gitr agonism enhances cellular metabolism to support cd8 t ...€¦ · in vivo tumor models for...

Research Article

GITR Agonism Enhances Cellular Metabolism toSupport CD8þ T-cell Proliferation and EffectorCytokine Production in a Mouse Tumor ModelSimran S. Sabharwal1, David B. Rosen1, Jeff Grein1, Dana Tedesco1,Barbara Joyce-Shaikh1, Roanna Ueda1, Marie Semana2, Michele Bauer2,Kathy Bang2, Christopher Stevenson2, Daniel J. Cua1, and Luis A. Z�u~niga1

Abstract

GITR is a costimulatory receptor currently undergoingphase I clinical trials. Efficacy of anti-GITR therapy insyngeneic mouse models requires regulatory T-cell deple-tion and CD8þ T-cell costimulation. It is increasingly appre-ciated that immune cell proliferation and function aredependent on cellular metabolism. Enhancement of diversemetabolic pathways leads to different immune cell fates.Little is known about the metabolic effects of GITR agonism;thus, we investigated whether costimulation via GITRaltered CD8þ T-cell metabolism. We found activated,GITR-treated CD8þ T cells upregulated nutrient uptake,lipid stores, glycolysis, and oxygen consumption rate (OCR)in vitro. Using MEK, PI3Kd, and metabolic inhibitors, weshow increased metabolism is required, but not sufficient,for GITR antibody (DTA-1)-induced cellular proliferationand IFNg production. In an in vitromodel of PD-L1–induced

CD8þ T-cell suppression, GITR agonism alone rescuedcellular metabolism and proliferation, but not IFNg pro-duction; however, DTA-1 in combination with anti–PD-1treatment increased IFNg production. In the MC38 mousetumor model, GITR agonism significantly increased OCRand IFNg and granzyme gene expression in both tumor anddraining lymph node (DLN) CD8þ T cells ex vivo, as well asbasal glycolysis in DLN and spare glycolytic capacity intumor CD8þ T cells. DLN in GITR-treated mice showedsignificant upregulation of proliferative gene expressioncompared with controls. These data show that GITR agon-ism increases metabolism to support CD8þ T-cell prolif-eration and effector function in vivo, and that understand-ing the mechanism of action of agonistic GITR antibodiesis crucial to devising effective combination therapies.Cancer Immunol Res; 6(10); 1199–211. �2018 AACR.

IntroductionImmunotherapies have revolutionized the treatment of various

cancers (1, 2). Current methods involve checkpoint receptorblockade on cytotoxic effector T cells, attenuating immune inhib-itory signals and leading to tumor eradication. Despite remark-able clinical success, the majority of patients still do not respondto these drugs (3). For this reason, the next generation of immu-notherapies aims to activate costimulatory receptors to helpinitiate antitumor responses.

The tumor necrosis factor (TNF) superfamily is a group ofrelated costimulatory receptors that have received much interestas potential cancer immunotherapies (4). These include 4-1BB(CD137), CD27, OX40 (CD134), and glucocorticoid-inducedTNFR family-related protein (GITR, CD357). All of these targetscurrently have drugs undergoing clinical trials as monotherapies,

in combination with checkpoint blockade therapy, or in combi-nation with additional costimulatory receptors (5–8). TNF recep-tors are characterized by their ability to bind TNF family ligandsand activate the NF-kB pathways via recruitment of TNF receptorassociated factors (TRAF), a family of six proteins that are recruitedto further transduce signals within the cell (9).

Regulatory T cells (Treg) have high expression of GITR.Much ofthe previous research investigating the mechanism of action ofanti-GITR therapy has focused on the antibody's ability to medi-ate Treg depletion within the tumor microenvironment (TME),reducing immunosuppression of tumor infiltrating lymphocytes(TIL).Despite this attentiononTreg reductionwithin the tumor, itis clear that the direct agonist effect of anti-GITR therapy oneffector cells is required for full antitumor efficacy seen in pre-clinical models (10, 11).

In CD8þ T cells stimulated with suboptimal anti-CD3 concen-trations, GITR agonism is associated with increased cellular pro-liferation and production of effector molecules, such as perforin,granzymes, and interferon gamma (IFNg ; ref. 12). Large energeticdemands are associated with the rapid expansion of stimulatedT cells, requiring increased glycolysis and mitochondrial respira-tion. Increased IFNg production is also linked with increasedglycolysis (13, 14). Modulating cellular metabolism is emergingas a central theme in elucidating how coinhibitory moleculesrepress T-cell activation and how costimulatory moleculesenhance T-cell receptor signaling, proliferation, and effector func-tion. Indeed, CD28, a costimulatory receptor on T cells, wasshown to potentiate T-cell activation via upregulation of

1Merck & Co., Inc., Palo Alto, California. 2Charles River Laboratories, InsourcingSolutions, Palo Alto, California.

Note: Supplementary data for this article are available at Cancer ImmunologyResearch Online (http://cancerimmunolres.aacrjournals.org/).

Current address for S.S. Sabharwal: Pfizer, South San Francisco, California.

Corresponding Author: Luis A. Z�u~niga, Merck & Company, Inc., 901 CaliforniaAvenue, Palo Alto, CA 94304. Phone: 650-496-1181; Fax: 650-496-1200; E-mail:[email protected]

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

�2018 American Association for Cancer Research.

CancerImmunologyResearch

www.aacrjournals.org 1199

on June 14, 2020. © 2018 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from

Published OnlineFirst August 28, 2018; DOI: 10.1158/2326-6066.CIR-17-0632

http://crossmark.crossref.org/dialog/?doi=10.1158/2326-6066.CIR-17-0632&domain=pdf&date_stamp=2018-9-19

http://cancerimmunolres.aacrjournals.org/

glycolysis and mitochondrial priming via enhanced fatty acidoxidation (FAO; refs. 15, 16). 4-1BB was also shown to enhanceglycolysis and FAO to support increased T-cell proliferation(17). Conversely, signaling along the PD-L1/PD-1 inhibitoryaxis prevents T-cell upregulation of glycolysis while promotinglipolysis and FAO, whereas CTLA-4 signaling prevents upregu-lation of glycolysis and FAO, keeping T cells in a na€�ve-like,quiescent state (18).

We hypothesized that anti-GITR agonist therapy augmentscellular metabolism in CD8þ T cells. In the current study, wedemonstrated that GITR antibody therapy enhances CD8þ T-cell activation and metabolism under both suboptimal andsupraoptimal stimulation conditions. Using small-moleculeand checkpoint inhibitors, we demonstrated that GITR ago-nist-induced metabolism is required, but not sufficient by itself,for rescuing T-cell activation, depending on what other signal-ing pathways are being perturbed. In vivo, anti-GITR treatmentalso enhanced CD8þ T-cell metabolism and upregulated pro-liferative gene expression. These data show GITR agonismincreases metabolism to support CD8þ T-cell effector functionand proliferation in vivo, and understanding the mechanism ofaction of anti-GITR antibodies is crucial to devising effectivecombination therapies.

Materials and MethodsMice and reagents

Wild-type C57BL/6J and Foxp3-GDL (C57BL/6J background)mice were obtained from The Jackson Laboratory and housed andbred under specific pathogen-free conditions in the Merck & Co.,Inc. animal facility. MC38 mouse colon carcinoma cell line wasobtained from the Developmental Therapeutics Program TumorRepository (Frederick National Laboratory, Frederick, MD) andauthenticated using genomic profiling (IDEXXRADILCell Check)and tested to be mycoplasma free (IMPACT I PCR Profile). Cellswere frozen down at passage five. For each experiment, cells werethawed and placed in T75 flasks, and two days later were expand-ed into several T175flasks. Threedays later, cellswere counted andresuspended at the appropriate concentration prior to injectioninto mice. Rat anti-mouse DTA-1 GITR antibody (S. Sakaguchi,Kyoto University, Kyoto, Japan) was murinized as previouslydescribed (19) for in vivo studies. A proprietary mouse anti–PD-1 (DX400) was made in-house at Merck & Co., Inc. (20). Allanimal studies were performed in accordance to protocolsapproved by Merck Research Laboratories' Ethics board.

In vivo tumor modelsFor syngeneic tumor experiments, 8- to 12-week old mice

were subcutaneously injected with 106 MC38 cells on the rightflank. Tumor diameter was measured by electronic calipers andtumor volume was calculated using the formula V ¼ (W2 � L)/2, where V is tumor volume, W is tumor width, and L is tumorlength. DTA-1 or isotype control was administered once at 5mg/kg subcutaneously when tumors reached 100 � 30 mm3.Tumor-draining lymph nodes (DLN) were harvested andmechanically disrupted to obtain a single-cell suspension. ForTIL isolation, tumors were mechanically disrupted and digestedfor 45 minutes at 37�C in the presence of collagenase 1 (300collagenase digestion units/mL; Sigma-Aldrich), DNase 1 (400domase 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 leuko-

cytes. CD8þ T cells were isolated using a positive selection kit(Miltenyi Biotec; cat #130-049-401).

In vitro T-cell isolation and activationLymphocytes were isolated from lymph nodes and spleens of

na€�ve C57BL/6J mice. Tissue was mechanically disrupted andpassed through a 70-mm filter, and red blood cells were removedusing ACK lysis buffer (Gibco; cat #A1049201). CD8þ T cells wereisolated using a negative selection kit (Miltenyi Biotec; cat #130-104-075) per manufacturer's instructions (typical purity �92-95% of live cells). Cells were plated in 6-well tissue culture plateswith plate-bound antibodies. Suboptimal conditions consisted oflow-dose plate-bound anti-CD3 (0.1 mg/mL). Supraoptimal con-ditions consisted of plate-bound anti-CD3 (10 mg/mL), anti-CD28 (2 mg/mL), and IgG1Fc (10 mg/mL). For PD-L1 inhibitedcells, PD-L1 (10 mg/mL) was used instead of IgG1Fc. Cells weretreated with either IgG2a (10 mg/mL; eBioscience; cat #16-4724-85 or in-house), or DTA-1 (10 mg/mL; eBioscience; cat #16-5874-83 or in-house). For small-molecule inhibitor studies, T cells wereactivated for 16 hours prior to addition of the inhibitors. Thirtyminutes later, antibodies were added, and experiments wereperformed after an additional 48 hours. Etomoxir (#E1905),PD98059 (#P215), SW30 (#526559), and oligomycin A(#75351) were purchased from Sigma-Aldrich. SB203580(#SYN-1074) was purchased from AdipoGen.

Western blottingCells were lysed in M-PER buffer (Thermo Fisher Scientific;

cat #78501) with Pierce Protease and Phosphatase InhibitorCocktail (Thermo Fisher Scientific; cat #88668). Lysates wereseparated on SDS-polyacrylamide gels (Bio-Rad) and trans-ferred to nitrocellulose membranes that were blotted withprimary antibodies. Blots were further incubated with second-ary horseradish peroxidase-conjugated antibodies (Cell Signal-ing Technology) and stained with ECL reagent (Amersham).Chemiluminescence was detected on film. All antibodies werepurchased from Cell Signaling Technology. Primary antibodiesused 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), and phospho-p70S6k(#9234).

Flow cytometryIsolated cells were stained for 30 minutes in PBS, washed, and

analyzed on an LSRII or LSRFortessa flow cytometer (BD Bios-ciences). All flow antibodies were purchased from BD Biosciencesas follows: CD44 (#559250), CD62L (#564108), IL7Ra(#560733), CD25 (#564021). Live/dead near-IR (#L10119) andCellTrace Violet (C34557) were purchased from Sigma-Aldrich.Data were acquired using the FACS DIVA software (BD Bios-ciences). All flow cytometry data were analyzed with FlowJo(TreeStar Software).

Nutrient uptake assaysAll fluorescent nutrient stains were purchased from Sigma-

Aldrich. Approximately 250,000 activated CD8þ T cells wereplaced in 400 mL of RPMI media with one of the markers at thefollowing concentrations: 2-NBDG (100 mg/mL; #N13195),BODIPY (1.25 mg/mL; D3922), C12-BODIPY (1 mmol/L;

Sabharwal et al.

Cancer Immunol Res; 6(10) October 2018 Cancer Immunology Research1200




#D3822), and C16-BODIPY (0.5 mmol/L; #D3821). Cells wereincubated for 30 minutes at 37�C prior to washing and surfacestaining for flow cytometry analysis.

Seahorse extracellular flux analysisSeahorse tissue culture plates were coated with Cell-Tak (Corn-

ing, 22.4 mg/mL) per manufacturer's instructions. Cells werecounted on a ViCell Analyzer, and 200,000 viable CD8þ T cellswere plated per well per manufacturer's instructions. Seahorsemedia used consisted of glucose (10 mmol/L), glutamine (2mmol/L), and sodium pyruvate (1 mmol/L). For in vitro assays,basalmetabolicmeasurements were taken followed by sequentialinjection of etomoxir (100 mmol/L; Sigma-Aldrich #E1905),oligomycin (1 mmol/L), and rotenone/antimycin A (0.5 mmol/L). For ex vivo assays, basal metabolic measurements were takenfollowed by sequential injections of oligomycin (1 mmol/L),FCCP (2 mmol/L), and rotenone/antimycin A (0.5 mmol/L).

Cell viability and sizeCell viability and size were assessed using a ViCell Analyzer

(Beckman Coulter) per manufacturer's instructions.

ELISA assaysCell culture supernatants were collected and interferon g levels

were assessed using a mouse IFNg DuoSet ELISA kit (R&DSystems; #DY485) per manufacturer's instructions and read ona SpectraMax microplate reader (Molecular Devices).

RNA expression analysisFor real-time PCR analysis, total RNA was isolated from cells

using Arcturus PicoPure RNA Isolation method, according tomanufacturer's protocol (Thermo Fisher Scientific).

Real-time quantitative PCR for gene expressionDNase-treated total RNA was reverse transcribed using Quan-

tiTect Reverse Transcription (Qiagen) according to the manufac-turer's instructions. Primers were obtained commercially fromThermo Fisher Scientific. Primer assay IDs were as follows: Ebi3¼Mm00469294_m1; Cxcl10/IP-10 ¼ Mm00445235_m1; Il2 ¼Mm00434256_m1; Icam1 ¼ m00516023_m1; Nt5e – CD73 ¼Mm00501917_m1; Tbx21 – Tbet ¼Mm00450960_m1; Socs1 ¼Mm00782550_s1; Bcl2l1 – Bcl-xl ¼ Mm00437783_m1; GITR –

Tnfrsf18 ¼ Mm00437136_m1; Plscr1 (exons 8–9) ¼Mm01228223_g1; Il2ra – CD25 ¼ Mm00434261_m1; Gzmb¼Mm00442834_m1; Ox40 – Tnfrsf4¼Mm00442039_m1; Gls2¼ Mm01164862_m1; Axl ¼ Mm00437221_m1; Gpt2 ¼Mm00558028_m1; Pdk1 ¼ Mm00554300_m1; Slc7a5 ¼Mm00441516_m1; Slc3a2 – CD98 ¼ Mm00500525_m1;Myc–c-myc ¼ Mm00487803_m1; Chek1 - Chk1 ¼Mm00432485_m1; Ccnb1 ¼ Mm00838401_g1; Aurkb - Aurb(Exons 7–8) ¼ Mm01718146_g1; Cdkn2c - p18 INK4c ¼Mm00483243_m1; Nusap1 ¼ Mm01324634_m1; Smc2 ¼Mm00484340_m1; Rrm1 ¼ Mm00485876_m1; Ccna2 ¼Mm00438064_m1;Ccnb2¼Mm00432351_m1; Birc5– Survivin¼ Mm00599749_m1; Gzma ¼ Mm00439191_m1; Gzmk ¼Mm00492530_m1; Ifng ¼ Mm01168134_m1; Klrg1 ¼Mm00516879_m1; Cpt1a ¼ Mm01231186_m1; Slc2a3 - Glut3¼Mm01184104_m1; Hif1a - MOP1¼Mm00468875_m1. Genespecific preamplification was done per Fluidigm Biomark man-ufacturer's instructions (Fluidigm). Real-time quantitative PCRwas performed on the Fluidigm Biomark using two unlabeled

primers at 900 nmol/L each were used with 250 nmol/L of FAM-labeled probe (Thermo Fisher Scientific) with TaqMan UniversalPCR Master Mix with UNG. Samples and primers were run on a96.96 Array per manufacturer's instructions (Fluidigm). Ubiqui-tin levels were measured in a separate reaction and used tonormalize the data by the D-D Ct method. Using the mean-cyclethreshold value for ubiquitin and the gene of interest for eachsample, the equation 1.8 ^ (Ct ubiquitin minus Ct gene ofinterest) � 104 was used to obtain the normalized values.

Statistical analysisStatistical analysis was performed using GraphPad Prism soft-

ware. Unless otherwise noted, two samples were compared usingStudent t test andmultiple sampleswere compared using two-wayANOVA followed by the Tukey multiple comparisons test.

ResultsLow-dose anti-CD3 plus GITR agonism enhances CD8þ T-cellactivation and metabolism

In addition to T-cell receptor stimulation, costimulatory signalsare needed to optimally activate CD8þ T cells (e.g., CD28).Previous studies investigating costimulatory effects of GITR agon-ism utilized suboptimal anti-CD3 stimulation only, showingGITR treatment enhanced cellular proliferation and effector mol-ecule production (12). In agreement with these studies, we showDTA-1 treatment of CD8þ T cells, under suboptimal stimulation,enhances cellular proliferation. GITR agonism increases the num-ber of actively proliferating cells and the number of divisions thatthe proliferating cells undergo (Fig. 1A and B).

As increased activation states of T cells often require increasedenergy demands to support augmented cellular proliferation andcytokine production, we tested if DTA-1 treatment would altercellular metabolism of activated CD8þ T cells. We observedsignificant increases in oxygen consumption rate (OCR; Fig.1C) and extracellular acidification rate (ECAR, a measure ofglycolysis; Fig. 1D) with DTA-1 treatment.

The concept of nutrient competition in the TME betweeneffector T cells and cancer cells posits that enhancing a T cell's"fitness" to access and utilize nutrients can enable better tumorclearance (21, 22). Hence, we tested whether GITR agonismwould increase nutrient uptake in CD8þ T cells. Using the fluo-rescent glucose analogue 2-NBDG, we show DTA-1 treatmentsignificantly increases glucose uptake (Fig. 1E). Further, anti-GITRagonism enhances CD8þ T-cell effector function as measured byIFNg production (Fig. 1F).

Our data confirm the costimulatory role of GITR signaling inCD8þ T cells, and demonstrate DTA-1 treatment leads toincreased metabolism. However, it is unsurprising that metabo-lism was affected under these conditions, as increases in prolif-eration and effector cytokine production require increased met-abolic function to meet the energy and biosynthetic demands ofrapid cellular expansion (13).

DTA-1 enhances CD8þ T-cell activation despite optimal anti-CD3/CD28 stimulation

We next sought to determine the effects of DTA-1 on CD8þ Tcells activated with supraoptimal stimulation in vitro. We wantedto create activation conditions that removed the proliferativeadvantage of GITR-stimulated cells to determine if DTA-1 treat-ment would enhance CD8þ T-cell activation andmetabolism in aproliferation-independent manner.

GITR Agonism Increases CD8þ T-cell Metabolism

www.aacrjournals.org Cancer Immunol Res; 6(10) October 2018 1201




Here, DTA-1 treatment increased cell size relative to IgG2aisotype-treated controls (Fig. 2A). Viability of control cellsdeclined by day 3, whereas DTA-1 attenuated this decrease (Fig.2B). These data are consistent with reports that GITR and otherTNFRs increase cell survival through regulation of antiapoptoticproteins such as Bcl-xL (23).

Surface activation markers were assessed to ascertain the extentthat DTA-1 treatment augments CD8þ T-cell stimulation underthese conditions. IL7Ra and CD25 expression were upregulatedby 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-1still upregulated IFNg expression (Fig. 2D).

TNFRs are defined by their ability to upregulate NF-kB signal-ing. Two NF-kB signaling pathways are known: the canonicalpathway (NF-kB1) and the noncanonical pathway (NF-kB2). Therelative extent bywhich the various TNFRmembers can potentiatethese two distinct pathways is unclear (24). Here, we showDTA-1treatment leads to elevated phosphorylation, and therefore acti-

vationofp105 (NF-kB1), p100 (NF-kB2), andp65 (RelA; Fig. 2E).Although both NF-kB pathways are activated, DTA-1 enhancedthe amount of total NF-kB2 protein, matching gene-expressiondata demonstrating significant DTA-1–induced upregulation ofNfkb2 message without upregulation of Nfkb1 message (Fig. 2F).Ikba andGadd45b, two target genes ofNF-kB, are also upregulated.These data demonstrate increased NF-kB activity downstream ofanti-GITR agonism.

Despite the lack of increased proliferation with DTA-1 underoptimal stimulation conditions, we observed significantincreases in both the ECAR and OCR (Fig. 2G). Two days afterstimulation, the DTA-1–induced increase in OCR is entirely dueto increased FAO, as indicated by the etomoxir-sensitive por-tion of basal OCR (Fig. 2G, panel 3). Etomoxir inhibits carni-tine palmitoyl transferase 1a (CPT1a), the rate-limiting step ofFAO. However, 3 days after activation, there is virtually noetomoxir effect in either control or DTA-1–treated cells, sug-gesting a shift in substrate utilization by the mitochondria (Fig.2G, panel 4).

Figure 1.

Costimulation with the mouse GITR agonist antibody, DTA-1, enhances activation and metabolism in CD8þ T cells stimulated with low-dose anti-CD3. A,Representative CellTrace Violet FACS plots of IgG2a control versus DTA-1–treated CD8þ T cells 3 days after activation. B, Proliferation results of 4 independentexperiments. Oxygen consumption rate (OCR; C) and glycolytic rate [extracellular acidification rate (ECAR)] (D); N ¼ 3. E, Uptake of the fluorescent glucoseanalogue 2-NBDG at 72 Hours; N ¼ 5. F, ELISA results for interferon g (IFNg) levels; N ¼ 3. Data are shown as mean � SEM. � , P � 0.05 using Student t test.

Sabharwal et al.





DTA-1 treatment significantly increased 2-NBDG uptake. Cellscan increase uptake of other nutrients to feed their energydemands, and DTA-1 treatment also increased C12 medium-chain and C16 long-chain fatty acid uptake and increased intra-cellular lipid stores, assessed by BODIPY staining (Fig. 2H). Lipidstores can be mobilized for ATP production via mitochondrialoxidative phosphorylation (OXPHOS). These data suggest thatanti-GITR treatment increases CD8þ T-cell fitness in vitro byimproving nutrient uptake and allowing cells to have increasedflexibility in altering the carbon sources they use to meet theirenergy and biosynthetic needs.

A panel of genes was associated with increased CD8þ T-cellproliferation, activation, or function (Fig. 2I). These includeupregulation of IL2 message and its receptor, CD25, down-regulation of inhibitory receptor CD73, and confirmation ofBcl-xl upregulation. DTA-1 treatment also upregulated Tbettranscripts, which is important for induction of a type I cyto-

toxic T-cell (Tc1) phenotype critical for CD8þ T-cell–mediatedtumor killing (25).

Transcripts for several metabolic targets were also upregulatedwith DTA-1 treatment (Fig. 2J). These include upregulation of themaster metabolic transcription factor c-myc, as well as severalother metabolic enzymes and solute transporters (26–28). Thesedata cumulatively suggest that DTA-1 treatment increases globalcellular metabolism, even when proliferation is not enhanced.

DTA-1–induced cellular proliferation requires increasedglycolytic and mitochondrial metabolism

We next performed experiments following stimulation condi-tions described above and using 2-deoxyglucose (2-DG), a com-petitive inhibitor of glycolysis, at a dose sufficient to highlyattenuate glycolysis but not completely abolish it. Under theseconditions, we demonstrate that DTA-1 is unable to rescue OCR(Fig. 3A), ECAR (Fig. 3B), 2-NBDGuptake (Fig. 3C), proliferation

Figure 2.

Mouse anti-GITR agonism by DTA-1 enhances CD8þ T-cell activation andmetabolism despite optimal anti-CD3/anti-CD28 stimulation.A, Cell size and (B) viability inIgG2a control versus DTA-1–treated CD8þ T cells; N ¼ 3. C, FACS plots of activation markers. D, ELISA IFNg concentrations; N ¼ 3. E, Representative NF-kBpathwayWestern blots from two separate experiments. F,NF-kB pathway gene expression;N¼ 3; ns, not significant.G, ECAR (first two panels) andOCR at baselineand after addition of 100 mmol/L etomoxir (last two panels). H, 2-NBDG uptake, intracellular lipid droplet staining by BODIPY, and C12 and C16 fatty aciduptake. I, Gene-expression heat map depicting DTA-1 regulation of proliferation and activation-associated genes and (J) metabolic gene transcripts. Individualcolor blocks represent an average of normalized gene expression from 3 individual experiments. FACS plots are representative of at least three individualexperiments. Data are shown as mean � SD. �, P � 0.05 using Student t test for comparing two groups or ANOVA for multiple groups.






(Fig. 3D; Supplementary Fig. S1A), or IFNg production (Fig. 3E)by CD8þ T cells. Although the metabolic and IFNg 2-DG isotypecontrols trend downward versus vehicle controls, there is nosignificant difference between these groups. Proliferation, how-ever, is significantly blunted in control 2-DG cells, and beingunable to rescue metabolic function, DTA-1 is incapable ofrescuing cellular proliferation.

Incubating cells with etomoxir to inhibit FAO did not signif-icantly decrease metabolic function, though there is a slight trenddownward when comparing DTA-1–treated groups (Fig. 3A–C).Although 100 mmol/L etomoxir is sufficient to fully inhibit FAOupon acute administration during Seahorse experiments, thatconcentration only partially inhibits FAO after 2 days of incuba-tion. This is supported by the fact that the DTA-1–inducedincrease inOCRof etomoxir-incubated cells (Fig. 3A) is complete-ly FAO dependent and etomoxir sensitive (Supplementary Fig.S1B).With only partial inhibition of FAO, there is still a significantdecrease in cellular proliferation (Fig. 3D; Supplementary Fig.S1A) that DTA-1 is unable to rescue. DTA-10s inability to rescue

proliferation in etomoxir-incubated cells may be dependent onincreasing OCR, which indicates that increased FAO followingDTA-1 treatment supports increased proliferation.

IFNg levels trend down in isotype controls with etomoxirtreatment, though there is a significant increase with DTA-1(Fig. 3E). This increase, however, is still significantly lower thanDTA-1–treated vehicle controls. These data suggest that FAOmayplay a role in IFNg production, though this effect may be due tothe proliferative advantage seen in control cells or confounded byonly partially inhibiting FAO with etomoxir incubation.

We next used oligomycin to inhibit ATP synthase and blockmitochondrial ATP synthesis. OCR was severely attenuated andDTA-1 could not rescue it (Fig. 3F). ECAR was significantlyupregulated in isotype control cells with oligomycin, whereasDTA-1 treatment further increased ECAR (Fig. 3G). DTA-1 treat-ment significantly upregulated 2-NBDG uptake compared withisotype and oligomycin-treated cells (Fig. 3H). Without mito-chondrial ATP production, there was a proliferative disadvantagein oligomycin-treated cells that DTA-1 administration was unable

Figure 3.

DTA-1–induced cellular proliferation requires increased glycolytic and mitochondrial metabolism, whereas increased IFNg 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 undergoing1–3 cell divisions or 4þ cell divisions. Graph representsN¼ 3 for 2-DGandN¼4 for other groups.E, IFNg ELISA levels for 2-DGandEto-treated cells. Cells treatedwiththe ATP synthase inhibitor oligomycin (Oligo) and their (F) OCR, (G) ECAR, (H) 2-NBDG uptake, (I) percent proliferating cells, and (J) IFNg concentration. K,Representative plot of cellular proliferationwith cells treatedwith Veh and oligo.N¼ 3 for oligo experiments. Data are shown asmean� SEM. � ,P�0.05; �� ,P�0.05compared with all other IgG2a treatment groups; z, P � 0.05 compared with all other DTA-1 treatment groups, as measured by ANOVA; ns, not significant.

Sabharwal et al.





to rescue, which highlights the importance of mitochondrialrespiration for basal and DTA-1–induced cellular proliferation(Fig. 3I; Supplementary Fig. S1C). There was a comparableamount of IFNg production versus vehicle controls (Fig. 3J),despite the reduced proliferation in oligomycin-treated isotypecontrols (93.7% vs. 47.6%; Fig. 3K), further demonstrating theimportance of increased glycolytic function on IFNg production.DTA-1 significantly increased IFNg production in cells treatedwith oligomycin. Although these DTA-1–induced levels weresignificantly lower than DTA-1 control levels, this is likely dueto the lower number of proliferating cells in the oligomycin group(95.6% vs. 45.6%). Collectively, these data underscore the centralrole of metabolism in cellular proliferation and IFNg production.

DTA-1 upregulatesMAPK signaling and can rescue CD8þ T cellsfrom MEK inhibition

TNFRs also signal through the p38, JNK, and ERK MAPKpathways. There are conflicting reports as to which pathways areactivated in specific T-cell subsets, depending on which TNFR isinvolved (12, 29, 30). Here, we demonstrate that phosphoryla-tion and activation of all three MAPK pathways are enhanced byDTA-1 (Fig. 4A). Activation of these pathways in control condi-tions appears to decrease between 48 and 72 hours, whereas DTA-1–treated cells display enhanced signaling during the same timeinterval.

To dissect whichMAPK pathways are involved in regulating theobserved DTA-1–induced changes, we used the p38 inhibitorSB203580 and the MEK inhibitor PD98059. We found thatp38 inhibition of isotype-treated cells had no effect on cellularmetabolism (Fig. 4B andC) or 2-NBDGuptake (Fig. 4D), whereasMEK inhibition significantly decreased all metabolic readouts.DTA-1 increased metabolic parameters of all treatment groups,including rescue of MEK-inhibited cells to vehicle/IgG2a controllevels.

Many receptor signals activate both the RAS-RAF-MEK-ERK andPI3K-AKT-mTOR pathways, which both play a role in cell growthand proliferation (31). We hypothesized that GITR agonism byDTA-1 may rescue MEK inhibition, in part, by upregulating thePI3K signaling axis. p70S6k is a kinase downstream ofmTOR thatis specifically activated by the Akt pathway (32). p70S6k levelsdecreased with MEK inhibition (Fig. 3E), likely due to cross-talkbetween the two pathways (31), but DTA-1 treatment rescued theamount of phosphorylated, activated enzyme. Phospho-Akt andphospho-4EBP1, another mTOR-regulated protein, wereincreased (Supplementary Fig. S2A).

We next used the PI3Kd inhibitor SW30 together with MEKinhibition (33). Both PD98059 and SW30 attenuated 2-NBDGuptake (47.3% and 40.5%, respectively), and DTA-1 rescuedboth to vehicle/IgG2a control levels (Fig. 4F). The combinationof the two drugs reduced 2-NBDG uptake further (59.7%reduction). Although DTA-1 increased 2-NBDG levels whenadministered with the inhibitor combination, the levels weresignificantly lower than DTA-1–rescued levels of either smallmolecule alone. OCR (Fig. 4G) and ECAR (Fig. 4H) werecomparably affected. As described earlier (Fig. 2G), the DTA-1–induced increase in OCR 2 days after dosing can be whollyattributed to an increase in FAO, as the increase is etomoxirsensitive. With small-molecule inhibitors, however, OCR is stillsignificantly higher in DTA-1–treated cells after etomoxiradministration, compared with isotype controls. This may bedue to additional impairment in access to or utilization of other

carbon sources for fuel in isotype-treated groups, though fur-ther studies are needed to explore this.

Increased pathway signaling and metabolic rescue via DTA-1treatment were sufficient to rescue cellular proliferation (Fig. 4I;Supplementary Fig. S2B–S2D). There was significant rescue ofIFNg levels by DTA-1 treatment when assessed using multipledifferent t tests (Fig. 4J, panel 1, untransformed data). The largeIFNg concentration in DTA-1 vehicle control cells required a logtransformation of the data to compare values between treat-ment groups. ANOVA of the transformed data showed thatDTA-1 did rescue MEK inhibition IFNg levels to isotype vehiclecontrol levels (Fig. 4J, panel 2, transformed data). DTA-1 alsosignificantly increased IFNg levels in PI3Kd inhibitor-treatedcells; though this was significantly lower than the MEK-inhib-ited and vehicle control cells treated with DTA-1. DTA-1 did notrescue the IFNg levels in the combination treatment group.These data show that, although metabolic rescue may besufficient for increased proliferation, increased metabolismalone is not sufficient to rescue effector function, as indicatedby IFNg levels.

Combined checkpoint blockade and anti-GITR therapyovercome PD-L1–induced T-cell inhibition

As current clinical immunotherapy strategies involve combi-nation treatment with immune-checkpoint inhibitors, we soughtto test if anti-GITR treatment would beneficially combine withanti–PD-1 administration in an in vitro system. In addition tostimulating CD8þ T cells with anti-CD3/anti-CD28, we usedplate-bound PD-L1 to inhibit activation and simulate PD-1–associated immunosuppression that T cells may experience incertain TMEs.

PD-L1 inhibition decreased cell viability (Fig. 5A). Monother-apy with either anti-GITR agonism or PD-1 blockade partiallyrescued viability, whereas combination therapy significantlyrestored cell viability to uninhibited levels. A similar pattern wasseenwith basalOCR (Fig. 5B), basal ECAR (Fig. 5C), and 2-NBDGuptake (Fig. 5D). Cellular proliferation showed a similar response(Fig. 5E). PD-L1 attenuated the percentage of cells undergoingfour or five cell divisions (gate 5 and 6, respectively), whileincreasing the percentage of cells that underwent only one or nocell divisions (gates 2 and 1, respectively). Monotherapy partiallyrescued PD-L1–associated inhibition of proliferation, whereascombination therapy rescues it further.

Although PD-L1 signaling abrogated IFNg production, DTA-1 treatment alone did not significantly rescue production of thiscytokine, whereas anti–PD-1 monotherapy displayed partialrescue (Fig. 5F). Combination therapy combined to fullyrestore IFNg production to non-PD-L1–treated levels. Althoughenhanced glycolytic flux can lead directly to enhanced IFNgproduction, here, DTA-1 monotherapy rescues ECAR and 2-NBDG uptake (Fig. 5C and D, respectively), but not IFNgproduction. These data suggest that metabolic enhancementalone is not sufficient to rescue IFNg production, indicatingthat other signals through the PD-L1/PD-1 axis inhibit IFNgproduction.

DTA-1 treatment in a mouse model enhances CD8þ T-cellactivation and proliferation in vivo

After demonstrating that anti-GITR agonism alters CD8þ T-cellactivation, 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 cancercells, which are known to respond well to anti-GITR therapy.Tumors were harvested 8 days after treatment, at a time wheretumor regression is just beginning to occur (Fig. 6A). DTA-1–treated TIL and DLN CD8þ T cells also had enhanced PI3K/MEK/mTOR signaling (Supplementary Fig. S3A and S3B). DTA-1–treated DLN were significantly larger than IgG2a-treated DLN,suggesting substantially more cellular proliferation was occurringin the DLN from DTA-1–treated mice (Fig. 6B). This increase incellular proliferation is further verified by upregulation of a panelof proproliferative genes in DLN CD8þ T cells (Fig. 6C) and Ki67staining (Supplementary Fig. S3C).

Gene transcripts for cytotoxic effector molecules were signifi-cantly upregulated in both TIL and DLNCD8þ T-cell populations(Fig. 6D). Ifng, Gzma, Gzmb, and Gzmk transcripts were signifi-cantly elevated in response toDTA-1 treatment. These data suggest

that DTA-1 promotes a Tc1 phenotype that enhances antitumorimmunity.

GITR and other TNFRs are also associated with enhancedmemory cell formation (34). As expected, DTA-1 treatmentincreased the CD44þCD62L� effector memory andCD44þCD62Lþ central memory pools relative to IgG2a controls(Fig. 6E). Enhanced memory formation by DTA-1 was supportedby increased gene transcript of the memory marker Klrg1 (35) inboth TIL and DLN (Fig. 6F). This indicated that the T-cell pop-ulation within the DLN was a complex mixture of na€�ve, newlyactivated, and memory cells. The shift from na€�ve cells towardeffector and memory cells, along with our previous in vitro data,implies GITR agonism may participate in the priming phase ofCD8þ T cells (36).

Because DTA-1 treatment depletes TIL, not DLN Tregs (19), theincreased CD8þ T-cell proliferation in the DLN, and the increased

Figure 4.

DTA-1 upregulates MAPK signaling and can rescue CD8þ T cells from MEK inhibition, in part due to increased PI3K/AKT/mTOR signaling. A, Representative MAPKpathway Western blots from two separate experiments. B, OCR, (C) ECAR, and (D) 2-NBDG uptake of cells incubated with DMSO vehicle (Veh), the p38inhibitor SB203580 (SB), or theMEK inhibitor PD98059 (PD).E,p70S6kWesternblot representativeof twoexperiments.F,2-NBDGuptake (N¼ 3), (G) basalOCR (N¼ 4), and (H) basal ECAR (N ¼ 4) of cells incubated with Veh, PD, the PI3Kd inhibitor SW30 (SW), or the PD/SW combination (Com). I, CellTrace plotsrepresentative of three separate experiments. J, IFNg levels (left; multiple t tests using Holm–Sidakmethod). The large difference in concentrations between controland treatment groups required the log transformation of data to compare results between treatment groups (right, N ¼ 3). For F, average percent change isdepicted in red. Data are shown asmean� SEM. � , P� 0.05; �� , P� 0.05 comparedwith all other IgG2a treatment groups; z, P� 0.05 compared with all other DTA-1treatment groups, as measured by ANOVA; ns, not significant.

Sabharwal et al.





effector molecule transcript levels can be attributed to GITRagonist effects of the DTA-1 antibody. This suggests that GITRagonism contributes to CD8þ T-cell expansion and priming in theDLN to enhance antitumor immunity.

GITR agonism increases CD8þ T-cell metabolism in the DLNand tumor of MC38-bearing mice

DTA-1 treatment in MC38-bearing mice significantly increasedboth OCR and ECAR in DLN CD8þ T cells (Fig. 7A and B,respectively). TIL CD8þ T cells also had significantly increasedOCR (Fig. 7C). Although reports indicate improved effectorfunction is generally accompanied by increased glycolysis (13,14), the increase we saw in TIL CD8þ T-cell ECAR with DTA-1treatment was not significant, although 2 of 3 experimentsshowed increases (Fig. 7D). Several reports have highlighted theimportance of mitochondrial function on proper effector T-cellperformance (37–40), althoughno clearmechanismof action hasyet been described. It is possible that the increased OCR seen withDTA-1 allows effector CD8þ T cells to function properly in theTME. TIL CD8þ T cells fromDTA-1–treatedmice had significantlyincreased spare glycolytic capacity (Fig. 7E), which indicates a

cell's ability to respond to cellular stress and increased energeticdemands.

TIL CD8þ T cells did not display the same proliferative genesignature seen in DLN CD8þ T cells but did regulate severalmetabolic gene transcripts affected by DTA-1 treatment (Fig. 7F).

DTA-1 also significantly increased BODIPY staining of internallipid stores in DLN CD8þ T cells, whereas TIL CD8þ T cells trendupward (Fig. 7G).

These data suggest that anti-GITR agonism significantlyincreases CD8þ T-cell metabolism during the priming phase inthe DLN, as well as during the effector phase inside the TME,thereby increasing T-cell fitness and enhancing the CD8þ T-cell–mediated antitumor response.

DiscussionIn this study, we aimed to better understand the mechanism of

action of anti-GITR agonism, as opposed to the contribution ofTreg depletion, on antitumor efficacy. Here, we show that DTA-1treatment upregulated OCR and ECAR in CD8þ T cells both invitro and in vivo. In vitro, we demonstrated that GITR agonism

Figure 5.

Checkpoint blockade therapy and anti-GITR therapy combine to overcome inhibition of CD8þ T-cell activation by PD-L1 signaling in vitro. A, Cell viability at72 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 CD8þ T cells,4 technical replicates representative of N ¼ 3 separate experiments. D, 2-NBDG uptake and (E) cellular proliferation of PD-L1–inhibited CD8þ T cells;representative of N ¼ 3. F, IFNg concentrations by ELISA; N ¼ 5 individual experiments. Data are shown as mean � SD. � , P � 0.05 by ANOVA.






increases cellular proliferation and IFNg production, and thatmetabolic changes elicited byDTA-1 treatment were required, butnot sufficient by themselves, for those changes. In vivo, enhancedmetabolism was accompanied by increased proliferation in theDLN and increased effector molecule transcription in the DLNand TIL populations.

Understanding the mechanism of action of DTA-1–mediatedsignaling will better inform upon how GITR agonist therapy willcombine with other immuno- and chemotherapies.

Costimulatory signals are reported to increase FAO, whichsupports multiple CD8þ T-cell functions (16, 17). CD8þ T cellsin hypoxic and hypoglycemic TMEs enhance fatty acid catabolismto maintain effector function, mainly by utilizing endogenousfatty acids (41). Our in vitro data show that DTA-1 increased FAOand internal lipid stores. Our in vivo data also demonstrateincreased lipid droplet formation in DLN CD8þ T cells. WhetherDTA-1–induced increases in lipid stores during activation in theDLN are then mobilized upon entry into the TME to help fueleffector function is not yet known.

Tumor cells outcompeting T cells for nutrients in the TME is onemechanism of action of tumor immunosuppression (22). Onestudy shows that enhancing tumor cell metabolism converts aregressive murine cancer line into a progressive cancer (21). Incases of nutrient competition, boosting T-cell metabolism with aGITR agonist antibodymay prove beneficial. However, repressionof T-cell metabolism can also result from direct interactionbetween T cells and tumor cells or suppressive immune cells, orindirect interaction, via metabolites such as adenosine, or tryp-tophan depletion via IDO overexpression (42, 43). In these cases,GITR agonism may prove insufficient in rescuing an antitumorimmune response. It is vital to understand which immune inhib-itory pathways can or cannot be overcome by GITR agonism inorder to devise the most effective combination therapy strategies.

Our finding that GITR agonism can potentiate T-cell activationand function has potential therapeutic relevance. MEK inhibitorsare FDA-approved against melanomas with certain mutations,and ongoing clinical trials are testing these inhibitors with check-point blockade therapy. Previous work showed that MEK

Figure 6.

DTA-1 treatment in a syngeneic mouse tumor model enhances CD8þ T-cell activation and proliferation in vivo. A, Tumor mass and (B) DLN mass on day8 after treatment; individual masses from N ¼ 4 separate experiments (8–13 mice per experiment), � , P � 0.05 by Student t test. C, Gene-expression heat mapdepicting DTA-1 regulation of proliferation-associated genes in DLN. Individual color blocks represent an average of normalized gene expression from4 individual experiments. D, Granzyme and IFNg gene transcript levels; � , P � 0.05 by ANOVA. F, Klrg1 gene transcript levels; N ¼ 4; � , P � 0.05 by Student t test.E, Effector/memory staining from DLN. Representative plot from N ¼ 4 separate experiments. Data are shown as mean � SD.

Sabharwal et al.





inhibition reduces T-cell-receptor–induced apoptosis that typi-cally occurs in exhausted T cells in the TME, leading to increasedefficacy when paired with anti–PD-1 blockade (44). The study,however, notes that T-cell priming in the DLN is suppressed byMEK treatment. Our data show that GITR agonism can rescue theMEK inhibitor-associated decreases in metabolism, proliferation,and IFNg production, which suggests that adding anti-GITRtreatment to the MEK/anti–PD-1 combination may boost anti-tumor clearance further by enhancing activation in the DLN.Indeed, other murine studies show that triple combination ther-apy of TNFR agonist antibodies with MEK inhibitor/anti–PD-1therapy improves tumor clearance significantly compared withMEK/anti–PD-1 combination alone (45, 46). The TNFR antibo-dies in these studies target 4-1BB and OX40, but a GITR agonistantibody is likely to act similarly. Although these studies did notidentify a molecular mechanism of action, it is probable that theenhanced PI3Kd/Akt/mTOR signaling and augmented metabolicfunction that we have ascribed to GITR agonism plays a role inrescuingMEK inhibition of T-cell activation in the DLN; however,further studies are required to confirm this hypothesis.

Althoughmuchof the focushas remainedon the Tregdepletioneffects of DTA-1, it is still unclear to what extent Treg depletioncontributes to efficacy. Several reports demonstrate that Tregdepletion alone does not account for the antitumor effects ofGITR treatment. Our study substantiates that GITR agonist effectsof DTA-1 are necessary for proper tumor clearance (19, 47, 48).

Immune cell metabolism is increasingly appreciated for its rolein influencing immune cell function. Here, we elucidated some ofthe metabolic effects of anti-GITR agonism to better understandthe mechanism of action of GITR agonism-induced tumor clear-ance. Current cancer treatment strategies are increasingly focusedon combination therapies, with anti–PD-1 therapy as the foun-dation (49). Understanding the mechanism by which anti-GITRtreatment increases metabolic function, and circumstances bywhich this increased metabolism can rescue T-cell proliferationand effector function canprovide improved insight into the effectsof combining small molecules and immunotherapies to modu-late immune cell metabolism. These insights may lead toenhanced therapeutic strategies that will improve patientoutcomes.

Figure 7.

GITR agonism increasesmetabolism in CD8þ T cells in the DLN and tumor of MC38-bearingmice.A,OCRand (B) ECAR in DLN (N¼ 4), and TIL (C andD, respectively;N ¼ 3) CD8þ T cells. E, TIL spare glycolytic reserve (basal ECAR minus oligomycin-treated ECAR). F, Gene expression of TIL CD8þ metabolic genes.G, BODIPY staining. Data shown are mean � SD. � , P � 0.05 by Student t test; ns, not significant.






Disclosure of Potential Conflicts of InterestD.B. Rosen reports receiving commercial research support from and has

ownership interest (including stock, patents, etc.) in Merck Inc.. No potentialconflicts of interest were disclosed by the other authors.

Authors' ContributionsConception and design: S.S. Sabharwal, D.B. Rosen, B. Joyce-Shaikh,L.A. Z�u~nigaDevelopment of methodology: S.S. Sabharwal, D.B. Rosen, L.A. Z�u~nigaAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): S.S. Sabharwal, D.B. Rosen, D. Tedesco, B. Joyce-Shaikh, M. Semana, M. Bauer, K. Bang, C. Stevenson, L.A. Z�u~nigaAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): S.S. Sabharwal, D.B. Rosen, J. Grein, D. Tedesco,K. Bang, L.A. Z�u~nigaWriting, review, and/or revisionof themanuscript: S.S. Sabharwal,D. Tedesco,D.J. Cua, L.A. Z�u~niga

Administrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): K. Bang, C. StevensonStudy supervision: S.S. Sabharwal, R. Ueda, L.A. Z�u~niga

AcknowledgmentsThe authors are grateful to the Merck Research Laboratories (MRL) Postdoc-

toral Research Fellow Program for financial support provided by a fellowship toS.S. Sabharwal.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received October 30, 2017; revised May 11, 2018; accepted August 23, 2018;published first August 28, 2018.

References1. Letendre P, Monga V,MilhemM, Zakharia Y. Ipilimumab: from preclinical

development to future clinical perspectives in melanoma. Future Oncol2017;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 inipilimumab-refractory advanced melanoma: a randomised dose-comparison cohort of a phase 1 trial. Lancet 2014;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 ClinOncol 2016;34:4102–9.

4. Waight JD, Gombos RB, Wilson NS. Harnessing co-stimulatory TNFreceptors for cancer immunotherapy: current approaches and future oppor-tunities. Hum Antibodies 2017;25:87–109.

5. Makkouk A, Chester C, Kohrt HE. Rationale for anti-CD137 cancer immu-notherapy. Eur J Cancer 2016;54:112–9.

6. Knee DA, Hewes B, Brogdon JL. Rationale for anti-GITR cancer immuno-therapy. Eur J Cancer 2016;67:1–10.

7. Aspeslagh S, Postel-Vinay S, Rusakiewicz S, Soria JC, Zitvogel L, MarabelleA. Rationale for anti-OX40 cancer immunotherapy. Eur J Cancer 2016;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 2015;7:655–67.

9. Croft M, Benedict CA, Ware CF. Clinical targeting of the TNF and TNFRsuperfamilies. Nat Rev Drug Discov 2013;12:147–68.

10. Coe D, Begom S, Addey C, White M, Dyson J, Chai JG. Depletion ofregulatory T cells by anti-GITR mAb as a novel mechanism for cancerimmunotherapy. Cancer Immunol Immunother 2010;59:1367–77.

11. Ramirez-Montagut T, Chow A, Hirschhorn-Cymerman D, Terwey TH,Kochman AA, Lu S, et al. Glucocorticoid-induced TNF receptor familyrelated gene activation overcomes tolerance/ignorance to melanoma dif-ferentiation antigens and enhances antitumor immunity. J Immunol2006;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 costimulatoryto 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 aerobicglycolysis. Cell 2013;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 cellresponses. Cell 2015;162:1217–28.

15. Frauwirth KA, Riley JL,HarrisMH, Parry RV, Rathmell JC, PlasDR, et al. TheCD28 signaling pathway regulates glucose metabolism. Immunity2002;16:769–77.

16. Klein Geltink RI, O'Sullivan D, Corrado M, Bremser A, Buck MD, BuescherJM, et al. Mitochondrial priming by CD28. Cell 2017;171:385–97.

17. Choi BK, Lee DY, Lee DG, Kim YH, Kim SH, Oh HS, et al. 4-1BB signalingactivates glucose and fatty acid metabolism to enhance CD8þ T cellproliferation. Cell Mol Immunol 2017;14:748–57.

18. Patsoukis N, Bardhan K, Chatterjee P, Sari D, Liu B, Bell LN, et al. PD-1alters T-cell metabolic reprogramming by inhibiting glycolysis and pro-moting lipolysis and fatty acid oxidation. Nat Commun 2015;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 inagonistic GITR targeting for tumor immunotherapy. Cancer Res 2017;77:1108–18.

20. Lindauer A, ValiathanCR,Mehta K, SriramV, deGreef R, Elassaiss-Schaap J,et al. Translational pharmacokinetic/pharmacodynamic modeling oftumor growth inhibition supports dose-range selection of the anti-PD-1antibody pembrolizumab. CPT Pharmacometrics Syst Pharmacol 2017;6:11–20.

21. Chang CH, Qiu J, O'Sullivan D, Buck MD, Noguchi T, Curtis JD, et al.Metabolic competition in the tumormicroenvironment is a driver of cancerprogression. Cell 2015;162:1229–41.

22. Siska PJ, Rathmell JC. T cell metabolic fitness in antitumor immunity.Trends Immunol 2015;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 expansionand mouse survival following severe influenza infection. J Immunol2010;185:7223–34.

24. Hauer J, Puschner S, Ramakrishnan P, SimonU, BongersM, Federle C, et al.TNF receptor (TNFR)-associated factor (TRAF) 3 serves as an inhibitor ofTRAF2/5-mediated activation of the noncanonical NF-kappaB pathway byTRAF-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 cellsand imprints a T-bet-dependent Tc1 program with repressed IL-6/TGF-beta1 signaling. J Immunol 2013;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 mediatedby CD8þ T cells. Nat Immunol 2014;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 catabolismin cancer cells. Cell Rep 2016;17:821–36.

28. Ip H, Sethi T. CD98 signals controlling tumorigenesis. Int J Biochem CellBiol 2016;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 throughglucocorticoid-induced tumor necrosis receptor (GITR) is dependent on c-Jun N-terminal kinase activation. J Biol Chem 2012;287:17100–8.

30. Tang X, Tian J, Ma J, Wang J, Qi C, Rui K, et al. GITRL modulates theactivities of p38 MAPK and STAT3 to promote Th17 cell differentiation inautoimmune arthritis. Oncotarget 2016;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.

Sabharwal et al.





32. Pullen N, Thomas G. The modular phosphorylation and activation ofp70s6k. FEBS Lett 1997;410:78–82.

33. WilliamsO,HousemanBT, Kunkel EJ, Aizenstein B,HoffmanR, Knight ZA,et al. Discovery of dual inhibitors of the immune cell PI3Ks p110delta andp110gamma: a prototype for new anti-inflammatory drugs. Chem Biol2010;17:123–34.

34. Croft M. The TNF family in T cell differentiation and function–unansweredquestions and future directions. Semin Immunol 2014;26:183–90.

35. FuertesMarraco SA, Neubert NJ, Verdeil G, Speiser DE. Inhibitory receptorsbeyond T cell exhaustion. Front Immunol 2015;6:310.

36. ZhouP, L'Italien L,HodgesD, SchebyeXM. Pivotal roles ofCD4þ effector Tcells in mediating agonistic anti-GITR mAb-induced-immune activationand 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 bythe inhibitory receptor PD-1 are an early driver of CD8(þ) T cell exhaus-tion. Immunity 2016;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 metabolicprogramming. Cell 2016;166:63–76.

39. ScharpingNE,Menk AV,Moreci RS,Whetstone RD,Dadey RE,Watkins SC,et al. The tumor microenvironment represses T cell mitochondrial bio-genesis to drive intratumoral T cell metabolic insufficiency and dysfunc-tion. Immunity 2016;45:701–3.

40. Scharping NE, Menk AV, Whetstone RD, Zeng X, Delgoffe GM. Efficacy ofPD-1 blockade is potentiated by metformin-induced reduction of tumorhypoxia. Cancer Immunol Res 2017;5:9–16.

41. Zhang Y, Kurupati R, Liu L, Zhou XY, Zhang G, Hudaihed A, et al.Enhancing CD8þ T cell fatty acid catabolism within a metabolicallychallenging tumor microenvironment increases the efficacy of melanomaimmunotherapy. Cancer Cell 2017;32:377–91.

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

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 incombination 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. Oncoimmunology2015;5:e1052212.

46. Hu-Lieskovan S, Mok S, HometMoreno B, Tsoi J, Robert L, Goedert L, et al.Improved antitumor activity of immunotherapy with BRAF and MEKinhibitors 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 2015;4:e992237.

48. Kim YH, Shin SM, Choi BK, Oh HS, Kim CH, Lee SJ, et al. Authentic GITRsignaling fails to induce tumor regression unless Foxp3þ regulatory T cellsare depleted. J Immunol 2015;195:4721–9.

49. Ott PA, Hodi FS, Kaufman HL, Wigginton JM, Wolchok JD. Combinationimmunotherapy: a road map. J Immunother Cancer 2017;5:16.






2018;6:1199-1211. Published OnlineFirst August 28, 2018.Cancer Immunol Res Simran S. Sabharwal, David B. Rosen, Jeff Grein, et al. Tumor ModelT-cell Proliferation and Effector Cytokine Production in a Mouse

+GITR Agonism Enhances Cellular Metabolism to Support CD8

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:

Cited articles

http://cancerimmunolres.aacrjournals.org/content/6/10/1199.full#ref-list-1

This article cites 49 articles, 11 of which you can access for free at:

Citing articles

http://cancerimmunolres.aacrjournals.org/content/6/10/1199.full#related-urls

This article has been cited by 4 HighWire-hosted articles. Access the articles at:

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

Subscriptions

Reprints and

[email protected]

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

Permissions

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

.http://cancerimmunolres.aacrjournals.org/content/6/10/1199To 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/content/6/10/1199.full#ref-list-1

http://cancerimmunolres.aacrjournals.org/content/6/10/1199.full#related-urls

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

mailto:[email protected]

http://cancerimmunolres.aacrjournals.org/content/6/10/1199


gitr agonism enhances cellular metabolism to support cd8 t ...€¦ · in vivo tumor models for...

Documents