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Immune signature of murine solid tumor models

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Immune signature of murine solid tumor models

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IMMUNE SIGNATURES OF MURINE SOLID TUMOR MODELS

by

Chern-Yu Yen

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
(BIOCHEMISTRY AND MOLECULAR BIOLOGY)

August 2009

Copyright 2009 Chern-Yu Yen

ii
ACKNOWLEDGEMENTS
First of all, I owe my deepest gratitude to my advisor, Dr. Alan Epstein. His
inspiration and encouragement always stimulated me to seek more knowledge.
Without his guidance and support, this thesis would have never been able to succeed.
My appreciation goes to my committee, Dr. Frank Markland and Dr. Zoltan
Tokes, who assisted me during my research endeavors. My appreciation also goes to
all the faculty members in the Department of Biochemistry and Molecular Biology
as well as the Department of Pathology, for their academic instruction and
motivation.
Furthermore, I would like to thank my lab mate, Melissa Lechner, for her
wisdom, patience and enthusiasm which gave me an invaluable learning experience
throughout my graduate studies. I would also like to thank Dr. Peisheng Hu, Manday
Han, Maggie Yun, and James Pang. This thesis would not have been possible
without their laboratory assistance.
Lastly, and most importantly, I would like to dedicate this thesis to my beloved
family and friends, especially my parents, Jennifer Yen and Simon Yen. Their
continuous love and support throughout my life, this thesis is simply impossible
without them.

iii
TABLE OF CONTENTS
Acknowledgements ................................................................................................. ii
List of Tables........................................................................................................... v
List of Figures ......................................................................................................... vi
Abbreviations .......................................................................................................... vii
Abstract ................................................................................................................... ix
CHAPTER ONE:
Introduction ..................................................................................................... 1
1.1 Background to Cancer Imuunotherapy ................................................. 1
1.1.1 Cancer immunoeditting ................................................................ 1
1.1.2 Immunotherapy ............................................................................ 1
1.2 Tumor Immunosurveillance .................................................................. 2
1.2.1 Myeloid-derived suppressor cells (MDSCs) ................................ 2
1.2.2 Regulatory T cells (Tregs) ........................................................... 3
1.3 Tumor Microenvironment ..................................................................... 4
1.4 Co-stimulation ....................................................................................... 4

CHAPTER TWO:
Materials and Methods .................................................................................... 7
2.1 Reagents ................................................................................................ 7
2.2 Cell Lines .............................................................................................. 7
2.3 Mice....................................................................................................... 8
2.4 Murine Tumor Models .......................................................................... 8
2.5 Real-time RT-PCR ................................................................................ 8
2.5.1 RNA isolation .............................................................................. 8
2.5.2 Immune signature analysis ........................................................... 9
2.6 Immunohistochemistry .......................................................................... 9
2.7 Microscope ............................................................................................ 10

CHAPTER THREE:
Results ............................................................................................................. 12
3.1 Immune Signature by Real-time RT-PCR ............................................ 12
3.1.1 Wehi 164 fibrosarcoma model ..................................................... 12
3.1.2 B16 melonoma model .................................................................. 13
3.1.3 RENCA renal carcinoma model................................................... 13
3.1.4 Colon 26 adenocarcinoma model ................................................. 14
3.1.5 MAD109 lung carcinoma model .................................................. 14

iv
3.1.6 Lewis lung carcinoma model ....................................................... 15
3.1.7 4T1 breast carcinoma model ........................................................ 15
3.1.8 D2F2 breast carcinoma model ..................................................... 15
3.2 Characterization of Infiltrating Cell Populations by
Immunohistochemistry .......................................................................... 19

CHAPTER FOUR:
Discussion ....................................................................................................... 26

CHAPTER FIVE:
Future Studies ................................................................................................. 36

REFERENCES........................................................................................................ 39

v
LIST OF TABLES
Table 1: Immune Signature Analysis Panel ....................................................... 11
Table 2: Summary Table for Immunohistochemistry Studies of
Murine Solid Tumor Models ................................................................ 21
Table 3: Summary Table for Infiltration Cell Populations in
Mouse Tumor Models by IHC ............................................................. 30
Table 4: Probable Immune Suppression Mechanisms in
Mouse Tumor Models .......................................................................... 32

vi
LIST OF FIGURES
Figure 1: Co-stimulatory Factors .......................................................................... 5
Figure 2: Expression of Microenvironment Genes in Mouse Tumor Models. ... 16
Figure 3: Expression of Immune Activation Genes in Mouse Tumor Models. .. 17
Figure 4: Expression of Immune Suppression Genes in
Mouse Tumor Models. ........................................................................ 18
Figure 5: IHC Demonstrating ARG1, CD11b, CD3 and Gr1 Expression in
Mouse Tumor Models. ........................................................................ 22
Figure 6: IHC Demonstrating CD25, FoxP3, TGFβ and IL10 Expression in
Mouse Tumor Models. ........................................................................ 23
Figure 7: IHC Demonstrating VEGF, GZMB, BM-2 and F4/80 Expression in
Mouse Tumor Model. ......................................................................... 24
Figure 8: IHC Demonstrating NK1/NK1.1 and PECAM-1 Expression in
Mouse Tumor Model. ......................................................................... 25

vii
ABBREVIATIONS
Ab: Antibody
APC: Antigen Presenting Cell
AGR-1: Arginase I
B16: B16 Malignant Melanoma
CD4: Cluster of Differentiation 4
CD3: Cluster of Differentiation 3
CD25: Cluster of Differentiation 25
Ct: Critical Threshold
CTL: Cytotoxic T Lymphocyte
CTLA-4: Cytotoxic T-Lymphocyte Antigen 4
C26: Colon 26 Adenocarcinoma
DC: Dendritic Cells
D2F2: D2F2 Breast Carcinoma
FoxP3: Forkhead Box P3
GAPDH: Glyceraldehyde-3-phosphate Dehydrogenase
GZMB: Granzyme B
HLA: Human Leukocyte Antigen
IDO: Indoleamine 2,3-dioxygenase
IHC: Immunohistochemistry
IL10: Interleukin 10
LLC: Lewis Lung Carcinoma

viii
MOM: Mouse-on-mouse
MDSC: Myeloid-derived Suppressor Cell
MAD109: Madison109 Lung Carcinoma
NK1: Natural Killer Cell
PECAM1: Platelet Endothelial Cell Adhesion Molecule
PlGF: Placental Growth Factor
TCR: T-cell Receptor
TGFβ: Transforming Growth Factor β
Th: T Cell Helper
TNF: Tumor Necrosis Factor
Treg: Regulatory T Cell
Real-time RT-PCR: Real-time polymerase chain reaction
RENCA: RENCA Renal Carcinoma
s.c.: Subcutaneous
VEGF: Vascular Endothelial Growth Factor
Wehi 164: Wehi 164 Fibrosarcoma
4T1: 4TI Breast Carcinoma

ix
ABSTRACT
This study characterized the major mechanisms of tumor escape at the tumor
site employed by eight frequently studied murine solid tumor models by quantitative
analysis of immune-related gene expression and immunohistochemistry procedures.
The results of these investigations showed that the tumor models used different
suppressor cell populations including Treg: Wehi164, B16, and D2F2; MDSC:
RENCA, MAD109 and LLC; both Treg and MDSC: 4T1, and a relative lack of
either cell population: C26. MAD109, in contrast to the other tumor models, showed
high activation of infiltrated lymphoid cells. D2F2, LLC, and RENCA showed
incomplete immune activation, with moderate DC activation and upregulation of
co-stimulatory factors except for specific deficits. These data suggest that each of the
eight tumor models displayed different activation thresholds or used different
suppressor mechanisms to escape host immunity. Based upon these data,
immunotherapeutic protocols can be tested to counter the evasive mechanisms seen
in each tumor model.

1
CHAPTER 1
INTRODUCTION
1.1 Background to Cancer Immunotherapy
1.1.1 Cancer immunoediting. The dynamic process for cancer development is
known as cancer immunoediting, which contains three main phases: elimination,
equilibrium, and escape. Innate and adaptive immune cells play critical roles to help
in removing pre-malignant and early stage malignant cells in elimination phase.
Equilibrium is the period of immune-mediated latency after incomplete tumor
destruction. Cancer immuoediting process may stop in this phase if the cancer
immunosurveillance process is successfully destroying a developing tumor. In the
final escape stage, tumors have overcome immunological pressure. Dunn et al.
propose three possible outcomes for a tumor that has entered the latent period of
equilibrium stage (Dunn, Old et al. 2004). First, tumor is eventually eliminated by
the immune system. Second, tumor permanently maintains in the equilibrium phase
by cellular and molecular controls of the immune system. Third, tumor escapes from
immune pressure and transits to the final escapes phase. The first two phases of
cancer immunoediting represent potential goals for immunotherapy.
1.1.2 Immunotherapy. Traditional ways for cancer treatments include surgery,
radiation and chemotherapy. However, these treatments have many side effects that
lower the efficiency to treat cancer and even fail to eradicate tumor. Therefore,
treatments that either directly or indirectly use the patient’s own immune system to
fight cancer or to lessen the side effects that may be caused by other cancer

2
treatments are needed. In 1891, the first successful report for immunotherapy was
published by Wiliam Coley, a clinician at Memorial Sloan Kettering Cancer Institute
in New York who used heat-killed endotoxin-containing bacteria to achieve a cure
rate of 10% in soft-tissue sarcomas (Coley 1891). In the last decade, monoclonal
antibodies have become an effective treatment for many diseases and formed a new
class of drugs approved for cancer treatment. Before recognizing potential
immunotherapeutic targets, it is important to know how a tumor is able to escape
from immune system.
1.2 Tumor Immunosurveillance.
The immune system has three primary roles to prevent tumor development.
First of all, the immune system can eliminate or suppress viral infection to protect
the host from various oncogenic viruses. Second, elimination of pathogens and
induced inflammation can prevent an inflammatory tumorgenic environment. Third,
cellular stress can induce the expansion of tumor-specific antigens or molecules
which the immune system can specifically recognize to eliminate tumor cells (tumor
immunosurveillance) (Swann and Smyth 2007). Tumor immunosurveillance can be
thwarted by active suppression of host immune responses by tumor derived factors
that suppress DC and T cells or the induction and recruitment of suppressor cell
populations.
1.2.1 Myeloid-derived suppressor cells (MDSCs). MDSCs are heterogeneous
population of innate immune cells that consist of myeloid progenitor cells and
immature myeloid cells (IMCs). In normal healthy conditions, IMCs are generated in

3
the bone marrow and can quickly differentiate into macrophages, dendritic cells, and
granulocytes. However, in pathological conditions such as cancer, MDSCs promote
tumor growth and suppress immune cell function. MDSCs in mice express CD11b
+
,
IL-4Rα
+
, and Gr-1
+
. They are characterized by poor antigen presentation (HLA-DR
-
)
and a suppressive phenotype (Curiel 2007; Liu, Wong et al. 2007). Their suppressive
mechanisms include enhanced expression of arginase-1 (ARG-1), reactive oxygen
and nitrogen species, and inhibitory surface molecules (Borsellino, Kleinewietfeld et
al. 2007; Miyara and Sakaguchi 2007).
1.2.2 Regulatory T cells (Tregs). Tregs are a subset of CD4
+
T cells present in
normal mice and humans that are involved in the maintenance of tolerance to
tissue-specific antigens (Takahashi, Kuniyasu et al. 1998; Beyer and Schultze 2006).
Tregs are CD4
+
CD25
+
FoxP3
+
cells that suppress effector T cell responses by
cell-contact dependent and independent mechanisms (e.g. IL-10, TGFβ, CTLA-4)
(Talmadge 2007; Talmadge, Donkor et al. 2007). Increased levels of TGFβ derived
from tumor cells or from MDSCs, plus indoleamine 2,3-dioxygenase (IDO) activity
and suboptimal antigen presentation by MDSCs, may each play a role in Tregs
expansion and functional activation in the tumor microenvironment (Shimizu,
Yamazaki et al. 2002; Talmadge 2007; Talmadge, Donkor et al. 2007). In both
mouse tumor models and human cancer patients, Treg and MDSC populations are
increased in tumors, peripheral blood, and tumor-draining lymph nodes (Valzasina,
Guiducci et al. 2005).

4
1.3 Tumor Microenviroment.
The tumor microenviroment is comprised of growing tumor cells, the tumor
stroma, blood vessels, and a variety of associated tissue cells. Some factors such as
IL10, TGFβ and VEGF can regulate tumor survival, growth, angiogenesis, and
metastasis. IL10 and TGFβ have been implicated in induction or conversion of Tregs
(Zhang, Berndt et al. 2008). TGFβ is a crucial cytokine that is required for CD4
+

CD25
-
cells converting into Treg while IL10 is required for induction of
antigen-specific Tr1 cells in vitro and in vivo. Tumor cells not only secrete IL10 and
TGFβ but also induce MDSC to secrete IL10 and TGFβ to promote CD4
+
CD25
-

converting to CD4
+
CD25
+
Tregs. VEGF is secreted by most tumors and is correlated
with tumor growth and angiogenesis. Yang at el. showed that MDSC could stimulate
tumor progression not only by suppressing the immune system but also by promoting
tumor angiogenesis (Yang, DeBusk et al. 2004). Previous studies have showed that
VEGF and PlGF affect the early stages of myeloid/DC differentiation. VEGFR1 is
the primary mediator for the VEGF leading to DC maturation, whereas VEGFR2
tyrosine kinase signaling is essential for hemopoietic differentiation but has only
little effect on DC maturation (Shibuya 2006). Tumor-infiltrating lymphocytes are
often a major component of the tumor microenvironment. These lymphocytes
include various proportions of CD3
+
CD4
+
and CD3
+
CD8
+
T cells (Whiteside 2008).
1.4 Co-stimulation.
Key components of immune activation in the tumor setting include
co-stimulatory receptors, which are present on dendritic cells (DC) and T cells. Upon

5
recognition of antigen, innate immune cells present immune epitopes in conjunction
with co-stimulatory signals, including cytokines and B7 surface molecules (CD80 or
B7.1, CD86 or B7.2), to adaptive immune cells to activate a robust and targeted
immune response. In addition, several members of the tumor necrosis factor super
family (TNFSF), including OX40L, CD137L (or 41BBL), and GITRL, are normally
expressed on antigen presenting cells (APC) and upon binding to their receptors,
provide critical signals to stimulate T-cell syntheses and sustain their response to
eventually produce immunological memory (Fig. 1 ). Similarly TNFSF ligand
CD40L (CD154) expressed on T helper cells binds to its target receptor on APCs to
provide costimulatory signals needed for optimal activation. It is now recognized
that some tumors block these mechanisms of immune activation to escape normal
host immune surveillance, and that restoration improves anti-tumor immunity.

Figure 1. Co-stimulatory Factors. The T-cell/Dendritic cell synapse is stabilized by
co-stimulation required to enable proper T-cell activation to antigen, CTLA-4 acts as
a negative signal in this response.

6
The immune systems are complicated and vary from different tumor models.
Previous studies have shown the value of using real-time RT-PCR (Sadun, Sachsman
et al. 2007) and IHC to identify immune signature. Therefore, this study seeks to
provide a comprehensive analysis of the mechanisms of tumor-induced immune
tolerance in eight frequently studied murine solid tumor models from a variety of
tissue types (Wehi 164 fibrosarcoma, 4TI breast carcinoma, D2F2 breast carcinoma,
Colon 26 adenocarcinoma, MAD109 lung carcinoma, Lewis lung carcinoma, B16
malignant melanoma and RENCA renal carcinoma). The data gained from this study
should inform the selection of experimental models for future studies of tumor
immunology and the development of novel cancer immunotherapies.

7
CHAPTER 2
MATERIALS AND METHODS
2.1 Reagents.
Immunological and cell culture reagents were purchased from Sigma-Aldrich
Chemical Co. (St. Louis, MO) or Pierce Biotechnology (Rockford, IL).
Characterized and dialyzed fetal calf sera and glutamine free hybridoma medium
(HyQ SFM4MAB) were purchased from Hyclone Laboratories (Logan, Utah). For
immunohistochemistry antibodies, primary mouse anti-mouse arginase 1 monoclonal
antibody (clone 19; 1:200) was purchased from BD Transduction. Mouse anti-mouse
FoxP3 (clone FJK-16s; 1:200) and Gr1 (clone RB6-8C5; 1:1000) were purchased
from eBioscience. Primary mouse anti-mouse CD11b (clone M1/70.15; 1:200) was
purchased from Novus Biological. Mouse anti-mouse CD3 (PC3/188A, 1:50), TGFβ
(V; 1:50), IL10 (NYRmIL-10; 1:50), VEGF (C-1; 1:50), PECMA-1 (ER-MP12;
1:500), F4/80 (6A545; 1:500), and Granzyme B (2C5; 1:100) were from Santa Cruz
Biotechnology. NK1.1, NK-1 (PK136) and BM-2 were from Dr. Alan Epstein. CD25
(clone 4C9; 1:200), Vector Mouse-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.
2.2 Cell Lines.
For tumor cell lines: Wehi 164 fibrosarcoma, 4TI breast carcinoma, D2F2
breast carcinoma, Colon 26 adenocarcinoma, MAD109 lung carcinoma, Lewis lung

8
carcinoma, B16 malignant melanoma and RENCA renal carcinoma were obtained
from American Tissue Culture Collection (Manasus, VA) and maintained in RPMI
1640 medium with 10% FBS, L-glutamine, and penicillin/streptomycin
2.3 Mice. Six-week-old female BALB/c and C57BL/6 mice were obtained from
Harlan Sprague Dawley (Indianapolis, IN). Institutional Animal Care and Use
Committee-approved protocols and guidelines were followed.
2.4 Murine Tumor Models.
Mice received s.c. injections of tumor cells (5 x 10
6
in 0.2mL) in the left flank.
Tumors from tumor-bearing mice for each tumor model around day 10 (tumor size
reaches around 1.0-1.5 cm) after implantation were removed (n = 3). Normal skin,
kidney, colon, lung, and mammary glands pooled from three healthy mice were used
as the tissue matched controls in these studies. Tumor tissue was harvested and
immediately placed in liquid nitrogen for RNA isolation or placed in formalin for
tissue fixation for IHC.
2.5 Real-time RT-PCR.
2.5.1 RNA isolation. 30 mg of tumor or control tissue was homogenized in RLT
buffer. RNA was extracted by Qiagen RNeasy Mini Kit (Qiagen) and DNase treated
with DNA-free kit (Ambion).
2.5.2 Immune signature analysis. A primer panel was created to examine 26
genes involved in immune activation, immune suppression and the tumor
microenvironment (Table 1). All real-time RT-PCR primers were designed using
qprimerdepot database (Cui, Taub et al. 2007) and were synthesized by the USC core

9
facility. Real-time RT-PCR reactions were performed with 50ng of DNase-treated
RNA using 1-step rtPCR SYBR green master mix kit (Applied Biosystems) and
detected by ABI PRISM 7900HT Sequence Detection System (Applied Biosystems).
Reactions were run for 40 cycles on a Stratagene Mx3000P cycler. Each sample was
amplified in triplicate runs and mean data used. GAPDH served as the housekeeping
gene control for normalization and data will be expressed as a fold change relative to
gene expression level in tissue matched control. For tumor tissue samples, fold
changes in gene expression compared to tissue matched control were calculated
using the formula 2
((TumorGAPDHct-TumorGENEct)-(NormalGAPDHct-NormalGENEct))
and then
averaged across each time point. Gene expression was also analyzed relative to
CD45, CD4, and CD11b expression to better understand changes in gene expression
within subpopulations of infiltrating leukocytes, T helper and Treg cells, and
immature DC, respectively.
2.6 Immunohistochemistry.
Tumors from tumor-bearing mice were removed on day 10 after tumor
implantation. Tumors were fixed in 10% neutral buffered formalin (VWR) overnight
and embedded in paraffin before being sliced. Slides were subjected to antigen
retrieval (citrate buffer, pH 6.0), and Vector MOM 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 4 ˚C, followed
by biotinylated secondary antibody for 45 minutes at room temperature (RT).
Avidin-biotin peroxidase was added for 30 minutes at RT, and slides were developed

10
with 0.03% diaminobenzidine for 10 minutes. 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% nd 100%
alcohol, a-terpineol xylene, and xylene.
2.7 Microscope
Observation, evaluation and image acquisition were made using Leica DM
2500 microscope connected to a SPOT RTke camera (SPOT Diagnostic Instrument
Inc.) and a PC with SPOT Advanced Software.

11
Table 1. Immune Signature Analysis Panel. A panel of 26 genes involved in immune activation, immune
suppression, and tumor microenvironment was examined by real-time RT-PCR to characterize the
unique immune profile of eight murine solid tumor models.

Immune Activation Forward Sequence Reverse Sequence
CD80 5'-ATG CTC ACG TGT CAG AGG A-3' 5'-GAC GGT CTG TTC AGC TAA TG-3'
CD86 5'-CAA CTG GAC TCT ACG ACT TC-3' 5'-TGC TTA GAC ATG CAG GTC AA-3'
CD83 5'-CCA GTT ACC TCC CCA AGC-3' 5'-AGG AGG TTG ACC AGA TAG C-3'
41BBL 5'-ATT CAC AAA CAC AGG CCA CA-3' 5'-GAT AAG CCC TCA GAC CCA C-3'
GITRL 5'-CAA GAC ATG CCA ACA ACA CC-3' 5'-AAG GCC TAG GGG AAA GTT CA-3'
CD40 5'-CAC TGA TAC CGT CTG TCA TCC CT-3' 5'-AGT TCT TAT CCT CAC AGC TTG TCC A-3'
CD11c 5'-CTG AGA GCC CAG ACG AAG ACA-3' 5'-TGA GCT GCC CAC GAT AAG AG-3'
OX40L 5'-ACG GAT CAA GGC CAA GAT TCA A-3' 5'-ACG GAT CAA GGC ACC GAT TCA A-3'
IL6R 5'-AAG CAG CAG GCA ATG TTA CC-3' 5'-CAT AAA TAG TCC CCA GTG TCG-3'
CD62L 5'-GGG AGC CCA ACA ACA AGA AG-3' 5'-CAC ATC AGC AGA TCG ATT TGA ATG T-3'
CD45 5'-GTT GTG CTT GGA GGG TCA GT-3' 5'-CTC AAA CTT CTG GCC TTT GG-3'
CD4 5'-GAG CTC TTG TTG GTT GGG AA-3' 5'-CGA ACA TCT GTG AAG GCA AA-3'
CD8 5'-GCC AGT CCT TCA GAA AGT GA-3' 5'-CCG ACA ATC TTC TGG TCT CT-3'
Immune Suppression
ARG-1 5'-CAG AAG AAT GGA AGA GTC AGT GT-3' 5'-CAG ATA TGC AGG AG TCA CC-3'
CD11b 5'-CTC CGG TAG CAT CAA CAA CAT-3' 5'-TGA TCT TGG C AGG GTT TCT-3'
iNOS 5'-CAG AAG CTG CAT GTG ACA TC-3' 5'-GCT GGT AGG TTC CTG TTG TT-3'
IDO 5'-TGG GCA GCT TTT CAA CTT CT-3' 5'-ATG AAG ATG TGG GCT TTG CT-3'
T G F β 5'-AA TTG GCA TGG TAG CC TT-3' 5'-GGA GAG CCC TGG ATA CCA AC-3'
IL10 5'-AGA CAC CTT GGT CTT GGA GC-3' 5'-TTT GAA TTC CCT GGG TGA GA-3'
FoxP3 5'-GTG GTC AGC TGG ACA ATC AC-3' 5'-CTG AGG CAC CTG TTT TAG CA-3'
CD25 5'-CAA AC CCT CTC CTA CAA GAA CG-3' 5'-AAC ACT CTG TCC TTC ACA GAA ATG-3'
CTLA-4 5'-CTG CTA GCC AAC ACC ACT GA-3' 5'-CCG GAG GTA CAA AGC TCA AC-3'
Microenvironment
VEGF 5'-TAC TGC TGT ACC TCC ACC AT-3' 5'-GCT CAT TCT CTC TAT GTC CTG G-3'
PlGF 5'-GGA CAC AGG ACG GAC TGA-3' 5'-TGC TGG GAA CAA CTC AAC AG-3'
VEGFR1 5'-CCG AAC TCC ACC TCC ATG TTT-3' 5'-TAT CTT CAT GGA GGC CTT GGC-3'
VEGFR2 5'-AGG CTC CAA CCA GAC CAG T-3' 5'-CTA AGC ACC TCT CTC GT-3'
GAPDH 5'-AAC TTT GGC ATT GTG GAA GG-3' 5'-CAC ATT GGG GGT AGG AAC AC-3'

12
CHAPTER 3
RESULTS
Eight murine tumor cell lines were implanted in BALB/c or C57/Black 6 mice
(n=3). When tumors reached 1.0-1.5 cm diameter (approximately 10 days) the
tumors were removed and analyzed for immune profile by real-time RT-PCR and
immunohistochemistry.
3.1 Immune Signature by Real-time RT-PCR.
Expression of immune activation, immune suppression, and microenvironment
genes in the tumor site was analyzed relative to the expresssion of housekeeping
gene GAPDH. Additionally, gene expression was further analyzed relative to
expression of CD45, the common leukocyte antigen, in order to describe the nature
and activation status of infiltrating leukocytes. CD4 and CD11b gene expression
were used to examine the relative expression of Treg and MDSC-related gene
products, respectively.
3.1.1 Wehi 164 fibrosarcoma model: In the Wehi 164 fibrosarcoma model,
analysis of infiltrating leukocytes demonstrated a paucity of T cells (CD4 and CD8)
and APCs (CD11c and CD11b). There is generally decreased DC maturation and
activation (CD80, CD86, CD83, and CD11c) with concomitant decreases in APC-T
cell co-stimulatory signals (GITRL, IL6R, CD40, and OX40L) (Fig. 3). There is
significant tumor induction of Treg cells (FoxP3 and CD25) with elevations in TGFβ
and IL-10 possibly related to mechanisms of Treg-mediated suppression (Fig. 4).
While ARG-1 and CD11b do not show appreciable increases in this model (Fig. 4),

13
myeloid cell-bound TGFβ may contribute to the accumulation of Tregs in the tumor
microenvironment (Zhang, Berndt et al. 2008). The increased expression in PlGF
and VEGF may contribute to low DC maturation. These data suggest that Wehi 164
is a poorly immunogenic tumor, with significant Treg-mediated suppression (Fig. 2).
3.1.2 B16 melanoma model: Like Wehi 164, B16 melanoma tumors
demonstrated few tumor infiltrating T cells (CD4 and CD8) or APCs (CD11c, and
CD11b). Infiltrating cells demonstrated a marked decrease in DC maturation and
activation markers, as well as low expression of several co-stimulatory markers
(OX40L, IL6R, GITRL, and CD40) (Fig. 3). Immune suppression demonstrates the
presence of Treg cells (FoxP3 and CD25) with strong expression of TGFβ and
CTLA-4, but not IL-10 (Fig. 4). In addition, myeloid-derived suppressor cells do not
appear to play a major role in this model (low ARG-1 and CD11b) (Fig. 4), but may
contribute to the generation of Treg cells through expression of IDO and TGFβ
(Fallarino, Grohmann et al. 2006). High expression of PlGF also may lead low DC
maturation (Fig. 2). These data suggest that B16 is a poorly immunogenic tumor,
with significant Treg-mediated suppression.
3.1.3 RENCA renal carcinoma model: Innate immune cell infiltration is seen in
RENCA renal carcinoma tumors, notably with increased CD11b+ cells, but T cell
infiltration is low (CD4 and CD8). DC appears to be presented with moderate
increases in maturation and activation of T cells (CD80, CD86, 41BBL, GITRL,
CD40, and IL6R). However, two markers of DC activation are notably
downregulated: CD83 and OX40L (Fig. 3). Direct MDSC suppression of T cell

14
immune responses via ARG-1 is likely present, and may serve to promote the
upregulation of inhibitory surface ligands (CTLA-4) and suppressive cytokines
(IL-10 and TGFβ) by helper T cells (Fig. 4). These data suggest that RENCA is a
mildly immunogenic tumor, with significant MDSC-mediated suppression.
3.1.4 Colon 26 adenocarcinoma model: Colon 26 adenocarcinoma murine
tumor model appears not to induce a significant infiltration of T cells or APC (CD4,
CD8, CD11c and CD11b). Markers of differentiated and activated APCs are largely
decreased (CD80, CD86 and CD83), and T cell-APC co-stimulation appears absent
(decreased 41BBL, OX40L, IL6R, and CD40), with the exception of a modest
increase in GITRL expression (Fig. 3). MDSC expression of iNOS may contribute to
immune suppression, but it appears unlikely that Tregs and MDSCs are keys to
tumor immune tolerance in this model (Fig. 4). These data suggest that C26 is a
poorly immunogenic tumor, with only a minor role for either Treg- or
MDSC-mediated suppression.
3.1.5 MAD109 lung carcinoma model: MAD109 lung carcinoma tumors
elicited significant T cell infiltration (CD4 and CD8) and CD11b
+
cells. Maturation
and activation of infiltrating APCs appear to be modestly augmented in general
(CD86, CD83, 41BBL, GITRL, OX40L and IL6R) (Fig. 3). While there appears to
be little to no contribution to tumor immune tolerance from Treg cells, MDSCs
appear to infiltrate the tumor and become activated (CD11b, ARG-1 and iNOS) (Fig.
4). These data suggest that MAD109 is a strongly immunogenic tumor, with
significant MDSC-mediated suppression.

15
3.1.6 Lewis lung carcinoma lung: Similar to MAD109 lung tumors, Lewis lung
carcinoma (LLC) tumors elicited active infiltration of T cells (CD4 and CD8).
Overall infiltration of APCs was decreased (CD11c), and DCs in the tumor
microenvironment had increased maturation (CD80 and CD86) but poor expression
of some T cell co-stimulatory surface receptors (CD83, OX40L and CD40) (Fig. 3).
MDSCs are likely to contribute to immune suppression in this model (increased
ARG-1 and iNOS), while Tregs do not appear to accumulate appreciably (Fig. 4).
These data suggest that LLC is a mildly immunogenic tumor, with significant
MDSC-mediated suppression.
3.1.7 4T1 breast carcinoma model: The murine breast carcinoma model 4T1
demonstrated low T cell (CD4 and CD8) infiltration with little APC (CD11c)
presence. Infiltrating DCs have decreased maturation and activation (CD80, CD86,
and CD83) with poor expression of T cell co-stimulatory ligands (OX40L, 41BBL,
GITRL, and IL6R) (Fig. 3). Tumor immune tolerance in this model appears to be
further exacerbated by significant Treg cell and moderate MDSC contributions
(increased FoxP3, CD25, TGFβ, IL-10, and CTLA-4 and increased ARG-1 and
iNOS expression, respectively) (Fig. 4). These data suggest that 4T1 is a mildly
immunogenic tumor in which T cell and APC activity are markedly suppressed by
tumor-infiltrating Treg cells and MDSC.
3.1.8 D2F2 breast carcinoma model: A different murine model of breast
carcinoma, D2F2, similarly showed low infiltration by T cells (CD4 and CD8) with
modestly increased infiltration of APC (CD11c). This breast carcinoma model

16
elicited an increase in DC maturation/activation markers (CD80 and CD86) and
some T cell co-stimulatory ligands (41BBL, GITRL and IL6R), with selective
down-regulation of co-stimulatory factors CD40 and OX40L (Fig. 3). This is in
contrast to the more uniform downregulation of DC activation and T cell
co-stimulation seen in 4T1. Additionally, while Treg cells appear to accumulate
significantly in the tumor microenvironment (FoxP3, CTLA4, TGF-β, and IL-10),
MDSCs seem to make a minor, if any, contribution (decreased CD11b and ARG-1,
but increased iNOS) to immune suppression (Fig. 4). These data suggest that D2F2 is
a poorly immunogenic tumor, with significant Treg-mediated suppression.
Figure 2. Expression of Microenvironment Genes in Mouse Tumor Models. Groups
of Balb/c and C57/Black mice (n=3) were injected with 4T1, D2F2, MAD 109, LLC,
B16, Wehi 164, C26, and RENCA (5 x 10
6
cells) s.c. into the left flank. Tumors
were removed when they reached 1.0-1.5cm for real-time RT-PCR analysis by a
murine immune signature panel (immune suppression) Mean fold change shown
(n=3), +SD.

17

Figure 3. Expression of Immune Activation Genes in Mouse Tumor Models. Groups
of Balb/c and C57/Black mice (n=3) were injected with 4T1, D2F2, MAD 109, LLC,
B16, Wehi 164, C26, and RENCA (5 x 10
6
cells) s.c. into the left flank. Tumors
were removed when they reached 1.0-1.5cm for real-time RT-PCR analysis by a
murine immune signature panel (immune suppression) Mean fold change shown
(n=3), +SD.

18

Figure 4. Expression of Immune Suppression Genes in Mouse Tumor Models.
Groups of Balb/c and C57/Black mice (n=3) were injected with 4T1, D2F2, MAD
109, LLC, B16, Wehi 164, C26, and RENCA (5 x 10
6
cells) s.c. into the left flank.
Tumors were removed when they reached 1.0-1.5cm for real-time RT-PCR analysis
by a murine immune signature panel (immune suppression) Mean fold change shown
(n=3), +SD.

19
3.2 Characterization of Infiltrating Cell Populations by Immunohistochemistry
Immunohistochemistry studies were performed on tumors to complement and
verify real-time RT-PCR data. Fourteen primary antibodies were used to stain
paraffin embedded tissue sections to identify populations of infiltrating leukocytes at
the tumor site and characterize the immunomodulating factors present in the tumor
microenvironment. A summary of all immunohistochemistry data is presented in
Table 2, and representative images for each tumor model and staining target are
shown in Figures 5-8.
Tumor-infiltrating MDSCs in mice are characterized by CD11b and Gr-1

surface expression and ARG-1 and iNOS activity. Positive ARG-1 staining was
observed in infiltrated cells in all tumor models except C26. MAD109 and LLC
tumor models showed strong ARG-1 positive staining throughout the tumor section,
while other models demonstrated more focal positivity. Gr-1 expression was greatest
in tumor models MAD109, LLC, Wehi164 and RENCA. For CD11b staining, LLC
and B16 showed stronger expression compare to other tumor models.
Infiltrating T cells, demonstrated by positive CD3 staining, were seen
appreciably in all tumor models. These T cells may represent active effector T cells,
suppressive Treg cells, or anergic T cells. The low affinity IL-2 receptor, CD25, is
expressed on activated T-cells and Treg cells. Immunohistochemistry studies showed
the strong presence of CD25
+
infiltrating cells in all models except for D2F2 and
B16, which showed only sparse CD25
+
staining and may be less immunogenic tumor
models. FoxP3 staining was not appreciable in any tumor model.

20
Additional subpopulations studied by immunohistochemistry included natural
killer (NK) cells (antibody: NK1/NK1.1), macrophages (antibody: F4/80), and
polymorphonuclear granulocytes (PMN; antibody: BM-2). NK1/NK1.1-positive
cells were apparent in all tumor models around areas of tumor cell necrosis. F4/80
positive staining was seen in six tumor models (B16, MAD 109, LLC, 4T1, C26,
RENCA) accumulated near sites of necrosis, but not in Wehi 164 and D2F2 tumor
models. All tumor models have strong positive staining for BM-2 especially C26.
PECAM-1 (CD31) is constitutively expressed on all vascular cells and has
provided a useful immunohistochemical marker of blood vessels, particularly for
angiogenesis. All tumor models showed positive PECAM-1 staining. Compared to
the dense microvascular and macrovasculature staining observed with normal tissue,
PECAM-1 staining in tumor samples was concentrated primarily in the
macrovasculature.
Immunosuppressive cytokines TGF-β, VEGF, and IL-10 contribute to tumor
immune evasion. These factors may be tumor-derived or produced by infiltrating
immune suppressor cells. All tumor models showed high expression of TGFβ and
VEGF in the tumor environment. IL-10 staining, while not as strong as that for
TGFβ or VEGF, was consistently positive across all tumor models except D2F2,
which is consistent with real-time RT-PCR data. Granzymes are serine proteases that
are secreted by CTL and NK cells and can induce cell apoptosis. Granzyme B
positive staining was observed in all tumor models except D2F2.

21

Table 2. Summary table for immunohistochemistry studies of murine solid tumor models.
Formalin-fixed paraffin-embedded tumor tissue from each tumor model was stained for
immunohistochemistry using 14 different antibodies to characterize the nature and activation status of
infiltrated immune cells and tumor-derived factors. Two individual tumors of each type were analyzed
by microscopy, and three to five high-power fields per slide were graded for the intensity of
immunostaining as ++, +, (+), or - for strong, moderate, weak and negative positive staining observed,
respectively.

22

Figure 5. IHC Demonstrating ARG1, CD11b, CD3 and Gr-1 Expression in Mouse
Tumor Models. All tumor section that had been fixed in 10% neutral buffered
formalin and applied antigen retrieval method before proceed for IHC. Original
magnification x 50

23

Figure 6. IHC Demonstrating CD25, FoxP3, TGFβ and IL10 Expression in Mouse
Tumor Models. All tumor section that had been fixed in 10% neutral buffered
formalin and applied antigen retrieval method before proceed for IHC. Original
magnification x 50

24

Figure 7. IHC Demonstrating VEGF, GZMB, BM-2 and F4/80 Expression in Mouse
Tumor Models. All tumor section that had been fixed in 10% neutral buffered
formalin and applied antigen retrieval method before proceed for IHC. Original
magnification x 50

25

Figure 8. IHC Demonstrating NK1/NK1.1 and PECAM-1 Expression in Mouse
Tumor Models. All tumor section that had been fixed in 10% neutral buffered
formalin and applied antigen retrieval method before proceed for IHC. Original
magnification x 50

26
CHAPTER 4
DISCUSSION
Studies in murine tumor models provide valuable in vivo information about
tumorigenesis, the tumor immune microenvironment and the potential efficacy of
novel therapies that can be applied to the study of human cancer. Evidence
supporting the anti-tumor potential of immunotherapy comes from work in murine
models. Treg-depletion or inhibition therapies were shown to have benefit in mice
bearing MAD 109 (Liu, Hu et al. 2005), RENCA (Liu, Hu et al. 2005; Webster,
Thompson et al. 2007), B16 (Shimizu, Yamazaki et al. 1999; Jones, Dahm-Vicker et
al. 2002) C26 (Liu, Hu et al. 2005), and LLC (Ozao-Choy, Ma et al. 2009; Xin,
Zhang et al. 2009) tumors. Additionally, MDSC-inhibitor treatments produced tumor
regression in mice bearing 4T1 (Le, Graham et al. 2009) and LLC (Suzuki, Kapoor
et al. 2005; Ozao-Choy, Ma et al. 2009) tumors. Furthermore, murine solid tumor
models have been used to characterize the myriad of tumor-derived factors thought
to modulate the host immune system, such as cytokine TGF β, which has been
reported in RENCA and 4T1 models (Liu, Wong et al. 2007; Zhang, Berndt et al.
2008). These studies and others suggest that variation exists in the mechanisms of
tumor immune evasion and suppression employed by different tumor models. They
further suggest that these variations have implications for selecting experimental
models in which to study tumor-induced immune changes or to test the efficacy of
new immunotherapies. Despite the routine use of such models, a comprehensive
understanding of tumor-induced immune modulations specific to commonly used

27
murine models is lacking. Therefore, the aim of this study is to provide a strong
foundation characterizing the murine tumor immune signatures to aid future
investigations.
Analysis of gene expression by real-time RT-PCR and immunohistochemical
studies together provide a picture of cellular activities and interactions at a given
point in time. By examining the expression of immune-related genes in the tumor or
tumor-draining lymphoid organs, it is possible to describe the immune status at these
sites, including the presence of activated lymphocytes and immune suppressor cells.
Our investigation of eight murine solid tumor models highlighted different patterns
of immune evasion among the models. The tumor models can be grouped into four
different classes according to the immunosuppressive cell populations present in the
tumor environment: Wehi164, B16, and D2F2 models show significant numbers of
Treg; RENCA, MAD109 and LLC models strongly induce MDSC accumulation;
4T1 model shows both Treg and MDSC at the tumor site; C26 model shows a
relative lack of either suppressor cell population (Fig. 2-4).
Immune activation appeared to be stimulated most in MAD109 tumors.
Real-time RT-PCR analysis of C26, 4T1, Wehi164, and B16 tumors showed
decreased expression of immune activation and co-stimulatory genes relative to
normal control tissue, suggesting a failure of immune activation. Tumor models
D2F2, LLC, and RENCA showed incomplete immune activation of
tumor-infiltrating leukocytes, with moderate DC activation and up-regulation of
some co-stimulatory factors in combination with specific deficits in others (i.e. low

28
OX40L, GITRL, or IL6R). In all tumor models studied, except Wehi 164 and
MAD109, infiltrating cells had decreased expression of OX40L. In two models,
D2F2 and RENCA, this deficiency in OX40L expression was accompanied by an
increase in all other co-stimulatory markers examined (GITRL, 4IBBL, CD40,
IL6R). Therefore, this specific deficiency in OX40L may provide a key target for
immunotherapy, particularly in D2F2 and RENCA tumor models.
Related to these data, we observed that the relative immunogenicity of a tumor
was related to the dominant suppressor cell population it recruited. In murine tumor
models with predominantly Treg recruitment (Wehi 164, B16 and D2F2), expression
of co-stimulatory signals is low (OX40L, 41BBL, and GITRL), suggesting a poorly
immunogenic tumor. However, in murine tumor models that recruit more MDSC
(MAD109, LLC and RENCA), the expression of co-stimulatory genes is increased
relative to control tissue, suggesting a more immunogenic tumor model. Previously
reports have shown that inflammation and tissue injury in the tumor setting may
cause accumulation of immune suppression populations. For example, PGE2, one of
the major products of inflammation, modulates tumor growth by producing the
accumulation of MDSC and also increases FoxP3 gene expression in Tregs. Another
example, the proinflammatory cytokine Interleukin-1β was also shown to induce
MDSC accumulation (Sharma, Yang et al. 2005; Sinha, Clements et al. 2007;
Nagaraj and Gabrilovich 2008; Serafini, Mgebroff et al. 2008).
Interestingly, tumor-infiltrating leukocytes in all eight tumor models
demonstrated attenuated CD62L, a marker of memory lymphocytes, with the most

29
significant decreases (>10 fold) seen in 4T1, D2F2, LLC, MAD109, and B16
models. This absence may reflect the failure to generate active anti-tumor immunity
with memory that allows tumor growth and progression in the mouse model.
Differential recruitment of infiltrating immune populations by different tumor
models may result in varying degrees of sensitivity to different immunotherapy
strategies. Murine effector cell populations play important roles in generating and
executing anti-tumor immune responses. Innate immune cells internalize tumor
antigens in the periphery and, if activated by surrounding stimuli, traffic to
secondary lymphoid organs where they present antigens to adaptive immune cells in
concert with appropriate co-stimulatory signals. Dendritic cells are particularly
important in this process, serving as professional APC and gatekeepers for adaptive
immune responses. If DC activation at the tumor site is incomplete or actively
suppressed by tumor-derived factors, subsequent adaptive helper T cell and cytotoxic
T lymphocyte responses will not be sufficient for anti-tumor immunity. Other innate
immune cells, including macrophages and natural killer cells, also contribute to the
balance of effective anti-tumor immunity versus tumor tolerance. Tumor-infiltrating
macrophages serve to phagocytize necrotic tumor cells and stimulate cytotoxic
activities by CTL and NK cells in effective immunity, but may also hinder immune
responses. Adaptive immune cells, in conjunction with innate NK cells and PMNs,
promote tumor cell elimination and the generation of memory cells. Specifically, NK
cells and CTL mediate tumor cell-specific cytotoxicity through Fas-FasL interactions
and granzyme-perforin-mediated killing.

30
Table 3. Summary Table for Infiltration Cell Populations in Mouse Tumor Models by IHC.
Polymorphonuclear granulocyte (PMN) was stained by BM-2 antibody, T cell was stained by CD3
antibody, NK cell was stained by NK-1/NK1. and Macrophage was stained by F4/80. Treg population
was characterized by CD25 and FoxP3 while MDSC population was characterized by CD11b, Gr-1
and ARG1. Grading for the intensity of immunostaining as ++, +, (+), or - for strong, moderate, weak
and negative positive staining observed, respectively.

Using real-time RT-PCR techniques and IHC studies, we characterized the
different immune populations infiltrating each tumor model (Table 3). In general,
almost all tumor models showed some type of immune cell infiltration. However,
tumor immune tolerance still occurred in those tumor models. As suggested by
others, tumor-derived factors such as TGFβ, IL10, VEGF or ARG1 disable the
function of infiltrating cell populations or Treg and MDSC suppressor cells hinder
immune cells infiltrating from tumor microenvironment. While unique markers for

31
Treg and MDSC have yet to be agreed upon, gene expression patterns associated
with Treg cell or MDSC populations can be used to identify the presence of these
cells in the tumor microenvironment. Given the increasing evidence that these
suppressor cell populations significantly hinder anti-tumor immune responses, the
ability to screen tumor biopsies for their presence by clinically-feasible RT-PCR
techniques would be advantageous in selecting therapy regimens. High expression of
CD11b, ARG-1, iNOS, and TGFβ, collectively, can reasonably be assumed to
indicate the presence of MDSC. Similarly, the collective elevation of FoxP3, CD25,
TGFβ, and IL-10, putative markers and suppressive mechanisms of tumor-induced
Treg cells (Zhang, Berndt et al. 2008) can be used to identify Treg cells in the tumor
microenvironment. In addition, CTLA-4, a T cell inhibitory marker, expression has
been shown to modulate Treg cell activity (Takahashi, Tagami et al. 2000). In the
D2F2 model, while CD25 was not appreciably elevated at the time of tumor
excision, CTLA-4 expression was greatly increased (>100 fold), suggesting that
perhaps CTLA-4 proceeds and induces later T cell-mediated suppression in this
model. In addition, recent studies have shown a role for MDSC in the induction of
Treg cells in the tumor microenvironment, through surface-bound TGFβ (Dumitriu,
Dunbar et al. 2009) and TGFβ-independent mechanisms (Serafini, Mgebroff et al.
2008). In the case of MAD109, which demonstrates small increases in CD25 and
FoxP3 (<10 fold) expression but highly increased CD11b expression (>10 fold), it is
possible that MDSC had been fully recruited to the tumor and were beginning to
induce the accumulation of Treg cells at the time of tumor excision. While these

32
studies demonstrate the presence of different immune cell populations in the tumor,
further functional assays for each cell population are needed in the future to confirm
the active contribution of each cell type.

Table 4. Probable Immune Suppression Mechanisms in Mouse Tumor Models. IHC and real-time RT-
PCR were applied for analysis. ARG-1, TGFβ and VEGF data are form fold change of gene expression
relative to tissue match control. Grading for the intensity of suppression as ++, +, (+), or - stands for
strong, moderate (> 10 fold), weak and negative respectively.

Correlation of data from real-time RT-PCR and IHC studies reveals general
concordance, with several notable areas of inconsistency (Table 4). ARG-1
expression as determined by real-time RT-PCR or IHC techniques are in agreement
in all tumor models except C26, D2F2 and B16. All tumor models expressed TGFβ

33
as assessed by IHC studies, in agreement with real-time RT-PCR data, with the
exceptions of MAD109 and D2F2 tumor models. VEGF, which showed strong
protein expression in all tumor models by IHC, shows significant upregulation of
transcripts only in B16, Wehi 164 and RENCA models. Such differences may reflect
transcriptional versus translational and post-translational changes in gene expression.
Furthermore, while an attempt was made in the analysis of real-time RT-PCR data to
examine gene expression relative to total cell expression (tumor and infiltrating
immune cells), as well as in relation to only infiltrating immune cells (CD45
+
cells)
and to subpopulations of immune cells (CD4
+
or CD11b
+
cells), additional studies
are needed to clarify which cell population(s) is responsible for producing AGR-1,
TGFβ and other chemoattractants in these tumor models. Future in vitro co-culture
experiments and multi-antigen IHC and immunofluorescence studies involving
different cell populations can be performed on tumor samples to help clarify these
issues.
Real-time RT-PCR is a sensitive, inexpensive, rapid, and reproducible
technique, available in most of medical centers. This technique allows the
simultaneous screening of a large number of gene transcripts in a single tissue
sample. Quantitative analysis of the expression of immune activation, immune
suppression, and microenvironment genes can be used to uncover the major
mechanisms of tumor escape being elicited by a given tumor at the time of biopsy
(Sadun, Sachsman et al. 2007). Morphological studies of tumor biopsies can further
describe the nature and activation status of tumor-infiltrating immune cells. Our

34
laboratory has demonstrated previously that treatment targeted to specific immune
deficits in a given tumor model identified by immune signature analysis can produce
improved anti-tumor immune responses (Sadun, Sachsman et al. 2007). In this study,
we chose controls based on tissues of origin of each murine solid tumor models
instead of skin control in order to understand the change in immunoregulatory gene
relatives to normal matched tissue (skin, kidney, colon, lung, and mammary glands).
Furthermore, to better understand gene expression change within subpopulations of
infiltrating leukocyte, Treg and MDSC, we analyzed our expression data relative to
CD45, CD4 and CD11b expression, respectively.
Angiogenesis plays one of the important roles for tumor progression. Yang et
al. showed that MDSC could stimulate tumor progression not only by suppressing
the immune system, but also by promoting tumor angiogenesis (Yang, DeBusk et al.
2004). PECAM-1 is a useful immunohistochemical marker of blood vessels,
particularly for angiogenesis (Weidner 1995). In our IHC result, all tumor models
studies showed positive staining for PECAM-1, but only concentrated in the
macrovasculature. Wang et al. point out that the choices of antibody and antigen
retrieval method have significant effect on IHC when studying angiogenesis (Wang,
Stockard et al. 2008). Therefore, better staining may be achieved in future IHC
studies with these tumor tissue sections by using different staining reagents and
protocols.

35
CHAPTER 5
FUTURE STUDIES
In summary, based upon our results, this study provides a foundation for a
description of the immune profile present in eight different murine solid tumor
models. These data describe the status of immune activation and suppression
mechanisms and the presence of different tumor-infiltrating immune cells using a
compilation of both gene transcript and protein expression analyses. Future
experiments, described below, will further describe cell-specific activities in the
tumor microenvironment and the functional status of different infiltrating cells.
Discrepancies between our data and previously reported findings may be due to
several factors. First, the use of transplanted versus spontaneous tumor models may
produce different tumor immunology settings. Additionally, the use of subcutaneous
tumor implantation (heterotopic), rather than orthotopic implantation, necessarily
alters the immune interaction between host and tumor. Orthotopically implanted
tumors may be undertaken in future studies to determine if this is important.
Secondly, host immune cells responding to a tumor, may accumulate in a variety of
sites, including the primary tumor site, tumor-draining lymph nodes, and the spleen.
Liu et al. observed an increase in the fraction of CD25
+
cells of CD4
+
cells in spleen
but not in peripheral blood in C26 colon carcinoma-bearing BALB/c mice (Liu,
Zhang et al. 2005). Youn et al. used flow cytometry and in vitro functional assays to
evaluate MDSC subpopulations in ten different murine models and detected the
expansion of functionally suppressive MDSC in the spleens of mice bearing all of

36
these tumor models (Youn, Nagaraj et al. 2008). The data from our current study
describes the immune profile of a tumor specifically at the tumor site. Immune
processes occurring in tumor-draining lymph nodes, the spleen, and other lymphoid
tissues may or may not be represented by the immune status at the tumor site. In fact,
a considerable amount of evidence may suggest that it is indeed different (Nagaraj
and Gabrilovich 2008; Youn, Nagaraj et al. 2008). Characterizing the immune
modulation seen in the tumor is a first step in a complete characterization of the host
immune response, and will provide specific immune targets for tumor-targeted
therapies. Previous study in our laboratory has shown that the targeting of LEC to
necrotic areas of tumors can be the new approach for immunotherapy (Li, Hu et al.
2003 a; Li, Hu et al. 2003 b). However, in order to understand its action, it may be
useful to study what changes are induced by LEC immunotherapy, not only localized
in the tumor site but also into tumor-draining lymph node, spleen, and peripheral
blood. Thirdly, the process of tumorigenesis involves many stages accompanied by
the infiltration of changing cell populations into tumor environment (Diaz-Montero,
Salem et al. 2009). Since our initial studies focused on the immune status of these
tumors at a specific stage of tumor development (1.0-1.5 cm diameter). Our data
likely only looks at the terminal stage of tumor progression. Thus, in order to get a
more complete picture of immune modulation in the tumor site, we could examine
tumors at different stages of development in the future. Finally, since the goal in
characterizing tumor-induced defects in the immune response in these murine tumor
models is to identify immunotherapeutic strategies to increase anti-tumor immunity,

37
the targeting should provide optimal and specific mechanisms of immune evasion
identified in each model effective immunotherapy. For example, the models that
showed profound deletion of OX40L expression might benefit most by OX40L
immunotherapy. Similarly, those models showing high TGFβ expression might be
best treated with anti TGFβ inhibitors. By using the data generated in these studies to
test the effects of specific immunotherapy protocols, it should be possible to identify
which therapies are most likely to translate successfully to human tumors with
similar immune profile as corresponding murine tumors.

38
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Abstract (if available)

Abstract This study characterized the major mechanisms of tumor escape at the tumor site employed by eight frequently studied murine solid tumor models by quantitative analysis of immune-related gene expression and immunohistochemistry procedures. The results of these investigations showed that the tumor models used different suppressor cell populations including Treg: Wehi164, B16, and D2F2

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Asset Metadata

Creator Yen, Chern-Yu (author)

Core Title Immune signature of murine solid tumor models

School Keck School of Medicine

Degree Master of Science

Degree Program Biochemistry and Molecular Biology

Degree Conferral Date 2009-08

Publication Date 08/06/2009

Defense Date 06/24/2009

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag cancer,immune,immunotherapy,OAI-PMH Harvest,tumors

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Tokes, Zoltan A. (committee chair), Epstein, Alan L. (committee member), Markland, Francis (committee member)

Creator Email chernyuy@usc.edu,sherryyen@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m2479

Unique identifier UC197338

Identifier etd-Yen-3145 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-172144 (legacy record id),usctheses-m2479 (legacy record id)

Legacy Identifier etd-Yen-3145.pdf

Dmrecord 172144

Document Type Thesis

Rights Yen, Chern-Yu

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