Identification of novel epigenetic biomarkers in ...€¦ · Identification of novel epigenetic biomarkers in colorectal cancer, GLDC and PPP1R14A. Deeqa Ahmed Mohamed Ali Thesis

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Identification of novel epigenetic biomarkers in colorectal cancer,

GLDC and PPP1R14A.

Deeqa Ahmed Mohamed Ali

Thesis for the master´s degree in Molecular Biosciences at

Department of Molecular Biosciences (IMBV), Faculty of

Mathematics and Natural sciences

UNIVERSITY OF OSLO February 2010

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Acknowledgements

The work presented in this thesis was carried out at the project Group of

Epigenetics, Department of Cancer Prevention, Institute for Cancer Research, Oslo

University Hospital-Radiumhospitalet from July 2008 to February 2010.

First and foremost, I would like to express my sincere gratitude to my supervisor

Guro Elisabeth Lind, for her encouragement, guidance and invaluable assistance

throughout the project. Her knowledge and enthusiasm for the field of epigenetics

has been a true inspiration. I would also like to thank my co-supervisor and head of

department, Professor Ragnhild Lothe, for including me in her outstanding

academic department and for contributing with objective comments.

I am grateful to my colleagues for creating a wonderful social environment and for

always being available to answer my questions. I would especially like to thank

Hilde, both for helping out in the lab as well as contributing in determining the final

scores, Stine for assisting me with the statistical analyses, and Anne Cathrine and

Marianne, for our many scientific discussions and for always putting a smile on my

face.

This thesis could not have been completed had it not been for the support and love I

have received from my friends and family. A special thanks to my parents for

supporting my dreams and aspirations, and for making me believe that I can achieve

anything I set my mind to.

Oslo, February 2010

Deeqa Ahmed M.Ali

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Table of contents

ACKNOWLEDGEMENTS ................................................................................................................. 2

TABLE OF CONTENTS ..................................................................................................................... 3

SUMMARY ........................................................................................................................................... 6

ABBREVIATIONS ............................................................................................................................... 8

GENE SYMBOLS .............................................................................................................................. 10

1. INTRODUCTION .................................................................................................................... 11

1.1 GENETIC AND EPIGENETIC ALTERATIONS IN CARCINOGENESIS ............................................... 11

1.2 EPIGENETIC REGULATION OF GENE EXPRESSION ..................................................................... 13

1.2.1 Defining epigenetics .................................................................................................... 13

1.2.2 The interplay of epigenetic regulators ........................................................................ 20

1.2.3 RNA-mediated gene silencing ..................................................................................... 23

1.2.4 Epigenetics: Nature or nurture or both? .................................................................... 25

1.3 COLORECTAL CANCER ............................................................................................................ 26

1.3.1 Molecular developmental pathways............................................................................ 27

1.3.2 Histopathology and morphological pathways ............................................................ 29

1.3.3 Tumour classification, treatment and outcome ........................................................... 31

1.4 CLINICAL RELEVANCE OF MOLECULAR BIOMARKERS ............................................................. 32

2. AIMS ......................................................................................................................................... 34

3. MATERIALS AND METHODS ............................................................................................. 35

3.1 MATERIALS ............................................................................................................................ 35

3.1.1 Colon cancer cell lines ................................................................................................ 35

3.1.2 Tissue samples – Colorectal carcinomas and normal mucosa ................................... 35

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3.2 METHYLATION-SPECIFIC METHODOLOGIES ............................................................................ 36

3.2.1 Strategy to select novel DNA methylation candidate genes ........................................ 36

3.2.2 Bisulfite modification .................................................................................................. 38

3.2.3 Qualitative methylation-specific polymerase chain reaction. ..................................... 39

3.2.4 Quantitative real-time methylation-specific polymerase chain reaction .................... 43

3.2.5 Capillary electrophoresis sequencing ......................................................................... 46

3.2.6 Bisulfite sequencing .................................................................................................... 48

3.2.7 Statistics ...................................................................................................................... 49

4. RESULTS .................................................................................................................................. 51

4.1 QUALITATIVE METHYLATION ANALYSES OF CANDIDATE GENES IN VITRO AND IN VIVO ........... 51

4.2 QUANTITATIVE METHYLATION PROFILES OF GLDC AND PPP1R14A .................................... 53

4.3 CONCORDANCE OF CONVENTIONAL MSP AND QUANTITATIVE REAL-TIME MSP .................... 56

4.4 BISULFITE SEQUENCING CONFIRMS THE PROMOTER METHYLATION STATUS OF GLDC AND PPP1R14A

60

4.5 ASSOCIATION OF TUMOUR METHYLATION WITH GENETIC AND CLINICO-PATHOLOGICAL FEATURES 63

5. DISCUSSION ........................................................................................................................... 65

5.1 METHODOLOGICAL CONSIDERATIONS .................................................................................... 65

5.1.1 Methylation-specific polymerase chain reaction ........................................................ 65

5.1.2 Bisulfite sequencing .................................................................................................... 67

5.2 CELL LINES VERSUS SOLID TUMOURS ..................................................................................... 68

5.3 NOVEL EPIGENETICALLY DEREGULATED GENES IN COLORECTAL CANCER ............................. 69

5.4 EARLY DETECTION AND DIAGNOSTICS ................................................................................... 74

6. CONCLUSIONS ...................................................................................................................... 79

7. FUTURE PERSPECTIVES .................................................................................................... 80

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8. REFERENCE LIST ................................................................................................................. 82

APPENDIX I - TUMOUR SAMPLES .............................................................................................. 90

APPENDIX II – NORMAL TISSUE SAMPLES ............................................................................ 92

APPENDIX III – QUALITATIVE MSP ANALYSES .................................................................... 94

APPENDIX IV – QUANTITATIVE MSP ANALYSES ................................................................. 96

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Summary

Colorectal cancer is one of the most common malignancies in the Western world,

with an incidence of 3500 new cases per year in Norway alone. There is a need for

improved early diagnostics as well as more precise cancer diagnosis to better guide

the choice of treatment.

CpG island hypermethylation of tumour-suppressor genes has been established as a

key molecular event in colorectal cancer. Furthermore, DNA hypermethylation

occurs early during tumor development, suggesting that it could be used as a

molecular marker for early detection of the disease. Determining the methylation

frequencies of target genes in colorectal cancer could therefore help discover novel

biomarkers with a diagnostic potential.

The objective of this study was to identify novel epigenetic biomarkers in colorectal

cancer. A set of candidate genes were selected after treatment of colon cancer cell

lines with AZA and TSA, and subsequent microarray gene expression analysis.

Then, in silico analyses was performed on candidate genes to search for the

presence of CpG islands in the promoter region of the gene. Ten genes were

investigated in vitro for promoter hypermethylation by methylation-specific PCR in

colon cancer cell lines (n = 20). Six of the ten genes were methylated in more than

14 of the cell lines and were subjected to an in vivo pilot methylation study of

primary colorectal carcinomas (n = 20) and normal mucosa samples (n = 10). The

two most promising genes, GLDC and PPP1R14A, were further investigated by

quantitative real-time methylation-specific PCR in an extended series of malignant

(n = 47) and normal (n = 49) colorectal tissue samples.

Promoter hypermethylation of GLDC and PPP1R14A had a sensitivity of 60% and

57% in colorectal carcinomas, whereas normal mucosa samples were unmethylated

for both genes, resulting in 100% specificity. Promoter methylation was

independent of tumour stage, age and gender of the patients. PPP1R14A was

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significantly more methylated in tumours with microsatellite instability and thus in

tumours located on the right side of the colon.

In the present study GLDC and PPP1R14A are identified as novel methylated gene

targets in colorectal cancer.

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Abbreviations

ACF Aberrant crypt foci ATP Adenosine triphosphate AZA 5-aza-2´-deoxycytidine bp Base pairs CIMP CpG Island Methylator Phenotype CIN Chromosomal instability CpG Cytosine phosphate guanine CRC Colorectal cancer Ct Cycle treshold ddNTP Dideoxyribonucleotide triphosphate DNA Deoxyribonucleic acid DNMT DNA methyltransferase dNTP Deoxyribonucleotide triphosphate FAP Familial adenomatous polyposis FOBT Fecal occult blood test HNPCC Hereditary non-polyposis colorectal cancer ICF Immunodeficiency centromeric instability facial anomalies LOH Loss of heterozygosity LOI Loss of imprinting MBD Methyl binding domain MeCP2 Methyl CpG binding protein 2 MGB Minor groove binder miRNA Micro ribonucleic acid MMR DNA mismatch repair mRNA Messenger ribonucleic acid MSI Microsatellite instability MSP Methylation-specific polymerase chain reaction MSS Microsatellite stable nt Nucleotides PCR Polymerase chain reaction PMR Prosent methylated referance qMSP Quantitative methylation-specific polymerase chain reaction RNA Ribonucleic acid ROC Receiver Operating Characteristics siRNA Small interfering ribonucleic acid SWI/SNF SWItch/Sucrose NonFermentable TAE buffer Tris-acetate ethylenediaminetetraacetate buffer Tm Melting temperature

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TSA Trichostatin A XIST X-inactive specific transcript

1Gene symbols and full names are approved by the HUGO Gene Nomenclature Committee

(http://www.genenames.org). Approved gene symbols are used throughout the thesis.

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Gene symbols1

BNIP3 BCL2/adenovirus E1B 19kDa interacting protein 3 BRAF v-raf murine sarcoma viral oncogene homolog B1 CBS Cystathionine-beta-synthase CD44 CD44 molecule (Indian blood group CDKN2A Cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) CTNNB1 Catenin (cadherin-associated protein), beta 1, 88kDa DDX43 DEAD (Asp-Glu-Ala-Asp) box polypeptide 43

EGFR Epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian)

GLDC Glycine dehydrogenase (decarboxylating) H19 H19, imprinted maternally expressed transcript (non-protein coding) HRAS v-Ha-ras Harvey rat sarcoma viral oncogene homolog IGF2 Insulin-like growth factor 2 (somatomedin A) IQCG IQ motif containing G KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog MAL Mal, T-cell differentiation protein MGMT O-6-methylguanine-DNA methyltransferase MLH1 MutL homolog 1, colon cancer, nonpolyposis type 2 (E. coli) MSH2 MutS homolog 2, colon cancer, nonpolyposis type 1 (E. coli) PEG10 Paternally expressed 10 PIK3CA Phosphoinositide-3-kinase, catalytic, alpha polypeptide PPP1R14A Protein phosphatase 1, regulatory (inhibitor) subunit 14A PTEN Phosphatase and tensin homolog RASSF4 Ras association (RalGDS/AF-6) domain family member 4 RB Retinoblastoma 1 RBP7 Retinol binding protein 7, cellular SEPT9 Septin 9 SFRP2 Secreted frizzled-related protein 2 TFP12 Tissue factor pathway inhibitor 2 TP53 Tumor protein p53 VIM Vimentin WDR21B DDB1 and CUL4 associated factor 4-like 1 WT1 Wilms tumor 1

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1. Introduction

1.1 Genetic and epigenetic alterations in carcinogenesis

A tight network of controls regulates the mechanisms that govern normal cell

proliferation and homeostasis, and disruption of these controls may lead to

successful tumour development. Hanahan and Weinberg have described six

essential changes that constitute the hallmarks of cancer: insensitivity to anti-growth

signals, evading apoptosis, self-sufficiency in growth signals, sustained

angiogenesis, limitless replicative potential and tissue invasion and metastasis. It

has long been accepted that genetic alterations can cause cancer, however,

throughout the last decades the importance of epigenetic changes in initiation and

progression of cancer has been widely acknowledged. The genetic and epigenetic

processes seem to be interconnected in driving the development of tumours (Figure

1).

Figure 1. Epigenetic plasticity and genetic lesions drives tumour progression. Cancer evolves through a series of genetic and epigenetic alterations. These acquired changes will eventually give rise to successive subclones with selective advantages over neighboring clones. The gained biological properties could include the six hallmarks of cancer, as well as other important survival or growth advantages.

In 1976, Nowell proposed that tumour development is caused by acquired genetic

variability in a cell which may allow natural selection of subgroups, resulting in

monoclonal tumours [1]. This clonal expansion model for tumour development is

now widely accepted. The model has similarities with Darwinian evolution, in

which several successful changes and “survival of the fittest” cells may lead to a

Introduction

12

monoclonal population. However, cytogenetic studies have shown that the

acquisition of new mutant alleles by the cells in the population in some cases may

result in genetic heterogeneity within the population, leading to polyclonality [2].

The cancer stem cell hypothesis was first proposed 150 years ago. Cell surface

marker expression analysis indicates that cells of tumours can be sorted into a major

and a minor population, where the latter constitutes less than 1% of the cells in the

tumour [3]. The cells of the minor population display several abilities which

resemble those of stem cells, i.e. self-renewal and differentiation, both crucial

properties in driving malignancy. Self-renewal drives tumourigenesis, whereas

differentiation contributes to the heterogeneity phenotype of the tumours. Because

stem cells have an unlimited ability to proliferate, it is likely that the tumourigenic

cancer stem cells are the drivers of multistep tumourigenesis [4].

The multistep tumourigenesis pathway is a consequence of alterations in three

different types of gene families, proto-oncogenes, tumor-suppressor genes, and

DNA repair genes. A proto-oncogene specifies proteins that stimulate cell growth,

and when altered, it may increase the ability of the cell to divide extensively. Thus,

proto-oncogenes can become cancer-causing oncogenes, having the ability to

transform normal cells and induce cancer. Dominant gain-of-function mutation or

hypomethylation of proto-oncogenes render the genes constitutively active or active

under conditions where the wild type gene is not. Tumour-suppressor genes code

for proteins that restrain cell growth, thereby reducing the possibility that a cell will

develop into a tumour. Recessive loss-of-function mutations or hypermethylation,

leading to partial or complete inactivation of tumour-suppressor genes, may reduce

the ability of the genes to constrain cell proliferation. The DNA repair genes, also

referred to as caretaker genes, are under normal circumstances responsible for

maintaining the integrity of the genome [5]. A gain-of-function alteration in one

allele of a proto-oncogene is sufficient to activate an oncogene, while both of the

copies of a recessive tumour-suppressor gene must be eliminated to gain a

phenotypic change. Alfred Knudson elegantly postulated this “two-hit” hypothesis

Introduction

13

for inactivation of tumour-suppressor genes in 1971, using the retinoblastoma gene

as an example [6].

1.2 Epigenetic regulation of gene expression

1.2.1 Defining epigenetics

Epigenetics is defined as stable changes in gene expression inherited through

subsequent cell divisions, which is not due to a change in DNA sequence [7]. The

term “epigenetics” was first introduced by C.H Waddington in 1942 to describe

“the casual interactions between genes and their products, which bring the

phenotype into being” [8]. However, recent publications indicate that experiments

by Paul Kammerer, performed already in the early 1900s, revealed an epigenetic

mechanism [9,10]. Epigenetic inheritance includes DNA methylation, histone

modifications and RNA-mediated silencing [11]. DNA methylation is the best-

known epigenetic marker, and global hypomethylation was the first epigenetic

abnormality to be identified in cancer cells [12].

The molecular role of DNA methylation DNA methylation affects the packing of chromatin and the overall architecture of

the nucleus, and has thereby a critical role in the control of gene expression [13].

The chemical modification, represented by methyl groups, occurs on cytosines (C)

that are located upstream of guanines (G); these sites are termed CpG dinucleotides.

In vertebrates, the genome is depleted of CpG dinucleotides as a consequence of

spontaneous deamination of 5-methylcytosines to thymines (T), leading to C-T

transition mutations [14]. The remaining CpG dinucleotides are unequally

distributed throughout the genome and are especially common in centromeric

regions as well as in repetitive sequences. Stretches of sequences containing the

theoretically expected frequency of CpG dinucleotides are frequently located in the

5´ end of the promoter regions. These stretches are called “CpG islands” [15] and

Introduction

14

are present in approximately half of all human genes [16] According to Takai and

Jones, a CpG island is defined as a region of minimum 200 base pairs, with a GC-

content higher than 55% and an observed to expected ratio of CpG higher than 0.65

[17].

Cytosine methylation is catalysed by DNA methyltransferases, and generated by an

enzymatic transfer of a methyl group from the universal methyl donor S-

adenosylmethionine to the carbon-5 position of cytosine (Figure 2). Maintenance

and restoration of methylation patterns of hemi-methylated strands after DNA

replication is carried out by DNA methyltransferase 1 (DNMT1) [18]. The enzymes

function is mainly to sustain the methylation patterns in proliferating cells.

DNMT3A and DNMT3B, on the other hand, are required to initiate de novo

methylation, and thereby establish new DNA methylation patterns [19].

Figure 2. The process of DNA methylation. Methylation of cytosine is an epigenetic mechanism which provides an extra layer of transcriptional control. The cytosine is methylated in the carbon-5 position by the action of DNA methyltransferases.

The role of DNA methylation in cancer

Global genomic hypomethylation in tumours Hypomethylation is defined as a decrease in the level of DNA methylation at CpG

sites in a given sample, in comparison to normal tissue [20]. In 1982, Feinberg and

Vogelstein found that a considerable number of CpGs methylated in normal tissue,

were unmethylated in cancer cells [12]. Further research confirmed that the global

level of 5-methylcytosine was reduced in all tumour types, both benign and

malignant, compared to their normal counterparts [21]. The overall decrease in

DNA methylation is mainly due to hypomethylation of repetitive sequences, and the

Introduction

15

degree of hypomethylation increases during the neoplastic development of a benign

lesion to an invasive cancer.

Several alternative mechanisms have been suggested to explain how DNA

hypomethylation can contribute to the development of a cancer cell (Figure 3).

First, methylation of repetitive sequences has been thought to impair their ability to

promote chromosomal rearrangements. Undermethylation of these regions can

favour mitotic recombination. This theory has been confirmed in experiments where

loss of DNMT and the resulting DNA hypomethylation led to chromosomal

instability in human cancer cells [22]. In addition, intragenomic parasitic DNA

sequences, such as the L1 (long interspersed nuclear element), are inactivated by

methylation in normal cells. Demethylation of these transposons may lead to

translocation, insertions, and deletions, further disrupting the genome [23]. A study

of genomic hypomethylation in colorectal neoplasia showed that global

hypomethylation occurs early in the tumour development and that the frequency or

extent of hypomethylation was independent of tumour subtype [24]

Second, hypomethylation of DNA may lead to inappropriate gene activation by

demethylating CpG islands located in the promoter of proto-oncogenes. This leads

to expression of normally inactivated genes, such as the HRAS oncogene [25].

Testis-cancer antigens (TCA) are also reactivated by this mechanism. These genes

are generally methylated and not expressed in normal tissue, with the exception of

the testis. Due to hypomethylation, protein expression of this gene family can be

used as antigens in cancer cells. Finally, loss of methyl groups may disrupt genomic

imprinting and consequently increase risk of cancers (See section below).

Introduction

16

Figure 3. The effect of epigenetic alterations in cancer. The figure illustrates the increasing difference in methylation patterns and histone modifications during tumourigenesis. Both mechanisms play an important role in normal development and loss of epigenetic control in cancer cells can affect closely regulated mechanisms, including genomic imprinting, x-chromosome inactivation and silencing of repetitive sequences.

Gene specific DNA hypermethylation The retinoblastoma (RB) gene was not only the first gene to be characterised as a

tumour-suppressor, but was also the first gene to be identified as hypermethylated.

In 1994, Horsthemke et al documented the connection between reduced expression

of the RB gene in tumours and hypermethylation of the promoter [26].

Hypermethylation of CpG islands located in promoter regions of genes is a major

event in the development of the majority of cancer types, due to the subsequent

aberrant silencing of important tumour-suppressor genes (Figure 4) [13]. The

importance of promoter hypermethylation for gene expression has been

exemplified by the ability of the demethylating agens 5-aza-2´-deoxycytidine to

reactivate the genes and to restore the active transcription in cultured cancer cells

[16].

Introduction

17

Epigenetic inactivation may affect all of the molecular pathways involved in

transformation of a cell. Promoter hypermethylation-associated gene silencing has

amongst others been observed in the APC/β-catenin route (APC), DNA repair

(hMLH1, MGMT), cell cycle (CDKN2A) and other pathways and processes that

together constitutes the hallmarks of cancer (see previous). Some tumour-

suppressor genes are hypermethylated across several cancer types. However,

several genes are tumour specifically methylated constituting a distinct DNA

methylation profile for each cancer type. It is likely that some of the epigenetic

events are contributing to the neoplastic process (drivers), while others are mere

passengers. When comparing the hypermethylation profiles of distinct cancer types,

the general observation is that tumours arising in the gastrointestinal tract, such as

the colon, rectum, and gastric share a set of genes that undergo hypermethylation,

while tumours arising in other tissues, such as the lung, neck, and/or head have a

different “hypermethylome” pattern [27].

Figure 4. Normal versus cancer epigenome. In normal mammalian cells, CpG islands located in proximal gene promoter regions are protected from DNA methylation (cytosines shown as open lollipops) and reside in an open chromatin conformation or euchromatin regions, where transcription of the downstream gene is constitutive. However, in cancer cells, the CpG islands in the promoter tend to be hypermetylated (cytosines shown as black lollipops) and reside in a closed chromatin conformation, or heterochromatin. This condition does not favour transcription, thus the gene is in an inactive state. Modified after Baylin and Herman, 2003 [16].

Introduction

18

DNA methylation in normal development DNA methylation is one of the mechanisms that control the normal development of

a fertilized egg into an embryo, as well as maintaining the normal pattern of gene

expression in all cells after the embryo is fully developed. In most mammalian

genomes, CpG islands are generally unmethylated in normal tissue except for a few,

well-known exceptions such as genomic imprinting, X-chromosome inactivation

and intragenomic repetitive and parasitic sequences (Figure 3). These cases will be

discussed briefly in the following.

Genomic imprinting Genomic imprinting refers to genes that are differentially expressed depending on

whether they are inherited from the maternal or paternal genome. The imprints are

established during the development of germ cells into either sperm or eggs. These

imprints are maintained during fertilisation, as the chromosomes duplicate and

segregate. The germ cells of the new organism will erase all of the imprinted marks,

and a new pattern will be established, one that is maintained and modified in all

somatic cells during development [28].

The role for DNA methylation in maintaining allele-specific expression was put

forward by Li and colleagues in 1993 [29], where they demonstrated that

methylation patterns may be inherited in a parent-of-origin specific manner.

Imprinted genes have further been shown to be associated with CpG islands [30],

and local epigenetic modifications of these sites as well as of the surrounding

histone tails protruding from the chromatin contribute in determining differential

gene expression. Loss of imprinting (LOI) of the insulin-like growth factor II gene

(IGF2) has been discovered in embryonic tumours, such as Wilms tumour [31]. LOI

of IGF2 has been linked to hypermethylation or a deletion in the differentially

methylated region localised upstream to the reciprocally imprinted paternal H19

allele, leading to biallelic expression [21,32]. H19 and IGF2 are expressed in a

monoallelic fashion from the maternal and paternal chromosomes, respectively.

Introduction

19

Conversely, a study carried out by Feinberg et al showed that loss of IGF2

imprinting is associated with an increased risk of developing colorectal cancer [31].

X-chromosome inactivation X-chromosome inactivation is a mechanism that equalises the gene dosage between

females (harbouring two X chromosomes; XX) and males (harbouring one X

chromosome; XY) by inactivating one of the X-chromosomes in female cells [33].

One of the two copies will be subjected to genetic and epigenetic regulatory

mechanisms, leading to transcriptional silencing and inactivation. The inactivated X

chromosome expresses the XIST (X-inactive specific transcript) gene, a nuclear

non-coding RNA that originates in the X-inactivation centre. XIST coats the

chromosome, which subsequently forms a condensed and clearly visible

heterochromatin-like structure called the Barr body [34]. Berg et al showed that

once the XIST RNA has covered the chromosome, epigenetic modifications leading

to X-chromosome inactivation are induced. The epigenetic mechanisms include

hypoacetylation of lysine 9 at histone H3 (H3K9ac) and trimethylation of lysine 27

at histone H3 (H3K25Me3) [33]. The inactivated state of the Xi chromosome is

clonally inherited through several rounds of cell division.

Methylation has long been though to be a key mechanism in maintaining the

inactivated state of the X-chromosome. Mohandas et al found that treatment with

the demethylating 5-azacytidine reactivated several genes on the Xi chromosome

[35]. The importance of CpG methylation in stabilising and maintaining Xi is

further demonstrated in patients with ICF (immunodeficiency centromeric

instability facial anomalies). The syndrome is caused by a defect in DNA

methyltransferase DNMT3B [36], and as a consequence, several CpG islands

analysed in the Xi of these patients are hypomethylated. It is proposed that the

escape of Xi is limited to candidate genes which are involved in pathways that are

altered in ICF [37].

Introduction

20

Silencing of parasitic and repetitive sequences Repetitive elements constitute approximately 45% of the human genome and loss of

methylation of these sequences is thought to account for most of the global

hypomethylation observed in close to all human cancer types [38]. This

phenomenon is probably related to the fact that most mammalian repeat elements

are maintained in the heavily methylated and inactivated state in normal somatic

tissues [39]. While methylation inactivates the transcription and movement of the

mobile repeat elements, demethylation may result in transcriptional interference and

dysregulation of normal gene expression, leading to destabilisation and

chromosomal translocations. Loss of heterozygosity (LOH) could occur as a result

of the rearrangements, and LOH is a strong driver for the neoplastic process of a

cell into a malignant tumour. A study carried out by Ward et al showed that

hypomethylation of the retrotransposon L1 (LINE-1) was a frequent feature of

colorectal cancer, possibly participating in chromosomal instability [23].

1.2.2 The interplay of epigenetic regulators

To understand the overall role of epigenetic alterations on gene activity, the

molecular role of histone modification in organising and maintaining the chromatin

structure must also be mentioned. Positioning of the nucleosomes together with

different modifications of the histone tails protruding from the nucleosome

modulates the normal epigenome in terms of maintaining normal gene expression

and chromosome structure and function [40]. The balance of the histone

modifications in conjunction with DNA methylation is strictly regulated and even

slight changes may alter the transcriptional state of a gene.

Histone modification and chromatin remodelling The underlying unit of the chromatin, the nucleosome, has the same type of design

in all eukaryotic cells, and consists of approximately 147 base pairs DNA wrapped

around an octamer of core histone proteins, including H2A, H2B, H3 and H4 [41].

Each of the histones is present in duplicates in the octamer. Covalent modification

Introduction

21

of the N-terminal of certain amino acids on the histone tails has been proposed to

create a histone code, in which the sum of different modifications determines

whether the chromatin exists in an open and actively transcribed state (euchromatin)

or in an inactive, closed state (heterochromatin). Among the post-translational

modifications that constitute the code are methylation, acetylation, phosphorylation,

ubiquitination and sumoylation. Acetylation of histone lysines is carried out by

histone acetylases (HATs) and is generally associated with active transcription.

Methylation of the residues, distributed by histone methyltransferases (HMTs), is

associated with both active or inactive state, depending upon the modified residue

(lysine or arginine) and modification site. This is exemplified by methylation of

lysine four on histone H3, which is associated with transcription, whereas

methylation of lysine nine on the same histone tail is associated with repression

[16].

Chromatin remodelling, the general process of inducing changes in chromatin

structures, consists of mechanisms that provide energy to displace nucleosomes. By

relocating the nucleosomes at the promoter of a gene, the accessibility of

transcription factors to DNA is increased. The SWI/SNF is an ATP-dependent

remodelling complex that uses ATP hydrolysis to increase the accessibility to

nucleosomal DNA, which is an essential requirement to activate transcription [42].

Candidate players for epigenetic regulation of gene expression There is a tight interdependence between DNA methylation, histone modification

and chromatin remodelling in packaging DNA and determining how available the

regulatory regions are for transcription factors. Methylated CpG dinucleotides in

promoters attract proteins that “read” the methylation pattern, and repress the

transcription of the downstream gene [18]. These proteins contain a methyl

cytosine-binding domain which recognises and binds to methylated CpG sites, and

often recruits histone modifying and chromatin remodelling complexes. One such

methyl binding domain (MBD) containing protein is MeCP2. MeCP2 has been

shown to form a complex with HDACs (histone deacetylases) and the co-repressor

Introduction

22

SIN3, leading to transcriptional repression in a methylation-dependent matter. In

addition, MBD containing proteins may also interact with histone

methyltransferases [18]. Harikrishnan et al showed that Brahma (Brm), which is a

catalytic subunit of the SWI/SNF complex, associates with MeCP2, providing a

potential link between DNA methylation and chromatin silencing [43]. All of these

finding suggests that DNA methylation, histone modification and chromatin-

remodelling are connected (Figure 5).

Figure 5. Interaction between DNA methylation, histone modification and chromatin remodelling can silence gene transcription. Methylated DNA recruits proteins with methyl-CpG binding domains (MBDs) such as MeCP2. MeCP2 usually occurs in a complex with histone deacetylases (HDACs) and co-repressors, such as SIN3. HDACs removes acetylation from histone tails protruding from the chromatin, contributing to gene inactivation. The inactive state of the gene may be sustained by methylation of lysine 9 at histone H3, performed by histone methyltransferases (HMTs). Chromatin remodelling complexes, such as SWI/SNF, can be recruited by MeCP2, leading to rearrangement of the chromatin structure into a more densely packed conformation. Modified after Wang, 2005 [44].

Introduction

23

With the exception of DNA methylation, the establishment and maintenance of

modification patterns after replication is not well understood. It has, however, been

stated that DNA methylation patterns and various modifications of histone tails

have a mutually dependent biological relationship. The relationship might work

both ways. Histone modifications may direct DNA methylation, while DNA

methylation, on the other hand, might serve as template for diverse histone

modifications. Bergman et al postulated that mono-, di- and trimethylation of lysine

4 at histone three (H3K4) precede de novo methylation before implantation of the

embryo. At the time of implantation, DNMT3A and DNMT3B will be expressed

and directed to the methylated H3K4 residues, where it will perform de novo

methylation of nearby CpG sites.

1.2.3 RNA-mediated gene silencing

Small, endogenous RNA molecules, named microRNAs (miRNAs) and small

interfering RNAs (siRNAs), regulate the stability and translation of target

messenger RNAs (mRNAs). In recent years, altered expression of miRNAs in

various cancers due to epigenetic regulation has been shown to be an additional

hallmark of carcinogenesis. This will be further described in the following.

Micro-RNA MicroRNAs (miRNAs) are small, single-stranded, non-coding RNAs of

approximately 22 nucleotides (nt) that negatively regulate gene expression in

eukaryotic cells through translational inhibition or degradation of messenger RNA

[45]. Most miRNAs are derived from primary miRNA transcripts produced by RNA

polymerase II, and cleaved into a 70 nt long pre-miRNA by a multiprotein complex

in the nucleus. The complex is transported back into the cytoplasm and cleaved into

a mature, 22 nt miRNA, which is subsequently incorporated into a RNA-induced

silencing complex (RISC). RISC selectively guides this complex to and targets 3´

untranslated region of specific mRNAs [46]. miRNAs downregulate the expression

of target genes depending on the level of complimentarity to the target mRNA.

Introduction

24

Perfect or near-perfect complimentarity induces mRNA degradation, whereas

imperfect binding results in translational inhibition.

miRNAs have recently been shown to participate in the control of numerous cellular

processes, such as apoptosis, differentiation, proliferation and development.

miRNAs are either downregulated or upregulated in carcinomas when compared to

normal tissue, resembling the actions of the inactivation of tumour-suppressor genes

or activation of proto-oncogenes during carcinogenesis. Hence, miRNAs can serve

as both tumour-suppressor genes and oncogenes, and disruptions of the miRNAs

involved in maintaining the normal activity of the cell may contribute to the

formation of tumours [47].

Epigenetic dysregulation of microRNAs in cancer Epigenetic profiling of miRNAs has revealed new insights into the altered

epigenetic regulation of these molecules in diseases, including cancer. In recent

years, silencing of miRNA gene expression due to hypermethylation of associated

CpG islands has lead to speculations regarding whether miRNAs could potentially

be utilised as diagnostic and prognostic biomarkers. Downregulation of miRNAs

and potential histone modifications and subsequent transcriptional inactivation is

now a widely accepted feature of several cancer types [45]. MiR-34b and miR-34c,

two components of the TP53 pathway, are epigenetically inactivated in colorectal

cancer, and treatment with the demethylating chemical 5-aza-2´-deoxycytidine has

been shown to restore their expression [48]. MiR-143 has been shown to target

DNMT3A in colorectal cancer, leading to reduced growth of colon cancer cells

[49]. Among the oncogenic miRNAs, miR-21 and miR-31 has been shown to be

upregulated in colorectal cancer. Upregulation of miR-21 has been discovered in

the advanced stages, indicating that it may play an important role in invasiveness of

the cancer. Furthermore, mir-21 has been suggested to post-transcriptionally

downregulate the translation of genes involved in suppressing tumor progression.

MiR-31 is another potential micro-oncogene in colorectal tumours. Interestingly,

members of the Wnt signalling pathway are among the target genes of miR-31 [50].

Introduction

25

1.2.4 Epigenetics: Nature or nurture or both?

Epigenetic events play a fundamental role in normal physiological responses to

environmental stimuli, which affects the epigenome and subsequently directs

alterations in the epigenetic state of the genome. Dietary and environmental

substances have been shown to affect the epigenetic patterns, leading to changes

that accumulate over a longer period of time. Factors that induce such changes are

termed “epigenetic carcinogens” (epimutagens) [51]. Chemical and physical

epimutagens, such as tobacco smoke and irradiation, are examples of factors that

contribute to development of cancer by inducing epigenetic, as well as genetic,

changes. In terms of nutrition and diet, deficiency in folate and methionine, which

are involved in the processes that supply methyl groups for DNA methylation, may

affect the level of methylated CpG sites [52]. Accordingly, epidemiologic research

suggests that diets providing higher levels of folate may reduce the risk of

developing colorectal cancer. However, large doses of folic acid have been shown

to lead to unmetabolised folic acid in the peripheral blood, and subsequent

reduction in natural killer cells [53]. In addition, intake of folic acid

supplementation in early pregnancy has been associated with epigenetic alterations

of IGF2 in the child, which may affect the growth, development and health of the

child [54].

The risk of having cancer increases with age, probably because cells progressively

accumulate enough errors to evade the homeostatic control mechanisms that govern

normal cell behaviour and tissue contexts. A recently published article by Kelsey et

al showed that CpG-island loci in a wide range of normal tissues gain methylation

with age, and they hypothesise that the reduced fidelity of maintenance

methyltransferases with aging could be one potential explanation for this

phenomenon [55]. Several twin studies on epigenetic profiles have investigated to

which extent age, environment and lifestyle can impact gene expression [56]. Fraga

and co-workers were the first to use monozygotic twins for this purpose and found

that twins who had spent less of their lives together, and had different natural

Introduction

26

health-medical history, were those with the greatest epigenetic differences [57]. In

addition, the older twin pairs included in the study seemed to have the greatest

epigenetic differences. Taken together, these findings demonstrate that age, diet,

lifestyle and environmental factors, affect the epigenetic pattern in individuals with

identical genetic make-up. Thus, distinct epigenetic profiles may contribute to

explain the phenotypic differences and different disease susceptibility among

monozygotic twins.

1.3 Colorectal cancer

Colorectal cancer (CRC) is the third most commonly diagnosed cancer among men

and women, with approximately 1 million new cases each year world-wide [58].

The incidence in Norway is approximately 3500 [59]. An almost equal number of

men and women develop CRC, indicating that the cancer is gender independent.

The risk of developing CRC increases with age, and the disease affects primarily

older individuals (median age 70 years). The significant difference in CRC

occurrence between industrialised and developing countries emphasize the

importance of lifestyle and environmental factors in CRC development. Dietary risk

factors, including red and processed meat, as well as alcohol consumption, tobacco,

diabetes, obesity and physical inactivity are associated with greater risk of

developing CRC [60].

While the majority of CRCs are sporadic, a small group (~5%) arises as a

consequence of defects in single hereditary components, such as hereditary non-

polyposis colorectal cancer (HNPCC) and familial adenomatous polyposis (FAP).

HNPCC, also known as Lynch syndrome, is an autosomal dominant disease, which

is caused by a germ line mutation in one of the DNA mismatch repair (MMR) genes

[61]. Consequently, DNA replication errors occur at a higher frequency in repetitive

sequences, know as microsatellites, leading to microsatellite instability. HNPCC

counts for 1-5% of colorectal cancers, and is associated with an 80% lifetime risk

Introduction

27

for developing colorectal cancer [62]. HNPCC is associated with a better prognosis

when compared to sporadic cancers.

FAP is an autosomal dominant disease which accounts for less than 1% of all CRC

cases, and is associated with nearly 100% lifetime risk of developing colorectal

cancer, unless the colon is removed by surgery [61]. The disease is characterised by

the presence of more than 100 adenomatous polyps, and the number of polyps

increases with age. Truncating mutations in the Adenomatous polyposis coli (APC)

tumour-suppressor gene has been shown to be the cause of most FAP cases. The

APC gene participates in the Wnt signalling pathway, where it is involved in the

degradation of β-catenin (CTNNB1). Mutations in APC affect the ability to maintain

normal growth, subsequently leading to uncontrolled overgrowth of cells.

1.3.1 Molecular developmental pathways

Two distinct colorectal cancer pathways are suggested to explain the step-wise

process from benign neoplasms to adenocarcinoma, the chromosomal instability

(CIN) pathway and the microsatellite instability (MSI) pathway.

Chromosomal instability is the most common type of genomic instability and

accounts for more than 80% of colorectal carcinomas. Chromosomal instability

occurs mainly as a consequence of either missegregation of normal chromosomes or

structural rearrangements [63]. Inactivation of proteins that regulate the mitotic

spindle checkpoints, DNA damage checkpoints, chromosome metabolism,

centrosome function and DNA replication has been hypothesized to potentially

cause CIN in cancer [64]. Aneuploidy1 serves as a hallmark of chromosomal

instability [64], and tumours that exhibit CIN phenotype are most often located at

the distal, or the left, side of the colon. Furthermore, the CIN molecular pathway is

associated with better prognosis for the patient when compared to MSI.

Introduction

28

Microsatellite instability occurs in ~15% of sporadic colorectal carcinomas.

Microsatellites are stretches of DNA where a short pattern of 1-6 bases is repeated

several times, and these motifs are spread throughout the genome. HNPCC is, as

mentioned previously, caused by either a germline mutation in the DNA mismatch

repair genes MLH1 or MSH2, while MSI in sporadic cancers are primarily caused

by biallelic hypermethylation and subsequent inactivation of the mismatch repair

gene MLH1 [65]. These genetic and epigenetic changes will give rise to a defective

DNA mismatch repair system, leading to an inability to repair base-base

mismatches and small insertions and deletions. This will in turn cause an increased

genomic mutation rate, which is why MSI has been referred to as the mutator

phenotype [62]. The defective repair system may ultimately facilitate malignant

transformation by allowing rapid accumulation of alterations in genes that

ordinarily have key functions in the cell. Sporadic MSI colorectal carcinomas have

distinct clinicopathological features, such as location in the proximal colon, diploid

or near-diploid karyotype, association with the female gender and poor

differentiation [66].

In addition to these molecular phenotypes, an alternative third molecular route for

colon cancer development was suggested by Toyoto et al in 1999. The CpG Island

Methylator Phenotype (CIMP) is based on the identification of a subset of

colorectal cancers with concordant hypermethylation of several CpG loci. Toyoto

and co-workers showed that CIMP positive tumours include the majority of

sporadic colorectal cancers with MSI related to MLH1 hypermethylation [67].

Furthermore, CIMP has been showed to be strongly associated with BRAF mutation

[68]. Hierarchal clustering has identified three groups with distinct genetic and

epigenetic profiles (Figure 6). CIMP negative tumours display rare methylation and

TP53 mutation. CIMP1 tumours are methylated at multiple loci and display MSI

and BRAF mutations, while CIMP2 is methylated at a limited number of age-related

1 Aneuploidy - having or being a chromosome number that is not an exact multiple of the usually haploid number.

Introduction

29

genes and display mutations in KRAS [68,69]. CIMP tumours are characterised by

many of the features typical of MSI tumours, such as proximal location, BRAF

mutation, poor differentiation, female gender and old age.

Figure 6. Integrated genetic and epigenetic analysis identifies three different subclasses of CIMP. Shen and co-workers utilised integrated analysis of the mutation status of BRAF, KRAS and TP53, as well as MSI status and methylation status of a panel of genes, to identify three distinct subgroups of CIMP (from [69]).

1.3.2 Histopathology and morphological pathways

The transition from normal epithelium to carcinoma in human colon can be arrayed

into a series of increasing abnormality. Several lines of evidence indicate that

adenomas can develop into carcinomas; however, this does not imply that all

adenomatous polyps will undergo malignant transformation. Aberrant crypt foci

(ACF) represent one of the earliest steps in the development of colorectal cancer,

followed by other morphological precursors. Each of these pre-malignant

outgrowths differs in size, level of dysplasia and villous complexity [70]. Colorectal

polyps are associated with different genetic and epigenetic alterations when

compared to normal tissue, thus, an accurate description of the phenotype is

important to achieve correct phenotypic and (epi)genotypic classification.

The adenoma-carcinoma sequence refers to the evolution of normal epithelia cells

to adenocarcinomas as a progression of histological changes and concurrent genetic

and epigenetic alterations. According to this model, adenomas can develop into

either MSI or CIN tumours, depending on the alterations [71]. The concept that

Introduction

30

inactivation of the APC tumour-suppressor gene initiaties colorectal neoplasia has

been shown to be an oversimplification. Research findings support the theory that

adenomas only gives rise to CIN and CIMP negative tumours, whereas the sessile

serrated adenomas, a subgroup of the hyperplastic polyps, has emerged as

precursors to MSI and CIMP tumours (Figure 7). Mutation in the BRAF proto-

oncogene is considered to be the “gatekeeper”2 in this pathway [72-74].

Figure 7. Molecular developmental pathways in colorectal cancer. Colorectal cancer is thought to develop through two molecular and morphological distinct pathways, the sessile serrated pathway giving rise to CIMP positive, MSI-tumours (red), and the chromosomal instability pathway giving rise to CIN-tumours (blue). The histological diverse steps are associated with distinct genetic (bold) and epigenetic (bold, italic) alterations. The “gatekeeper” gene APC has long been known to initiate the adenoma-carcinoma sequence, whereas BRAF is though to initiate the sessile serrated pathway.

2 A class of genes which directly regulate tumor growth by inhibiting growth or by promoting cell death.

Introduction

31

1.3.3 Tumour classification, treatment and outcome

Cuthbert E. Dukes proposed the original staging system for tumours of the colon

and the rectum in 1932 [75], and a modified version is today used to divide colon

and rectal cancer into four Dukes´ stages. Dukes´ A tumours are confined to the

intestinal mucosa and submucosa, whereas Dukes´ B tumours have grown through

these layers, and into the muscle layers of the bowel wall. Dukes´ C tumours have

spread to the regional lymph nodes, and Dukes´ D tumours have distant metastasis

in other organs of the body (Figure 8). The TNM staging system is also widely

used. TNM stands for “tumour, node, metastasis”, and the model describes the size

of the primary tumour (T), whether any lymph nodes contain cancer cells (N) and

whether the cancer has spread to another part of the body (M).

Figure 8. Carcinogenesis of colorectal cancer and Dukes´ classification. The figure illustrates the steps in which a benign precursor, adenoma, develops into a malignant polyp. The polyp may progress into a tumour, and according to the level of penetration of the bowel wall, may be classified as either Dukes´ A, B, C or D tumour. In Dukes´ A colorectal cancer, the tumour has only affected the innermost lining of the colon (mucosa and submucosa), whereas Dukes´ B cancer has grown through the muscle layers. Dukes´ C cancer has spread to at least one local lymph node, while Dukes´ D cancer has metastasised to distant organs.

Survival among CRC patients depends on the tumour stage at the time of diagnosis.

Patients diagnosed with localised tumours (Dukes´ A and B) have a five-year

Introduction

32

survival close to 90%, while patients diagnosed with spread to regional lymph

nodes (Dukes´ C) have a five- year survival of 65%. Patients diagnosed with distant

metastasis (Dukes´ D) have the worst prognosis, with a five-year survival of only

10% [59].

Colorectal cancer patients in Norway are today treated with surgery and/or adjuvant

chemotherapy3, depending on the tumour stage at diagnosis. Patients with localised

tumours (Dukes´ A and B), receive surgery alone. Adjuvant chemotherapy (surgery

in combination with chemotherapy) is given to patients with Dukes´ C cancer.

Additionally, some Dukes´ B patients receive adjuvant treatment when an

inadequate amount of lymph nodes are analysed for presence of cancer cells. The

most common regime is 5-fluoruracil/leukovorin (calsiumfolinate) in combination

with other drugs, such as oxaliplatin.

1.4 Clinical relevance of molecular biomarkers

The identification of molecular biomarkers has been the focus of extensive research

where the ultimate goal is to discover markers with a diagnostic and/or therapeutic

value. Molecular biomarkers are defined as indicators of normal biological

processes, pathogenic processes or pharmacologic responses to therapeutic

intervention, and can be DNA, RNA or protein based, which is exemplified in the

following. Epigenetic changes, including DNA hypermethylation, are potentially

good indicators of existing diseases. DNA hypermethylation of ADAMTS1,

CRABP1 and NR3C1 has been found in 71%, 49% and 25% of colorectal

carcinomas [76]. Interestingly, epigenetic gene regulation can also be utilised to

predict response to treatment. Alkylating agents are frequently used as treatment

against malignant brain tumours. Methylation of the MGMT promoter has been

shown to predict the response of brain tumours to alkylating agents [77,78].

3 From the web-page of the Norwegian Gastro Intestinal Cancer Group: http://ngicg.no/wp/

Introduction

33

RNA expression analysis has identified distinct molecular subtypes of breast

cancer, predicted response to neoadjuvant therapy (treatment before primary

surgery) and discovered gene-expression signatures that distinguish primary

tumours from metastatic adenocarcinoma [79-81]. Furthermore, alternative splicing

of RNA molecules can generate cancer-specific splice variants which may serve as

diagnostic disease biomarkers. Through alternative splicing, a single gene is able to

generate several transcript variants from one type of precursor messenger RNA

(pre-mRNA), which may produce different protein isoforms. CD44 and WT1 have

been characterised as cancer-related genes that undergo extensive alternative

splicing [82]. Splice variants that are overexpressed in cancer are usually expressed

as hyperoncogenic proteins, which often correlate with poor prognosis [83].

Conversely, cancer therapy directed to correct malfunctioning splicing machinery

can prevent production of oncogenic, mRNA splice variants [84].

Proteins that are produced in increased amounts can serve as biomarkers to detect

specific diseases, exemplified by GOLM1 which has been found to be upregulated

in urine of patients with prostate cancer, suggesting that GOLM1 levels in urine can

serve as a predictor of prostate cancer [85]. Unlike genomic measurements, which

generally require biopsy, blood provides important insight into the presence and

activity of proteins in diseases. However, protein biomarker research is complicated

by the difficulty of identifying medium or low abundance of proteins in the plasma.

Many biomarkers with clinical value have concentrations five to seven orders of

magnitude lower in abundance than the most highly concentrated plasma proteins.

It is possible to overcome this obstacle if the biomarkers arise locally (e.g.

malignant tumour), by analysing fluid close to or in contact with the site of disease

[86].

34

2. Aims

The overall aim of the present study was to identify novel epigenetic biomarkers

with a diagnostic potential in colorectal cancer.

The first objective was to identify yet undiscovered candidate target genes

inactivated by promoter hypermethylation.

Second, the candidate gene list identified needed to be evaluated for its cancer

specificity through experimentally optimised assays in suitable normal and tumor

samples.

The final objective of this thesis was to evaluate the suitability of any novel target

genes to be developed as biomarkers for colorectal cancer.

35

3. Materials and methods

3.1 Materials

3.1.1 Colon cancer cell lines

Twenty colon cancer cell lines were analysed in this project. The cell lines included

eleven microsatellite stable (MSS; ALA, Colo320, EB, FRI, HT29, IS1, IS2, IS3,

LS1034, SW480, V9P) and nine microsatellite unstable (MSI; Co115, HCT15,

HCT116, LoVo, LS174T, RKO, SW48, TC7, TC71) cell lines, thereby representing

both of the phenotypical subgroups of colorectal cancer.

3.1.2 Tissue samples – Colorectal carcinomas and normal mucosa

Forty-seven primary colorectal carcinoma samples, including 27 MSS and 20 MSI

tumours, were subjected to DNA promoter methylation analysis in the present

study. Twenty-four of the samples derived from a series which was collected at

seven hospitals in the South-Eastern part of Norway from 1987-1989 [87]. The

remaining 23 samples were collected at Aker University Hospital from 2005-2007.

For detailed clinico-pathological data, see Appendix I.

Also included in the present project were 49 normal colorectal mucosa samples

derived from deceased colorectal cancer-free individuals (Institute of Forensic

Medicine, Rikshospitalet University Hospital). These clinico-pathological data are

listed in Appendix II.

Genomic DNA from cell lines, primary tumours and normal tissue had previously

been isolated using a standard phenol/chlorophorm extraction method [88].

Materials and methods

36

3.2 Methylation-specific methodologies

3.2.1 Strategy to select novel DNA methylation candidate genes

Genome-wide gene expression analysis In contrast to genetic changes, epigenetic modifications can be reversed. Treatment

of cancer cells with the demethylating agens 5-AZA-2´-deoxycytidine (AZA) and

the histon deacetylase inhibitor Trichostatin A (TSA) has been shown to reactivate

tumor suppressor genes inactivated by promoter hypermethylation and histon

deacetylation [89]. AZA is a cytosine analogue which is incorporated into the DNA

and forms an irreversible covalent complex with DNMT1[90]. This will in turn lead

to depletion of DNMT1 in the cell during DNA replication, causing a passive

demethylation and subsequent reactivation of genes [91]. Histone deacetylase

inhibitors, such as TSA, modulate the expression of genes by causing an increase in

histone acetylation, thereby relieving the transcriptional repression of the

chromatin.

The AB1700 microarray platform (Applied Biosystems, Foster City, CA, USA) was

utilised to analyse the gene expression of colon cancer cell lines before and after

treatment with AZA and TSA, identifying novel gene targets epigenetically

inactivated in colorectal tumorigenesis. The analyses were performed prior to this

master thesis. Six cell lines were analysed, including three MSI (SW48, RKO,

HCT15) and three MSS (SW480, LS1034, HT29) cell lines. Only genes up-

regulated four or more times after treatment in at least five of the six cell lines

analysed were chosen for further investigation. In order to increase the likelihood of

selecting true epigenetic targets, the gene expression of the same genes were

analysed in primary colorectal carcinomas and normal tissue samples, using the

same microarray platform. Only those genes that responded in cell lines and

simultaneously were down-regulated in the carcinomas as compared to normal

tissue were chosen for further analysis. The selection process for the discovery of

new, hypermethylated genes is summarised in Figure 9.


37

Figure 9. Strategy to select novel DNA methylation candidate genes. Prior to the start of the masterproject, six cell lines and their AZA and TSA treated counterparts were analysed by the AB1700 microarray platform. Genes upregulated four or more times after treatment in at least five of six cell lines, while simultaneously being down-regulated in primary colorectal carcinomas relative to normal colon mucosa, were choosen for downstream methylation analyses. The masterproject included in silico analyses of potential targets for DNA methylation. Only genes containing one or more CpG islands in the promoter were subjected to analyses in cell lines and clinical samples.

Analysing gene promoters for the presence of CpG islands Because loss of gene expression often is associated with aberrant methylation of

promoter CpG islands, suitable target genes for DNA methylation analysis should

contain a CpG island in their promoter region. The CpG Island searcher4 was

applied to analyse the candidate genes for the presence of one or more islands. The

algorithm and criteria used in the program was described by D. Takai and P.A Jones

in 2002 [17].

4 http://www.uscnorris.com/cpgislands


38

3.2.2 Bisulfite modification

The principle of bisulfite modification of DNA was first described in 1970 [92], but

the protocols on how this application could be used for epigenetic analyses was not

published until the 1990s [93,94]. The modification of DNA translates the

methylation event to a genetic change, which can then be analyzed using various

polymerase chain reaction-based methods. The principle behind this treatment is

that the bisulfite will deaminate unmethylated cytosines (C) to uracil under

conditions with low pH and high bisulfite salt concentration, while 5-

methylcytosines remains protected from this conversion. During PCR the uracils are

replaced with thymine, while the methylated Cs remains the same. Hence, this

treatment serves as a chemical modification resulting in differences in the sequences

that can be used to determine the methylation pattern in subsequent methods.

In this thesis, bisulfite mediated conversion was performed using the EpiTect

Bisulfite Kit supplied from Qiagen (Qiagen Co., Valencia, California, USA).

During the conversion procedure, sample DNA was mixed with the bisulfite

solution and a DNA protection buffer. Incubation of the DNA samples in high salt

concentration, high temperature, and low pH will eventually lead to fragmentation

and loss of DNA. The DNA protection buffer contains an indicator which confirms

that the reaction pH is suitable for complete conversion of unmethylated cytosines,

while at the same time limiting the degradation of DNA. The reaction was

performed in a thermo cycler (MJ Mini Personal Thermal Cycler, BIO-RAD,

Hercules, CA, USA), in a program consisting of a series of denaturation and

incubation steps. This ensures proper denaturation of the DNA, and the subsequent

sulfonation and cytosine deamination. Following the reaction procedure is the

clean-up of the bisulfite converted DNA, where the samples are desulfonated and

washed. The input amount of DNA for the bisulfite conversion was 1.3 µg, and the

DNA was eluted in 40 µl elution buffer with a final concentration of 32.5 µg/µl. For

standardisation of the cleaning process, the Qiacube (Qiagen) automated pipette

system was utilised.


39

A fully denatured DNA prior to bisulfite treatment is important due to the reaction

being highly single strand specific. Unsuccessful denaturing may lead to incomplete

conversion, and the subsequent downstream analyses may wrongly interpret

unconverted, unmethylated cytosines as cytosines, producing false positive results.

Several important factors contribute in ensuring successful and effective bisulfite

conversion. First, the DNA must be of high quality and fully denatured. Second,

correct pH and incubation temperature for the various steps are crucial to gain

optimal conversion conditions. Third, because bisulfite can oxidize automatically

with oxygen, a free radical should be included in the reaction mixture to minimize

oxidative degradation. The rate of conversion is extremely effective, with an

estimated rate of 99%. However, a conversion rate of 95%-98% is more frequent

due to the varying DNA quality [95].

3.2.3 Qualitative methylation-specific polymerase chain reaction.

In 1996, Herman and colleagues introduced methylation-specific PCR (MSP) [96].

MSP is using bisulfite treated DNA as template and two primer sets with distinct

specificities (Figure 10). One primer set is designed to anneal to and amplify the

unmethylated sequence, and the other primer set to anneal to and amplify the

methylated sequence. The sequential differences can be visualised by UV

irradiation following ethidium bromide staining and gel electrophoresis. This

technique can detect as little as 1 methylated allele among 1000 unmethylated

alleles, making MSP among the most sensitive methylation analysis methods [97].


40

Figure 10. Methylation specific polymerase chain reaction assay. The principle of qualitative methylation specific polymerase chain reaction, with specific primers annealing to either the methylated or the unmethylated fragment.

Ten genes were analysed by MSP using DNA isolated from 20 colon cancer cell

lines. Normal blood and human placenta treated in vitro with SssI methyltransferase

were used as positive controls for the unmethylated and methylated reactions,

respectively. Milli-Q water was used as a negative control. If the results from the

cell line analysis indicated a high frequency of methylation, MSP was performed on

colorectal carcinomas and normal tissue samples as well. The samples for each gene

were scored relative to the intensity of the positive control. The samples were

scored as either weakly metylated (intensity is less than the positive control) or

heavily methylated (intensity is equal to or higher than the positive control). For

this thesis, only tumour samples scored as heavily methylated were considered

methylated, while samples scored as weakly methylated were classified as

unmethylated. This ensures a conservative classification with a low number false

positive. The scorings were performed independently by the author and another

group member, Hilde Honne. All results were verified by another round of analysis,


41

and in cases with diverging results from the two rounds of MSP and/or discrepancy

in the scoring by the two authors, a third run of MSP was performed.

Primer design for MSP The most critical parameter defining the specificity and success of a MSP assay

relies on the binding of the primers to their target sequence, and their ability to

discriminate between methylated and unmethylated fragments. To ensure proper

discrimination, the primers should contain as many CpG sites as possible, including

one or more CpG sites on the 3´ region of the primer. Potential amplification of

unconverted CpG sites may give false positives with regards to the methylation

level, which is why non-CpG sites also should be included in the primer sequences.

Finally, the overall choice of region amplified by the primers is also important for

the methylation analysis. The aim is to amplify a representative region of the

promoter where methylation most likely will have an effect on transcriptional

activity. It is therefore important to select an area surrounding the transcription start

site of the gene.

In this thesis, all ten MSP primer sets were designed using Methyl Primer Express

1.0 (Applied Biosystems) and purchased from MedProbe (Oslo, Norway). For

detailed primer information, see Appendix III.

Optimisation of primer sets for MSP analysis The primer sets must be optimised with regards to magnesium concentration,

annealing temperature, and annealing- and elongation time. This will ensure that the

primers function optimally and amplify the correct PCR fragment. The

unmethylated and the methylated fragments must be optimised separately, using

bisulfite converted DNA from normal blood as positive controls for the

unmethylated reaction, and human placenta DNA treated in vitro with SssI

methyltransferase as positive control for the methylated reaction (see Figure 11).


42

Figure 11. Temperature and magnesium gradient for the IQCG methylated fragment. A range of different temperatures and MgCl2 concentrations were tested for all genes analysed. For this gene, 48° and 1.5 mM MgCl2 was selected for downstream analysis. M, 100 base pair marker, degrees are in Celsius, 1.5, 1.7 and 2.0 are mM MgCl2.

Magnesium The MSPs were performed using a thermo stable enzyme polymerase (HotStar Taq

DNA polymerase; Qiagen). Magnesium functions as a cofactor for the polymerase,

and may increase the efficiency in which the enzyme performs it catalytic activity.

However, increased amount of magnesium may lead to unspecific PCR products.

To avoid this, a gradient of various magnesium concentrations (1.5, 1.7 and 2.0

mM) were tested for all primer sets. The overall result indicated that the lowest

quantity of magnesium was sufficient. Consequently, 1.5mM was used as a standard

in all reactions.

Annealing temperature The annealing temperatures of the primers are among the key factors determining

how efficient PCR amplification is. Primer sets work best at distinct temperature

ranges, reflecting their ability to bind to the template within those ranges. Too high

melting temperatures (Tm) may give a low PCR yield resulting from insufficient

primer-template hybridization, while too low Tm may give unspecific products as a

consequence of base pair mismatches. Two algorithms were used to calculate

melting temperatures of the primer sets and a temperature gradient was set up for

the unmethylated and methylated fragments.

Reaction time and cycles Generally, the majority of the primer sets amplify adequate amount of MSP

products with 30 seconds of annealing, 30 seconds of elongation and 35 cycles.


43

Some reactions may, however, require an increase in these parameters. This will

improve the efficiency for the PCR reaction. The overall aim is to generate

comparable band intensities for the methylated and unmethylated positive controls.

MSP experimental assay The MSP mix consisted of ca 24 ng/µl bisulphite treated template DNA, 1 x Qiagen

PCR buffer (containing 1.5 mM MgCl2), 0.8 µM of each of the primers

(Medprobe), 0.2 mM of each of the four dNTPs (Amersham Biosciences,

Piscataway, NJ, USA), 0.2 mM MgCl2 solution (Qiagen) for some of the methylated

reactions, one unit Hotstar Taq Polymerase (Qiagen) and Milli-Q water to a total

reaction volume of 25 µl. The DNA was amplified using a Robocycler Gradient 96

thermo cycler (Stratagene, La Jolla, California, USA). The cycling conditions

consisted of a denaturation step at 95° for 15 minutes to activate the enzyme,

followed by 35 cycles of denaturation at 95° for 30 seconds, 30 seconds of

annealing at 48°-56°, 30 seconds of elongation at 72°, and a final extension step at

72° for 7 minutes. The range of annealing temperatures used was adapted to the

individual melting temperatures for each primer set (see table III in the Appendix).

The PCR products were mixed with five µl gel loading buffer (1 x TAE buffer and

0.1% xylen cyanol) and separated on a 2% agarose gel (400 ml 1 x TAE and 8 gr

agarose; BIO-RAD) stained with ethidium bromide (a fluorescent intercalating

dye). The electrophoresis was performed for 24 minutes at 200 V and the PCR

products were visualized on an UV trans-illuminator (Chemidoc XRS Gel

Documentation System; BIO-RAD and Gene Genius; Syngene, Cambride, UK).

3.2.4 Quantitative real-time methylation-specific polymerase chain reaction

The real-time polymerase chain reaction is a technique used to measure the amount

of product formed during each PCR cycle. This is in contrast to regular, qualitative

PCR where the amount of end-product is measured. The quantitative MSP assays

(qMSP) used in this thesis include primers and probes designed specifically to


44

amplify bisulfite-converted DNA. The probe has a fluorescent reporter dye attached

at its 5´ end and a non-fluorescent quencher attached to its 3´ end. When the probe

is intact, the proximity between the quencher and the reporter dye will result in

suppression of the fluorescence emitted by the reporter. During the PCR process,

the enzyme polymerase will extend the primers, cleave the probe due to its nuclease

activity and release the reporter dye, which is detected as fluorescence by the real-

time machine. The amount of fluorescence is directly proportional to the amount of

product [98].

The Cycle threshold (Ct) value is defined as the number of cycles needed for the

fluorescent signal to cross the threshold, i.e. exceed the background level.

Consequently, the number of cycles required for a sample to cross the threshold can

be used to measure the quantity of initial target fragment. In this thesis, serial

dilutions of samples with known concentrations were made to generate a standard

curve. The Ct values of the dilutions were plotted against the samples´

concentration, creating a linear relationship that was used to determine the quantity

of a target sequence in an unknown sample [99]. Ct levels are inversely proportional

to the amount of target sequence in the sample. The lower the Ct level, the greater

amount of target sequence is present in the sample. High Ct values indicate a

minimal amount of target sequence and could represent a possible contamination.

We determined a cut-off at cycle 35, all PCR products equal to or above this Ct

were censored.

Methylation-specific quantification assay For real-time PCR-based quantification, primers and probes were designed

manually using Primer Express Software 3.0 (Applied Biosystems). Probes were

labelled with 6-FAM and a minor groove binder non-fluorescent quencher. The

PCR was carried out in a reaction volume of 20 µl in 384 well plates, using the

7900HT Fast Real-Time PCR machine (Applied Biosystems). The final reaction

mixture consisted of 0.9 µM of each primer (Medprobe), 0.2 µM probes (Applied

Biosystems), 1 x Taqman Universal PCR Mastermix (No AmpErase UNG; Applied


45

Biosystems), and 30 ng/µl of bisulphite-treated template DNA. Thermal cycling

was initiated with a denaturising step of 95° for 10 minutes. The amplification

protocol was 45 cycles of 95° for 15 seconds and 60° for 60 seconds. Each plate

included several water blanks as non-template controls, normal blood as

unmethylated control and in vitro methylated DNA as methylated control. The

samples were run in triplicates in 384-well plates and the median value was used for

data analysis. A standard curve was generated from 1:5 serial dilutions using

bisulfite-converted commercially available methylated DNA (CpGenome Universal

Methylated DNA; Millipore Billerica, MA, USA). The same methylated DNA

sample was used as a positive control for the qMSP reactions. Alu repetitive

element was utilised as an internal reference to normalise for input DNA. The data

was calculated as percent of methylated reference (PMR) values. The median

GENE: ALU ratio of a sample was divided by the median GENE: ALU ratio of the

positive control and multiplied by 100.

Two important factors should be taken into consideration when determining the

threshold value from quantitative analyses. Increasing the threshold value will give

a higher sensitivity, while the specificity will decrease. A lower threshold value will

increase the specificity, but reduce the sensitivity. Because the majority of the

carcinoma samples analysed in the current project had higher PMR values than the

normal mucosa samples, we chose to set a threshold value which would give the

highest specificity. Consequently, the threshold values to score samples as

methylated were set according to the PMR values for the normal mucosa samples.

The highest PMR value from the normal mucosa samples for PPP1R14A was PMR

= 3.4.Consequently, the threshold was set at PMR = 3.5 for this gene. All samples

with a PMR value above this threshold were scored positive for methylation. The

corresponding threshold for GLDC was PMR = 2.5.

PCR products resulting from qualitative and quantitative MSPs were subjected to

DNA sequencing to confirm that the correct fragments had been amplified.


46

All qMSP primer sets and probes were designed using Primer Express Software

v3.0 (Applied Biosystems). The primers were purchased from MedProbe whereas

the probes were purchased from Applied Biosystems. For detailed information

about the primers and probes used for quantitative analyses, see Appendix IV.

3.2.5 Capillary electrophoresis sequencing

In 1977, Atkinson et al demonstrated that the attachment of a dideoxynucleotide

(ddNTP) in the place of a deoxyribonucleic acid in a growing oligonucleotide chain

had an inhibitory effect on DNA synthesis. The dideoxynucleotides lack a 3´-

hydroxylgroup, thus the chain cannot be extended further after insertion and the

synthesis is terminated [100]. Based on this discovery, Sanger et al developed a

new system for DNA sequencing in the mid 1970s [101], and this method has been

succeeded by several new sequencing techniques, such as next generation

sequencing.

PCR product purification To remove excess primers and dNTPs prior to sequencing, the MSP and qMSP

products were purified using EXOSAP-IT (GE HEALTHCARE, USB Corporation,

Ohio, USA), which contains the hydrolytic enzymes exonuclease I and shrimp

alkaline phosphatase. One point five µl ExoSAP-IT was added to 10 µl of PCR

product and the reaction was incubated at 37° for 15 minutes to perform the

treatment, followed by an inactivation step at 80° for 15 minutes. The purification

was conducted on an Eppendorf Mastercycler Gradient PCR machine.

Sequencing reaction The sequencing reaction mix consisted of 0.25 µl forward or reverse primer, 2 µl

BigDye Terminator v1.1 Cycle Sequencing Kit (Applied Biosystems), 2 µl 5 x Big

Dye Terminator v1.1 Sequencing Buffer (Applied Biosystems), 2 µl purified PCR

product, and MilliQ-water to a total reaction volume of 10 µl. Each of the four

dideoxynucleotides present in the sequencing kit is labelled with fluorescent dyes


47

that emit fluorescence at different wavelengths. When one of the four ddNTPs is

incorporated instead of the dNTP, the synthesis will be terminated, giving

fragments with different lengths. The DNA bases will be excited by a laser beam,

which causes them to fluoresce, thus visualising the DNA. The sequencing reaction

was performed on a Robocycler Gradient 96 thermo cycler (Stratagene) and the

thermal cycling conditions involved the following steps: an initial denaturation at

96° for 2 minutes, followed by 25 cycles of denaturation at 96° for 15 seconds,

annealing at 50° for 5 seconds, elongation at 60° for 4 minutes and a final extension

step at 6°.

Purification of sequencing products A gel filtration method based on Sephadex G-50 Superfine (Amersham

Biosciences), was utilized to remove excess ddNTPs and primers prior to

electrophoresis and laser exposure. The method is based on differential separation

of molecules. The molecules will pass through a porous gel matrix, with the speed

of diffusion depending on their size. Smaller molecules, like ddNTPs and primers,

will diffuse further into the pores of the gel, and will therefore move relatively

slowly and be retained in the column. Larger molecules, like the sequence products,

will either enter less into the pores or not enter at all, and will therefore move more

quickly through the gel and be eluted with the buffer.

Sephadex powder was poured onto a 96-well Multiscreen HV plate (Millipore) and

300 µl MilliQ-water was added to each well. The plate was left at room temperature

for at least 2 hours, allowing the Sephadex to swell. The Sephadex plate was placed

on a 96 well Optical Reaction Plate (Applied Biosystems) and centrifuged at 910

rpm for 5 minutes. To rinse the columns, 150 µl MilliQ-water was added to the

wells and the plate was once again centrifuged at 910 rpm for five 5 minutes. The

Sephadex plate was then placed on a new 96 well Optical Reaction Plate (Applied

Biosystems), 10 µl MilliQ-water and 10 µl product were added, and a final

centrifugation at 910rpm for 6 minutes was conducted.


48

ABI PRISM 3730 Sequencer was used to separate the oligonucletide fragments

according to their sizes. The laser beam exits the fluorescently labelled ddNTPs

which then emits lights of different wavelengths. The software interprets the

fluorescence data and visualises the results as electropherograms. All

electropherograms were analysed manually using Sequencing Analysis 5.2 (Applied

Biosystems).

3.2.6 Bisulfite sequencing

Bisulfite sequencing - the gold standard of DNA methylation analysis MSP relies on the match and mismatch of primers to bisulfite treated DNA. The

method can however, in some instances, produce false positive results, exemplified

by designing of primers that potentially anneal to non-bisulfite converted

unmethylated cytosines. Thus, the samples may wrongly be scored as methylated. It

is therefore necessary to ensure that the promoter area indeed is methylated, and

that the MSP primers and qMSP primers and probe anneal to relevant regions of the

promoter. A representative promoter region of GLDC and PPP1R14A was

subjected to bisulfite sequencing in colon cancer cell lines. To calculate the

approximate amount of methylation of each CpG site, the peak height of the

cytosine signal was divided with the sum of cytosine and thymine peak height

signals and multiplied by 100 to convert ratios to percentages. CpG sites with

methylation frequencies ranging between 0% - 20% were classified as

unmethylated, CpG sites with frequencies in the range of 21% - 80% were classified

as partially methylated, and CpG sites with frequencies ranging from 81% - 100%

were classified as hypermethylated.

Designing bisulfite sequencing primers Unlike MSP primers, bisulfite sequencing primers must be designed so that they do

not discriminate between methylated and unmethylated sequences, the primers

should therefore be designed to anneal to sequences where there are no CpG sites.


49

The amplified fragment should cover the area amplified by the MSP primers.

Additionally, monorepeats consisting of 9 or more bases should be avoided, as the

enzyme polymerase can make slippage mistakes that may lead to insertions or

deletions in the amplified product, eventually leading to errors in the sequence. The

bisulfite sequencing primers were designed using Methyl Primer Express 1.0

(Applied Biosystems), and the reactions were optimised for magnesium

concentration and annealing temperature to ensure equal amplification efficiency

for the unmethylated and methylated DNA fragments.

Bisulfite sequencing reaction Prior to bisulfite sequencing, a PCR reaction was performed and 5 µl product

together with 1 µl loading buffer (1 x TAE buffer and 0.1% xylen cyanol) was

loaded onto a 2% gel for visualization. Ten µl of the remaining product were

purified by EXOSAP-IT. The sequencing reaction and post-sequencing Sephadex

purification was performed as previously described. For the bisulfite sequencing

reaction, dGTP BigDye Terminator v3.0 Cycle Sequencing Ready Reaction Kit (for

GC-rich areas) was utilized.

3.2.7 Statistics

The Pearson Chi-square ( χ2) and Fisher´s exact test are used to establish whether or

not the observed frequency distribution differs from a theoretical distribution, given

that the H0 hypothesis (no association between the variables) is true. The more the

observed outcome diverges from the expected outcome, the less likely it is that the

null hypothesis is true, thus giving a low P value.

For this thesis, all 2 x 2 contingency tables were analysed using a two-sided

Fisher´s exact test, whereas a two-sided Pearson Chi-square test was used on 2 x 3

and 2 x 4 contingency tables. P values less than or equal to 0.05 (5%) were

considered significant. Binary regression analyses were used to examine possible

association between DNA methylation and patient age. Receiver Operating


50

Characteristics (ROC) curves for individual genes were created using PMR values

and tissue type (carcinoma and normal) as input. All calculations are derived from

two-tailed statistical tests using the SPSS 16.0 software.

51

4. Results

4.1 Qualitative methylation analyses of candidate genes in vitro and in vivo

Ten genes were analysed for promoter hypermethylation in colon cancer cell lines.

The promoters of BNIP3, CBS, DDX43, GLDC, PEG10, PPP1R14A and WDR21B

were frequently hypermethylated with frequencies of 14/20 (70%), 14/19 (74%),

17/19 (89%), 15/20 (75%), 18/20 (90%), 19/20 (95%) and 20/20 (100%). RBP7

displayed an intermediate level of methylation with a frequency of 9/20 (45%),

while RASSF4 and IQCG displayed low levels of methylation, with frequencies of

1/19 (5%) and 0/20 (0%), respectively. These results are presented in Table 1 and

Table 2. Several of the cell lines showed biallelic methylation (one allele is

methylated whereas the other is unmethylated). However monoallelic methylation

(both alleles are methylated) was found for three MSI cell lines (Co115, RKO,

SW48) and three MSS cell lines (EB, HT29, IS2) across all ten genes.

Table 1. Promoter methylation statuses of candidate genes in 20 colon cancer cell lines, stratified according to their MSI status. Abbreviations: MSI, microsatellite instability; MSS, microsatellite stable; U, unmethylated; M, methylated; U/M, partially methylated;ND, not determined

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Table 2. Methylation frequencies among MSI and MSS colon cancer cell lines. Abbreviations: MSI, microsatellite unstable; MSS, microsatellite stable.

The genes that were hypermethylated in cell lines were subjected to downstream in

vivo methylation analysis in primary colorectal carcinomas. BNIP3, DDX43,

GLDC, PEG10, PPP1R14A and WDR21B were methylated in 8/19, 19/20, 14/20,

18/19, 11/20 and 20/20 carcinomas, respectively. Additionally, normal mucosa

samples from deceased, cancer-free individuals were analysed. DDX43, PEG10 and

WDR21B were methylated in all normal samples, BNIP3 was methylated in 1/10

(10%) of the samples, whereas GLDC and PPP1R14A were unmethylated. The

qualitative data from these analyses are summarised in Table 3. Conversely, GLDC

showed a higher degree of methylation among MSI primary colorectal carcinomas

than in MSS tumours (P = 0.05).

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Table 3. Methylation frequencies in primary colorectal carcinomas and normal mucosa samples. Abbreviations: MSI, microsatellite unstable; MSS, microsatellite stable; CRC, colorectal cancer.

4.2 Quantitative methylation profiles of GLDC and PPP1R14A

Conventional MSP analyses revealed that GLDC and PPP1R14A were among the

most frequently hypermethylated genes in the qualitative pilot study. Furthermore,

the results obtained indicated that the methylation profiles were cancer-specific,

meaning that genes were differentially methylated in primary colorectal carcinomas

versus normal mucosa. In order to validate these findings, GLDC and PPP1R14A

were further investigated by real-time, quantitative PCR in a larger series of

malignant and normal colorectal tissue samples, as well as in the 20 colon cancer

cell lines that were analysed qualitatively. Promoter hypermethylation for GLDC

and PPP1R14A were found in 28/47 (60%) and 27/47 (57%) of the primary

colorectal tumours. The corresponding number in cell lines were 15/20 (75%) and

19/20 (95%), respectively. As previously mentioned, samples were scored as

positive for methylation if the PMR value was > 3.5 for PPP1R14A and 2.5 for

GLDC (see section 3.2.4). With these cut-off values, none of the normal mucosa

samples for either gene were scored as methylated, resulting in 100% specificity for

both assays. The results are visualised in Figure 12 and Figure 13. ¨

Results

54

Figure 12. Amplification plots displaying quantitative methylation measurements in colorectal carcinoma and normal mucosa samples. The upper part of the figure illustrates the successful amplification plots for GLDC in colorectal carcinoma and normal mucosa samples. The lower part of the figure shows the results for PPP1R14A. Fluorescence intensity (y-axis) is plotted versus the number of PCR cycles (x-axis).The red line indicates the cycle threshold (Ct), while the vertical, stapled line indicates the cut-off value (Ct=35). All PCR products with Ct values equal to or above 35 were censored (see material and methods).

Figure 13. Box plots showing median PMR values as assessed by qMSP. The box plots show the distribution of PMR values according to the median, upper and lower quartiles. The lines inside the boxes denote median values whereas whiskers represent the interval between the 10th and 90th percentiles. Circles indicate outliers, and the star indicates an extreme outlier.

Results

55

ROC curve analysis was applied to provide a statistical method to assess the

diagnostic accuracy of the genes as biomarkers. GLDC had a sensitivity of 64% and

a specificity of 100%, with an area under the curve (AUC) of 0.819 (P = 7 · 10-8).

PPP1R14A had a sensitivity of 57.5% and a specificity of 100%, with an AUC of

0.792 (P = 8.59 · 10-7). The ROC curves visualise the unbiased trade-off between

sensitivity and specificity. Interestingly, the sensitivity and specificity values

obtained from the ROC curve analyses are concordant with the estimates we

obtained by visual determination of cut-off values.The ROC curves are visualised in

Figure 14 and Figure 15.

Figure 14. ROC curve analysis from quantitative methylation-specific PCR results of GLDC. ROC curve was designed for the qMSP assay on the basis of PMR values for colorectal carcinomas (n = 47) and normal mucosa samples (n = 49). The AUC was 0.819, and the sensitivity and specificity were 64% and 100%, respectively.

Results

56

Figure 15. Roc curve analysis from quantitative methylation-specifc PCR results of PPP1R14A. ROC curve was designed for the qMSP assay on the basis of PMR values of colorectal carcinomas (n = 47) and normal mucosa samples (n = 49). The AUC was 0.792, and the sensitivity and specificity were 57.5% and 100%, respectively.

4.3 Concordance of conventional MSP and quantitative real-time MSP

The results of qMSP analyses were compared with those obtained by conventional

MSP in colon cancer cell lines (n = 20), primary tumours (n = 11) and normal tissue

samples (n = 8). While conventional MSP scores samples as methylated, partially

methylated or unmethylated for cell lines, and methylated or unmethylated for

tissue samples, qMSP data gives a quantitative measurement of DNA methylation

levels ranging from 0 to 100 (Figure 16). The cut-off values of 2.5 for GLDC and

3.5 for PPP1R14A resulted in good concordance between data obtained from qMSP

and conventional MSP analyses. For GLDC, 39/39 (100%) of the samples were

concordant (P = 0.000). PPP1R14A, however, had one sample which was scored as

unmethylated from qualitative, gel-based MSP and as methylated from the

quantitative real-time MSP analysis. Consequently, the methylation status was in

Results

57

agreement for 38/39 (97%) of the samples (P = 2 · 10-9). The results are illustrated

in Figure 17 and Figure 18 and and summarised in Table 4 and Table 5.

Figure 16. Comparativ analysis of convential MSP and qMSP results. Qualitative MSP scores are compared with quantitative PMR values as a proof-of-principle. The figure reveals that there is a concordance between the data obtained by the two methods. Red indicates highly methylated samples, while blue indicates unmethylated samples. The figure illustrates the quantitative differences between traditional MSP and qMSP. While MSP samples are scored as either unmethylated (blue) or methylated (red), PMR values reveal the absolute quantitative value of a sample. NB, normal blood (positive control for unmethylated samples); IVD, in vitro methylated DNA (positive control for methylated samples).

Results

58

Figure 17. Comparison of scores obtained from conventional MSP and quantitative MSP analysis of GLDC. Box-plots denote the PMR values (y-axis) determined for 39 samples by qMSP to the scores obtained by conventional MSP unmethylated (green) and methylated (red) samples (x-axis). The dashed line represents the cut-off value (PMR = 2.5).

Quantitative real-time MSP with cut-off = 2.5 Conventional MSP Unmethylated Methylated Total Unmethylated 15 0 15 Methylated 0 24 24 Total 15 24 39

Table 4. Concordance of classification of the GLDC status by the two methods.

Results

59

Figure 18. Comparison of scores obtained from conventional MSP and quantitative MSP of PPP1R14A. Box-plots denote the PMR values (y-axis) determined for 39 samples by qMSP to the scores obtained by conventional MSP unmethylated (green) and methylated (red) samples (x-axis). The dashed line represents the cut-off value (PMR = 3.5).

Quantitative real-time MSP with cut-off = 3.5 Conventional MSP Unmethylated Methylated Total Unmethylated 11 1 12 Methylated 0 27 27 Total 11 28 39

Table 5. Concordance of classification of the PPP1R14A status by the two methods.

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60

4.4 Bisulfite sequencing confirms the promoter methylation status of GLDC and PPP1R14A

Bisulfite sequencing of GLDC and PPP1R14A in colon cancer cell lines showed

that all non-CpG cytosines were fully converted to thymine. These results, along

with detailed sequencing results and MSP status are shown in Figure 19 and Figure

20. In general, the majority of the cell lines that were scored as fully methylated by

MSP, were also fully methylated from the bisulfite sequencing analyses. For GLDC,

a good association was seen between the MSP scores and bisulfite sequencing

results. Similarly, a good association was also seen for the MSP scores and bisulfite

sequencing analyses of PPP1R14A, except for V9P. This cell line was scored as

partially methylated by MSP analyses of PPP1R14A; however, all CpG sites were

unmethylated from analyses of the bisulfite sequencing electropherograms.

Results

61

Figure 19. Bisulfite sequencing verifies site specific methylation within the GLDC promoter. A) The upper part of the figure is a schematic presentation of the CpG sites amplified by bisulfite sequencing primers. The arrows indicate the location of the MSP primers, transcription start site is represented by +1 and the vertical bars indicate the location of the individual CpG sites. For the lower part of figure A, filled circles represent methylated CpGs; open circles represent unmethylated CpGs; and grey circles represent partially methylated CpG sites. The right column of U, M and U/M lists the methylation status of the cell lines as assessed by MSP analyses. B) Representative bisulfite sequencing electropherograms of the GLDC promoter in colon cancer cell lines. A subsection of the reverse complimentary bisulfite sequence electropherogram, covering CpG sites -1 to -4 relative to transcription start site. Cytosines residing in CpG sites are indicated by a lollipop, whereas cytosines residing in non-CpG sites are underlined. Black lollipop indicates methylated CpGs, whereas open lollipop indicates unmethylated CpGs. The GLDC promoter sequencing electropherogram illustrated here are from the unmethylated Colo320 cell line and the hypermethylated IS1 cell line.

Results

62

Results

63

Figure 20. Bisulfite sequencing verifies site specific methylation within the PPP1R14A promoter. A) The upper part of the figure is a schematic presentation of the CpG sites amplified by bisulfite sequencing primers. The arrows indicate the location of the MSP primers, transcription start site is represented by +1 and the vertical bars indicate the location of the individual CpG sites. For the lower part of figure A, filled circles represent methylated CpGs; open circles represent unmethylated CpGs; and grey circles represent partially methylated CpG sites. The right column of U, M and U/M lists the methylation status of the cell lines as assessed by MSP analyses. B) Representative bisulfite sequencing electropherograms of the PPP1R14A promoter in colon cancer cell lines. A subsection of the reverse complimentary bisulfite sequence electropherogram, covering CpG sites -22 to -15 relative to transcription start site. Cytosines residing in CpG sites are indicated by a lollipop, whereas cytosines residing in non-CpG sites are underlined. Black lollipop indicates methylated CpGs, whereas open lollipop indicates unmethylated CpGs. The PPP1R14A promoter sequencing electropherogram illustrated here are from the hypermethylated RKO and EB cell lines.

4.5 Association of tumour methylation with genetic and clinico-pathological features

DNA methylation status for GLDC and PPP1R14A were compared with genetic

and clinico-pathological features of the tumours. DNA methylation frequencies

were higher among MSI tumours, statistically significant for PPP1R14A (P =

0.001). DNA methylation of both genes was associated with proximal location,

however, only statistically significant for PPP1R14A (P = 0.0008). Tumour

methylation was associated with wild-type BRAF with P = 0.015 for GLDC and P =

0.0004 for PPP1R14A. There was no association between mutation of KRAS,

PTEN, PIK3CA, TP53 and methylation of either gene. No significant association

was found between DNA methylation and clinico-pathological data such as Dukes´

staging or sex of the patient. No association was observed between patient age and

tumour methylation for PPP1R14A. However, binary regression analysis showed an

increase in age among colorectal cancer patients who were positive for methylation

of GLDC (mean 74, 95% confidence interval 77.98-70.09, Std deviation 10.163)

compared with patients who were negative for methylation (mean 64.7, 95%

confidence interval 58.18-71.19, Std deviation 13.499; P = 0.02).

Co-methylation of the two genes was seen in 34/47 samples and was not associated

with Duke´s staging, or sex or age of the patient. Not surprisingly, co-methylation

was more frequent in MSI tumours (P = 0.05) and tumours harboring wild-type

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64

BRAF (P = 0.021). Furthermore, carcinomas located proximal in the colon showed

more frequent co-methylation than carcinomas located in the distal colon (P =

0.018). Co-methylation was not associated with mutation of KRAS, PTEN, PIK3CA

or TP53.

65

5. Discussion

5.1 Methodological considerations

5.1.1 Methylation-specific polymerase chain reaction

The method by which converted DNA is analysed can influence the interpretation

of the methylation status of the DNA. For this thesis, both qualitative and

quantitative methylation-specific PCR was applied. Differences between these

methods lie in detection of the amplified PCR products, as well as in the assay

design. In the first cycles of a PCR reaction, all samples will amplify exponentially.

However, the reaction will eventually reach a plateau phase caused by i.e depletion

of reagents and product renaturation competing with primer binding. The point at

which the reaction reaches the plateau phase varies between samples, and might

even vary between replicates. Consequently, samples that started out with different

amount will reflect the same quantity when measured at the end phase. To precisely

determine the initial sample quantity, measurements from the exponential phase

should be used.

Traditional MSP is based on detection of the end-product using agarose gel stained

with an intercalating dye, such as ethidium bromide. Furthermore, end-point

detection is based on visual estimation of the quantitatity of the target sequence. In

contrast to traditional MSP, quantitative MSP measures the data at the exponential

phase of the PCR reaction and the need for post-PCR processing, such as staining

and separation on agarose gel, is eliminated. The MSP and qMSP assays are

designed to recognise the fully methylated version of the sequence, and the results

can therefore be considered as highly conservative. However, the qMSP assay also

contains an oligonucleotide probe, which ensures an even greater degree of

specificity for the methylated target sequence.

Discussion

66

Traditional MSP assay serves as a robust and sensitive screening process to

discover genes that have high methylation frequencies in cell lines and primary

tissues. The method does not include fluorescence and is therefore less expensive

than qMSP.

A high concordance between the quantitative and qualitative analyses of GLDC and

PPP1R14A indicates that the data obtained from qMSP and MSP analyses were

highly concordant. Even though the scoring of traditional MSP is visual, the good

concordance with the qMSP results indicates that the band intensities indeed are

semi-quantitative. Sample 1047 was scored as methylated from the qMSP analyses,

while the sample was scored as unmethylated from MSP analyses. This emphasizes

the increased sensitivity of real-time quantitative analyses, which detects one

methylated allele in a pool of 10,000 unmethylated alleles. In comparison,

conventional MSP detects one methylated allele in a pool of 1,000 unmethylated

alleles.

In qMSP, each sample must be normalised for input DNA, using a CpG-

independent, bisulfite specific control. Single-copy housekeeping genes have

traditionally been used for this purpose. However, rearrangements, duplications and

deletions of genes or chromosomes are frequent events in human cancers and may

profoundly affect the PCR yield for these genes. In the present thesis, a part of the

ALU repetitive element was used as an internal reference. As apposed to the single-

copy genes, ALU is present in approximately 1 million copies in a haploid genome,

and is dispersed throughout the genome. Copy number changes due to the

alterations mentioned above are therefore less likely to affect this internal control

[38].

The normalised quantity value of the samples must be compared with a fully

methylated human genomic DNA sample. For this thesis, commercially available in

vitro methylated DNA (IVD), which is methylated at minimum 99% of the CpG

sites in the genome, was utilised as a positive control for methylation. Hence, the

PMR values reflect the degree of methylation per sample according to the IVD. A

Discussion

67

few of the PMR values obtained for the GLDC and PPP1R14A assays were greater

than 100%. A possible explanation for this could be incomplete SssI treatment of

the reference sample. The assay could incidentally be designed to cover a CpG site

which is unmethylated in the reference IVD sample, but methylated for the genes of

interest. Consequently, the degree of methylation for the genes will be higher than

the reference sample. None of the assays amplified bisulfite-treated unmethylated

DNA (normal blood) or non-template controls (water), confirming the specificity of

the assays.

5.1.2 Bisulfite sequencing

Bisulfite sequencing serves two purposes when analysing samples for DNA

methylation. As mentioned in section 3.2.6, MSP analysis may produce false

positive results due to amplification of unconverted, unmethylated cytosines, and

bisulfite sequencing is performed to verify the methylation statuses. Second,

bisulfite sequencing confirms whether or not the MSP assay is designed to amplify

a representative region of the gene promoter. The importance of MSP study design

and verification by DNA sequencing has been exemplified by articles on DNA

methylation of the MAL tumour-suppressor gene. Mori et al reported a methylation

frequency of 6% in colorectal carcinomas, while a study published by Lind et al

found hypermethylation of the MAL promoter in approximately 80% [102-104].

While Mori and co-workers designed primers that were located a couple of hundred

base pairs upstream of the transcription start site, the assay designed by Lind et al

included primers located very close to the transcription start site.

In the present thesis, a concordance was seen between the MSP results and the

methylation statuses of individual CpG sites as assessed by bisulfite sequencing.

The cell line V9P was scored as partially methylated from qualitative analyses and

as methylated from quantitative MSP analyses of PPP1R14A. However, the

bisulfite sequencing electropherograms revealed a completely unmethylated region.

This discrepancy could not be caused by poor primer design, as both MSP and

Discussion

68

qMSP (with an increased specificity due to the probe) gave the same score. If the

bisulfite conversion of V9P was sub-optimal, unmethylated cytosines would be

wrongly scored as methylated in the subsequent MSP analysis. However all non-

CpG cytosines were deaminated to uracil and amplified as thymine, confirming that

the bisulfite modification indeed was successful. All of these factors mentioned

above highlights the importance of optimal design and optimisation of the assays.

A possible solution to the above mentioned problem would be to clone the PCR

product. Direct sequencing, such as bisulfite sequencing, gives an average value for

methylation of the analysed sample, ensuring a representative methylation profile of

the sample. However, cloning of the PCR product and subsequent sequencing of the

individual clones will give a more accurate profile, as individual clones might

contain different degrees of methylation. This approach is therefore suitable to

elucidate the level of methylation heterogeneity in a sample

5.2 Cell lines versus solid tumours

A cell line is a permanently established cell culture that in contrast to normal cells

that reach senescense will proliferate indefinitely. Human cancer cell lines resemble

the phenotypic, genetic and epigenetic characteristics of their original tumour, and

are consequently important experimental tools in understanding the behaviour of

the primary tumours. Cancer cell lines are easy to culture in vitro, they yield

substantial amounts of high-quality DNA and RNA, and as opposed to

heterogeneous primary tumours, cancer cell lines are usually not “contaminated” by

normal cells [105]. An additional advantage is the commercial availability of a

broad series of tumour types.

A number of immortalisation methods have been developed in order to obtain

permanent cell lines, including exposure to a DNA tumor virus such as SV40 virus,

EBV, and papilloma virus, cell fusion between the cell with a limited lifespan and a

permanent cell line and treatment with carcinogenic chemicals [106]. Conversely,

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differences between subtypes within a cell line population can develop. When cells

are grown over numerous generations, faster growing cells will eventually

predominate over the other cells. If these clones have acquired new genetic and/or

epigenetic characteristics, a biased selection could occur, resulting in a cell line

population that is less representative of the original tumour. Furthermore, exposure

to environmental variations, such as relocation to other laboratories with different

temperatures, may lead to a selection of cell lines that thrive better under specific

conditions.

For this thesis, cell lines were initially cultured with AZA and/or TSA and the gene

expression levels were measured. The cell lines were subsequently utilised in a pilot

analyses to determine which genes that should be subjected to methylation analyses

in tissue samples, which is a unique and valuable material. Several studies have

reported a higher prevalence of DNA methylation in cancer cell lines when

compared to matching primary tumours, while others have seen an equal extent of

methylation [107,108]. Notably, colon cancer cell lines are among the cell lines that

most closely resemble their matching primary tumours. We therefore expect the

resulting cell line methylation frequencies to be a good indication of the frequency

in primary tumours.

5.3 Novel epigenetically deregulated genes in colorectal cancer

In the present study a comprehensive, genome-wide microarray analysis in

combination with downstream methylation analysis was used to identify target

genes inactivated by DNA hypermethylation. All ten genes analysed in the present

study, were upregulated in colon cancer cell lines after treatment with

demethylating chemicals. All of the genes were further found to be methylated in

colon cancer cell lines, with the exception of IQCG, and RASSF4 (partially

methylated in a single cell line). These genes might be considered as “false

positives” in our resulting gene list. This apparently false response to the epigenetic

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drug treatment might be due to an associated hypermethylated enhancer element, or

possibly a cellular response to the toxic effect of the chemicals. However, our

approach is generally very successful and has a lower rate of false positives when

compared with other studies using variants of the same approach. This indicates that

our fairly conservative approach is highly effective.

Six of the ten genes analysed in the current project were hypermethylated in cell

lines, and were subjected to methylation analyses in primary tumours and normal

tissue samples. Validation analyses were performed on the two most promising

genes, GLDC and PPP1R14A. Both of these genes displayed a frequent and cancer-

specific methylation profile. Notably, the genes presented in this thesis were not as

promising as biomarkers as other genes previously identified in our lab. A possible

explanation for this could be that gene expression analyses which identified the

previous biomarkers were compared with cDNA gene expression microarrays,

while the candidate genes in this project were compared with the AB1700

microarray platform. The same approach was used; however, a lower concentration

of AZA was used during cell culture to avoid possible cytotoxic effect of the drug.

BNIP3, a pro-apoptotic member of the Bcl-2 family, has been shown to be

frequently methylated in gastric and colorectal cancer. The expression of the gene is

induced in hypoxic regions of tumours. The presence of hypoxic regions in tumours

is often associated with poor prognosis, because hypoxic tumour cells can develop

resistance to chemotherapy and radiation treatment [109,110]. Although tumours

often contain hypoxic regions, the cells of the tumours must survive and continue to

grow. One possible mechanism by which the tumour accomplishes this, is by

induction of hypoxia-inducible factor-1 (HIF-1), which upregulates genes that are

involved in glycosis, angiogenesis and cell survival. However, HIF-1 has also been

shown to have a pro-apoptotic function, by activating apoptotic signalling pathways

involving HIF-1-mediated expression of BNIP3 [109]. Over-expression of BNIP3

leads to opening of the mithocondrial permeability transition pore, thereby

abolishing the proton electrochemical gradient, which is followed by chromatin

Discussion

71

condensation and DNA fragmentation [110]. Thus, the cancer cells must find a way

to escape this apoptotic function. Inactivation of BNIP3 by promoter

hypermethylation could be one mechanism by which the cancer cells can escape

apoptosis. The cancer-specific methylation frequencies observed in the present

thesis indicates that BNIP3 possibly contributes in tumourigenesis of colorectal

cancer, possibly by limiting the susceptibility of cancer cells to hypoxia-induced

apoptosis.

PEG10 is a maternally imprinted and paternally expressed gene, which has been

reported to be overexpressed in hepatocellular carcinoma and B-cell chronic

lymphocytic leukemia [111]. It has been suggested that the PEG10 protein may

block TGF-β signalling by binding to TGF-β receptor II [112]. The transforming

growth factor β (TGF-β) pathway is important in several cellular processes,

including cell growth, cell differentiation and apoptosis. In relation to cancer, it is

possible that the genes associated with this pathway are subjected to different

selective pressure, and are selected for loss of function at early stages but gain of

function in late stages of the disease. In this context, inactivation of PEG10 in late

cancer would lead to gain of function for the genes involved in the pathway. These

findings suggests that PEG10 inactivation might be of general importance in late

cancer, as well as in normal tissue, as promoter hypermethylation is found in 95%

of the colorectal carcinomas and 100% of the normal mucosa samples analysed in

the present thesis.

DDX43 was to our surprise a testis cancer antigen (TCA). Generally, we would

prefer to avoid such genes by not selecting them for downstream methylation

analyses. The reason for this is because, as briefly mentioned in the introduction,

TCAs are usually not expressed in normal tissue, with the exception of the testis,

due to hypermethylation of the gene promoter [113]. The protein encoded by this

specific gene is an ATP-dependent RNA helicase in the DEAD-box family. It is

well understood that dysregulation of the molecules that participate in RNA

processing can potentially affect normal cellular homeostasis and contribute to

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72

cancer development and/or progression. Based on the findings found in the current

project, we propose that hypermethylation of DDX43 is an important feature of

normal mucosa and colorectal cancer.

In the present thesis we report that hypermethylation of WDR21B, is present in

tumour and normal samples. WDR21B codes for a protein containing a conserved

WD40 domain. The domain covers a wide range of functions including regulation

of signal transduction, pre-mRNA processing and cytoskeleton assembly. All of

these cellular processes are important in cancer development, and alterations in

either of them can give the cancer cell selective advantages.

The data provided in this thesis presents GLDC and PPP1R14A as novel

hypermethylated target genes in colorectal cancer. To our knowledge, GLDC and

PPP1R14A have not been reported to be silenced through promoter

hypermethylation in any physiological or biological context. GLDC codes for the

enzyme system needed to cleave glycine into smaller pieces. The system is

composed of four protein components and a defect in either one of these

components may cause glycine encephalopathy (OMIM #238300). Mutations in

GLDC accounts for most of the cases for glycine encephalopathy [114]. The

breakdown of excess glycine is necessary for the normal development and function

of nerve cells in the brain and spinal cord. Furthermore, dietary glycine has been

found to inhibit angiogenesis during tumour growth, indicating that glycine is a

necessary precursour in building new blood vessels [115]. Some GLDC mutations

may lead to the production of a nonfunctional version of the glycine cleavage

system, thus preventing the system from breaking down glycine. Inactivation of

GLDC due to promoter hypermethylation may give the same consequences. As a

result, excess glycine can build up, and cancer cells can exploit this in the process

of angiogenesis.

PPP1R14A on the other hand is a phosphorylation-dependent inhibitor of smooth

muscle myosin phosphatase. Inhibition of PPP1R14A leads to increased myosin

phosphorylation and enhances smooth muscle contraction in the absence of

Discussion

73

increased intracellular Ca2+ concentration (PPP1R14A, OMIM #608153). Smooth

muscles are found in blood vessels where it regulates the flow of blood in arteries.

The cancer-specific hypermethylation of PPP1R14A observed in this thesis

underlies the importance of smooth muscle contractions in tumour blood flow,

which is necessary in order to supply the cancer cells with oxygen and other factors

needed to survive and grow. Furthermore, the smooth muscle contractions may also

help to travel cancer cells to other parts of the body during metastasis. Single cancer

cells can break away from an established solid tumor, enter the blood vessel, and be

carried to a distant site by muscle contractions, where they can implant and begin

the growth of a secondary tumor.

It has been debated whether promoter hypermethylation play a direct causal role in

tumour development and progression, or if it is merely a consequence of abnormal

phenotype of cancer cells. Several lines of evidence suggest that DNA methylation

play a direct and causative role in tumourigenesis. This is exemplified by the

discovery of promoter hypermethylation in aberrant crypt foci, underlining that

epigenetic events occur early in colorectal cancer [116]. Furthermore, reduced DNA

methylation was found to suppress the formation of intestinal polyps in mice [117].

Additionally, sporadic cases of colorectal cancer displayed high frequencies of

deregulation of the mismatch repair gene hMLH1. Treatment of cell lines with AZA

resulted in re-expression of hMLH1 and restoration of the mismatch repair ability,

indicating that inactivation of hMLH1 was the primary inactivating event [118]. All

of these results support the fact that DNA hypermethylation serves as one of the

initiating events contributing to tumourigenesis. However, it is important to

highlight that the effect of DNA hypermethylation and its contribution to cancer

development is gene-specific, meaning that the function of the gene determines

whether inactivation by hypermethylation is important in cancer development and

progression.

As mentioned previously, our objective with this study was to identify biomarkers

for colorectal cancer. In this setting, cancer-specific methylation (defined as the

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74

unique presence of methylation in cancer cells) has been shown to be highly

suitable. Age-specific methylated target genes, such as ER, MyoD and N33, are

found to be methylated also in normal tissue in elderly patients and the methylation

increases with age. This non-cancer specific methylation is thereby not suitable for

determining the presence of cancer cells. This is because some tumour-suppressor

genes become aberrantly methylated after exposure to environmental factors and

aging. Consequently, these factors could lead to false positives in the study of

tumour-specific methylation in colorectal cancer. We did not observe statistically

significant associated age-related methylation for PPP1R14A in the current project.

However, binary regression analysis revealed a statistical significant association

between methylation of GLDC and elderly patients. It is important to emhasize that

the observed association does not indicate that the methylation is age-specific, as

this is defined by methylation of normal tissue samples. Conversely, all of the

normal samples were unmethylated for GLDC. Both of the genes analysed were

more frequently methylated in MSI tumours than in MSS tumours. Furthermore,

because MSI tumours are generally more common in the proximal colon, it is not

surprising that we found an association between gene-specific methylation of

GLDC and PPP1R14A and the proximal colon. This indicates that

hypermethylation of these genes could be more important in proximal colon

tumourigenesis than in distal colon tumourigenesis.

5.4 Early detection and diagnostics

Screening for colorectal cancer (CRC) has been shown to reduce cancer-related

death by detecting early stage CRC and pre-malignant lesions. As mentioned in the

introduction, survival among CRC patients depends on tumour stage at time of

diagnosis. This is because discovering the patients at a stage where the tumour is

localised would mean that the majority of the patients could be cured by surgery

alone. Consequently, detection of early stage CRC can increase survival rate

dramatically. Several countries, such as Germany and Italy, are using colonoscopy

Discussion

75

as the primary screening tool. This is a method that accurately can detect early

cancerous lesions and is by many considered to be the gold standard of colorectal

cancer screening. However, the invasive nature of the procedure limits its

effectiveness, as well as the need for a skilled physician performing the procedure.

In contrast, a non-invasive screening test, using patient’s blood or fecal samples

would be simple and more cost effective, and also increase patient compliance.

The ideal samples for early diagnosis are material collected in a non-invasive way,

which at the same time contains methylated DNA. Stool blood test, also known as

faecal occult blood test (FOBT), detect the presence of occult blood in the stool.

FOBT has shown to reduce CRC-related mortality with 15% to 33%, with reported

sentivities ranging from 5% to 98% [119]. It is however important to emphasize that

presence of blood in the stool could derive from other intestinal or gastric changes

than CRC, which leads to a low specificity for the FOBT. In addition, biannual

testing is recommended because adenomatous polyps do usually not bleed and

bleeding from larger polyps or cancers may not always be detectable in a single

stool sample [120]. However, cells of the colon are continually exfoliated and shed

into the stool. Genomic DNA can be isolated from these cells and alterations in

DNA methylation patterns can be analysed.

When determining the efficiency of a biomarker, the sensitivity and specificity must

be taken into consideration. The sensitivity refers to the proportion of individuals

with confirmed disease, who test positive for the biomarker. The specificity refers

to the proportion of individuals without the disease, who test negative for the

biomarker. These two measures can serve as the selection criteria when determining

which potential biomarkers should be further investigated. An ideal biomarker

assay for detection of DNA methylation, would be highly sensitive and specific

[20]. Molecular biomarkers represent a promising non-invasive approach in

detecting cancer, determining prognosis and monitoring disease progression or

therapeutic response [121].

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76

Several studies have identified DNA methylation markers with a diagnostic

potential in colorectal cancer. VIM is a promising biomarker with a sensitivity of

73% and a specificity of 86 % when analysed in fecal DNA samples. However,

when combined with a DNA integrity assay (measures long DNA stretches, more

abundant in patients with a tumour) the sensitivity increased to 88% whereas the

specificity was 82%. A single marker test for VIM promoter hypermethylation is

today the only commercially available stool DNA test. However, other research

groups have reported a lower sensitivity and specificity for methylation of VIM in

fecal samples, reducing its potential to be used as a clinical biomarker.

Hypermethylation of SFRP2 in stool from patients with CRC have been reported to

have a sensitivity of 77% to 90%, with a specificity of 77% [122]. A recent article

published by Ahuja et al presents data indicating that aberrant methylation of

TFPI2 is a frequent and early event in CRC tumourigenesis. Hypermethylation of

TFPI2 was detected in 97% of the adenomas and 99% of the carcinomas.

Interestingly, stool methylation analysis revealed a sensitivity of 76% to 89%, and a

specificity of 79% to 93% [123].

DNA methylation analysis in blood samples represents another promising non-

invasive approach for early diagnosis. Cancer-specific DNA methylation can be

detected in circulating tumor DNA. Studies have reported an elevated level of free

DNA in serum of cancer patients, which is most likely due to DNA released from

necrotic tumour cells [20]. Abberant DNA methylation can be detected in

circulating DNA, and is minimally invasive to obtain from patients. However, there

are several disadvantages associated with DNA methylation biomarkers in blood. It

is questioned whether there is enough methylated DNA present in the blood to

efficiently detect tumours at an early stage. Moreover, blood is not organ-specific,

meaning that methylated DNA in the bloodstream could point to cancer in any of

several organs [124]. So far, hypermethylation of SEPT9 is the most promising

blood based CRC methylation biomarker with a sensitivity of 72% and a specificity

of 90%. Methylation of SEPT9 was also detected in some patients who had large

polyps, although with a sensitivity of only 20%. This implies that the low sensitivity

Discussion

77

of SEPT9 reduces its ability to be utilised as a diagnostic tool for early detection

[125]. Interestingly, recent research has focused on the elevated levels of certain

miRNAs in the plasma of CRC patients. A study by Sung and co-workers identified

significantly higher level of miR-92 in such patients, with a reported sensitivity and

specificity of 89% and 70% [126]. The biomarkers that have been identified in the

literature are promising; however more biomarkers need to be identified in order to

increase the sensitivity and specificity.

A promising biomarker panel consisting of six methylated genes with a high

sensitivity and specificity for CRC and adenomas have recently been identified in

our lab (Lind et al, unpublished, [102,103]). The combined panel has a sensitivity

of 93% in CRC and 88% in adenomas, and a specificity of 99% and has therefore

the potential of performing well in early detection of colorectal tumors. Stool

samples are currently being analysed, and if the high sensitivity and specificity

measurements are validated in these non-invasive samples, this biomarker panel

could be a suitable assay for a non-invasive methylation-based fecal test. The two

novel methylated gene targets presented in this thesis were not highly sensitive.

However, the genes were 100% specific, which implies that they can be included in

a diagnostic test together with other genes that are highly sensitive and specific,

thus increasing the robustness of the test. It would also be an advantage if the

cancer-specific methylation profile of the genes were not present in other cancer

types or inflammatory conditions, as this may reduce the specificity of the test.

The choice of threshold for scoring of methylated samples in quantitative

methylation-specific analysis will ultimately affect the sensitivity and specificity of

an assay. Setting a high threshold for a cancer-specific marker will increase the

specificity, but reduce the sensitivity. On the contrary, setting the threshold lower

will increase the sensitivity, but reduce the specificity. A high specificity is

preferred in diagnostic tests because a low specificity will increase the number of

false positives, subsequently affecting not only the patient’s life-quality, but also

lower the cost-efficiency due to follow-up of these patients. Receiver Operating

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78

Characteristics (ROC) curve is a statistical tool which is used to evaluate the

performance of a biomarker assay. ROC curves are also a useful tool to guide the

choice of threshold values in order to reach the most optimal sensitivity and

specificity. The area under the ROC curve (AUC) is a measure of the ability of a

biomarker to accurately classify tumour and non-tumour tissue (normal tissue) [20].

DNA methylation as a biomarker has several advantages over other molecular

biomarkers, such as RNA and protein (see section 1.4 in introduction). Several

DNA methylation changes have been shown to occur early in carcinogenesis and

are therefore good biomarkers for early cancer development. Furthermore, promoter

hypermethylation of important tumour-suppressor genes is a key event in human

cancers and is often associated with transcriptional silencing. Research indicates

that each type of human cancer is associated with a distinct methylation profile,

which can be used to identify the tissue of origin from a particular neoplasm. In

addition, DNA methylation is easier to handle methodologically than RNA

followed by reverse-transcription PCR, mainly because DNA is more stable than

RNA and protein [95]. An advantage of DNA methylation over protein-based

markers is that DNA is readily amplifiable and easily detectable, whereas protein

can not be amplified. Furthermore, cancer-specific mutations can occur anywhere in

a gene, while DNA methylation usually occurs in defined regions (promoter region)

of a gene.

79

6. Conclusions

Using a combined approach of microarray analysis and in vitro, in vivo and in silico

analysis we have successfully identified GLDC and PPP1R14A as novel

epigenetically deregulated genes in colorectal cancer. Both genes were

unmethylated in normal mucosa samples, and frequently methylated in colorectal

tumors, resulting in a sensitivity of 60% and 57%, respectively, and a specificity of

100%.

80

7. Future perspectives

Time of methylation onset during tumor development for the two novel genes

The two novel methylated target genes in CRC identified in the present thesis will

additionally be analysed in colorectal adenomas in order to see whether they are

early changes in the tumorigenesis or markers for fully developed carcinomas.

Diagnosis of CRC at an early stage can dramatically improve the patient survival;

therefore, our main aim in previous studies has been to identify early changes.

However, markers classifying whether tumors are malignant or benign would also

be highly interesting in a clinical setting.

Are the two novel genes methylated in various cancer types?

To investigate whether the candidate genes are specific for colorectal cancer, or if

they are epigenetically deregulated across several cancer diseases in the

gastrointestinal tract, a series of cancer cell lines derived from different tissues will

be analysed and the methylation profiles will be compared. The methylation

frequency should preferably be specific for colorectal cancer; however, the

discovery of epigenetic master keys for the gastrointestinal tract will certainly be of

interest.

The functional significance of the two novel genes?

We will apply reverse transcription-PCR, which measures the mRNA level

transcribed from the gene, to confirm whether hypermethylation is associated with

reduced or loss of gene expression, and if treatment with AZA leads to re-

expression. We will also consider performing functional studies to explore if loss of

protein expression has any effect on cell growth. This is important to determine if

methylation of the genes has a direct role in driving the tumourigenesis, or if they

are mere passengers in the process.

Future perspectives

81

Additional new epigenetic markers in CRC

In the present thesis, candidate genes were selected on the basis of a microarray

approach where colon cancer cell lines were cultured with both a demethylating

agent (AZA) and a histone deacetylase (TSA). This approach has previously been

utilised in our lab, and has resulted in the discovery of promising biomarkers with

high sensitivity and specificity. In the present study, we did not get the methylation

frequencies as high as expected; possibly due to low concentration treatment of the

cell lines (see discussion). However, the criteria set to select candidate genes

obviously influence the resulting gene list, and we may identify target genes with

higher methylation frequencies by changing these criteria.

Additionally, colon cancer cell lines have also been cultured with either AZA or

TSA, and it would be of interest to compare the gene lists from the combined and

individual treatment strategies and analyse potential novel candidate genes

generated also from these individual treatments. The pipeline will include the same

technical validation strategy as introduced in this thesis, including qualitative and

quantitative MSP analyses, as well as bisulfite sequencing, of cell lines and clinical

samples. If the biomarkers have high methylation frequencies in adenomas and

carcinomas, while at the same time being unmethylated in normal samples, fecal

samples may be analysed to determine the sensitivity and specificity in non-

invasive material.

Building an optimal epigenetic biomarker panel for non-invasive testing

Using only markers identified from own lab imply the possibility to participate in

development of a marker set for screening purposes and for monitoring of CRC

patients. The biomarker panel identified in our lab may possibly be improved by

including GLDC and/or PPP1R14A.

82

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Appendix I - Tumour samples

COLORECTAL CARCINOMAS Sample MSI status Gender Age Localisation Duke´s stage Differentiation BRAF KRAS PIK3CA PTEN TP53

GIM_c884 MSI Female 90 Right B Medium Mutated Wild-type Wild-type Mutated Wild-type GIM_c887 MSS Female 82 Rectum B High Wild-type Mutated Mutated Wild-type Wild-type GIM_c894I MSI Male 80 Right B Medium Wild-type Mutated Wild-type Wild-type Wild-type GIM_c896 MSS Female 71 Rectum C Medium Wild-type Wild-type Wild-type Wild-type Mutated GIM_c910 MSI Female 65 Right B High Wild-type Wild-type Wild-type Wild-type Wild-type GIM_c912I MSI Female 66 Left B Low Wild-type Mutated Mutated Wild-type Wild-type GIM_c946 MSS Male 77 Left B Medium Wild-type Wild-type Wild-type Wild-type Wild-type GIM_c955 MSI Female 84 Right B Medium Mutated Wild-type Wild-type Wild-type Wild-type

GIM_c1013 MSS Female 66 Rectum B Medium Mutated Wild-type Wild-type Wild-type Mutated GIM_c1044II MSI Female 63 Rectum A Medium Mutated Wild-type Wild-type Mutated Wild-type GIM_c1045 MSS Female 62 Left A Medium Wild-type - - - Mutated

GIM_c1047III MSI Male 70 Rectum C Medium Wild-type Mutated Wild-type Wild-type Wild-type GIM_c1117I MSI Male 78 Right C Medium Wild-type Wild-type Wild-type Wild-type Wild-type GIM_c1121 MSS Male 71 Left B Medium Wild-type Mutated Mutated Wild-type Mutated GIM_c1141II MSI Female 76 Right D Medium Wild-type Wild-type Wild-type Mutated Wild-type GIM_c1166 MSS Male 77 Left B Medium Wild-type Mutated Wild-type Wild-type Wild-type GIM_c1167 MSS Male 73 Left C Medium Wild-type Wild-type Wild-type Wild-type Mutated GIM_c1193 MSI Female 69 Right C Low Mutated Wild-type Mutated Wild-type Wild-type GIM_c1194 MSS Male 44 Left C Medium Wild-type Wild-type Wild-type Wild-type Mutated

GIM_c1268III MSI Male 71 Right B Low Mutated Wild-type Mutated Mutated Wild-type GIM_c1341I MSI Female 89 Right B Medium Mutated Wild-type Wild-type Mutated Wild-type

Appendix I

91

GIM_c1342 MSI Male 49 Right B Medium Wild-type Wild-type Wild-type Wild-type Mutated GIM_c1363 MSI Male 70 Right A Medium Wild-type Mutated Wild-type Wild-type Wild-type GIM_c1369 MSS Female 82 Left B Low Wild-type Mutated - - Wild-type AUS_001 MSS Female 71 Left A Medium Wild-type Mutated Wild-type Wild-type Mutated AUS_003 MSS Female 79 Right B Medium Wild-type Mutated Wild-type Wild-type Wild-type AUS_006 MSS Male 62 Right A Medium Wild-type Mutated Wild-type Wild-type Mutated AUS_007 MSS Female 87 Rectum A Medium Wild-type Mutated Wild-type Wild-type Mutated AUS_008 MSS Female 39 Left A Medium Wild-type Wild-type Wild-type Wild-type Mutated AUS_011 MSS Female 67 Left C Medium Wild-type Wild-type Wild-type Wild-type Mutated AUS_012 MSI Female 84 Right B Low Mutated Wild-type Wild-type Mutated Wild-type AUS_015 MSI Female 66 Right C Low Mutated Wild-type Wild-type Wild-type Wild-type AUS_017 MSS Female 73 Rectum B Medium Wild-type Wild-type Wild-type Wild-type Mutated AUS_018 MSS Male 78 Left C Medium Wild-type Wild-type Wild-type Wild-type Mutated AUS_019 MSI Male 71 Right C Low Wild-type Wild-type Wild-type Wild-type Wild-type AUS_020 MSS Male 42 Right D Medium Mutated Wild-type Wild-type Wild-type Mutated AUS_021 MSS Male 77 Left A Medium Wild-type Wild-type Wild-type Wild-type Mutated AUS_024 MSS Male 77 Right C Medium Wild-type Mutated Wild-type Wild-type Mutated AUS_025 MSS Male 71 Left A Medium Wild-type Wild-type Wild-type Wild-type Mutated AUS_026 MSS Female 62 Transversum B Medium Wild-type Wild-type Wild-type Wild-type Mutated AUS_030 MSI Female 84 Right B High Mutated Wild-type Wild-type Wild-type Wild-type AUS_032 MSI Male 69 - A High Mutated Wild-type Wild-type Wild-type Wild-type AUS_035 MSS Female 81 Left A Medium Wild-type Wild-type Wild-type Wild-type Mutated AUS_036 MSS Male 35 Rectum C Medium Wild-type Wild-type Wild-type Wild-type Mutated AUS_037 MSI Male 69 Transversum B Medium Wild-type Wild-type Mutated Wild-type Wild-type AUS_040 MSS Male 69 Left D Medium Wild-type Wild-type Wild-type Wild-type Wild-type AUS_111 MSS Male 64 Rectum C Medium Wild-type Wild-type Wild-type Wild-type Wild-type

92

Appendix II – Normal tissue samples

Normal mucosa Sample Gender Age Localisation

N1 Male 44 Distal N2 Male 54 Proximal N3 Male 33 Distal N9 Female 63 Proximal

N11 Female 40 Rectum N12 Male 40 Coecum N13 Male 38 Rectum N14 Male 38 Coecum N15 Female 82 Rectum N16 Male 54 Proximal N17 Female 60 Distal N18 Female 75 Coecum N19 Female 39 Rectum N20 Female 48 Coecum N21 Male 54 Rectum N22 Female 86 Coecum N24 Male 40 Rectum N26 Male 74 Rectum N27 Male 51 Rectum N28 Female 79 Rectum N29 Male 53 Proximal N33 Female 76 Distal N36 Male 85 Distal N37 Male 38 Proximal N38 Female 22 Distal N42 Female 46 Proximal N47 Female 62 Proximal N49 Female 73 Proximal N50 Male 61 Coecum N51 Male 43 Coecum N52 Male 62 Coecum N53 Female 69 Coecum N55 Female 78 Coecum N57 Male 81 Coecum N58 Male 38 Rectum N59 Female 79 Rectum N61 Male 55 Rectum N62 Male 68 Rectum N63 Male 54 Rectum N64 Male 28 Rectum N65 Male 62 Rectum N66 Male 60 Coecum N67 Female 55 Coecum N68 Male 72 Coecum N69 Male 64 Coecum N70 Male 85 Rectum N71 Male 57 Rectum

Appendix II

93

N72 Male 66 Coecum N73 Male 42 Rectum

94

Appendix III – Qualitative MSP analyses

Primer information

Primer set Forward primer Reverse primer Fragment size (base pairs)

Annealing temperature (°C)

Annealing and elongation time (sec) Accession number

BNIP3-MSP-M ACGCGTCGTACGTGTTATAC ACTACGCTCCCGAACTAAAC 158 52 30 NM_004052

BNIP3-MSP-U GTATGTGTTGTATGTGTTATAT ACTACACTCCCAAACTAAACAA 158 52 30

CBS-MSP-M GTTACGAGATATTGGTCGGC CTACGACGAAACGAAAACG 127 48 60 NM_000071

CBS-MSP-U TTTGTTATGAGATATTGGTTGGT ACTACAACAAAACAAAAACAAC 127 48 60

DDX43-MSP-M GGCGTTTGGAAAAAGTTTTAC CCAATCGATTTTCTAAACCG 110 50 60 NM_018665

DDX43-MSP-U TTGGGTGTTTGGAAAAAGTTTTAT TAACCAATCAATTTTCTAAACCA 110 50 60

GLDC-MSP-M CGTCGTTTAAAGTGTGC CAATCGACCGAACAAATAAA 122 48 60 NM_000170

GLDC-MSP-U AGGGTGTTGTTTAAAGTGTGT AAACAATCAACCAAACAAATAAA 122 48 60

GLDC BS GGGTAGGATTGGAGATGGTAGT CTCTTAACCCCTCTCCTAACCTC 364 56 30

IQCG-MSP-M GGTAGACGGAGGGTTTAGTC CATTTATTAACCGACTTCGC 133 48 30 NM_032263

IQCG-MSP-U GGGGTAGATGGAGGGTTTAGTT AACATTTATTAACCAACTTCAC 133 48 30

PEG10-MSP-M GAGTACGTTGGGATTTGGC ACTCGATAAACCTTCTCCGC 152 52 30 NM_001040152

PEG10-MSP-U GGAGTATGTTGGGATTTGGT AACTCAATAAACCTTCTCCAC 152 52 30

PPP1R14A-MSP-M TTAGAGGGCGTAGATAGGTC CTACGTCGACTTAAAACACG 157 52 30 NM_033256

PPP1R14A-MSP-U GTTAGAGGGTGTAGATAGGTT CTACATCAACTTAAAACACAC 157 52 30

PPP1R14A BS TTAGTTTGGGYGATAAAGAGAG CCTCAAACCTCAATTTCCC 382 56 30

RASSF4-MSP-M TTATCGGCGTTTTTAGAGC CCGACACGACCAAAAATA 113 56 60 NM_032023

Appendix III

95

RASSF4-MSP-U AATTTATTGGTGTTTTTAGAGT CCCAACACAACCAAAAATACC 113 56 60

RBP7-MSP-M TTTGGTTTATAGGTTTCGGTTC AACCCTCGAAATTATCGCTA 123 54 45 NM_052960

RBP7-MSP-U GTTTGGTTTATAGGTTTTGGTTT TAACCCTCAAAATTATCACTA 123 54 45

WDR21B-MSP-M ATTTTCGTTTGTATTCGGAC TCCTACGAAATATTCCTCGT 105 48 60 NM_001029955

WDR21B-MSP-U AATATTTTTGTTTGTATTTGGAT TCCTCCTACAAAATATTCCTCAT 105 48 60 Abbreviations: BS, bisulfite sequence; MSP, methylation-specific PCR; U, unmethylated; M, methylated.

96

Appendix IV – Quantitative MSP analyses

Primer and probe information

Assay Forward primer Reverse primer Probe Fragment size (base pairs)

GLDC qMSP GGGCGTCGTTTAAAGTGTGC GCGAACAATAAATAAACGCTACGC 6FAM-GGTGGAGTTATAATTTTGCGCGA-MGB 98 PPP1R14A qMSP ACGAAGGAATAAGTGATCGTTCG CGCCCTCTAACGATAACGAAA 6FAM-GATAGCGGCGTAGGC-MGB 94

Abbreviations: qMSP, quantitative methylation-specific PCR; MGB, minor groove binder.

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