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Characterization of Kallikrein 6 N-glycosylation Patterns and
Identification of Sialylated Glycoproteins in Ovarian Cancer
by
Uros Kuzmanov
A thesis submitted in conformity with the requirements
for the degree of Doctor of Philosophy
Department of Laboratory Medicine and Pathobiology
University of Toronto
© Copyright by Uros Kuzmanov 2013
ii
Characterization of Kallikrein 6 N-glycosylation Patterns and
Identification of Sialylated Glycoproteins in Ovarian Cancer
Uros Kuzmanov
Doctor of Philosophy
Laboratory Medicine and Pathobiology
University of Toronto
2013
Abstract
Ovarian cancer is the leading cause of death among all gynecological disorders. Aberrant
glycosylation, or more specifically, increased sialylation of proteins has been observed in this
malignancy. Several sialyltransferase genes have been shown to be up-regulated at both mRNA
and and protein levels in a number of cancers, including that of the ovary. In the present study,
we have analyzed the glycosylation patterns of kallikrein 6 in the context of ovarian cancer. We
have discovered that the carbohydrate structures found at the single N-glycosylation site of
kallikrein 6 derived from ovarian cancer cells found in the ascites fluid of ovarian cancer patients
is enriched in sialic acid moieties and has an increased branching pattern when compared to
controls. We have also developed a reliable anion-exchange HPLC-based methodology capable
of quantifying different glycoform subpopulations of kallikrein 6 in serum and other biological
fluids, which was capable of differentiating between samples from ovarian cancer patients and
healthy controls. A variety of classic molecular biology and mass spectrometry based techniques
were utilized in these experiments. Based on the results of the analysis of kallikrein 6
glycosylation and other literature reports showing upregulated sialylation of proteins in ovarian
cancer, we have also identified sialylated glycoproteins from ovarian cancer proximal fluids and
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conditioned media of ovarian cancer cell lines. Sialylated proteins were enriched utilizing lectin
affinity or hydrazide chemistry. In total, 333 sialylated glycoproteins and 579 glycosylation sites
were identified. A list of 21 potential candidate ovarian cancer biomarkers was produced from
proteins that were identified solely in ovarian cancer proximal fluids, which could form the basis
for any future studies.
iv
Acknowledgments
First and foremost, I would like to thank Dr. Eleftherios P. Diamandis for creating an almost
perfect environment in which graduate students are encouraged and nurtured to grow as scientists
but also as human beings. The years I have spent in his laboratory have been some of my
happiest, and I will always look at them with nothing but the greatest fondness. Rarely has there
been a day I was not happy and excited to go to the lab. Thank you, Dr. D.
I would also like to express my appreciation to my advisory committee members, Dr. Jim
Dennis and Dr. Isabelle Aubert. Their support and guidance has helped me greatly in the course
of my graduate studies.
Over the course my studies I have grown to look at the other members of the Diamandis
lab not as colleagues, but as friends. They have all contributed greatly to the creation of an
environment where scientific pursuits can be undertaken in the most supportive, comfortable,
and productive fashion. I would like to send out special thanks to Antoninus Soosaipillai whose
indomitable spirit and impeccable management of the lab were of incalculable value to me.
Thanks, Antonio.
Above all, I want express my unending appreciation for the love and unquestionable
support of my family. My parents, sister and her family, and the Loncar family have shown
great understanding during the course of my studies.
Finally, I would like to dedicate this work to my amazing wife Natasha and our daughter
Lara. I love you more than words can describe.
v
Table of Contents
Abstract .................................................................................................................................... ii
Acknowledgments .................................................................................................................... iv
Table of Contents ..................................................................................................................... v
List of Tables ........................................................................................................................... ix
List of Figures ........................................................................................................................... x
List of Abbreviations .............................................................................................................. xii
CHAPTER 1: Introduction ...................................................................................................... 1
1.1 Overview ....................................................................................................................... 2
1.2 Glycosylation ................................................................................................................. 3
1.2.1 General information .......................................................................................... 3
1.2.2 Glycosylation in Cancer .................................................................................... 7
1.2.3 Glycoprotein Cancer Biomarkers ..................................................................... 8
1.2.4 The Glycobiomarker Potential ....................................................................... 11
1.3 Ovarian cancer ............................................................................................................ 13
1.3.1 The ovary ......................................................................................................... 13
1.3.2 Ovarian cancer classification .......................................................................... 14
1.3.3 Ovarian cancer detection and treatment ........................................................ 18
1.3.4 Glycosylation in ovarian cancer ...................................................................... 20
1.3.5 Mass Spectrometry-based Glycoproteomics of Ovarian Cancer ................... 22
1.4 Kallikrein 6 in ovarian cancer .................................................................................... 25
1.4.1 Kallikrein family ............................................................................................. 25
1.4.2 Kallikreins as ovarian cancer biomarkers...................................................... 29
1.4.3 Kallikrein 6 ...................................................................................................... 30
1.4.4 Kallikrein 6 as a biomarker in ovarian cancer ............................................... 33
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1.5 Purpose and Aims of Study ........................................................................................ 34
1.5.1 Rationale .......................................................................................................... 34
1.5.2 Hypothesis........................................................................................................ 35
1.5.3 Aims of study ................................................................................................... 36
CHAPTER 2: Characterization of KLK6 Glycosylation ..................................................... 37
2.1 Short Overview ........................................................................................................... 38
2.2 Introduction ................................................................................................................ 38
2.3 Materials and Methods ............................................................................................... 41
2.3.1 Anion-Exchange Chromatography ................................................................. 41
2.3.2 KLK6 Immunoisolation .................................................................................. 42
2.3.3 Site-directed mutagenesis, Mutant expression and purification.................... 43
2.3.4 SDS-PAGE Western Blot Analysis ................................................................. 44
2.3.5 Glycosidase Digestion ...................................................................................... 45
2.3.6 Lectin ELISA Assay ........................................................................................ 45
2.3.7 Sample preparation for Mass Spectrometry .................................................. 46
2.3.8 Mass Spectrometry Conditions ....................................................................... 47
2.3.9 MS/MS Glycan Structure Identification ........................................................ 48
2.4 Results ......................................................................................................................... 49
2.4.1 Anion-Exchange Chromatography ................................................................. 49
2.4.2 Western Blot Analysis, Glycosidase Treatment and Site-Directed
Mutagenesis ..................................................................................................... 51
2.4.3 Lectin-Antibody Sandwich ELISA ................................................................. 53
2.4.4 Structure Characterization by Tandem Mass Spectrometry ........................ 55
2.5 Discussion and Conclusions ........................................................................................ 62
CHAPTER 3: Development of Methodology for Detection of Alterations in KLK6
Glycosylation Patterns in Complex Biological Fluids ....................................................... 68
3.1 Short Overview ........................................................................................................... 69
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3.2 Introduction ................................................................................................................ 69
3.3 Materials and Methods ............................................................................................... 73
3.3.1 Clinical Samples .............................................................................................. 73
3.3.2 Recombinant KLK6 Production ..................................................................... 73
3.3.3 Anion Exchange Methodology ........................................................................ 74
3.3.4 Lectin Detection ............................................................................................... 75
3.3.5 Sample Preparation for Mass Spectrometry .................................................. 76
3.3.6 MS/MS Glycopeptide Structure Identification .............................................. 77
3.3.7 Glycopeptide Product Ion Monitoring ........................................................... 78
3.4 Results ......................................................................................................................... 79
3.4.1 Anion-Exchange Chromatography of Biological Samples ............................. 79
3.4.2 Lectin Analysis of rKLK6 peaks ..................................................................... 85
3.4.3 MS/MS Analysis of rKLK6 ............................................................................. 87
3.4.4 Quantification of rKLK6 glycopeptides by Product Ion Monitoring ............ 94
3.5 Discussion and Conclusions ........................................................................................ 96
CHAPTER 4: Glycoproteomic analysis of ovarian cancer cell lines and proximal fluids .103
4.1 Short Overview ..........................................................................................................104
4.2 Introduction ...............................................................................................................104
4.3 Materials and Methods ..............................................................................................108
4.3.1 Microarray Profiling ......................................................................................108
4.3.2 Clinical Samples .............................................................................................109
4.3.3 Cells Line Supernatants .................................................................................110
4.3.4 Elderberry lectin sialoglycopeptide enrichment ...........................................111
4.3.5 Hydrazide bead sialoglycopeptide enrichment ..............................................113
4.3.6 ESI-LTQ-Orbitrap Tandem mass spectrometry ..........................................115
4.3.7 Database searches and glycopeptide assignment ..........................................116
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4.4 Results ........................................................................................................................117
4.4.1 Sialyltransferase expression ...........................................................................117
4.4.2 Identification of Sialylated Glycoprproteins .................................................119
4.4.3 Subcellular Localization .................................................................................124
4.5 Discussion and Conclusions .......................................................................................126
CHAPTER 5: Discussion and Future Directions ................................................................131
5.1 Conclusions in Brief ...................................................................................................132
5.2 Discussion ...................................................................................................................134
5.2.1 The Pitfalls of Glycobiomarker Quantification .............................................134
5.2.2 Lectin-based Quantification ...........................................................................136
5.2.3 Mass spectrometry-based Quantification ......................................................140
5.2.4 Alternative Strategies .....................................................................................148
5.2.5 Concluding Remarks ......................................................................................150
5.2.6 Future Directions............................................................................................151
REFERENCES ......................................................................................................................152
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List of Tables
Table Title/Short Description Page
1.1 List of common serological tumor markers in clinical use containing a glycan
component.
3.1 Results of anion-exchange separation of KLK6 subpopulations in biological
fluids and clinical information of patients
4.1 Proteins identified in ovarian cancer proximal fluids alone or both in cancer
proximal fluids and cell line supernatants
4.2 Proteins identified in the ovarian cancer proximal fluids previously studied in
ovarian cancer
x
List of Figures
Figure Title/Short Description Page
1.1 Life span of glycoproteins from translation to circulation
1.2 Gene expression of AFP, beta-hCG, and PSA by tissue
1.3 Characteristics of the kallikrein locus, genes, and proteins
1.4 Kallikrein 6 gene, mRNA, and protein organization and tissue expression
2.1 Anion-exchange chromatography of biological fluids
2.2 KLK6 Western Blot Analysis
2.3 SNA-antibody lectin ELISA
2.4 Glycopeptide characterization workflow
2.5 KLK6 glycopeptide mass spectra
2.6-2.9 KLK6 glycopeptide fragmentation
3.1 Methodology outline
3.2 Anion-exchange chromatography of biological fluids
3.3 Peak area integration
3.4 SNA lectin affinity to recombinant KLK6 from chromatographic peaks
3.5 Total ion current chromatograms
3.6 Composite MS1 spectra of recombinant KLK6 glycopeptides
3.7-3.10 Composite mass spectra of KLK6 glycopeptides in chromatographic peaks
3.11 Glycopeptide product ion monitoring.
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4.1 Lectin-based glycopeptide enrichment
4.2 Hydrazide chemistry-based enrichment of glycopeptides
4.3 Sialyltransferase expression in ovarian cancer
4.4 Study outline
4.5 Identified sialylated glycoproteins
4.6 Subcellular localization of identified sialoglycoproteins
5.1 Glycopeptide MRM/SRM
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List of Abbreviations
Abbreviation Description
ACN acetonitrile
AFP alpha-fetoprotein
AFP alpha-fetoprotein
ALP alkaline phosphatase
BSA bovine serum albumin
CA 15-3 carcinoma antigen 15-3
CA 19-9 carbohydrate antigen 19-9
CA125 cancer antigen 125
CEA carcinoembryionic antigen
CNS central nervous system
CSF cerebrospinal fluid
CT computed axial tomography
DFP diflunisal phosphate
DTT dithiothreitol
EBL Elderberry bark lectin, Sambucus nigra agglutinin
EDTA ethylenediamine tetra-acetic acid
ELISA enzyme-linked immunosorbent assay
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ELLA enzyme-linked lectinsorbent assay
ESI electrospray ionization
FIGO International Federation of Gynecology and Oncology
FT-ICR fourier-transform ion-cyclotron resonance
GO gene ontology
hCG human chorionic gonadotropin
IPI international protein index
kDa kilodalton
KLK kallikrein
KLK6 kallikrein 6
m/z mass to charge ratio
MALDI matrix-assisted laser disorption/ionization
MRM multiple reaction monitoring
MS mass spectrometry
MS/MS tandem mass spectrometry
PIM product ion monitoring
PSA prostate specific antigen
PTM post-translational modification
P-value probability value
rKLK6 recombinant KLK6
xiv
SCX strong cation exchange
SDS-PAGE sodium dodecyl sulfate polyacrylamide gel electrophoresis
SNA Sambucus nigra agglutinin; Elderberry bark lectin
SRM selected/single reaction monitoring
TBS Tris-buffered saline
TFA Trifluoroacetic acid
TOF time of flight
WHO world health organization
1
CHAPTER 1: Introduction
2
1.1 Overview
The thesis presented herein focuses on the application of recorded observations that disturbances
in protein glycosylation in ovarian and other cancers are a common event, towards the
improvement of the diagnostic potential of kallikrein 6 and the discovery of novel glycoprotein
biomarker candidates. We have utilized the full scope of classical and novel mass spectrometry-
based techniques to characterize the glycosylation patterns of kallikrein 6, quantify the
intramolecular heterogeneity associated with kallikrein 6 glycosylation in clinical samples, and
identify potential ovarian cancers based on the enrichment of a glycan structure over-expressed
in ovarian cancer cells. The following introductory section will cover: protein glycosylation and
how it is affected in cancer in general, ovarian cancer basics and the disturbances of
glycosylation pathways in this disease, and the role of kallikrein 6 in ovarian cancer. These are
the major topics, which converge and form the basis of the studies presented in this dissertation.
3
1.2 Glycosylation
1.2.1 General information
It is a well-established concept that gene and protein expression are not the sole factors
responsible for phenotype determination. The discovery of varying roles of post-translational
modifications (PTMs) of proteins has provided us with another level at which functional
information is stored. Of the over 200 different types of protein PTMs, glycosylation occurs
often and holds great importance (1-4). It has been shown to have an important role in a number
of physiological processes, including: protein folding and trafficking, cell-cell and cell-matrix
interaction, cellular differentiation, fertilization, and the immune response (5-9).
Approximately half of all mammalian proteins are glycosylated with as much as an
estimated 3000 different glycan structures, which can vary to a large degree based on differences
in tissue, cell type, and disease state (10, 11). It is estimated that 250 to 500 genes are involved
in the protein glycosylation process (12). Carbohydrate molecules on proteins can be attached to
asparagine residues within the N-X-S/T consensus sequence where X is not a proline (N-
glycosylation), or to serine or threonine residues (O-glycosylation). This occurs during or after
translation as the nascent protein is shuttled through the ER and subsequent organelles in the
classical secretory pathway (Figure 1.1). However, glycosylation is not a template-based process
such as DNA, RNA, or protein synthesis, but is rather based on the balance achieved by the
expression levels of the different glycan attachment and processing enzymes, and availability of
precursor monosaccharide molecules, which in turn is dependent on nutrient resources and
expression of other metabolic enzymes responsible for their synthesis and inter-conversion. This
greatly increases the complexity of the protein glycosylation process resulting in extensive
molecular microheterogeneity of glycoproteins requiring a specialized set of tools for their study.
4
The glycan constituents of secreted and membrane glycoproteins play a number of roles
in the extracellular space and cell surface. For example, ligand-independent EGFR dimerization
and subsequent activation is normally blocked by the presence of N-glycans on 4 of its 12 N-
glycan sites, which get removed in some human glioblastomas (13). The initial co-translational
glycosylation and glycan processing of nascent ER bound proteins plays an important part in
protein folding and quality control through interaction with ER resident chaperones such as
calnexin and calreticulin (14). In addition to this role, N-linked glycans also serve as localization
tags for directing proteins to different cellular locations, and affect the mobility of membrane-
bound glycoproteins through their interactions with lattice-forming lectins, such as galectins
(13). As is the case with lysosomal proteins Lamp 1 and 2, increased glycosylation can also
provide glycoproteins protection from proteolytic cleavage (14).
5
Figure 1.1. Life span of glycoproteins from translation to circulation. The translation of
signal peptide-containing membrane and secreted protein occurs on the surface of the ER with
the growing peptide chain being shuttled through the translocon complex into the lumen of the
ER. In the ER lumen, core N-glycosylation of accessible N-X-S/T sites is performed by the
oligosaccharide transferase component of the translocon complex as the nascent protein is being
6
translated and folded. Following the completion of translation, folding and core glycan
processing, the protein is shuttled to the Golgi apparatus where further N- and O-glycosylation is
performed by different glycosyltransferases. In the Golgi, glycoproteins are packaged into
secretory vesicles bound for fusion with the plasma membrane, where secreted proteins are
released into the extracellular space and membrane proteins presented on the surface of the cell
making them accessible for cleavage and release by proteolytic enzymes. Once in the
extracellular space these glycoproteins can then enter the circulation.
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1.2.2 Glycosylation in Cancer
Since the initial observation in 1969 showing that membrane glycoproteins of higher molecular
weight were found in transformed mouse fibroblasts when compared to their normal counterparts
(15, 16), aberrant glycosylation patterns have been established as a common characteristic of
oncologic malignancies. It has been observed in almost all types of experimental and human
cancers. Even under non-malignant conditions, individual glycoproteins are produced in a
number of different glycoforms (17). The differences in these forms can arise from differential
occupancy of glycosylation sites or variability in attached glycan structures. This allows for
great heterogeneity in glycosylation of single proteins even under normal physiological
conditions. However, under normal physiologic conditions the distribution of these glycoforms
is stable and reproducible. Once malignant transformation occurs, when under-, over-, or neo-
expression of glycan moieties can occur, this balance is disturbed and can expand the degree of
pre-existing microheterogeneity of individual proteins (18). In tumors, the changes in glycan
structures most often arise from disturbances in the expression levels of different
glycosyltransferases along the secretory pathway, in the ER and Golgi of cancer cells. This can
lead to changes in the structures of both N- and O- linked glycans. For example, increased
activity or expression of N-acetylglucosaminyltransferase V (MGAT5) has been shown in a
number of tumors resulting in increased glycan branching on proteins resulting in increased
tumor growth and metastasis (19-23). Alteration in terminal glycan residues can also occur
during malignancy, which is often the case with the upregulation of different sialyltransferase
enzymes in tumors (24-29). However, it must be noted that altered glycosylation does not only
occur on proteins produced by the tumor itself, but may reflect the host’s response to the disease.
In cancer patients, acute phase proteins and IgGs have been shown to have glycosylation patterns
distinct from those found under normal physiological conditions (30). Therefore, the detection
8
and quantification of the disturbances in protein glycosylation can aid in the screening and
diagnosis of virtually all cancer types.
1.2.3 Glycoprotein Cancer Biomarkers
Some of the oldest and most commonly clinically utilized serological biomarkers for cancer
diagnosis and monitoring of malignant progression are glycoproteins. Some of these include
biomarkers widely monitored in patients with prostate cancer (PSA), ovarian cancer (CA125),
colon cancer (CEA), and nonseminomatous testicular carcinoma (hCG-) (Table 1). Although
all of these proteins have been shown to have aberrant glycosylation patterns in malignancy (25-
29, 31-34), only their total protein levels are clinically monitored, but not their different
glycoform subpopulations in a simultaneous fashion, which could increase the diagnostic
potential of these molecules. For two other common tests, -fetoprotein (AFP) for
hepatocellular carcinoma and CA 15-3 (mucin 1 epitope) for breast cancer, specific glycan
structures on these proteins are monitored, as is discussed below.
There are three general approaches, utilizing a variety of techniques, with which
glycoproteins or carbohydrate epitopes can be quantified. The most commonly used approach
involves the measurement of total levels of a given glycoprotein biomarker. This usually
involves the production of monoclonal antibodies against a given glycoprotein facilitating the
development of an assay capable of quantifying total protein levels in a biological fluid of
interest. This is the case with PSA, CA125, hCG-b, and CEA quantification (Table 1).
However, this type of methodology is not capable of detecting the changes occurring in the
glycosylation patterns of the target glycoprotein as a result of malignant transformation thereby
missing out on another level of information, which could lead to improved diagnosis and
monitoring of disease. Therefore, even though a glycoprotein is being measured, its glycan
9
moiety is completely ignored. Another approach involves the detection and quantification of
particular glycan structure shown to be associated with cancer, such as the antibody-based
measurement of blood group antigen Lewisa in the CA 19-9 assay. This type of approach does
not yield any information on the identity or quantity of the glycoprotein with the particular
carbohydrate epitope, thereby also not including the full scope of information which could lead
to improved diagnosis, especially if the protein is directly produced by the tumor. The third,
most seldomly used, and most difficult type of approach to develop allows for the detection and
quantification of both total protein levels and associated glycan structure(s), such as the
measurement of the core-fucosylated species of AFP in hepatocellular carcinoma (35, 36). This
type of assay can yield the most information and not suffer from the weaknesses of the other two
approaches mentioned above. Therefore, the development of this type of methodology would
have the most diagnostic benefit.
10
Table 1.1. List of common serological tumor markers in clinical use containing a glycan
component.*
Biomarker Type of Detection Cancer type(s) Clinical applications Ref.
AFP Protein and core
fucosylation (for
AFP-L3)
Germ-cell
hepatoma;
nonseminomato
us testicular
carcinoma
Diagnosis
Staging
Detecting recurrence
Monitoring therapy
(37, 38)
hCG Protein alone Testicular Diagnosis
Staging
Detecting recurrence
Monitoring therapy
(38, 39)
CA 125 Protein alone Ovarian Prognosis
Detecting recurrence
Monitoring therapy
(38, 40)
CA 15-3 Sialylated O-
glycan on
MUC1
Breast Monitoring therapy (41-43)
CA 19-9 SLea on mucin
glycoproteins
and
gangliosides**
Pancreatic Monitoring therapy (44, 45)
CEA Protein alone Colon Detecting recurrence
Monitoring therapy
(38, 41,
45)
HER2 Protein alone Breast Therapy selection (41, 46,
47)
PSA Protein alone Prostate Screening
Diagnosis
(with digital rectal
examination)
(38, 48)
Thyroglobuli
n
Protein alone Thyroid Monitoring (49, 50)
CA 27-29 MUC1 protein
alone
Breast Monitoring (45, 51)
* Modified and adapted from Kulasingam and Diamandis (52).
** SLea : Sialyl Lewis a antigen.
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1.2.4 The Glycobiomarker Potential
In the past decade or so, there have been significant advancements in the characterization of
glycosylation patterns of individual proteins and glycoproteomic identification of glycoproteins
in a number of complex biological fluids. This has occurred mostly through the development
and refinement of mass spectrometry techniques and equipment, which when used in concert
with the traditional methods used for characterization of protein glycosylation can provide a
powerful complement of tools to tackle the problem of fully understanding the complexity and
heterogeneity associated with protein glycosylation and applying the gained knowledge in a
clinical setting. However, there has been limited progress in tapping the full potential of
glycobiomarkers and their dual nature in developing an assay capable of simultaneously
delivering information on the absolute quantity of the protein and of its associated glycan
structures in complex matrices, such as serum, which is the preferred sample type for high
throughput clinical analysis.
Some of the best a most widely recognized cancer biomarkers are highly tissue specific,
such as PSA for prostate tissue, hCG for the placenta, and AFP for the developing fetus (Figure
1.2). Therefore, a malignant transformation of cells in a single organ causing the over- or neo-
expression of a protein is detected and monitored more reliably and earlier in the progression of
the disease, when compared to a protein expressed ubiquitously or in multiple tissues. However,
proteins with such characteristics are quite rare. When considering that glycosylation patterns of
the same protein can differ between tissues, and between normal and transformed cells, the
capability to detect and quantify these differences could confer tissue/tumor-specific profiles on
a large number of glycoproteins. The ability to perform such a task reliably and in a routine
fashion could greatly expand the field of potential biomarkers and the chances of their
application in the clinical setting.
12
Figure 1.2. Gene expression of AFP, beta-hCG, and PSA by tissue. Tissue expression
profiles of AFP, PSA, and hCG-beta based on mRNA expression. Adapted and modified from
the BioGPS Application (53), using the HG_U133A/GNF1H Gene Atlas (54).
13
1.3 Ovarian cancer
1.3.1 The ovary
The ovary is the female reproductive organ responsible for the production of ova (the female
gamete. In addition to serving as gonads, ovaries also have an endocrine role and are responsible
for the production of female sex hormones (estrogen and progesterone). They are located lateral
to the uterus in relation to the pelvic wall, and are suspended by peritoneal folds and ligaments
on the sides of the uterus. They are also connected to the back of the broad ligament dorsal to
the uterine tubes. They weigh between 2 and 4 g and are approximately 3 to 5 cm in length.
Based on the location, there are three major types of cells that are found in the ovary: surface
epithelial, stromal, and oocytes. The epithelial cells are found as a single-cell layer on the
external surface of the ovaries. The ovarian stroma is more complex and is composed of highly
vascularized soft tissue. It is mainly made up of spindle-shaped cells with interspersed
connective tissue. Immediately below the surface epithelium, the stromal tissue is more
condensed and forms the tunica albuginea layer that is composed of short connective tissue fibres
and fusiform cells. Interstitial cells, similar to those found in the testes, can also be found in the
ovarian stroma. The germ cells (oocytes) are located in the periphery of the ovary and are
surrounded granulose cells that form the follicles. These are further surrounded by theca cells,
which are responsible for the production of secretion of androgens upon stimulation by
luteinizing hormone.
14
1.3.2 Ovarian cancer classification
Among all gynecological malignancies, ovarian cancer has been shown to be the most lethal. It
accounts for 5-6% of all cancer-related deaths. In the United States, women have a 1 in 59
lifetime probability of developing an ovarian tumor (38). Worldwide, the approximate incidence
of ovarian cancer is 200 000 with approximate 125 000 related deaths (38). The median age of
ovarian cancer patients is 60 years (55). The most important risk factor for developing ovarian
cancer is family history, but the majority of cases are sporadic and only 5% have an identifiable
genetic predisposition, mostly related to mutations in BRCA1/2 genes (55). Endocrine,
environmental, and dietary factors can also contribute to the development of the disease (56),
including: advanced age, nulliparity, hormonal therapy and exposure to talc (57). Oral
contraceptives, pregnancy, and lactation have been shown to have protective effects.
Ovarian carcinomas normally present as pelvic masses. In excess of 80% of patients
show symptoms even when the disease is confined to the ovaries. These can include: abdominal
bloating and pain, fatigue, indigestion, frequent urination, pelvic pain, constipation, urinary
incontinence, back pain, pain with intercourse, early satiety, weight loss, nausea, bleeding with
intercourse, deep venous thrombosis and diarrhea (56). However, these are relatively common
symptoms, which occur in benign conditions, and are not suitable for early diagnosis.
Although classified as a single disease, ovarian cancer can be stratified into several
related but distinct categories. These are: sex-cord stromal tumors, germ cell tumors, and
surface epithelial tumors (38). Each category of tumor is also subdivided into several
histological subtypes. The most common are epithelial ovarian cancers accounting for 80% of
diagnosed cases, followed by sex-cord stromal tumors (10-15%) and germ cell tumors (5-10%).
Stromal cell tumors are mostly seen in postmenopausal women (~50% of cases),
although they may also occur in younger women. In some cases, reproductive hormones may be
15
produced by stromal tumors often resulting in vaginal bleeding in postmenopausal women or
premature puberty in young girls. Male hormones may be produced in rare cases, which can
result in menstrual irregularities, hirsutism, and virilism. Germ cell tumors arise from oocytes
and are most common in adolescent women accounting for 60% of ovarian cancer cases in
women below the age of 20 years (58). These can present as teratomas, dysterminomas,
endodermal sinus tumors, and choriocarcinoms.
Epithelial ovarian cancers arising from the ovarian surface epithelium are the most lethal
among all ovarian malignancies (59, 60). Considering that this category of ovarian cancer
accounts for a large majority of total cases, it is the focus of most research dealing with the
diagnosis and treatment of this disease. Epithelial ovarian tumors can be benign or malignant.
Benign tumors can be serous adenomas, mucinous adenomas, and Brenner tumors (61).
Malignant tumors on the ovarian surface, known as carcinomas, have a high potential to spread
to proximal or distant sites in the body. Epithelial ovarian cancers can be divided into four
distinct subtypes based on tissue morphology: serous, endometrioid, mucinous, or clear-cell
carcinomas (62).
Serous carcinomas account for 40-60% of epithelial ovarian tumors and are considered as
the most aggressive subtype. Less than 25% of cases are detected at stages I or II. There are a
number of different histological presentations of serous carcinomas, which is a reflection of the
genetic heterogeneity characteristic of most aggressive tumors. Papillary and micropapillary
architecture with interspersed solid masses and chamber-like open spaces are most common.
The cellular content is varied, most often including columnar cells with a mixed contribution of
clear cells, eosinophilic cells, signet ring cells, and spindle cells. Some molecular characteristics
of serous ovarian carcinomas include the expression of WT1 (63), p53 overexpression and
mutations (64, 65), and loss of BRCA1 expresion in some high –grade tumors (66).
16
The second most common subtype of epithelial ovarian cancer accounting for 10-20% of
cases are endometrioid tumors. In the context of endometrial tissue-tissue like background, these
present with structural patterns containing tubules, cribriform structures, papillae, and exhibit
sheet-like growth. Due to their similarity to endometrioid tissue, these tumors are often
associated with endometriosis, endometrioid borderline tumors, or co-existing tumor of the
endometrium (67, 68). Endometrioid tumors may also exhibit sex cord properties and spindle
cells. Molecular features include: estrogen and progesterone receptor expression, and mutations
in genes encoding for -catenin, PI3K, and PTEN (69-73).
Mucinous tumors account for less than 3% of all epithelial ovarian cancers. The tumor is
composed of cells that often resemble intestinal or endocervical epithelium. Malignant
mucinous tumors normally contain cysts with papillary projections in the lumen, and large solid
areas of necrosis and hemorrhage. They normally lack the estrogen receptor, mesotheiln, and
fascin expression, but mutations in K-ras are common (74-77).
Clear-cell tumors occur in 5-10% of patients with ovarian malignancies. They often
contain polyp-like masses protruding into the lumen of tumors, which are predominantly solid or
cystic. These tumors are most often malignant and are the most lethal of all epithelial ovarian
carcinomas. They are also characterized by low estrogen and progesterone receptor, WT1, p53,
and mib-1 expression (73). K-ras and PTEN mutations can also be present (78, 79).
Some types of epithelial ovarian carcinomas are classified as undifferentiated. They are
characterized by absence of any specific differentiation and presence of diffuse solid areas. They
are considered to grow and spread faster than the other subtypes. Borderline tumors account for
up to 10% of cases. These normally do not appear cancerous and are characterized as having a
low malignant potential. They mainly occur in young women and are histologically similar to
serous and mucinous tumors. Borderline tumors generally have good prognosis, but can recur
17
after surgical removal or metastasize within the abdominal cavity. Therefore, ovarian cancer is a
complex and heterogeneous disease, which makes it difficult to diagnose, treat, and monitor.
However, there are standard diagnostic and treatment options available in the clinic.
18
1.3.3 Ovarian cancer detection and treatment
When diagnosed at early stages (I and II), the 5-year survival of ovarian cancer patients reaches
rates of close to 90%. However, when the disease is diagnosed at late stages (III and IV) the
overall 5-year survival rate drops down to between 10 and 30%. Unfortunately, this is the case
with the vast majority of ovarian cancer patients. Only 25% of ovarian cancer patients are
diagnosed at stages I or II when the tumors is confined to the ovary, 7% are diagnosed with
pelvic spread, and 68% of patients are diagnosed with distant metastases (80). No current test
has the required sensitivity or specificity to be used for screening of ovarian cancer.
This is due to the location of ovaries in the body, which in the early stages of the disease
do not interfere with any vital organs and local irritation is not detectable. Although
considerably limited, current approaches towards the detection of ovarian cancer include pelvic
examination, ultrasonography, and serum tumor marker screening. These tests are regularly
performed on women with a strong familial history of ovarian cancer (80-82). Pelvic
examination fails to detect early stage disease and ultrasound cannot reliably differentiate
between benign and malignant forms, leaving serum biomarker detection as a promising strategy
for early screening.
To date, there is only one validated ovarian cancer biomarker, named CA125, a large
mucin-type glycoprotein whose expression level is elevated in ovarian cancer (83). This protein
has proven to be essential for monitoring the response of patients to treatment but has shown less
promise as a screening tool since CA125 can be elevated in a number of other malignancies and
benign conditions, as well as during menstruation and pregnancy. Therefore, a great need still
exist for a biomarker capable of clinical use in screening or diagnosis of early stage ovarian
cancer, which could affect disease outcome.
19
When the presence of an ovarian tumor is suspected, based on physical symptoms and
pelvic examination, transvaginal and abdominal ultrasonography is performed, and serum levels
of CA125 are measured. As well, a CT (computed axial tomography) scan of the abdomen and
pelvis is undertaken. Following a diagnosis of ovarian cancer, an exploratory laparotomy may
lead to the surgical resection of ovaries, fallopian tubes, and/or the uterus. Lymph nodes, liver,
and sites within the abdomen are also checked for metastases. Surgery allows for the
determination of the histology and staging of the tumor, and is performed to improve the
patient’s response to chemotherapy. Patients with well differentiated stage IA or IB tumors that
are surgically removed normally do not require any further treatment. Patients with poorly
differentiated stage I, or stage II-IV tumors require chemotherapy after surgery. Normally,
taxane or paclitaxel therapy is combined with platinum-based compunds such as cisplatin or
carboplatin (55, 84, 85).
The stage of the tumor is the main prognostic factor for patient outcome, but
histological type, age of patient, and patient’s overall condition also play an important role.
Tumors histologically classified as clear-cell, mucinous, or poorly differentiated genereally have
the worst prognosis (56). Therefore, biomarker-based early detection and stratification based on
molecular biomarker profiles of ovarian tumors could greatly affect the patients with ovarian
cancer.
20
1.3.4 Glycosylation in ovarian cancer
Branching and sialylation of protein-associated glycans is increased in ovarian cancer. More
specifically, increases in the agalactosylated biantennary glycans on IgG molecules, and the
sialylated Lewis x structures on haptoglobin, 1-acid glycoprotein, and1-antichymotrypsin
have been recorded (86). It must be noted that all of these proteins are involved with chronic
inflammation and are likely not produced by tumor cells at nearly as high levels (or at all) as
from other sites of expression such as the liver and immune cell types.
However, there is strong evidence that increased sialylation in ovarian tumors cells also
occurs. Namely, in addition to carcinomas of the brain, breast, cervix, and the colon, the
expression of ST6GAL1 (a sialyltransferase responsible for attachment of 2-6-linked terminal
sialic acids) is increased in ovarian tumors (87, 88). Increased expression of 2-6-linked sialic
acids generally correlates with cancer progression and metastasis (87). The part ST6GAL1
performs in these events is poorly understood, with possible roles in enhancing 1-integrin
function (87, 89) and blocking of Fas- and TNFR1-mediated apoptosis by sialylation of these
receptors (90, 91). Direct mRNA expression analysis of multiple sialyltransferases in ovarian
tumor tissues has indicated the preference for the attachment of 2-6- over 2-3-linked sialic
acids on glycoproteins produced by cancer cells (88). Specifically, ST6GAL1 levels were shown
to be increased while the levels of ST3GAL6, a competing enzyme which normally attaches
sialic acid in a 2-3 linkage, were decreased. Additionally, sialyltransferases attaching sialic
acid in an 2-3 linkage to O-glycans were shown to be upregulated while enzymes with the same
linkage specificity for N-glycans were down regulated (88). These observations lend significant
support to other studies showing that 2-6-linked sialic acids are overexpressed on glycoproteins
secreted or shed from membranes of ovarian cancer cells. However, this in contrast to recent
21
study that examined the mRNA expression of 6 different sialyltransferases and their correlation
to CA125 expression, which did not show any evidence for ST6GAL1 dysregulation in ovarian
cancer tissues (92).
Glycosylation of CA125 has also been examined. The CA125-associated glycans in
protein purified from the OVCAR3 ovarian cancer cell line have been elucidated. The
glycoform population was found to be 20% high mannose and 80% complex type (93). Mono-
fucosylated bi-, tri-, and tetra-antennary bisecting structures with single or no sialic acid residues
were identified. CA125 from OVCAR3 cells and amniotic fluid was also examined by multiple
lectin chromatography, showing differential binding patterns to a panel of lectins, which could
improve the diagnostic potential of CA125 by distinguishing the increases its serum levels
between benign conditions, such as pregnancy, from ovarian cancer-related increases (32).
22
1.3.5 Mass Spectrometry-based Glycoproteomics of Ovarian Cancer
Mass spectrometry-based identification of proteins relies on the measurement of mass and charge
of individual molecules and atoms. This occurs in the gas phase of ionized analytes (94). A
mass spectrometer consists of an ion source, mass analyzer, and a detector (95). The most
common ion sources utilize electrospray ionization (ESI) or matrix-assisted laser
desorption/ionization (MALDI) to volatilizes and ionizes the protein or peptide analytes before
mass analysis. ESI-based ion sources ionize the molecules of interest out of a solution as it
passes through an electrostatic field. MALDI sublimates and ionizes analytes from a dry,
crystalline matrix with short pulses of a laser beam (94). ESI sources are used for complex
sample analysis while MALDI is normally used for more uniform types of analytes. Following
ionization, the analyte molecules enter the first mass analyzer (MS1) where the gas-phase ions
are separated based on their mass to charge ratio (m/z). They are then directed into the collision
cell, where the ions collide with neutral gas molecules and fragment. The fragment m/z values
are then measured in the second mass analyzer resulting in a tandem mass spectrum. In the
proteomics field, the analytes are most often peptides derived from the protease-based (most
often trypsin) cleavage of proteins. The generated MS1 and MS2 spectrums for analyzed
peptides are then examined by various bioinformatic algorithms (MASCOT, SEQUEST,
X!TANDEM) based on the predicted fragmentation patterns of different amino acid sequences.
This allows for the identification of the peptide sequence from which the identity of the protein
can usually be deduced by matching against translated genomic sequences (96).
There have been a considerable number of studies that have used shotgun mass
spectrometry-based proteomics to mine a variety of samples for potential ovarian cancer
biomarkers. Our group has investigated cell line supernatants and ascites fluids form ovarian
cancer patients. Gunawardana et al. identified 2039 proteins in the conditioned media of four
23
cell lines representing the four major subtypes of epithelial ovarian cancer (97), and a study by
Kuk et al. identified 445 proteins in ascites fluid (98). Some other, more recent, proteomics
studies have included: in excess of 700 proteins were identified in ascites and interstitial tissue
fluids of ovarian cancer patients and controls (99), 508 proteins were identified in benign and
malignant ascites by Elschenbroich et al. (100), endometrial cancer tissue was analyzed by
Maxwell et al. (101), and the proteome of paclitaxel-resistant and –sensitive cell lines has also
been elucidated (102). Therefore, thousands of proteins have been identified in ovarian cancer
related samples, from which lists of potential biomarkers have been produced but as of yet no
candidate has been able to match the performance of CA125 in independent validation studies.
In what can be considered a subset of shotgun proteomic studies, glycoproteomic studies
have focused on the mass spectrometry-based identification of glycosylated proteins. The
general rationale is that glycoproteins have properties desirable in tumor biomarkers, such as the
fact they are most often secreted or membrane proteins that can be shed from the tumor surface
and present in the general circulation. This field has expanded considerably over the last decade
and glycoproteomic analysis has been conducted in the context of a large number of human
cancers. In the case of ovarian cancer, glycoproteins from different tumor subtypes were
identified using hydrazide chemistry-based enrichment of glycopeptides (103). Lectin affinity
enrichment was used to identify and relatively quantify glycoproteins in ovarian cancer and
normal ovarian tissue (104). However, the major weakness of these and similar studies is that
the focus is placed on the gross and undiscriminating capture of all glycoproteins rather than the
phenotypic changes in the glycosylation of proteins that occur during oncogenic transformation
and progression.
Studies that characterize or measure the glycosylation of individual proteins or identify
relative glycan levels of all glycoproteins in a given sample also fall under the glycoproteomics
24
umbrella. For example, a study has examined the changes in N-glycan structures in sera of
normal women and patients with ovarian cancer undergoing an experimental treatment. The
results showed increased levels of tri- and tetra-branched structures with varying degrees of
sialylation and fucosylation, increased levels of terminal fucosylation, and decreased levels of
"bisecting" glycans in patient samples compared to normal controls (105). Several recent studies
have also examined the status of MUC1 glycosylation in ovarian cancer tissues (106), the O-
linked glycome of high molecular weight proteins in ovarian cancer ascites (107), and the total
cellular protein N-glycome of the SKOV3 ovarian cancer cell line (108). However, there still
appears to be a need for more focused studies, examining glycosylation of individual tumor
derived proteins and the identification of potential glycoprotein biomarkers carrying glycan
structures reflective of the glycosylation status of tumor cell-derived proteins.
25
1.4 Kallikrein 6 in ovarian cancer
1.4.1 Kallikrein family
The kallikrein (KLK) gene family is comprised of homologous secreted serine proteases with
trypsin or chymotrypsin-like activities (109). Unlike other trypsin-like proteases which are
spread across multiple loci, the kallikreins map to a single locus located on the long arm of
chromosome 19 (19q13.4), which is composed of 15 uninterrupted and contiguous kallikrein
genes spanning approximately 300 kb (Figure 1.3A). It includes the tissue kallikrein (KLK1)
and kallikrein-related peptidase genes (KLK2-KLK15) (110, 111). KLK genes are 4-10 kb in
length and are all composed of 5 coding exons and 4 introns of variable lengths, with a
conserved catalytic triad of histidine, aspartic acid, and serine (Figure 1.3B). Variability
between the genes is found within the 5’ untranslated region. The KLK proteins are single-chain
serine proteases translated as pre-proenzymes containing an N-terminal signal peptide that
targets them for secretion through the classical secretory pathway, a propeptide that keeps them
as inactive zymogens until proteolytic activation by cleavage of the pro sequence, and a serine-
protease domain for catalytic activity (Figure 1.3C).
KLK genes are expressed in a variety of tissues and have been shown to be involved in
several proteolytic cascades (112-116). They are often co-expressed in the skin, breast, prostate,
pancreas and brain. They are primarily produced by secretory epithelial cells and accumulate in
bodily fluids such as sweat, milk, saliva, seminal plasma, cerebrospinal fluid or pericellular
spaces. Proteinase-activated receptors (PARs), matrix metalloproteases (MMPs), insulin-like
growth factor binding proteins, latent transforming growth factor beta, fibronectins, and
collagens are known KLK enzyme substrates (117). The patterns of kallikrein expression can be
tissue-restrictive for some members of the family (117-120), or broad for other groups within the
26
family (121, 122). For example, KLK2, KLK3 (PSA, prostate specific antigen), KLK4, and
KLK15 have prostate-specific expression patterns. Variable expression of other kallikrein
family members has been documented in normal tissues (122).
A number of different mechanisms have been shown to affect KLK gene or protein
expression. Gene copy-number and methylation effects have been studied (123-125), but the
main focus of research has been on the influence of hormones on kallikrein expression (126-
129). Hormone response elements in the proximal promoter and enhancer regions of KLK2 and
KLK3 have been shown to be sensitive to androgen stimulation (130). Recently, single
nucleotide polymorphisms have been identified for each family member and these variations are
now being studied in different diseases (131-138). The action of different microRNAs has been
put forward recently as an important mechanism of post-transcriptional regulation of KLK
expression (139, 140). Different serpin family members and 2-macroglobulin have been shown
to act as inhibitors of activated kallikreins (141).
The participation of kallikreins in almost all human cancers has been studied extensively
and their roles in oncogenesis, tumor progression, metastasis, and angiogenesis have been shown
(117, 142-145). Aberrant kallikrein expression has been shown in a number of malignancies,
mostly hormone-dependent ones such as breast, ovarian, and prostate cancer. KLK3, or better
known as PSA, has been shown to be one of the most valuable tumor biomarkers due to its use in
the diagnosis and monitoring of prostate cancer. Protein and mRNA expression levels of
kallikreins 4, 5, 10, 11, 14 and 15 have also been investigated and found to be dysregulated in the
context of prostate cancer (145). In breast cancer patients, elevated KLK5 and KLK14 gene
expression has been shown to be indicative of poor prognosis, while loss of KLK10 appears to
be a favorable factor for tumor progression (146-148). Decreased mRNA expression levels of
kallikreins 5, 10, 11, 13, and 14 have been shown in testicular cancer (145). Overexpression of
27
KLK6 and KLK10 has been shown in an in-silico study of kallikrein family member gene
expression in pancreatic cancer (149). The same study also found the overexpression of
kallikreins 6, 8, and 10 in colon cancer. Therefore, it is clear that almost all members of the
kallikrein family are aberrantly expressed in a number of different oncologic malignancies
making them potentially valuable targets for application in the diagnosis, prognosis, and
monitoring of cancer.
28
Figure 1.3. Characteristics of the kallikrein locus, genes, and proteins. The organization of
the kallikrein locus on the long arm of chromosome 19 (A). General structure of a typical
kallikrein gene (B). A schematic of the different regions of kallikrein proteins (C). Adapted
from Borgono and Diamandis (117).
29
1.4.2 Kallikreins as ovarian cancer biomarkers
Kallikreins are known to play an important role in the progression and metastasis of a number of
human cancers (117, 142), and most members of this family (kallikreins 4, 5, 6, 7, 8, 9, 10, 11,
13, 14, and 15) have been shown to have some value in the detection, diagnosis, prognosis
prediction, and monitoring of ovarian carcinomas (145, 150-152). Kallikrein 4 is expressed by a
majority of serous carcinomas but not by normal epithelial cells. Its expression is associated
with higher tumor stage and grade, and patients with kallikrein 4-expressing tumors have an
increased risk for relapse and death (153). Kallikrein 5 has been shown to have prognostic utility
in patients with stage I and II disease (154). Expression of kallikrein 7 is associated with poorer
prognosis in patients with lower grade disease (155). Opposite to the situation in breast cancer,
the up-regulated expression of KLK10 is an unfavorable prognostic characteristic for ovarian
cancer patients (145). On the other hand, kallikreins 8, 9, and 11 are favorable prognostic
markers in ovarian cancer (145). Some kallikreins, in addition to their presence in ovarian tumor
tissue, can also be detected in serum and can serve as potential serological markers of disease
(38). Of these, kallikreins 6, 7, 8, and 11 are the four most specific secreted kallikreins in
effusions from ovarian cancer patients (156).
30
1.4.3 Kallikrein 6
Kallikrein 6 has previously been identified under several different names, including: Bssp,
neurosin, Protease M, PRSS9, PRSS19, serine protease 9, serine protease 19, SP59, and ZYME
(109). It was originally cloned and named as Protease M from primary breast cancer cell lines
(157). The KLK6 gene spans approximately 11 kb and is composed of 6 introns and 7 exons, of
which the first two are untranslated. KLK6 mRNA expression has been shown in a number of
tissues: bone marrow, breast, kidney, lung, ovary, pituitary, salivary gland, spleen, testis,
thymus, thyroid, and uterus. However, the highest expression is found in tissues of the central
nervous system (109). The gene, mRNA, and protein organization and tissue expression of
KLK6 can be seen in Figure 1.4. Overexpression of the KLK6 gene transcript has been observed
in several malignancies: ovarian, breast, uterine, pancreatic, colorectal, gastric, skin, and bladder
cancers (109). On the other hand, in Alzheimer’s and Parkinson’s disease patients KLK6 mRNA
levels are decreased in their brain tissue (158, 159).
Much like the other members of the kallikrein family, the KLK6 protein is translated as
a single-chain inactive prepro-enzyme. It has trypsin-like activity and is 244 amino acids in
length with a 16 amino acid long N-terminal signal sequence and a 5 amino acid long activation
peptide (109). Regulation of KLK6 activity is achieved through proteolytic (auto) activation of
the pro-enzyme, (auto) degradation of the mature enzyme, or binding to protease inhibitors. The
possibility of KLK6 auto-activation has been previously shown, and activation of KLK6 by
enterokinase, plasmin, and kallikreins 4, 5, 11, and 14 has also been reported (116, 160-163). An
autolytic mechanism has been implicated in the inactivation of the enzymatic activity of KLK6
through protein degradation (160, 164, 165). As well, a number of inhibitors of KLK6 catalytic
activity have been identified, including: protein-C inhibitor, aprotonin, soybean trypsin inhibitor,
PMSF, leupeptin, antipain, antithrombin III, 2-antiplasmin, 1-antitrypsin, and 1-
31
antichymotrypsin (166-169). LEKTI has been suggested as a potential inhibitor of KLK6
activity in skin desquamation (141). Several studies have been conducted to determine the
physiological substrates of KLK6. Degradation of myelin basic proteins and amyloid precursor
protein by KLK6 have been reported (164, 170, 171). Also, components of the extracellular
matrix have been shown to be targets of KLK6 enzymatic activity, including: plasminogen,
fibrinogen, fibronectin, vitronectin, collagen type I, II, III, and IV, and laminin (117, 171-173).
The measurement of KLK6 protein expression in various biological samples has been
pursued actively since purification of recombinant proteins, subsequent production of antibodies,
and development of increasingly sensitive and specific ELISAs (125, 174, 175). A large number
of tissues and bodily fluids have been examined for KLK6 expression by ELISA and
immunohistochemistry (122, 174, 176). With the use of in-house developed antibodies and
ELISA, the Diamandis group has measured KLK6 in ascites, breast cyst, cerebrospinal, and
nipple aspirate fluids (174). In a study examining adult and fetal tissues, KLK6 protein
expression was found to be the highest in brain tissue cell lysates and CSF (122). In other adult
tissues, the levels of KLK6 were considerably lower (<10% of the brain) with the exception of
the spinal cord, which exhibited 50% of brain tissue expression. This appears to be in
concordance with mRNA expression. In addition, immunohistochemical analysis has revealed
that KLK6 expression is often different between different cells of the same organ (176-178).
Changes in KLK6 protein expression have been noted in oncologic and neurodegenerative
pathological conditions as well, including: Alzheimer’s and Parkinson’s diseases, renal cancer,
gliomas, lung cancer, pancreatic ductal carcinoma, uterine cancer, ovarian cancer (109).
32
Figure 1.4. Kallikrein 6 gene, mRNA, and protein organization and tissue expression.
Adapted an modified from Bayani and Diamandis (109).
33
1.4.4 Kallikrein 6 as a biomarker in ovarian cancer
Similarly to PSA (KLK3), KLK6 has great potential as a biomarker. It is a secreted protein that
can be easily measured in routinely available clinical samples, such as blood, urine, or other
bodily fluids. In ovarian cancer, KLK6 is overexpressed in all of the four major subtypes, with
patients carrying serous and undifferentiated tumors showing the greatest percentage of
identifiable disease based on the KLK6 levels (126, 179, 180). The potential of KLK6 as an
ovarian cancer marker was demonstrated in a study showing that it is capable of identifying the
disease in approximately 22% of cases not detected by CA125 (181) and that the diagnostic
power of each of these molecules is improved when they are used in combination (179, 182-
184). KLK6 expression has been shown to correlate with late stage disease and that KLK6-
positive patients exhibited an increased risk of relapse and death (185). As well, increases in
mRNA levels and copy number variations in the KLK locus have been examined as potential
causes for KLK6 protein overexpression (123-125, 180, 185). Because of these findings, KLK6
was included in the biomarker validation studies conducted by the Early Detection Research
Network (EDRN) and Specialized Programs of Research Excellence (SPORE), where it was
shown that KLK6 performed well by ranking in the top 10-20 out of 49 tested markers (186,
187). It ranked 9th in terms of sensitivity (95% confidence interval), and 15th in Area Under the
Curve (AUC – 95% confidence interval). However, in the case of early detection, it ranked 29th
for sensitivity (95% confidence interval) and 28th AUC (95% confidence interval). Therefore,
even a relatively modest improvement in the diagnostic potential of KLK6 could potentially
result in its application in the clinic.
34
1.5 Purpose and Aims of Study
1.5.1 Rationale
As discussed in the previous sections, the ability to detect ovarian cancer more reliably and at
earlier stages in the progression of the disease is of crucial importance. Application of treatment
options to patients with early stage cancer allows for an extremely good prognosis. At the
present time, despite availability of ovarian cancer screening and diagnostic modalities, which
include pelvic examination, ultrasonography and the serum marker CA125, the ability of these
technologies, alone or in combination, to diagnose adnexal masses and distinguish between
benign and malignant growths is not at a satisfactory level. Therefore there appears to be a
desperate need for improvement of existing and discovery of novel biomarkers, which can alone,
or in combination with existing techniques be utilized for improved and earlier diagnosis of
ovarian carcinoma. Based on the very well established observations of disruptions in the protein
glycosylation pathways in ovarian cancer, the detection and concurrent measurement of
disturbances in glycosylation patterns of existing or novel biomarkers provides for a strong
avenue of research directed towards that goal. Considering that it is expressed in virtually all
ovarian cancers, is detected in serum, has clearly delineated biomarker statistics and an extensive
set of recombinant and immunological reagents available for its study, kallikrein 6 was chosen as
the most suitable target of examination in our laboratory. Based on the observations from our
KLK6 experience and supporting research from other groups, we were able to identify other
potential candidate glycoprotein biomarkers based on their altered glycosylation status in ovarian
cancer.
35
1.5.2 Hypothesis
Kallikrein 6 protein is expressed in many tissues but the major site of expression is the central
nervous system and it is present in high amounts in the cerebrospinal fluid (1 mg/L), which is in
up to thousand-fold excess than the levels in serum. Therefore, it is believed that the kallikrein 6
found in the general circulation is mostly derived by passive diffusion from the cerebrospinal
fluid. Extremely high levels of kallikrein 6 have also been detected in the ascites fluid of ovarian
cancer patients, which is derived from tumor cells.
We hypothesize here that the central nervous system-derived and ovarian tumor-derived
kallikrein 6 proteins have different glycosylation patterns, and that the differences in these
patterns will be detected in the serum population of kallikrein 6 in ovarian cancer patients, which
could form the basis for the development of an improved test measuring the changes in kallikrein
6 glycosylation. We further hypothesize that the knowledge of the changes in the glycosylation
patterns of kallikrein 6 could form the basis for a biomarker discovery study, which would be
based on mass spectrometry-based proteomic identification of glycoproteins.
36
1.5.3 Aims of study
1. Isolation and purification of kallikrein 6 from cerebrospinal fluid of healthy individuals
and ascites fluid of ovarian cancer patients, and characterization of kallikrein 6-associated
glycosylation with chromatography, molecular biology and tandem mass spectrometry
techniques (Chapter 2).
2. Development of a quantitative anion-exchange-based high performance liquid
chromatography methodology capable of differentiating kallikrein 6 glycoform
subpopulations in serum and other complex biological fluids (Chapter 3).
3. Tandem mass spectrometry-based glycoproteomic identification of candidate
glycoprotein biomarkers exhibiting similar glycosylation patterns (ie. sialylation) as
ovarian cancer-derived kallikrein 6 that were identified in aims 1 and 2 (Chapter 4).
37
2 CHAPTER 2: Characterization of KLK6 Glycosylation
Kuzmanov U, Jiang N, Smith CR, Soosaipillai A, Diamandis EP. Differential N-Glycosylation
of kallikrein 6 derived from ovarian cancer cells or the central nervous system. Mol Cell
Proteomics. 2009 Apr;8(4):791-8.
N Jiang assisted with majority of experiments as a summer/undergraduate student.
A Soosaipillai provided insight and assistance with ELISA and HPLC methodology.
CR Smith operated and maintained mass spectrometry equipment.
38
2.1 Short Overview
In the present chapter, N-glycosylation of KLK6 immunoisolated from ascites fluid of ovarian
cancer patients was compared to KLK6 isolated from CSF of healthy individuals. Differences in
the molecular weights were examined by SDS-PAGE Western blot. Differential charge status of
KLK6 from the two different sources was examined by anion-exchange HPLC. The sole N-
glycosylation site of KLK6 was identified by site-directed mutagenesis. Glycan content was
determined by endoglycosidase treatment and lectin affinity, and tandem mass spectrometry was
utilized for glycan structure determination.
2.2 Introduction
Disturbed glycosylation patterns have been observed in the majority of human cancers. Over the
past 40 years, a number of physiologically expressed proteins containing abnormal glycan
structures have been shown to be tumor-associated antigens (188). For example, prostate-
specific antigen (PSA) and ribonuclease 1 (RNase 1) were found to be differentially glycosylated
in prostate and pancreatic cancers, respectively (189, 190). It has been suggested that the
disturbed glycosylation of proteins is an early event of oncogenic transformation, aiding in the
invasion and metastasis of tumor cells (188, 191-198). As such, a selective advantage might be
conferred on tumor cells with increased glycan structures, allowing them to evade immune
response during the invasion and metastasis processes (199).
In ovarian cancer, a number of proteins are found to be aberrantly glycosylated, including:
CA125 (32), 1-proteinase inhibitor (200), haptoglobin (200), other acute phase proteins (86),
and IgGs (86). In particular, there is mounting evidence of increased sialylation of proteins and
deregulated sialylation pathways in ovarian cancer (201). Altered sialylation of proteins in this
39
disease is indicated by increased levels of the sialyl LewisX (SLeX) and sialyl-Tn antigens in
ovarian carcinoma, even at early stages of progression (86, 202, 203). This coincides with
findings showing disrupted sialyltransferase protein expression (204-206) and altered mRNA
expression of several sialyltransferases in ovarian cancer cells (88).
Human tissue kallikreins are a family of 15 secreted serine proteases with trypsin or
chymotrypsin-like activities. Through the use of RT-PCR, ELISA, immunohistochemical and
bioinformatic techniques, most kallikreins have been shown to be deregulated in a number of
malignancies including breast, ovarian, prostate and testicular cancer (142, 144, 207). Elevated
levels of kallikrein 6 (KLK6), a trypsin-like protease, in serum and tissue extracts have been
shown to forecast for poor prognosis in ovarian cancer (120, 153-155, 183, 208, 209). KLK6 has
a wide expression pattern at both the mRNA and protein levels. However, immunohistochemical
and ELISA studies have shown that the major site of KLK6 expression is the central nervous
system (CNS), with very high (mg/L) levels of the protein detected in cerebrospinal fluid (CSF)
(122, 156, 185). As such, the major source of KLK6 in the circulation of normal individuals is
the CNS.
The upregulation of KLK6 in ovarian cancer and its unfavorable prognostic value have been
well-established (181, 185, 210). It has been previously shown that virtually all ovarian tumors
express KLK6, some of them at extremely high levels (181, 185). During ovarian cancer
development and progression, tumor-derived KLK6 diffuses into the general circulation (185,
210). Despite these highly favorable characteristics of KLK6 as an ovarian cancer biomarker,
the sensitivity of the test performed in serum (for both early and late stage disease) has been
shown not to exceed that of the classical ovarian cancer biomarker, CA125 (210). The
combination of KLK6 and CA125 resulted in modest increases in sensitivity (10-30% over and
above CA125 alone) for both early and late stage disease (210). At early stages, the increase of
40
serum KLK6 contributed by ovarian cancer cells is usually not sufficient to raise KLK6 above
the normal serum levels. Therefore, the ability to differentiate KLK6 originating from the CNS
(normally found in the serum of healthy individuals) and KLK6 originating from ovarian tumors
could potentially increase the diagnostic value of KLK6 as an ovarian cancer biomarker.
Towards this purpose, the differential N-glycosylation patterns of KLK6 from ascites fluid of
ovarian cancer patients and CSF of healthy individuals were examined. Initially, anion-exchange
chromatography with the two biological fluids resulted in different elution patterns, indicative of
differential post-translational modifications or processing. Different N-glycosylation patterns of
the two isoforms of KLK6 were confirmed by glycosidase digestion followed by gel shift
mobility assays. Additionally, the presence of sialylation on the two isoforms was determined by
lectin-antibody sandwich ELISA methodology. The composition and structure of the glycans
present on the two subpopulations of KLK6 were elucidated by monitoring KLK6 glycopeptides
by electrospray ionization-Orbitrap tandem mass spectrometry (MS/MS). Our main finding is
that KLK6 from ovarian cancer ascites (but not CSF) is extensively sialylated. This difference in
sialylation may be exploited in the future for developing a specific biomarker for ovarian
carcinoma.
41
2.3 Materials and Methods
2.3.1 Anion-Exchange Chromatography
Anion-exchange chromatography was performed using a Mono Q 4.6/100 PE Tricorn high
performance column (GE Healthcare) attached to an Agilent 1100 series High Performance
Liquid Chromatography (HPLC) system. The running buffer used was a 20 mM Tris solution at
pH = 8.6. Biological fluids (100 L ) were diluted 1:1 in running buffer and loaded onto the
column for 5 min with a 0.5 ml/min flow rate. Maintaining the same flow rate, bound proteins
were eluted with a linear gradient of increasing NaCl concentration in running buffer (0–400
mM) over the next 35 min. Fractions were collected every min. KLK6 levels in each fraction
were measured using a previously described sandwich-type ELISA method(174), utilizing two
mouse monoclonal antibodies.
42
2.3.2 KLK6 Immunoisolation
Monoclonal mouse antibody against KLK6 (developed in-house; code 27-4) was coated on the
NHS-activated Sepharose 4 Fast Flow beads as per manufacturer’s instructions (1 mg of
antibody per 1 ml of beads). Immunoisolation was performed by incubating 10 ml of biological
fluid with 1 ml of NHS beads for 2 hours at room temperature with slow end to end rotation.
The beads were then washed with 20 ml of a TBS solution with 1 M urea at pH=7.5. The
antibody-bound KLK6 was eluted using 10 ml of a 1 M glycine solution at a pH of 2.5. The
isolated KLK6 was subjected to buffer exchange with TBS, pH 7.5, and concentrated down to
150 L using the Millipore Amicon Ultracel spin column with a 10 kDa molecular weight cutoff.
The biological fluids were leftovers of samples submitted for routine biochemical testing, or
collected with informed consent and institutional review board approval, and stored at –80
°C
until use. The CSF samples were clear in appearance, without any visible blood contamination
and were pools from approximately 100 male and female patients. Ovarian cancer ascites used
were pools from three late stage ovarian cancer patients.
43
2.3.3 Site-directed mutagenesis, Mutant expression and purification
A C-terminally his-tagged KLK6 genomic clone construct in a pcDNA5/FRT/V5-HIS-TOPO
backbone (Invitrogen) was generously provided by Dr. Yves Courty (Faculte de Medicine,
F3700 Tours, France). The asparagine residue at position 134 of KLK6 in this construct was
mutated to glycine by site-directed mutagenesis using standard T7 forward and BGH reverse as
terminal primers, and 5’-GAC TGC TCA GCC GGC ACC ACC AGC TGC-3’ and 5’-GCA
GCT GGT GGT GCC GGC TGA GCA GTC-3’ as mutagenic internal primers. Human
embryonic kidney cells (HEK 293) at 80% confluency were transiently transfected with wild-
type and mutant constructs in T175 tissue culture flasks with 70 g of plasmid DNA and 170 L
of the Lipofectamine 2000 transfection reagent as per manufacturer’s instructions (Invitrogen).
Following transfection and a 72 h growth period, the supernatant was collected, concentrated 10-
fold as described above, and the his-tagged KLK6 protein was purified using agarose-bound Ni-
NTA in batch, as per the manufacturer’s protocol (Qiagen).
44
2.3.4 SDS-PAGE Western Blot Analysis
All samples were run on pre-cast NuPAGE 12% Bis-Tris gels in MES/SDS running buffer as per
manufacturer’s protocol (Invitrogen). The gels were run for 2 hours at 200 V. The resolved
proteins were transferred onto Hybond-C Extra nitrocellulose membrane (GE Healthcare) at 30
V for 1 h. Membrane blocking was performed by incubation with TBS-T (0.1 mol/L Tris-HCl
buffer (pH 7.5) containing 0.15 mol/L NaCl and 0.1% Tween 20) supplemented with 5% nonfat
dry milk for 1 h at room temperature. The membrane was then probed with anti-KLK6
polyclonal rabbit antibody (produced in-house; diluted 1:2000 in TBS-T with 5% nonfat dry
milk) for 1 h at room temperature. The membrane was washed three times
for 15 min with TBS-
T and incubated with alkaline phosphatase-conjugated goat anti-rabbit antibody (1:2000
in TBS-
T with 5% nonfat dry milk; Jackson ImmunoResearch) for 1 h at room temperature. Finally, the
membranes were washed again as above, and the signal was detected on x-ray film using
a
chemiluminescent substrate (Diagnostic Products Corp.).
45
2.3.5 Glycosidase Digestion
Immunoisolated KLK6 was treated with N-Glycosidase F (PNGase F) from Flavobacterium
meningosepticum and acetyl-neuraminyl hydrolase (Neuraminidase) from Cloistridium
perfringens as per manufacturer’s instructions (New England Biolabs), where the enzymes were
used in 10-fold excess for 2 hours at 37 oC.
2.3.6 Lectin ELISA Assay
Sambucus nigra agglutinin (SNA, Vector Labs) in 50 mM Tris-HCl, pH 7.8 was coated on a 96-
well white polystyrene microtiter plate (100 L of 5ng/L SNA per well) by overnight
incubation at room temperature and washed twice in wash buffer (10 mmol/L Tris-HCl, pH 7.4,
containing 150 mmol/L NaCl and 0.5 ml/L Tween 20). Different dilutions of immunoisolated
KLK6 in 100 L of 50 mM Tris-HCl, pH 7.8 were incubated on the plate for 2 hours at room
temperature with continuous shaking followed by 6 wash steps, as described above. To detect
the presence of SNA-bound KLK6, 100 µL/well of biotinylated mouse monoclonal detection
antibody E24 (50 ng) diluted in 6% BSA were added to each well, incubated at room temperature
for 1 hour and washed 6 times. Subsequently, 100 µL (5 ng) of alkaline phosphatase–conjugated
streptavidin diluted in 1% BSA were added to each well, incubated for 15 minutes with
continuous shaking, and washed 6 times. 100 µL of diflunisal phosphate solution (0.1
mol/L Tris-
HCl, pH 9.1, containing 1 mmol/L diflunisal phosphate, 0.1 mol/L NaCl, and 1 mmol/L MgCl2)
were then added to each well, and incubated for 10 min with continuous shaking followed by the
addition of 100 µL of developing solution (1 mmol/L Tris, 0.4 mol/L NaOH, 2 mmol/L
TbCl3,
and 3 mmol/L EDTA) to each well and mixed for 1 min. Fluorescence was measured with the
PerkinElmer EnVision 2103 Multilabel Reader.
46
2.3.7 Sample preparation for Mass Spectrometry
Immunoisolated KLK6 (1 g) was resolved on a pre-cast NuPAGE 12% Bis-Tris as described
above. The gel was stained with SimplyBlue SafeStain (Invitrogen) and destained in water, as
per manufacturer’s protocol. The KLK6 bands were excised from the gel and dehydrated with
acetonitrile (ACN) for 10 min at room temperature. ACN was aspirated and the bands were
reduced in 300 L of 10 mM dithiothreitol (DTT, Sigma-Aldrich) in a 50 mM NH4HCO3
solution for 30 min at 60 oC and allowed to cool to room temperature for 10 min. Following the
removal of the reducing solution, the reduced protein in the gel bands was alkylated by addition
of 300 L of a 100 mM iodoacetamide in 50 mM NH4HCO3 solution for 1 h at 37 oC in the dark.
Upon removal of the alkylating solution, the gel bands were shrunk with ACN and rehydrated
with 50 mM NH4HCO3. This was repeated 3 times. After the last ACN dehydration step, the gel
bands were resuspended in 100 L of 50 mM NH4HCO3 solution containing 1 g of sequencing
grade modified trypsin (Promega) and left overnight at 37 oC for digestion. 40 L of this
solution were used for each MS/MS run.
47
2.3.8 Mass Spectrometry Conditions
KLK6-derived tryptic peptides were initially bound to a 2 cm C18 pre-column with a 200 m
diameter and eluted onto a resolving 5 cm analytical C18 column (75 m diameter) with a 15
mm tip (New Objective). The liquid chromatography setup was connected to a Thermo LTQ
Orbitrap XL mass spectrometer with a nanoelectrospray ionization source (Proxeon). Analysis
of the eluted peptides was done by tandem mass spectrometry in positive-ion mode. A two
buffer system was utilized where Buffer A (running) contained 0.1% formic acid, 5% ACN, and
0.02% TFA in water and Buffer B (elution) contained 90% ACN, 0.1% formic acid, and 0.02%
TFA in water. For structure determination a parent mass list was created for the glycopeptides of
interest, and each glycopeptide was fragmented with 25, 30, and 35 percent normalized collision
energy in HCD mode and 35 percent normalized collision energy in CID mode. Charge state
rejection was enabled to reject charge states 1+, 2+ and unassigned charge states. HCD collision
energy was optimized in the calibration procedure according to manufacturer’s instructions. All
data-dependent scan events had isolation width set to 3.0.
48
2.3.9 MS/MS Glycan Structure Identification
The glycan structure of KLK6 was determined by MS/MS analysis of the
DCSANTTSCHILGWGK glycopeptide. The retention time of the KLK6 glycopeptides was
determined by observing the presence of common diagnostic oxonium ions in MS2 spectra (ie.
204.08 for N-acetylglucosamine or 366.13 for a hexose linked N-acetylglucosamine). Once this
was determined, MS1 spectra over that period of time were combined in a single spectrum using
QualBrowser on Xcalibur software (Version 2.0) and individual peaks (corresponding to visually
chosen monoisotopic masses of each ion) were inspected as indicators for the presence of
glycosylation on the KLK6 glycopeptide. Only triply charged ions were inspected.
Corresponding monoisotopic masses were referenced against the Glycomod tool
(www.expasy.org) which provided the output of glycan composition on the given glycopeptide
within 5 parts per million mass tolerance. The glycan composition allowed for the inference of
the glycan structures, which were further confirmed (where available) against glycan structure
databases available at www.glycosciences.de and www.functionalglycomics.org. For most of
the observed ions, analysis of MS2 data for the presence of fragment glycopeptides and glycans
was used to further confirm that these were indeed the suspected molecules (data not shown).
49
2.4 Results
2.4.1 Anion-Exchange Chromatography
Anion-exchange chromatography was used to examine the differential elution patterns of KLK6
from CSF and ovarian cancer ascites fluid. Following the chromatography step, eluted fractions
were analyzed for the presence of KLK6 by sandwich ELISA methodology. The elution patterns
for KLK6 from CSF and ascites samples were distinctly different (Figure 2.1). These patterns
were consistent when CSF and ascites fluids from different subjects were used. These results
suggested that the ascites form of KLK6 has an overall higher negative charge, suggesting
presence of more complex glycosylation.
50
Figure 2.1. Anion-exchange chromatography of biological fluids. Results of ELISA-based
quantification of KLK6 in fractions collected after elution of ovarian cancer ascites fluid and
CSF from a MonoQ anion-exchange column. The data for each fraction is presented as
percentage of total eluted KLK6. Results are representative of the same analysis performed on 3
different ascites fluids and CSFs.
0
5
10
15
20
25
30
35
40
45
Fraction #
% o
f to
tal
KL
K6 Ascites
CSF
15 17 19 21 23 25 27 29 31 33 35 37 39
51
2.4.2 Western Blot Analysis, Glycosidase Treatment and Site-Directed
Mutagenesis
To determine differences in molecular masses between the KLK6 from the two different
biological fluids, SDS-PAGE followed by Western blot analysis was performed on KLK6 from 3
ovarian cancer ascites fluids and three CSFs from different female subjects. In all cases, KLK6
from ascites fluid had a higher molecular mass than KLK6 from the CSF, further suggesting a
differential pattern of post-translational modifications between the two KLK6 isoforms (Figure
2.2A). The smearing of the ascites-derived KLK6 bands raises the possibility of
microheterogeneity of the protein present in this fluid.
To confirm that the molecular mass differences between the two isoforms of KLK6 were
due to differential glycosylation, immunoisolated KLK6 from pools of ascites fluids and CSFs
was treated with PNGase F (removes all N-glycans at asparagine residues) and Neuraminidase
(catalyzes hydrolysis of 2-3, 2-6, and 2-8 linked sialic acid residues), and analyzed for
changes in gel mobility by Western blot. Treatment with PNGase F resulted in a shift of both the
ascites and CSF forms of the protein to the same molecular mass, suggesting that the initial
molecular mass differences between these two KLK6 isoforms were due to their differential N-
glycosylation patterns (Figure 2.2B). Neuraminidase treatment resulted in a shift to a lower
molecular mass of the ascites form, but the CSF form. The shift resulted in a molecular mass
between the fully glycosylated and completely deglycosylated (as produced by PNGase F
treatment) forms of KLK6 (Figure 2.2B). These results suggested presence of terminal sialic
acid residues on ascites-derived KLK6 but not on the CSF-derived KLK6.
KLK6 was further shown to contain only one site of N-glycosylation at residue N134 by
site-directed mutagenesis and transient expression in HEK293 cells. The N134G mutant KLK6
displayed a lower molecular mass than the wild-type protein (Figure 2.2C). As well, upon
52
treatment with PNGase F there was no molecular mass shift for the mutant KLK6, while the
wild-type protein showed a drop to the apparent molecular mass of the unglycosylated mutant
protein. These results confirm in human cells, similar findings reported previously with the
Baculovirus/insect cell line expression system (211).
C1 A1 C2 C3A2 A3
N - P - PN
ascites CSF
P - P -
N134G WT
A
C
B
Figure 2.2. KLK6 Western Blot Analysis. (A) Western blot of 10 L of three different ascites
fluids from ovarian cancer patients (A1-3) and three CSFs from women (C1-3). (B)
Immunoisolated KLK6 from pools of ovarian cancer ascites fluids and CSFs following mock (-),
neuraminidase (N), and PNGase treatment (P). (C) Purified KLK6 from supernatant of HEK
293 cells transiently transfected with wild type (WT) and N134G KLK6 constructs following
mock (-) and PNGaseF (P) treatment. For more details see text.
53
2.4.3 Lectin-Antibody Sandwich ELISA
The presence of 2-6 linked sialic acid on KLK6 glycoisoforms was further confirmed using a
lectin-antibody sandwich ELISA method. Identical and increasing concentrations of
immunoisolated KLK6 (confirmed by a total KLK6 ELISA) from ascites fluid and CSF were
assayed for the presence of 2-6 linked sialic acid by capturing any sialylated protein with
immobilized Sambucus nigra agglutinin (SNA) and detecting specifically the KLK6 moiety with
a monoclonal KLK6 antibody. Signal intensity was corrected for background by subtracting the
average signal from 12 repeats from wells without added KLK6. Only the glycoisoform of
KLK6 from ovarian cancer ascites fluid showed a concentration-dependent increase in signal that
was above background noise (Figure 2.3), further confirming the presence, and absence, of sialic
acid on KLK6 from ovarian cancer ascites and CSF, respectively. Similar results were obtained
with the reverse approach, where a KLK6-specific antibody was used for capture and
biotinylated SNA was used for detection (data not shown). Related experiments where SNA was
substituted with Maackia amurensis Lectin II (binds sialic acid in an -2,3 linkage) showed no
signal above background for either glycoisoform of KLK6 (data not shown).
54
Figure 2.3. SNA-antibody lectin ELISA. Measurement of 2-6 linked sialic acid on
increasing concentrations of immunoisolated KLK6 from pools of ovarian cancer ascites fluid
and CSFs using SNA lectin-monoclonal mouse antibody ELISA methodology. Signal is
expressed as raw fluorescence counts. For each protein concentration, analysis was repeated 3
times and mean with standard deviation error bars is presented.
55
2.4.4 Structure Characterization by Tandem Mass Spectrometry
The general workflow outline of sample preparation and MS/MS analysis can be seen in Figure
2.4. Consistent with results described above, the glycan structures present on KLK6 derived
from ovarian cancer ascites fluid were shown to be highly heterogeneous and almost exclusively
sialylated, save for one identified non-sialylated glycopeptide (Figure 2.5A). The majority of the
identified structures were core-fucosylated bi-, tri-, or tetra-antennary glycans with a varying
number of terminal galactose-linked sialic acids. Two exceptions were observed, the ion at m/z
1192.48 lacked a terminal sialic acid residue and the ion at m/z 1400.22 contained a terminal
sialic acid linked to an N-acetylglucosamine directly, instead through galactose. Conversely, a
single major peak at m/z 1152.14 was observed for KLK6 from CSF, corresponding to a tri-
antennary core-fucosylated glycopeptide (Figure 2.5B). The minor peaks identified were
indicative of tri- and tetra-antennary structures heterogeneous in respect to core-fucosylation and
terminal fucosylation and galactosylation. Two minor peaks (at m/z 1303.19 and 1370.88) were
found to contain terminal sialic residues. However, although present, these sialylated forms of
the protein would account for a relatively very small proportion of the total KLK6 present in the
CSF, as indicated by the other presented data. Examples of the MS2 scans for one of the
identified glycopeptides (m/z = 1152.48) can be seen in Figures 2.6 through 2.9.
56
Figure 2.4. Glycopeptide characterization workflow. A schematic representation of the steps
involved in the sample preparation of KLK6 immunoisoalted from biological fluids required for
the tandem mass spectrometry-based identification and characterization of glycopeptides.
57
Figure 2.5. KLK6 glycopeptide mass spectra. Analysis of the DCSANTTSCHILGWGK
tryptic glycopeptide of KLK6 isolated from ovarian cancer ascites (A) and CSF (B) by
electrospray ionization-Orbitrap mass spectrometry. The m/z values presented are visually
chosen monoisotopic masses used for glycan composition determination.
58
Figure 2.6. Glycopeptide fragmentation. Sample MS2 scan of the fragmentation of the triply
charged glycopeptide at m/z = 1152.48. The fragmentation was performed in HCD mode with
17% normalized collision energy. The predicted charge states and fragmentation of selected m/z
peaks are annotated.
59
Figure 2.7. Glycopeptide fragmentation. Sample MS2 scan of the fragmentation of the triply
charged glycopeptide at m/z = 1152.48. The fragmentation was performed in HCD mode with
24% normalized collision energy. The predicted charge states and fragmentation of selected m/z
peaks are annotated.
60
Figure 2.8. Glycopeptide fragmentation. Sample MS2 scan of the fragmentation of the triply
charged glycopeptide at m/z = 1152.48. The fragmentation was performed in HCD mode with
30% normalized collision energy. The predicted charge states and fragmentation of selected m/z
peaks are annotated.
61
Figure 2.9. Glycopeptide fragmentation. Sample MS2 scan of the fragmentation of the triply
charged glycopeptide at m/z = 1152.48. The fragmentation was performed in CID mode with
35% normalized collision energy. The predicted charge states and fragmentation of selected m/z
peaks are annotated.
62
2.5 Discussion and Conclusions
The utility of protein glycosylation for diagnostic purposes in cancer has been reported
previously and is widely studied. The diagnostic potential of -fetoprotein (AFP) for detection
of hepatocellular carcinoma was improved by the specific measurement of its monosialylated
form (212-214). The glycosylation patterns of pancreatic ribonuclease were used to differentiate
the protein from normal pancreatic tissue and cancer cells (189). The core fucosylation status of
serum haptoglobin glycoforms can be used to differentiate between pancreatic cancer and
chronic pancreatitis (215). Serum (1)-acid glycoprotein (AGP) N-glycosylation patterns in
conjunction with linear discriminant analysis were recently used to differentiate between normal,
lymphoma, and ovarian cancer cases (216).
Ovarian cancer is the fifth most common cause of all cancer deaths among women in the
United States and has the highest morbidity rate among gynecological malignancies (217). This
is mainly due to the fact that this cancer cannot be detected at early stages. Detection of early
disease can dramatically improve the long-term survival of patients afflicted with ovarian cancer.
Deregulation of glycosylation pathways is one of the hallmarks of cancer, including cancer of the
ovary. Culture media from several ovarian cancer cell lines and serum from ovarian cancer
patients were used to identify a number of unique oligosaccharides corresponding to
glycoproteins shed from tumor cells (218). A recent study concentrated on the glycoproteomic
analysis of three major serum proteins (apolipoprotein B-100, fibronectin, and immunoglobulin
A1) and found them all to be aberrantly glycosylated in ovarian cancer patients (219). Acute-
phase proteins and IgG were also shown to have increased core fucosylation and sialylation in
serum of advanced ovarian cancer patients (86). Therefore, it is not surprising that similar
modifications were found in tumor-derived KLK6.
63
To date, there is only one validated ovarian cancer biomarker, CA125, a large
glycoprotein whose expression level is elevated in ovarian cancer. This protein is essential for
monitoring the response of patients to treatment but has shown less promise as a screening tool
since it can be elevated in a number of other malignancies and benign conditions, as well as
during menstruation and pregnancy (220-223). In the case of CA125, there has been only
limited interest in the study of the N-glycosylation patterns of this protein, which were compared
using non-malignant and tumor sources. The glycan structures of CA125 isolated from the
OVCAR3 cell line were studied in detail by a multiplexed approach of molecular biology and
mass spectrometry techniques (93). The predominant types of N-glycans were found to be bi-,
tri-, and tetraantennary bisecting oligosaccharides. However, the approach that was used
involved the release of glycans from immobilized glycopeptides by PNGase F which does not
allow for site-specific assignment of N-glycosylation structures. Another study distinguished the
CA125 from OVCAR3 cells and amniotic fluid by multiple lectin chromatography, showing
differential binding patterns to a panel of lectins, of the protein from the two different sources
(32). The relative scarceness of information on CA125 glycosylation most likely stems from the
fact that it is a protein with extensive O- and N-glycosylation. When the glycan structure
microheterogeneity at each glycosylation site is taken into account, it becomes an increasingly
difficult and tedious task to obtain consistent and clear patterns of glycan differences of CA125
from different sources, which could allow for improvement in its role as a biomarker. This is not
the case with KLK6, as it contains a single N-glycosylation site that is modified differentially in
the CNS and ovarian tumor tissue, allowing for much clearer determination of the source of the
protein based on its N-glycosylation pattern.
Previously, several members of the kallikrein family of proteases have been associated
with various forms of malignancy, including ovarian cancer (120, 145, 174). Among these,
64
KLK6 appears to be the most promising candidate for the detection ovarian carcinoma.
Considering that the majority of KLK6 in the circulation comes from a single source, the CNS,
and that the vast majority of malignant ovarian tumors express it at high levels (181, 185, 224),
the ability to differentiate KLK6 from these two sources could improve its value as a diagnostic
biomarker for early detection of ovarian cancer.
Excluding PSA (also known as kallikrein 3) glycan structural patterns of other members
of the kallikrein family of proteases have not been studied in detail. There have been a number
of reports, sometimes contradictory, on the N-glycosylation status of PSA under normal and
prostate cancer conditions, with the purpose of improving the efficiency of PSA as a biomarker
for diagnosis of prostate cancer. A variety of approaches with different sources of PSA, ranging
from cultured cell lines to tissue extracts and biological fluids, were employed to differentiate
PSA from non-malignant and malignant sources, based on differential N-glycosylation patterns.
Over the last two decades a number of studies utilizing a variety of lectin affinity approaches,
chromatofocusing and two-dimensional electrophoresis have shown an increase in
multiantennary complex type glycans and sialic acid content in PSA derived from prostate
cancer tissue, seminal plasma and serum from prostate cancer patients (225-228). A recent study
utilizing a glycopeptide monitoring approach, similar to the one used in the present study,
compared PSA from serum of prostate cancer patients and seminal plasma, suggesting that the
presence of 2-3-linked sialic acid on PSA could potentially differentiate between benign and
malignant conditions (229). In spite of these and other findings, a clinically applicable assay has
not been developed to date. However, the potential advantage of KLK6, and the use of one of its
glycoforms as a biomarker for ovarian cancer, is based on the fact that KLK6 is highly expressed
in a tissue (CNS) unrelated to the ovarian tumor. This potentially further minimizes the
similarity in the glycan profiles of the glycoforms and their tissue-specific microheterogeneity.
65
Elevated sialylation of cancer cell membranes is well established (230). It can occur due
to the upregulation of one of the sialyltransferases or more extensive branching of N-linked
glycans, which resulted in more termini available for modification with sialic acid (231).
Increased expression of 2-6-linked sialic acid is usually a poor prognosticator for outcome in
cancer and has been found to correlate with increased expression of the ST6GAL1
sialyltransferase gene in a number of malignant conditions (232, 233). In ovarian cancer, the
expression of several sialyltransferases was shown to be deregulated at both the protein and
mRNA levels (88, 204-206). Therefore, it is not surprising that total protein bound sialic acid
was found to be increased in the circulation of ovarian cancer patients (201). As well, it has been
well-established that both the sialyl LewisX and sialyl-Tn antigens are present at increased levels
in malignant ovarian tumors and serum of ovarian cancer patients (86, 202, 203).
Mass spectrometry has emerged as the most powerful tool for characterization of individual
protein glycosylation, surpassing the ability of classical molecular and biological techniques in
the detailed delineation of glycan structures. Two major approaches, with a number of variations
for each, have arisen for studying glycosylation patterns of individual proteins. One of them
involves the chemical or enzymatic cleavage of glycans from the target glycoprotein, followed
by purification and MS analysis (234, 235). However, this approach is limited when multiple
glycosylation sites are present on the protein, because different glycan structures cannot be
assigned to a specific site. As well, the degree of difficulty in the preparation and purification of
the sample is increased because any contaminating glycoproteins will contribute to the identified
glycan structures. This is particularly an issue when dealing with relatively small amounts of
protein being isolated from complex biological fluids where even the best protein preparations
will contain a significant degree of contamination. These two issues are minimized when the
alternative method, glycopeptide monitoring, is utilized, as in the present study. In this
66
approach, proteolytic glycopeptides are characterized by composition with no ambiguity
regarding the localization of the inspected glycan due to the peptide portion of each glycopeptide
(2, 236, 237). In addition, due to the site-specificity of this approach any contaminating
glycopeptides would not impact the output of the experiment.
In the present study, a combined approach of molecular and mass spectrometry
techniques was utilized to elucidate the differences in the N-glycosylation of KLK6 derived from
CSF and ascites fluid of ovarian cancer patients. Considering the mRNA upregulation of several
sialyltransferases and the general deregulation of sialylation pathways in ovarian cancer (86, 88,
193, 201-206) it was not surprising that KLK6 isolated from ascites fluid of ovarian cancer
patients was found to be modified with glycan structures containing 2-6 linked sialic acid. On
the other hand, KLK6 from CSF of healthy individuals was, for the most part, lacking in sialic
acid groups. These findings were supported by lectin affinity and MS/MS monitoring of the
glycan structure on the single KLK6 tryptic glycopeptide.
These results could pave the way for similar studies with other members of the kallikrein
family that have been reported to be up-regulated in ovarian cancer, which could potentially lead
to a panel of glycoisoform-specific biomarkers. In the future, we will attempt to develop a
KLK6 glycoisoform-specific quantitative assay, which can be applied to a large set serum
samples. Towards this purpose, the sialic acid content of KLK6 in serum can be measured using
a lectin ELISA approach similar to the one previously used for serum transferrin (238), where an
antibody was used to capture the protein and SNA was used to detect 2-6-linked sialic acid. As
well, a “product ion monitoring” method for isoform-specific glycopeptides can be utilized
(239).
In conclusion, we here report the unique patterns of N-glycosylation of KLK6 found in
ascites fluid of ovarian cancer patients and CSF of healthy individuals. The almost exclusive
67
presence of sialic acid moieties on KLK6 derived from ovarian cancer cells could, in the future,
serve to the further development and refinement of KLK6 as an improved ovarian cancer
biomarker.
68
3 CHAPTER 3: Development of Methodology for
Detection of Alterations in KLK6 Glycosylation
Patterns in Complex Biological Fluids
Kuzmanov U, Smith CR, Soosaipillai A, Batruch I, Diamandis A, Diamandis EP. Separation of
KLK6 glycoprotein subpopulations in biological fluids by anion-exchange chromatography
coupled to ELISA. Proteomics 2012 Mar;12(6):799-809.
A Soosaipillai and A Diamandis provided assistance with ELISA methodology.
CR Smith and I Batruch operated and maintained mass spectrometry equipment.
69
3.1 Short Overview
Based on the finding from Chapter 2, a method was developed capable of quantifying different
subpopulations of KLK6 in serum and other biological fluids. The method involved the anion-
exchange HPLC-based fractionation of KLK6 from biological fluids and detection by ELISA in
resulting fractions. This resulted in a four peak elution profile, which was highly variable in
ovarian cancer patient samples but stable in samples from healthy individuals. The methodology
was characterized using recombinant KLK6 purified from a stably transfected HEK293 cell line,
which was also established to have a four peak elution profile. Tandem mass spectrometry-
based glycosylation analysis revealed that the recombinant protein carried most of the glycan
structure identified on KLK6 from physiological sources. The glycan content of the four anion
exchange peaks was determined using tandem mass spectrometry. Specific glycan structures in
each peak were relatively quantified using lectin affinity and targeted mass spectrometry (ie.
product ion monitoring).
3.2 Introduction
Ovarian cancer is the worldwide leading cause of death among common gynecological
malignancies with more than 200,000 new cases and 125,000 deaths every year (240, 241). Such
a high mortality rate (4.2% of all cancer deaths among women) can be attributed to the lack of
symptoms in incipient disease resulting in only 25% of early stage ovarian cancer being
diagnosed (83, 240-242). Early stage diagnosis allows for a 90% 5-year survival rate among
patients, but current means of disease detection by ultrasonography and/or the serum biomarker
CA125 (MUC16) have shown only modest success as early screening tools (220-223, 242, 243).
70
Aberrancies in protein glycosylation patterns have been observed in a majority of cancers
and over the past four decades a number of different protein glycoforms have been identified as
tumor-associated antigens (188, 243). Dysregulation of glycosylation pathways can be an early
event in oncogenesis, resulting in increased glycan structures on the surface of tumor cells,
aiding them in evading the immune response during invasion and metastasis (188, 191-199).
Several glycoproteins of varying biological functions and sites of expression have been shown to
have disturbed glycosylation patterns in ovarian cancer (243). These include a number of acute
phase proteins, CA125, and IgGs (32, 86, 200, 243, 244). More specifically, the addition and
processing of sialic acids on glycoproteins seems to be disturbed in ovarian cancer (201, 243).
This is supported by evidence showing altered sialyltransferase enzyme activity and mRNA
expression in ovarian cancer and other gynecological tumors (88, 204-206). Not surprisingly,
overexpression of sialyl Lewis X and sialyl-Tn antigens has been recorded in this disease (86,
202, 203).
Kallikrein 6 (KLK6) is a secreted trypsin-like member of the human tissue kallikrein family of
serine proteases. Although it is expressed in a number of tissues in the body, the major site of
KLK6 expression is the central nervous system with high levels of the protein (mg/L) detected in
cerebrospinal fluid (CSF), making it the major source of KLK6 in serum (122, 156, 185).
Increased levels of KLK6 and five other members of the kallikrein family have been shown to be
prognostic of negative outcome in ovarian cancer (148, 155, 183, 208, 209, 224). The majority
of ovarian tumors produce KLK6, which is belived to enter the circulation as the cancer
progresses (122, 156, 185). However, measurement of KLK6 levels alone has not shown any
improvement over CA125 in detection of ovarian cancer, and when used in combination, these
two tests show only a small improvement in sensitivity (224). In the early stages of ovarian
cancer, the contribution of KLK6 from tumor tissue is not sufficient to raise the total serum
71
levels of this protein above the defined normal diagnostic range. Therefore, a method capable of
detecting tumor-derived KLK6 could improve the diagnostic value of this molecule. The N-
glycosylation patterns of KLK6 immunoisolated from CSF of normal individuals and ascites
fluid of ovarian cancer patients have been elucidated (245). Through a combination of molecular
biology, lectin affinity, and mass spectrometry techniques, KLK6 from ascites fluid was found to
be highly branched and enriched with terminal 2-6 galactose linked sialic acid glycans when
compared to the protein from CSF (245). However, these techniques were not sensitive enough
to characterize or quantify glycoforms of KLK6 in serum due to the low levels of protein present
in this fluid (5<ng/ml in normal individuals).
Here, we report the development of a KLK6 ELISA-coupled anion exchange method,
which can distinguish between KLK6 glycoform subpopulations at physiologically relevant
levels in biological fluids, including serum. Namely, high-performance anion exchange liquid
chromatography was employed to fractionate directly injected samples. KLK6 in the resulting
fractions was quantified using an in-house developed ELISA, resulting in an elution profile
composed of four distinct peaks. Utilizing this methodology, the KLK6 elution profiles of
matched ovarian cancer patient sera and asictes fluids were found to be different from serum and
CSF of healthy subjects. As well, electrospray ionization-Orbitrap tandem mass spectrometry
was used to characterize recombinant KLK6 (rKLK6) purified from an immortalized human cell
line. This preparations was found to consist of a highly heterogenous KLK6 population, which
encompassed the majority of KLK6 glycoforms previously detected in protein isolated from
ascites fluid and CSF(245). When subjected to anion exchange, the elution profile of rKLK6
was found to contain all of the four diagnostic peaks observed in assayed biological fluids. The
glycoform composition of each of the rKLK6 peaks was analyzed with Elderberry (Sambucus
72
nigra, SNA) lectin affinity and relative glycopeptide quantification by product ion monitoring
with mass spectrometry (239).
73
3.3 Materials and Methods
3.3.1 Clinical Samples
The biological fluids used were collected with informed consent and institutional review board
approval, or leftovers submitted for routine medical testing. They were stored at -80oC until use.
CSF samples were chosen to be clear in appearance to ensure no blood contamination, and
ranged in KLK6 concentration from 44 to 280 ng/ml. Ovarian cancer serum samples and
matched ascites fluids were from patients with FIGO stage III and IV serous ovarian carcinomas.
The total KLK6 values ranged from 2.6 to 30 ng/ml for the ovarian cancer sera, and from 40 to
355 ng/ml for the ascites fluids. Normal control sera analyzed were from women in the age
range between 37 and 66, with KLK6 values ranging from 1 to 2.6 ng/ml. Serum samples from
patients with renal failure ranged in total KLK6 from 2.0 to 7.3 ng/ml.
3.3.2 Recombinant KLK6 Production
Recombinant KLK6 (rKLK6) was purified from the serum-free medium of human embryonic
kidney (HEK-293) cells transfected with the inactive zymogen form of KLK6, as described
previously (170, 171, 246). Briefly, the supernatant medium was collected and concentrated
down 10-fold using a 10 kDa cutoff nitrocellulosse membrane with the Centricon ultrafiltration
device (Millipore, Waltham, MA, USA). The rKLK6 protein from the concentrated medium was
purified using a cation exchange chromatography column (5-ml HiTrap CM FF column, GE
Healthcare Bio-Sciences) connected to the AKTA FPLC system (GE Healthcare Bio-Sciences,
Uppsala, Sweden). Liquid chromatography was performed using 50 mM sodium acetate, pH 5.3
as the running buffer over a 0-1M NaCl linear gradient. The resulting fractions were analyzed
for the presence and purity of KLK6 by SDS-PAGE and ELISA.
74
3.3.3 Anion Exchange Methodology
A Mono Q 4.6/100 PE Tricorn high performance column (GE Healthcare) connected to an
Agilent 1100 series High Performance Liquid Chromatography (HPLC) system was used for
anion exchange chromatography of the selected biological fluids. 20 mM Tris-HCl (pH 8.6)
solution was used as running buffer, and the elution buffer contained 1M NaCl. Samples (100
L) were injected directly into the column and the protein was allowed to bind to the column for
5 min at a 0.5 ml/min flow rate. The same flow rate was maintained throughout. The elution
buffer was brought to 8% (80 mM NaCl) over the next 5 min. This concentration of elution
buffer was maintained for another 15 min, then increased to 9% (90 mM NaCl) where it was
maintained for another 15 min. Following this, another step up in NaCl concentration was made
to 25% (250 mM NaCl) which was kept for another 15 min. Subsequent 15 min washing (100%
NaCl) and re-equilibration (running buffer alone) steps followed. Fractions (500 L) were
collected every minute into glass vials containing 100 L of a 6% BSA solution. 200 L of each
fraction were analyzed in duplicate with a previously described in-house KLK6 ELISA (174).
Peak areas of the resulting KLK6 elution chromatograms were integrated using OriginPro 8.0
software (OriginLab Corp.) by manually selecting peak centers and using automatically provided
parameters. 100 L of undiluted serum samples, 100 L of CSF- or ascites-spiked KLK6
immunodepleted serum (to final concentration of 10 ng/ml of KLK6), and 100 L of 2 mg/ml
solution of rKLK6 were used for anion-exchange chromatography.
75
3.3.4 Lectin Detection
rKLK6 from chromatographic peaks was diluted in 100 L of 50 mM Tris-HCl, pH 7.8 at
different concentrations and coated overnight at room temperature on a 96 well polystyrene
microtiter plate (Greiner Bio-One). The plate was washed twice in wash buffer (10mmol/L Tris-
HCl, pH 7.4, containing 150 mmol/L NaCl and 0.5ml/L Tween 20) and blocked for 1 hour at
room temperature with 1X CarboFree solution (Vector Labs). The plate was washed 6 times
with wash buffer and 500 ng of biotinylated Sambucus nigra agglutinin (SNA, Vector Labs) in
100 L of 1X CarboFree buffer was added to each well and incubated at room temperature for 1
hour. Following another 6 wash steps, 100 L of 50 ng/ml alkaline phosphatase-conjugated
streptavidin in 1X CarboFree solution was added to each well, incubated with shaking for 15
min, and washed 6 times. For 10 min, each well was incubated with 100 L of diflunisal
phosphate solution (0.1 mol/L Tris-HCl, pH 9.1, containing 1 mmol/L diflunisal phosphate, 0.1
mol/L NaCl, and 1 mmol/L MgCl2). 100 L of developing solution (1 mmol/L Tris, 0.4 mol/L
NaOH, 2 mmol/L TbCl3, and 3 mmol/L EDTA) was then added and left on the shaker for 1 min.
The PerkinElmer EnVision 2103 Multilabel Reader was used to measure the fluorescence signal
in each well, in time-resolved mode.
76
3.3.5 Sample Preparation for Mass Spectrometry
Purified or anion exchange-fractionated rKLK6 (1-5 g) was run on a pre-cast NuPAGE 4-12%
10 well Bis-Tris gel (Invitrogen). As per the manufacturer's protocol, the gel was exposed to
SimplyBlue SafeStain (Invitrogen) and destained in water. The excised KLK6 bands were
dehydrated for 10 min with acetonitrile (ACN) at room temperature and reduced in 150 L of a
solution containing 10 mM dithiothreitol (DTT) and 50 mM NH4HCO3 for 30 min at 60oC and
cooled to RT for 10 min. The gel band was dehydrated with ACN and rehydrated with 50 mM
NH4HCO3 another 3 times. The final rehydration was performed in 300 L of 50 mM
NH4HCO3 with 100 mM iodoacetamide, followed by incubation at room temperature in the dark
for 1h. After another 3 dehydration steps with ACN the gel band rehydrated in 50 mM
NH4HCO3 with 1 mg of sequencing grade modified trypsin (Promega) and left overnight at
37oC. The resulting solution was used for MS/MS analysis.
77
3.3.6 MS/MS Glycopeptide Structure Identification
KLK6 tryptic peptides were subjected to MS/MS analysis as previously described (245).
Following liquid chromatography with a 2 cm C18 pre-column (200 m diameter) and a 5 cm
resolving analytical C18 column (75 m diameter) with a 15 mm tip (New Objective), the eluted
peptides were injected with a nanoelectrospray ionization source (Proxeon) into a Thermo LTQ
Orbitrap XL mass spectrometer set to positive-ion mode. Liquid chromatography was performed
over a 90 minute linear gradient using a running buffer containing 0.1% formic acid, 5% ACN,
and 0.02% TFA in water and elution buffer 90% ACN, 0.1% formic acid, and 0.02% TFA in
water. Parent ion frgamentation conditions were set to reject 1+, 2+ and unassigned charge
states and only the peptides in the 1000 to 1800 m/z range were chosen. Peptides were
fragmented in HCD mode (17, 24, and 30 percent normalized collision energy) and in CID mode
using 35 percent normalized collision energy. The isolation width was set to 3.0 for all data
dependent events. The retention time of the glycopeptides associated with the
DCSANTTSCHILGWGK seqence was determined by observing the common oxonium ions (i.e.
204.08 for N-acetylglucosamine or 366.13 for a hexose linked N-acetylglucosamine) in MS2
spectra. Xcalibur 2.0 software (Thermo) was used to combine MS1 spectra over the
glycopeptide specific retention time period and visually selected monoisotopic masses of triply
charged ions were referenced against the Glycomod tool at 5 parts per million mass tolerance.
To further confirm the identity of the glycopeptides, MS2 data was examined for the presence of
fragment glycopeptides and glycan in a majority of cases.
78
3.3.7 Glycopeptide Product Ion Monitoring
Relative quantification of rKLK6 glycopeptides was performed by product ion monitoring
methodology (239). Briefly, several major DCSANTTSCHILGWGK peptide-based
glycopeptides from the rKLK6 protein in different chromatographic peaks were subjected to LC-
MS/MS analysis in CID mode with 35% collision energy on the LTQ-Orbitrap XL instrument.
All of the parent masses chosen were of triply charged glycopeptides previously identified (ie.
1152.49, 1352.55, 1454.91, 1673.65). For each fragmented parent ion, at least three daughter
ions (transitions) were monitored and quantified in MS2. For the 1152.49 parent mass, the
1524.85, 1545.35 and 1626.39 transitions were monitored. Transitions 1255.43, 1525.12,
1606.00, 1679.05 and 1699.75 were monitored for the 1352.55 parent mass. For the 1454.91
parent ion, 1357.76, 1524.87, 1707.53 and 1853.10 daughter ions were monitored, while
1270.80, 1343.30, 1526.26, 1606.85 and 1653.73 were monitored for the 1673.65 parent m/z.
The instrument setup and in line liquid chromatography were performed in the same fashion as
described above. Additionally, each MS/MS run also included the monitoring of the triply
charged LSELIQPLPLER non-glycosylated KLK6 tryptic peptide (parent mass of 704.44 with
684.40, 724.49, 852.53 transitions) as an indicator of the total KLK6 quantity. The MS2
transitions were quantitated using Xcalibur 2.0 software (Thermo) by peak area integration using
boxcar type smoothing over 7 points.
79
3.4 Results
3.4.1 Anion-Exchange Chromatography of Biological Samples
When biological fluids were subjected to anion-exchange chromatography separation, four
distinct peaks were observed when plotting KLK6 concentration (determined by ELISA) against
elution time, over the course of the described method (Figure 3.1). Based on their retention time,
from earliest to latest, these peaks were designated as A, B, C, and D (Figure 3.2). In relation to
each other, the peaks were found to be of varying intensities in the different fluids analyzed. To
ensure that the differences in the KLK6 distribution across the chromatographic peaks was not
due to matrix and background effects, CSF and ascites fluid samples were spiked (to a final
concentration of 10 ng/ml) into a serum pool of normal individuals, which was immunodepleted
of KLK6 (serum concentration <0.1 ng/ml). CSF-spiked sera and sera from patients with no
ovarian malignancy had similar distribution of KLK6 across the peaks between different
samples. Namely, intermediate peaks B and C were of low (and sometimes undetectable)
intensity while the majority of KLK6 was found in peaks A and D, with peak A consistently
showing a higher content of KLK6, on average. When serum and ascites samples from ovarian
cancer patients were analyzed, no such consistency was observed. In these samples, there was a
relative increase in the intensities of the last three peaks (B, C, and D). However, between
different patient samples, the increase in these peaks was variable, with different peak(s) being
up-regulated in different patients. Considering the expected microheterogeneity of post-
translational modifications between individual cancer cells or patient subpopulations, these
results are not surprising. As well, additional samples were included in the non-malignant group
from patients with renal failure and with increased levels of serum KLK6 due to lack of
clearance, but with no diagnosis of ovarian cancer. This group of samples showed the same
80
pattern as the normal set of sera, indicating that the pattern of distribution of KLK6 across the
observed peaks is not a function of the absolute levels of KLK6 in serum, but rather their origin
(ie. malignant vs. non-malignant conditions).
These observations can be summarized by monitoring the area of peak A as a percentage of the
total area under the KLK6 elution chromatogram (Figure 3.3). For CSFs, sera of healthy
individuals and renal failure patients the mean and median average values were between 50%
and 55%, indicating that the majority of the KLK6 in serum of normal individuals have the same
(or closely similar) glycosylation pattern as the one found in the CSF. Ovarian cancer ascites
fluid KLK6 exhibited an elution pattern where the KLK6 glycoforms found in Peak A were
minimally represented (compared to CSF) for all of the samples analyzed. The area of peak A
was at less than 10% of total. Analysis of sera collected from the same patients had a more
diverse distribution but the representation of peak A was significantly lower than that found in
normal control samples (Figure 3.3). This heterogeneity is to be expected due to the differential
contribution of normal (CSF-derived) and ovarian cancer (ascites-derived) KLK6 in the
circulation. The individual peak areas of each sample analyzed can be seen in Table 3.1.
81
Figure 3.1. Methodology outline. A schematic representation of the ELISA-coupled anion-
exchange method for separation of glycoform subpopulations of KLK6. Biological fluids are
injected into the HPLC connected to a Mono Q analytical anion exchange column and a 3 step
elution gradient is applied (see Materials and Methods). Eluting fractions are collected and
analyzed for KLK6 content by sandwich ELISA. This data is used to plot a KLK6 elution
chromatogram.
82
Figure 3.2. Anion-exchange chromatography of biological fluids. ELISA quantification of
KLK6 in collected fractions after elution from an anion-exchange column. The value for each
fraction is presented as percentage of total eluted KLK6. The presented data is from a single
representative runs for a control serum of a woman with no ovarian cancer diagnosis,
cerebrospinal fluid (CSF), ovarian cancer ascites fluid, and serum from a woman with ovarian
cancer (OC). Peaks A, B, C, D representing glycoforms of KLK6, are discussed in the text.
83
Figure 3.3. Peak area integration. Results of peak area integration of individual anion
exchange runs for different biological fluids (RF, renal failure serum; OC, ovarian cancer serum)
represented as the percentage of total area under the curve attributed to the first chromatographic
KLK6 peak (Peak A). See Figure 1 for peak identification. The top and bottom borders of the
box represent 25th and 75
th percentiles, respectively. The whiskers are outlier values, with the
mean and median average values represented by a small box and a bisecting line, respectively.
Serum from ovarian cancer patients and ascites fluid contains significantly less peak A-related
KLK6 than CSF, RF, and normal serum samples. n = number of samples per category.
Differences between OC and ascites groups and CSF, normal and RF groups were statistically
significant (p<0.05) by ANOVA test.
84
Table 3.1. Results of anion-exchange separation of KLK6 subpopulations in biological fluids and
clinical information of patients. The data is represented as area of each chromatographic peak,
expressed as a percentage (%) of total area under the curve.
Sample peak A peak B peak C peak D [KLK6]
(ng/ml)
FIGO
stage Grade Sex
CSF 1 55.5 ND ND 24.7 44 - - M
CSF 2 62.7 ND ND 21.4 61 - - F
CSF 3 55.9 3.4 ND 24.7 100 - - F
CSF 4 57.2 4.5 1.2 22.4 280 - - M
CSF 5 50.4 4.2 2.2 27.3 185 - - F
CSF 6 49.5 4.5 1.8 29.4 137 - - F
CSF 7 44.8 5.7 1.9 31.8 85 - - F
ascites 1 4.5 7.9 37.7 39.3 63 III 2 F
ascites 2 0.4 13.3 22.8 48.6 215 III 2 F
ascites 3 10.5 21.8 20.9 37.8 41 III 2 F
ascites 4 3.2 16.7 36.7 32.8 76 III 1 F
ascites 5 2.3 19.7 44.2 23.3 49 III 2 F
ascites 6 7.6 31.0 26.6 26.1 220 IV 3 F
ascites 7 6.6 4.5 3.5 71.6 335 IV 3 F
normal serum 1 56.2 9.8 7.6 21.1 1.7 - - F
normal serum 2 45.8 11.2 6.8 25.7 2.6 - - F
normal serum 3 42.7 15.7 8.4 19.1 1.7 - - F
normal serum 4 50.6 12.3 3.9 23.6 2.1 - - F
normal serum 5 58.3 13.5 7.5 19.5 2.5 - - F
normal serum 6 55.3 13.3 10.4 19.4 1.8 - - F
normal serum 7 47.6 15.1 9.9 21.7 1.0 - - F
OC serum 1 34.1 12.6 19.3 24.2 4.9 III 2 F
OC serum 2 5.7 13.7 34.4 41.9 11.4 III 2 F
OC serum 3 36.2 12.2 10.0 30.1 2.7 III 2 F
OC serum 4 25.9 18.8 33.7 16.8 4.5 III 1 F
OC serum 5 19.4 14.0 24.5 33.5 3.1 III 2 F
OC serum 6 23.7 29.0 26.5 13.0 8.4 IV 3 F
OC serum 7 7.6 10.6 27.4 41.7 30 IV 3 F
RF serum 1 39.9 16.1 9.3 26.0 6.7 - - F
RF serum 2 34.1 12.6 10.8 32.2 7.3 - - F
RF serum 3 55.4 14.6 2.7 17.4 5.5 - - M
RF serum 4 55.4 11.1 3.3 22.1 2 - - F
RF serum 5 58.6 10.8 4.6 17.4 4.4 - - M
RF serum 6 55.7 10.0 5.2 27.2 3.7 - - F
rKLK6 31.9 38.5 15.4 8.5 - - - -
CSF = cerebrospinal fluid, OC = ovarian cancer, RF = renal failure
85
3.4.2 Lectin Analysis of rKLK6 peaks
Due to the limited quantity of native KLK6 protein available for study (ie. low g/L levels in
serum and ~100 g/L levels in CSF and ovarian cancer ascites fluid) and the difficulty associated
with isolation of this protein with sufficient purity from biological fluids, the tandem mass
spectrometry (MS/MS) and lectin-based characterization of the KLK6 found in the different
peaks following anion-exchange chromatography was performed using purified recombinant
KLK6 (rKLK6). This protein was purified from transformed human embryonic kidney cells
(HEK293) stably expressing the inactive zymogen version of the protein, thereby increasing the
probability of detecting similar glycosylation patterns normally found in humans and minimizing
the possibility of autolytic degradation of KLK6. However, rKLK6 may not fully represent the
glycan composition of KLK6 found in biological fluids.
To characterize the sialic acid content of KLK6 found in each of the four peaks, purified
recombinant KLK6 was subjected to anion-exchange chromatography separation as described for
biological samples and fractions were immobilized on a microtiter plate and tested for SNA
lectin affinity, as described in Materials and Methods (Figure 3.4). SNA preferentially binds 2-
6 sialylated glycans. KLK6 eluting in Peak A was found not to bind the SNA lectin, whereas the
other three peaks contained sialic acid, but at differing levels. The relative degree of sialylation
increased with the retention time of each peak, with peak D containing the highest and peak B
the lowest amount of sialic acid. This is not surprising, considering that an overall increase in
negative charge due to more extensive glycan branching and terminal sialylation, would cause
stronger binding to the anion-exchange matrix and therefore result in later elution times over an
increasing salt gradient.
86
Figure 3.4. SNA lectin affinity to recombinant KLK6 from chromatographic peaks. The
quantity of galactose linked terminal sialic acid moities on microtiter plate-immobilized
recombinant KLK6 from the different chromatographic peaks as determined by SNA lectin
affinity. Peaks A, B, C, and D are shown in Figure 1. The data is presented as raw fluorescence
counts subtracted from fluorescence counts recorded with no KLK6 present. Error bars represent
standard deviation. The results presented are from duplicate measurements of the different
concentrations of KLK6 from the four chromatographic peaks. Note that peak D contains more
sialic acid, as determined by SNA lectin affinity, than the other peaks.
87
3.4.3 MS/MS Analysis of rKLK6
Trypsin-digested rKLK6 was subjected to MS/MS analysis and pertinent retention times of the
DCSANTTSCHILGWGK-based glycopeptides (containing the sole KLK6 N-glycosylation site;
underlined) were identified by the presence of diagnostic oxonium ions (204.08 for N-
acetylglucosamine or 366.13 for a hexose-linked N-acetylglucosamine) in the resulting MS2
scans. The glycopeptide retention times were further determined by comparing mock and
PNGase F-treated rKLK6 in the total ion current (TIC) spectra in MS1 of each run. For the
PNGase F-treated sample, there was a loss of spectra in the same specific time period where the
majority of oxonium ions were identified (Figure 3.5). The identified glycan structures
associated with the DCSANTTSCHILGWGK peptide seen in Figure 3.6 exhibit great diversity.
The glycan structures range from a tri-antennary core-fucosylated glycopeptide with terminal N-
acetylglucosamine residues (1152.47) to tetra-antennary core-fucosylated structure with three
terminal galactose-linked sialic acid residues (1673.65), encompassing a number of structures
previously identified in KLK6 from CSF and ascites fluid of ovarian cancer patients (245). What
should be noted is that the absolute values for the relative abundance of the indicated masses
cannot be taken as a quantitative measure, considering that glycopeptides with different glycan
moieties may ionize differently, generally following the trend that ionization efficiency decreases
with increased glycan branching and sialylation. The identified glycan structures in each of the
anion-exchange chromatographic peaks of rKLK6 can be seen in Figures 3.7 through 3.10.
88
Figure 3.5. Total ion current chromatograms. Resulting total ion current (TIC) MS1
chromatograms of mock (top panel) and PNGase F treated (bottom panel) recombinant KLK6.
The predicted elution period of the DCSANTTSCHILGWGK-based glycopeptides is boxed.
89
Figure 3.6. Composite MS1 spectra. Combined MS1 spectra over the
DCSANTTSCHILGWGK glycopeptide retention time period for mock (top panel) and PNGaseF
treated (bottom panel) recombinant KLK6. For comments see text. A heterogeneous population
of glycan structures is detected, varying in sialic acid content and branching pattern.
90
Figure 3.7. Chromatographic Peak A MS1 spectra. Combined MS1 spectra over the
DCSANTTSCHILGWGK glycopeptide retention time period for recombinant KLK6 protein
eluting in anion-exchange chromatographic peak A Figure 3.2.
91
Figure 3.8. Chromatographic Peak B MS1 spectra. Combined MS1 spectra over the
DCSANTTSCHILGWGK glycopeptide retention time period for recombinant KLK6 protein
eluting in anion-exchange chromatographic peak B from Figure 3.2.
92
Figure 3.9. Chromatographic Peak C MS1 spectra. Combined MS1 spectra over the
DCSANTTSCHILGWGK glycopeptide retention time period for recombinant KLK6 protein
eluting in anion-exchange chromatographic peak C from Figure 3.2.
93
Figure 3.10. Chromatographic Peak D MS1 spectra. Combined MS1 spectra over the
DCSANTTSCHILGWGK glycopeptide retention time period for recombinant KLK6 protein
eluting in anion-exchange chromatographic peak D from Figure 3.2.
94
3.4.4 Quantification of rKLK6 glycopeptides by Product Ion Monitoring
Product ion monitoring assays were developed for several of the representative rKLK6
glycopeptides, four of which are shown to be highly enriched in each of the four diagnostic
peaks (Figure 3.11). These were used as indicators of the glycan content in each of the KLK6
peaks as it relates to branching extent and sialic acid presence. The selected glycopeptides were
quantified relative to the amount of the LSELIQPLPLER tryptic peptide of KLK6 (amino acids
118-129 in the protein sequence), which served as an indicator of total KLK6 quantity. These
data should be considered to be semi-quantitative, especially considering the several orders of
magnitude difference in the absolute values (area under the curve) recorded for glycopeptides
and the non-glycosylated LSELIQPLPLER peptide. This effect is due to the much weaker
ionization of glycopeptides when compared to unmodified peptides.
The core-fucosylated tri-antennary glycopeptide with terminal N-acetylglucosamines
(1152) was shown to be mostly present in peak A (Figure 3.11). Peak B showed enrichment of
the glycopeptide with a bi-antennary glycan structure with terminal galactose and N-
acetylgalactosamine-linked terminal sialic acids (1352). The triply charged glycopeptides with
m/z values of 1454 and 1673 were shown to be enriched in peaks C and D, respectively (Figure
3.11). These have more extensive glycan branching with galactose-linked terminal sialic acid
(Figure 3.6). Therefore, it appears that with increasing retention time of KLK6 during the anion
exchange method, the extent of glycan branching and sialic acid content also increases,
indicating (in concert with other data presented herein) that KLK6 produced by ovarian cancer
cells has preferentially higher glycan branching and sialylation.
95
Figure 3.11. Glycopeptide product ion monitoring. Product ion monitoring (PIM)-based
relative quantification of glycopeptides in the chromatographic peaks of recombinant KLK6.
The abundance of the different glycopeptides in each KLK6 chromatographic peak is expressed
as an absolute ratio to the LSELIQPLPLER non-glycosylated KLK6 peptide. Peaks A, B, C, and
D are identified in Figure 3.2.
96
3.5 Discussion and Conclusions
Protein glycosylation is one of the most common post-translational modifications. The majority
of secreted and membrane proteins are glycosylated. Alterations in protein glycosylation occur in
a number of human disorders, including autoimmune diseases, cancer, and immunodeficiency
(243). Aberrant glycosylation patterns in cancer can present as over-, under-, or neo-expression
of embryonic glycan structures, which result from changes in expression of glycosyltransferase
enzymes in the classical secretory pathway (247). One of the most common changes is the
increased branching of N-glycans, which, in the presence of increased sialyltransferase
expression, opens more sites for attachment of terminal sialic acids and results in increased
protein sialylation (247, 248).
Ovarian cancer has the highest morbidity rate among all gynecological disorders and is
the fifth leading cause of cancer deaths among women in the United States (249). However,
early detection of this disease drastically improves the long-term outcome and survival rate
among patients. Alterations in the protein glycosylation processes are well established in ovarian
cancer(243). In the serum of ovarian cancer patients, IgG exhibits a decrease in galactosylation
and several acute phase proteins, including haptoglobin, 1-acid glycoprotein and 1-
antichymotrypsin, have been found to overexpress the sialyl Lewis-X antigen (86, 200). As well,
three major serum proteins (apolipoprotein B-100, fibronectin, and immunoglobulin A1) display
unique glycan structures in the presence of ovarian cancer (219). However, these proteins are
not expressed directly by the tumor, making them less suitable as biomarker candidates. The only
routinely used clinical biomarker for ovarian carcinoma is CA125, also a glycoprotein. It has
only limited utility as a screening tool and is mostly used for monitoring of patient response to
treatment, because its levels in serum can be elevated in other malignancies, benign conditions,
menstruation and pregnancy (220-223). CA125 has high carbohydrate content, estimated at 24-
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28% of total mass, that is located at a number O- and N-glycosylation sites whose corresponding
glycan structures have only been partially characterized in a non site-specific fashion, either by
MS-based identification of PNGase F-released glycans or lectin affinity (32, 93). Taking into
account the innate heterogeneity of cancer, variability in site occupancy and microheterogeneity
of glycans occupying each site, the prospect of fully characterizing particular glycan structures of
CA125 to improve its diagnostic potential becomes an extremely difficult proposition. With its
single N-glycosylation site and characterized glycan structures, and well established
immunoreagents available, KLK6 has a clear advantage when considering the practicalities of
developing a clinically applicable assay capable of quantitating both protein and associated
glycan levels.
Some of the most well recognized clinically utilized cancer biomarkers are glycoproteins
(45, 250). Aberrant glycosylation patterns of some of these proteins have been elucidated when
normal and disease states were compared. Measurement of the monosialylated form of -
fetoprotein (AFP), instead of total AFP protein levels, has been suggested to improve its
diagnostic potential as a biomarker for hepatocellular carcinoma (212-214). Prostate-specific
antigen (PSA) was shown to be aberrantly glycosylated in prostate cancer (190), and
measurement of 1,2 linked fucose on PSA has shown improvement in sensitvity and specificity
over the existing test (251). Quantification of fucosylated haptoglobin seems to be a promising
avenue for diagnosing pancreatic cancer (252).
In spite of these observations, few clinically applied tests utilize the potential of the binary nature
of glycoprotein biomarkers. The majority of clinical tests are based on sandwich ELISA
methodology, relying on glycoprotein immunocapture and detection with antibodies that do not
have epitope specificities for glycan structures. Therefore, only total protein levels are
measured, without taking into account any information on protein microheterogeneity
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represented by its glycoform subpopulations. As such, the majority of glycoprotein biomarkers
utilized today are not taking advantage of this secondary diagnostic level, which could improve
their clincal utility. However, there are a number of challenges, which have impeded progress
in this direction.
The majority of challenges preventing reliable, clinically applicable binary measurement
of glycoprotein biomarkers are of a technical nature. More specifically, there is only a very
limited set of tools capable of performing this task, each with its own set of associated
limitations and difficulties (253). Currently, the options for concurrent quantification of protein
and its attached glycans are narrowed down to a combination of antibody mediated protein
capture and detection with glycan specific antibodies, lectins, or mass spectrometry. The
advancement of these approaches is hindered by the absence of a suitable recombinant
technology capapble of relaiable and convenient production of glycoproteins with desired glycan
structures, which would allow for more convenient and detailed studies. However, considering
that protein glycosylation is not a template driven linear sequence based process, such as DNA or
protein synthesis, a suitable solution to this problem does not seem to be on the horizon, even
though some advancements have been made. Due to a large number of combinations of
branched oligosaccharide structures that can be created from available monosaccharides in
eukaryotic cells, and especially cancer cells, where target protein production and normal
glycosylation processes are highly disturbed, the staggering glycan microheterogeneity can
significantly impede precise binary measurement of glycobiomarkers. Therefore, a quantitative
detection system encompassing the heterogeneity of glycan structures of a single protein in a
single output is required to bring use of glycobiomarkers to a respectable (clinically testable)
level. To broach the issue of KLK6 glycoform heterogeneity, towards the purpose of quantifying
specific overexpressed glycoforms of the protein in ovarian cancer, we chose to resolve the
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subpopulations of KLK6 based on the differential charge status conferred by differences in
glycosylation (ie. anion exchange chromatography). We managed to identify four separate
glycoform populations that stem from glycan variability at the single N-glycosylation site of
KLK6. However, the chance of success for this approach would be significantly decreased if it
is attempted with a glycoprotein containing multiple glycosylation sites. This is due to the fact
that the complexity of the assay output (ie. number of possible peaks) would likely increase
exponentially with each additional glycosylation site.
The majority of high abundance proteins, that account for more than 90% of protein
content in serum, are glycoproteins. These include such proteins as the Ig family members,
haptoglobin, antitrypsin and transferrin, among others. However, the majority of potential
biomarkers are found at significantly (several orders of magnitude) lower levels in the serum
(254). Taking into consideration that a specific glycan profile on one protein might indicate a
malignant condition, but not on another (ie. one of the high abundance proteins) the specificity of
detection by lectins, or even glycan-specifc antibodies of low concentration serum glycoproteins
can be hindered by high levels of background from high abundance glycoproteins. As such,
these methods of detection lag behind the gold standard (sandwich ELISA) in sensitivity,
especially when taking into account that only a subset of the target protein's total population is
being measured. These issues are magnified in lectin based assays, because the quality and
source of lectins has been brought into question, and the extensive washing required for this type
of detection causes concern when reliability and reproducibility are considered (255, 256). As
well, antibody-lectin based sandwich assays can be hindered by narrow affinity of certain lectins
for specific glycan structures, which may be highly variable in cancer. Therefore, even if there is
a disturbance in the glycosylation pattern of a protein, a lectin might detect only a single variant
among many abberant glycan structures. However, this issue may be ameliorated with the use of
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antibody arrays using multiple lectins for detection of glycan epitopes (257), but even this
technique cannot detect subtle changes of a few monosaccharides in the glycan structure.
Although the possibility of using glycoform-specific antibodies might appear attractive,
the development of these molecules is difficult. Again, the issue of microheterogeneity at each
glycosylation site of a protein is at the heart of the problem. Although not impossible, there is no
reliable approach to ensure the production of a monoclonal antibody against a particular epitope
that encompasses both the glycan structure and a peptide portion of a glycoprotein, and the
majority of glycospecific antibodies simply identify a particular carbohydrate structure, much
like lectins. The absence of recombinant technology, which would allow for the production of a
single glycoform of a target protein that could be utilized as an antigen in the antibody
production process is the major challenge in manufacture of true glycoprotein antibodies with
dual (glycan and protein, together) recognition. Even if such an antibody is produced, the
epitope it recognizes would be just one of a number of possible ones with diganostic potential.
This approach would be cumbersome when considering a protein with a single N-glycosylation
site such as KLK6. The real world application to a multiply glycosylated protein such as CA125
would be highly improbable.
There has been a number of promising MALDI-TOF mass spectrometry-based efforts at
detecting glycan variability in a number of malignancies (258-261). These approaches have been
proven to be successful at detecting alterations in glycans released from individual or multiple
proteins. Glycopeptide quantitation by MS also appears promising when considering approaches
for the future. Non-glycosylated peptides have been measured reliably in a number biological
fluids from low abundance proteins (239, 262-264). However, the same is not true for
glycopeptides and the reasons for this are two fold. Firstly, glycopeptides ionize more weakly
when compared to their non-glycosylated counterparts, which is especially true for sialic acid
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containing glycopeptides (265, 266) . Secondly, measurement of each non-glycosylated peptide
remains constant for the total population of a particular glycoprotein, whereas for the equal
quantity of the same glycoprotein the detection signal is divided among the different glycoform
subpopulations. Attempts have been made towards quantifying heterogenous glycopeptide
populations, but these approaches involved extensive sample preparation, such as lectin or
hydrazide bead enrichment, not suitable for reliable and reproducible high-throughput analysis of
large sets of samples (267-269). Nonetheless, as technology advances multiple reaction
monitoring (MRM) methodologies will become the tools of choice for measuring glycoproteins.
Although we have had only moderate success with quantifying different glycoforms of KLK6 in
a similar approach, the ability to even semi-quantitatively measure the representation of several
closely related glycoforms in the total KLK6 population is beyond the reach of other detection
methods described above.
We are aware of the limitations of the methodologies utilized within. The majority of the
data reported should be considered as semi-quantitative. Anion-exchange chromatography can
be prone to retention time disturbances, especially when monitoring such minute changes on the
single protein being monitored, causing potentially significant variability. This can affect the
robustness of the methodology if careful and precise calibration is not employed. As well, the
cumbersome and time consuming methodology of this approach limits its potential for use in a
clinical setting, where a high number of samples need to be analyzed. Utilization of the product
ion monitoring glycopeptide relative quantification methodology is also far from being
applicable to analyzing complex samples such as biological fluids. The signal from the
glycopeptides is approximately 3 orders of magnitude less than the unglycosylated peptide, even
in the high quantity and purity preparation of KLK6 used in this study. Also, considering that
the ovarian cancer samples used in this study were from patients with late stage disease (III and
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IV), it can be questioned how sensitive this approach will be when attempting to detect early
stage ovarian carcinoma. Also in this feasibility study, the number of samples analyzed is small
and the conclusions need to be verified with analysis of a larger dataset.
In conclusion, even with these concerns, this is the first study to indicate that aberrant
glycosylation of KLK6 occurs in the serum of individuals with ovarian cancer and we developed
a reliable method of measuring these changes. As such, further refinement of the analytical
method may lead to the improvement of KLK6 and other cancer glycoprotein biomarkers with
similar properties in their diagnostic potential.
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4 CHAPTER 4: Glycoproteomic analysis of ovarian
cancer cell lines and proximal fluids
Kuzmannov U, Musrap N, Kosanam H, Smith CR, Batruch I, Diamandis EP. Glycoproteomic
dentification of potential glycoprotein biomarkers in ovarian cancer proximal fluids. (in
preparation).
N Musrap proliferated cell lines and prepared lyophilized protein from serum-free media.
H Kosanam assisted in the design of workflow.
CR Smith and I Batruch operated and maintained mass spectrometry equipment.
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4.1 Short Overview
Based on literature reporting increased expression of sialyltransferases in ovarian cancer cells
and the corresponding discoveries from the previous two chapters showing the increased
sialylation of ovarian cancer cell-derived KLK6 we conducted a study with the purpose of
identifying other sialylated proteins in ovarian cancer proximal fluids that could serve as
candidate biomarkers. This was performed by enriching for sialylated glycopeptides derived
from biological fluids and conditioned media from ovarian cancer cell lines. Sialoglycopeptides
were enriched for by utilizing Elderberry lectin affinity and hydrazide chemistry.
4.2 Introduction
Approximately 30,000 North American women are diagnosed with ovarian cancer every year
and over 50% of these women die within 5 years following diagnosis (270). As such, ovarian
cancer has the highest fatality rate and is the most common of all gynecological malignancies in
the developed world. Current approaches utilized towards the detection and diagnosis of ovarian
cancer include pelvic examination, ultrasonography, and serum tumor marker screening (242).
Pelvic examination fails to detect early stage disease and ultrasound cannot reliably differentiate
between benign and malignant forms, leaving serum biomarker detection as a promising strategy
for early screening (220, 242). To date, only CA125, a large mucin-type glycoprotein, is widely
used as an ovarian cancer biomarker in the clinical setting. It is used for monitoring the
response of patients to treatment but has shown less promise as a screening tool since CA125
can be elevated in a number of other malignancies and benign conditions, as well as during
menstruation and pregnancy (83, 181, 220, 271). Therefore, there still exists a great need for
ovarian cancer biomarkers.
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It is currently a well-established concept that gene and protein expression are not the sole
factors responsible for phenotype determination, and that post-translational modifications
(PTMs) of proteins provide us with another level where functional information can be stored.
There is over 200 different types of protein PTMs, with glycosylation being one of the most
common (1-4). It has been shown to have an important role in a number of physiological
processes, including: protein folding and trafficking, cell-cell and cell-matrix interaction,
cellular differentiation, fertilization, and the immune response (5-9). Approximately half of all
mammalian proteins are glycosylated with as much as an estimated 3000 different glycan
structures, which can vary to a large degree based on differences in tissue, cell type, and disease
state (10, 11). An estimated 250 to 500 genes are involved in the protein glycosylation process
(12).
Disruption of glycosylation pathways has been established as a common characteristic of
oncologic malignancies. It has been observed in almost all types of experimental and human
cancers. Under normal physiological conditions, glycoproteins are produced in a number of
different glycoforms and the differences in these forms can arise from differential occupancy of
glycosylation sites or variability in attached glycan structures (17). In cells undergoing
malignant trasformation under-, over-, or neoexpression of glycan moieties can occur on
glycoproteins (18). These changes most often arise from disturbances in the expression levels of
different glycosyltransferases. For example, the increased activity or expression of N-
acetylglucosaminyltransferase V (MGAT5) results in increased glycan branching on proteins
resulting in increased tumor growth and metastasis (19-23). Changes in terminal glycan residues
also often occurs in malignant cells, as is often the case with the upregulation of different
sialyltransferase enzymes in tumors (24-29). Therefore, the detection and quantification of the
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disturbances in protein glycosylation can aid in the screening and diagnosis of virtually all cancer
types.
In ovarian cancer, a number of acute phase proteins have exhibited aberrant glycosylation
patterns, including: haptoglobin, 1-antitrypsin, 2-macroglobulin, transferrin and IgGs
generally following the trend of increased branching and sialylation (243). However, there is
strong evidence that increased sialylation in ovarian tumors cells also occurs. Namely, in
addition to carcinomas of the brain, breast, cervix, and the colon, the expression of ST6GAL1 (a
sialyltransferase responsible for attachment of 2-6-linked terminal sialic acids) is increased in
ovarian tumors (87, 88). Direct mRNA expression analysis of multiple sialyltransferases in
ovarian tumor tissues has indicated the preference for the attachment of 2-6- over 2-3-linked
sialic acids on glycoproteins produced by cancer cells (88). Specifically, ST6GAL1 levels were
shown to be increased while the levels of ST3GAL6, a competing enzyme which normally
attaches sialic acid in a 2-3 linkage, were decreased. Additionally, sialyltransferases attaching
sialic acid in an 2-3 linkage to O-glycans were shown to be upregulated while enzymes with
the same linkage specificity for N-glycans were downregulated (88). These observations lend
significant support to other studies showing that 2-6-linked sialic acids are overexpressed on
glycoproteins secreted or shed from membranes of ovarian cancer cells. Therefore, the
identification, and subsequent characterization and quantification of proteins with 2-6-linked
sialic acids in ovarian cancer proximal fluids or cell lines has great potential in the identification
of new and improvement of existing ovarian cancer biomarkers by including their glycosylation
patterns in their measurement.
Considering the need for further ovarian cancer biomarkers, we have developed a tandem
mass spectrometry-based strategy for elucidating potential biomarker candidates. Due to the
well-established upregulation of sialylation in ovarian cancer we have conducted a study with the
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purpose of identifying the N-linked sialome (sialic acid containing glycoproteins) of ovarian
cancer proximal fluids and cell line conditioned media with the purpose of mining for novel
biomarkers of this disease. Towards this purpose, we have analyzed two types of samples from
ovarian cancer patients, ascites and malignant cyst fluids, in addition to supernatants from four
different ovarian cancer-derived cell lines. As non-malignant controls, we have also analyzed
benign ovarian cyst and peritoneal effusion fluids. Proteominer technology was utilized for the
enrichment of low-abundance proteins in the biological fluids analyzed. Sialylated
glycopeptides in analyzed samples were enriched by two well-established methodologies, lectin
affinity and hydrazide chemistry. In total, 333 proteins and 579 distinct glycosylation sites with
the sialic acid moiety were identified. Of these, 21 were unique to the biological fluids from
ovarian cancer patients and formed the candidate biomarker list to be studied in future
endeavors.
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4.3 Materials and Methods
4.3.1 Microarray Profiling
Microarray profiles of normal and cancerous ovarian tissues were extracted from previously
published studies on Affymetrix HGU-133A platform. The meta-analysis on 5372 human
samples representing 369 different tissue types was used to select a total of 104 samples from
human ovary including normal and pathological tissue (272). The expression data had been
previously normalized using RMA and log2 intensities for each probe were used for the analysis.
Probe to gene mappings based on current Affymetrix annotations were used. In cases where
multiple probes hybridize to the same gene, the most informative probe was selected as the one
having the largest range of expression. Ratios for comparison between groups were obtained as
ratios of the means of each group. Significance analysis was performed using the limma package
(273), comparing the complete set of tumour (n=87) and benign ovarian polycystic syndrome
(n=5) samples to normal ovarian tissue (n=12). Tumor samples were derived from patients with
clear cell (n=7), serous (n=34), mucinous (n=11), and endometrioid (n=35) subtypes of ovarian
cancer.
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4.3.2 Clinical Samples
The biological fluids used in the study were collected with informed consent and institutional
review board approval, or were leftovers submitted for routine medical testing. All samples used
were stored at -80oC prior to use. Ovarian ascites and malignant cyst fluids were collected from
patients with FIGO stage III and IV of the major histological subtypes of epithelial ovarian
carcinomas and pooled based on total protein content. In total 13 ascites fluids from ovarian
cancer patients with different histological presentation were used: 3 serous, 3 endometrioid, 3
mucinous, and 4 undifferentiated. 12 malignant ovarian cyst fluids were also pooled (4 serous, 4
endometrioid, 3 mucinous, and 3 undifferentiated). As non-malignant controls, fluid from
benign ovarian cysts (n=10) and peritoneal effusions from patients with peritonitis (n=20) were
utilized and pooled in the same fashion. All pooled biological fluid samples were subjected to
size exclusion chromatography or Proteominer low abundance protein enrichment (BioRad).
Size exclusion chromatography was performed using a 0.75 x 60-cm TSK-Gel G3000SW
column (Tosoh Bioscience) attached to an Agilent 1100 HPLC system. 0.5 ml of pooled
biological fluids were loaded onto the system equilibrated with PBS and run for 1 hour at a flow
rate of 0.5 ml/min. Fractions were collected every minute, and only sub-50 kDa fractions were
pooled and concentrated using centrifugal spin columns with a 3 kDa cuttof (Millipore). These
were used for subsequent analyses. Proteominer enrichment was performed with 1 ml of pooled
biological fluids, per manufacturer’s protocol (BioRad).
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4.3.3 Cells Line Supernatants
The human ovarian cancer cell lines, OVCAR-3 (HTB-161), ES-2 (CRL-1978), and TOV-112D
(CRL-11731) were purchased from the American Type Culture Collection (ATCC, Manassas,
VA). OVCAR-5 cells were obtained from the Fox Chase Cancer Centre (Philadelphia, PA). ES-
2, TOV-112D, and OVCAR-5 cells were grown in RPMI 1640 (Wisent) medium supplemented
with 10% characterized fetal bovine serum (FBS) (Thermo Scientific). RPMI 1640 containing
20% FBS was used to maintain OVCAR-3 cells. All cells were cultured in a humidified
incubator adjusted to 37°C with an atmosphere of 5% CO2. Each cell line was cultured in
duplicate using T-175 cm2 flasks. Upon reaching 80% confluency, culture media was removed
and the attached cells were washed gently three times using 25mL of phosphate buffered saline
(PBS) (Wisent). Following the washes, cells were grown in 30 mL of CD CHO chemically
defined medium (Invitrogen) supplemented with 8mM L-glutamine (Invitrogen) for 48 hours.
After this period, the conditioned medium was collected and centrifuged at 1200 rpm for 5
minutes to remove cellular debris. All cell line supernatants were dialyzed using a 3.5 kDa
molecular weight cut-off porous membrane (Spectrum Laboratories, Inc., Compton, CA) in 4
litres of 1mM ammonium bicarbonate (NH4HCO3) buffer overnight at 4°C. The buffer was
exchanged for a total of three times before the dialyzed samples were frozen at -80°C. Frozen
samples were then lyophilized to complete dryness using a ModulyoD Freeze Dryer (Thermo
Electron Corporation).
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4.3.4 Elderberry lectin sialoglycopeptide enrichment
100 g of total protein from all samples was brought to 300 l volume and 50 mM ammonium
bicarbonate concentration. 10 l of 400 mM DTT was added and the solution was incubated at
60oC for 45 min and subsequently brought to room temperature. 11 l of 800 mM
iodoacetamide solution was added an the solution was incubated at room temperature with no
exposure to light for 45 min. 2 g of sequencing grade trypsin was added and solution was left
overnight (12 h) at 37oC. Following trypsin digestion of the samples, the resulting peptide-
containing reaction mixture was heated to 90oC for 15 min. The samples were stored at -80
oC
until futhere use. 150 l of agarose-bound Elderberry lectin slurry (Vector Labs) was added to
500 l spin columns (Fisher). The storage solution was removed by centrifugation (300xg) and
the beads were washed three times with 1X lectin binding buffer (10 mM HEPES, 150 mM
NaCl, 0.1 mM Ca++, pH 8). Trypsinized samples were diluted 1:1 in 2X lectin binding buffer
and incubated with lectin-bound agarose beads for 2 hours. Flow through was removed with
centrifugation (300xg) and the beads were washed 6 times with 1X lectin binding buffer. The
beads were resuspended in 300 l of 50 mM ammonium bicarbonate containing 500 units of
mass spectrometry grade PNGase F (New England Biolabs) and left overnight (12h) at 37oC
with shaking. The eluate was collected by centrifugation (300xg), acidified to pH 3 with formic
acid (0.1%), and used for MS analysis as described below. The general schematic of the
procedure can be seen in Figure 4.1.
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Figure 4.1. Lectin-based glycopeptide enrichment. Schematic representation of lectin
affinity-based pull-down of glycosylated peptides following tryptic digestion of whole protein(s),
and tandem mass spectrometry-based identification. Proteins in the biological fluids of interest
are digested by trypsin and the resulting sialylated tryptic glycopeptides are bound to agarose-
immobilized elderberry lectin. After washing of the agarose beads, the peptide portion of the
lectin bound glycopeptides is released by PNGase F treatment and analyzed by tandem mass
spectrometry. For more details see Materials and Methods.
113
4.3.5 Hydrazide bead sialoglycopeptide enrichment
100 g of total proteins from all biological fluid samples were brought up to 500 l volume with
phosphate buffered saline (PBS) and sodium periodate (Sigma) was added to a final
concentration of 2 mM and the reaction mixture was inbubated on ice for 10 min. The oxidation
reaction was stopped by addition of 5 l of glycerol. The solution was concentrated to 300 l
and buffer exchanged with Sodium Aceteate solution (100mM Sodium Acetate, 100 mM NaCl,
pH 5.5) using ultracentrifugal spin columns with a 3 kDa molecular weight cut-off (Millipore).
The sodium presiodate-treated samples were added to hydrazide-conjugate magnetic beads
(Dynal) and incubated at room temperature for 14h with shaking. The supernatant was removed
and the magnetic beads were washed 3 times with a 50 mM ammonium bicarbonate solution
containing 8M urea. The beads were washed again 6 times with a 50 mM ammonium
bicarbonate solution. 400 l of ammonium bicarbonate solution was added to the beads
followed by the addition of 50 l of 50 mM DTT. The solution was incubated with shaking for
45 min at 60oC and allowed to cool to room temperature. Next, 60 l of 100 mM iodoacetamide
was added and the reaction mixture was left for 45 min at room temperature with no exposure to
light. 1 g of sequencing grade trypsin was added and the solution was incubated at 37oC
overnight (14h) with shaking. Supernatant was removed from the beads and they were washed 6
times with 50 mM ABC. The beads were resuspended in 300 l of 50 mM ammonium
bicarbonate containing 500 units of mass spectrometry grade PNGase F (New England Biolabs)
and left overnight (12h) at 37oC with shaking. The eluate was collected, acidified to pH 3 with
formic acid (0.1%), and used for MS analysis as described below. The general schematic of the
procedure can be seen in Figure 4.2.
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Figure 4.2. Hydrazide chemistry-based enrichment of glycopeptides. A schematice
representation of sialoglycopeptde processing and enrichment using hydrazide-conjugated
magnetic beads, from periodate-mediated oxidation of sialic acid residues, whole protein binding
to beads, on-bead tryptic digestion, PNGase F-mediated release of glycosylated peptides, to their
identification by tandem mass spectrometry.
115
4.3.6 ESI-LTQ-Orbitrap Tandem mass spectrometry
Formerly glycosylated tryptic peptides from the different samples described above were initially
desalted by binding to ZipTip pipette tips containing C18 beads (Millipore) and elutes in 5 l of
buffer containing 64.5 % ACN, 35.4% H2O, 0.1% formic acid, 0.02% trifluoroacetic acid. 80 l
of a 95% H2O, 0.1% fromic acid, 5% ACN, 0.02% trifluoroacetic acid was added to the eluate.
Tryptic peptides from 40 l of each sample were then bound to a 2 cm C18 pre-column with a 5
mm diameter and eluted onto a resolving 5 cm analytical C18 column (3 mm diameter) with a 8
mm tip (New Objective). The liquid chromatography setup was connected to a Thermo LTQ
Orbitrap XL mass spectrometer with a nanoelectrospray ionization source (Proxeon) in data
dependent mode. A two buffer system was utilized where Buffer A (running) contained 0.1%
formic acid, 5% ACN, and 0.02% trifluoroacetic acid in water and Buffer B (elution) contained
90% ACN, 0.1% formic acid, and 0.02% trifluoroacetic acid in water. Each MS run was
conducted over a 90 min linear gradient and eluted peptides were scanned once in the Orbitrap
(450-145 m/z range), followed by top six (by m/z peak intensity) data-dependent MS/MS scans
in the LTQ. Unassigned, 1+, and 4+ charge states were rejected.
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4.3.7 Database searches and glycopeptide assignment
XCalibur RAW mass spectrometry files were processed using Mascot Daemon (version 2.2.0)
and extract_msn, and subsequently searched with Mascot (Matrix Science, London, UK; version
2.2). The data was searched against the concatenated non-redundant IPI.Human v.3.71 database
with parent and fragment tolerances of 7 ppm and 0.4 Da, respectively. Searches were
performed for tryptic peptides with a fixed carbamidomethylation of cysteines, a single missed
cleavage allowed, variable asparagine deamidation, and variable methionine oxidation.
Resulting data files were uploaded into Scaffold (Proteome Software Inc., Portland, OR; v.2.6)
and a further search using X!Tandem was conducted. Potential formerly glycosylated peptides
were filtered for in several steps. Only peptides with reported N deamidation within the NxS/T
consensus sequence were selected for further analysis. Due to reports of chemical deamidation
during trypsin digestion of glycan-unoccupied asparagines with a glycine in the NxS/T
consensus sequence, peptides with NGS or NGT sequences alone were omitted from further
analysis. As well, peptides with R or K in the NxS/T sequence where tryptic cleavage had
occurred were also included in the analysis.
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4.4 Results
4.4.1 Sialyltransferase expression
Although previous reports have shown the dysregulation of gene expression of some
sialyltransferase genes in ovarian tumors (88, 206), these studies were limited when considering
the number of sialyltransferase genes inspected and the patient samples used (ie. only the serous
subtype). Therefore, we had undertaken the task of searching an existing gene expression
microarray database (272) containing information from several different subtypes of ovarian
cancer and examining the expression patterns of the most common sialyltransferase genes. In
support of previous reports, only the ST6GAL1 gene showed consistent overexpression across
different tumor subtypes when compared to normal tissue and benign cysts (Figure 4.3). The
expression of the majority of other sialyltransferase genes was either undisturbed or decreased in
tumor samples. This is in further support of the hypothesis that a shift in the sialylation patterns
on proteins occurs in ovarian cancer cells leading to the preferential and/or increased addition of
sialic acid monosaccharides in an 2-6 linkage. Therefore, the comparison of sialylated proteins
present in biological fluids from ovarian cancer patients and fluids from patients with benign
conditions may yield a list of prospective biomarkers for future study.
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Figure 4.3. Sialyltransferase expression in ovarian cancer. A heatmap of relative
sialyltransferase mRNA expression in different tumor subtypes normalized against normal
ovarian tissue (A). Box plot of ST6GAL1 mRNA expression in normal and ovarian cancer
tissue (B). Values are represented as the log2 corrected raw counts.
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4.4.2 Identification of Sialylated Glycoprproteins
Sialylated glycoproteins were identified in clinical samples from ovarian cancer patients (ascites
and malignant ovarian cyst fluids), patients with no ovarian malignancy (peritoneal effusions
from peritonitis patients and fluids from benign ovarian cysts), and conditioned media from four
ovarian cancer cell lines (OVCAR3, OVCAR5, ES-2, and TOV-112D). For each type of
biological fluid analyzed, pools of samples from individual patients were used (see Materials and
Methods). To increase coverage, sialylated glycoproteins/peptides were enriched for using lectin
affinity (Sambucus nigra agglutinin) or hydrazide chemistry. As well, in addition to analyzing
undisturbed clinical samples, we also analyzed the sub-50 kDa and Proteominer enriched
fractions of the clinical samples in the same fashion. The general study outline can be seen in
Figure 4.4.
Initially, close to 700 proteins were identified in all sample types. However, data
filtering steps were undertaken due to the presence of high-abundance protein contaminants,
other non-specifically bound proteins, and environmental contaminants during the sample
preparation process. The initial step was to select only proteins that exhibited deamidation at
asparagine residues as a result of the catalytic activity of PNGase F. Due to the established
observations that even non-glycosylated asparagines may be deamidated as a result of PNGase F
activity, any identified peptides that exhibited deamidation at asparagine residues not found
within the N-glycosylation consensus sequence (N-X-S/T) were excluded. Artificial
deamidation at N-G-T and N-G-S sites resulting from prolonged trypsin exposure has been
reported. Therefore, identified peptides exhibiting deamidation at these sites were excluded from
the final list. Peptides satisfying other criteria with terminal N-K and N-R residues followed by
S or T were included in the final list. This resulted in a final list of 333 proteins and 579
sialylated glycosylation sites identified between the different types of samples analyzed (Figure
120
4.5). Of these, 151 proteins and 291 glycosylation sites were identified in cancer associated
fluids, 174 proteins and 315 sites in peritoneal effusions and benign ovarian cysts, and 222
proteins and 329 sites in conditioned media from ovarian cancer cell lines (Figure 4.5). 279
proteins (465 glycopeptides) were identified using hydrazide magnetic bead pull-down
methodology and 225 proteins (381 glycosylation sites) were identified by elderberry lectin
affinity (Figure 4.5). Therefore, more proteins and glycosylation sites were identified using
hydrazide chemistry, which is not surprising considering that this methodology does not
discriminate between the types of linkages sialic acid is attached to the glycan core, unlike the
elderberry lectin which preferentially binds 2-6-linked sialic acid. When comparing the
identified proteins from the three different sample types (Figure 4.5), a list of 21 candidate
protein biomarkers was produced (Table 4.1). These were selected as proteins that were only
identified in both the cell line supernatants and cancer ascites and cyst fluids, or only in the
cancer-associated fluids. Some of these, such as kallikrein 6, have been previously studied for
their biomarker properties and glycosylation status in ovarian cancer.
121
Figure 4.4. Study outline. A schematic representation of the general steps taken towards the
identification of sialylated glycoproteins in ovarian cancer proximal fluids and cell line
supernatants. Sialylated glycopeptide were enriched separately from all sample types by
utilizing hydrazide chemistry or Elderberry lectin affinity (see Materials and Methods). Proteins
from biological fluid samples were either untreated or pretreated with Proteominer or size-
exclusion chromatography. Serum-free media from ovarian cancer cell lines was lyophilized
prior to enrichment. Resulting peptides were subjected to tandem mass spectrometry analysis.
122
Figure 4.5. Identified sialylated glycoproteins. Venn diagrams representing the number and
distribution of identified sialylated glycoproteins (large circles) and glycopeptides (small
circles). Comparisons for identified glycoproteins between different samples analyzed (top) and
glycopeptide enrichment methods (bottom) is presented.
123
Table 4.1. Proteins identified in ovarian cancer proximal fluids alone or both in cancer proximal
fluids and cell line supernatants.
*number of transmembrane domains; **presence of signal peptide (S = signal peptide)
GENE DESCRIPTION IPI# aa# TM* SP** IDENTIFIED PEPTIDES
AMBP Protein AMBP IPI00022426 352 0 S WNITMESYVVHTNYDEYAIFLTK
CDH6 Isoform 1 of Cadherin-6 IPI00024035 790 1 S ETLLWHNITVIATEINNPK, EDAQINTTIGSVTAQDPDAAR
CFB complement factor b IPI00947496 1115 0 S
LTDTICGVGNMSANASDQER,LGSYPVGGNVSFECEDGFILR,TMFPNLTDVR,SPYYNVSDEISFHCYDGYTLR
CFHR5 Complement factor H-related 5 IPI00006543 593 0 S EQFCPPPPQIPNAQNMTTTVNYQDGEK
CTSD Cathepsin D IPI00011229 412 0 S GSLSYLNVTR
DKK3 cDNA FLJ52545, highly similar to Dickkopf-related protein 3
IPI00002714 364 0 S VGNNTIHVHR, ASSEVNLANLPPSYHNETNTDTK
DSG2 Desmoglein-2 IPI00028931 1118 1 S INATDADEPNTLNSK
HGFAC Hepatocyte growth factor activator
IPI00029193 655 1 S DSVSVVLGQHFFNR
ICAM2 Intercellular adhesion molecule 2 IPI00009477 275 1 S QESMNSNVSVYQPPR,AAPAPQEATATFNSTADR,GNETLHY
ETFGK
INHBC Inhibin beta C chain IPI00023314 352 1 S EQECEIISFAETGLSTINQTR
KLK6 Isoform 1 of Kallikrein-6 IPI00023845 244 1 S DCSANTTSCHILGWGK
KRT10 Keratin, type I cytoskeletal 10 IPI00009865 584 0
NVSTGDVNVEMNAAPGVDLTQLLNNMR
LBP Lipopolysaccharide-binding protein
IPI00032311 481 0 S LSVATNVSATLTFNTSK
LCAT Phosphatidylcholine-sterol acyltransferase
IPI00022331 440 1 S AELSNHTRPVILVPGCLGNQLEAK
LILRB2
Isoform 1 of Leukocyte
immunoglobulin-like receptor subfamily B member 2
IPI00303952 598 1 S QPQAGLSQANFTLGPVSR
MARCO Macrophage receptor MARCO IPI00009521 520 0 S VDNFTQNPGMFR
MINPP1 Isoform 2 of Multiple inositol polyphosphate phosphatase 1
IPI00028553 312 0 S NATALYHVEAFK
POSTN Isoform 1 of Periostin IPI00007960 836 0 S EVNDTLLVNELK
SERPINA10 Protein Z-dependent protease inhibitor
IPI00007199 444 1 S
QLAHQSNSTNIFFSPVSIATAFAMLSLGTK,YLGNATAIFFLPDEGK,ADTHDEILEGLNFNLTEIPEAQIHEGFQELLR
SVEP1 sushi, von Willebrand factor type A, EGF and pentraxin domain-
containing protein 1 precursor
IPI00301288 3571 2 S GAVNISACGVPCPEGK
VASN Vasorin IPI00395488 673 1 S LHEITNETFR
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4.4.3 Subcellular Localization
For the proteins identified we also performed an analysis of subcellular localization using Gene
Ontology (GO) annotation (Figure 4.6), secretion status (presence of signal peptide), and
presence of characterized transmembrane domains using the Protein Centre software. 288
proteins were shown to have a signal peptide, which would destine them for the classical
secretory pathway through the endoplasmic reticulum and subsequent organelles where
glycosylation occurs. 175 proteins were shown to have at least a single transmembrane domain.
329, or 98.8 %, of the 333 proteins identified in all samples were shown to have a signal peptide
and/or GO subcellular localizations to the extracellular space, cell surface, or membrane. This is
of great significance considering these characteristics are highly desirable in potential protein-
based cancer biomarkers, because of the heightened likelihood of their presentation in
circulation. Therefore, this suggests that our methodology was highly successful in enriching for
potential biomarkers with desirable properties.
125
Figure 4.6. Subcellular localization of identified sialoglycoproteins. Number of Gene
Ontology annotations to cellular locations associated with all of the sialoglycoproteins identified
in the present study. Extracted from ProteinCentre.
126
4.5 Discussion and Conclusions
The disruption of the protein glycosylation processes has been established as a common event in
oncogenic transformation. The changes in glycan structures of glycoproteins expressed by tumor
cells most often arise from disturbances in the expression levels of different glycosyltransferases,
glycosylation processing enzymes, or due to altered nutrient levels in the tumor
microenvironment. This can result in the under-, over-, or neoexpression of specific glycan
moieties.
The majority of the most commonly clinically utilized serological biomarkers for cancer
diagnosis and monitoring of malignant progression are glycoproteins. Some of these include
biomarkers widely monitored in patients with prostate cancer (PSA), colon cancer (CEA),
nonseminomatous testicular carcinoma (hCG-), hepatocellular carcinoma (AFP), breast cancer
(CA 15-3/MUC1), and ovarian cancer (CA125). Considering that these proteins are produced by
tumor cells, it is not surprising that these proteins have shown disturbed glycosylation patterns in
malignancy (33, 93, 106, 213).
Ovarian cancer is the leading cause of death among women with gynecological disorders
and is the fifth most common cause of all cancer-related deaths among women in the United
States (249). Disturbed glycosylation of proteins during the course of this malignancy is well
established (243). Overexpression of the sialyl Lewis-X antigen has been recorded on several
acute phase proteins including haptoglobin, 1-acid glycoprotein and 1-antichymotrypsin (86,
200). Disturbances of normal glycosylation patterns on apolipoprotein B-100, fibronectin,
immunoglobulin A1, and IgG have also been shown under malignant conditions (86, 219).
Currently, CA125 is the only routinely used ovarian cancer biomarker in the clinic.
However, its use is limited to therapy response monitoring due to the fact its levels can be
127
elevated in other malignancies, benign conditions affecting the ovary, menstruation, and
pregnancy (220-223). Therefore, a great need still exists for new biomarkers to be used for
ovarian cancer screening, diagnosis, and monitoring of progression.
There is strong evidence showing that sialylation of glycoproteins produced by ovarian
tumor cells is increased (243). Namely, mRNA expression of the ST6GAL1 gene, encoding for
an2-6 sialyltransferase, was shown to be increased by real-time quantitative PCR in ovarian
tumour tissues (88). This is supported by other data, as shown by our analysis of existing
microarray experiments in Figure 4.3 (272). Increased expression of 2-6-linked sialic acids
generally correlates with cancer progression and metastasis (87). The part ST6GAL1 performs
in these events is poorly understood, with possible roles in enhancing 1-integrin function (87,
89) and blocking of Fas- and TNFR1-mediated apoptosis by sialylation of these receptors (90,
91). Although the full picture of the importance of sialic acid in the oncogenic process is not
complete, it is evident that proteins produced by transformed ovarian cells are enriched with the
2-6 linked sialic acid moiety. An example of this is kallikrein 6, one of the glycoproteins
identified in this study, previously shown to exhibit enrichment in 2-6 linked sialic acid under
malignant conditions (Chapters 2 and 3). Therefore, the identification of other sialylated
proteins in the proximal fluids of ovarian cancer patients and ovarian cancer cell lines could
provide an narrowed pool of biomarker candidates for this malignancy. This is in contrast to
mass spectrometry-based global proteomic analyses of tissues, cell lines, or cell line supernatants
that have been previously performed and produced candidate lists of thousands of proteins.
Therefore, candidates for further evaluation based on these studies were normally chosen by
arbitrary filtering criteria such as predicted subcellular localization, secretion status, or removal
of high abundance proteins, rather than experimental setup.
128
With the a priori knowledge that proteins produced by the tumor cells are enriched with a
particular moiety (ie. sialic acid), the identification of proteins with this type of glycan in
biological fluids of ovarian cancer patients would enrich for the proteins produced by the
transformed cells. Further comparison of these to the proteins identified in the same fashion
from similar biological fluids of individuals with no ovarian malignancy has produced a compact
list of candidates based on experimental, rather than arbitrary selection criteria (Table 4.1).
When considering future studies, these candidates also provide more potential than those chosen
from global proteomic identification studies. In the subsequent studies for these candidates, the
next logical step would be the measurement of total protein levels in serum by ELISA or in
tissue by immunohistochemistry, as is the case with other proteomic data verification studies.
However, considering that a subpopulation of these proteins is uniquely produced by ovarian
cancer cells with the sialic acid-containing glycans, this knowledge could be used for the
development of hybrid, binary assays that can measure the levels of both protein and associated
glycan.
Other sialoglycoproteins identified in this study that did not meet the criteria to be
included in the candidate list (Table 4.1) should not be summarily discounted. This is due to the
fact that some well-known and previously studied proteins in the context of ovarian cancer were
also identified (Table 4.2). These include the classical ovarian cancer biomarker CA125 and
others such as mesothelin, WFDC2, a2-macroglobulin, cathepsin D, and clusterin, which have
shown promise as ovarian cancer biomarkers (38, 97, 98). The glycosylation status of a number
of these proteins has been studied in various contexts and some were reported to be sialylated
(Table 4.2) lending further support to our methodology.
Some of the most widely recognized and utilized cancer biomarkers exhibit a highly
tissue specific expression pattern, such as PSA for prostate tissue, hCG for the placenta, and AFP
129
for the developing fetus. Therefore, the over- or neo-expression of a protein as a result of
malignant transformation can be detected and monitored with greater confidence and earlier in
the progression of the malignancy in comparison to a protein produced ubiquitously or in
multiple tissues. However, such proteins are rare. If it is taken into account that glycosylation
patterns of individual proteins can be different between tissues, or between normal and tumor
cells, the ability to detect and quantify these differences could confer tissue/tumor-specificity on
a considerable number of glycoproteins. This could greatly widen the number of potential
biomarkers or improved the clinical performance of existing ones. We believe that the candidate
list found in this study identifies proteins with such properties. These proteins would benefit
from further development of quantitative assays, similar to the one developed for KLK6 (Chapter
3), capable of binary measurement of both protein and associated glycan levels that can be
applied toward diagnostic purposes.
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Table 4.2 Proteins identified in the ovarian cancer proximal fluids that were previously studied
in ovarian cancer.
Gene, protein name recorded
sialylation
Ref.
A2M,2-macroglobulin yes (274, 275)
AFM, afamin precursor no (276, 277)
CD59 yes (278, 279)
CLU, Clusterin yes (280, 281)
CP, ceruloplasmin yes (274, 282)
CTSD, Cathepsin D no (283)
FGB, Fibrinogen yes (284, 285)
HP, Haptoglobin yes (86, 243)
ICAM1 no (286)
KLK6, kallikrein 6 yes (245, 287)
LUM, Lumican yes (107, 288)
MSLN, Mesothelin no (83)
MUC16, CA125, mucin 16 yes (32, 83, 289)
MUC5B yes (290-292)
ORM1/2, -1-acid glycoprotein yes (216, 293, 294)
PLAUR, urokinase plasminogen activator surface receptor
precursor
yes (295, 296)
PTGDS no (297)
RBP4, plasma retinol-binding protein precursor no (298)
S100A9 no (275, 290, 299)
SERPINA1 no (300)
SERPINA3 no (301)
SPON1, Spondin no (302)
TF, Serotransferrin yes (238, 243, 303)
THBS1, thrombospondin-1 precursor no (304)
TIMP1, metalloproteinase inhibitor 1 precursor yes (305, 306)
ITIH4, isoform 1 of inter--trypsin inhibitor heavy chain H4 precursor
no (278, 307)
WFDC2, isoform 1 of whey acidic protein four-disulfide
core domain protein 2 precursor
no (308, 309)
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5 CHAPTER 5: Discussion and Future Directions
132
5.1 Conclusions in Brief
In summary, through the application of classical molecular biology and tandem mass
spectrometry techniques we have been able to complete the main objectives stated in the
introductory sections. In support of the initial hypothesis the N-glycosylation patterns of KLK6
were found to be different between normal individuals and patients with ovarian cancer, and
differences in the sialylation of glycoproteins in ovarian cancer proximal fluids were also
observed. The key findings of the study:
1. Kallikrein 6 derived from ovarian cancer cells exhibits altered glycosylation patterns.
2. The glycosylation patterns of ovarian cancer-related kallikrein 6 are highly heterogeneous
with a propensity for increased branching and sialic acid content.
3. The changes in kallikrein 6 in serum and other biological fluids can be monitored by an
HPLC anion-exchange ELISA-coupled methodology developed as a part of this study.
4. Through the use of glycoproteomic techniques we have identified the sialylated subset of
the ovarian cancer secreted or shed glycoproteome, and identified a candidate list of
glycoproteins manifesting differential sialylation patterns between malignant and non-
malignant samples, which could be utilized in future studies.
When considering the future applications of the present study one issue above all must be
addressed. Namely, although we have identified the glycosylation differences in kallikrein 6 and
other proteins in ovarian cancer, our ability to capitalize on these discoveries is considerably
limited. The kallikrein 6 assay we have developed (Chapter 3) is applicable only for smaller
pilot studies, such as the one we conducted. Specifically, this assay is far from being as
sensitive, reliable, or high throughput enough to be applied in larger clinical studies which could
examine the true potential of the differential glycosylation of kallikrein 6 in screening, diagnosis,
and monitoring of cancer. The same would appear to hold true for the other candidates we have
133
identified and glycoprotein biomarkers in general, but at this particular moment in time there
does not appear to be a viable alternative. However, some promising novel techniques and
approaches allow for alternatives which may be viable in the future. These and related topics are
described in the sections to follow.
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5.2 Discussion
5.2.1 The Pitfalls of Glycobiomarker Quantification
The screening, diagnostic, prognostic, and disease monitoring potential of binary (protein and
glycan) measurement of glycobiomarkers is undisputable. However, there are a series of
technical and biological obstacles to developing quantitative assays that reflect the full picture of
the status of a glycoprotein biomarker. The majority of challenges preventing reliable, clinically
applicable binary measurement of glycoprotein biomarkers are of a technical nature. More
specifically, there is only a very limited set of tools capable of performing this task, each with its
own set of associated limitations and difficulties. Currently, the options for concurrent
quantification of a protein and its associated glycans are narrowed down to a combination of
antibody mediated protein capture and detection with glycan specific antibodies, lectins, or mass
spectrometry. The advancement of these approaches is hindered by the absence of a suitable
recombinant technology capable of reliable and convenient production of glycoproteins with
desired glycan structures, which would allow for more convenient and detailed studies.
However, considering that protein glycosylation is not a template driven linear sequence based
process, such as DNA or protein synthesis, a suitable solution to this problem does not seem to
be on the horizon, even though some advancements have been made (253). Due to a large
number of combinations of branched oligosaccharide structures that can be created from
available monosaccharides in eukaryotic cells, and especially cancer cells, where target protein
production and normal glycosylation processes are highly disturbed, the staggering glycan
microheterdogeneity can significantly impede precise binary measurement of individual
glycobiomarkers (190). That is why the majority of proteins for which the development these
types of assays has been attempted are high-bundance proteins themselves (eg. transferrin,
135
haptoglobin, IgGs, -1-acid glycoproteins, etc.). Therefore, a quantitative detection system
encompassing the heterogeneity of glycan structures of a single protein in a single output holds
great potential for bringing the use of more glycobiomarkers to a respectable (clinically testable)
level.
The majority of the top 22 plasma high abundance proteins, which account for 99% of
protein content in serum, are glycoproteins (254). These include such proteins as the Ig family
members, haptoglobin, antitrypsin and transferrin, among others. However, the majority of
potential biomarkers are found at significantly (several orders of magnitude) lower levels in the
serum. Taking into consideration that a specific glycan profile on one protein might indicate a
malignant condition, but not on another (ie. one of the high abundance proteins) the specificity of
detection by lectins, or even glycan-specifc antibodies of low concentration serum glycoproteins
can be hindered by high levels of background from contaminating high abundance glycoproteins.
As such, these methods of detection lag far behind the gold standard (sandwich ELISA) in
sensitivity, especially when taking into account that only a subset of the target protein's total
population is being measured. Therefore, the technologies with the capability or with a strong
potential for binary (protein and carbohydrate) measurement of glycoprotein cancer biomarkers
in serum are still at a rudimentary stage and require much improvement.
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5.2.2 Lectin-based Quantification
The existence of lectins has been known for over 100 years since the discovery of ricin by
Herrmann Stillmark in 1888 (310). However, wider application in research did not occur until
the early 1970s (311, 312). Lectins are proteins with a proven affinity and selectivity for specific
carbohydrate structures, which they can bind in a reversible fashion. They can recognize
carbohydrates conjugated to protein and lipids, or free mono- or polysaccharides. In excess of
500 lectins have been discovered, mostly from plant origin, and over 100 are commercially
available (310). They have been used in a wide variety of technical formats, including lectin
blots, immunohistochemistry, liquid chromatography, enzyme-linked lectin assay (ELLA), and
lectin microarrays. Despite extensive characterization and the many years of experience in lectin
research, there has been only a precious few applications where lectins have been used in a
clinically applicable high throughput fashion to detect and quantify serological biomarkers in
cancer. They are the oldest and most reliable tool for glycoprotein characterization and are
indispensible in any endeavor involving analysis of protein associated glycans. However, the
lectin journey from an analytical to a quantitative tool has been a long one, with many obstacles
in the way and few successes.
Enzyme-linked lectin detection approaches for detection of carbohydrates have been
known and utilized for close to 3 decades (313, 314). These types of quantitative assays have
been ported into a high-throughput multi-well plate format, similar to the common ELISA. In
difference to the ELISA, where a protein of interest is captured and/or detected by an antibody,
these roles are performed by lectins. Over the years, there have been several types of assays,
which can be grouped under the common name of enzyme-linked lectin assay (ELLA). In one
format, serum or cell bound proteins are non-specifically immobilized and the global levels of a
particular glycan structure are detected using a specific lectin. This has been performed for sera
137
of patients with squamous cell carcinoma of the uterine cervix by measuring the levels of the
Thomsen-Friendenreich antigen (T-Ag) using peanut agglutinin (PNA) lectin for detection (315).
The reactivity of a number of lectins to serum glycoproteins from lung cancer patients was also
measured using this general approach (316). It has also been used extensively for detection and
differentiation of a number of species of bacteria (317-319). In another use of lectins in an
ELLA-type approach, an immobilized lectin is used to capture all glycoconjugates with a
particular glycan structure from a complex biological sample, and a particular proteins presence
and quantity is then determined by antibody detection. An example of this approach was a study
detecting wheat germ agglutinin (WGA) bound mucins in the serum of pancreatic cancer patients
(320). However, this approach requires that the target glycoprotein accounts for a significantly
large proportion of the total glycoprotein content in the sample, which is often not the case.
Another, most desirable approach involves the antibody-based capture of a single protein and
subsequent detection of associated glycan components by lectins. This approach has been used
to measure sialylation of transferrin (238), fucosylation of PSA in prostate cancer patients (251),
sialylation of recombinant erythropoietin (321), WGA and ConA reactivity to p185 in serum of
breast cancer patients (322), and fucosylation of haptoglobin in sera of pancreatic cancer patients
(252).
It must be noted that the antibody-lectin sandwich approaches are plagued by a number of
technical issues, which can be addressed with varying degrees of success. A major issue is the
inherent glycosylation of the antibodies used to capture a specific glycoprotein, which can cause
a non-specific background signal from lectin binding often masking the signal from the
glycoprotein of interest. This effect can be minimized by the enzymatic or chemical
derivatization of the antibody associated carbohydrates prior to use in the assay (238, 257, 323).
Another issue is the limited recognition range of any given lectin for a particular glycan structure
138
thereby not allowing for the detection of the full scope of the heterogeneity of glycosylation on
any particular glycoprotein. Use of multiple lectins for detection in an array format can
ameliorate this issue (see below). When considering serum as the analyte matrix, another
significant source of background signal in this type of assay comes from the non-specific
contamination by high-abundance glycoproteins. This often masks the signal from low-
abundance glycoprotein analytes. This is not an issue when measuring other high abundance
serum glycoproteins, such as transferrin (238) or haptoglobin (252), where the dilution of the
serum sample would lower the background noise to a minimal level. For low-abundance
glycoproteins, where sample dilution is not an option, more rigorous washing and blocking steps
are required (324).
The greatest success for use of lectins in diagnosis of malignant conditions has been the
discovery and quantification of the Lens culinaris agglutinin (LCA)-reactive species of -
fetoprotein (AFP-L3). It has been shown to improve the specificity for hepatocellular carcinoma
(HCC) when compared to total AFP levels, which can be elevated in pregnancy, hepatitis, and
liver cirrhosis (35, 36, 325, 326). However, in an ingenious departure from the ELLA type
approach where a lectin replaces an antibody in an ELISA format, the AFP-L3 test relies on the
liquid phase capture of AFP reactive to LCA and subsequent measurement of bound and
unbound portions of the protein by an ELISA for total AFP. Therefore, the lectin is not used for
detection but for fractionation of the AFP glycoprotein populations in the serum of patients, and
the quantification is performed by a standard ELISA developed with antibodies recognizing
peptide (non-glycosylated) epitopes. It should be considered highly fortuitous that only the core
fucosylation status of the single N-glycosylation site of AFP, as detected by LCA, is sufficient
for successful diagnosis when considering the micro-heterogeneity associated with AFP
glycosylation in HCC (213, 327).
139
Over the past decade, a new role has been re-invented for lectins in the characterization
and quantification of serum glycoproteins under malignant conditions. In a re-imagination of the
ELLA approach, multiple lectins are now being used to simultaneously detect different
carbohydrate structures on antibody-captured glycoproteins in a microarray format. Several
groups have created methodologies whereby an antibody is immobilized in an array format and
lectins are used to measure glycosylation of the captured proteins (323, 328-330). The major
advantage of this approach is the ability to detect a glycan profile of any given glycoprotein and
to compare it between different samples in a high-throughput fashion. Aberrant glycosylation
patterns of mucins, CEACAMs, and -1-beta glycoprotein in clinical samples from pancreatic
cancer patients have been detected using similar methodologies by different groups (331-333).
This type of approach goes a long way in detecting the heterogeneity of glycan structures of
individual glycoproteins, but at the core it is only a simple multiplexing of the ELLA
methodology, with its associated restrictions, that has been known and applied with limited
success over the past 3 decades.
140
5.2.3 Mass spectrometry-based Quantification
Advancements in mass spectrometry have revolutionized the field of carbohydrate research. It
has led to the initiation of a large number of studies dealing with the identification, analysis, and
quantification of glycoconjugates (18, 334). When considering glycosylated proteins, these
studies range from inspections of individual glycoproteins to elucidation of whole
glycoproteomes. Towards these purposes, MS has been coupled to a number of well established,
as well as some novel technologies dealing with chemical modification, chromatographic
separation and affinity purification of glycans to achieve the best results. These studies were
conducted on multiple MS platforms, including ion trap (IT), linear trap quadrupole (LTQ), time
of flight (TOF), quadrupole/triple quadrupole (Q), Orbitrap, and Fourier transform ion cyclotron
resonance (FTICR) mass analyzers (250). As a result of its proven utility, MS analysis has
become an almost absolute requirement for any study dealing with the identification and analysis
of protein glycosylation. MS-based approaches for glycoprotein identification and
characterization have been reviewed extensively and in great detail in a number of publications
(18, 250, 334, 335). However, the quantification of glycoproteins and their associated glycans
using MS techniques is at a nascent stage, with no clinical applications to date. Similarly to the
strategies that are utilized to identify and characterize protein glycosylation, MS can also be used
to quantitate glycoproteins alone, only glycoprotein-associated glycans, or simultaneously
measure both the quantity of the protein and its associated carbohydrate structure. These
quantification strategies have followed the same trend as the established MS-based techniques
for quantifying proteins. These can be further separated in label-based or label-free approaches.
Most common labeling methodologies have involved stable isotopic labeling techniques, such as
16O/
18O,
12C/
13C, stable isotope labeling with amino acids in culture (SILAC), isobaric tags
(iTRAQ), isotope coded affinity tags (ICAT) (250). These strategies are regularly applied to
141
comparison and relative quantification of glycoprotein analytes between samples. Label-free
approaches have included spectral counting, ion intensity measurement, and multiple/selected
reaction monitoring (MRM/SRM). However, as can be seen from the majority of the recent
examples in literature shown below, all of these approaches, and their combinations, have been
limited to quantification of glycoproteins that are highly purified, in background matrices much
less complex than serum or other biological fluids of interest, or dealing with one of the high-
abundance proteins.
Although routinely used for identification and characterization purposes, an established
application of MS in the glycomics field is the quantification of carbohydrates released,
chemically or enzymatically, from individual or multiple glycoproteins. MALDI-MS
instrumentation has been shown to be invaluable for this type of approach. This platform was
used by two different groups to quantitate sialylated glycans enzymatically-released (PNGase F
treated) glycoproteins in a high-throughput fashion. Gil et al. (2010) quantified amidated acidic
glycans derivatized with 2-Aminobezoic acid or Girard’s T reagent (based on hydrazide
chemistry) from purified human IgG (336). Another group, was able to perform relative
quantification of sialylated glycans released from CHO-expressed IgG and Fc-fusion protein
differentially labeled using reductive amination with the 12
C7 and 13
C7 analogs of anthranilic acid
(337). Another MALDI-TOF-based methodology was also developed for absolute and relative
measurement of up to 34 major N-glycans released from (mostly high-abundance) serum
proteins by optimization of glycan release conditions through development of novel detergent
reagents (338). The diagnostic and stage stratification potential of MS-based quantification of
permethylated glycans from serum proteins of breast cancer patients was shown by study able to
identify and quantify close to 50 different glycan structures (339). Relative quantification of
anthranilic acid-derivatized glycans enzymatically released from -1-acid glycoprotein purified
142
from serum in combination with linear discriminant analysis has been shown to have the
potential of discriminating between normal individuals and patients with ovarian cancer and
lymphoma (216). Similar approaches have also led to the identification of serum haptoglobin
glycans with diagnostic potential in lung cancer (259) and liver disease (260).
Quantification of proteins, including some glycoproteins, by MRM/SRM has been
performed in a number of biological fluids (239, 262-264). Great advances have been made with
approaches utilizing immunoaffinity enrichment of peptides or proteins followed by
MRM/SRM-based quantification, achieving levels of sensitivity (~ng/ml) applicable to the
concentration range at which low-abundance tumor biomarkers are found (340-344). This type
of methodology has also been used in combination with different types of glycan affinity
enrichment strategies thereby producing hybrid assays where classic glycoprotein enrichment
strategies are used for capture of specific glycoforms and MS is used for detection and
quantification of the protein in those subpopulations by monitoring the MS2 fragmentation of
non-glycosylated tryptic peptides. One such example was the quantification of
phytohemagglutinin-L4 (L-PHA) enriched fraction of TIMP1 from the serum of patients with
colorectal carcinoma and supernatant of colon cancer cell lines (267, 345). Also, a method for
the measurement of total glycosylated and sialylated PSA has been recently developed whereby
periodate oxidized PSA tryptic glycopeptides are captured using immobilized hydrazide,
released by PNGase F, and quantified by SRM using a triple quadrupole LC-MS (346).
However, it must be noted that these types of studies are not utilizing the full potential of MS in
detection of the heterogeneity of glycan structures associated with any given glycoprotein, but
are rather utilizing this technology solely for protein quantification, which could be performed
more conveniently and reliably by classical methods such as the ELISA.
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The true potential of MS in the quantification of protein glycosylation lies in the
measurement of total glycoprotein amounts while simultaneously measuring its heterogeneously
glycosylated subpopulations. The ultimate goal is the development of site-specific label-free
methods with the capability of simultaneously quantifying multiple glycopeptides encompassing
multiple glycosylation sites and their different glycoforms, with the use of a non- glycosylated
peptide from the glycoprotein of interest or a labeled exogenous peptide standard, which could
serve as an indicator of the total glycoprotein concentration. Considering that SRM assays have
been developed for simultaneous measurement of dozens of tryptic (or other proteolytic)
peptides from dozens of proteins, it is not beyond grasp to do the same for glycopeptides with
different glycan structures from a single or even multiple proteins. In our labporatory, we have
achieved this by developing an SRM assay for several glycopeptides and a non-glycosyalted
peptide derived from a highly purified preparation of recombinant kallikrein 6 (Chapter 4). A
general schematic of a glycopeptide-targeted MRM from a single glycoprotein can be seen in
Figure 5.1A. However, to improve the sensitivity of this methodology further development and
technical advances will be required.
In addition to the general problems with quantifying glycoproteins described above, there
is a set of technical limitations that are currently preventing the application of this type of
approach to glycoproteins found in samples of clinical interest. The major issue is the much
lower ionization efficiency of glycopeptides compared to their non-glycosylated counterparts,
generally following the trend that ionization efficiency decreases with glycan branching and
sialylation (265, 266). This can result in differences of several orders of magnitude in absolute
signal values between glycopeptides and non-glycosylated peptides (265, 266). As well, in
comparison to the measurement of non-glycosylated peptides, for the same quantity of the
144
protein analyte the SRM signal for any individual glycopeptide will be significantly diminished
due to the fact that it represents only a subset of a heterogeneous glycoform population.
The verification of candidate biomarkers in non-serological bio-fluids using selected-
reaction monitoring (SRM) assays has become a standard practice in biomarker discovery
laboratories. The challenges associated with the development and optimization of SRM assays
were significantly eased with the advent of SRM-transition-prediction and data analysis software
such as Pinpoint (Thermo Scientific) and Skyline (Open-source software, MacCoss lab,
University of Washington, Seattle). Owing to the absence of such invaluable tools, the SRM
development of glycan bound peptides is still a daunting task, as evidenced from lack of
publications in the area. Predicting the glycopeptide SRM transitions and their optimal collision
energies is a major obstacle to the method development.
In our laboratory, to identify cancer-specific glycoforms, the protein of interest is
immuno-isolated from both healthy and malignant bio-fluids and differential glycosylation
patterns are analyzed with high-resolution LTQ-Orbitrap mass spectrometry. The peptide and
glycan sequences are assigned based on the fragment ion information collected from collision-
induced dissociation (CID), high-energy collision dissociation (HCD) and electron-transfer
dissociation (ETD). Due to the similar fragmentation behavior of glycopeptide ions in linear
iontrap and triple quadrupole, it may be possible that the most intense fragment ions from LTQ
could be transformed into SRM transitions. However, the linear traps do not allow for trapping
of fragment ion masses below 1/3 of precursor ion mass and therefore given the high mass of
glycopeptide ions, the high intensity low mass oxonium ions (m/z 163 and 204 etc.) may not be
detectable. Excluding these oxonium ions could significantly hamper the SRM assay sensitivity.
An alternative approach is to use triple-quadrupole to perform product ion scans on specific
glycopeptide precursor ions to identify all the possible daughter ions including low mass
145
oxonium ions. From this fragment ion pool, three or more intense fragment ions can be chosen to
formulate high sensitive SRM transitions. Additionally, the collision energy (CE) needs to be
optimized to maximize the fragment ion intensity which could directly impact the sensitivity of
the SRM quantitative assay. The precursor ions are fragmented with increasing collision energies
from 15-50% in increments of 2 units per step until an optimal value for each SRM pair is found.
In the Diamandis lab the feasibility of this methodology has been tested and an SRM assay was
developed which could be applied to quantitate human KLK6 glycoforms (manuscript in
preparation). In a futuristic scenario, to construct high-throughput platforms for the verification
of cancer-exclusive glycoforms, these SRM-mass spectrometry assays could be coupled to
robotic immuno-affinity enrichment methods (347).
However, in spite of these considerable obstacles, some proof-of-concept studies have
been performed. For example, Kurogochi et al. (2010) have be able to develop MRM assays for
quantification of 25 glycopeptides from 16 glycoproteins found in serum of mice (Figure 5.1B)
(268). Specifically, sialic acid moieties on glycopeptide were oxidized with sodium periodate,
enriched for by hydrazide chemistry, labeled with 2-aminopyridine, and the resulting labeled
sialoglycopeptides were subjected to MS. Preliminary studies have also been performed with
purified RNase B and asialofetuin (348). Haptoglobin glycopeptides were characterized and
relatively quantified in serum samples of psoriasis (349) and pancreatic cancer patients (215).
Ion current intensities were used to quantitate glycopeptides from alpha-1-acid glycoprotein
(350). The core fucosylated subpopulations of several glycoproteins were quantified using
partial deglycosylation with Endo F3 in conjunction with glycopeptide MRMs (351). With the
improvement and evolution of MS technology and sample preparation techniques, these types of
assays will play a more prominent role in the quantification of glycoproteins. In a futuristic
scenario, to construct high-throughput platforms for the verification of cancer-exclusive
146
glycoforms, these MRM-mass spectrometry assays could be coupled to robotic immuno-affinity
enrichment methods (347).
147
Figure 5.1. Glycopeptide MRM/SRM. (A) General schematic representation of MRM
methodology. Peptides and glycopeptides from a protease (normally trypsin)-cleaved
glycoprotein are subjected to triple quadrupole MS. Only selected parent ion ions were selected
for fragmentation and the resulting fragment ion intensities were used for (glyco)peptide
quantification. (B) Representative chromatogram from simultaneous MRMs of 25 pyridyl
amineated sialoglycopeptides found on 16 glycoproteins in mouse serum. Adapted and modified
from Kurogochi et al. (268).
148
5.2.4 Alternative Strategies
Although lectin and MS-based approaches for quantification of glycoproteins are the most
common, there are other technologies commonly applied and new ones being developed, alone
or in combination with each other. The most established affinity binding agents for
quantification of proteins and other molecules are antibodies, and the ELISA still remains the
gold standard in the clinical measurement of serological targets. However, glycan-specific
antibodies are extremely rare in comparison to antibodies recognizing peptide epitopes, and their
use in the field is limited when compared to lectins. This is because carbohydrates have been
shown to be poor immunogens and have affinities comparable to the lectins, but with a much
more difficult development and generation process. As well, antibodies that detect an epitope
which encompasses a part of a given protein’s sequence while at the same time recognizing its
glycan structure thereby giving site- and glycoprotein-specificity are extremely rare. Therefore,
the possible advantage of using a glycan-specific antibody over a comparable lectin is minor.
The issue of cross-reactivity has been brought up for Tn antigen-recognizing antibodies
(352). In a recent study, 27 commonly used carbohydrate-binding antibodies against histo-blood
group, Lewis, and tumor antigens were examined for their specificity using a
glycan/glycoprotein array (353). Although some showed high specificity and affinity for their
targets, almost half of them exhibited cross-reactivity for other glycan structures. In cancer
research, their role has been mostly limited to indirect quantitation by immunohistochemistry
and blotting. When considering applications of glycan-specific antibodies for serological
markers of malignancy, the CA 19-9 and CA 15-3 tests stand out. By using a sandwich ELISA,
the CA 19-9 test measures the serum levels of sialyl Lewisa antigen on glycoproteins and
glycolipids, and is used for monitoring of pancreatic cancer progression and recurrence, and for
differentiation from pancreatitis (354-356). The CA 15-3 test is used to quantitate a sialylated O-
149
glycosylation epitope on mucin 1 (MUC1) and is used for prognosis and monitoring of treatment
for breast cancer patients (357, 358).
Other, chromatography-based, strategies have been employed with some success. Ion
exchange chromatography is being utilized clinically for separation and quantification of serum
transferrin glycoforms to test for congenital disorders of glycosylation (359, 360). We have
measured KLK6 glycoforms in a number of biological fluids of ovarian cancer patients,
including serum, at low concentrations (down to 1 ng/ml) using strong anion-exchange for
separation and ELISA for quantification (Chapter 3). Novel strategies are also being employed
for the development of new carbohydrate recognizing agents, which could be utilized in a
quantitative fashion. Phage display technology has been used to improve and alter the binding
properties of glycan-binding modules of glycan processing enzymes and for development of
carbohydrate-binding peptides (361-365). Systemic evolution of ligands by exponential
enrichment (SELEX) technology has been applied to the development of aptamers, single-
stranded DNA or RNA oligonucleotides, which have been proven as binding agents for a number
of carbohydrate moieties (363, 366-370). The more recent advancements and nascet
technologies developed for carbohydrate detecteion, also referred to as glyco-biosensors, have
been reviewed extensively (1, 371, 372). Some of these include electrochemical impedance
spectroscopy (373-376), ’molecular tweezers’ (377), nanoparticle displacement methods (378),
quartz crystal microbalance (379, 380), and surface plasmon resonance (381-383). However,
these technologies are garnered towards highly controlled in vitro systems, and will require
further testing before application in a clinical setting.
150
5.2.5 Concluding Remarks
The clinical application potential of glycoprotein biomarkers in cancer is undisputable. Some
great successes have been achieved in the field, yet there is much room for improvement. The
majority of the tools currently available have proven their utility beyond a shadow of a doubt
when used for qualitative and characterization purposes. However, for each of these
technologies the leap from analytical to quantitative has not been sufficiently successful. It
appears that no single methodology will be sufficient for new breakthroughs, but rather a
combination (eg. lectin affinity and MS, immunoaffinity and MS/lectin detection). The major
goal is the detection and quantification of the full scope of glycan heterogeneity on any particular
glycoprotein of interest, and the ability to differentiate these patterns between homeostatic and
disease conditions. That is why development of novel glycan recognition agents (such as lectins)
is desired and their arrangement into multiplexed/array platforms has been shown to be essential.
MS holds the greatest potential, but it is still hampered by a number of technical limitations,
which will require significant progress in technology before it will be sufficiently reliable and
applicable in the most appropriate manner. The future appears bright and progress in the field is
inevitable, the only uncertainty is how long it will take.
151
5.2.6 Future Directions
When considering the future, it becomes clear, as discussed in the previous sections, that the
major bottleneck in the progress towards the full utilization of glycoprotein tumor biomarkers in
a clinical setting will be the development of reliable high-throughput quantitative assays capable
of measuring the full scope of glycan heterogeneity of any particular protein. In the case of
KLK6, we are at this junction. Although not described in detail in this dissertation, we had
invested a significant effort in developing a hybrid antibody-lectin assay for the quantification of
KLK6-associated 2-6 sialic acid in clinical samples. However, we were not successful due to a
number of reasons that were outlined in this chapter (ie. low signal to noise ratio, low levels of
KLK6 in serum, single glycan chain on KLK6, etc.). We believe that the best hope lies in the
development of a MS-based multiple MRM assay capable of quantifying all or a majority of
glycopeptides derived from serum-derived KLK6. This will likely require some form of
immuno-enrichment of KLK. We had clearly taken some early steps in this direction by
developing MRMs for some of the glycopeptides found on recombinant KLK6 , but this was
performed using a highly purified preparation of KLK6 in quantities 1000-fold excess of what
would be available from clinical samples, as described in Chapter 3. Similar issues should be
expected in the further development of candidates discovered in Chapter 4. Although, the
individual properties of those candidates may be more conducive to the development of a
workable assay. Therefore, from our personal experience and literature in general, it is clear
that identification of candidate biomarkers based on their different glycosylation patterns under
malignant conditions and even the detailed characterization of the full glycan-associated
heterogeneity of glycoproteins has become almost routine. But the next major steps will require
significant advances in technology, which we can only hope will come sooner than later.
152
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