Identification of novel epigenetic biomarkers in colorectal cancer

Transcription

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
1
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
2
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
1.3
1.4
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
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
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
3
3.2
4.
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
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
5.
ASSOCIATION OF TUMOUR METHYLATION WITH GENETIC AND CLINICO-PATHOLOGICAL FEATURES 63
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
4
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
5
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
6
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.
7
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
8
TSA
Trichostatin A
XIST
X-inactive specific transcript
9
Gene symbols1
BNIP3
BRAF
CBS
CD44
CDKN2A
CTNNB1
DDX43
EGFR
GLDC
H19
HRAS
IGF2
IQCG
KRAS
MAL
MGMT
MLH1
MSH2
PEG10
PIK3CA
PPP1R14
A
PTEN
RASSF4
RB
RBP7
SEPT9
SFRP2
TFP12
TP53
VIM
WDR21B
WT1
BCL2/adenovirus E1B 19kDa interacting protein 3
v-raf murine sarcoma viral oncogene homolog B1
Cystathionine-beta-synthase
CD44 molecule (Indian blood group
Cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4)
Catenin (cadherin-associated protein), beta 1, 88kDa
DEAD (Asp-Glu-Ala-Asp) box polypeptide 43
Epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene
homolog, avian)
Glycine dehydrogenase (decarboxylating)
H19, imprinted maternally expressed transcript (non-protein coding)
v-Ha-ras Harvey rat sarcoma viral oncogene homolog
Insulin-like growth factor 2 (somatomedin A)
IQ motif containing G
v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog
Mal, T-cell differentiation protein
O-6-methylguanine-DNA methyltransferase
MutL homolog 1, colon cancer, nonpolyposis type 2 (E. coli)
MutS homolog 2, colon cancer, nonpolyposis type 1 (E. coli)
Paternally expressed 10
Phosphoinositide-3-kinase, catalytic, alpha polypeptide
Protein phosphatase 1, regulatory (inhibitor) subunit 14A
Phosphatase and tensin homolog
Ras association (RalGDS/AF-6) domain family member 4
Retinoblastoma 1
Retinol binding protein 7, cellular
Septin 9
Secreted frizzled-related protein 2
Tissue factor pathway inhibitor 2
Tumor protein p53
Vimentin
DDB1 and CUL4 associated factor 4-like 1
Wilms tumor 1
1
Gene symbols and full names are approved by the HUGO Gene Nomenclature Committee
(http://www.genenames.org). Approved gene symbols are used throughout the thesis.
10
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
11
Introduction
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
12
Introduction
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 bestknown 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
13
Introduction
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 GCcontent 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 Sadenosylmethionine 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
14
Introduction
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).
15
Introduction
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].
16
Introduction
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 tumoursuppressor 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].
17
Introduction
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.
18
Introduction
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].
19
Introduction
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
20
Introduction
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
21
Introduction
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 chromatinremodelling 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].
22
Introduction
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.
23
Introduction
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].
24
Introduction
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
25
Introduction
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 nonpolyposis 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
26
Introduction
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.
27
Introduction
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.
28
Introduction
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
29
Introduction
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 protooncogene 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 adenomacarcinoma 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.
30
Introduction
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
31
Introduction
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/
32
Introduction
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].
33
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.
34
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].
35
Materials and methods
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 upregulated 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.
36
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
37
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 5methylcytosines 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.
38
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].
39
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,
40
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).
41
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.
42
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
chain reaction
methylation-specific
polymerase
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
43
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 realtime 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
44
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.
45
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
46
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.
47
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.
48
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
49
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.
50
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
51
Results
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).
52
Results
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
PPP1R14A
methylation
profiles
of
GLDC
and
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. ¨
53
Results
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 cutoff 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.
54
Results
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.
55
Results
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
56
Results
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).
57
Results
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).
Conventional MSP
Unmethylated
Methylated
Total
Quantitative real-time MSP with cut-off = 2.5
Unmethylated
Methylated
Total
15
0
15
0
24
24
15
24
39
Table 4. Concordance of classification of the GLDC status by the two methods.
58
Results
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).
Conventional MSP
Unmethylated
Methylated
Total
Quantitative real-time MSP with cut-off = 3.5
Unmethylated
Methylated
Total
11
1
12
0
27
27
11
28
39
Table 5. Concordance of classification of the PPP1R14A status by the two methods.
59
Results
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.
60
Results
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.
61
Results
62
Results
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
63
Results
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.
64
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.
65
Discussion
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 CpGindependent, 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 singlecopy 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
66
Discussion
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
67
Discussion
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 nonCpG 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,
68
Discussion
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
69
Discussion
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 cancerspecific 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
70
Discussion
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
71
Discussion
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
72
Discussion
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
73
Discussion
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
74
Discussion
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].
75
Discussion
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 noninvasive 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
76
Discussion
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
77
Discussion
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.
78
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%.
79
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 reexpression. 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.
80
Future perspectives
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 noninvasive 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.
81
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89
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
90
Appendix I
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
91
Appendix II – Normal tissue samples
Normal mucosa
Sample
Gender
Age
Localisation
N1
N2
N3
N9
N11
N12
N13
N14
N15
N16
N17
N18
N19
N20
N21
N22
N24
N26
N27
N28
N29
N33
N36
N37
N38
N42
N47
N49
N50
N51
N52
N53
N55
N57
N58
N59
N61
N62
N63
N64
N65
N66
N67
N68
N69
N70
N71
Male
Male
Male
Female
Female
Male
Male
Male
Female
Male
Female
Female
Female
Female
Male
Female
Male
Male
Male
Female
Male
Female
Male
Male
Female
Female
Female
Female
Male
Male
Male
Female
Female
Male
Male
Female
Male
Male
Male
Male
Male
Male
Female
Male
Male
Male
Male
44
54
33
63
40
40
38
38
82
54
60
75
39
48
54
86
40
74
51
79
53
76
85
38
22
46
62
73
61
43
62
69
78
81
38
79
55
68
54
28
62
60
55
72
64
85
57
Distal
Proximal
Distal
Proximal
Rectum
Coecum
Rectum
Coecum
Rectum
Proximal
Distal
Coecum
Rectum
Coecum
Rectum
Coecum
Rectum
Rectum
Rectum
Rectum
Proximal
Distal
Distal
Proximal
Distal
Proximal
Proximal
Proximal
Coecum
Coecum
Coecum
Coecum
Coecum
Coecum
Rectum
Rectum
Rectum
Rectum
Rectum
Rectum
Rectum
Coecum
Coecum
Coecum
Coecum
Rectum
Rectum
92
Appendix II
N72
N73
Male
Male
66
42
Coecum
Rectum
93
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
NM_004052
BNIP3-MSP-M
ACGCGTCGTACGTGTTATAC
ACTACGCTCCCGAACTAAAC
158
52
30
BNIP3-MSP-U
GTATGTGTTGTATGTGTTATAT
ACTACACTCCCAAACTAAACAA
158
52
30
CBS-MSP-M
GTTACGAGATATTGGTCGGC
CTACGACGAAACGAAAACG
127
48
60
CBS-MSP-U
TTTGTTATGAGATATTGGTTGGT
ACTACAACAAAACAAAAACAAC
127
48
60
DDX43-MSP-M
GGCGTTTGGAAAAAGTTTTAC
CCAATCGATTTTCTAAACCG
110
50
60
DDX43-MSP-U
TTGGGTGTTTGGAAAAAGTTTTAT
TAACCAATCAATTTTCTAAACCA
110
50
60
GLDC-MSP-M
CGTCGTTTAAAGTGTGC
CAATCGACCGAACAAATAAA
122
48
60
GLDC-MSP-U
AGGGTGTTGTTTAAAGTGTGT
AAACAATCAACCAAACAAATAAA
122
48
60
GLDC BS
GGGTAGGATTGGAGATGGTAGT
CTCTTAACCCCTCTCCTAACCTC
364
56
30
IQCG-MSP-M
GGTAGACGGAGGGTTTAGTC
CATTTATTAACCGACTTCGC
133
48
30
IQCG-MSP-U
GGGGTAGATGGAGGGTTTAGTT
AACATTTATTAACCAACTTCAC
133
48
30
PEG10-MSP-M
GAGTACGTTGGGATTTGGC
ACTCGATAAACCTTCTCCGC
152
52
30
PEG10-MSP-U
GGAGTATGTTGGGATTTGGT
AACTCAATAAACCTTCTCCAC
152
52
30
PPP1R14A-MSP-M
TTAGAGGGCGTAGATAGGTC
CTACGTCGACTTAAAACACG
157
52
30
PPP1R14A-MSP-U
GTTAGAGGGTGTAGATAGGTT
CTACATCAACTTAAAACACAC
157
52
30
PPP1R14A BS
TTAGTTTGGGYGATAAAGAGAG
CCTCAAACCTCAATTTCCC
382
56
30
RASSF4-MSP-M
TTATCGGCGTTTTTAGAGC
CCGACACGACCAAAAATA
113
56
60
NM_000071
NM_018665
NM_000170
NM_032263
NM_001040152
NM_033256
NM_032023
94
Appendix III
RASSF4-MSP-U
AATTTATTGGTGTTTTTAGAGT
CCCAACACAACCAAAAATACC
113
56
60
RBP7-MSP-M
TTTGGTTTATAGGTTTCGGTTC
AACCCTCGAAATTATCGCTA
123
54
45
RBP7-MSP-U
GTTTGGTTTATAGGTTTTGGTTT
TAACCCTCAAAATTATCACTA
123
54
45
WDR21B-MSP-M
ATTTTCGTTTGTATTCGGAC
TCCTACGAAATATTCCTCGT
105
48
60
WDR21B-MSP-U
AATATTTTTGTTTGTATTTGGAT
TCCTCCTACAAAATATTCCTCAT
105
48
60
NM_052960
NM_001029955
Abbreviations: BS, bisulfite sequence; MSP, methylation-specific PCR; U, unmethylated; M, methylated.
95
Appendix IV – Quantitative MSP analyses
Primer and probe information
Assay
Forward primer
Reverse primer
Probe
GLDC qMSP
PPP1R14A qMSP
GGGCGTCGTTTAAAGTGTGC
ACGAAGGAATAAGTGATCGTTCG
GCGAACAATAAATAAACGCTACGC
CGCCCTCTAACGATAACGAAA
6FAM-GGTGGAGTTATAATTTTGCGCGA-MGB
6FAM-GATAGCGGCGTAGGC-MGB
Fragment size
(base pairs)
98
94
Abbreviations: qMSP, quantitative methylation-specific PCR; MGB, minor groove binder.
96
97

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