Blockade of TIGIT/CD155 signaling reverses T-cell exhaustion and enhances
antitumor capability in head and neck squamous cell carcinoma
Lei Wu 1
, Liang Mao 1, Jian-Feng Liu
1, Lei Chen
1,Guang-Tao Yu
1, Lei-Lei Yang
1,
Hao Wu 1, Lin-Lin Bu
1, Ashok B. Kulkarni
3, Wen-Feng Zhang
1, 2 , and Zhi-Jun Sun
1,2,3
1 The State Key Laboratory Breeding Base of Basic Science of Stomatology
(Hubei-MOST) & Key Laboratory of Oral Biomedicine, Ministry of Education,
School and Hospital of Stomatology, Wuhan University, Wuhan 430079, China
2 Department of Oral Maxillofacial-Head Neck Oncology, School and Hospital of
Stomatology, Wuhan University, Wuhan 430079, China
3 Functional Genomics Section, Laboratory of Cell and Developmental Biology,
National Institute of Dental and Craniofacial Research, National Institutes of Health,
Bethesda, MD, 20892, USA.
Running title: Blockade of TIGIT/CD155 signaling in HNSCC
This work was supported by the National Natural Science Foundation of China
(NFSC): 81672668, 81472528, 81472529 and the Fundamental Research Funds for
the Central Universities (2042017kf0171)
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L. Wu and L. Mao contributed equally to this article.
Corresponding Author: Prof. Zhi-Jun Sun, School and Hospital of Stomatology,
Wuhan University, Wuhan, 430079, China.
Tel: +86-27-8768-6336
Fax: +86-27-8787-3260
E-mail: [email protected] (Z.J. S.)
No potential conflicts of interest were disclosed.
Abstract
Immunosuppression is common in head and neck squamous cell carcinoma
(HNSCC). In previous studies, the TIGIT/CD155 pathway was identified as an
immune checkpoint signaling pathway that contributes to the “exhaustion” state of
infiltrating T cells. Here, we sought to explore the clinical significance of
TIGIT/CD155 signaling in HNSCC and identify the therapeutic effect of
TIGIT/CD155 pathway in transgenic mouse model. TIGIT was overexpressed on
tumor-infiltrating CD8+ and CD4
+ T cells in both HNSCC patients and mouse models,
and was correlated with immune checkpoint molecules (PD-1, TIM-3, LAG-3).
TIGIT was also expressed on murine regulatory T cells (Tregs) and correlated with
immune suppression. Using a human HNSCC tissue microarray, we found that
CD155 was expressed in tumor and tumor-infiltrating stromal cells, and also indicated
poor overall survival. Multispectral immunohistochemistry indicated that CD155 was
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coexpressed with CD11b or CD11c in tumor-infiltrating stromal cells. Anti-TIGIT
treatment significantly delayed tumor growth in transgenic HNSCC mouse models
and enhanced antitumor immune responses by activating CD8+ T-cell effector
function and reducing the population of Tregs. In vitro coculture studies showed that
anti-TIGIT treatment significantly abrogated the immunosuppressive capacity of
MDSCs, by decreasing Arg1 transcripts, and Tregs, by reducing TGFβ1 secretion. In
vivo depletion studies showed that the therapeutic efficacy by anti-TIGIT mainly
relies on CD8+ T cells and Tregs. Blocking PD-1/PD-L1 signaling increased the
expression of TIGIT on Tregs. These results present a translatable method to improve
antitumor immune responses by targeting TIGIT/CD155 signaling in HNSCC.
Introduction
Immune therapies are considered low toxicity, high affinity, and targeted treatment
options that can harness the activity of the host’s immune system to prevent tumor
escape (1,2). When used as a monotherapy or in combination with standard therapies,
immune therapies have been demonstrated to be an effective therapeutic approach in
multiple advanced cancers, including head and neck squamous cell carcinoma
(HNSCC) (3-6). HNSCC accounts for 3 to 5 % of all cancers (7), with the common
features of tumor-mediated immunosuppression and high mutational burden (8).
HNSCC has been shown to develop multiple immune escape mechanisms, attributed
to these characteristics (8). Preclinical studies, clinical trials, and our previous work
have suggested that immunosuppressive cells contribute to the poor survival of
HNSCC patients, whereby immune therapies (such as immune checkpoint blockades,
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adoptive cell therapies, and neoantigen-targeting vaccines) can improve clinical
outcomes by reversing the immunosuppressive state (9-13). However, the limited
efficacy of some immune therapies indicates that new applicable immune checkpoints
and therapeutic strategies need to be investigated to overcome the pervasive immune
suppression in HNSCC (14).
T-cell immunoglobulin and ITIM domain (TIGIT), which is also known as Vstm3
and VSIG9, is an immunoglobulin superfamily member (15). TIGIT is expressed
restrictedly on subsets of activated T cells and natural killer (NK) cells, and interacts
with CD155 to induce immunosuppression (16). However, the expression profile and
immunological effect of TIGIT/CD155 signaling in HNSCC are poorly characterized.
Analogous to the B7/CD28/CTLA-4 pathway, which contains both costimulatory and
coinhibitory receptors, TIGIT competes with CD226 (also known as DNAM-1) to
bind CD155 with a high affinity (15). Multiple groups have shown that the genetic
knockout or antibody ablation of TIGIT-enhanced NK cell killing and augmented
CD8+ T-cell activity against tumors (17-20). In addition, TIGIT
+ regulatory T cells
(Tregs) may display a stronger immunosuppressive activity than TIGIT– Tregs (21). It
was reported that CD155 was highly expressed in multiple tumor cells and
tumor-associated myeloid cells (22-24), and that TIGIT/CD155 signaling may
contribute to the potential suppression of conventional NK cells by Myeloid-derived
suppressor cells (MDSCs) (25). Thus, blocking TIGIT/CD155 signaling might
provide a promising complement to current immune checkpoint–based antitumor
immunotherapies for clinical intervention.
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In this study, we investigated the expression and function of TIGIT+ T cells and
CD155+ myeloid cells in HNSCC patients and mouse models. We observed an
elevated number of TIGIT+ T cells in HNSCC compared to healthy controls, as well
as an increase in CD155-expressing tumor cells and myeloid cells. We also provide
evidence that blocking TIGIT/CD155 signaling promoted antitumor immunity, aided
in immune homeostasis, and reduced the tumor burden in mouse models by an in vivo
TIGIT mAb administration. Our data demonstrate that TIGIT/CD155 signaling is a
potential immunotherapeutic target for HNSCC.
Materials and Methods
Mice
Six- to 8-week-old male Tgfbr1/Pten 2cKO mice were used in this study. The time
inducible tissue-specific Tgfbr1/Pten double-knockout mice (K14-CreERtam+/–
;
Tgfbr1flox/flox
; Ptenflox/flox
, Tgfbr1/Pten 2cKO) were maintained and genotyped
according to previously published protocols (26). All animal studies were carried out
in accordance with the NIH guidelines for the use of laboratory animals in a
pathogen-free ASBL3 animal center at Wuhan University. All mouse procedures were
approved by the Animal Care and Use Committee of Wuhan University
(2014LUNSHENZI06 and 2016LUNSHENZI62). The tamoxifen treatment was
performed as previously described (27). All the mice had a mixed background of
FVBN/CD1/129/C57BL/6J.
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Human samples
Study cohort 1. A retrospective series of 210 primary HNSCC cases, 35 HNSCC
cases with lymph node metastasis, 68 oral epithelial dysplasia (DYS) cases and 42
normal oral mucosa (MUC) cases was obtained from the Hospital of Stomatology,
Wuhan University. All the included HNSCC patients underwent primary surgery
(without preoperative adjuvant chemotherapy or radiotherapy) between 2011 and
2016. The specimens were used to generate human HNSCC tissue microarrays.
Additionally, 201 primary HNSCC cases were included in the survival analysis due to
9 patients lost to follow-up.
Study cohort 2. A prospective series of the whole blood of 16 primary HNSCC cases
was obtained from the Hospital of Stomatology, Wuhan University. Additionally, 12
matched fresh surgically resected tumor tissues were obtained to isolate
tumor-infiltrating T cells (TILs). All the included HNSCC patients underwent primary
surgery (without preoperative adjuvant chemotherapy or radiotherapy) from October
2017 to March 2019. The whole blood of 10 healthy donors was used as a normal
control.
Informed consent was obtained for all the patients and the study was approved by
the Institutional Medical Ethics Committee of School and Hospital of Stomatology,
Wuhan University (2014LUNSHENZI06,2016LUNSHENZI62) and was conducted
in agreement with the Helsinki Declaration. The pathological diagnosis was made by
two independent pathologists of the Department of Oral Pathology, Wuhan
University.
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Immunohistochemistry
IHC was performed as previously (28). Briefly, the sections of HNSCC tissue
microarray were deparaffinized, rehydrated, and subjected to antigen retrieval by
sodium citrate (pH 6.0), followed by blocking endogenous peroxidase. Then the
sections were incubated with CD155 antibody (1:200; Cell Signaling Technology)
overnight. On the second day, the sections were incubated with secondary antibodies
and stained with ABC kits (Vector). The slides were scanned with Aperio ScanScope
CS scanner (Vista, CA, USA) and analyzed by Aperio ScanScope quantification
software (Version 9.1). The detailed quantification procedures were performed as
before (28).
Multispectral IHC, imaging and analysis
Multispectral IHC was performed on formalin-fixed paraffin embedded (FFPE)
HNSCC samples with PerkinElmer Tyramide Plus (Opal) reagents according to Opal
serial immunostaining manual. Briefly, paraffin sections were first deparaffinized and
rehydrated. After antigen retrieval with AR buffer (pH = 6.0; PerkinElmer), the
sections were covered with blocking buffer (PerkinElmer) for 20 minutes at room
temperature, and then were incubated with a primary antibody, followed by the
horseradish peroxidase–conjugated secondary antibody (PerkinElmer). Sections were
washed three times for 2 minutes each in 0.02% Tris-buffered saline–Tween 20
(TBST) followed by signal generation using 100 µl of Opal Fluorophore Working
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Solution (PerkinElmer) per slide at a dilution of 1:100 in 1× amplification diluent,
incubated at room temperature for 10 minutes as specified by the manual
(PerkinElmer). Opal 520 Fluorophore, Opal 540 Fluorophore, Opal 620 Fluorophore,
Opal 650 Fluorophore, and Opal 690 Fluorophore (all from PerkinElmer) were
applied to each antibody. Multispectral images were acquired by PerkinElmer Vectra
platform at ×20 magnification. The following primary antibodies were used in this
panel: CD155 (1:200; Cell Signaling Technology), CD11c (1:1000; Cell Signaling
Technology), CD11b (1:400; Cell Signaling Technology), Pan-CK (1:2000; Cell
Signaling Technology), PD-L1(1:1000; Cell Signaling Technology), and DAPI
(PerkinElmer).
In vivo treatments
After 5 consecutive days of tamoxifen administration, all the Tgfbr1/Pten 2cKO
mice were randomized into a treatment group, which was treated with TIGIT mAbs
(10 mg/kg; BE0274; Bio X Cell), and a control group which was treated with IgG1
isotype antibody (10 mg/kg; BE0083; Bio X Cell) by intraperitoneal injection three
times a week from day 12 to day 40. Tumors were measured by micrometer caliper,
and photos were taken every other day. Mice were euthanized on day 40, and tissues
were harvested for flow cytometry and functional analysis.
For the in vivo T cell, Treg, or MDSC depletion, mice received CD4 (200μg;
BE0003-1; Bio X Cell), CD8 (200μg; BE0004-1; Bio X Cell), CD25 (250μg; BE0012;
Bio X Cell), or Gr-1 (500μg; BE0075; Bio X Cell) targeting antibodies on the day
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before tamoxifen administration and on day 4 of tamoxifen administration . On day 5,
the blood or lymph nodes were obtained to verify the depletion efficiency by flow
cytometry.
For the in vivo PD-1 antibody treatment, all Tgfbr1/Pten 2cKO mice harboring
tumors were randomized into a group treated with PD-1 mAb (10mg/kg; BE0146; Bio
X Cell), and a control group treated with IgG2a isotype control (10mg/kg; BE0089;
Bio X Cell) by intraperitoneal injection three times a week during day 12 to day 40.
Mice were euthanized on day 40, and tissues were harvested for flow cytometry and
functional analysis.
PBMC separation
Peripheral blood mononuclear cells (PBMCs) were separated from the whole blood
samples of patients and healthy donors with LymphoprepTM
(STEMCELL
Technologies) and used for flow cytometry analysis. Briefly, blood was diluted with
an equal amount of Dulbecco’s Phosphate-Buffered Saline with 2% Fetal Bovine
Serum and was layered on top of Lymphoprep™. Then, the sample was centrifuged at
800 x g for 20 minutes at room temperature and mononuclear cell layer was retained.
Isolation of tumor-infiltrating lymphocytes (TILs)
Tumor tissues were harvested and manually minced into small pieces (smaller than
2 mm), digested in RPMI medium containing collagenase D at 1 mg/ml (Roche),
hyaluronidase at 0.1 mg/ml (Sigma-Aldrich), and DNases at 0.2 mg/ml
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(Sigma-Aldrich) for 2 hours at 37℃, and were then filtered with 70 μm cell strainers
(Becton & Dickinson). The filtered cells were collected and separated with
LymphoprepTM
(STEMCELL Technologies). Then the TILs were collected and stored
in liquid nitrogen until flow cytometry analysis.
Flow cytometry
The following human antibodies were used for staining: CD45- APC-eFluor 780
(HI30), CD3-Alexa Fluor 700 (UCHTI), CD4-FITC (OKT4), CD8-PE-Cy7 (SK1),
TIGIT-PE (MPSA43), purchased from eBioscience and PD-1-Alexa Fluor 700
(EH12.2H7), purchased from BioLegend.
For the TILs, splenocytes and lymph node cells of mice were pre-incubated with
purified anti-mouse CD16/CD32 (eBioscience) before membrane staining. The
following mouse antibodies were used for membrane staining: CD3-FITC (17A2),
purchased from BD Biosciences; CD8-PerCP-Cy5.5 (53-6.7), TIGIT-APC (1G9),
TIM3-PE (8B.2C12), LAG3-BV421 (C9B7W), Ly6G-PE (1A8), Ly6C- PE-Cy7
(HK1.4), CD11b-FITC (M1/70), Gr-1-APC (RB6-8C5), purchased from BioLegend;
CD4-PE-Cy7 (GK1.5), PD-1-PE (RMP1-30), CD155-APC (TX56), purchased from
eBioscience.
For regulatory T-cell staining, Mouse Regulatory T-cell Staining Kit #3
(eBioscience) was used. Cells were first stained with CD4-FITC (RM4-5) and
CD25-PE (PC61.5) surface marker antibodies, fixed with fixation/permeabilization
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buffer, and stained with anti-Foxp3-PerCP-Cy5.5 (FJK-16s) in 1X permeabilization
buffer.
For mouse intracellular cytokine staining, the TILs were first stimulated with Cell
Activation Cocktail with Brefeldin A (BioLegend) in vitro for 6 hours. The cells were
collected for CD8-PerCP-Cy5.5 (53-6.7), and CD4-PE-Cy7 (GK1.5) staining, fixed
with fixation buffer (BioLegend), and permeabilized with 1X intracellular staining
permeabilization wash buffer (BioLegend). Fixed cells were stained with IFNγ-PE
(XMG1.2), IL2-PE (JES6-5H4), and TNFα-APC (MP6-XT22), which were purchased
from BioLegend.
All samples were analyzed on a CytoFLEX flow cytometer (BECKMAN
COULTER), and data were analyzed using CytoExpert software (BECKMAN
COULTER). Dead cells were excluded based on Fixable Viability Dye-eFluor 506
(eBioscience).
Cell isolation
For mouse MDSC isolation, a mouse Myeloid-Derived Suppressor Cell Isolation
Kit (Miltenyi Biotec) was used to enrich Gr-1high
Ly6G+ cells and Gr-1
dimLy6G
- cells
from the TILs or splenocytes of the anti-TIGIT treatment and control groups. Then,
the CD11b+Ly6G
+Ly6C
lo PMN-MDSCs were sorted from Gr-1
highLy6G
+ cells, and
CD11b+Ly6G
-Ly6C
hi M-MDSCs were sorted from Gr-1
dimLy6G
- cells by flow
cytometry. Flow cytometry cell sorting was performed on a Moflo XDP (BECKMAN
COULTER).
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For mouse Treg isolation, the CD4+CD25
hi Tregs were sorted from lymph node
cells or TILs of mice with an EasySep™ Mouse CD4+CD25+ Regulatory T-cell
Isolation Kit (STEMCELL Technologies). Foxp3 staining was performed to analyze
the purity. Cells of over 90% purity could be used for the next step.
For mouse CD8+ T-cell isolation, CD8
+ T cells were sorted from the lymph node
cells of wildtype mouse with a CD8α+ T-cell isolation Kit (Miltenyi Biotec). The
purity was analyzed by flow cytometry.
Coculture assays
The sorted CD8+ T cells were labeled with CFSE and stimulated with 5 μg/ml
anti-CD3, 2.5 μg/ml anti-CD28, and 20 ng/ml rIL2 (BD PharmingenTM
). Then, the
activated T cells (1 x 105/well) were cocultured with sorted PMN-MDSCs, M-MDSCs,
and Tregs, respectively, at different concentration gradients in 96-well round-bottom
plates. After 72 hours, the cells were collected for CFSE dilution analysis by flow
cytometry. Dead cells were excluded based on Fixable Viability Dye-eFluor 506
staining.
Apoptosis assays
The sorted PMN-MDSCs, M-MDSCs, and Tregs (1 x 105/well) from the control
group were cultured in RPMI with 10% FBS, 5 mM glutamine, 25 mM HEPES, and 1%
antibiotics (Invitrogen). Recombinant GM-CSF (Invitrogen) was added to the media
of PMN-MDSCs and M-MDSCs at 10 ng/ml. Then, the TIGIT mAb (10 μg/ml) or
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isotype control antibody (10 μg/ml) was administered. After 20 hours, the cells were
assessed and analyzed by flow cytometry. Annexin V-FITC/PI staining was
performed in staining buffer (BD Biosciences) according to the manufacturer’s
protocol.
Enzyme-linked immunosorbent assay (ELISA)
The sorted Tregs (2 x 105/well) from the control group were cultured in RPMI with
10% FBS, 5 mM glutamine, 25 mM HEPES, and 1% antibiotics (Invitrogen). Then,
the TIGIT mAb (10 or 20 μg/ml) or isotype control antibody (10 μg/ml) was
administered respectively. After 48 hours, the supernatants were harvested, and the
concentrations of TGFβ1 were detected with an ELISA Kit (Neobioscience)
according to the manufacturer’s protocol. Briefly, appropriately diluted samples were
added to each well with precoated capture antibody. Then, diluted detection antibody
and conjugated secondary antibody were added to each well successively. After that,
the substrate solution was dispensed to per well. Finally, the absorbance was recorded
at 450 nm on a plate reader.
Real Time-PCR
Total RNA of MDSCs were extracted by the RNeasy Mini Kit (Qiagen). The
cDNA was synthesized by PrimeScriptTM
RT reagent Kit (TaKaRa). The target genes
of samples were analyzed by CFX Connect™ Real-Time PCR Detection System. The
following primers were used: β-actin-F: 5’-GTGACGTTGACATCCGTAAAGA-3’,
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β-actin-R: 5’-GCCGGACTCATCGTACTCC-3’; ARG1-F:
5’-TTGGGTGGATG-CTCACACTG-3’, ARG1-R:
5’-GTACACGATGTCTTTGGCAGA-3’. The expression of arginase-I (ARG1) was
calculated by the 2-∆∆ct method and β-actin was used as a normalized control.
Statistical analysis
GraphPad Prism 7.0 for windows (GraphPad Software, Inc., La Jolla, CA) was
used to conduct statistical analyses. Data analyses were conducted by the unpaired (or
paired where specified) Student t test for two-group comparisons or by one-way
analysis of variance (ANOVA) for multiple group comparisons. All values are
presented as the mean ± SD. P < 0.05 considered statistical significance. The Kaplan–
Meier method followed by long-rank test was used to analyze the overall survival of
patients with HNSCC and the significance of observed differences was assessed by
log-rank test.
Results
High expression of TIGIT on tumor-infiltrating T cells
TIGIT expression has been shown to be increased on T cells in multiple types of
malignant tumors (17,18). To confirm that TIGIT was expressed in HNSCC, we
performed flow cytometry to assess the surface expression of TIGIT within human
PBMCs and HNSCC tissues. We found that TIGIT expression on HNSCC patient
PBMCs was higher than that on CD4+
and CD8+ T cells from healthy donor PBMCs
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(Fig. 1A and B). Furthermore, TIGIT was expressed by a large percentage of
HNSCC-infiltrating CD4+
and CD8+ T cells, and the expression was significantly
higher than those on the matched PBMC (Fig. 1A and B). The coexpression of
immune checkpoint molecules may drive T lymphocyte exhaustion (29,30). Therefore,
we examined the expression of TIGIT with coinhibitory receptor PD-1 in human
PBMCs and HNSCC tissues. We observed that TIGIT was coexpressed with PD-1 on
CD4+
and CD8+
T cells from human PBMC and TILs. We found that the coexpression
of TIGIT and PD-1 on TILs was higher than that on PBMCs (Fig. 1C). Considering
these data from human HNSCC, we investigated TIGIT expression in a Tgfbr1/Pten
2cKO HNSCC mouse model. Analogously, TIGIT was expressed on 23.96% of CD4+
TILs and 50.15% of CD8+ TILs, which was significantly higher than that in the spleen
in WT and in tumor-bearing mice (Fig. 2A and B). Moreover, we found that the
coexpression of TIGIT/PD-1, TIGIT/TIM-3, and TIGIT/LAG-3 was upregulated on
CD4+ and CD8
+ TILs in the mouse model compared to that in the spleen (Fig. 2C and
D; Supplementary Fig. S1). These data indicated that TIGIT was highly expressed by
HNSCC TILs and correlated with expression of other immune checkpoint molecules,
especially PD-1.
It was reported that TIGIT predominantly regulates the function of regulatory T
cells (31). Thus, we then characterized TIGIT expression on Tregs in our HNSCC
mouse model. We found that CD4+CD25
+Foxp3
+ Tregs from tumor-bearing mice
expressed more TIGIT than that in WT mice (Fig. 3A and B). We observed that the
CD25hi or med
Foxp3+ subset expressed the majority of TIGIT among the CD4
+ T cells,
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compared with the CD25–Foxp3
– subset and CD25
medFoxp3
– subset (Fig. 3A and B).
Suppression assays indicated that Tregs sorted from tumor-bearing mice showed an
increased ability to suppress the proliferation of CD8+ T cells that were activated by
CD3/CD28 antibodies compared with that from WT mice (Fig. 3C and D;
Supplementary Fig. S2). These results suggested that TIGIT might act as a negative
immune checkpoint to generate an exhausted phenotype in HNSCC.
High expression of CD155 in patients and in a mouse model
TIGIT might transduce negative signals to effector T cells by binding to their
inhibitory receptors, such as CD155 (16). Thus, we assessed the expression and
functional consequences of CD155 in HNSCC. First, using the TCGA database via
GEPIA (32), we found that the CD155(PVR) mRNA expression in HNSCC tissues
was significantly higher than that in normal tissues (Supplementary Fig. S3A).
Survival analysis indicated that HNSCC patients with high CD155 expression
demonstrated a worse overall survival than that of patients without CD155 expression
(Supplementary Fig. S3B). We detected constitutive expression of CD155 on the
epithelial and interstitial cells of human HNSCC tissue (Fig. 4A - C). High CD155
expression on the malignant cells or stromal cells of HNSCC patients was associated
with poor survival (Fig. 4B and C). Furthermore, higher expression of CD155 in
epithelial cells was correlated with the pathologic grade (Fig. 4D; Supplementary Fig.
S4A) and lymph node metastasis (Fig. 4E). However, there was no significant
difference in CD155 expression between tumors of different sizes (Supplementary Fig.
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S4B), and no significance difference in CD155 expression was observed between
patient HNSCC tissue and the matched metastatic lymph nodes (Supplementary Fig.
S4C). To investigate further, multiplexed IHC analysis showed that CD155 (green)
was highly expressed in Pan-CK+ (red) HNSCC (Fig. 4F). We also found that CD155
was coexpressed with myeloid cell markers, such as CD11b (yellow) and CD11c
(pink), at the invasive front (Fig. 4F). We observed that CD155 and PD-L1
coexpressed on CD11b+ myeloid cells (Supplementary Fig. S5). Based on the CD155
expression profiles in patient tissues, we observed that CD155 was similarly
overexpressed on CD11b+Ly6G
+Ly6C
lo PMN-MDSCs and CD11b
+Ly6G
-Ly6C
hi
M-MDSCs in the tumor-bearing Tgfbr1/Pten 2cKO HNSCC mouse model compared
with that in the bone marrow and MDSCs of WT mice (Fig. 4G and H;
Supplementary Fig. S6A). In our previous work, we verified that these two subsets of
MDSCs in tumor-bearing mice had immunosuppressive activity (28). To further
confirm this observation, we sorted these cells to detect their capacity to produce
arginase-1(Arg-1). Higher expression of arginase-1 was found in the PMN-MDSCs
and M-MDSCs in the tumor-bearing mice, whereas this gene was lowly expressed in
the MDSCs of WT mice (Fig. 4I; Supplementary Fig. S6B). These data confirmed the
high prevalence of CD155 in human and mouse HNSCC and suggested that CD155
may correlate with an immunosuppressive function in HNSCC. Thus, we
hypothesized that blocking TIGIT/CD155 signaling may activate antitumor immunity
in HNSCC.
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Blocking TIGIT/CD155 signaling inhibits tumor progression
To test our hypothesis, tumor-bearing HNSCC mice were subjected to in vivo
TIGIT/CD155 blockade by treatment with a TIGIT mAb. Tgfbr1/Pten 2cKO mice
were administered five consecutive days of tamoxifen. When the papilloma had
formed, the mice were intraperitoneally injected with a TIGIT mAb (Fig. 5A). The
results indicate that blocking TIGIT led to a significant delay in tumor progression
compared to that of the control (Fig. 5B ). We did not observe significant differences
between the anti-TIGIT treatment group and control group in liver and kidney using
H&E staining (Supplementary Fig. S7A), indicating that there was no detectable
cytotoxicity in our mouse model upon anti-TIGIT treatment, which was consistent
with previous study (33). We also observed a lower frequency of Tregs infiltrating in
the peripheral immune organs, local circulation, and the tumor microenvironment
(TME) after treatment compared to those in the controls (Fig. 5C). A higher
frequency of tumor-infiltrating CD8+ T cells expressing IL2/TNFα/IFNγ and CD4
+ T
cells expressing IL2 were also observed after TIGIT mAb treatment relative to those
in the control mice (Fig. 5D). However, treatment with a TIGIT mAb did not reduce
tumor-infiltrating MDSCs frequencies (Supplementary Fig. S7B). These data
suggested that blocking TIGIT/CD155 signaling alleviated CTL exhaustion and
delayed tumor growth in HNSCC mouse models.
Blocking TIGIT/CD155 signaling decreases suppression by Tregs and MDSCs
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To determine the potential mechanism of the TIGIT/CD155 signaling blockade, we
selectively sorted MDSCs and Tregs from Tgfbr1/Pten 2cKO mice for in vitro
investigations. Coculture assays indicated that the immunosuppressive activity of
MDSCs was abrogated by in vivo TIGIT mAb treatment (Fig. 6A). In addition, Arg-1
transcripts were also decreased by anti-TIGIT treatment compared to those in the
controls (Fig. 6B). Consistent with the in vivo data, anti-TIGIT treatment in vitro did
not induce apoptosis in MDSCs (Fig. 6C). These data suggested that blocking
TIGIT/CD155 may partially regulate the immunosuppression of MDSCs by reducing
Arg-1 production.
Then, CD4+CD25
+Foxp3
+ Tregs that were isolated from the spleens of TIGIT or
isotype antibody–treated mice were cocultured with CD8+ effector T cells. The results
indicated that the anti-TIGIT treatment decreased the suppressive function of Tregs
compared to that in the controls (Fig. 6D). The in vitro analysis revealed that the
anti-TIGIT treatment did not induce the apoptosis of Tregs (Fig. 6E), but the
treatment reduced the secretion of TGFβ1 in cell supernatants from Tregs compared
to that in the controls (Fig. 6F). Overall, these data suggested that blocking
TIGIT/CD155 signaling may partially regulate the immunosuppression of Tregs by
downregulating TGFβ1 secretion.
Anti-TIGIT therapeutic efficacy is mainly dependent on CD8+ T cells and Tregs
As the anti-TIGIT treatment may partially influence the function of CD4+ T cells,
CD8+ T cells, MDSCs, and Tregs, we determined whether the effect of anti-TIGIT
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was mainly dependent on one of these cell populations. Next, tumor-bearing HNSCC
mice were subjected to in vivo depletion of CD4+ T cells, CD8
+ T cells, MDSCs, or
Tregs by depleting antibodies before the blockade of TIGIT antibody. Using depleting
antibodies, we found that antitumor effects in tumor-bearing HNSCC mice were
abrogated when CD8+ T cells, but not CD4
+ T cells, were depleted (Fig. 7A).
Moreover, we found that when depleting CD25+ Tregs, there were no differences in
the antitumor effects between the anti-CD25 alone and the combination of anti-CD25
and anti-TIGIT. However, the tumor growth was slower with the combination of
anti-Gr1 and anti-TIGIT than that with anti-Gr1 alone (Fig. 7B). These results
indicated that the therapeutic efficacy by anti-TIGIT mainly relies on CD8+ T cells
and Tregs.
Blocking PD-1/PD-L1 signaling increases the expression of TIGIT on Tregs.
Our data showed that PD-1 was coexpressed with TIGIT on human and mouse
TILs. We therefore examined the expression of TIGIT on CD4+
T cells, CD8+ T cells,
and Tregs after blocking PD-1/PD-L1 signaling to investigate whether there is a
possible rationale to combine the TIGIT and PD-1 treatment in HNSCC. The results
showed that blockade of PD-1 increased the expression of TIGIT on Tregs compared
with the isotype (Fig. 7C). However, there were no differences on the expression of
TIGIT on CD8+ T cells between anti–PD-1 treatment group and the control group
(Supplementary Fig. S8D). Collectively, these data indicated that blocking PD-1 and
TIGIT corporately may elicit better antitumor effects.
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Discussion
Cancer immunotherapy with immune checkpoint blockade has been one of the
most successful strategies for cancer therapy (34). Although blocking PD-1, PD-L1,
and CTLA-4 have shown to generate antitumor immunity and durable responses, drug
resistance still occurs in some patients (14,35), emphasizing the need for
supplementary strategies. TIGIT is an inhibitory checkpoint receptor that has been
demonstrated to have immunosuppressive effects on antitumor immunity in several
solid tumors and in leukemia (15,17,20). The coexpression of TIGIT and other
immune checkpoints can lead to an exhausted phenotype in cytotoxic lymphocytes
(29). TIGIT+ Tregs are believed to be a distinct Treg subset capable of strong
suppression (21). However, direct evidence suggesting a clinical role for TIGIT in
HNSCC patients has not been presented. In this study, we found that TIGIT was
highly expressed by both human and mouse tumor-infiltrating CD4+ and CD8
+ T cells,
and was related to several key T-cell checkpoints. In addition, we also demonstrated
that TIGIT was more highly expressed on Foxp3+ Tregs than that on Foxp3
– CD4
+ T
cells in our HNSCC mouse model, which could be associated with the high amount of
suppression on CD8+ T-cell proliferation. CD155 is a ligand of TIGIT that interacts
with TIGIT with high affinity. The loss of both host- and tumor-derived CD155 leads
to decreased tumor growth and metastasis and increased response to immunotherapy
(22). Here, we showed that CD155 was widely expressed in the TME of HNSCC
patients, and the overexpression of CD155 in both cancer cells or in tumor-infiltrating
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stromal cells could predict poor overall survival. The overexpression of CD155 was
also associated with pathological grade and lymph node metastasis, which indicated
that the assessment of CD155 expression could be used as an approach to predict the
prognostic outcomes of HNSCC patients. These results were consistent with several
previous studies in other types of malignant tumors (20,36,37). In addition, high
CD155 expression on tumor-infiltrating myeloid cells was observed in human and
murine HNSCC. In these studies, the blockade of TIGIT/CD155 signaling enhanced
the antitumor CTL responses and downregulated the immunosuppressive function of
Tregs and MDSCs by decreasing the production of suppressive cytokines. To our
knowledge, this is the first evidence regarding the role of TIGIT/CD155 signaling in
HNSCC pathogenesis and immunotherapy.
Current studies have shown that the depletion of CD8+ CTLs or the absence of NK
cells might abrogate the therapeutic effects of anti-TIGIT blockade (17,19). However,
the contribution of tumor-infiltrating immunosuppressive cells was not been studied
in these investigations. Two previous studies indicated that the expression of PD-L1
on host DCs and macrophages may predict the clinical therapeutic efficacy of
PD-L1/PD-1 blockade (38,39). These data provide evidence that the interaction
between CTLs and tumor-associated stromal cells may play an essential role in
immune checkpoint inhibition. In line with our previous work, CD4+CD25
+Foxp3
+
Tregs, CD11b+Ly6G
+Ly6C
lo PMN-MDSCs, and CD11b
+Ly6G
–Ly6C
hi M-MDSCs
are the major immune suppressive cells in the Tgfbr1/Pten 2cKO mouse model
(28,40). In this work, a decrease in the suppressive function of Tregs and MDSCs was
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observed in an HNSCC mouse model by blocking TIGIT, which indicates that CD8+
CTL exhaustion was reversed as a result of the reduction of the immunosuppression
of the TME. In pathologic conditions, MDSCs highly express multiple
anti-inflammatory cytokines and immunosuppressive factors, inhibiting adaptive
immunity and supporting tumor progression (41). In a mouse model, rapid tumor
growth and inflammatory infiltrates can result in expansion of the MDCS populations
(42). Although the blockade of TIGIT did not induce apoptosis or decrease the
MDSCs in this study, the downregulation of ARG1 transcripts were observed in the
mouse model. These data indicated that TIGIT mAbs may not directly affect the
expansion of MDSCs in the HNSCC TME, but may reduce immunosuppression by
inhibiting ARG1 production. The complete mechanism needs to be further
investigated. In addition, Tregs are recruited into the TME early and play a prominent
role in the regulation of the immune response to tumors via cytokine production or
surface molecule interactions (43,44). Previous studies also revealed that apoptotic
Tregs could mediate enhanced immunosuppression via the adenosine pathways (45).
In this study, we found that TIGIT mAbs did not directly affect the apoptosis of Tregs
in vitro but could downregulate the secretion of typical suppressive cytokine TGFβ1.
Paradoxically, we also observed that the blockade of TIGIT/CD155 signaling in vivo
could significantly decrease the Treg population in our mouse model. These results
may be explained by previous studies, which showed that TIGIT-deficient T cells
generate less TGFβ-mediated Treg differentiation (21), but the exact mechanism
needs to be further studied.
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In summary, TIGIT/CD155 signaling was enhanced in HNSCC patients and in
mouse models and was correlated with immunosuppression. Targeting TIGIT/CD155
signaling may be a potential therapeutic strategy for HNSCC treatment.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Authors' Contributions
Conception and design: L. Wu, L. Mao, Z. Sun
Development of methodology: L. Wu, L. Mao, Z. Sun
Acquisition of data (provided animals, acquired and managed patients, provided
facilities, etc.): L. Wu, L. Mao, J. Liu, L. Chen, G. Yu, L. Yang, H. Wu, Z. Sun
Analysis and interpretation of data (e.g., statistical analysis, biostatistics,
computational analysis): L. Wu, L. Mao, J. Liu, L. Chen, G. Yu, L. Yang, H. Wu, L.
Bu, Z. Sun
Writing, review, and/or revision of the manuscript: L. Wu, L. Mao, AB. Kulkarni,
Z. Sun
Administrative, technical, or material support (i.e., reporting or organizing data,
constructing databases): AB. Kulkarni, W. Zhang, Z. Sun
Study supervision: W. Zhang, Z. Sun
Acknowledgments
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This work was supported by the National Natural Science Foundation of China
(NFSC): 81672668, 81472528, 81472529 and the Fundamental Research Funds for
the Central Universities (2042017kf0171).
We are grateful to Prof. Ashok B. Kulkarni for kindly proof editing. And we also
want to thank Shu-Yan Liang and Yin Liu from Wuhan Institute of Biotechnology
and Ya-Zhen Zhu from Tongji Hospital for their excellent technical assistance on
flow cytometry and cells isolation.
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Figure legends
Figure 1.
TIGIT is expressed on human TILs. A, Representative flow cytometry contour plots
of TIGIT expression on CD4+ T cells from healthy donor peripheral blood (HD, n =
10), human HNSCC peripheral blood (n = 16), and matched human HNSCC TILs (n
= 12) (left). Quantitation of TIGIT expression percentage in total CD4+ T cells is
shown at right. B, Representative flow cytometry plots of TIGIT expression on CD8+
T cells from healthy donor peripheral blood (HD, n = 10), human HNSCC peripheral
blood (n = 16), and matched human HNSCC TILs (n = 12) (left). Quantitation of
TIGIT expression as percentage of total CD8+ T cells is shown at right. C,
Representative flow cytometry plots of TIGIT and PD-1 coexpression on CD8+ T
cells from human HNSCC peripheral blood and TILs (n = 6) (left). Quantitation of
TIGIT and PD-1 coexpression as the percentage of total CD8+ or CD4
+ T cells is
shown at right. Data represent mean ± SD.
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Figure 2.
TIGIT is expressed on murine TILs and coordinately with immune checkpoints. A,
Representative flow cytometry contour plots of TIGIT expression on CD4+ T cells by
wild-type mice spleen (WT, n = 6), tumor-bearing mice spleen (TB, n = 6), and
tumor-bearing mice TILs (n = 6) (left). Quantitation of TIGIT expression as a
percentage of total CD4+ T cells is shown at right. B, Representative flow cytometry
contour plots of TIGIT expression on CD8+ T cells by wild-type mice spleen (WT, n
= 6), tumor-bearing mice spleen (TB, n = 6), and tumor-bearing mice TILs (n = 6)
(left). Quantitation of TIGIT expression as a percentage of total CD8+ T cells is
shown at right. C, Representative flow cytometry plots of TIGIT and PD-1
coexpression on CD4+ or CD8
+ T cells by wild-type mice spleen (WT, n = 6),
tumor-bearing mice spleen (TB, n = 6), and tumor-bearing mice TILs (n = 6) (left).
Quantitation of TIGIT and PD-1 coexpression as a percentage of total CD4+ or CD8
+
T cells is shown at right. D, Quantitation of TIGIT/LAG3 or TIGIT/TIM3
coexpression percentage in total CD4+ T cells or CD8
+ T cells is shown. Data
represent mean ± SD with two independent biological duplications.
Figure 3
TIGIT is expressed on murine Tregs and correlated with highly immune suppression.
A, Representative flow cytometry contour plots of TIGIT expression on CD25–
Foxp3–, CD25
medFoxp3
–, and CD25
hi or medFoxp3
+ of wild-type (WT) or tumor-bearing
(TB) mice spleen CD4+ T cells (n = 6, respectively). B, Quantitation of TIGIT
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expression in Tregs. C, Representative suppression assay of wild-type (WT) or
tumor-bearing (TB) mice Tregs cocultured with CFSE-labeled CD8+ effector T cells
for 72 hours. D, Quantitation of suppression percentage in CD8+ effector T cells. Data
represent mean ± SD with two independent biological duplications.
Figure 4
CD155 is highly expressed on malignant cells and tumor-infiltrating myeloid cells in
human and murine HNSCC, and correlated with poor overall survival. A,
Representative IHC images of CD155 expression on human primary HNSCC and oral
mucosa samples in the HNSCC tissue microarrays (Scale bar, 50 μm). B, Quantitation
of CD155 expression score in epithelial cells according to oral mucosa (MUC),
dysplasia (DYS), and HNSCC (left). Kaplan-Meier survival curves for overall
survival for 201 HNSCC patients according to the presence of a low or high
expression of CD155 by median cut-off approach, P = 0.0337 (right). C, Quantitation
of CD155 expression score in interstitial cells according to oral mucosa (MUC),
dysplasia (DYS), and HNSCC (left). Kaplan-Meier survival curves for overall
survival for 201 HNSCC patients according to the presence of a low or high
expression of CD155 by median cut-off approach, P = 0.0149 (right). D, Quantitation
of CD155 expression score in epithelial cells according to pathological grade (I, n =
53; II + III, n = 157). E, Quantitation of CD155 expression score in epithelial cells
according to lymph node status (N0, n = 136; N1 + N2, n = 72). F, Representative
multiplexed IHC image of human primary HNSCC samples. CD155 (green) was
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distributed broadly within the carcinoma element of human HNSCC, identified by
Pan-CK positivity (red). Tumor-infiltrating myeloid cells were revealed by CD11b
(yellow) or CD11c (pink) positivity. The merged image shows colocalization of
CD155 and CD11c (bottom left) or CD11b (bottom right). Scale bars: 50 μm (above).
Nuclei were stained with DAPI (blue). G, Flow cytometry histogram representative of
CD155 expression by CD11b+Ly6G
+Ly6C
lo PMN-MDSCs and CD11b
+Ly6G
-Ly6C
hi
M-MDSCs of wild-type (WT) or tumor-bearing (TB) mice spleens (n = 6,
respectively). H, Quantitation of CD155 mean fluorescent intensity (MFI) on MDSCs.
I, Relative quantification of arginase-1 (Arg1) transcripts in CD11b+Ly6G
+Ly6C
lo
PMN-MDSCs and CD11b+Ly6G
–Ly6C
hi M-MDSCs subsets sorted from wild-type
(WT) or tumor-bearing (TB) mice. The relative mRNA expression was counted as the
ratio of TB to WT. Data represent mean ±SD with two (G-I) independent biological
duplications.
Figure 5
TIGIT blockades elicit tumor rejection and reverses T cells exhaustion. A,
Experimental protocol. Beginning on day 0, Tgfbr1/Pten 2cKO mice were
administered tamoxifen each day for 5 consecutive days. On day 12, TIGIT mAb or
isotype antibody was administered i.p. three times per week. On day 40, the mice
were euthanized (n = 6, respectively). B, Quantitation of tumor size of isotype and
anti-TIGIT treatment groups. C, Representative flow cytometry plots of
CD25+Foxp3
+ Treg cells in CD4
+ T cells of TILs from isotype and anti-TIGIT
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treatment groups (left). Quantitation of CD25+Foxp3
+ Treg cells as a percentage of
CD4+ T cells from spleens, lymph nodes, peripheral blood, and TILs from isotype and
anti-TIGIT treatment groups is shown at right. D, Representative flow cytometry plots
of IL2, IFNγ, and TNFα expression on CD4+ and CD8
+ T cells of TILs from isotype
and anti-TIGIT treatment groups (left). Quantitation of IL2-producing, and
IFNγ/TNFα dual–producing, CD4+ and CD8
+ T cells as percentages of total CD4
+ and
CD8+ TILs is shown at right. Data represent mean ± SD with two independent
biological duplications.
Figure 6
Blocking TIGIT/CD155 signaling increased T-cell resistance to MDSC– and Treg–
mediated suppression. A, Representative of suppression assay of PMN-MDSCs
isolated from isotype and anti-TIGIT treatment groups cocultured with CFSE-labeled
effector T cells for 72 hours (left). Quantitation of suppression assay of PMN-MDSCs
and M-MDSCs subsets isolated from isotype and anti-TIGIT treatment groups is
shown at right. B, Relative quantification of arginase-1 (Arg1) transcripts in
PMN-MDSCs and M-MDSCs subsets sorted from isotype and anti-TIGIT treatment
groups. The relative mRNA expression was counted as the ratio of anti-TIGIT
treatment groups to isotype groups. C, Representative contour plots of annexin V+PI
+
apoptotic PMN-MDSCs. Isolated MDSCs were cultured with TIGIT mAb or isotype
antibody in vitro for 20 hours and stained annexin V and PI to assess apoptotic
percentage by flow cytometry (left). Quantitation of annexin V+PI
+ apoptotic
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PMN-MDSCs and M-MDSCs from TIGIT mAb or isotype antibody treatment in vitro
is shown at right. Data represent mean ± SD with two independent biological
duplications. D, Representative of suppression assay of Tregs isolated from isotype
and anti-TIGIT treatment groups cocultured with CFSE-labeled CD8+ effector T cells
for 72 hours (left). Quantitation of suppression assay of Tregs isolated from isotype
and anti-TIGIT treatment groups is shown at right. E, Representative contour plots of
annexin V+PI
+ apoptotic Tregs. Isolated Tregs were cultured with TIGIT mAb or
isotype antibody in vitro for 20 hours, and stained annexin V, and PI to assess
apoptotic percentage by flow cytometry (left). Quantitation of annexin V+PI
+
apoptotic Tregs from TIGIT mAb or isotype antibody treatment in vitro is shown at
right. F, Quantitation of TGFβ1 concentrations in the supernatants from TIGIT mAb
(10 or 20 μg/ml) or isotype antibody (10 μg/ml) in vitro treated Tregs by ELISA. Data
represent mean ± SD with two independent biological duplications.
Figure 7
The therapeutic efficacy by anti-TIGIT is mainly dependent on CD8 T cells and Tregs.
(A and B), Beginning on day 0, Tgfbr1/Pten 2cKO mice were administered tamoxifen
each day for 5 consecutive days. On the day before tamoxifen administration and on
day 4, mice were treated with anti-CD4, anti-CD8, anti-CD25, anti-Gr-1, or isotype
control. On day 5, the efficacy of depletion was detected by flow cytometry
(Supplementary Fig. S8A-C). On day 12, mice were treated with TIGIT mAb or
isotype antibody i.p. three times per week for three weeks as described in Figure 5.
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Quantitation of tumor volume in Tgfbr1/Pten 2cKO mice over time (n = 4,
respectively). C, Representative flow cytometry contour plots of TIGIT expression on
CD4+CD25
hiFoxp3
+ Tregs cells of TILs from isotype and anti-PD-1 treatment groups
(left). Quantitation of TIGIT expression on Tregs of spleens, lymph nodes, and TILs
from isotype and anti-PD-1 treatment groups is shown at right (n = 5, respectively).
Data represent mean ± SD.
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Published OnlineFirst August 6, 2019.Cancer Immunol Res Lei Wu, Liang Mao, Jian-Feng Liu, et al. squamous cell carcinomaand enhances antitumor capability in head and neck Blockade of TIGIT/CD155 signaling reverses T-cell exhaustion
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