cancer immunology copyright © 2018 tumor immune evasion arises through loss … · kearney et al.,...

Kearney et al., Sci. Immunol. 3, eaar3451 (2018) 18 May 2018

S C I E N C E I M M U N O L O G Y | R E S E A R C H A R T I C L E

1 of 14

C A N C E R I M M U N O L O G Y

Tumor immune evasion arises through loss of TNF sensitivityConor J. Kearney,1,2* Stephin J. Vervoort,2,3* Simon J. Hogg,2,3 Kelly M. Ramsbottom,1 Andrew J. Freeman,1 Najoua Lalaoui,4 Lizzy Pijpers,1 Jessica Michie,1 Kristin K. Brown,2,5,6 Deborah A. Knight,2 Vivien Sutton,7 Paul A. Beavis,2,8 Ilia Voskoboinik,2,9 Phil K. Darcy,2,8 John Silke,4 Joseph A. Trapani,2,7 Ricky W. Johnstone,2,3†‡ Jane Oliaro1,2†‡

Immunotherapy has revolutionized outcomes for cancer patients, but the mechanisms of resistance remain poorly defined. We used a series of whole-genome clustered regularly interspaced short palindromic repeat (CRISPR)–based screens performed in vitro and in vivo to identify mechanisms of tumor immune evasion from cytotoxic lymphocytes [CD8+ T cells and natural killer (NK) cells]. Deletion of key genes within the tumor necrosis factor (TNF) signaling, interferon- (IFN-) signaling, and antigen presentation pathways provided protection of tumor cells from CD8+ T cell–mediated killing and blunted antitumor immune responses in vivo. Deletion of a number of genes in the TNF pathway also emerged as the key mechanism of immune evasion from primary NK cells. Our screens also identi-fied that the metabolic protein 2-aminoethanethiol dioxygenase (Ado) modulates sensitivity to TNF-mediated killing by cytotoxic lymphocytes and is required for optimal control of tumors in vivo. Remarkably, we found that tumors delete the same genes when exposed to perforin-deficient CD8+ T cells, demonstrating that the dominant immune evasion strategy used by tumor cells is acquired resistance to T cell–derived cytokine- mediated antitumor effects. We demonstrate that TNF-mediated bystander killing is a potent T cell effector mechanism capable of killing antigen-negative tumor cells. In addition to highlighting the importance of TNF in CD8+ T cell– and NK cell–mediated killing of tumor cells, our study also provides a comprehensive picture of the roles of the TNF, IFN, and antigen pre-sentation pathways in immune-mediated tumor surveillance.

INTRODUCTIONCancer immunotherapies, such as checkpoint blockade, have had re-markable success in the clinic; however, acquired resistance often devel-ops through mechanisms that are not well defined (1–3). Neoantigens presented through major histocompatibility complex (MHC)–I facili-tate detection of tumor cells by cytotoxic CD8+ T cells and underpin the success of immune checkpoint blockade therapy (4–7). Thus, disrup-tion of antigen presentation is a key mechanism of tumor immune eva-sion, demonstrated by loss-of-function mutations in the genes encoding 2-microglobulin (B2M), interferon- (IFN-) and Janus kinases (JAK1/2) in patients that fail to respond to immunotherapy (8–10).

Cytotoxic lymphocytes, such as CD8+ T cells and natural killer (NK) cells, promote antitumor immunity through a combination of direct perforin-dependent tumor cell killing and by increasing “tumor im-mune sensitivity” through the release of inflammatory cytokines such as IFN- and tumor necrosis factor (TNF), which act on both tumor and

immune effector cells (11–13). However, the relative contribution of these additional mechanisms to tumor cell killing by cytotoxic lympho-cytes is still unclear. Clustered regularly interspaced short palindromic repeat (CRISPR)–based loss-of-function screens are a powerful approach to identify genes that sensitize to, or protect tumors from, T cell– and NK cell–driven antitumor immunity. Here, we have used a series of whole- genome CRISPR- based screens to identify the genes and pathways that confer resistance to both CD8+ T cell– and NK cell–mediated killing. We found that suppression of cytokine signaling and antigen presentation are key mechanisms by which tumors evade attack by cytotoxic lympho-cytes. In particular, we found that disruption of TNF-induced tumor cell death by CD8+ T cells was a major immune evasion mechanism.

RESULTSImmune evasion occurs through loss of TNF, IFN-, or antigen presentation pathwaysTo uncover, in an unbiased manner, the genes and signaling pathways that regulate sensitivity to T cell–mediated attack, we first performed genome-wide CRISPR/CRISPR-associated protein 9 (Cas9) screening in vitro using two tumor lines—MC38 colon adenocarcinoma and B16 melanoma cells, both of which are responsive to IFN-, engineered to present the chicken ovalbumin (Ova) antigen on H-2Kb (MC38Ova, B16Ova), and recognized and killed by transgenic OT-I CD8+ T cells (fig. S1A). The expression of key molecules on the T cells and tumor cells is presented in fig. S1 (B to D). MC38Ova cells expressing Cas9 and sta-bly transduced with a whole- genome guide RNA (gRNA) library were serially incubated with activated OT-I T cells, and the result-ing population was sequenced to identify single-guide RNAs (sgRNAs) that provided resistance to T cell–mediated killing (Fig. 1A). In this model, anti–PD-1 (programmed death 1) treatment enhanced both

1Immune Defence Laboratory, Cancer Immunology Program, Peter MacCallum Can-cer Centre, Melbourne, Victoria 3000, Australia. 2Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria 3052, Australia. 3Gene Regu-lation Laboratory, Translational Haematology Program, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia. 4Cell Signalling and Cell Death Division, Walter and Eliza Hall Institute for Medical Research, Parkville, Victoria 3050, Australia. 5Cancer Therapeutics and Cancer Metabolism Program, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia. 6Department of Biochemistry and Mo-lecular Biology, University of Melbourne, Parkville, Victoria 3052, Australia. 7Cancer Cell Death Laboratory, Cancer Immunology Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3000, Australia. 8Immuno therapy Laboratory, Cancer Immunology Program, Peter MacCallum Cancer Center, East Melbourne, Victoria 3000, Australia. 9Killer Cell Biology Laboratory, Cancer Immunology Program, Peter MacCallum Can-cer Centre, East Melbourne, Victoria 3000, Australia.*These authors contributed equally to this work.†These authors contributed equally to this work.‡Corresponding author. Email: [email protected] (R.W.J.); [email protected] (J.O.)

Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works

by guest on June 14, 2020http://im

munology.sciencem

ag.org/D

ownloaded from

http://immunology.sciencemag.org/



2 of 14

A

C D

B

E

F

G H I

J K M

L

Fig. 1. Immune evasion occurs through loss of TNF, IFN-, or antigen presentation pathways. (A) Experimental design of CRISPR/Cas9 screening. (B) OT-I T cell–MC38Ova killing and TNF secretion assays in the presence or absence of a neutralizing –PD-1 antibody (50 g/ml). (C) MC38Ova cells were subjected to three rounds of exposure to OT-I T cells, as described in (A), followed by sequencing for the top enriched genes. (D) The screen from (C) was carried out in parallel, in the presence of –PD-1 (50 g/ml), followed by sequencing for the top enriched genes. (E) Comparison of top scoring genes in screens described in (C) and (D). (F) Normalized sgRNA counts from the screen described in (C). unt, untreated. (G) Validation of screen hits using the indicated individual sgRNAs in MC38Ova upon an 18-hour exposure to OT-I T cells. (H) Protein network analysis of the screen hits from (D). (I) GO term analysis from the screen hits from (D). (J) The screen described in (A) was performed using B16Ova cells. After three rounds of exposure to OT-I T cells, the cells were sequenced for the top enriched genes. WT, wild type. (K) Protein network analysis from the top hits identified in (J). (L) GO term analysis from the top hits identified in (J). (M) The screen described in (A) was performed using MDA-MB-231 cells. After three rounds of exposure to HER2-directed human CAR T cells, the cells were sequenced for the top enriched genes.


munology.sciencem

ag.org/D

ownloaded from




3 of 14

OT-I T cell killing and cytokine production after recognition of MC38Ova cells (Fig. 1B). Parallel screens were conducted with the ad-dition of an anti–PD-1 antibody to identify genes that confer resistance to checkpoint blockade therapy. Under these screening conditions, the surviving tumor cells were almost completely refractory to the in-creased cell death conferred by anti–PD-1 (fig. S1E).

Deletion of genes from three principal pathways—antigen presen-tation, IFN- signaling, and TNF signaling—provided protection from T cell killing (Fig. 1, C to F). Identification of Jak1, B2m, Ifngr1, and Tap1, key genes in antigen presentation and IFN- signaling, and re-cently identified in similar screens (14, 15), validated our approach. We also uncovered genes, such as Casp8 and Tnfrsf1a, as crucial T cell effectors using our genome-wide screening approach (Fig. 1, C to F). Other less well-characterized genes, such as Sqstm1, Gosr1, and Ado, were also identified (Fig. 1, C to E). Subsequent depletion of selected genes from each pathway using two independent sgRNAs (fig. S2A) confirmed that they provided protection from T cell killing (Fig. 1G) without affecting MHC-I expression (fig. S2B). Nonbiased gene in-teraction analysis using the top hits confirmed that the dominant immune evasion genes clustered in the TNF, IFN-, and antigen pre-sentation pathways (Fig. 1H), and Gene Ontology (GO) term anal-ysis classified antigen presentation, death receptor–mediated killing, and cytokine signaling as the key pathways affected (Fig. 1I).

Consistent with the screen carried out in MC38Ova cells, we iden-tified Ifngr1/2, Jak1/2, and Stat1 as potent mediators of T cell killing using the B16Ova melanoma cell line (Fig. 1J), with enrichment of the IFN- and antigen presentation pathways but not TNF signaling (Fig. 1, K and L, and fig. S2C). To test a model system where antigen presentation is not required for T cell killing of target cells, we per-formed a genome-wide CRISPR/Cas9 screen using HER2-directed human CAR T cells as effectors and HER2+ MDA-MB-231 tumors as targets. We failed to significantly enrich for deletion of IFN- or TNF pathway genes but successfully selected for sgRNAs targeting HER2 (ERBB2) expression (Fig. 1M and fig. S2D). These results emphasized the importance of the IFN- pathway in regulating antigen presen-tation, which is not required in the CAR T cell model, and highlighted that tumors selectively disrupt pathways that confer the highest lev-els of vulnerability to T cell killing to evade immune attack.

NK cell immune evasion also occurs through loss of TNF pathway membersNK cells also recognize and kill tumor cells through specific receptor- ligand interactions, without the need for neoantigen presentation (16). We therefore performed a whole-genome CRISPR screen using pri-mary mouse NK cells and MC38 tumor cells as target cells to identify genes that confer resistance to immune-mediated killing in the ab-sence of the need for antigen presentation (Fig. 2A). NK cells effi-ciently killed MC38 target cells and produced substantial amounts of both IFN- and TNF (Fig. 2, B and C). As seen in our immune eva-sion CRISPR screens using CD8+ T cells, deletion of genes involved in TNF-mediated cell death was also the predominant mechanism of immune evasion against NK cell–mediated killing (Fig. 2, D to F). These data demonstrated that well-characterized genes in this path-way, such as Casp8 and Tnfrsf1a, and previously poorly character-ized genes, such as Ado, were crucial for both CD8+ T cell and NK cell antitumor effector function using two unbiased genome-wide CRISPR screens (Fig. 2G). This suggested that TNF derived from CD8+ T cells and NK cells upon target recognition plays an important role in mediating tumor cell death by these cells (Fig. 2H).

Immune evasion occurs independent of perforin-mediated killingNotable in our results was that genes involved in direct CD8+ T and NK cell killing by the perforin-mediated granule-exocytosis path-way were not identified in any of our immune evasion screens. We subsequently repeated the screen using perforin knockout (Prf1−/−) OT-I T cells, which efficiently killed MC38Ova and B16Ova cells in the presence of antigen over 18 hours (Fig. 3A and fig. S1A). sgRNAs tar-geting the TNF signaling molecules Tradd, Casp8, and Fadd were heavily enriched using MC38Ova tumor cells as target cells and Prf1−/− OT-I T cells as effector cells (Fig. 3, B and H). Consistent with the data in Fig. 1, in a CRISPR screen using B16Ova as target cells and Prf1−/− OT-I T cells as effectors, sgRNAs targeting Ifngr1/2, Jak1/2, and Stat1 were also enriched (Fig. 3, C and K). In the absence of perforin-mediated tumor cell killing, Tnfrsf1a was revealed as a common T cell sensi-tivity gene between the two tumor types (Fig. 3, D and E), suggesting that, in the absence of perforin, the TNF pathway was engaged to kill B16 cells. Pathway interaction and GO term analysis confirmed that both TNF and IFN- signaling were the key T cell–mediated effector pathways affected (Fig. 3, F, G, I, and J).

T cell–derived cytokines drive immune evasionGiven the identification of cytokine networks in our tumor immune evasion screens, we performed 3′ RNA sequencing on MC38Ova cells exposed to T cells in the presence or absence of anti–PD-1, which re-sulted in robust changes in gene transcription (Fig. 4A). Gene set enrichment analysis (GSEA) identified preferential regulation of the genes that respond to TNF and IFN- signaling (Fig. 4, B and C). Incubation of MC38 cells with supernatant from the T cell–MC38Ova coculture (Fig. 4D) or recombinant TNF and/or IFN- (fig. S3) elic-ited a similar transcriptional signature to that identified after direct effector-target engagement. Furthermore, killing of MC38Ova and the breast tumor line E0771Ova using Prf1−/− OT-I T cells and coculture supernatant was abrogated in the presence of anti-TNF blocking antibody (Fig. 4E and fig. S4A) but not an anti-FasL blocking antibody (fig. S4B). Finally, recombinant TNF, but not IFN-, was shown to di-rectly kill MC38Ova and E0771Ova tumor cells (Fig. 4E and fig. S4A).

We next introduced a customized sgRNA pool, encompassing the top scoring 2000 guides identified in Fig. 1, into Cas9-expressing MC38Ova cells, which were then exposed to three independent rounds of TNF treatment. As expected, and consistent with our CRISPR screen using wild-type and Prf1−/− T cells as immune effectors, this screen also en-riched for genes known to modulate TNF- induced cell death, including casp8, tradd, and tnfrsf1a, as well as uncharacterized genes in the TNF pathway, such as Ado (Fig. 4, F and G, and fig. S4C). We also sorted for cells that failed to up-regulate MHC-I upon IFN treatment. This re-vealed that the Jak1/2- Stat1-Tap1 axis, but not TNF signaling, was critical for optimal antigen presentation, and highlighted the role of IFN- in this process (Fig. 4, H to J).

To determine whether T cell–mediated “bystander killing” of tumors not directly engaged by a T cell occurred, we used time-lapse microscopy. As expected, MC38Ova cells (green) were rapidly eliminated by the OT-I T cells (unlabeled), an effect that was delayed by a TNF-neutralizing antibody (Fig. 5A and movie S1). Notably, MC38Vec cells (blue) were similarly killed over a 24-hour time period, demonstrating significant bystander killing that was also inhibited by neutralizing TNF (Fig. 5A and movie S2). TNF-mediated by stander killing was also confirmed in killing assays using chromium- labeled MC38Vec cells mixed in a 50:50 ratio with unlabeled MC38Ova cells, using perforin wild-type or


munology.sciencem

ag.org/D

ownloaded from




4 of 14

Fig. 2. NK cell immune evasion occurs through loss of TNF pathway mem-bers. (A) Experimental design of CRISPR/ Cas9 screening for resistance to NK cell killing. (B) NK cell–MC38 killing assay (18 hours) at the indicated E:T ratios. (C) NK cells were cocultured with MC38 cells at the indicated E:T ratios. After 6 hours, cytokines were measured by cytometric bead array. (D) MC38 cells were subjected to three rounds of ex-posure to primary mouse NK cells, as described in (A), followed by sequencing for the top en ri ched genes. (E) Protein network analysis from the top hits iden-tified in (D). (F) GO term analysis from the top hits identified in (D). TRAIL, TNF-related apoptosis- inducing ligand. (G) Compari-son of the top scoring genes in screens described in (D) and the OT-I T cell screen described in Fig. 1C. (H) Schematic rep-resentation of immune evasion from NK attack. DISC, death- inducing signaling complex.

Tnfrsf1bTnfrsf1bBirc3Birc3

Casp8Casp8

TraddTraddCd40Cd40

CflarCflar

Traf2Traf2Birc2Birc2

TnfTnf

AdoAdo

Tnfrsf1aTnfrsf1aFaslFasl FasFasSqstm1Sqstm1

FaddFadd

2

4

−Log

10 (P

val

ue)

−Log10P

−Log

10 (P

) NK

−Log10 (P) OT-I

Genes10,000 20,0005000 15,000

MC38 + NK

10.10.01Effector : Target

51C

r rel

ease

(%)

50

25

75

MC38 + NK100

Death-inducing signaling complex assembly

Death effector domain binding

TRAIL-activated apoptotic signaling pathway

Protein heterooligomerization

Death receptor binding

5 100

MC38 + NK

Effector : Target10 5 2.5 10 5 2.5

Effector : Target

10

20

pg/m

lIFN-γTNF

4

8

pg/m

l

B

C

D

F

2

4

2 4 6

NK v OTI

E

G

Genome-widesgRNA library

Cas9+

tumor

Puromycin selection Cas9+

sgRNA+ tumorNatural killer cells

+In vitro

cocultureImmune evasion

FADD

FADD

TNF

TRAD

D

TRAD

DTN

FRSF

1A

Apoptosis

CASP

8

CASP

8FA

DD

FADDTR

ADD

TRAD

D

Caspase activation

DISC assembly

TNF

TNFTNF

NK cells

Tumor

H

A


munology.sciencem

ag.org/D

ownloaded from




5 of 14

LtaLtaMap3k7Map3k7

Tnfrsf1aTnfrsf1aTraf2Traf2Ripk1Ripk1

FaddFadd

Stat3Stat3Il10raIl10raStat1Stat1

IfngIfngStat5aStat5a

Ifngr2Ifngr2Socs1Socs1Jak2Jak2PrlrPrlr

Socs3Socs3Jak1Jak1GhrGhr Ifngr1Ifngr1Stat5bStat5b

Ptpn11Ptpn11

Ptpn1Ptpn1

2

4

−Log

10 (P

) WT

−Log

10 (P

) WT

6

2 4 6

MC38Ova Prf1+/+ versus Prf1

2

4

Genes10,000 20,0005000 15,000

6

2

4

−Log

10 (P

val

ue)

−Log

10 (P

val

ue)

Genes10,000 20,0005000 15,000

6

101102103

Target : Effector

51C

r rel

ease

(%)

40

20

60MC38 + OT-I Prf −/−

VectorOva

B16Vector

2 % 55 %

105

104

103

105

104

103

Fsc

DA

PI (

dead

)

B16Ova

Mapk11 Ifngr1Fadd

IfngFas Jak1Ripk1Casp8

Rpp25Tnfrsf1a Il10raTraf2Cflar

Tradd

PrkczSqstm1

TNF signalingMC38Ova + OT-I Prf1

B16Ova + OT-I Prf1

2

4

6

2 4 6

Tnfrsf1a

6

10Jak1

6

10

Tnfrsf1a Ifngr1

7

11Jak1

unt OT-I unt OT-I unt OT-I

unt OT-I unt OT-I unt OT-I

910

10

8

Log 2 c

ount

s

Toxoplasmosis

Purine ribonucleotide catabolic process

Sphingolipid signaling pathway

Ceramide metabolic process

Log 2 c

ount

s

Log 2 c

ount

s

Log 2 c

ount

s

Log 2 c

ount

s

Log 2 c

ount

s

Death-inducing signaling complex assembly

0 5 10

Regulation of IFN-γ signaling

Positive regulation of reactive oxygen species metabolic process

Positive regulation of lipid metabolic process

Homophilic cell adhesion via plasma membrane adhesion molecules

Connective tissue development

0 5 10

MC38Ova + OT-I Prf −/−

B16 + OT-I Prf −/−

8

10

Jak1Casp812

87

9

12

Ado

TNF signaling

2

4MC

38O

va

−Log10 (P) B16

−Log

10 (P

)

−Log10P

−Log10P

−Log10 (P) KO

−Log10 (P) KO

Ova

6

2 4 6

MC38Ova versus B16Ova + Prf1−/− OT-I

B16Ova + OT-I Prf −/−

B16Ova + OT-I Prf1−/−

+ OT-I Prf1−/−

−/−

−/−

−/−

Prf1+/+ versus Prf1−/−

MC38Ova

B16Ova

IFN-γ signaling

IFN-γ signaling

A B C

D E

F G H

I J K

Fig. 3. Immune evasion occurs independently of perforin-mediated killing. (A) MC38Ova/Vector chromium release assay (18 hours) at the indicated E:T ratios with Prf1−/− OT-I T cells. B16Ova/ Vector coculture (48 hours) with Prf1−/− OT-I T cells (2:1 E:T ratio). DAPI, 4′,6-diamidino-2- phenylindole. (B) MC38Ova screen as in Fig. 1A, but using Prf1−/− OT-I T cells. (C) B16Ova screen, as in Fig. 1J, but using Prf1−/− OT-I T cells. (D) Comparison of top enriched genes in screens described in (B) and (C). (E) Selected sgRNA counts from (B) and (C). (F) Protein network analysis of the top scoring genes from (B). (G) GO term analysis from the top scoring genes from (B). (H) Comparison of top enriched genes in screens described in Figs. 1C and 2C. KO, knockout. (I) Protein network analysis of the top scoring genes from (B). (J) GO term analysis of the top scoring genes from (C). (K) Comparison of top enriched genes in screens described in Fig. 1J and (C).


munology.sciencem

ag.org/D

ownloaded from




6 of 14

2−Log

10 (P

val

ue)

Genes

400200 600

MC38Ova TNF killing screen

−Log

10 (P

) MC

38 +

OT-

I

−Log10 (P) MC38 + TNF2 4

MC38 OT-I versus TNF

2

4

6

2 4

Coculture TNF signaling via NFκB

IFN-γ response

MC3

8

MC3

8 +

OT-

IM

C38

+ OT-

I

+

PD-1

51C

r rel

ease

(%)

60

40

80 Supernatant

1:11:101:100Supernatant dilution

Vec supOva supOva sup + α-IFN-γOva sup + α-TNF

20

101102103

Target : Effector

51C

r rel

ease

(%)

40

10

30

20

IgG

α-TNFα-IFN-γ

1010.1Cytokine (ng/ml)

unt

TNFIFN-γ

51C

r rel

ease

(%)

40

20

60

80 Cytokine

0

0.4

0.8

Enr

ichm

ent s

core

0

0.4

0.8

Enr

ichm

ent s

core

0.4

0

Enr

ichm

ent s

core

0.2

0

0.4

0.8

Enr

ichm

ent s

core

A B C

F

G

Log FC

TNF signaling via NFκB

IFN-γ response

−Log

10 (P

)

nes = 3.09

P < 0.001

P < 0.001

nes = 3.35

nes = 2.03

nes = 3.35

P < 0.001

P < 0.001

UntTN

F

T+γ

Sup

Cd274

1

2

3

4

5

Log

CP

M

Tap1

3

4

5

Log

CP

M

6

Cxcl10

5

6

7

8

9

Log

CP

M

Nfkbia

7.5

8.5

9.5

Log

CP

M

0

2

4

6

UntTN

F

T+γ

Sup

UntTN

FIF

N- γIF

N- γ

IFN- γ

IFN- γT+

γSup UntTN

F

T+γ

Sup

MC38Ova + OT-I Prf1−/−

4

D

E

8

10

10

unt unt unt

7.5

Log 2

cou

nts

Log 2

cou

nts

Log 2

cou

nts12

129

unt unt

Log 2

cou

nts

Log 2

cou

nts

1213 10

Targeted MHC-I screen - MC38

B2m10

5

Ifngr1

Sort Sort Sort Sort Sort

14Jak1 Tap1

1110

Casp8

876

2

4

−Log

10 (P

val

ue)

Genes400200 600

B16Ova MHC-I negative screenMC38Ova MHC-I negative screen

2

4

−Log

10 (P

val

ue)

400200 600Genes

H

J

I

Ado

unt

Log 2

cou

nts

Sort

910

8

7

Fig. 4. T cell–derived cytokines drive immune evasion. (A) RNA-seq (triplicate samples) of MC38Ova cells that were cocultured with OT-I T cells for 6 hours. (B) Volcano plot of top regulated genes from (A). (C) GSEA from (A). (D) MC38Ova cells were treated with supernatant (6 hours) derived from an overnight MC38Ova coculture with OT-I T cells (1:1 E:T ratio) followed by 3′ RNA-seq. GSEA is displayed. (E) MC38Ova killing assay with Prf1−/− OT-I T cells ± -TNF or –IFN- (25). MC38Ova killing assay using super-natant from an MC38Ova/vec coculture with OT-I T cells ± -TNF or –IFN- (25 g/ml). MC38 Ova killing assay upon TNF or IFN- treatment at the indicated concentrations for 18 hours. (F) Custom sgRNA pool screen in MC38Ova cells. Cells were treated with TNF (1 ng/ml) three consecutive times. (G) Comparison of the top scoring genes from (F) to the OT-I T cell screen described in Fig. 1C. (H and I) MC38 and B16 cells carrying the custom gRNA library were treated with IFN- overnight and then cell-sorted for MHC-I negatives (repeated three times). Cells were then sequenced for gRNA enrichment. (J) Selected gRNAs from the MC38 screen in (H).


munology.sciencem

ag.org/D

ownloaded from




7 of 14

knockout effector OT-I T cells (Fig. 5B). Thus, upon antigen recognition, CD8+ T cells can efficiently kill antigen-negative bystander tumor cells through TNF. In support of this, we found that anti–PD-1 therapy also enhanced antitumor immunity in perforin-deficient mice in vivo (Fig. 5C), an effect most likely due to enhanced TNF production upon checkpoint blockade, and enhanced TNF bystander killing in the absence of perforin.

Immune evasion occurs through loss of TNF signaling, IFN- signaling, or antigen presentation in vivoTo confirm our findings in vivo, Cas9-expressing MC38Ova cells were transduced with the customized 2000-sgRNA pool used in Fig. 4 (F and G), injected into NSG mice, and tumor-bearing mice were adoptively transplanted with OT-I T cells. Sequencing of tumors har-

vested 4 to 7 days after OT-I T cell transfer revealed enrichment in sgRNAs targeting genes involved in IFN- signaling (stat1), antigen presentation (tap1), and TNF signaling (caspase-8, tnfrsf1a, and ado) (Fig. 6, A and B, and fig. S5A). To validate these findings, we individually depleted an immune evasion gene from each path-way in MC38Ova cells and transplanted these into NSG mice that subsequently received adoptively transferred OT-I T cells. The proliferation in vitro and growth in vivo of these tumor lines were not significantly different (fig. S5B). How-ever, depletion of tap1, tnfrsf1a, or jak1 sig-nificantly reduced the efficacy of adoptive cellular therapy (Fig. 6C), highlighting the importance of these three pathways in tumor immune evasion in vivo (Fig. 6D). We identified that low expression of at least two genes from each of these three pathways was associated with significant-ly poorer prognosis in patients with colo-rectal cancer (Fig. 6E).

Because we had identified Ado as im-portant for tumor cell sensitivity to death induced by CD8+ T and NK cells, using both our in vitro and in vivo CRISPR screen-ing (Figs. 1E, 2D, 3H, 4G, and 6B), and because our targeted screen suggested that Ado modulates TNF sensitivity (Fig. 4F) but not IFN- signaling pathways (Fig. 4J), we investigated this gene further. Ado plays a role in the cysteine metabolism pathway, where it converts cysteamine to hypotaurine (17). To investigate the potential role of Ado in regulating the metabolic state of MC38 tumor cells, we first performed com-prehensive metabolomic profiling of Ado control and knockout MC38 tumor cells (Fig. 7). We identified disrupted cysteine metabolism and polyamine synthesis path-ways upon Ado depletion. We next per-formed transcriptional profiling of Ado control and knockout MC38 tumor cells,

under both steady state and TNF stimulation, to investigate altered gene expression under steady-state Ado loss (Fig. 8A). The rapid induction of classical TNF-induced target genes was not affected by Ado deple-tion, suggesting that Ado was not directly involved in TNF-induced signal transduction (Fig. 8B). However, depletion of Ado in MC38Ova cells using two independent sgRNAs significantly impaired killing by OT-I T cells (Fig. 8C) and significantly reduced TNF-induced killing of MC38Ova cells (Fig. 8D and fig. S6A) but not granzyme B–induced killing (fig. S6B). Depletion of Ado in two additional tumor lines, the mouse breast line EO771 and the human breast line AU565, also con-ferred decreased sensitivity to TNF-mediated cell death (figs. S6C and S7D). To determine whether deletion of Ado suppressed T cell killing in vivo, we injected Ado-depleted MC38Ova cells into NSG mice and monitored the efficacy of adoptive T cellular therapy. Ado-depleted

A

MC38Vec - Prf1+/+

51C

r rel

ease

(%)

20

10

30

40

101102103

Target : Effector

50

MC38Vec - Prf1−/−

51Cr negative MC38Ova - bystander Killing

MC38Ova

MC38Vec

OT-I T cellPI (dead)

MC38Ova

MC38Vec

OT-I T cellPI (dead)

0 hours 10 hours 24 hoursIs

otyp

eA

nti-T

NF

Isotyp

e

Anti-T

NF

40

20

60

80

100

Per

cent

age

of P

I pos

itive *

MC38Vec bystander killing

101102103

Target : Effector

51C

r rel

ease

(%)

20

10

30

40

50

IgGα-TNF

IgGα-TNF

B

50

100

150

Tum

or s

ize

(mm

2 )

10 20 30

Therapy

Immune control

200

50

100

Ove

rall

surv

ival

(%)

IgG

α-PD-1

20 40 60Days

P < 0.01

Days

IgG

α-PD-1

50

100

Tum

or s

ize

(mm

2 )

* * * * ***

IgG

α-PD-1

10 20 30

Immune control

Days

150

MC38Ova in Pfn−/− BL6

Therapy

C

Fig. 5. T cell–derived TNF kills tumor cells in a bystander effect. (A) MC38Ova cells were labeled with CFSE and MC38Vec with Cell Trace Violet and then mixed (50:50) in the presence or absence of neutralizing -TNF (50 g/ml). OT-I T cells were then overlaid, and killing was monitored by time-lapse microscopy. Percentage of MC38Vec PI-positive cells that occurred in the presence or absence of T cells ± -TNF. Pooled experimental data, n = 3; *P < 0.05, Student’s t test. (B) MC38Vec cells were labeled with chromium and then mixed (50:50) with unlabeled MC38Ova ± -TNF (50 g/ml). Prf1+/+ or Prf1−/− OT-I T cells were then added for 18 hours. *P < 0.05, Student’s t test. (C) MC38Ova cells (1 × 106) were injected subcutaneously into Prf1 knockout mice. Mice were treated with anti–PD-1 (200 g) twice per week for the time span indicated. Tumor growth and overall survival are displayed.


munology.sciencem

ag.org/D

ownloaded from




8 of 14

tumors were significantly protected from the anti tumor activity of T cells in vivo (Fig. 8E). Depletion of another uncharacterized mole-cule, Golgi SNAP receptor complex member 1 (Gosr1), also impaired killing of MC38Ova cells by OT-I T cells (fig. S7A) and impaired tu-mor control in vivo (fig. S7B). Reduced expression of Gosr1 was also found to be associated with poor prognosis in a number of cancer types (fig. S7C). These data confirmed that deletion of Ado or Gosr1 protected tumor cells from T cell killing, suppressed antitumor im-mune responses in an in vivo tumor model, and is associated with poor patient prognosis in different cancer types.

DISCUSSIONUsing a series of whole-genome and customized CRISPR screens, we identified TNF, IFN-, and antigen presentation as the major pathways necessary for, and used by, CD8+ T cells to kill tumor cells in vitro and in vivo. Our study also found that TNF was a potent NK cell effector molecule and that the TNF-mediated apoptotic pathway was engaged to kill tumors in the context of NK cell attack. We did not find a role for IFN- in the context of NK cell–driven tumor immune evasion. These results suggest that tumor cells dampen the effects of IFN- signaling predominantly as a strategy to limit presentation of antigen

Contro

l

Tap1

Tnfrs

f1a Jak1

50

100

150

200

250

Tum

or s

ize

(mm

2 )

*

*

*

Prf1+/+ cellular therapy

gRNA:

Casp8

Stat1

Sqstm1Ado

Stat1Tnfrsf1a

Stat1

Stat1

Prf1+/+ OT-I cellular therapy Prf1−/− OT-I cellular therapy

Tap1

Ado

100 200 300 400Genes

100 200 300 400Genes

100 200 300 400Genes

100 200 300 400Genes

100 200 300 400Genes

100 200 300 400Genes

1

2

1

2

3

1

2

3

2

1

3

1

2

3

2

1

−Log

10 (P

vau

e)

C Immune evasion pathways

6

10

32

30 30

3Stat1

Casp8Sqstm1

Ado

Tnfrsf1a

6

E

FAD

D

FAD

DJAK1 JAK2

IFN

GR

1IF

NG

R2

IFN-

STAT1P

TS

TA1P

P

TS

TA1

Interferon signaling

GAS

Antigen presentation

TAP1/2MHC-IMHC-I

ER

Proteosome

Peptides

Protein

MH

C I

MH

C I

TNF signalingTNF

TR

AD

D

TR

AD

DT

NF

RS

F1A

Apoptosis

CA

SP

8

CA

SP

8FA

DD

FAD

D

TR

AD

D

TR

AD

D

Caspase activation

DISC assembly

IRF1

ADO

A

B D

Prf1−/− transfer × 2Prf1+/+ transfer × 1 Prf1+/+ transfer × 2

TNFRSF1A CASP8 IFNGR1 JAK1 TAP1 B2M

2 4 6 8 10 2 4 6 8 10 2 4 6 8 10 2 4 6 8 10 2 4 6 8 10 2 4 6 8 10

Years Years Years Years Years Years

0.2

0.4

0.6

0.8

1.0

Dis

ease

-free

sur

viva

l

0.2

0.4

0.6

0.8

1.0

Dis

ease

-free

sur

viva

l

0.2

0.4

0.6

0.8

1.0

Dis

ease

-free

sur

viva

l

0.2

0.4

0.6

0.8

1.0

Dis

ease

-free

sur

viva

l

0.2

0.4

0.6

0.8

1.0

Dis

ease

-free

sur

viva

l

0.2

0.4

0.6

0.8

1.0

Dis

ease

-free

sur

viva

l

Mouse 1 Mouse 2 Mouse 3 Mouse 1 Mouse 2 Mouse 3

Fig. 6. Immune evasion occurs through loss of TNF signaling, IFN- signaling, or antigen presentation in vivo. (A) MC38Ova custom sgRNA library–containing cells (1 × 106) (Fig. 3F) were subcutaneously injected into NSG mice. Prf1+/+ or Prf1−/− OT-I T cells (5 × 106) were adoptively transferred either once (Prf1+/+) or again 5 days later (Prf1+/+ and Prf1−/−). Tumors were then isolated followed by polymerase chain reaction of sgRNAs and sequencing for sgRNA enrichment. (B) Overlapping hits from (A). (C) Control, Tap1-, Tnfrsf1a-, or Jak1-depleted MC38Ova cells (1 × 106) were injected subcutaneously into NSG mice. On day 12, 5 × 106 Prf1+/+ OT-I T cells were adoptively transferred. This was repeated on days 16 and 25. Tumor size on day 28 is displayed. (D) Schematic representation of immune evasion pathways. (E) The Cancer Genome Atlas (TCGA) survival plots for colorectal adenocarcinoma for two genes from each of the immune evasion pathways. HR, hazards ratio; CI, confidence interval.


munology.sciencem

ag.org/D

ownloaded from




9 of 14

to CD8+ T cells via MHC-I, not to minimize the potential cytostatic effects of this cytokine (18). Consistent with this, evasion from CAR T cell–mediated killing of tumor cells was mediated by selective loss of the CAR T cell target molecule (Her2) and not through any ef-fects on the IFN- pathway. Thus, tumor cell suppression of these pathways may result in potent immune evasion and highlights the importance of cytokines in driving antitumor immunity.

Using similar genetic approaches, recent studies also identified genes within the IFN- and antigen presentation pathways as key immune evasion genes (14, 15). However, these genes were identi-fied using a tumor cell line with preexisting resistance to TNF- induced cell death; thus, the global immune evasion network was not fully appreciated. Cell line selection for genome-wide screens is critical, because cell lines diverge in their sensitivity to different as-pects of immune pressure. Here, we selected a variety of tumor cell lines for our immune-based screens to allow for a more comprehen-sive analysis of the pathways that are necessary for robust antitumor immune responses. We posit that our screens in B16 melanoma cells and MC38 colon cancer cells have provided extensive, but certainly not exhaustive, insight into immune evasion mechanisms. CRISPR screening itself is a powerful, nonbiased method for identifying genes that play an important role in a biological process. Here, we have used immune pressure on tumor cells to identify genes that, when deleted, provide resistance to immune cell killing. Although it

is known that tumor cells frequently suppress expression of par-ticular genes as an immune escape mechanism, including MHC-I, they often mutate key genes such as tp53 to provide a growth advan-tage. Gene mutations that reduce the efficacy of immunotherapy have recently been identified, including mutations in Janus kinases (JAK) providing resistance to anti–PD-1 therapy (9). Thus, a limitation of CRISPR screening that may arise as the effect of gene deletion, not genetic mutation, is investigated. However, because we detected JAK1/2 as key immune evasion genes in our screens, the phenotype that arises through CRISPR-based deletions can mimic loss-of-function muta-tions that arise in cancer patients. As previously reported (14, 15), our screens identified the IFN- and antigen presentation pathways, in addition to the TNF pathway, as important for antitumor immunity. The presence of genetic alterations in each of these pathways in can-cer patients with poor prognosis (9, 19) provides confidence in the physiological significance of our discoveries. However, we recognize that other proteins and pathways important for immune cell–mediated tumor surveillance very likely exist and await further discovery and analysis.

Our screening approach revealed a somewhat unappreciated role for the TNF pathway, and bystander TNF-mediated cell death, in antitumor immunity. Cytotoxic lymphocytes use a variety of ef-fector mechanisms to control tumors, including direct target cell kill-ing via synapse-dependent granzyme delivery (20). However, our

Control

ADO KO 1

ADO KO 2

0.0

0.4

0.8

1.2

Abu

ndan

ce(r

elat

ive

to c

ontr

ol)

Taurine

** ****

Control

ADO KO 1

ADO KO 2

0.0

0.4

0.8

1.2

Abu

ndan

ce(r

elat

ive

to c

ontr

ol)

Cystine

*** ****

Control

ADO KO 1

ADO KO 2

0.0

0.5

1.0

1.5

Hypotaurine

Abu

ndan

ce(r

elat

ive

to c

ontr

ol)

NS

*

Control

ADO KO 1

ADO KO 2

0.0

0.4

0.8

1.2

Abu

ndan

ce(r

elat

ive

to c

ontr

ol)

Cysteine

NSNS

Control

ADO KO 1

ADO KO 2

0.0

0.5

1.0

Abu

ndan

ce(r

elat

ive

to c

ontr

ol)

Pantothenic acid

****

***

Contro

l

ADO KO 1

ADO KO 2

0.0

0.4

0.8

1.2

1.6

Ab

un

dan

ce

(rela

tive t

o c

on

tro

l)

Spermine

*****

Contro

l

ADO KO 1

ADO KO 2

0.0

0.5

1.0

1.5

2.0

Putrescine

Ab

un

dan

ce

(rela

tive t

o c

on

tro

l)

*

****

Contro

l

ADO KO 1

ADO KO 2

0.0

0.4

0.8

1.2

Ab

un

dan

ce

(rela

tive t

o c

on

tro

l)

Proline

****

****

Polyamine synthesis

Cysteine, metabolism

Ado

Fig. 7. Ado loss alters cellular metabolism. Control or Ado knockout MC38 cells were subjected to amine derivatization/liquid chromatography–MS (cysteamine, cystamine, taurine, and glutathione) and GC-MS polar metabolomics (cystamine, taurine, cysteine, and hypotaurine). ATP, adenosine 5′-triphosphate; ADP, adenosine 5′- diphosphate; AMP, adenosine 5′-monophosphate. *P < 0.01, **P < 0.001, ***P < 0.0001, ****P < 0.00001. NS, not significant. by guest on June 14, 2020

http://imm

unology.sciencemag.org/

Dow

nloaded from




10 of 14

Untrea

ted TNF

Untrea

ted TNF

77

8

9

10

Log

CP

M

Irf1

Untrea

ted TNF

Untrea

ted TNF

8

7

9

6Log

CP

M

JunB

Untrea

ted TNF

Untrea

ted TNF

789

10

Log

CP

M

NFkBia

Con Ado

gRNA

Con Ado

gRNA

Con Ado

gRNA

5

1112

A Control versus Ado KO - steadystate Control versus Ado KO - TNF treatmentB

C D

MHC I TCR

IFN-

TNF-

CD8+

T cell

Cytotoxicgranules

PerforinGranzymes

Tumorantigen

TNFR

IFNGR

Bystander killing

Tumor cells

Loss of IFN, TNF,or antigen presentation2.510Unt

TNF (ng/ml)

PI u

ptak

e (%

)

40

20

60

80

Con 1

MC38Ova

5

Con 2Ado 1Ado 2

gRNA

** *

Rel

ativ

e ki

lling

(%)

40

20

60

80

MC38Ova + OT-I*

100

Contro

l 1Con

trol 2

Ado 1

Ado 2

gRNA:

50

*

100

Day 21

Day 23

Day 25

150

200

Tum

or s

ize

(mm

2 )

ControlAdo

gRNA

*

*

E Bystander immune evasionPrf1+/+ cellular therapy F

Contro

l 1Con

trol 2

Ado 1

Ado 2

gRNA:

2

0

−2

4

-4

0

Thyroid Cancer Low-grade glioma

2 4 6 8 10

Years

0.2

0.4

0.6

0.8

1.0

Dis

ease

-free

sur

viva

l

Esophageal Prostate adenocarcinoma

2 4 6 8 10

Years2 4 6 8 10

Years2 4 6 8 10

Years

0.2

0.4

0.6

0.8

1.0

Dis

ease

-free

sur

viva

l

0.2

0.4

0.6

0.8

1.0

Dis

ease

-free

sur

viva

l

0.2

0.4

0.6

0.8

1.0

Dis

ease

-free

sur

viva

l

G TCGA - Ado

Fig. 8. Ado loss drives TNF-dependent immune evasion. (A and B) Control or Ado knockout MC38 cells were left untreated or treated with TNF (10 ng/ml) for 1 or 6 hours, followed by 3′ RNA-seq. The top regulated genes are displayed with comparisons of control gRNA and Ado knockout (two individual gRNAs for each, performed in dupli-cate). Representative box plots for the top three TNF-induced genes are displayed. (C) Ado was targeted in MC38Ova with two individual sgRNAs. Prf1+/+ OT-I T cells were then added, and cell death was measured at 18 hours. Data are pooled from three independent experiments. (D) Cells from (C) were treated with TNF (1 ng/ml). At 18 hours, cell death was measured by PI uptake. Data are representative of three independent experiments. (E) Control or Ado-depleted MC38Ova cells were injected subcutaneous-ly into NSG mice. On day 12, and again on day 16, 5 × 106 Prf1+/+ OT-I T cells were adoptively transferred. (F) Schematic representation of bystander immune evasion. *P < 0.05, Student’s t test. (G) TCGA data analyses of Ado expression and disease-free survival for different cancer types.


munology.sciencem

ag.org/D

ownloaded from




11 of 14

results suggest that this is not a process disengaged by tumors to avoid T cell– and NK cell–mediated death. Instead, we propose a model whereby the cytotoxic synapse with a tumor cell triggers TNF and IFN- production from the T or NK cell (21, 22). If the tumor is intrinsically TNF-sensitive, then TNF will initiate apoptosis of surrounding tumor cells in a bystander effect (22–25), which may be a more effective way to kill a larger tumor cell mass. However, as a consequence, tumor cells may acquire resistance to bystander T or NK cell attack, having never been the subject of direct recognition through the cytotoxic synapse (9, 10, 22, 26). Many tumor cell lines are intrinsically resistant to TNF treatment in vitro, but the molec-ular mechanism that determines this sensitivity threshold is not ful-ly understood. The resistance to TNF-mediated killing may be due to the relative expression levels of TNF receptor–associated anti- apoptotic proteins such as cFLIP, cIAPs, and caspase-8 (27–29). Our screens revealed that loss of caspase-8 was the most frequently tar-geted TNF receptor complex component that drives immune eva-sion. Caspase-8 is frequently lost or functionally mutated in several tumor types (19, 30, 31) and represents an attractive protein for tu-mor cells to target, because loss of its enzymatic activity entirely eliminates the apoptotic potential of TNF, but not the nuclear factor B (NFĸB) activation signal, which can promote cell proliferation and survival (27).

Our study also identified a number of genes not previously impli-cated in antitumor immunity or resistance to immunotherapy. These included Gosr1, a trafficking membrane protein that transports pro-teins among the endoplasmic reticulum and Golgi apparatus (32). Our results demonstrated that loss of Gosr1 facilitates resistance to T cell–mediated tumor control both in vitro and in vivo. Although our study did not address the mechanism behind this, Gosr1 was identified as a prognostic marker in a number of cancer types and highlights the power of unbiased screens to identify new markers of resistance to antitumor immunity and immunotherapy. One gene encoding an uncharacterized protein that scored highly in our screens was Ado. Deletion of Ado provided protection against TNF-mediated cell death in a number of tumor lines and provided resistance to immune- mediated tumor control in vivo. Depletion of Ado did not affect transcriptional responses to TNF but did result in disrupted cysteine metabolism and polyamine synthesis pathways. Polyamine path-way metabolites can modulate sensitivity to TNF-induced cell death (33), and this may provide a functional link between depletion of Ado and immune evasion through loss of TNF-mediated cell death, al-though this remains to be formally demonstrated in our experimen-tal systems. This finding highlights the potentially underappreciated role of cysteine metabolism in TNF biology. Further studies will be required to determine exactly how loss of Ado inhibits TNF-induced apoptosis through deregulated cellular metabolism and how this af-fects tumor cell survival. We also found that depletion of two other genes encoding proteins associated with TNF signaling and cell death, sequestosome-1 (Sqstm1) (34) and spermatogenesis-associated protein 2 (Spata2) (35, 36), resulted in resistance to T cell– or NK cell–mediated killing. Spata2, in particular, was identified in a number of recent studies as a key molecule required for TNF-induced, receptor- interacting pro-tein kinase 1 (RIPK1)–dependent apoptosis (36). In addition to direct inhibition of TNF-mediated apoptosis through death receptor sig-naling, these findings provide an additional pathway for tumors to target to evade cell death mediated by cytotoxic lymphocytes.

Our study, and those previously reported (14, 15), identified the IFN- and antigen presentation pathways as important for anti-

tumor immunity, but our additional finding that the TNF pathway is targeted as an immune evasion mechanism may be important thera-peutically. Agents that sensitize tumor cells to TNF-induced apop-tosis, such as smac mimetics, are currently undergoing clinical trials for a variety of solid and hematological cancers (37, 38). Thus, com-bining immunotherapies, such as anti–PD-1 (which enhances T cell TNF production), with smac mimetics may yield potent antitumor responses (11, 39). Similarly, using smac mimetics to increase the cytotoxic potential of adoptive cellular therapies is an exciting possi-bility. Understanding the major effector pathways used by cytotoxic lymphocytes through functional screens, such as those presented here, is critical for the generation of future targeted immune-based therapies.

MATERIALS AND METHODSStudy designAll animal studies were conducted under a protocol approved by the Peter MacCallum Cancer Centre Animal Experimentation Ethics Committee and according to their guidelines.

Antibodies and reagentsNeutralizing antibodies used were as follows: anti-TNF (BioLegend) clone MP6-XT22, anti–PD-1 clone RMP1-14, and anti–IFN- clone R4-6A2 (BioXCell). Recombinant TNF and IFN- were from PeproTech.

Cells and cell linesThe cell lines MC38 and B16, and derivatives expressing chicken Ova, were cultured in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal calf serum (FCS) and penicillin/streptomycin (Gibco) and incubated at 37°C in 5% CO2. OT-I T cells from wild-type or perforin- deficient (Prf1−/−) mice were activated from spleens with the chicken Ova peptide SIINFEKL. Activated T cells were used on days 5 to 10 and had a typical effector phenotype (CD8+CD69+CD25+CD62L−CD44+). OT-I T cells and NK cells were cultured in RPMI supplemented with 10% FCS, l-glutamine, penicillin/streptomycin, nonessential amino acids, sodium pyruvate, Hepes, 2-mercaptoethanol, and interleukin-2 (100 IU/ml).

Time-lapse microscopyMC38Ova cells were labeled with carboxyfluorescein diacetate suc-cinimidyl ester (CFSE) or CellTrace Violet for 20 min and then washed and mixed together in a 50:50 ratio. Cells were then seeded into each well of an eight-well chamber slide (Ibidi, Munich, Germany) and incubated overnight at 37°C/10% CO2. OT-I T cells were then added to adherent targets, in medium containing 100 M propidium iodide (PI). Chamber slides were mounted on a heated stage within a temperature-controlled chamber maintained at 37°C, and constant CO2 concentrations (5 or 7%) were infused using a gas incuba-tion system with active gas mixer (“The Brick”; Ibidi). Optical sections were acquired through sequential scans or brightfield/differential interference contrast on a TCS SP5 confocal microscope (Leica Mi-crosystems, Deerfield, IL) using a ~40 Å (numerical aperture, 0.85) air objective and Leica LAS AF software. Image analysis was performed using MetaMorph Imaging Series 7 software (Universal Imaging, Downingtown, PA).

3′ mRNA sequencingRNA extraction, library preparation, and data analysis were performed as described previously (1). Briefly, total RNA was isolated as per


munology.sciencem

ag.org/D

ownloaded from




12 of 14

the manufacturer’s instructions (NucleoSpin RNA Extraction kit, Macherey-Nagel, Bethlehem, PA). Subsequently, the RNA was analyzed on the TapeStation (Agilent TapeStation 2200), and only RNA with RNA integrity number values greater than 9 were used for down-stream library preparation. The QuantSeq 3′ mRNA Library Prep Kit (Lexogen) was used to prepare libraries. The 3′ mRNA sequencing libraries were sequenced single-end 75 base pairs (bp) on the NextSeq 500 (Illumina). The resulting reads were demultiplexed using CASAVA 1.8, and sample quality control was performed using FastQC (Babraham Bioinformatics, Babraham Institute). The reads were trimmed using cutadapt (v1.7) and subsequently aligned to the mouse reference genome (GRCm38/mm10) using HISAT2 (v2.1.0), after which read counting was performed using FeatureCounts from the Subread package (v1.5.0). Differential gene expression analysis was performed using Voom- LIMMA. GSEA was performed using GSEA (v3.0).

Genome-wide and targeted CRISPR screensGenome-wide CRISPR/Cas9 screens were performed using MC38, MC38Ova, B16Ova, and MDA-MB-231 cells transduced with mCherry- Cas9 (FUCas9Cherry). The mouse cell lines MC38-Cas9, MC38Ova- Cas9, and B16Ova-Cas9 cells were transduced with the Brie genome-wide sgRNA library (40), and the human cell line MDA-MB-231 was trans-duced with the Brunello genome-wide sgRNA library. Transductions were performed at a multiplicity of infection of 0.3 to ensure integra-tion of single sgRNA constructs per cell. After transduction, the cells were selected with puromycin (5 g/ml) for 5 days. The immune eva-sion screening was started at 7 days after transduction, after which the T0 time point was taken as a reference. The MC38Ova-Cas9 cells were cocultured with activated Prf1+/+ and Prf1−/− OT-I T cells, or NK cells, at effector to target (E:T) ratios of 1:20 and 1:10, respectively. The B16Ova-Cas9 cells were cocultured with Prf1+/+ and Prf1−/− OT-I T cells at E:T ratios of 1:5 and 1:1, respectively. MDA-MB-231 cells were cocul-tured with HER2-directed CAR T cells at an E:T ratio of 1:5. The tumor cells were exposed to a total of three hits with either OT-I T or NK cells, after which the pellets were snap-frozen. An untreated control was taken along as a reference for all cell lines. Genomic DNA extraction was subsequently performed using the DNeasy Blood & Tissue Kit (Qiagen), and libraries were prepared as described previously (41). The libraries were subsequently multiplexed and run on the NextSeq 500 (Illumina) generating 75-bp single-end reads. After demultiplex-ing with CASAVA (v1.8), the vector-derived sequence reads were removed, and only reads of exactly 20 bp were extracted using cutadapt (v1.7). Subsequently, MAGeCK (v0.5.6) was used to count the reads and perform gene/sgRNA enrichment and statistical analysis (42). The resulting data were visualized using the R package ggplots2. The targeted CRISPR library was composed of the top scoring genes from the MC38 screens, and the libraries were custom-cloned from oligo pools obtained from CustomArray Inc.

In vivo CRISPR screensMC38Ova cells expressing Cas9 mCherry were transduced with a custom-cloned immune evasion library into pLenti-puro sgRNA vector as described above. After puromycin selection for 7 days, 1 × 106 cells were implanted subcutaneously into recipient NSG mice. When tumors reached 30 to 50 mm2, mice were injected with 5 × 106 SIINFEKL- activated OT-I T cells (days 6 to 8 after activation). Four days after mice received either a single dose (Prf1+/+) or two doses (Prf1−/−) of OT-I T cells, tumors were isolated. Genomic DNA isolation and li-brary preparation were conducted as described above.

Metabolomics profilingControl or Ado knockout cells were maintained in full growth medium. Medium was aspirated, and wells were washed with normal saline. Cells were snap-frozen by addition of liquid nitrogen to cell culture plates immediately after washing. For metabolite extraction, ice-cold MeOH/ CHCl3 (9:1, v/v) containing 13C-sorbitol and 13C,15N-valine internal standards was added. Cells and metabolite-containing supernatants were collected, and insoluble material was pelleted by centrifugation at 16,000g for 5 min. Polar metabolites were prepared for analysis by gas chromatography–mass spectrometry (GC-MS) as previously described (43) and analyzed using a Shimadzu GC-TQ8040 instru-ment with metabolites identified and quantified using the Shimadzu Smart Database. Primary and secondary amines were identified and quantified relative to authentic standards, using a Shimadzu LC-TQ8050 instrument after derivatization of amine-containing metabolites, as previously described (44). Raw peak areas of individual metabolites were normalized to the total peak area in each sample. Adjusted data are presented as fold change in metabolite abundance relative to control.

T cell killing assaysSpecific OT-I T cell killing was measured using chromium release assays at the indicated target to effector (T:E), as previously described in detail (45).

SUPPLEMENTARY MATERIALSimmunology.sciencemag.org/cgi/content/full/3/23/eaar3451/DC1Fig. S1. Additional validation and control for CRISPR screen.Fig. S2. Confirmation of CRISPR gene deletion.Fig. S3. Transcriptional analysis of cytokine-treated tumor cells.Fig. S4. Additional cytokine treatment data.Fig. S5. Further adoptive T cell therapy screen data.Fig. S6. Validation of Ado knockout TNF response in additional tumor lines.Fig. S7. Confirmation of Gosr1 as an immune evasion gene.Movie S1. Time-lapse imaging of T cells killing MC38Ova or MC38Vec cells.Movie S2. Time-lapse imaging of T cells killing MC38Ova or MC38Vec cells in the presence of anti-TNF antibody.Table S1. Raw data.Table S2. CRISPR screen data, B16 perforin knockout OT-I screen.Table S3. CRISPR screen data, B16 OT-I screen.Table S4. CRISPR screen data, MC38 OT-I screen.Table S5. CRISPR screen data, MDA CAR T cell screen.Table S6. CRISPR screen data, OT-I IgG versus T0.Table S7. CRISPR screen data, OT-I PD-1 versus T0.Table S8. CRISPR screen data, MC38 NK cell.

REFERENCES AND NOTES 1. J. M. Pitt, M. Vétizou, R. Daillere, M. P. Roberti, T. Yamazaki, B. Routy, P. Lepage,

I. G. Boneca, M. Chamaillard, G. Kroemer, L. Zitvogel, Resistance mechanisms to immune-checkpoint blockade in cancer: Tumor-intrinsic and -extrinsic factors. Immunity 44, 1255–1269 (2016).

2. J. S. O’Donnell, M. J. Smyth, M. W. Teng, Acquired resistance to anti-PD1 therapy: Checkmate to checkpoint blockade? Genome Med. 8, 111 (2016).

3. M. Bellone, A. R. Elia, Constitutive and acquired mechanisms of resistance to immune checkpoint blockade in human cancer. Cytokine Growth Factor Rev. 36, 17–24 (2017).

4. N. A. Rizvi, M. D. Hellmann, A. Snyder, P. Kvistborg, V. Makarov, J. J. Havel, W. Lee, J. Yuan, P. Wong, T. S. Ho, M. L. Miller, N. Rekhtman, A. L. Moreira, F. Ibrahim, C. Bruggeman, B. Gasmi, R. Zappasodi, Y. Maeda, C. Sander, E. B. Garon, T. Merghoub, J. D. Wolchok, T. N. Schumacher, T. A. Chan, Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer. Science 348, 124–128 (2015).

5. D. T. Le, J. N. Durham, K. N. Smith, H. Wang, B. R. Bartlett, L. K. Aulakh, S. Lu, H. Kemberling, C. Wilt, B. S. Luber, F. Wong, N. S. Azad, A. A. Rucki, D. Laheru, R. Donehower, A. Zaheer, G. A. Fisher, T. S. Crocenzi, J. J. Lee, T. F. Greten, A. G. Duffy, K. K. Ciombor, A. D. Eyring, B. H. Lam, A. Joe, S. P. Kang, M. Holdhoff, L. Danilova, L. Cope, C. Meyer, S. Zhou,


munology.sciencem

ag.org/D

ownloaded from

http://immunology.sciencemag.org/cgi/content/full/3/23/eaar3451/DC1




13 of 14

R. M. Goldberg, D. K. Armstrong, K. M. Bever, A. N. Fader, J. Taube, F. Housseau, D. Spetzler, N. Xiao, D. M. Pardoll, N. Papadopoulos, K. W. Kinzler, J. R. Eshleman, B. Vogelstein, R. A. Anders, L. A. Diaz Jr., Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017).

6. V. Anagnostou, K. N. Smith, P. M. Forde, N. Niknafs, R. Bhattacharya, J. White, T. Zhang, V. Adleff, J. Phallen, N. Wali, C. Hruban, V. B. Guthrie, K. Rodgers, J. Naidoo, H. Kang, W. Sharfman, C. Georgiades, F. Verde, P. Illei, Q. K. Li, E. Gabrielson, M. V. Brock, C. A. Zahnow, S. B. Baylin, R. B. Scharpf, J. R. Brahmer, R. Karchin, D. M. Pardoll, V. E. Velculescu, Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer. Cancer Discov. 7, 264–276 (2017).

7. N. McGranahan, A. J. S. Furness, R. Rosenthal, S. Ramskov, R. Lyngaa, S. K. Saini, M. Jamal-Hanjani, G. A. Wilson, N. J. Birkbak, C. T. Hiley, T. B. K. Watkins, S. Shafi, N. Murugaesu, R. Mitter, A. U. Akarca, J. Linares, T. Marafioti, J. Y. Henry, E. M. Van Allen, D. Miao, B. Schilling, D. Schadendorf, L. A. Garraway, V. Makarov, N. A. Rizvi, A. Snyder, M. D. Hellmann, T. Merghoub, J. D. Wolchok, S. A. Shukla, C. J. Wu, K. S. Peggs, T. A. Chan, S. R. Hadrup, S. A. Quezada, C. Swanton, Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).

8. N. P. Restifo, F. M. Marincola, Y. Kawakami, J. Taubenberger, J. R. Yannelli, S. A. Rosenberg, Loss of functional beta2-microglobulin in metastatic melanomas from five patients receiving immunotherapy. J. Natl. Cancer Inst. 88, 100–108 (1996).

9. J. M. Zaretsky, A. Garcia-Diaz, D. S. Shin, H. Escuin-Ordinas, W. Hugo, S. Hu-Lieskovan, D. Y. Torrejon, G. Abril-Rodriguez, S. Sandoval, L. Barthly, J. Saco, B. Homet Moreno, R. Mezzadra, B. Chmielowski, K. Ruchalski, I. P. Shintaku, P. J. Sanchez, C. Puig-Saus, G. Cherry, E. Seja, X. Kong, J. Pang, B. Berent-Maoz, B. Comin-Anduix, T. G. Graeber, P. C. Tumeh, T. N. Schumacher, R. S. Lo, A. Ribas, Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).

10. D. S. Shin, J. M. Zaretsky, H. Escuin-Ordinas, A. Garcia-Diaz, S. Hu-Lieskovan, A. Kalbasi, C. S. Grasso, W. Hugo, S. Sandoval, D. Y. Torrejon, N. Palaskas, G. A. Rodriguez, G. Parisi, A. Azhdam, B. Chmielowski, G. Cherry, E. Seja, B. Berent-Maoz, I. P. Shintaku, D. T. Le, D. M. Pardoll, L. A. Diaz Jr., P. C. Tumeh, T. G. Graeber, R. S. Lo, B. Comin-Anduix, A. Ribas, Primary resistance to PD-1 blockade mediated by JAK1/2 mutations. Cancer Discov. 7, 188–201 (2017).

11. C. J. Kearney, N. Lalaoui, A. J. Freeman, K. M. Ramsbottom, J. Silke, J. Oliaro, PD-L1 and IAPs co-operate to protect tumors from cytotoxic lymphocyte-derived TNF. Cell Death Differ. 24, 1705–1716 (2017).

12. S. P. Cullen, S. J. Martin, Mechanisms of granule-dependent killing. Cell Death Differ. 15, 251–262 (2008).

13. I. Voskoboinik, J. C. Whisstock, J. A. Trapani, Perforin and granzymes: Function, dysfunction and human pathology. Nat. Rev. Immunol. 15, 388–400 (2015).

14. R. T. Manguso, H. W. Pope, M. D. Zimmer, F. D. Brown, K. B. Yates, B. C. Miller, N. B. Collins, K. Bi, M. W. LaFleur, V. R. Juneja, S. A. Weiss, J. Lo, D. E. Fisher, D. Miao, E. Van Allen, D. E. Root, A. H. Sharpe, J. G. Doench, W. N. Haining, In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature 547, 413–418 (2017).

15. S. J. Patel, N. E. Sanjana, R. J. Kishton, A. Eidizadeh, S. K. Vodnala, M. Cam, J. J. Gartner, L. Jia, S. M. Steinberg, T. N. Yamamoto, A. S. Merchant, G. U. Mehta, A. Chichura, O. Shalem, E. Tran, R. Eil, M. Sukumar, E. P. Guijarro, C.-P. Day, P. Robbins, S. Feldman, G. Merlino, F. Zhang, N. P. Restifo, Identification of essential genes for cancer immunotherapy. Nature 548, 537–542 (2017).

16. C. Chester, K. Fritsch, H. E. Kohrt, Natural killer cell immunomodulation: Targeting activating, inhibitory, and co-stimulatory receptor signaling for cancer immunotherapy. Front. Immunol. 6, 601 (2015).

17. J. E. Dominy Jr., C. R. Simmons, L. L. Hirschberger, J. Hwang, R. M. Coloso, M. H. Stipanuk, Discovery and characterization of a second mammalian thiol dioxygenase, cysteamine dioxygenase. J. Biol. Chem. 282, 25189–25198 (2007).

18. M. R. Zaidi, G. Merlino, The two faces of interferon- in cancer. Clin. Cancer Res. 17, 6118–6124 (2011).

19. S. Hopkins-Donaldson, J.-L. Bodmer, K. B. Bourloud, C. B. Brognara, J. Tschopp, N. Gross, Loss of caspase-8 expression in highly malignant human neuroblastoma cells correlates with resistance to tumor necrosis factor-related apoptosis-inducing ligand-induced apoptosis. Cancer Res. 60, 4315–4319 (2000).

20. J. A. Trapani, Dual mechanisms of apoptosis induction by cytotoxic lymphocytes. Int. Rev. Cytol. 182, 111–192 (1998).

21. B. S. Parker, J. Rautela, P. J. Hertzog, Antitumour actions of interferons: Implications for cancer therapy. Nat. Rev. Cancer 16, 131–144 (2016).

22. A. Ratner, W. R. Clark, Role of TNF- in CD8+ cytotoxic T lymphocyte-mediated lysis. J. Immunol. 150, 4303–4314 (1993).

23. B. Zhang, T. Karrison, D. A. Rowley, H. Schreiber, IFN-- and TNF-dependent bystander eradication of antigen-loss variants in established mouse cancers. J. Clin. Invest. 118, 1398–1404 (2008).

24. C. H. Poehlein, H.-M. Hu, J. Yamada, I. Assmann, W. G. Alvord, W. J. Urba, B. A. Fox, TNF plays an essential role in tumor regression after adoptive transfer of perforin/IFN- double knockout effector T cells. J. Immunol. 170, 2004–2013 (2003).

25. R. M. Zwacka, L. Stark, M. G. Dunlop, NF-kappaB kinetics predetermine TNF-alpha sensitivity of colorectal cancer cells. J. Gene Med. 2, 334–343 (2000).

26. C.-F. Lin, C.-M. Lin, K.-Y. Lee, S.-Y. Wu, P.-H. Feng, K.-Y. Chen, H.-C. Chuang, C.-L. Chen, Y.-C. Wang, P.-C. Tseng, T.-T. Tsai, Escape from IFN--dependent immunosurveillance in tumorigenesis. J. Biomed. Sci. 24, 10 (2017).

27. J. Silke, The regulation of TNF signalling: What a tangled web we weave. Curr. Opin. Immunol. 23, 620–626 (2011).

28. M. Lork, K. Verhelst, R. Beyaert, CYLD, A20 and OTULIN deubiquitinases in NF-B signaling and cell death: So similar, yet so different. Cell Death Differ. 24, 1172–1183 (2017).

29. Y. Tsuchiya, O. Nakabayashi, H. Nakano, FLIP the switch: Regulation of apoptosis and necroptosis by cFLIP. Int. J. Mol. Sci. 16, 30321–30341 (2015).

30. B. Liu, D. Peng, Y. Lu, W. Jin, Z. Fan, A novel single amino acid deletion caspase-8 mutant in cancer cells that lost proapoptotic activity. J. Biol. Chem. 277, 30159–30164 (2002).

31. C. Pingoud-Meier, D. Lang, A. J. Janss, L. B. Rorke, P. C. Phillips, T. Shalaby, M. A. Grotzer, Loss of caspase-8 protein expression correlates with unfavorable survival outcome in childhood medulloblastoma. Clin. Cancer Res. 9, 6401–6409 (2003).

32. V. N. Subramaniam, F. Peter, R. Philp, S. H. Wong, W. Hong, GS28, a 28-kilodalton Golgi SNARE that participates in ER-Golgi transport. Science 272, 1161–1163 (1996).

33. L. C. Penning, R. G. Schipper, D. Vercammen, A. A. Verhofstad, T. Denecker, R. Beyaert, P. Vandenabeele, Sensitization of TNF-induced apoptosis with polyamine synthesis inhibitors in different human and murine tumour cell lines. Cytokine 10, 423–431 (1998).

34. G. Y. Kim, P. Nigro, K. Fujiwara, J. Abe, B. C. Berk, p62 binding to protein kinase C regulates tumor necrosis factor -induced apoptotic pathway in endothelial cells. Arterioscler. Thromb. Vasc. Biol. 32, 2974–2980 (2012).

35. P. R. Elliott, D. Leske, M. Hrdinka, K. Bagola, B. K. Fiil, S. H. McLaughlin, J. Wagstaff, N. Volkmar, J. C. Christianson, B. M. Kessler, S. M. Freund, D. Komander, M. Gyrd-Hansen, SPATA2 Links CYLD to LUBAC, activates CYLD, and controls LUBAC signaling. Mol. Cell 63, 990–1005 (2016).

36. S. A. Wagner, S. Satpathy, P. Beli, C. Choudhary, SPATA2 links CYLD to the TNF- receptor signaling complex and modulates the receptor signaling outcomes. EMBO J. 35, 1868–1884 (2016).

37. A. M. Noonan, K. P. Bunch, J. Q. Chen, M. A. Herrmann, J. M. Lee, E. C. Kohn, C. C. O’Sullivan, E. Jordan, N. Houston, N. Takebe, R. J. Kinders, L. Cao, C. J. Peer, W. D. Figg, C. M. Annunziata, Pharmacodynamic markers and clinical results from the phase 2 study of the SMAC mimetic birinapant in women with relapsed platinum-resistant or -refractory epithelial ovarian cancer. Cancer 122, 588–597 (2016).

38. R. K. Amaravadi, R. J. Schilder, L. P. Martin, M. Levin, M. A. Graham, D. E. Weng, A. A. Adjei, A phase I study of the SMAC-mimetic birinapant in adults with refractory solid tumors or lymphoma. Mol. Cancer Ther. 14, 2569–2575 (2015).

39. S. T. Beug, C. E. Beauregard, C. Healy, T. Sanda, M. St-Jean, J. Chabot, D. E. Walker, A. Mohan, N. Earl, X. Lun, D. L. Senger, S. M. Robbins, P. Staeheli, P. A. Forsyth, T. Alain, E. C. LaCasse, R. G. Korneluk, Smac mimetics synergize with immune checkpoint inhibitors to promote tumour immunity against glioblastoma. Nat. Commun. 8, 14278 (2017).

40. J. G. Doench, N. Fusi, M. Sullender, M. Hegde, E. W. Vaimberg, K. F. Donovan, I. Smith, Z. Tothova, C. Wilen, R. Orchard, H. W. Virgin, J. Listgarten, D. E. Root, Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184–191 (2016).

41. J. Joung, S. Konermann, J. S. Gootenberg, O. O. Abudayyeh, R. J. Platt, M. D. Brigham, N. E. Sanjana, F. Zhang, Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening. Nat. Protoc. 12, 828–863 (2017).

42. W. Li, H. Xu, T. Xiao, L. Cong, M. I. Love, F. Zhang, R. A. Irizarry, J. S. Liu, M. Brown, X. S. Liu, MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

43. D. P. De Souza, Detection of polar metabolites through the use of gas chromatography-mass spectrometry. Methods Mol. Biol. 1055, 29–37 (2013).

44. B. A. Boughton, D. L. Callahan, C. Silva, J. Bowne, A. Nahid, T. Rupasinghe, D. L. Tull, M. J. McConville, A. Bacic, U. Roessner, Comprehensive profiling and quantitation of amine group containing metabolites. Anal. Chem. 83, 7523–7530 (2011).

45. I. G. House, K. Thia, A. J. Brennan, R. Tothill, A. Dobrovic, W. Z. Yeh, R. Saffery, Z. Chatterton, J. A. Trapani, I. Voskoboinik, Heterozygosity for the common perforin mutation, p.A91V, impairs the cytotoxicity of primary natural killer cells from healthy individuals. Immunol. Cell Biol. 93, 575–580 (2015).

Acknowledgments: We acknowledge the Victorian Centre for Functional Genomics and the Molecular Genomics Core at the Peter MacCallum Cancer Centre and Metabolomics Australia for their contribution to this work. Funding: S.J.V. was funded by a Rubicon fellowship from the Netherlands Organization for Scientific Research; R.W.J. by a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellow Fellowship, project and program grants, Cancer Council Victoria, and the Victorian Cancer Agency grants; C.J.K. by an NHMRC Early Career Fellowship; S.J.H. by a Peter MacCallum Foundation grant; and J.O. and I.V. by


munology.sciencem

ag.org/D

ownloaded from




14 of 14

NHMRC project grants. General funding and support provided by the Australian Cancer Research Foundation. Author contributions: C.J.K., S.J.V., S.J.H., K.M.R., A.J.F., P.A.B., V.S., D.A.K., L.P., and J.M. conducted experimental work. C.J.K. and S.J.V. performed the CRISPR screens and analyzed the screening and genomic data sets. K.K.B. performed the metabolomics analyses. N.L., J.S., I.V., P.K.D., and J.A.T. provided reagents and advice. C.J.K., S.J.V., R.W.J., and J.O. designed the study, analyzed the data, and wrote the manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability: CRISPR screen data have been added as tables S2 to S7, and sequencing data have been deposited into the Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo/) under accession number GSE112253.

Submitted 27 October 2017Accepted 29 March 2018Published 18 May 201810.1126/sciimmunol.aar3451

Citation: C. J. Kearney, S. J. Vervoort, S. J. Hogg, K. M. Ramsbottom, A. J. Freeman, N. Lalaoui, L. Pijpers, J. Michie, K. K. Brown, D. A. Knight, V. Sutton, P. A. Beavis, I. Voskoboinik, P. K. Darcy, J. Silke, J. A. Trapani, R. W. Johnstone, J. Oliaro, Tumor immune evasion arises through loss of TNF sensitivity. Sci. Immunol. 3, eaar3451 (2018).


munology.sciencem

ag.org/D

ownloaded from

http://www.ncbi.nlm.nih.gov/geo/


Tumor immune evasion arises through loss of TNF sensitivity

John Silke, Joseph A. Trapani, Ricky W. Johnstone and Jane OliaroPijpers, Jessica Michie, Kristin K. Brown, Deborah A. Knight, Vivien Sutton, Paul A. Beavis, Ilia Voskoboinik, Phil K. Darcy, Conor J. Kearney, Stephin J. Vervoort, Simon J. Hogg, Kelly M. Ramsbottom, Andrew J. Freeman, Najoua Lalaoui, Lizzy

DOI: 10.1126/sciimmunol.aar3451, eaar3451.3Sci. Immunol.

dampening the effects of cytokines, not direct killing via perforin.mechanisms arose upon screening with perforin-deficient CTLs, suggesting that tumors evade the immune system bysignaling as a key effector mechanism for both CTL and NK cell antitumor activity. The same immune evasion

signaling and antigen presentation to be critical for CTL-mediated killing of cancer cells and uncovered TNFγfound IFN-identify mechanisms of tumor immune evasion from cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells. They

carried out a series of genome-wide CRISPR screens toet al.exactly how tumors evade this form of therapy. Kearney centric cancer immunotherapies, there is considerable interest in understanding−Given the success of T cell

Killing without poking holes

ARTICLE TOOLS http://immunology.sciencemag.org/content/3/23/eaar3451

MATERIALSSUPPLEMENTARY http://immunology.sciencemag.org/content/suppl/2018/05/15/3.23.eaar3451.DC1

REFERENCES

http://immunology.sciencemag.org/content/3/23/eaar3451#BIBLThis article cites 45 articles, 16 of which you can access for free

Terms of ServiceUse of this article is subject to the

is a registered trademark of AAAS.Science ImmunologyNew York Avenue NW, Washington, DC 20005. The title (ISSN 2470-9468) is published by the American Association for the Advancement of Science, 1200Science Immunology

Science. No claim to original U.S. Government WorksCopyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of


munology.sciencem

ag.org/D

ownloaded from

http://immunology.sciencemag.org/content/3/23/eaar3451

http://immunology.sciencemag.org/content/suppl/2018/05/15/3.23.eaar3451.DC1

http://immunology.sciencemag.org/content/3/23/eaar3451#BIBL

http://www.sciencemag.org/about/terms-service


cancer immunology copyright © 2018 tumor immune evasion arises through loss … · kearney et al.,...

Documents