed a robust prognostic signature (GPS) score model to predict the survival of GBM patients and identified four drugs with subclass-specific efficacy and the GBM classification and GPS score model as potential biomarkers for immunotherapy response, advancing individual-based therapies in GBM.

 

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