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immunology.sciencemag.org/cgi/content/full/3/29/eaat7061/DC1
Supplementary Materials for
Combination cancer immunotherapy targeting PD-1 and GITR can rescue CD8+ T
cell dysfunction and maintain memory phenotype
Bei Wang, Wen Zhang, Vladimir Jankovic, Jacquelynn Golubov, Patrick Poon, Erin M. Oswald, Cagan Gurer, Joyce Wei, Ilyssa Ramos, Qi Wu, Janelle Waite, Min Ni, Christina Adler, Yi Wei, Lynn Macdonald, Tracey Rowlands,
Susannah Brydges, Jean Siao, William Poueymirou, Douglas MacDonald, George D. Yancopoulos, Matthew A. Sleeman, Andrew J. Murphy, Dimitris Skokos*
*Corresponding author. Email: [email protected]
Published 2 November 2018, Sci. Immunol. 3, eaat7061 (2018)
DOI: 10.1126/sciimmunol.aat7061
The PDF file includes:
Fig. S1. T cell depletion with Abs. Fig. S2. Bioinformatic pipeline rpsTCR. Fig. S3. Combination therapy expands intratumoral high-frequency tumor-reactive CD8+ T cell clones. Fig. S4. Combination treatment expands tumor antigen–specific CD8+ T cells with effector function. Fig. S5. Mean fluorescence intensity of markers for dysfunctional cell clusters identified in Fig. 2. Fig. S6. Mean fluorescence intensity of markers for effector/memory cell clusters identified in Fig. 3. Fig. S7. TIGIT expression at single-cell RNA level and FACS analysis of TIGIT/CD226 expression level on different T cell subsets. Fig. S8. GITR and PD-1 combination treatment significantly reduced highly activated Treg subsets. Fig. S9. GITR and PD-1 combination treatment induced intratumoral CD8+ T cell subsets distinct from CD25 and PD-1 combination therapy. Fig. S10. CD226−/− mice show normal T cell development and homeostatic function. Fig. S11. Effectiveness of combination treatment does not rely on CD28, OX40, and 4-1BB pathway. Fig. S12. Expression level of CD155. Table S1. Negative controls for rpsTCR. Table S2. Comparison of TCR detection rate. Table S3. Selected genes differentially regulated by each treatment. Table S4. Abs for flow cytometry. Table S5. Primers for TCRα/β repertoire sequencing.
References (48–52) Other Supplementary Material for this manuscript includes the following: (available at immunology.sciencemag.org/cgi/content/full/3/29/eaat7061/DC1)
Table S6 (Microsoft Excel format). Raw data.
Supplementary Methods
Cell lines and tissue culture. MC38 mouse colon carcinoma cells and RENCA mouse renal
adenocarcinoma cells were obtained from American Type Culture Collection (ATCC) and were
cultured at 37°C, 5% CO2 in DMEM media supplied with 10% FBS, 100U mL-1 penicillin and
100 μg ml-1 streptomycin, 2 mM L-glutamine, 100 μM NEAA (ThermoFisher Scientific). Tumor
cell lines were tested negative for Mycoplasma and common rodent pathogens by IMPACT test
(IDEXX BioResearch). MC38-OVA-β2m-Kb were generated by transducing MC38 tumor cells
with lentiviral vector (LV) encoding a single chain trimer consisting of SIINFEKL peptide-
spacer-β2 microglobulin-spacer MHC class I (Kb) heavy chain. Surface expression of single
chain trimer was confirmed with 25D-1.16 Ab (eBioscience, Extended Data Fig. 6a). MC38-
OVA-β2m-Kb were maintained with selection media containing 1.25 g ml-1 puromycin
(ThermoFisher Scientific).
In vivo mouse studies. For tumor studies, 3x105 MC38 cells or 1x106 RENCA cells were
subcutaneously injected on the right flank of age-matched C57BL/6 or Balb/c respectively (day
0). On day 6 after tumor implantation, mice (randomly distributed in different groups) were
grouped based on tumor size and treated by intraperitoneal injection with 5 mg kg-1 anti-GITR
(DTA-1) and/or anti-PD-1 (RPM1-14) Ab or isotype control IgGs (rat IgG2b, LTF-2 and rat
IgG2a, 2A3) at indicated doses (antibodies were obtained from Bio X Cell). Antibodies were
administered again on day 13. For antibody depletion experiments, mice treated with either
combination therapy or isotype control IgG were treated with 300 μg depleting or isotype control
mAbs, including anti-CD4 (clone GK1.5); anti-CD8 (clone 2.43) and rat IgG2b isotype (clone
LTF-2), rat IgG1 isotype (clone HPRN, Bio X Cell) and anti-CD25 (clone PC61, eBioscience).
Depletion Ab were given at one day prior of tumor challenge (day -1) and twice weekly for total
eight doses. The depletion efficiency was confirmed by FACS analysis of peripheral blood
samples (Fig. S1B). Blocking antibodies used in this study include anti-CD226 Ab (clone 10E5,
rat IgG2b, eBioscience, 25 mg/kg), CD28 blocking (CTLA4-Fc, Orencia, BMS, 10 mg/kg), anti-
OX40L (clone RM134L, rat IgG2b, Bio X Cell, 10 mg/kg) and anti-4-1BBL (clone TKS-1, rat
IgG2a, Bio X Cell, 10 mg/kg).Blocking Ab were given twice weekly by i.p. injection starting 1-2
days prior to immunotherapy, for two weeks. Perpendicular tumor diameters were measured
blindly 2-3 times per weeks using digital calipers (VWR, Radnor, PA). Volume was calculated
using the formula L × W x W × 0.5, where L is the longest dimension and W is the perpendicular
dimension. Differences in survival were determined for each group by the Kaplan-Meier method
and the overall p value was calculated by the log-rank testing using survival analysis by Prism
version 6 (GraphPad Software Inc.). An event was defined as death when tumor burden reached
the protocol-specified size of 2000 mm3 in maximum tumor volume to minimize morbidity.
Analysis of flow cytometry data. Flow cytometry data were analyzed using Cytobank software
(Cytobank, Santa Clara, CA). A range of 200 to >100,000 live cells was acquired. Samples were
run on equal numbers of events per sample. The range in events was determined by the sample
with the fewest events acquired. In each figure, all samples were derived from the same viSNE
run. Individual flow cytometry standard files from each viSNE run were combined into a single
flow cytometry standard file to assist in defining spatially distinct populations using the
concatenation tool. viSNE heat maps show fluorescent intensity of each marker for each event.
Scales on the heat maps are individually generated for each surface marker from low to high
expression. To cluster T cells automatically based on specific markers, SPADE analysis and
CITRUS analysis from Cytobank was used (25, 26, 48).
Targeted TCR sequencing. Libraries containing both TCR-a and TCR-b sequences were
generated from 100 ng human PBMC RNA using the SMARTer Human TCRa/b Profiling Kit
(Clontech). Libraries were produced using 21 amplification cycles for PCR1 and 20
amplification cycles for PCR2. Sequencing was performed on MiSeq (Illumina) by multiplexed
paired-read run with 2X300 cycles. Mouse TCRA and TCRB sequences were amplified by a 5’
RACE based method from T cells with constant region-specific primers and sequenced using
Illumina MiSeq. Specifically, total RNA was isolated from T cells using the Mag/Max-96 Total
RNA Isolation kit (Thermo Fisher Scientific) according to manufacturer’s instructions. Reverse
transcription was performed to generate cDNA containing TCRA or TCRB constant region
sequence, using a SMARTer™ RACE cDNA Amplification Kit (Clontech) and a TCRA or
TCRB specific primer. During this process, a DNA sequence, which is reverse compliment to 3’
of primer PE2-PIIA, was attached to the 3′ end of the newly synthesized cDNAs. Purified TCR
cDNAs were then amplified by the 1st round PCR (semi-nested) using the PE2-PIIA primer and
an TCRA or TCRB constant specific primer listed. PCR products between 450-700bp were
isolated using Pippin Prep (SAGE Science). These products were further amplified by a 2nd
round PCR using primers. All primers used are listed here (Supplementary Method Table 2).
PCR products between 400bp-700bp were isolated, purified, and quantified by qPCR using a
KAPA Library Quantification Kit (KAPA Biosystems) before loading onto a Miseq sequencer
(Illumina) for sequencing using Miseq Reagent Kits v3 (600 cycles).
TCR sequence analysis bioinformatics pipeline, rpsTCR, and its validation. We developed a
new bioinformatics pipeline rpsTCR (rps stands for Random Priming Sequencing) for
assembling and extracting TCR-CDR3 sequences from random priming short RNA sequencing
reads (fig. S2). The rpsTCR took paired- and single-end short reads and mapped these reads to
mouse or human genomes and transcriptomes, but not TCR gene loci and transcripts using
Tophat (49) with default parameters. Mapped reads were discarded, and unmapped reads were
recycled for extraction of TCR sequences. Low quality nucleotides in the unmapped reads were
trimmed. Then reads with length less than 35bp were filtered out using HTQC toolkit (50). QC
passed short reads were assembled into longer reads using iSSAKE default setting. TCRklass,
which was reported to have best performance of retrieving CDR3 sequences(51), was used to
identify CDR3 sequences with Scr (conserved residue support score) set from default 1.7 to 2.
We used targeted TCR-seq data sets generated from a healthy human PBMC and a mouse whole
blood samples as positive controls to evaluate whether the extra steps introduced to our pipeline
result in higher false positive or false negative rates comparing to TCRklass alone. Majority of
unique CDR3 sequences from TCRB (64,031) or TCRA (51,448) were detected by both rpsTCR
and TCRklass. The squared correlations between rpsTCR and TCRklass were 0.9999 and 0.9365
for TCRB-CDR3 and TCRA-CDR3, respectively (fig. S3). Six TCR-negative cancer or non-
cancer cell lines were used as negative controls. rpsTCR didn’t detected any CDR3 sequences,
while TCRklass extracted a few CDR3 sequences from some of these TCR-negative cancer cell
lines (Table S1). To further validate the performance of our pipeline, we sequenced a heathy
mouse whole blood sample using both targeted TCR-seq and random priming RNA-seq
approaches (200M, 2x100bp). Although the number of CDR3 sequences assembled from RNA-
seq data was much smaller than that from the targeted TCR-seq approach, about 45% of the
CDR3 sequences identified from RNA-seq data using rpsTCR were also observed among CDR3
sequences from targeted TCR-seq. Because of the technique limitation of targeted TCR-seq, it is
not surprising that a fraction of the CDR3 sequences extracted from RNA-seq data were not
present in the TCR-seq results. For example, the efficiency of 5’ RACE adapter used for targeted
TCR-seq is generally low and the multiply PCR tends to amplify high frequency TCRs, thus only
a small portion of TCRs can be targeted. As expected, much higher percentage (~ 70%) of the
CDR3 sequences identified from RNA-seq data using rpsTCR were also observed among high
frequency CDR3 sequences (>= 0.1%) from targeted TCR-seq, while only about 40% CDR3
extracted using TCRklass alone. Moreover, we cut the 100bp read length into 50bp segments and
randomly selected 200M reads. Among the top 10 CDR3 sequences ranked by targeted TCR-seq
approach, 8 CDR3 sequences were detected by our rpsTCR, while only 3 were detected by
TCRklass. We then applied our rpsTCR pipeline to extracting CDR3 sequences from the single
cell RNA-seq data generated from intratumoral CD8+ T cells of MC38 treated with different
antibodies. Our detection rates of CDR3_beta and CDR3_alpha sequence detection rates were
comparable to published data (52) (Table S2) using targeted TCR-seq approach to detect TCR
sequences from single cell sequencing of T cells.
Large Unilamellar Vesicles (LUVs).
Phospholipids (79.7% POPC + 10% POPS + 10% DGS-NTA-Ni + 0.3% Rhodamine-PE) were
dried under a stream of Argon, desiccated for at least 1 hour and suspended in 1x Reaction buffer
(50 mM HEPES-NaOH, pH 7.5, 150 mM NaCl, 10 mM MgCl2, 1 mM TCEP). LUVs were
prepared by extrusion 20 times through a pair of polycarbonate filters with a pore size of 200 nm,
as described previously (45).
Phosphotyrosine Western Blot. 50 μg of protein for each sample was used for the Western
Blot. The samples were heated at 95 °C for 5 min and subjected to SDS-PAGE. Proteins were
transferred to nitrocellulose membranes using iBlotTM Dry Blotting system (ThermoFisher
Scientific). The membranes were blocked with 5%BSA in Tris-buffered saline (pH 7.4) with
0.1% Tween-20, incubated with desired phosphotyrosine specific antibodies, and detected with
HRP-based enhanced chemiluminescence. The following primary antibodies we used: anti-
pY142-CD3ζ (BD Biosciences #558402), anti-pY20 (Santa Cruz Biotechnology #sc-1624, for
detection of tyrosine phosphorylated CD28), anti-pY418-Src (BD Biosciences #560095, for
detection of pY394-Lck), anti-pY505-Lck (Cell Signaling #2751), anti-pY315-ZAP70 (Abcam
#ab60970), anti-pY493-ZAP70 (Cell Signaling #2704).
Supplementary Figure Legend:
Fig. S1. T cell depletion with Abs. (A) Representative FACS plots showing depletion efficiency
by CD4, CD8 and CD25 antibodies. (B) Similar as in Fig. 1B, C57BL/6 mice bearing
MC38 tumors were treated with anti-CD8, CD4 or anti-CD25 depletion antibody
followed by treatment with control IgG. Data shown is average tumor growth curve upon
treatment with different depletion Ab (n=6 mice/ group).
Control IgG CD4 depletion CD8 depletion
Control IgG CD25 depletion
Tu
mo
r siz
e (
mm
3)
0 5 10 15 20
0
500
1000
1500
2000Isotype + Isotype
Isotype + anti-CD8
Isotype + anti-CD4
Isotype + anti-CD25
Days after tumor challenge
B
A
Fig. S2. Bioinformatic pipeline rpsTCR. (A) Schematic of rpsTCR, a bioinformatics pipeline
for TCR repertoire analysis using random priming short RNA-seq data. (B) rpsTCR
platform validation using human and mouse primary blood cells. Targeted TCR-seq data
from healthy human PBMC samples or mouse whole blood were used as a positive
control to evaluate false positive or false negative rates comparing to TCRklass alone.
Majority of unique CDR3 sequences were detected by both our pipeline and TCRklass, as
indicated by the number in Venn diagram. The squared correlations (R2) between our
pipeline and TCRklass were indicated in the Figure.
Fig. S3. Combination therapy expands intratumoral high-frequency tumor-reactive CD8+
T cell clones. (A) Intratumoral CD8+ T cell clonal analysis based on single cell-sorted
RNA-seq data on day 8 and 11 post tumor challenge. Each circle represents a single
CD8+ T cell. T cell sharing the same TCR sequence is color-coded, number indicates
frequency of individual clone followed by the sequence of CDR3 region of TCR chain.
(B) Quantitative analysis of T cell clonality. Data depicts cumulative frequency of
expanded CD8+ T cell clones from each group (*, p <0.05, **, p < 0.01, ***, p<0.001,
Fisher’s test).
Day 8
Day 1
1
0
10
20
30
40
% E
xpa
nd
ed c
lone
s
Day 8
Day 11
**** *
***
Tumor Spleen
Anti-PD1 -
-
+
-
-
+
+
+Anti-GITR
-
-
+
-
-
+
+
+
B
A
Fig. S4. Combination treatment expands tumor antigen–specific CD8+ T cells with effector
function. (A) Validation of surface expression of OVA peptide-Kb complex on MC38-
OVA-β2m-Kb cells by FACS. MC38-OVA-β2m-Kb or empty vector control MC38 cells
are stained with isotype or anti-Kb-SIINFEKL Ab. Representative histogram is shown.
(B) Frequency (left) and counts normalized to tumor weight (cell count/mg, right) of
tumor and spleen (total cell count) OVA-specific CD8+ T cells from MC38-OVA-β2m-Kb
bearing mice treated with anti-GITR and/or anti-PD-1 Ab. (Representative of two
experiments, n = 9-10 mice/ group). (C) Increase of OVA-specific recall response in
spleen and tumor CD8+ T cells with combination treatment. Cells were restimulated with
or without SIINFEKL peptide in the presence of BFA. Intracellular expression of IFN-
was analyzed by FACS. (Accumulating data from 6-13 mice/ group) (*, p <0.05, **, p <
0.01; ***, p < 0.001, B, One-way ANOVA, C, Two-way ANOVA, Tukey’s multiple
comparison test).
Tumor
0
5
10
15
20
% o
f IF
Ng+
CD
8 No peptide
SIINFEKL**
Anti-PD1 -
-
+
-
-
+
+
+Anti-GITR
0
1
2
3
4
% o
f IF
Ng+
CD
8 No peptide
SIINFEKL
****
Anti-PD1 -
-
+
-
-
+
+
+Anti-mGITR
C
Anti-Kb-SIINFEKL
MC38 Parental + Isotype
MC38-OVA-B2M-K(B) + Isotype
MC38 Parental + Ab
MC38-OVA-B2M-K(B) + Ab
A
B
0
10
20
30
40
50
Cell
count / m
g *
Anti-PD1 -
-
+
-
-
+
+
+Anti-mGITR
0
10
20
30
40
50
% o
f C
ells
**
Anti-PD1 -
-
+
-
-
+
+
+Anti-GITR
Percentage of OVA-specific CD8
0
2
4
6
8
10
% o
f C
ells
****
Anti-PD1 -
-
+
-
-
+
+
+Anti-mGITR
0.0
5.0×104
1.0×105
1.5×105
2.0×105
Cell
co
un
t
***
**
-
-
+
-
-
+
+
+
Number of OVA-specific CD8
Spleen
Fig. S5. Mean fluorescence intensity of markers for dysfunctional cell clusters identified in
Fig. 2.
Fig. S6. Mean fluorescence intensity of markers for effector/memory cell clusters identified
in Fig. 3.
14 12 7 100
1000
2000
3000
Cluster
MF
I
KLRG1
14 12 7 100
5000
10000
15000
20000
Cluster
MF
I
PD1
14 12 7 100
1000
2000
3000
Cluster
MF
I
TIM3
14 12 7 100
1000
2000
3000
4000
5000
Cluster
MF
I
LAG3
14 12 7 100
1000
2000
3000
4000
ClusterM
FI
Tbet
14 12 7 100
1000
2000
3000
4000
Cluster
MF
IEomes
14 12 7 100
1000
2000
3000
4000
Cluster
MF
I
CD244
11 Tcm Teff0
100
200
300
400
MF
I
Sca1
11 Tcm Teff0
200
400
600
MF
I
CD95
11 Tcm Teff0
20
40
60
80
MF
I
CD127
11 Tcm Teff0
50
100
150
MF
I
CD122
11 Tcm Teff0
100
200
300
400
500
MF
I
Eomes
11 Tcm Teff0
200
400
600
MF
I
CD226
11 Tcm Teff0
50
100
150
MF
I
Ki67
Fig. S7. TIGIT expression at single-cell RNA level and FACS analysis of TIGIT/CD226
expression level on different T cell subsets. (A) Cumulative distribution function (CDF)
plots showing TIGIT RNA expression comparing total CD8+ T cells from isotype control
group versus indicated CD8+ T cell subsets from treatment groups. (B) FACS analysis,
(representative of two experiments, n = 9~10 mice per group) showing expression of
TIGIT (MFI) in total CD8+ T cells from isotype control group versus, clonally expanded
(OVA-specific) CD8+ T cells from different treatment groups. (C-D) FACS analysis of
TIGIT expression on tumor T cell subsets 8 days after MC38 tumor implantation,
percentage of TIGIT+ cells within each population are shown. (C) Representative
histogram (D) % of TIGIT positive cells (top) and MFI of TIGIT (bottom) in each cell
subset (data shown from two independent experiments combined. n=16 mice for Isotype
and anti-PD-1 group; n=17 mice for anti-GITR and Combo groups). (E) FACS analysis
of CD226 expression on spleen and tumor T cell subsets 11 days after MC38 tumor
implantation, percentage of CD226+ cells within each population are shown. (n=17 mice/
group for spleen samples and n=16 mice/ group for tumor samples) (F) FACS plot, % of
TIGIT positive and TIGIT MFI showing TIGIT expression level on T cell clusters
responsive to combination treatment. (*, p <0.05, **, p < 0.01, ***, p<0.001, One-way
ANOVA, Tukey’s multiple comparison test).
A
TIGIT Expression (RPKM)
Cu
mula
tive
Fre
qu
en
cy
Non-expanded CD8 Clonal expanded CD8Total CD8
Isotype total CD8
Anti-PD1 CD8 Subset
Anti-GITR CD8 Subset
Combo CD8 Subset
C
E
0
20
40
60
80
% C
ells
Isotype
Anti-PD1
Anti-GITR
Anti-PD1+Anti-GITR
CD4 Teff CD4 TeffTreg Treg
Spleen Tumor
CD226
CD
22
6
FoxP3
CD4 T cells
TregCD4 TeffCD8
TIGIT
Isotype
Anti-PD1
Anti-GITR
Combo
F
TIG
IT
OVA-Pentamer
Fig 2
c14
Fig 3
c11
0
20
40
60
80
100
% o
f C
ell
TIGIT+_%
C14
C11
0
500
1000
1500
2000
2500
MF
I
TIGIT_MFI
C14
C11
0
20
40
60
80
% T
IGIT
+ C
ells
Anti-PD1 -
-
+
-
-
+
+
+
*******
*******
Anti-GITR
0
20
40
60
80
100
% T
IGIT
+ C
ells
Anti-PD1 -
-
+
-
-
+
+
+
********
*******
Anti-GITR
0
20
40
60
80
100
% T
IGIT
+ C
ells
Anti-PD1 -
-
+
-
-
+
+
+
**
**
Anti-GITR
0
100
200
300
400
TIG
IT M
FI
Anti-PD1 -
-
+
-
-
+
+
+
********
*******
Anti-GITR
0
100
200
300
400
TIG
IT M
FI
Anti-PD1 -
-
+
-
-
+
+
+
********
*******
Anti-GITR
0
1000
2000
3000
TIG
IT M
FI
Anti-PD1 -
-
+
-
-
+
+
+
****
****
Anti-GITR
DTregCD4 TeffCD8
0
100
200
300
400
TIG
IT M
FI
Isotype Total CD8 T cells
Anti-PD1 OVA-specific
Anti-GITR OVA-specific
Combo OVA specific
***
TIGITB
1.0
0.8
0.6
0.4
0.2
0.0
0 1 32 1024 0 1 32 1024 0 1 32 1024
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
Fig. S8. GITR and PD-1 combination treatment significantly reduced highly activated Treg
subsets. (A) Density viSNE plots of Treg cells from each treatment group day 12 post MC38
tumor challenge. (B) viSNE plot of Treg cells overlaid with color-coded T cell clusters
identified by SPADE. (C) viSNE plot of tumor-infiltrating Treg cells overlaid with the
expression of selected markers and MFI of each cluster is shown next to the viSNE plot. (D)
Frequency of selected T cell clusters displayed on a per-mouse basis with mean ± S.E.M. (*,
p <0.05, **, p < 0.01, ***, p<0.001, ****, p<0.0001; One-way ANOVA, Tukey’s multiple
comparison test).
A
Anti-CD25
Isotype Anti-PD1
Anti-GITR
Anti-PD1 +
Anti-CD25
Anti-PD1 +
Anti-GITR
1
2
3
4 5
67
8
TIGIT
TIM3
KLRG1
LAG3
C
c2 c5 c7 c80
20
40
60
% o
f C
ells
Isotype
PD1
CD25
GITR
PD1+CD25
PD1+GITR
*
*****
***
*
****
****
*
****
* *
Treg
Intratumoral Treg
c1 c2 c3 c5 c4 c6 c7 c82000
2500
3000
3500
TIG
IT M
FI
c1 c2 c3 c5 c4 c6 c7 c80
2000
4000
6000
8000
10000
TIM
3 M
FI
c1 c2 c3 c5 c4 c6 c7 c80
5000
10000
15000
KLR
G1 M
FI
c1 c2 c3 c5 c4 c6 c7 c80
5000
10000
15000LA
G3 M
FI
tSNE2
tSN
E1
tSNE2
tSN
E1
tSN
E1
tSN
E1
tSNE2
tSN
E1
tSNE2
tSN
E1
tSNE2
tSN
E1
tSNE2
tSNE2B
D
tSN
E1
Fig. S9. GITR and PD-1 combination treatment induced intratumoral CD8+ T cell subsets
distinct from CD25 and PD-1 combination therapy. (A) Density viSNE plots of OVA-
specific CD8+ T cells from each treatment group day 12 post MC38-OVA tumor challenge
(n=10). (B) viSNE plot of T cells overlaid with color-coded T cell clusters identified by
SPADE. (C) viSNE plot of tumor-infiltrating T cells overlaid with the expression of
selected markers and MFI of each cluster is shown next to the viSNE plot. (D) Frequency of
selected T cell clusters displayed on a per-mouse basis with mean ± S.E.M. (E) Expression
level (MFI) of TIGIT on intratumoral CD8+ T cells. (F) Expression level (MFI) of CD226
on intratumoral OVA-specific CD8+ T cells. (*, p <0.05, **, p < 0.01, ***, p<0.001, ****,
p<0.0001; One-way ANOVA, Tukey’s multiple comparison test).
PD1
CD62L
TIM3 LAG3 CD244 KLRG1
CD44 Sca1 CD127 CD95 CD122
Eomes Tbet TIGIT
CD226 Ki67
1 6 8 100
10
20
30
Cluster
% o
f C
ells
Isotype
Anti-PD1
Anti-CD25
Anti-GITR
Anti-PD1 + Anti-CD25
Anti-PD1 + Anti-GITR
***
**
****
*
*
*******
****
***
*
***** ****
**
*
*
Anti-CD25
Isotype Anti-PD1
Anti-GITR
Anti-PD1 +
Anti-CD25
Anti-PD1 +
Anti-GITR
B
CA
D
0
2000
4000
6000
CD
226 M
FI
Isotype
Anti-PD1
Anti-CD25
Anti-GITR
Anti-PD1 + Anti-CD25
Anti-PD1 + Anti-GITR
*
E F
Tumor infiltrating OVA-specific CD8+ T
1 23
4 56 7
89 10
11
tSN
E1
tSNE2
tSN
E1
tSN
E1
tSNE2
E
0
50
100
150
200
250
TIG
IT M
FI
Isotype
Anti-PD1
Anti-CD25
Anti-GITR
Anti-PD1 + Anti-CD25
Anti-PD1 + Anti-GITR
*** ***
*
*
******
******
***
Fig. S10. CD226−/− mice show normal T cell development and homeostatic function. (A)
Targeting strategy. Coding exons 1 to 2 of mouse CD226 was replaced with self-deleting
eGFP-Neo cassette (eGFP-polyA-hUb-EM7-neo-polyA-Prm-Crei-polyA), beginning just
3’ to the start ATG in coding exon 1 to 13 bp before the 3’ end of coding exon 2. The
intron between coding exons 1-2 is also deleted. After cassette deletion, eGFP, polyA
WT CD226 KO
CD8 CD8
CD
226
B
CD4 Tconv Treg DP CD8 SP DN0
20
40
60
80
100
% C
ells
CThymus
D
0
5
10
15%
Cells
CD8 CD4
Teff
Treg
Spleen
CD8 CD4
Teff
Treg
Blood
0
50
100
150
200
MF
I
CD8 CD4
Teff
Treg
Spleen
CD8 CD4
Teff
Treg
Blood
0
1000
2000
3000
4000
MF
I
CD8 CD4
Teff
Treg
Spleen
CD8 CD4
Teff
Treg
Blood
PD1 MFI GITR MFI
Medium CD3/CD280
10
20
30
pg
/mL
IL-5
Medium CD3/CD280
200
400
600
800
pg
/mL
IL-6
Medium CD3/CD280
100
200
300
400
500
pg
/mL
TNFa
Medium CD3/CD280
500
1000
1500
2000
2500
pg
/mL
IL-2
Medium CD3/CD280
500
1000
1500
pg
/mL
IFNg
E
F
Endogenous
Cd226 knockout, contains eGFP-Neo self-deleting cassette
Cd226 knockout, cassette deleted, eGFP and LoxP site remain
Deletion (754 bp)
ATG
ATG STOP
ATG STOP
ATG
A
LoxP and cloning sites (1141 bp) remain. (B) FACS validation of CD226 deletion on T
cell subsets. (C) FACS analysis of T cell development in thymus (Tconv, conventional T
cells; DP, CD4/CD8 double positive; SP, single positive; DN, CD4/CD8 double
negative). (D) T cell subsets in spleen and blood analyzed by FACS. (E) Expression level
of PD-1 and GITR on spleen and blood T cell subsets from CD226-/- or WT mice. Data
shown is Mean fluorescence intensity (MFI) (n=3 mice/ group). (F) Inflammatory
cytokine secretion upon TCR stimulation. Splenocytes from CD226-/- or wild type (WT)
mice were stimulated ex vivo with anti-CD3 and anti-CD28 Ab for 16 hours. Supernatant
was collected for indicated cytokine release.
Fig. S11. Effectiveness of combination treatment does not rely on CD28, OX40, and 4-1BB
pathway. MC38 tumor bearing mice were treated with either indicated blocking Ab or
isotype IgG prior to immunotherapy with anti-GITR + anti-PD-1 or isotype IgGs.
Percentage of survival are shown here. (A) Blocking CD28 signaling with CTLA-4-Ig
(10 mg/kg). (B) Blocking OX40 signaling with OX40L blocking antibody (10 mg/kg).
(C) Blocking 4-1BB signaling with 4-1BBL blocking antibody (10 mg/kg). Data shown
are survival curves (n=8-10 mice/ group). (**, p < 0.01; ***, p < 0.001; ****, p <
0.0001).
Fig. S12. Expression level of CD155. Open: isotype control; filled: Anti-CD155 Ab staining.
MC38 RENCA
CD155
Table S1. Negative controls for rpsTCR.
Table S2. Comparison of TCR detection rate.
TCR type TotalCDR3 detected
by our pipeline
Detection rate of
our pipeline
Detection rate reported
by Han A. et al.
TRB-CDR3 1,379 1,186 86.0% 92%
TRA-CDR3 1,379 1,078 78.2% 87%
TRA&B-CDR3 1,379 1,009 73.1% 82%
Table S3. Selected genes differentially regulated by each treatment.
Cluster GeneIDAnti-
GITR
Anti-
PD1Combo
cluster1 Il2rb 2.86 2.35 2.61
cluster2 Cblb 2.85 1.78 1.61
Cd200 2.30 4.02 0.88
Eomes 2.74 2.17 1.44
Lyn 6.84 0.64 0.74
Havcr2 6.30 0.88 1.62
Lat2 5.76 0.77 1.71
Gzmd 4.44 0.65 2.01
Gzme 3.19 0.25 1.07
Klra3 3.06 0.54 0.66
Tnfrsf1b 2.86 0.16 1.03
Ly9 2.68 0.59 0.28
Prf1 2.67 0.68 1.44
Tigit 2.42 1.19 0.50
Il2ra 2.34 0.53 0.46
Txk 2.33 0.34 0.34
Gata3 0.64 3.32 1.11
Pdcd4 0.29 2.61 1.56
Pten 0.74 2.59 1.93
Cd226 0.11 0.67 7.82
Pde4d 0.42 0.02 5.39
Vav1 0.11 0.12 3.27
Mki67 0.58 0.02 2.53
Id2 0.70 1.55 2.44
cluster3
cluster4
cluster5
cluster6
Table S4. Abs for flow cytometry. Antibodies for flow cytometry used in the mouse and human
experiments are provided in the tables below.
Antibody Clone Flurophore Conjugation Company
B220 RA3-6B2 eFluor 605 eBioscience
CD101 Moushi101 PE eBioscience
CD11a M17/4 Alexa Fluor 647 eBioscience
CD11b M1/70 BUV395 BD Bioscience
CD122 5H4 BB700 BD Bioscience
CD127 A7R34 BV711 Biolegend
CD137 17B5 Alexa Fluor 647 eBioscience
CD226 10E4 PE-Cy7 Biolegend
CD244 2B4 PE BD Bioscience
CD25 PC61 Alexa Fluor 647 and Alexa Fluor 700 eBioscience
CD3 17A2 Alexa Fluor 700 eBioscience
CD3 17A2 FITC Biolegend
CD38 90 PErCP-eFluor 710 eBioscience
CD4 RM4-5 BV510 and BV786 BD Bioscience
CD44 IM7 BV711 BD Bioscience
CD44 IM7 PerCP-Cy5.5 eBioscience
CD45 103138 BV510 Biolegend
CD45.2 104 APC eFluor 780 eBioscience
CD5 53-7.3 BUV737 BD Bioscience
CD62L MEL-14 BUV737 and BV786 BD Bioscience
CD62L MEL-14 PE eBioscience
CD8a 53-6.7 BUV805 BD Bioscience
CD8a 53-6.7 APC eFluor 780 eBioscience
CD95 Jo2 BV510 BD Bioscience
CTLA-4 UC10-4F10-11 PE-CF594 BD Bioscience
Eomes Dan11Mag eFluor 660 eBioscience
FoxP3 FJK-16s eFluor 450 eBioscience
GITR DTA1 BV711 BD Bioscience
Granzyme A GzA-3G8.5 PErCP-eFluor 710 eBioscience
Granzyme B NGZB PE eBioscience
Helios 22F6 Alexa Fluor 647 Biolegend
ICOS C398.4A BV510 BD Bioscience
IFNg XMG1.2 BUV737 BD Bioscience
Ki67 B56 BUV395 BD Bioscience
Ki67 B56 PE-Cy7 eBioscience
LAG3 C9B7W FITC eBioscience
NK1.1 PK136 BV650 and BUV395 BD Bioscience
PD1 J43 PerCP-Cy5.5 eBioscience
PD1 J43 BV605 BD Bioscience
Sca1 D7 APC eFluor 780 eBioscience
TIGIT GIGD7 PerCP-Cy5.5 Biolegend
TIM3 RMT3-23 PE-Cy7 eBioscience
TNFa MP6-XT22 BV711 BD Bioscience
Table S5. Primers for TCRα/β repertoire sequencing.
RT primers
TCR 5′ - GCAGGTGAAGCTTGTCTGGTTGCT - 3′
TCR 5′ - CGAGGGTAGCCTTTTGTTTGTTTGC - 3′
1st round PCR
primers
TCR
5’ - ACACTCTTTCCCTACACGACGCTCTTCCGATCT
TCAAAGTCGGTGAACAGGCAGAG - 3’
TCR
5′ - ACACTCTTTCCCTACACGACGCTCTTCCGATCT
GACCTTGGGTGGAGTCACATTTCTC - 3′
PE2-PIIA
5′ - GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
AAGCAGTGGTATCAACGCAGAGT - 3′
2nd round PCR
Primers
Forward
5′ - AATGATACGGCGACCACCGAGATCTACACXXXXXX
ACACTCTTTCCCTACACGACGCTCTTCCGATCT- 3′
Reverse
5′ - CAAGCAGAAGACGGCATACGAGATXXXXXX
GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT- 3′
