Immune landscape-based machine-learning–assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma - Li, Haiyan; He, Jian; Li, Menglong; Li, Kun; Pu, Xuemei; Guo, Yanzhi (Frontiers in Immunology, 2022)
As a malignant brain tumor, glioblastoma (GBM) is characterized by intra-tumor heterogeneity, worse prognosis, high invasive, lethal and refractory natures. Immunotherapy has been becoming a promising strategy to treat diverse cancers. It has been known that there are highly heterogeneous immunosuppressive microenvironments among different GBM molecular subtypes that mainly include classical (CL), mesenchymal (MES) and proneural (PN) respectively. Therefore, an in-depth understanding of immune landscapes among them is essential for identifying novel immune markers of GBM. In present study, based on collecting the largest number of 109 immune signatures, we aim to achieve the precise diagnosis, prognosis and immunotherapy prediction for GBM by performing a comprehensive immunogenomic analysis. Firstly, machine-learning (ML) methods were proposed to evaluate the diagnostic values of these immune signatures and the optimal classifier was constructed for accurate recognition of three GBM subtypes with the robust and promising performance. Then the prognostic values of these signatures were also confirmed and a risk score was established to divide all GBM patients into high, median and low-risk groups with the high predictive accuracy of overall survival (OS). Therefore, complete differential analysis across GBM subtypes were performed in terms of the immune characteristics along with clinicopathological and molecular features, which indicates that MES shows much higher immune heterogeneity compared to CL and PN, but it has significantly better immunotherapy responses, although MES patients may have an immunosuppressive microenvironment and be more proinflammatory and invasive. Finally, MES subtype is proved to be more sensitive to 17-AAG, docetaxel and erlotinib using drug sensitivity analysis and three compounds of AS-703026, PD-0325901 and MEK1-2-inhibitor might be potential therapeutic agents. Overall, the findings of this research could help enhancing our understanding of the tumor immune microenvironment and providing new insights for improving the prognosis and immunotherapy of GBM patients.