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Identification and characterization of immune-escape mechanisms in solid tumors
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Identification and characterization of immune-escape mechanisms in solid tumors
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Content
IDENTIFICATION AND CHARACTERIZATION OF IMMUNE-ESCAPE
MECHANISMS IN SOLID TUMORS
by
Scott Anthony Bergfeld
A Thesis Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(EXPERIMENTAL AND MOLECULAR PATHOLOGY)
August 2008
Copyright 2008 Scott Anthony Bergfeld
ii
Dedication
For my wife, Kat, whose love and support were indispensible in the creation of
this manuscript.
iii
Acknowledgements
I would like to thank my mentor, Dr. Alan Epstein, for his guidance and support
throughout the course of my studies. I would also like to thank the other members of my
committee, Dr. Clive Taylor and Dr. Harvey Kaslow, for their advice on furthering my
professional development.
I am very grateful to my fellow laboratory members, especially Ryan Park, Sherry
Yen and Melissa Lechner, for their assistance with PCR and in vitro studies. Additional
thanks go to Rebecca Sadun, for her foundational work, and to Dr. Peisheng Hu, Maggie
Yun, Mandy Han and James Peng, for their technical support.
A final thank you goes to Lillian Young, Dr. Mohammad Alavi and Anthony
Williams, of Dr. Clive Taylor‟s laboratory, for their help with immunohistochemistry and
PCR studies.
iv
Table of Contents
Dedication ii
Acknowledgements iii
List of Figures v
Abbreviations vi
Abstract vii
Introduction 1
Materials and Methods 4
Table 1. Primers for SYBR Green PCR 7
Results 8
Discussion 14
Bibliography 24
Appendix 28
v
List of Figures
Fig 1. Expression of Suppressive Cell Markers 8
Fig 2. Expression of Chemokines 10
Fig 3. Immunohistochemical Analysis 11
Fig 4. In Vitro Expression Assay 13
vi
Abbreviations
Ab Antibody
Arg1 Arginase I
ATRA All-Trans Retinoic Acid
ch-TNT3 Chimeric Tumor Necrosis Targeting-3
Ct Critical Threshold
DC Dendritic Cells
GAPDH Glyceraldehyde-3-phosphate Dehydrogenase
GM-CSF Granulocyte/Macrophage Colony Stimulating Factor
IHC Immunohistochemistry
LEC Liver-expressed Chemokine (CCL16)
MCP-2 Monocyte Chemoattractant Protein-2
MOM Mouse-on-mouse
MSC Myeloid Suppressor Cell
NOHA N-hydroxy-L-Arginine
PF4 Platelet Factor 4
RT-PCR Real-time Polymerase Chain Reaction
s.c. Subcutaneous
SPR Surface Plasmon Resonance
TCR T-cell Receptor
Treg Regulatory T-cell
vii
Abstract
Tumor development depends on immunomodulatory signals produced by multiple
cells within the microenvironment, resulting in the recruitment of suppressive cell
populations which limit anti-tumor immunity. Expression markers for suppressive cells
can indicate their relative contribution to tumor immunoregulation and highlight potential
treatments to limit proliferation and spread. With this goal in mind, Rt-PCR analysis was
carried out on murine tumor cell lines grown in vivo and in vitro, along with
immunohistochemical analysis of expression markers. In vivo, murine tumors of lung,
breast and colorectal origin were found to upregulate the Treg marker FoxP3, the
suppressive DC marker B7-H3, and the MSC marker arginase I. IHC studies were
inconclusive, but in vitro coculturing lung or breast tumor with myeloid cells induced
production of arginase I by myeloid cells. Chemokines found to recruit myeloid cells
were also upregulated. MSC recruitment pathways may provide a novel target for future
combination therapies.
1
Introduction
Cancer continues to be a leading cause of death in the developed world (1).
Conventional anti-cancer therapies have mostly targeted rapidly dividing neoplastic cells
with highly toxic DNA-damaging compounds, in the hope that transformed cells will
expire at a faster rate than healthy cells of the body. The results of such therapy are often
disappointing, as fully regressed tumors can regrow as new primary or metastatic lesions.
The remarkable ability of cancer cells to manipulate normal cells in the environment, and
thus create growth-supportive conditions, is partly to blame (2).
Tumor growth is accompanied by strong signaling to the microenvironment,
leading to recruitment of stromal, endothelial, and immune-effector cells (3, 4, 5). Active
T-lymphocytes are part of the milieu, drawn in by antigen presenting cells displaying
tumor antigens (6). However, these T-cells exhibit weak anti-tumor immunity and are
unable to prevent proliferation and spread (7). This deficiency can be attributed to
inhibition of T-cell activation and cytotoxicity, achieved via immunomodulatory
conditions produced by tumor cells and suppressor cells recruited to the tumor site (8).
In recent years, regulatory T-cells (Tregs) have received much attention for their
role in tumor immunosuppression. This population of cells exerts powerful suppressive
effects on T-cells through expression of CTLA-4, which blocks CD28 costimulation of
T-cells, and CD25, the alpha chain of the IL-2 receptor which increases Treg affinity for
IL-2 and limits T-cell access to IL-2 (9). Tregs are beneficial when they reduce
autoimmunity in the periphery, but when recruited by tumors they prevent immune-attack
on transformed cells (10).
2
The adaptive T-cell response cannot occur without antigen presentation and
costimulation. This first step is usually accomplished by dendritic cells (DCs), which
recognize foreign and tumor-produced antigens and present them to CD4-positive helper
T-cells, along with co-stimulatory ligands like B7.1 and B7.2 (11). Immunomodulatory
conditions in the tumor microenvironment can influence DCs to produce inhibitory
ligands, such as murine B7-family member B7-H3, instead of co-stimulatory molecules,
thus limiting anti-tumor immunity (12).
A final, very newly discovered population of immature myeloid cells, called
myeloid suppressor cells (MSCs), can create strong T-cell suppression through the
depletion of L-Arginine, an amino acid critical for production of the T-cell receptor
(TCR) zeta chain (13). Without this transmembrane protein, the TCR kinase-signalling
cascade cannot occur, leading to tolerogenic effects in T-cells. Reduced L-Arginine
levels were first correlated with T-cell dysfunction in rats and patients after trauma to the
liver (14), the primary expression site of the catabolic enzyme arginase I.
Arginase I (Arg1) is primarily expressed by hepatocytes as part of the urea cycle,
which detoxifies ammonia from protein metabolism into urea (15). Arg1 specifically
catabolizes L-arginine into L-ornithine and urea (16). Though it is essentially a liver-
specific enzyme, Arg1 is also found in low levels in certain mature myeloid cells, such as
human polymorphonuclear cells and murine macrophages, where it is expressed in
response to pathogens and depletes arginine in phagocytic vesicles (17, 18). Tumors can
induce populations of immature myeloid cells to migrate and express arginase1 in the
microenvironment, stimulating transcription via COX-2 signaling (19, 20, 21). The
genetic variability of tumor cells may also enable them to express arginase I on their own.
3
Cells present in the microenvironment do not possess the genetic instability of
cancer cells and thus cannot alter cellular features used to specifically deliver
immunotherapeutic agents to the tumor. As such, suppressive infiltrating immune cells
are a very strong target for immunotherapies which seek to block their activity or influx
into the tumor. Patterns of gene expression associated with different populations of
suppressive immune cells should identify possible immunotherapeutic strategies to
increase anti-tumor immunity. Indeed the arginase inhibitor N-hydroxy-L-arginine
(NOHA) reduced growth of murine tumors of lung origin which attract high levels of
arg1-expressing myeloid cells (22, 23). Additionally, murine colorectal tumors have
shown complete regression in response to a targeted chemokine (LEC) and anti-Treg
antibody PC61, again demonstrating the importance of immunosuppressive cell
populations on tumor growth (24).
Using a panel of 114 immunodulatory genes, we have undertaken expression
analysis of murine 4TI (breast), MAD109 (lung), and C26 (colon) tumors in order to
identify strong targets for immunotherapeutic intervention. We attempted
immunohistochemical (IHC) localization of myeloid and Arginase1 positive cells in
tumors showing significant arginase upregulation. Additionally, arginase upregulation
was characterized as MSC or tumor produced using a newly developed in vitro assay in
which tumor and myeloid cell lines and supernatants were mixed to simulate the tumor
microenvironment. MSC dependence on COX2 and sources of potential MSC-recruiting
chemokines were also characterized by this assay.
4
Materials and Methods
Antibodies, Cell Lines, and Reagents
For immunohistochemistry, primary mouse anti-mouse arginase 1 monoclonal
antibody (clone 19) was purchased from BD Transduction. Primary rat anti mouse Gr-1
monoclonal antibody (clone RB6-8C5) was purchased from eBioscience. VectorMouse-
on-Mouse Peroxidase kit (including blocking reagent, secondary biotinylated anti-mouse
IgG polyclonal antibody, and ABC peroxidase) and secondary biotinylated rabbit anti-rat
IgG polyclonal antibody were purchased from VectorLabs.
The murine tumor cell lines 4TI, MAD109, Colon26, and 3LL were obtained
from American Type Culture Collection. The murine pre-monocyte cell line J777.4A1
was obtained from Dr. Stephen Stohlman (Cleveland Clinic, Cleveland, OH). Cells were
maintained in RPMI 1640 supplemented with 2.5% fetal bovine serum, L-glutamine, and
penicillin/streptomycin as previously described (24).
Mice
Six-week-old female BALB/c and C57BL/6 mice were obtained from Harlan
Sprague Dawley. Experiments were carried out in accordance with Institutional Animal
Care and Use Committee-approved protocols and guidelines for proper care and use of
animals in research. Mice received s.c. injections of 4TI, MAD109, or C26 cells (5 x 10
6
in 0.2mL) in the left flank. Tumor-bearing mice were sacrificed on days 4, 7, 10, and 14
after injection (n = 3 mice per time point). Normal control mice were sacrificed on day
14 (n = 2 mice). Tumor tissue was harvested and preserved in RNALater (Qiagen).
5
Immunohistochemistry
Tumors from MAD109, 4TI, and C26–implanted mice were removed on day 15
after tumor implantation. Tumors were fixed in 10% neutral buffered formalin (VWR)
overnight and embedded in paraffin. Sections were mounted on poly-L-lysine–coated
slides, deparaffinized with Histoclear, and rehydrated using 100% and 95% alcohol.
Endogenous peroxidase was quenched with 3% hydrogen peroxide in 100% methanol for
20 min. Slides were subjected to antigen retrieval in a microwave oven (citrate buffer,
pH6.0) for 30 minutes and cooled at room temperature for 15 min. VectorMOM blocking
reagent was added as per the manufacturer‟s instructions to block nonspecific binding of
endogenous mouse IgG. Slides were incubated with primary antibody overnight at room
temperature, followed by biotinylated secondary antibody for 45 min. Avidin-biotin
peroxidase was added for 30 min, and slides were developed with 0.03%
diaminobenzidine for 10 min. Slides were washed for 10 minutes between each step
using phosphate buffer solution. Prior to mounting, slides were counterstained with
hematoxylin for 2 minutes and dehydrated using 95% and 100% alcohol, a-terpineol
xylene, and xylene.
In-vitro induction assay
The MAD109, 4TI, and 3LL tumor cell lines, as well as the J777.4A1 pre-
monocyte cell line, were grown in T75 flasks to approximately 70% confluence. Culture
medias were harvested, centrifuged and filtered to remove cells, and maintained at 4°C
for later use. Cells were trypsinized, collected by centrifugation, and resuspended in
RPMI 1640 supplemented media. Resuspended cells of each line were then transferred to
T25 flasks and grown singly, as a mixed-culture of the J777.4A1 line and a tumor cell
6
line, or as a mixed-culture treated with the Cox-2 inhibitor celecoxib (1ul/10mL media),
obtained from Dr. Axel Schonthal (Dept. of Biochemistry, USC Keck School of
Medicine). After three days, cells were harvested by trypsinization, resuspended, and
counted via hemacytometer. Approximately 3 x 10
6
cells were then pelleted and retained
for expression analysis. A second set of T25 flasks, containing resuspended cells of each
tumor cell line and passaged media from cultured J777.4A1 cells, and vice-versa, was
grown for four days before harvest and pelleting.
RNA isolation and reverse transcription
Cell pellets and RNALater-preserved tissues were disrupted with a homogenizer
and processed with an RNeasy Mini Kit (Qiagen) to isolate RNA. RNA samples were
DNase treated with an RNAqueous 4-PCR kit (Ambion). Samples were reverse-
transcribed using the SuperScriptIII kit (Invitrogen) with 4uL RNA per reaction.
Synthesis of cDNA was verified by real-time PCR using primers for murine
glyceraldehyde-3-phosphate dehydrogenase (GAPDH). RNA purity was verified using
no-RT control reactions.
Real-time PCR primers
Primers for Real-time PCR were selected from the literature or designed using
Primer3 software (Table 1). Primers for murine Indoleamine-2,3-deoxygenase were
obtained from SuperArray.
Real-Time SYBR Green PCR
For analysis of tumor tissue samples, real-time PCR was carried out using 1uL
cDNA, 0.5uL forward and reverse primers (25-40 pmol/uL), 12.5uL Brilliant SYBR
Green Master Mix (Stratagene), and 10.5uL Millipore water. Reactions were run for 40
7
Table 1 – Primers for SYBR Green PCR (sequences appear in appendix)
T-cell Activity Markers DC Activity Markers
T-cell
Activation
CD28
41BB
Lag3
GITR
IL-6
IL-6R
Ox40
CD40L
CD27
CD70
CD30
CD153
ICOS
T-cell
Inhibition
PD1
CTLA-4
TGF-b
TGF-bR
IL-10
IL-10R
FasL
Fas
TRAIL
TRAILR2
mDcTRAILR1
HVEM
LT-BR
Regulatory
T-cell (Treg)
FoxP3
CTLA-4
GITR
CD25
Gpr83
Ecm1
Glutaredoxin
Insulin-like 7
Helios
T-cell
Adhesion
LFA-1
CD2
VLA-4
PSGL-1
ESL-1
FUT9
T-cell
Maturation
CD45
IL-7R
CCR7
CD62L
T-cell
Trafficking
CCR4
CCR5
CCR7
CCL4
CCL5
CCL19
CCL21
CCL22
CXCR3
CXCR4
CXCL9
CXCL10
CXCL12
Stimulatory
DC
CD80
CD86
Ox40L
41BBL
GITRL
CD40
ICOS-L
DC
Adhesion
ICAM-1
ICAM-2
DC-SIGN
Suppressive
DC
PDL2
PDL1
IDO
B7H3
B7H4
TRAIL
LIGHT
DC
Maturation
CD83
CD1c
CD11c
H2Ea
DC
Inhibition
IL-10
COX2
Ptges1
PF4
DC
Trafficking
CCR1
CCR5
CCR7
CCL3
CCL4
CCL5
CCL19
CXCR4
Innate Activity Markers Microenvironment Activity Markers
NK cell
Activation
CD56
B3GAT
CD69
CD94
NKG2D
GranzymeB
KIR3DL1
NK-T Cell
Activation
CD1d
TCR Va14
Myeloid
Activation
CD16
MGSA
CXCR2
Myeloid
Adhesion
LFA-1
VLA-4
PSGL-1
ESL-1
ICAM-1
ICAM-2
Suppressive
Myeloid (MSC)
Arginase
iNOS
COX2
Ptges1
Myeloid
Maturation
CD11b
Gr-1
H2Ea
Myeloid
Trafficking
CCR1
CCR5
CCL3
CCL4
CCL5
CCL7
CCL8
Angiogenesis
VEGF-A
VEGFR1
VEGFR2
bFGF
FGFR1
Angiopoetin
TIE-1
TIE-2
Vascular
Adhesion
CD62E
CD62P
VCAM-1
CD31
Ep-CAM
Stromal
Activation
FAP
CD44
HVEM
LT-BR
TRAILR2
mDcTRAILR
cycles on an MX3000P (Stratagene), using GAPDH as an internal control for
normalization. For analysis of pelleted cell samples, 12.5uL FastStart SYBR Green
(Roche) was used in the real-time reaction.
Quantitative analysis
For tumor tissue samples, fold changes in gene expression compared to normal
tissue were calculated using the formula 2
((TumorGAPDHct-TumorGENEct)-(NormalGAPDHct-
NormalGENEct))
and then averaged across each time point. For pelleted cell samples, the
expression of target genes as a percentage of GAPDH was calculated with the formula
2
(GAPDHct-GENEct)
and then averaged across duplicate runs.
8
Results
Expression of suppressive-cell markers
Real-time PCR was used to analyze changes in expression of genes used to mark
immunosuppressive cell populations (Fig1). Only genes with verifiable Ct values for all
A
B
C
Fig 1 – Expression of
Suppressive Cell Markers
Tumors of each cell line
were harvested from tumor-
bearing mice on days 4, 7,
10, and 14 after injection of
tumor cells. Tissues were
subjected to RNA extraction
and Rt-PCR analysis for
suppressive cell marker
expression was carried out
on cDNA of each line. SE
bars are shown for n = 3
mice.
A) MAD109
B) 4TI
C) C26
9
three tumor models (MAD109, 4TI, C26) and normal controls could be used for analysis.
FoxP3 expression, used as a marker of regulatory T-cells (Tregs), increased by 4-16 fold
in the 4TI model and by 2-19 fold in the MAD109 model, the former showing a steady
climb and the latter a peak and decline. FoxP3 expression in the C26 model, which has
proven amenable to anti-Treg therapy, exhibited fold changes in the range of 2-7,
showing a peak followed by a small decline. Expression of B7-H3, used as a marker of
suppressive dendritic cells, increased in the range of 11-45 fold for MAD109, 19-71 fold
for 4TI, and 4-11 fold for C26. The MAD109 and 4TI models saw peaks in B7-H3
followed by a decline, while expression levels in C26 peaked, declined, and peaked
again. Arginase expression, used as a marker of myeloid suppressor cells (MSCs),
exhibited changes in the range of 8-45 fold for MAD109, 13-42 fold for 4TI, and 1-23
fold for C26 . Arginase expression peaked and declined in C26, steadily peaked in
MAD109, and peaked early, fell, and peaked again in 4TI.
Analysis of chemokine markers
Rt-PCR expression analysis was also carried out on chemokine genes to
determine these signal molecules‟ contribution to tumor immunomodulation (Fig2).
Only genes with clear Ct values for tumor and control groups could be analyzed.
Monocyte chemoattractant protein-2 (MCP-2, CCL8), which is chemotactic for
monocytes (25), showed expression increases of 5-12 fold for MAD109, 1-7 fold for 4TI,
and 2-44 fold for C26. Expression of MCP-2 peaked and declined in all models. Platelet
factor 4 (PF4, CXCL4), which is chemotactic for monocytes as well as neutrophils (26),
showed expression increases of 2-8 fold for MAD109, 4-7 fold for 4TI, and 5-33 fold for
C26. PF4 expression tended to peak early and then decline in all three models. CXCR4,
10
A
B
C
expressed on hematopoetic lineage cells (27), showed expression increases of 1-2 fold for
MAD109, 1-3 fold for 4TI, and 2-7 fold for C26. Expression of CXCR4 held steady in
MAD109, peaked steadily in 4TI, and peaked and declined in C26.
Fig 2 – Expression of
Chemokines
Tumors of each cell line
were harvested from
tumor-bearing mice on
days 4, 7, 10, and 14 after
injection of tumor cells.
Tissues were subjected to
RNA extraction and Rt-
PCR analysis for
chemokine expression was
carried out on cDNA of
each line. SE bars are
shown for n = 3 mice.
A) MAD109
B) 4TI
C) C26
11
Immunohistochemical analysis
Sections of MAD109, 4TI, and C26 tumors were stained with anti-mouse
arginase1 or anti-mouse Gr-1 antibody to localize myeloid cells and suppressive arginase
activity (Fig 3). Light background staining was observed in the no primary controls for
arginase. Dark-staining infiltrating cells could be distinguished in anti-arginase sections
of MAD109 and 4TI tumors, but background staining in C26 was too high to allow
distinct staining of an infiltrating population. Cells of the three tumors were also lightly
stained in the anti-arginase sections, so the cell population (tumor or infiltrate) most
A B C D
E F G H
I J K L
Fig 3 – Immunohistochemical analysis
FFPE sections of murine tumors were stained for Arginase I and Gr-1. All slides
appear at 20x magnification. No primary controls are also shown.
A)MAD109 Arg + B) MAD109 Arg - C)MAD109 Gr-1 + D) MAD109 Gr-1 -
E)4TI Arg + F) 4TI Arg - G)4TI Gr-1 + H) 4TI Gr-1 -
I)C26 Arg + J) C26 Arg - K)C26 Gr-1 + L) C26 Gr-1 -
12
responsible for arginase upregulation in these tumor models could not be distinguished.
For Gr-1, very little background staining was observed in the no primary controls. Only
weak positive staining was achieved in anti-Gr-1 sections, however, with C26 staining
lightest of all. A few small, Gr-1 positive cells could be seen deep in the tumors,
although localization of these cells with areas of strong arginase staining in anti-arginase1
sections proved unsuccessful.
In vitro culture analysis
Expression analysis of chemokine and arginase-related genes was carried out on
pelleted cells of the 4TI, MAD109, 3LL, and J777.4A1 lines to determine the levels of
target transcript vs. GAPDH transcript (Fig4). Expression levels for arginase and related
genes were very low in all single cell type cultures, save for a moderate level of COX2
transcript in the 4TI culture. Chemokine genes were also weakly expressed in these
single cell cultures, with the exception of a high level of PF4 transcript in the J777.4A1
culture.
Cell pellets were also collected from mixed cultures of J777.4A1 cells and tumor
cells, as well as mixed cultures treated with the COX2 inhibitor celecoxib. Expression
analysis of these pellets revealed moderately high levels of arginase transcript in the
mixed cultures. This increased expression was reduced by 10-fold or greater in the
presence of the COX-2 inhibitor celecoxib. Moderately high COX2 expression was seen
in the 4TI mixed cultures, and a slight increase in transcript level was seen in the 3LL
mixed cultures. None of the mixed cultures produced appreciable increases in the level
of MCP-2. Similar mixes were carried out with the RAW264-7 monocyte-macrophage
13
A
B
C
cell line, producing increased levels of arginase only in combination with the 4TI cell line
(data not shown).
Cells of each tumor line were grown in the presence of culture media harvested
from J777.4A1 cells (diluted 1:1 with fresh media) and pelleted after 4 days. J777.4A1
Fig4 – In vitro
expression assay
Cells of each line were
cultured alone or in the
presence of other cell
lines, supernatants, and
COX2 inhibitor
(celecoxib). Cell
pellets were harvested,
RNA extracted, and
expression analysis
carried out on
synthesized cDNA.
SD bars are shown for
duplicate runs.
A) Single culture
B) Mixed culture
with celecoxib
treatment
C) Media transfer
culture
14
cells were similarly grown in culture media harvested from each of the tumor lines and
pelleted. Expression analysis revealed moderately high to high levels of arginase in
J777.4A1 cells treated with tumor cell media. Conversely, arginase expression was very
low in tumor cells exposed to J777.4A1 media. Among arginase-related genes, only
COX2 reached moderately high expression levels, specifically in 4TI and 3LL cells
treated with J777.4A1 media. PF4 was very weakly expressed by tumor cells treated
with J777.4A1 media, while J777.4A1 cells showed moderately high levels of PF4
transcript after treatment with tumor cell media, although higher PF4 expression levels
were observed in cultures of J777.4A1 cells alone. Similar media transfer experiments
were carried out with the RAW264-7 line, which failed to induce Arginase after
treatment with tumor cell media (data not shown).
Discussion
Gene expression data for the MAD109, 4TI, and C26 murine tumor models
provides a window into the immunosuppressive signals present in the tumor
microenvironment. It is unfortunate that such a large number of paneled genes exhibited
weak or unreliable Rt-PCR amplification curves due to low transcript, as this made fold-
change analysis by our methodology impossible. However, considering that many of
these genes are only strongly upregulated during the inflammatory response, weak or
nonexistent expression in the steady state is not too surprising. Some information can be
gleaned from comparison of target gene vs. GAPDH expression in tumor-bearing mice
alone, though genes which create powerful effects with very low levels of expression
become difficult to analyze. Subcutaneous tissue controls, rather than controls based on
tissues of origin for each murine tumor model (lung, breast, colon), were chosen in order
15
to understand the changes in immunoregulatory gene balance induced at the site of tumor
growth. These changes result not only from tumor gene expression, but from gene
expression in fibroblasts, endothelial cells, and infiltrating immune cells as well.
Infiltrating immune cells exhibiting suppressive phenotypes appear to make a
strong contribution to immunoregulatory gene balance in all three tumor models tested.
Treg cells, suppressive DCs, and MSCs seem to be recruited to the tumor site not as a
result of signaling specific to each tumor, but as part of a general, prolonged
inflammatory response tending toward Th2 polarization. Activity of cytotoxic T-cells
against tumor cells is greatly reduced due to this suppression, driven by cellular pathways
intended to protect self-antigens from immune attack.
FoxP3-expressing Treg cells are apparent in all three tested models. It is therefore
quite interesting that combination therapies involving anti-CD25 Ab-mediated depletion
of Tregs have only led to 100% tumor regression in the C26 model (24), which saw the
smallest fold increases in FoxP3 expression throughout the growth of the tumor.
MAD109 showed the largest fold increases in FoxP3 expression, while fold increases for
4TI were only slightly reduced below MAD109 levels. Anti-CD25 combination
therapies used to treat MAD109 and 4TI tumors produced 60% and 80% tumor
regression, respectively (unpublished results). While it is doubtful that Tregs are the only
infiltrating cell type making an important contribution to immunosuppression in the C26
model, these other modes of suppression are clearly weak enough to allow an effective
immune response once Tregs are eliminated. It is possible that Treg cells in the MAD109
and 4TI models exhibit different patterns of gene expression and thus induce different
regulatory effects. In this way, loss of Treg cells in MAD109 and 4TI tumors may prime
16
other populations of immunosuppressive cells for increased activity, whereas loss of
Tregs in C26 leaves these populations in a weakly suppressive state that is easily
overcome by T-cell activating signals. The increased anti-Treg susceptibility of the C26
model as compared to 4TI and MAD109 may result from a reduced level of Treg
infiltration, indicated by the relatively lower fold changes in FoxP3 expression for the
C26 model. One reason for this may be due to the level of T-cell suppression seen in
these various tumor models. Tregs infiltrating C26 may be more potent than those
infiltrating MAD109 or 4TI, a hypothesis testable by studying infiltrating Tregs in
functional Treg assays.
Levels of expression of B7-H3 in the murine tumor models indicate a tendency
for DCs at the tumor site to exhibit an inhibitory phenotype rather than stimulate T-cell
activity. This behavior probably results from signaling pathways intended to reduce
autoimmunity, limiting T-cell cytotoxicity should antigen presentation occur without
detection of foreign antigen. The antigenicity of murine tumors, combined with a lack of
innate immune signaling through pattern recognition receptors, may result in independent
induction of suppressive DCs. It is also possible that signals generated by a different
class of suppressive infiltrating cell are responsible for the continued generation of
suppressive DCs at the site of the tumor, making these cells secondary mediators of the
immunoregulatory environment.
Arginase expression was upregulated in all three murine tumor models, providing
evidence for new immunotherapeutic strategies in cancer treatment. Although arginase-
mediated inhibition of T-cells is likely to contribute to immunosuppression at the tumor
site, it is difficult to tell from expression data alone whether this upregulation of arginase
17
is due chiefly to recruitment of MSCs or to production by tumor cells due to genetic
instability. IHC studies reveal infiltrating populations of arginase-positive cells in the
tumor, but also a tendency for tumor cells themselves to express some arginase. Some
co-localization of Gr-1 positive myeloid cells with arginase-expressing cells is seen,
although the contribution of these myeloid cells to arginase production in the tumor is
still difficult to distinguish from the tumor cell contribution. In-vitro culture analyses of
tumor cell lines alone show almost no arginase-production capability, although
expression of COX2, thought to be an important mediator of arginase production, is
moderately high in 4TI cells. Similarly weak arginase expression is observed in cultures
of the pre-monocyte cell line J777.4A1 alone. Only when tumor and myeloid cells are
combined is arginase induced, an effect which can be blocked by COX2 inhibition as
suggested by Ochoa et al. (21). Media transfer experiments reveal arginase to be strongly
upregulated in myeloid cells in response to soluble factors produced by tumor cells, and
not vice versa. This indicates that the major arginase-producing cell type in 4TI,
MAD109, and 3LL tumors is of myeloid origin, creating strong evidence for MSC-based
immunosuppression in these models. Unpublished in vitro culture results for the C26
model revealed similar arginase production by myeloid cells. The tendency of the
RAW264-7 monocyte-macrophage cell line to upregulate arginase in response to 4TI, but
only in a contact-dependent fashion, may reflect innate-immune stimulation of mature
monocytes triggered by non-self antigens, as opposed to induction of MSCs. The
differential antigenicity of 4TI cells compared to MAD109, 3LL, and C26 cells remains
to be elucidated.
18
Chemokine expression in vivo and in vitro provides clues as to the regulation of
infiltrating cell populations in tumors. A large percentage of chemokines are only
expressed during the inflammatory response, so in vivo fold-change analysis of these
genes was not possible as normal, steady state Ct values could not be obtained. Very
small expression increases were achieved for CXCR4, shown to be representative of
genes with low transcript level in both the steady-state and inflammatory state which
nonetheless display strong immunomodulatory effects. CXCR4 is expressed at low
levels by almost all immune cells (27), and thus cannot aid in specific recruitment of cell
populations by tumors. As such, the small fold increases observed for this gene are
indicative of a general inflammatory response and do not constitute a direct mode of
tumor-induced suppression. In contrast, notable increases in expression compared to
normal controls occurred for both MCP-2 and PF4. Control levels of these genes were
relatively high compared to other chemokines, indicating that these molecules contribute
to steady state balance of immunoregulatory factors. Both of these chemokines are
chemotactic for monocytes (24, 25), so it may be that increased expression of these genes
in tumor-bearing mice results in increased influx of mature and immature monocytes into
the tumor, increasing the population of cells which can be induced to form MSCs. In
vitro culture analysis indicated almost no MCP-2 production from tumor cell lines or
J777.4A1 cells, grown singly or as a mixed culture. This indicates that a different cell
population is responsible for MCP-2 production in the steady-state condition as well as
the tumor condition. Endothelial cells and fibroblasts are good candidates for
maintaining homeostatic levels of MCP-2 and upregulating production in response to
tumors. PF4 was detected at high levels in J777.4A1 cells cultured alone or in the
19
presence of tumor media. This level of expression was commensurate with fairly stable
production of PF4 regardless of culture condition. It may be that myeloid cells are
responsible for maintaining homeostatic levels of PF4 through tightly regulated
expression in the steady state, a balance which is shifted to favor increased PF4 levels
once the tumor has begun to excessively recruit monocytes. This increased recruitment
would tend to create a vicious circle as infiltrating monocytes lead to higher levels of PF4
in the tumor and trigger even greater recruitment.
Future Directions
Further inquiry into immunosuppressive pathways in murine tumor models may
help refine current treatments or provide the basis for new therapies. Already, clinical
trials using COX2 inhibitors in addition to chemotherapy are in progress (28). These
inhibitors can have very powerful, but very broad, effects, and systemic toxicity is
usually of concern. Future therapies targeting signal molecules which serve as more
direct mediators of arginase upregulation in tumors may prove clinically efficacious
while limiting side effects. Larger scale expression studies, perhaps involving microarray
analysis, are needed to elucidate the specific regulators of this gene expression pathway.
Recent evidence has indicated the existence of multiple populations of Treg, some
thought to acquire their suppressive phenotype through a normal process of T-cell
differentiation (natural Tregs) and some through inflammatory stimuli (induced Tregs)
(29). Differential recruitment of these Treg subtypes by different tumors may result in
varying degrees of sensitivity to anti-Treg therapy, as previously observed in the
MAD109, 4TI, and C26 tumor models. Expression analysis of markers of natural vs.
induced Tregs in murine tumor models was attempted but proved inconclusive. Isolation
20
experiments intended to purify separate Treg populations are warranted, as this will allow
analysis of immunosuppressive gene activity in each population help their role in normal
vs. tumor immunoregulation.
Rt-PCR analysis of immunomodulatory gene expression in murine tumor models
should pave the way for more sensitive analysis of expression levels. The low levels of
transcript observed for some genes makes specific quantitation of transcript difficult due
to nonspecific primer interactions. Expression analysis by internal standard or by
standard curve is thus infeasible. Surface plasmon resonance (SPR) has proven to be
effective at detecting target-trap binding events at very low concentrations of the target
molecule (30). It may be necessary to utilize this technique in order to characterize the
expression changes in genes with consistently low transcript levels. Recent advances in
nanotechnology have also produced chip-based detection systems which can detect
molecules at extremely low concentration in very small samples (31). These new devices
may one day provide a fast, reproducible, and inexpensive means of analyzing
immunomodulatory changes in samples from both the clinic and the bench.
Our methodology is being used to gather expression data from clinical tumor
samples, which afford the opportunity to study the natural course of tumor growth and
immunoregulation in the tissue of origin. Mouse models involving subcutaneous
implantation provide good foundational support but fail to replicate the tumor-
environment interactions necessary for the growth of a spontaneously formed lesion.
Further RT-PCR expression studies of the MAD109, 4TI, and C26 mouse models should
be undertaken using orthotopic injection of tumor cells to allow the growth of tumors in
their tissue of origin. Initial studies may be forced to begin sampling once tumors have
21
grown large enough in the deep tissue to be visible, which will restrict analysis of the
early stages of tumor immunomodulation. As the sensitivity of small animal imaging
techniques improves, the growth of small, orthotopic tumors will traceable at earlier and
earlier stages, allowing these still “young” tumors to be harvested and profiled for
immune modulation (32).
The artificial induction of arginase expression in pre-monocytes is a first step
toward creating a replenishable supply of murine MSCs for further studies. Human MSC
populations might be similarly induced through mixed culture with human tumor cell
lines. However, granulocytes are the only human myeloid lineage cells which
constitutively express arginase in an antimicrobial fashion, as opposed to monocytes-
macrophages in mice (18). It stands to reason then that pre-granulocytes are recruited to
form the human MSC compartment instead of pre-monocytes. Human arginase induction
experiments will require isolation of large numbers of immature granulocytes, or the
development of a human pre-granulocyte cell line.
Should human expression studies show evidence of myeloid suppression in the
tumor microenvironment, new treatment options will need to be explored in murine and
human systems. Depletion therapies, such as those utilizing anti-CD25 antibodies to
remove Tregs, cannot be directed against MSCs at this time as no cell-specific marker for
this population has been identified. Blocking arginase activity could prove
therapeutically effective if specific, relatively inexpensive inhibitors of arginase or its
production can be developed. Dosages for such treatments must be determined carefully,
as excessive inhibition of arginase in the liver could result in toxic levels of ammonia
systemically. It may be possible to stop MSC production of arginase by chemically
22
inducing the differentiation of these immature cells into more mature myeloid cells,
which express arginase to a much smaller degree in response to immune stimuli.
Compounds such as all-trans retinoic acid (ATRA) and granulocyte-macrophage colony
stimulating factor (GM-CSF) can induce differentiation and expansion of myeloid cells
and may reduce levels of MSCs in tumors (33, 34). Targeted delivery of these
compounds to tumor sites, either through antibody conjugation or fusion protein antibody
generation, could increase their therapeutic effect. Antibodies which readily bind tumor
tissue, such as necrosis-targeting ch-TNT3, have already been developed for drug-
delivery and would make excellent vehicles for anti-MSC therapies (35).
Prolonged influx of immune cells contributes to tumor progression, so stopping
recruitment of MSCs by tumors is a logical pathway for therapeutic intervention. Our
analysis identified a number of chemokines upregulated by murine tumors which may
influence MSC influx, such as MCP-2 and PF-4, and more sensitive expression analysis
techniques may yet identify other chemokines which contribute to MSC recruitment at
very low transcription levels. Co-culture experiments involving different cell populations
can be undertaken to elucidate the types of cells responsible for production of these
important chemoattractants in tumors. Additionally, creating transwell cultures of
immature myeloid cells separated from tumor and microenvironment cells by a
membrane will allow the relative affinity of MSCs for different tumor-produced
chemokines to be analyzed. Signals which strongly recruit MSCs will trigger more
migration of immature myeloid cells across the transwell membrane. Once the molecules
most responsible for MSC recruitment have been identified, neutralizing abs can be
generated and utilized to prevent the action of these signals. Targeting signal molecules
23
produced by infiltrating immune cells or microenvironment cells should meet with better
results than targeting tumor-produced signals, as these non-cancerous cells are not subject
to the same genetic instability as cancer cell and cannot readily change their
transcriptional profile to exploit different immunosuppressive paths.
24
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28
Appendix
Primer Sequences for SYBR Green PCR
CD28 F: 5„-CTCTGGAATCTGCACGTCAA-3„ R: 5„-AACAGGACTCCAGCAACCAC-3„
4-1BB F: 5„-GGTGTCCTGTGCATGTGA-3„ R: 5„-AGTTATCACAGGAGTTCTGC-3‟
Lag3 F: 5„-GACCCCTTCTTTGCTCATTG-3„ R: 5„-CCAGGTAACCCGAAGGATTT-3„
GITR F: 5„-GACGGTCACTGCAGACTTTG-3„ R: 5„-GCCATGACCAGGAAGATGAC-3„
IL-6 F: 5„-TCCAGTTGCCTTCTTGGGAC-3„ R: 5„-GTGTAATTAAGCCTCCGACTTG-3„
IL-6R F: 5„-AAGCAGCAGGCAATGTTACC-3„ R: 5„-CATAAATAGTCCCCAGTGTCG-3„
OX40 F: 5„-GTGTACACAGTGCAACCATCG-3„ R: 5„-TTCTGTCCTCACAGACTGCG-3„
CD40L F: 5„-CGAGTCAACGCCCATTCATC-3„ R: 5„-GTAATTCAAACACTCCGCCC-3„
CD27 F: 5„-CGAGTCAACGCCCATTCATC-3„ R: 5„-GTAATTCAAACACTCCGCCC-3„
CD70 F: 5„-TGGCTGTGGGCATCTGCTC-3„ R: 5„-ACATCTCCGTGGACCAGGTATG-3„
CD30 F: 5„-CAGTGATCGTGGGCTCTGTA-3„ R: 5„-CTTTTCCTCCTTCCTCCACC-3„
CD153 F: 5„-AGGATCTCTTCTGTACCCTGAAAAGTA-3„ R: 5„-GTTTGGTATTGTTGAGATGCTTTGA-3„
ICOS F: 5„-GCCACCATCTGTCCTCATTT-3„ R: 5„-CAGGCATCTAAGCCCTGAAG-3„
PD1 F: 5„-ATTCGTAGACTGGGGGACTG-3„ R: 5„-CATGCAGAAGGACAGCAGAT-3„
CTLA-4 F: 5„-CAGGTGACCCAACCTTCAGT-3„ R: 5„-CAGTCCTTGGATGGTGAGGT-3„
TGF-b F: 5„-TGCTTCAGCTCCACAGAGAA-3„ R: 5„-TGGTTGTAGAGGGCAAGGAC-3„
TGF-bR F: 5„-CGTCTGCATTGCACTTATGC-3„ R: 5„-AGCAGTGGTAAACCTGATCC-3„
IL-10 F: 5„-CGGGAAGACAATAACTG-3„ R: 5„-CATTTCCGATAAGGCTTGG-3„
IL-10R F: 5„-AGTCTCAAGGGATGGCTTCT-3„ R: 5„-TCAAGTTTATGGGCAAACCT-3„
FasL F: 5„-ATCCCTCTGGAATGGGAAGA-3„ R: 5„-CCATATCTGTCCAGTAGTGC-3„
Fas F: 5„-ATGCTGTGGATCTGGGCTGTCCT-3„ R: 5„-GCATAATGGTTCTTGTCCATG-3„
TRAIL F: 5„-GAAGACCTCAGAAAGTGGC-3„ R: 5„-GACCAGCTCTCCATTCCTA-3„
DR5 (TRAILR2) F: 5„-AAGTGTGTCTCCAAAACGG-3„ R: 5„-AATGCACAGAGTTCGCACT-3„
mDcTRAILR1 F: 5„-TCTCCAGTCTGAGTCACTGG-3„ R: 5„-TCCTGGGTGACACTTCTCAC-3„
HVEM F: 5„-GTGTCCATCCTTTTGCCACT-3„ R: 5„-CAGTTGGAGGCTGTCTCCTC-3„
LT-BR F: 5„-TTATCGCATAGAAAACCAGACTTGC-3„ R: 5„-TCAAAGCCCAGCACAATGTC-3„
FoxP3 F: 5„-GTGGTCAGCTGGACAATCAC-3„ R: 5„-CTGAGGCACCTGTTTTAGGA-3„
CTLA-4 F: 5„-CAGGTGACCCAACCTTCAGT-3„ R: 5„-CAGTCCTTGGATGGTGAGGT-3„
GITR F: 5„-GACGGTCACTGCAGACTTTG-3„ R: 5„-GCCATGACCAGGAAGATGAC-3„
CD25 F: 5„-CAAAGCCCTCTCCTACAAGAACG-3„ R: 5„-AACACTCTGTCCTTCCACGAAATG-3„
Gpr83 F: 5„-GAAGATGCTGGTGCTTGTGGTAGTC-3„ R: 5„-AAGTGGTGATTAGGTAGTGGAGCCC-3„
Ecm1 F: 5„-ACTACCTGCTCCGACCCTGC-3„ R: 5„-CCTGTTCTGGATATGGAAGCTCG-3„
Glutaredoxin F: 5„-GGACATCACAGCCACTAACAACACC-3„ R: 5„-ATCTGCTTCAGCCGAGTCATCAG-3„
Insulin-like7 F: 5„-TCGCTGATGGAGAAGCCAATAC-3„ R: 5„-AGAAAGCCTGGGGGGATTTG-3„
Helios F: 5„-ACTCCTCAGAAGTTTGTGGGGG-3„ R: 5„-GCTGGGCTTTGTTTCCTCTTG-3„
LFA-1 F: 5„-ACTATGTAGTGTTGACCTGGA-3„ R: 5„-CTGAGCCCACCAGGCTTC-3„
CD2 F: 5„-ATTCAGTGGCGCTCCAAG-3„ R: 5„-TCTTCTTCTGCTGGTGCTCA-3„
VLA-4 F: 5„-CGCTGTTTGGCTACTCGGT-3„ R: 5„-GGAGCTGTTCGCAGGTCTG-3„
PSGL-1 F: 5„-GCAGAGACCTCAAAACCAGC-3„ R: 5„-TCAGCAGACATTGCTTCACC-3„
ESL-1 F: 5„-CAAGATGACGGCCATCATTTTCA-3„ R: 5„-TTCCCCAAGACGAATGCTGC-3„
FUT9 (Sialyl-LewisX) F: 5„-CAAATCCCATGCGGTCCTGAT-3„ R: 5„-TGCTCACCGTCAAGAAGCCATAA-3„
CD45 F: 5„-ACCATGGGTTTGTGGCTCAA-3„ R: 5„-CACAGTAATGTTCCCAAACATGGC-3„
IL-7R F: 5„-CGAAACTCCAGAACCCAAGA-3„ R: 5„-GGAAGATCATTGGGCAGAAA-3„
CCR7 F: 5„-GCATCAGCATTGACCGCTA-3„ R: 5„-GGTACGGATGATAATGAGGTAGCA-3„
CD62L F: 5„-GGGAGCCCAACAACAAGAAG-3„ R: 5„-ACACTGGACCACATACTGACACTG-3„
CD44 F: 5„-CACAGCAGCAGATCGATTTG-3„ R: 5„-GAGGAGCTGAGGCATTGAAG-3„
CCR4 F: 5„-ATTTGCTGTTCGTCCTGTCCC-3„ R: 5„-TGATGAAGAAGATGCCGCTGT-3„
CCR5 F: 5„-CATCGATTATGGTATGTCAGCACC-3„ R: 5„-CAGAATGGTAGTGTGAGCAGG-3„
CCR7 F: 5„-GCATCAGCATTGACCGCTA-3„ R: 5„-GGTACGGATGATAATGAGGTAGCA-3„
CCL4 F: 5„-ATGAAGCTCTGCGTGTCTGC-3„ R: 5„-TCAGTTCAACTCCAAGTCACTCAT-3„
CCL5 F: 5„-TGCTTTGCCTACCTCTCCCTAG-3„ R: 5„-CGAGTGACAAACACGACTGCA-3„
CCL19 F: 5„-GGTGCTAATGATGCGGAAGAC-3„ R: 5„-ATAGCCCCTTAGTGTGGTGAACA-3„
CCL21 F: 5„-AACAGACACAGCCCTCAAGA-3„ R: 5„-CCTCTTTGCCTGTGAGTTGGA-3„
CCL22 F: 5„-ATCTGCTGCCAGGACTACATC-3„ R: 5„-GTTATCAAAACAACGCCAGGC-3„
CXCR3 F: 5„-TGAACGTCAAGTGCTAGATGC-3„ R: 5„-GGCAGGAAGGTTCTGTCAAA-3„
CXCR4 F: 5„-TCAGTGGCTGACCTCCTCTT-3„ R: 5„-CTTGGCCTTTGACTGTTGGT-3„
CXCL9 F: 5„-GAACCCTAGTGATAAGGAATGCA-3„ R: 5„-CTGTTTGAGGTCTTTGAGGGATT-3„
CXCL10 F: 5„-AGTGCTGCCGTCATTTTCTG-3„ R: 5„-ATTCTCACTGGCCCGTCAT-3„
CXCL12 F: 5„-CAGAGCCAACGTCAAGCAT-3„ R: 5„-CAGGTACTCTTGGATCCACTTT-3„
CXCL13 F: 5„-TGAAGTTGTGATCTGGACCAAGA-3„ R: 5„-ACAGACTTTTGCTTTGGACATGTC-3„
CD11b F: 5„-CTCCGGTAGCATCAACAACAT-3„ R: 5„-TGATCTTGGGCTAGGGTTTCT-3„
CD16 F: 5„-ATGTTTCAGAATGCACACTCTGGA-3„ R: 5„-GTCCAGTTTCACCACAGCCTT-3„
CD56 F: 5„-GGAAGGGAACCAAGTGAACA-3„ R: 5„-ACGGTGTGTCTGCTTGAACA-3„
B3GAT1 (CD57) F: 5„-ATGCCGAAGAGACGGGACAT-3„ R: 5„-TTGTGCACAGCAAGCAGAGG-3„
CD69 F: 5„-AAGTACAATTGCCCAGGCTT-3„ R: 5„-ATGTCCTCTTGTATGAAATCCACT-3„
CD94 F: 5„-AATCAACACCTTCTCCAACCA-3„ R: 5„-GATGCCCAACCCACTTGTCC-3„
NKG2D F: 5„-CGATTCACCCTTAACACATTGATG-3„ R: 5„-GGGACTTCCTTGTTGCACAATAC-3„
GranzymeB F: 5„-AAGCTGAAGAGTAAGGCCAA-3„ R: 5„-CCAGCCACATAGCACACATC-3„
29
Primer Sequences for SYBR Green PCR continued
KIR3DL1 F: 5„-CCTCGTGTGTTCTGGTTTCT-3„ R: 5„-CAGGCAGATAGGAATGGTTT-3„
CD1d F: 5„-AATTACACCTTCCGCTGCC-3„ R: 5„-CTTCGTGAAGCTGATGGTGG-3„
TCRa Va14 F: 5„-TGGGAGATACTCAGCAACTCTGG-3„ R: 5„-CAGGTATGACAATCAGCTGAGTCC-3„
CD80 F: 5„-ATGCTCACGTGTCAGAGGA-3„ R: 5„-GACGGTCTGTTCAGCTAATG-3„
CD86 F: 5„-CAACTGGACTCTACGACTTC-3„ R: 5„-TGCTTAGACATGCAGGTCAA-3„
B7H3 F: 5„-TACAGCTGCCTGGTACGCAA-3„ R: 5„-CAGAGGGTTTCAGAGGCCGTA-3„
Ox40L F: 5„-ACGGATCAAGGCCAAGATTCAA-3„ R: 5„-CTGGTAACTGCTCCTCTGAGTCT-3„
41BBL F: 5„-ATTCACAAACACAGGCCACA-3„ R: 5„-GATAAGCCCTCAGACCCAC-3„
GITRL F: 5„-CAAGACATGCCAACAACACC-3„ R: 5„-AAGGCCTAGGGGAAAGTTCA-3„
CD40 F: 5„-CACTGATACCGTCTGTCATCCCT-3„ R: 5„-AGTTCTTATCCTCACAGCTTGTCCA-3„
ICOS-L F: 5„-TCTTGGAAGAGGTGGTCAGG-3„ R: 5„-TCCAAGGGAGCCTTAATGTG-3„
PDL2 F: 5„-CGTGACAGCCCCTAAAGAAG-3„ R: 5„-GATGACCAGGCAACGGTACT-3„
PDL1 F: 5„-CGAATCACGATGAAAGTCAA-3„ R: 5„-GCTGGTCACATTGAGAAGCA-3„
IDO Super Array:PPH05363A:proprietary Super Array:PPM05363A:proprietary
B7H4 F: 5„-TGGCTTTGGCATTTCAGGC-3„ R: 5„-CCGTTGAGTTTGATGTCAGGTTC-3„
TRAIL F: 5„-GAAGACCTCAGAAAGTGGC-3„ R: 5„-GACCAGCTCTCCATTCCTA-3„
LIGHT F: 5„-CTGCATCAACGTCTTGGAGA-3„ R: 5„-GATACGTCAAGCCCCTCAAG-3„
Arginase F: 5„-CAGAAGAATGGAAGAGTCAGTGT-3„ R: 5„-CAGATATGCAGGGAGTCACC-3„
iNOS F: 5„-GACAAGCTGCATGTGACATC-3„ R: 5„-GCTGGTAGGTTCCTGTTGTT-3„
CD83 F: 5„-CCAGTTACCTCCCCAAGC-3„ R: 5„-AGGAGGTTGACCAGATAGC-3„
CD11c F: 5„-CTGAGAGCCCAGACGAAGACA-3„ R: 5„-TGAGCTGCCCACGATAAGAG-3„
Gr-1 F: 5„-TGGACTCTCACAGAAGCAAAG-3„ R: 5„-GCAGAGGTCTTCCTTGCAAC-3„
H2-Ea (IAd) F: 5„-GGCGATGAGATTTTCCATGT-3„ R: 5„-TCACAGGGCTTCTGGAGAGT-3„
IL-10 F: 5„-CGGGAAGACAATAACTG-3„ R: 5„-CATTTCCGATAAGGCTTGG-3„
MGSA (CXCL1) F: 5„-CTATCGCCAATGAGCTGCG-3„ R: 5„-CTTGGGGACACCTTTTAGCATCTT-3„
CXCR2 F: 5„-TCTGGCATGCCCTCTATTCTG-3„ R: 5„-AAGGTAACCTCCTTCACGTATG-3„
COX2 F: 5„-TTCGGGAGCACAACAGAGTG-3„ R: 5„-TAACCGCTCAGGTGTTGCA-3„
Ptges1 (PGE2 synthase 1) F: 5„-GAGTTTTCACGTTCCGGTGT-3„ R: 5„-GGTAGGCTGTCAGCTCAAGG-3„
Platelet Factor 4 F: 5„-AGTCCTGAGCTGCTGCTTCT-3„ R: 5„-GGCAAATTTTCCTCCCATTCTT-3„
ICAM-1 F: 5„-CATCCCAGAGAAGCCTTCCT-3„ R: 5„-TCAGCCACTGAGTCTCCAAG-3„
ICAM-2 F: 5„-ACCATTGAGTGCACGGTGTC-3„ R: 5„-GCTCCCCCAAAGGTCTGATT-3„
DC-SIGN F: 5„-CATGAGTGATTCTAAGGAAATGGG-3„ R: 5„-TGTGAAGCTACTGAATCCAGA-3„
CCR1 F: 5„-GCAAGCTTCTCTCTGGGTTTTA-3„ R: 5„-GGTGATGATGCCAAAAGTAACA-3„
CCL7 F: 5„-GCTCATAGCCGCTGCTTTC-3„ R: 5„-GCTTTGGAGTTGGGGTTTTC-3„
CCL8 F: 5„-GCTTTCATGTACTAAAGCTGAAGA-3„ R: 5„-CAGAGAGACATACCCTGCTT-3„
CCL3 F: 5„-ATGAAGGTCTCCACCACTGC-3„ R: 5„-AGCTCCATATGGCGCTGAGAA-3„
CD62E (E-selectin) F: 5„-GGACTGTGTAGAGATTTACATCCA-3„ R: 5„-GCAGGTGTAACTATTGATGGTCT-3„
CD62P (P-selectin) F: 5„-GTGCAGAGCGGTCAAATGC-3„ R: 5„-CTGAGACGCTTTCTTAGCAGAGC-3„
VCAM1 F: 5„-AACGACCTTCATCCCCACC-3„ R: 5„-TCTGCCTCTGTTTGGGTTCAG-3„
EpCAM F: 5„-TTGCTCCAAACTGGCGTCT-3„ R: 5„-TGTTGACACACCAGCACGT-3„
PECAM (CD31) F: 5„-GCAAAGAGTGACTTCCAGACT-3„ R: 5„-GTACCTCGTTACTCGACAGG-3„
VEGF1 F: 5„-TACTGCTGTACCTCCACCAT-3„ R: 5„-GCTCATTCTCTCTATGTGCTGG-3„
VEGFR1 F: 5„-CCGAACTCCACCTCCATGTTT-3„ R: 5„-TATCTTCATGGAGGCCTTGGG-3„
VEGFR2 F: 5„-AGGCTCCAACCAGACCAGT-3„ R: 5„-CTAAGCAGCACCTCTCTCGT-3„
bFGF F: 5„-AGCGACCCACACGTCAAACT-3„ R: 5„-CGTCCATCTTCCTTCATAGCAAG-3„
FGFR F: 5„-TGCCAAGACGGTGAAGTTCA-3„ R: 5„-CAATTCGGTGGTCAGGCTTA-3„
Angiopoietin F: 5„-TACAACACCGGGAAGATGGAAG-3„ R: 5„-GTCGTTATCAGCATCCTTCGT-3„
TIE-1 F: 5„-ACTGCCCTCCTGACTGGAC-3„ R: 5„-CGATGTACTTGGATATAGGCCCA-3„
TIE-2 F: 5„-TTGAAGTGACGAATGAGATTTTCAC-3„ R: 5„-ATTTAGAGCTGTCTGGCTTTTTG-3„
FAP F: 5„-ATGAAGACATGGCTGAAAACT-3„ R: 5„-GTAAACTCTTGAGGGACGTAAGA-3„
GAPDH F: 5„-AACTTTGGCATTGTGGAAGG-3„ R: 5„-CACATTGGGGGTAGGAACAC-3„
Abstract (if available)
Abstract
Tumor development depends on immunomodulatory signals produced by multiple cells within the microenvironment, resulting in the recruitment of suppressive cell populations which limit anti-tumor immunity. Expression markers for suppressive cells can indicate their relative contribution to tumor immunoregulation and highlight potential treatments to limit proliferation and spread. With this goal in mind, Rt-PCR analysis was carried out on murine tumor cell lines grown in vivo and in vitro, along with immunohistochemical analysis of expression markers. In vivo, murine tumors of lung, breast and colorectal origin were found to upregulate the Treg marker FoxP3, the suppressive DC marker B7-H3, and the MSC marker arginase I. IHC studies were inconclusive, but in vitro coculturing lung or breast tumor with myeloid cells induced production of arginase I by myeloid cells. Chemokines found to recruit myeloid cells were also upregulated. MSC recruitment pathways may provide a novel target for future combination therapies.
Linked assets
University of Southern California Dissertations and Theses
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Asset Metadata
Creator
Bergfeld, Scott Anthony
(author)
Core Title
Identification and characterization of immune-escape mechanisms in solid tumors
School
Keck School of Medicine
Degree
Master of Science
Degree Program
Experimental and Molecular Pathology
Publication Date
07/31/2008
Defense Date
06/24/2008
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
arginase I,monocyte chemoattractant protein-2,myeloid suppressor cell,OAI-PMH Harvest,platelet factor 4
Language
English
Advisor
Epstein, Alan L. (
committee chair
), Kaslow, Harvey R. (
committee member
), Taylor, Clive R. (
committee member
)
Creator Email
bergfeld@usc.edu
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-m1475
Unique identifier
UC1398840
Identifier
etd-Bergfeld-20080731 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-89318 (legacy record id),usctheses-m1475 (legacy record id)
Legacy Identifier
etd-Bergfeld-20080731.pdf
Dmrecord
89318
Document Type
Thesis
Rights
Bergfeld, Scott Anthony
Type
texts
Source
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Repository Name
Libraries, University of Southern California
Repository Location
Los Angeles, California
Repository Email
cisadmin@lib.usc.edu
Tags
arginase I
monocyte chemoattractant protein-2
myeloid suppressor cell
platelet factor 4