Novel hexavalent GITR agonists stimulate T cells and enhance memory formation
GITR agonism enhances cellular metabolism to support CD8 ...€¦ · 28/08/2018 · PI3Kδ, and...
Transcript of GITR agonism enhances cellular metabolism to support CD8 ...€¦ · 28/08/2018 · PI3Kδ, and...
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GITR agonism enhances cellular metabolism to support CD8+ T cell proliferation and effector
cytokine production in a mouse tumor model
Simran S. Sabharwal1, 2
, David B. Rosen1, Jeff Grein
1, Dana Tedesco
1, Barbara Joyce-Shaikh
1, Roanna
Ueda1, Marie Semana
3, Michele Bauer
3, Kathy Bang
3, Christopher Stevenson
3, Daniel J. Cua
1, Luis A.
Zúñiga1
1Merck & Co., Inc., Palo Alto, CA 94304 USA
2Current address: Pfizer, South San Francisco, CA 94080 USA
3Charles River Laboratories – Insourcing Solutions
Running Title: GITR agonism increases CD8+ T cell metabolism
Keywords: GITR, DTA-1, glycolysis, fatty acid oxidation, metabolism
Corresponding Author: Luis A. Zúñiga, Merck & Co., Inc., 901 S. California Ave., Palo Alto, CA, USA
94304. Phone: 650-496-1181; Fax: 650-496-1200; E-mail: [email protected]
Conflicts of Interest: All authors are employees of Merck & Co., Inc., Kenilworth, NJ USA
Précis: Further understanding of the mechanism of action of GITR agonist antibodies can provide
insight into appropriate combination therapies and help interpret future clinical trial data.
Word Count: 4,996 (Intro: 531; Results; 2,831; Discussion/Acknowledgements: 756; Figure Legends;
878)
Number of Figures: 7, + 3 Supplemental
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Abstract
GITR is a costimulatory receptor currently undergoing Phase I clinical trials. Efficacy of anti-GITR
therapy in syngeneic mouse models requires regulatory T-cell depletion and CD8+ T-cell costimulation.
It is increasingly appreciated that immune cell proliferation and function is dependent on cellular
metabolism. Enhancement of diverse metabolic pathways leads to different immune cell fates. Little is
known about the metabolic effects of GITR agonism; thus, we investigated whether costimulation via
GITR altered CD8+ T-cell metabolism. We found activated, GITR-treated CD8
+ T cells upregulated
nutrient uptake, lipid stores, glycolysis and oxygen consumption rate (OCR) in vitro. Using MEK,
PI3Kδ, and metabolic inhibitors, we show increased metabolism is required, but not sufficient, for GITR
antibody (DTA-1)-induced cellular proliferation and IFNγ production. In an in vitro model of PD-L1-
induced CD8+ T-cell suppression, GITR agonism alone rescued cellular metabolism and proliferation,
but not IFNγ production; however, DTA-1 in combination with anti-PD-1 treatment increased IFNγ
production. In the MC38 mouse tumor model, GITR agonism significantly increased OCR and IFNγ
and granzyme gene expression in both tumor and draining lymph node (DLN) CD8+ T cells ex vivo, as
well as basal glycolysis in DLN and spare glycolytic capacity in tumor CD8+ T cells. DLN in GITR-
treated mice showed significant upregulation of proliferative gene expression compared to controls.
These data show that GITR agonism increases metabolism to support CD8+ T-cell proliferation and
effector function in vivo, and that understanding the mechanism of action of agonistic GITR antibodies
is crucial to devising effective combination therapies.
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Introduction
Immunotherapies have revolutionized the treatment of various cancers (1,2). Current methods involve
checkpoint receptor blockade on cytotoxic effector T cells, attenuating immune inhibitory signals and
leading to tumor eradication. Despite remarkable clinical success, the majority of patients still do not
respond to these drugs (3). For this reason, the next generation of immunotherapies aims to activate
costimulatory receptors to help initiate antitumor responses.
The tumor necrosis factor (TNF) superfamily is a group of related costimulatory receptors that have
received much interest as potential cancer immunotherapies (4). These include 4-1BB (CD137), CD27,
OX40 (CD134), and glucocorticoid-induced TNFR family-related protein (GITR, CD357). All of these
targets currently have drugs undergoing clinical trials as monotherapies, in combination with checkpoint
blockade therapy, or in combination with additional costimulatory receptors (5-8). TNF receptors are
characterized by their ability to bind TNF family ligands and activate the NF-B pathways via
recruitment of TNF receptor associated factors (TRAFs), a family of six proteins that are recruited to
further transduce signals within the cell (9).
Regulatory T cells (Tregs) have high expression of GITR. Much of the previous research investigating
the mechanism of action of anti-GITR therapy has focused on the antibody’s ability to mediate Treg
depletion within the tumor microenvironment (TME), reducing immunosuppression of tumor infiltrating
lymphocytes (TILs). Despite this attention on Treg reduction within the tumor, it is clear that the direct
agonist effect of anti-GITR therapy on effector cells is required for full antitumor efficacy seen in
preclinical models (10,11).
In CD8+ T cells stimulated with suboptimal anti-CD3 concentrations, GITR agonism is associated with
increased cellular proliferation and production of effector molecules, such as perforin, granzymes, and
interferon gamma (IFNγ) (12). Large energetic demands are associated with the rapid expansion of
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stimulated T cells, requiring increased glycolysis and mitochondrial respiration. Increased IFNγ
production is also linked with increased glycolysis (13,14). Modulating cellular metabolism is emerging
as a central theme in elucidating how coinhibitory molecules repress T-cell activation and how
costimulatory molecules enhance T-cell receptor signaling, proliferation, and effector function. Indeed,
CD28, a costimulatory receptor on T cells, was shown to potentiate T-cell activation via upregulation of
glycolysis and mitochondrial priming via enhanced fatty acid oxidation (FAO) (15,16). 4-1BB was also
shown to enhance glycolysis and FAO to support increased T-cell proliferation (17). Conversely,
signaling along the PD-L1/PD-1 inhibitory axis prevents T-cell upregulation of glycolysis while
promoting lipolysis and FAO, whereas CTLA-4 signaling prevents upregulation of glycolysis and FAO,
keeping T cells in a naïve-like, quiescent state (18).
We hypothesized that anti-GITR agonist therapy augments cellular metabolism in CD8+ T cells. In the
current study, we demonstrated that GITR antibody therapy enhances CD8+ T-cell activation and
metabolism under both suboptimal and supraoptimal stimulation conditions. Using small molecule and
checkpoint inhibitors, we demonstrated that GITR agonist-induced metabolism is required, but not
sufficient by itself, for rescuing T cell activation, depending on what other signaling pathways are being
perturbed. In vivo, anti-GITR treatment also enhanced CD8+ T cell metabolism and upregulated
proliferative gene expression. These data show GITR agonism increases metabolism to support CD8+ T
cell effector function and proliferation in vivo, and understanding the mechanism of action of anti-GITR
antibodies is crucial to devising effective combination therapies.
Materials and Methods
Mice and reagents
Wildtype C57BL/6J and Foxp3-GDL (C57BL/6J background) mice were obtained from The Jackson
Laboratory and housed and bred under specific pathogen-free conditions in the Merck & Co., Inc., Palo
Alto, CA USA animal facility. MC38 mouse colon carcinoma cell line was obtained from the
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Developmental Therapeutics Program Tumor Repository (Frederick National Laboratory) and
authenticated using genomic profiling (IDEXX RADIL Cell Check) and tested to be Mycoplasma free
(IMPACT I PCR Profile). Cells were frozen down at passage five. For each experiment, cells were
thawed and placed in T75 flasks, and two days later were expanded into several T175 flasks. Three days
later, cells were counted and resuspended at the appropriate concentration prior to injection into mice.
Rat anti-mouse DTA-1 GITR antibody (S. Sakaguchi, Kyoto University), was murinized as previously
described (19) for in vivo studies. A proprietary mouse anti-PD-1 (DX400) was made in-house at Merck
& Co., Inc., Palo Alto, CA USA (20). All animal studies were performed in accordance to protocols
approved by Merck Research Laboratories’ Ethics board.
In vivo tumor models
For syngeneic tumor experiments, 8 to 12 week old mice were subcutaneously injected with 106
MC38 cells on the right flank. Tumor diameter was measured by electronic calipers and tumor volume
was calculated using the formula V = (W2 × L)/2, where V is tumor volume, W is tumor width, and L is
tumor length. DTA-1 or isotype control was administered once at 5mg/kg subcutaneously when tumors
reached 100 ± 30 mm3. Tumor draining lymph nodes (DLNs) were harvested and mechanically
disrupted to obtain a single cell suspension. For TIL isolation, tumors were mechanically disrupted and
digested for 45 minutes at 37°C in the presence of collagenase 1 (300 Collagenase Digestion Units/mL;
Sigma), DNase 1 (400 Domase Units/mL; Calbiochem) and Dispase II (1 mg/mL; Roche). The digested
tumor material was centrifuged in 40% Percoll for 10 minutes at 2000 RPM to further enrich leukocytes.
CD8+ T cells were isolated using a positive selection kit (Miltenyi Biotec; Cat#130-049-401).
In vitro T-cell isolation and activation
Lymphocytes were isolated from lymph nodes and spleens of naïve C57BL/6J mice. Tissue was
mechanically disrupted and passed thru a 70µM filter, and red blood cells were removed using ACK
lysis buffer (Gibco, Cat#A1049201). CD8+ T cells were isolated using a negative selection kit (Miltenyi
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Biotec, Cat#130-104-075) per manufacturer’s instructions (typical purity ~92-95% of live cells). Cells
were plated in 6-well tissue culture plates with plate bound antibodies. Suboptimal conditions consisted
of low dose plate-bound anti-CD3 (0.1 µg/mL). Supraoptimal conditions consisted of plate-bound anti-
CD3 (10 µg/mL), anti-CD28 (2 µg/mL), and IgG1Fc (10 µg/mL). For PD-L1 inhibited cells, PD-L1 (10
µg/mL) was used instead of IgG1Fc. Cells were treated with either IgG2a (10 μg/mL; eBioscience
Cat#16-4724-85 or in-house), or DTA-1 (10 μg/mL; eBioscience Cat#16-5874-83 or in-house). For
small molecule inhibitor studies, T cells were activated for 16 hours prior to addition of the inhibitors.
Thirty minutes later, antibodies were added, and experiments were performed after an additional 48
hours. Etomoxir (#E1905), PD98059 (#P215), SW30 (#526559), and Oligomycin A (#75351) were
purchased from Sigma. SB203580 (#SYN-1074) was purchased from Adipogen.
Western blotting
Cells were lysed in M-PER buffer (ThermoFisher Cat#78501) with Pierce Protease and Phosphatase
Inhibitor Cocktail (ThermoFisher Cat#88668). Lysates were separated on SDS-polyacrylamide gels
(Bio-Rad) and transferred to nitrocellulose membranes that were blotted with primary antibodies. Blots
were further incubated with secondary horseradish peroxidase-conjugated antibodies (Cell Signaling)
and stained with ECL reagent (Amersham). Chemiluminescence was detected on film. All antibodies
were purchased from Cell Signaling. Primary antibodies used were: p105/p50 (Cat#3035), phospho-
p105 (#4806), p100/p52 (#4882), phospho-p100 (#4810), p65 (#8242), phospho-p65 (#3033), Erk1/2
(#4695), phospho-Erk1/2 (#4370), Jnk (#9252), phospho-Jnk (#9255), p38 (#9212), phospho-p38
(#9211), p70S6k (#2708), phospho-p70S6k (#9234).
Flow cytometry
Isolated cells were stained for thirty minutes in PBS, washed, and analyzed on an LSRII or LSR
Fortessa flow cytometer (BD). All flow antibodies were purchased from BD as follows: CD44
(#559250), CD62L (#564108), IL-7Ra (#560733), CD25 (#564021). Live/Dead near-IR (#L10119) and
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CellTrace Violet (C34557) were purchased from Sigma. Data was acquired using the FACS DIVA
software (BD Biosciences). All flow cytometry data were analyzed with FlowJo (TreeStar Software,
Ashland, OR, USA).
Nutrient uptake assays
All fluorescent nutrient stains were purchased from Sigma. Approximately 250,000 activated CD8+ T
cells were placed in 400uL of RPMI media with one of the markers at the following concentrations: 2-
NBDG (100 μg/mL; #N13195), BODIPY (1.25 μg/mL; D3922), C12-BODIPY (1 μM; #D3822), and
C16-BODIPY (0.5 μM; #D3821). Cells were incubated for 30 minutes at 37°C prior to washing and
surface staining for flow cytometry analysis.
Seahorse extracellular flux analysis
Seahorse tissue culture plates were coated with Cell-Tak (Corning, 22.4 μg/mL) per manufacturer’s
instructions. Cells were counted on a ViCell Analyzer, and 200,000 viable CD8+ T cells were plated per
well per manufacturer’s instructions. Seahorse media used consisted of glucose (10 mM), glutamine (2
mM), and sodium pyruvate (1 mM). For in vitro assays, basal metabolic measurements were taken
followed by sequential injection of etomoxir (100 μM; Sigma #E1905), oligomycin (1 μM), and
rotenone/antimycin A (0.5 μM). For ex vivo assays, basal metabolic measurements were taken followed
by sequential injections of oligomycin (1 μM), FCCP (2 μM), and rotenone/antimycin A (0.5 μM).
Cell viability and size
Cell viability and size was assessed using a ViCell Analyzer (Beckman Coulter) per manufacturer’s
instructions.
ELISA assays
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Cell culture supernatants were collected and interferon gamma levels were assessed using a mouse IFNγ
DuoSet ELISA kit (R&D Systems; #DY485) per manufacturer’s instructions and read on a SpectraMax
microplate reader (Molecular Devices).
RNA expression analysis
For real-time PCR analysis, total RNA was isolated from cells using Arcturus PicoPure RNA Isolation
method, according to manufacturer's protocol (Thermo Fisher Scientific, Foster City, CA).
Real-time quantitative PCR for gene expression
DNase-treated total RNA was reverse-transcribed using QuantiTect Reverse Transcription (Qiagen,
Valencia, CA) according to manufacturer's instructions. Primers were obtained commercially from
Thermo Fisher Scientific (Foster City, CA). Primer assay IDs were as follows: Ebi3 =
Mm00469294_m1; Cxcl10 / IP-10 = Mm00445235_m1; IL-2 = Mm00434256_m1; Icam1 =
m00516023_m1; Nt5e – CD73 = Mm00501917_m1; TBX21 – Tbet = Mm00450960_m1; Socs1 =
Mm00782550_s1; BCl2l1 – Bcl-xl = Mm00437783_m1; Gitr – Tnfrsf18 = Mm00437136_m1; Plscr1
(exons 8-9) = Mm01228223_g1; IL-2ra - Cd25 = Mm00434261_m1; Gzmb = Mm00442834_m1; Ox40
– Tnfrsf4 = Mm00442039_m1; Gls2 = Mm01164862_m1; Axl = Mm00437221_m1; Gpt2 =
Mm00558028_m1; Pdk1 = Mm00554300_m1; Slc7a5 = Mm00441516_m1; Slc3a2 - Cd98 =
Mm00500525_m1; Myc - c-myc = Mm00487803_m1; Chek1 - Chk1 = Mm00432485_m1; Ccnb1 =
Mm00838401_g1; Aurkb - Aurb (Exons 7-8) = Mm01718146_g1; Cdkn2c - p18 INK4c =
Mm00483243_m1; Nusap1 = Mm01324634_m1; Smc2 = Mm00484340_m1; Rrm1 =
Mm00485876_m1; Ccna2 = Mm00438064_m1; Ccnb2 = Mm00432351_m1; Birc5 – Survivin =
Mm00599749_m1; Gzma = Mm00439191_m1; Gzmk = Mm00492530_m1; Ifng = Mm01168134_m1;
Klrg1 = Mm00516879_m1; Cpt1a = Mm01231186_m1; Slc2a3 - Glut3 = Mm01184104_m1; Hif1a -
MOP1 = Mm00468875_m1. Gene specific pre-amplification was done per Fluidigm Biomark
manufacturer's instructions (Fluidigm, Foster City). Real-time quantitative PCR was performed on the
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Fluidigm Biomark using two unlabeled primers at 900 nM each were used with 250 nM of FAM-labeled
probe (Thermo Fisher Scientific, Foster City, CA) with Taqman Universal PCR Master Mix with
UNG. Samples and primers were run on a 96.96 Array per manufacturer's instructions (Fluidigm, Foster
City). Ubiquitin levels were measured in a separate reaction and used to normalize the data by the Δ-Δ
Ct method. Using the mean cycle threshold value for ubiquitin and the gene of interest for each sample,
the equation 1.8 ^ (Ct ubiquitin minus Ct gene of interest) x 104 was used to obtain the normalized
values.
Statistics
Statistical analysis was performed using GraphPad Prism software. Unless otherwise noted, two
samples were compared using Student t test and multiple samples were compared using 2-way ANOVA
followed by the Tukey’s multiple comparisons test.
Results
Low-dose anti-CD3 plus GITR agonism enhances CD8+ T cell activation and metabolism
In addition to T-cell receptor stimulation, costimulatory signals are needed to optimally activate CD8+ T
cells (e.g. CD28). Previous studies investigating costimulatory effects of GITR agonism utilized
suboptimal anti-CD3 stimulation only, showing GITR treatment enhanced cellular proliferation and
effector molecule production (12). In agreement with these studies, we show DTA-1 treatment of CD8+
T cells, under suboptimal stimulation, enhances cellular proliferation. GITR agonism increases the
number of actively proliferating cells and the number of divisions that the proliferating cells undergo
(Fig. 1A-B).
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As increased activation states of T cells often require increased energy demands to support augmented
cellular proliferation and cytokine production, we tested if DTA-1 treatment would alter cellular
metabolism of activated CD8+ T cells. We observed significant increases in oxygen consumption rate
(OCR) (Fig. 1C) and extracellular acidification rate (ECAR, a measure of glycolysis) (Fig.1D) with
DTA-1 treatment.
The concept of nutrient competition in the TME between effector T cells and cancer cells posits that
enhancing a T cell’s “fitness” to access and utilize nutrients can enable better tumor clearance (21,22).
Hence, we tested whether GITR agonism would increase nutrient uptake in CD8+ T cells. Using the
fluorescent glucose analog 2-NBDG, we show DTA-1 treatment significantly increases glucose uptake
(Fig. 1E). Further, anti-GITR agonism enhances CD8+ T cell effector function as measured by IFNγ
production (Fig. 1F).
Our data confirm the costimulatory role of GITR signaling in CD8+ T cells, and demonstrate DTA-1
treatment leads to increased metabolism. However, it is unsurprising that metabolism was affected
under these conditions, as increases in proliferation and effector cytokine production require increased
metabolic function to meet the energy and biosynthetic demands of rapid cellular expansion (13).
DTA-1 enhances CD8+ T-cell activation despite optimal anti-CD3/CD28 stimulation
We next sought to determine the effects of DTA-1 on CD8+ T cells activated with supraoptimal
stimulation in vitro. We wanted to create activation conditions that removed the proliferative advantage
of GITR-stimulated cells to determine if DTA-1 treatment would enhance CD8+ T-cell activation and
metabolism in a proliferation-independent manner.
Here, DTA-1 treatment increased cell size relative to IgG2a isotype-treated controls (Fig. 2A). Viability
of control cells declined by Day 3, whereas DTA-1 attenuated this decrease (Fig. 2B). These data are
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consistent with reports that GITR and other TNFRs increase cell survival through regulation of anti-
apoptotic proteins such as Bcl-xL(23).
Surface activation markers were assessed to ascertain the extent that DTA-1 treatment augments CD8+
T-cell stimulation under these conditions. IL7Ra and CD25 expression were upregulated by DTA-1
treatment under these optimal activation conditions. DTA-1 treatment also decreased expression of
CD62L (Fig. 2C). Despite the supraoptimal conditions used in this study, DTA-1 still upregulated IFNγ
expression (Fig. 2D).
TNFRs are defined by their ability to upregulate NF-B signaling. Two NF-B signaling pathways are
known: the canonical pathway (NF-B1) and the non-canonical pathway (NF-B2). The relative extent
by which the various TNFR members can potentiate these two distinct pathways is unclear (24). Here
we show DTA-1 treatment leads to elevated phosphorylation, and therefore activation, of p105 (NF-
B1), p100 (NF-B2), and p65 (RelA) (Fig. 2E). Although both NF-kB pathways are activated, DTA-1
enhanced the amount of total NF-B2 protein, matching gene expression data demonstrating significant
DTA-1–induced upregulation of Nfkb2 message without upregulation of Nfkb1 message (Fig. 2F). Ikba
and Gadd45b, two target genes of NF-B, are also upregulated. These data demonstrate increased NF-
B activity downstream of anti-GITR agonism.
Despite the lack of increased proliferation with DTA-1 under optimal stimulation conditions, we
observed significant increases in both the ECAR and OCR (Fig. 2G). Two days post-stimulation, the
DTA-1–induced increase in OCR is entirely due to increased FAO, as indicated by the etomoxir-
sensitive portion of basal OCR (Fig. 2G, panel 3). Etomoxir inhibits carnitine palmitoyl transferase 1a
(CPT1a), the rate-limiting step of FAO. However, three days post-activation there is virtually no
etomoxir effect in either control or DTA-1–treated cells, suggesting a shift in substrate utilization by the
mitochondria (Fig. 2G, panel 4).
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DTA-1 treatment significantly increased 2-NBDG uptake. Cells can increase uptake of other nutrients to
feed their energy demands, and DTA-1 treatment also increased C12 medium-chain and C16 long-chain
fatty acid uptake, and increased intracellular lipid stores, assessed by BODIPY staining (Fig. 2H). Lipid
stores can be mobilized for ATP production via mitochondrial oxidative phosphorylation (OXPHOS).
These data suggest anti-GITR treatment increases CD8+ T cell fitness in vitro by improving nutrient
uptake and allowing cells to have increased flexibility in altering the carbon sources they use to meet
their energy and biosynthetic needs.
A panel of genes was associated with increased CD8+ T-cell proliferation, activation, or function (Fig.
2I). These include upregulation of IL-2 message and its receptor, CD25, downregulation of inhibitory
receptor CD73, and confirmation of Bcl-xl upregulation. DTA-1 treatment also upregulated Tbet
transcripts, which is important for induction of a type 1 cytotoxic T-cell (Tc1) phenotype critical for
CD8+ T cell–mediated tumor killing (25).
Transcripts for several metabolic targets were also upregulated with DTA-1 treatment (Fig. 2J). These
include upregulation of the master metabolic transcription factor c-myc, as well as several other
metabolic enzymes and solute transporters (26-28). These data cumulatively suggest that DTA-1
treatment increases global cellular metabolism, even when proliferation is not enhanced.
DTA-1–induced cellular proliferation requires increased glycolytic and mitochondrial metabolism
We next performed experiments following stimulation conditions described above and using 2-
deoxyglucose (2-DG), a competitive inhibitor of glycolysis, at a dose sufficient to highly attenuate
glycolysis but not completely abolish it. Under these conditions, we demonstrate that DTA-1 is unable
to rescue OCR (Fig. 3A), ECAR (Fig. 3B), 2-NBDG uptake (Fig. 3C), proliferation (Fig. 3D and
Supplementary Fig. S1A), or IFNγ production (Fig. 3E) by CD8+ T cells. Although the metabolic and
IFNγ 2-DG isotype controls trend downward versus vehicle controls, there is no significant difference
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between these groups. Proliferation, however, is significantly blunted in control 2-DG cells, and being
unable to rescue metabolic function, DTA-1 is incapable of rescuing cellular proliferation.
Incubating cells with etomoxir to inhibit FAO did not significantly decrease metabolic function, though
there is a slight trend downwards when comparing DTA-1–treated groups (Fig. 3A-C). Although 100
µM etomoxir is sufficient to fully inhibit FAO upon acute administration during Seahorse experiments,
that concentration only partially inhibits FAO after two days of incubation. This is supported by the fact
that the DTA-1–induced increase in OCR of etomoxir-incubated cells (Fig. 3A) is completely FAO-
dependent and etomoxir-sensitive (Supplementary Fig. S1B). With only partial inhibition of FAO, there
is still a significant decrease in cellular proliferation (Fig. 3D and Supplementary Fig. S1A) that DTA-1
is unable to rescue. DTA-1’s inability to rescue proliferation in etomoxir-incubated cells may be
dependent on increasing OCR, which indicates that increased FAO following DTA-1 treatment supports
increased proliferation.
IFNγ levels trend down in isotype controls with etomoxir treatment, though there is a significant
increase with DTA-1. This increase, however, is still significantly lower than DTA-1–treated vehicle
controls. These data suggest that FAO may play a role in IFNγ production, though this effect may be
due to the proliferative advantage seen in control cells or confounded by only partially inhibiting FAO
with etomoxir incubation.
We next used oligomycin to inhibit ATP synthase and block mitochondrial ATP synthesis. OCR was
severely attenuated and DTA-1 could not rescue it (Fig. 3F). ECAR was significantly upregulated in
isotype control cells with oligomycin, whereas DTA-1 treatment further increased ECAR (Fig. 3G).
DTA-1 treatment significantly upregulated 2-NBDG uptake compared to isotype and oligomycin-treated
cells (Fig. 3H). Without mitochondrial ATP production, there was a proliferative disadvantage in
oligomycin-treated cells that DTA-1 administration was unable to rescue, which highlights the
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importance of mitochondrial respiration for basal and DTA-1–induced cellular proliferation (Fig. 3I and
Supplementary Fig. S1C). There was a comparable amount of IFNγ production versus vehicle controls
(Fig. 3J), despite the reduced proliferation in oligomycin-treated isotype controls (93.7% vs. 47.6%; Fig.
3K), further demonstrating the importance of increased glycolytic function on IFNγ production. DTA-1
significantly increased IFNγ production in cells treated with oligomycin. Although these DTA-1–
induced levels were significantly lower than DTA-1 control levels, this is likely due to the lower number
of proliferating cells in the oligomycin group (95.6% vs. 45.6%). Collectively, these data underscore the
central role of metabolism in cellular proliferation and IFNγ production.
DTA-1 upregulates MAPK signaling and can rescue CD8+ T cells from MEK inhibition
TNFRs also signal through the p38, JNK, and ERK MAP-Kinase pathways. There are conflicting
reports as to which pathways are activated in specific T-cell subsets, depending on which TNFR is
involved (12,29,30). Here we demonstrate that phosphorylation and activation of all three MAPK
pathways are enhanced by DTA-1 (Fig. 4A). Activation of these pathways in control conditions appears
to decrease between 48 and 72 hours, whereas DTA-1–treated cells display enhanced signaling during
the same time interval.
To dissect which MAPK pathways are involved in regulating the observed DTA-1–induced changes, we
used the p38 inhibitor SB203580 and the MEK inhibitor PD98059. We found that p38 inhibition of
isotype-treated cells had no effect on cellular metabolism (Fig. 4B-C) or 2-NBDG uptake (Fig. 4D),
whereas MEK inhibition significantly decreased all metabolic readouts. DTA-1 increased metabolic
parameters of all treatment groups, including rescue of MEK-inhibited cells to vehicle/IgG2a control
levels.
Many receptor signals activate both the RAS-RAF-MEK-ERK and PI3K-AKT-mTOR pathways, which
both play a role in cell growth and proliferation (31). We hypothesized that GITR agonism by DTA-1
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may rescue MEK inhibition, in part, by upregulating the PI3K signaling axis. p70S6k is a kinase
downstream of mTOR that is specifically activated by the Akt pathway (32). p70S6k levels decreased
with MEK inhibition (Fig. 3E), likely due to cross-talk between the two pathways (31), but DTA-1
treatment rescued the amount of phosphorylated, activated enzyme. Phospho-Akt and phospho-4EBP1,
another mTOR-regulated protein, were increased (Supplementary Fig. S2A).
We next used the PI3Kδ inhibitor SW30 together with MEK inhibition (33). Both PD98059 and SW30
attenuated 2-NBDG uptake (47.3% and 40.5%, respectively), and DTA-1 rescued both to vehicle/IgG2a
control levels (Fig. 4F). The combination of the two drugs reduced 2-NBDG uptake further (59.7%
reduction). Although DTA-1 increased 2-NBDG levels when administered with the inhibitor
combination, the levels were significantly lower than DTA-1-rescued levels of either small molecule
alone. OCR (Fig. 4G) and ECAR (Fig. 4H), were comparably affected. As described earlier (Fig. 2G),
the DTA-1–induced increase in OCR two days post-dosing can be wholly attributed to an increase in
FAO, as the increase is etomoxir-sensitive. With small molecule inhibitors, however, OCR is still
significantly higher in DTA-1–treated cells after etomoxir administration, compared with isotype
controls. This may be due to additional impairment in access to or utilization of other carbon sources
for fuel in isotype-treated groups, though further studies are needed to explore this.
Increased pathway signaling and metabolic rescue via DTA-1 treatment were sufficient to rescue
cellular proliferation (Fig. 4I, Supplementary Fig. S2B-D). There was significant rescue of IFNγ levels
by DTA-1 treatment when assessed using multiple different t tests (Fig. 4J, panel 1, untransformed
data). The large IFNγ concentration in DTA-1 vehicle control cells required a log transformation of the
data to compare values between treatment groups. ANOVA of the transformed data showed that DTA-1
did rescue MEK-inhibition IFNγ levels to isotype vehicle control levels (Fig. 4J, panel 2, transformed
data). DTA-1 also significantly increased IFNγ levels in PI3Kδ inhibitor-treated cells; though this was
significantly lower than the MEK-inhibited and vehicle control cells treated with DTA-1. DTA-1 did
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not rescue the IFNγ levels in the combination treatment group. These data show that, although metabolic
rescue may be sufficient for increased proliferation, increased metabolism alone is not sufficient to
rescue effector function, as indicated by IFNγ levels.
Combined checkpoint blockade and anti-GITR therapy overcome PD-L1-induced T-cell inhibition
As current clinical immunotherapy strategies involve combination treatment with immune checkpoint
inhibitors, we sought to test if anti-GITR treatment would beneficially combine with anti-PD-1
administration in an in vitro system. In addition to stimulating CD8+ T cells with anti-CD3/anti-CD28,
we used plate-bound PD-L1 to inhibit activation and simulate PD-1-associated immunosuppression that
T cells may experience in certain TMEs.
PD-L1 inhibition decreased cell viability (Fig. 5A). Monotherapy with either anti-GITR agonism or PD-
1 blockade partially rescued viability, whereas combination therapy significantly restored cell viability
to uninhibited levels. A similar pattern was seen with basal OCR (Fig. 5B), basal ECAR (Fig. 5C), and
2-NBDG uptake (Fig. 5D). Cellular proliferation showed a similar response (Fig. 5E). PD-L1
attenuated the percentage of cells undergoing four or five cell divisions (gate 5 and 6, respectively),
while increasing the percent of cells that underwent only one or no cell divisions (gates 2 and 1,
respectively). Monotherapy partially rescued PD-L1-associated inhibition of proliferation, whereas
combination therapy rescues it further.
Although PD-L1 signaling abrogated IFNγ production, DTA-1 treatment alone did not significantly
rescue production of this cytokine, whereas anti-PD-1 monotherapy displayed partial rescue (Fig. 5F).
Combination therapy combined to fully restore IFNγ production to non-PD-L1–treated levels. Although
enhanced glycolytic flux can lead directly to enhanced IFNγ production, here, DTA-1 monotherapy
rescues ECAR and 2-NBDG uptake (Fig. 5C and 5D, respectively), but not IFNγ production. These
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data suggest that metabolic enhancement alone is not sufficient to rescue IFNγ production, indicating
that other signals through the PD-L1/PD-1 axis inhibit IFNγ production.
DTA-1 treatment in a mouse model enhances CD8+ T-cell activation and proliferation in vivo
After demonstrating that anti-GITR agonism alters CD8+ T-cell activation, PI3K/MEK/mTOR signaling,
and metabolism in vitro, we wanted to test whether similar changes are observed in vivo. To this end,
we challenged mice with syngeneic MC38 colon cancer cells, which are known to respond well to anti-
GITR therapy. Tumors were harvested eight days post-treatment, at a time where tumor regression is
just beginning to occur (Fig. 6A). DTA-1–treated TIL and DLN CD8+ T cells also had enhanced
PI3K/MEK/mTOR signaling (Supplementary Fig. S3A, B). DTA-1–treated DLN were significantly
larger than IgG2a-treated DLN, suggesting substantially more cellular proliferation was occurring in the
DLN from DTA-1–treated mice (Fig. 6B). This increase in cellular proliferation is further verified by
upregulation of a panel of pro-proliferative genes in DLN CD8+ T cells (Fig. 6C), and Ki67 staining
(Supplementary Fig. S3C).
Gene transcripts for cytotoxic effector molecules were significantly upregulated in both TIL and DLN
CD8+ T cell populations (Fig. 6D). Ifng, Gzma, Gzmb, and Gzmk transcripts were significantly elevated
in response to DTA-1 treatment. These data suggest that DTA-1 promotes a Tc1 phenotype that
enhances antitumor immunity.
GITR and other TNFRs are also associated with enhanced memory cell formation (34). As expected,
DTA-1 treatment increased the CD44+CD62L
– effector memory and CD44
+CD62L
+ central memory
pools relative to IgG2a controls (Fig. 6E). Enhanced memory formation by DTA-1 was supported by
increased gene transcript of the memory marker Klrg1 (35) in both TIL and DLN (Fig. 6F). This
indicated that the T cell population within the DLN was a complex mixture of naïve, newly activated,
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and memory cells. The shift from naïve cells towards effector and memory cells, along with our
previous in vitro data, implies GITR agonism may participate in the priming phase of CD8+ T cells (36).
Since DTA-1 treatment depletes TIL, not DLN Tregs (19), the increased CD8+ T cell proliferation in the
DLN, and the increased effector molecule transcript levels can be attributed to GITR agonist effects of
the DTA-1 antibody. This suggests that GITR agonism contributes to CD8+ T cell expansion and
priming in the DLN to enhance antitumor immunity.
GITR agonism increases CD8+ T cell metabolism in the DLN and tumor of MC38-bearing mice
DTA-1 treatment in MC38-bearing mice significantly increased both OCR and ECAR in DLN CD8+ T
cells (Fig. 7A and 7B, respectively). TIL CD8+ T cells also had significantly increased OCR (Fig 7C).
Although reports indicate improved effector function is generally accompanied by increased glycolysis
(13,14), the increase we saw in TIL CD8+ T cell ECAR with DTA-1 treatment was not significant,
although 2 of 3 experiments showed increases (Fig. 7D). Several reports have highlighted the
importance of mitochondrial function on proper effector T cell performance (37-40), although no clear
mechanism of action has yet been described. It is possible that the increased OCR seen with DTA-1
allows effector CD8+ T cells to function properly in the TME. TIL CD8
+ T cells from DTA-1–treated
mice had significantly increased spare glycolytic capacity (Fig. 7E), which indicates a cell’s ability to
respond to cellular stress and increased energetic demands.
TIL CD8+ T cells did not display the same proliferative gene signature seen in DLN CD8
+ T cells, but
did regulate several metabolic gene transcripts affected by DTA-1 treatment (Fig. 7F).
DTA-1 also significantly increased BODIPY staining of internal lipid stores in DLN CD8+ T cells,
whereas TIL CD8+ T cells trend upwards (Fig. 7G).
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19
These data suggest anti-GITR agonism significantly increases CD8+ T cell metabolism during the
priming phase in the DLN, as well as during the effector phase inside the TME, thereby increasing T cell
fitness and enhancing the CD8+ T cell–mediated antitumor response.
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20
Discussion
In this study we aimed to better understand the mechanism of action of anti-GITR agonism, as opposed
to the contribution of Treg depletion, on antitumor efficacy. Here we show DTA-1 treatment
upregulated OCR and ECAR in CD8+ T cells both in vitro and in vivo. In vitro, we demonstrated that
GITR agonism increases cellular proliferation and IFNγ production, and that metabolic changes elicited
by DTA-1 treatment were required, but not sufficient by themselves, for those changes. In vivo,
enhanced metabolism was accompanied by increased proliferation in the DLN and increased effector
molecule transcription in the DLN and TIL populations.
Understanding the mechanism of action of DTA-1-mediated signaling will better inform upon how
GITR agonist therapy will combine with other immuno- and chemotherapies.
Costimulatory signals are reported to increase FAO, which supports multiple CD8+ T cell functions
(16,17). CD8+ T cells in hypoxic and hypoglycemic TMEs enhance fatty acid catabolism to maintain
effector function, mainly by utilizing endogenous fatty acids (41). Our in vitro data shows that DTA-1
increased FAO and internal lipid stores. Our in vivo data also demonstrate increased lipid droplet
formation in DLN CD8+ T cells. Whether DTA-1–induced increases in lipid stores during activation in
the DLN are then mobilized upon entry into the TME to help fuel effector function is not yet known.
Tumor cells outcompeting T cells for nutrients in the TME is one mechanism of action of tumor
immunosuppression (22). One study shows that enhancing tumor cell metabolism converts a regressive
murine cancer line into a progressive cancer (21). In cases of nutrient competition, boosting T-cell
metabolism with a GITR agonist antibody may prove beneficial. However, repression of T cell
metabolism can also result from direct interaction between T cells and tumor cells or suppressive
immune cells, or indirect interaction, via metabolites like adenosine, or tryptophan depletion via IDO
overexpression (42,43). In these cases, GITR agonism may prove insufficient in rescuing an antitumor
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21
immune response. It is vital to understand which immune inhibitory pathways can or cannot be
overcome by GITR agonism in order to devise the most effective combination therapy strategies.
Our finding that GITR agonism can potentiate T-cell activation and function has potential therapeutic
relevance. MEK inhibitors are FDA-approved against melanomas with certain mutations, and ongoing
clinical trials are testing these inhibitors with checkpoint blockade therapy. Previous work showed
MEK inhibition reduces T cell-receptor–induced apoptosis that typically occurs in exhausted T cells in
the TME, leading to increased efficacy when paired with anti-PD-1 blockade (44). The study, however,
notes that T-cell priming in the DLN is suppressed by MEK treatment. Our data show that GITR
agonism can rescue the MEK inhibitor-associated decreases in metabolism, proliferation, and IFNγ
production, which suggests that adding anti-GITR treatment to the MEK/anti-PD-1 combination may
boost antitumor clearance further by enhancing activation in the DLN. Indeed, other murine studies
show that triple combination therapy of TNFR agonist antibodies with MEK inhibitor/anti-PD-1 therapy
improves tumor clearance significantly compared to MEK/anti-PD-1 combination alone (45,46). The
TNFR antibodies in these studies target 4-1BB and OX40, but a GITR agonist antibody is likely to act
similarly. Although these studies did not identify a molecular mechanism of action, it is probable that
the enhanced PI3Kδ/Akt/mTOR signaling and augmented metabolic function that we have ascribed to
GITR agonism plays a role in rescuing MEK inhibition of T-cell activation in the DLN; however,
further studies are required to confirm this hypothesis.
Although much of the focus has remained on the Treg depletion effects of DTA-1, it is still unclear to
what extent Treg depletion contributes to efficacy. Several reports demonstrate that Treg depletion
alone does not account for the antitumor effects of GITR treatment. Our study substantiates that GITR
agonist effects of DTA-1 are necessary for proper tumor clearance. (47). (19). (48).
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22
Immune cell metabolism is increasingly appreciated for its role in influencing immune cell function.
Here we elucidated some of the metabolic effects of anti-GITR agonism to better understand the
mechanism of action of GITR agonism-induced tumor clearance. Current cancer treatment strategies are
increasingly focused on combination therapies, with anti-PD-1 therapy as the foundation (49).
Understanding the mechanism by which anti-GITR treatment increases metabolic function, and
circumstances by which this increased metabolism can rescue T-cell proliferation and effector function,
can provide improved insight into the effects of combining small molecules and immunotherapies to
modulate immune cell metabolism. These insights may lead to enhanced therapeutic strategies that will
improve patient outcomes.
Acknowledgements
We are grateful to the MRL Postdoctoral Research Fellows Program for financial support provided by a
fellowship (S.S.S.).
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23
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Figure Legends
Fig. 1: Co-stimulation with the mouse GITR agonist antibody, DTA-1, enhances activation and
metabolism in CD8+ T cells stimulated with low-dose anti-CD3. A) Representative CellTrace
Violet FACS plots of IgG2a control versus DTA-1 treated CD8+ T cells 3 days post-activation. B)
Proliferation results of 4 independent experiments. Oxygen consumption rate (OCR) (C), and glycolytic
rate (extracellular acidification rate [ECAR]) (D); N=3. E) Uptake of the fluorescent glucose analog 2-
NBDG at 72 Hours; N=5. F) ELISA results for Interferon γ (IFNγ) levels; N=3. Data are shown as
mean ± SEM. * p ≤ 0.05 using Student t test.
Fig. 2: Mouse anti-GITR agonism by DTA-1 enhances CD8+ T cell activation and metabolism
despite optimal anti-CD3/anti-CD28 stimulation. A) Cell size and (B) viability in IgG2a control
versus DTA-1–treated CD8+ T cells; N=3. C) FACS plots of activation markers. D) ELISA IFNγ
concentrations ; N=3. E) Representative NF-κB pathway western blots from two separate experiments.
F) NF-κB pathway gene expression; N=3; ns=not significant. G) ECAR (first two panels) and OCR at
baseline and after addition of 100μM etomoxir (last two panels). H) 2-NBDG uptake, intracellular lipid
droplet staining by BODIPY, and C12 and C16 fatty acid uptake. I) Gene expression heat map
depicting DTA-1 regulation of proliferation and activation-associated genes and (J) metabolic gene
transcripts. Individual color blocks represent an average of normalized gene expression from 3
individual experiments. FACS plots are representative of at least three individual experiments. Data are
shown as mean ± SD. * p ≤ 0.05 using Student t test for comparing two groups or ANOVA for multiple
groups.
Fig. 3: DTA-1–induced cellular proliferation requires increased glycolytic and mitochondrial
metabolism, whereas increased IFNγ is glycolysis-dependent. A) OCR, (B) ECAR, and (C) 2-NBDG
uptake of cells treated with Veh, 2-Deoxyglucose (2-DG), or etomoxir (Eto). D) Proliferating cells were
gated into cells undergoing 1-3 cell divisions or 4+ cell divisions. Graph represents N=3 for 2-DG and
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27
N=4 for other groups. E) IFNγ ELISA levels for 2-DG and Eto treated cells. Cells treated with the ATP
Synthase inhibitor oligomycin (Oligo) and their (F) OCR, (G) ECAR, (H) 2-NBDG uptake, (I) percent
proliferating cells, and (J) IFNγ concentration. K) Representative plot of cellular proliferation with cells
treated with Veh and oligo. N=3 for oligo experiments. Data are shown as mean ± SEM. * p ≤ 0.05,
**p≤0.05 compared to all other IgG2a treatment groups, ‡p≤0.05 compared to all other DTA-1
treatment groups, as measured by ANOVA.
Fig. 4: DTA-1 upregulates MAPK signaling and can rescue CD8+ T cells from MEK inhibition, in
part due to increased PI3K/AKT/mTOR signaling. A) Representative MAPK pathway western blots
from two separate experiments. B) OCR, (C) ECAR, and (D) 2-NBDG uptake of cells incubated with
DMSO vehicle (Veh), the p38 inhibitor SB203580 (SB), or the MEK inhibitor PD98059 (PD). E)
p70S6k western blot representative of two experiments. F) 2-NBDG uptake (N=3), (G) basal OCR
(N=4), and (H) basal ECAR (N=4) of cells incubated with Veh, PD, the PI3Kδ inhibitor SW30 (SW), or
the PD/SW combination (Com). I) CellTrace plots representative of three separate experiments. J)
IFNγ levels (left panel; multiple t tests using Holm-Sidak method). The large difference in
concentrations between control and treatment groups required the log transformation of data to compare
results between treatment groups (right panel, N=3). For (F), average percent change is depicted in red.
Data are shown as mean ± SEM. * p ≤ 0.05, **p≤0.05 compared to all other IgG2a treatment groups,
‡p≤0.05 compared to all other DTA-1 treatment groups, as measured by ANOVA.
Fig. 5: Checkpoint blockade therapy and anti-GITR therapy combine to overcome inhibition of
CD8+ T cell activation by PD-L1 signaling in vitro. A) Cell viability at 72 Hours. *p ≤ 0.05 versus
all other groups via ANOVA. ** p ≤ 0.05 versus all other PD-L1-inhibited groups via ANOVA. B)
OCR and, C) ECAR, in CD8+ T Cells, 4 technical replicates representative of N=3 separate experiments.
D) 2-NBDG uptake and E) cellular proliferation of PD-L1-inhibited CD8+ T cells; representative of
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28
N=3. F) IFNγ concentrations by ELISA; N=5 individual experiments. Data are shown as mean ± SD. *
p ≤ 0.05 by ANOVA.
Fig. 6: DTA-1 treatment in a syngeneic mouse tumor model enhances CD8+ T cell activation and
proliferation in vivo. A) Tumor mass and (B) DLN mass on day 8 post-treatment; individual masses
from N=4 separate experiments (8-13 mice per experiment), * p ≤ 0.05 by Student t test. C) Gene
expression heat map depicting DTA-1 regulation of proliferation-associated genes in DLN. Individual
color blocks represent an average of normalized gene expression from 4 individual experiments. D)
Granzyme and IFNγ gene transcript levels, * p ≤ 0.05 by ANOVA. F) Klrg1 gene transcript levels;
N=4, * p ≤ 0.05 by student’s t-test. E) Effector/memory staining from DLN. Representative plot from
N=4 separate experiments. Data are shown as mean ± SD.
Fig. 7: GITR agonism increases metabolism in CD8+ T cells in the DLN and tumor of MC38-
bearing mice. A) OCR and (B) ECAR in DLN (N=4), and TIL (C and D, respectively; N=3) CD8+ T
cells. E) TIL spare glycolytic reserve(basal ECAR minus oligomycin-treated ECAR). F) Gene
expression of TIL CD8+ metabolic genes. G) BODIPY staining. Data shown is mean ± SD. * p ≤ 0.05
by Student t test; ns=not significant.
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A
B
C F E D
IgG2a DTA-1 Figure 1
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A B
C1 8 H o u rs 4 2 H o u rs 6 6 H o u rs
6
8
1 0
1 2
1 4
Ce
ll S
ize
(n
m)
N a iv e Ig G 2 a D T A -1
**
1 8 H o u rs 4 2 H o u rs 6 6 H o u rs
0
5 0
1 0 0
1 5 0
Ce
ll V
iab
ilit
y (
%)
N a iv e Ig G 2 a D T A -1
*
F
G
B a s a l + E to m o x ir
0
1 0 0
2 0 0
3 0 0
OC
R (
pm
ol/
min
)
Ig G 2 a D T A -1
4 2 H o u rs
***
B a s a l + E to m o x ir
0
5 0
1 0 0
1 5 0
2 0 0
2 5 0
OC
R (
pm
ol/
min
)
Ig G 2 a D T A -1
6 6 H o u rs
* *
H
JIED
Figure 2
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A C B D E
F I H G J
K
Figure 3
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A
C B D E
F
I
H G
J
Figure 4
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CD3/CD28 IgG1Fc PD-L1
- + + + + + + - + + - - - - - - - + + + +
*
**
Naiv
e
IgG
2a
DT
A-1
IgG
1 +
Ig
G2a
IgG
1 +
DT
A-1
DX
400 +
Ig
G2a
DX
400 +
DT
A-1
6 0
7 0
8 0
9 0
1 0 0
Ce
ll V
iab
ilit
y (
%)
Naiv
e
IgG
2a
DT
A-1
IgG
1 +
Ig
G2a
IgG
1 +
DT
A-1
DX
400 +
Ig
G2a
DX
400 +
DT
A-1
0
5 0
1 0 0
1 5 0
2 0 0
2 5 0
OC
R (
pm
ol/
min
)
CD3/CD28 IgG1Fc PD-L1
- + + + + + + - + + - - - - - - - + + + +
Naiv
e
IgG
2a
DT
A-1
IgG
1 +
Ig
G2a
IgG
1 +
DT
A-1
DX
400 +
Ig
G2a
DX
400 +
DT
A-1
0
5 0
1 0 0
1 5 0
EC
AR
(m
pH
/min
)
CD3/CD28 IgG1Fc PD-L1
- + + + + + + - + + - - - - - - - + + + +
A C B
D F E
DX400 + DTA-1: 16182 DX400 + IgG2a: 11480 IgG1 + DTA-1: 12263 IgG1 + IgG2a: 8632
72 Hour 2-NBDG
1 2
3
4
5
6
1+2 3+4 5+6 15.57 59.5 25.04 21.7 59.2 19.0 15.08 63.6 21.19 22.8 63.9 13.22
72 Hour CellTrace
IgG
2a
DT
A-1
IgG
1 +
Ig
G2a
IgG
1 +
DT
A-1
DX
400 +
Ig
G2a
DX
400 +
DT
A-1
0
2 0 0 0
4 0 0 0
6 0 0 0
8 0 0 0
1 0 0 0 0
IFN
g
Co
nc
en
tra
tio
n (
ng
/mL
) 2 4 H o u rs
4 8 H o u rs
+ + - - - - - - + + + +
IgG1Fc PD-L1
*
* *
Figure 5
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N a iv e Ig G 2 a D T A -1
0
2 0
4 0
6 0
8 0
D ra in in g L y m p h N o d e M a s s
Ma
ss
(m
g)
G z m a G z m b G z m k IF N g
0
5 0 0
1 0 0 0
1 5 0 04 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
D L N
Min
(N
orm
ali
ze
d V
alu
e)
N a iv e
Ig G 2 a
D T A -1
*
*
*
*
G z m a G z m b G z m k IF N g
0
2 0 0 0
4 0 0 0
6 0 0 0
8 0 0 0
T IL
Min
(N
orm
ali
ze
d V
alu
e)
Ig G 2 a
D T A -1
A
C
B
*
*
D
E Ig G 2 a D T A -1
0
2 0 0
4 0 0
6 0 0
8 0 0
T u m o r M a s s
Ma
ss
(m
g)
*
(1.42)
(2.74)
F
T u m o r D L N
0
1 0
2 0
3 0
4 0
5 0
K lr g 1
Min
(N
orm
ali
ze
d V
alu
e)
Ig G 2 a D T A -1
* *
Figure 6
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A D C B
F
E
G
TIL
Figure 7
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Published OnlineFirst August 28, 2018.Cancer Immunol Res Simran S. Sabharwal, David B. Rosen, Jeff Grein, et al. tumor modelcell proliferation and effector cytokine production in a mouse GITR agonism enhances cellular metabolism to support CD8+ T
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