The effect of IL-6 on the level of N-myc downstream regulated gene 2 and the growth

and survival of colon cancer cells Master thesis in molecular biology & medical biology

Louise Dannemann Jørgensen

Department of Science, Systems, and Models

Roskilde University

Supervisor Anders Blomkild Lorentzen

Title: The effect of IL-6 on the level of N-myc downstream regulated gene 2 and the growth and survival of colon cancer cells

Author: Louise Dannemann Jørgensen

Institution: Roskilde University, Denmark

Supervisor:

Anders Blomkild Lorentzen, Substitute Assistant Professor, Ph.D.

Department of Science, Systems and Models, Roskilde University, Denmark

Date of submission: May 31, 2014

Preface

This master thesis has been conducted over a time period from December 2012 to June 2014 at

the Department of Science, Nature, Systems and Models, Roskilde University.

I wish to express my deep and sincere gratitude to my supervisor, Anders Blomkild Lorentzen, for

his endless support, patience, and guidance throughout this master thesis.

I would like to thank Kirsten Olesen for her valuable help, support, and advice in the laboratory.

Further, I would like to address a special greeting to my friend, Annette Weber, for always

supporting me and believing in me throughout this project.

Abstract

Cancer is a leading cause of death, and tumorigenesis is dependent on different mechanisms,

including genomic changes and the immune system. Silencing of tumor suppressor genes is an

important mechanism. The recently suggested tumor suppressor gene, NDRG2, has been

correlated with down-regulation and decrease proliferation in cancers. Additionally, chronic

inflammation and cancer development have been connected. Oncogenic transformation occurs in

tumor cells exposed to repeated chronic inflammation, which may lead to epigenetic alterations

and changed expression of tumor suppressor genes. The immune system supplies the tumor

environment with cytokines including IL-6, which has been connected with chronic inflammation

through its ability to activate STAT3 leading to inhibition of anti-tumor immunity. NDRG2

expression has been reported to modulate SOCS3 and STAT3 activity and further induce SOCS1

expression, which leads to down-regulation of STAT3 in breast cancer. It would therefore be

interesting to study a possible connection between NDRG2 expression and IL-6 levels in colon

cancer cells.

The objective of this thesis was to study the expression of NDRG2 and examine a possible

correlation between IL-6 and NDRG2 expression in colon cancer cell lines, SW480 and HCT116, by

use of RT-qPCR and western blotting. Furthermore, distinct growth assays were performed to

evaluate the effect on the growth rate in colon cancer cells of presence of IL-6 and NDRG2,

separately and together.

The expression of NDRG2 in colon cancer cells was found to be down-regulated both on mRNA

and protein level, and the same was observed after treatment with IL-6 on mRNA level. The

growth assays provided results indicating that SW480 cells transfected with the plasmid pcDNA6-

NDRG2L-V5 had an increased growth rate, when compared with normal SW480 cells, and the

same tendency was seen after treatment with IL-6, both in normal SW480 cells and transfected

SW480 cells.

Altogether, these results suggest that NDRG2 is down-regulated in both colon cancer cell lines,

and that transfected cells treated with IL-6 show increased growth rate. This may indicate a

potential correlation between NDRG2 and IL-6 in relation to growth in colon cancer cells.

Resume

Cancer er en af de hyppigste dødsårsager, og udviklingen af tumorer er afhængig af forskellige

mekanismer, inklusiv genom-ændringer og immunsystemet. Inhibition af tumor suppressor gener

er en vigtig mekanisme, og den nyligt foreslåede tumor suppressor kandidat, NDRG2, er blevet set

nedreguleret i mange forskellige cancer typer og forbundet med nedsat vækst hos cancer celler.

Endvidere er kronisk inflammation og cancer udvikling blevet forbundet gennem transformation af

tumor celler, der har været udsat for gentagen kronisk inflammation, hvilket har medført

epigenetiske ændringer og ændring i udtrykket af tumor suppressor gener. Immunsystemet

supplerer tumormiljøet med cytokiner, herunder IL-6, som er blevet forbundet med kronisk

inflammation gennem dets evne til at aktivere STAT3 og medføre inhibition af anti-tumor respons.

NDRG2 ekspression har vist sig at medføre modulation af SOCS3 og STAT3s aktivitet, og derudover

induceres ekspressionen af SOCS1, hvilket medfører en nedregulering af STAT3 i bryst cancer.

Derfor vil det være yderst interessant at undersøge, om der er en mulig forbindelse mellem

NDRG2 ekspression og IL-6 niveau i colon cancer celler.

Formålet med dette speciale var at undersøge ekspressionen af NDRG2 på mRNA og protein

niveau og undersøge om en mulig sammenhæng mellem IL-6 og NDRG2 ekspression er til stede i

cancer cellelinierne SW480 og HCT116 ved at bruge metoder som RT-qPCR og western blot.

Derudover blev vækstforsøg udført for at undersøge om IL-6 og NDRG2, adskilt og sammen,

påvirker væksten af SW480 celler.

En nedregulering af NDRG2 ekspression på både mRNA og protein niveau blev fundet i cancer

celler, og det samme blev observeret på mRNA niveau efter behandling med IL-6. Resultaterne af

de udførte vækstforsøg viste, at SW480 cellers, transfekteret med plasmid pcDNA6-NDRG2L-V5,

vækst er øget i forhold til normale SW480 celler og den samme tendens kunne ses efter

behandling med IL-6 i både normale og transfekteret SW480 celler. Disse resultater indikerer, at

NDRG2 er nedreguleret i begge cancer cellelinier, og at transfekterede SW480 celler behandlet

med IL-6 viser en øget vækst, hvilket kunne indikere en potentiel forbindelse mellem NDRG2 og IL-

6 i forhold til væksten af colon cancer celler.

Table of Contents

1. Introduction 1

1.1 Cancer 1

1.2 The hallmarks of cancer 1 1.2.1 Sustaining proliferative signaling 2 1.2.2 Evading growth suppressors 2 1.2.3 Resisting cell death 3 1.2.4 Enabling replicative immortality 4 1.2.5 Inducing angiogenesis 5 1.2.6 Activating invasion and metastasis 6

1.3 Enabling characteristics and emerging hallmarks in cancer 9 1.3.1 Genome instability and mutations 9 1.3.2 Genetic modifications in the genome of cancer cells 10 1.3.3 Epigenetics 11 1.3.4 Epigenetic changes in normal cells 12 1.3.5 Epigenetic modifications and cancer 14 1.3.6 N-Myc downstream-regulated family of genes 16 1.3.7 NDRG2 17 1.3.8 NDRG2 & Cancer 19

1.4 Avoiding of immune destruction 21 1.4.1 Immune system 21 1.4.2 Innate immunity 22 1.4.3 Recognition of pathogens by the innate immune system 23 1.4.4 The interplay between the innate immune system and the adaptive immune system 23 1.4.5 Adaptive immunity 24 1.4.6 B cell and T cell function and activation 24 1.4.7 The Adaptive immunity is able to respond in two different ways 26 1.4.8 Cancer immunology 27 1.4.9 Immunoediting: from immune surveillance to immune escape 28 1.4.10 From elimination to escape 28 1.4.11 Cancer cells’ most common strategies to avoid the immunity 29

1.5 Tumor-promoting inflammation 33 1.5.1 Inflammation and cancer 33 1.5.2 Function of IL-6 and its role in cancer 36 1.5.3 Interleukin 6 and NDRG2 37

1.6 Short summary 38

2. The aim of the Master’s thesis 39

3. Materials & Methods 40

3.1 Experimental Design 40

3.2 Cells Culture - growth, harvesting, and treatment 41 3.2.1 Treatment of Cells 41

3.3 Quantification of NDRG2 mRNA level 43 3.3.1 RNA extraction 43 3.3.2 cDNA synthesis 44 3.3.3 Quantitative real time polymerase chain reaction (qRT-PCR) 45 3.3.4 Standard curve 46

3.4 Detection of NDRG2 protein expression 47 3.4.1 Transfection 47 3.4.2 Whole cell protein extraction 48 3.4.3 Western Blot 48 3.4.4 Protein measurement 49 3.4.5 Separation of proteins by gel electrophoresis 49 3.4.6 Transfer of proteins by electro-blotting 50 3.4.7 Antigen binding 51 3.4.8 Protein detection 51

3.5 Quantification of cell proliferation under varied conditions 52 3.5.1 Growth assay for transfected cancer cell line SW480 52 3.5.2 Growth assay for transfected cancer cell line SW480 treated with IL-6 52

3.6 Statistic 53

4. Results 54

4.1 NDRG2 expression in cancer cell lines SW480 and HCT116 at the mRNA level 54

4.2 NDRG2 expression in cancer cell lines SW480 and HCT116 at the protein level 55

4.3 Analysis of the most efficient concentration and time for the treatment with IL-6 57

4.4 NDRG2 expression in treated cancer cell lines SW480 and HCT116 at mRNA level 58

4.5 Analysis of transfection efficiency 60

4.6 Analysis of NDRG2 protein level after transfection 60

4.7 Growth assays comparing cancer cell lines SW480 growth rate +/- NDRG2 62

4.8 Growth assays comparing growth rates for SW480 cells untreated or treated with IL-6 63

4.9 Growth assay comparing transfected cancer cell line SW480 treated and untreated with IL-6 64

4.10 Short summary 66

5. Discussion 67

5.1 The demonstration of the observed down-regulation of NDRG2 expression 67

5.2 Possible impact of IL-6 on the expression of NDRG2 68

5.3 The growth rate of cancer cell lines and the influence of NDRG2 expression 69

5.4 The growth rate and the influence of IL-6 treatment 70

6. Conclusion 71

7. Perspectives 72

8. References 73

9. Appendices 87

9.1 Appendix I 87

9.2 Appendix II 89

List of abbreviations

Abbreviation Clarification

RB Retinoblastoma-associated protein

Bcl-2 B-Cell lymphoma 2

Bax Bcl-2 associated X protein

Bak Bcl-2 antagonist/Killer protein

VEGF-A Vascular endothelial growth factor-A

TSP-1 Thrombospondin-1

ECM Extracellular matrix

EMT Epithelial-mesenchymal transition

TAMs Tumor-associated macrophages

EGF Epidermal growth factor

CIN Chromosomal instability

miRNA microRNA

NDRG N-MYC downstream-regulated gene

BMP Bone morphogenetic protein

MMP Matrix metallopeptidase

TGF-β Transforming growth factor beta

CCRCC Clear cell renal cell carcinoma

MHC Major histocompatibility complex

APCs Antigen-presenting cells

B cells B lymphocytes

T cells T lymphocytes

DCs Dendritic cells

NK cells Natural killer cells

PAMPs Pathogen-associated molecular patterns

PRRs Pattern recognition receptors

TLRs Toll-like receptors

Tc cells Cytotoxic T cells

Th cells Helper T cells

Treg cells Regulatory T cells

TCRs T cell receptors

IFN-γ Interferon gamma

TNF-α Tumor necrosis factor alpha

KIR Killer inhibitory receptor

ER Endoplasm reticulum

B2M Beta-2 microglobulin

FasL Fas ligand

IDO Indolamine-2-3-dioxygenase

FasR Fas receptor

TILs Tumor-infiltrating lymphocytes

IL Interleukin

sIL-6R Soluble IL-6 receptor

RT-qPCR Real time quantitative polymerase chain reaction

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1. Introduction

1.1 Cancer

According to the WHO, cancer affected 54.9 million people worldwide in year 2012. Cancer is the

term for a large group of diseases with different origins in the body, and they are also called

neoplasms [WHO, 2014]. Neoplasia refers to new tissue composed by cells with heritable capacity

to do uncontrolled and abnormal growth beyond normal growth patterns [Halazonetis et al.,

2008]. Neoplasia exists in two types; malign and benign, where the malign neoplasia is also named

cancer [Halazonetis et al., 2008]. The process of neoplasia in humans is a multistep process, and

genetic and epigenetic modifications are highly involved in the transformation of normal cells into

cancer cells [Hanahan & Weinberg, 2000; Iacobuzio-Donahue, 2009; Baylin & Ohm, 2006]. Cancer

is caused by alterations in three types of genes, including oncogenes, tumor suppressor genes, and

stability genes, and unlike most other genetic-dependent diseases, cancer does not arise because

of a single gene defect [Vogelstein & Kinzler, 2004]. Cancer can be defined as a hyper-proliferative

disorder and involves morphological cellular transformation, uncontrolled cellular proliferation,

dysregulation of apoptosis, invasion, angiogenesis, and metastasis [Lin & Karin, 2007; Zitvogel et

al., 2006]. Hanahan & Weinberg’s six hallmarks constitute and provide understanding of the

diversity in the processes behind cancer [Hanahan & Weinberg, 2011].

1.2 The hallmarks of cancer

Six hallmarks (see Figure 1) that

describe cancer were proposed in

2000 by Hanahan & Weinberg to

support the description of the

processes behind the development

of cancer cells and the complexity of

the tumor tissue [Hanahan &

Weinberg, 2011]. The hallmarks will

be described more thoroughly in the

next sections.

Figure 1 shows the six hallmarks of cancer [Hanahan & Weinberg, 2011]

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1.2.1 Sustaining proliferative signaling

Under normal conditions the production and release of growth-promoting signals are strictly

controlled by several mechanisms. These control mechanisms ensure homeostasis of cell number

and maintenance of normal tissue structure and function [Clarke & Fuller, 2006; Hanahan &

Weinberg, 2011]. Cancer cells are characterized by their ability to sustain proliferative signaling

and avoid normal growth patterns. They obtain the capability to sustain proliferative signaling in

several ways including; production of growth factor ligands, stimulation of normal cells to produce

and release growth factors, de-regulation of receptor signaling, and structural alteration of

receptor molecules [Cheng et al., 2008; Bhowmick et al., 2004; Hanahan & Weinberg, 2011]. DNA

sequencing of cancer cells genomes has shown somatic mutations in almost all human tumors,

and they may affect the constitutive activation of signaling pathways trigged by activated growth

factor receptors and thus support sustained proliferative signaling [Stratton et al., 2009; Hanahan

& Weinberg, 2011]. Furthermore, the importance of negative-feedback loops has been shown,

and under normal conditions they function by suppressing various types of signaling to ensure

homeostasis in the regulation of signaling through the intracellular circuitry. However, in cancer

cells defects in these negative-feedback loops enable them to enhance proliferative signaling

[Hanahan & Weinberg, 2011; Wertz & Dixit, 2010; Cabrita & christofori, 2008; Amit et al., 2007;

Mosesson et al., 2008].

1.2.2 Evading growth suppressors

Just like the controlled production and release of growth factor signals, cell proliferation is

negatively regulated by specific programs, and many of them depend on the actions of tumor

suppressor genes [Hanahan & Weinberg, 2011]. Tumor suppressor genes are characterized by

their ability to prevent proliferation and growth of tumor cells, and studies have shown that they

are inactivated in many human cancer types [Park & Vogelstein, 2003]. One of the most well-

documented tumor suppressor genes is TP53, which receives signals from stress and abnormality

sensors and then suppress further cell cycle progression until the right conditions are present.

Furthermore, TP53 can trigger the cell to undergo apoptosis, whenever irreparable or

overwhelming damage is observed [Macleod, 2000; Vogelstein et al., 2000; Oren, 2003]. Another

important tumor suppressor gene is the RB gene encoding the retinoblastoma-associated (RB)

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protein, which also functions as a central part of the control mechanisms in growth and cell

division. The RB protein determines if the cell may proceed through the cell cycle, and defects

found in this pathway are connected with permitted persistent cell proliferation in cancer cells

[Burkhart & Sage, 2008; Deshpande et al., 2005; Sherr & McCormick, 2002]. Several studies have

indicated that both TP53 and RB operate through larger networks. Experiments with TP53 null

mice confirm this redundancy in that they show normal development, tissue homeostasis, and

proper cell development [Ghebranious & Donehower, 1998].

Contact inhibition is observed in normal cells and is a consequence of cell-to-cell contact in cell

populations, where two-dimensional cultures are formed. In various types of cancer this

mechanism disappears during the course of tumorigenesis, which suggests that contact inhibition

ensures normal tissue homeostasis in normal cells [Hanahan & Weinberg, 2011]. One of the

mechanisms involved in contact inhibition is through the protein product of NF2. When the

expression of the NF2 gene is lost, the formation of neurofibromatosis is trigged. By coupling of

cell-surface adhesion molecules to the transmembrane receptor tyrosine kinases, the adhesivity of

cell-to-cell attachments is strengthened and the ability to release mitogenic signals is limited by

isolation of growth factor receptors [Curto et al. 2007; Okada et al., 2005].

1.2.3 Resisting cell death

Normal programmed cell death serves as a natural barrier to cancer development, but elevated

levels of oncogene signaling can result in signaling imbalances and lead to hyperproliferation

[Adams & Cory, 2007; Lowe et al., 2004; Evan & Littlewood, 1998]. The apoptotic machinery is

regulated by both extracellular death-inducing signals (the extrinsic apoptotic program) and

signals of intracellular origin (the intrinsic apoptotic program). Both signaling programs induce

activation of proteases, which leads to initiation of a cascade of proteolysis, and the cell is

progressively disassembled and consumed by neighboring cells [Adams & Cory, 2007; Hannahan &

Weinberg, 2011]. Regulatory proteins of the B-Cell lymphoma 2 (Bcl-2) family function as

controllers of apoptotic triggers, and they are inhibitors of apoptosis by binding to the

proapoptotic triggering proteins, Bcl-2 associated X protein (Bax) and Bcl-2 antagonist/Killer

protein (Bak). When no proapoptotic proteins are present, Bax and Bak disrupt the integrity of the

outer mitochondrial membrane, which leads to release of proapoptotic signaling proteins

4

including cytochrome c. A cascade of caspases is activated by cytochrome c and induces the

cellular changes leading to apoptosis [Adams & Cory, 2007; Willis & Adams, 2005]. Furthermore,

several abnormality sensors involved in tumor development have been identified and associated

with apoptosis [Adams & Cory, 2007; Lowe et al. 2004]. The most well-known DNA-damage

sensors function through the TP53 tumor suppressor and induce apoptosis by up-regulation of

Noxa and Puma BH3-only proteins, when DNA breaks and chromosomal abnormalities are

detected [Junttila & Evan, 2009]. Tumor cells have developed several strategies to avoid apoptosis

and the most common one is the loss of TP53 tumor suppressor function [Hanahan & Weinberg,

2011].

Autography is another cell degenerating mechanism, which is induced by cellular stress especially

at low levels of nutrient, where it enables cells to break down cellular organelles leading to

recycling for use in biosynthesis and energy metabolism [Levine & Kromer, 2008; Mizushima,

2007]. Cancer cells are able to generate metabolites that support survival in stressed nutrient-

limited environments and ensure further proliferation of cancer cells [Hanahan & Weinberg,

2011].

Necrosis of cells is described as the system-wide exhaustion and breakdown, which leads to

release of proinflammatory signals to the surrounding microenvironment. As a consequence of the

proinflammatory signals, inflammatory cells of the immune system are recruited, and this process

has recently been associated with tumor promotion by inducing angiogenesis, proliferation, and

invasiveness [Grivennikov et al., 2010; White et al., 2010; Galluzzi & Kroemer, 2008].

1.2.4 Enabling replicative immortality

Under normal conditions, cells are only able to pass through a limited number of cell cycles to

prevent uncontrolled cell proliferation and tumorigenesis [Wai, 2004]. At the end of every

chromosome are protective structures called telomeres, which are composed of long repetitive

sequences of TTAGGG [Wai, 2004]. The protection of the chromosomes by telomeres indicates

that telomeres may be involved in unlimited proliferation and thus connected with cancer

development [Blasco, 2005; Shay & Wright, 2000]. Senescence and crisis are the two telomere-

dependent pathways of cell mortality, which prevent proliferation and involve irreversible

5

entrance into the cell cycle and cell death [Hanley, 2008]. The first step to prevent further

proliferation is induction of senescence, and for those cells that succeed in circumventing this

barrier, the crisis phase will be induced and the cells will undergo cell death. Unlimited

proliferation has been associated with telomerase activity and expression in immortalized cells

[Wai, 2004]. Both senescence and crisis are suppressed by telomerase activity, and suppression of

telomerase activity thus leads to shortening of telomeres and activation of senescence or crisis

[Hanahan & Weinberg, 2011]. Up-regulation of telomerase expression is the most common

procedure in immortalized cells to maintain telomeric DNA at lengths sufficient to prevent

senescence or apoptosis. Excessive or unbalanced oncogene signaling has been shown to induce

another form of cell senescence and function as a protective mechanism against development of

neoplasia. Thereby cell senescence is a part of the protective barrier against neoplastic expansion

and is triggered by various proliferation-associated abnormalities and shortening of telomeres

[Hanahan & Weinberg, 2011].

1.2.5 Inducing angiogenesis

Angiogenesis is the process whereby new blood vessels are developed from existing ones. This

process is especially used by tumors to acquire nutrients and oxygen, and to evacuate waste

products and carbon dioxide. Under normal conditions angiogenesis is activated in processes like

wound healing, but only temporary [Hanahan & Weinberg, 2011]. In tumor progression,

angiogenesis is almost always activated and new vessels are developed to support the neoplastic

growth [Hanahan & Folkman, 1996]. This change in the activation of angiogenesis is called the

angiogenic switch and is controlled by countervailing factors that either induce or oppose

angiogenesis [Baeriswyl & Christofori, 2009; Bergers & Benjamin, 2003]. The regulation is

performed by signaling proteins, which bind to cell-surface receptors on vascular endothelial cells.

These regulatory proteins are classified as either inhibitor or stimulator proteins, and the most

well-known are vascular endothelial growth factor-A (VEGF-A) and thrombospondin-1 (TSP-1)

[Hanahan & Weinberg, 2011]. VEGF-A is a stimulator of angiogenesis and has been found to be up-

regulated by oncogene signaling and hypoxia, which indicate an important role in tumor

progression [Ferara, 2009; Mac Gabhann & Popel, 2008; Carmeliet, 2005]. Additional signaling

proteins have been associated with tumor angiogenesis based on their proangiogenic properties

6

and chronic up-regulation [Baeriswyl & Christofori, 2009]. TSP-1 and several other proteins have

been connected with inhibition of angiogenesis and are called endogenous inhibitors of

angiogenesis [Ribatti, 2009; Kazerounian et al., 2008; Folkman, 2006; Folkman, 2002; Nyberg et al.,

2005]. Many of these proteins can be detected in the circulation in both mice and humans and if

the levels are increased by overexpression, tumor growth is impaired; this indicates a role as an

intrinsic barrier to induction of angiogenesis [Ribatti, 2009; Nyberg et al., 2005].

Cells originating from the bone marrow have been shown to be important in the process of

angiogenesis [Qian & Pollard, 2010; Zumsteg & Christofobi, 2009; Murdoch et al., 2008; De Palma

et al., 2007]. Many cells from the innate immune system originate from the bone marrow

including macrophages, neutrophils, mast cells, and myeloid progenitors, and all of them are able

to infiltrate the tumor environment. These cells are also involved in the angiogenic switch, which

occurs in the early progression of the tumor and ensures facilitation of local invasion [Hanahan &

Weinberg, 2011]. After the migration to the neoplastic environment most of the bone marrow-

derived progenitor cells become pericytes or endothelial cells. Studies have shown an association

between pericytes and the neovasculature found in most tumors, where pericytes are important

for the maintenance of functional tumor neovasculature [Patenuade et al., 2010; Kovacic &

Boehm, 2009; Lamagna & Bergers, 2006; Raza et al., 2010; Bergers & Song, 2005].

1.2.6 Activating invasion and metastasis

Invasion and metastasis is a multistep process and is termed the invasion-metastasis cascade

[Talmadge & Fidler, 2010; Fidler, 2003]. Local invasion of the specific tissue is the first step,

followed by intravasion into blood and lymphatic vessels, which makes the cancer cells able to

escape through the lymphatic and hematogenic systems and invade distant tissues. When the

cancer cells have reached the distant tissue, the formations of small nodules of cancer cells

(micrometastases) takes place and by further growth they develop into macroscopic tumors

[Hanahan & Weinberg, 2011].

The ability of cancer cells to invade and develop metastases involves alterations in shape and

attachment to other cells and the extracellular matrix (ECM). A well-known key cell-to-cell

adhesion molecule is E-cadherin, and the expression of this molecule has been found to be missing

7

in carcinoma cells. E-cadherin works by forming junctions with adjacent epithelial cells to

assemble and maintain epithelial cell sheets, and the down-regulation of E-cadherin in carcinomas

support its role as a key suppressor of invasion and metastasis [Berx & van Roy, 2009; Cavallaro &

Christofori, 2004]. The gene expression of many cell-to-ECM adhesion molecules has been found

altered in aggressive forms of carcinomas and especially adhesion molecules involved in cell

migration are up-regulated [Cavallaro & Christofori, 2004].

Epithelial cells become transformed by epithelial-mesenchymal transition (EMT) to acquire the

ability to invade, resist apoptosis, and disseminate [Klymkowsky & Savagner, 2009; Polyak &

Weinberg, 2009; Thiery et al., 2009; Yilmaz & Christofori, 2009; Barrallo-Gimeno & Nieto, 2005].

Carcinoma cells are able to co-opt multiple attributes from the EMT program, which enable the

invasion and metastasis, and further carcinoma cells can activate the EMT program transiently or

stably throughout the invasion and metastasis. The activation of EMT is organized by several

transcriptional factors, which are all expressed in different combinations in different malignant

tumor types [Micalizzi et al., 2010; Taube et al., 2010; Schmalhofer et al., 2009; Yang & Weinberg,

2008]. These transcription factors affect the cells by loss of adherence junctions, expression of

matrix-degrading enzymes, increased motility, and resistance to apoptosis, all processes involved

in invasion and metastasis [Hanahan & Weinberg, 2011]. The gene expression of E-cadherin has

been found repressed by several of these transcription factors, which lead to increased

invasiveness in the neoplastic epithelial cells [Peinado et al., 2004].

The collaboration between cancer cells and cells of the neoplastic stroma has been shown to be

strongly associated with the ability of cancer cells to invade and develop metastases [Egeblad et

al., 2010; Qian & Pollard, 2010; Joyce & Pollard, 2009; Kalluri & Zeisberg, 2006]. Experiments with

metastatic breast cancer have shown collaboration between tumor-associated macrophages

(TAMs) and breast cancer cells, where TAMs supply the cancer cells with epidermal growth factor

(EGF), and the cancer cells supply TAMs with CSF-1. Intravasion into the circulatory system and

development of metastases happen through this communication between normal cells and cancer

cells [Qian & Pollard, 2010; Wyckoff et al., 2007].

The invasion process can be split up in to different modes; collective invasion and amoeboid

invasion [Friedl & Wolf, 2008; Friedl & Wolf, 2010]. Collective invasion is when a small mass of

8

cancer cells invades an adjacent tissue, and this type of invasion is characteristic for squamous cell

carcinomas. Individual cancer cells can also perform invasion typically in already existing

interstices in the extracellular matrix, but the knowledge about this amoeboid invasion is limited

[Madsen & Sahai, 2010; Sabeh et al., 2009]. Inflammatory cells have been shown to produce

extracellular matrix-degrading enzymes and other important factors and thereby facilitate cancer

cell invasion and growth [Kessenbrock et al., 2010; Qian & Pollard, 2010; Joyce & Pollard, 2009].

The cancer cells recruit the inflammatory cells by release of chemoattractants, and thus they avoid

producing the matrix-degrading enzymes themselves [Hanahan & Weinberg, 2011].

After the invasion the next step is the development of metastases, which can be described by two

phases; the dissemination of cancer cells from the primary tumor to distant tissues, and the

successful colonization leading to development of micrometastases into macroscopic tumors

[Hanahan & Weinberg, 2011]. The development from micrometastases to macroscopic tumors can

be controlled by systemic suppressor factors, which are released by the primary tumor in some

cancer types [Demicheli et al., 2008; Folkman, 2002]. In those cases, when the primary tumor is

removed the metastatic growth will explode and the macroscopic tumor develop as a

consequence of the missing systemic suppressor factors [Demicheli et al., 2008; Folkman, 2002].

Other reasons for delayed development of micrometastases are inability to activate tumor

angiogenesis, antigrowth signals, tumor suppression by the immune system, nutrient starvation

inducing autophagy, and poor adaption to new microenvironments in the tissue [Naumov et al.,

2008; Aguirre-Ghiso, 2007; Kenific et al., 2010; Lu et al., 2008; Barkan et al., 2010; Teng et al.,

2008; Gupta et al., 2005].

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1.3 Enabling characteristics and emerging hallmarks in cancer

Hanahan & Weinberg have proposed some emerging hallmarks and enabling characteristics of the

development of cancer [Hanahan & Weinberg, 2011]. Enabling characteristics make the process of

tumorigenesis possible, and these

characteristics describe how the

development of cancer cells occurs.

Emerging hallmarks ensure the

progression of the cancer cells by

deregulation of cellular energetics and

the ability to avoid immune

destruction, see Figure 2 [Hanahan &

Weinberg, 2011]. Throughout this

master thesis, the main focus will be on

genome instability and mutations,

avoiding immune destruction, and

tumor-promoting inflammation.

1.3.1 Genome instability and mutations

The first enabling characteristic for cancer development discovered was genome instability and

mutations, which trigger tumor progression [Hanahan & Weinberg, 2011]. Clonal expansion can be

achieved through changes in the genome and are caused by genetic and epigenetic mechanisms

including inactivation of different cancer related genes [Berdasco & Esteller, 2010; Esteller, 2007;

Jones & Baylin, 2007; De Visser et al., 2006; Abbas et al., 2007; Jones, 2007; Shama et al., 2010].

Normally, the genome’s maintenance system is able to detect and resolve defects in the DNA and

thus ensure low rate of spontaneous mutations during each cell generation, but cancer cells are

capable of modifying the maintenance system and increase the rate of mutations in order to

ensure the development of tumorigenesis [Negrini et al., 2010; Salk et al., 2010]. By breakdown in

one or several components of the genomic maintenance machinery, the genome becomes more

sensitive to mutagenic agents and as a result the mutation rate is increased. The surveillance

Figure 2 shows the enabling and emerging hallmarks of cancer [Hanahan & Weinberg, 2011]

10

systems of the genome normally monitor genomic integrity and force damaged cells into either

senescence or apoptosis, but the altered mutation rate can compromise these systems and

accumulation of mutations is accelerated [Jackson & Bartek, 2009; Kastan, 2008; Sigal and Rotter,

2000]. A special group of genes of the DNA-maintenance machinery are named caretakers of the

genome and defects in these genes and theirs products are involved in detection of DNA damage,

activation of the repair system and direct repair of damaged DNA, and inactivation of mutagenic

molecules before damaged DNA [Kinzler & Vogelstein, 1997; Negrini et al., 2010; Ciccia & Elledge,

2010; Jackson & Bartek, 2009; Kastan, 2008; Harper & Elledge, 2007; Friedberg et al., 2006].

Introduction of mutant copies of these caretaker genes into mice results in increased cancer

incidence and thus supports their involvement in human cancer development [Barnes & Lindalh,

2004]. Both genetic and epigenetic modifications drive the tumor progression, and the large

numbers of defects in genes of the maintenance and repair systems together with findings of

silencing of tumor suppressor genes are all supporting the enabling characteristic of genome

instability in cancer development [Hanahan & Weinberg, 2011].

1.3.2 Genetic modifications in the genome of cancer cells

Genomic instability is characteristic for almost all human cancer types and is caused by genetic

modifications [Negrini et al., 2010; Stratton et al., 2009]. Different forms of genomic instability are

found, and the most common form in human cancer is called chromosomal instability (CIN), which

refers to a high rate of changes found in chromosome structure and number, when cancer cells

are compared with normal cells [Negrini et al., 2010; McGranahan et al., 2012]. Other forms of

genomic instability have been found, including forms that are characterized by expansion or

contraction of the number of oligonucleotide repeats present in microsatellite sequences, and

increased frequencies of base-pair mutations [Fisher et al., 1993; Leach et al., 1993; Al-Tassan et

al., 2002].

Cancer types can be classified as hereditary or sporadic cancers depending on their origin and

development. Both CIN and non-CIN forms of genomic instability have been associated with

mutations in DNA repair genes and are characteristic for hereditary cancers [Fishel et al., 1993;

Negrini et al., 2010]. The study of mutations in DNA repair genes in hereditary cancers has

provided evidence for the mutation hypothesis concerning the presence of genomic instability in

11

precancerous lesions and increased mutation rate leading to tumor development [Nowell, 1976;

Loeb, 1991]. As mentioned earlier, genomic instability has been linked to mutations in caretaker

genes, and they include DNA repair genes and mitotic checkpoint genes. Furthermore, the tumor

suppressor gene TP53 and ataxia telangiectasia mutated gene have been considered as caretaker

genes because of their function in DNA damage responses [Negrini et al., 2010]. The presence of

genomic instability caused by inactivation of caretaker genes in sporadic cancers has not been

confirmed successfully, and the molecular basis of genomic instability in sporadic cancers is still

unclear [Negrini et al., 2010]. Several studies have investigated target sequences hoping to find

mutations in DNA repair and mitotic checkpoint genes with higher mutation frequency, and

thereby explain the genomic instability and development of sporadic cancers [Rajagopalan &

Lengauer, 2004; Cahill et al., 1999; Wang et al., 2004; Cahill et al., 1998]. The low frequency of

mutations in caretaker genes observed in sporadic cancers may be underestimated because of the

repression of gene function caused by epigenetic mechanisms [Esteller, 2008]. These epigenetic

abnormalities together with genetic alterations are important processes in the transformation of

normal cells to cancer cells [Sharma et al., 2010].

1.3.3 Epigenetics

All DNA in cells is packaged into chromatin forms, and these structures define the state of

organization of genetic information within the cell [Sharma et al., 2010]. The chromatin structure

is made by nucleosomes, where every unit contains 146 base pairs of DNA. The DNA is wrapped

around a histone octamer

consisting of four histone

proteins, named H3, H4,

H2A, H2B, see Figure 3

[Luger et al., 1997;

Iacobuzio-Donahue, 2009;

Momparler, 2003].

Epigenetics was defined by

C.H. Waddington in the 1940s based on epigenetics in embryonic development. Later the

definition was modified, so that epigenetic changes mean heritable changes in gene expression

Figure 3 shows the chromatin structure, nucleosome and histone composition [Füllgrabe et al., 2010]

12

which are not accomplished by changes in the primary DNA sequence, and the definition of

epigenetics is modifications of DNA [Jones & Baylin, 2007; Momparler, 2003; Baylin & Ohm, 2006;

Iacobuzio-Donahue, 2009; Sharma et al., 2010; Rodriquez-Paredes & Esteller, 2011]. The

epigenetic processes are essential for key biological processes, such as proper development,

cellular differentiation, imprinting, and silencing of large chromosomal domains, which include

histone modifications, genome imprinting, and DNA methylation [Sharma et al., 2010; Rodriquez-

Paredes & Esteller, 2011; Jones & Baylin, 2007; Iacobuzio-Donahue, 2009; Jaenisch & Bird, 2003;

Momparler, 2003]. Studies have found that genetic and epigenetic alterations interact at all stages

of cancer development and promote cancer progression [Sharma et al., 2010; Jones & Laird,

1999]. Epigenetic modifications have further been connected with suppression of tumor

suppressor genes in several cancer types. Thereby, epigenetic modifications are of highest interest

for this project because of the connection between cancer development and repression of tumor

suppressor genes such as the potential tumor suppressor gene, NDRG2.

1.3.4 Epigenetic changes in normal cells

The function of the genome is regulated through different epigenetic mechanisms such as DNA

methylation, histone modification, and miRNAs, which all modify the chromatin structure [Sharma

et al., 2010]. All these modification mechanisms work together and regulate the genome by

altering the local structure of chromatin, thereby creating an “epigenome” that ensures the

cellular identity by the way the genome manifests itself in different cell types [Jones & Baylin,

2007; Bernstein et al., 2007; Suzuki & Bird, 2008; Kouzarides, 2007; Zhang et al., 2007; Jiang et al.,

2009].

Normally, the primary sequence of DNA consists of

four bases named adenine, guanine, cytosine and

thymine, but a fifth base called 5-methylcytosine can

be produced by covalent modification of post-

replicative DNA. This process is also called DNA

methylation, in which S-adenosyl-methionine

function as the methyl donor and the process is

catalyzed by the enzyme DNA methyltransferase (DNMTs). The methylation is finished when the

Figure 4 shows the DNA methylation process [Meehan, 2013]

13

methyl group is added to the cytosine ring, see Figure 4, and the cytosine becomes methylated

[Herman & Baylin, 2003]. The process of DNA methylation takes place after DNA replication and

cell division, and the process is performed by a maintenance DNA methylase, also called DNMT1

[Momparler, 2003]. DNA methylation provides stable gene silencing and plays an important role

in regulation of gene expression [Sharma et al., 2010]. This epigenetic modification primarily

occurs in cytosines of the dinucleotid sequence CpG, and CpG-rich regions are called CpG islands

[Herman & Baylin, 2003; Weber et al., 2007; Jaenisch & Bird, 2003]. The distinction between CpG

sites and CpG islands is based on size, with CpG islands defined as a 1 kb stretch of DNA containing

the sequence more frequently than the rest of the genome [Momparler, 2003]. CpG islands are

primarily located in the 5’end of the gene and in 60 % of all human gene promoters, and they are

normally not methylated in normal cells, whereas CpG sites have been found to be methylated to

prevent chromosome instability [Herman & Baylin, 2003; Weber et al., 2007; Suzuki & Bird, 2008;

Wang et al., 2004].

Another important epigenetic mechanism is histone modification. The histone is composed of four

core histone proteins with DNA wrapped around them. The nucleosome has an N-terminal tail and

a C-terminal domain, and the N-terminal tail can undergo many post-translational modifications

such as methylation, acetylation, ubiquitylation and phosphorylation [Kouzarides, 2007]. All these

modifications are added or removed by different enzymes, including histone acetyltransferases

(HATs), deacetylases (HDACs), methyltransferases (HMTs), demethylases (HDMs), and so on

[Kouzarides, 2007; Allis et al., 2007]. Different combinations of modifications in specific genomic

regions may lead to a more open or closed state of the chromatin structure, and unlike DNA

methylation this leads to either activation or repression of the genes [Li et al., 2007; Sharma et al.,

2010]. Furthermore, these specific patterns of histone modifications may play a potential role in

determining cellular identity by affecting gene expression [Mikkelsen et al., 2007; Ringrose et al.,

2007].

Interactions between DNA methylation and histone modifications are necessary for each of them

to perform their individual roles in gene regulation, and the complexity of epigenetic regulation is

further enhanced by these interactions [Cedar & Bergman 2009].

14

MicroRNAs (miRNA) are small noncoding RNAs that are able to regulate gene expression through

posttranscriptional silencing by binding to the 3’ untranslated region of mRNAs [Baer et al., 2013;

Sharma et al., 2010]. This binding leads to inhibition of protein synthesis or RNA degradation, both

affecting the expression of the target gene [Baer et al., 2013; He & Hannon, 2004]. Like other

epigenetic mechanisms, miRNAs are also part of the control of different biological processes,

including cell proliferation, apoptosis, and differentiation, and the miRNAs can be regulated like

normal genes by epigenetic modifications [Saito & Jones, 2006]. Furthermore, miRNAs are able to

modulate epigenetic regulatory mechanisms by targeting enzymes involved in DNA methylation

and histone modifications [Sharma et al., 2010].

1.3.5 Epigenetic modifications and cancer

Several studies have stated the fact that epigenetic changes play an important role in

tumorigenesis [Momparlet 2003; Sharma et al., 2010; Rodriguez-Paredes & Esteller, 2011]. The

first connection between epigenetic abnormality and cancer was found by Feinberg and

Vogelstein in 1983, who observed a reduction in methylation in colon cancer cells when compared

with normal tissue [Feinberg & Vogelstein, 1983]. Additionally, Gama-Sosa et al. demonstrated a

reduction of 5-methylcytosine content, and both reductions where observed in pre-invasive and

invasive cancer tissues [Gama-Sosa et al., 1983]. DNA methylation is a normal event in gene

regulation, but aberrant DNA methylation can lead to silencing of tumor suppressor genes and

also to activation of oncogenes, which are two important groups of genes in cancer development

[Momparlet, 2003; Sharma et al., 2010; Esteller, 2007]. The activation of oncogenes happens by a

mechanism called DNA hypomethylation, which means loss of methylation, and which can lead to

demethylation of specific coding regions [Feinberg & Tycko, 2004]. Furthermore, DNA

hypomethylation differ from the more site-specific DNA hypermethylation by affecting many

genomic sequences in the genome and thus leading to genomic instability [Esteller, 2007]. The

degree of hypomethylation increases through the progression of cancer [Fraga et al., 2004]. In

both gastric cancer and colon cancer, growth-promoting genes such as R-Ras, MAPSIN and S-100

have been found activated by hypomethylation [Sharma et al., 2010]. Also, promoter regions have

been found to be demethylated allowing normally repressed genes to become expressed, for

15

example the gene PAX2 known to encode a transcription factor involved in proliferation of cells

[Wu et al., 2005; Brueckner et al., 2007].

Opposite to DNA hypomethylation, DNA hypermethylation works by silencing especially tumor

suppressor genes and thereby inducing cancer progression [Costello et al., 2000; Esteller et al.,

2001]. The first DNA hypermethylations were found in the CpG Island of the promoter region of

the Rb tumor suppressor gene [Greger et al., 1989; Sakai et al., 1991]. This finding was further

supported by hypermethylation in other tumor suppressor genes, such as P16, MLH1, VHL (Hippel-

Lindau disease) and BRCA1 (Breast cancer) [Herman & Baylin, 2003; Esteller et al., 2000; Baylin,

2005; Jones & Baylin., 2002; Jones & Baylin, 2007]. The mentioned tumor suppressor genes are all

involve in DNA repair, cell cycle control, cell adhesion, apoptosis, and angiogenesis, which are all

important steps in the cancer development and progression [Sharma et al., 2010; Feinberg, 2005;

Howard et al., 2008]. DNA hypermethylation of the CpG islands in tumor suppressor genes are

specific for each cancer type [Esteller et al., 2001; Grady et al., 2000]. How these specific

hypermethylation “patterns” occur for each cancer type is still unclear. Besides affecting tumor

suppressor genes, hypermethylation can also indirectly silence genes encoding transcription

factors and DNA repair genes [Sharma et al., 2010].

A new potential tumor suppressor gene is NDRG2, which has been found to be involved in cell

growth, initiation and progression of cancer, cell differentiation, and apoptosis [Shi et al., 2009;

Yao et al., 2008; Choi et al., 2003]. Down-regulation or inactivation of NDRG2 expression has been

linked to transcriptional repression by MYC, post-translational inactivation by microRNA, and

epigenetic silencing through promoter methylation [Oh et al., 2012; Zhang et al., 2006; Shi et al.,

2009; Tepel et al., 2008; Lusis et al., 2005; Piepoli et al., 2009; Furuta et al., 2010; Zhao et al.,

2008; Shi et al., 2009]. Hypermethylation is one of the most important reasons for down-

regulation and loss of NDRG2 expression, and hypermethylation of the NDRG2 promoter is

significantly associated with meningioma, breast cancer, and colorectal cancer [Yao et al., 2008;

Feng et al., 2011; Piepoli et al., 2009; Tepel et al., 2008; Liu et al., 2007; Lusis et al., 2005].

16

1.3.6 N-Myc downstream-regulated family of genes

One of the earliest identified oncogenes was MYC, which has been associated especially with

regulation of cell proliferation and differentiation. A small fraction of Myc-repressed genes have

shown the ability to affect the interaction and communication between the cells and their external

environment, and several of these have been associated with tumor suppressor and metastatic

properties [Vervoorts et al., 2006; Grandori et al., 2000; Dang, 1999; O’Connell et al., 2003]. One

of the families of Myc-repressed genes are the N-Myc downstream-regulated family of genes

(NDRG), which consists of four genes called NDRG1-4 and are all found in humans [Qu et al., 2002;

Liu, 2012; Yao et al., 2008]. Because of the potential as tumor suppressors, the NDRG gene family

has been given special attention in cancer research [Yao et al., 2008].

The NDRG genes are localized on four different chromosomes, and they code for proteins of

varying sizes ranging from 339 – 394 amino acids, see Table 1 [Lorentzen & Mitchelmore, 2012;

Zhao et al., 2001]. The sequences of the four genes have sequence homologies between 57 and 65

% [Lorentzen & Mitchelmore, 2012; Chu et al., 2011; Melotte et al., 2010]. The different isoforms

of the NDRG genes are uniquely expressed in tissues between species, which is especially apparent

for NDRG4 in human and mice [Melotte et al., 2010].

Name Chromosomal location Isoform Number of exon Protein length (aa) NDRG1 8q24 0 16 394 NDRG2 14q11.1-11.2 1

2 14 13

371 357

NDRG3 20q11.21-q11.23 1 2

16 15

375 363

NDRG4 16q21-q22.1 1 2 3

17 16 15

371 352 339

Table 1 shows the comparison of human N-myc downstream-regulated gene (NDRG) gene family [Lorentzen & Mitchelmore, 2012; Melotte et al., 2010; Yao et al., 2008]

Common for NDRG proteins is the α/β hydrolase fold domain and NDR-domain, where the α/β

hydrolase fold domain has showed no catalytic function in any of the genes [Zheng et al., 2010;

Lorentzen & Mitchelmore, 2012; Bhaduri et al., 2003; Shaw et al., 2002]. The sequence

differences are primarily located in the N-and C-terminal regions of the NDRG genes, and NDRG1

differs from the rest of the genes by having three 10-aa tandem repeats in the C-terminal region,

17

see Figure 5 [Melotte et al., 2010]. All family

members have a CpG island in their

promoter, which is an important feature in

DNA methylation of the genes [Melotte et

al., 2010].

All the proteins of the NDRG gene family

have been correlated with the regulation of

cell proliferation, differentiation,

development, and stress responses [Kim et

al., 2009; Melotte et al., 2010]. The

function of NDRG1 is the most well-known,

and alterations in the protein have been observed, when specific mutations are present in the

gene. Especially two mutations have been linked with Charcot-Marie-Tooth disease, which is also

known as a demyelinating disorder [Hunter et al., 2003; Kalaydjieva et al., 2000]. NDRG1

expression is induced by cellular stresses, and it is involved in inflammatory processes, regulation

of cell growth, metastasis suppression, and nerve myelination [Taketomi et al., 2003; Piquemal et

al., 1999; Kalaydjieva et al., 2000; Kim et al., 2009]. Important roles in neurodegenerative diseases,

cell differentiation, and cancer have been found for all genes of the NDRG family, and especially in

studies on NDRG2 expression, significantly low levels of NDRG2 were found in tumors and cancer

cell lines, when compared with normal benign tissues. This may indicate a potential role for

NDRG2 as a tumor suppressor and as a prognostic marker in some cancer types [Lorentzen &

Mitchelmore, 2012; Wang et al., 2012; Yang et al., 2011; Shi et al., 2009; Lusis et al., 2005; Park et

al. 2008].

1.3.7 NDRG2

Two isoforms of NDRG2 are found located on chromosome 14q 11.1-11.2, and they differ in

numbers of exons and amino acids, see Table 1 [Dake, 2011 & Libo, 2008; Feng et al., 2011]. In the

C-terminal region of NDRG2, several potential phosphorylation sites are found, and they may have

influence on regulatory mechanisms by a phosphorylation-dephosphorylation cycle [Lorentzen et

al., 2011; Kim et al., 2009; Yao et al., 2008].

Figure 5 shows the variation between the genes in the NDRG family [Melotte et al., 2010]

18

The expression of the NDRG2 gene has been analyzed on mRNA level, and high expression of

NDRG2 mRNA was found in brain, heart, skeletal muscle, liver, and kidney, and low expression was

found in colon, spleen, placenta, and lung tissue [Yao et al., 2008; Wang et al., 2012; Feng et al.,

2011; Lorentzen et al., 2011]. The observed expression pattern of NDRG2 suggests a correlation

between the level of NDRG2 and the rate of cell proliferation, and during development the

expression level of NDRG2 is increased [Hu et al., 2006]. The protein product of NDRG2 is found in

cytoplasm, cell membranes, adherence junctions, and the nucleus [Deng et al., 2003; Qu et al.,

2002; Lachat et al.,2002; Hu et al., 2006; Shen et al., 2008; Okuda et al., 2008; Yang et al., 2011].

Beside the potential role in cancer development, an up-regulation of NDRG2 has been observed in

patients with Alzheimer disease and linked with neural differentiation, synapse formation, and

axon survival [Mitchelmore et al., 2004; Nichols et al., 2005]. NDRG2 is also up-regulated under

hypoxic conditions in cancer cell lines, which may indicate a role as a cell stress responding

molecule. This observation is further supported by experiments where NDRG2 silencing reduced

hypoxia-induced apoptosis suggesting that NDRG2 function as a positive regulator of hypoxia-

induced apoptosis [Melotte et al., 2010; Wang et al., 2008]. Furthermore, NDRG2 has been

associated with insulin-production, aldosterone-mediated epithelial sodium channel function, and

dendritic cell differentiation [Choi et al., 2003; Boulkroun et al., 2002; Burchfield et al., 2004;

Wielpütz et al., 2007].

As a member of the N-myc downstream-regulated gene family, NDRG2 is transcriptionally

regulated by Myc, which function as a master switch molecule in cell proliferation and

differentiation [Dang et al., 2008; Wierstra & Alves, 2008]. Shi et al. have also shown that C-Myc

can repress human NDRG2, and in experiments with colorectal cancer an increased level of Myc

was observed, whereas the level of NDRG2 was decreased [Shi et al., 2009]. This may indicate a

potential role for NDRG2 as an inhibitor of cancer cell proliferation [Shi et al., 2009].

In several types of cancer, the level of NDRG2 has been found to be decreased or undetectable,

and these observations may indicate an important role in initiation and progression of cancer cells.

Supporting the statement of NDRG2 as a tumor suppressor gene, NDRG2 was found to suppress

cell proliferation, cell survival, and induce apoptosis through regulation of cyclin D1 and T cell

19

factor (TCF)/β-catenin activity [Shi et al., 2009; Lorentzen & Mitchelmore, 2012; Yao et al., 2008;

Chu et al., 2011; Zheng et al., 2010; Liang et al., 2012].

1.3.8 NDRG2 & Cancer

Several experiments have confirmed different expression patterns for NDRG2 in tumors and

normal tissue [Deng et al., 2003; Lusis et al., 2005; Lorentzen et al., 2007; Choi et al., 2007; Liu et

al., 2007; Hu et al., 2004; Assämäki et al., 2007; Felsberg et al., 2006; Hummerich et al., 2006]. The

connection between low expression of NDRG2 and proliferation of cancer cells was first observed

in glioblastoma cells, and the NDRG2 expression was reduced by 56 % in human glioblastoma

tissue, when compared to normal tissue samples [Deng et al., 2003]. Furthermore, Deng et al.,

showed that NDRG2 was able to inhibit proliferation of glioblastoma cells, when NDRG2 was

expressed in the tissue [Deng et al., 2003]. Down-regulation or absence of NDRG2 expression on

both mRNA and protein level have been observed in several types of cancer, including colorectal

cancer, breast cancer, lung cancer, hepatocellular cancer, glioma, oral squamous cell carcinoma,

thyroid cancer, liver cancer, pancreas cancer, meningioma, clear cell renal cell carcinoma (CCRCC),

prostate cancer, gallbladder cancer, gastric cancer, and myeloid leukemia [Chu et al., 2011; Hu et

al., 2004; Lee et al., 2008; Lorentzen et al., 2007; Piepoli et al., 2009; Lorentzen et al., 2011; Zhao

et al., 2008; Ma et al., 2008; Wang et al., 2012; Chang et al., 2012]. In addition, nineteen different

types of cancer tissues have been studied, and an up-regulation of NDRG2 mRNA was only

observed in around 8 % of the tissues, whereas 62 % of the tissues showed unchanged expression

of NDRG2 mRNA. Every third sample of cancer tissue tested showed a down-regulation of NDRG2

expression when compared with normal tissue [Lorentzen et al., 2011].

In studies of breast cancer, low or no expression of NDRG2 was observed, and this may be of

importance for the metastatic potential by inducing bone morphogenetic protein 4 (BMP-4)

expression thereby suppressing matrix metallopeptidase 9 (MMP-9) activity, which has influence

on metastasis and angiogenesis [Zheng et al., 2010; Lorentzen et al., 2011]. Oh et al. have shown

clinically that NDRG2 suppresses tumor metastasis by decreasing the active autocrine

transforming growth factor beta (TGF-β) production, which leads to a significantly higher

recurrence and survival rate in patients [Oh et al., 2012]. CD24 glycoprotein is expressed on the

surface of most B lymphocytes and differentiated neuroblasts and function as an adhesion

20

molecule for P-selectin, which is related to tumor growth and metastasis. The CD24/P-selectin

binding pathway causes interactions with platelets and endothelial cells, which lead to spreading

of cancer cells. NDRG2 has thereby been identified as a regulator of adhesion and invasion

processes in breast cancer, hepatocellular carcinoma cancer, lung cancer, and gallbladder

carcinoma [Zheng et al., 2010; Song et al., 2012; Wang et al., 2012]. Lung cancer studies have also

shown a correlation between high CD24 levels and metastasis. NDRG2 is highly expressed in the

early stages of cancer development without any pathological metastasis in lung cancer patients.

Reduced NDRG2 expression is thereby associated with CD24 up-regulation and poor prognosis in

breast cancer, lung cancer, hepatocellular carcinoma, and gallbladder carcinoma [Wang et al.,

2012; Zheng et al., 2010; Zheng et al., 2011; Song et al., 2012]. High levels of CD24 have been

correlated with lymph node metastases, high TNM status, and lower survival rate [Song et al.,

2012].

Studies with hyperthermia have show changes in invasion capacity and apoptosis rate in both

hepatocellular carcinoma and gastric cancer [Tao et al., 2013; Guo et al., 2013]. Hyperthermia

leads to overexpression of NDRG2 by inhibition of MMP-2, MMP-9, and invasion in hepatocellular

carcinoma. Suppression of MMP-9 is correlated with a higher metastasis rate, but the

overexpression of NDRG2 suppresses the function of MMP-9 [Guo et al., 2013; Zheng et al., 2010;

Oh et al., 2012]. Knockdown of NDRG2 expression reverses the effect of the hyperthermia reaction

by inducing invasion [Guo et al., 2013]. Apoptosis rate in gastric cancer is increased by

approximately 8.3% after one hour of treatment [Tao et al., 2013].

In clear cell renal cell carcinoma (CCRCC), NDRG2 has been associated with inhibition of CCRCC cell

lines growth rates and induction of cell cycle arrest at G1 in vitro [Liang et al., 2012]. Furthermore,

NDRG2 expression was found to be down-regulated, which has been linked with oncogenic

properties, and NDRG2 may function as a potential prognostic biomarker. Additionally, the

decrease in NDRG2 expression has been associated with higher TNM stages [Song et al., 2011; Ma

et al., 2012; Liang et al., 2012].

Metastasis in cancer patients is the leading cause of death, and it is necessary to find a prognostic

marker to detect the different types of cancer early in their progression phase. Based on these

studies, NDRG2 is correlated with inhibition of invasion and metastasis and may function as a

21

prognostic marker in different cancer types [Zheng et al., 2010; Lorentzen et al., 2011]. High

expression of NDRG2 have also been connected with better survival chances and smaller risk of

developing metastases [Chu et al., 2011; Li et al., 2011; Oh et al., 2012; Zheng et al.,2010].

1.4 Avoiding of immune destruction

One of the hallmarks of cancer development is avoidance of immune destruction, and under

normal cellular conditions the immune system is responsible for protection against foreign

pathogens [De Visser et al., 2006; Hanahan & Weinberg, 2011]. All foreign pathogens, including

tumor cells, express antigens and the immune system is introduced to these antigens by major

histocompatibility complex (MHC) molecules on antigen-presenting cells (APCs) [Pardoll, 2012].

Tumor cells have developed different strategies to avoid the immune system, and the relationship

between them is described by a theory called immunoediting [Dunn et al., 2002; Bhardwaj, 2007;

Schreiber al., 2011].

1.4.1 Immune system

All living organisms have evolved

strategies to protect themselves

against pathogens, and these

strategies are collectively referred

to as the immune system

[Hoffmann et al., 1999; Murphy et

al., 2008; Waller et al., 2005]. The

system consists of the immediate

innate immune system and the highly specific adaptive immune system, and by collaboration of

these two systems the immune system is able to recognize and eliminate invading pathogens

[Palm & Medzhitov, 2009]. The system works through recognition, reaction, regulation, and

memory functions, which differ between the innate immune system and the adaptive immune

system [Medzhitov & Janeway, 1997; Cooper & Alder, 2006]. The innate immune system detects

infections by recognition of unique molecular structures, and the adaptive immune system uses

Figure 6 shows the different types of cells in the immune system [Dranoff, 2004]

22

highly specific receptors to recognize nearly any antigen and their clonal expression [Medzhitov &

Janeway, 1997; Cooper & Alder, 2006]. Both systems are composed and react through several

different cell types, see Figure 6 [Dranoff, 2004].

1.4.2 Innate immunity

The innate immune system is the first defense against pathogens, and it consists of several cell

types including the white blood cells. Besides B lymphocytes (B cells) and T lymphocytes (T cells),

both dendritic cells (DCs) and macrophages are important for the interplay between the innate

immune system and the adaptive immune system [Abbas et al., 2007]. All cell types mentioned in

Table 2 express specific recognition receptors and become activated during an inflammatory

response. After activation, the cells become effector cells, whose primary role is to combat

detected pathogens [Janeway & Medzhitov, 2002].

Cell types Function Reaction

Neutrophils The most important cell in the innate

immunity. Acts on many different

pathogens

Early phagocytosis and killing of pathogens

Dendritic cells

(DCs)

Linked to both innate and adaptive

immunity. Present antigens for T cells

Release cytokines, when introduced to pathogens

Macrophages Present antigens for T cells Efficient phagocytosis and killing pathogens,

secretion of cytokines that stimulate inflammation

Natural killer

(NK) cells

Recognize abnormal cells and kill them,

e.g. tumor cells.

Lysis of infected cells and activation of macrophages

Table 2 shows the most important cell types of the innate immune system and further describe their function and reaction pattern [Abbas et al., 2007; Murphy et al., 2008; Wood, 2006; Clancy, 1998]

The epithelial barrier and circulating plasma proteins have also been considered a part of the

innate immunity [Janeway & Medzhitov, 2002; Abbas et al., 2007]. The epithelial barrier produces

antibiotics, and together with lymphocytes it helps to prevent penetration into the host.

Circulating plasma proteins are a varied group of proteins e.g. the proteins of the complement

system, which are also able to recognize pathogens and serve as effector molecules [Abbas et al.,

2007].

23

1.4.3 Recognition of pathogens by the innate immune system

The innate immunity uses special structures called pathogen-associated molecular patterns

(PAMPs) to detect pathogens. These patterns are unique for each pathogen, and they are products

of pathways that are unique for the microbe, which allows for discrimination between self and

non-self molecules [Janeway & Medzhitov, 2002; Abbas et al., 2007; Medzhitov, 2007]. The innate

immune system uses a variety of pattern recognition receptors (PRRs), which are expressed on the

cell surface, in intracellular compartments, or in the circulating system, and the most well-known

are Toll-like receptors (TLRs) [Janeway & Medzhitov, 1997]. When the PRRs are bound to the

PAMPs, the PRRs become activated and induce one or two responses; activation of antimicrobial

and proinflammatory functions in the cells and/or facilitating pathogen uptake into the cell. Some

PRRs are soluble and ensure clearance of pathogens in blood and extracellular fluids [Abbas et al.,

2007]. As mentioned earlier, other mechanisms have evolved in the innate immune system to

recognize pathogens, and these include the complement system, specialized receptors for NK

cells, and other intracellular sensors [Hoebe et al., 2004]. The activation of the innate immune

system is important for the development of the adaptive immune systems responses [Takeda &

Akira, 2005].

1.4.4 The interplay between the innate immune system and the adaptive

immune system

Activation of the adaptive immune system requires two specific signals, where one is provided by

the innate immune system, and the interplay between these two systems is therefore of highest

importance in the recognition and elimination of pathogens [Kindt et al., 2007]. The adaptive

immune system recognizes pathogens through antigens presented by antigen-presenting cells

such as DCs and macrophages, which are part of the innate immune system [Hoebe et al., 2004;

Lydyard et al., 2001; Akira et al., 2006]. T cells and B cells are a major part of the adaptive immune

system, and they are also able to recognize antigens and produce appropriated responses. Besides

antigen presentation, an additional signal is needed for full activation of T cells and B cells to

ensure correct distinction between foreign antigens and self-antigens. This signal is provided by

different soluble molecules released by the innate immune system, e.g. cytokines [Abbas et al.,

2007].

24

1.4.5 Adaptive immunity

All responses by the adaptive immune system are primarily produced by lymphocytes, including B

cells and T cells, which are further described in a later section [Kindt et al., 2007; Werling et al.,

2003]. T cells and B cells become activated when they are presented to foreign antigens, which are

only expressed when pathogens are present in the host. All antigens have their own unique

molecular structure, and these structural variations are called the antigenic variation [Lydyard et

al., 2001; Wood, 2006; Murphy et al., 2008]. Antigens are able to interact with antibodies

produced by the host as a response against pathogens. Antibodies are glycoproteins grouped in

five distinct classes, and they interact with antigens with a high specificity and affinity [Lydyard et

al., 2001; Wood, 2006; Murphy et al., 2008]. Antigens are presented on the surface of APCs, and

APCs are only able to present the antibodies through MHC glycoprotein molecules. The MHC

molecules are encoded by large clusters of genes called MHC genes and are organized in three

classes of molecules [Reche & Reinherz, 2003]. MHC genes are expressed by different cell types.

MHC Class I molecules are expressed by almost every nucleated cell and assist in the presentation

of antigens to T cytotoxic (Tc) cells. MHC Class II molecules are expressed primarily on APCs and

present antigens to T helper (Th) cells. MHC Class III genes code for proteins e.g. in the

complement system, immune receptors, TNF, and regulatory receptors [Trowsdale, 2001; Kindt et

al., 2007]. When antigens are presented on APCs by MHC molecules, the adaptive immune system

will be able to distinguish between self and foreign antigens and produce a proper response. After

the antigen-antibody interaction occurs, the antigens are removed by specific antibody responses

leading to phagocytosis of foreign cells [Kumagai & Tsumoto, 2001; Wood, 2006; Lydyard et al.,

2001].

1.4.6 B cell and T cell function and activation

B cells and T cells are developed in the bone marrow and belong to the lymphocytes, which are

the only cells in the body capable of recognize and distinguishing foreign antigens. Furthermore,

they are responsible for the adaptive immune system’s characteristics, which include specificity

and memory [Abbas et al., 2007]. Both classes of cells are described in Table 3.

25

Class Function Antigen presentation

CD4+ T Helper (Th)

lymphocytes

B cell differentiation (Humoral immunity)

Macrophage activation (cell-mediated immunity)

Able to recognized antigens present

by MHC class II complexes

CD8+ T cytotoxic (Tc)

lymphocytes

Killing of cells infected by pathogens

Killing of tumor cells

Able to recognized antigens present

by MHC class I complexes

T Regulatory (Tregs)

cells

Suppress function of other T cells (regulation of

immune responses, maintenance of self-

tolerance)

B lymphocytes (B cells) Antibody production (humoral immunity) Surface antibody

Table 3 shows the most important cell types of the adaptive immunity, their function and antigen presentation [Abbas et al., 2007; Murphy et al., 2008; Wood, 2006; Clancy, 1998]

B cells are not fully developed, when they are released from the bone marrow, and they are called

naïve B cells until they are activated. They become activated by antigen-antibody interactions

through B cell receptors presenting membrane-bound antibodies. After the interaction, the

proliferation and differentiation stages are completed, and the B cells become memory B cells or

effector B cells also called plasma B cells. Memory B cells express the same membrane-bound

antibodies as naïve B cells, whereas plasma B cells produce antibodies released to the

environment as part of the humoral immunity [Kindt et al., 2007; Wood, 2006; Murphy et al.,

2008].

The production of T cells also starts in the bone marrow, but unlike B cells, T cells migrate to the

thymus in order to mature completely. Two types of T cells are produced, and both cell types

express T cell receptors (TCRs), see Table 3. The two T cells types differ from each other by

expressing different membrane glycoproteins on the surface. Th cells and Treg cells both express

CD4 on their surface, but differ in surface markers and activation. Tc cells express CD8 on their

surface. Most TCRs are only able to recognize antigens through MHC molecules [Kindt et al., 2007;

Wood, 2006; Murphy et al., 2008].

26

1.4.7 The Adaptive immunity is able to respond in two different ways

The adaptive immune system can react by using two different response mechanisms; humoral and

cell-mediated response. The humoral immunity only detects extracellular antigens, whereas the

cell-mediated immunity only detects intracellular pathogens, see Figure 7 [Kindt et al., 2007;

Abbas et al., 2007]. Humoral immunity is performed by activated B cells, which bind antigens

expressed by extracellular microbes through their receptors. Before the humoral response can be

launched, Th cells have to present the extracellular antigens to the B cells, which then leads to

activation and maturation of the B cells. The activated B cells mature into plasma B cells and

memory B cells, where plasma B cells release antibodies against the extracellular pathogens and

memory B cells recognize and remember the antigen specificity. The released antibodies interact

with the antigens and facilitate the

clearance of the pathogens [Kindt et al.,

2007; Murphy et al., 2008].

Cell-mediated immunity recognizes and

eliminates intracellular pathogens and

detects genetic modifications in cells, e.g.

modifications seen in tumor cells [Lydyard

et al., 2001; Kindt et al., 2007].

For activation of T cells, two different

signals are necessary provided by APCs

and co-stimulatory molecules. TCRs can

only bind to antigens presented by MHC

class II molecules and the co-stimulatory

molecules; B7 is expressed by APCs, which interact with CD28 on T cells [Chen et al., 1992; Guinan

et al., 1994]. Proliferation of naïve T cells begins, when both signals are present, and the activated

T cells become effector T cells, which include both Th cells and Tc cells. Memory T cells can be

developed instead of effector T cells during the proliferation, and they have almost the same

function as memory B cells, see Figure 7 [Lydyard et al., 2001; Kindt et al., 2007]. Activated Th cells

are separated into two different subtypes, Th1 and Th2 cells, where Th1 cells are primarily

Figure 7 shows the two types of adaptive immunity [Abbas et al., 2007]

27

developed to fight against infections caused by intracellular bacteria [Kindt et al., 2007]. They

trigger a phagocyte-mediated host response through the production of high levels of interferon

gamma (IFN-γ) and tumor necrosis factor alpha (TNF-α). Parasites and allergens activate Th2 cells

and trigger a phagocyte-independent host response by producing cytokines such as IL-4, IL-5 and

IL-6, which are all involved in B cell differentiation and maturation, thus indicating that Th2 cells

are primary involved in humoral immunity [Romagnani, 1995; Lydyard et al., 2001].

Many cytokines produced by Th cells are essential for activation of Tc cells, and naïve Tc cells are

incapable of eliminating any target cells. Tc cells recognize antigens presented by MHC class I

molecules expressed on all nucleated cells. Thereby, Tc cells are able to recognize and eliminate

almost any cell expressing MHC class I molecules [Kindt et al., 2007]. Before Tc cells can be

activated, they need three signals. Two of them are the same as mentioned for Th cells activation,

and the third signal is induced by inflammatory cytokines [Curtsinger & Mescher, 2010].

1.4.8 Cancer immunology

One of the issues about tumor formation is how cancer cells are able to progress and avoid the

immune system. The theory about immunosurveillance proposes that cells and tissues are

monitored constantly, and thereby the immune system is able to recognize and eliminate

precancer cells and nascent tumors [Hanahan & Weinberg, 2011]. Several studies support this

theory. Experiments have shown increased tumor development and higher progression rate in

mice that are genetically engineered for deficiency of various components of the immune system

when compared to mice with an intact immune system. These observations were made in mice

with engineered deficiencies in development or function of cytotoxic T cells, Th1 cells, or NK cells.

Furthermore, mice with combined deficiencies in both T cells and NK cells were even more

susceptible to cancer development [Bui & Schreiber, 2007; Finn, 2008; Vajdic & van Leeuwen,

2009; Teng et al., 2008; Kim et al., 2007]. Over time several theories and hypotheses have

attepted to answer the question of how tumor cells are still able to survive. The most recent

theory is called immunoediting, which includes three essential phases; elimination, equilibrium,

and escape [Kim et al., 2007].

28

1.4.9 Immunoediting: from immune surveillance to immune escape

In 1909, Ehrlich proposed a theory on the role of the immune system in protection against cancer

[Ehrlich, 1909]. This theory was later modified by Burnet and Thomas, who provided the theory

about immunosurveillance, which has later been validated by findings of tumor-associated

antigens in tumor transplantation models [Burnet, 1964 &Thomas, 1959]. It has now become clear

that the immune system has three distinct roles in preventing cancer development, which include

protection of the host against viral infections and virus-induced tumors, preventing establishment

of an inflammatory environment to prevent production of inflammatory components, and

elimination of nascent tumor cells [Schreiber et al., 2011]. In 2001, it became clear that the

immune system was controlling both tumor quantity and quality, also termed immunogenicity

[Shankaran et al., 2001; Dunn et al., 2002]. This was based on results of studies with

immunocompetent mice (intact immune system) and immunodeficient mice (lack of immune

components), which showed that tumors transplanted from immunocompetent mice into naïve

wild type recipients were able to develop in the new host, whereas tumors received from

immunodeficient mice were not able to continue to grow in the new host [Shankaran et al., 2001].

Furthermore, these results also revealed that tumors from immunocompetent mice were more

immunogenic than those from immunodeficient mice, and they indicate that the immune system

not only protects against tumor development, but also affects the immunogenicity in tumor cells

[Schreiber et al., 2011]. These statements form the basis of the cancer immunoediting hypothesis,

which brings us closer to a possible explanation of how tumor cells are able to survive and prevent

immune responses, including three different phases; elimination, equilibrium, and escape [Dunn

et al., 2002; Bhardwaj, 2007; Kim et al., 2007; Vesely et al., 2011; Dunn et al., 2004; Smyth et al.,

2006; Swann & Smyth, 2007].

1.4.10 From elimination to escape

The elimination of cancer cells is supported by antitumor immune responses, and experiments

with infiltration of Tc cells and NK cells in the tumor have shown better prognosis for the survival

[Bindea et al., 2010; Ferrone & Dranoff, 2010; Nelson, 2008]. It has been observed that

immunosuppressed organ transplant recipients can develop cancer after transplantation of a

29

tumor-free organ, which indicates that an intact immune system is important to hold the cancer

cells in check [Strauss & Thomas, 2010].

In the phase called equilibrium, the cancer cells are living in equilibrium with the immune system

of the host. Increased sculpting of cancer cells leads to production of cells with resistance to

immune effector cells, and through immune selection cancer cells with reduced immunogenicity

are able to survive the immune responses [Kim et al. 2007]. Tumor cells with reduced

immunogenicity are more capable of surviving in hosts with an intact immune system, and several

studies in mice with a range of deficiencies have indicated different degrees of immune selection

pressure [Kim et al., 2007]. The tumor-host equilibrium can be explained by the possibility that

highly immunogenic cancer cells are able to avoid the immune system through disabling of

important immune components involved in elimination of cancer cells [Shields et al., 2010;

Hanahan & Weinberg, 2011].

The phase of cancer cell escape can be described as the tumors’ ability to avoid and prevent

immune recognition. Several tumor-derived soluble factors are able to induce different

mechanisms, all important in escape from immune attacks [Kim et al., 2007]. This phase includes

multiple steps and strategies, such as loss of antigenic presentation, tumor antigens, sensitivity

against immune-effector molecules, and induction of Treg cells, which are all further described in

the section below.

1.4.11 Cancer cells’ most common strategies to avoid the immunity

All strategies presented in this section are the most well-known and documented, and they

represent many aspect of the immune system.

1.4.11.1 NK cells and the NKG2D receptor

Under normal conditions NK cells’ ability to kill directly is inhibited by the presence of MHC class I

molecules. Inhibition of NK cells happens when MHC class I molecules bind to NK cells through a

receptor called killer inhibitory receptor (KIR) [Cerwenka & Lanier, 2001]. Without the expression

of MHC class I molecules on the cell surface, NK cells are activated to kill the cell [Weinberg, 2013].

NK cells recognize tumor cells through different NK receptors, NKp46, NKp50 and NKG2D, where

the NKG2D receptors are the most well known [Gasse & Raulet, 2006]. As a result of genetic

30

damage, viral infection, and neoplastic transformation many cells express stress signal proteins,

and these can be detected by NK cells through the NKG2D receptors [Bui & Schreiber, 2007;

Weinberg, 2013]. Interaction between the NKG2D receptor and the stress signal proteins leads to

a release of IFN-γ, which consequently elicits several distinct responses. Many components of the

immune system are recruited by release of IFN-γ including macrophages, which are able to kill

either directly or indirectly through APCs. Additionally, IFN-γ also increases the expression of MHC

class I molecules on tumor cells leading to expression of tumor antigens and further adaptive

responses [Weinberg, 2013]. Under normal conditions tumor cells would be expected to release

stress signal proteins as a consequence of the neoplastic transformation leading to activation of

the NKG2D receptor and an up-regulation of stress signal proteins, but a down-regulation of these

stress signal proteins is observed in tumor cells instead [Bui & Schreiber, 2007; Gasser & Raulet,

2006]. This is supported by experiments in mice capable of expressing NKG2D, which have shown

that tumor cells suppress the expression of the Rae1 stress antigen in order to avoid NK cell attack

[Weinberg, 2013].

1.4.11.2 MHC molecules

NK cells and Tc cells use the same killing mechanism by introducing a protease to target the cell

and induce apoptosis. Tumor cells are able to avoid the attack from Tc cells by increasing the level

of inhibitor-of-apoptosis proteins [Krajewska et al., 2003]. Normally Tc cells recognize tumor cells

through tumor antigens presented by MHC class I molecules on APCs, but tumor cells have also

shown ability to down-regulate the expression of MHC class I molecules [Finn, 2008; Houghton &

Guevara, 2004; Hicklin et al., 1999; Garrido et al., 1997]. Down-regulation of MHC class I molecules

makes the tumor cells less antigenic, and thereby they avoid immune responses because of the

missing detection of tumor antigens. The expression of antigens is inhibited by promoter

methylation of antigen-coding genes, which makes the tumor cells invisible to effector immune

cells [Bicknell et al., 1994]. The migration of MHC class I molecules from the endoplasmic

reticulum (ER) to the cell surface depend on beta-2 microglobulin (B2m) proteins. Synthesis of

B2m proteins is inhibited by some tumor cells and the transport of MHC class I molecules is

prevented, which leads to repressed expression of antigens [Weinberg, 2013]. Another mechanism

seen especially in highly invasive and metastatic tumors is inhibition of the transcription of MHC

class I genes, which leads to reduced mRNA and prevents further synthesis of MHC class I

31

molecules [Weinberg, 2013]. In some cases, tumor antigens are essential for neoplastic

proliferation, and a down-regulation of the MHC class I molecules will lead to activation of NK cells

and elimination of the tumor cells. Six types of MHC class I molecules have been classified and

some tumor cells are able to selectively suppress expression of only one of these molecules, which

are normally expressed concomitantly by cells throughout the body. This may block the

presentation of tumor antigens and thereby prevent attack by Tc cells and NK cells [Weinberg,

2013].

1.4.11.3 Treg cells

A population of T cells known to express high levels of CD25 and other proteins are called Treg

cells, and under normal conditions they are produced to suppress autoreactive T cells through

contact-dependent mechanisms [Bhardwaj, 2007; Curiel, 2007]. Improved immune-mediated

tumor rejection and tumor antigen-specific immunity have been found in studies with mice lacking

Treg cells [Curiel, 2007]. Two different types of Treg cells have been found including natural and

adaptive Treg cells [Curiel, 2007]. Natural Treg cells are produced in the thymus to prevent

autoimmunity, whereas adaptive Treg cells are produced under inflammatory conditions such as

infections or cancer [Curiel, 2007]. Adaptive Treg cells suppress effector T cells through direct

contact and production of immune-suppressive cytokines, IL-10, and growth factor beta (TGF-β)

[Curiel, 2007; Zou, 2006; Bhardwaj, 2007; De Visser et al., 2006; Finn, 2008]. Tumors recruit

adaptive Treg cells by the chemokine CCL22, and they infiltrate the tumor microenvironment by

interaction with adaptive Treg receptors called CCR4. Located in the middle of the tumor they

inhibit the action of effector T cells [Weinberg, 2013]. The production of TGF-β in mice with

tumors has shown conversion of antitumor effector T cells into Treg cells [Woo et al., 2001 & Liu et

al., 2007]. Furthermore, TGF-β and tumor cells have been found to induce the differentiation of

Treg cells [Curiel, 2007].

1.4.11.4 Transforming growth factor beta (TGF-β)

Tumor cells produce different immunosuppressive products, such as TGF-β, Fas ligand (FasL), and

indolamine-2,3-dioxygenase (IDO), which are all produced to prevent or decrease immune

responses [Finn, 2008; Elgert et al., 1998; Chouaib et al., 1997]. TGF-β is a cytokine involved in

proliferation, activation, differentiation, and apoptosis of innate and adaptive immune cells and

32

functions by inhibition of antitumor immune responses [Igney & Krammer, 2002; Siegel &

Massagué, 2003]. TGF-β is able to reduce the presentation of antigens through APCs by inducing

apoptosis and thereby decreasing the number of APCs [Weinberg, 2013]. Furthermore, TGF-β has

an immunosuppressive cytostatic effect on T cells, and NK cells, and prevents maturation of DCs

by inhibition of MHC class II molecules expression [Weinberg, 2013; Siegel & Massagué, 2003].

1.4.11.5 Indolamine-2-3-dioxygenase (IDO)

Tumor cells are able to develop an immune-suppressive enzyme called IDO [Uyttenhove et al.,

2003 & Muller & Prendergast, 2007]. Earlier, the enzyme has been connected with tolerance

between mother and fetus, but later it has been found to regulate auto-immunity by T cell

activation [Munn et al., 2002]. IDO is up regulated in both tumor cells and DCs, and its function is

dependent on activation signals from APCs [Bhardwaj, 2007; Prendergast, 2008]. Most IDO is

found in the tryptophan catabolism function as a rate-limiting enzyme, whereby it induces a fall in

tryptophan level and has a negative consequence for T cell proliferation [Bhardwaj, 2007; Finn,

2008]. Studies with inhibitors of IDO in mice have shown an induction of immunity [Finn, 2008].

1.4.11.6 Fas receptor and Fas ligand

Fas receptor (FasR) and FasL molecules play important roles in immune escape and tolerance

[O’Connell et al., 1996; Zhang et al., 2007]. FasL is able to induce apoptosis in immune cells leading

to limited immune responses [Nguyen & Rusell, 2001; O’Connell et al., 1996]. Activation of

apoptosis happens when FasL interact with FasR, but tumor cells are able to develop resistance

against FasL-mediated mechanisms by releasing soluble FasL. The attention is then removed from

the tumor cells over to the different lymphocytes, where FasR will interact with the soluble FasL

[Connell et al., 1996; Weinberg, 2013]. Furthermore, tumor cells can attack Fas-sensitive tumor-

infiltrating lymphocytes (TILs) by increasing the level of FasL, which leads to tumor cell immunity

and cancer progression [Lin et al., 2001; Zhang et al., 2007].

33

1.5 Tumor-promoting inflammation

A second enabling characteristic is tumor-promoting inflammation, and this involves the balance

between the immune system and the tumor microenvironment. Chronic inflammation has been

associated with cancer progression and inadequate pathogen eradication, prolonged

inflammatory signaling, and defects in anti-inflammatory mechanisms [Han & Ulevitch, 2005]. The

tumors are infiltrated with cells from both the innate and adaptive immunity, and the

inflammation response contributes with bioactive molecules to the tumor microenvironment.

These bioactive molecules, including growth factors, survival factors and extracellular matrix-

modifying enzymes, can all lead to proliferation, continued survival, angiogenesis, invasion, and

metastasis [Hanahan & Weinberg, 2011; DeNardo et al., 2010; Grivennikov et al., 2010; Qian &

Pollard, 2010; Karnoub & Weinberg, 2006-2007]. The network of the tumor microenvironment

includes inflammatory cytokines, growth factors, and chemokines produced by tumor cells or

tumor-associated leukocytes and platelets [Balkwill & Mantovani, 2001; Grivennikov & Karin,

2010; Hsu & Chung, 2006; Lin & Karin, 2007].

1.5.1 Inflammation and cancer

Since the 19th century inflammatory cells have been observed within tumors, and tumors often

occur at sites of chronic inflammation, which provides the first indication of a possible link

between inflammation and cancer [Schetter et al., 2010; Grivennikov et al., 2010; Balkwill &

Mantovani, 2001]. Epidemiological studies now support these findings and suggest that up to 25 %

of cancer cases are related to inflammation, and that 15-25 % of all deaths from cancer are linked

to infections and inflammation [Aggarwal et al., 2009; Perwez Hussain & Harris, 2007; Balkwill &

Mantovani, 2001]. Several chronic inflammatory diseases have been connected with increased risk

of cancer development including Crohn’s disease, ulcerative colitis, chronic pancreatitis, and

chronic bronchitis [Ekbom et al., 1990; Gillen et al., 1994; Ekbom et al., 1993; Wu et al., 1995;

Mayne et al., 1999]. Furthermore, chronic inflammation caused by microbial or parasitic infections

is involved in development of cancer by viral hepatitis B and C, Helicobacter pylori, and parasitic

worms, which all lead to chronic inflammation and promotion of several cancer types [Schetter et

al., 2010; Tsukuma et al., 1993; Parsonnet et al., 1991; Grivennikov et al., 2010].

34

Studies have shown that 90 % of all cancers are associated with somatic mutations and

environmental factors, and many of these factors are related to chronic inflammation

[Grivennikov et al., 2010]. Acute inflammation often occurs as a response to microbial infection

and tissue damage, however whether chronic inflammation is triggered by microbial infections,

autoimmune disease, viral infections, exposure to allergens, toxic chemicals, obesity, and

inflammation is still unknown [Mantovani et al., 2008; Schetter et al., 2010]. Tissue homeostasis is

monitored by the immune system, and tissue damage or infections interrupt the balance and lead

to an immune response. Sometimes an imbalance of the response provided by the innate immune

system leads to constant activation of the immune system and subsequently to chronic

inflammation [De Visser et al., 2006]. Cells exposed frequently to chronic inflammation will

undergo oncogenic transformation, and the causes of this are many, e.g. induction of genomic

instability, increased angiogenesis, epigenetic altering, and increased proliferation. Furthermore,

inflammation is able to change the gene expression of oncogenes and tumor suppressor genes in

order to promote neoplastic transformation [Schetter et al., 2010]. Chronic inflammation can

provide the tumor environment with a supply of inflammatory mediators, which include growth

factors, survival factors, proangiogenic factors, extracellular matrix-modifying enzymes, and

inductive signals, all involved in either proliferation, apoptosis, angiogenesis, invasion, or

metastasis [DeNardo et al., 2010; Grivennikov et al., 2010; Qian & Pollard, 2010; Karnoub &

Weinberg, 2006-2007]. The outcome of an inflammatory immune response depends on the

balance between proinflammatory and anti-inflammatory mediators of the adaptive and innate

immune system, and inappropriate responses of the immune system can affect this balance, see

Figure 8 [Ben-Baruch, 2006; Kim et al., 2006; Schetter et al., 2010].

Figure 8 shows the importance of the overall balance between proinflammatory and anti-inflammatory mediators [Schetter et al., 2010]

35

The connection between cancer and inflammation

can be explained by the intrinsic and extrinsic

inflammation pathways, see Figure 9 [Mantovani et

al., 2008; Schetter et al., 2010]. The difference

between the two pathways is the way they are

activated. The intrinsic pathways are primarily

activated by genetic events including genetic

alterations of oncogenes and tumor suppressor

genes leading to cancer, whereas the extrinsic

pathways involved in cancer are caused by infection

and chronic inflammation [Mantovani et al., 2008;

Schetter et al., 2010]. The two pathways only differ

in their activation and cause of cancer promotion,

but both of them result in activation of

transcription factors such as NF-κB, STAT3 and

HIF1α in both tumor cells and immune cells [Karin,

2006; Yu et al., 2007]. When the transcription

factors are activated, they ensure production of

prostaglandins, chemokines, and cytokines by the

tumor cells, where the cytokines are signaling

molecules involved in many cellular functions especially

in inflammation and immune responses [Schetter et al., 2010; Mantovani et al., 2008]. Cytokines

are classified as either pro-inflammatory or anti-inflammatory mediators, and they are important

for the balance between promotion and suppression of cancer mentioned earlier, but also for the

recruitment and activation of leukocytes [Schetter et al., 2010]. Thereby the same transcription

factors are activated again, but this time in both inflammatory cells and tumor cells, which leads to

production of more inflammatory mediators, and the environment for cancer development is

generated [Mantovani et al., 2008]. One of the inflammatory mediators is the cytokine interleukin

6 (IL-6), which is important in transition from acute inflammation to chronic inflammation and

triggers the activation of the transcription factor STAT3 [Erreni et al., 2011].

Figure 9 shows the intrinsic and extrinsic inflammation pathways that connect cancer and inflammation [Mantovani et al., 2008]

36

1.5.2 Function of IL-6 and its role in cancer

The family of IL-6 cytokines consists of IL-6, leukemia inhibitory factor, oncostatin M,

cardiotrophin-1, ciliary neurotrophic factor, and cardiotrophin-like cytokine [Taga & Kishimoto,

1997; Derouet et al., 2004]. Common for all is the ability to activate the signal transducing

receptor protein Gp130, and thereby activation of target genes involved in differentiation,

survival, apoptosis, and proliferation [Heinrich, 2003].

IL-6 is a multifunctional cytokine with functions in both immune and non-immune cells produced

by lymphoid and non-lymphoid cells such as T cells, B cells, monocytes, fibroblasts, endothelial

cells, and several tumor cells [Heinrich, 2003; Becker et al., 2004; Grivennikov & Karin, 2008

Kishimoto, 1989]. IL-6 has both pro- and anti-inflammatory properties and is important for

immune responses, cell survival, apoptosis, proliferation, and production of acute phase proteins

[Heinrich, 2003 & Kishimoto, 1989]. The expression of IL-6 is regulated both negatively and

positively by different cytokines. TNF-α and IL-1 are able to increase the production of IL-6,

whereas glucocorticoids affect the production of IL-6 negatively [Kishimoto, 1989].

The group of IL-6 receptors is arranged into two subunits of receptors, non-signaling α-receptors

(IL-6Rα, IL-11Rα and CNTFRα) and signal transduction receptors (gp130, LIFR and OSMR) [Heinrich,

2003]. In order to establish the complex, IL-6 interacts with the α-receptor (IL-6Rα) integrated in

the cell membrane. The IL-6/ IL-6Rα complex then interacts with the gp130 glycoprotein on the

cell surface thus forming an activating receptor unit, which leads to activation of the STAT

pathway. Gp130 glycoproteins are expressed on almost every cell in the organisms, whereas the

expression of IL-6Rα is restricted and tightly regulated leading to the degree of cytokine sensitivity

in the cell [Grivennikov & Karin, 2008; Heinrich et al., 2003].

Cells missing the membrane-bound receptor can be activated by trans-signaling through the

soluble IL-6 receptor (sIL-6R). Together, sIL6R and IL-6 form a complex called the IL-6/sIL-6R

complex. This interaction shows exactly the same activation of the JAK/STAT pathway as seen

when interacting with IL-6Rα units [Lin & Karin, 2007; Becker et al., 2004]. When the JAK/STAT

pathway is activated, JAK1 handles the phosphorylation of STAT proteins including STAT1 and

STAT3, see Figure 10. Nuclear translocation and activation of specific target genes happen through

37

phosphorylation dimerization, and target genes are primarily involved in cell cycle progression and

suppression of apoptosis. STAT3 proteins are predominant in IL-6 signaling transduction and

further involved in malign cell proliferation and survival. STAT1 has been shown to inhibit the

growth of tumor cells, and studies in lung cancer cells have shown that knockdown and inhibition

of STAT3’s phosphorylation lead to delayed cell growth [Lin & Karin, 2007]. Moreover, a role as a

pro-tumor agent has been suggested for IL-6 supported by poor prognosis related to significantly

elevated levels of IL-6 found in lung and breast cancer

patients [Grivennikov & Karin, 2008].

In addition to activation of the JAK/STAT pathway, IL-6

can work directly with some immune cells in response

to pathogens. Further studies have shown IL-6

involvement in the final step in differentiation of B cells

and stimulation of B cells to become plasma B cells

[Abbas et al., 2007]. Kishimoto has suggested a

potential role for IL-6 as a factor for T cell activation and

proliferation, based on the fact that resting T cells

express IL-6Rα [Kishimoto, 1989]. Thereby IL-6 is an

important part of the immune system and the activation

of the JAK/STAT pathway.

1.5.3 Interleukin 6 and NDRG2

Immune responses in the tumor microenvironment can promote or inhibit cancer progression,

and the STAT proteins have a central role in determining the response outcome [Yu et al., 2009].

STAT3 and NF-kB function as nuclear transcription factors for genes involved in tumor

proliferation, survival, angiogenesis, and invasion, and NF-kB further targets genes coding for IL-6,

which functions as a STAT3 activator. STAT3 induces the expression of cytokines, growth factors,

and angiogenesis factors leading to activation of theirs associated receptors, which result in a

reactivation of STAT3, including IL-6 and IL-10 [Naugler & Karin, 2007; Yu et al., 2009]. Thereby a

feed-forward loop is created between tumor cells and immune cells in the tumor

microenvironment, and the persistent activation of STAT3 promotes tumor-inflammation and

Figure 10 shows JAK/STAT pathway [Shuai & Liu, 2003]

38

inhibits anti-tumor immunity [Yu et al., 2009]. IL-6 and IL-10 are both key activators of STAT3, and

NDRG2 expression has been found to induce SOCS1, which negatively regulates STAT3 activation

in breast cancer cells [Park et al., 2007]. Furthermore, NDRG2 expression has been observed to

modulate SOCS3 and STAT3 activity, which may lead to inhibition of IL-10 [Lee et al., 2010]. IL-6

and IL-10 are strongly connected, and therefore it would be interesting to see if a connection

between NDRG2 expression and IL-6 levels exists.

1.6 Short summary

Various mechanisms have been connected to tumorigenesis including emerging hallmarks and

enabling characteristics, which describe the relationship between the immune system and cancer

and genomic changes involved in tumorigenesis [Hanahan & Weinberg, 2011]. Silencing of tumor

suppressor genes is an important mechanism of tumorigenesis and can lead to inhibition of tumor

suppressor genes’ normal function in suppression of cancer development [Hanahan & Weinberg,

2011]. NDRG2 has been suggested as a potential tumor suppressor candidate, and the observation

of down-regulation of NDRG2 in several cancer types supports this statement [Chu et al., 2011; Hu

et al., 2004; Lee et al., 2008; Lorentzen et al., 2007; Piepoli et al., 2009; Lorentzen et al., 2011;

Zhao et al., 2008; Ma et al., 2008; Wang et al., 2012]. Under normal cellular conditions, the

immune system is responsible for protection against foreign pathogens including tumor cells [De

Visser et al., 2006; Hanahan & Weinberg, 2011]. Furthermore, the responses of the immune

system against inflammation and chronic inflammation have been associated with cancer

development. The oncogenic transformation of tumor cells exposed to frequent chronic

inflammation has been connected with epigenetic alterations and changed expression of tumor

suppressor genes [Schetter et al., 2010]. Additionally, the tumor environment is supplied with

inflammatory mediators including cytokines as a result of immune responses and chronic

inflammation [DeNardo et al., 2010; Grivennikov et al., 2010; Qian & Pollard, 2010; Karnoub &

Weinberg, 2006-2007]. An important cytokine is IL-6, which has been connected with chronic

inflammation through its ability to activate STAT3, and persistent activation of STAT3 leads to

inhibition of anti-tumor immunity [Yu et al., 2009]. NDRG2 expression has been reported to induce

SOCS1 expression leading to down-regulation of STAT3 in breast cancer, therefore it would be

39

interesting to study a possible connection between NDRG2 expression and IL-6 levels in colon

cancer cells [Lee et al., 2010].

2. The aim of the Master’s thesis

The aims of this Master’s thesis are to study the influence that IL-6 and NDRG2 may have on colon

cancer cells, and to study the impact that IL-6 may have on colon cancer cells proliferation with

and without the expression of NDRG2

Experimental description:

- To use colon cancer cell lines to analyze the expression level of NDRG2 on both mRNA and

protein level

- To treat colon cancer cell lines with IL-6 and then analyze the expression of NDRG2 on

mRNA level

- To establish stable cell lines expressing NDRG2

- To analyze if the expression of NDRG2 affects the growth rates of colon cancer cells

- To analyze if colon cancer cells expressing NDRG2 may have different growth rates

compared to cells without the expression of NDRG2

- To analyze if transfected colon cancer cells expressing NDRG2 have different growth rates

when treated with IL-6

To perform the above-mentioned analyses, the methods and techniques described in the next

section were used.

40

3. Materials & Methods

The sections provide a short introduction to the theory behind the methods used, followed by a

description of the particular use in this thesis. The experimental design is described briefly to give

a better understanding of the process.

3.1 Experimental Design

In the present study, the expression of NDRG2 and the growth of colon cancer cells were

examined under various conditions. The mRNA and protein expression of NDRG2 in the cell lines

were examined by reverse transcription quantitative polymerase chain reaction (RT-qPCR) and

western blotting. The growth rate of the cell line was examined by growth assays.

The conducted experiments are categorized into two groups and investigate different aspects of

NDRG2 and the presence of IL-6, see Figure 11.

Figure 11 shows a schematic overview of the two groups of experiments conducted through this master thesis

NDRG2 expression

Expression in colon cancer cells

at mRNA level

Expression in colon cancer cells

at protein level

Expression in colon cancer cells

after IL-6 treatment

Growth assays

Normal colon cancer cells

Colon cancer cells with NDRG2

Colon cancer cells treated with IL-6

Colon cancer cells with NDRG2 and treated with IL-6

41

3.2 Cells Culture - growth, harvesting, and treatment

All cell experiments were conducted with colon cancer cell lines SW480 and HCT116. The colon

cancer cells lines SW480 and HCT116 were originally isolated from an adenocarcinoma and a

colorectal carcinoma. The cells were cultured in T25 or T75 culture flasks supplied with McCoy’s

5A medium (Sigma-aldrich, #M9309), 10% fetal bovine serum (Sigma-aldrich, #F0804), and 1%

Penicillin-streptomycin (Sigma-aldrich, #P0781) to provide nutrition and avoid infections. To

ensure optimal growth conditions, the cells were incubated at 37°C with 5% CO2 in a humidified

atmosphere.

The cells were passaged twice a week to ensure space for continued growth, and the frequency

was based on the individual growth rate for the cell lines. The splitting procedure was as follows:

wash with 10 ml PBS (Lonza, #BE17-512F) twice to remove the rest of the medium followed by

trypsinization with 2 ml Trypsin 1X (Sigma-aldrich, #T3924) to loosen the cells from the bottom of

the flasks. The cells were resuspended in 8 ml McCoy’s 5A medium to separate the cells

completely, and around 1.5-2 ml cell suspension was kept for further culture.

The rest of the cell suspension was used for isolation of RNA, DNA and protein. The cell suspension

was transferred to a 15 ml tube and spun down for five minutes at 1500 rpm to isolate the cells.

The supernatant was removed and the cells were resuspended in 3 ml PBS to remove any

remaining medium. 1 ml of the cell suspension was transferred to three 1.5 ml eppendorf tubes

and spun down for five minutes at 1500 rpm to isolate the washed cells. After removing the

supernatant the pellets were stored at -80 °C.

3.2.1 Treatment of Cells

Cancer cell lines HCT116 and SW480 were treated with recombinant human IL-6 (Sigma-Aldrich,

#H7416) in order to analyze the effect that different IL-6 levels may have on colon cancer cell lines

and the expression of NDRG2.

The treatment efficiency was first tested with different concentration and over a time period of 72

hours, in order to find the most efficient concentration and time. Hsu & Chung have tested

different concentrations for IL-6 treatment of colon cancer cells and based on their results the

42

following concentrations (10 ρM, 50 ρM and 10.000 ρM) were chosen for this study [Hsu & Chung,

2006]. The cells were plated at a density of 1 x 105 cells per well in 24-well plates for 24 hours, and

medium was changed before initiation of IL-6 treatment. All samples were handled in duplicates

or triplicates and treated with the following concentrations: 10 ρM, 50 ρM and 10.000 ρM over

distinct time periods of 24, 48, and 72 Hours. Hereafter, the cells were harvested for further

analysis. See Figure 12 for further information on the following procedure.

Figure 12 shows the whole procedure for the treated cells

When the most efficient concentration was found, the colon cancer cell line SW480 was treated

with IL-6 to analyze if the cytokine had an impact on the growth rate of SW480 cells with and

without expression of NDRG2. Only SW480 cells were used for treatment, because of the fact that

the HCT116 cells do not express NDRG2 protein. Cells were plated at a density of 1 x 104 cells per

well in 24-well plate for 24 hours, and the medium was changes before the treatment of IL-6

(10,000 ρM) was started. All samples were handled in duplicates and treated over a time period of

24, 96, and 168 hours.

Treatment of cellsMeasurement of

expression by qPCR

Growth assays

43

3.3 Quantification of NDRG2 mRNA level

The methods used to analyze the level of NDRG2 mRNA are described in the following section.

These include RNA extraction, cDNA synthesis, and qRT-PCR.

3.3.1 RNA extraction

Before initiation of RNA extraction, all materials used were cleaned

with RNaseZap (Sigma-aldrich, #R2020). RNA extraction includes the

steps: homogenization, extraction, precipitation, and solubilization,

see Figure 13:

1. The cells were disrupted to separate RNA from the cells

2. The homogenate was separated in an organic and an aqueous

phase. RNA is found in the aqueous phase

3. RNA was precipitated and the isolation of RNA was completed

after the wash steps

4. RNA was stored in Rnase-free water at -80°C until use

Throughout the whole procedure, the pellet must stay on ice. The pellet was resuspended in 1 ml

TRI reagent® solution (Ambion, #AM9738) and incubated at room temperature (RT) for five

minutes. TRI reagent® solution contains phenol and guanidine thiocyanate, which inhibit the

Rnase activity. The isolation of RNA from the cells is performed by centrifugation at 12000 x g, 4 °C

for 15 minutes, and hereafter the supernatant was transferred to new tubes and work continued

with these tubes.

To separate RNA and waste, 100 μL of Chloroform (100%) was added to the supernatant and the

tubes were vortexed shortly. The tubes were then incubated at RT for 10 minutes to ensure the

phase division, and the tubes were then centrifuged at 12,000 x g, 4°C for 15 minutes to enable

transfer of the RNA-containing phase to new tubes. Then 500 μL Isoprophanol (100%) was added

and the samples were vortexed shortly. Samples were incubated at RT for 5 minutes followed by

centrifugation at 12,000 x g, 4 °C for 20 minutes. Supernatants were discarded.

Figure 13 shows the RNA extraction in steps [Ambion, 2014]

44

Pellets were washed with 1 ml Ethanol 75% and centrifuged at 12000 x g, 4 °C for 20 minutes. The

ethanol was gently removed, and the tubes were air-dried for maximum 10 minutes. The samples

were then resolved in 50 μL of Rnase-free water and vortexed shortly, and the concentration of

RNA was measured at 260 nm on the NanoDrop® spectrophotometer ND-1000. Pellets were

stored at -80 °C or used immediately.

3.3.2 cDNA synthesis

By cDNA synthesis double stranded cDNA was

produced and used downstream for quantification

of mRNA levels of specific genes by qRT-PCR.

Random primers annealed to all RNA sequences,

and reverse transcriptase performed the synthesis

of cDNA. Hydrolysis of RNA strands happen when

the synthesis of cDNA is completed and the cDNA is

now ready to use. See Figure 14.

For cDNA synthesis, High capacity cDNA reverse

transcription kit (Applied Biosystems, #4368814)

was used, and the total volume of the mastermix for

each reaction was 10 μL. The mastermix was

prepared as follows: 2.0 μL 10X Reverse

transcriptase buffer, 0.8 μL 25X dNTP mix (100 mM),

2.0 μL 10X Reverse transcriptase Random Primers,

1.0 μL MultiScribe™ reverse transcriptase and 4.2 μL nuclease-free water. Then, the mastermix

was gently mixed and kept on ice until use to avoid activation of the reverse transcriptase.

In preparation, the RNA samples were diluted to a concentration of 100 ng/μL in order to ensure

an equal amount of RNA in all cDNA samples. 10 μL mastermix was transferred to 0.2 ml PCR tubes

with 10 μL of diluted RNA and mixed to resolve the RNA in the mastermix.

The thermal cycler was set to run at 25 °C for 10 minutes, 37 °C for 120 minutes, 85 °C for 5 sec

and paused at 4 °C. The cDNA was stored at -20 °C.

Figure 14 shows the cDNA synthesis step by step [Addison Wesley Longman, 1999]

45

To minimize the risk of contamination and to ensure that only the desired RNA was in the samples,

two different cDNA control reactions were performed; one without RNA and one without

MultiScribe™ reverse transcriptase.

3.3.3 Quantitative real time polymerase chain reaction (qRT-PCR)

Expression of mRNA of a preferred gene was measured by use of qRT-PCR, which is a procedure

including several steps, see Figure 15:

1. By denaturation at 95°C, double stranded DNA was separated.

2. By primer annealing, forward and reverse primers were attached to each of the DNA

strands at varied temperatures depending on the specific primer set.

3. Extension was performed by DNA polymerase enzyme at 72°C.

In this study the SYBR Green technique was used, which is a commonly used fluorescent DNA

binding dye. Through binding to all double-stranded DNA, the increase in fluorescence can be

measured through the cycle and is equal to the amount of DNA amplified over time.

Figure 15 shows the different steps in the qRT-PCR procedure [Lu et al., 2011 (modified)]

For qPCR, Quantitech® SYBR® Green PCR kit (Quigen, #204141) was used, and for each reaction

the mastermix contained; 5 μL 2X Quantitech SYBR Green PCR mastermix, 0.5 μL (10 ρmol/μL)

forward primer, 0.5 μL (10 ρmol/μL) reverse primer, and 3 μL nuclease-free water. Each reaction

consisted of 1 μL cDNA and 9 μL mastermix, giving a total volume of 10 μL. Primer sequences and

annealing temperatures can be seen in

46

Table 4 shows primer sequences and annealing temperatures (Tm) for quantitative Real Time Reverse Transcriptase

Polymerase Chain Reaction (qRT-PCR)

Table 4 shows primer sequences and annealing temperatures (Tm) for quantitative Real Time Reverse Transcriptase Polymerase Chain Reaction (qRT-PCR)

The program at the thermo cycler was as follows; 95°C for 15 minutes, 95°C for 15 sec, annealing

temperature (varied with primers) for 30 sec, and lastly 72°C for 30 sec, repeated for 40 cycles.

3.3.4 Standard curve

To assess the qPCR reaction, the efficiency of the primers is important, and by use of standard

curves it is possible to optimize the qPCR. A template known to express the gene of interest was

diluted at different concentrations to constitute a dilution series. The standard curve gives a linear

regression formula, which can be used to determine the reaction efficiency. The optimal slope of

the standard curve has a correlation coefficient (R2) of 0.999, but an R2 value greater than 0.98 is

ok for an optimized PCR reaction. The slope of the standard curve can be analyzed and reaction

efficiency can be evaluated over a range of different concentrations. Normal human liver cDNA

(BD biosciences, #636742) was used for the standard curve for NDRG2 expression and normal

human heart cDNA (BD biosciences, #636742) was used for RPLP0. The standard curve for MCL-1

was prepared from human colon cancer cell line HCT116.

Gene Forward Primer 5’ 3’ Reverse Primer 3’ 5’ Tm (°C)

NDRG2 GCTACAACAACCGCCGAGAC ACAGGCGAGTCATGCAGGAT 55

RPLP0 GCTTCCTGGAGGGTGTCC GGACTCGTTTGTACCCGTTG 52

MCL-1 TAAGGACAAAACGGACTGG CCTCTTGCCACTTGCTTTTC 60

47

3.4 Detection of NDRG2 protein expression

Methods to detect NDRG2 protein are described in the following sections. These include

transfection, whole cell protein extraction, and Western blotting. The NDRG2 protein levels were

investigated in the cancer cell lines HCT116 and SW480. Furthermore, these cells were transfected

with three different plasmids, pCMV-EGFP, pcDNA6V5-HIS-A-EGFP and pcDNA6-NDRG2L-V5, see

appendix II. After the transfection the NDRG2 protein level was investigated again. All further

information on the antibodies and solutions used in the study can be found in Appendix I.

3.4.1 Transfection

Specific DNA is transfected into cells to control

the gene expression. In stable transfection, the

DNA is introduced into cells long-term. The

plasmid used for transfection contains the gene

of interest and a blasticidin resistance gene.

Selection is possible through blasticidin

treatment, because of the blasticidin resistance

gene. The process of transfection can be seen

in Figure 16.

For this project the transfection was prepared

by plating cells at a density of 3x105 per well in

6-well plates for 24 hours. Hereafter they were

ready for transfection, which was conducted

with three different plasmids; pCMV-EGFP, pcDNA6V5-HIS-A-EGFP and pcDNA6-NDRG2L-V5.

Transfection mastermix was prepared by diluting 1 μg of plasmid in 100 μL McCoy’s 5A medium. 2

μL gently vortex Turbofect Transfection Reagent (Thermo Scientific, #R0539) was added to the

diluted plasmid. The transfection mastermix was resuspended and incubated at RT for 20 minutes.

100 μL Transfection mastermix was added drop-wise to each well, and the plate was gently

rocked. The plate was then incubated at 37°C with 5% CO2.

Figure 16 shows the transfection step by step [Ibidi, 2014]

48

After 24 hours, the transfection was controlled and medium changed to ensure the best growth

conditions for the cells. Control of the transfection was performed by use of fluorescence

microscopy (Leica DMIRB) and the cells tranfected with the pCMV-EGFP plasmid. Hereafter, the

transfected cells were grown in a selected medium for at least 14-21 days, and 12 μL (1mg/ml)

blasticidin S HCI (Invitrogen, #46-1120) was used for selection.

When the cells had been exposed to the selection factor for sufficient time, they were harvested

by the followed procedure: washing with 1 ml PBS, trypsinizing with 100 μL trypsin for 5 minutes,

resuspension in 900 μL McCoy’s 5A medium, and again resuspended in 1 ml PBS and spun down at

14000 rpm for 5 minutes. Supernatant was removed and the pellet was stored at -80°C.

3.4.2 Whole cell protein extraction

To isolate protein from whole cells, a whole cell protein extraction method was used. All steps

were performed on ice.

Lysis Buffer++ was prepared using 1 ml Lysis Buffer (see Appendix I for the lysis buffer solution), 2

μL 0.5M DTT, and 2 μL protease inhibitor (Sigma-aldrich, #P8340). The cell pellet was resuspended

in 30 μL Lysis Buffer++ and incubated on ice for 30 minutes. Then the sample was spun down at

14000 x g for 10 minutes at 4°C. Supernatant containing the protein fraction was transferred to a

new tube and stored at -80°C until use.

3.4.3 Western Blot

Western Blotting is used to investigate the expression of a specific protein. Proteins are separated

by size through gel electrophoresis. Proteins are then transferred to a membrane, and by using

primary and secondary antibodies the proteins can be detected by the chemiluminescent

detection method. The western blot is incubated with substrate that will luminesce when exposed

to the reporter on the secondary antibody, and by use of Biospectrum® imaging system (UVP) the

light makes the protein visible, see Figure 17.

49

3.4.4 Protein measurement

Protein content was measured by use of Bradford standard curve assay. Bradford is an acidic

solution of Coomassie Brilliant Blue dye, which interacts with the protein and changes the

absorbance from 465 nm to 595 nm. This absorbance shift can be measured by a

spectrophotometer to determine the protein concentration in the samples. The standard curve

was prepared based on known concentrations of Bovine Serum Albumin (BSA). The concentration

of unknown proteins can be determined by the absorbance given from the standard curve at

known concentrations. See Appendix I for the Bradford solution.

The standard curve was established by: 1 ml Bradford solution and varied concentrations of BSA of

0, 1, 2, 4, 6, and 8 μg/μL. All samples were prepared in duplicates and incubated in darkness for 10

minutes. The concentrations were then measured at the spectrophotometer (Biophotometer from

eppendorf) at 595 nm.

Protein samples with unknown concentrations could now be measured. All samples were

prepared by adding of 1 ml Bradford solution and 2 μL protein lysate. A blank sample was

prepared by use of 2 μL Lysis Buffer instead of protein. Then all samples were incubated at 10

minutes in complete darkness and measured on the spectrophotometer at 595 nm.

3.4.5 Separation of proteins by gel electrophoresis

All protein samples were prepared by mixing 30 μg protein, 3.75 μL 4X Nupage® LDS sample Buffer

(Invitrogen, #46-5030), 1.5 μL 0.5M DTT, and H2O to a total of 15 μL. Nupage® LDS sample Buffer

had been incubated at 30°C before use. Before loading the gel, all samples were heated at 70°C for

Figure 17 shows the antibody binding and the luminesce of light [Advansta, 2014]

50

10 minutes. Nupage® 4-12% Bis-Tris Gel (Novex, #NP0322BOX) was prepared while the samples

were heated. First, the comb was gently removed from the wells, then the wells were washed by

deionized water and the tape was peeled off. The gel was placed in an XCell Surelock System and

the chambers were filled with Running Buffer. The samples and markers were loaded and the gel

was run at 200V for 1 hour. The marker used was PageRuler™ prestained protein ladder

(Fermentas, #SM1811).

3.4.6 Transfer of proteins by electro-blotting

The membrane was prepared before the end of the gel run. All blotting pads were soaked in

Transfer Buffer. The membrane was soaked in 96% ethanol for 30 sec, flushed with deionized

water, and then placed in the transfer buffer until use.

After the gel electrophoresis, the gel was separated from the cassette and the XCell II Blot module

was prepared. All blotting pads were squeezed to remove air bubbles, filter paper was soaked in

transfer buffer shortly before use, and blotting pads, filter paper, membrane, and gel was pressed

together in layers as illustrated in Figure 18.

When the XCell II Blot Module was prepared, it was filled with Transfer buffer and ran at 25V for 1

Hour.

Figure 18 shows the composition in the XCell II Blot Module

51

3.4.7 Antigen binding

After transfer, the membrane was separated in two. Both parts were loaded with the same

samples, and detection of two different proteins could therefore be made at the same time.

Membranes were washed briefly in transfer buffer and then incubated in 20 ml blocking buffer for

30 minutes at RT. A rocking table was used at all steps to ensure full cover of the membranes.

Membranes were then washed 2 x 2 minutes in 20 ml Wash buffer. The primary antibody was

diluted in Dilution buffer and then added to the membranes after wash. The membranes were

incubated overnight with the primary antibodies at 4°C. All solutions can be found in Appendix I.

The secondary antibody was also prepared and diluted in Dilution buffer. The next day, the

membranes were washed 2 x 10 minutes in 20 ml Wash buffer. The secondary antibody was

added, and the membranes were incubated for 1 hour at RT. After incubation the membranes

were washed 4 x 10 minutes in 20 ml Wash buffer. Antibodies and dilutions can be found in

appendix I.

3.4.8 Protein detection

To detect the proteins the membranes were incubated for 10 minutes with 1 ml SuperSignal ®

West Dura Extended Duration Substrate mix (Thermo Scientific, #34076) for each membrane. In

order to obtain the best results, the incubation had to be performed in completely darkness and

the proteins were detected by photography of the membranes by use of Biospectrum® imaging

system (UVP).

52

3.5 Quantification of cell proliferation under varied conditions

The description of growth assays can be found in the follow section.

The growth assays were performed with cancer cell line SW480 with both +/- NDRG2 expression

and +/- IL-6 treatment. Only the cell line SW480 was selected for further work, based on the

undetectable level of NDRG2 expression in transfected HCT116 cells.

3.5.1 Growth assay for transfected cancer cell line SW480

The growth rate for the transfected cancer cell line SW480 (pcDNA6V5-His-A-EGFP plasmid) and

SW480 (pcDNA6-NDRG2L-V5 plasmid) were analyzed to define the growth of the cell line without

NDRG2 and with NDRG2. Because the pcDNA6V5-His-A-EGFP plasmid does not contain NDRG2,

the cells are considered to grow as normal SW480 cells. All cells were plated at a density of 1x104

cells per well in 24-well plates, and all samples were conducted in triplicates. The cells were

harvested and counted over a time period from 24-168 hours. The counting was performed by use

of Beckmann Coulter Z2 Particle Count and size Analyzer, which had to be cleaned before use by

primer and flushed with 10 ml 0.9 % biological salt water. Hereafter a blank sample consisting of

10 ml 0.9 % biological salt water was counted, and now the unknown samples consisting of 200 μL

sample diluted in 10 ml 0.9 % biological salt water could be counted.

3.5.2 Growth assay for transfected cancer cell line SW480 treated with IL-6

The growth rates for the transfected cancer cell line SW480 (pcDNA6-NDRG2L-V5 and pcDNA6V5-

His-A-EGFP plasmid) were analyzed in order to define the effect of IL-6 with NDRG2 and without.

The cells were plated in duplicate at a density of 1x104 cells per well in 24-well plates. After 24h

the cells were treated with IL-6 and set to grow for a further 24h, 96h, and 168h before being

harvested. The counting was performed by use of Beckmann Coulter Z2 Particle Count and size

Analyzer as described above.

53

3.6 Statistic

Some of the results could be evaluated through statistical tests, and student’s t-test and F-test

were used. The F-test is used to compare the variance between two data sets and can only be

used in normal distributed populations, and high values of F indicate a difference between the

dispersions in the two populations. All F-values over 0.05 were not significant and reversed. The

student’s t-test can be used to study whether average values in two populations can be presumed

to be identical. It is a condition for the use of the t-test that the populations are normal

distributed. Different t-tests are available depending on the result of the F-test. If the variance was

significant for the F-test, the populations were assumed to be equal. Thereby two different t-tests

were available; paired t-test for two populations with equal variance, and unpaired t-test for two

populations without equal variance. Both were used in this study and like the F-test, all t-values

above 0.05 were considered not significant.

54

4. Results

Results obtained in order to evaluate the hypotheses of the study are examined and analyzed in

the following sections.

4.1 NDRG2 expression in cancer cell lines SW480 and HCT116 at the mRNA

level

Previous studies have reported a down-regulation of NDRG2 expression in colorectal cancer and

several other types of cancer [Chu et al., 2011; Hu et al., 2004; Lee et al., 2008; Lorentzen et al.,

2007; Piepoli et al., 2009; Lorentzen et al., 2011; Zhao et al., 2008; Ma et al., 2008; Wang et al.,

2012], and therefore it could be interesting to examine if the same tendency could be seen in

human cancer cell lines SW480 and HCT116. This result was important for further analyzes

through the study and was analyzed by use of qRT-PCR.

To be able to evaluate the NDRG2 expression level in the cell lines, a tissue with a known high

expression of NDRG2 was used for normalization. Normal brain tissue was selected for the

normalization, because brain tissue express high levels of NDRG2 [Deng et al., 2003]. A reference

gene, RPLP0, was used and worked as an invariable endogenous control. The results of the

examination of NDRG2 mRNA expression in the cell lines SW480 and HCT116 are illustrated in

Figure 19.

Figure 19 shows the expression levels of NDRG2 mRNA in cancer cell lines SW480 and HCT116 normalized to normal brain tissue. Columns represent the fold expression of NDRG2 mRNA expression. Standard deviations are included on the graph

When the expression of NDRG2 mRNA was compared with normal brain tissue levels, the results

show low expression of NDRG2 mRNA in cell lines SW480 and HCT116. In order to evaluate the

00,20,40,60,8

11,2

Brain SW480 HCT116

NDR

G2

mRN

A ex

pres

sion

55

obtained results, a student’s t-test was used to analyze the observed difference between normal

brain tissue and the cell lines and to conclude if the difference was significant. All calculated p-

values can be seen in Table 5, and all values below 0.05 mean that the differences were significant.

Based on the observed results and p-values, NDRG2 mRNA expression was significantly lower in

both cell lines when compared to normal brain tissue.

Table 5 shows the p-values for the student’s t-test. Down-regulation of NDRG2 were significant for both cell lines, because of p-values below 0.05

4.2 NDRG2 expression in cancer cell lines SW480 and HCT116 at the

protein level

Previous studies have reported decreased levels of NDRG2 protein in different cancer types and

cancer cell lines. The precise biological function of the NDRG2 protein is still unknown, but

expression of NDRG2 protein has been observed to decrease the growth of cancer cells [Kim et al.,

2009]. Even though NDRG2 mRNA was low in both cell lines, this may not affect the expression of

NDRG2 protein. Therefore, it was of interest to examine the levels of NDRG2 protein in both cell

lines to see if the same tendency, as seen in previous studies, exists in these cell lines.

Furthermore, the presence of NDRG2 was important for analyses of the growth rates of the cell

lines.

The protein level of NDRG2 could be examined and evaluated through extraction of total protein

from both cell lines and western blotting. Before the cell lines could be examined and evaluated,

they was transfected with an empty plasmid, pcDNA6V5-His-A-EGFP, which was assumed not to

effect the normal NDRG2 protein level. This assumption could be confirmed by western blotting.

The plasmid was constructed with a V5 and histidine-tag, which is approximately 2000 dalton ≈ 2

kilodalton (kDa), and this construction made it possible to perform the analyses with both NDRG2-

and V5 specific antibodies. The samples of interest were named by the cell lines name with V5

added in the end. One positive control was included, which has previously shown NDRG2 protein

Cell line p-value SW480 2.4 x 10-15

HCT116 1.1 x 10-17

56

expression. The results of the western blotting with specific NDRG2- and V5 antibodies are

illustrated below in Figure 20.

Membrane A was analyzed with specific NDRG2 antibody, and if the cell lines expressed NDRG2

protein, the expected band would appear at approximately 41 kDa. All samples in membrane A

named with a V5 in the end did not show any bands at all, which means that both cell lines did not

express NDRG2 protein, and that the empty vector pcDNA6-V5-HIS-A-EGFP did not affect the

expression of NDRG2. Furthermore, the positive control only showed a clear band at the

approximate size for NDRG2. Membrane B was analyzed with a specific V5 antibody, but because

the V5-His-tag is only around 2 kDa it is not visible and therefore no bands were expected. Again

the samples with a V5 in the end were of interest, and the result of the analysis with the specific

V5 antibody did not show any bands as expected.

Based on these results it must be concluded that neither of the cell lines express NDRG2 at the

protein level, and therefore a transfection with a plasmid containing NDRG2 was necessary for

further analysis of how the presence of NDRG2 protein affects the growth rate, and how IL-6

treatment affects the NDRG2 expression.

Figure 20 shows the two membranes from the Western blotting result with SW480 and HCT116 cells. Picture A, shows the membrane analyzed with NDRG2 specific antibody and Picture B, shows the membrane analyzed with V5 specific antibody

57

4.3 Analysis of the most efficient concentration and time for the treatment

with IL-6

In order to optimize the treatment with IL-6, the cell lines were treated with different

concentrations over a given time period. This was important for optimal treatment of the cell

lines, when the effect of IL-6 on NDRG2 expression and growth rate was evaluated.

In order to find the most efficient concentration and time, the following concentrations of 10 ρM,

50 ρM, and 10,000 ρM of recombinant human IL-6 were used over a time period of 72 hours.

When IL-6 is used for treatment, it is known to affect the activation of the JAK/STAT pathway,

which normally leads to increased expression of Mcl-1 [Grivennikov et al., 2009; Jourdan et al.,

2003]. Therefore, to be able to estimate the efficiency of the IL-6 treatment, the mRNA expression

level of Mcl-1 in human cancer cell lines SW480 and HCT116 was also analyzed. In preparation,

cDNA was synthesized and then analyzed by qRT-PCR. The expression levels of Mcl-1 were

normalized to RPLP0.

Figure 21 shows the expression levels of IL-6 mRNA in cancer cell lines SW480 at different concentrations and times. Columns represent the fold expression of Mcl-1 mRNA expression. Standard deviations are included.

The obtained levels of Mcl-1 mRNA after treatment of SW480 cells with different IL-6

concentrations are illustrated in Figure 21. The highest fold increase of Mcl-1 mRNA expression

was observed after treatment with 10,000 ρM of IL-6 for 72 hours.

When HCT116 cells were treated with different concentrations of IL-6, the obtained levels of Mcl-1

mRNA were different from the result with SW480 cells. The results are illustrated in Figure 22

0

5

10

15

24 48 72

Mcl

-1 m

RNA

fold

incr

ease

Hours

SW480

10 ρM

50 ρM

10000 ρM

58

below, and they show that HCT116 cells express more Mcl-1 mRNA, when treated with 50 ρM of

IL-6 for 72 hours.

Figure 22 shows the expression levels of IL-6 mRNA in cancer cell lines HCT116 at different concentrations and times. Columns represent the fold expression of Mcl-1 mRNA expression. Standard deviations are included

Therefore, all further treatments of the cell lines SW480 and HCT116 were made with 10,000 ρM

for SW480 cells and 50 ρM for HCT116 cells respectively in order to obtain the most efficient

treatment.

4.4 NDRG2 expression in treated cancer cell lines SW480 and HCT116 at

mRNA level

Association between IL-6 and colon cancer has been observed in previous studies, where elevated

levels of IL-6 in serum were reported in patients with colon cancer and have been further

connected with prognosis and survival. Furthermore, IL-6 has been connected with promotion of

proliferation and invasion in colon cancer [Hsu et al., 2011; Nikiteas et al., 2005; Chung & Chang,

2003; Hsu & Chang, 2006; Becker et al., 2004]. All these connections together with the observed

functions of NDRG2 in colon cancer, makes it interesting to examine if NDRG2 expression is

affected by elevated levels of IL-6.

The expression of NDRG2 mRNA was evaluated in both cell lines when treated with appropriate

concentrations of recombinant human IL-6. Expression levels of NDRG2 mRNA in treated cell lines

were examined using qRT-PCR to evaluate if IL-6 treatment had any influence on the expression

level of NDRG2 mRNA. All expression levels were normalized to the reference gene, RPLP0. The

results of the qRT-PCR analysis are illustrated in Figure 23.

0

20

40

60

24 48 72

Mcl

-1 m

RNA

fold

incr

ease

Hours

HCT116

10 ρM

50 ρM

10000 ρM

59

Figure 23 shows the expression levels of NDRG2 mRNA in cancer cell lines SW480 and HCt116 treated and untreated with IL-6. Columns represent the fold expression of NDRG2 mRNA expression. Standard deviations were included in the figure

The results of the analyses show that the expression of NDRG2 mRNA in both cell lines was

increased after treatment with IL-6. To evaluate if the observed increase was significant, a

student’s t-test was used. The p-values for both cell lines show that the small increases observed

in NDRG2 mRNA expression for treated cells were not significant, when compared with untreated

cells. The p-values can be seen in Table 6.

Table 6 shows the p-values. All values over 0.05 indicate no significantly difference between treated and untreated SW480 and HCT116 cell lines

These results indicate that elevated IL-6 levels in both cell lines did not affect the expression of

NDRG2 mRNA levels.

0

0,0002

0,0004

0,0006

0,0008

0,001

0,0012

SW480 treated SW480 untreated HCT116 treated HCT116 untreated

NDR

G2

mRN

A fo

ld in

crea

se

Cell lines p-value

SW480 0.237

HCT116 0.402

60

4.5 Analysis of transfection efficiency

The results of western blotting with an empty plasmid showed that none of the cell lines express

NDRG2 protein. In order to examine if the presence of NDRG2 affects the growth rate in the cell

lines, both cell lines were therefore transfected with the following plasmids; pcDNA6V5-His-A-

EGFP, pcDNA6-NDRG2L-V5, and pCMV-EGFP. A plasmid containing enhanced green fluorescence

protein (EGFP) was used to verify transfection efficiency. EGFP functions as a powerful reporter

molecule for estimation of transfection efficiency. All cells who have obtained the plasmid will

fluoresce, when exposed to light by a fluorescence microscope camera at 470 nm. A picture of

cells transfected with pCMV-EGFP plasmids is shown in Figure 24.

The protocol for the transfection kit recommends transfection efficiency of minimum 10% before

the work with the cells continues. When cells were exposed to fluorescence, around 40% were

positive for fluorescence, and the transfection efficiency was approved for further work.

4.6 Analysis of NDRG2 protein level after transfection

In order to evaluate if the transfection had been successful, the protein level of NDRG2 was

analyzed through western blotting. If the cell lines do not express any NDRG2 protein, it does not

necessarily mean that the transfection was unsuccessful, because the plasmid can be incorporated

in parts of the genome, which are not activated. Then no NDRG2 protein will be detectable, but it

is possible to perform selections, which show if the plasmid has been obtained. The result was

important for further analysis in order to evaluate the effect of the presence of NDRG2 in the cell

Figure 24 shows pictures of the EGFP-transfected cell samples obtained with and without a fluorescence microscope, the two pictures are not from the same day and comparison cannot be made

61

lines. In this western blot, only plasmid pcDNA6-NDRG2L-V5 was examined because of the

conduction with NDRG2. The plasmid contained the long version of NDRG2 protein and was

approximately 41 kDa. A V5-his-tag was coupled to NDRG2L and functioned as positive control for

the presence of NDRG2. Therefore, proteins detected with a specific V5 antibody were expected

to be approximately the same size as proteins detected with a specific NDRG2 antibody. The

results of the western blot can be seen below in Figure 25, which includes both membranes

analyzed with NDRG2 antibody and V5 antibody, respectively. The samples of interest were named

with NDRG2 in the end.

The results of the analysis with specific NDRG2 antibody are shown in membrane A and only the

sample with SW480 cells showed an intense band at approximately 40-45 kDa, which was

consistent with the expected size of NDRG2 protein. The fact that the sample with HCT116 cells

did not show any band does not necessarily mean that the transfection was not successful, which

was based on a successful selection. This can be explained by the fact that the plasmid with

NDRG2 could be taken up by the cells without being activated in the genome. Membrane B was

analyzed with a specific V5 antibody, where the V5-tag coupled to NDRG2L function as a positive

control for the presence of NDRG2. This analysis showed the same observations as seen in

membrane A and the only difference was that the observed band for the sample with SW480 cells

Figure 25 shows the result of western blotting and all samples named with N (NDRG2) are of interest. The picture (A) show the membrane analyzed with NDRG2 antibody and picture (B) show the membrane analyzed with V5 antibody. Only samples named with NDRG2 in the end are of interest

62

was weaker when analyzed with the V5 antibody, but approximately the same size as NDRG2

protein.

Because HCT116 cells did not express NDRG2 protein after the transfection, all further work was

continued with SW480 cells expressing NDRD2 protein.

4.7 Growth assays comparing cancer cell lines SW480 growth rate +/-

NDRG2

To examine if the presence of NDRG2 in the cells affect the growth rate, growth assays were

performed with transfected SW480 cells +/- NDRG2 in order to compare the growth rates. In

previous studies, the presence of NDRG2 has been associated with decreased growth, and

therefore it would be interesting to see if the same observations could be seen in SW480 cells.

Transfected SW480 cells +/- NDRG2 were grown over a time period at 168 hours and counted

every day. Figure 26 illustrates the growth rates for transfected SW480 cells +/- expression of

NDRG2. The highest increase in growth rate was observed after 168 hours. The observed

difference in growth rates was around 60 % after 168 hours of growth, when the growth rates for

SW480 cells +/- NDRG2 were compared.

Figure 26 shows the result for the growth assay performed with SW480 cell line +/- NDRG2. Small standard deviations are illustrated

0

200000

400000

600000

800000

1000000

24 48 72 96 120 144 168

Cells

per

ml

Hours

SW480

Untreated SW480 cells with NDRG2

Untreated SW480 cells without NDRG2

63

The measured p-values further support the results illustrated in Figure 26 by showing a significant

difference in growth rates between SW480 cells +/- NDRG2, see Table 7. The significant difference

was first observed after 96 hours and up to 168 hours.

Table 7 shows the F-values and p-values for SW480 growth assay +/- NDRG2. Significant differences (values under 0.05) are seen for the cells growing over 96 hours up to 168 hours

Therefore, the conclusion of the growth assay is that cells with NDRG2 show increased growth

rate, when compared with cells without NDRG2.

4.8 Growth assays comparing growth rates for SW480 cells untreated or

treated with IL-6

Based on the knowledge from previous studies, which have shown a correlation between

increased levels of IL-6 and proliferation in colon cancer, it would be interesting to examine if IL-6

affects the growth rate of SW480 cells.

The cell line was treated with a concentration of IL-6 selected based on knowledge from previous

analyses in this study, over a time period at 168 hours. Figure 27 illustrates the results of the

growth assays, where the highest increase was observed after 96 hours of treatment and a smaller

increase was seen after 168 hours of treatment. Treatment of the cells at 24 hours did not show

any difference in growth rates, which indicate that IL-6 does not have any effect on the growth

rate, when treated for less than 24 hours.

Hours of growth p-value

24 0.408

48 0.052

72 0.241

96 0.003

120 6.2 x 10-4

144 0.001

168 5 x 10-4

64

Figure 27 shows the result of the growth assay with untreated and treated SW480 cells. Small standard deviations were seen

The p-values show that the observed increase in growth rates after 96 and 168 hours in treated

SW480 cells was significant, when compared with untreated SW480 cells, see Table 8.

Hours of growth p-value

24 0.318

96 0.003

168 0.022

Table 8 shows the P-values for untreated and treated SW480 cells

Therefore it can be concluded that treatment of SW480 cells with IL-6 does affect the proliferation

significantly after at least 96 hours of treatment.

4.9 Growth assay comparing transfected cancer cell line SW480 treated

and untreated with IL-6

In order to examine how SW480 cells grow in the presence of NDRG2 and elevated levels of IL-6,

the transfected cells were treated with IL-6 and comparison of the growth rates were made with

untreated SW480 cells. In previous analyses through this study, both NDRG2 and IL-6 were

observed to increase the growth rates, and the same concentration as previously was used for the

treatment over a time period at 168 hours.

The results of this growth assay are illustrated in Figure 28, where an increased growth rate was

observed for transfected SW480 cells after at least 96 hours of treatment with IL-6. The biggest

0100000200000300000400000500000600000

24 96 168

Cells

per

ML

Hours

SW480

Treated SW480 cells

Untreated SW480 cells

65

difference between untreated and treated SW480 cells was seen after 96 hours, and the highest

increase was observed after 168 hours of treatment, which was confirmed by the p-values

showing significant difference between treated and untreated SW480 cells at all times, see Table

9.

Figure 28 shows the results for growth assays with SW480 cells containing NDRG2 and treated with and without IL-6. Small standard deviations are illustrated

This indicates that the presence of NDRG2 together with treatment with IL-6 increases the growth

rate significantly for SW480 cells.

Hours of growth p-value

24 0.049

96 1 x 10-5

168 0.0001

Table 9 shows the p-values

0200000400000600000800000

1000000

24 96 168

Cells

per

ml

Hours

SW480 cells

Untreated SW480 cells with NDRG2

Treated SW480 cells with NDRG2

66

4.10 Short summary

Based on the results in this section, it was observed that both SW480 and HCT116 cells did not

show natural expression of NDRG2 at both mRNA and protein level, and further that treatment

with IL-6 did not affect the expression of NDRG2 at mRNA level. Through transfection with a

plasmid containing NDRG2 it was possible to get SW480 cells to express NDRG2 protein, which

was required for most of the remaining analyses through this study. The presence of NDRG2 in

SW480 cells affected the growth rate by increasing it, and the same tendency was observed in

SW480 cells treated with IL-6, which did not express NDRG2. Furthermore, the presence of NDRG2

and increased levels of IL-6 combined affected the growth rate for transfected SW480 cells by

increasing the proliferation.

67

5. Discussion

Cancer is a leading cause of death, and more than 30 million people are living with the disease

worldwide [WHO, 2014]. Therefore research of the molecular mechanisms behind tumorigenesis

is of great importance in order to elucidate, how to treat and prevent the events of tumorigenesis.

Various mechanisms have been connected to cancer development and growth including emerging

hallmarks and enabling characteristics, which describe the relationship between the immune

system and cancer and genomic changes behind tumorigenesis [Hanahan & Weinberg, 2011]. One

of the genomic aspects in cancer development is tumor suppressor genes, such as the newly found

tumor suppressor gene, NDRG2, which has been reported to be down-regulated in several cancer

types and epigenetically modulated [Piepoli et al., 2009; Chu et al., 2011; Hu et al., 2004; Lee et

al., 2008; Lorentzen et al., 2007; Piepoli et al., 2009; Lorentzen et al., 2011; Zhao et al., 2008; Ma

et al., 2008; Wang et al., 2012]. Other important mediators of tumorigenesis are inflammatory

factors including the cytokine IL-6, which has been connected with induction of progression,

growth, and invasiveness in human colon carcinoma [Hsu et al., 2011; Hsu & Chung, 2006].

Through this study human cancer cell lines HCT116 and SW480 were used to validate the

previously observed down-regulation of NDRG2 in colon cancer, and further to investigate if IL-6

had any impact on the NDRG2 expression and growth rate.

5.1 The demonstration of the observed down-regulation of NDRG2

expression

In both cells lines, the NDRG2 expression at mRNA level was compared with the expression in

normal brain tissue. In previous studies with colon cancer tissue and different cell lines, a down-

regulation of the NDRG2 expression had been reported [Feng et al., 2011; Lorentzen et al., 2007;

Chu et al., 2011; Kim et al., 2009]. Consistent with these findings obtained by other groups, the

result of this study showed a significantly lower NDRG2 expression in HCT116 and SW480 cancer

cell lines, when compared to normal brain tissue. A possible explanation of the observed low

expression of NDRG2 could be the elevated evidence of transcriptional silencing of NDRG2

through hypermethylation of NDRG2 promoter region [Feng et al., 2011]. Furthermore, studies

with colon cancer have shown a significant increase of NDRG2 mRNA expression after treatment

68

with a demethylating agent, which indicates that methylation, can affect the expression of NDRG2

mRNA [Piepoli et al., 2009; Feng et al., 2011].

Proteins have many distinct biological functions. They are the resulting product of the gene

expression, and therefore the protein expression of NDRG2 is of interest. In order to investigate

the protein level, protein extraction was performed and analyzed by western blotting. The

obtained results show that none of the cell lines express NDRG2 protein, and this observation is

supported by other studies with colon cancer cells [Shi et al., 2009; Lorentzen & Mitchelmore,

2012]. miRNAs have been connected with regulation of gene expression through

posttranscriptional silencing by binding to the 3’ untranslated region of mRNA [Baer et al., 2013;

Sharma et al., 2010]. Feng et al. have investigated miRNAs impact on the expression of NDRG2 and

found that down-regulation of NDRG2 was inversely associated with up-regulation of miRNAs

[Feng et al., 2011]. This may indicate a possible role in aberrant expression of NDRG2 mRNA and

protein, because of miRNAs ability to inhibit protein synthesis and RNA degradation [Baer et al.,

2013; He & Hannon, 2004]. Furthermore, miRNAs have been associated with biological processes

as proliferation, apoptosis, and differentiation, also supporting a potential role in tumorigenesis

[Saito & Jones, 2006].

5.2 Possible impact of IL-6 on the expression of NDRG2

In order to elucidate the impact of IL-6 on NDRG2 expression, both cell lines were transfected with

plasmid (pcDNA6-NDRG2L-V5) containing NDRG2 to ensure NDRG2 expression and then treated

with recombinant human IL-6. The doses used were determined and modified based on a previous

published study, where the authors have treated colon cancer cells with IL-6 [Hsu & Chung, 2006].

As already mentioned IL-6 functions as a key activator of the JAK/STAT pathway leading to

activation of transcription factor STAT3. The expression of Mcl-1 was used to measure the

efficiency of the treatment with IL-6, because Mcl-1 expression is induced by STAT3. The most

efficient doses for treatment of both cell lines were high, when compared with normal levels of IL-

6 in humans. Becker et al. have showed that treatment with hyper-IL-6 (IL-6 and covalently linked

soluble IL-6 receptor) affect proliferation and phosphorylation of STAT3. Additionally, they showed

a down-regulation of IL-6R in colon cancer patients [Becker et al., 2004]. This down-regulation of

69

IL-6R may affect the activation of the JAK/STAT pathway and thereby the expression of Mcl-1,

which could explain the necessity of high doses of recombinant human IL-6 for treatment.

NDRG2 expression has been reported to modulate SOCS3 and STAT3 activity and inhibit the

production of the cytokine IL-10, which is strongly connected with IL-6 through the JAK/STAT

pathway [Lee et al., 2010; Hamidullah et al., 2012]. Because of the important role of IL-6 in the

activation of the JAK/STAT pathway, it may also be involved in the production and regulation of IL-

10 [Lee et al., 2010]. No studies have investigated IL-6 impact on NDRG2 expression in cancer, and

for that reason the results of this experiment are completely unknown. In both cell lines a small

increase in NDRG2 mRNA were observed after the treatment with IL-6, but the observation were

not significant. This may indicate that IL-6 does not have an influence on the regulation of NDRG2

expression, but further knowledge of NDRG2’s impact on IL-6 is needed to be able to elucidate a

possible connection.

5.3 The growth rate of cancer cell lines and the influence of NDRG2

expression

To investigate how NDRG2 expression affects the growth of colon cancer cell lines, a growth assay

was performed. Normally the NDRG2 expression is transcriptionally regulated by MYC, which is

important for cell proliferation and differentiation [Dang et al., 2008; Wierstra & Alves, 2008]. In

colorectal cancer an increase in the MYC level has been observed together with a decrease in

NDRG2 expression, which may indicate that MYC is able to repress human NDRG2 [Shi et al.,

2009]. Furthermore, several studies with cancers have reported a down-regulation of NDRG2 and

suppression of proliferation and induced apoptosis through regulation of cyclin D1 and T cell

factor/β-catenin activity, all supporting a potential role for NDRG2 as an inhibitor of cancer cell

proliferation [Shi et al., 2009; Lorentzen & Mitchelmore, 2012; Yao et al., 2008; Chu et al., 2011;

Zheng et al., 2010; Liang et al., 2012]. The results of the growth assay does not consist with other

observations, in that a significantly increased growth rate was observed in both cell lines, when

NDRG2 was present. Because HCT116 did not show any protein expression of NDRG2 after the

transfection, the result of the growth assay for this cell line was unclear. The increased growth

rate for SW480 cell line may be explained by the fact that other studies with colon cancer cell lines

have shown that an increase in expression of MYC leads to a decrease in the expression of NDRG2.

70

This may lead to increased growth rate, because NDRG2 expression has been correlated with

proliferation [Shi et al., 2009; Zhang et al., 2006]. In order to evaluate this theory, the expression

of MYC has to be examined in SW480 cells.

5.4 The growth rate and the influence of IL-6 treatment

To elucidate if IL-6 has an impact on the growth rate of the colon cancer cell line SW480, two

growth assays were performed; one with the presence of NDRG2 and one without. Studies have

shown an association between elevated levels of IL-6 in patients with colon carcinoma and tumor

growth [Hsu et al., 2011; Hsu & Chung, 2006; Becker et al., 2004]. Also, IL-6 signaling regulates

many cellular functions, including growth, differentiation, and angiogenesis, and IL-6 has been

observed to increase the proliferation of colorectal carcinoma cells [Hirano et al., 2000; Jee et al.,

2004; Leu et al., 2003; Köbel et al., 2005; Lahm et al., 1992]. The results of the first growth assay

with no expression of NDRG2 showed an increase in growth rate, when compared with untreated

cells, and this result is consistent with other studies.

The second growth assay was performed with cells expressing NDRG2 to investigate if IL-6 and

NDRG2 together affect the growth rate. As mentioned earlier, NDRG2 is known to decrease the

growth rate of colon cancer cells, while IL-6 has been shown to increase the growth rate

previously in this study. The NDRG2-presenting cells treated with IL-6 showed a significant

increase in growth rate, when compared to cells without NDRG2. Based on previous results in this

study, both the presence of NDRG2 and treatment with IL-6 were expected to affect the growth

rate positively by an increase in proliferation. This is also consistent with the results. For further

analyzes it would be necessary to performed growth assays with NDRG2-presenting cell lines

treated with IL-6 and cell lines without NDRG2 present but still treated with IL-6 to investigate the

exact role of NDRG2 in IL-6 treated cell lines.

Through this study it was found that NDRG2 was down-regulated in colon cancer cell lines both at

mRNA and protein level, that IL-6 did not affect the NDRG2 expression at the level of mRNA, that

an increase in growth was seen both in cells with NDRG2 and treated with IL-6, and lastly that IL-6

may affect the growth rate positively when NDRG2 is present.

71

6. Conclusion

The present thesis has contributed with further evidence of the reported down-regulation of

NDRG2 expression in colon cancer cell lines. In the human cell lines HCT116 and SW480, a

significantly lower expression of NDRG2 was shown, when compared to expression levels in

normal human brain tissue. These findings correlated with an undetectable level of NDRG2 protein

in both cell lines. Treatment of both cell lines with IL-6 showed a small increase in NDRG2 mRNA

expression, but the increase was not significant, and further research is necessary in order to

investigate if IL-6 has an impact on NDRG2.

When NDRG2 was expressed in the human cell line SW480 through transfection, an increase in

growth rate was observed. These observations were not consistent with other studies, and they

do not support the theory of NDRG2 as a potential tumor suppressor gene. However, this thesis

was based on small experimental data sets, and this may affect the strength of the results.

Additionally, treatment of the human cell line SW480 with IL-6 caused a significantly increased

growth rate after 96 hours, which was consistent with other published results. In order to evaluate

if IL-6 treatment and presence of NDRG2 affected the growth rate of human cell line SW480, a

growth assay was performed and a significantly increased growth rate was observed, when NDRG2

was present in the cell line and treatment with IL-6 had been performed. This may indicate a

potential correlation between NDRG2 and IL-6, but further knowledge, larger experimental data

sets, and more research are needed.

72

7. Perspectives

At present, the exact mechanisms behind a possible connection between IL-6 and NDRG2 still

remains unknown. To be able to elucidate this matter and examine a possible connection, further

research and knowledge of both IL-6 and NDRG2s function in development, proliferation of cancer

cells, and regulation are required. Several different cancer types have been associated with down-

regulated NDRG2 expression, whereas IL-6 has only been associated with a few types, including

colon cancer. If the published findings of NDRG2 expressions involvement in the JAK/STAT

pathway in breast cancer are correct, it might be possible to investigate if this involvement also

appears to influence the progression of colon cancer cells. Additionally, NDRG2 expression has

already been connected with decreased proliferation of colon cancer cells, whereas the levels of

IL-6 have been reported increased in colon cancer cells.

A possible extension of the present work would be to optimize the experiment of the impact of IL-

6 on the NDRG2 mRNA level by examining the expression level of NDRG2 mRNA in normal colon

cancer cells in order to be able to compare the expression of NDRG2 mRNA with the presence in

colon cancer cells. Hereafter, treatment with IL-6 could be performed to see if the treatment

affects the expression of NDRG2 in normal colon cells. These studies would make it possible to

examine, if IL-6 has an impact on the NDRG2 expression in colon cancer cells, and thus provide

important information in order to develop new immunotherapeutic agents against colon cancer.

Although an association between NDRG2 expression and IL-6 treatment was not supported by

data obtained in the present thesis, findings in other studies indicate a potential role of TGF-β in

the connection between NDRG2 and IL-6 in colon cancer. Therefore, it would be interesting to

investigate if TGF-β affects NDRG2 expression and its function in inhibition of IL-6-trans signaling in

colon cancer.

73

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9. Appendices

9.1 Appendix I

Lysis Buffer

200 μL 1M Hepes (PH 7.9)

15 μL 1M MgCl2

4.2 ml 1M NaCl

2.9 ml 87% Glycerol

2.7 ml Milli-Q water

Bradford Reagent (AppliChem, #A3480,0010)

200 mg Coomassie Brilliant Blue

100 ml 96% Ethanol

200 ml 85% Phosphoric acid

Milli-Q water until 2 Liters

Transfer Buffer (Life technologies, #NP0006-1)

50 ml 20X NuPAGE® Transfer Buffer

200 ml 96% Ethanol

750 ml Milli-Q water

Blocking Buffer (GE healthcare, #RPN2125V)

5 % 20 ml PBS-T

1 g Blocker

Dilution Buffer 2.5% (GE healthcare, #RPN2125V)

20 ml PBS-T

0.5 g Blocker

Running Buffer (Life technologies, #NP0001)

50 ml 20X NuPAGE® MOPS SDS

950 ml Milli-Q water

PBS 10X (Ph 7.3)

1.4 M NaCl

27 mM KCl

101 mM Na2HPO4

18 mM KH2PO4

TE-Buffer

10 mM TrisCl (PH 8.0)

1 mM EDTA

TENS-Buffer:

100 mM TrisCl (PH 8.5)

5 mM EDTA

200 mM NaCl

0.2% SDS

Wash Buffer (PBS-T 0.01%) (Sigma-Aldrich, #P2287)

1 liter 1X PBS

1 ml Tween-2

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Antigens Molecular

Weight (kDa)

Primary antibody/

Secondary antibody

Dilution Supplier

NDRG2 41 Goat polyclonal IgG/

Donkey IgG-HRP

1:5000 Santa-Gruz Biotechnology,

#sc-19568/Santa-Gruz

Biotechnology, #sc2056

V5 14 + 41 (NDRG2) Rabbit monoclonal IgG/

Goat IgG-HRP

1:5000/

1:10000

Sigma-Aldrich, #V8137/Pierce,

#1858415

Β-actin 42 Mouse monoclonal IgG/

Goat IgG-HRP

1:5000 Sigma-Aldrich, #A5411/Pierce,

#1858413

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9.2 Appendix II

Plasmid pCMV-EGFP

Plasmid pcDNA6V5-His-A-EGFP

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Plasmid pcDNA6-NDRG2L-V5