Abstract

   There has been no significant improvements for immune checkpoint
   inhibitors since its first use. Tumour-associated macrophages (TAMs)
   are critical mediators in the PD-1/PD-L1 axis, contributing to the
   immunosuppressive tumour microenvironment. This study aims to
   investigate the potential role of PD-L1 in regulating TAMs in
   glioblastoma. Gene expression data and clinical information of glioma
   patients were collected from TCGA (n = 614) and CGGA (n = 325)
   databases. Differentially expressed genes between PD-L1^high and
   PD-L1^low groups were identified and subjected to bioinformatical
   analysis. We found that PD-L1 was frequently expressed in gliomas with
   a grade-dependent pattern. Higher PD-L1 expression predicted shorter
   overall survival. Moreover, PD-L1 was positively correlated with
   immunosuppressive cells (macrophage, neutrophil and immature DC) and
   negatively correlated with cytocidal immune cells (CD8^+ T cell and
   Th1). Importantly, PD-L1 high expression was significantly correlated
   with M2-polarization of macrophages (M2-TAMs). We conclude that PD-L1
   is an unfavourable prognostic marker for patients with glioblastoma;
   PD-L1-mediated immunosuppression may attribute to the infiltration and
   M2-polarization of TAMs.

   Keywords: immune checkpoint inhibitor, PD-1/PD-L1 axis, glioblastoma
   tumor microenvironment, immunosuppression, tumor-associated macrophages

Introduction

   Glioblastoma multiforme (GBM) is the most common malignant primary
   brain tumor in adults. It accounts for 30% of all the brain tumors and
   more than 50% of gliomas ([35]1, [36]2). The current standard of care
   for GBM patients includes surgical tumor resection followed by
   radiotherapy with concomitant and adjuvant temozolomide ([37]3). Even
   with aggressive and comprehensive treatment, tumor recurrence is
   inevitable. The median overall survival of patients diagnosed with GBM
   is less than two years ([38]1). Given the poor survival of GBM patients
   and inefficiency of the current therapy regimen, alternative treatment
   strategies, and novel therapeutic targets are clearly needed.

   Immunotherapy is a revolutionary anti-cancer therapy in the past
   decade. Various immunotherapy modalities have been established,
   including immune checkpoint inhibitors, CAR-T, vaccine and oncolytic
   virus ([39]4). The most well-known checkpoint inhibitors are antibodies
   of cytotoxic T lymphocyte antigen 4 (CTLA-4), programmed cell death
   protein 1 (PD-1), and its ligand programmed death-ligand 1 (PD-L1)
   ([40]5). Upon activation, the checkpoints induce inhibitory or even
   apoptotic effects within immune cells (mainly effector T cells) ([41]6,
   [42]7). Immune-checkpoint inhibitors can block the interaction between
   the ligands and the immune-repressive receptors, thus overcome the
   inhibition of immune cells and reactivate the cytocidal immune response
   ([43]8). Currently, immune-checkpoint inhibitors have shown remarkable
   benefits in prolonging survival in many cancers such as lung cancer,
   breast cancer, and melanoma ([44]9–[45]11).

   In GBM, however, therapeutic response to checkpoint inhibitors is
   variant and mostly ineffective. All the large phase 3 clinical studies
   on PD-1 inhibitors in GBM have failed to show survival benefits
   ([46]12, [47]13). Less than 10% of patients with recurrent GBM respond
   to PD-1 inhibitors ([48]14). It is recognized that GBM is highly
   genetically heterogeneous and unresponsive to immunotherapy approaches
   ([49]15). The underlying mechanisms remain elusive and can be
   multifaceted, wherein the immunosuppressive tumor microenvironment
   (TME) represents a critical factor ([50]13). The engagement of PD-1 and
   PD-L1 is an essential mechanism that contributes to the
   immune-suppressive TME. Multiple suppressive effects can be triggered,
   such as the induction of cellular apoptosis, the impairment of T
   lymphocyte proliferation, and the inhibition of “effector” cytokine
   generation ([51]7). The majority of PD-L1 expression is contributed by
   tumor-infiltrating myeloid cells (TIMs, including TAM, tumor associated
   neutrophils and myeloid-derived suppressor cells) ([52]16). Recent
   studies reported that PD-L1 inhibitor could skew TAMs towards a
   pro-inflammatory M1 status ([53]17, [54]18), expanding its canonical T
   cell suppression function. Currently, the relationship between PD-L1
   and TAMs in GBM remains poorly understood.

   The dissatisfied efficacy of anti-PD-1 antibodies in GBM necessitates
   the basic research on PD1/PD-L1 axis-mediated immune resistance. This
   study aims to delineate the role of PD-L1 in the immunosuppressive TME,
   focusing on its relationship with TAMs. By using transcriptional gene
   expression data from CGGA (The Chinese Glioma Genome Atlas) and TCGA
   (The Cancer Genome Atlas) databases, we depicted the immune landscape
   associated with PD-L1 in GBM and proposed that PD-L1-mediated
   immunosuppression may correlate with the infiltration and
   M2-polarization of macrophages.

Methods and Materials

Data Acquisition

   Glioma gene expression profile and patients’ clinical information were
   downloaded from TCGA ([55]http://cancergenome.nih.gov) and CGGA
   ([56]http://www.cgga.org.cn) databases. In the TCGA dataset, the
   transcriptome expression data of 150 GBM and 464 lower gliomas were
   collected. In the CGGA dataset, the total number of samples is 325,
   including 144 GBM and 181 lower gliomas. Patients’ information on age,
   gender, World Health Organization (WHO) grade, diagnosis with molecular
   characteristics, treatment, and patient prognoses were organized ([57]
   Table S1 ). PD-L1 and other gene expression profiles were obtained as
   described previously ([58]19, [59]20). Gene expression level was
   presented as PRKM (reads per kilobase transcriptome per million reads).

Bioinformatic Analysis

   Genes that showed consistent differential expression in both TCGA and
   CGGA cohorts were extracted. Overlapped highly expressed genes in the
   PD-L1^high (807 over-expressed genes) and PD-L1^low (559 over-expressed
   genes) GBMs were subjected to KEGG enrichment analysis ([60] Table S2
   ). Gene annotation and pathway enrichment analysis were performed by
   Database for Annotation, Visualization, and Integrated Discovery
   (DAVID), and Kyoto Encyclopedia of Genes and Genomes (KEGG,
   [61]http://www.kegg.jp/kegg/pathway.html). Gene Set Variation Analysis
   (GSVA package of R [62]http://www.bioconductor.org/) was used to
   explore the correlation between PD-L1 and the predefined, highly
   distinctive transcriptional profile of each immune cell type
   ([63]21–[64]24). The classical chemokines and surface markers of both
   M1- and M2- macrophages were also included ([65]25–[66]28). Twenty-six
   types of immune cells with corresponding gene signatures were utilized
   for analyses ([67] Table S3 ).

Statistical Analysis

   R language (v. 3.4.3, AT&T BellLaboratories, Lucent Technologies), SPSS
   software (v. 22.0, IBM company), and GraphPad Prism (v. 8.0, LLC) for
   Windows were used for statistical analyses and figure generation.
   Samples from the TCGA and CGGA datasets were analysed separately. Genes
   differentially expressed between PD-L1 high and low groups (PD-L1^high
   and PD-L1^low, stratified by median value) were estimated by a
   two-tailed Student’s t-test. The Significance Analysis of Microarrays
   (SAM) package of R was performed to control the FDR (False Discovery
   Rate). Statistically significant was considered when FDR <0.01 and norm
   p <0.05. The prognostic value of these differentially expressed genes
   was evaluated by the survival package of R. A multivariate Cox
   proportional hazard model was performed to evaluate the independent
   prognostic variables. Kaplan-Meier curves were utilized to depict the
   survival distributions. PD-L1-correlated genes and immune cells were
   explored by Pearson’s correlation coefficient (r) using R. A
   significant correlation was indicated by an absolute r-value greater
   than 0.3 and a p-value less than 0.05.

Results

PD-L1 Is Frequently Expressed in Gliomas and Predicts Unfavorable Overall
Survival in GBM

   Firstly, we found that PD-L1 mRNA was frequently expressed in different
   grades of gliomas with a grade-dependent pattern in TCGA cohorts ([68]
   Figure 1A ). A similar trend was found in CGGA cohort, although the
   difference was of no statistical significance ([69] Figure S1A ). Of
   note, when compared with other GBM subtypes, the proneural subtype has
   particularly lower PD-L1 expression whereas the mesenchymal subtype has
   a relatively higher level ([70] Figure 1B , [71]S1B ). In both cohorts,
   GBM patients with higher PD-L1 expression had shorter overall survival
   ([72] Figure 1C TCGA, median survival: PD-L1^high vs PD-L1^low, 375 vs
   453 days, p = 0.0272; [73]Figure 1D CGGA, median survival: PD-L1^high
   vs PD-L1^low, 315 vs 567 days, p = 0.0021). Cox regression analysis
   further showed that PD-L1 was an independent prognostic marker in GBM
   ([74] Table S4 ). The results suggested that PD-L1 was frequently
   expressed in gliomas and could serve as a prognostic biomarker in GBM.

Figure 1.

   [75]Figure 1
   [76]Open in a new tab

   The expression of PD-L1 in gliomas in TCGA dataset. (A). The expression
   of PD-L1 in each grade of glioma, G2 (Grade II) mean = 3.321, G3 (Grade
   III) mean = 3.870, G4 (Grade IV) mean = 4.915. (B). Expression of PD-L1
   in GBM subtypes, proneural mean = 3.365, neural mean = 4.669, classical
   mean = 5.078, mesenchymal mean = 5.442; one-way ANOVA followed by
   Tukey’s multiple comparisons test was used. (C, D). Kaplan-Meier
   analysis showed the prognosis value of PD-L1 in GBM, median survival
   days of PD-L1^high vs. PD-L1^low: 375 days vs 453 days (C, TCGA cohort)
   and 315 days vs 567 days (D, CGGA cohort); Log-rank test was used;
   ****p < 0.0001.

PD-L1 Is Positively Correlated With Immunosuppressive M2-Macrophage and
Suppresses Effector T Cells in GBM

   PD-L1 is the ligand of the well-known immune checkpoint PD-1, which
   mediates the suppression on effector T cell ([77]7). To investigate the
   immune cells that may correlate with PD-L1 in GBM, we identified
   PD-L1-associated immune components in GBM microenvironment by utilizing
   GSVA analysis ([78] Figure 2 and [79]Table S5 ). The results showed
   that PD-L1 expression was positively correlated with the infiltration
   of macrophages (CGGA r = 0.3024, p = 0.0002; TCGA r = 0.30818, p =
   0.0001), M1-macrophage (CGGA r = 0.25567, p = 0.002; TCGA r = 0.36842,
   p < 0.0001), M2-macrophage (CGGA r = 0.30057, p = 0.0003; TCGA r =
   0.36069, p < 0.0001), neutrophil (CGGA r = 0.33619, p < 0.0001; TCGA r
   = 0.26032, p = 0.0013), and Treg (CGGA, r = 0.02204, p > 0.05; TCGA, r
   = 0.21973, p = 0.0069). In contrast, PD-L1 was negatively correlated
   with the infiltration of CD8^+ T cell (CGGA, r = -0.19901, p = 0.0168;
   TCGA, r = -0.35739, p < 0.0001), Tfh (CGGA, r = -0.3092, p = 0.0002;
   TCGA, r = -0.25058, p = 0.0020), Tgd (CGGA, r = -0.30892, p = 0.0002;
   TCGA, r = -0.36625, p < 0.0001), and B cell (CGGA r = -0.32434, p <
   0.0001; TCGA r = –0.26846, p = 0.0009). These results indicated that
   PD-L1 was positively correlated with the infiltration of macrophages in
   GBM and negatively correlated with effector T immune cells.

Figure 2.

   [80]Figure 2
   [81]Open in a new tab

   Heatmap indicates the correlation of PD-L1 with 26 immune cell
   subpopulations in CGGA dataset (A) and TCGA dataset (B). Each coloured
   square illustrates the correlation between PD-L1 and the
   transcriptional profile of the corresponding immune cell type. Red
   colour illustrates a positive correlation (r = 1), white indicates no
   correlation (r = 0), and blue a negative correlation (r = −1). Immune
   cells in the ranking list: (A), eosinophil, Tcm, Tem, CD8^+T cell, Tfh,
   Th, Th2, B cell, Tgd (γδT cell), Th1, NK CD56bright cell, iDC, NK cell,
   NK CD56dim cell, DC, pDC, T cell, Cytotoxic T cell, Microglia, aDC,
   Treg, Th17, neutrophil, M2-like-Macrophage,
   Macrophage,M1-like-Macrophage; (B), neutrophil, M2-like-Macrophage,
   M1-like-Macrophage, Microglia, Macrophage, DC, T cell, Cytotoxic T
   cell, iDC, pDC, Th, Th2, CD8^+T cell, Tgd, Tcm, Tem, Th1, B cell, NK
   CD56dim cell, Treg, aDC, Th17, Tfh, eosinophil.

PD-L1 Correlates With M2-Macrophages-Related Chemokines

   The M2-polarization of TAM is recognized as an immune-suppressive
   phenotype ([82]29). It is striking to notice the correlation between
   PD-L1 and M2-macrophages. To verified the above findings, we
   investigated the correlation between PD-L1 and canonical chemokines of
   both M1-macrophages (IL-12, IL-23, TNF, IFNG) and M2 macrophages
   (IL-10, TGF-β, IL-4, IL-13). In TCGA cohort ([83] Figure 3 ), PD-L1 was
   positively correlated with M2-macrophage chemokines, such as TGF-β1 (r
   = 0.34433, p < 0.0001), TGF-β3 (r = 0.22328, p = 0.0049), and IL-10 (r
   = 0.18208, p = 0.0225). However, no significant correlation was found
   between PD-L1 and M1-macrophage chemokines (|r| < 0.1, p > 0.05). In
   CGGA cohort, PD-L1 also showed positive correlation with M2 chemokines,
   although the differences were not statistically significant ([84]
   Figure S2B ). These findings supported that PD-L1 was intimately
   correlated with M2-macrophages in the microenvironment of GBM.

Figure 3.

   [85]Figure 3
   [86]Open in a new tab

   Pearson’s correlation of PD-L1 and TAM-related chemokines in TCGA
   glioblastoma dataset. (A) Chemokine of M1-TAM: IL12A (r = 0.117796, p >
   0.05), IL12B (r = 0.140694, p > 0.05), IL23A (r = 0.149971, p > 0.05),
   IL23R (r = 0.107516, p > 0.05), TNF (r = -0.00741, p > 0.05), IFNG (r =
   0.129588, p < 0.0001). (B) Chemokine of M2-TAM: TGF-β1 (r = 0.34433, p
   < 0.0001), TGF-β2 (r = 0.12637, p = 0.1148), TGF-β3 (r = 0.22328, p <
   0.0049), IL-10 (r = 0.18208, p = 0.0225), IL-4 (r = 0.0748, p > 0.05),
   IL-13 (r = -0.05977, p > 0.05).

PD-L1 Is Associated With Signalling Pathways That Modulate Macrophage
Polarization

   In order to investigate the function of PD-L1 in modulating TAM M2
   polarization, genes highly expressed in PD-L1^high and PD-L1^low GBMs
   were used to identify potential biological pathways ([87] Table S2 ).
   As shown in [88]Figure 4 , the pathways enriched in PD-L1^high GBM
   include (1) NF-kappa B signalling pathway; (2) apoptotic process; (3)
   positive regulation of interferon-gamma production; (4) Fc gamma
   R-mediated phagocytosis; (5) positive regulation of interleukin-10
   production; (6) response to interferon-gamma; (7) negative regulation
   of ERK1 and ERK2 cascade; (8) mTOR signalling pathway; (9) innate
   immune response; (10) Cytokine-cytokine receptor interaction. Enriched
   pathways in PD-L1^low GBM contain (1) nervous system development; (2)
   regulation of transcription, DNA-templated; (3) spinal cord
   oligodendrocyte cell fate specification; (4) negative regulation of
   transcription from RNA polymerase II promoter; (5) neurogenesis; (6)
   mRNA splicing, via spliceosome; (7) negative regulation of neuron
   differentiation; (8) Oxidative phosphorylation. In sum, critical
   pathways involved in macrophage polarization were enriched in
   PD-L1^high GBMs, while biological pathways enriched in PD-L1^low GBMs
   were less relevant with macrophages functions.

Figure 4.

   Figure 4
   [89]Open in a new tab

   The top bioinformatics hits of biological pathways derived from genes
   enriched in GBM patients with PD-L1^high (A) and PD-L1^low (B). Plot
   sizes show gene counts enriched in the enrichment of pathway. Colour
   depth indicates the p value from low (red) to high level (blue). The p
   values of all presented hits are less than 0.05.

Discussion

   The continuous failure of clinical trials on PD-1 antibodies in GBM
   necessitates basic researches on the mechanism of immunotherapies
   resistance. This study depicts the immune features associated with
   PD-L1 in the TME of GBM. Firstly, the PD-L1 mRNA expression shows a
   grade-dependent pattern in gliomas. Higher PD-L1 expression predicted a
   poorer outcome in patients with GBM. Moreover, PD-L1 expression is
   associated with the infiltration of immune-suppressive macrophages and
   neutrophils. We further found that PD-L1 high expression was positively
   correlated with the M2-polarization of TAMs, evidenced by the increased
   M2-related gene signatures and canonical chemokines. Signalling
   pathways that correlated with macrophage polarization were enriched in
   PD-L1^high GBMs, indicating a critical role of PD-L1 in modulating
   macrophage activation. The present study provides preliminary evidence
   on the intimate correlation between PD-L1 and M2-TAMs, supporting the
   notion that PD-L1 inhibitors could enhance the efficacy of prevalent
   PD-1 antibodies for GBM therapy.

   It is important to determine the expression pattern of PD-L1 in GBM.
   The protein level of PD-L1 has been considered as a critical predictive
   marker for therapeutic response to PD-1/PD-L1 antibody in multiple
   types of cancer ([90]30). However, the positive rate and expression
   level of PD-L1 in GBM can be influenced by many factors, such as the
   selected anti-PD-L1 antibody; the positive criteria; and the intrinsic
   tumoral heterogeneity ([91]31). For instance, the percentage of GBM
   patients with detectable PD-L1 protein expression level varies from 61
   to 88% according to different reports ([92]32, [93]33), while the
   median percentage of PD-L1-expression cells in GBM is only 2.77%
   ([94]32). Thus, a more comprehensive landscape of PD-L1 expression in
   glioma is needed. In this study, we found that PD-L1 mRNA was
   frequently expressed in all grades of gliomas and exhibited a
   grade-dependent manner. This finding is in line with previous studies
   that PD-L1 is positively correlated with glioma grades ([95]34). We
   also noticed that the proneural GBM subtype had lower PD-L1 expression
   among all the GBM subgroups whereas the mesenchymal subtype had a
   relatively higher level. These findings are in agreement with other
   reports that the proneural subtype has a better outcome and the
   immunosuppressive genes are predominant in mesenchymal subtype ([96]35,
   [97]36).

   Whether PD-L1 represents a stable prognosis predictor in glioma is
   still under debate. Over half of the published reports proposed the
   negative correlation of PD-L1 expression and survival time of glioma
   patients, while other studies showed no significant correlation between
   PD-L1 and patient survival ([98]31, [99]33, [100]37). This study shows
   that higher PD-L1 mRNA expression is correlated with shorter overall
   survival. The Cox regression analysis further indicates that PD-L1 is
   an independent unfavourable prognostic marker in GBMs.

   Intra-tumor heterogeneity and unresponsive to immunotherapy represent
   the major obstacles for immune-checkpoint antibodies in GBM. The WHO
   2016 glioma diagnosis scheme based on molecular characteristics
   represents a big step towards precise diagnosis and tailored therapy
   for patients with diffused glioma ([101]38). GBMs are well-known
   insensitive “cold” tumors with relatively low tumor mutation burden and
   quiescent immune reactivity ([102]13, [103]39). The highly
   immune-suppressive TME with a paucity of infiltrating CTLs has been
   considered a pivotal mediator of the insensitivity ([104]40), wherein
   TAMs play an indispensable role ([105]16). Classically, TAMs can
   polarize to M1 macrophages (the classical activation) which exhibit
   pro-inflammatory and cancer-inhibiting effects. Alternatively, stimuli
   such as IL-4, IL-14, IL-10 can induce macrophages towards an
   anti-inflammatory and cancer-promoting M2 phenotype ([106]41, [107]42).
   In GBM, TAMs were the predominant infiltrating immune cells and usually
   polarized to an immunosuppressive M2-like phenotype ([108]43, [109]44).
   This study shows that PD-L1 may be an important regulator of
   macrophages in GBM, supported by the positive correlation between them.
   Moreover, PD-L1 is positively correlated with the canonical markers of
   M2 macrophages, while has insignificant correlation with M1 markers.
   Thus, PD-L1 may participate in the induction and maintenance of
   macrophage M2-polarization.

   The tumor infiltrating neutrophils could also inhibit T cells activity
   via the PD-1/PD-L1 pathway in hepatocellular carcinoma ([110]45) and
   was associated with pro-inflammatory cytokine in lung cancer ([111]46).
   The positive correlation between PD-L1 and neutrophil in the study
   implies that neutrophil-mediated immunosuppression may also occur in
   GBM. Regulatory T cells (Treg) are emerging as a mediator of
   immunosuppression in glioblastoma by inhibiting the generation of IL-12
   and IFN-γ and suppressing tumor infiltrating T cells ([112]47).
   PD-L1-mediated immunosuppression may also involve Treg.

   In contrast, PD-L1 shows negative correlations with CD8^+ cytotoxic T
   cells, T follicular helper cells (Tfh), gamma delta (γδ) T cells (Tgd),
   and B cells. CD8^+ T cells represent the major tumoricidal T
   lymphocyte. The immunosuppressive TME is feathered by the exhaustion,
   anergy, and apoptosis of CD8^+ T cells ([113]48). Tfh belongs to CD4^+
   T cells which play a critical immune protective role via facilitating B
   cell generating antibody ([114]49). Tgd produces various cytokines and
   chemokines (IFN-γ, TNF-α, IL-17) and can lysate infected or malignant
   cells ([115]50). Thus, the immunosuppression mediated by PD-L1 may
   involve in a broader range of anti-tumor lymphocytes.

   Genes that highly expressed in PD-L1^high GBMs enriched in multiple
   critical polarizing-related signalling pathways. For instance, The
   classical activation of macrophages (M1) is induced by LPS and
   interferon-gamma (IFN-γ) ([116]51). NF-kappa B is a critical
   transcription factor that transduces activation signals from LPS/TLR4
   ([117]52). Meanwhile, Shen J et al. also showed that NF-kappa B
   mediated IL-17 induced M2 macrophage polarization ([118]53). IL-10
   represents one of the canonical M2 stimuli. Upon ligating with its
   receptor IL-10R, IL-10 activates the transcription factor STAT3, which
   suppresses the production of pro-inflammatory cytokine ([119]54,
   [120]55). The key role of mTORC1 in macrophage polarization has been
   reported, constitutive mTORC1 activation impaired the M2 polarization
   and increase pro-inflammatory response ([121]56); depletion of mTORC1
   decreased inflammation and pathogenic immune response during infection
   ([122]57).

   Translationally, targeting PD-L1 may represent a promising approach to
   re-educate TAM towards the anti-tumor M1 phenotype. Inhibition of PD-L1
   would, therefore, abrogate the immune-suppression induced by M2
   macrophage. It has been reported that PD-L1 involved in constitute
   negative signals pathways and led to immune-suppressive phenotypes in
   macrophages. Inhibition of PD-L1 promoted the proliferation, survival,
   and activation of macrophages ([123]18). A similar remodeling of
   intratumoral macrophages was observed in a murine sarcoma model treated
   with anti-PD1/anti-CTLA-4 antibodies ([124]17). Our study and others
   imply that targeting the PD-1/PD-L1 axis may yield additional
   anti-cancer effects mediated by TAM polarization. This notion can be
   exploited in the treatment of GBM since the combinations of PD-L1 and
   PD-1 antibodies have shown potent anti-GBM efficacy in pre-clinical
   studies ([125]58–[126]60).

   Experimental studies are needed to validate the findings of this study.
   Ex vivo profiling of the cell components in GBM tissues would provide
   valuable information of TME. In addition, the correlation analysis can
   only provide preliminary evidence of the relationship rather than
   determine the causal relationship between PD-L1 and M2 macrophages. One
   of the major challenges of exploiting PD-L1 for prognosis prediction in
   GBM is to determine the positive criteria and cut-off value of PD-L1
   expression, which necessitates large-scale studies. Lastly, it remains
   elusive whether M2-TAM polarization causally induces PD-L1 expression
   or vice versa, further studies are needed to delineate the relationship
   between PD-L1 and macrophage polarization.

   This study provides preliminary evidence that PD-L1 is intimately
   correlated the infiltration and M2-polarization of macrophages. This
   notion represents an underappreciated immunosuppressive mechanism in
   the context of GBM. One brave but reasonable speculation is that PD-L1
   inhibitors may enhance the efficacy of the prevalent PD-1 antibodies in
   GBM. It would be worthy to evaluate the efficacy of combining PD-L1 and
   PD-1 antibodies in future clinical trials.

Data Availability Statement

   The original contributions presented in the study are included in the
   article/[127] Supplementary Material . Further inquiries can be
   directed to the corresponding authors.

Author Contributions

   ZYZ and MG designed the study and wrote the manuscript. HZ designed the
   study, interpreted the results, and revised the manuscript. XL and MG
   performed the analysis and prepared the figures. BC, SZ, and ZTZ
   designed and supervised the study. All authors contributed to the
   article and approved the submitted version.

Funding

   This work was supported by the funding: Basic research of Shenzhen
   Science and technology plan project (No. JCYJ2017306091310788).

Conflict of Interest

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest.

Supplementary Material

   The Supplementary Material for this article can be found online at:
   [128]https://www.frontiersin.org/articles/10.3389/fimmu.2020.588552/ful
   l#supplementary-material
   [129]Click here for additional data file.^ (18.6KB, docx)
   [130]Click here for additional data file.^ (26.6KB, xlsx)
   [131]Click here for additional data file.^ (17.6KB, xlsx)
   [132]Click here for additional data file.^ (21KB, docx)
   [133]Click here for additional data file.^ (11.7KB, xlsx)
   [134]Click here for additional data file.^ (146.3KB, pdf)

References