Prognostic and Predictive Value of an Immunoscore Signature in Glioblastoma Multiform - Tang, Xiangjun; Xu, Pengfei; Chen, Ann; Deng, Gang; Zhang, Shenqi; Gao, Lun; Dai, Longjun; Chen, Qianxue (Frontiers in Genetics, 2020)
Background: Increasing studies show that immune infiltration has essential role in glioblastoma tissue, but current prognostic indicators do not accurately represent the risk of glioblastoma patients. Thus, it is urgent to identify specific markers to predict the prognosis of glioblastoma. We investigated the effectiveness of immunoscore in improving risk stratification and prediction of chemotherapy benefit for these patients. Methods: Using mRNA microarrays and CIBERSORT, we analyzed 22 types of immune cell fraction in 517 glioblastoma samples and identified an immunoscore u sing the LASSO Cox regression model based on the fraction of immune cell types and patients’ overall survival. The prognostic and predictive accuracy of immunoscore were verified in the validation cohort and the entire cohort. Results: Using the LASSO model, an immunoscore was developed to classify patients into High- and Low- immunoscore groups in the training cohort (P< 0.0001) based on the fraction of 8 immune cell types. The performance of immunoscore was validated in validation cohort (P< 0.0001) and entire cohort (P< 0.0001) respectively. Furthermore, a nomogram comprising age, IDH1 status and immunoscore was generated to predict 1- and 3- year survival rates in training cohort. The predictive value of the immunoscore was also confirmed in validation cohort and entire cohort (C-index: 0.66, 0.67, and 0.68, respectively). In addition, the patients in the low-immunoscore group might benefit from adjuvant chemotherapy for glioblastoma. Conclusion: Immunoscore, an immune-infiltration-based signature, is a reliable prognostic and predictive tool for glioblastoma.