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The effect of inhibiting the NFkB pathway on myeloid derived suppressor cell cytokine production
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The effect of inhibiting the NFkB pathway on myeloid derived suppressor cell cytokine production
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Content
THE EFFECT OF INHIBITING THE NFB PATHWAY ON MYELOID
DERIVED SUPPRESSOR CELL CYTOKINE PRODUCTION
by
Sabrina Zhong
A Thesis Presented to the
FACULTY OF THE USC KECK SCHOOL OF MEDICINE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(APPLIED BIOSTATISTICS AND EPIDEMIOLOGY)
May 2024
Copyright 2024 Sabrina Zhong
ii
Dedication
This thesis is dedicated to my friends and family who supported me throughout my
academic journey. I could not be where I am today without them.
iii
Acknowledgements
I would like to thank Dr. Wendy Mack for being my advisor and committee chair and
providing me with the necessary support and knowledge to be successful in this program.
I would also like to thank my Principal Investigator, Dr. Evanthia Roussos Torres, for
providing me with the opportunity to join her research for this thesis and allowing me to become
a well-rounded researcher. In addition, I would like to thank Aaron Baugh for being a great
mentor who allowed me to excel in performing laboratory techniques and build upon my
knowledge of immunology during my time at Dr. Roussos Torres’s lab.
Lastly, I would like to thank my committee member, Dr. Mariana Stern, for her expertise
and guidance.
iv
Table of Contents
Dedication....................................................................................................................................... ii
Acknowledgements........................................................................................................................ iii
List of Tables ...................................................................................................................................v
List of Figures................................................................................................................................ vi
Abstract......................................................................................................................................... vii
Chapter 1: Introduction....................................................................................................................1
Chapter 2: Methods..........................................................................................................................7
Chapter 3: Results..........................................................................................................................10
CCL2 & CCL5...................................................................................................................10
NOS2..................................................................................................................................18
TNFα & IL-6......................................................................................................................19
Chapter 4: Discussion ....................................................................................................................22
CCL2 & CCL5...................................................................................................................22
NOS2..................................................................................................................................23
TNFα & IL-6......................................................................................................................24
Limitations and Future Directions.....................................................................................25
Implications........................................................................................................................26
References......................................................................................................................................27
v
List of Tables
Table 1. Descriptive statistics of the log(ΔΔCt) values for each target cytokine by 24-hour
treatment ....................................................................................................................................... 13
Table 2. Descriptive statistics of the log(ΔΔCt) values for each target cytokine by 72-hour
treatment ....................................................................................................................................... 15
vi
List of Figures
Figure 1. The two NFB signaling pathways in regulating expression of target cytokines. ......... 5
Figure 2. Graphical illustration of JSH-23 effect on target cytokine gene expression. ............... 14
Figure 3. Graphical illustration of 72-hour JSH-23 treatment effect on target cytokine gene
expression.. ................................................................................................................................... 16
Figure 4. Bar graph of average log2 fold change in cytokine expression for the inhibitortreated cells. .................................................................................................................................. 17
vii
Abstract
Immune checkpoint inhibitors (ICIs) have been shown to reactivate T cells to eliminate
cancer cells in various cancer types. However, suppressive immune cells have been shown to
prevent the anti-tumor effects of T cells in breast cancers which have a suppressive tumor
microenvironment (TME), posing a challenge for ICI effectiveness. Myeloid-derived suppressor
cells (MDSCs) are a significant contributor to the suppressive environment in breast tumors.
NFB signaling is activated in MDSCs and increases the production of suppressive cytokines
that allow tumor cells to evade immune system attacks, proliferate, and invade. Thus, I
hypothesize that inhibiting the NFκB signaling pathway in MDSCs will decrease the expression
of suppressive cytokines, which could decrease MDSC suppressive function. J774M, an MDSC
cell line, was treated with various concentrations of JSH-23, an NFB inhibitor. RNA was
collected and reverse transcribed into cDNA for qPCR analysis of CCL2, CCL5, Il-6, NOS2, and
TNFα gene expression. ANOVA p-values were calculated and subsequent pairwise comparisons
using Tukey’s test were conducted on mean log(ΔΔCt) values among samples. The 72-hour
inhibitor treatments illustrated significant changes in the expression of certain cytokines such as
CCL5. However, further log2 fold change investigation determined these changes were
insignificant independent of DMSO, the inhibitor vehicle. Higher concentrations of JSH-23 did
lower the expression of most cytokines, but this decrease was not statistically significantly
different from the control cells. Future studies can evaluate changes in cytokine production on
the protein level or with respect to a different transcription factor involved in the inflammation of
breast TME.
1
CHAPTER 1: INTRODUCTION
Female breast cancer had the highest incidence rate amongst all other cancers worldwide
in 2020 and is one of the leading causes of death in women. In 2020, breast cancer contributed to
25.8% of all new cancer cases in women worldwide. This percentage was magnitudes higher
than any other cancer type in the same year (World Health Organization, 2022). Because this
cancer negatively affects so many women and some men around the world, it is imperative to
understand the mechanisms behind breast cancer development and find effective treatments to
improve survival rates.
The immune system plays an important role in the elimination and proliferation of cancer
cells and contributes to many hallmarks of cancer such as evading immune destruction, tumor
promoting inflammation, and activating invasion and metastasis (Hanahan, 2022). The highly
accepted immunoediting hypothesis of the interaction between the immune system and
cancerous cells describes the phases of tumor progression in the immune system. The first phase
is the elimination phase where the immune system’s natural killer (NK) and CD8+ T cells can
identify and remove cancerous cells. The cancerous cells that slip past the elimination phase go
into the equilibrium phase where they can remain dormant for many years. The last phase is the
escape phase where the cancer cells can proliferate rapidly and appear as a tumor. The immune
system can no longer provide an antitumor response and may also contribute to tumor growth
through inflammation (Lasek, 2022). The immunoediting hypothesis and its emphasis on the
importance of an immune system that eliminates cancerous cells instead of aiding them has been
the basis for many cancer treatments.
2
Immune checkpoint inhibitors (ICIs) have been developed to counteract the escape phase
of the immunoediting hypothesis and have shown promising therapeutic effects in certain cancer
types. In a normally functioning immune system, T cells are activated by the immune system to
eliminate pathogens and tumors (Kumar et al., 2018). However, there must be inhibition of T cell
activity after they eliminate antigens to prevent T cells from attacking normal cells. Immune
checkpoints inhibit T cell activity by binding to partner proteins on T cells. Cancer cells can take
advantage of these checkpoints by upregulating these partner proteins to suppress the function of
T cells. ICIs, such as CTLA-4 and PD-L1, work by blocking the binding of checkpoint proteins
with their partner proteins. Through this process, T cells can be reactivated to recognize and
eliminate tumor cells (He & Xu, 2020). Although ICIs demonstrate promising therapeutic results
in eliminating cancer cells for some cancer types, there are still limitations to these types of
therapies. Some cancers, such as breast cancer, have highly suppressive cell types within their
tumor microenvironment (TME) that prevent ICIs from reactivating T cells. Therefore, it is
imperative to find other drugs that can be used with ICIs as combination therapy to sensitize the
TME to facilitate T cell elimination of breast cancer cells by ICIs and tackle the proliferation and
suppressive nature of breast cancer tumors.
The suppressive nature of the breast cancer TME can be attributed to tumor-associated
myeloid cells such as myeloid-derived suppressor cells (MDSCs). There are two types of
MDSCs: monocytic (M-MDSC) and granulocytic (G-MDSC). M-MDSCs are immature
mononuclear cells similar to monocytes whereas G-MDSCs are polymorphonuclear cells similar
to neutrophils (Shimizu et al., 2018). MDSCs aid cancer cells in escaping immune surveillance
and elimination by secreting suppressive cytokines such as IL-10 and TGF and upregulating the
expression of immune checkpoints like PD-L1 to deactivate T cell activity (Cha & Koo, 2020;
3
Shimizu et al., 2018). Specifically, M-MDSCs suppress T cell activity by upregulating inducible
nitric oxide synthase (iNOS) and releasing high levels of nitric oxide in addition to suppressive
cytokines. G-MDSCs, on the other hand, produce high levels of reactive oxygen species (ROS)
and arginase 1 to induce cell cycle arrest (Cha & Koo, 2020). Because MDSCs can also
upregulate the expression of immune checkpoints, they contribute in part to the intrinsic
resistance of breast cancer to ICIs (Hou et al., 2020). Primary MDSC characterization can be
studied through derivation of MDSCs from the bone marrow or bead removal from tumors. The
J774M cell line is a single, stable MDSC-like cell line derived from the macrophage cell line
procured from the ATCC-J774A.1 cell. This cell line was characterized by Dr. Kebin Liu’s Lab
and will be utilized for this thesis as primary MDSCs exhibit tremendous plasticity especially
when cultured ex-vivo. J774M cells are MDSC-like as they are highly suppressive of T cell
proliferation and have qualities shown through flow cytometry of immature monocytes such as
expression of CD11b and lack of MHC-II expression. Moreover, J774M cells are not
representative of a certain subtype of MDSCs as they express both Ly6C and Ly6G which are
markers produced by M-MDSCs and G-MDSCs respectively to differentiate the two MDSC
subtypes from each other (Liu et al., 2016). This thesis will utilize the J774M cell line given their
stable phenotype and functional characteristics.
Recently, the Roussos Torres lab found that Entinostat, an oral class I histone deacetylase
inhibitor, improved various immunosuppressive tumor models’ responses to ICIs. Using MDSC
cell lines treated with Entinostat, they discovered that the inhibitor altered signaling pathways
such as STAT3 and NFB. Additionally, they reported decreased immunosuppression illustrated
by measuring suppressive cytokine and chemokine production (Sidiropoulos et al., 2022).
However, since Entinostat affects the signaling pathways together, it is still unknown which
4
cytokines and chemokines are decreased from the alteration of which specific transcription factor
and associated signaling pathway. This thesis will focus on the inhibition of the NFB
transcription factor and the NFkB signaling pathway.
The NFB signaling pathway is highly involved in the regulation of immune responses
and cell proliferation. NFB is a proinflammatory transcription factor composed of 5 subunits:
Rel (cRel), p65 (RelA, NFκB3), RelB, p105/p50 (NFκB1), and p100/p52 (NFκB2). This
transcription factor is regulated by IBs and IKKs which bind to and inactivate NFB. The
NFB pathway has two different pathways: canonical and non-canonical. Both pathways start
with an inactivated NFB that remains in the cytoplasm of the cell. The canonical NFB
pathway can be activated by many different extracellular signals such as TNF⍺ and IL-1. The
activation of the canonical pathway starts with IKK-mediated phosphorylation of IB which then
signals for the degradation of IB by proteasomes. Once the IB is degraded, NFB subunits,
mainly the p60/p65 heterodimers, are activated and translocated into the nucleus to bind to and
regulate the expression of target genes (Xia et al., 2018). The heterodimers of the canonical
pathway mainly bind to the B site and activate genes to regulate inflammatory and immune
responses (Fig. 1A). Conversely, the non-canonical pathway can only be activated by a smaller
group of receptors within the TNF-receptor (TNFR) family members including lymphotoxin-β
receptor (LTβR). When these receptors are activated, it triggers the phosphorylation of IKK⍺, a
protein complex that deactivates NFB when attached. p100/RelB heterodimer is then
phosphorylated by the activated IKK⍺. This triggers the degradation of the C-terminal of p100’s
IB-like structure, which then causes the release of p52 from cleaving p100. Subsequently, this
allows for the translocation of the p52/RelB heterodimer into the nucleus and binding to its
specific B site to transcribe target genes. This heterodimer regulates the expression of target
5
genes involved in biological functions such as B-cell maturation and survival and dendritic cell
maturation (Khongthong et al., 2019) (Fig. 1B).
Figure 1. The two NFB signaling pathways in regulating expression of target cytokines.
The canonical and non-canonical pathways start with inactivated NFB residing in the
cytoplasm. A. The canonical NFB signaling pathway is initiated with the phosphorylation of
IB⍺ by the IKK complex. This causes the IKK complex to dissociate from the RelA (p65)/p50
heterodimer and degrade. The p65/p50 heterodimer is now activated and translocated to the
nucleus to regulate the gene expression of targeted genes. B. The non-canonical pathway
involves the phosphorylation of IKK⍺ that subsequently phosphorylates the p100/RelB
heterodimer. The C-terminal of p100’s IB-like structure is then degraded which causes the
release of p52 from cleaving p100. The p52/RelB heterodimer is translocated into the nucleus to
transcribe target genes at its specific B site.
The NFB signaling pathway is also an important mediator of MDSC development.
MDSCs can activate the NFB transcription factor along with the STAT3 transcription factor to
inhibit the synthesis of p53. The activation of these transcription factors also causes the
production of suppressive cytokines such as chemokine ligand 2 (CCL2), chemokine ligand 5
(CCL5), interleukin 6 (IL-6), nitric oxide synthase 2 (NOS2), and tumor necrosis factor alpha
(TNFα). These factors can allow tumor cells to evade immune system attacks, proliferate, and
invade (Shrihari, 2017). Therefore, it is important to understand how the inhibition of the NFB
signaling pathway affects immunosuppressive TMEs, such as in breast cancer, through
measurements of suppressive cytokine production. Many inhibitors of the NFB signaling
A B
6
pathway have been developed to alter different steps of the pathway. This thesis will use an
inhibitor called JSH-23, an aromatic diamine. JSH-23 inhibits the transcriptional activity of
NFB by preventing the p65 subunit from translocating to the nucleus. JSH-23 does not prevent
IB degradation (MedChemExpress, n.d.).
I hypothesize that inhibiting NFB signaling pathways using JSH-23 in MDSCs will
decrease the expression of functional cytokines: CCL2, CCL5, NOS2, IL-6, and TNF⍺.
Additionally, I hypothesize that the higher the concentration of JSH-23 added to the cells, the
lower the production of suppressive cytokines will be.
7
CHAPTER 2: METHODS
J774M Culturing
J774M cell lines, murine myeloid derived suppressor cells, were supplied by the Kebin
Liu Lab from Augusta University. 1 mL of J774M cells were thawed from liquid nitrogen tanks.
The cells were first cultured in T75 flasks with 10 mL of J cell media. The J Cell media consisted
of RPMI-1640, 10% FBS, 1% L-Glutamine, 1% Pen/Strep, 1% NEAA, 1% Na Pyruvate, 1.5%
HEPES, and 0.00004% BME. After 2 days or 70-80% cell confluency, the cells were passed and
expanded to T175 flasks with a total volume of 30 mL of cells and media. The cells were then
passaged every 3 days and used in experiments when needed. Cells were kept in media for no
longer than 25 passages. The cells were checked every 6 months for mycoplasma contamination.
Treating J774M Cells with NFB inhibitor-JSH-23 and Stimulating with TNFα
2 x 106
cells were plated in 6 well plates. Cells were pre-treated with media, DMSO
vehicle, 1 µM JSH-23 (MedChemExpress, cat. HY-13982), 5 µM JSH-23, or 10 µM JSH-23 for
either 24 hours with stimulation using 20 ng/mL of TNFα after 2 hours or 72 hours with TNFα
stimulation after 48 hours. One well treated with media was not stimulated for the 24-hour trials.
8
RNA Extraction and Reverse Transcription
RNA was extracted from treated and stimulated cells using the Quick-RNA miniprep kit
from Zymo Research (Zymo Research; cat. R1050). The concentration of RNA extracted from
each well was measured using a Nanodrop machine and subsequently reversed transcribed into
cDNA. 5x reaction mix and maxima enzyme mix from the Maxima First Strand cDNA Synthesis
Kit for RT-qPCR (ThermoFisher; cat. K1642) along with 1 µg of RNA template were combined
to perform reverse transcription of each RNA sample into cDNA. The mixtures were then gently
mixed and centrifuged. The mixtures were incubated at 25°C for 10 minutes followed by 50°C
for 15 minutes. To terminate the reaction process, the mixtures were heated at 85°C for 5
minutes. The cDNA product was then used in qPCR.
Quantitative (q)PCR
qPCR was performed using PowerUp™ SYBR™ Green Master Mix (ThermoScientific;
cat. A25742) with 5 ng of cDNA as the sample. The target primer sets used were from Integrated
DNA Technologies and the sequences were as follows: Actb (5’-
GACTCATCGTACTCCTGCTTG-3’ and 5’-GATTACTGCTCTGGCTCCTAG-3’), TNFα (5’-
GAGCAGAGGTTCAGTGATGTAG-3’ and 5’-CTACCTTGTTGCCTCCTCTTT-3’), Il6 (5’-
TCCTTAGCCACTCCTTCTGT-3’ and 5’-AGCCAGAGTCCTTCAGAGA-3’), Nos2 (5’-
CACTTCTGCTCCAAATCCAAC-3’ and 5’-GACTGAGCTGTTAGAGACACTT-3’), CCL2
(5’-CATCCACGTGTTGGCTCA-3’ and 5’-AACTACAGCTTCTTTGGGACA-3’), and CCL5
(5’-GCTCCAATCTTGCAGTCGT-3’ and 5’-CCTCTATCCTAGCTCATCTCCA-3’). The
samples were mixed with each target primer in triplicate wells on the qPCR plate. The plates
9
were run on the QuantStudio(TM) 6 Flex System (ThermoScientific) with the real-time PCR
cycling conditions as 50 °C for 2 min, 95 °C for 2 min, 40 cycles of 95 °C for 15 s, 52°C for 15
s, and 72°C for 1 min. Each qPCR run was repeated 4 times.
Statistical Analyses
The ΔΔCt method was used to quantify the gene expression that was normalized to Actb
as the reference gene using Microsoft Excel version 2309. The ΔΔCt values were logtransformed to achieve a normal distribution. A one-way ANOVA was conducted between wells
with media only without TNFα stimulation, media with stimulation, DMSO, 1 µM JSH-23, 5
µM JSH-23, and 10 µM JSH-23 for each target cytokine (CCL2, CCL5, Il-6, NOS2, and TNFα).
There were 4 wells for each of the 6 treatment groups. Subsequent pairwise comparisons using
Tukey’s test were conducted on cytokines in which the ANOVA p-value for overall group
comparisons showed statistically significant (p < 0.05) or marginally significant differences
(0.05 < p < 0.10) in mean ΔΔCt values among groups. A log2 fold change was computed for the
72-hour treatment groups in reference to the DMSO treated group for each cytokine ΔΔCt value
to determine the effect of JSH-23 on cytokine expression independent of its vehicle (DMSO).
Subsequent Wilcoxon signed rank tests were performed for the log2 fold change values to test
for differences from the null value of zero. The SAS software version 9.4 and a 2-sided
significance level of 0.05 were used for all statistical analyses.
10
CHAPTER 3: RESULTS
The MDSC-like cell line J774M was utilized to examine changes in expression of
functional cytokines: chemokine ligand 2 (CCL2), chemokine ligand 5 (CCL5), interleukin 6
(IL-6), nitric oxide synthase 2 (NOS2), and tumor necrosis factor alpha (TNFα). To measure
changes in functional cytokines, MDSCs require stimulation to initiate an immune response.
There are numerous different methods in which MDSCs can be stimulated. TNFα was used in
this thesis to stimulate cells because it plays a crucial role in activating the NFB pathway. To
determine the role of NFB signaling in production of suppressive cytokines, JSH-23, an NFB
inhibitor, was implemented which works by preventing the p65 subunit from translocating to the
nucleus. Following inhibition, the production of cytokines was investigated at the mRNA level.
To determine the changes in the production of functional cytokines regulated by the NFB
signaling pathways, the gene expression was quantified through ΔΔCt analysis calculated from
qPCR data. A one-way ANOVA and subsequent pairwise comparisons were conducted to
identify statistically significant differences in cytokine expression among the six treatment
conditions. The following are the results from this study:
CCL2 & CCL5
CCL2 and CCL5 expression were analyzed given their role in promoting the recruitment
of immune cells such as monocytic-MDSCs (M-MDSCs) into the tumor microenvironment.
While M-MDSCs are essential to the balance of immune system activity, excess M-MDSC
generation leads to inhibition of T cell activity to fight off cancerous cells (Umansky et al.,
2016). Higher levels of CCL2 and CCL5 were hypothesized in this thesis to relate to the
11
suppressive function of M-MDSCs. CCL2 and CCL5 expression was first examined in J774M
cells, which are not purely representative of M-MDSCs, with plans to validate findings in
primary M-MDSCs in the future.
24-Hour Inhibitor Treatment
Of the three different concentrations of JSH-23 inhibitor used, the 5 µM concentration
led to the lowest median expression of CCL2 while the cells treated with 10 µM led to
intermediate expression and the cells treated with 1 µM had the highest expression. The
differences in the mean expression of the different samples were not statistically significant (p =
0.85). CCL2 expression for each sample had high variability in the media without stimulation,
media, and DMSO-treated cells as illustrated in the boxplots. The median expression for these
cells was relatively similar with the cells treated with media without stimulation being slightly
higher. Cells treated with 10 and 5 µM of JSH-23 showed the lowest variability. The median
expression for the inhibitor-treated cells (those with different concentrations of JSH-23) was also
relatively similar to the non-inhibitor-treated cells (Fig. 1A).
For CCL5 expression, the cells treated with media without stimulation had the highest
median expression. Of the cells treated with the inhibitor, the cells treated with 5 µM of JSH-23
had the lowest median CCL5 expression. Additionally, the cells treated with 10 µM of JSH-23
had the highest median expression. The difference in mean CCL5 expression for each treatment
condition was not statistically significant (p = 0.10). There was some variability noted among
technical replicates in the expression of CCL5: cells treated with DMSO showed the highest
variability and 5 µM JSH-23 with the least as demonstrated by the boxplot (Fig. 1B).
12
72-Hour Inhibitor Treatment
Of all the inhibitor-treated cells, the cells treated with 10 µM of JSH-23 led to the lowest
median expression of CCL2 and the cells treated with 1 µM of JSH-23 had the highest. While
there was a decreasing trend in CCL2 expression as the concentration of the inhibitor increases,
the differences were not statistically significant (p = 0.46). There was high variability in the
expression of CCL2 among the cells treated with DMSO, 1 µM of JSH-23, and 10 µM of JSH23. None of the mean expression levels had a statistically significant difference from each other
(p = 0.46) (Fig. 2A). The change in CCL2 expression independent of DMSO was not statistically
significant for 1, 5, or 10 µM of JSH-23 (p = .25, p = 1.0, p = 0.5 respectively) (Fig. 4A).
Looking at CCL5 expression, cells inhibited with 10 µM of JSH-23 led to the lowest
median expression and cells treated with 1 µM of JSH-23 led to the highest. Additionally, the
ANOVA test provided evidence of a statistically significant difference in the mean CCL5
expression of at least one treatment sample (p = 0.0084). Observing the pairwise comparisons,
the control cells (media) had a statistically significant difference in mean CCL5 expression from
cells treated with 10 µM of JSH-23 (p = 0.041) and a marginally significant difference from cells
treated with 5 µM of JSH-23 (p = 0.093). The cells treated with 1 µM of JSH-23 also had a
statistically significant difference in CCL5 expression from cells treated with 5 µM of JSH-23 (p
= 0.029) (Fig. 3B). Looking at the difference in expression of CCL5 for inhibitor-treated cells
independent of DMSO, there was not a statistically significant change in CCL5 expression for 1,
5, or 10 µM of JSH-23 (p = 1.0 for all three treatments) (Fig. 4B).
13
Table 1. Descriptive statistics of the log(ΔΔCt) values for each target cytokine by 24-hour
treatment
Target Cytokine Mean (SD) log(ΔΔCt) Median (IQR) log(ΔΔCt) ANOVA p-value
CCL2
Media w/o Stim
Media w/ Stim
DMSO
1 µM JSH-23
5 µM JSH-23
10 µM JSH-23
0.0369 (1.3194)
0.0000 (2.0310)
0.4028 (2.7112)
-0.6389 (0.9098)
-0.8747 (0.3631)
-0.4244 (0.3936)
-0.3481 (-0.8579, 0.9316)
-0.6521 (-1.2080, 1.2080)
-0.5376 (-1.4399, 2.2454)
-0.3253 (-1.1707, -0.1071)
-0.8413 (-1.1695, -0.5799)
-0.5694 (-0.6967, -0.1521)
0.85
CCL5
Media w/o Stim
Media w/ Stim
DMSO
1 µM JSH-23
5 µM JSH-23
10 µM JSH-23
0.0574 (0.6546)
0.0000 (0.8968)
-0.1685 (1.5761)
-1.1358 (1.0741)
-1.6192 (0.3975)
-0.4987 (0.3182)
0.1711 (-0.4755, 0.5904)
-0.1624 (-0.5876, 0.5875)
-0.5060 (-1.2305, 0.8935)
-1.2124 (-2.0389, -0.2328)
-1.5976 (-1.9513, -1.2872)
-0.5477 (-0.7488, -0.2486)
0.10
IL-6
Media w/o Stim
Media w/ Stim
DMSO
1 µM JSH-23
5 µM JSH-23
10 µM JSH-23
0.8741 (1.7625)
0.0000 (1.5693)
-0.1165 (2.2117)
-0.6196 (1.5773)
-0.8900 (1.7388)
-1.2115 (0.3317)
0.6911 (-0.2185, 1.9667)
-0.3894 (-1.2002, 1.2002)
-0.6520 (-1.7823, 1.5493)
-0.1639 (-1.774, 0.5346)
-0.9380 (-2.3582, 0.5782)
-1.1956 (-1.4963, -0.9267)
0.55
NOS2
Media w/o Stim
Media w/ Stim
DMSO
1 µM JSH-23
5 µM JSH-23
10 µM JSH-23
-0.5352 (0.5481)
0.0000 (0.8605)
0.4082 (1.5535)
-0.2572 (0.8030)
-0.4907 (0.6834)
-1.7197 (0.3660)
-0.4986 (-0.8865, -0.1839)
0.0133 (-0.7421, 0.7421)
-0.1114 (-0.6095, 1.4259)
-0.3596 (-0.8308, 0.3163)
-0.7014 (-0.9530, -0.0284)
-1.6810 (-1.9927, -1.4466)
0.06
TNF⍺
Media w/o Stim
Media w/ Stim
DMSO
1 µM JSH-23
5 µM JSH-23
10 µM JSH-23
-1.0749 (1.7877)
0.0000 (0.3216)
-1.1236 (1.6789)
-0.7093 (1.3069)
-0.6709 (0.7168)
0.5015 (0.2128)
-0.9916 (-2.6148, 0.4650)
0.0490 (-0.2542, 0.2542)
-0.4935 (-2.1041, -0.1431)
-0.7081 (-1.7666, 0.3480)
-0.6939 (-1.2820, -0.0598)
0.4747 (0.3683, 0.6348)
0.36
The ΔΔCt values were calculated using Actb as the reference gene and media with TNF⍺
stimulation as the untreated control. A log transformation was conducted on the values to
approximate a normal distribution. The p-value corresponds to the one-way ANOVA test on the
differences in mean log(ΔΔCt) between each treatment for each target cytokine.
14
Figure 2. Graphical illustration of JSH-23 effect on target cytokine gene expression.
The ΔΔCt values were measured in cell lines to quantify gene expression. Sample refers to the
treatment that was applied to cells growing in the wells. Cells treated with ‘Media w/o St’ were
not stimulated with TNF⍺. A. Boxplot of log(ΔΔCt) values for CCL2. B. Boxplot of log(ΔΔCt)
values for CCL5. C. Boxplot of log(ΔΔCt) values for IL-6. D. Boxplot of log(ΔΔCt) values for
NOS2. E. Boxplot of log(ΔΔCt) values for TNF⍺.
p = 0.85 p = 0.10
p = 0.85 p = 0.06
p = 0.36
A B
C D
E
15
Table 2. Descriptive statistics of the log(ΔΔCt) values for each target cytokine by 72-hour
treatment
Target Cytokine Mean (SD) log(ΔΔCt) Median (IQR) log(ΔΔCt) ANOVA p-value
CCL2
Media
DMSO
1 µM JSH-23
5 µM JSH-23
10 µM JSH-23
0.0000 (0.6529)
-1.1268 (1.1116)
0.0447 (1.3930)
-1.2795 (0.3934)
-0.8931 (1.5106)
-0.0134 (-0.6461, 0.6595)
-0.5025 (-2.4102, -0.4676)
0.4333 (-1.5014, 1.2021)
-1.3776 (-1.6146, -0.8463)
-1.6611 (-1.8653, 0.8472)
0.46
CCL5
Media
DMSO
1 µM JSH-23
5 µM JSH-23
10 µM JSH-23
0.0000 (0.1410)
-0.5116 (0.3804)
0.2300 (0.4771)
-0.8366 (0.0669)
-1.1139 (0.5483)
-0.0631 (-0.0985, 0.1616)
-0.5976 (-0.8416, -0.0955)
0.4816 (-0.3203, 0.5286)
-0.8307 (-0.9062, -0.7729)
-1.1139 (-1.5016, -0.7261)
0.0084
IL-6
Media
DMSO
1 µM JSH-23
5 µM JSH-23
10 µM JSH-23
0.0000 (1.1210)
-1.0175 (1.3890)
0.4071 (0.2880)
0.2213 (0.8298)
-2.7993 (0.7893)
-0.4378 (-0.8361, 1.2739)
-1.5356 (-2.0729, 0.5561)
0.3866 (0.1299, 0.7048)
-0.0605 (-0.4310, 1.1553)
-2.7993 (-3.3574, -2.2411)
0.0332
NOS2
Media
DMSO
1 µM JSH-23
5 µM JSH-23
10 µM JSH-23
0.0000 (0.5864)
-0.9284 (0.8250)
-0.1672 (1.5035)
-1.5993 (0.9323)
-1.3994 (1.2158)
0.0393 (-0.6050, 0.5657)
-1.0351 (-1.6949, -0.0552)
0.4280 (-1.8771, 0.9477)
-1.8897 (-2.3518, -0.5564)
-1.6571 (-2.4658, -0.0754)
0.31
TNF⍺
Media
DMSO
1 µM JSH-23
5 µM JSH-23
10 µM JSH-23
0.0000 (0.3167)
-1.2396 (0.1489)
0.2916 (0.4305)
-0.0776 (0.5639)
0.5427 (0.7787)
-0.0062 (-0.3135, 0.3197)
-1.3120 (-1.3386, -1.0684)
0.4686 (-0.1992, 0.6053)
0.2155 (-0.7277, 0.2794)
0.1335 (0.0539, 1.4407)
0.0117
The ΔΔCt values were calculated using Actb as the reference gene and media with TNF⍺
stimulation as the untreated control. A log transformation was conducted on the values to fit a
normal distribution. Outliers were removed from the data set according to studentized residuals.
The p-value corresponds to the one-way ANOVA test on the differences in mean log(ΔΔCt)
between each treatment for each target cytokine.
16
Figure 3. Graphical illustration of 72 hour JSH-23 treatment effect on target cytokine gene
expression.
The ΔΔCt values were measured in cell lines to quantify gene expression. Sample refers to the
treatment that was applied to cells growing in the wells. A. Boxplot of log(ΔΔCt) values for
CCL2. B. Boxplot of log(ΔΔCt) values for CCL5. C. Boxplot of log(ΔΔCt) values for IL-6. D.
Boxplot of log(ΔΔCt) values for NOS2. E. Boxplot of log(ΔΔCt) values for TNF⍺.
C D
E
A B
p = 0.46 p = 0.0084
p = 0.0332 p = 0.31
p = 0.0117
17
Figure 4. Bar graph of average log2 fold change in cytokine expression for the inhibitortreated cells. The log2 fold change values were calculated using ΔΔCt values of DMSO-treated
cells. The error bars depict the range of log2 fold change values used to calculate the average.
The log2 fold change illustrates the effect of JSH-23 on the expression of each functional
cytokine independent of its vehicle, DMSO. A. Bar graph of average log2fold change values for
CCL2. B. Bar graph of average log2fold change values for CCL5. C. Bar graph of average
-4
-3
-2
-1
0
1
2
3
1 5 10
Average Log2 Fold Change
Concentration of JSH-23 (uM)
Target = CCL2
-3
-2
-1
0
1
2
3
1 5 10
Average Log2 Fold Change
Concentration of JSH-23 (uM)
Target = CCL5
-6
-4
-2
0
2
4
6
1 5 10
Average Log2 Fold Change
Concentration of JSH-23 (uM)
Target = IL-6
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
1 5 10
Average Log2 Fold Change
Concentration of JSH-23 (uM)
Target = NOS2
0
0.5
1
1.5
2
2.5
3
3.5
4
1 5 10
Average Log2 Fold Change
Concentration of JSH-23 (uM)
Target = TNFa
A B
C D
E
18
log2fold change values for IL-6. D. Bar graph of average log2fold change values for NOS2. E.
Bar graph of average log2fold change values for TNFα.
NOS2
The expression of NOS2 was of interest due to its relevance in depleting the tumor
microenvironment of L-arginine, an amino acid that is actively metabolized by activated T cells.
The downstream metabolites of L-arginine are essential for T cell survival (Fultang et al., 2021).
Furthermore, a study done by Geiger and colleagues in 2016 illustrated that increased L-arginine
concentration induced a metabolic switch from glycolysis to oxidative phosphorylation, which
can oppose the Warburg effect. The relevance for expression of NOS2 by MDSCs is related to
this study in that, high levels of NOS2 represent high levels of suppressive function by MMDSCs.
24-Hour Inhibitor Treatment
The difference in the mean NOS2 expression among all groups was marginally
significant (p = 0.058). Looking at the pairwise comparisons, only cells treated with DMSO and
cells treated with 10 µM JSH-23 had a statistically significant difference in mean expression
levels (p = 0.032). Additionally, the variability in NOS2 expression for the cells treated with
DMSO was the highest as shown from the boxplots. Of the cells treated with media, the cells that
were not stimulated with TNF⍺ had a lower median expression than the cells that were
stimulated. The cells treated with the inhibitor had decreasing expression of NOS2 as the
concentration of the inhibitor increased (Fig. 2D).
19
72-Hour Inhibitor Treatment
The median NOS2 expression for cells in media was lower than for the cells treated with
1 µM of JSH-23, but higher than cells treated with 5 µM and 10 µM of JSH-23 (Table 2).
Overall, there were no pairs of mean NOS2 expression that had a statistically significant
difference from each other (p = 0.31) (Fig. 3D). There was also no statistically significant
difference in NOS2 expression for 1, 5, or 10 µM of JSH-23 independent of DMSO (p = 1.0, p =
0.25, p = 1.0 respectively) (Fig. 4D). Moreover, the expression of NOS2 for every sample group
had high variability as depicted in the width of the boxplots (Fig. 3D).
IL-6 & TNFα
IL-6 and TNFα inflammatory cytokines were evaluated as they are responsible for the
recruitment of MDSCs to the tumor microenvironment. The secretion of TNFα induces the
production of IL-6. IL-6 then promotes inflammation by upregulating the expression of T cellattracting chemokines such as CCL4 and CCL5. However, since IL-6 has been shown to also
recruit immunosuppressive cells to the tumor site, a high expression of IL-6 would be beneficial
to cancer proliferation and survival (Weber et al., 2021). This thesis also focused on examining
the expression of IL-6 and TNFα as higher levels of these cytokines play a role in the
suppressive function of MDSCs.
24-Hour Inhibitor Treatment
The cells treated with media without stimulation and the cells treated with 1 µM of JSH23 had the highest median IL-6 expression of all the other cell samples. The cells treated with 10
µM of JSH-23 had the lowest median IL-6 expression compared to other cell groups. The
20
median IL-6 expression of the inhibitor-treated cells decreased as the concentration of the
inhibitor increased. The non-significance of the difference in the mean expression of IL-6 in each
cell treatment group was confirmed with the one-way ANOVA test (p = 0.55). The expression of
IL-6 among the different treated wells had high variability except for the cells treated with 10
µM of JSH-23 (Fig. 2C).
The cells treated with 10 µM of JSH-23 had the highest median expression of TNF⍺. The
cells treated with media and stimulated had the second highest median expression. The median
TNFα expression of the other cell treatment groups was similar to each other. The nonsignificant
difference in mean TNFα expression was further confirmed through the one-way ANOVA test (p
= 0.36). The cells treated with media without stimulation, DMSO, 1 µM JSH-23, and 5 µM JSH23 illustrated high variability in expression (Fig. 1E).
72-Hour Inhibitor Treatment
The median expression of IL-6 for the cells treated with 10 µM of JSH-23 were the
lowest whereas the median expression for the cells treated with 5 µM of JSH-23 were the
highest. Among the control wells, the cells treated with DMSO had a lower median IL-6
expression than the cells in media only (Table 2). The control cells (media and DMSO) had high
variability among IL-6 expression. The ANOVA test suggested that there was at least one cell
treatment group that had a statistically significant difference in mean expression (p = 0.033).
Looking closer at the pairwise comparisons, the mean IL-6 expression for control cells in media
had a marginally significant difference from the median expression for cells treated with 10 µM
of JSH-23 (p = 0.068). The cells treated with 10 µM of JSH-23 had a statistically significant
difference in median expression from both the cells treated with 1 µM of JSH-23 (p = 0.035) and
21
5 µM of JSH-23 (p = 0.048) (Fig. 3E). For the expression of IL-6 independent of DMSO, there
was not a statistically significant difference for 1, 5, or 10 µM of JSH-23 (p =1.0, p = 0.75, p =
1.0 respectively) (Fig. 4C).
Looking at TNFα, the expression of TNFα for the cells treated with DMSO were the
lowest of all the cell samples. The medians for the inhibitor-treated cells showed a decreasing
trend as the concentration of inhibitor treatment increased. The one-way ANOVA test suggested
that at least one of the mean expressions of TNFα in a treatment group had a statistically
significant difference from another treatment group (p = 0.012). The pairwise comparisons
showed that the cells in DMSO had a statistically significant difference in mean expression from
cells treated with 1 µM (p = 0.024) and 10 µM of JSH-23 (p = 0.009). None of the inhibitortreated cells had a mean expression of TNFα that was statistically significantly different from
cells in media. Looking at the expression of TNFα for 1, 5, or 10 µM of JSH-23-treated cells,
there was not a statistically significant difference independent of DMSO (p = 0.25, p = 1.0, p =
1.0) (Fig. 4E).
22
CHAPTER 4: DISCUSSION
The NFB transcription factor is responsible for controlling the gene expression of
functional cytokines such as TNF-⍺, Il-6, CCL2, CCL5, and NOS2, which can aid in tumor
proliferation, immune evasion, and invasion. The purpose of this thesis was to determine the role
of NFB signaling on the expression of these specific suppressive cytokines as an indirect
measure of the effect of Entinostat on MDSC suppressive function via signaling through the
NFB pathway. Overall, these findings will contribute to our understanding of how Entinostat
treatment can work to dampen the immunosuppressive response of breast cancer cells which will
in turn, improve their response to ICIs.
CCL2 & CCL5
It was expected that inhibition of the NFB pathway in MDSCs would decrease CCL2
and CCL5 expression which would decrease the inhibition of T cell activity and allow them to
eliminate tumor cells. However, the expression of CCL2 and CCL5 after 24 hours of inhibition
did not change levels of expression. After 72 hours of inhibition, there was still no difference in
the expression of CCL2 compared to the control cells. A study done by Nakatsumi and
colleagues in 2017 found that while the NFB transcription factor regulated the expression of
CCL2 from previous studies, the expression of CCL2 is also regulated by the mTORC1-FOXK1
pathway in tumor cells, independent of NFB signaling. This could explain why there was no
significant difference in the expression of CCL2 for cells treated with the JSH-23 inhibitor as
this alternative signaling pathway could be engaged via treatment with Entinostat. Future studies
can evaluate the effect of Entinostat on the mTORC1-FOXK1 pathway and its subsequent effect
on CCL2 expression and ultimately immunosuppression. Additionally, the cells treated with 72
23
hours of the inhibitor showed a decrease in CCL5 expression between the control versus cells
treated with 10 µM of JSH-23. The cells treated with 1 µM and 5 µM of JSH-23 also had a
decrease in CCL5 expression, however this was only in comparison to cells treated with DMSO
as there was no difference compared to untreated cells. This could potentially illustrate that
inhibiting the NFB signaling pathway with higher concentrations of JSH-23 affected the
production of CCL5. Many articles recognize CCL5 as a target gene for the NFB transcription
factor. One study in particular conducted by Yeo and colleagues in 2020 also illustrated a
decrease in the production of CCL5 when inhibiting the NFB pathway using a different
inhibitor, chrysin. While chrysin acts upon an earlier step in the NFB pathway compared to
JSH-23, the outcome is still the same: the NFB transcription factor cannot be translocated into
the nucleus. However, the expression of CCL5 did not decrease when observing the effect of
JSH-23 independent of DMSO. This suggests that DMSO could have influenced the change in
CCL5 expression when the cells were treated with JSH-23.
NOS2
Changes to the production of NOS2 in MDSCs by inhibiting the NFB pathway could
affect the depletion of L-arginine and promote the survival of T cells in the elimination of cancer
cells. For both the 24- and 72-hour inhibitor-treated cells, the expression of NOS2 compared to
the control cells in media did not change dramatically. The only decrease in NOS2 expression
occurred between the DMSO-treated cells and 10 µM of JSH-23-treated cells for the 24-hour
inhibitor treatment. This is unexpected because NOS2 expression is regulated by NFB
homodimers, p65/p65 or p50/p50, that bind to the NOS2 promoter (Redd et al., 2015). JSH-23
inhibits the pathway by preventing p65 from translocating into the nucleus which should
24
theoretically affect the expression of NOS2. Further research on the regulation of NOS2
expression by other transcription factors could be done to understand this discrepancy. Higher
concentrations of JSH-23 can also be utilized to illustrate changes in NOS2 expression compared
to DMSO.
IL-6 & TNFα
Changes to IL-6 and TNFα expression using JSH-23 could result in a change in the
suppressive nature of breast tumors and allow for the activation of T cells to attack cancer cells.
However, while the 24-hour treatment of JSH-23 did result in a lower expression of IL-6 with
increasing concentrations of JSH-23, these differences were not statistically significant from
each other or the control cells. The expression of TNFα also did not have any statistically
significant differences between each cell sample. For the 72-hour treatment, the expression of
IL-6 in 10 µM of JSH-23 was significantly lower than the other concentrations of inhibitortreated cells. This could suggest that higher concentrations of JSH-23 were needed to lower the
expression of IL-6. For TNFα expression, the cells treated with DMSO had a significantly lower
expression than the cells treated with 1 µM and 10 µM of JSH-23. The vehicle of the inhibitor
may be affecting the expression of TNFα rather than the inhibitor itself as observed from the
log2 fold change analysis. These results were unexpected as the NFB transcription factor
directly regulates the expression of IL-6 and TNFα (Liu et al., 2017). A study conducted by
Liang and colleagues found that intervertebral disc degeneration patients with higher expression
of NFB transcription factor also had higher expressions of IL-6 and TNFα. Inhibiting this
signaling pathway should have resulted in a change in the production of IL-6 and TNFα. The
25
contrasting results from this thesis could suggest that IL-6 and TNFα expression is regulated by
other transcription factors in tumor cells.
Limitations & Future Directions
Limitations to this study may have contributed to the non-significance of the results.
Almost all the control wells (media without stimulation, media with stimulation, and DMSO)
had a wide range of ΔΔCt values which made it difficult to tell which values were more
representative of the gene expression of cytokines in control cells. The wide range of values also
made it difficult to see a difference between the gene expression of each cytokine between
control and treatment wells. In the future, additional measurements of ΔΔCt values can be made
for each treatment to give a better understanding of the gene expression of cytokines for cells in
the control wells. Potential outliers or influential points can also be more confidently identified
with more trials and be removed if needed.
Furthermore, there was evidence to suggest that JSH-23 did affect the expression of
specific cytokines from the pairwise comparisons, but the log2 fold change analysis deemed
these changes to be insignificant when eliminating the effect of the inhibitor vehicle, DMSO.
Since DMSO can heavily disrupt the function of the cell, future investigations can utilize a
different inhibitor vehicle such as PBS or ethanol that is less toxic to mammalian cell lines. This
would prevent the expression of cytokines from being influenced by DMSO and allow the ΔΔCt
values to be more representative of the effect of JSH-23.
Additionally, the variation in ΔΔCt values only illustrates the difference in cytokine
production of cells with each treatment. These values cannot indicate whether the suppressive
function of the cells has changed or not. Future experiments can utilize ELISA technology to
26
understand changes to the indirect suppressive function of cells by measuring changes in protein
production. Furthermore, suppression assays can help to understand changes in the direct
suppressive function of cells by measuring changes in T-cell proliferation.
Implications
Breast cancer affects hundreds of thousands of women in the U.S. annually. Breast
cancer’s immunosuppressive tumor microenvironment makes it difficult for treatments such as
immune checkpoint inhibitors (ICIs) to reactivate T cells to eliminate cancer cells. Therefore,
decreases in suppressive cytokines from inhibiting specific inflammation-related pathways such
as the NFκB signaling pathway are pertinent to initiating a better immune response and
identifying more effective treatment with Entinostat for immunosuppressive tumor
microenvironments. Other inflammation pathways can also be evaluated to see whether
inhibition of the pathway leads to a better immune response. Finding more effective treatments
will improve the quality and longevity of life for patients suffering from breast cancer or even
other immunosuppressive cancer types such as lung and ovarian cancer.
27
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Abstract (if available)
Abstract
Immune checkpoint inhibitors (ICIs) have been shown to reactivate T cells to eliminate cancer cells in various cancer types. However, suppressive immune cells have been shown to prevent the anti-tumor effects of T cells in breast cancers which have a suppressive tumor microenvironment (TME), posing a challenge for ICI effectiveness. Myeloid-derived suppressor cells (MDSCs) are a significant contributor to the suppressive environment in breast tumors. NFkB signaling is activated in MDSCs and increases the production of suppressive cytokines that allow tumor cells to evade immune system attacks, proliferate, and invade. Thus, I hypothesize that inhibiting the NFkB signaling pathway in MDSCs will decrease the expression of suppressive cytokines, which could decrease MDSC suppressive function. J774M, an MDSC cell line, was treated with various concentrations of JSH-23, an NFkB inhibitor. RNA was collected and reverse transcribed into cDNA for qPCR analysis of CCL2, CCL5, Il-6, NOS2, and TNFa gene expression. ANOVA p-values were calculated and subsequent pairwise comparisons using Tukey’s test were conducted on mean log(ddCt) values among samples. The 72-hour inhibitor treatments illustrated significant changes in the expression of certain cytokines such as CCL5. However, further log2 fold change investigation determined these changes were insignificant independent of DMSO, the inhibitor vehicle. Higher concentrations of JSH-23 did lower the expression of most cytokines, but this decrease was not statistically significantly different from the control cells. Future studies can evaluate changes in cytokine production on the protein level or with respect to a different transcription factor involved in the inflammation of breast TME.
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The effect of inhibiting the NFkB pathway on myeloid derived suppressor cell cytokine production
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Applied Biostatistics and Epidemiology
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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 author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.
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
Repository Email
cisadmin@lib.usc.edu
Tags
breast cancer
CCL2
CCL5
cytokines
gene expression
ICIs
IL-6
immune checkpoint inhibitors
immunoediting hypothesis
immunology
immunosuppression
inflammation
J774M
MDSCs
myeloid derived suppressor cells
NFKb
NOS2
TNFa
tumor microenvironment