Abstract

The glioma tumor microenvironment (TME), composed of several noncancerous cells and biomolecules is known for its complexity of cancer-immune system interaction. Given that, novel risk signature is required for predicting glioma patient responses to immunotherapy. In this study, we systematically evaluated the TME infiltration pattern of 2877 glioma samples. TME phenotypes were determined using the Partitioning Around Medoid method. Machine learning including SVM-RFE and Principal component analysis (PCA) were used to construct a TME scoring system. A total of 857 glioma samples from four datasets were used for external validation of the TME-score. The correlation of TME phenotypes and TME-scores with diverse clinicopathologic characteristics, genomic features, and immunotherapeutic efficacy in glioma patients was determined. Immunohistochemistry staining for the M2 macrophage marker CD68 and CD163, mast cell marker CD117, neutrophil marker CD66b, and RNA sequencing of glioma samples from the XYNS cohort were performed. Two distinct TME phenotypes were identified. High TME-score correlated with a high number of immune infiltrating cells, elevated expression of immune checkpoints, increased mutation rates of oncogenes, and poor survival of glioma patients. Moreover, high TME-score exhibited remarkable association with multiple immunomodulators that could potentially mediate immune escape of cancer. Thus, the TME-score showed the potential to predict the efficacy of anti-PD-1 immunotherapy. Univariate and multivariate analyses demonstrated the TME-score to be a valuable prognostic biomarker for gliomas. Our study demonstrated that TME could potentially influence immunotherapy efficacy in melanoma patients whereas its role in immunotherapy of glioma patients remains unknown. Therefore, a better understanding of the TME landscape in gliomas would promote the development of novel immunotherapy strategies against glioma.

Highlights

  • According to the 2016 World Health Organization (WHO) classification criteria, gliomas are classified into low-grade glioma (LGG) and glioblastoma (GBM)

  • Chinese Glioma Genome Atlas (CGGA) datasets were downloaded from the CGGA website, while The Cancer Genome Atlas (TCGA) datasets were downloaded from UCSC Xena

  • Partitioning Around Medoid (PAM) was subsequently performed in the TCGA cohort (1027 patient samples), and two phenotypes were separated by different clinical factors (Figure S2B and Table S6)

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Summary

Introduction

According to the 2016 World Health Organization (WHO) classification criteria, gliomas are classified into low-grade glioma (LGG) and glioblastoma (GBM). The prognosis of GBM patients is dismal and the median overall survival (OS) is about 15 months following concomitant chemoradiotherapy, which can be attributed to the excessive heterogeneity of GBMs, rendering traditional therapies ineffective. Immune checkpoint blockers such as PD-1/L1 and CTLA-4 have demonstrated remarkable clinical efficacy in the management of multiple cancers [2, 3]. The current checkpoint immunotherapy is only effective in a limited number of glioma patients. It is, important to develop more effective immunotherapies for gliomas

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