PD-L1 Expression and Combined Status of PD-L1/PD-1–Positive Tumor Infiltrating Mononuclear Cell Density Predict Prognosis in Glioblastoma Patients

BACKGROUND: Programmed death ligand 1 (PD-L1) in tumor cells is known to promote immune escape of cancer by interacting with programmed cell death 1 (PD-1) in tumor infiltrating immune cells. Immunotherapy targeting these molecules is emerging as a new strategy for the treatment of glioblastoma (GBM). Understanding the relationship between the PD-L1/PD-1 axis and prognosis in GBM patients may be helpful to predict the effects of immunotherapy. METHODS: PD-L1 expression and PD-1–positive tumor infiltrating mononuclear cell (PD-1+tumor infiltrating mononuclear cell [TIMC]) density were evaluated using tissue microarray containing 54 GBM cases by immunohistochemical analysis; the associations with patient clinical outcomes were evaluated. RESULTS: PD-L1 expression and high PD-1+TIMC density were observed in 31.5% and 50% of GBM cases, respectively. High expression of PD-L1 in tumor cells was an independent and significant predictive factor for worse overall survival (OS; hazard ratio, 4.958; p = .007) but was not a significant factor in disease-free survival (DFS). PD-1+TIMC density was not correlated with OS or DFS. When patients were classified based on PD-1 expression and PD-1+TIMC density, patients with PD-L1+/PD-1+TIMC low status had the shortest OS (13 months, p = .009) and DFS (7 months, p = .053). CONCLUSIONS: PD-L1 expression in GBM was an independent prognostic factor for poor OS. In addition, combined status of PD-L1 expression and PD-1+TIMC density also predicted patient outcomes, suggesting that the therapeutic role of the PD-1/PD-L1 axis should be considered in the context of GBM immunity.

The Importance of Interface Irregularity between the Tumor and Brain Parenchyma in Differentiating between Typical and Atypical Meningiomas: Correlation with Pathology

PURPOSE: To understand clinical significance of irregular interface between meningioma and adjacent brain parenchyma in predicting histological grading of tumor, focusing on brain parenchymal invasion. MATERIALS AND METHODS: Pathologically confirmed 79 cases with meningiomas with pathological reports about the presence of parenchymal invasion were included. We defined the presence of interface irregularity as either spiculations or fuzzy margins between the tumor and brain parenchyma. We counted number of spiculations and measured ratio of fuzzy margin length to whole length of mass with consensus of two neuroradiologists. We classified the patients into Present group and Absent group, and the two groups were compared by using the Mann-Whitney U test. Statistical correlations between the presence of an interface irregularity and brain parenchymal invasion by the tumor as well as meningioma histological grade were tested with chi-square test. The optimal cutoff values of spiculation numbers and the ratio of fuzzy margins were determined. The sensitivity and specificity of number of spiculations, ratio of fuzzy margin and the presence of irregular interface as combined parameters for predicting the parenchymal invasion were calculated using ROC curve analysis. RESULTS: Statistically significant differences were noted between the Present and Absent groups for number of spiculations and ratio of fuzzy margin (P = 0.038 and P = 0.028, respectively). The optimal cutoff value for number of spiculations (> 4.5 with 61.1% sensitivity and 68.9% specificity) and the ratio of fuzzy margin (> 0.24 with 66.7% sensitivity and 65.6% specificity) were determined. The sensitivity and specificity of interface irregularity as the combined parameters were 72% and 59%, respectively. The interface irregularity between tumor and brain parenchyma significantly correlated with not only brain parenchymal invasion (P = 0.001) and but also histological grade (P < 0.001). CONCLUSION: The interface irregularity between tumor and brain parenchyma in MRI can be a strong predictive factor for brain parenchymal invasion and high grade meningioma.