MERTK acts as a costimulatory receptor on human CD8+ T cells · 2019. 7. 2. · MERTK acts as a...

MERTK acts as a costimulatory receptor on human CD8+ T cells

Authors: Marlies J.W. Peeters1

*, Donata Dulkeviciute1†, Arianna Draghi

1

, Cathrin Ritter2

, Anne

Rahbech1

, Signe K. Skadborg1

, Tina Seremet1

, Ana Micaela Carnaz Simões1

, Evelina

Martinenaite1

, Hólmfridur R. Halldórsdóttir1

, Mads Hald Andersen1

, Gitte Holmen Olofsson1

,

Inge Marie Svane1,5

, Lene Juel Rasmussen4

, Özcan Met1,3,5

, Jürgen C. Becker2

, Marco Donia1,5

,

Claus Desler4

, Per thor Straten1,3

*

1Center for Cancer Immune Therapy, Department of Hematology, University Hospital Herlev,

Copenhagen, Denmark, 2Translational Skin Cancer Research, University Hospital Essen, German Cancer

Consortium (DKTK) Partner Site Essen and German Cancer Research Center (DKFZ), Heidelberg,

Germany, 3Department of Immunology and Microbiology, Inflammation and Cancer Group, and

4Center

for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen,

Denmark, 5Department of Oncology, University Hospital Herlev, Copenhagen, Denmark, † Deceased.

*Corresponding authors: Per thor Straten, Center for Cancer Immune Therapy, Department of

Hematology, University Hospital Herlev, email: [email protected], and Marlies J.W. Peeters,

Center for Cancer Immune Therapy, Department of Hematology, University Hospital Herlev, email:

[email protected]

Running title: MERTK receptor function in human CD8+ T cells

Keywords: TAM receptors, MERTK, PROS1, CD8+ T lymphocytes, costimulation

Financial support: This study was supported by the Danish Council for Independent Research

(grant no. DFF-1331-00095B), Danish Cancer Society (grant no. R72-A4396-13-S2), Training

Network for the Immunotherapy of Cancer funded by the EU (IMMUTRAIN) (H2020 grant no.

641549 to M.J.W.P and P.t.S.), The Danielsen Foundation, Axel Musfeldts fond, Dagmar

Marshalls Fond, Else og Mogens Wedell-Wedellsborg Fond, AP Møller Fonden, and Den

Bøhmske Fond.

Competing interests: P.t.S. and M.J.W.P. are authors on a pending European patent 18168195.8

pertaining the use of MERTK in adoptive T-cell therapy. The other authors declare that they have

no competing interests

Abstract: 182 words, Text: 4595 words, Figures: 7, References: 50

Supplementary data: 5 figures, 2 tables

on July 25, 2021. © 2019 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 July 2, 2019; DOI: 10.1158/2326-6066.CIR-18-0841

http://cancerimmunolres.aacrjournals.org/

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Abstract

The TAM family of receptor tyrosine kinases (TYRO3, AXL, and MERTK) is known to be

expressed on antigen-presenting cells and function as oncogenic drivers and as inhibitors of

inflammatory responses. Both human and mouse CD8+ T cells are thought to be negative for

TAM receptor expression. In this study, we show that TCR-activated human primary CD8+ T

cells expressed MERTK and the ligand PROS1 from day two post-activation. PROS1-mediated

MERTK signaling served as a late costimulatory signal, increasing proliferation and secretion of

effector and memory-associated cytokines. Knockdown and inhibition studies confirmed that this

costimulatory effect was mediated through MERTK. Transcriptomic and metabolic analyses of

PROS1-blocked CD8+ T cells demonstrated a role of the PROS1-MERTK axis in differentiation

of memory CD8+ T cells. Finally, using tumor infiltrating lymphocytes (TILs) from melanoma

patients, we show that MERTK-signaling on T cells improved TIL expansion and TIL-mediated

autologous cancer cell killing. We conclude that MERTK serves as a late costimulatory signal

for CD8+ T cells. Identification of this costimulatory function of MERTK on human CD8

+ T

cells suggests caution in the development of MERTK inhibitors for hematological or solid cancer

treatment.




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Introduction

The TAM receptor kinases - TYRO3, AXL, and MERTK – are negative regulators of

inflammatory responses on antigen-presenting cells (APCs), like macrophages and dendritic cells

(DCs) (1). TAM receptor signaling is induced by the ligands growth arrest–specific gene 6

(GAS6), and protein S (PROS1), which act as bridging molecules by binding to

phosphatidylserine (PtdSer) (2). The PtdSer-GAS6 or PtdSer-PROS1 complex subsequently

activates the TAM receptors (3). TAM receptors are reported to be expressed on DCs,

macrophages, human platelets, NK(T) cells and B cells (4-8). On cells of the innate immune

system, TAM signaling dampens activation and promotes silent engulfment of apoptotic cells

(9,10). As for T cells, the ligand PROS1 is expressed by activated human and mouse CD4+ T

cells. The previous thought that T cells did not express TAM receptors was challenged with the

report of MERTK being expressed by activated human CD4+ T cells (11). In contrast, another

study reported that activated mouse T cells do not express MERTK (12).

The TAM receptors also act as oncogenes. Many solid and hematological cancers express TAM

receptors, PtdSer and ligands PROS1 or GAS6 (13). These cancer cells are capable of TAM

auto-signaling which is associated with oncogenic traits such as survival, invasion, chemo-

resistance, and metastasis (13-15). As a consequence, overexpression of TAM receptors in

cancer is associated with a poor prognosis (reviewed in (13)). A range of inhibitors of TAM

receptor signaling are in development or clinical testing for treatment of cancers such as

leukemia (16-18).




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Cytotoxic CD8+ T cells establish and maintain anti-tumor immune responses. Some cancer

immunotherapies are based on the capacity of T cells to recognize and kill cancer cells (19). The

use of checkpoint antibodies that block inhibitory signaling on T cells in the tumor

microenvironment (TME) is associated with tumor regression in a variety of cancers. Here we

characterize another molecule that impacts T-cell functionality.

We show that human CD8+ T cells expressed MERTK and its ligand PROS1 upon TCR-

mediated activation. PROS1-mediated MERTK activation delivered a costimulatory signal in

cytotoxic CD8+ T cells. We show that PROS1-MERTK signaling in CD8

+ T cells impacted the

function, gene expression, and metabolism of the cell. Finally, we show that PROS1-MERTK

signaling benefited tumor-infiltrating lymphocyte (TIL) expansion and autologous tumor cell–

killing by TILs from metastatic melanoma patients. Such TAM receptor function on activated

CD8+ T cells could have therapeutic implications.




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Materials and Methods

Clinical specimens, peripheral blood cells, and cell lines

All procedures were approved by the Scientific Ethics Committee for the Capital Region of

Denmark. Written informed consent was obtained from all patients according to the Declaration

of Helsinki. All cells were cultured in a humidified 37C, 5% CO2 incubator. Cell lines PC-3,

FM82 and MDA-MB-231 were cultured in RPMI 1640 + 10% heat-inactivated fetal calf serum

(FCS, Gibco). Cell lines were obtained from ATCC or ESTDAB in 2014 or later, and frozen

upon initial expansion. The cell lines used in experiments were cultured for a maximum of 20

passages. Cells were not re-authenticated and were tested as mycoplasma-negative. PBMCs from

healthy donor buffy coats were isolated by gradient centrifugation and used immediately or

cryopreserved.

Biopsies from metastatic lesions of patients with AJCC 7th

edition stage III or IV melanoma were

collected from 2006-2013 and used for expansion of TILs. TILs and autologous tumor cell lines

used for in vitro cell killing assays were generated as described previously (20,21). Autologous

tumor cell lines were cultured in RPMI medium supplemented with 10% FCS.

T-cell isolation and stimulation

Human CD8+ T cells were isolated from human PBMCs by negative selection (Magnisort,

Invitrogen). Purified CD8+ T cells or PBMCs were cultured with anti-CD3/anti-CD8–coated

Dynabeads (Gibco) in X-VIVO 15 medium (Lonza) or CEF peptide pool (MabTech),

supplemented with 5% heat-inactivated human AB serum (Sigma-Aldrich) and 50 U/ml hIL2

(Proleukin). Alternatively, cells were cultured in serum free medium with 50 nM PROS1

(Haematologic Technologies) or 250 nM MERTK-inhibitor UNC2025 (Sigma-Aldrich). For




6

PROS1-blocking experiments or PtdSer-blocking experiments, cells were cultured in medium

with 5% human serum with PROS1-blocking antibody (anti-PROS1) (10 μg/ml, clone PS7,

SCBT) or unconjugated Annexin V (5 μg/ml, BD Biosciences), respectively.

Flow cytometry

For proliferation assays, cells were labelled with proliferation dye CellTrace Violet (Invitrogen).

For surface staining, single-cell suspensions were stained with the following: anti-CD3 (clone

UCHT1), anti-CD8 (RPA-T8), anti-CD45RO (UCHL-1, all BD Biosciences), anti-CD4 (SK3),

anti-CD137 (VIC7) anti-CCR7 (G043H7, all Biolegend), anti-PROS1 (PS7, SCBT), anti-Mertk

(125518), anti-Tyro3 (96201) and anti-Axl (108724, all R&D Systems). Sample acquisition was

performed using a FACSCanto or LSR II (BD Biosciences) and data was analyzed using FlowJo

v10.

Cytokine measurements

Amounts of IFNγ and TNFα (Invitrogen) or PROS1 (Abcam) were measured in culture

supernatants using enzyme-linked immunosorbent assays (ELISA) according to manufacturer’s

instructions. Results were analyzed using Epoch plate reader (BioTek) and Gen5 Take3 software

(v1.00.4, BioTek). Alternatively, culture supernatants were tested using the Bio-Plex Pro Human

Cytokine 27-Plex Immunoassay (Bio-Rad). Samples were acquired on a Bio-Plex 200 system

and analyzed with Bio-Plex Manager v.6 software. Samples analyzed with the Bio-Plex system

which were below or above the standard curve range were excluded from analysis.

Real-time qPCR

RNA was isolated with NucleoSpin® RNA kit (Macherey-Nagel) and reverse-transcribed using




7

SuperScript® VILO™ cDNA Synthesis Kit (Invitrogen). qPCR was performed in Agilent

AriaMX System using the Brilliant III Ultra-Fast QPCR Master Mix (Agilent). Amplified

products were checked by dissociation curves and expression was normalized to a housekeeping

gene. Primers sequences used are in Supplementary Table S1.

Western blotting

Western blotting was performed according to standard protocols. Briefly, cells were lysed using

RIPA lysis buffer (Pierce) supplemented with protease and phosphatase inhibitor cocktails

(Thermo Scientific). Proteins were quantified by BCA assay (Pierce) and separated using precast

4-12% Bolt Bis-Tris Plus SDS-PAGE gels (Invitrogen). Proteins were transferred to

nitrocellulose membranes using the iBlot 2 system (Invitrogen). The following primary

antibodies were used for protein detection: rabbit anti-human MERTK (D21F11), rabbit anti-

human TYRO3 (D38C6, both Cell Signaling), rat anti-human PROS1 (PS7), mouse anti-human

AXL (B-2), and mouse anti-human actin (C4, all SCBT). Proteins were visualized using

SuperSignal West ECL Kit (GE Healthcare) and Bio-Rad ChemiDoc Molecular Imager.

Quantification of signal was done using Fiji ImageJ (v.1.49).

siRNA gene knockdown

A set of three Stealth siRNA duplexes for targeted silencing of human MERTK were obtained

from Invitrogen. siRNA duplex sequences are listed in Supplementary Table S1. For control

experiments three siRNAs with scrambled sequences possessing similar GC content (Invitrogen)

were used. Following magnetic bead removal, three-day stimulated CD8+ T cells were

transfected with MERTK or mock siRNA with the ECM830 square wave electroporation system

(BTX) using electroporation parameters as previously described (22). Knockdown of protein

content was confirmed for every individual experiment.




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Transcriptomic analysis of CD8+ T cells

Sorted CD8+ T cells from three healthy donors were cultured in the presence or absence of anti-

CD3/anti-CD8 beads or 10 µg/ml anti-PROS1 for 3 days. Subsequently, cells were incubated

with Brefeldin A (Biolegend) for four hours. Final input for transcriptomic analysis was 1x105

viable cells per condition. RNA and protein samples were split and processed separately.

Transcriptomic analysis was performed using the nCounter Vantage 3D RNA:Protein Immune

Cell Signaling Panel (NanoString Technologies). Samples were subsequently processed in the

fully automated nCounter Prepstation (NanoString Technologies) and analyzed in the nCounter

Digital Analyzer (NanoString Technologies). The nSolver4 software (NanoString Technologies)

was used for data normalization and differential gene expression analyses. Data analysis was

performed according to NanoString gene expression data analysis guidelines and the nCounter

Advanced Analysis 2.0. Selected housekeeper mRNAs and proteins are listed in Supplementary

Table S2. The significance of differential gene expression between paired groups was estimated

using a mixed module significance testing with the algorithm included in the nCounter Advanced

Analysis. In this module, a negative binomical mixture model for low expression probes or a

simplified negative binomial model for high expression probes is used. If both models fail, a log-

linear model is applied. Differential expression is indicated as the log2 fold change in gene or

protein expression and the obtained p-values were adjusted for multiple testing by the Benjamini

and Hochberg method (BH. p-value) to control the false discovery rate. Differentially expressed

genes and proteins were depicted as volcano plot using R/RStudio v1.0.44.

Measurements of bioenergetics

The bioenergetics from CD8+ T cells were measured in the presence or absence of costimulatory

MERTK signaling in real-time using an XF-96 Extracellular Flux Analyzer (Seahorse




9

Bioscience, Agilent). Anti-CD3/anti-CD8–stimulated CD8+ T cells were grown in the presence

or absence of 50 nM PROS1 (serum free medium) or anti-PROS1 (10 µg/ml, serum-containing

medium) for three days prior to use. Cells were resuspended in Seahorse assay media (Seahorse

Bioscience, Agilent), supplemented with 1 mM pyruvate, 2 mM glutamine, adjusted to pH 7.4,

and subsequently seeded in a Seahorse 96-well plate using Cell-Tak adherent (Corning). Oxygen

consumption rates (OCR) and extracellular acidification rates (ECAR) were measured, then

wells were treated with 1 µM oligomycin and 10 mM 2-deoxy-D-glucose to measure ATP

turnover and glycolytic capacity from the changes in OCR and ECAR respectively, or with 0.4

µM carbonyl cyanide p-(trifluoromethoxy) phenylhydrazone (FCCP) to determine reserve

respiratory capacity from change in OCR. All wells received a final treatment with 2µM

Antimycin A.

Determination of ATP content

Whole-cell ATP content was measured in three-day anti-CD3/anti-CD8–stimulated CD8+ T cells

grown in the presence or absence of 50 nM PROS1 or anti-PROS1 (10 µg/ml) using a luciferase-

based assay (ViaLight MDA Plus Detection Kit, Lonza). Luminescence was quantified in a

MicroBeta2 Scintillation Counter (Perkin Elmer).

Expansion of TILs

For ‘young’ TIL outgrowth, biopsy material was cut into small fragments (1-2 mm2) and cultured

overnight. The following day, tumor fragments and cells were washed and used immediately or

cryopreserved. TILs were expanded in RPMI 1640 supplemented with 10% heat-inactivated

Human AB serum), IL2 (6000 IU/ml), penicillin, streptomycin and fungizone. Expansion

conditions included culture medium in the presence or absence of 50 nM PROS1 or 10 μg/ml

anti-PROS1. Outgrowth of ‘young’ TILs was measured by manual, unblinded counting of live




10

cells and fold expansion was calculated.

TILs designated for in vitro killing assays were isolated and expanded in vitro from metastatic

melanoma lesions with a two-step process as described previously (21). Expanded TILs with

high specificity for the HLA-A2 restricted MART-1/MelanA peptide analog ELAGIGILTV

(>90% specific with peptide-MHC multimer staining) were obtained through electronic sorting

of relevant CD8+ ‘young’ TILs, using peptide-MHC multimers. TILs were subsequently

subjected to the rapid expansion protocol, as previously described (20).

In vitro killing assay and cocultures

The tumor-specific killing ability of TILs was assessed with an impedance-based cytotoxicity

assay (23). Briefly, antigen-specific TILs were thawed and rested in IL2 free media (RPMI 1640

supplemented with 10% human serum, penicillin and streptomycin) for 72 hours. Autologous

tumor cells were seeded on E-plate 96 plates (ACEA Biosciences Inc) which were loaded onto

RTCA SP real-time cell analyzer (ACEA Biosciences Inc). After 24 hours, TILs were added with

50 nM PROS1 or a titration ranging from 0-100 nM PROS1.

For coculture experiments, high TAM receptor-expressing MDA-MB-231 cells were cultured in

serum-free X-VIVO medium for 1 week prior to coculture. Subsequently, MDA-MB-231 cells

were plated in a flat-bottom 96-well plate and left to adhere for approximately 4 hours. Sorted

allogenic non-reactive CD8+ T cells and anti-CD3/anti-CD8 beads were added in a 1:10 tumor

cell:T cell ratio. A PROS1 titration was added in the range of 0-100 nM PROS1. After 4 days of

coculture, supernatants were harvested and analyzed by ELISA.

Statistical analysis

Data are plotted as mean ± SEM. Comparisons between groups were analyzed with two-tailed




11

paired Student’s t tests or two-way ANOVA with Bonferroni’s multiple comparisons tests, as

appropriate. Data analysis was performed with GraphPad Prism (v8.00) software unless specified

otherwise. Used statistical tests and number of biological replicates are indicated in the figure

legends. *p<0.05, **p<0.01, ***p<0.001.

Results

Human CD8+ T cells express ligand PROS1 and TAM receptor MERTK upon activation

We analyzed the expression of the TAM receptors and ligand PROS1 upon CD8+ T-cell

activation. anti-CD3/anti-CD8-mediated activation of sorted human CD8+ T cells led to an

increase in surface staining of PROS1 on CD8+ T cells from day two onwards (Fig. 1A-B). This

was correlated with an approx. 60-fold induction of PROS1 mRNA expression and an increase in

endogenous protein expression (Fig. 1C-D). PROS1 surface staining was partly reversible by

blockage of PtdSer (Fig. S1A-B). Furthermore, activated CD8+ T cells significantly increased

TAM receptor MERTK surface expression from day two onwards, only on PROS1-positive cells

(Fig. 1E and Supplementary Fig. S1). On day three post-activation, approx. 25% of CD8+ T cells

were MERTK-positive, whereas resting T cells remained MERTK-negative (Fig. 1E-F).

MERTK expression was confirmed by mRNA and protein expression on three-day activated

CD8+ T cells (Fig. 1G-H). To assess if MERTK expression was limited to a certain CD8

+ T-cell

subset, we analyzed MERTK expression on three-day activated CD8+

T cells which were

costained with subset markers CCR7 and CD45R0. These data show that MERTK expression

was significantly higher on TCM CD8+ T cells (Fig. 1I -J). Finally, to confirm that MERTK

upregulation was not due to persistent stimulation by CD3/CD28, human PBMCs were activated

with a pool of 23 peptides derived from cytomegalovirus, Epstein-Barr virus and influenza.

Using CD137, recently TCR-activated, naturally occurring, CD8+ T cells can be identified (24).




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Only recently activated CD137+ CD8

+ T cells expressed MERTK (Fig. 2A-E). Additionally, we

found that resting or activated CD8+ T cells expressed little TYRO3 and do not express AXL

(Supplementary Fig. S2A-D).

PROS1 positively regulates CD8+ T-cell proliferation and cytokine release

Next, we asked whether PROS1 acts as a costimulatory signal for CD8+ T cells, as previously

shown for CD4+ T cells (11). We confirmed that PtdSer would not be a limiting factor of TAM

signaling in our culture conditions, as presence of PtdSer is essential for optimal TAM receptor

activation. We excluded apoptosis as the sole PtdSer source by using a caspase inhibitor

(Supplementary Fig. S3A-D). To study the effect of PROS1-mediated MERTK signaling on

CD8+ T cells, we activated CD8

+ T cells in the presence of 50 nM PROS1. PROS1 acted as a

costimulatory molecule by significantly increasing proliferation, but only when CD8+ T cells

were activated (Fig. 3A-B). PROS1-mediated costimulation also increased secretion of effector

cytokines IFNγ, TNFα and chemokine CXCL10 (Fig. 3C). In addition, PROS1-costimulated

CD8+ T cells secreted more memory cytokine IL7 (Fig. 3C). To substantiate these findings, we

studied the effect of a PROS1-blocking antibody (anti-PROS1) which led to a significant

inhibition of CD8+ T-cell proliferation (Fig. 3D-E).

PROS1 costimulation on CD8+ T cells acts via MERTK

To verify if the functional changes related to PROS1 were due to signaling through MERTK, we

inhibited MERTK signaling in activated CD8+ T cells. We established a siRNA-mediated

knockdown of MERTK. As our earlier results have shown that resting T cells do not express

MERTK, CD8+ T cells were activated for three days prior to siRNA electroporation. We




13

confirmed that siRNA knockdown resulted in a 70% reduction in MERTK protein expression

compared to control (Fig. 4A-B). When re-activated, MERTK-knockdown cells produced less

IFNγ (Fig. 4C). Moreover, IL7, but not IL15, secretion was significantly decreased (Fig. 4C).

We verified these results using UNC2025, a MERTK-inhibitor which is in development for the

treatment of leukemia (18). MERTK inhibition significantly decreased anti-CD3/anti-CD8–

mediated CD8+ T-cell proliferation with no decrease in cell viability (Fig. 4D-E).

Correspondingly, MERTK inhibition could reverse the positive effects of PROS1 on IFNγ

secretion (Fig. 4F).

PROS1-MERTK signaling in CD8+ T cells is associated with changes in gene expression

To investigate the intracellular effects of this PROS1-MERTK axis on CD8+ T cells, we

analyzed the transcriptome of three-day anti-CD3/anti-CD8–activated CD8+ T cells in the

presence or absence of anti-PROS1. An overview of the resulting differential regulation of

approx. 800 genes and 30 proteins is depicted in Fig 5A. The most differentially upregulated

genes and proteins in PROS1-blocked cells versus control were LTA, TNFSRF9, IL2, and IFNγ,

whereas the most differentially downregulated genes were IL4R, DUSP4, CD99, ITGAL and

CCL5 (Fig. 5B-F). These results, along with the observation that activation-associated MERTK

expression was more pronounced on TCM cells, led us to hypothesize whether PROS1-MERTK

signaling could influence differentiation of “long-lived” memory cells.

PROS1-MERTK signaling influences CD8+ T-cell metabolism

The gene encoding the transcription factor IRF4 was downregulated in PROS1-blocked cells

versus control (Fig. 6A). IRF4 has been correlated with metabolic programming of CD8+ T cells




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where it induces a metabolic shift, essential for antigen-specific responses and effector

differentiation and function (25). Subsequently, we studied the metabolism of activated CD8+ T

cells in the presence or absence of PROS1-MERTK signaling. Bioenergetic phenotypes are

shown to be predictive for CD8+ T-cell differentiation into the various memory subsets (26). The

basal respiration rate of PROS1-blocked cells was significantly decreased to 35% of control-

stimulated cells (Fig. 6B). Accordingly, the ATP turnover of PROS1-blocked cells was reduced

to 31% (Fig. 6C). Finally, the spare respiratory capacity (SRC) of PROS1-blocked cells was

significantly decreased (Fig. 6D). This contrasts with activated CD8+ T cells supplemented with

PROS1, where no significant changes were found (Supplementary Fig. S4A-F). For both

PROS1-blocked and PROS1-supplemented cells no significant change of glycolytic reserve

capacity was discovered (Fig. 6E and Supplementary Fig. S4D). To test whether this effect was

due to a lack of overall energy, we measured the whole cell content of ATP, which increased by

140% in PROS1-blocked CD8+ T cells (Fig. 6F). Representative plots of metabolic experiments

are depicted in Fig. 6G. This demonstrates that the decreased activity of oxidative

phosphorylation and mitochondrial respiration in PROS1-blocked cells was not a result of

starvation of ATP. Taken together, these results indicate that when PROS1-MERTK signaling is

absent in activated CD8+ T cells, the mitochondrial respiration capacity, necessary for long-lived

TCM cells, is significantly decreased.

PROS1-MERTK signaling affects melanoma TIL outgrowth and functionality

TAM receptors are expressed on cancer cells (13,16). Cytotoxic CD8+ T cells have been thought

to be negative for these TAM receptors. Our identification of potential joint expression of TAM

receptors on T cells and cancer cells could indicate a situation of ligand competition. As a proof




15

of principle, we measured PROS1 consumption from culture medium by T cells and cancer cells.

As cancer cell lines express higher more TAM receptors, their PROS1 consumption was higher

than that of activated T cells (Supplementary Fig. S5A-B). We aimed to study the ligand

competition in a T cell/cancer cell coculture using activated CD8+ T cells and the MDA-MB-231

cancer cell line (highly expressing TAM receptors) (Fig. 7A-B). Scarcity of PROS1 in low

concentrations resulted in an increased inhibition by cancer cells on CD8+ T-cell activation.

Correspondingly, once an excess of PROS1 was present, this PROS1-competive inhibitory effect

was abrogated.

Next, we sought to examine the clinical relevance of TAM signaling in T cells in relation to

adoptive T-cell therapy, where T cell-mediated anti-tumor immunity is essential. We studied if

PROS1-signaling had an impact on the primary expansion of ‘young’ TILs from metastatic

melanoma patients. Treatment of TILs with anti-PROS1 during the outgrowth phase led to a

significant decrease in fold expansion rate (Fig. 7C). Although PROS1-blocked conditions had a

reduced total number of cells, analysis of T-cell subsets revealed that no specific subset was

depleted (Fig. 7D). Finally, we asked whether joint TAM receptor expression on TILs and cancer

cells could affect anti-tumor immunity. For this study, we screened cancer cells from three

metastatic melanoma patients for TAM receptor expression (Fig. 7E). Next, rapidly expanded

(‘REP’) antigen-selected TILs from the highest MERTK-expressing patient were cocultured with

their autologous TAM receptor-expressing tumor cells. Using xCELLigence technology, we

followed real-time in vitro tumor-cell killing by autologous TILs (Fig. 7F and Supplementary

Fig. S5C-D). PROS1 alone had no effect on cancer cell growth. PROS1 supplementation had no

significant effect on TIL mediated killing (Fig. 7G).




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Discussion

TAM receptors play a role in dampening immune responses by negative feedback mechanisms in

cells of the innate immune system (27). TAM receptor signaling thereby safeguards termination

of inflammation and promotes tissue repair (13). A variety of cells are reported to express TAM

receptors, but T cells have been reported negative (28). Our results demonstrate that CD8+ T

cells express MERTK upon activation, and that MERTK signaling has pro-proliferative,

costimulatory effects on human CD8+ T cells.

It was shown more than twenty years ago that crosslinking of PROS1 inhibited T-cell

proliferation, but PROS1’s function as a TAM receptor ligand was yet unknown and T-cell

inhibition effect was attributed to PROS1’s anti-coagulant role (29). In contrast, soluble PROS1

has pro-proliferative effects on human CD4+ T cells, an effect that was reversible with blocking

antibodies for PROS1 (11). T cells express PROS1 upon activation (7,30,31), and T cell-derived

PROS1 plays a role by negative feedback signaling to DCs (7). In this regard, low plasma

concentration of TAM ligands is associated with a range of autoimmune disorders, suggesting

that unbalanced TAM signaling could play a role in the pathogenesis of autoimmunity (27).

TAM receptors were described as absent on both human and mouse lymphocytes, in both resting

and PMA/ionomycin-activated cells (12,28,32). Cabezon et al. later showed that human CD4+ T

cells upregulate MERTK upon TCR-mediated activation after three days (11). In line with these

results, we demonstrate that human CD8+ T cells express not only PROS1 but also MERTK from

two days post-activation. MERTK upregulation was indeed induced by CD3/CD28 stimulation,

but also by physiologically relevant activation of naturally circulating CD8+ T cells specific for

common viruses such as cytomegalovirus and influenza. PROS1 costimulation is subsequently




17

mediated via MERTK. PtdSer was not a limiting factor for TAM signaling in the CD8+ T-cell

cultures. PtdSer has various non-apoptotic signaling functions on activated T cells (33,34).

Indeed, PtdSer can be ‘downregulated’ again on non-apoptotic cells, although why transient

PtdSer-exposing viable cells are not phagocytosed remains controversial (35,36).

Memory CD8+ T cells form a heterogenic population of cells (37,38). The metabolism of cells is

linked to their function (39). Our data suggests a role for IRF4-mediated MERTK signaling in

CD8+ T-cell mitochondrial respiration, which is necessary for TCM differentiation and longevity

(39,40). We have previously shown that the bioenergetics of even close differentiation stages can

differ in their activity of glycolysis and oxidative phosphorylation (41). The downregulation of

IRF4 and the change in bioenergetic phenotype demonstrated after anti-PROS1 treatment

indicates a shift in differentiation stage of CD8+

T cells. This supports our hypothesis that

costimulatory PROS1-MERTK signaling is needed for CD8+ T-cell differentiation. Along those

lines, the transcriptomic data showed that IL2 mRNA were increased in the absence of MERTK

signaling, which is needed for differentiation of effector T cells (37). The expression data also

suggests a role for the IL4 receptor. This coincides with earlier findings showing that IL4

amplified PROS1 expression upon TCR stimulation in mouse T cells, and that IL4 deficient

CD4+ T cells are not able to induce PROS1 expression (30,31). IL4, as well as being a cytokine

for CD4+

T cells, stimulates CD8+ T-cell memory differentiation (42,43).

TAM receptor signaling on cancer cells has been implicated in proliferation, epithelial to

mesenchymal transition, survival, and migration (13-15,44). Next to this, AXL and MERTK

have been suggested to play a role in therapy resistance. TAM receptor expression by cancer




18

cells – and any cell in the tumor microenvironment – could thus set the stage for ligand

competition. PROS1 secreted by T cells could be exploited by cancer cells for oncogenic TAM

receptor signaling. This is corroborated by our coculture experiments, suggesting that cytokine

secretion by T cells is inhibited at low concentrations of PROS1, supposedly due to ligand

competition. TAM receptor signaling in cancer has been shown to lead to upregulation of PD-L1

(14,45,46). Therefore, oncogenic TAM receptor signaling could not only jeopardize T-cell

costimulation, but also inhibit PD-1 expressing T cells. This could restrain natural as well as

treatment induced anti-cancer T-cell responses, for instance upon adoptive cell transfer (ACT)

(47). A hurdle for TIL-based ACT is outgrowth of T cells during the early phases of culture. We

showed that melanoma TILs show a decreased expansion if PROS1 is cleared during culture.

Together, our data on ex vivo and in vitro killing by TILs in an autologous setting support the

notion that MERTK signaling may be inflicted by cancer cells which may jeopardize the anti-

tumor T-cell response.

Several agonistic antibodies directed against costimulatory molecules are in development or

clinical testing as cancer treatments (48). Due to the widespread expression of TAM receptors,

the therapeutic use of agonists is less straightforward. However, delivery approaches such as

those using bi-specific antibodies could open possibilities for targeting to T cells (49). Another

therapeutic approach is the development of MERTK inhibitors (13). Due to their expression on

various cancer types, TAM receptor small-molecule inhibitors have been, and are currently being

developed (12,18) (reviewed in (16)). TAM receptor inhibitors have been suggested to be

combined with immunotherapy, as T cells are assumed not to express TAM receptors (45,50).

Lee-Sherick et al. showed that MERTK inhibition in mice indirectly lowered PD-1 expression on




19

T cells via effects on DCs and macrophages (12). As mouse T cells are assumed negative, there

are, correspondingly, no expected direct effects of MERTK-inhibition on (mouse) T cells. Data

on human T cells however, might paint a different picture. Highlighting this difference between

mice and men, Cabezon et al. showed that antibody-mediated MERTK inhibition decreased

human CD4+ T-cell proliferation (11), whereas our study found inhibition of both CD8

+ T-cell

proliferation and IFNγ secretion using MERTK-inhibitor UNC2025. Moreover, we confirmed

these effects using MERTK knockdown. Our data indicate that clinical development should be

halted or at least carefully monitored due the expression of MERTK on activated human CD8+ T

cells. Direct targeting of cancer cells could be positive, whereas inhibiting MERTK expressed on

DCs implies more potent induction of T cells. However, targeting of MERTK could jeopardize

both regular and anti-tumor T-cell responses and differentiation. Further studies are needed to

scrutinize pros and cons of MERTK inhibition in cancer.

Despite our findings, several questions remain. Although TCM cells are prone to express more

MERTK, the identity of signals other than TCR signaling that lead to expression of MERTK is

unknown. Furthermore, TAM receptor expression would be expected to influence T-cell activity

and T-cell numbers in the TME, which again could impact overall survival. Due to expression of

three TAM receptors and at least two ligands, these relationships are not trivial to study.

Our results reveal a role for TAM receptor MERTK in providing late costimulatory signaling to

human CD8+ T cells. The mechanisms and implications of TAM receptor expression and

signaling in T cells and effects on anti-tumor immunity remain unclear. Future studies are




20

needed to determine whether oncogenic TAM signaling in cancer cells, or costimulatory TAM

signaling in cytotoxic T cells will tip the scale towards anti-tumor immunity.




21

Acknowledgements: We would like to thank Center for Cancer Immune Therapy staff, in

particular M. Idorn, for technical assistance. We would also like to thank R. van Eijsden

(NanoString Technologies) and S. Schwengberg (ACEA Biosciences) for valuable help with

gene expression studies and xCELLigence analysis, respectively.

Funding: This study was supported by the Danish Council for Independent Research (grant no.

DFF-1331-00095B), Danish Cancer Society (grant no. R72-A4396-13-S2), Training Network for

the Immunotherapy of Cancer funded by the EU (IMMUTRAIN) (H2020 grant no. 641549 to

M.J.W.P and P.t.S.), The Danielsen Foundation, Axel Musfeldts fond, Dagmar Marshalls Fond,

Else og Mogens Wedell-Wedellsborg Fond, AP Møller Fonden, and Den Bøhmske Fond.

Data and materials availability: All data needed to evaluate the conclusions in the paper are

present in the paper and/or the Supplementary Materials.




22

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27

Figure Legends

Figure 1

PROS1 ligand and MERTK receptor are expressed by TCR-activated human CD8+ T cells.

(A) Representative histogram of (B) PROS1 surface expression (MFI) on unstimulated and anti-

CD3/anti-CD8 activated CD8+ T cells as analyzed by flow cytometry (n=3). (C) RT-qPCR

evaluated expression of PROS1 mRNA in three-day activated CD8+ T cells, normalized to

unstimulated (n=3). (D) PROS1 protein expression in day three of activation of CD8+ T cells, as

analyzed by western blot (representative of at least 3 independent experiments). β-actin (bottom)

served as a loading control. (E) % MERTK-positive CD8+ T cells on unstimulated and activated

CD8+ T cells, harvested daily (n=3). (F) Representative dot plot of MERTK-expressing CD4

+ or

CD8+ T cells upon activation. (G) % MERTK-positive CD8

+ T cells on day three after activation

(n=6). (H) RT-qPCR evaluated expression of MERTK mRNA in three-day activated CD8+ T

cells, normalized to unstimulated (n=3). (I) MERTK protein expression in day three of activation

of PBMCs or CD8+ T cells, as analyzed by western blot (representative of at least 3 independent

experiments). β-actin (bottom) served as a loading control. (J) Gating strategy for CD8+ subset

classification using CCR7 and CD45R0, gated on unstimulated CD8+CD3

+ live cells. (J) MFI of

MERTK on CD8+ T-cell subsets, as measured on day 3 of stimulation (n=4). CM = central-

memory, EM = effector-memory, TEMRA = terminally differentiated EM cells. Data are plotted

as mean ± SEM and statistical significance was determined with Student’s t tests (C,F,G) or two-

way ANOVA with Bonferroni’s multiple comparisons tests (B,D,J). *p<0.05, **p<0.01,

***p<0.001.




28

Figure 2

MERTK is expressed by naturally occurring activated peripheral CD8+ T cells. Human

PBMCs were stimulated for 48 hours with peptides derived from CMV, EBV and influenza to

track naturally occurring CD8+ T-cell activation. (A) Representative dot plots of CD137 and

MERTK coexpression on peptide-stimulated CD8+ T cells from 5 healthy human donors. (B)

Gating strategy for resting (CD137-) and activated (CD137

+) T cells. (C) Representative

histogram of MERTK on CD137- (grey) and CD137

+ (blue) CD8

+ T cells. (D) and (E)

Percentage (D) and MFI (E) of MERTK-expressing CD8+ T cells in resting and activated

peripheral CD8+ T cells as classified by CD137 expression (n=10). Data are plotted as mean ±

SEM and statistical significance was determined with Student’s t tests (D,E), ***p<0.001.

Figure 3

PROS1 positively regulates CD8+ T-cell proliferation and cytokine secretion. Human CD8

+

T cells were cultured in serum-free medium, stained with a proliferation dye (CTV, CellTrace

Violet), and activated for three (A,B,C) or five (D,E) days with anti-CD3/anti-CD8 in the

presence or absence of PROS1. Proliferation was measured by flow cytometry. (A)

Representative histograms of technical triplicates of 1 donor. (B) Relative proliferation, with

proliferated anti-CD3/anti-CD8–activated CD8+ T cells set as 100 (n=4). (C) IFNγ, TNFα, IL7,

IL15 and CXCL10 concentrations in culture supernatants (n=3 or n=4). (D) Representative

histogram of (E). (E) Relative proliferation of CD8+ T cells activated with anti-CD3/anti-CD8

for five days, treated with anti-PROS1 (n=5). Data are plotted as mean ± SEM and statistical

significance was determined with Student’s t tests (B,C,E). *p<0.05, **p<0.01, ***p<0.001.




29

Figure 4

MERTK acts as a costimulatory molecule on CD8+ T cells. (A) siRNA-mediated knockdown

(compared to control) of MERTK on three-day CD3/CD28-stimulated CD8+

T cells, followed for

24, 48 and 72h after siRNA knockdown as analyzed by MERTK protein expression via western

blot (representative of at least 3 independent experiments). β-actin (bottom) served as a loading

control. (B) Quantification of (A) using relative density compared to control (normalized with

loading control). (C) Cytokine concentrations (IFNγ, IL2, IL7, IL15) in supernatants of MERTK-

knockdown and control CD8+ T cells re-stimulated overnight with anti-CD3/anti-CD8, 48 hours

after siRNA knockdown (n=4). (D) Human CD8+

T cells were cultured in serum-free medium,

stained with a proliferation dye and activated for three days with anti-CD3/anti-CD8 in the

presence or absence of 250 nM MERTK-inhibitor UNC2025. Proliferation was measured by

flow cytometry and relative proliferation was calculated compared to control (n=3). (E) % of live

cells of CD8+ T cells activated with or without 200 nM MERTK-inhibitor UNC2025 (n=3). (F)

IFNγ concentration in culture supernatants of activated CD8+ T cells stimulated with or without

PROS1 or MERTK-inhibitor UNC2025 (n=3). Data are plotted as mean ± SEM and statistical

significance was determined with Student’s t tests (C,D,F). *p<0.05, **p<0.01, ***p<0.001.

Figure 5

PROS1 signaling in CD8+ T cells is associated with changes in gene expression. (A) Log2

fold change of differentially expressed genes in anti-PROS1-treated CD8+

T cells compared to a

paired control is depicted as volcano plot. Genes with a p-value <0.05 are marked as blue dots,

genes with a p-value <0.01 are labeled with gene names and the horizontal lines in the plot




30

indicate the adjusted Benjamini and Hochberg adjusted p value (BH. p-value). (B,C) Summary

of individual genes that are over- (B) or under expressed (C) in anti-PROS1-treated CD8+ T cells

compared to the control group and their respective p-value (cut-off 0.05) and BH. p-value. (D, E)

Most differentially down regulated (IL4R, DUSP4 and CD99) (D) or up regulated (LTA,

TNFRSF9 and IL2) (E) mRNAs are depicted for the individual donors A (blue), B (purple) and

C (green). (F) IFNγ protein expression of the individual donors A (blue), B (purple) and C

(green).

Figure 6

Blocking PROS1-MERTK axis decreases mitochondrial respiration in CD8+ T cells.

Bioenergetic properties of CD3/CD28-stimulated CD8+ T cells cultured for three days in the

presence or absence of anti-PROS1. (A) Nanostring-measured IRF4 mRNA expression in three-

day activated CD8+ T cells, analyzed as in Fig. 4. (B) Basal respiration was determined as initial

resting consumption of oxygen. (C) ATP turnover was measured as decrease of oxygen

consumption after addition of oligomycin. (D) Reserve respiratory capacity was measured as

percentage of basal respiration, after addition of FCCP. (E) Glycolytic capacity was measured

after addition of oligomycin. (F) . (F) Whole-cell ATP content was normalized to control. (G)

Representative raw levels of oxygen consumption. Cells were treated with either oligomycin

(decrease in oxygen consumption, downward lines) or FCCP (increase in oxygen consumption,

upward lines) at stage A and antimycin A at stage B. Data are plotted as mean ± SEM and

statistical significance was determined with Student’s t tests (A,B,C,D,F). *p<0.05, **p<0.01,

***p<0.001.




31

Figure 7

PROS1 impacts on anti-tumor tumor-infiltrating lymphocytes. (A) Experimental setup of

(B). (B) IFNγ concentrations in coculture supernatants (n=3). Significance shown in comparison

with 0 nM PROS1 condition. (C) Tumor infiltrating lymphocytes (TILs) from biopsies

originating from four metastatic melanoma patients were cultured and expanded according to the

‘young’ TIL protocol in the presence or absence of exogenous PROS1 or anti-PROS1. Fold

expansion was calculated on day 16 and 23 of culture, relative to day 0 (n=4). (D) Phenotypic

analysis on TILs was done on day 23 of expansion using CCR7 and CD45RO as T-cell subset

markers (n=4). CM = central-memory, EM = effector-memory, TEMRA = terminally

differentiated EM cells. (E) TAM receptor protein expression status of tumor cells from 3

metastatic melanoma patients. Actin was used a loading control. (F) Real-time in vitro cytolysis

of autologous cancer cells from metastatic melanoma patient 3 after addition of antigen-selected

autologous TILs (1:10 target:effector ratio) and PROS1 titration from ranging from 0-100 nM

PROS1. (G) % Cytolysis 12 hours post TIL addition. Data are plotted as mean ± SEM and

statistical significance was determined with two-way ANOVA with Bonferroni’s multiple

comparisons tests (B,C,D). *p<0.05, **p<0.01, ***p<0.001.




FMOUnstimulated

αCD3/CD28

PROS1 on CD8

Coun

t

sllec T +8DC no 1S

ORP IFM

0 2 4 6 80

500

1000

1500

2000

*

***

******

******

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UnstimulatedαCD3/CD28 A

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ORP evitaleR)egnahc dlof(

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20

40

60

80

100 *

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M %

Days0 1 2 3 4 5

0

10

20

30

40

50

***

*** ***

UnstimulatedαCD3/CD28

KTREM

Unstimulated

αCD3/CD28

CD8

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ERTK

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αCD3/CD28

0

2

4

6

8

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100

200

300

400

500 ***

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Figure 1




CD13

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MERTK

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CD137-

CD137+CD137

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CD137-

CD137+

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40

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400

500

600 ***

Esllec T +8

DC no KTREM fo IF

M

CD137- CD137+(resting) (activated)

Figure 2




Unstimulated αCD3/CD28

Unstimulated+ 50 nM PROS1

αCD3/CD28+ 50 nM PROS1

CTV

Coun

t

0

50

100

150

200

250 *

)%( noitarefilorp evitaleR

αCD3/CD28

+ 50 nM PROS1

αCD3/CD28

+ 50 nM PROS10

10

20

30**

IFN-γ

ng/m

l

0

5

10

15

20*

TNF-α

αCD3/CD28

+ 50 nM PROS10.5

1.0

1.5

2.0

2.5*noiterces evitaleR

IL-7

αCD3/CD28

+ 50 nM PROS10.5

1.0

1.5

2.0

2.5IL-15

αCD3/CD28

+ 50 nM PROS10.5

1.0

1.5

2.0

2.5 *CXCL10

αCD3/CD28

+ 50 nM PROS1

CTV

Coun

t

αCD3/CD28 αCD3/CD28+ αPROS1


αCD3/CD28

+ αPROS1

0

50

100

**

A B

C

ED

Figure 3




MERTK siRNA

α-MERTK

α-β-actin

Mock

24h 48h 72h 24h 48h 72h

24h 48h 72h

46.7%knockdown

71.4%knockdown

69.1%knockdown

0.0

0.5

1.0

1.5MockMERTK siRNA

ytisned .ler KTREM

)nitca ot desilamron(

A B

C

0

20

40

60

80

100

**


αCD3/CD28

+ MERTKi

0

20

40

60

80

100

αCD3/CD28

+ MERTKi

)%( sllec eviL

0.8

1.0

1.2

1.4

1.6*

*

ns

αCD3/CD28

+ 50 nM PROS1

+ 50 nM PROS1

+ MERTKi

noiterces γNFI evitaleR

0.0

0.5

1.0

1.5

0.0

0.5

1.0

1.5

*

0.0

0.5

1.0

1.5

*

0.0

0.5

1.0

1.5

αCD3/CD28

MERTK siRNA

noiterces evitaleR

αCD3/CD28

MERTK siRNA

αCD3/CD28

MERTK siRNA

αCD3/CD28

MERTK siRNA

IFN-γ IL-2

p=0.0575

IL-7 IL-15

D E F

Figure 4




Donor ADonor BDonor C

100

120

140

160

180

200

100

120

140

160

180

200

260

280

300

320

340

360

1000

1200

1400

1600

1800

250

300

350

400

450

500

100

150

200

250

0

500

1000

1500

2000

2500

Control αPROS1

Control

αPROS1

Control

αPROS1

Control

αPROS1

Control αPROS1 Control αPROS1

Control αPROS1 Control αPROS1 Control αPROS1

norm

. IL4

R m

RNA

norm

. DU

SP4

mRN

A

norm

. CD

99 m

RNA

norm

. LTA

mRN

A

norm

. TN

FRSF

9 m

RNA

norm

. IL2

mRN

A

norm

. IFN

γ pr

otei

n

1.510.50-0.5-1-1.5

A B C

D

E F

-log1

0 (p

-val

ue)

log2 (fold change)

IL4R -0.518 9.10E-05 0.0279CD99 -0.346 0.00023 0.0353DUSP4 -0.804 0.00043 0.044CCL5 -0.462 0.000748 0.0528ITGAL -0.298 0.000859 0.0528TIGIT -0.173 0.00197 0.0751IRF4 -0.187 0.0022 0.0751TMEM173 -0.475 0.00245 0.0751NFATC2 -0.311 0.003 0.0837TNFSF4 -0.74 0.00442 0.113MX1 -0.469 0.0052 0.123GZMA -0.58 0.00567 0.124HLA-DRA -0.858 0.00719 0.141NOTCH2 -0.642 0.00737 0.141SMAD3 -0.374 0.00926 0.158NFIL3 -0.19 0.0116 0.169IRAK1 -0.418 0.0123 0.169NFKBIA -0.18 0.0129 0.169ILF3 -0.229 0.013 0.169CTLA4_all -0.406 0.0134 0.169SH2D1A -0.2 0.0136 0.169PRF1 -0.309 0.0138 0.169ITGAE -0.211 0.0173 0.205TNFAIP3 -0.189 0.0185 0.208IL2RB -0.447 0.0196 0.208GNLY -0.195 0.0197 0.208RUNX1 -0.389 0.0207 0.212IRAK4 -0.339 0.0248 0.238BATF -0.186 0.0258 0.24CD59 -0.545 0.0291 0.243TRAF3 -0.231 0.0295 0.243CXCR4 -0.321 0.031 0.243CDKN1A -0.354 0.0317 0.243EGR2 -0.675 0.0345 0.252ZAP70 -0.165 0.0359 0.256ICOS -0.223 0.0428 0.286IRF1 -0.229 0.044 0.286LGALS3 -0.872 0.0441 0.286CD58 -0.331 0.0442 0.286IL16 -0.55 0.0465 0.286CCR5 -0.528 0.0497 0.299

LTA 0.458 0.00118 0.0605TNFRSF9 0.467 0.00173 0.0751IL2 0.554 0.00793 0.143IL7R 0.455 0.0221 0.219TRAF4 0.319 0.029 0.243TNF 0.244 0.0306 0.243IFNg-protein 0.655 0.0313 0.243SLAMF7 0.327 0.0338 0.252TCF7 0.221 0.045 0.286FCER1G 0.567 0.0463 0.286

Log2 fold change

Log2 fold change

Log2 fold changep-value p-valueBH.p-value BH.p-value

Figure 5




0

10

20

30

40

50 *

etar noitpmusnoc negyx

O)ni

m/lomp( )RC

O(

Basal respiration

αCD3/CD28

+ αPROS1

Donor ADonor BDonor C

ANR

m 4FRI .mron

αCD3/CD28

+ αPROS10

10

20

30

40

50*etar noitp

musnoc negyxO

)nim/lo

mp( )RCO(

αCD3/CD28

+ αPROS1

ATP turnover

0

20

40

60*etar noitp

musnoc negyxO

)nim/lo

mp( )RCO(

αCD3/CD28

+ αPROS1

Reserve respiratory capacity

0

10

20

30

40etar noitacifidica ralullecartxE)ni

m/Hp

m( )RACE(

αCD3/CD28

+ αPROS1

Glycolytic capacity

0

50

100

150

200*

noitartnecnoc PTA)lortnoc ot evitaler(

αCD3/CD28

+ αPROS1

50 0 100

0

50

100 A Betar noitpmusnoc negyx

O)ni

m/lomp( )RC

O(

αCD3/CD28

+ αPROS1

Minutes

A B C

D E F

600

650

700

G

Figure 6




MDA-MB-231

Allogenic CD8+ T cellsαCD3/CD28

PROS1 titration

0 100 nM

4 days: IFN-γ secretion

0

50

100

150 Control medium

αPROS1sLIT +3

DC fo tesbus %

NaiveEM CM

TEMRA0

5

10

15

20

25

***

htworgtuo LIT gnuoY)noisnapxe dlof(

Days0 16 23

50 nM PROS1

Control medium

αPROS1

A B

C D

α-MERTK

α-TYRO3

α-AXL

α-β-actin

Patient 1

Patient 2

Patient 3*

)%( sisylotyC

Time post TIL addition (hours)

0 nM PROS16.25 nM PROS1

50 nM PROS1100 nM PROS1

E F

)%( sisylotyC

0 100 nM PROS1

G

0.0

0.5

1.0

*

** ***

ns

0 PROS1100 nM

noiterces γ-NFI evitaleR

20 40 00

20

40

60

80

100

0

20

40

60

80

100

Figure 7




Published OnlineFirst July 2, 2019.Cancer Immunol Res Marlies J.W. Peeters, Donata Dulkeviciute, Arianna Draghi, et al. cellsMERTK acts as a costimulatory receptor on human CD8+ T

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