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

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.

Full text

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
and Gary Kohanbash
1,4
*
1
Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, United States,
2
Departments of Neurological
Surgery, University of California, San Francisco, CA, United States,
3
University of Pittsburgh Medical Center (UPMC) Hillman
Cancer Center Biostatistics Facility, University of Pittsburgh, Pittsburgh, PA, United States,
4
Department of Pediatrics,
University of Pittsburgh, Pittsburgh, PA, United States,
5
Departments of Neurology, University of Chicago, Chicago, IL,
United States,
6
Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States,
7
Department of Pediatrics,
Division of Health Informatics, Children’s Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh,
PA, United States
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 aPD1/aTIGIT immunotherapy.
Keywords: glioblastoma, immunotherapy, PD1, TIGIT, MDSCs, myeloid suppressor cell, gene network analyses
Frontiers in Immunology | www.frontiersin.org May 2021 | Volume 12 | Article 6371461
Edited by:
Sarah E. Church,
NanoString Technologies,
United States
Reviewed by:
Shane Curran,
EMD Millipore, United States
Patrick Wen,
Dana–Farber Cancer Institute,
United States
*Correspondence:
Gary Kohanbash
gary.kohanbash2@chp.edu
†
These authors have contributed
equally to the work
Specialty section:
This article was submitted to
Cancer Immunity
and Immunotherapy,
a section of the journal
Frontiers in Immunology
Received: 02 December 2020
Accepted: 12 April 2021
Published: 07 May 2021
Citation:
Raphael I, Kumar R, McCarl LH,
Shoger K, Wang L, Sandlesh P,
Sneiderman CT, Allen J, Zhai S,
Campagna ML, Foster A, Bruno TC,
Agnihotri S, Hu B, Castro BA,
Lieberman FS, Broniscer A, Diaz AA,
Amankulor NM, Rajasundaram D,
Pollack IF and Kohanbash G (2021)
TIGIT and PD-1 Immune Checkpoint
Pathways Are Associated With Patient
Outcome and Anti-Tumor
Immunity in Glioblastoma.
Front. Immunol. 12:637146.
doi: 10.3389/fimmu.2021.637146
ORIGINAL RESEARCH
published: 07 May 2021
doi: 10.3389/fimmu.2021.637146

INTRODUCTION
Malignant gliomas are the most common primary malignant
central nervous system (CNS) tumor in adults (1). Glioblastoma
(GBM) are highly aggressive brain cancers and the most
common type of high-grade glioma (HGG) (2). The current
standard of care for GBM patients include a combination of
surgery, radiation therapy, and chemotherapy. However, even
with standard of care, the median overall survival times remain
less than two years (3,4). Therefore, identification of novel GBM
treatment strategies is warranted.
Theimmunesystemcanmountspecific and durable
responses against tumors (5,6). However, cancer cells, tumor-
myeloid cells, and tumor infiltrating regulatory T cells (Tregs)
can express negative regulators of the immune system including
immune checkpoint (IC) molecules, thereby limiting effective
anti-tumor immunity (7,8). In recent years, the development of
immunoregulatory drugs that block IC pathways, such as PD1/
PD-L1 inhibitors, have emerged as a promising treatment
strategy against a variety of malignancies, including melanoma,
lung cancers, and head and neck cancers (9,10). Although anti-
PD1/PD-L1 immunotherapy shows durable response in other
types of malignancies, its efficacy is limited to approximately 10%
of GBM patients (11–13), thus highlighting the need for more
effective and novel approaches, including the combination of
additional IC inhibitors (ICIs) to target several IC
pathways simultaneously.
T cell immunoreceptor with Ig and ITIM domain (TIGIT) is
an IC receptor expressed on activated T cells, NK cells, and Tregs
(14). Elevated TIGIT expression on TILs correlates with reduced
TIL cytokine production and poor overall survival (14). TIGIT
binds with high-affinity to CD155 (PVR) and with low-affinity to
CD112 (PVRL2; nectin-2) which are expressed in the tumor
microenvironment (TME) by antigen presenting cells (APCs)
and tumor cells (15). The binding of TIGIT to CD155 suppresses
the activation of TILs. CD155 can also bind CD226, which is
expressed on T cells and provides a stimulatory signal which
promotes T cell activation, thus competing with TIGIT binding
to CD155. However, TIGIT has a significantly greater affinity to
CD155 than CD226 (15,16). While blocking the interaction
between TIGIT and CD155 has been identified as a potential
therapeutic target in treatment of malignancies, its effects in
GBM are poorly understood (17).
MDSCs are myeloid-lineage regulatory cell that act as negative
immune regulators in the TME (18). MDSCs consist of two major
subtypes based on phenotype:PMN-MDSCsmatchedwith
granulocytes, and Mo-MDSC resembling inhibitory monocytes
(18). In mice, PMN-MDSC are defined a CD11b
+
Ly6C
low
Ly6G
high
,
and Mo-MDSC as CD11b
+
Ly6C
high
Ly6G
low
whereas in human,
PMN-MDSC are defined as CD14
-
CD11b
+
CD33
+
CD15
+
and Mo-
MDSC as CD14
+
CD11b
+
HLA-DR
low
. Some studies have shown that
increased presence of MDSCs within the TME is related to poor
clinical outcome in patients treated with ICI (19). Consequently,
reduced infiltration of MDSCs in TME has shown enhanced anti-
tumor efficacy of ICI in pre-clinical tumor models (20,21).
In order to identify putative IC targets in GBM, we first
analyzed of The Cancer Genome Atlas (TCGA) dataset and
identified IC molecules whose expression is associated with poor
survival in GBM patients. We found that upregulated expression
of PD1 and TIGIT, but not other ICs or their ligands, are
associated with reduced patient survival. We demonstrate that
dual treatment with aPD1/aTIGIT prolonged survival in a
murine GBM model, at least in part by targeting MDSCs.
Together, our data provide new insights into mechanisms of
immunotherapy in GBM.
MATERIALS AND METHODS
TCGA Data Analysis
The Cancer Genome Atlas (TCGA) database was used to assess
survival of patients with GBM in accordance with gene
expression levels of immune checkpoint molecules. Survival
analysis was performed through the cBioPortal platform using
a z-score of 1.0 for all checkpoint receptors and their respective
ligands. The correlation of checkpoint gene expression with z
score >2.0 was considered upregulated expression. Kaplan-Meier
(KM) survival curves were generated to determine overall
survival (OS) and disease-free survival (DFS).
RNA Sequencing (RNA-seq) and
Pathway Analysis
The study uses RNA-seq datasets of GBM tissue from The Cancer
Genome Atlas (TCGA), and the raw expression files were
downloaded from TCGA Genomic Data Commons (GDC) Data
Portal. Reads were quantified and mapped to human genome
(Ensembl GRCh38 Homo sapiens) Salmon version 0.8.2 (22).
Transcript-per-kilobase-million (TPM) were used for gene-
correlation and pathway analyses. Pearson’s rank correlation
analysis was performed for TIGIT and PDCD1. Genes with
statistically significant correlation (p value < 0.05 and false
discovery rate (FDR) p < 0.05) were used to determine pathway
enrichment using Gene Ontology (GO) (23)forReactome
(version 65 Released 2020-11-17) (24)andPANTHER
Overrepresentation Test (Released 20200728) (25)curated
pathways. Pathway enrichment cutoff was set for p<0.05 using
Fisher’s Exact test and FDR p<0.05 and enrichment scores greater
than 1. Immunological network analysis was performed using
ClueGo v2.5.7 (26) and Cytoscape 3.8 (27) with the current
parameters: GO ImmuneSystemProcess EBI-UniPort, GO term
fusion, network specificity was set to medium-detailed, pathways’
p value <0.05 with Benjamini-Hochberg correction. Positively and
negatively correlated genes were used to determine positively and
negatively associated networks, respectively.
Single Cell RNA-seq (scRNA-seq) Analysis
scRNA-seq data were obtained from Wang et al. (28)and
processed as described previously (28). Briefly, the neoplastic
cells and non-neoplastic cells were separated via copy number
variation (CNV). The presence/absence of CNVs was assessed
with CONICSmat (29), and the primary cell types of non-
neoplastic cells (i.e. monocytes/myeloid) were identified by
using ELSA (30). CD11b
+
monocyte/myeloid cell population
was sampled for further analysis using Seurat package on
Raphael et al. aTIGIT/aPD1 Immunotherapy in GBM
Frontiers in Immunology | www.frontiersin.org May 2021 | Volume 12 | Article 6371462

Bioconductor (R) (31). Following Elbow Plot analysis, the
number of principal components analysis (PCA) was set up to
3 with 0.2 resolution for UMAP clustering.
Cell Lines
GL261 cells were cultured in Dulbecco’s Modified Eagle Medium
(DMEM, Gibco) supplemented with 10% fetal bovine serum
(FBS, Hyclone), 1x antimycotic-antimycotic solution (Gibco),
1% L-glutamine, ß-mercaptoethanol, 200 µg sodium pyruvate,
and 1x NEAA. Cell lines were kept in a 37°C humidified
incubator with 5% CO2. Cell number and viability were
measured using the trypan exclusion method (0.4% trypan
Blue, Gibco).
Mice
C57BL/6J mice (Stock No. 000664) and B6.Cg-Thy1 a/Cy Tg
(TcraTcrb)8Rest/J (PMEL; Stock No. 005023 (32)) were purchased
from the Jackson Laboratory and housed in animal facility of the
UPMC Children’s Hospital of Pittsburgh. Animals were kept in the
facility for at least one week prior to performing any procedures to
minimize stress-related symptoms. 5–6-week-old female were used
in the experiments. All experiments were conducted following
protocols approved by the University of Pittsburgh Institutional
Animal Care and Use Committee (IACUC).
Intracranial Tumor Model and
Antibodies Treatment
Mice were anesthetized by mask inhalation of 1.5% vaporized
isoflurane throughout the surgical procedure. GL261 cells
(100,000 cells in 2 mL DPBS) were stereotactically implanted
into the caudate nucleus using the following coordinates
relative to bregma: x = +2.5 mm (lateral), y = +1.5 mm
(anterior), and z = 2-3.0 mm (inferior) (33). MRI was
performed 7 days post tumor cell implantation to confirm
tumor presence, and again at day 40 to measure tumor size
growth in control-treated animals and aTIGIT & aPD1 dual
blockade-treated animals. All mice were randomized prior to
their separation into treatment groups. IgG1 (clone MOPC-21),
IgG2a (clone RTK2758), aPD1 (clone RMP1-14) and aTIGIT
(clone 1G9) antibodies were obtained from Bio-X-Cell
Antibodies were dosed at 200 mg per animal and administered
via intraperitoneal (i.p.) injection, as described previously (34)
for both the survival and immunophenotyping studies. Anti-
TIGIT and anti-PD1 treatments were given on the same day
twice per week starting on day 8, for a total of 7 doses. Mice were
euthanized after receiving seven doses of immune-checkpoint
inhibitor therapeutic antibodies (aTIGIT/aPD1) to investigate
biological endpoint and immune cell phenotype. Mice were
monitored for weight loss and morbidity symptoms for
survival study. All survival experiments were repeated in
triplicate with at 4-6 animals per group.
Mouse Immune Cell Isolation
For the biological endpoint study, mice were euthanized on day
22 (CO2 asphyxiation followed by cervical dislocation) post-
tumor inoculation. Brains were dissected and processed for flow
cytometry analysis. Brains were homogenized in Collagenase IV
Cocktail (3.2 mg/mL collagenase type IV, 1.0 mg/mL
deoxyribonuclease I, 2 mg/mL Soybean Trypsin Inhibitor).
Samples were centrifuged for 5 minutes at 1500 rpm, followed
by red-blood cell (RBC) lysis using ACK lysing buffer (Lonza).
Cell viability was measured using the trypan blue exclusion
method. Cells were resuspended in FACS Buffer (DPBS with
1% BSA) and centrifuged for 5 minutes at 300 g, after which the
pellet was resuspended in FACS buffer. The cells were then
stained with appropriate antibodies and acquired on a BD LSR
Fortessa flow cytometer.
Isolation of TILs From GBM Patients
Patient-derived GBM tissue was dissociated, using Accutase
(1:10), to form a single cell suspension (SCS). SCS was
centrifuged at 1500rpm for 5 mins. The pellet was resuspended
in 5mL of 70% Percoll solution. A Percoll gradient of 5mL of 37
and 5mL of 30% Percoll sequentially, was then overlaid onto the
tumor-containing 70% Percoll solution. The tumor gradient
solution was centrifuged at 2400 rpm for 20 minutes. Immune
cells at the interphase were collected and washed once with PBS.
The cells were then stained with appropriate antibodies and
acquired on a BD LSR Fortessa flow cytometer.
Isolation of PBMCs
Peripheral blood samples were collected in preservative-free
heparin tubes (10 U/mL) and layered into an equal volume of
Ficoll-Hypaque density gradient solution (Amersham Pharmacia
Biotech Ltd., Little Chalfont, UK). Samples were then centrifuged
at 2250 rpm for 20 minutes. After removal of the top layer
(plasma), the mononuclear cells (PBMCs) were collected and
washed twice with PBS (Hyclone™,GEHealthcare).Cell
viability was determined by trypan blue exclusion and
exceeded 95%. The cells were then stained with appropriate
antibodies and acquired on a BD LSR Fortessa flow cytometer.
Generation of Immunosuppressive Myeloid
Cells From Bone Marrow (BM)
Immunosuppressive myeloid cells were generated as described
previously (35). Briefly, tibia and femur-derived BM cell from
C57BL/6j mice were cultured in complete DMEM media
supplemented with 10 ng/ml each of GM-CSF and IL-4. On
day 3, floating cells were removed, and medium was replaced
with 1:1 complete DMEM media to GL261 tumor-derived
conditioned media (TCM), supplemented with GM-CSF and
IL-4 for 3 additional days prior to use.
Suppression of T Cell Proliferation Assay
T cell suppression assay was performed as described previously
(21,36). In brief, hGP100-restricted (B6.Cg-Thy1a/Cy TCR-
transgenic) CD8
+
T cells were isolated from PMEL-mice (32)
using magnetic bead separation (Miltenyi Biotec) and labeled
with Cell-Trace proliferation dye (Invitrogen. Cat. No C34557)
according to the manufacture guidelines. Feeder cells (antigen
presenting cells) were generated from non-CD8
+
cell fraction
and were treated with 10 mg/ml of mitomycin at 37°C for 1 hour
to cease proliferation (37). T cells and feeder cells were co-
cultured with BM-derived immunosuppressive myeloid cells in
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the presence of 100 U/mL hIL-2 (PeproTech), 100 µg/mL
hGP10025-33 peptide (antigen), and 10 µg/mL of either IgG2a
(RTK2758) –as control, or aPD1 (RPMI14) and aTIGIT (1G9).
Cells were collected and analyzed on day 4 by flow cytometry.
Flow Cytometry
Prior to cell surface staining, samples were stained with cell
viability dyes (GhostDye or 7AAD) in PBS for 20 minutes in 4°C
and then washed with FACS buffer. For mouse immune cell
staining, the cell suspensions were blocked with 1% anti-mouse
Fc-receptor (CD16/CD32) in FACS buffer for 20 minutes, then
washed and stained with fluorescently labeled anti-mouse
antibodies for 45 minutes in FACS buffer at 4°C. TILs (n=5)
and PBMCs from 2 matched, 3 unmatched and 3 healthy donor
(HD) patient samples (n=8) were washed with PBS and stained
with cell-surface antibodies for 30 minutes at 4°C per the
manufacture guidelines. After staining, cells were washed with
FACS buffer and fixed with fixation buffer (BD Cytofix/
Cytoperm buffer). The cells were washed with FACS buffer,
resuspended in FACS buffer and analyzed by flow cytometry.
The antibody clones were purchased from BioLegend or
eBioscience and used for flow cytometry as follows: For mouse
cell staining: CD4 (GK1.5), CD8 (53-6.7), CD11b (M1/70), CD45
(30-F11), Gr-1 (RB6-8C5), CD3 (17A2, and 145-2C11),
Granzyme B (QA16A02). For human cell staining: CD45
(2D1), CD11b (ICRF44), CD3 (C3e/1308), CD8 (OKT-8), PD-
1 (EH12.2H7), PD-L1 (MIH2), CD33 (WM53), CD226 (11A8),
TIGIT (A15153G), CD155/PVR (SKII.4). GhostDyes
(TONOBO) UV450 and Red-780, and 7AAD were used to
stain for cell viability (live/dead) according to the manufacture
guidelines. Gating was performed on live CD45
+
cells to
designate all immune cells. All samples were analyzed on a BD
LSRFortessa (BD Biosciences). Data were analyzed using BD
FACSDiva (BD Biosciences) and FlowJo V10 data analysis
software (FlowJo LLC).
Statistical Analysis and Software
Kaplan-Meier survival curves were generated to determine
survival and then compared using the log-rank Mantel Cox
test. One-way analysis of variance (ANOVA) with Kruskal
Wallis multiple comparisons test was used to compare assays
containing more than two groups. Statistical significance was
considered as p <0.05. Normal distribution was assumed unless
specified overwise in the text or figure legend. The analyses were
performed using GraphPad Prism 8 or Bioconductor (R
programing) on RStudio.
RESULTS
High Expression Level of Immune
Checkpoint Molecules Associated With
Overall Survival (OS) and Disease-Free
Survival (DFS) in GBM
To identify putative immunotherapy targets for GBM, we
evaluated the expression of IC genes and their ligands in RNA
sequencing (RNA-seq) data of 153 GBM tumor samples in the
TCGA database (38). We first assessed the correlation of 15
established IC gene expression levels with overall survival (OS)
and disease-free survival (DFS) (39,40). Upregulated expression
was defined as expression z score greater than 2. Our data
demonstrate that upregulated expression (red lines) of TIGIT
and PDCD1 (gene encoding PD1) were associated with poor
patient outcome and increased mortality as compared with
patients who had no change in TIGIT and PDCD1 RNA
expression (green lines) (Figures 1A, B). Upregulated ICOS
expression was also associated with reduced OS and DFS,
although the data did not reach significance (Figures 1A, B).
However, upregulated expression of other IC receptor genes,
including CTLA4,LAG3, TIM3 (HVAC1), BTLA4, and CD224
were not associated with changes in OS and DFS. Interestingly,
expression of genes for CD155 (PVR), PD-L1, and ICOS-L, the
ligands for TIGIT, PD1, and ICOS, respectively, was also
significantly associated with decreased OS and DFS, whereas
upregulated expression of other IC gene ligands did not affect
these parameters in GBM patients (Figures 2A, B). Although our
survival analysis assessed patients with elevated expressed based
on Z score (i.e. compared to mean expression of that gene), we
further examined the absolute expression of each gene to
determine the extent of therapeutic utility among all patients.
Our data show that a large portion of patients showed to have
physiologically relevant expression levels (TPM>1) of the genes
encoding to the checkpoint receptors PD-L1 (94%) and CD155
(PVR; 100%) (Supplemental Figure 1). Additionally, PD1 and
TIGIT were reported to be expressed by large frequencies of
GBM CD4
+
and CD8
+
TILs (34,41). Taken together these data
suggest that PD1/TIGIT-targeted therapy may be relevant for
many patients with GBM.
TIGIT and PD1 Are Co-Expressed, Share
Common Gene Networks, but Are Also
Associated With Distinct Pathways in GBM
Our data revealed that TIGIT/CD155 and PD1/PD-L1
checkpoint genes were significantly associated with GBM
clinical outcome, thus we next analyzed RNA-seq data from
these patients to identify genes and pathways which may be
involved with TIGIT and PD1 expression in GBM. Notably, the
expression of TIGIT and PDCD1 were significantly correlated
with each other (Figure 3A), suggesting a rationale for dual
blockade of these checkpoint molecules in GBM patients. Despite
their significant correlation, TIGIT and PDCD1 may be
associated with unique gene networks and pathways (42).
Therefore, we next interrogated the gene networks associated
with the expression of TIGIT and/or PDCD1 in GBM. We
identified a total of 6347 genes which correlated with TIGIT
and PDCD1 expression with high statistical significance (p<0.05
and FDR<0.05) (Figure 3B). While many genes correlated with
both TIGIT and PD1 expression, we also identified a large
number of genes and pathways uniquely correlated with either
TIGIT or PD1 (Figures 3B, C).
TIGIT/PD1 (shared)-associated pathways included immune
related pathways, such as Toll-like receptor (TLR) signaling,
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Frontiers in Immunology | www.frontiersin.org May 2021 | Volume 12 | Article 6371464

interleukins signaling (such as IL-10 and IL-2), TCR signaling
and T cell activation, and innate immune system pathways
(Figure 3D). Interestingly, TIGIT-associated pathways included
Treg development, MHC class I presentation, caspases and
death-receptors signaling, control of cell cycle transition,
regulation of TLR and Nf-kB signaling, and p53 regulation
(Figure 3D). PD1-associated pathways included cell motility,
oxidation and phagocytosis, IL-12 mediated Jak-STAT signaling,
MHC class II and antigen presentation, and EGF receptor
signaling (Figure 3D). Immunological network analysis showed
that many immune responses were strongly associated with the
expression of TIGIT and PD1, mostly T cell activating and
regulation of immunity, but also innate immune functions such
as leukocytes degranulation, and functions of macrophages and
dendritic cells (Figure 3E).
Together, these data suggest that upregulated expression of
TIGIT and PD1 may confer immunosuppression and tumor
aggression in GBM patients through both shared and distinct
pathways, and therefore targeting both these pathways may be
beneficial for improving clinical outcome of GBM patients.
Combination of aTIGIT and aPD1
Immunotherapy Increases Numbers of TIL
Cytolytic CD8
+
T Cells and Prolongs Long-
Term Survival of GBM-Bearing Mice
Our data suggest a beneficial outcome for IC blockade of TIGIT
and/or PD1 in GBM. To investigate this hypothesis, C57BL/6
mice were intracranially injected with syngeneic GL261 cells,
followed by 7 doses of immunotherapy with (1) isotype control
antibodies, (2) aPD1, (3) aTIGIT, or (4) a combination of
aTIGIT/aPD1 therapeutic antibodies, administered twice per-
week starting on day 8 post-tumor injection (Figure 4A).
Analysis of immune cells was performed uniformly across
groups on day 22 post-tumor implantation (biological
endpoint) followed by MRI analysis for tumor size on day 40
for control and dual aTIGIT/aPD1-treatment groups (Figure
4A). Control mice (Isotype; black line) displayed median survival
of 33 days (range: 29-51 days) with severe morbidity signs and
did not reach long-term survival endpoint (Figure 4B). While
aTIGIT monotherapy (green line) moderately improved
survival, treatment with aPD1 (blue line) or a combination
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.
Raphael et al. aTIGIT/aPD1 Immunotherapy in GBM
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treatment of aTIGIT/aPD1 (red line) significantly prolonged
animal survival as compared with isotype treated animals
(Figure 4B). The median survival of aTIGIT treatment was 34
days (range: 32-43 days) while aPD1 monotherapy (green line)
was 37 days (range 32-74 days). Notably, aTIGIT/aPD1 dual
treatment most significantly prolonged mice survival with
median survival of 48 days (range 39-74 days) (Figure 4B).
MRI analysis showed that in aTIGIT/aPD1 treated animals the
tumor size was significantly smaller than tumors in isotype-
treated animals (Figure 4C). These data confirm previous results
in which immunotherapy combination of aPD1 with aTIGIT
reduced tumor burden and improved survival of mice with
glioma (34).
To examine a mechanism by which the combination therapy
improved anti-tumor immunity, we explored the effect of
treatment on tumor-infiltrating lymphocytes (TILs) and their
cytolytic phenotype on day 22 post tumor implantation.
Although aPD1 monotherapy did not significantly affected the
percentages of CD4
+
TILs, treatment with either aTIGIT or
aTIGIT/aPD1 resulted in a significant increase of CD4
+
TILs as
compared with control animals (Figure 4D). Additionally, we
noted a significant increase in percentages of CD8
+
TILs in
tumors in animals treated with aPD1 or aTIGIT/aPD1, but not
in aTIGIT-monotherapy treated animals (Figure 4E). Analysis
showed that treatment with either aPD1 or aTIGIT
monotherapy resulted in a mild increase in the percentages of
CD8
+
granzyme-B
+
TILs, while this effect was significantly
increased in aTIGIT/aPD1 combination treatment
(Figure 4F). These data are complementary to previous results
showing that aPD1 or aTIGIT immunotherapy enhances the
expression of TNFaand IFNgin TILs from GBM (34) as well as
other cancers (43), and suggest that the therapeutic effect of
checkpoint blockade with aTIGIT/aPD1 may work through
distinct mechanisms to affect CD4
+
and CD8
+
TILs and
promote anti-glioma immunity.
Dual Blockade of TIGIT and PD1
Regulates MDSCs in GBM Murine Model
Previous reports have shown that MDSCs stimulate suppressive
mechanism to develop a pre-metastatic niche, promote tumor
growth, inhibit anti-tumor function of TILs, and negate
immunotherapy which results as resistance to IC blockade (44,
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.
Raphael et al. aTIGIT/aPD1 Immunotherapy in GBM
Frontiers in Immunology | www.frontiersin.org May 2021 | Volume 12 | Article 6371466

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.
Raphael et al. aTIGIT/aPD1 Immunotherapy in GBM
Frontiers in Immunology | www.frontiersin.org May 2021 | Volume 12 | Article 6371467

45). Furthermore, MDSCs have been shown to contribute to
immunosuppressive microenvironment in gliomas, including
GBM (45–47). MDSCs were shown to express PD-L1 (48).
Additionally, inhibition of TIGIT was reported to abrogate
MDSC immunosuppressive capacity in vitro (49). Together,
these data suggest that targeting PD1 and TIGIT pathways may
affect MDSCs in GBM. However, the effects of these checkpoint on
MDSC infiltration in gliomas are ill defined (45). We, therefore,
investigated if MDSCs were affected by immunotherapy in our
model on day 22 (biological endpoint; end of immunotherapy).
Shown in Figure 5A,gliomainfiltrating MDSC subsets were
characterized by the expression of Gr1 and CD11b as follows:
PMN MDSCs were defined as CD11b
+
Gr1
high
cells, whereas Mo
MDSCs were defined as CD11b
+
Gr1
intermediate (int)
cells (18). We
evaluated the levels of MDSCs and their subsets following
immunotherapy (Figure 5A; lower panel). Our data show, that
compared with isotype treatment, both aTIGIT monotherapy and
dual blockade of TIGIT & PD1 significantly reduced the
frequencies of GL261 glioma infiltrating MDSCs
(CD45
+
CD11b
+
Gr1
+
cells), most strikingly for aTIGIT/aPD1
combination therapy (Figure 5B). Treatment with aPD1
showed a trend of decreasing MDSC percentages, though the
results did not achieve statistical significance (Figure 5B). Analysis
of MDSC subsets revealed that aTIGIT/aPD1 dual treatment
significantly decreased the frequencies of PMN MDSCs (Figure 5C),
while Mo MDSCs levels remained mostly unaltered (Figure 5D).
Furthermore, we observed a statistically significant increase in ratios
of CD8
+
T cells over total MDSCs in tumors when mice were treated
with aTIGIT monotherapy or aTIGIT/aPD1 combination therapy
(Figure 5E). Blockade of PD1 alone did not significantly increase the
CD8
+
T cells/MDSCs ratios (Figure 5E). Together, our data reveal a
mechanism of TIGIT/PD1 blockade in glioma and suggest distinct
roles of these ICs on MDSC subsets and in regulating
tumor immunity.
Myeloid Cells Upregulate PD-L1 and
TIGIT-Ligands in GBM Which Inhibit
T Cell Functions
We next evaluated the potential of aTIGIT/PD1-immunotheraphy
to impact MDSC-like cell in GBM patients. For that, we first
analyzed single-cell (sc)RNA-seq data of CD11b
+
myeloid cells
from GBM patients (28) for the expression of PD1, TIGIT, and their
ligands. Myeloid cells were confirmed based on the expression of
CD45 (PTPRC), CD14,andCSF1R genes (Figure 6A)(28,50).
Of note, we identified 4 unique clusters of tumor-associated
myeloid/macrophage cells (TAMs) in GBM, which had distinct
expression profiles (Figure 6A and Supplemental Figure 2A). The
expression of CD33, an hematopoietic progenitor cell marker which
commonly used to identify pan-MDSCs (51), was distributed
throughout the TAM clusters. Nonetheless, the expression of
inhibitory and suppressive markers, including genes for IL-4R,
IL-10, IL-6, VEGFA, CCL2 and IL-1b(Figure 6A and
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 ttest 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. Pvalues are as followed: *≤0.05, **≤0.01, ***≤0.001. NS, not significant.
Raphael et al. aTIGIT/aPD1 Immunotherapy in GBM
Frontiers in Immunology | www.frontiersin.org May 2021 | Volume 12 | Article 6371468

Supplemental Figure 2A), were mostly expressed by cluster 0,
suggesting that these cells had a tumor-promoting and immune-
suppressing functions, which resemble MDSC-like cells (52).
Interestingly, PDCD1 (PD1), CD274 (PD-L1), and CD226,were
also predominantly expressed by cluster 0 (Figure 6B and
Supplemental Figure 2B). CD155 (PVR) was also associated and
expressed by cluster 0, although less frequent than CD226, PD1, and
PD-L1 (Figure 6B and Supplemental Figure 2B). PVRL2, another
inhibitory receptor that bind to TIGIT (53), was also expressed by
TAMs, with highest expression levels in cluster 0 (Figure 6B and
Supplemental Figure 2B). We did not detect TIGIT expression in
TAMs by scRNA-seq (Figure 6B). Additionally, we noted high
expression and associated of ICOS-L with cluster 0 (Supplemental
Figure 2B), which interestingly was also associated with GBM
patientOSandDFS(Figures 2A, B). These data suggest that
immunosuppressive TAMs, such as MDSCs, express genes for PD1,
PD-L1 and TIGIT-ligands. Therefore, evaluated the protein
expression of these markers on CD45
+
immune cells in patient
derived GBM tissue and PBMCs, and healthy donor (HD) PBMCs.
The frequencies of CD11b
+
CD33
+
cells in GBM TILs were on
average higher compared to GBM PBMCs, and were significantly
higher than of HD PBMCs (Figure 6C and Supplemental Figure
2C), suggesting that MDSCs are present at high levels in GBM and
could contribute to the TME immunosuppression (46). Consistent
with our scRNA-seq data, CD11b
+
CD33
+
cells had higher
expression levels of PD1, PD-L1, PVR, and CD226 in TILs, as
compared with CD11b
+
CD33
+
cells from PBMCs of GBM patients
and HDs, most notably for PD-L1 and CD226 expression (Figure
6D). Moreover, as compared to PBMCs samples we noted an
increased expression of PD1, PD-L1 and TIGIT on CD8
+
TILs,
while CD4
+
TILs had mostly upregulated expression of TIGIT
(Figure 6E). Together, these data suggest that CD11b
+
CD33
+
TAMs may promote immunosuppressive functions at-least in part
through expression of PD1/TIGIT-checkpoint ligands. To test this
hypothesis, hGP100-restricted naïve T cells isolated from pmel mice
(32) were activated in vitro with hGP100
25–33
peptide and feeder cell
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
+
Gr1
high
)(C), and Mo MDSCs
(CD11b
+
Gr1
low
)(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. Pvalues are as followed: *≤0.05,
**≤0.01, NS, not significant.
Raphael et al. aTIGIT/aPD1 Immunotherapy in GBM
Frontiers in Immunology | www.frontiersin.org May 2021 | Volume 12 | Article 6371469

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 aTIGIT and aPD1. 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.
Raphael et al. aTIGIT/aPD1 Immunotherapy in GBM
Frontiers in Immunology | www.frontiersin.org May 2021 | Volume 12 | Article 63714610

(antigen presenting cells; APCs) in the presence of bone-marrow
derived myeloid cells (putatively MDSC-like cells) cultured in
GL261 cell-derived tumor-conditioned media and treated with
aTIGIT and aPD1. Our data indicated that glioma-conditioned
immunosuppressive myeloid cells significantly inhibited CD8
+
T
cell proliferation, which was restored by the addition of aTIGIT or
aPD1 (Figure 6F). In summary, these new data suggest that
immunosuppressive myeloid cells, and presumably MDSCs,
suppress anti-tumor immunity by inhibiting antigen-specificT
cell function in GBM, at least in part via TIGIT and PD1
pathways, which may have major implication to patient
treatments by immunotherapy.
DISCUSSION
In cancer, the dysregulationof immune checkpoints, such as TIGIT
and PD1, is directly associated with tumor progression and
enhanced immune evasion (54–57). In the past decade, an
increasing number of IC-targeted immunotherapies have proven
to have substantially beneficial outcomes for a wide variety of
malignancies and provide durable tumor immunity and long-
term patient survival (58). Nonetheless, evidence supporting the
efficacy of IC immunotherapy in glioma remains insufficient (11).
In this study we interrogated RNA-seq data of 153 GBM
patients in the TCGA database to identify IC genes whose
upregulated expression is associated with poor outcome. We
found that upregulated expression of TIGIT and PDCD1, as well
as their ligands CD155 (PVR) and PD-L1 (respectively), was
significantly correlated with poor DFS and OS. Other checkpoint
pathways with inhibitors currently in development, including
LAG3 and TIM3, were not associated with either patient OS or
DFS. We posit that interrogation of TIGIT and PD1 -associated
regulatory gene networks in responding and non-responding GBM
patients would be of great interest to identify biomarker of ICIs.
PD1isanimmunecheckpointexpressedonactivated
immune cells, including CD4
+
and CD8
+
TILs. The binding of
PD1 to its ligand, PD-L1 on tumor and stromal cells, delivers a
signal that inhibits effector functions such as cytokine production
and cytolytic activity in the tumor microenvironment (TME)
(59). PD-L1, like many other IC ligands, is hijacked by tumor
cells in order to evade anti-tumor immunity. Accordingly,
blockade of PD1/PD-L1 pathway with antibodies have been
shown to improve T cell function and reduce tumor burden in
several types of tumors (60,61). Previous studies demonstrated
elevated levels of TIGIT expression in human gliomas (34);
however, the therapeutic effects of targeting this pathway in
glioma patients remain poorly understood. TIGIT has recently
emerged as an important checkpoint that is also expressed by
activated CD4
+
and CD8
+
TILs. TIGIT has a higher binding
affinity to CD155 than CD226; thus, once TIGIT is upregulated,
the inhibitory signal becomes more dominant (62–64). Similarly,
interfering with TIGIT/CD155 interaction has been identified as
a potential therapeutic target for malignancies (65). Interestingly,
blocking PD1/PD-L1 signaling was shown to increase the
expression of TIGIT on Tregs in head and neck squamous cell
carcinoma (HNSCC) patients (49), suggesting a resistant
mechanism for aPD1 immunotherapy mediated by TIGIT.
Accordingly, our data support prior studies that combination
immunotherapy treatment targeting the PD1 and TIGIT
pathways leads to prolonged survival in GBM murine models
(34,66). Furthermore, we showed that aTIGIT/aPD1 dual
treatment increased the numbers of CD8
+
TILs and enhanced
their lytic function in GBM, supporting previous findings that
this treatment can enhances IFN-gexpression in glioma-
infiltrating T cells (34). Importantly, our data indicate that
combined immunotherapy with aTIGIT/aPD1 affects MDSCs
in the glioma TME.
MDSCs are a heterogenous population of immature myeloid
cells that contribute to tumor growth, accumulation of
additional immunosuppressive cells, and immunotherapy
resistance (66,67). Furthermore, MDSCs express large amounts
of immunosuppressive factors, multiple anti-inflammatory
cytokines and chemokines that directly stimulate tumor
progression (68). Notably, a long-term survival study in
melanoma patients showed that elevated numbers of MDSCs
were highly associated with ICI resistance and negative
therapeutic outcomes (69). Additionally, elevated numbers of
tumor infiltrating MDSCs are correlated with CD8
+
TIL
dysfunction and induced tumor cell expression of IC ligands;
thus, MDSCs may promote and sustain an immunosuppressive
glioma TME (70–72). Here, we showed that TIGIT blockade
stimulated anti-tumor cytotoxic T cell (CTL) responses and
reduced the immunosuppressive MDSCs in a murine model of
GBM. Moreover, we found that PMN MDSC, but not Mo MDSC
accumulation was reduced by dual blockade of TIGIT and PD1,
compared with controls. Thus, our data suggest that PMN and Mo
MDSCs might have different mechanisms to confer resistance
against ICI immunotherapy, but may also be a target of ICI in
glioma.We positedthat future studies shouldfocus on unveiling the
crosstalk and mechanisms by which ICIs affect MDSCs in glioma.
Along these lines, we showed that suppressive myeloid cells express
PD1, PD-L1, and TIGIT-ligands in human GBM tissue. Moreover,
we demonstrated that antigen specific T cell proliferation is
inhibited by immunosuppressive myeloid cells can be restored by
TIGIT/PD1 blockade. This suggests that CTL exhaustion might be
regulatedat least in part bythe expression of IC ligands on MDSCs
in GBM.
Treg cells are major components of the immune suppressive TME
which express many ICs (73). The expression of TIGIT and PD1 by
Treg cells was shown to enhances their immunosuppressive
functions and contribute to tumor progression both in glioma
murine models and GBM patients (74). Importantly, Treg cells are
major source of IL-10 in GBM (74,75), and IL-10 can induce MDSC
development and enhance their suppressive functions (76,77), as well
as increasing the expression of PD1 myeloid cells (78). Additionally,
TIGIT is important for IL-10 expression by Treg cells (55). Therefore,
it is possible that aTIGIT might also regulate MDSC cell numbers
and functions by suppressing Treg expression of IL-10. Future studies
should focus on the mechanisms and crosstalk between Treg cells and
MDSCs via checkpoint molecules in the GBM TME and their
contribution to ICI resistant.
Raphael et al. aTIGIT/aPD1 Immunotherapy in GBM
Frontiers in Immunology | www.frontiersin.org May 2021 | Volume 12 | Article 63714611

In summary, our data support the concept of treating GBM
patients with dual blockade of PD1 and TIGIT and provides new
insights into mechanisms of GBM immunotherapy to facilitates
the development of novel treatments.
DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in https://www.
ncbi.nlm.nih.gov/gap/ online repository, under accession
number phs000178.
ETHICS STATEMENT
The animal study was reviewed and approved by University of
Pittsburgh Institutional Animal Care and Use Committee.
AUTHOR CONTRIBUTIONS
Performed experiments and collected data: IR, RK, LM, KS, PS,
CS, JN, NA, MLC, AF and TB. Formal data analysis and figures:
IR and RK. Statistical analysis: IR, SZ, and DR. RNA-seq and
single cell RNA-seq analysis: IR, LW, and DR. Resources,
concepts and/or manuscript revisions: IR, BH, SA, AB, FSL, IP,
NA, BC, AD and GK. IR wrote the manuscript. IR revised the
manuscript with assistance from GK. IR and GK designed the
experiments. GK supervised and financed the study. All authors
contributed to the article and approved the submitted version.
FUNDING
This research was supported by National Institute of Health
(NIH)/National Cancer Institute (NCI) grants R01CA244520
and NIH R01CA222804 (to GK), NIH/National Institute of
Biomedical Imaging and Bioengineering (NIBIB) R21EB029650
(to GK), The Walter L.Copeland Fund of The Pittsburgh
Foundation (to IR, GK) and The Brain Tumor Funders’
Collaborative (GK). IR was supported by a fellowship from
UPMC Children’s Hospital of Pittsburgh. This work utilized
the Hillman Cancer Center Flow Cytometry Core, a shared
resource at the University of Pittsburgh supported by the
CCSG P30 CA047904.
ACKNOWLEDGMENTS
The results shown here are in whole or part based upon data
generated by the TCGA Research Network: https://www.cancer.
gov/tcga. The authors would like to thank Dr. Yijen Wu, director
of Rangos Research Center Animal Imaging Core for assistance
with MRI imaging and analysis.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fimmu.2021.
637146/full#supplementary-material
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