gitr agonism enhances cellular metabolism to support cd8 ...€¦ · 28/08/2018 · pi3kδ, and...

1

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

on January 25, 2021. © 2018 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on August 28, 2018; DOI: 10.1158/2326-6066.CIR-17-0632

mailto:[email protected]

http://cancerimmunolres.aacrjournals.org/

2

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.




3

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




4

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




5

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




6

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




7

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




8

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




9

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).




10

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




11

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).




12

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




13

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




14

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




15

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




16

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




17

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,




18

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).




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.




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




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).




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.).




23

References

1. Letendre P, Monga V, Milhem M, Zakharia Y. Ipilimumab: from preclinical development to future clinical perspectives in melanoma. Future Oncol;13:625-36

2. Robert C, Ribas A, Wolchok JD, Hodi FS, Hamid O, Kefford R, et al. Anti-programmed-death-receptor-1 treatment with pembrolizumab in ipilimumab-refractory advanced melanoma: a randomised dose-comparison cohort of a phase 1 trial. Lancet;384:1109-17

3. Daud AI, Wolchok JD, Robert C, Hwu WJ, Weber JS, Ribas A, et al. Programmed Death-Ligand 1 Expression and Response to the Anti-Programmed Death 1 Antibody Pembrolizumab in Melanoma. J Clin Oncol;34:4102-9

4. Waight JD, Gombos RB, Wilson NS. Harnessing co-stimulatory TNF receptors for cancer immunotherapy: Current approaches and future opportunities. Hum Antibodies

5. Makkouk A, Chester C, Kohrt HE. Rationale for anti-CD137 cancer immunotherapy. Eur J Cancer;54:112-9 6. Knee DA, Hewes B, Brogdon JL. Rationale for anti-GITR cancer immunotherapy. Eur J Cancer;67:1-10 7. Aspeslagh S, Postel-Vinay S, Rusakiewicz S, Soria JC, Zitvogel L, Marabelle A. Rationale for anti-OX40

cancer immunotherapy. Eur J Cancer;52:50-66 8. van de Ven K, Borst J. Targeting the T-cell co-stimulatory CD27/CD70 pathway in cancer immunotherapy:

rationale and potential. Immunotherapy;7:655-67 9. Croft M, Benedict CA, Ware CF. Clinical targeting of the TNF and TNFR superfamilies. Nat Rev Drug

Discov;12:147-68 10. Coe D, Begom S, Addey C, White M, Dyson J, Chai JG. Depletion of regulatory T cells by anti-GITR mAb as

a novel mechanism for cancer immunotherapy. Cancer Immunol Immunother;59:1367-77 11. Ramirez-Montagut T, Chow A, Hirschhorn-Cymerman D, Terwey TH, Kochman AA, Lu S, et al.

Glucocorticoid-induced TNF receptor family related gene activation overcomes tolerance/ignorance to melanoma differentiation antigens and enhances antitumor immunity. J Immunol 2006;176:6434-42

12. Ronchetti S, Zollo O, Bruscoli S, Agostini M, Bianchini R, Nocentini G, et al. GITR, a member of the TNF receptor superfamily, is costimulatory to mouse T lymphocyte subpopulations. Eur J Immunol 2004;34:613-22

13. Chang CH, Curtis JD, Maggi LB, Jr., Faubert B, Villarino AV, O'Sullivan D, et al. Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell;153:1239-51

14. Ho PC, Bihuniak JD, Macintyre AN, Staron M, Liu X, Amezquita R, et al. Phosphoenolpyruvate Is a Metabolic Checkpoint of Anti-tumor T Cell Responses. Cell;162:1217-28

15. Frauwirth KA, Riley JL, Harris MH, Parry RV, Rathmell JC, Plas DR, et al. The CD28 signaling pathway regulates glucose metabolism. Immunity 2002;16:769-77

16. Klein Geltink RI, O'Sullivan D, Corrado M, Bremser A, Buck MD, Buescher JM, et al. Mitochondrial Priming by CD28. Cell 2017;171:385-97 e11

17. Choi BK, Lee DY, Lee DG, Kim YH, Kim SH, Oh HS, et al. 4-1BB signaling activates glucose and fatty acid metabolism to enhance CD8+ T cell proliferation. Cell Mol Immunol

18. Patsoukis N, Bardhan K, Chatterjee P, Sari D, Liu B, Bell LN, et al. PD-1 alters T-cell metabolic reprogramming by inhibiting glycolysis and promoting lipolysis and fatty acid oxidation. Nat Commun;6:6692

19. Mahne AE, Mauze S, Joyce-Shaikh B, Xia J, Bowman EP, Beebe AM, et al. Dual Roles for Regulatory T-cell Depletion and Costimulatory Signaling in Agonistic GITR Targeting for Tumor Immunotherapy. Cancer Res

20. Lindauer A, Valiathan CR, Mehta K, Sriram V, de Greef R, Elassaiss-Schaap J, et al. Translational Pharmacokinetic/Pharmacodynamic Modeling of Tumor Growth Inhibition Supports Dose-Range Selection of the Anti-PD-1 Antibody Pembrolizumab. CPT Pharmacometrics Syst Pharmacol 2017;6:11-20




24

21. Chang CH, Qiu J, O'Sullivan D, Buck MD, Noguchi T, Curtis JD, et al. Metabolic Competition in the Tumor Microenvironment Is a Driver of Cancer Progression. Cell;162:1229-41

22. Siska PJ, Rathmell JC. T cell metabolic fitness in antitumor immunity. Trends Immunol;36:257-64 23. Snell LM, McPherson AJ, Lin GH, Sakaguchi S, Pandolfi PP, Riccardi C, et al. CD8 T cell-intrinsic GITR is

required for T cell clonal expansion and mouse survival following severe influenza infection. J Immunol;185:7223-34

24. Hauer J, Puschner S, Ramakrishnan P, Simon U, Bongers M, Federle C, et al. TNF receptor (TNFR)-associated factor (TRAF) 3 serves as an inhibitor of TRAF2/5-mediated activation of the noncanonical NF-kappaB pathway by TRAF-binding TNFRs. Proc Natl Acad Sci U S A 2005;102:2874-9

25. Grange M, Verdeil G, Arnoux F, Griffon A, Spicuglia S, Maurizio J, et al. Active STAT5 regulates T-bet and eomesodermin expression in CD8 T cells and imprints a T-bet-dependent Tc1 program with repressed IL-6/TGF-beta1 signaling. J Immunol;191:3712-24

26. Chou C, Pinto AK, Curtis JD, Persaud SP, Cella M, Lin CC, et al. c-Myc-induced transcription factor AP4 is required for host protection mediated by CD8+ T cells. Nat Immunol;15:884-93

27. Smith B, Schafer XL, Ambeskovic A, Spencer CM, Land H, Munger J. Addiction to Coupling of the Warburg Effect with Glutamine Catabolism in Cancer Cells. Cell Rep;17:821-36

28. Ip H, Sethi T. CD98 signals controlling tumorigenesis. Int J Biochem Cell Biol;81:148-50 29. Joetham A, Ohnishi H, Okamoto M, Takeda K, Schedel M, Domenico J, et al. Loss of T regulatory cell

suppression following signaling through glucocorticoid-induced tumor necrosis receptor (GITR) is dependent on c-Jun N-terminal kinase activation. J Biol Chem;287:17100-8

30. Tang X, Tian J, Ma J, Wang J, Qi C, Rui K, et al. GITRL modulates the activities of p38 MAPK and STAT3 to promote Th17 cell differentiation in autoimmune arthritis. Oncotarget;7:8590-600

31. Mendoza MC, Er EE, Blenis J. The Ras-ERK and PI3K-mTOR pathways: cross-talk and compensation. Trends Biochem Sci 2011;36:320-8

32. Pullen N, Thomas G. The modular phosphorylation and activation of p70s6k. FEBS Lett 1997;410:78-82 33. Williams O, Houseman BT, Kunkel EJ, Aizenstein B, Hoffman R, Knight ZA, et al. Discovery of dual

inhibitors of the immune cell PI3Ks p110delta and p110gamma: a prototype for new anti-inflammatory drugs. Chem Biol 2010;17:123-34

34. Croft M. The TNF family in T cell differentiation and function--unanswered questions and future directions. Semin Immunol;26:183-90

35. Fuertes Marraco SA, Neubert NJ, Verdeil G, Speiser DE. Inhibitory Receptors Beyond T Cell Exhaustion. Front Immunol;6:310

36. Zhou P, L'Italien L, Hodges D, Schebye XM. Pivotal roles of CD4+ effector T cells in mediating agonistic anti-GITR mAb-induced-immune activation and tumor immunity in CT26 tumors. J Immunol 2007;179:7365-75

37. Bengsch B, Johnson AL, Kurachi M, Odorizzi PM, Pauken KE, Attanasio J, et al. Bioenergetic Insufficiencies Due to Metabolic Alterations Regulated by the Inhibitory Receptor PD-1 Are an Early Driver of CD8(+) T Cell Exhaustion. Immunity;45:358-73

38. Buck MD, O'Sullivan D, Klein Geltink RI, Curtis JD, Chang CH, Sanin DE, et al. Mitochondrial Dynamics Controls T Cell Fate through Metabolic Programming. Cell;166:63-76

39. Scharping NE, Menk AV, Moreci RS, Whetstone RD, Dadey RE, Watkins SC, et al. The Tumor Microenvironment Represses T Cell Mitochondrial Biogenesis to Drive Intratumoral T Cell Metabolic Insufficiency and Dysfunction. Immunity;45:701-3

40. Scharping NE, Menk AV, Whetstone RD, Zeng X, Delgoffe GM. Efficacy of PD-1 Blockade Is Potentiated by Metformin-Induced Reduction of Tumor Hypoxia. Cancer Immunol Res;5:9-16

41. Zhang Y, Kurupati R, Liu L, Zhou XY, Zhang G, Hudaihed A, et al. Enhancing CD8+ T Cell Fatty Acid Catabolism within a Metabolically Challenging Tumor Microenvironment Increases the Efficacy of Melanoma Immunotherapy. Cancer Cell 2017;32:377-91 e9

42. Allard D, Allard B, Gaudreau PO, Chrobak P, Stagg J. CD73-adenosine: a next-generation target in immuno-oncology. Immunotherapy 2016;8:145-63




25

43. Platten M, Wick W, Van den Eynde BJ. Tryptophan catabolism in cancer: beyond IDO and tryptophan depletion. Cancer Res 2012;72:5435-40

44. Ebert PJR, Cheung J, Yang Y, McNamara E, Hong R, Moskalenko M, et al. MAP Kinase Inhibition Promotes T Cell and Anti-tumor Activity in Combination with PD-L1 Checkpoint Blockade. Immunity 2016;44:609-21

45. Homet Moreno B, Mok S, Comin-Anduix B, Hu-Lieskovan S, Ribas A. Combined treatment with dabrafenib and trametinib with immune-stimulating antibodies for BRAF mutant melanoma. Oncoimmunology;5:e1052212

46. Hu-Lieskovan S, Mok S, Homet Moreno B, Tsoi J, Robert L, Goedert L, et al. Improved antitumor activity of immunotherapy with BRAF and MEK inhibitors in BRAF(V600E) melanoma. Sci Transl Med 2015;7:279ra41

47. Zhu LX, Davoodi M, Srivastava MK, Kachroo P, Lee JM, St John M, et al. GITR agonist enhances vaccination responses in lung cancer. Oncoimmunology;4:e992237

48. Kim YH, Shin SM, Choi BK, Oh HS, Kim CH, Lee SJ, et al. Authentic GITR Signaling Fails To Induce Tumor Regression unless Foxp3+ Regulatory T Cells Are Depleted. J Immunol;195:4721-9

49. Ott PA, Hodi FS, Kaufman HL, Wigginton JM, Wolchok JD. Combination immunotherapy: a road map. J Immunother Cancer;5:16




26

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




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




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.




A

B

C F E D

IgG2a DTA-1 Figure 1




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 (

%)


*

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




A C B D E

F I H G J

K

Figure 3




A

C B D E

F

I

H G

J

Figure 4




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

)


- + + + + + + - + + - - - - - - - + + + +

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

)


- + + + + + + - + + - - - - - - - + + + +

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





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




A D C B

F

E

G

TIL

Figure 7




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

Updated version

10.1158/2326-6066.CIR-17-0632doi:

Access the most recent version of this article at:

Material

Supplementary

http://cancerimmunolres.aacrjournals.org/content/suppl/2018/08/28/2326-6066.CIR-17-0632.DC1

Access the most recent supplemental material at:

Manuscript

Authoredited. Author manuscripts have been peer reviewed and accepted for publication but have not yet been

E-mail alerts related to this article or journal.Sign up to receive free email-alerts

Subscriptions

Reprints and

[email protected] at

To order reprints of this article or to subscribe to the journal, contact the AACR Publications

Permissions

Rightslink site. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC)

.http://cancerimmunolres.aacrjournals.org/content/early/2018/08/28/2326-6066.CIR-17-0632To request permission to re-use all or part of this article, use this link



http://cancerimmunolres.aacrjournals.org/lookup/doi/10.1158/2326-6066.CIR-17-0632

http://cancerimmunolres.aacrjournals.org/content/suppl/2018/08/28/2326-6066.CIR-17-0632.DC1

http://cancerimmunolres.aacrjournals.org/cgi/alerts

mailto:[email protected]

http://cancerimmunolres.aacrjournals.org/content/early/2018/08/28/2326-6066.CIR-17-0632


gitr agonism enhances cellular metabolism to support cd8 ...€¦ · 28/08/2018 · pi3kδ, and...

Documents