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. 2021 May 7:12:637146.
doi: 10.3389/fimmu.2021.637146. eCollection 2021.

TIGIT and PD-1 Immune Checkpoint Pathways Are Associated With Patient Outcome and Anti-Tumor Immunity in Glioblastoma

Itay Raphael  1 , Rajeev Kumar  1 , Lauren H McCarl  1 , Karsen Shoger  1 , Lin Wang  2 , Poorva Sandlesh  1 , Chaim T Sneiderman  1 , Jordan Allen  1 , Shuyan Zhai  3 , Marissa Lynn Campagna  1 , Alexandra Foster  1 , Tullia C Bruno  4 , Sameer Agnihotri  1 , Baoli Hu  1 , Brandyn A Castro  5 , Frank S Lieberman  6 , Alberto Broniscer  7 , Aaron A Diaz  2 , Nduka M Amankulor  1 , Dhivyaa Rajasundaram  7 , Ian F Pollack  1 , Gary Kohanbash  1   4
Affiliations

TIGIT and PD-1 Immune Checkpoint Pathways Are Associated With Patient Outcome and Anti-Tumor Immunity in Glioblastoma

Itay Raphael et al. Front Immunol. .

Abstract

Glioblastoma (GBM) remains an aggressive brain tumor with a high rate of mortality. Immune checkpoint (IC) molecules are expressed on tumor infiltrating lymphocytes (TILs) and promote T cell exhaustion upon binding to IC ligands expressed by the tumor cells. Interfering with IC pathways with immunotherapy has promoted reactivation of anti-tumor immunity and led to success in several malignancies. However, IC inhibitors have achieved limited success in GBM patients, suggesting that other checkpoint molecules may be involved with suppressing TIL responses. Numerous IC pathways have been described, with current testing of inhibitors underway in multiple clinical trials. Identification of the most promising checkpoint pathways may be useful to guide the future trials for GBM. Here, we analyzed the The Cancer Genome Atlas (TCGA) transcriptomic database and identified PD1 and TIGIT as top putative targets for GBM immunotherapy. Additionally, dual blockade of PD1 and TIGIT improved survival and augmented CD8+ TIL accumulation and functions in a murine GBM model compared with either single agent alone. Furthermore, we demonstrated that this combination immunotherapy affected granulocytic/polymorphonuclear (PMN) myeloid derived suppressor cells (MDSCs) but not monocytic (Mo) MDSCs in in our murine gliomas. Importantly, we showed that suppressive myeloid cells express PD1, PD-L1, and TIGIT-ligands in human GBM tissue, and demonstrated that antigen specific T cell proliferation that is inhibited by immunosuppressive myeloid cells can be restored by TIGIT/PD1 blockade. Our data provide new insights into mechanisms of GBM αPD1/αTIGIT immunotherapy.

Keywords: MDSCs; PD1; TIGIT; gene network analyses; glioblastoma; immunotherapy; myeloid suppressor cell.

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Conflict of interest statement

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.

Figures

Figure 1
Figure 1
Immune-checkpoint receptor genes associated with GBM patient outcome. TCGA patient survival data obtained from cBioPortal, and patients were grouped based on gene expression z-scores to upregulated expression (z ≥2; red line) or no change expression (z <2; green line). The (A) overall survival rate and (B) disease free survival rate, were plotted using Kaplan-Meier survival curves. P values reflect one-way ANOVA with Kruskal Wallis comparison test. n=153.
Figure 2
Figure 2
Immune-checkpoint ligand genes associated with GBM patient outcome. TCGA patient survival data obtained from cBioPortal, and patients were grouped based on gene expression z-scores to upregulated expression (z ≥2; red line) or no change expression (z <2; green line). The (A) overall survival rate, and (B) disease free survival rates were plotted using Kaplan-Meier survival curves. P values reflect one-way ANOVA with Kruskal Wallis comparison test. n=153.
Figure 3
Figure 3
TIGIT and PDCD1 (PD1) exhibit shared immunological networks but have unique regulatory pathways in GBM. GBM patients’ RNA-seq data was obtained from TCGA, transcript per million (TPM) normalized reads were calculated per each patient and Pearson’s correlation analysis was performed. n=153. Genes with a statistically significant (p<0.05 and FDR<0.05) positive correlation and negative correlation to TIGIT and PDCD1 expression were identified. (A) Pearson’s correlation analysis of TIGIT and PDCD1 expression. (B) Venn diagrams showing number of statistically significant correlated genes unique and overlapping within TIGIT and PD1 gene groups. (C) Number of statistically significant (p<0.05 and FDR<0.05) pathway enriched in each corresponding gene group. (D) Representative pathways which are positively and negatively enriched in the shared-gene group, TIGIT-associated group, and PD1-associated group. (E) Network analysis for Gene Ontology (GO) Immunological Processes associated with TIGIT and PDCD1 positively correlated gene network. Statistically significant gene correlation and pathway enrichments were corrected for false discovery rate (FDR) using Benjamini-Hochberg test.
Figure 4
Figure 4
Anti-TIGIT and anti-PD1 combination improves survival of GL261 glioma bearing mice. GL261 glioma cells were injected stereotactically in the caudate putamen of C56BL/6J mice followed by immunotherapy treatment starting on day 8 post tumor injection. Mice were evaluated for T cell responses on day 22 (biological endpoint) and for tumor size by day MRI on day 40. (A) Schematic showing induction of GL261 glioma in mice following treatment regimen using anti-PD1 and anti-TIGIT immunotherapies. (B) Survival curves with Log-rank (Mantel-Cox) curve comparison test. Pooled data from 3 independent experiments. (C) Pooled data and representative MRI images of tumor growth in murine GL261 glioma model in anti-PD1/anti-TIGIT treated group and isotype (control) treated animals. Unpaired t test with Welch’s correction. n=5 per group. (D–F) Percentages (%) of CD45+ glioma-infiltrating CD4+ T cells (D), CD8+ T cells (E), and CD8+ granzyme B+ T cells (F), on day 22 following anti-TIGIT/anti-PD1 immunotherapy. Representative data of 3 experiments. n=5 per group. One-way ANOVA with multiple comparisons test corrected for false discovery rate (FDR) using Benjamini-Hochberg test. P values are as followed: *≤0.05, **≤0.01, ***≤0.001. NS, not significant.
Figure 5
Figure 5
Anti-PD1/TIGIT immunotherapy is associated with altered myeloid-derived suppressor cells (MDSCs) in GL261 murine model. GL261 glioma cells were injected stereotactically in the caudate putamen of C56BL/6J mice followed by immunotherapy treatment starting on day 8 post tumor injection, and the frequencies of MDSCs were determined on day 22. (A) Representative flow cytometry plots showing gating strategy of PMN MDCSs and Mo MDCSs based on the expression of Gr1 and CD11b. (B–D) Percentages (%) of CD45+ glioma-infiltrating total MDSCs (CD11b+ Gr1+) (B), PMN MDSCs (CD11b+ Gr1high) (C), and Mo MDSCs (CD11b+ Gr1low) (D), on day 22 following treatment. (E) Ratios of tumor infiltrating CD8+ T cell to total MDSCs. Representative data of 3 experiments. n=5 per group. One-way ANOVA with multiple comparisons test corrected for false discovery rate (FDR) using Benjamini-Hochberg test. P values are as followed: *≤0.05, **≤0.01, NS, not significant.
Figure 6
Figure 6
PD1, PD-L1 and TIGIT-ligands are expressed on myeloid suppressor cells in GBM and contribute to T cell dysfunction. Single cell (sc) RNA-seq analysis was performed on myeloid cells from GBM patients. (A) UMAP clustering and expression (z-scores) of suppressive myeloid cell markers. (B) Expression z-scores of PD1/TIGIT-associated checkpoint molecules in the scRNA-seq clusters. (C–E) Healthy donor (HD) PBMCs and GBM patient PBMCs and TILs analyzed flow cytometry for myeloid cells, T cells, and IC markers. n = 4 HD; n = 5 GBM patients. (C) Representative flow cytometry plots and percentages (%) of CD11b+ CD33+ myeloid cells. (D) Representative histograms and mean fluorescence intensity (MFI) of PD1, PD-L1, PVR, and CD226 on CD11b+ CD33+ cells. (E) MFI of PD1, PD-L1, PVR, and CD226 on CD8+ T cells and CD4+ T cells. (F) T cell proliferation assay of murine hGP100-reactive CD8+ T cells cultured with immunosuppressive myeloid cells with αTIGIT and αPD1. Representative histogram plots and percentages (%) of proliferated CD8+ T cells at different culture conditions as indicated in the table lagend. n=4 per group. One-way ANOVA with Tukey multiple comparisons correction. ns, not significant. p = *<0.05, **<0.01, ***<0.001, ****<0.0001.
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