proteomic analysis of prostate cancer cell line … · 2013-10-11 · cancer cell lines to identify...
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PROTEOMIC ANALYSIS OF PROSTATE CANCER CELL LINE CONDITIONED MEDIA FOR THE DISCOVERY OF CANDIDATE BIOMARKERS FOR
PROSTATE CANCER
by
Girish Sardana
A thesis submitted in conformity with the requirements for the Degree of Doctor of Philosophy
Graduate Department of Laboratory Medicine and Pathobiology University of Toronto
© Copyright by Girish Sardana 2008
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PROTEOMIC ANALYSIS OF PROSTATE CANCER CELL LINE
CONDITIONED MEDIA FOR THE DISCOVERY OF CANDIDATE
BIOMARKERS FOR PROSTATE CANCER
Girish Sardana
Doctor of Philosophy 2008
Department of Laboratory Medicine and Pathobiology
University of Toronto
ABSTRACT
Early detection of prostate cancer is problematic due to the lack of a marker
that has high diagnostic sensitivity and specificity. The prostate specific antigen test,
in combination with digital rectal examination, is the gold standard for prostate
cancer diagnosis. However, this modality suffers from low specificity. Therefore,
specific markers for clinically relevant prostate cancer are needed. Our objective
was to proteomically characterize the conditioned media from human prostate
cancer cell lines to identify secreted proteins that could serve as novel prostate
cancer biomarkers.
An initial proof of principle study of the PC3 prostate cancer cell line was
conducted. From this study over 200 proteins were identified in the conditioned
media. Through gene ontology analysis and literature searches Mac-2 binding
protein was selected as a candidate biomarker for validation in the serum of prostate
cancer patients. A preliminarily validation showed that Mac-2 binding protein has
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discriminatory ability in prostate cancer diagnosis. However, an extended validation
did not confirm this.
Based on our proof of principle study we optimized our workflow and
extended our analysis by culturing three different prostate cell lines [PC3 (bone
metastasis), LNCaP (lymph node metastasis), and 22Rv1 (localized to prostate)].
We conducted a bottom-up analysis of each cell line by 2-dimensional liquid
chromatography and tandem mass spectrometry. Of the 2124 proteins identified,
12% (329) were classified as extracellular and 18% (504) as membrane-bound.
Among the identified proteins were known prostate cancer biomarkers such as PSA
and KLK2. To select the most promising candidates for further investigation, tissue
specificity, biological function, disease association based on literature searches, and
comparison of protein overlap with the proteome of seminal plasma and serum were
examined. Based on these results, several candidates were selected for validation in
serum of patients with and without prostate cancer. Of these four novel candidates:
follistatin, chemokine (C-X-C motif) ligand 16, pentraxin 3 and spondin 2 showed
discriminatory ability.
Of the four candidates, follistatin was further studied in an extended validation
in serum of patients with biopsy confirmed prostate cancer and tissues of prostate
cancer patients of low and high grade tumours by immunohistochemistry. In
addition, follistatin was also investigated in the tissue of colon and lung cancer
where intense staining was observed in one specimen of lung squamous carcinoma.
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ACKNOWLEDGEMENTS
First and foremost I would like to thank my supervisor Dr. Eleftherios P.
Diamandis for his guidance and vision for this project. It has been a maturing and
character building experience and I am truly grateful for the opportunity to have been
a part of the ACDC Lab.
I would like to also thank my advisory committee members Drs. Alex
Romaschin and Pui-Yuen Wong for their continued review of my work throughout my
project. As well as Drs. Robert Nam and Oliver John Semmes for their perspectives
and constructive criticism on my thesis. I would like to give special thanks to Drs.
John Marshall, Theodorus van der Kwast and Carsten Stephan for their
collaboration on this project. I am also grateful to the following for providing the
necessary resources to conduct my research: The Department of Laboratory
Medicine and Pathobiology, The University of Toronto, The Samuel Lunenfeld
Research Institute, Mt. Sinai Hospital and Proteomic Methods Inc.
There is a saying - “It’s the people that make the lab”. I would not have been
able to experience the true meaning of graduate studies without the friends and
colleagues that I met along the way. Special thanks to all the members of the ACDC
Lab past and present.
Finally, I would like to thank my family for their support during my graduate
studies.
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TABLE OF CONTENTS
ABSTRACT ii ACKNOWLEDGEMENTS iv TABLE of CONTENTS v LIST of TABLES viii LIST of FIGURES ix LIST of ABBREVIATIONS xi CHAPTER 1: INTRODUCTION 1.1 Prostate Cancer 2
1.1.1 Epidemiology and Statistics 2 1.1.2 Anatomy and Histology of the Prostate 5 1.1.3 Pathobiology of Prostate Cancer 7 1.1.4 Current Methods for Diagnosis 10 1.1.5 Current Treatments 13
1.2 Cancer Biomarkers 19
1.2.1 Introduction to Cancer Biomarkers 19 1.2.2 Methods for Identifying Novel Cancer Biomarkers 20
1.2.2.1 Genomic Approaches 21 1.2.2.2 Proteomic Approaches 24
1.2.3 Clinical and Analytical Properties of a Cancer Biomarker 27 1.2.4 Applications of Cancer Biomarkers 29 1.2.5 Current and Emerging Prostate Cancer Biomarkers 31
1.3 Prostate Cancer Model Systems 51 1.3.1 Mouse Models 51 1.3.2 Cell Culture Models 52
1.4 Continued Need for Novel Prostate Cancer Biomarkers 54 1.5 Rationale and Objectives of the Present Study 55
1.5.1 Rationale 55 1.5.2 Hypothesis 57 1.5.3 Objectives 57
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CHAPTER 2: PROOF OF PRINCIPLE: PROTEOMIC ANALYSIS OF THE PC3 CELL LINE CONDITIONED MEDIA
2.1 Introduction 60 2.2 Materials and Methods 61
2.2.1 Roller Bottle Cell Culture 61 2.2.2 Dialysis 63 2.2.3 Fast Performance Liquid Chromatography of CM 63 2.2.4 Lyophilization and Digestion of Fractions 63 2.2.5 Liquid Chromatography–MS Analysis 64 2.2.6 Database, Genome Ontology, and Literature Search 65 2.2.7 ELISAs for Kallikrein 5, 6, and 11 66 2.2.8 Protein Recovery 66 2.2.9 Mac-2BP ELISA 66
2.3 Results 67
2.3.1 Total Protein, KLK5 and KLK6 Concentrations in Culture Over Time
67
2.3.2 Recovery of KLK5 and KLK6 During Sample Preparation 69 2.3.3 Proteins Identified by Mass Spectrometry 69 2.3.4 Overlap of Proteins Identified in Batches 1 and 2 73 2.3.5 Mac-2BP Concentrations in Patients with Prostate Cancer vs. Healthy Men and in the Conditioned Media of the PC3(AR)6 cell line
74
2.4 Discussion 79 CHAPTER 3: OPTIMIZATION OF CELL CULTURE AND PROTEOMIC WORKFLOW 3.1 Introduction 83 3.2 Materials and Methods 84
3.2.1 Cell Culture 84 3.2.2 Measurement of Total Protein, LDH and PSA, KLK5 and KLK6
84
3.2.3 Sample Preparation 85 3.3 Results 86
3.3.1 Cell Culture Optimization 86 3.3.2 Sample Preparation Optimization 90
3.4 Discussion 92
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CHAPTER 4: COMPARATIVE PROTEOMIC ANALYSIS OF THE CONDITIONED MEDIA OF THREE PROSTATE CANCER CELL LINES
4.1 Introduction 96 4.2 Materials and Methods 98
4.2.1 Cell Culture 98 4.2.2 Measurement of Total Protein, Lactate Dehydrogenase and PSA, KLK5, KLK6
99
4.2.3 Conditioned Media Sample Preparation and Trypsin Digestion
99
4.2.4 Strong Cation Exchange High Performance Liquid Chromatography
101
4.2.5 Online Reversed Phase Liquid Chromatography – Tandem Mass Spectrometry
101
4.2.6 Database Searching and Bioinformatics 1024.2.7 Validation of Candidates 104
4.3 Results 105
4.3.1 Proteins Identified by Mass Spectrometry 1054.3.2 Identification of Internal Control Proteins 1064.3.3 Reproducibility between Replicates 1064.3.4 Differences in Proteins Identified Between Cell Lines 1094.3.5 Genome Ontology Distributions of Proteins 1094.3.6 Secreted and Membrane Proteins 1144.3.7 Overlap with Previous Data 1144.3.8 Overlap with Seminal Plasma Proteins 1144.3.9 Biological Network Analysis 1154.3.10 Overlap with Breast Cancer Secretome 1184.3.11 Validation of Follistatin, Chemokine (C-X-C motif) ligand 16, Pentraxin 3 and Spondin 2
118
4.4 Discussion 121 CHAPTER 5: VALIDATION OF CANDIDATE BIOMARKERS IN PROSTATE CANCER PATIENT SAMPLES
5.1 Introduction 128 5.2 Materials and Methods 130
5.2.1 Conditioned Media, Serum Samples and Tissue Specimens
130
5.2.2 Quantification of candidates in biological fluids 1315.2.3 Immunohistochemistry of Follistatin 1345.2.4 Statistical Data Analysis 134
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5.3 Results 1365.3.1 Chemokine (C-X-C motif) ligand 5 1365.3.2 Chemokine (C-X-C motif) ligand 1 1385.3.3 Lipocalin-2 1405.3.4 Tumour necrosis factor receptor superfamily, member 6b, decoy
142
5.3.5 Extended Validation of Follistatin 144 5.4 Discussion 151 CHAPTER 6: SUMMARY AND FUTURE DIRECTIONS 6.1 Summary 157 6.2 Key Findings 157 6.3 Future Directions 159 REFERENCES 161 APPENDIX: Assay Reproducibility and Precision 201
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LIST OF TABLES
Table Title Page
1.1 List of candidate biomarkers for prostate cancer and their possible clinical utility.
50
2.1 Extracellular candidate tumor markers identified in culture medium of the PC3 (AR)6 roller bottle culture
71
4.1 Known prostate biomarkers identified in the conditioned media of PC3, LNCaP, and 22Rv1 cell lines
107
4.2 List of candidate biomarkers selected based on selection criteria. 127
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LIST OF FIGURES Figure Title Page
2.1 Schematic representation of the workflow for proteomic analysis of
roller bottle CM 62
2.2 Measurement of KLK5, KLK6 and total protein 68 2.3 Cellular location of proteins from PC3(AR)6 CM 70 2.4 Mac-2BP concentrations and the correlation between serum Mac-
2BP concentrations and PSA 76
2.5 Concentrations of KLK5, KLK6, and KLK11 in serum of 26 CaP patients and 17 healthy men
77
2.6 Serum concentrations of Mac-2BP in CaP, BPH and normal patients
78
3.1 Measurements of PSA and LDH in the CM of the 22Rv1 cell line 87 3.2 Measurements of PSA and LDH in the CM of the LNCaP cell line 88 3.3 Measurements of KLK5, KLK6 and LDH in the CM of the PC3 cell
line 89
3.4 Overview of the optimized sample preparation workflow 91 4.1 Workflow of proteomic method employed 100 4.2 Overlap of the 3 replicates from PC3, LNCaP and 22Rv1
conditioned media 108
4.3 Overlap of proteins identified between each cell line 110 4.4 Overlap of the extracellular and membrane proteins identified in
each cell line 111
4.5 Classification of proteins by cellular location 112 4.6 Classification of proteins by cellular location for each cell line 113 4.7 Molecular functions related to diseases associated with Follistatin 116 4.8 Molecular functions related to diseases associated with
Chemokine (C-X-C motif) ligand 16, Pentraxin 3 and Spondin 2 117
4.9 Concentrations of each candidate in the conditioned media of each cell line and the control flask
119
4.10 Validation of Follistatin, Chemokine (C-X-C motif) ligand 16, Pentraxin 3 and Spondin 2 in serum
120
5.1 Measurement of CXCL5 in CM of CaP cell lines and serum of CaP patients and normals
137
5.2 Measurement of CXCL1 in CM of CaP cell lines and serum of CaP patients and normals
139
5.3 Measurement of LCN2 in CM of CaP cell lines and serum of CaP patients and normals
141
5.4 Measurement of TNFRSF6B in CM of CaP cell lines and serum of CaP patients and normals
143
5.5 Follistatin serum levels and correlations with PSA, Gleason score and age
146
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Figure Title Page
5.6 PSA, %fPSA, KLK2 and KLK11 serum concentrations in patient serum
147
5.7 Prostate tissue specimens stained for follistatin 148 5.8 Lung tissue specimens stained for follistatin 149 5.9 Colon tissue specimens stained for follistatin 150
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LIST OF ABBREVIATIONS
2-DE two-dimensional gel electrophoresis
A2M α-2-macroglobulin
ACN acetonitrile
ACT α-1-antichymotrypsin
AIPC androgen independent prostate cancer
AMACR α-methylacyl CoA racemase
ANN artificial neural networks
ANXA3 annexin A3
API α1-protease inhibitor
AR androgen receptor
AUC area under curve
BPH benign prostate hyperplasia
BPSA BPH associated PSA
CaP prostate cancer
CDCHO chemically-defined Chinese Hamster Ovary
CGH comparative genomic hybridization
cPSA complexed PSA
CM conditioned medium
CXCL16 chemokine (C-X-C motif) ligand 16
DiEtOH diethanolamine
DRE digital rectal exam
DTT dithiothreitol
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ELISA enzyme linked immunosorbent assay
EPCA early prostate cancer antigen
ESI electrospray ionization
EZH2 enhancer of zeste homolog gene 2
FCS fetal calf serum
fPSA free PSA
FISH fluorescence in situ hybridization
FPLC fast-performance liquid chromatography system
FPR false positive rate
FSH follicle-stimulating hormone
GO gene ontology
GSTP1 glutathione S-transferase pi
IGF insulin-like growth factors
IGFBP insulin-like growth factor binding proteins
IL-6 interleukin 6
iPSA intact PSA
KLK1 pancreatic/renal kallikrein 1
KLK2 human kallikrein-related peptidase 2
LCM laser-capture microdissection
LC-MS/MS liquid chromatography-tandem mass spectrometry
LDH lactate dehydrogenase
LH luteinizing hormone
LHRH LH releasing hormone
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Mac-2BP mac 2 binding protein
MALDI matrix assisted laser desorption ionization
MS mass spectrometry
MS/MS tandem MS
NCBI national center of biotechnology information
NGEP new gene expressed in the prostate
NPV negative predictive value
%fPSA percent free PSA
PAP prostatic acid phosphatase
PBS phosphate-buffered saline
PCA3 prostate cancer antigen 3
PCPT the prostate cancer prevention trial
PIN prostatic intraepithelial neoplasia
PPV positive predictive value
PSA prostate specific antigen
PSAD PSA density
PSAD-TZ transitional zone PSA density
PSMA prostate specific membrane antigen
PSAV PSA velocity
proGRP progastrin-releasing peptide
PSCA prostate stem cell antigen
PSP94 prostate secretory protein 94
PSPBP PSP94-binding protein
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xv
PTEN phosphatase and tensin homologue deleted from chromosome 10
PTX3 pentraxin 3
RB retinoblastoma
RT-PCR real-time polymerase chain reaction
ROC receiver operating characteristic curve
SAX strong anion exchange
SCX strong cation exchange
SELDI-TOF surface enhanced laser desorption ionization time of flight
SFM serum-free media
SILAC stable isotopic labelling with amino acids in cell culture
SPON2 spondin 2
TBST tris-buffered saline with tween 20
TGF-β1 transforming growth factor β1
TFA trifluoroacetic acid
tPSA total PSA
TMN tumour node metastasis
TRAMP transgenic adenocarcinoma of the mouse prostate
TRUS trans-rectal ultrasound
TZ transitional zone
uPA urokinase-plasminogen activation
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Chapter 1: Introduction 1
CHAPTER 1:
INTRODUCTION
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Chapter 1: Introduction 2
1.1 Prostate Cancer
The prostate is recognized for having a relatively high occurrence of tumours
in men, especially older men. As a result, prostate cancer (CaP) is the most
prevalent malignancy in men and is the second leading cause of cancer deaths in
North America with one in six men having a lifetime risk of being diagnosed and a
3.4% chance of death due to CaP(1). Most patients currently are being diagnosed
with early stages of the disease with no symptoms(2). As a result of this increase in
early stage tumour diagnoses or stage migration, the classical ways of prognosis,
such as the Partin Tables and Kattan Nomograms are no longer as effective. The
focus has now moved from early detection to the determination of the clinical
significance of these early stage tumours. One objective is to find ways of
distinguishing clinically relevant tumours that have the ability to metastasize.
Currently, 30% of tumours removed by radical prostatectomy are deemed clinically
insignificant and would not have required such invasive treatment. Most patients
diagnosed have a latent, non-aggressive form of CaP(3), thus it is important that
these patients are not over-treated. Currently, little is known about the molecular
pathogenesis of CaP(4). Control of CaP could be achieved through early detection
and appropriate selection of treatment; however we have yet to reach this level of
sophistication.
1.1.1 Epidemiology and Statistics
It is estimated that there will be over 500,000 new cases worldwide of CaP
making it the sixth cancer in the world in terms of incidence. It is the most common
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Chapter 1: Introduction 3
cancer in North America, Europe and parts of Africa. Current statistics from the
Canadian Cancer Society show that 24,700 estimated new cases with 4,300 deaths
in 2008(5). Before the age of 40, CaP is rarely diagnosed however its clinical
incidence increases with age. The mean age of diagnosis of CaP is 72-74 with the
majority of patients diagnosed after the age of 65. Prostate cancer incidence rates
are expected to rise as life expectancies continue to increase. The rise in incidence
rates since the mid 1990’s has been attributed to the implementation of the prostate
specific antigen (PSA) test for population screening in Western countries. While
incidence rates have been rising, mortality rates in Canada have been shown to
decrease significantly by 2.9% from 1995 to 2004(5). While this has been presumed
to be a result of screening and improved treatment there are two prospective clinical
trials underway that are assessing this: the European Randomized study of
Screening for Prostate Cancer and Prostate, Lung, Colorectal and Ovarian Cancer
Screening Trial.
While CaP claims the highest incidence rates among all cancers in men, this
does not take into account the prevalence of small adenocarcinomas that are found
incidentally upon autopsy or during other surgical procedures. The prevalence of
these ‘latent’ tumours is thought to be over 50% in men over 50(6). This highlights
that men are more likely to die with CaP rather than from it. This attribute of CaP
makes it very difficult in its diagnosis where the high prevalence of clinically
insignificant tumours could result in an increase in unnecessary treatment.
Geographically the highest incidence in the world of CaP is centered mainly in
the United States and Scandinavian countries and the lowest in Mediterranean
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Chapter 1: Introduction 4
countries and Japan(7). Within countries, CaP differs in ethnic groups. With respect
to the US, African-American males harbour an increased incidence versus
Caucasian, native Chinese and native Japanese(8). An increase in the incidence of
CaP in Japanese males who move to Western countries suggests that life-style
factors may play a role in the development of detectable CaP(9). Other factors that
may account for these differences include access to health care and how CaP is
reported.
Differences in genetic susceptibility are also attributed as being a
predisposing factor for CaP. The chance for developing CaP increases by two to
three times when a first degree relative is afflicted with the disease and increases
when there are more affected individuals in a family, between 10-15% of CaP
patients have a relative who is affected(10). Twin studies have also assessed the
link between genetics and environmental factors showing a possible autosomal
dominant inheritance of a high risk gene(11). Strong candidate genes for genetic
association of CaP are those involved with androgen regulation(12) and
metabolism(13).
Androgen hormones are required for the development of the normal prostate
and have been linked to CaP progression. As a result anti-androgen therapies have
proven to be of great use in treating CaP in 75-80% of patients with metastatic CaP.
While testosterone and dihydrotestosterone have shown to induce CaP tumours
there is very little epidemiological data to support androgen levels as a risk factor for
CaP(14).
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Chapter 1: Introduction 5
In addition to genetics and hormonal influences, nutritional factors have been
shown to play a role in CaP incidence. Studies have shown that CaP is associated
with the Western lifestyle with respect to a diet that contains high fat, red meat and
dairy(15). Studies continue to show conflicting data but there are some consistently
reoccurring components such as α-linolenic acid(16) and calcium(17). Conversely,
protective effects of some diets such as those found in Asia have been attributed to
high intakes of phyto-oestrogens from soybeans which have a preventative effect on
CaP(18). Intake of lycopene from tomato based products has also been shown to
reduce risk of CaP by acting as an antioxidant(19). However, the most promising
data of nutritional prevention of CaP comes from selenium and vitamin E. Both are
considered antioxidants that also enhance the immune system and induce apoptosis
in cancerous cells. The value of these two nutrients in prevention of CaP is currently
being studied in the Selenium and Vitamin E Cancer Prevention Trial (SELECT) and
final results will be available by 2013(20).
1.1.2 Anatomy and Histology of the Prostate
The prostate is an accessory reproductive organ that consists of exocrine
glands that are organized in a fibromuscular architecture surrounding the proximal
urethra. It is located in the abdominal cavity inferior to the bladder where the urethra
from the bladder passes through the prostate before entering the penis. The portion
of the urethra in the prostate is termed the prostatic urethra. Connected to the
prostate are two seminal vesicles which connect via two deferential ducts that
originate from the epididymis. The seminal vesicles and deferential ducts connect to
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Chapter 1: Introduction 6
the prostatic urethra via two ejaculatory ducts within the prostatic gland. Within the
pelvis the prostate is surrounded by striated musculature as well as fatty tissue and
neurovascular bundles.
The glandular tissue within the prostate can be divided into different regions
anatomically. These are the five lobes of the prostate: the anterior, middle, posterior
and two lateral lobes. The prostate can also be subdivided functionally into two
zones. These are the centrally located transition zone, the posteriorly and laterally
located peripheral zone. The transition zone is the main site for benign prostate
hyperplasia (BPH) development while the peripheral zone is where malignant
tumours are found(21).
The prostate produces substances that add to those produced by the seminal
vesicles and the spermatozoa. Approximately 60% of semen consists of
seminogelin – a protein produced by the seminal vesicles. Thirty percent is produced
by the prostate which adds proteins and enzymes that affect the liquidity of the
semen, and the remaining 10% are secretions from the bulbourethral glands that lie
‘downstream’ from the prostate. These secretions contribute to the protective liquid
environment of the semen and for nourishment of the sperm.
The histology of the prostate is similar in all regions consisting of ducts and
acini which are lined by a double epithelial layer. The luminal epithelial cells are
cuboidal or columnar in shape and the basal epithelial cells are flattened. Below
these two layers is the basal membrane. Throughout the gland are scattered
neuroendocrine cells that lie above the basal cell layer. The glands are supported
by a dense stroma of smooth muscle fibres and fibrous connective tissue which
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Chapter 1: Introduction 7
exerts its main function during ejaculation when prostatic secretions are ejected
together with semen.
The luminal cells in the ducts and acini produce proteins that become
constituents of semen. These include enzymes such as prostatic acid phosphatase
(PAP) and PSA. Prostate specific antigen is responsible for liquefying the seminal
clot by digesting seminogelin and thus increasing the motility of sperm. Basal cell
function is still not yet known but have been hypothesized to act as either stem-like
cells to replenish the luminal epithelium or act as functional cell barrier between
luminal cells and the stroma and are involved in growth and differentiation of the
epithelium(22). Neuroendocrine cells in the prostate produce peptides such as
chromogranin A, serotonin and progastrin-releasing peptide. The exact function of
this is currently unknown, however rare tumours are derived from neuroendocrine
cells and these peptides have been shown to be useful as tumour markers in their
diagnosis(23,24).
1.1.3 Pathobiology of Prostate Cancer
Cancers including CaP have been shown to develop from accumulating
genetic alterations that translate into increased cell growth and propensity to invade
surrounding tissues. Morphologic alterations in prostate tissue histology are evident
that indicate it is a multistep process that involve genetic mutations including
deletions, amplifications, rearrangements and epigenetic modifications(25).
Phenotypic changes during progression of CaP have been characterized to follow
transition states from benign glands to the malignant phenotype. Initially, epithelial
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Chapter 1: Introduction 8
cells are seen to progress to prostatic intraepithelial neoplasia (PIN) within benign
glands. Here we begin to see dysplastic epithelial cells that can be categorized as
high and low grade PIN(26). High grade PIN is considered a precursor to CaP
however not all PIN lesions detected progress to CaP(27). It is hypothesized that
tumour cells are themselves genetically instable and are thus prone to genetic
mutations(28). Transformed cells that develop a survival advantage then proliferate
over the surrounding cells and form a tumour.
Results from epidemiologic studies have shown a genetic susceptibility risk
for men with a family history of CaP as well as through twin studies(29,30). From
linkage analysis there have been several CaP susceptibility loci that have been
reported(31). Candidate genes have emerged that may play a role in CaP
progression. These include RNASEL which encodes a ribonuclease that can induce
apoptosis upon viral infection(32) and MSR1 which is a lipopolysaccharide receptor
found at sites of inflammation in the prostate(33). While the prevalence of familial
CaP is low, most tumours arise from somatic mutations that accumulate over several
decades. An alteration that occurs frequently in the CaP genome is a gain or loss of
a region of DNA. Common gains are at 7p, 7q, 8q, Xq and common losses are at 8p,
10q, 13q and 16q(34-36). The frequency of these alterations varies with reports;
however, by identifying functions of disrupted genes a molecular pathway may be
developed to explain the process of the growth, metastasis and progression to
androgen independent prostate cancer (AIPC). However, due the heterogeneity of
the tumours this process is complicated and requires careful dissection(37).
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Chapter 1: Introduction 9
Deletion, inactivation by mutation or suppression of genes by promoter
methylation that function in regulating apoptosis and cell proliferation may act to
permit the progression of CaP. These tumour suppressor genes have been shown to
be important in several aspects of CaP cell growth. A gene lost early at the stage of
PIN due to promoter methylation is the glutathione S-transferase pi-1 (GSTP1) gene
which helps to neutralize oxidative stress in basal and epithelial cells(38). Additional
loss of function of the retinoblastoma (RB) and TP53 gene that regulate cell division
have been shown in CaP cell lines and metastatic disease(39). A prominent tumour
suppressor gene lost in many CaP tumours is the phosphatase and tensin
homologue deleted from chromosome 10 (PTEN). This gene encodes a
phosphatase that inactivates the PI3 kinase and Akt pathways that result in
regulating apoptotic activity(40).
In addition to inhibition of tumour suppressor genes, activation of proto-
oncogenes in CaP are observed. Gain of function mutations are evident in these
genes that result in their over-expression or constitutive activation resulting in either
increased proliferation or decreased cell death. Growth factors and their receptors
that have been shown to be overexpressed include epidermal growth factor,
transforming growth factor-α and β, platelet-derived growth factor and insulin-like
growth factor-1 and 2(41-44). The Her-2/neu gene encodes a transmembrane
tyrosine kinase where overexpression in breast cancer has shown prognostic and
therapeutic significance. Her-2/neu amplification is uncommon in CaP, however
overexpression has been observed in metastatic CaP and AIPC(45,46). Another
potent oncogene is c-myc amplification which is associated with AIPC(47). The
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Chapter 1: Introduction 10
cyclin-dependent kinases, a family of protein kinases that regulate the cell cycle
have also been shown to have altered expression in AIPC lesions(48). Mutations in
genes involved in cell survival pathways such as telomerase, BCL-2 and the ras
family of oncogenes have also been implicated in CaP progression(47,49,50).
The androgen receptor (AR) plays a pivotal role in the development of the
prostate and the progression of CaP(51). Androgen resistant tumours are thought to
arise through selection pressure for cells containing mutations in the AR gene
allowing it to grow independently of testosterone. One mechanism is the increased
expression of the AR in AIPC that would permit the growth of the tumour in a low
testosterone environment(52). Mutations in the ligand binding domain of the AR
result in gain of function of the receptor by allowing it to be able to bind and be
activated by a variety of ligands(53).
1.1.4 Current Methods for Diagnosis and Prognosis
The diagnosis of CaP is usually based on symptoms, an abnormal digital
rectal exam (DRE) or PSA test. Confirmation of diagnosis is then performed by a
trans rectal ultrasound (TRUS)-guided biopsy, and histological examination of the
prostate tissue is performed to assess the presence of cancer. It is common
practice in North America that men over the age of 50 have a routine DRE and PSA
test performed. Men that are at high risk may have the exams performed earlier.
This form of screening for CaP has resulted in the stage migration to organ confined
tumours, however, the benefit in terms of increasing overall survival is hotly debated.
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Chapter 1: Introduction 11
A CaP patient may present with symptoms stemming from urinary
obstruction. These can be hesitancy, nocturia, incomplete emptying, hematuria,
diminished urinary stream or impotence. These symptoms are also commonly
associated with BPH and warrant further examination by DRE and PSA test.
Currently, patients rarely present with symptoms of metastatic disease such as
anemia, bone pain or neurologic related symptoms associated with cord
compression or metastases to the brain.
A DRE is usually performed to assess the physical features of the prostate
and to determine areas of the prostate that have hardened and to determine if there
is extension to the area surrounding the prostate. This exam is subjective and the
results do not correlate well with tumour volume and extent of disease progression.
However, it is useful in determining if the tumour has spread to outside the capsule
of the prostate or whether it is confined.
Accompanying a DRE is usually the PSA test which is a serum blood test for
the presence of elevated levels of PSA which is a highly prostate specific protein
that is produced in large amounts by prostate epithelial cells. It has been shown that
normal and BPH tissue produce more PSA than CaP tissue with poorly differentiated
CaP producing less than well-differentiated CaP(54). Elevated serum levels of PSA
due to CaP growth result from disruption of the basement membrane surrounding
the epithelial cells and leakage of into capillaries and lymphatics. The normal range
of PSA is less than 4 ng/mL, however, it has been realised that PSA can not be used
as a dichotomous marker and levels are considered as a continuum. As a result,
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Chapter 1: Introduction 12
there is considerable overlap between levels of PSA in benign conditions and CaP,
thus reducing the specificity of PSA.
Based on the results of the PSA and DRE a TRUS is performed of the
prostate to determine staging, prostate volume and to determine sites for needle
biopsy. Cancerous tissue is usually hypoechoic compared to the surrounding
prostate tissue and thus can be visualized with TRUS, however, this method can not
predict CaP with certainty(55). Based on the TRUS results, a needle biopsy is
performed transrectally using a TRUS-guided spring-loaded needle gun. Areas that
are felt or visualized to be abnormal are sampled with at least 10 to 12 cores. Each
core is then assessed by a pathologist to determine the spread of the tumour within
the prostate. Biopsies are usually taken from the peripheral or transition zone of the
prostate.
To assess the presence and differentiation of CaP the Gleason grading
system is used(56). Gleason grading is based on the pattern of the glands and
combines in the first and second most prominent patterns evident. Grading is on a
scale of 1 to 5 increasing from well differentiated tissue architecture to poorly
differentiated structures. The grading from each pattern is then summed to attain the
Gleason score from 2 to 10. Based on the Gleason score further imaging of the
surrounding tissues may be warranted to determine staging and spread of the
tumour outside the prostate. Imaging modalities that are currently used include:
ultrasound, computed tomography, magnetic resonance imaging in combination with
contrast agents are used to determine spread to the lymph nodes and bone. Based
on these results the tumour may be staged based on the Tumour Node Metastasis
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Chapter 1: Introduction 13
(TNM) staging system. From this a prognosis may be determined and appropriate
treatment decisions can be made.
1.1.5 Current Treatments
Clinical treatment decisions for CaP, especially in CaP that is localized and
detected early, are problematic when determining whether to treat or not to treat the
patient. Several factors contribute to determining this decision namely the risk
benefit ratio of the adverse events of treatment versus the life expectancy of the
patient. Due to the slow growing nature of CaP it is important to determine the
prognosis of the patient. Recommendations for treatment based on risk stratification
has recently been suggested by the National Institute for Health and Clinical
Excellence(57). Ultimately it is up to the healthcare professional to ensure the
patient receives adequate information so as to make an informed decision.
Initially, a conservative management approach is taken that is referred to as
“watchful waiting”. Here patients enter a clinical follow-up program in which the
tumour size and progression is measured by serum PSA testing. If there is evidence
of CaP progression then curative or palliative treatments may be considered. For a
substantial number of CaP patients, especially older patients with localised and low
grade tumours, a watchful waiting approach might be the better option. Data from a
recent study from the Surveillance, Epidemiology, and End Results Program showed
that patients who undertook conservative management of clinically localized CaP
had a 10-year survival of 94% for Gleason 2 to 4 tumours and 45% for Gleason 8 to
10(58).
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Chapter 1: Introduction 14
Radical Prostatectomy
Due to the use of PSA testing there has been an increase in the number of
organ confined tumours, the majority of which are deemed to be clinically
relevant(59). Thus, these tumours are at the stage where they may be removed to
cure a patient’s CaP. Advances in surgical techniques over the decades have
revolutionized the ability to cure locally confined CaP. Radical prostatectomy is a
surgical procedure where all or part of the prostate gland is removed either as a
result from a progressing tumour or if there is urinary obstruction due to enlargement
of the prostate from BPH or CaP that restricts the flow of urine in the urethra. The
ideal candidate for radical prostatectomy would have a life expectancy of 10 to 20
years post surgery, should be free of co-morbidities and have a clinically relevant
tumour that is confined to the prostate and thus curable by surgery. Current surgical
techniques allow for nerve sparring where erectile function is preserved and
incontinence can be prevented. However, these are still possible long term side
effects from the procedure. Radical prostatectomy is not recommended for patients
with metastatic disease.
Radiation Therapy
Similar to surgery, the objective of radiation therapy is to cure patients with
localized CaP by eliminating the tumour from the prostate. In this case, external-
beam radiation or implanted radioactive ‘seeds’ termed brachytherapy are delivered
to the prostate with the least possible harm to surrounding tissues. The ionizing
radiation given during radiation therapy damages the DNA in cells. Cancer cells
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Chapter 1: Introduction 15
have a reduced ability to repair this damage and thus are unable to proliferate, while
normal cells that receive this radiation are able to repair any DNA damage.
Since the advent of external-beam radiation for treatment of localized CaP
there have been several advances that have allowed improvements in treatment
planning and delivery. As a result, the curative ability of external-beam radiation for
localized CaP has improved over the past decades(60). The type of radiation used is
usually X-rays or gamma rays and is given daily over the course of 6 to 10 weeks.
The implementation of three-dimensional conformal radiation therapy has been able
to incorporate more precise definition of tumour volume and thus calculation of dose
delivered. The ability to exclude normal tissues from receiving high-dose radiation
allows for higher tumour doses without increased surrounding tissue toxicity(61). To
obtain three dimensional images, computed tomography images are taken to
produce high-resolution three dimensional images of the prostate and surrounding
tissues. From these decisions on delivery and dose of radiation are determined(62).
A further advancement has been the implementation of intensity-modulated radiation
therapy which allows dose distributions that conform to the tumour area with
enhanced precision(63).
The use of brachytherapy relies on the principle that high does of radiation
can be given directly within tumours without the radiation leaking to surrounding
tissues. Currently, the mode of delivery is through TRUS-guided implantation of the
radioactive isotopes 125I or 103Pd with or without external-beam radiation(64). Use of
permanent metal seed implants in combination with external-beam radiation therapy
has shown improvements in survival(65). Some side effects of both external-beam
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Chapter 1: Introduction 16
and brachytherapy include incontinence, impotence, infertility, bowel problems and
fatigue.
Androgen Ablation Therapy
Steroid hormones such as testosterone and other androgens have been
shown to play an essential role in the growth and development of the prostate.
Circulating testosterone is converted to its more potent form in the prostate by 5α-
reductase to dihydrotestosterone. This molecule then enters prostate epithelial cells
and binds the androgen-binding region of the AR which then binds to androgen
response elements on DNA promoting transcription of androgen-responsive genes
and prostate cell growth. The major source of testosterone production is from the
testes, with a small proportion coming from the adrenal glands. Testosterone
secretion is regulated by secretion of luteinizing hormone (LH) and follicle-
stimulating hormone (FSH) from the anterior pituitary which stimulates the Leydig
cells in the testes and causes them to produce testosterone. Secretion of LH is
regulated by LH releasing hormone (LHRH) also known as gonadotropin-releasing
hormone which is secreted from the hypothalamus. The secretion of LH and LHRH
is under the control of a negative feedback loop where the concentration of
testosterone in the circulation acts to inhibit the secretion of LH and LHRH.
The relationship with prostate and CaP growth and androgens was
discovered over sixty years ago(66) and was marked with the awarding of the 1966
Nobel Prize. Since then we know that CaP cells deprived of androgens slow in
proliferation and under apoptosis while normal prostate tissue undergoes atrophy
and a reduction in prostate gland size. Thus, methods to block the action of
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Chapter 1: Introduction 17
androgens on the prostate were developed and have since been used in the
treatment of metastatic CaP.
The gold standard for androgen ablation therapy and treatment for metastatic
CaP has been bilateral orchiectomy (removal of the testes). By this method serum
levels of testosterone are reduced by 95% and due to the reduction in testosterone
there is permanent rise in LH and FSH. Administration of estrogens also reduces
testosterone levels by down-regulating the secretion of LH and FSH by the pituitary.
Diethylstilbestrol is used during this therapy with castrate levels of testosterone
being achieved between 3 to 9 weeks(67). Side effects from estrogen therapy
include nausea, vomiting, gynecomastia, fluid retention as well as cardiovascular
effects. Anti-androgen therapy has also been used to reduce the effects of
androgens to treat CaP. There are two classes of anti-androgens: steroidal and
non-steroidal. Steroidal anti-androgens include cyproterone acetate and megestrol
acetate which act by inhibiting C21-9 decarboxylase – an enzyme that synthesises
androgens, and by inhibiting LHRH release. Non-steroidal anti-androgens act by
competitively blocking the binding of dihydrotestosterone to the androgen receptor.
Three agents that are commonly used are flutamide, bicalutamide and nilutamide.
The agents will decrease the intracellular levels of testosterone and
dihydrotestosterone but in contrast to steroidal anti-androgens there will be
increased serum levels of testosterone due to increased LHRH. The chemical
androgen ablation therapy of choice has become LHRH agonists. Two agonists that
are used are leuprolide acetate and goserelin acetate which are taken
subcutaneously. They function by negatively inhibiting LHRH release in the pituitary
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Chapter 1: Introduction 18
through a negative feedback loop and by downregulating the receptors for LH in the
pituitary. An initial rise in LH and FSH is observed initially and this is compensated
for by the administration of anti-androgens. A meta-analysis of monotherapies used
in androgen ablation showed that LHRH agonists were equivalent to patients treated
with orchiectomy(68).
Approximately 70% of CaP patients respond well initially to hormonal
treatment(69). However, most tumours eventually acquire androgen independence
and can grow without the presence of androgens. The prognosis of these patients is
poor with median survival time of less than 10 months(69). New chemotherapeutic
approaches have been developed that combat CaP at this stage.
Cytotoxic Chemotherapy for Androgen Independent CaP
Owing to the slow growing nature of early CaP, chemotherapeutic agents are
not effective in targeting these cells preferentially. As a result, uses of
chemotherapies are restricted to treatment of patients with AIPC. Current
chemotherapeutic agents used for treatment of AIPC have not been able to show a
survival benefit, however, they have been found useful for palliative care and to
improve overall quality of life. Current chemotherapies for AIPC include:
mitoxantrone and prednisone for the palliation of pain of bone metastases(70).
Mitoxantrone acts by inhibiting type II topoisomerase thus disrupting DNA synthesis
and repair. Doxorubicin and etoposide act in similar ways by intercalating with DNA
and disrupting topoisomerase activity. Estramustine, which is a derivative of
estrogen and acts as an alkylating agent that binds and disrupts DNA, has been
shown with etoposide to decrease PSA and show a non-statistical survival
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Chapter 1: Introduction 19
benefit(71). Vinblastine is a microtubule inhibitor and functions by binding tubulin
and halting cell cycle progression at M phase. Paclitaxel and its semi-synthetic
derivative docetaxel are both from the drug class of taxanes and both bind tubulin in
microtubules with high affinity thus stabilizing the structure of the cytoskeleton and
preventing depolymerisation and as a result, freezing the cell from undergoing cell
division. Docetaxel has shown reductions in PSA and non-statistical increases in
progression-free survival(72).
1.2 Cancer Biomarkers
1.2.1 Introduction to Cancer Biomarkers
Cancer biomarkers, or tumour markers, can be defined as measurable
analytes in biological tissues or fluids that act as a surrogate indication for the
presence of a tumour. They may be produced by the tumour itself or by the host in
response to the presence of the tumour. The analyte may be measured qualitatively
or quantitatively by several methods such as proteomic, genomic or chemical to
determine its presence. The ideal tumour marker should be both specific and
sensitive for the detection of a certain type of tumour early in its progression.
Currently, most tumour markers are not sensitive or specific enough to be used in
routine population screening and are used in monitoring patients after therapy.
Tumour biomarkers have been shown to span many different forms. These
include small molecule, gene or protein based analytes. Small molecule metabolites
have been identified resulting from the metabolic pathways of cancer cells. In
addition, some cancers have shown higher concentrations of certain micronutrients.
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Chapter 1: Introduction 20
Nucleic acid based analysis of cancers has resulted in a plethora of differing forms
of cancer biomarkers. These include DNA based mutations in cancer cells, single
nucleotide polymorphisms, altered expression of certain genes measurable by their
transcript levels and epigenetic modifications that alter gene expression. Changes in
peptide and protein levels have also been assayed as cancer biomarkers. These
include altered expression of peptides and proteins, post-translational modifications,
variants of protein isoforms, mutations in proteins, as well as autoantibodies. Both
genomics and proteomics have been utilized in multi-parametric approaches such as
gene and protein microarrays and mass spectrometric proteomic patterns. In
addition, computational approaches to the formation of multi-panel biomarkers
through the use of artificial neural networks have resulted in increased performance
of several biomarkers. With evolving technologies such as high throughput genomics
and proteomics, the search for novel biomarkers that are of high quality is ongoing.
1.2.2 Methods for Identifying Novel Cancer Biomarkers
The opportunity to identify candidate cancer biomarkers has materialized in
the past decade with the completion of the human genome sequence and the advent
of high throughput sequencing technologies, microarrays and mass spectrometric
approaches for the identification of proteins. As a result, there have been large
efforts in developing methods to identify novel tumour markers that show clinical
utility for diagnosis, prognosis or therapeutic monitoring. Here we discuss two main
approaches, genomics and proteomics that have been applied to the search for
cancer biomarkers.
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Chapter 1: Introduction 21
1.2.2.1 Genomic Approaches
Of recent there has been an emergence of genetic tools that have made
available the comparison of genetic profiles of normal and cancerous tissue and cell
cultures. The gold standard to assess gene transcript levels has been the use of
real-time polymerase chain reaction (RT-PCR) which is able to quantitatively
determine levels of mRNA transcripts in several samples simultaneously. However,
the most common high throughput tool in use is the DNA microarray which can
assess either DNA gene copy number alterations or transcript levels of a gene’s
mRNA on a genome wide scale. Microarrays have thus provided several insights
into the molecular mechanisms of cancer biology, as well as led to the discovery of
new markers for disease and therapeutic response. The application of microarrays
to CaP was thought to help elucidate the genetic underpinnings of the differences
between indolent tumours and tumours with a clinically aggressive phenotype. Due
to the several challenges posed by the nature of CaP this has resulted in limited
success.
There are unfortunately limited models for CaP such as cell lines which are
mainly attained from metastatic sites and patients with AIPC(73). Several animal
models established have been studied but their relevance to human CaP
progression is controversial, albeit recent transgenic models have shown promise for
study(74). When using fresh frozen tissue samples for genomic analysis the tissue
samples are invariably contaminated with normal glandular architecture and stromal
cells, thus confounding results obtained due to high background from normal cells.
Issues also occur when selecting normal tissues for control since a field effect may
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Chapter 1: Introduction 22
be present that affects normal epithelium or the presence of PIN lesions(75).
Therefore, tissue samples should be carefully examined histologically before being
used for RNA extraction in a genomic study. Improvements in tissue dissection
including laser-capture microdissection (LCM) have shown to improve the purity of
tissue samples obtained(76). However, this procedure has been subject to
contamination with artefacts.
In the quest for the identification of differentially expressed biomarkers,
several studies have compared gene expression of cancerous to normal tissues or
BPH tissue. A number of novel genes have been discovered that have shown
differential expression. One gene, AMACR, was found to be expressed in almost all
CaP and confirmed by immunohistochemistry and has since become a routine
diagnostic tool for pathologists(77). Due to the increased number of datasets
emerging from microarray studies of CaP, discrepancies between datasets were
seen. This may in part have been due to selection bias of samples used for each
dataset, or differences in sample preparation or the genes represented on
microarrays used. To overcome these differences a meta-analysis of independent
data sets was conducted to validate and establish a model for CaP gene
expression(78). From this 50 genes were identified to be overexpressed and 103
genes were underexpressed in CaP with 11 novel genes being found to score high
that were not noted in their original datasets. Value has also been gained in the
reanalysis of a public data set of Lapointe et al.(79). Tomlins et al.(80) used cancer
profile outlier analysis to identify translocations of gene fusions. This involved
searching multiple microarray data sets for gene expression differences that were
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Chapter 1: Introduction 23
consistently different between normal and CaP tissues. Through this they discovered
the novel gene fusion of the TMPRSS2 serine protease gene to two ETS family
transcription factor genes which were found to be overexpressed in CaP; ERG and
ETV1, which is under current investigation as being a prognostic marker for CaP.
Correlating clinical outcome with transcript levels has also been an
approached used for stratification of tumour subtypes. An example is hepsin which is
seen to be elevated in CaP relative to normal, but tumours with relatively lower
levels of hepsin have a worse prognosis(81). A comparison of normal, localized and
metastatic tumours resulted in identification of EZH2 which was correlated by
immunohistochemistry with recurrent CaP after surgery(82). In contrast to identifying
single genes that discriminate CaP from normal, another approach has been to use
gene expression patterns to identify subtypes of tumours and predict prognosis. In
this instance unsupervised hierarchical clustering is used to separate tumours based
on clinical outcome(79). From the data used by Lapointe et al. they were able to
distinguish 4 genes positively and 19 genes negatively associated with recurrence of
CaP tumours. Of these MUC1 And zinc-α-2-glycoprotein were also confirmed by
immunostaining to have poor prognosis and tumour recurrence. The comparison of
gene expression data from cell lines and animal models with CaP tissue samples
has also proven useful. A tumour stem cell gene signature was identified by
Glinskey et al. that predicted failure of therapy(83).
In addition to gene expression changes, alterations in gene copy number due
to chromosomal aberrations are associated with malignant transformation(4).
Several platforms are available that are used to assess DNA copy number
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Chapter 1: Introduction 24
alterations by comparative genomic hybridization (CGH) including BAC arrays, SNP
chips, and cDNA microarrays. Numerous studies have been performed to analyze
copy number alterations including those done using cell lines(84,85) and with tumour
samples(86,87). The significance of these changes will facilitate the identification of
genes correlating with clinical outcome and identify the critical regions of genetic
alteration in CaP progression.
1.2.2.2 Proteomic Approaches
High-throughput technologies that are used to proteomically profile biological
specimens have evolved tremendously over the last decade. A large focus has
been on the identification of novel biomarkers for disease. The rationale used for a
proteomic approach versus a genomic approach to biomarker discovery is that
proteins are the effector molecules of the cell, and differences caused during the
progression to a malignant phenotype will be evident in differences in the proteome.
These proteomic alterations are then candidates for identification as surrogate
biomarkers either in tissues or biological fluids.
Proteomic studies first involved the use of two dimensional gel
electrophoresis (2-DE), however, novel technologies that have emerged include
protein microarrays, ‘soft’ ionization techniques such as electrospray ionization (ESI)
and matrix assisted laser desorption ionization (MALDI), improvements in mass
spectrometer design such as the LTQ-Orbitrap (ThermoFisher Scientific), and
quantitative isotopic labelling approaches such as iTRAQ(88) and stable isotopic
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Chapter 1: Introduction 25
labelling with amino acids in cell culture (SILAC)(89). All of these have combined to
contribute to the search for protein biomarkers.
Several approaches can be taken to biomarker discovery utilizing proteomics.
The most common, as in genomic studies, is to utilize cancer and normal tissue or
biological fluids from patients and perform a proteomic analysis on each to
determine differences in the abundance of proteins or protein modifications. The
most commonly used sources for searching for biomarkers for prostate cancer has
been serum, prostate tissue and seminal fluid. However, one of the main issues with
proteomic analysis of biological fluids is the large dynamic range of proteins present.
Usually, the proteins of interest are low abundance tumour proteins and the
presence of high abundance serum proteins is seen to suppress their ionization thus
precluding them from detection. In an effort to simplify this complex mixture
debulking or chromatographic methods are used to remove the interference of high
abundance proteins.
A common approach that was initially employed was the use of surface
enhanced laser desorption ionization time of flight (SELDI-TOF) mass spectrometry
(MS) analysis of serum. It was thought to have low sample preparation, was able to
simplify the sample mixture prior to MS analysis and have a high throughput. The
technology relies on the use of chips with affinity-based chromatographic surfaces
where serum samples would be deposited and then washed off. The bound proteins
and peptides to the chip surface would then be subjected to MALDI-TOF analysis.
This would generate a collection of unknown peptide mass spectra from each
sample of which discriminatory algorithms would be used to determine spectral
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Chapter 1: Introduction 26
peaks that corresponded to normal and cancerous states. This method was used as
an approach in the discovery of CaP biomarkers(90-92), and has since been
debunked as a method for biomarker discovery due to its poor reproducibility and
artifactual results(93).
The proteomic analysis of tissues also presents with similar hurdles as with
serum but to a lesser extent. Current approaches have been to use LCM to capture
pure populations of cancerous and normal cells and perform a comparative
proteomic analysis(94). In addition, the use of archived formalin fixed prostate tissue
biopsies have also been used for proteomic analysis(95). A simpler approach, albeit
in vitro, has been to use CaP cell lines and study their response to stimuli(96) or
their secreted proteins in culture(97,98).
To identify protein biomarkers that may be secreted by the tumour and end up
in the circulation, it is relevant to study the proximal fluids that are produced by the
prostate gland. Since, proteins present in this fluid would contain secreted proteins
from tumour glands as well as normal secretions; it is highly likely that these
secreted proteins would be detectable in the circulation through tissue leakage. Two
recent studies highlight this approach, each using prostatic fluid(99) or urine(100)
containing prostatic fluid from patients with and without CaP. Bioinformatics analysis
of the proteins identified resulted in selection of novel candidates for further
validation.
It has also been speculated that antibodies are generated to tumour antigens
and are then amplified and present in the circulation. A novel approach to identify
autoantibodies to tumour antigens utilizes protein microarrays that are spotted with
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Chapter 1: Introduction 27
antibodies from the serum of patients with CaP. This method measures the humoral
response to tumour proteins. This approach was used to detect a response against
AMACR a well known CaP biomarker(101). Wang et al. demonstrated that they were
able to generate phage-display library of proteins derived from CaP tissue. Using
this they identified a 22-phage peptide detector for autoantibodies in CaP
patients(102). Thus, the clinical significance of autoantibodies in CaP has been
shown to be prominent.
1.2.3 Clinical and Analytical Properties of a Cancer Biomarker Assay
Clinical efficacy and analytical robustness are two of the main criteria used in
selecting tumour makers for use. A tumour marker should be specific for its
application be it screening, prediction or therapeutic response monitoring. Overall it
should exhibit high sensitivity and specificity with a high area under curve (AUC) in a
receiver operating characteristic curve (ROC). It should also be well defined
biochemically with the availability of detection reagents such as antibodies. The
analytical method for measuring the marker should be accurate and precise with no
cross-reactivity and should be automated for efficient execution.
When developing a test for a tumour marker in a biological fluid for example it
is important that the reproducibility of the assay first be optimized. Next the dynamic
range of the assay should be established such that analytical sensitivity and limit of
detection be determined along with the maximal value measurable that is accurate.
After a working assay is developed the clinical validation of the marker may be
assessed. It is important to establish reference values of the tumour marker in
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Chapter 1: Introduction 28
healthy individuals to determine upper limits of the marker in the healthy population.
This will in turn help in deciding cut-offs for the tumour marker in determining clinical
diagnosis.
The clinical sensitivity and specificity of a tumour marker assay can be
defined as the percentage of people tested with the disease that the test correctly
finds positive and percentage of people with the disease that the test correctly finds
negative, respectively. These values are in essence indications of the true positive
and false positive rates of the test. Calculations for each are shown below:
Sensitivity = True Positives / (True Positives + False Negatives)
Specificity = True Negatives / (False Positives + True Negatives)
Based on these values a ROC curve may be drawn depicting the range of sensitivity
and specificity across a range of cut-off values. The sensitivity values are plotted on
the y-axis with 1-specifity on the x-axis. This aids in determining the trade off
between sensitivity and specificity and helps in choosing a cut-off value. It is
important to note the consequences of a false positive or false negative when
choosing the appropriate cut-off. The added value of a ROC curve is that multiple
tests can be compared on the same graph to determine which has the highest AUC
which translates the ability of the test to discriminate those with and without the
disease. These values range from 0.5 (a test that has a 50% probability of being
correct) and 1 (a test that has a 100% of being correct).
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Chapter 1: Introduction 29
A measure of depicting how clinically relevant the tumour marker assay is of
detecting disease in the patient is shown in its predictive value. The positive
predictive value (PPV) of a test is the percentage of people testing positive for the
test that indeed have the disease. The negative predictive value (NPV) of a test is
the percentage of people testing negative for the test who indeed don’t have the
disease. The calculations of which are shown below:
PPV = True Positives / (True Positives + False Positives)
NPV = True Negatives / (False Negatives + True Negatives)
Taken together in combination with the prevalence of the disease will determine if
the test is suitable for population screening or in just high risk populations.
1.2.4 Applications of Cancer Biomarkers
There are several uses for tumour markers in aiding clinical management of
patients with cancer. Based on the properties of the marker and the test used to
measure it dictates which application the marker is suitable for. These include
population screening, diagnosis, prognosis, prediction of therapeutic response and
monitoring effectiveness of therapy, determining tumour recurrence or remission, or
being used as an imaging marker in localizing the tumour. Below we discuss the
requirements of the marker and assay for each application.
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Chapter 1: Introduction 30
Screening for cancer in the population requires that a marker is elevated
when the disease is in its early stages and curable. With the exception of PSA, most
markers are elevated in late stage disease and have a low diagnostic sensitivity. It is
also beneficial if the marker is specific to a particular tissue since elevations in other
tissues due to other diseases or inflammatory conditions will reduce its specificity
and increase its false positives. It is also required that the test for the marker be non-
invasive and low in cost to increase compliance, e.g. a blood test. Screening must
also demonstrate that it delivers a clear benefit to the patient in reducing morbidity
and mortality associated with the disease.
A tumour marker used in diagnosis of cancer involves the same
considerations as a marker for screening. Most markers used have either low
diagnostic sensitivity or specificity. However, for patients in high-risk groups where
prevalence is relatively high, using a diagnostic marker will aid in deciding whether
more invasive tests are required.
Determining the prognosis of a tumour is essential in deciding the course of
therapy to be taken. Most tumour markers are correlated with cancer prognosis and
other prognostic indications such as tumour grade and staging. However, the
clinical decision to determine a therapeutic course of action can not be taken based
on the levels of the tumour marker alone. This is evident with PSA and its
derivatives.
Tumour markers may also be used to predict and monitor therapeutic
response. With a marker such as PSA, its high tissue specificity allows it to be used
to monitor a patient after radical prostatectomy and other therapies. This determines
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Chapter 1: Introduction 31
if the therapy was effective and also determines if there is tumour recurrence as the
PSA level will be seen to increase again. Tumour markers have also found use as
targets for therapy. The most prominent marker being Her-2/neu in breast cancer as
a target for the antibody based therapy Herceptin. Tumours overexpressing Her-
2/neu are responsive and tumours lacking will not respond, thus this tailors the type
of therapy to the patient.
1.2.5 Current and Emerging Prostate Cancer Biomarkers
The current biomarker used for CaP diagnosis is PSA. It is considered both
the best tumour marker available for any cancer and as a marker with many
shortcomings. Originally, it was used for monitoring patients with CaP and was
subsequently implemented for screening.
Prostate Specific Antigen-Derived Forms:
It has become clear that the operating characteristics of PSA need to be
improved. One approach has been to measure PSA derivates that include PSA rate
change over time (PSA velocity, PSAV), ratio of PSA to prostate volume (PSA
density, PSAD), and age specific PSA ranges. In addition, novel improvements in
measuring PSA and PSA related proteins have allowed the measuring of percent
free PSA (%fPSA) which is a ratio of free to total PSA (tPSA); complexed PSA which
measures how much PSA in serum is bound to either α-2-macroglobulin (A2M), α1-
protease inhibitor (API) or α-1-antichymotrypsin (ACT); as well as different cleavage
isoforms of PSA.
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Chapter 1: Introduction 32
Age-Specific PSA:
Age-specific PSA ranges adjust PSA thresholds based on age with the
assumption that older men have higher normal PSA levels due to prostate
enlargement since PSA correlates linearly with prostate size(103). Thus, a lower cut-
off for younger men should be employed to improve sensitivity, and a higher cut-off
in older men would reduce false positives, increasing specificity. Recently, a
retrospective study showed that men <50 years with a PSA of 0.51-1ug/L compared
to <0.5ug/L had a 2.5 fold greater risk of CaP up to 25 years later. In addition, men
with PSA of 2-3ug/L had a 19 times greater chance of CaP(104). Thus, this test can
serve to stratify patients based on risk and to monitor and tailor therapy. This test
still remains controversial as studies have shown reduction in sensitivity and lower
diagnostic accuracy compared to the 4.0ug/L cut-off(105,106).
PSA Density:
PSA density takes into account the volume of the peripheral and transitional
zone (TZ) of the prostate gland and is calculated by dividing the serum PSA level by
prostate size determined by TRUS. A higher PSAD has been shown in men with
CaP versus BPH(107,108), while others have not been able to reproduce these
results(109,110). In BPH the TZ is mostly enlarged and not a frequent site of
carcinoma. Taking into account TZ size specifically (PSAD-TZ) has shown
improvements in specificity but has not been reproducible(111-114). A PSAD ratio of
more than 0.15 is used to indicate an increased risk of CaP(108,115,116). This test
would increase specificity and sensitivity in large prostates in men with small
prostates respectively. While the use of PSAD is not in clinical use for screening due
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Chapter 1: Introduction 33
the cumbersome need for TRUS, utility may be derived in selecting men for repeat
biopsy.
PSA Velocity:
PSA velocity is calculated by measuring serial PSA measurements over time
and determining the rate increase. It has been seen that BPH patients show a linear
increase in PSA, while CaP patients display exponential increases in PSA(117).
However, variations in PSA levels due to biological or analytical variability can result
in imprecision of this test(118,119). In addition, infection and chronic inflammation
are confounding factors that can increase PSA levels. A consensus on the
appropriate parameters to use has still yet to be determined. Carter et al.(120,121)
showed that at least three measurements over a period of at least two years are
required and a PSAV >0.75ug/L per year was significantly associated with CaP with
men with 4-10ug/L showing specificities of >90% PSA. In addition, a 2.0ug/L yearly
PSAV has been associated with a shorter time to PSA relapse and mortality
following surgery for localized CaP(122-124). Thus, this could identify patients who
may not benefit from localized therapy. Initial results from the European Randomized
Study of Screening for Prostate Cancer confirmed PSAV values differ significantly
between men with and without CaP and was limited in value in predicting biopsy
outcome(125).
Free PSA:
Measuring the ratio of fPSA to PSA (%fPSA) has shown to be beneficial by
improving the operating characteristics of PSA in the ranges of 4-10ug/L and <4ug/L
in several retrospective and prospective studies(126-129). Higher %fPSA correlates
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Chapter 1: Introduction 34
with decreased risk of CaP and increased risk of BPH and this ratio is now being
used routinely since the mid 1990’s and has been approved by the USA Food and
Drug Administration in 1998. Sensitivity for %fPSA testing has been shown at 90-
95% resulting in reduction of unnecessary biopsies by 15-20%(126,130). However,
confounding variables such as prostate volume(131), stage and grade(132), total
PSA(133), PIN lesions(134), race, age and drug treatment history should be
considered(135). A recent study utilizing 10-12 core biopsy versus the sextant
biopsy method depicted %fPSA as having reduced diagnostic efficacy(136).
Conflicting studies(137-139) have resulted in a debate as to what the optimal %fPSA
threshold should be.
Complexed PSA:
Prostate specific antigen circulating in serum has been shown to be mostly
bound to protease inhibitors, with the majority (~75%) consisting of the PSA-ACT
complex(140-142). Measuring these forms of complexed PSA (cPSA) have shown to
improve specificity marginally in the PSA range of 2-10ug/L in a prospective trial as
well as in meta-analyses of CaP patients(143,144). Currently there are two assays
available: one is for PSA bound to ACT (PSA-ACT) and one is for cPSA which
measures PSA-ACT and PSA bound to API (PSA-API) but not bound to
A2M(144,145). This assay is of limited use since PSA-ACT increases(146) where as
PSA-API decreases in CaP patients(147). Currently, it is very difficult to measure
PSA-A2M since the levels are low and A2M sterically hinders access to PSA
epitopes(148). Complexed PSA however, in combination with tPSA (cPSA/tPSA)
results in comparable sensitivity and specificity as %fPSA(149-152). In addition,
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Chapter 1: Introduction 35
there have been conflicting multi-center studies depicting either benefits of cPSA
compared to tPSA(143,144) or showing no advantage when used
independently(147,153). Thus, the context of the usefulness of cPSA is still debated.
Measurements of PSA-A2M(154) and PSA-API(155) complexes in serum have been
reported(156) and have both shown elevation in BPH patients vs. CaP, while PSA-
API has shown to be increased and an improvement over tPSA but not fPSA.
Cleavage Isoforms of PSA:
Prostate specific antigen is secreted from prostate epithelial cells as a pro-
enzyme with a seven amino acid activation peptide that is cleaved after secretion.
However, there are several different isoforms and cleavage products of PSA that
result and that have been assayed(157,158). The non-clipped ‘intact’ PSA (iPSA)
has been assayed and shown to be informative in discriminating CaP from
BPH(159,160). This free form of PSA is enzymatically inactive and the ratio with
fPSA has been shown to be higher in CaP patients. proPSA contains 244 amino
acids (-7)(161) compared to fPSA (237 amino acids) and can be differentially
cleaved to produce different forms termed (-7), (-5), (-4), (-2), (-1)(162). proPSA in
combination with fPSA has been shown to improve detection of CaP in ranges less
than 4ug/L(163) and in the 4-10ug/L range and associated with aggressive CaP’s
more so than tPSA, fPSA and cPSA alone(157,164,165). (-2)proPSA has shown to
be increased in serum of CaP patients compared to BPH and can discriminate CaP
from BPH in patients with elevated fPSA(166). The efficacy of the (-5, -7) proPSA
forms has been evaluated and has shown inadequate ability compared with %fPSA
to enhance detection of CaP(167,168). However, in combination with fPSA it was
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Chapter 1: Introduction 36
correlated with increased Gleason grade and metastatic disease and was able to
improve specificity in PSA levels of 2-4ug/L(169).
Benign or BPH associated PSA (BPSA) is a cleaved sub-form of fPSA that
has been correlated with presence of benign prostate tissue in the TZ of the
prostate(170). Benign PSA is unable to discriminate between BPH and CaP in
serum independently(171), however it could serve as a marker for BPH when used
in conjunction with %fPSA and therapeutic monitoring of BPH patients(172). The
proPSA/BPSA ratio was measured in CaP patients with fPSA less than 15% and
attained a sensitivity and specificity of 90% and 46% respectively(173). BPSA is
elevated in patients with symptomatic BPH versus asymptomatic patients and
correlates with prostate enlargement more closely than tPSA and %fPSA(174). In
healthy men BPSA is almost undetectable and is not affected by age.
Molecular derivatives of PSA still need to be validated in larger sample sets to
determine their clinical utility for diagnosing BPH or CaP. Currently, there is still
debate on which combination of derivatives produces the optimal diagnostic and
prognostic characteristics. Thus, there is a great need for novel identification of
biomarkers to aid in increasing the specificity of CaP detection and prognostication.
Human Kallikrein-Related Peptidase 2:
Human kallikrein-related peptidase 2 (KLK2) is a secreted serine protease
from the same gene family as PSA and shares an 80% sequence homology. Like
PSA it’s highest expression is in the prostate, albeit it is expressed in other
tissues,(175) it is also androgen regulated in the prostate however its concentrations
in the circulation are 100 times less than PSA, albeit its mRNA levels are half that of
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Chapter 1: Introduction 37
PSA. Unlike PSA, KLK2 differs in enzymatic activity and is mostly found in the free
unbound form. Prostate cancer tissue data has shown that KLK2 is relatively
elevated during CaP progression and thus may be a useful CaP
biomarker(176,177). Serum studies have displayed improvements in diagnosis of
CaP(178) in combination with tPSA(179) and fPSA(180-185) specifically with
respect to extracapsular extension and tumour volume(186,187). KLK2 also showed
improved independent prognostic information versus PSA for risk of biochemical
recurrence in men with PSA <10ug/L(188). Like PSA, KLK2 is also found in a bound
and free form(189), where the bound form has been shown to diagnose CaP with
higher specificity in complex with protease inhibitor 6(190). Additional validation
studies are required to elucidate the full prognostic potential of KLK2.
Other Tissue Kallikreins:
Up until recently PSA, KLK2 and pancreatic/renal kallikrein 1 (KLK1) were the
only genes identified in the kallikrein locus on chromosome 19. Now we know that
the locus spans 300kb and consists of 15 genes that share significant homology and
sequence similarity at the DNA and amino acid sequence(175,191). In addition to
KLK2, other kallikreins have shown utility as biomarkers for CaP and other
diseases(192-196). There are 8 kallikreins that are expressed relatively highly in
prostate tissue – KLK2-4, 10-13, and 15(175). Of these KLK11 shows promise as a
serum biomarker for CaP(197). In combination with tPSA and %fPSA showed
improvement in prediction of CaP(198,199).
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Chapter 1: Introduction 38
Prostate Specific Membrane Antigen:
Prostate specific membrane antigen (PSMA), formally known as folate
hydrolase 1, is a membrane glycoprotein highly expressed in epithelial cells of
normal and CaP. It was first discovered from a monoclonal antibody derived from
mice immunized with the membrane fraction of the CaP cell line LNCaP(200). It was
found that there was increased relative expression of PSMA in epithelial cells of CaP
tissue; as well PSMA was shown to have folate hydrolase and carboxypeptidase
activity. The function of PSMA in the prostate is currently unknown; however, in the
brain it has shown to release glutamate from N-acetylaspartylglutamate. A
commercial imaging test for PSMA was developed (Prostascint, Cytogen Corp.) by
using the 7E11 antibody to PSMA conjugated to indium-111 and used for
radioimmunoscintigraphy. This approach was validated against computed
tomography, magnetic resonance imaging, and biopsy results and was shown to be
more sensitive and specific in detecting metastatic spread to the lymph nodes(201).
Currently, there has been conflicting data showing reproducible use of PSMA as a
serum marker for CaP(202-205). Detection of PSMA versus PSA via RT-PCR for the
identification of circulating tumour cells did not show a strong correlation with
CaP(206). Finally, PSMA has been studied as a target for therapy by using
antibodies conjugated to radioisotopes or toxins, or the activation of dendritic cells
against PSMA(207-211). The use of PSMA has not been adopted into clinical
practice as yet and its role as a diagnostic and therapeutic tool is still evolving.
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Chapter 1: Introduction 39
Prostate Cancer Antigen 3:
Also known as DD3, prostate cancer antigen 3 (PCA3) is a non-coding RNA
that is expressed almost exclusively in the prostate and shown to be highly
overexpressed in CaP tissue, including metastasis, versus BPH(212). Several
assays can measure PCA3 mRNA in urine sediment. The only commercially
available test is APTIMA® (Gen-Probe Inc., San Diego, CA) which utilizes
transcription-mediated amplification(213). A PCA3 score is derived from PCA3
mRNA levels normalized to PSA. A recent large multi-institutional study of patients
undergoing biopsy included PCA3 analysis in voided urine after prostatic massage;
an AUC of 0.66 versus 0.57 for PSA using a PCA3 score cut-off of 58 was
obtained(214). In a study of 233 patients undergoing repeat biopsy after a negative
biopsy, PCA3 had an AUC of 0.68 and a sensitivity of 58% and specificity of
72%(215). This test could potentially be useful in improving the specificity of PSA. A
combination of PCA3 and another three urinary biomarkers, GOLPH2, SPINK1 and
TMPRSS2:ERG fusion, improved the sensitivity and specificity of PCA3 alone.(216)
Early Prostate Cancer Antigens:
Changes in nuclear matrix proteins have been shown to be associated with
carcinogenesis. Early prostate cancer antigen (EPCA) is a nuclear matrix protein
that was initially detected by proteomic profiling of rat prostate tissue(217). It has
since then been shown promise as a diagnostic marker for CaP.
Immunohistochemical studies by auto-antibodies to EPCA in biopsies of CaP tissue
showed increased staining relative to non-cancerous specimens(218). A field effect
was also seen in non-cancerous adjacent areas to tumour tissue, in 86% of CaP
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Chapter 1: Introduction 40
tissue and aids in identifying patients at risk who have a negative biopsy. A blood-
based assay for EPCA was developed showing a sensitivity of 92% with 94%
specificity in a small cohort of 12 CaP and 34 healthy patients(219). Another study
measuring the EPCA-2 protein in serum showed a 92% specificity and 94%
sensitivity for identifying CaP and was able to differentiate localized from metastatic
CaP with an AUC of 0.89(220). Larger and other independent studies are awaited to
confirm this promising data. However, methodological deficiencies were identified
with such markers, casting doubt on their actual validity(221).
Αlpha-Methylacyl-CoA Racemase:
Alpha-methylacyl CoA racemase (AMACR) is an enzyme involved in the
oxidative metabolism and synthesis of branched chain fatty acids found in dairy
products and red meat(222). As well as being strongly expressed in CaP tissue, it is
located in a gene region (5p13.3) that contains polymorphisms that are associated
with CaP(223). A meta-analysis of microarray data showed AMACR to be up-
regulated in CaP with high confidence(78). A multi-institutional study of
immunohistochemical staining of AMACR helped in distinguishing benign from
cancerous prostate tissue with a 97% sensitivity and 92% specificity(224). Recently,
decreased AMACR expression was also shown to have prognostic value in
predicting biochemical recurrence and death due to CaP(225). Circulating levels of
AMACR mRNA have been measured by RT-PCR in serum and urine(226). Protein
levels are low in serum, however, AMACR protein has been detected by Western
blot in urine(227). Increased levels of autoantibodies to AMACR were able to
discriminate CaP patients from healthy subjects, in the PSA range 4-10ug/L, and
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Chapter 1: Introduction 41
showed a sensitivity of 62% and specificity of 72%(101). Additional studies are
underway to fully elucidate the potential of AMACR as a biomarker for CaP.
Urokinase Plasminogen Activator and Receptor:
The degradation of the extracellular matrix has been associated with cancer
progression and the urokinase-plasminogen activation (uPA) cascade has been
shown to participate in this. Plasminogen is converted to its active form plasmin
through the activation of the serine protease uPA, binding to its receptor uPAR. One
study demonstrated increased levels in BPH and CaP compared to normal, albeit
there was no statistically significant association with CaP(228). Detection of isoforms
of uPAR, in combination with PSA isoforms and KLK2 showed improved prediction
of biopsy outcome in patients with elevated PSA in both univariate and multivariate
models(229). Increased tissue levels in CaP were associated with osteoblastic
metastases(230), as well as advanced CaP progression(231). Recently a study has
shown increased serum levels of uPA and uPAR in patients with metastatic bone
CaP(232). These studies depicted preoperative plasma uPA as a predictor of
biochemical recurrence and metastatic disease indicating the presence of distant
disease at time of localized therapy. Large prospective studies are needed to
elucidate the full prognostic potential of uPA and uPAR for preoperative models of
disease progression and metastases.
Insulin-Like Growth Factors and Binding Proteins:
Serum levels of insulin-like growth factors (IGF) and their binding proteins
(IGFBP) have been found to be associated with CaP. The IGF family consists of two
ligands (IGF-1, IGF-2), two receptors (IGFR-1, IGFR-2) and six binding proteins
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Chapter 1: Introduction 42
(IGFBP-1 to 6). Increased levels of IGF-1 and decreased levels of IGFBP-3 have
been correlated with increased risk of developing CaP(233). Another prospective
study found that IGF-1 levels were increased slightly with CaP risk but did not
outperform PSA(234). However, others have failed to reproduce these results and
found no association with CaP progression. In addition, the main IGFBP produced
by the prostate is IGFBP-2 and was shown to be increased in CaP. Albeit, levels in
localized tumours were correlated inversely to tumour size and indicators of CaP
progression(235). Serum levels of IGFBP-3 were shown to be inversely correlated
with presence of metastases to the bone, however did not show any difference to
localized CaP and normals(235).
TMPRSS2:ERG/ETV Gene Fusion:
Gene rearrangements have been implicated in cancers, in particular
haematological malignancies. One such rearrangement between the transcription
factor genes ERG (21q22.2) and ETV1 (7p21.1) with membrane-anchored serine
protease TMPRSS2 (21q22.3) was shown to occur in 80% of CaP by cancer outlier
profile analysis(80). The fusion product of these genes tested in urine has been
shown in 42% of CaP patients and in 20% of patients with PIN and rarely in
BPH(236). A prospective study which followed 252 men with stage T1a/b CaP for 9
years showed that the TMPRSS2:ERG fusion was associated more than the
TMPRSS2:ETV fusion in Gleason sums>7, metastatic disease and death due to
CaP(237). An isoform of the TMPRSS2:ERG fusion has been shown by
fluorescence in situ hybridization (FISH) analysis to be present in 80-95% of CaP
tissue, and could be a potential target for therapy.
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Chapter 1: Introduction 43
Transforming Growth Factor Beta:
Transforming growth factor β1 (TGF-β1) is a widely acting growth factor
involved in a variety of molecular processes such as cellular differentiation, immune
response, angiogenesis and proliferation. Studies with model systems of CaP have
shown a role of TGF-β1 in CaP progression(238). Increased levels of TGF-β1 in CaP
tissue have been correlated with tumour grade and stage and lymph node
metastasis(239). An ELISA for TGF-β1 was used to measure pre-operative plasma
levels, which have shown to be increased in CaP patients(240) and correlate with
extracapsular extension, seminal vesicle invasion, metastasis(241) and biochemical
recurrence(242). Thus, TGF-β1 could prove useful as a prognostic marker for CaP.
Enhancer of Zeste Homolog Gene 2:
Enhancer of zeste homolog gene 2 (EZH2) is part of the polycomb family of
proteins involved with regulation of gene expression. It has been shown that EZH2 is
expressed more in metastatic CaP compared to localized CaP and BPH through
gene expression profiling of CaP tissue from autopsies of men who died from
metastatic CaP(82). In addition, it was found to outperform preoperative PSA and
Gleason score for determination of CaP progression. It was also shown in
combination with E-cadherin to predict CaP recurrence after localized therapy(243).
Development of a serum assay would aid in the validation of this candidate
biomarker to identify patients at risk of developing metastatic disease.
Glutathione S-Transferase Pi hypermethylation:
Hypermethylation of tumour suppressor genes at their promoter regions at
cytosine/guanine (CpG) nucleotide islands have been implicated in CaP. Glutathione
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Chapter 1: Introduction 44
S-transferase pi (GSTP1) is an enzyme that protects DNA from free radical damage.
Reduced expression of the GSTP1 gene due to hypermethylation of the promoter
has been consistently shown in CaP and has been studied in urine sediment to
determine the need for biopsy(244). This assay has been improved through prostatic
massage before collecting urine(245). Panels of genes, including GSTP1, have been
studied in a similar manner(246).
Prostate Secretory Protein 94 and Binding Protein:
Prostate secretory protein 94 (PSP94), also known as h-microseminoprotein,
is a highly abundant protein in semen and plays a role in regulation of cell
proliferation and apoptosis. Bound forms of PSP 94 exist that are complexed with
PSP94-binding protein (PSPBP). Serum levels of PSP94 / free PSP94 and PSPBP
in CaP patients after local surgery were associated with Gleason sum, biochemical
recurrence and surgical margin status(247). Large prospective studies are still
required to validate this candidate biomarker.
Markers for Neuroendocrine Differentiation:
Chromogranin A is a peptide produced by the neuroendocrine cells in the
prostate and is currently used for diagnosis and prognosis of CaP tumours that show
neuroendocrine differentiation. Increased chromogranin A levels in serum have
been correlated with androgen independent CaP progression and poor
prognosis(23), and have been shown to precede PSA elevation(248), and improve
specificity in combination with fPSA(249).
Progastrin-releasing peptide (proGRP) is a growth factor found to be released
in the neuroendocrine type of CaP. Increased levels were detected in metastatic
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Chapter 1: Introduction 45
CaP and was associated with its progression(24). It was also shown to be predictive
of the androgen-independent phenotype(250). Thus, both chromogranin A and
proGRP may be used to monitor patients with late stage disease for hormone-
refractory CaP in a subset of patients that display neuroendocrine differentiation.
E-cadherin:
Cell-cell adhesion plays an important role in normal tissue architecture and
carcinogenesis. E-cadherin is a cell adhesion molecule expressed in epithelial cells
and whose expression has been shown to predict CaP prognosis. An
immunohistochemical study showed reduced expression of E-cadherin in 50% of
CaP tumours, where normal prostate tissue showed uniform expression(251). This
was then further studied and correlated with grade, tumour stage, and survival. A
lower immunohistochemical expression of E-cadherin was associated with CaP
patients with a shorter survival(252).
Annexin A3:
Annexin A3 (ANXA3) is a calcium-binding protein that is part of the annexin
family of proteins. ANXA3 has been shown to be involved with the activation of the
immune response as well as membrane trafficking and lymphocyte
migration(253,254). Recently, ANXA3 has been studied as a promising prognostic
tissue marker for CaP and was shown to have lower expression in CaP versus BPH
and PIN and normal tissues by immunohistochemistry(255,256). ANXA3 was able to
stratify a large group of intermediate-risk patients in to high and low risk subgroups.
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Chapter 1: Introduction 46
Prostate Stem Cell Antigen:
Prostate stem cell antigen (PSCA) is a membrane glycoprotein that shows
fairly specific expression to the prostate. PSCA was detected by
immunohistochemistry in CaP tissues and its RNA was found in blood samples(257).
Increased expression of PSCA was correlated with increased risk of CaP, Gleason
score, stage and presence of metastasis(258). PSCA was also investigated as a
target for therapy(259). However, larger validation studies are required to confirm its
clinical utility.
Hepsin:
Hepsin is a membrane serine protease that is highly expressed in prostate
tissue and was first identified in the human liver from cDNA libraries(260).
Overexpression of the hepsin gene has been shown in 90% of CaP tumours through
mRNA expression profiling studies(261). Immunohistochemical staining of hepsin in
one study showed it to be highly expressed in PIN lesions of the prostate, and
preferentially expressed in CaP versus BPH(262). Further studies in serum and
urine are required to fully elucidate its diagnostic potential.
Interleukin-6 Ligand and Receptor:
Interleukin 6 (IL-6) is a cytokine secreted by a variety of cell types and
involved in the immune and acute-phase responses. Elevated levels of IL-6 and its
receptor IL-6R have been shown in metastatic and AIPC(263), and has thus far have
been suggested as a candidate markers for CaP progression(264). Studies with IL-6
in combination with TGF-β1 have also shown promise in CaP diagnosis(265).
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Chapter 1: Introduction 47
Circulating Tumour-associated DNA:
Dissemination of tumour cells is a prerequisite to metastasis; thus early
detection of these cells in the circulation can be useful in prognosis of CaP patients.
This has been shown by RT-PCR which has proven to be sensitive and used to
increase the accuracy of staging and prediction of disease recurrence by using
markers specific to the prostate(257,266).
Autoantibodies:
It is known that the immune system elicits an autoantibody response to some
antigens overexpressed by tumours. Humoral responses to huntingtin-interaction
protein 1, prostasomes and AMACR have been reported(101,267,268). By using
phage display and protein microarrays, Wang et al.(102) were able to identify
autoantibodies to peptides derived from CaP tissue in a new approach termed
“cancer immunomics”. They were able to generate a 22 phage peptide array that
was able to discriminate 68 CaP serum samples from 60 controls with 88.2%
specificity and 81.6% sensitivity with an AUC of 0.93, which was superior to PSA
(AUC 0.8). Studies are underway to further validate this detection tool in a larger
cohort. A recent study by the same group applied a similar approach, followed by
biological network analysis to determine deregulated pathways in CaP
progression(269). One concern is that the needle biopsies themselves may be
eliciting an autoimmune response.
Nomograms:
Nomograms are multi-parametric tools that combine clinical features such as
tumour grade/stage and biomarkers to provide physicians with standardized patient
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Chapter 1: Introduction 48
care. They utilize evidence-based approaches for decisions regarding treatment at
each stage of disease management. The value of a nomogram is derived from its
performance characteristics and user-friendliness. There are numerous nomograms
that have been developed for CaP including a TGF-β1 and IL-6 standard nomogram
for biochemical recurrence(270), as well as nomograms predicting outcome of
biopsy(271). A review by Karakiewicz and Hutterer summarizes nomograms that
have been developed for CaP(272).
Multi-Parametric Tests / Artificial Neural Networks:
The use of combined biomarkers for improvement of disease prediction has
been used widely for many different disease states. There is heterogeneity among
individuals and as a result, disease states within these individuals differ in their
biology. Thus, the use of multi-parametric tests will most likely be more applicable
for population screening versus one marker. Recently, Parekh et al.(273) used a
biomarker panel consisting of 54 proteins that included adipokines,
metalloproteinases, adhesion molecules and growth factors. They used age-
matched controls and measured pre-diagnostic serum concentrations of patients
later diagnosed with CaP. Their results did not prove that the marker panel was able
to outperform the risk factors from the The Prostate Cancer Prevention Trial (PCPT)
calculator. Artificial neural networks (ANN) have been used to model complex
relationships between variables and find patterns in data. Stephan et al. have used
the ANN approach to assess various combinations of kallikrein biomarkers to
determine their clinical utility for CaP diagnosis(274).
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Chapter 1: Introduction 49
Proteomic Patterns:
High throughput proteomic analysis of biological fluids, tissues and cell lines
has recently become a popular approach for the identification of novel biomarkers. In
particular, the application of SELDI-TOF has been used frequently to profile
biological samples. With respect to CaP, Adam et al.(91) used a decision tree
algorithm to ascertain a peak fingerprint that could discriminate CaP from normal
individuals with a sensitivity and specificity of 83% and 97%, respectively. Petricoin
et al.(275) used 266 serum samples from CaP patients and controls to achieve 95%
sensitivity and 78% specificity. Qu et al.(92) used a boosted decision tree algorithm
for analysis of their SELDI-TOF data and were able to achieve 97% sensitivity and
specificity. Other studies have also used proteomic profiling for CaP diagnosis
showing usefulness of the approach(276). However, the use of proteomic pattern
fingerprinting has come under scrutiny and as a result the NCI/EDRN has conducted
a multi-institutional study to objectively validate this approach and the results were
recently published(93). Even though stage 1 of the validation confirmed the
analytical reproducibility of the approach, stage 2 was unable to determine if the
approach could predict CaP in a case-control series across institutions. The cause of
this failure has been attributed to pre-analytical, analytical and bioinformatics biases,
as described in previous literature(277,278).
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Chapter 1: Introduction 50
Table 1.1: List of candidate biomarkers for prostate cancer and their possible clinical utility.
Candidate CaP Biomarker Assessed Clinical Utility References
KLK2 Diagnostic and prognostic predictor of extracapsular extension, tumour volume and biochemical recurrence (186,188)
KLK11 Early predictor of CaP in serum (199) PSMA Imaging marker and target for therapy (201,209,210)
PSP94 Predictor of Gleason sum, surgical margin status and biochemical recurrence after local surgery (247)
PSCA Immunohistochemical marker associated with Gleason sum and stage. Target for therapy. (258,259)
PCA3 Urinary biomarker for detection of CaP (214,215)
EPCA/EPCA2 Immunohistochemical detection of CaP, serum marker to differentiate local from metastatic CaP (218-220)
Hepsin Immunohistochemical detection in PIN, CaP vs. BPH (262)
AMACR Increased detection of autoantibodies in CaP,
immunohistochemical expression as a prognostic factor for biochemical recurrence and death
(101,224,225)
EZH2 CaP tissue gene expression predicts progression (82)
uPA/uPAR Increased tissue and serum levels predicts biochemical recurrence and metastasis (230,232)
IGF/IGFBP IGF-1 slightly increased in CaP serum, IGFBP’s inversely correlated to CaP progression (234,235)
TGF-β1 Increased immunohistochemical and serum levels with
CaP progression and biochemical recurrence (239-241)
TMPRSS2:ERG/ETV Increased detection in urine of CaP, PIN patients vs. BPH, gene fusion present in CaP tissue by FISH (236,237)
GSTP1 Hypermethylation of promoter detected in urine to assess for biopsy (244)
IL-6 Elevated serum levels in late stage CaP (263,264)
Chromogranin A Monitoring of patients with androgen independent late stage CaP with neuroendocrine differentiation (23,248,249)
Annexin A3 Decreased expression in tissues of CaP by immunohistochemistry, prognostic risk marker (255,256)
Progastrin-releasing peptide
Monitoring of patients with metastatic CaP with neuroendocrine and androgen-independent phenotype (24,250)
E-cadherin Reduced immunohistochemical expression in CaP correlated with stage and reduced survival (251,252)
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Chapter 1: Introduction 51
1.3 Prostate Cancer Model Systems
The utilization of mouse and cell culture based model systems for CaP has
allowed the portrayal of this disease phenotype and has enabled the understanding
of the cancer biology and aided in developing novel therapies. Here we discuss both
mouse model and cell culture based approaches to CaP model systems.
1.3.1 Mouse Models
Mouse models have been shown to be immensely valuable in comprehending
CaP cancer biology. Knockouts of genes in a prostate specific fashion have
elucidated the associations with CaP progression. One of the most commonly used
mouse models for CaP is the transgenic adenocarcinoma of the mouse prostate
(TRAMP) mouse in which the Simian virus SV40 large and small T antigens are
expressed under the regulation of a rat probasin promoter(279). In the TRAMP
mouse, CaP pathogenesis develops within 12 weeks of birth with initial PIN lesions
being seen and metastatic spread occurring at 30 months. Another model also
utilizing the SV40 large t-antigen is the LADY mouse model. This is an early model
of CaP progression with PIN lesions developing at 20 months and no metastatic
spread observed(280). Of use have also been models using c-myc(281) and a
heterozygous PTEN mouse model(282). The PTEN model shows that loss of PTEN
recapitulates features of human CaP progression and mice also lacking p27 present
with more aggressive tumours with earlier onset, albeit without metastasis. A rat
model of CaP, known as the Dunning rat model, was one of the first to be developed
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Chapter 1: Introduction 52
and used widely(283). It has shown utility for the study of AIPC growth of CaP cells
and the molecular determinants of metastases.
Due to the in vivo nature of the mouse model the interplay between host
responses and the tissue microenvironment can result in a representative phenotype
that recapitulates the morphology in humans. However, due the complex nature of
these interactions, elucidating the mechanisms involved become more of a
challenge, added to this is the maintenance of a colony of mice. A canine model for
PIN development has also been used, albeit their long life span makes them
unsuitable for many studies(284). With respect to the identification of circulating
protein biomarkers for CaP in animal models, similar issues are present as with
analyzing human specimens. In this case a simplified cell culture based approach
may be warranted to study CaP cells in a homogenous environment.
1.3.2 Cell Culture Models
The use of cell lines derived from prostate tumours allows the analysis of
prostate tumour cells in a simplified and controlled environment. There have been
numerous cell lines derived from prostate tumours, albeit most have been from
metastatic sites(73). Prominent cell lines that are commonly used include the PC3,
LNCaP, DU-145, 22Rv1 and MDA PCa 2b. All are obtained from metastatic sites
except for 22Rv1 which was obtained from a localized tumour to the prostate.
Normal epithelial phenotype cells have been developed such as the RWPE-1 cell
line series, albeit their sustained growth in culture draws question to the extent of the
normal phenotype.
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Chapter 1: Introduction 53
There are several advantages and disadvantages to using cancer cell lines
over animal models. These then dictate the nature of the experiment that can be
conducted. Firstly, the cost involved with maintaining them is significantly less than
maintaining mice. They are readily available and studies can be performed relatively
quickly. Large quantities and volumes of cells may be propagated to create high-
throughput studies. Cell lines are extremely versatile in the types of studies they may
be used in. Not only can they be grown in vitro but also can be injected into mice to
form xenograft models of CaP progression. They can be modified and studied over
time to determine sequential events that occur as a result of specific stimulus. As
well as the products produced from the cells such as their ‘secretome’ can be
analyzed readily. Disadvantages that are associated with cell lines are that they do
not represent the heterogeneity of the tumour microenvironment as well as the
necessarily heterogeneous nature of tumours with a patient and between patients.
As a result multiple cell lines may be required to address the full heterogeneity seen
in a tumour phenotype. Cell lines are also subject to genetic alterations in culture
that may alter their phenotype over the course of a long experiment. The path to the
progression of the tumour is lost and does not provide insight in the pathogenic
process necessarily.
Developing CaP cell lines has been regarded as being notoriously difficult.
As a result the CaP cell lines that are utilized today have been around for quite some
time and have been characterized very well. Each cell has shown to have its own
unique phenotype which has become useful in comparing the proteins secreted by
each in the search for CaP biomarkers(285).
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Chapter 1: Introduction 54
1.4 Continued Need for Novel Prostate Cancer Biomarkers
The discovery of PSA and its introduction in the clinic in the early 1990’s has
had a profound impact on the early diagnosis of CaP and resulted in an increase in
CaP incidence(286). PSA is currently used as a dichotomous marker for diagnosis
but it is now being realized that its values represent a relative degree of risk for
CaP(287). The upper limit of normal set at 4ug/L fails to detect a significant number
of cancers and the PCPT determined that there is no level of PSA where cancer can
be ruled out(288). Measurement of total PSA has been shown to be useful as a
prognostic tool, with high preoperative values associated with advanced disease and
poor clinical outcome(104). The controversy surrounding the use of this marker is
being currently debated since it is not clear if PSA screening has led to a decline in
mortality due to CaP(289). In 2008 and 2009 two major randomized prospective
clinical trials will report if PSA screening reduces mortality: the European
Randomized study of Screening for Prostate Cancer and Prostate, Lung, Colorectal
and Ovarian Cancer Screening Trial. The relationship of PSA with tumour grade is
also not clear. It has been shown that tissue PSA decreases with increasing
Gleason sum(54), albeit serum levels increase due to disruption of the basement
membrane surrounding the prostate epithelial cells and the overall prostate tissue
architecture. PSA is not specific for prostate cancer and can serve as a marker for
benign prostate hyperplasia and prostate volume growth(290). Key statistics for the
test have been shown to be inadequate, with positive predictive values of 37% and
patients in the grey zone of 4-10ug/L having 25% chance of displaying CaP(291)
and 15% of men with levels of <4ug/L displaying CaP(287). Due to the inadequacies
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Chapter 1: Introduction 55
of PSA there is a need for novel markers of CaP to prevent over treatment of
indolent tumours. In addition to diagnosis, prognostic, predictive and therapeutic
markers are needed to act as surrogate endpoints in forecasting disease severity,
choosing treatments and monitoring response to therapies, respectively.
1.5 Rationale and Objectives of the Present Study
1.5.1 Rationale
Advancements in sequencing technologies for genomics have led to the
completion of many genomes including the human(292). Coupled with advances in
mass spectrometry, particularly soft ionization techniques for MS such as ESI and
MALDI, has of recent made a large impact in the field of biomarker
discovery(293,294). These new advances have enabled the generation of large
databases housing theoretical protein sequences of all the genes in an organism.
With these advancements, the identification of complex mixtures of proteins by MS
has become routine. In our global analysis of proteins present in the CM of CaP cell
lines we will utilize this technology in our search for novel prostate tumour markers.
We have chosen a cell line based model for identifying secreted proteins for
our discovery approach. By collecting and concentrating CM produced from cell
lines, the proteins secreted from the cells will accumulate in the CM and be present
at higher levels than those found in tissues, thereby making their identification
through MS more facile. In addition, given that the cell lines to be used are specific
to epithelial metastatic CaP cells, the proteins present in the CM will originate from
the cancer cell specifically and not from the the stroma environment, thereby
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Chapter 1: Introduction 56
avoiding unnecessary complications in our analysis. This will eliminate any
extraneous proteins identified that are not specific to the tumour. To select our
candidates, our focus will be on secreted or shed membrane proteins, since in the
past they have proven to be the most useful as biomarkers(295). In addition, using
prostate cell lines derived from clinically relevant tumours will aid in our discovery of
tumour markers that are specific to clinically relevant tumours.
Through qualitative analysis of the PC3, LNCaP and 22Rv1 malignant
prostate cell lines, proteins identified will be investigated for their potential as
candidate tumour markers. We assume that these molecules have not yet been
identified because their concentration in serum is too low and therefore cannot be
measured or purified, unless specific immunological reagents and highly sensitive
enzyme linked immunosorbent assay (ELISA) methods are available.
To facilitate our analysis of the CM, we will use protein and peptide free
chemically defined serum-free media (SFM) in our cell culture system. This will
provide us with a distinct advantage over fetal calf serum (FCS) containing media,
since all the proteins secreted into the media will be from the cell line and can thus
be easily identified without worry of contaminating proteins from the FCS.
Conditioned cell culture media is also significantly easier to characterize compared
to human serum or plasma which contains many substances that interfere with its
proteomic analysis such as lipids. In addition, there are many high abundance
proteins in human serum that will mask the detection by MS of lower abundant
proteins. There are also concerns regarding sample acquisition and handling that
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Chapter 1: Introduction 57
makes blood samples an imperfect source when performing biomarker discovery
studies(296).
1.5.2 Hypothesis
Proteins produced by tumour cells are secreted or shed into the circulation
and may act as surrogate tumour markers. These tumour markers can be used to
aid in the diagnosis and prognosis of CaP patients. We hypothesize that candidate
protein tumour markers for the early detection of clinically significant CaP are
secreted in vitro by CaP cell lines into their tissue culture media. These proteins can
subsequently be identified through qualitative proteomic analysis of the CM by MS
based proteomics. Candidate tumour markers identified will then be selected to
validate their clinical utility in serum and tissues of patients with and without CaP.
1.5.3 Objectives
1) Perform a proof of principle study to demonstrate the proteomic analysis
of the secretome of the PC3(AR)6 cell line by mass spectrometry
a) Optimize the culture of PC3(AR)6 in large volumes of SFM for an
extended period of time by measuring control proteins KLK5, KLK6
and total protein
b) Collect and prepare the CM for fractionation by fast performance
liquid chromatography
c) Process the fractions for liquid chromatography tandem mass
spectrometry
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Chapter 1: Introduction 58
d) Analyze the identified proteins and select candidates for further
validation through bioinformatics and literature searches
e) Validate candidates in serum of patients with and without CaP
2) Conduct and extended proteomic analysis of the CM of three CaP cell
lines and a control flask
a) Optimize culture of each cell line in SFM to optimize secreted
protein production and reduce release of intracellular proteins
b) Collect and prepare CM for peptide fractionation through high
performance liquid chromatography
c) Process samples for analysis by liquid chromatography tandem
mass spectrometry
d) Analyze the identified proteins and determine protein identification
probabilities and false positive rates
e) Subject the list of proteins to bioinformatics and literature searches
and select novel candidates for validation
3) Perform a pre-clinical validation on selected candidates in serum of
patients with and without CaP
4) Conduct and extended validation on the most promising candidate in the
serum and tissues of patients with and without CaP
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Chapter 2: Proof of Principle: Proteomic Analysis of the PC3 Cell Line Conditioned Media
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CHAPTER 2:
PROOF OF PRINCIPLE: PROTEOMIC ANALYSIS OF THE PC3 CELL LINE CONDITIONED MEDIA
The work presented in this chapter was published in Clinical Chemistry:
Sardana, G., Marshall J. and Diamandis, E.P. Discovery of Candidate Tumor Markers for Prostate Cancer via Proteomic Analysis of Cell
Culture–Conditioned Medium Clinical Chemistry, 2007 Mar;53(3):429-37
Copyright permission has been granted.
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2.1 Introduction
Prostate cancer is the most common malignancy in men and the 2nd leading
cause of cancer-related deaths (297). Early diagnosis of cancer improves clinical
outcomes, but detection methods for clinically relevant preclinical CaP are limited
(298). Tumour markers can be used to detect cancer, determine prognosis, or
monitor treatment (299). The established CaP tumour marker PSA, has low
diagnostic specificity (300); increased concentrations are also seen in BPH and
prostatitis (301). The sensitivity and specificity of the PSA test for CaP have been
improved by modifications such as measuring PSAV and measuring the ratio of
fPSA to tPSA or KLK2 in addition to PSA (302-304). Other methods for detecting
CaP are not highly specific for CaP and are uncomfortable to patients (305). Serum
proteomic profiling has emerged as a new method for detecting CaP (306,307) but
has not been adequately validated (308,309).
Determining the clinical significance of a prostate tumour is a major concern
of CaP testing (310). An autopsy study of men who died of other causes revealed
CaP or precursor lesions in 29% of men 30 to 40 years old and 64% of those >60
years old (311). Because treatments for CaP (androgen ablation, radical
prostatectomy, radiation, and chemotherapy) have serious side effects, there is a
need to differentiate patients who require treatment from those who do not. Mass
spectrometry for biomarker discovery (312-314) has generated large databases of
protein sequences. We used a cell culture–based proteomic approach to search for
novel candidate prostate tumour markers in the proteins secreted into the CM of the
CaP cell line PC3(AR)6.
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2.2 Materials and Methods
2.2.1 Roller Bottle Cell Culture
We grew the CaP epithelial cell line PCR(AR)6, kindly provided by Dr.
Theodore Brown (Toronto, Ontario, Canada), in a humidified incubator at 37°C and
5% CO2 in RPMI 1640 (Gibco) with 80 mL/L fetal calf serum (Hyclone) to confluence
(20 X 106 cells/flask) in two 175-cm2 tissue culture flasks (Nunc). The cells were
trypsinized and transferred to an 850-cm2 roller bottle flask with a vented cap
(Corning), placed on a roller culture apparatus (Wheaton Science Products), and
incubated for 2 days in 150 mL RPMI with 8% FCS to allow the cells to adhere
(Figure 2.1). Afterwards, the medium was discarded and the interior was rinsed
twice with 150 mL phosphate-buffered saline (PBS) (137 mmol/L NaCl, 10 mmol/L
phosphate, 2.7 mmol/L KCl, pH 7.4). Next, 400 mL of chemically defined Chinese
hamster ovary medium (CDCHO) (Gibco), supplemented with glutamine (8 mmol/L)
(Gibco), was added, and the roller bottle was incubated for 14 days. During the
culture period, we measured total protein by the Coomassie (Bradford) assay
(Pierce Biotechnology) and KLK5 and KLK6 by ELISA (192,315). The CM was
collected, spun down (3,000g) to remove cellular debris, and frozen at –20°C for
later use. We processed 2 replicates for MS analysis; these replicate cultures are
referred to as batch 1 and batch 2.
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Figure 2.1
Figure 2.1: Schematic representation of the workflow for proteomic analysis of roller bottle CM. CM from roller bottles was collected and dialyzed overnight. The dialyzed medium was directly loaded onto a SAX column and eluted by FPLC. Ten fractions were collected, lyophilized, and trypsin-digested. The resulting peptides were ZipTip desalted and separated by reversed-phase C-18 chromatography coupled online to an ion-trap mass spectrometer. The acquired MS/MS data were searched by Mascot, and identified proteins were manually categorized by Genome Ontology and literature searches through NCBI.
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2.2.2 Dialysis
The thawed CM was dialyzed in tubing with a molecular weight cut-off of 3.5
kDa (Spectra/Por) in 10 L of 20 mmol/L diethanolamine (DiEtOH) (Sigma-Aldrich),
pH 8.9, overnight at 4°C. A sample aliquot was taken after dialysis.
2.2.3 Fast Performance Liquid Chromatography of CM
The dialyzed CM was loaded onto an HR10/10 column (GE Amersham)
containing SOURCE15 strong anion exchange (SAX) beads (GE Amersham). An
AKTA fast-performance liquid chromatography system (FPLC) was used running
Unicorn v4.12 software equipped with a P-960 sample pump and Frac-900 fraction
collector (GE Amersham), at a flow rate of 1 mL/min followed by a 2-stage elution
gradient at a flow rate of 3 mL/min (0% to 60% elution buffer within 40 min followed
by a ramp from 60% to 100% within 10 min) using 20 mmol/L DiEtOH, pH 8.9
running buffer, and 1 mol/L NaCl elution buffer. Absorbance was monitored at 214
nm. The first 10 fractions of 10mL each were collected, taking sample aliquots from
each, and the flow-through. A SAX protein standard (Bio-Rad) was run each time to
evaluate the quality of the column before each sample loading.
2.2.4 Lyophilization and Digestion of Fractions
The collected fractions were lyophilized overnight to dryness, resuspended in
1 mL dH2O, and trypsin-digested using a 10X digest buffer [5% acetonitrile (ACN),
200 mmol/L urea, and 50 mmol/L tricine, pH 8.8] to digest ~100μg protein from each
lyophilized fraction; 1μg trypsin (Promega) was used per digest. The digests were
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incubated overnight at 37°C and reduced the following day with dithiothreitol (DTT)
(1 mmol/L) for 1 h at 25°C. We added a final 1μg trypsin, and the samples were
incubated at 37°C for approximately 3 h.
2.2.5 Liquid Chromatography–MS Analysis
Digested samples were collected on a C-18 ZipTip (Millipore) to purify and
desalt the peptides. The ZipTip was primed with 50% ACN in 0.1% acetic acid and
washed with 0.1% acetic acid before the digested samples were passed through the
ZipTip. The peptides were eluted from the ZipTip with 2μL of 0.1% acetic acid in
65% ACN, and dH2O was added to give a final volume of 20μL. The desalted
peptides were injected at 2μL/min onto a C-18 reversed-phase (RP) chromatography
column (Vydak 300μm X 15 cm) via an Agilent 1100 series HPLC system coupled to
a Bruker HCT ion-trap ESI mass spectrometer (Bruker Daltronics) via a metal
electrospray needle. The sample was injected in 5% acetonitrile in 0.1% acetic acid
and, after loading for 5 min, a 1-min gradient to 12.5% acetonitrile was followed by a
90-min gradient to 65% acetonitrile in 0.1% acetic acid. We analyzed the eluted
peptides by tandem MS (MS/MS), and data were acquired and mass spectral peaks
were deconvoluted to consolidate different charge states observed for each peptide
with the software supplied by Bruker. The instrument was standardized with a tryptic
digest of alcohol dehydrogenase, cytochrome C, and glycogen phosphorylase to
assess mass accuracy and sensitivity of the instrument before and after each set of
runs.
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2.2.6 Database, Genome Ontology, and Literature Search
The resulting MS/MS spectra was searched in MGF format from both batch 1
and 2 using the Mascot algorithm search engine (version 2.1) with default variables
and trypsin specified. Specifically, one missed cleavage was allowed, a variable
oxidation of methionine residues and a fixed modification of carbamidomethylation of
cysteines was set with a fragment tolerance of 0.4 Da and a parent tolerance of 3.0
Da. The database used was a custom-built non-redundant compilation of human,
mouse, and rat sequences from GenBank, Ensembl, and SwissProt, compiled
January 2005. A bioinformatics program was used through Protana Inc.
(Mississauga) to identify peptides from the MS/MS spectra present from each
fraction, giving each peptide a score. The identified peptides from all the fractions
within each respective batch were clustered with other peptides that were common
to a particular protein, and each group of peptides was then given a cluster score.
Any peptide with a Mascot score <20 and any protein with a score <40 was removed
from the data. The identified proteins were manually analyzed and classified by their
genome ontology cellular component classification and PubMed literature searches
were conducted on each protein.
The false-positive rate of protein identification was measured by searching a
random database, in which every sequence entry from the “normal” database was
randomly shuffled. The number of hits from each search was categorized based on
score, and for each scoring interval, the false-positive rate was calculated as number
of random hits/(number of random hits + number of normal hits).
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2.2.7 ELISAs for Kallikrein 5, 6, and 11
Kallikreins 5, 6, and 11 were measured by sandwich-type ELISA, 96-well
plates, as described earlier (192,198,315) with use of biotinylated detection
antibodies, alkaline phosphatase-conjugated streptavidin and diflunisal phosphate
substrate. Plates were read by time-resolved fluorescence(316).
2.2.8 Protein Recovery
To assay for sample recovery of proteins during dialysis and fractionation, we
analyzed sample aliquots that had been taken during this procedure for KLK5 and
KLK6 by the aforementioned ELISA assays.
2.2.9 Mac-2BP ELISA
We obtained the s90K/Mac-2BP ELISA reagent set from Bender
Medsystems. Briefly, serum samples were diluted 1:500 and CM samples 1:10 in
sample diluent buffer (provided by the manufacturer) and loaded onto 96-well strips
pre-coated with anti–Mac-2BP antibody. Samples were incubated at 37°C for 45 min
with shaking at 100 rpm. The wells were washed 3 times with wash buffer (as
provided), a detection antibody was added, and the plate was incubated for 45 min.
The plate was washed and substrate solution (as provided) was added to each well,
and the plate was incubated with shaking at room temperature for 10 min, following
which a stop solution (as provided) was added to each well. Absorbance was
measured at 490 nm by a Wallac–Victor2 plate reader (Perkin-Elmer). An extended
validation of Mac-2BP was performed with 210 CaP, 53 BPH and 17 normal serum
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samples donated by Carsten Stephan from the Department of Urology, Charité -
Universitätsmedizin Berlin, Germany.
2.3 Results
2.3.1 Total Protein, KLK5 and KLK6 Concentrations in Culture Over Time
To demonstrate the accumulation of secreted proteins over time in the roller
bottle culture, we measured 2 secreted proteins that are known to be produced by
the PC3(AR)6 cell line, namely KLK5 and KLK6, over the 14-day culture period
(Figure 2.2A, B). The concentrations of KLK5 and KLK6 increased with time in
culture and began to plateau after ~10 days. We measured the total protein in the
CM, which increased steadily throughout the culture period (Figure 2.2C).
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Figure 2.2
Figure 2.2: Measurement of KLK5, KLK6 and total protein. KLK5 (A), KLK6 (B), and total protein (C) concentrations over time in CM of the PC3(AR)6 roller bottle culture. The concentrations of KLK5 and KLK6 were monitored by ELISA. Two replicates are shown.
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2.3.2 Recovery of KLK5 and KLK6 During Sample Preparation The recoveries of KLK5 and KLK6 were 25% and 17%, respectively. The
major protein losses were seen after lyophilization. In addition, for KLK5, a
significant amount went in the flow-through after column loading.
2.3.3 Proteins Identified by Mass Spectrometry
After LC-MS/MS and searching by Mascot from both batches, we identified
262 proteins from the SAX FPLC fractions that had a Mascot score of at least 40.
Each protein identified was tabulated and cross-referenced with the genome
ontology database for cellular components (317) (Figure 2.3). A large percentage
(39%) of all proteins identified are classified as extracellular (23%) or membrane
(16%) proteins. Many identified proteins are classified as intracellular (50%),
whereas 11% were unclassified. From the list of the 262 proteins, we selected
candidate biomarkers (Table 2.1) based on the following criteria:
1. Proteins were searched manually against the Genome Ontology database
(317) for their cellular localization. Proteins that were classified as secreted
and membrane-bound were selected.
2. Literature searches through the National Center of Biotechnology Information
(NCBI) PubMed database were then performed to determine:
a. if these proteins are novel molecules that have yet to be explored as
potential biomarkers;
b. if these proteins are known to participate in critical pathways implicated
in cancer initiation and progression.
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Figure 2.3
Figure 2.3: Cellular location of proteins from PC3(AR)6 CM. Classification of proteins by cellular location from PC3(AR)6 CM, batches 1 and 2. Each protein identified after Mascot searching was classified by its cellular location using Genome Ontology classifiers (www.geneontology.org).
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Table 2.1: Extracellular candidate tumor markers identified in culture medium of the PC3 (AR)6 roller bottle culture.
Gene Protein Description Mascot Score
GDF15 Prostate differentiation factor; PTGF-β 40.81 COL2A1 Alpha 1 type II collagen, isoform 1, preproprotein; collagen II, 44.96 PAM* Peptidylglycine α-amidating monooxygenase, isoform c, preproprotein 45.64 IGFBP2 Insulin-like growth factor binding protein 2 precursor (IGFBP-2) (IBP-2) 45.7 S100A8* S100 calcium-binding protein A8; cystic fibrosis antigen; calgranulin A 47.9 TLR9* Toll-like receptor 9, isoform A precursor 49.44 TPT1* Tumor protein, translationally controlled 1 50.73 CTSL* Cathepsin L 52.09 PLG Plasminogen 54.92 LGALS3 Lectin, galactoside-binding, soluble, 3 (galectin 3); Lectin, galactose-binding 55.53 LTBP2* Latent transforming growth factor β binding protein 2 65.36 MUC5B* Mucin 5, subtype B, tracheobronchial 66.33 GALNT6* Polypeptide N-acetylgalactosaminyltransferase 6 66.35 IGFBP6 Insulin-like growth factor binding protein 6 72.26 INHBB Inhibin β B subunit precursor; Inhibin, β-2 74.86 TIMP2 Tissue inhibitor of metalloproteinase 2 precursor 75.21 LAMC2 Laminin, γ 2, isoform a precursor; nicein (100 kDa); 75.96 CXCL3* Macrophage inflammatory protein-2-β precursor (MIP2-β) 80.8 AZGP1 α-2-glycoprotein 1, zinc; α-2-glycoprotein 82.29 NPC2* Niemann-Pick disease, type C2; Niemann-Pick disease, type C2 gene 82.8 MDK* Midkine (neurite growth-promoting factor 2) 90.34 SERPINA1 Serine (or cysteine) proteinase inhibitor 93.92 KLK6* Kallikrein 6 (neurosin, zyme); protease M; protease, serine, 9 (neurosin) 95.71 SEMA3F* Semaphorin 3F precursor (Semaphorin IV) (Sema IV) (Sema III/F) 100.2 C19orf10* Chromosome 19 open reading frame 10; interleukin 25; interleukin 27 103.18 LRG1 Leucine-rich α-2-glycoprotein 109.66 COL6A2 α 2 type VI collagen, isoform 2C2 109.73 GALNT2 Polypeptide N-acetylgalactosaminyltransferase 2; UDP-GalNAc transferase 2 114.5 IGFBP5 Insulin-like growth factor binding protein 5 120.38 RNASET2* Ribonuclease 6 (EC 3.1.27.—) precursor 124.53 FBLN1 Fibulin 1 isoform C precursor 137.3 DAG1* Dystroglycan 1 precursor; α-dystroglycan; Dystrophin-associated glycoprotein-1 145.92 S100A9 S100 calcium-binding protein A9; calgranulin B 152.1 TGFB2 Transforming growth factor, β 2 152.59 SPINT1* Hepatocyte growth factor activator inhibitor precursor 154.24 B2M β-2-microglobulin 177.47 LCN2* Lipocalin 2 (oncogene 24p3) 181.58 KLK5* Kallikrein 5; stratum corneum tryptic enzyme 181.94 LCN7* P3ECSL; glucocorticoid-inducible protein; oxidized-LDL responsive gene 2 184.67 DF Complement factor D preproprotein; adipsin; properdin factor D 187.96 SFN* Stratifin 193.8 APP* Amyloid β (A4) precursor protein (protease nexin-II, Alzheimer disease) 215.47
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TIMP1 Tissue inhibitor of metalloproteinase 1 precursor; Erythroid-potentiating activity 227.55 C1S Complement component 1, s subcomponent 254.89 GRN Granulin 258.53 PLTP* Phospholipid transfer protein 282.02 STC2* Stanniocalcin 2; stanniocalcin 2; stanniocalcin-related protein 303 PTX3* Pentaxin-related gene, rapidly induced by IL-1β; Pentraxin-3 308.44 CYR61* Cysteine-rich, angiogenic inducer, 61; cysteine-rich heparin-binding protein 61 327.5 IL6 Interleukin 6 (interferon, β 2) 336.16 PSAP* Saposin precursor 359.65 PLAU Plasminogen activator, urokinase 361.2 FSTL1* Follistatin-like 1 precursor; follistatin-related protein 367.11 P4HB* Procollagen-proline, 2-oxoglutarate 4-dioxygenase (proline 4-hydroxylase) 375.33 AGRN* Agrin 486.69 HSPG2* Basement membrane-specific heparan sulfate proteoglycan core protein precursor 506.82 INHBA* Inhibin β A subunit precursor; Inhibin, β-1; EDF 507.77 NUCB1* Nucleobindin 1 precursor (CALNUC) 631.69 LGALS3BP* Galectin 3 binding protein; L3 antigen; Mac-2-binding protein; serum protein 90K 632.55 COL6A1 Collagen, type VI, α 1 precursor; collagen VI, α-1 polypeptide 766.46 CTSD Cathepsin D (lysosomal aspartyl protease) 771.08 THBS1 Thrombospondin 1 precursor 865.57 BF Complement factor B preproprotein; B-factor, properdin; C3 proactivator 1071.18FN1 Fibronectin 1 isoform 2 preproprotein; cold-insoluble globulin 1978.92C3 Complement component 3 3339.99
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We determined the overlap of the proteins identified between the 2 batches through
visual inspection of the Mascot data.
Proteins in this list (Table 2.1) that are marked with an asterisk have not been
previously evaluated as serum biomarkers for CaP as determined through PubMed
literature searches specific for the protein.
We calculated false-positive protein identification rates based on a random
database search. False-positive rates for specific scoring intervals were as follows:
40–50, 44%; 50–60, 35%; 60–70, 7%; 70–80, 8%; 90–150, 7%; and >150, 0%. The
presence of a higher false-positive rate in the 70–80 scoring interval is the result of a
statistical fluctuation attributable to additional protein identification in the random
database search.
2.3.4 Overlap of Proteins Identified in Batches 1 and 2
To determine the reproducibility of the method, the proteins identified from
batches 1 and 2 were manually compared for overlap. We identified 145 proteins in
both batches (55% overlap). Additionally, we identified 78 proteins only in batch 1
and 39 proteins only in batch 2. Combined, the total number of identified proteins
was 262. As expected, the more abundant proteins were preferentially identified in
both batches.
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Chapter 2: Proof of Principle: Proteomic Analysis of the PC3 Cell Line Conditioned Media
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2.3.5 Mac-2BP Concentrations in Patients with Prostate Cancer vs. Healthy Men and in the Conditioned Media of the PC3(AR)6 Cell Line
From the proteins identified, Mac-2BP was chosen as one novel biomarker
candidate for further validation. The concentrations of Mac-2BP increased in the CM
over time (Figure 2.4A), as expected and in a similar fashion to KLK5 and KLK6
(Figure 2.2). We measured serum concentrations of Mac-2BP from 26 men with CaP
and 17 healthy men using a Mac-2BP ELISA. The median Mac-2BP concentrations
in CaP patients were almost twice as high as those in healthy men (Figure 2.4B),
with 50% of the CaP patients having increased Mac-2BP concentrations compared
with the healthy men (at the 100th percentile of healthy men as a cutoff). The
difference in medians of the 2 populations by Mann-Whitney test was highly
significant (P = 0.003). The negative correlation between Mac-2BP and PSA in these
26 patients was also significant, with a Spearman correlation coefficient (rs) of –0.63
(P < 0.001) (Figure 4C).
We further measured KLK5, KLK6, and KLK11 (a previously identified
prostate and ovarian cancer biomarker) (318) in the same set of patients and
controls, as above. The data (Figure 2.5) showed decreased concentrations of KLK5
in CaP (P < 0.0001 by Mann-Whitney test), decreased concentrations of KLK6 in
CaP (P = 0.03 by Mann-Whitney test), and increased concentrations of KLK11 in
CaP (P < 0.0001 by Mann-Whitney test).
Spearman correlations for all pairs of measured concentrations (Mac-2BP,
KLK5, KLK6, KLK11, and PSA) included only one statistically significant negative
correlation between Mac-2BP and PSA (Figure 2.4C).
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Chapter 2: Proof of Principle: Proteomic Analysis of the PC3 Cell Line Conditioned Media
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An extended validation of Mac-2BP was performed using serum samples that
were confirmed cases of CaP, BPH and healthy males. As can be seen from Figure
2.6 the trend initially observed in the preliminary validation is reversed with the
median levels of CaP patients showing lower Mac-2BP serum concentrations than
normals. Levels are also lower in BPH patients. A significant difference is observed
by the Kruskal-Wallis non-parametric one-way ANOVA of p = 0.0028. Post-hoc
analysis by the Dunn’s multiple comparisons test showed there was a significant
difference between the CaP and normal group, p < 0.01.
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Chapter 2: Proof of Principle: Proteomic Analysis of the PC3 Cell Line Conditioned Media
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Figure 2.4
Figure 2.4: Mac-2BP concentrations and the correlation between serum Mac-2BP concentrations and PSA. (A), Mac-2BP concentrations in CM of PC3(AR)6 cell line over time. (B), Mac-2BP concentrations in serum of 26 CaP patients and 17 healthy men. Horizontal lines indicate medians. P value was calculated with the Mann–Whitney test. (C), Correlation between serum Mac-2BP concentrations and PSA in the 26 cancer patients. rs = Spearman correlation coefficient.
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Chapter 2: Proof of Principle: Proteomic Analysis of the PC3 Cell Line Conditioned Media
77
Figure 2.5
Figure 2.5: Concentrations of KLK5, KLK6, and KLK11 in serum of 26 CaP patients and 17 healthy men. Horizontal lines indicate medians. P value was calculated with the Mann–Whitney test.
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Chapter 2: Proof of Principle: Proteomic Analysis of the PC3 Cell Line Conditioned Media
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Figure 2.6
p = 0.0028
Figure 2.6: Serum concentrations of Mac-2BP in CaP, BPH and normal patients. Horizontal lines indicate median values. Significance was determined using the Kruskal-Wallis non-parametric one-way ANOVA test.
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Chapter 2: Proof of Principle: Proteomic Analysis of the PC3 Cell Line Conditioned Media
79
2.4 Discussion
We used a proteomic method for identification of secreted proteins from the
CM of the metastatic CaP cell line PC3(AR)6 as a model to discover novel markers
for CaP. Because large amounts of cells are needed for confident MS detection of
low-abundance cellular proteins, we determined if a cell line could be grown in a
large volume of SFM for an extended period. The use of protein- and peptide-free
chemically defined Chinese hamster ovary serum-free medium simplified analysis,
providing a distinct advantage over fetal calf serum–containing medium, which would
contaminate the CM.
The loss of KLK5 in the flow-through during SAX FPLC was attributable to
incomplete capture of KLK5 by the SAX column. The incomplete capture is
consistent with its relatively high pI of ~ 8. Appreciable losses of both KLK5 and
KLK6 also occurred after lyophilization of the FPLC fractions, possibly attributable to
incomplete solubilization of the freeze-dried protein (319).
Independent MS detection of KLK5 and KLK6, proteins known to be secreted
by PC3(AR)6, was confirmed for batch 1 and 2 in the expected fractions. The
complex mixture of proteins present at varying concentrations in CM necessitated
fractionation before MS to increase the depth of identification. However, not all
proteins in a mixture can be ionized and detected in 1 run, with the lower abundance
proteins not being identified in both batches(320).
Fifty percent of the proteins identified were intracellular. Their presence in the
CM is to be expected because of cell death and their high abundance within cells.
Because we were primarily interested in investigating proteins that are secreted or
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Chapter 2: Proof of Principle: Proteomic Analysis of the PC3 Cell Line Conditioned Media
80
shed from CaP cells in vivo, we selected the extracellular and membrane proteins
for further evaluation. Each protein was examined to establish if it had been
previously evaluated as a CaP biomarker or if it had any link to cancer. The selected
candidates are listed in Table 2.1.
We performed preliminary validation of Mac-2BP by ELISA. Mac-2BP has
been shown to be a serum prognostic marker in lymphoma (321), and lung (322),
breast (323), hepatocellular (324), ovarian (325), and colon (326,327) carcinoma.
Serum concentrations have not been evaluated in CaP, however, despite the
correlation of immunohistochemical staining for Mac-2BP with Gleason grade (328).
Serum Mac-2BP was increased in 50% of the CaP patients. (Figure 2.4). The
correlation of Mac-2BP concentrations and PSA concentrations in these patients
was weak and negative (Figure 2.4C). As can be seen in our extended validation of
Mac-2BP, the trend seen in the preliminary validation was reversed (Figure 2.6).
Even though Mac-2BP is seen to be elevated at the tissue level in CaP this was not
translated into an elevation seen in serum in our larger sample set. Upon further
study of Mac-2BP it was realised that it is ubiquitously expressed in almost every
tissue in the human body (Unigene database). As a result, the elevations due to
CaP most likely are not discernable among the background of endogenous Mac-2BP
in serum from other tissues. As a result we did concluded that Mac-2BP would not
be a useful serum tumour marker for CaP as it is not specific to the prostate and
eleveations due to CaP are not detectable in serum. Two kallikreins (KLK5 and
KLK6) were also present at lower concentrations in CaP, whereas KLK11 was
present at increased concentrations. These data support the theory that secreted
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Chapter 2: Proof of Principle: Proteomic Analysis of the PC3 Cell Line Conditioned Media
81
proteins are adjunct biomarkers for CaP, although with less diagnostic accuracy than
PSA. No correlation was seen between any pairs of these markers in serum.
The results of this and other similar studies (97,329) suggest that a wealth of
knowledge is obtainable by analyzing the CM of cell lines. We observed minimal
overlap of our data with those of Martin et al.(97) and Lin et al.(330), who studied the
proteins secreted and present in the LNCaP cell line, with 67 proteins from the
Martin et al. study and 27 proteins from the Lin et al. study overlapping. This
highlights the heterogeneity of cell lines and the data that can be derived from each.
Obviously, much work is needed to further evaluate the identified candidate
biomarkers, but this study will form the basis of future communications.
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Chapter 3: Optimization of Cell Culture and Proteomic Workflow
82
CHAPTER 3:
OPTIMIZATION OF CELL CULTURE AND PROTEOMIC WORKFLOW
Sections of this chapter were published in:
Sardana, G., Jung, K., Stephan, C. and Diamandis, E.P. Proteomic Analysis of Conditioned Media from the PC3, LNCaP and 22Rv1 Prostate Cancer
Cell Lines: Discovery and Validation of Candidate Prostate Cancer Biomarkers Journal of Proteome Research, 2008 Aug 1;7(8):3329-3338
Copyright permission has been granted.
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Chapter 3: Optimization of Cell Culture and Proteomic Workflow 83
3.1 Introduction
As with most proof of principle studies, based on the results obtained an
optimization is needed before further experiments are conducted. From the data
shown in the previous chapter there were some key issues that were identified that
required improvement.
First, proteins identified from one cell line are not indicative of the
heterogeneity of a disease phenotype. Second, the culture volume and amount of
total protein produced was not controlled for the capacity of the mass spectrometer
used. Third, the amount of cell death was not controlled for during culture with ~50%
of the identified proteins identified were from an intracellular source. Fourth, the
sample preparation workflow employed was not in agreement with the published
literature as an optimal method for sample preparation. Fifth, the amount of protein
loss during our sample preparation steps was not quantified. Sixth, the type of mass
spectrometer used was considered to have a relatively low sensitivity and accuracy
in identifying peptides. Seventh, our bioinformatics approach did not fully take
advantage of multiple search engines and assessing prediction probabilities as well
as determining false positive rates (FPR).
Taking all these concerns into account, a modified approach was devised
based on the best practices used for proteomic analysis from the literature. These
changes were then incorporated and applied to modify the proteomic workflow from
our proof of principle study.
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Chapter 3: Optimization of Cell Culture and Proteomic Workflow 84
3.2 Materials and Methods
3.2.1 Cell Culture
The PC3, LNCaP and 22Rv1 cell lines were purchased from the American
Type Culture Collection (Rockville, MD). All cell lines were grown in T-175 culture
flasks (Nunc) in RPMI 1640 culture medium (Gibco) supplemented with 8% fetal
bovine serum (FBS) (Hyclone). Cells were cultured in a humidified incubator at 37oC
and 5% CO2. Cells were seeded at varying densities in duplicate. PC3 and 22Rv1
cells were grown for 2 days in 30mL of RPMI + 8% FBS. Afterwards, the medium
was removed, and the flask was gently washed 3 times with 30mL of PBS (137 mM
NaCl, 10 mM phosphate, 2.7 mM KCl, pH 7.4). Thirty milliliters of CDCHO (Gibco)
medium supplemented with glutamine (8mmol/L) (Gibco) were added to the flasks
and incubated for 2 days. The LNCaP cell line was grown as above, except that the
cells were incubated for 3 days in RPMI + 8% FBS before the media were changed
to CDCHO. The cell lines were grown in duplicate.
After incubation in CDCHO, the CM was collected and spun down (3,000 X g)
to remove cellular debris. Measurement of total protein, lactate dehydrogenase
(LDH) and PSA, KLK5, KLK6 were taken from each culture.
3.2.2 Measurement of Total Protein, LDH and PSA, KLK5 and KLK6
The total protein of the CM was measured using the Coomassie (Bradford)
assay (Pierce Biotechnology) as recommended by the manufacturer.
Lactate dehydrogenase levels in the CM were measured via an enzymatic
assay based on conversion of lactate to pyruvate. NADH production from NAD+
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Chapter 3: Optimization of Cell Culture and Proteomic Workflow 85
during this reaction was monitored at 340mm with an automated method and
converted to Units per litre (U/L) (Roche Modular Systems).
Kallikrein 3 (PSA), KLK5 and KLK6 were measured with in-house ELISAs as
described earlier(192,315,331).
3.2.3 Sample Preparation
We devised a new approach to sample preparation where we monitored the
level of PSA by ELISA after each sample preparation step as a marker for overall
protein recovery. The sample preparation procedure was devised from the published
literature. Approximately 30mL of CM from the LNCaP cell line, which corresponded
to 1mg of total protein, were dialyzed overnight using a 3.5kDa cut-off dialysis tubing
(Spectra/Por) at 4oC in 5L of 1mM of ammonium bicarbonate solution with one buffer
exchange. The dialyzed CM was lyophilized overnight to dryness followed by
resolubilization with 322µL of 8M urea, 25µL of 200mM DTT, and 25µL of 1M
ammonium bicarbonate. The sample was vortexed thoroughly and incubated at 50oC
for 30min. One hundred and twenty five micro litres of 500mM iodoacetamide were
added and the sample was incubated in the dark at room temperature for 1h. The
sample was then desalted using a NAP-5 column (GE Healthcare) and lyophilized to
dryness.
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Chapter 3: Optimization of Cell Culture and Proteomic Workflow 86
3.3 Results
3.3.1 Cell Culture Optimization
Growth conditions of the three cell lines were optimized in order to reduce cell
death and maximize secreted protein levels. Cells were incubated in SFM for 2 days
at different seeding densities. Total protein, LDH and the concentration of KLK5 and
KLK6 in the CM of PC3, and PSA in the CM of LNCaP and 22Rv1 cells were
measured. The ratio of PSA, 5, and 6 concentrations with LDH levels for each
culture condition (Figures 3.1, 3.2, 3.3) were compared. The optimal seeding
concentrations were 7.5 X 106, 22 X 106, and 75 X 106 cells for PC3, LNCaP and
22Rv1 cell lines, respectively, as these gave the highest ratio of KLK production
(indicator of secreted proteins) to LDH (indicator of cell death). The total protein of
the CM for the optimized seeding densities was 33 µg/mL, 39 µg/mL and 39 µg/mL
for PC3, LNCaP and 22Rv1 cells, respectively. Thus, 30mL of media contained
approximately 1mg of total protein.
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Chapter 3: Optimization of Cell Culture and Proteomic Workflow 87
Figure 3.1
Figure 3.1: Measurements of PSA and LDH in the CM of the 22Rv1 cell line. Concentrations of PSA (A) and LDH (B) at different seeding densities. (C) Ratio of PSA to LDH at different seeding densities.
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Chapter 3: Optimization of Cell Culture and Proteomic Workflow 88
Figure 3.2
Figure 3.2: Measurements of PSA and LDH in the CM of the LNCaP cell line. Concentrations of PSA (A) and LDH (B) at different seeding densities. (C) Ratio of PSA to LDH at different seeding densities.
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Chapter 3: Optimization of Cell Culture and Proteomic Workflow 89
Figure 3.3
Figure 3.3: Measurements of KLK5, KLK6 and LDH in the CM of the PC3 cell line. Concentrations of KLK5 and KLK6 (A) and LDH (B) at different seeding densities. (C) Ratio of KLK5 and KLK6 to LDH at different seeding densities.
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Chapter 3: Optimization of Cell Culture and Proteomic Workflow 90
3.3.2 Sample Preparation Optimization
The consensus reached from the literature search to optimize our sample
preparation workflow is shown in Figure 3.4 with the total recovery of PSA after each
step also shown. The proteomic workflow of choice was found to be the MuDPiT
(multi-dimensional protein identification technology) approach first shown by
Washburn et al.(332) in 2001. This involved trypsin digestion of the sample followed
by orthogonal chromatography steps. Strong cation exchange (SCX) followed by
LC-MS/MS reversed-phase C18 capillary chromatography. To employ this approach
with our requirements for processing the CM, we utilized dialysis and lyophilization
as intermediate steps to concentrate the sample prior to denaturing with urea, DTT
and iodoacetamide. A desalting step was added to remove any salts in the sample,
followed by a lyophilization step prior to resuspension in Buffer A for SCX
chromatography.
In order to evaluate sample recovery PSA was measured by ELISA at each
step. The results are shown in Figure 3.4. As can be seen the overall recovery is
~20% with the majority of sample loss at the lyophilization steps. Based on the
results obtained here, we determined our starting total protein concentration should
be 1mg. Thus, ~200ug of total protein would be fractionated prior to LC-MS/MS, and
if ~10 fractions were collected, each would contain ~20ug of peptides. Considering
the capillary column used during electrospray ionization into the mass spectrometer
can only hold 1ug of peptides, the total concentration of peptides in each fraction
would be more than enough.
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Chapter 3: Optimization of Cell Culture and Proteomic Workflow 91
Figure 3.4
Figure 3.4: Overview of the optimized sample preparation workflow. Levels of PSA are measured by ELISA after each step of the sample preparation procedure to assess sample recovery.
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Chapter 3: Optimization of Cell Culture and Proteomic Workflow 92
3.4 Discussion
In order to improve our sample coverage and efficiency of identification of
proteins the methods employed in the proof of principle study needed to be
optimized. This required analyzing our approach at each step of our workflow and
determining the most robust procedure for sample preparation and analysis. We
addressed each of our concerns raised in the proof of principle study before
continuing on with our extended analysis of CaP cell lines.
Initially, our primary concern was the sample preparation procedure used to
process the CM prior to LC-MS/MS analysis was not the most commonly used for
this type of ‘shotgun’ proteomics application. Instead a multi-dimensional
chromatographic approach that fractionated peptides rather than proteins was
investigated(332). This MuDPiT approach reduced the sample handling of samples
by performing one initial trypsin digest prior to the first dimension of chromatography
and in addition employed a high resolution HPLC for the first chromatographic
dimension. It was agreed upon that this method provided a robust approach to
protein identification.
Our next concern was the culture volume used to grow the cells was too large
and we did not optimize the seeding density and duration of culture for the amount of
total protein produced to meet our requirements for LC-MS/MS. Based on the
amount of total protein required for LC-MS/MS analysis we worked backwards and
determined what would be the initial amount of total protein required before cell
culture and the sample processing steps. To aid in determining this we first
quantified our protein recovery for our new proteomic workflow. By measuring PSA
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Chapter 3: Optimization of Cell Culture and Proteomic Workflow 93
after each step up to trypsin digestion we were able to determine the overall
recovery to be ~20% (Figure 3.4). Using this information and based on the fact that
the reversed phase capillary used for LC-MS/MS can only bind 1ug of peptides, we
determined that having 1mg of total protein starting material would be sufficient for
our purposes.
To encompass more of the heterogeneity of the CaP phenotype we chose to
expand the number of cell lines studied. In this respect, we chose to use the PC3,
LNCaP and 22Rv1 CaP cell lines since each had been derived from a different
metastatic site or localized from the prostate and exhibited a unique morphology and
phenotype in culture. As a result each cell line had to be optimized to grow in culture
to produce the optimal amount of total protein while minimizing cell death and
maximizing secreted protein produced. We grew each cell line at different seeding
densities for a short period in SFM. The CM was then collected and the levels of
KLK’s for each cell line were measured as well as the level of LDH in the CM. The
amount of KLK and LDH in the CM acted as a surrogate markers for the amounts of
secreted and intracellular proteins produced by the cell respectively. To determine
the optimum seeding density for each cell line the ratio of KLK to LDH was
determined and the seeding density with the highest ratio was chosen. The results of
the optimization for each cell line are shown in Figures 3.1, 3.2, and 3.3. As can be
seen, each cell line had different growth rates and required differing seeding
densities. Based on these results the appropriate seeding density for each cell line
was chosen.
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Chapter 3: Optimization of Cell Culture and Proteomic Workflow 94
In addition to improvements in our sample preparation and workflow, we also
aimed to improve the mass spectrometric platform used. From the Bruker ion trap
used in our proof of principle study we transitioned to using the Thermo LTQ linear
ion trap which has enhanced sensitivity. This is due to its ability to trap more ions
and analyze them faster due to its shortened duty cycle. This translates into an
increase in the number of peptides identified as well as the number of spectra
identified per peptide.
To manage the large amounts of data that will be obtained from the analysis
of three cell lines and the replicates semi-automated database applications were
designed to assist in the annotation and comparison of the data. These included a
parsing program to assist with annotation by genome ontology of proteins identified
which was designed in collaboration with Adrian Pasulescu at the Samuel Lunenfeld
Research Institute; a database application to sort the genome ontology annotated
proteins into unique groups, by Peter Bowden at Ryerson University; a protein list
comparison database application to compare proteins identified between cell lines
an replicates, designed with David Ngyuen at Mt. Sinai Hospital. Together these
semi-automated applications will increase the efficiency at which data is analyzed.
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Chapter 4: Comparative Proteomic Analysis of the Conditioned Media of Three Prostate Cancer Cell Lines
95
CHAPTER 4:
COMPARATIVE PROTEOMIC ANALYSIS OF THE CONDITIONED MEDIA OF THREE PROSTATE CANCER
CELL LINES
The work presented in this chapter was published in part in the Journal of Proteome Research:
Sardana, G., Jung, K., Stephan, C. and Diamandis, E.P. Proteomic Analysis of Conditioned Media from the PC3, LNCaP and 22Rv1 Prostate Cancer
Cell Lines: Discovery and Validation of Candidate Prostate Cancer Biomarkers Journal of Proteome Research, 2008 Aug 1;7(8):3329-3338
Copyright permission has been granted.
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4.1 Introduction
Currently, serum PSA levels, combined with DRE, are the recommended
screening tests for early detection of CaP in asymptomatic men over the age of
50(333). However, there is considerable controversy surrounding the efficacy of the
PSA test in reducing the overall mortality of CaP(334,335). These concerns stem
from over-diagnosis and the lack of specificity of PSA in discriminating CaP from
BPH(335). While PSA has been shown to correlate very well with tumour
volume(336), it is unable to predict with certainty the biological aggressiveness of
the disease. Several refinements of the PSA test have been shown to increase its
sensitivity and specificity(337-341). However, there is still a need to develop non-
invasive tests to identify clinically relevant CaP(342).
Mass spectrometry-based proteomic technologies are currently in the
forefront of cancer biomarker discovery. Serum or plasma is usually the discovery
fluid of choice(343) however, several studies have employed MS for biomarker
discovery in various other biological fluids(344,345). In addition, a number of
alternative approaches have been used, such as MS spectra profiling(275,346),
which refers to the identification of discriminatory mass spectral features without
identification of the peptides being identified, peptide profiling(347,348), which refers
to the identification of differentially cleaved or modified peptide sequences, and
isotopic labelling(349). There are many inherent limitations to using MS for
biomarker discovery in complex biological mixtures such as serum(296). The main
concern is the suppression of ionization of low abundance proteins by high
abundance proteins such as albumin and immunoglobulins. Depletion strategies
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have been used to remove high abundance proteins which have aided in improving
the detection limit of MS(350,351). One approach to overcome the limitations posed
by biological fluids is to study the secretome of cell lines grown in SFM. The proteins
identified from the CM are specific to the cell line being cultured as there are no
other contaminating proteins, therefore, greatly simplifying MS analysis. Studies
analyzing the CM from prostate(97), colon(352), endothelial(353), adipose(354),
nasopharyngeal(355) and retinal epithelial cells(356), have already been conducted,
thus demonstrating the versatility of this approach.
Previously, we have shown the PC3(AR)6 CaP cell line can be grown in a
serum-free environment and the secreted proteins present can be readily identified
by MS-based methods(98). In this study, we performed a detailed proteomic
analysis of the CM of three CaP cell lines; PC3, LNCaP and 22Rv1. From this
analysis we identified 2,124 proteins by using a bottom-up approach, consisting of
offline strong cation exchange (SCX) chromatography followed by capillary C-18
reversed-phase liquid chromatography-tandem mass spectrometry (LC-MS/MS).
Our extensive lists of proteins, and their cellular and biological classifications, may
form the basis for discovering novel CaP biomarkers.
The utility of this approach was examined by validating four candidates by
ELISA. Each was found elevated in serum of a subset of CaP patients; a positive
correlation was also observed with serum PSA levels. Furthermore, our approach
identified many known CaP biomarkers including PSA, KLK2, KLK11, prostatic acid
phosphatase and prostate specific membrane antigen, further supporting the view
that this unbiased approach may aid in new CaP biomarker discovery efforts.
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4.2 Materials and Methods
4.2.1 Cell Culture
The PC3, LNCaP and 22Rv1 cell lines were purchased from the American
Type Culture Collection (Rockville, MD). All cell lines were grown in T-175 culture
flasks (Nunc) in RPMI 1640 culture medium (Gibco) supplemented with 8% fetal
bovine serum (FBS) (Hyclone). Cells were cultured in a humidified incubator at 37oC
and 5% CO2. Cells were seeded at 7.5 X 106, 22 X 106, and 75 X 106 cells for PC3,
LNCaP and 22Rv1 cell lines, respectively. PC3 and 22Rv1 cells were grown for 2
days in 30mL of RPMI + 8% FBS. Afterwards, the medium was removed, and the
flask was gently washed 3 times with 30mL of PBS (137 mM NaCl, 10 mM
phosphate, 2.7 mM KCl, pH 7.4). Thirty milliliters of CDCHO (Gibco) medium
supplemented with glutamine (8mmol/L) (Gibco) were added to the flasks and
incubated for 2 days. The LNCaP cell line was grown as above, except that the cells
were incubated for 3 days in RPMI + 8% FBS before the media were changed to
CDCHO. All cell lines were grown in triplicate and independently processed and
analyzed. A negative control was also prepared with the same procedures as above,
except no cells were seeded.
After incubation in CDCHO, the CM was collected and spun down (3,000 X g)
to remove cellular debris. Aliquots were taken for measurement of total protein,
lactate dehydrogenase (LDH) (a marker of cell death), and kallikreins 3, 5, 6 (internal
control proteins). The remainder was frozen at -80oC until further use.
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4.2.2 Measurement of Total Protein, Lactate Dehydrogenase and PSA, KLK5, KLK6
The total protein of the CM was measured using the Coomassie (Bradford)
assay (Pierce Biotechnology) as recommended by the manufacturer.
Lactate dehydrogenase levels in the CM were measured via an enzymatic
assay based on conversion of lactate to pyruvate. NADH production from NAD+
during this reaction was monitored at 340mm with an automated method and
converted to Units per litre (U/L) (Roche Modular Systems).
Kallikrein 3 (PSA), KLK5 and KLK6 were measured with in-house ELISAs as
described earlier(192,315,331).
4.2.3 Conditioned Media Sample Preparation and Trypsin Digestion
Our general protocol is shown in Figure 4.1. Approximately 30mL of CM from
each cell line, which corresponded to 1mg of total protein, was dialyzed overnight
using a 3.5kDa cut-off dialysis tubing (Spectra/Por) at 4oC in 5L of 1mM of NH4HCO3
solution with one buffer exchange. The dialyzed CM was lyophilized overnight to
dryness followed by resolubilization with 322µL of 8M urea, 25µL of 200mM DTT,
and 25µL of 1M NH4HCO3. The sample was vortexed thoroughly and incubated at
50oC for 30min. 125uL of 500mM iodoacetamide was added, the sample was
incubated in the dark at room temperature for 1h. The sample was desalted with a
NAP-5 column (GE Healthcare), lyophilized and resuspended in 120µL of 50mM
NH4HCO3, 100µL methanol, 150µL H2O and 5µg of trypsin (Promega), vortexed,
and incubated for 12h at 37oC.
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Figure 4.1
Figure 4.1: Workflow of proteomic method employed. For additional information, refer to Materials and Methods section.
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4.2.4 Strong Cation Exchange High Performance Liquid Chromatography
The tryptic digests were lyophilized and resuspended in 120µL of 0.26M
formic acid in 10% ACN (mobile phase A). The sample was fractionated using an
Agilent 1100 HPLC system connected to a PolySULFOETHYL ATM column
containing a hydrophilic, anionic polymer (poly-2-sulfoethyl aspartamide) with a 200Å
pore size and a diameter of 5µm (The Nest Group Inc.). A one hour linear gradient
was used, with 1M ammonium formate and 0.26M formic acid in 10% acetonitrile
(mobile phase B) at a flow rate of 200µL/min. Fractions were collected via a fraction
collector every 5 min (12 fractions per run), and frozen at -80oC for further use. A
protein cation exchange standard, consisting of four peptides, was run at the
beginning of each day to assess column performance (American Peptide).
4.2.5 Online Reversed Phase Liquid Chromatography – Tandem Mass Spectrometry
Each 1mL fraction was C18 extracted using a Zip TiPC18 pipette tip (Millipore)
and eluted in 4µL of 90% ACN, 0.1% formic acid, 10% water, 0.02% trifluoroacetic
acid (TFA) (Buffer B). Upon which 80µL of 95% water, 0.1% formic acid, 5% ACN,
0.02% TFA (Buffer A) was added and half of this volume (40µL) was injected via an
auto-sampler on an Agilent 1100 HPLC. The peptides were first collected onto a 2
cm C18 trap column (inner diameter 200 µm), then eluted onto a resolving 5 cm
analytical C18 column (inner diameter 75 µm) with an 8 micron tip (New Objective).
The HPLC was coupled online to an LTQ 2-D Linear Ion Trap (Thermo Inc.). A 120
min gradient was used on the HPLC and peptides were ionized via nano-
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electrospray ionization. The peptides were subjected to MS/MS and DTAs were
created using the Mascot Daemon v2.16 and extract_msn (Matrix Science).
Parameters for DTA creation were: min mass 300, max mass 4000, automatic
precursor charge selection, min peaks 10 per MS/MS scan for acquisition and min
scans per group of 1.
4.2.6 Database Searching and Bioinformatics
Mascot, v2.1.03 (Matrix Science)(357) and X!Tandem v2.0.0.4 (GPM, Beavis
Informatics Ltd.)(358) database search engines were used to search the spectra
from the LTQ runs. Each fraction was searched separately against both search
engines using the IPI Human database V3.16(359) with trypsin specified as the
digestion enzyme. One missed cleavage was allowed, a variable oxidation of
methionine residues and a fixed modification of carbamidomethylation of cysteines
was set with a fragment tolerance of 0.4 Da and a parent tolerance of 3.0 Da.
The resulting DAT files from Mascot and XML files from X!Tandem were
inputed into Scaffold v01_05_19 (Proteome Software)(360) and searched by
allocating all DAT files into one biological sample and all XML files into another
biological sample. This was repeated three times for each cell line. The cut-offs in
Scaffold were set for 95% peptide identification probability and 80% protein
identification probability. Identifications not meeting these criteria were not included
in the displayed results. The sample reports were exported to Excel, and an in-
house developed program was used to extract Gene Ontology (GO) terms for
cellular component for each protein and the proportion of each GO term in the
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dataset. Proteins that were not able to be classified by GO terms were checked with
Swiss-Prot entries and against the Human Protein Reference Database(361) and
Bioinformatics Harvester (http://harvester.embl.de) to search for cellular component
annotations. Finally, the overlap between proteins identified from each cell line and
within each of the replicates per cell line was determined by in-house built software.
Each protein was also searched against the Plasma Proteome Database
(www.plasmaproteomedatabase.org). The list of proteins were also compared with
those found in seminal plasma by Pilch et al.(362) and in breast cancer cell line CM
by Kulasingam et al.(363) In addition, we used Ingenuity Pathway Analysis software
(Ingenuity Systems) to determine differences in biological networks in the
extracellular and membrane proteins of each cell line, as well as overlay molecular
functions with respect to disease conditions associated with each of the biomarker
candidates.
The same set of spectra produced by the LTQ was searched with the same
parameters as above, but against a reversed IPI Human database V3.16 which was
created using the Reverse.pl script from The Wild Cat Toolbox (Arizona Proteomics).
The DAT and XML files from this “reversed” search were input into Scaffold as
before and the identified peptides meeting the pre-set cut-offs were identified. The
false positive rate (FPR) was calculated as such: FPR = # False peptides / (# True
peptides + # False peptides).
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4.2.7 Validation of Candidates
A pre-clinical validation was conducted where the concentrations of four
candidate proteins (follistatin, chemokine (C-X-C motif) ligand 16, pentraxin 3 and
spondin 2) were measured by ELISA in the serum of patients with or without
prostate cancer and in the CM of each cell line. These candidates were selected for
validation using similar criteria as Kulasingam et al.(363) Follistatin (R&D Systems),
chemokine (C-X-C motif) ligand 16 (R&D Systems) and pentraxin 3 (Alexis
Biochemicals) were measured using their respective manufacturer’s protocol.
Spondin 2 was measured at diaDexus Corporation, San Francisco, CA using an in-
house developed assay. Serum samples from healthy males and CaP patients were
collected at the Toronto Medical Laboratories, Toronto, Canada. The patient groups
were classified by their PSA levels. Healthy males were patients with PSA levels
<1ug/L and CaP patients were those with PSA levels >10ug/L. The study was
carried out after Institutional Review Board approval, median age of patients were 75
and 67 for the cancer and non-cancer groups, respectively.
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4.3 Results
4.3.1 Proteins Identified by Mass Spectrometry
A schematic of the sample preparation and bioinformatics utilized in this study
is shown in Figure 4.1. Each cell line was independently cultured in triplicate to
determine the reproducibility of our method in identifying proteins between the
replicates. After setting a cut-off of 95% peptide probability and 80% protein
probability in Scaffold, 2,124 proteins were identified that met the criteria from all
three cell lines combined. In total, from the three replicates per cell line, 1,157,
1,285, and 1,116 proteins were found in the PC3, LNCaP and 22Rv1 cell lines,
respectively.
A control flask was also prepared that did not contain any cells but were
treated with the same procedure. A total of 69 proteins were identified in the
negative control flask, which represented FCS-derived proteins from incomplete
washing of the tissue culture flasks. These proteins were removed from the list of
identified CM proteins of each cell line and were not considered further in the data
analysis.
Furthermore, to empirically determine false positive rate (FPR) of peptide
detection, the dataset was searched against a reversed IPI Human v3.16 database
using the same search parameters and Scaffold cut-offs. A total of 4, 4, and 6
proteins from the PC3, LNCaP and 22Rv1 cell lines, respectively were observed.
Each protein was identified by one peptide with Mascot and with no peptides
identified by X!Tandem (0% FPR). A FPR of <1% for all 3 cell lines was calculated
by Mascot.
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4.3.2 Identification of Internal Control Proteins
As an internal control, we sought to identify by LC-MS/MS three proteins that
were known to be secreted by these cell lines and were monitored by ELISA during
our cell culture optimization. The approximate initial concentrations of these proteins
in CM are given in brackets, below. We confidently identified PSA in the CM of
LNCaP (~550 µg/L) and 22Rv1 (~3 µg/L) with several peptides (Table 4.1). PC3
cells do not secrete any detectable PSA by ELISA and, as expected, this protein
was not identified in its CM. One KLK6 peptide from the CM of PC3 (~1.5 µg/L) was
identified, but no peptides from KLK5 were identified by MS. Together, these data
suggest that the detection limit of our MS-based method for protein identification is in
the low µg/L range.
4.3.3 Reproducibility between Replicates
Next, we investigated the reproducibility of our method by culturing each cell
line in triplicate. The Mascot and X!Tandem results from the PC3, LNCaP and
22Rv1 cell lines are shown in Figure 4.2. In general, a 56% overlap of identified
proteins in all three replicates from both Mascot and X!Tandem for each of the cell
lines was observed. Approximately 20% of proteins were found in two replicates and
24% were exclusive to one replicate. This data highlights the ionization efficiency of
the mass spectrometer, and thus the need for replicate analysis of samples by MS to
obtain a more comprehensive list of the secretome of these cell lines.
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Table 4.1: Known prostate biomarkers identified in the conditioned media of PC3, LNCaP, and 22Rv1 cell lines1
1 Protein name and number of peptides identified by Mascot and X!Tandem for each cell line are listed. KLK2 – Human kallikrein 2, PSA – Prostate specific antigen, KLK11 – Human kallikrein 11, ACPP – Prostatic acid phosphatase, Mac-2BP – Mac 2 binding protein, Zn-α2-GP – Zinc alpha 2 glycoprotein, PSMA – Prostate specific membrane antigen, NGEP – New gene expressed in prostate.
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Figure 4.2
Figure 4.2: Overlap of the 3 replicates from PC3, LNCaP and 22Rv1 conditioned media: Number of proteins identified from each cell culture replicate that overlapped with other replicates for both Mascot and X!Tandem results are depicted. Each circle represents a replicate.
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4.3.4 Differences in Proteins Identified Between Cell Lines Following this, the proteins identified from each of the three replicates of each
cell line were combined to form a non-redundant list per cell line. These three lists
were compared to determine their overlap. The results are shown in Figure 4.3.
About 54% of proteins were unique to one of the cell lines, 21% were common in all
3 cell lines, with 24% were identified in two of the cell lines. This data highlights the
heterogeneity of CaP cell lines and the need to investigate multiple cell lines, to
obtain a comprehensive picture of the CaP proteome. The overlap among the cell
lines for extracellular and membrane proteins, rather than total proteins yielded
similar heterogeneity (Figure 4.4).
4.3.5 Genome Ontology Distributions of Proteins
As shown in Figure 4.5, 12% of the proteins identified were classified as
extracellular, 18% as membrane and 12% as unclassified. The remainder of the
proteins identified in the CM were classified as intracellular, nucleus, golgi,
endoplasmic reticulum (ER), endosome or mitochondria (58%). Classification by
cellular localization is redundant since a protein can be classified in more than one
compartment. A similar distribution was found with the cellular component
distribution of each cell line (Figure 4.6).
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Figure 4.3
Figure 4.3: Overlap of proteins identified between each cell line: Each circle represents a cell line.
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Figure 4.4
Figure 4.4: Overlap of the extracellular and membrane proteins identified in each cell line: Extracellular and membrane proteins respectively were compared from each cell line. The numbers of proteins common and unique to each cell line are depicted.
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Figure 4.5
Figure 4.5: Classification of proteins by cellular location: Each protein identified after MASCOT and X!Tandem searching was classified by its cellular location using Genome Ontology classifiers (www.geneontology.org).
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Figure 4.6
Figure 4.6: Classification of proteins by cellular location for each cell line: Each protein identified after MASCOT and X!Tandem searching was classified by its cellular location using Genome Ontology classifiers (www.geneontology.org). Distribution of proteins identified in the conditioned media of (A) PC3, (B) LNCaP, and (C) 22Rv1 cell lines.
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4.3.6 Secreted and Membrane Proteins After GO annotation, 329 proteins were classified as extracellular, 504 as
membranous and 339 as unclassified. From the list of extracellular and membrane
proteins several previously known CaP biomarkers were identified. These included
PSA(364), KLK2(337,338), KLK11(365), PAP(366), Mac-2BP(367), zinc-α-2-
glycoprotein(368), a new gene expressed in the prostate (NGEP)(369) and
PSMA(370) (Table 4.1). Comparing all extracellular, membrane and unclassified
proteins with the Plasma Proteome Database yielded 93 membrane proteins, 98
extracellular proteins, and 27 unclassified proteins as being found previously in
serum.
4.3.7 Overlap with Previous Data
Given that we had previously analyzed the CM of the PC3(AR)6 cell line(98),
the overlap between this set of data and our previous one was examined. In brief,
the current data yielded 5-fold more proteins owing in part to the improvements in
sample preparation, the type of mass spectrometer used (more sensitive) and the
powerful bioinformatics (higher confidence of protein probability).
4.3.8 Overlap with Seminal Plasma Proteins
Recently, Pilch and Mann(362) conducted an in-depth proteomic survey of
the seminal plasma proteome and identified 932 unique proteins. Comparing our list
of extracellular, membrane and unclassified proteins found in the CM of prostate
cancer cell lines with those reported in a biological fluid such as seminal
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plasma(362) resulted in 108 extracellular, 120 membranous, and 40 unclassified
proteins as being common.
4.3.9 Biological Network Analysis
The extracellular and membrane proteins from each cell line were analyzed
using Ingenuity Pathway Analysis software v5.51 (Ingenuity Systems) and functional
networks were developed. Cancer, cellular movement and pathways involved in
tissue development were listed among the top networks for each of the cell lines.
However, in the case of 22Rv1 these were listed secondary to the “housekeeping”
functional networks. Next, the functions and disease association of each of the four
candidates selected were examined. Follistatin displayed the most direct
connections to cancer such as prostate(371,372), colon(373), and ovarian
cancer(374) (Figure 4.7). Chemokine (C-X-C motif) ligand 16 (CXCL16) displayed
connections to inflammation and chemotaxis(375) (Figure 4.8A). Pentraxin 3 (PTX3)
showed multiple connections to tissue and embryonic development(376) (Figure
4.8B). Few studies have been conducted on spondin 2 (SPON2); it was shown to
be involved in neuronal guidance and lung cancer(377) (Figure 4.8C).
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Figure 4.7
Figure 4.7: Molecular functions related to diseases associated with Follistatin: Web diagram depicting the biological functions that follistatin is associated with, in the context of disease. Diagram generated through Ingenuity Pathway Analysis software (Ingenuity Systems).
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Figure 4.8
Figure 4.8: Molecular functions related to diseases associated with Chemokine (C-X-C motif) ligand 16, Pentraxin 3 and Spondin 2: Web diagram depicting biological functions associated with in the context of disease for CXCL16 (A), PTX3 (B) and SPON2 (C). Diagram generated through Ingenuity Pathway Analysis software (Ingenuity Systems).
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4.3.10 Overlap with Breast Cancer Secretome The proteins identified by Kulasingam et al.(363) in the CM of two breast
cancer cell lines were compared with those identified in our analysis of CaP CM.
There was an overlap of 256 proteins that were present in the BT474, MDA468,
PC3, LNCaP and 22Rv1 cell line CM. These proteins were then subjected to
biological network analysis in a similar manner as stated above. Pathways related to
cancer and cell growth were among the top identified.
4.3.11 Validation of Follistatin, Chemokine (C-X-C motif) ligand 16, Pentraxin 3 and Spondin 2
From the list of proteins identified, pre-clinical validation of four candidates
were performed in the CM of PC3, LNCaP and 22Rv1 cell lines (Figure 4.9) and in
42 serum samples from patients with or without CaP (Figure 4.10). The
concentration of each candidate in the CM correlates semi-quantitatively with the
number of unique spectra (shown in brackets) identified from each peptide of each
candidate after database searching by Mascot/X!Tandem: Follistatin (PC3, 100/128;
22Rv1, 0/1); CXCL16 (LNCaP, 3/4, 22Rv1, 2/2); PTX3 (PC3, 45/52); SPON2
(LNCaP, 152/149, 22Rv1, 2/2). A significant difference (Mann-Whitney Test) in sera
of patients with or without CaP in all four candidates (Figure 4.9A, C, E, G) by ELISA
was observed. In addition, the correlation between PSA levels and candidate levels
in serum of patients with CaP was significant and positive by Spearman analysis
(Fig 4.9B, D, F, H).
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Figure 4.9
Figure 4.9: Concentrations of each candidate in the conditioned media of each cell line and the control flask. Levels of Follistatin (A), CXCL16 (B), Pentraxin 3 (C), Spondin 2 (D) in conditioned media is shown with corresponding number of spectra identified for each candidate by Mascot/X!Tandem.
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Figure 4.10
Figure 4.10: Validation of Follistatin, Chemokine (C-X-C motif) ligand 16, Pentraxin 3 and Spondin 2 in serum: Levels of Follistatin, CXCL16, PTX3 and SPON2 measured in serum of patients with or without CaP (A, C, E, G respectively). Median values are shown by a horizontal line. p values were calculated using a Kruskal-Wallis test. Correlations of each candidate with PSA levels (B, D, F, H), p values calculated by the Spearman correlation. (r = Spearman correlation coefficient)
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4.4 Discussion LC-MS/MS analysis allows for the elucidation of the identity of thousands of
proteins in complex mixtures, in a high-throughput fashion. This technology has
been applied to cancer biomarker discovery, where biological fluids, tissues or cell
cultures have been analyzed for differences in protein expression(97,352,378-380).
However, the dynamic range of current LC-MS/MS methods is not adequate to
identify all proteins in a complex mixture such as serum. Even with depletion of
highly abundant proteins and extensive fractionation, this is a major challenge still
faced today(296).
Prostate cancer, when diagnosed early, is associated with favourable clinical
outcomes(381). Our objective was to identify proteins secreted by three
tumourigenic CaP cell lines of differing origin and phenotype. The purpose of
examining the secretome of cell lines of differing origin was to obtain a more
complete picture of the proteome of CaP since CaP is a heterogeneous
disease(382,383) and it requires a diverse model system for biomarker discovery.
Thus, we chose to focus on shed and secreted proteins from three CaP cell lines,
since this approach is amenable to MS and these proteins will most likely be
produced by the tumour in measurable amounts to be detected via a blood test.
In our previous study of the CM of the PC3(AR)6 cell line(98) we found a large
number of intracellular proteins. In another study, similar data were seen with the
LNCaP cell line (97). We sought to reduce the amount of intracellular proteins by
optimizing the cell culture (Figures 3.1, 3.2, 3.3). Yet our current data (Figure 4.5)
revealed a similar distribution of proteins by cellular component. From this, we
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deduce that cell death during culture is an unavoidable contaminant when analyzing
CM by proteomics. However, previous studies from our laboratory utilizing a similar
cell culture-based approach found that the proteins identified in cell lysates do not
contain as many extracellular proteins as the CM for that cell line(363). Furthermore,
the extracellular proteins identified in the cell lysate displayed minimal overlap to the
proteins identified in the CM illustrating that analyzing the CM, despite the amount of
cell death occurring in SFM, leads to a significant enrichment of secreted proteins
which may be novel serological markers. We also believe that our method may
include microsomal proteins that were not removed during centrifugation of the CM
after harvesting.
From our previous work we determined that replicate analysis expanded the
coverage and increased the number of identified proteins(98). As can be seen in
Figure 4.2, there are proteins that are uniquely identified by only one replicate. This
is most likely due to the incomplete ionization of certain peptides during an LC-
MS/MS run. Many studies have shown that cell lines do represent the tumour from
which they originated. Hence, the proteins that they secrete should reflect the
genetic alterations that they harbor. Given that the biological triplicates yielded a
more complete coverage of the secretome for a cell line, it is highly probable that the
differences in the proteins identified among the 3 cell lines (Figure 4.3) indeed
reflects the heterogeneity of that cell line.
We confirmed the presence of two internal control proteins (PSA and KLK6)
in the CM at µg/L concentrations as measured by ELISA and identified by MS
(Figures 3.1, 3.2, 3.3). We were not able to identify by MS KLK5 which was also
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present in PC3 at about the same concentration as KLK6 (2 µg/L). It is possible that
the stringent peptide and protein probability cut-offs utilized in this study was too
strict to allow identification of KLK5 by MS. Alternatively, it is possible that a
concentration of 2 µg/L is close to the detection limit of our methodology and hence
it was not identified. We thus conclude that our method can identify proteins in CM of
approx. low µg/L or higher, a detection limit which nevertheless is 2-3 orders of
magnitude better than the ones achieved by using serum(384). Furthermore, based
on the currently used biomarkers in the clinic, this is the expected concentration
range that potential tumour markers should be observed in serum.
The use of multiple search engines has been shown to increase confidence,
as well as expand coverage(385). In this study, Mascot and X!Tandem were used
since these search engines use different algorithms to determine if a mass spectrum
matches an entry in the database. The use of both search engines served to provide
an independent confirmation of the results. The use of peptide(386) and protein
prophet algorithms(387) contained within Scaffold allowed for increased confidence
of the protein identification probabilities. To further increase confidence, we
performed a search against a reversed IPI human database(388) and obtained FPR
of <1% for the cell lines by Mascot. The low FPR highlights the fidelity of the search
approach used. In addition, to eliminate contaminants left over from the FCS we
processed a control flask that did not contain any cells and deleted from the list of
proteins identified from the CaP cell lines the FCS-derived proteins.
Moreover, from the extracellular, membrane and unclassified proteins
identified in this study, 98, 93, and 26 proteins in extracellular, membrane and
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unclassified protein lists, respectively were found in the plasma proteome. It is
possible that the other proteins are of low abundance and have not yet been
identified in circulation. This list was also compared against the list of proteins found
in seminal plasma(362). We reasoned that if a protein identified from our CaP cell
lines is also detected in seminal plasma, it is likely to be secreted or shed at
relatively high concentration by prostate cells. We found 108 extracellular, 120
membranous and 40 unclassified proteins in our CM, as well as in the seminal
plasma proteome.
To discern functional differences between the “secretome” of the three cell
lines, we performed a biological analysis of the extracellular and membrane proteins
identified from each cell line’s CM. First, we compared the extracellular and
membrane proteins identified between each cell line (Figure 4.4). We found 18%
and 20% of proteins identified as extracellular and membrane, respectively, were
common to all three cell lines. This is a significantly lower overlap than the overall
number of proteins which were common between the cell lines, indicating that there
are significant differences specifically within the “secretome” from each cell line. We
further investigated functional differences between the cell lines using the Ingenuity
Pathway Analysis. The top ranked networks were those involved with cell
movement, signalling, cancer and the cell cycle. Furthermore, 22Rv1 was not
represented as having any extracellular proteins involved with cellular movement,
whereas PC3 and LNCaP did. This is interesting and relevant since 22Rv1 was
derived from an organ-confined prostate tumour while PC3 and LNCaP were derived
from metastatic tumours. In addition, each cell line differs with its sensitivity to
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androgens, a major component to prostate development: PC3 does not express the
AR and is a model for androgen insensitive tumours, 22Rv1 expresses the AR but is
considered AR insensitive, and LNCaP, which expresses AR, is considered
androgen dependent. In addition, we also investigated the common proteins in
breast CM identified by Kulasingam et al.(363) with those identified in this study to
look for similarities between these two hormonally regulated cancers. Here we see
that cancer and cell proliferation networks are among the top pathways identified to
be common. This commonality between the cell lines highlights that these tumours
utilize similar processes during carcinogenesis and warrants further study to
delineate biomarkers that could be useful for diagnosis.
Using information from this study, we developed criteria to narrow down our
list of candidate biomarkers: (a) we considered proteins that showed relatively
prostate-specific mRNA expression by searching through the UniGene expressed
sequence tag online database; (b) we selected proteins that were classified as
extracellular or membrane and have been identified in seminal plasma(362); (c) we
performed literature searches to ensure that these proteins have not been validated
before as CaP biomarkers and highlighted proteins that have shown biological links
to CaP and other cancers; (d) we then selected candidates that had commercially
available immunoassays. The list of narrowed down candidates are listed in Table
4.2.
From these, follistatin, CXCL16, PTX3 and SPON2 were chosen for further
validation based on the above criteria. Each candidate showed several links to
CaP(371,389-391) or carcinogenesis(377,392-397). Each candidate showed a
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significant difference between patients with or without CaP. In addition, a positive
correlation with increasing PSA was also observed (Figure 4.10 B, D, F, H). From
these results, we speculate that these candidates show an association with CaP
progression. Future studies will determine if in combination they can improve the
specificity of the PSA test.
To determine the biological association with CaP we profiled each of the
candidate’s links to functions and diseases (Figure 4.7, 4.8). With the exception of
SPON2, each of the candidates displayed several links to cancer development,
tissue development, inflammation or chemotaxis. All of these processes have shown
to play a role in the malignant development of tumours. However, the involvement of
each candidate with respect to CaP pathobiology will need to be further studied to
elucidate their role during progression.
In summary, we present a robust method of proteomic analysis of cell culture
CM and bioinformatics for new biomarker discovery. The four candidates validated
have not been previously shown to be serum markers for CaP but require further
study to fully elucidate their roles in CaP progression. Additional candidates from this
large database are worth validating in the future.
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Table 4.2: List of candidate biomarkers selected based on selection criteria.
Protein name Gene 64 kDa protein C1orf116Abhydrolase domain containing 14A ABHD14AADAMTS-1 precursor ADAMTS1Cartilage intermediate layer protein 2 precursor CILP2CEGP1 protein SCUBE2c-Myc-responsive protein Rcl C6orf108Creatine kinase B-type CKBchemokine (C-X-C motif) ligand 16 CXCL16Epiplakin EPPK1Epoxide hydrolase 2 EPHX2GAGE-2 protein GAGE2Galanin precursor GALglucosaminyl (N-acetyl) transferase 2, I-branching enzyme isoform A GCNT2Glycoprotein endo-alpha-1,2-mannosidase (Fragment) MANEAGroup 3 secretory phospholipase A2 precursor PLA2G3Growth-regulated protein alpha precursor CXCL1Hypothetical protein RPL22L1Hypothetical protein C1orf80 C1orf80Secretogranin-1 precursor CHGBSecretogranin-3 precursor SCG3Semaphorin 3F variant (Fragment) SEMA3FSplice Isoform 1 of Follistatin precursor FSTSplice Isoform 1 of Heme-binding protein 2 HEBP2Splice Isoform PTPS of Receptor-type tyrosine-protein phosphatase S precursor PTPRSSpondin-2 precursor SPON2von Willebrand factor A domain-related protein isoform 1 VWA1glucosaminyl (N-acetyl) transferase 2, I-branching enzyme isoform A GCNT2Splice Isoform 1 of Zinc transporter SLC39A6 precursor SLC39A6Neurexin-3-beta precursor NRXN3Tumor-associated calcium signal transducer 2 precursor TACSTD2protein tyrosine phosphatase, receptor type, sigma isoform 3 precursor PTPRSlatent transforming growth factor beta binding protein 1 isoform LTBP-1L LTBP1Pentraxin-related protein PTX3 precursor PTX3Splice Isoform 1 of Kallikrein-15 precursor KLK15Interferon-induced 17 kDa protein precursor ISG15Lysyl oxidase homolog 1 precursor LOXL1Spondin-2 precursor SPON264 kDa protein C1orf116CEGP1 protein SCUBE2ATP-binding cassette, sub-family C (CFTR/MRP), member 10 ABCC10hypothetical protein LOC255101 isoform 1 CCDC108PREDICTED: ephrin receptor EphA6 EPHA6Similar to Hepatocyte nuclear factor 1a dimerization cofactor isoform PCBD2von Willebrand factor A domain-related protein isoform 1 VWA1Tetraspanin-1 TSPAN1NGEP long variant TMEM16GSorbitol dehydrogenase SORD
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CHAPTER 5:
VALIDATION OF CANDIDATE BIOMARKERS IN PROSTATE CANCER PATIENT SAMPLES
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5.1 Introduction
In addition to the four candidates validated in the previous chapter there were
four additional candidates that were selected for further validation. Based on the
criteria outlined in the previous chapter for the selection of candidates identified in
the CM of the CaP cell lines, a preliminary validation was preformed on chemokine
(C-X-C motif) ligand 5 (CXCL5), lipocalin-2 (LCN2), chemokine (C-X-C motif) ligand
1 (CXCL1) and tumor necrosis factor receptor superfamily, member 6b, decoy
(TNFRSF6B). However, the preliminary results did not qualify these candidates for
further study. Their association to cancer and pathological conditions are
summarized below.
Chemokine (C-X-C motif) ligand 5 also known as epithelial neutrophil-
activating protein 78 is a member of the CXC chemokine family and has been shown
to act as an inflammatory chemoattractant and activator of neutrophil function. It has
been seen to be elevated in serum of patients with rheumatoid arthritis, inflammatory
bowl conditions such as Crohn’s disease and ulcerative colitis. High expression of
CXCL5 was also seen in tissues of patients with chronic pancreatitis(398). Unlike
other chemokines, expression of CXCL5 is not limited to immune cells and is seen in
epithelial, endothelial and fibroblastic cell types. Expression of CXCL5 has been
associated with both CaP and gastrointestinal malignancies and related
inflammatory conditions(399-402).
Lipocalin 2 also known as neutrophil gelatinase-associated lipocalin is a 25
kDa secreted glycoprotein that is part of the lipocalin family of proteins and has been
shown to function in cell proliferation and survival. An immunohistochemical study
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has shown the expression of LCN2 in a variety of tissues including tumour tissues of
the prostate, lung, colon, pancreas and kidney(403). In addition, LCN2 has been
studied as a marker for kidney injury(404) and ovarian cancer(405) and has shown
to be a poor prognostic indicator in breast cancer(406). The role of LCN2 in
carcinoma progression has also been studied and has shown to be promote breast
cancer and esophageal squamous cell carcinoma invasion by stabilizing
extracellular matrix enzymes(407,408) and inhibit cell invasion in colon cancer
cells(409).
Chemokine (C-X-C motif) ligand 1 also known as growth-related oncogene-α
like CXCL5 is part of the CXC family of chemokines. As with CXCL5, CXCL1 is also
elevated in serum of patients with inflammatory bowl conditions(410). Studies in
several different cancers have revealed that CXCL1 is a mediator of angiogenic and
tumourigenic activity in CaP cells(400,411) as well as has shown to promote the
malignant phenotype in colon cancer(412).
Tumor necrosis factor receptor superfamily, member 6b, decoy, also known
as decoy receptor 3, is a soluble receptor that is part of the tumour necrosis factor
receptor family. This soluble receptor has been shown to be overexpressed in some
cancers and thought to compete with binding Fas-L death receptors, thus inhibiting
apoptosis(413). Amplification of the TNFRSF6B gene has been shown in lung and
colon cancers(414), lymphomas(415), gastric cancer(416), glioblastoma(417) and
hepatocellular carcinoma(418). Serum levels of the soluble decoy receptor have
been measured in several malignancies and been shown to have diagnostic and
prognostic ability(397,419,420).
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An extended validation of follistatin was also performed in patient serum and
tissue samples. From all the candidates previously validated, follistatin was
determined to be the strongest. Here we measure its levels in serum of patients with
biopsy confirmed CaP, BPH, PIN, inflammatory conditions and disease free. In
addition, we show immunohistochemical staining of follistatin in prostate, colon and
lung tumours.
5.2 Materials and Methods
Four candidates were validated in the biological fluids of patients with and
without CaP as well as other disease conditions. In addition, an extended validation
of follistatin was conducted in a larger patient cohort, as well as its levels in CaP and
other cancer tissues were determined by immunohistochemistry.
5.2.1 Conditioned Media, Serum Samples and Tissue Specimens
Aliquots from the CM of the PC3, LNCaP, 22Rv1 and the control flask were
used for measuring of CXCL5, LCN2, CXCL1 and TNFRSF6B. The cell lines were
grown at their optimized seeding densities as stated in the previous chapter.
Serum samples used for measurement of CXCL5, LCN2, CXCL1 and
TNFRSF6B were collected from patients at the Toronto Medical Laboratories,
Toronto, Canada. The study was carried out after Institutional Review Board
approval. Samples were screened based on PSA levels and levels above 10ng/mL
were grouped in the CaP group, while PSA levels below 1ng/mL were considered
normal. Serum samples used for measurement of follistatin, KLK2, PSA, fPSA and
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KLK11 were collected from patients admitted to The Prostate Biopsy Clinic, in the
Prostate Centre at Princess Margaret Hospital, Toronto, for prostate biopsy.
Informed consent was obtained from each patient for participation in this study and
blood samples were collected prior to biopsy in yellow top serum separator tubes.
Whole blood was allowed to clot for 30 minutes at room temperature and
subsequently aliquoted and stored at -80oC until needed. Patients were diagnosed
with either CaP, BPH, PIN, inflammation or free of disease (Benign) based on biopsy
results. Data regarding age, PSA level, Gleason score, number of biopsy cores
taken, reason for biopsy, tumour location and if the patient had initial or recurrent
CaP was recorded. The mean ages for each group were: Benign – 60, BPH – 63,
CaP – 64, PIN – 60, Inflammation – 60. The majority of the patients in each group
had PSA levels in the gray zone (4-10ug/L).
Paraffin embedded tissue samples of prostate, lung and colon carcinomas
were obtained from the Toronto General Hospital, Toronto, Canada pathology
laboratory after institutional review board approval.
5.2.2 Quantification of Candidates in Biological Fluids
Chemokine (C-X-C motif) ligand 5
The CXCL5 sandwich ELISA kit was purchased from R&D Systems (Cat#:
DX000). The assay was performed using the kit protocol. Briefly, 200ul of assay
diluent was added to each pre-coated well in a 96 well plate followed by 50ul of
standard or sample in duplicate. The plate was incubated at room temperature for 2
hours followed by washing 3 times using an automated plate waster. Two hundred
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microlitres of the secondary detection antibody was added to each well and the plate
was incubated at room temperature for 2 hours. Two hundred microlitres of
substrate solution was added to each well and the plate was covered with foil and
left for 30 minutes at room temperature. Fifty microlitres of stop solution was then
added to each well and the absorbance was read by a plate reader at 450nm
wavelength with a correction of 570nm.
Lipocalin-2
The human lipocalin-2 sandwich ELISA kit was purchased from R&D Systems
(Cat#:DLCN20) and was performed according to the kit protocol. The methodology
is similar to CXCL5 with some minor differences.
Chemokine (C-X-C motif) ligand 1
The human CXCL1 ELISA kit was purchased from R&D systems
(Cat#:DGR00) and was performed according to the kit protocol. The methodology is
similar to that of CXCL5 and LCN2.
Follistatin
The human follistatin ELISA kit was purchased from R&D systems
(Cat#:DFN00) and was performed according to the kit protocol. The methodology is
similar to that of CXCL5, LCN2 and CXCL1.
Tumor necrosis factor receptor superfamily, member 6b, decoy
The human tumor necrosis factor receptor superfamily, member 6b, decoy
(TNFRSF6B) ELISA kit was purchased from R&D systems (Cat#:DY142) and was
performed according to the kit protocol with some minor modifications. Briefly,
200uL of the capture antibody was coated overnight at a concentration of 4ug/mL.
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The antibody was aspirated off and 100uL of standard or sample was aliquoted in
duplicate and incubated shaking at room temperature for 2 hours. The plate was
then washed 6 times and the biotinylated detection antibody was added and
incubated for 2 hours shaking at room temperature. The plate was washed 6 times
and a streptavidin-alkaline phosphatase conjugate was added and incubated for 15
minutes. The plate was washed 6 times again and 100ul of a difusinal phosphate
solution was added for 10 mins followed by 100ul of developing solution. The plate
was read by time-resolved fluorescence.
Prostate specific antigen, Kallikrein-related peptidase 2, Kallikrein-related peptidase
11
The assays for human PSA, KLK2 and KLK11 were conducted using in-
house developed ELISAs and were conducted as published
previously(198,331,421).
Free PSA
Free PSA was measured using the Elecsys modular analytic system by
Roche at Mt. Sinai Hospital, Toronto. Briefly, 20uL of sample and two monoclonal
antibodies, one biotinylated and one labelled with a ruthenium complex react to form
a complex. Streptavidin-coated microparticles are added to the mixture and bind the
complex. The mixture is aspirated and dispensed into a measuring cell where the
microparticles are capture by a magnet onto an electrode. Unbound substances are
removed and a voltage is applied to the electrode inducing chemiluminescence that
is measured by a photomultiplier. The duration of the assay is 18 minutes.
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5.2.3 Immunohistochemistry of Follistatin
Paraffin sections were dewaxed in 5 changes of xylene and brought down
with water through graded alcohols. Sections were then heat-retrieved in Tris-EDTA
buffer at pH 9.0 for 2 minutes at 120°C using a decloaking chamber (Biocare).
Sections were allowed to cool off at room temperature for 20 minutes before
washing well in running tap water. Endogenous peroxidase and biotin activities were
blocked respectively using 3% hydrogen peroxide and an avidin/biotin blocking kit
(Lab Vision, Cat# TA-015-BB). Sections were then treated for 10 mins with 10%
normal horse serum (Vector Labs, Cat# S2000) before incubated overnight with the
mouse monoclonal anti-Follistatin antibody (R&D, Clone 85918, Cat# MAB669) at
1/200 in a moist chamber. This was followed by 30 mins each with biotinylated horse
anti-mouse IgG (Vector Labs. Inc. Cat# BA-2001) and HRP-conjugated Ultra
Streptavidin (ID Labs. Inc. Cat# BP2378). Colour development was done with freshly
prepared NovaRed solution (Vector Labs. Inc: Cat# SK-4800) and counterstained
with Mayer’s hematoxylin. Finally, sections were dehydrated through graded
alcohols, cleared in xylene and mounted in Permount (Fisher: Cat# SP15-500).
5.2.4 Statistical Data Analysis
To determine if there were significant differences between the median serum
levels measured for normal and cancer patient groups tested for CXCL5, LCN2,
CXCL1 and TNFRSF6B the Mann-Whitney non-parametric test was employed using
95% confidence intervals and two-sided P values. To determine if there were
differences between the multiple groups measured by follistatin, KLK2, PSA, fPSA
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and KLK11 the Kruskal-Wallis test was employed using 95% confidence intervals. A
Dunn’s multiple comparisons test was performed post-hoc to determine if there were
any differences between individual groups. A Spearman correlation was utilized with
two-tailed P values and 95% confidence intervals to determine if there was an
association between follistatin with age, PSA or Gleason score. All tests were
conducted using GraphPad Prism software (GraphPad Software Inc.).
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5.3 Results
5.3.1 Chemokine (C-X-C motif) ligand 5
The CXCL5 protein was identified by MS in the CM of the PC3 cell line with
both Mascot and X!Tandem proteomic search engines. Two unique peptides and 8
unique spectra were identified by each search engine. To confirm our MS results we
measured the concentration of CXCL5 in the CM of each cell line and control flask
by ELISA (Figure 5.1A). As can bee seen our ELISA data corroborates with our MS
data as CXCL5 was only detected in the PC3 CM by both methods. An initial
validation of CXCL5 was also conducted in serum samples from patients considered
to not harbour CaP and have CaP (Figure 5.1B). From this preliminary validation we
observed no statistical difference in median values between the two groups
(p=0.7493). As a result, this candidate was not evaluated further.
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Figure 5.1
A
B
Figure 5.1: Measurement of CXCL5 in CM of CaP cell lines and serum of CaP patients and normals. (A) CXCL5 is measured by ELISA in CM of each cell line and only identified in PC3 CM. Number of spectra identified by Mascot and X!Tandem are also shown. (B) Levels of CXCL5 in serum of normal and CaP patients. Horizontal bars represent median values. P value calculated by Mann-Whitney test.
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5.3.2 Chemokine (C-X-C motif) ligand 1
Another candidate preliminarily validated was CXCL1. Similarly CXCL1 was
only identified in the CM of the PC3 cell line by MS. The MS results were validated
by measuring CXCL1 concentrations by ELISA (Figure 5.2A). The number of unique
spectra identified by Mascot and X!Tandem were 30 and 28 respectively, with 3 and
4 unique peptides identified by each search engine. The concentrations of CXCL1
were measured in the serum samples of men with and without CaP (Figure 5.2B),
where there was no significant difference determined by the Mann-Whitney test
(p=1.0000). Likewise this candidate was not studied further.
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Figure 5.2
A
B
Figure 5.2: Measurement of CXCL1 in CM of CaP cell lines and serum of CaP patients and normals. (A) CXCL1 is measured by ELISA in CM of each cell line and only identified in PC3 CM. Number of spectra identified by Mascot and X!Tandem are also shown. (B) Levels of CXCL1 in serum of normal and CaP patients. Horizontal bars represent median values. P value calculated by Mann-Whitney test.
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5.3.3 Lipocalin 2
Lipocalin 2 was identified in the CM of two of the CaP cell lines, PC3 and
LNCaP by MS. This was validated by measuring the concentration of LCN2 in the
CM of each cell line and the control flask. Once again the spectral counts derived
from Mascot and X!Tandem agreed with our ELISA data (Figure 5.3A). In addition,
LCN2 was also a protein identified by Pilch et al. in seminal plasma(362). We
examined the serum concentrations of LCN2 in the serum of patients with and
without CaP (Figure 5.3B). No significant difference was observed between groups
and the protein was not examined further.
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Figure 5.3
A
B
Figure 5.3: Measurement of LCN2 in CM of CaP cell lines and serum of CaP patients and normals. (A) LCN2 is measured by ELISA in CM of each cell line and identified in PC3 and LNCaP CM. Number of spectra identified by Mascot and X!Tandem are also shown. (B) Levels of LCN2 in serum of normal and CaP patients. Horizontal bars represent median values. P value calculated by Mann-Whitney test.
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5.3.4 Tumor necrosis factor receptor superfamily, member 6b, decoy
The final candidate investigated, TNFRSF6B was identified solely in the CM
of the PC3 CaP cell line by 1 peptide and 7 unique spectra each by Mascot and
X!Tandem. Results from the ELISA measurement of TNFRSF6B in the CM of each
cell line and control flask also agree with our MS data (Figure 5.4A). Serum
samples from patients with and without CaP were measured for TNFRSF6B by
ELISA and the results are shown in Figure 5.4B. As can be seen no significant
difference was observed between the median values of each group and this
candidate was not investigated further.
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Figure 5.4
Figure 5.4: Measurement of TNFRSF6B in CM of CaP cell lines and serum of CaP patients and normals. (A) TNFRSF6B is measured by ELISA in CM of each cell line and identified in PC3 CM. Number of spectra identified by Mascot and X!Tandem are also shown. (B) Levels of TNFRSF6B in serum of normal and CaP patients. Horizontal bars represent median values. P value calculated by Mann-Whitney test.
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5.3.5 Extended Validation of Follistatin
Measurement of follistatin in the serum of patients pre-biopsy was assessed
(Figure 5.5). Median levels of follistatin in each group are shown by horizontal lines
and were analyzed by the Kruskal-Wallis non-parametric one-way ANOVA test and
no significant difference was observed between the groups (p = 0.9521) (Figure
5.5A). To determine if there was a correlation of follistatin in patients diagnosed with
CaP with PSA, age or Gleason score a Spearman correlation was performed (Figure
5.5B, C, D respectively). No significant correlation was observed. In addition, post-
hoc analysis of the 95% confidence intervals showed overlapping intervals for each
median value, thus futher confirming the validity of our result.
The concentrations of PSA, %fPSA, KLK2 and KLK11 were also measured in
the same patient cohort (Figure 5.6). The Kruskal-Wallis non-parametric one-way
ANOVA test was performed. The only significant difference that was observed was
with KLK11 (Figure5.6D). When examined further by post-hoc pair-wise analysis of
the groups using the Mann-Whitney test it was determined that there were significant
differences between the benign and CaP group (p=0.0100) and the BPH and CaP
group (p=0.0085).
Follistatin expression was also preliminarily investigated in the tissues of
prostate, colon and lung cancers by immunohistochemical staining (Figure 5.7, 5.8.
5.9). In the prostate, staining was observed in the luminal epithelial cells and not in
the stroma of the benign, PIN, BPH and cancerous glands. All glands stained
positive however, staining of prostatic carcinoma (Figure 5.7A) in particular high
grade carcinoma (Figure 5.7E) was seen to be more intense relative to benign and
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BPH glands (Figure 5.7A, B, D). Interestingly, certain basal cells of benign (Figure
5.7B) and BPH glands displayed a more intense staining (Figure 5.7D). In addition,
PIN lesions were seen to stain equally to low grade carcinoma (Figure 5.7C).
Follistatin staining was also evaluated in lung tissue and was localized to the
cytoplasm of normal bronchial epithelial cells (Figure 5.8A), pulmonary carcinoma
(Figure 5.8C) as well as pulmonary squamous carcinoma (Figure 5.8D, E).
Macrophages in the lung were also seen to stain positive (Figure 5.8B). In addition,
one out of four specimens from different patients of pulmonary squamous carcinoma
was seen to stain intensely (Figure 5.8E) with staining seen in the cytoplasm as well
as in the surrounding membrane of the epithelial cells. Tissue specimens from
normal and cancerous colon were also investigated for follistatin staining (Figure
5.9). Here we see a more intense stain in the luminal mucosal cells of the colon
carcinoma (Figure 5.9C, D) versus the normal mucosal epithelium (Figure 5.9A, B).
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Figure 5.5
Figure 5.5: Follistatin serum levels and correlations with PSA, Gleason score and age. (A) Concentrations of follistatin in patients with benign, BPH, Inflammation, PIN and CaP. Horizontal bars represent median levels. P value calculated by Kruskal-Wallis one-way ANOVA test. (B, C, D) Correlation of follistatin with PSA Gleason score and age in CaP patients, respectively. r = Spearman coefficient, P values are calculated using the non-parametric Spearman correlation.
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Figure 5.6
Figure 5.6: PSA, %fPSA, KLK2 and KLK11 serum concentrations in patient serum. (A, B, C, D) Serum concentrations of PSA, %fPSA, KLK2 and KLK11 respectively in serum of patients with benign, BPH, inflammation, PIN and CaP disease. Horizontal lines represent median values. P values are calculated using the Kruskal-Wallis non-parametric one-way ANOVA test.
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Figure 5.7
Figure 5.7: Prostate tissue specimens stained for follistatin. (A) CaP specimen containing benign gland and cancerous gland with perineural invasion (CaP-PNI). (B) Benign and cancerous prostate glands, basal cell staining is evident. (C) CaP specimen displaying PIN lesions and cancerous glands. (D) Prostate specimen displaying a BPH gland with evident basal cell staining (E) Specimen of high grade carcinoma (HG-CaP).
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Figure 5.8
Figure 5.8 Lung tissue specimens stained for follistatin. (A) Normal lung bronchial epithelium (BrEp). (B) Normal lung alveoli, macrophages (Mac) are seen to stain positive for follistatin. (C) Specimen of pulmonary carcinoma. (D, E) Specimens of pulmonary squamous carcinoma, Intense staining of follistatin is seen in the latter specimen, with concentrations of intense membranous staining.
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Figure 5.9
Figure 5.9: Colon tissue specimens stained for follistatin. (A, B) Normal colon mucosa. (C, D) Specimens of colon cancer. Staining of follistatin is evident in the cytoplasm of the columnar mucosal epithelium.
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5.4 Discussion
There have been significant efforts to stop the progression of CaP by
detecting it at an early stage, but these have been curtailed by lack of a sensitive
and specific marker for the disease when it is organ-confined. In addition, there is a
lack of an effective therapy that does not involve significant side effects and cause
morbidity. Improvements in the prognosis of patients with organ-confined disease
are needed to determine those patients that require active management. Therefore,
the importance in delineating the role of growth and regulatory mechanisms in CaP
translates into targeting the disease earlier through innovative therapies and
improved diagnostics.
While the role of androgens in stimulation of CaP is well studied, the roles of
associated growth factors are still emerging. Growth factors are seen as playing
almost independent roles to androgen stimulation and provide potential targets for
therapy for AIPC. One family of proteins, the transforming growth factor β (TGFβ)
family have been implicated in progression of many cancers and are key in acting as
tumour suppressors(422). Development of resistance to this pathway is an important
event in malignant transformation(423). While the involvement of TGFβ has been
well determined, the involvement of other members of the family such as follistatin is
less well known.
Follistatin is a glycosylated secreted protein that is part of a larger group of
proteins with a conserved “follistatin” domain that is rich in cysteines. Follistatin has
been shown to be alternatively spliced with two isoforms of proteins FS288 and
FS315. The biological function of follistatin was first shown to act to inhibit the
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secretion of FSH by binding to proteins known as activins(424). The activins along
with inhibins regulate production of gonadotropins with activins stimulating
production and inhibins inhibiting production. While follistatin also binds to inhibin it
does so in a weaker fashion than to activin where the binding is irreversible(425).
The action of follistatin is not limited to the FSH secretion axis as it has been shown
to be expressed in a variety of tissues(426) as well as shown to bind other
molecules such as bone morphogenic proteins(427). With respect to CaP follistatin
has been shown to suppress the growth inhibiting effects of activins.
The localization of follistatin in prostate tissues has been shown in normal,
BPH and CaP. Using a polyclonal antibody Thomas et al.(390) were able to localize
follistatin in the stroma, basal and epithelial cells of high grade CaP. Expression in
BPH was also determined by the same group to be present in the stromal
tissue(428). While our study results do not show stromal staining, our results do
confirm basal cell staining in the prostate of some benign and BPH glands (Figure
5.7B, D) as well as staining of the epithelial cells in high grade CaP (Figure 5.7E).
While the immunohistochemical staining of follistatin in the prostate does not show
any obvious diagnostic significance, the interactions between its ligand activin has
been shown to play a role in CaP progression.
Co-localisation of activin and follistatin in the prostate support the theory that
follistatin reduces the growth inhibitory effects of activin(428). In addition, studies in
cell lines have demonstrated the direct effects of follistatin’s ability to neutralise the
action of activin(371,372). The PC3 cell line has been shown to secrete both activin
and follistatin(371), which was confirmed by our MS and ELISA data. In the PC3 cell
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line the inhibitory effect of activin is thought to be neutralised by follistatin in an
autocrine manner that promotes cell growth. This was tested by treating PC3 cells
with a neutralising antibody to follistatin and adding activin. This resulted in a 60%
decrease in cell proliferation thus confirming the theory of follistatin’s neutralising
ability over activin(371).
Follistatin has also been shown to have a heparin-binding region and have
been shown to interact with the heparin-sulfate chains on proteglycans on the cell
surface(429). This was thought to increase its ability to bind activin and sequester it
and prevent it from binding its receptor(430). However, another mechanism showed
the activin-follistatin complex being internalised and degraded by lysosomes(431).
The localization of follistatin to the cell membrane was apparent in one specimen
from lung cancer (Figure 5.8E).
While follistatin has been shown to be expressed in several tissues(426),
immunohistochemical location in colon and lung tissues has not been shown.
Studies with respect to lung cancer have shown follistatin to play an inhibitory role in
the progression of metastasis of lung tumours(432). Similar to prostate, the
cancerous epithelial cells of both lung and colon stain more intensely than the
normal epithelium (Figure 5.8, 5.9). One specimen of lung cancer displayed an
intense staining in the epithelial tumour cells. The prognostic significance of this will
need to be evaluated by further study to elucidate the prognostic potential of
follistatin as a lung tissue biomarker.
While there have been studies on the expression of follistatin in CaP there
have not been any determining its potential as a prostate biomarker in serum given
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that it has shown to play a role in promoting CaP progression. Our preliminary
validation of follistatin in the serum of patients with and without CaP (Figure 4.5A)
showed follistatin to have some discriminatory ability. However, this was not
confirmed in an extended validation conducted with biopsy confirmed patients
(Figure 5.5). The contradictory nature of the results is most likely due to the patients
used in the preliminary validation were selected from those who had PSA values
greater than 10ug/L. These patients in all probability harbour clinically advanced
CaP and those with highly elevated PSA greater that 50ug/L are likely to have
metastases. Measuring follistatin in this study set confirmed that follistatin is
elevated in a patient population with elevated PSA and that it also correlates
positively with increasing PSA (Figure 4.5B). The significance of these results were
not seen in serum of patients with biopsy confirmed CaP. In the extended patient
population the range of PSA values were mostly in the grey region of 4-10ug/L and
the tumours identified by biopsy had Gleason scores in the range of 5-9.
Correlations with PSA and Gleason score also did not show any significance with
follistatin measured in serum (Figure 5.7B, C). This could be attributed to the fact
that these are early stage tumours and levels of follistatin leaking into the circulation
are not enough to be measured by ELISA. To ensure there was no bias with age a
correlation of serum follistatin concentrations and age of CaP was shown to also be
non-significant.
The challenge of discriminating CaP from benign and BPH is seen even with
the use of PSA and %fPSA in this population (Figure 5.8A, B). There was no
significant difference seen between the groups using PSA or %fPSA. In addition,
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two well studied biomarkers for CaP, KLK2 and KLK11 were also measured (Figure
5.8C, D). In this case KLK11 was the only one to show a significant difference
between the benign and CaP groups and BPH and CaP groups. However, the
clinical relevance of this distinction is minimal considering there is significant overlap
in the levels of KLK11 measured in each group.
In conclusion, we show that follistatin is unable to distinguish CaP patients
from benign and inflammatory conditions in a patient cohort with PSA levels in the
grey region. However, we show for the first time the expression of follistatin in
tissues of colon and lung cancer with intense staining in one specimen of lung
squamous carcinoma. The clinical significance of this finding will need to be
elucidated with further studies in lung tissue specimens and serum samples of
patients with and without lung cancer.
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Chapter 6: Summary and Future Directions
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CHAPTER 6:
SUMMARY AND FUTURE DIRECTIONS
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Chapter 6: Summary and Future Directions 157
6.1 Summary
This thesis presents an optimized and validated approach to the discovery of
novel candidate CaP biomarkers through proteomic analysis of CaP cell line CM.
The use of CaP cell lines as model systems for the study of their secreted proteins
by MS approaches resulted in the identification of hundreds of proteins. These
proteins were assessed by literature and bioinformatics searches to elucidate their
potential as candidates for validation. Select candidates were validated in the serum
and tissues of patients with and without CaP.
6.2 Key Findings
1. Proof of Principle
a. Established a procedure for long term growth of the CaP cell line
PC3(AR)6 in large volume roller bottle culture in SFM
b. Processed CM in a suitable way for strong anion exchange fast
performance liquid chromatography
c. Prepared fractions for MS analysis by C18 reversed phase HPLC
coupled to a linear ion trap mass spectrometer.
d. Identified 262 proteins from combining proteins identified in two
replicates of culture by searching mass spectra with MASCOT
e. Measured the novel candidate Mac-2BP in the serum of CaP, BPH and
normal patients
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Chapter 6: Summary and Future Directions 158
2. Optimization of Culture Conditions
a. Seeding density for each of the three CaP cell lines used (PC3, LNCaP
and 22Rv1) was varied to optimize the amount of secreted proteins vs.
intracellular proteins. Marker proteins for secreted and intracellular
fractions were compared and total protein measured to determine
optimal seeding densities.
3. Comparative Proteomic Analysis of CM of Three CaP Cell Lines
a. Cultured and harvested the CM from each CaP cell line in triplicate
b. Concentrated and prepared the CM for each replicate via peptide
fractionation by HPLC SCX chromatography
c. Analyzed each fraction by MS/MS via C18 reversed phase capillary
electrophoresis coupled online to a linear ion trap mass spectrometer
d. Identified 2124 proteins by searching mass spectra with MASCOT and
X! Tandem proteomic search databases and combining results through
Scaffold
e. Determined candidate proteins for validation through a set of criteria
4. Validation of Candidate Biomarkers
a. Novel candidates were pre-clinically validated in serum of CaP and
healthy males.
b. Four novel candidates were selected for further pre-clinical validation
in serums of CaP and healthy males, each showed a positive
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Chapter 6: Summary and Future Directions 159
correlation with PSA serum levels and a statistically significant
increase in serum of CaP patients.
i. Chemokine (C-X-C) motif-16
ii. Spondin 2
iii. Pentraxin 3
iv. Follistatin
c. Follistatin was validated in an extended serum sample set along with
KLK2, KLK11 and %fPSA. No discriminatory ability was seen with any
marker.
d. Follistatin was evaluated as a tissue marker in the tissues of men with
high and low grade CaP and PIN and BPH and was not shown to offer
any discriminatory utility.
e. Evaluation of follistatin in the tissues of colon and lung cancers
revealed and intense positive staining in one lung cancer specimen
6.3 Future Directions
The approach of this study yielded several novel candidates for validation as
CaP biomarkers. The most promising of which was follistatin which showed slight
discriminatory staining between CaP and BPH glands. However, even though the
clinical utility of this as a tissue marker is not significant follistatin may play a role in
CaP progression and further elucidation of its role in CaP is required to determine
this. This would involve transcript levels and post translational modification analysis.
In addition, follistatin was shown to be expressed in other tissues and we show
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Chapter 6: Summary and Future Directions 160
staining in other cancers. Thus, more information on the role of follistatin in these
cancers is also required.
While we show that CXCL16, SPON2 and PTX3 show significant differences
between CaP and healthy males, their roles in CaP progression have not been fully
elucidated as well as their roles as tissue markers.
There is a need to examine the list of proteins produced in this study to a
greater extent to extract more novel candidates that would require validation. There
were several promising candidates that were not validated based on a lack of an
ELISA. Developments of antibodies and an ELISA to these candidates would be of
value. In addition, development of MS methods to quantify these candidates using
techniques such as multiple reaction monitoring would aid in their validation.
Further study of more CaP cell lines would add to the coverage of the
‘secretome’ of CaP and would aid in uncovering more candidates for validation. It is
likely that there will not be one biomarker for all tumours and thus a multi-parametric
panel is more than likely required to properly assess risk of CaP. Quantitative MS
proteomic comparisons of tumour tissue from matched tissue samples from tumour
and normal tissue by using isotopic tagging reagents such as iTRAQ would aid in
uncovering proteins that show quantitative differences between normal and CaP
specimens.
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References
161
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Appendix: Assay Reproducibility and Precision
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APPENDIX
ASSAY REPRODUCIBILITY AND PRECISION
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Appendix: Assay Reproducibility and Precision 202
Mac-2BP
0 25 50 75 100 125 150 175 2000
255075
100125150175200
n = 2
Theoretical Concentrationsng/mL
Expe
rim
enta
lC
once
ntra
tions
ng/m
L
KLK5
0.01 0.1 1 10 1000.01
0.1
1
10
100
n = 2
Theoretical Concentrationsug/L
Expe
rim
enta
lC
once
ntra
tions
ug/L
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Appendix: Assay Reproducibility and Precision 203
KLK6
0.1 1 10 1000.1
1
10
100
n = 2
Theoretical Concentrationsug/L
Expe
rim
enta
lC
once
ntra
tions
ug/L
KLK2
10 100 1000 1000010
100
1000
10000
n = 2
Theoretical Concentrationsng/L
Expe
rim
enta
lC
once
nrat
ions
ng/L
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Appendix: Assay Reproducibility and Precision 204
KLK11
100 1000 10000 100000100
1000
10000
100000
n = 2
Theoretical Concentrationsng/L
Expe
rim
enta
lC
once
ntra
tions
ng/L
PSA
10 100 1000 1000010
100
1000
10000
n = 2
Theoretical Concentratinsng/L
Expe
rim
enta
lC
once
ntra
tions
ng/L
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Appendix: Assay Reproducibility and Precision 205
SPON 2
0 25 50 75 100 125 150 175 2000.00
0.25
0.50
0.75
1.00
1.25
1.50
n = 2
Theoretical Concentrationng/mL
Expe
rim
enta
l Cou
nts
CXCL16
0 100 200 300 400 500 600 700 800 90010000
250
500
750
1000
n = 2
Theoretical Controlsng/L
Expe
rim
enta
l Con
trol
sng
/L
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Appendix: Assay Reproducibility and Precision 206
PTX3
0 400 800 1200 1600 2000 24000
400
800
1200
1600
2000
2400
n = 2
Theoretical Concentrationsng/L
Expe
cted
Con
cent
ratio
nsng
/L
Follistatin
0 4000 8000 12000 160000
4000
8000
12000
16000
n = 2
Theoretical Concentrationsng/L
Expe
rim
enta
lC
once
ntra
tions
ng/L
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Appendix: Assay Reproducibility and Precision 207
CXCL1
0 100 200 300 400 500 600 700 800 90010000
100200300400500600700800900
1000
n = 2
Theoretical Concentrationsng/L
Expe
rim
enta
lC
once
ntra
tions
ng/L
CXCL5
0 250 500 750 1000 1250 1500 1750 20000
250500750
10001250150017502000
n = 2
Theoretical Concentrationsng/L
Expe
rim
enta
lC
once
ntra
tions
ng/L
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Appendix: Assay Reproducibility and Precision
208
TNFRSF6B
0 2000 4000 6000 8000 10000 120000
2000
4000
6000
8000
10000
12000
n = 2
Theoretical Concentrationsng/L
Expe
rim
enta
lC
once
ntra
tions
ng/L
Lipocalin 2
0 1 2 3 4 5 6 7 8 9 100123456789
10
n = 2Theoretical Concentration
ng/L
Expe
rim
enta
lC
once
ntra
tion
ng/L