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Investigation of immune escape paths in the tumor microenvironment

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Investigation of immune escape paths in the tumor microenvironment

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Content INVESTIGATION OF IMMUNE ESCAPE PATHS IN THE TUMOR
MICROENVIRONMENT

by

Ryan Park

A Thesis Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(EXPERIMENTAL AND MOLECULAR PATHOLOGY)

May 2013

Copyright 2013 Ryan Park
ii
EPIGRAPH

What the mind of man can conceive and believe, it can achieve.
-Napoleon Hill

iii
DEDICATION

This thesis is decicated to my daughter Stevie Kate and wife Evelyn. It is with
their support, encouragement and entertainment that I complete this Master’s thesis.

iv
ACKNOWLEDGEMENTS

I would like to thank my mentor, Dr. Alan Epstein, who has continued to provide
guidance, patience, and funding for this project to reach its completion. I would also like
to thank my Master’s committee members, Dr. Florence Hofman and Dr. Martin Kast
who have provided guidance throughout this process.
Also, I would like to thank Scott Bergfeld, my friend and collaborator, who
worked on the murine primers used in the RT-qPCR and alongside me for forethought
and planning throughout these experiments. I thank Dr. Rebecca Sadun for her
encouragement and her previously published work, which was our foundation to expand
our series in mice. I thank Melissa Lechner, who provided a great amount of teaching \
and training for flow cytometry, ADCC, and other experiments that were not included in
this thesis. I thank Dr. Peisheng Hu for his guidance, Maggie for ordering all of our
supplies and keeping us within budget, and James and Mandy for their assistance with the
animals and cells. I thank Dr. John Dixon-Gray who provided assistance for the cell
assays and immune activation experiments and even though they did not end up in this
thesis, I wanted to ensure that his contribution to my education was also acknowledged.
And lastly, I would like to thank Lisa Doumak for providing all of the information from
an administration perspective for the completion of my degree.

v
TABLE OF CONTENTS

Epigraph ii
Dedication iii
Acknowledgements iv
List of Tables vi
Table of Figures vii
Abbreviations viii
Abstract ix
Introduction 1
Materials and Methods 6
Mice and tumor cell lines 6
RNA extraction 6
cDNA synthesis 7
Real Time SYBR Green RT-qPCR 8
Quantitative Analysis 10
Results 12
Melanoma 13
Renal Carcinoma 14
Lewis Lung Carcinoma 15

Discussion: 16
Conclusion 21
Appendix 22
Bibliography 24

vi
LIST OF TABLES

Table 1. Gene Expression Groups for RT-qPCR
Table 2. B16 Gene Expression as Fold Change from Day 4
Table 3. RENCA Gene Expression as Fold Change from Day 4
Table 4. LLC Gene Expression as Fold Change from Day 4

vii
TABLE OF FIGURES

Figure 1. Immunoediting Process in the Tumor Microenvironment
Figure 2. Anti-CD25 Treg depletion study
Figure 3. Heat map for 113 genes in B16, RENCA, and LLC tumors

viii
ABBREVIATIONS

CD Cluster of Differentiation
Cq Quantification Cycle
DC Dendritic Cell
GOI Gene of Interest
NK Natural Killer Cell
NTC Non-template Control
mRNA Messenger Ribonucliec Acid
DNA Deoxyribonuclein Acid
RT-qPCR Reverse Transcriptase Real Time Polymerase Chain Reaction
SYBR SYBR Green DNA chelator for PCR reactions
T-reg Regulatory T-cell

ix
ABSTRACT

Fold changes in 113 genes were measured using RT-qPCR in B16, RENCA, and
LLC tumors of syngeneic mice for mechanisms of immune escape. Fold changes were
compared at Day 7, 10 and 14 to gene expressions at Day 4 to evaluate sustained up or
downregulation of immunostimulation or immunosuppression. RENCA (11) had the
largest number of gene expression changes throughout all three timepoints than B16 (7)
or LLC (4). Escape mechanisms were identified in B16 tumors from a lack of
immunostimulation signalling while analysis in RENCA and LLC revealed
immunoinhibitory activity from regulatory T-cells.

1
INTRODUCTION
Cancer ranks second for cause of mortality in the United States. At the time of
publication, the American Cancer Society reported an estimated 580,350 deaths and
1,660,290 new cases expected to occur in 2013 ((ACS) 2013)(accessed March 12, 2013:
http://www.cancer.org/acs/groups/content/@epidemiologysurveilance/documents/docum
ent/acspc-036845.pdf). Given this prevalence of the disease, success in treatment remains
mixed, which is believed to be due to immunosuppressive challenges provided by its
complex escape mechanisms that result from interaction to favor tumor cell survival from
the host immune response (Stewart and Abrams 2008).
Hanahan and Weinberg identified cancer as a result of multi-hit processes where
each hit, or dysfunction in the cell, provides an increasing ability to obtain cellular
immortality, also defined as hallmarks. The six hallmarks for tumor growth include (1).
apoptosis evasion; (2) self-sufficiency from growth signals; (3) insensitivity to anti-
growth signals; (4) increased metastatic potential; (5) cellular immortality; (6)
angiogenesis and have recently been added with two emerging hallmarks: (7)
deregulation of cellular energetics and (8) avoiding immune destruction (Hanahan and
Weinberg 2011). The original six hits were believed to be necessary for tumor
development independent to its surroundings while the emerging hallmark, avoidance of
immune destruction, involves interaction between tumors and their host immune
responses. It is the control of this hallmark that provides hope for approaches in
immunotherapy.
Although Hanahan and Weinberg listed immune escape as a potential necessity
for tumorigenesis, preclinical and clinical evidence suggest the failed attempt by the host
2
immune response allows for tumor proliferation. For example, tumor infiltrating
lymphocytes (TIL) were correlated with positive response from tumors, especially in high
CD8/CD4 ratios of TIL (Sato, Olson et al. 2005). In addition, immunocompetent mice
when challenged with MCA-induced tumors from immunodeficient RAG-2
-/-
mice were
able to resolve tumors while other immunodeficient mice grew tumors from
immunocompetent mice (Shankaran, Ikeda et al. 2001).
Immunotherapy seeks to provide the host immune response with the proper
signals for elimination of malignant cells. Elimination occurs when the immune response
is engaged through growth of tumor cells leading to pro-
inflammatory/immunostimulatory signals (ie. T-cell activation, DC and NK activation,
myeloid activation, angiogenesis). When activated, the elimination process forces the
balance of immune response towards immunostimulation from immunosuppression. In
addition immunotherapy can also target the immunosuppression by removal or blockade
of these engaged factors (ie. T-cell inhibition, regulatory T-cell (T-reg), suppressive DC,
myeloid derived suppressor cells) in order to mount an immune response. The ability of
tumors to create the favorable environment of decreased immunostimulation and/or
increased immunosuppression is a broad process termed immunoediting.
Cancer immunoediting was described as occurring in three E phases (Dunn, Bruce
et al. 2002). (1) Elimination by the host immune system with proper signaling for
immunostimulation; (2) Equilibrium where the host provides a sufficient immune
response initially but followed by a slower selection process where cells with the proper
mix of gene expression survive and remain resistant to future immune responses; and (3)
Escape where surviving cells from the equilibrium phase gain ability to evade through
3
genetic or epigenetic expression leading to an unchecked proliferation of cells through
genetic variations (Figure 1). Although tumors grown from cell lines have already
performed immunoediting during initial tumor formation, we have utilized murine tumor
cell lines to investigate the tumor microenvironment to understand the paths of immune
escape but list immunoediting here for a more complete understanding in regards to
spontaneously forming tumors.

Figure 1. Immunoediting process in the tumor microenvironment. Tumor immunoediting
involves multiple mechanisms for the three phases of Elimination, Equilibrium, and
Escape. Photo from (Dunn, Bruce et al. 2002).

In our laboratory, previous data were published with syngeneic murine tumors
4
after regulatory T-cell (Treg) depletion. Using cytotoxic anti-CD25 antibodies, T-reg
populations were removed and resulted in regression of tumors as well as lack of tumor
growth during subsequent rechallenge experiments using in the RENCA and Colon26
tumor models (Liu, Hu et al. 2005). By decreasing the inhibitory signals provided by
CD25
+
Tregs, implanted Colon26 and RENCA tumor cells failed to grow. The anti-CD25
and Fc-B7.1 combination therapy produced 100% remission of tumors whereas anti-
CD25 (0% regression) or Fc-B7.1 (80% regression) alone did not produce complete
tumor remission (Figure 2).

Figure 2. Anti-CD25 Treg depletion study. Rechallenge experiments after Colon
26 (L, top and bottom), RENCA (R, top), and Mad109 (R, bottom) lung cancer
were implanted without (A and C) and with B7.1-Fc and anti-CD25 (T-reg
depletion) treatment (B, D).

5

A similar result was found for Gajewski et. al. CD25 depletion in adoptively
transferred splenic cells provided transient B16 melanoma suppression but an eventual
growth of the tumor. In these studies, Treg depletion alone did not complete tumor
elimination (Gajewski, Meng et al. 2006).
In addition, our laboratory has also utilized fusion proteins to provide co-
stimulation for tumor regression or delay. Administration of fusion proteins providing co-
stimulation with H60, OX40L, and CD137L have resulted in delayed growth or remission
using B7.1 and LEC with anti-CD25 (Li, Hu et al. 2003; Liu, Hu et al. 2005; Flanagan,
Khawli et al. 2006; Zhang, Sadun et al. 2007; Hu, Arias et al. 2008; Sadun, Hsu et al.
2008) providing promise for co-stimulation and removal of immunosuppressive elements.
Despite the varied factors contributing to tumor escape or progression,
immunotherapy will improve using cataloged genetic sequencing methods. The Cancer
Genome Atlas (TCGA) started in 2005 allowed for the sequencing, identification, and
cataloging of genetic expressions in tumors and multi-dimensional analyses (TCGA
2008). Through the use of technologies such as RT-qPCR and tumor screening,
immunotherapeutic approaches can be delivered for maximum effectiveness based on
genetic profiles in each patient’s tumor to maximize efficacy and provide a more tailored
and personalized approach to medicine.

6
MATERIALS AND METHODS

Mice and tumor cell lines
BALB/c mice and C57Bl/6 mice were used in accordance with procedures
approved by the University of Southern California Institutional Animal Care and Use
Committee policies. The murine tumor cell lines RENCA (renal carcinoma) and Lewis
Lung Carcinoma (LLC) were obtained from American Type Culture Collection
(Manassas, VA), while the B16 melanoma cell line was obtained from the National
Cancer Institute (Frederick, MD). All cells were maintained in accordance with their
respective vendor’s guidance. Six-week old female mice were implanted with syngeneic
tumors. BALB/c mice were used for RENCA while C57Bl/6 mice were used for LLC
and B16 melanoma models. All mice (n=3/tumor) were administered 5x10
6
cells in
0.2mL into the left flank subcutaneously and grown for 4, 7, 10, and 14 days before
harvesting for RNA extraction, cDNA synthesis, and reverse transcriptase real-time
quantitative PCR (RT-qPCR).

RNA extraction
At 4, 7, 10, and 14 days post-implantation, tumors were harvested and
immediately placed into 1mL of RNAlater RNA Stabilization Reagent (Qiagen GmbH,
Hilden, Germany) and stored at -20°C prior to RNA extraction. RNA extractions were
performed with the RNeasy Mini Kit (Qiagen Sciences, Germantown, Maryland) without
modifications under RNase free conditions with RNase Zap (Ambion Inc., Austin, TX).
Macrodissections of 3x3x3mm
3
harvested tumor tissues were placed into -
7
mercaptoethanol treated Buffer RLT and homogenized using the Pellet Pestle mortar and
pestle driven by the *Pellet Pestle* Cordless Motor (Kontes, Vineland, New Jersey).
Tissue homogenate was placed in a microcentrifuge and spun at 10,700g to separate
tissue debris. Supernatants were placed into a new microcentrifuge tube with 600uL of
70% Ethanol in DEPC-treated water to precipitate the nucleic acid. The nucleic acid
solution was loaded on an RNeasy mini column in 700uL volumes and centrifuged for
15sec at 8,200g to isolate the nucleic acid. Column washes were performed using the
enclosed Buffer RW1 (700uL) and Buffer RPE (500uL) and spun for 15s at 8,200g to
wash the column. A final wash of Buffer RPE (500uL) was added to the column and
centrifuged for 2min at 8,200g to dry the column before elution. Nucleic acid elution was
performed using 80uL of RNase free water and centrifuged for 1min at 8,200g in two
volumes of 50uL and 30uL. The eluted nucleic acid samples were stored at -80°C until
cDNA synthesis was performed.

cDNA synthesis
Purified RNA extractions were used as templates for cDNA syntheses. All cDNA
syntheses were performed under RNase free conditions using RNase Zap. 2uL of DNase
(Ambion Inc., Austin, TX) and 2.88uL of Buffer DNase was added to 24uL of the tumor
nucleic acid extract to digest genomic DNA. After 10min of incubation, 3.32uL of
DNase inactivator was added and incubated further for 2min. The samples were
centrifuged at 8,000g for 1min and 4uL was added to each well of an 8-well strip tube.
To each well were added: 4uL DEPC treated water, 1uL oligo dT primer, 1uL of 10nM
dNTP to create cDNA from mRNA. The strip tubes were placed in an iCycler
8
thermocycler at 65°C (Bio-Rad Laboratories, Inc., Hercules, CA) for 5min to bind the
oligodT primer to the mRNA. Afterwards, a superscript solution was added to each well:
2uL 10x RT buffer, 4uL 25mM MgCl
2
, 2uL 0.1M dithiothreitol, 1uL RNase OUT, 1uL
Superscript III (Invitrogen, Carlsbad, California). For a negative control of cDNA
synthesis, 1uL Superscript III was substituted with 1uL of DEPC treated water. The
samples were placed into the iCycler thermocycler for 50°C for 50min, 85°C for 5min
and 4°C for infinite time. Afterwards, each well received 0.66uL of RNase H (Invitrogen,
Carlsbad, California) and placed in the thermocycler at 37°C for 20min and 4°C for
infinite time. Samples were then stored at -20°C until RT-qPCR. RNA purity was
verified using no-RT control reactions.

Real Time SYBR Green RT-qPCR
All cDNA was tested for gene expression using the Stratagene Mx3000p qPCR
System (La Jolla, CA) and used the Stratagene Brilliant SYBR Green Master Mix (La
Jolla, CA). Primers were designed by Scott Bergfeld using Primer 3 PCR Primer Design
software developed by S. Rozen and H. Skaletsky. Polypropylene 96-well tube plates
(ThermoGrid C-18096, Denville Scientific Inc., South Plainfield, NJ) were filled with
1uL of cDNA, 0.5uL forward and reverse primers (25-40 pmol/mL, 12.5uL Brilliant
SYBR Green Master Mix (Stratagene) and 10.5uL Millipore 18Ω water then sealed with
an ultraclear sealing film (B1212-RT1, Denville Scientific Inc., South Plainfield, NJ) for
RT-qPCR applications. Primer sequences (forward and reverse) were synthesized by the
USC Molecular Genomics Core (listed in Table 1 with forward and reverse sequences in
the Appendix). Each 96-well plate set aside two wells for non-template controls (NTC)
9
and the GAPDH reference gene. To accommodate 114 (113 immunoregulation gene + 1
reference gene) genes on 96-well plates, two plates were used. The second plate was
partially filled and also utilized wells for NTC and GAPDH. For all RT-qPCR, forty
cycles were performed with a thermal profile of 95°C for 15min warm up before 40
cycles, 40 cycles (95°C for 30seconds, 55°C for 30sec), and completed with 72°C for one
minute. Endpoint collections occurred at 55°C and the 72°C plateaus.

10
Table 1. Gene Expression Groups for RT-qPCR
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
LIGHT
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

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
Gr-1

Myeloid
Maturation
CD11b
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
mDcTRAIL
R

Quantitative Analysis
Quantification cycle (Cq) values from Stratagene Mx3000p analysis software
reports were imported into Microsoft Excel, averaged, and statistically tested for
probability (p < 0.05) using the Student’s t-test for two-tails and heteroscedastic data.
Fold changes in gene expression were calculated using the Cq method. Calibration of
relative gene expression, Cq, was performed using the Cq from each triplicate sample
11
gene of interest (GOI) subtracted by the Cq value of GAPDH as the internal reference
gene. The Cq were averaged in their non-logarithmic quantity before use in the Cq
equation to account for the exponential increase in mRNA. Standard deviations from the
average Cq in each time point were squared, added, then the square root was taken for
the new Cq standard deviation. The average Cq values for Day 7, 10, and 14 were
compared to Day 4 as Cq. The Cq values were used to determine fold change in
gene expression with +/- standard deviations for the 95% confidence interval. The
formulas used were:

Cq = GOI
Day 4, 7, 10, 14
– GAPDH
Day 4, 7, 10, 14
Average DCq = – Log
2
[(2
–
Cq
1, 2, 3
) / 3]
Cq = Average Cq
Day 7, 10, 14
– Average Cq
Day 4

Fold change in gene expression = 2

–
Cq

Values of fold changes in gene expression for Day 7, 10, and 14 were compared
to Day 4. Student’s t-test (two-tailed, unpaired, heteroscedastic data) were calculated for
each fold change value and values with a probability limit of <0.05 used to evaluate
trends in the three time points. Data were plotted into heat maps using Java TreeView
(version 1.1.6r2, http://jtreeview.sourceforge.net, accessed March 1, 2013).

12
RESULTS

In these studies, we utilized reverse transcriptase real-time polymerase chain
reaction (RT-qPCR) to identify immune escape mechanisms through the investigation of
113 genes for T-cell, dendritic cell (DC), innate system, and microenvironment activities.
Using syngeneic tumors of B16 melanoma, RENCA renal carcinoma, and Lewis Lung
Carcinoma (LLC) lung carcinoma in mice, the progression of genetic expression was
monitored over the course of two weeks and analyzed. Fold changes in gene expression
were compared to early time points (Day 4) to compare the mechanisms involved in
melanoma, renal cancer, and lung cancer for tumor escape pathways leading to host
immune evasion and provide future paths towards routes of therapy. Gene expressions
were noted if they had a Student’s t-test with a probability <0.05.
From the B16, RENCA, and LLC tumors investigated, RENCA tumors were
found to contain the most number of genes (11) compared to B16 (7), and LLC (4) where
all three time points down-regulated or up-regulated expression throughout the 14-day
period as shown in the heatmap below (Figure 3). B16 tumors, compared to the other
two, were found to have an overall decrease in expression of genes monitored. All
down/up-regulations represented for each tumor and time point are represented as mean
value followed by 95% confidence intervals in parentheses.

13
Figure 3. Heat map for 113 genes in B16, RENCA, and LLC tumors. Each cell
corresponds to a gene. Of the three tumors tested, RENCA showed an overexpression of
genes while B16 was down-regulated when fold changes were compared to Day 4.

Melanoma Tumor Model
B16 tumors had an overall down-regulation when comparing tumor
microenvironments to Day 4 (Table 2). Consistent down-regulation was observed for all
three time-points in DC, microenvironment. and T-cell activity markers, including
PGEsynthase1, LIGHT, CCL3, bFGF, OX40L, LFA-1, and CCR7.

14
Table 2: B16 Gene Expression as Fold Change from Day 4

Renal Carcinoma Tumor Model
RENCA tumors had an overall up-regulation when comparing tumor
microenvironments to Day 4 (Table 3). Consistent up-regulation was observed for all
three time-points in DC and T-cell activity markers, including CD11c, CD83, GITRL,
CD69, C40L, ICOS, PD1, CXCL9, FoxP3, and Insulin-like 7.

Gene Regulation
Day 7
vs.
Day 4
Mean
Day 7 vs.
Day 4
95% CI
Day 10
vs.
Day 4
Mean
Day 10
vs. Day 4
95% CI
Day
14 vs.
Day 4
Mean
Day 14
vs. Day 4
95% CI
PGE
Synthase
1
Down 3.62
(5.26,
2.50)
8.60
(11.35,
6.52)
54.55
(87.03,
34.19)
LIGHT Down 5.04
(8.71,
2.92)
17.18
(23.87,
12.36)
24.89
(33.34,
18.58)
CCL3 Down 2.88
(4.64,
1.79)
6.77
(10.71,
4.27)
19.56
(30.38,
12.59)
bFGF Down 3.41
(6.09,
1.91)
12.45
(17.96,
8.63)
53.32
(81.57,
34.85)
Ox40L Down 18.72
(26.23,
13.37)
18.47
(23.08,
14.79)
135.9
8
(186.23,
99.29)
LFA-1 Down 2.59
(4.06,
1.65)
6.23
(8.78,
4.41)
18.14
(26.80,
12.28)
CCR7 Down 3.52
(5.33,
2.33)
15.83
(27.62,
9.07)
49.97
(69.83,
35.76)
15

Table 3: RENCA Gene Expression

Lung Carcinoma Tumor Model
LLC tumors had an overall mix of up-regulation and down-regulation when
comparing tumor microenvironments to Day 4 (Table 4). Consistent up-regulation was
observed for all three time-points in DC, and T-cell activity markers, including GITR,
and Glutaredoxin as well as in the Myeloid Derived Suppressor cell marker, Gr-1.
Consistent down-regulation was observed for all three time-points in the T-cell activity
marker CXCL12.

Gene Regulation
Day 7
vs.
Day 4
Mean
Day 7 vs.
Day 4
95% CI
Day 10
vs.
Day 4
Mean
Day 10
vs. Day 4
95% CI
Day
14 vs.
Day 4
Mean
Day 14
vs. Day 4
95% CI
CD11c Up 2.66
(1.68,
4.21)
8.57
(5.17,
13.39)
8.07
(4.89,
13.33)
CD83 Up 2.07
(1.15,
3.73)
3.48
(2.07,
6.35)
3.79
(2.35,
6.10)
GITRL Up 2.89
(1.75,
4.78)
4.48
(2.77,
4.68)
2.90
(1.77,
4.73)
CD69 Up 2.90
(1.80,
4.67)
4.55
(2.93,
14.14)
9.11
(6.52,
12.73)
CD40L Up 7.11
(5.45,
9.27)
9.09
(6.91,
11.95)
9.99
(7.16,
13.92)
ICOS Up 6.22
(4.65,
8.33)
10.03
(7.37,
13.65)
9.99
(7.50,
13.32)
PD1 Up 5.04
(3.34,
7.61)
13.61
(8.62,
21.48)
19.67
(14.59,
26.52)
CXCL9 Up 60.03
(45.75,
78.78)
46.12
(32.02,
66.44)
126.1
3
(95.30,
166.93)
FoxP3 Up 2.86
(1.86,
4.39)
12.26
(8.21,
18.29)
4.90
(3.00,
8.03)
Insulin-
like 7
Up 8.18
(5.90,
11.36)
18.40
(13.13,
25.78)
16.17
(12.05,
21.70)
16

Table 4: LLC Gene Expression
Gene Regulation
Day 7
vs.
Day 4
Mean
Day 7 vs.
Day 4
95% CI
Day 10
vs.
Day 4
Mean
Day 10
vs. Day 4
95% CI
Day
14 vs.
Day 4
Mean
Day 14
vs. Day 4
95% CI
Gr-1 Up 3.36
(2.16,
5.25)
12.68
(8.92,
18.02)
5.87
(4.00,
8.62)
GITR Up 3.07
(1.87,
5.05)
10.23
(7.09,
14.76)
3.47
(2.25,
5.33)
Glutared-
oxin
Up 3.23
(2.14,
4.86)
7.72
(5.21,
11.44)
3.08
(2.05,
4.62)
CXCL12 Down 5.15
(7.35,
3.60)
11.84
(16.82,
8.33)
64.22
(78.46,
52.57)

DISCUSSION

Gene expression in B16 melanoma, LLC Lewis lung carcinoma, and RENCA
renal cancer were measured using RT-qPCR. The expectation was to assess the escape
mechanism of each tumor in the tumor microenvironment for a potential path towards
immunotherapy. Immunoediting mechanisms by the tumor tip the immune response
balance from equilibrium to escape which results in proliferation of the tumor.
Identification of these specific mechanisms may provide strategies for immunotherapy to
shift the equilibrium phase towards elimination.
B16 melanoma showed a predominantly overall decrease in immunostimulatory
genes. The down-regulation from Day 4 occurred in DC, microenvironment, and T-cell
activity. Also, of all three tumors, B16 measured an overall decrease in gene expressions
for immunosuppression (PGEsynthase1) as well as for immunostimulation (OX40L,
17
LFA-1, CCR7, and LIGHT). By comparison, B16 did not possess an active Treg
response and the escape mechanism may be due to a lack of immunostimulation
signaling.
In the three tumors investigated, RENCA’s expression profile possessed the most
active changes in the 113 genes categorized into DC, innate, microenvironment, and T-
cell activity. The increase of DC, NK, and T-cell activation signals included DC
maturation, DC stimulation, NK activation, and T-cell activation. Despite all of the
increased immunostimulatory genes, RENCA was still able to grow in these mice. The
escape mechanism identified RENCA was not due to a decreased expression of
immunostimulatory genes but rather was due to overexpression from immunoinhibitory
genes.
Despite the growth of RENCA, immunostimulatory genes were identified and
shown to be increasing in expression over the 14 day period. The up-regulation of these
genes such as CD11c, CD83, and GITRL, suggests RENCA should have provided a
sufficient stimulatory immune response towards the tumors. In addition, NK activation
(CD69) and T-cell activation (CD40L, ICOS) were also shown to be up-regulated.
Higher expression of DC and T-cell signals suggest that there was a request for an
immune response created in the tumor microenvironment.
Immunoinhibitory gene expression was also measured in RENCA and the up-
regulation of PD1, an inhibitory receptor present on activated T-cells, revealed how the
potent T-cell activation from APCs and T-helper cells was countered by the tumor. The
PD-1/PDL-1 pathway has been identified for tumor immunotherapy and is currently
undergoing clinical trials as an anti-PD1 therapy including BMS-936558 (Bristol-Myers
18
Squibb) in the treatment of renal cell carcinoma, non-small cell lung cancer, and
melanoma (Hall, Gray et al. 2013; Oncology 2013).
In addition, up-regulation of FoxP3 and Insulin-like 7 genes in RENCA suggest a
role from regulatory T-cells (Treg) in addition to T-cell anergy from PD-1 alone. This
also verifies our previously published data where Treg depletion with anti-CD25 in
RENCA resulted in lack of tumor growth by the murine immune response (Liu, Hu et al.
2005). In addition, although only up-regulated for two time-points (Day 10 and 14 up-
regulation), other inhibitory signals such as TGF-β, CTLA-4, and Glutaredoxin
expression which suggests that an overexpression may have already been present even at
Day 4 post-implant. Inhibitory signals may be stronger than stimulatory signals to
provide effective and lasting immunosuppression. In support of this, evidence from the
RT-qPCR data in RENCA suggests that concurrent overexpression of
immunostimulatory and immunoinhibitory genes results in overall immune response
inhibition and tumor escape.
The LLC tumors possessed the smallest number of significant changes. While Gr-
1 identified the presence of Myeloid Derived Suppressor Cells, T-cell trafficking was
decreased (CXCL12) and Treg related signaling was increased through GITR and
Glutaredoxin. In addition, an expanded number of genes may be necessary to identify the
escape mechanism in more detail for LLC.
The early work produced by Dr. Rebecca Sadun was expanded from an 11 murine
gene panel to 113 genes. Although Sadun did not measure active immunesuppression in
the tumor microenvironment using RT-qPCR, she did find elevation of gene expression
in tumor draining lymph nodes (Sadun, Sachsman et al. 2007). By contrast, the present
19
study was able to identify immunosuppression in the tumor microenvironment and seen
as fold change comparisons to Day 4 for genes. Presumably, if the tumor already
mounted an immune response by Day 4, the use of normal tissue of tumor origin for
control should produce a higher fold change and may provide a better control of gene
expression in tumor tissue.
The strategy employed in this project was to identify the genes involved in
immunoregulation consistently down-regulated or up-regulated throughout the fourteen-
day period. Although fold change from an early time-point (Day 4) was a different
strategy than using normal subcutaneous tissue for controls, this strategy was employed
to monitor the progression in the tumor microenvironment and is likely a better estimate
of changes performed by the tumor. Use of normal subcutaneous tissue for reference
may not capture the escape mechanism utilized by the tumor but was chosen previously
to investigate gene expression in the subcutaneous tumors. The use of Day 4 gene
expression as controls then ignores any prior immune response due to the tumor presence
itself. For this study, genes were noted if they sustained statistically significant changes
throughout the three dates of Day 7, 10, and 14. Although there were many genes with
statistically significant changes for two of the three time-points compared, these were
considered below the threshold for sustained immune escape in order to identify the best
possible routes for therapeutic intervention. It may be best noted that decreased
expression may also attribute to another known escape mechanism known as immune
exhaustion. T-cells have been shown to decrease their cytokine expression and effector
function due to chronic signaling (Crespo, Sun et al. 2013). Unfortunately, immune
exhaustion was not be addressed using the criteria listed above. Lastly, normal organ
20
tissue would be the ideal control in order to target immunoediting in spontaneous forming
tumors as opposed to Day 4, but since cell lines were utilized here, this study was focused
on sustained routes of immune escape.
The short timeframe should also be noted. The malignant growths occurring in
human tumors follow a much longer timeline than 14 days. However, as the mouse was
utilized with a large tumor cell burden, the rapid growth would reach IACUC limits and
burden the animal beyond humane limits. As is the case, a later timeframe was not
chosen for this set of experiments. For longer time points, it may be necessary to use
smaller number of cells in orthotopic areas to create a more realistic tumor progression,
long term escape past Day 14, as well as the use of spontaneous tumor model to provide a
tumor microenvironment more relevant to human cases.
Translation of these results to clinical cases will take more effort beyond the
scope of this project. Identification of therapeutic pathways in patients can be visualized
using two methods: (1) identification of the greatest and most consistent path for immune
escape in each particular tumor type and utilize the information for a therapeutic
approach based upon tumor type and location, and (2) procurement of biopsies from the
patient at multiple time points to determine if the current therapeutic strategy is causing
further change in the efficacy due to the immunoediting process. In this way, it may be
possible to identify otimized therapy that is tailored to each individual patient and their
unique tumor profile to provide a more personalized treatment approach.

21

CONCLUSION

Quantification of immunoregulatory changes in tumor progression was observed
using RT-qPCR. Over two weeks, B16 melanoma, RENCA renal carcinoma, and LLC
lung carcinoma were observed and compared for gene expression levels in tumor tissue
to assess the paths of escape.

22
APPENDIX
Appendix. Primer Sequences for SYBR Green RT-qPCR

1. CD28
2. 4-1BB
3. Lag3
4. GITR
5. IL-6
6. IL-6R
7. OX40
8. CD40L
9. CD27
10. CD70
11. CD30
12. CD153
13. ICOS
14. PD1
15. TGF-b
16. TGF-bR
17. IL-10
18. IL-10R
19. FasL
20. Fas
21. TRAIL
22. DR5 (TRAILR2)
23. mDcTRAILR1
24. HVEM
25. LT-BR
26. FoxP3
27. CTLA-4
28. CD25
29. Gpr83
30. Ecm1
31. Glutaredoxin
32. Insulin-like7
33. Helios
34. LFA-1
35. CD2
36. VLA-4
37. PSGL-1
38. ESL-1
39. FUT9 (Sialyl-LewisX)
40. CD45
41. IL-7R
42. CD62L
43. CD44
44. CCR4
45. CCR5
46. CCR7
47. CCL4
48. CCL5
49. CCL19
50. CCL21
51. CCL22
52. CXCR3
53. CXCR4
54. CXCL9
55. CXCL10
56. CXCL12

1. F: 5’-CTCTGGAATCTGCACGTCAA-3’
2. F: 5’-GGTGTCCTGTGCATGTGA-3’
3. F: 5’-GACCCCTTCTTTGCTCATTG-3’
4. F: 5’-GACGGTCACTGCAGACTTTG-3’
5. F: 5’-TCCAGTTGCCTTCTTGGGAC-3’
6. F: 5’-AAGCAGCAGGCAATGTTACC-3’
7. F: 5’-GTGTACACAGTGCAACCATCG-3’
8. F: 5’-CGAGTCAACGCCCATTCATC-3’
9. F: 5’-CGAGTCAACGCCCATTCATC-3’
10. F: 5’-TGGCTGTGGGCATCTGCTC-3’
11. F: 5’-CAGTGATCGTGGGCTCTGTA-3’
12. F: 5’-AGGATCTCTTCTGTACCCTGAAAAGTA-3’
13. F: 5’-GCCACCATCTGTCCTCATTT-3’
14. F: 5’-ATTCGTAGACTGGGGGACTG-3’
15. F: 5’-TGCTTCAGCTCCACAGAGAA-3’
16. F: 5’-CGTCTGCATTGCACTTATGC-3’
17. F: 5’-CGGGAAGACAATAACTG-3’
18. F: 5’-AGTCTCAAGGGATGGCTTCT-3’
19. F: 5’-ATCCCTCTGGAATGGGAAGA-3’
20. F: 5’-ATGCTGTGGATCTGGGCTGTCCT-3’
21. F: 5’-GAAGACCTCAGAAAGTGGC-3’
22. F: 5’-AAGTGTGTCTCCAAAACGG-3’
23. F: 5’-TCTCCAGTCTGAGTCACTGG-3’
24. F: 5’-GTGTCCATCCTTTTGCCACT-3’
25. F: 5’-TTATCGCATAGAAAACCAGACTTGC-3’
26. F: 5’-GTGGTCAGCTGGACAATCAC-3’
27. F: 5’-CAGGTGACCCAACCTTCAGT-3’
28. F: 5’-CAAAGCCCTCTCCTACAAGAACG-3’
29. F: 5’-GAAGATGCTGGTGCTTGTGGTAGTC-3’
30. F: 5’-ACTACCTGCTCCGACCCTGC-3’
31. F: 5’-GGACATCACAGCCACTAACAACACC-3’
32. F: 5’-TCGCTGATGGAGAAGCCAATAC-3’
33. F: 5’-ACTCCTCAGAAGTTTGTGGGGG-3’
34. F: 5’-ACTATGTAGTGTTGACCTGGA-3’
35. F: 5’-ATTCAGTGGCGCTCCAAG-3’
36. F: 5’-CGCTGTTTGGCTACTCGGT-3’
37. F: 5’-GCAGAGACCTCAAAACCAGC-3’
38. F: 5’-CAAGATGACGGCCATCATTTTCA-3’
39. F: 5’-CAAATCCCATGCGGTCCTGAT-3’
40. F: 5’-ACCATGGGTTTGTGGCTCAA-3’
41. F: 5’-CGAAACTCCAGAACCCAAGA-3’
42. F: 5’-GGGAGCCCAACAACAAGAAG-3’
43. F: 5’-CACAGCAGCAGATCGATTTG-3’
44. F: 5’-ATTTGCTGTTCGTCCTGTCCC-3’
45. F: 5’-CATCGATTATGGTATGTCAGCACC-3’
46. F: 5’-GCATCAGCATTGACCGCTA-3’
47. F: 5’-ATGAAGCTCTGCGTGTCTGC-3’
48. F: 5’-TGCTTTGCCTACCTCTCCCTAG-3’
49. F: 5’-GGTGCTAATGATGCGGAAGAC-3’
50. F: 5’-AACAGACACAGCCCTCAAGA-3’
51. F: 5’-ATCTGCTGCCAGGACTACATC-3’
52. F: 5’-TGAACGTCAAGTGCTAGATGC-3’
53. F: 5’-TCAGTGGCTGACCTCCTCTT-3’
54. F: 5’-GAACCCTAGTGATAAGGAATGCA-3’
55. F: 5’-AGTGCTGCCGTCATTTTCTG-3’
56. F: 5’-CAGAGCCAACGTCAAGCAT-3’

1. R: 5’-AACAGGACTCCAGCAACCAC-3’
2. R: 5’-AGTTATCACAGGAGTTCTGC-3‟
3. R: 5’-CCAGGTAACCCGAAGGATTT-3’
4. R: 5’-GCCATGACCAGGAAGATGAC-3’
5. R: 5’-GTGTAATTAAGCCTCCGACTTG-3’
6. R: 5’-CATAAATAGTCCCCAGTGTCG-3’
7. R: 5’-TTCTGTCCTCACAGACTGCG-3’
8. R: 5’-GTAATTCAAACACTCCGCCC-3’
9. R: 5’-GTAATTCAAACACTCCGCCC-3’
10. R: 5’-ACATCTCCGTGGACCAGGTATG-3’
11. R: 5’-CTTTTCCTCCTTCCTCCACC-3’
12. R: 5’-GTTTGGTATTGTTGAGATGCTTTGA-3’
13. R: 5’-CAGGCATCTAAGCCCTGAAG-3’
14. R: 5’-CATGCAGAAGGACAGCAGAT-3’
15. R: 5’-TGGTTGTAGAGGGCAAGGAC-3’
16. R: 5’-AGCAGTGGTAAACCTGATCC-3’
17. R: 5’-CATTTCCGATAAGGCTTGG-3’
18. R: 5’-TCAAGTTTATGGGCAAACCT-3’
19. R: 5’-CCATATCTGTCCAGTAGTGC-3’
20. R: 5’-GCATAATGGTTCTTGTCCATG-3’
21. R: 5’-GACCAGCTCTCCATTCCTA-3’
22. R: 5’-AATGCACAGAGTTCGCACT-3’
23. R: 5’-TCCTGGGTGACACTTCTCAC-3’
24. R: 5’-CAGTTGGAGGCTGTCTCCTC-3’
25. R: 5’-TCAAAGCCCAGCACAATGTC-3’
26. R: 5’-CTGAGGCACCTGTTTTAGGA-3’
27. R: 5’-CAGTCCTTGGATGGTGAGGT-3’
28. R: 5’-AACACTCTGTCCTTCCACGAAATG-3’
29. R: 5’-AAGTGGTGATTAGGTAGTGGAGCCC-3’
30. R: 5’-CCTGTTCTGGATATGGAAGCTCG-3’
31. R: 5’-ATCTGCTTCAGCCGAGTCATCAG-3’
32. R: 5’-AGAAAGCCTGGGGGGATTTG-3’
33. R: 5’-GCTGGGCTTTGTTTCCTCTTG-3’
34. R: 5’-CTGAGCCCACCAGGCTTC-3’
35. R: 5’-TCTTCTTCTGCTGGTGCTCA-3’
36. R: 5’-GGAGCTGTTCGCAGGTCTG-3’
37. R: 5’-TCAGCAGACATTGCTTCACC-3’
38. R: 5’-TTCCCCAAGACGAATGCTGC-3’
39. R: 5’-TGCTCACCGTCAAGAAGCCATAA-3’
40. R: 5’-CACAGTAATGTTCCCAAACATGGC-3’
41. R: 5’-GGAAGATCATTGGGCAGAAA-3’
42. R: 5’-ACACTGGACCACATACTGACACTG-3’
43. R: 5’-GAGGAGCTGAGGCATTGAAG-3’
44. R: 5’-TGATGAAGAAGATGCCGCTGT-3’
45. R: 5’-CAGAATGGTAGTGTGAGCAGG-3’
46. R: 5’-GGTACGGATGATAATGAGGTAGCA-3’
47. R: 5’-TCAGTTCAACTCCAAGTCACTCAT-3’
48. R: 5’-CGAGTGACAAACACGACTGCA-3’
49. R: 5’-ATAGCCCCTTAGTGTGGTGAACA-3’
50. R: 5’-CCTCTTTGCCTGTGAGTTGGA-3’
51. R: 5’-GTTATCAAAACAACGCCAGGC-3’
52. R: 5’-GGCAGGAAGGTTCTGTCAAA-3’
53. R: 5’-CTTGGCCTTTGACTGTTGGT-3’
54. R: 5’-CTGTTTGAGGTCTTTGAGGGATT-3’
55. R: 5’-ATTCTCACTGGCCCGTCAT-3’
56. R: 5’-CAGGTACTCTTGGATCCACTTT-3’

23
Appendix. Primer Sequences for SYBR Green RT-qPCR (continued)

57. CXCL13
58. CD11b
59. CD16
60. CD56
61. B3GAT1 (CD57)
62. CD69
63. CD94
64. NKG2D
65. GranzymeB
57. KIR3DL1
58. CD1d
59. TCRa Va14
60. CD80
61. CD86
62. B7H3
63. Ox40L
64. 41BBL
65. GITRL
66. CD40
67. ICOS-L
68. PDL2
69. PDL1
70. IDO
71. B7H4
72. LIGHT
73. Arginase
74. iNOS
75. CD83
76. CD11c
77. Gr-1
78. H2-Ea (IAd)
79. MGSA (CXCL1)
80. CXCR2
81. COX2
82. Ptges1 (PGE2 synthase 1)
83. Platelet Factor 4
84. ICAM-1
85. ICAM-2
86. DC-SIGN
87. CCR1
88. CCL7
89. CCL8
90. CCL3
91. CD62E (E-selectin)
92. CD62P (P-selectin)
93. VCAM1
94. EpCAM
95. PECAM (CD31)
96. VEGF1
97. VEGFR1
98. VEGFR2
99. bFGF
100. FGFR
101. Angiopoietin
102. TIE-1
103. TIE-2
104. FAP
105. GAPDH
57. F: 5’-TGAAGTTGTGATCTGGACCAAGA-3’
58. F: 5’-CTCCGGTAGCATCAACAACAT-3’
59. F: 5’-ATGTTTCAGAATGCACACTCTGGA-3’
60. F: 5’-GGAAGGGAACCAAGTGAACA-3’
61. F: 5’-ATGCCGAAGAGACGGGACAT-3’
62. F: 5’-AAGTACAATTGCCCAGGCTT-3’
63. F: 5’-AATCAACACCTTCTCCAACCA-3’
64. F: 5’-CGATTCACCCTTAACACATTGATG-3’
65. F: 5’-AAGCTGAAGAGTAAGGCCAA-3’
66. F: 5’-CCTCGTGTGTTCTGGTTTCT-3’
67. F: 5’-AATTACACCTTCCGCTGCC-3’
68. F: 5’-TGGGAGATACTCAGCAACTCTGG-3’
69. F: 5’-ATGCTCACGTGTCAGAGGA-3’
70. F: 5’-CAACTGGACTCTACGACTTC-3’
71. F: 5’-TACAGCTGCCTGGTACGCAA-3’
72. F: 5’-ACGGATCAAGGCCAAGATTCAA-3’
73. F: 5’-ATTCACAAACACAGGCCACA-3’
74. F: 5’-CAAGACATGCCAACAACACC-3’
75. F: 5’-CACTGATACCGTCTGTCATCCCT-3’
76. F: 5’-TCTTGGAAGAGGTGGTCAGG-3’
77. F: 5’-CGTGACAGCCCCTAAAGAAG-3’
78. F: 5’-CGAATCACGATGAAAGTCAA-3’
79. Super Array:PPH05363A:proprietary
80. F: 5’-TGGCTTTGGCATTTCAGGC-3’
81. F: 5’-CTGCATCAACGTCTTGGAGA-3’
82. F: 5’-CAGAAGAATGGAAGAGTCAGTGT-3’
83. F: 5’-GACAAGCTGCATGTGACATC-3’
84. F: 5’-CCAGTTACCTCCCCAAGC-3’
85. F: 5’-TGGACTCTCACAGAAGCAAAG-3’
86. F: 5’-GGCGATGAGATTTTCCATGT-3’
87. F: 5’-CGGGAAGACAATAACTG-3’
88. F: 5’-CTATCGCCAATGAGCTGCG-3’
89. F: 5’-TCTGGCATGCCCTCTATTCTG-3’
90. F: 5’-TTCGGGAGCACAACAGAGTG-3’
91. F: 5’-GAGTTTTCACGTTCCGGTGT-3’
92. F: 5’-AGTCCTGAGCTGCTGCTTCT-3’
93. F: 5’-CATCCCAGAGAAGCCTTCCT-3’
94. F: 5’-ACCATTGAGTGCACGGTGTC-3’
95. F: 5’-CATGAGTGATTCTAAGGAAATGGG-3’
96. F: 5’-GCAAGCTTCTCTCTGGGTTTTA-3’
97. F: 5’-GCTCATAGCCGCTGCTTTC-3’
98. F: 5’-GCTTTCATGTACTAAAGCTGAAGA-3’
99. F: 5’-ATGAAGGTCTCCACCACTGC-3’
100. F: 5’-GGACTGTGTAGAGATTTACATCCA-3’
101. F: 5’-GTGCAGAGCGGTCAAATGC-3’
102. F: 5’-AACGACCTTCATCCCCACC-3’
103. F: 5’-TTGCTCCAAACTGGCGTCT-3’
104. F: 5’-GCAAAGAGTGACTTCCAGACT-3’
105. F: 5’-TACTGCTGTACCTCCACCAT-3’
106. F: 5’-CCGAACTCCACCTCCATGTTT-3’
107. F: 5’-AGGCTCCAACCAGACCAGT-3’
108. F: 5’-AGCGACCCACACGTCAAACT-3’
109. F: 5’-TGCCAAGACGGTGAAGTTCA-3’
110. F: 5’-TACAACACCGGGAAGATGGAAG-3’
111. F: 5’-ACTGCCCTCCTGACTGGAC-3’
112. F: 5’-TTGAAGTGACGAATGAGATTTTCAC-3’
113. F: 5’-ATGAAGACATGGCTGAAAACT-3’
114. F: 5’-AACTTTGGCATTGTGGAAGG-3’
106. R: 5’-ACAGACTTTTGCTTTGGACATGTC-3’
107. R: 5’-TGATCTTGGGCTAGGGTTTCT-3’
108. R: 5’-GTCCAGTTTCACCACAGCCTT-3’
109. R: 5’-ACGGTGTGTCTGCTTGAACA-3’
110. R: 5’-TTGTGCACAGCAAGCAGAGG-3’
111. R: 5’-ATGTCCTCTTGTATGAAATCCACT-3’
112. R: 5’-GATGCCCAACCCACTTGTCC-3’
113. R: 5’-GGGACTTCCTTGTTGCACAATAC-3’
114. R: 5’-CCAGCCACATAGCACACATC-3’
66. R: 5’-CAGGCAGATAGGAATGGTTT-3’
66. R: 5’-CTTCGTGAAGCTGATGGTGG-3’
67. R: 5’-CAGGTATGACAATCAGCTGAGTCC-3’
68. R: 5’-GACGGTCTGTTCAGCTAATG-3’
69. R: 5’-TGCTTAGACATGCAGGTCAA-3’
70. R: 5’-CAGAGGGTTTCAGAGGCCGTA-3’
71. R: 5’-CTGGTAACTGCTCCTCTGAGTCT-3’
72. R: 5’-GATAAGCCCTCAGACCCAC-3’
73. R: 5’-AAGGCCTAGGGGAAAGTTCA-3’
74. R: 5’-AGTTCTTATCCTCACAGCTTGTCCA-3’
75. R: 5’-TCCAAGGGAGCCTTAATGTG-3’
76. R: 5’-GATGACCAGGCAACGGTACT-3’
77. R: 5’-GCTGGTCACATTGAGAAGCA-3’
78. Super Array:PPM05363A:proprietary
79. R: 5’-CCGTTGAGTTTGATGTCAGGTTC-3’
80. R: 5’-GATACGTCAAGCCCCTCAAG-3’
81. R: 5’-CAGATATGCAGGGAGTCACC-3’
82. R: 5’-GCTGGTAGGTTCCTGTTGTT-3’
83. R: 5’-AGGAGGTTGACCAGATAGC-3’
84. R: 5’-GCAGAGGTCTTCCTTGCAAC-3’
85. R: 5’-TCACAGGGCTTCTGGAGAGT-3’
86. R: 5’-CATTTCCGATAAGGCTTGG-3’
87. R: 5’-CTTGGGGACACCTTTTAGCATCTT-3’
88. R: 5’-AAGGTAACCTCCTTCACGTATG-3’
89. R: 5’-TAACCGCTCAGGTGTTGCA-3’
90. R: 5’-GGTAGGCTGTCAGCTCAAGG-3’
91. R: 5’-GGCAAATTTTCCTCCCATTCTT-3’
92. R: 5’-TCAGCCACTGAGTCTCCAAG-3’
93. R: 5’-GCTCCCCCAAAGGTCTGATT-3’
94. R: 5’-TGTGAAGCTACTGAATCCAGA-3’
95. R: 5’-GGTGATGATGCCAAAAGTAACA-3’
96. R: 5’-GCTTTGGAGTTGGGGTTTTC-3’
97. R: 5’-CAGAGAGACATACCCTGCTT-3’
98. R: 5’-AGCTCCATATGGCGCTGAGAA-3’
99. R: 5’-GCAGGTGTAACTATTGATGGTCT-3’
100. R: 5’-CTGAGACGCTTTCTTAGCAGAGC-3’
101. R: 5’-TCTGCCTCTGTTTGGGTTCAG-3’
102. R: 5’-TGTTGACACACCAGCACGT-3’
103. R: 5’-GTACCTCGTTACTCGACAGG-3’
104. R: 5’-GCTCATTCTCTCTATGTGCTGG-3’
105. R: 5’-TATCTTCATGGAGGCCTTGGG-3’
106. R: 5’-CTAAGCAGCACCTCTCTCGT-3’
107. R: 5’-CGTCCATCTTCCTTCATAGCAAG-3’
108. R: 5’-CAATTCGGTGGTCAGGCTTA-3’
109. R: 5’-GTCGTTATCAGCATCCTTCGT-3’
110. R: 5’-CGATGTACTTGGATATAGGCCCA-3’
111. R: 5’-ATTTAGAGCTGTCTGGCTTTTTG-3’
112. R: 5’-GTAAACTCTTGAGGGACGTAAGA-3’
113. R: 5’-CACATTGGGGGTAGGAACAC-3’
24
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Abstract (if available)

Abstract Fold changes in 113 genes were measured using RT-qPCR in B16, RENCA, and LLC tumors of syngeneic mice for mechanisms of immune escape. Fold changes were compared at Day 7, 10 and 14 to gene expressions at Day 4 to evaluate sustained up or downregulation of immunostimulation or immunosuppression. RENCA (11) had the largest number of gene expression changes throughout all three timepoints than B16 (7) or LLC (4). Escape mechanisms were identified in B16 tumors from a lack of immunostimulation signalling while analysis in RENCA and LLC revealed immunoinhibitory activity from regulatory T-cells.

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Asset Metadata

Creator Park, Ryan (author)

Core Title Investigation of immune escape paths in the tumor microenvironment

School Keck School of Medicine

Degree Master of Science

Degree Program Experimental and Molecular Pathology

Publication Date 05/06/2013

Defense Date 03/21/2013

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

Tag B16 melanoma,fold change,immune,LLC Lewis lung carcinoma,microenvironment,OAI-PMH Harvest,RENCA renal carcinoma,RT-qPCR,Tumor

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Epstein, Alan L. (committee chair), Hofman, Florence M. (committee member), Kast, W. Martin (committee member)

Creator Email rpark@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-252101

Unique identifier UC11288094

Identifier etd-ParkRyan-1661.pdf (filename),usctheses-c3-252101 (legacy record id)

Legacy Identifier etd-ParkRyan-1661-0.pdf

Dmrecord 252101

Document Type Thesis

Rights Park, Ryan

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA