CD80 and CD86 molecules play an essential role in influencing the immune recognition of GBM cells. They bind to the CD28 molecule with a costimulatory signal for T-lymphocytes activation. On the other hand, they bind to CTLA-4, resulting in an immunosuppressive effect. CTLA-4 has a higher affinity to CD80 and CD86, making these molecules' role in immunosuppressive effect higher than their costimulatory effect . The current study has linked CD80 and CD86 expression on GBM tumor microenvironment to PFS. We observed a low correlation between mRNA and protein expression of CD80. However, a better correlation was observed between CD86 protein and mRNA expression. Low correlation between the mRNA and protein expression might be due to post-transcriptional mechanisms involved in turning mRNA into protein. Another reason could be related to the stability of both mRNA and protein in our patient’s samples. Finally, there is a possible error and noise in protein quantification and mRNA extraction that could influence mRNA stability and protein expression .
The number of patients (n = 47) in the ONT cohort is lower than the number of patients in the TCGA dataset (n = 121). The higher number of TCGA GBM samples could be one reason that affected the statistical analysis and provided a better prognostic value than the ONT cohort. Indeed, GBM samples' availability with comprehensive clinical and biological annotations and fulfilling the inclusion criteria is a limitation for a larger cohort. Larger patient cohort is needed to evaluate the prognostic value of CD80 and CD86 expression in GBM samples. In our protein analysis, co-staining of CD80 and CD86 is needed to determine these proteins' expression in different immune cell populations. Furthermore, other immune checkpoint proteins could be evaluated in future studies.
The expression of 50 immune checkpoint molecules was investigated in breast cancer. The study showed that high expression of costimulatory immune checkpoint molecules was associated with better PFS. However, no significant effect on prognosis was associated with CD80 and CD86 expression in the selected cohort . Feng et al. reported that low expression of CD80 is a predictive biomarker for poor prognosis in adenocarcinoma . Furthermore, CD80 and CD86 were found to be potential biomarkers for better prognosis survival in nasopharyngeal carcinoma . Additionally, the molecular characterization of PDL1 expression was correlated with other checkpoint proteins, i.e., CD80, highlighting that higher levels of immunosuppression are associated with GBM than lower-grade gliomas (LGG) . In myeloma cell lines, silencing the CD28-CD86 pathway resulted in myeloma cells' significant cell death . A recent study constructed a more robust model, using GBM and LGG data from the TCGA and CGGA (Chinese Glioma Genomic Atlas), and identified that low expression of CD86 molecules is a good prognostic indicator for OS. PFS analysis was not applied in this study .
In 2017, Berghoff et al. described a specific signature to predict the success of TMZ in MGMT-methylated patients. They showed that the TME signature could be used to indicate an individual's TMZ sensitivity. The TME was identified to be different between IDH mutant and wildtype. A richer tumor infiltrative lymphocyte (TIL) and a higher expression of PDL1 were observed in IDH-wildtype tumors . However, to date, no studies have linked MGMT promoter methylation with the TME. A recent research article has studied the expression of immune checkpoint inhibitor Tim3 and MGMT methylated status. They identified that a high expression of Tim3 in MGMT-unmethylated patients is linked to poor prognosis . Pratt et al have reported that the expression of PD-L1 is a negative prognostic biomarker in recurrent IDH-wildtype GBM Pratt, Dominah, Lobel, Obungu, Lynes, Sanchez, Adamstein, Wang, Edwards, Wu, Maric, Giles, Gilbert, Quezado and Nduom . In line with these findings, our study supports that the expression of immune checkpoint inhibitors may inhibit T-lymphocyte and anti-tumor reaction.
CD86 expression could be used as potential biomarkers predicting the efficacy of ipilimumab in GBM patients. Furthermore, it could be used as a biomarker for patients’ stratification for future clinical trials. Our study suffers from the limitation of retrospective studies with a limited number of patients. Nonetheless, our results were validated in an independent dataset and support investigations of immune checkpoint molecules as potential prognostic biomarkers and potential predictive biomarkers of response to immunotherapies in GBM.