IDENTIFICATION AND VALIDATION OF CANDIDATE BREAST CANCER BIOMARKERS: A MASS SPECTROMETRIC APPROACH
by
Vathany Kulasingam
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 Vathany Kulasingam 2008
ii
IDENTIFICATION AND VALIDATION OF CANDIDATE BREAST
CANCER BIOMARKERS: A MASS SPECTROMETRIC APPROACH
Vathany Kulasingam
Doctor of Philosophy 2008
Department of Laboratory Medicine and Pathobiology
University of Toronto
ABSTRACT
One of the best ways to diagnose breast cancer early or to predict therapeutic
response is to use serum biomarkers. Unfortunately, for breast cancer, we do not
have effective serological biomarkers. We hypothesized that novel candidate tumor
markers for breast cancer may be secreted or shed proteins that can be detected in
tissue culture supernatants of human breast cancer cell lines. A two-dimensional
liquid chromatography-tandem mass spectrometry (2D-LC-MS/MS) strategy was
utilized to identify and compare levels of extracellular and membrane-bound proteins
in the conditioned media. Proteomic analysis of the media identified in excess of 600,
500 and 700 proteins in MCF-10A, BT474 and MDA-MB-468, respectively. We
successfully identified the internal control proteins, kallikreins 5, 6 and 10 (ranging in
concentration from 2-50 µg/L), as validated by ELISA and confidently identified HER-
2/neu in BT474 cells. Sub-cellular localization was determined based on Genome
Ontology (GO) for the 1,139 proteins, of which 34% were classified as extracellular
and membrane-bound. Tissue specificity, functional classifications and label-free
quantification were performed. The levels of eleven promising molecules were
iii
measured in biological samples to determine its discriminatory ability for control
versus cases. This screen yielded activated leukocyte cell adhesion molecule
(ALCAM) as a promising candidate. The levels of ALCAM, in addition to the classical
breast cancer tumor markers carbohydrate antigen 15-3 (CA 15-3) and
carcinoembryonic antigen (CEA) were examined in 300 serum samples by
quantitative ELISA. All three biomarkers effectively separated cancer from non-
cancer groups. ALCAM, with area under the curve (AUC) of 0.78 [95% CI: 0.73,
0.84] outperformed CA15-3 (AUC= 0.70 [95% CI: 0.64, 0.76]) and CEA (AUC= 0.63
[95% CI: 0.56, 0.70]). The incremental values of AUC for ALCAM over that for CA15-
3 were statistically significant (Delong test, p <0.05). Serum ALCAM appears to be a
new biomarker for breast cancer and may have value for disease diagnosis.
iv
DEDICATION
I dedicate this PhD thesis to my beloved Swami whose guidance and grace
enabled me to complete this degree and to my late father, who had taught me
through example to face adversity and to conquer it, no matter the nature of the
challenge.
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ACKNOWLEDGEMENTS
“Matha, Pitha, Guru, Daivam” in Sanskrit means 'Mother, Father, Teacher,
God. It represents the order of significance and respect to be accorded to these
people. Therefore, first and foremost, with utmost humility, I would like to thank my
mother and late father for investing in me. There were many sacrifices made along
the way and I am truly indebted to my family for providing me with unconditional
support and love. Thank you for instilling in me the values of hard work, dedication
and determination.
My PhD journey has been a process of experiencing, learning and maturing,
both in terms of scientific knowledge and personal growth. This thesis would not
have been possible without the confidence, passion and motivation of my
teacher/supervisor. I am grateful to Dr. Eleftherios P. Diamandis for his guidance,
mentorship, patience, encouragement and friendship. Though I do not say it often,
THANK YOU, for giving me the opportunity to work in your laboratory and to explore
the field of scientific research.
I would also like to acknowledge the members of my PhD advisory and oral
examination committee for their mentorship and valuable advice: Drs. Andrew Emili,
Hilmi Ozcelik, Joe Minta, James Scholey and K.W. Michael Siu. I would like to
extend my acknowledgements to the Department of Laboratory Medicine and
Pathobiology, University of Toronto, and to my funding sources: Natural Sciences
and Engineering Research Council of Canada and Proteomic Methods Inc.
vi
In addition, many thanks to all members of the Advanced Centre for Detection
of Cancer (ACDC) laboratory, past and present, for their continued support and
friendship. Thank you for making my stay in the laboratory so much more than a
scientific exercise. In particular, I am grateful to Christopher R. Smith, Ihor Batruch,
Girish Sardana, Anton Soosaipillai and Tammy Earle for their technical support,
expertise and friendship.
Last, but certainly not least, my most humble salutations at the divine lotus
feet of my beloved Swami. May your grace always be with me and may your light
guide me in the right path.
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TABLE OF CONTENTS ABSTRACT ii DEDICATION iv ACKNOWLEDGEMENTS v TABLE of CONTENTS vii LIST of TABLES xi LIST of FIGURES xii LIST of ABBREVIATIONS xiv CHAPTER 1: INTRODUCTION 1 1.1 Breast Cancer 2
1.1.1 Statistics / Epidemiology 2 1.1.2 Anatomy and types of breast cancer 3 1.1.3 Current breast cancer screening methods 4 1.1.4 Early diagnosis of breast cancer is essential 8
1.2 Cancer biomarkers 9
1.2.1 Definition and types of biomarkers 9 1.2.2 Characteristics of an ideal tumor marker 12 1.2.3 Historical overview of cancer biomarkers 12 1.2.4 Current applications of tumor markers and their clinical utility
13
1.2.5 Currently-available breast cancer biomarkers: Clinical utility and limitations
14
1.2.5.1 MUC-1 15 1.2.5.2 CEA 18 1.2.5.3 Circulating levels of HER-2/neu 18 1.2.5.4 Other promising serological breast cancer markers 19 1.2.5.5 Non-serological markers for breast cancer 20
1.2.6 Renewed interest in discovering novel breast cancer biomarkers
21
1.3 Mechanisms of biomarker elevation in biological fluids 22
1.3.1 Gene over-expression 23 1.3.2 Increased protein secretion and shedding 24 1.3.3 Angiogenesis, invasion and destruction of tissue architecture
25
1.4 Strategies for discovering novel cancer biomarkers 26
1.4.1 Gene-expression profiling 27 1.4.2 Mass spectrometry-based profiling 30 1.4.3 Peptidomics 32 1.4.4 Cancer biomarker family approach 33 1.4.5 Secreted protein approach 34
viii
1.4.6 Other prominent strategies 35 1.5 Emergence of proteomics and relevance to breast cancer 36
1.5.1 Basic components of a mass spectrometer 36 1.5.2 Breast cancer proteomics: Sources to mine for biomarkers 39 1.5.3 Tissue culture based biomarker discovery platform 42
1.6 Purpose and aims of the present study 46
1.6.1 Rationale 46 1.6.2 Hypothesis 49 1.6.3 Objectives 50
CHAPTER 2: ANALYSIS OF THE CONDITIONED MEDIA OF THREE BREAST CELL LINES
52
2.1 Introduction 53 2.2 Materials and Methods 58
2.2.1 Cell lines 58 2.2.2 Cell culture 58 2.2.3 Sample preparation 59 2.2.4 Strong cation exchange liquid chromatography 60 2.2.5 Tandem mass spectrometry (LC-MS/MS) 60 2.2.6 Data analysis 61 2.2.7 Spectral counting 62 2.2.8 Total protein and lactate dehydrogenase assay 63 2.2.9 Quantification of KLK5, KLK6 and KLK10 63
2.3 Results 64 2.3.1 Optimization of cell culture 64 2.3.2 Identification of proteins by MS 66 2.3.3 Identification of internal control proteins in CM by MS 70 2.3.4 Cellular localization of identified proteins 71 2.3.5 Overlap of proteins between the three cell lines 71 2.3.6 Cell lysate proteome 74 2.3.7 Spectral counting and identification of differentially expressed proteins
76
2.4 Discussion 84 CHAPTER 3: BIOINFORMATICS and CANDIDATE SELECTION 89 3.1 Introduction 90 3.2 Materials and Methods 91
3.2.1 Tissue-specific expression 91 3.2.2 Biological functions analysis 91 3.2.3 Comparison of proteins identified from CM with other publications
92
3.2.4 Single nucleotide polymorphisms (SNPs) and human 92
ix
Plasma Proteome database 3.2.5 Selection of candidates 92
3.3 Results 94 3.3.1 Tissue-specific expression 94 3.3.2 Biological functions analysis 95 3.3.3 Comparison of proteins identified from CM with other publications
99
3.3.4 SNPs and human Plasma Proteome database 1033.3.5 Selection of candidates 104
3.4 Discussion 111 CHAPTER 4: VERIFICATION PHASE 1134.1 Introduction 1144.2 Materials and Methods 118
4.2.1 Quantification of Elafin and Kallikrein 5, 6 and 10 1184.2.2 Verification strategy 1194.2.3 Quantification of Cystatin C, Lipocalin-2 and Transforming growth factor beta-2
119
4.2.4 Quantification of ALCAM, BCAM, NrCAM and Fractalkine 1214.3 Results 122
4.3.1 Elafin 1224.3.2 Kallikrein 5, 6, 10 1244.3.3 Cystatin C 1264.3.4 Lipocalin-2 1284.3.5 Transforming growth factor beta-2 1304.3.6 B-cell adhesion molecule (BCAM) 1324.3.7 Neuronal cell adhesion molecule (NrCAM) 1344.3.8 Fractalkine 1364.3.9 Activated leukocyte cell adhesion molecule (ALCAM) 138
4.4 Discussion 140 CHAPTER 5: VALIDATION OF ALCAM AS A SEROLOGICAL BREAST CANCER DIAGNOSTIC MARKER
142
5.1 Introduction 1435.2 Materials and Methods 146
5.2.1 Patients and specimens 1465.2.2 Measurement of ALCAM, CA 15-3 and CEA in serum 1475.2.3 Data analysis and statistics 147
5.3 Results 1505.3.1 ALCAM ELISA assay development 1505.3.2 Association of biomarkers with age 1525.3.3 Correlations among biomarkers 1525.3.4 Association of biomarkers with tumor characteristics for cases
155
x
5.3.5 Association of biomarkers with breast cancer 1585.3.6 The diagnostic values of the three markers 162
5.4 Discussion 165 CHAPTER 6: SUMMARY AND FUTURE DIRECTIONS 1716.1 Summary 1726.2 Future Directions 174 References 176
xi
LIST OF TABLES
Table Title Page
1.1 Definitions and specifications of biomarkers
11
1.2 Advantages and disadvantages of a cell culture-based model for biomarker discovery
45
2.1 Total number of proteins identified per number of peptides
69
2.2 Top 100 differentially expressed extracellular and membrane proteins
80
3.1 Proteins elevated in breast tumor tissue proteome and found in CM analysis
96
3.2 Proposed filtering criteria for candidate selection 106
3.3 Top 30 candidates from discovery phase
108
3.4 Our candidate selection criteria 109
3.5 Top 11 candidates selected for verification phase 110
5.1 Spearman's rank correlation coefficients among 3 markers for female controls and cases
154
5.2 Marker distributions by tumor characteristics for cases
156
5.3 Results from logistic regression models
161
5.4 ROC analysis for biomarkers 164
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LIST OF FIGURES Figure Title Page
2.1 Cell number, total protein, LDH and kallikrein levels in 24 hour
conditioned media of cell lines
65
2.2 Outline of experimental workflow
67
2.3 Number of proteins identified in CM by LC-MS/MS for the 3 cell lines
68
2.4 Cellular localization for the 3 cell lines
72
2.5 Overlap of proteins in CM
73
2.6 Proteome of MDA-MB-468 cell lysate by LC-MS/MS
75
2.7 Overlap of proteins between cell lines and cellular localization using label-free quantification
79
3.1 Biological functions analyses
97
3.2 Canonical pathways and known genes linked to breast cancer
98
3.3 Overlap among other publications
102
4.1 Levels of elafin in biological samples
123
4.2 Levels of KLK5, KLK6 and KLK10 in biological samples
125
4.3 Levels of cystatin C in serum
127
4.4 Levels of lipocalin-2 in serum
129
4.5 Levels of transforming growth factor beta-2 (TGF-β2) in serum
131
4.6 Levels of B-cell cell adhesion molecule (BCAM) in serum
133
4.7 Levels of neuronal cell adhesion molecule (NrCAM) in serum
135
4.8 Levels of fractalkine in serum
137
4.9 Levels of activated leukocyte cell adhesion molecule (ALCAM) in serum
139
xiii
Figure Title Page
5.1 Model of homophilic ALCAM-ALCAM interactions between cells
145
5.2 Schematic of a sandwich ELISA assay
151
5.3 Scatter plot of individual markers for cases and female controls versus age
153
5.4 Scatter plot of ALCAM distribution by tumor grade
157
5.5 Distribution of markers: ALCAM, CA 15-3 and CEA
160
5.6 ROC curves for the three markers (CA 15-3, CEA, ALCAM)
163
xiv
LIST OF ABBREVIATIONS
2-DE, 2-dimensional electrophoresis
ACN, acetonitrile
AFP, alpha-feto protein
ALCAM, activated leukocyte cell adhesion molecule
ASCO, American Society of Clinical Oncology
AUC, area under the curve
BCAM, B-cell adhesion molecule
CA 15-3, carbohydrate antigen 15-3
CAMs, cell adhesion molecules
CBE, clinical breast examination
CDCHO, Chemically Defined Chinese Hamster Ovary
CEA, carcinoembryonic antigen
CI, confidence interval
CIS, carcinoma in situ
CM, conditioned media
CV, coefficient of variation
DFP, diflunisal phosphate
DFS, disease-free survival
DTT, dithiothreitol
ECD, extracellular domain
ELISA, enzyme-linked immunosorbent assay
ER, estrogen receptor
xv
ESI, electrospray ionization
FBS, fetal bovine serum
FDA, Food and Drug Administration
GO, genome ontology
HCG, human chorionic gonadotropin-β
HE4, human epididymis protein 4
HPLC, high performance liquid chromatography
ICAT, isotope-coded affinity tags
IGFBP, insulin-like growth factor binding protein
Ig-SF, immunoglobulin superfamily
IHC, immunohistochemistry
IPA, Ingenuity Pathways Analysis
KLK, kallikrein gene
KLK, kallikrein protein
LC-MS/MS, liquid chromatography tandem mass spectrometry
LDH, lactate dehydrogenase
MALDI, matrix-assisted laser desorption ionization
MMP, matrix metalloproteinase
MRI, magnetic resonance imaging
MRM, multiple reaction monitoring
MS, mass spectrometry
NAF, nipple aspirate fluid
NrCAM, neuronal cell adhesion molecule
xvi
NSCLC, non-small cell lung carcinoma
OR, odds ratio
OS, overall survival
PBS, phosphate buffered saline
PCR, polymerase-chain reaction
PDAC, pancreatic ductal adenocarcinoma
PEM, polymorphic epithelial mucin
PgR, progesterone receptor
PSA, prostate-specific antigen
ROC, receiver operating characteristic
SCX, strong cation exchange
SFM, serum-free media
SNP, single nucleotide polymorphism
TFA, trifluoroacetic acid
TGF- β2
TIF, tumor interstitial fluid
TOF, time-of-flight
uPA, urokinase plasminogen activator
Chapter 1: Introduction 1
CHAPTER 1:
INTRODUCTION
Sections of this chapter were published in Nature Clinical Practice Oncology:
Kulasingam, V. and Diamandis, E.P. Strategies for Discovering Novel Cancer Biomarkers by Utilizing Emerging Technologies.
Nat Clin Pract Oncol. Accepted: November 2007 (In Press)
Copyright permission has been granted.
Chapter 1: Introduction 2
1.1 Breast Cancer
1.1.1 Statistics / Epidemiology
Breast cancer is an important public health issue. It is both a heterogeneous
disease and the most common cancer affecting women worldwide, with
approximately one million new cases diagnosed each year1. Globally, it accounts for
22% of all new cancer diagnoses in women and represents 7% of the more than 7.6
million cancer-related deaths worldwide2. It is the most common cancer in Canadian
women, both before and after menopause, and is a major cause of premature death.
In Canada, an estimated 22,000 women will be diagnosed with breast cancer and
5,000 will die of it every year. In fact, 1 in 9 Canadian women is expected to develop
breast cancer during her lifetime and 1 in 27 will die of it. It is a disease of the middle
and late ages of life, as 75% of breast cancer is diagnosed in women over the age of
50 (www.cancerfacts.com). While breast cancer is less common at a young age,
younger women tend to have a more aggressive form of the disease than older
women. Some of the risk factors for breast cancer include age, family history,
reproductive history, obesity, hormone usage, radiation exposure and diet. However,
70% of all breast cancer cases have no identifiable risk factor3.
After long-term increases in women aged 40 and over, incidence rates are
beginning to either stabilize or drop since the 1990s4. Earlier, incidence rate of
breast cancer was increasing while mortality rates were declining5. This was partly
due to both the increased use of screening for early disease and the widespread
administration of systemic adjuvant therapy. The five-year survival rate is close to
98% when the cancer is confined to the breast6. However, when breast cancer has
Chapter 1: Introduction 3
metastasized at the time of diagnosis, the five-year survival rate is ~27%.
Fortunately at the time of diagnosis, 60% of cases are still localized to the breast
hence yielding excellent five-year survival rates but 40% of individuals are
diagnosed when the cancer is regional or distant6. For metastatic disease, the
median time to treatment failure is 9 months and the median survival is 2 years.
Therefore, the prognosis for women with metastatic breast cancer is not good.
1.1.2 Anatomy and types of breast cancer
The breast is organized around the mammary gland. It is made up of 10-20
lobes, and within each lobe, there are many smaller lobules. Lobules contain groups
of glands that can produce milk7. The lobules are all linked by tubes called ducts,
and all ducts lead to the nipple. Fat and connective tissue surround the ducts and
lobules. The cells forming the ducts and lobules are epithelial cells whose main
function is to produce and to secrete the various constituents of milk. In addition,
epithelial cells are surrounded by a layer of myoepithelial cells, attached on a basal
membrane, whose role is to maintain the tubular structure of ducts and lobules.
The histologic classification of breast carcinoma can be broadly grouped into
two categories: in situ carcinoma (CIS, tumor cells are confined by the basement
membrane) which accounts for 15-30% of all cases and invasive carcinoma (tumor
cells have invaded beyond the basement membrane) which accounts for 70-85% of
all cases. CIS is further subtyped into ductal CIS and lobular CIS. Invasive
carcinoma is subdivided into invasive ductal carcinoma (70-80% of all cases that are
invasive) and invasive lobular carcinoma (5-10%). Moreover, 5-10% of breast
Chapter 1: Introduction 4
cancers are caused by the inheritance of a germline mutation in a cancer
predisposing gene. The most important of these genes are BRCA1 and BRCA2.
BRCA1 protein functions in regulating transcription, inhibiting cellular proliferation
and repairing DNA8. BRCA2 appears to have similar functions to BRCA1. Studies
have shown that women who carry either of these genes have a 80-85% lifetime risk
of developing breast cancer9.
The main presenting features in women with symptomatic breast cancer
include a lump in the breast, nipple change or discharge and skin contour changes.
Patients with primary breast cancers are offered surgery, often followed by adjuvant
therapeutics. The use of additional systemic anticancer treatment given to patients
after a cancer is surgically removed is referred to as adjuvant systemic therapy. The
goal of adjuvant therapy is to eliminate any remaining tumor cells in the body
(micrometastases). This form of therapy improves the likelihood of surviving the
cancer because it decreases the chance that the cancer will return. Despite these
treatments though, 40% of patients with lymph node-positive disease will experience
a relapse, and the majority of these patients will die from disseminated cancer10.
Although adjuvant systemic therapy has led to considerable improvements of the
prognosis of the breast cancer population, it also carries the significant adverse
effect of overtreatment11.
1.1.3 Current breast cancer screening methods
The primary goal of screening is to prevent lethal, progressive disease by
detecting cancer at an earlier, more treatable stage or by detecting precursor lesions
Chapter 1: Introduction 5
that can be removed before they develop into invasive cancers. Screening is a
presumptive identification for disease; it is not a diagnostic tool. Screening alerts
individuals for further testing. The current screening methods used to detect breast
tumors either benign or malignant, include clinical breast examination (CBE),
mammography and ultrasound. Mammography remains the cornerstone of breast
cancer screening and early diagnosis. Calcifications, masses and distortions can
potentially be detected via mammography. The introduction of mammography in
1983 was followed by a steady increase in the age-adjusted incidence rates,
particularly among in situ (stage 0) and early-stage (stage 1) patients12. Certainly the
overall incidence of breast cancer is significantly higher than that of the pre-
mammographic era. The sensitivity and specificity of mammography for women over
the age of 50 is 77-84% and 90-94%, respectively12. It is lower in women aged 40-49
yrs (70% sensitivity with 90% specificity). Mammographic screening for women aged
50–69 years is effective in reducing breast cancer mortality, and reductions in
mortality have been observed where screening has been introduced13. For example,
with the advent of mammographic screening, two-thirds of newly diagnosed breast
cancer patients are node-negative14. Approximately 70% of these patients are cured
of breast cancer by surgery while the remaining 30% develop recurrent disease
within 10 years of diagnosis15.
However, there are a number of limitations to mammography. Specifically,
mammography will not detect all breast cancers, and some breast cancers detected
with mammography may still have poor prognosis. Furthermore, it suffers from high
false positive and negative rates, hazardous exposure and patient discomfort16,17.
Chapter 1: Introduction 6
For women under the age of 40, mammographic screening yields a poor sensitivity
of only 33%18,19. Not only does it suffer from low positive predictive value of
approximately 25%20, its benefit for early detection in pre-menopausal women is still
debated. Mostly it is argued that breasts in younger women may be more dense
(dense breast tissue decreases the performance of mammography), and
corresponding tumor rates may be higher than predicted. Thus a portion of
mammographically detected tumors in younger women that undergo regular
screening may already be disseminated. Lastly, mammography may be harmful to
women that carry germ-line mutations such as ataxia telangiectasia or BRCA1/2,
possibly because of their increased sensitivity to radiation21,22. In addition, the
incidence of stage 2 and 3 disease has not fallen commensurately, suggesting a
bias in the detection of indolent cancers rather than aggressive cancers by
mammography12.
For early detection of breast cancers, the current recommendations are that
average-risk women (aged >40 years) should begin annual mammography in
addition to performing CBE annually23. Women in their 20s and 30s are
recommended to perform CBE every 3 years23. Finally, for women at significantly
increased risk for breast cancer, it has been recommended that they may benefit
from earlier initiation of screening (age 25), screening at shorter intervals, and the
screening with additional modalities such as ultrasound or magnetic resonance
imaging (MRI)23. Screening modalities such as ultrasound and MRI are available but
are not recommended for use as a population screening tool due to a lack of
Chapter 1: Introduction 7
evidence for its benefit if used, operator dependence, non-reproducible results and
high false-positive rates.
As well, there are 4 different biases that need to be considered when
evaluating cancer screening: 1) Lead time bias which is the time period by which
screening advances the diagnosis of the disease. 2) Length bias which states that
slow progressing cases are more likely to be detected at screening than rapidly
progressing cases. 3) Selection bias which states that individuals who accept
screening are, in general, a healthier group than those who decline the screening
method. Finally, the fourth bias states that some lesions identified as cancers would
not have been presented clinically in individuals in the absence of screening.
It is possible that mammographic screening has significantly contributed to an
increase in the detection of early-stage disease that may have little chance of
progressing or becoming lethal over the course of a person’s lifetime12. Thus, while
mammography may have increased the incidence of breast cancer and while it may
have good sensitivity and specificity for women over the age of 40, the mortality
rates for breast cancer have not changed significantly since its introduction6. Hence
it is crucial to be able to differentiate indolent/low-risk tumors from aggressive/higher
risk tumors, so that an individual is not over-treated. Mammographic screening does
not provide information regarding the prognosis of the lesion detected. What is
needed are screening strategies that are less biased towards indolent cancers.
Chapter 1: Introduction 8
1.1.4 Early diagnosis of breast cancer is essential
The concept of early detection of various forms of cancer before they spread,
and become incurable, has enticed physicians and research scientists for decades24.
40% of breast cancers have regional or distant spread of their disease at the time of
diagnosis6. Moreover, survival rates for people diagnosed with advanced breast
cancer have changed little over the past 20 years. It is known that survival is
excellent for breast cancer when early-stage disease is treated with existing
therapies24. Without doubt, shifting all cases to early detection will have a profound
impact on overall mortality and economic burden.
Unfortunately, other than definitive diagnosis by biopsy and histopathology,
no diagnostic or screening test is presently suitable for the early detection of
clinically relevant breast cancer. This is because sufficiently high sensitivity (the
probability of the test being positive in individuals with the disease) and specificity
(the probability of the test being negative in individuals without the disease) are
usually both not attributes of the same test; an increase in sensitivity tends to result
in a reduction in specificity, and vice versa. Newer diagnostic methods with improved
sensitivity and specificity are clearly needed to identify women with early stage
breast cancer.
The criteria for effective early detection state that the disease must be
common with a high mortality rate. Second, the screening test must accurately
detect early-stage disease. Third, the treatment after detection through screening
must demonstrate improvements in prognosis and finally, the potential benefits must
Chapter 1: Introduction 9
outweigh the potential harms and costs of screening24. One of the most promising
ways to achieve this is through the use of cancer biomarkers.
1.2 Cancer biomarkers
1.2.1 Definition and types of biomarkers
The ability to detect human malignancy via a simple blood test has long been
a major objective in medical screening. The advantages of such an easy to use,
relatively non-invasive and operator-independent test are self-evident. In this respect,
cancer biomarkers can be DNA, mRNA, proteins, metabolites, or processes such as
apoptosis, angiogenesis or proliferation25. The markers are produced either by the
tumor itself or by other tissues, in response to the presence of cancer or other
associated conditions, such as inflammation. Such biomarkers can be found in a
variety of fluids, tissues and cell lines. Tumor markers can be used for screening the
general population, for differential diagnosis in symptomatic patients, and for clinical
staging of cancer. Additionally, they can be used to estimate the tumor volume, to
evaluate response to treatment, to assess recurrence through monitoring or as
prognostic indicators for disease progression (Table 1.1). Given the low prevalence
of cancer in any given population, no known marker meets all of these criteria.
A number of different types and forms of tumor markers exist. These markers
include hormones and different functional subgroups of proteins such as enzymes,
glycoproteins, oncofetal antigens and receptors. Furthermore, other tumor changes
such as genetic mutations, amplifications, translocations and changes in microarray-
generated profiles (signatures) are also forms of tumor markers. Regardless of the
Chapter 1: Introduction 10
types of tumor markers/profiles, the use of a tumor marker in a clinic must be
associated with proven improvements in patient outcomes, such as increased
survival or enhanced quality of life25. As mentioned earlier, other than definitive
diagnosis by biopsy and histopathology, no diagnostic or screening test is presently
suitable for the early detection of clinically relevant breast cancer.
Chapter 1: Introduction 11
Table 1.1: Definitions and specifications of biomarkers
Diagnostic (screening) biomarker A marker that is used to detect and identify a given type of cancer in an individual. These types of markers are expected to have high specificity and sensitivity. For example, the presence of Bence-Jones protein in urine remains one of the strongest diagnostic indicators of multiple myeloma. Prognostic biomarker This is used once the disease status has been established. These biomarkers are expected to predict the likely course of the disease, its recurrence, and thus they have an important influence on the aggressiveness of the therapy. For example, the traditional prognostic factors in breast cancer include tumor size, tumor grade and nodal status26. Stratification (predictive) biomarker This serves to predict the response to a drug before starting treatment. It classifies individuals as likely responders or non-responders to a particular treatment. These biomarkers mainly arise from array-type experiments that make is possible to predict clinical outcome from the molecular characteristics of the patient’s tumor. Specificity The proportion of control/normal subjects who test negative for the biomarker. Sensitivity The proportion of individuals with confirmed disease that test positive for the biomarker. Receiver operating characteristic (ROC) curve A graphical representation of the relationship between sensitivity and specificity. It is used to evaluate the efficacy of a tumor marker at various cut-off points. An ideal graph is the one giving the maximum area under the curve (AUC). Diagonal line represents a useless test (AUC = 0.5). Curved line represents a useful (AUC < 1.00) but not perfect (AUC = 1.00) test.
Tumor Marker Value
Frequency Distribution
Chosen Cut-Off
Increases SpecificityIncreases Sensitivity
A = True NegativesB = False NegativesC = False PositivesD = True Positives
A
B C
D
0% 100%
100%
Tru
e P
ositi
ve R
ate
False Positive Rate
Chapter 1: Introduction 12
1.2.2 Characteristics of an ideal tumor marker
An ideal tumor marker should be measured easily, reliably and cost-
effectively using an assay with high analytical sensitivity and specificity24. In
particular, an ideal tumor marker should be produced by the tumor cells and enter
the circulation and it should be present at low levels in serum of healthy or benign
disease patients and increase significantly in cancer (preferably in one cancer type).
Moreover, an ideal tumor marker should be present in detectable (or higher than
normal) quantities at early or preclinical stages and the quantitative levels of the
tumor marker should reflect the tumor burden. Finally, it should demonstrate high
diagnostic sensitivity (few false negatives) and specificity (few false positives).
A caveat to currently used tumor markers is that generally, they suffer from
low diagnostic specificity and sensitivity. Only a few markers have entered routine
use, and only for a limited number of cancer types and clinical settings. In the
majority of cases, the current markers are used in conjunction with imaging, biopsy
and associated clinicopathological information before a clinical decision is made.
1.2.3 Historical overview of cancer biomarkers
The first cancer marker ever reported was the presence of the light chain of
immunoglobulin in the urine, of 75% of myeloma patients27. Since its discovery in
1847, the test is still employed by clinicians today, but with use of modern
quantification techniques. From 1930–1960, scientists identified numerous
hormones, enzymes, and other proteins whose concentration was altered in
biological fluids from cancer patients. The modern era of monitoring malignant
Chapter 1: Introduction 13
disease, however, began in the 1960s with the discovery of alpha-fetoprotein
(AFP)28 and carcinoembryonic antigen (CEA)29, which was facilitated by the
introduction of immunological techniques such as the radioimmunoassay. In the
1980s, the era of hybridoma technology enabled development of the ovarian
epithelial cancer marker, carbohydrate antigen 125 (CA 125)30. In 1980, prostate-
specific antigen (PSA), considered one of the best cancer markers, was
discovered31.
1.2.4 Current applications of tumor markers and their clinical utility
One of the applications of a tumor marker is for population screening. A
screening test should have very high sensitivity and exceptional specificity, to avoid
too many false positives in low cancer prevalence populations. Furthermore, the test
must demonstrate a benefit in terms of clinical outcome. Unfortunately, current
biomarkers suffer from low diagnostic sensitivity and specificity to serve as
screening markers. With the exception of PSA, current tumor markers are more
frequently elevated at late stages of disease. Hence, the current clinical utility of any
marker to serve as a screening tool is limited. Another application of a tumor marker
is for diagnosis. Similar to its utility as a screening marker, the current biomarkers
suffer from low diagnostic sensitivity and specificity to serve as diagnostic markers.
A further application of a tumor marker is as a prognostic marker. Most cancer
markers have some prognostic value however; specific therapeutic interventions
cannot be issued since their accuracy of prediction is rather poor. In addition, some
markers can serve as a predictive indicator of therapeutic response. In this respect,
Chapter 1: Introduction 14
very few markers have predictive power (exceptions include steroid hormone
receptors and HER-2 amplification for breast cancer) but the provided information
helps for therapy selection. Yet another application of a tumor marker is for tumor
staging. Besides AFP and human chorionic gonadotropin-β (HCG) for use of staging
testicular cancer, the accuracy of the other markers to determine tumor staging is
poor. Two more current applications of tumor markers exist which include detecting
early tumor recurrence and monitoring effectiveness of cancer therapy. The
usefulness of the current markers to serve the former role is controversial as lead
time is short and does not significantly affect outcome. In addition, therapies for
treating recurrent disease are not usually effective and clinical relapses could occur
without biomarker elevation or biomarker elevation is non-specific. With respect to
the latter application (monitoring effectiveness of cancer therapy), current
biomarkers provide information on therapeutic response (effective or non-effective)
that is readily interpretable and more economical than imaging modalities. Hence
current markers play a very essential clinical role in this application.
1.2.5 Currently-available breast cancer biomarkers: Clinical utility and
limitations
The currently used serological breast cancer markers include carbohydrate
antigen 15-3 (CA 15-3) and carcinoembryonic antigen (CEA)32. Briefly, CA 15-3 and
BR 27.29 (also known as CA 27.29) serum assays detect the same antigen, i.e.
MUC-1 protein and provide similar clinical information33. CA 15-3 has however, been
more widely investigated than BR 27.29. CA 15-3 and CEA levels in serum are
Chapter 1: Introduction 15
related to tumor size and nodal involvement and are recommended by international
bodies such as American Society of Clinical Oncology (ASCO) for monitoring
patients with metastatic disease during active therapy34. Both these markers have
been recommended to be used in conjunction with diagnostic imaging, history and
physical examination. In general, serum marker levels reflect tumor burden and for
this reason, the current markers are not sensitive enough to be used for screening
and early diagnosis of primary breast cancer32,35,36. However, the role of tumor
markers in diagnosis of recurrent disease and in the evaluation of response to
treatment is well established. This is desirable since tumor marker elevation
represents a simple, objective method for monitoring of therapeutic response, which
has significant advantages over conventional imaging methods, particularly in
relation to cost-effectiveness of the tests. Indeed, prospective randomized studies
are required to demonstrate any survival benefit when earlier therapeutic
interventions are instituted upon elevation of serum markers. In the following,
detailed descriptions of the currently available serum breast cancer markers are
described.
1.2.5.1 MUC-1
CA 15-3 and CA 27-29 both measure the serum levels of MUC-1. The major
difference between the two tests is the different immunoreagents used,
predominantly the monoclonal antibodies utilized. CA 15-3 is the most widely used
test to assay MUC-1 and can be considered the gold standard. It consists of a
sandwich capture assay which uses the monoclonal antibody 115D8 (raised against
Chapter 1: Introduction 16
human milk fat globule membranes) and DF3 (raised against a membrane-enriched
fraction of metastatic human breast carcinoma)37,38. On the other hand, CA 27.29 is
measured using a solid-phase competitive immunoassay in which the monoclonal
antibody B27.29 is used either as a catcher or as a tracer. This antibody recognizes
the same epitope as DF3 but the binding of B27-29 is not influenced by the
presence of glucidic residues39. In either case, the test measures serum levels of
MUC-1. MUC-1 is a polymorphic epithelial mucin (PEM), a large glycoprotein found
on the apical surface of polarized epithelial cells40. Normally, MUC-1 is expressed in
the ducts and acini, from where it is released into the milk in soluble form or bound
on milk fat globules. However in cancer, with disruption of normal cell polarization
and tissue architecture, MUC-1 is shed into the bloodstream and hence its levels
can be measured by this immunoassay. MUC-1 is a high molecular weight (250-
1000 kDa) protein that can activate membrane receptors for growth factors, reduce
E-cadherin-mediated cell adhesion, thereby promoting cell migration, and reduce the
cellular apoptotic response to oxidative stress41,42. For CA 15-3, the diagnostic
sensitivity of the test is 10%, 20% and 40% in patients with stage I, stage II and
stage III disease, respectively. In addition to lacking sensitivity for early disease, CA
15-3 also lacks specificity for breast cancer as it is found elevated in 5% of healthy
individuals43,44. Furthermore, increased levels of CA 15-3 can be observed in several
non-neoplastic conditions, including benign breast pathology, chronic liver disorders
and immunological disorders45. As stated earlier, currently CA 15-3 is used for post-
operative surveillance in patients with no evidence of disease and for monitoring
therapy in advanced disease. Some studies have shown that pre-surgical CA 15-3
Chapter 1: Introduction 17
level is a prognostic factor with both disease-free survival (DFS) and overall survival
(OS) being shorter in patients with a high value for this marker46,47. Despite this, it
has not been proven that CA 15-3 is an independent prognostic factor and hence is
not currently used in the clinic to fulfill this role.
Serial tumor marker determinations can be useful tools in the diagnosis of
metastatic breast cancer. In the presence of distant metastases, the clinical
sensitivity of CA 15-3 in different studies has ranged from 50-90% depending on the
anatomical site. For example, liver metastases are associated with the highest
sensitivity followed by skeletal and lung33. Some studies have also suggested that
about two-thirds of patients display elevations of CA 15-3 either before or at the time
of recurrence, with a lead time ranging from 2-9 months45. Unfortunately, some other
studies were not as positive.
CA 15-3 is useful in the monitoring of response to either endocrine therapy or
cytotoxic therapy48. It should be noted that ASCO states that a marker cannot, in any
situation, stand alone to define response to treatment34. To further complicate the
matter, the magnitude of variation (also called the “critical difference”) between
successive marker levels that constitutes a clinically significant change is not well
defined. This “critical difference” depends on both the analytical imprecision of the
assay and the normal intra-individual biological variation. In general for CA 15-3, it
has been estimated that at least a 30% change is required before successive marker
concentrations can be regarded as significantly altered49. Having stated this, another
point to consider is the phenomena of “tumor marker spike”50. This refers to an
increase in tumor marker levels following initiation of chemotherapy due to massive
Chapter 1: Introduction 18
neoplastic cell necrosis induced by cytotoxic agents. In fact, this can be observed in
30% of patients who show a response to the therapy. Normally, the peak usually
occurs within 30 days from commencement of therapy but marker levels can remain
elevated for as long as 3 months. Therefore, marker values should be evaluated with
caution.
1.2.5.2 CEA
CEA is a single-chained glycoprotein, 640 amino acids, with a mass of 150-
300 kDa. It is an adhesion molecule that is part of the immunoglobulin superfamily51.
Therefore, CEA might play a role in cellular-matrix recognition. CEA can be
measured by commercially available immunoassays using either a radioisotope or a
non-radioactive (enzyme or chemiluminenscent) label. Currently, CEA is used in the
clinic for post-operative surveillance in patients with no evidence of breast cancer.
Overall, CEA appears to be less sensitive than CA 15-3/BR 27.29. Furthermore,
CEA is also used in the clinic for monitoring therapy in advanced disease, especially
if CA 15-3/BR 27.29 is not elevated34. The ability of CEA to predict prognosis has
been conflicting47. Only 50-60% of patients with metastatic disease will have
elevated CEA levels, compared with 75-90% who have elevated levels of CA 15-352.
1.2.5.3 Circulating levels of HER-2/neu
Some markers are able to predict response to or resistance to a specific
therapy. A classic example of this is the circulating levels of HER-2, an oncoprotein
of 185kDa. It is a transmembrane glycoprotein whose overexpression is present in
Chapter 1: Introduction 19
20-30% of primary breast cancers and is associated with poor prognosis, short
survival and recurrence. HER-2 can be proteolytically cleaved and circulating HER-2
ectodomain can be detected in 80% of patients with tumors overexpressing HER-2
compared with 3% of those with tumors not overexpressing the oncoprotein. The
negative prognostic effect of high circulating levels of HER-2 ectodomain seems to
be related to the resistance to chemotherapy such as paclitaxel (a mitotic inhibitor)
and doxorubicin (a DNA interacting drug)53,54.
1.2.5.4 Other promising serological breast cancer markers
High circulating levels of different hormones have been found to represent a
risk factor for breast cancer development. Elevated concentrations of prolactin,
insulin, insulin-like growth factor type I and androgens (testosterone) are frequently
detectable in subjects who finally develop malignant breast neoplasms. Moreover,
several circulating molecules have been revealed to be associated with patient
outcome including cyclins and p53 (cell cycle controllers), matrix metalloproteinases
(MMPs), urokinase plasminogen activator (uPA) and its inhibitor PA 1-1, cathepsins
(involved in local invasion and metastasis) and vascular endothelial growth factors
(angiogenesis). However for most of these potential markers, their real impact of
their application in clinical practice is currently unknown. Recently, it was
recommended that uPA/PA1 measurements by ELISA on breast cancer tissue may
be used for evaluating prognosis in patients newly diagnosed with node negative
breast cancer34. Low levels of these markers are associated with low risk of
recurrence.
Chapter 1: Introduction 20
In summary, measurement of circulating tumor marker levels in breast cancer
is most established in advanced disease so its clinical roles are to detect
recurrences in asymptomatic patients and to monitor anti-neoplastic treatments55.
1.2.5.5 Non-serological markers for breast cancer
Currently, the hormone receptors estrogen and progesterone are used in the
clinic as breast cancer tissue-based markers. Estrogen receptors (ER) are used for
predicting response to hormone therapy in both early and advanced breast cancer
and in combination with other factors for assessing prognosis in breast cancer34. ER
alone is a relatively weak prognostic factor. In a meta-analysis, it was found that ER-
positive patients were 7-times less likely to develop recurrent disease than ER-
negative patients after at least 5 years of adjuvant tamoxifen treatment10.
Progesterone receptor (PgR) is usually combined with ER for predicting response to
hormone therapy. The last tissue-based marker used in the clinic for breast cancer is
HER-2/neu. It is used to determine prognosis and it is most useful in node-positive
patients. There is conflicting data in node-negative patients. HER-2 tissue levels is
also used for selecting patients with either early or metastatic breast cancer for
treatment with Trastuzumab (Herceptin). Finally, the genetic markers for breast
cancer include BRCA1 and BRCA2, which is used in the clinic in some specialized
centers. Both of these markers are used for identifying individuals who are at high
risk of developing breast or ovarian cancer in high risk families.
Chapter 1: Introduction 21
1.2.6 Renewed interest in discovering novel breast cancer biomarkers
Rational management of this disease requires the availability of reliable
diagnostic, prognostic and predictive markers. Current therapies for advanced
cancers are elusive. Novel non-invasive methods for detecting breast cancer early in
the course of the disease have the potential to reduce morbidity and mortality, as
well as receive a higher compliance rate by patients undergoing screening24. In
addition, the clinical course of breast cancer is highly variable, so it is also crucial to
be able to predict the course of the disease in individual patients (prognosis) to
ensure adequate treatment and surveillance. Moreover, metastatic breast cancer is
regarded as incurable and thus, the goal of treatment is generally palliative at this
stage. In this context, the use of serial measurements of serum tumor markers is
potentially useful in deciding whether to persist in using a particular type of therapy,
to terminate its use or to switch to an alternative therapy. Therefore, optimal
management of patients with breast cancer requires the use of tumor markers. As
well, the risk of cancer recurrence is high in those who have previously had cancer,
even for those who have been in remission for five years. Cancer survivors
constitute a high-risk group that would benefit from improved tests for early detection
of disease recurrence. Novel biomarkers for breast cancer should have utility for
early breast cancer diagnosis, prognosis and/or prediction of therapeutic response.
Every era of biomarker discovery seems to be associated closely with the
emergence of a new and powerful analytical technology. The past decade has
witnessed an impressive growth in the field of large-scale and high-throughput
biology, which has contributed to an era of new technology development. The
Chapter 1: Introduction 22
completion of a number of genome sequencing projects, the discovery of oncogenes
and tumor-suppressor genes, and recent advances in genomic and proteomic
technologies, together with powerful bioinformatics tools, will have a direct and major
impact on the way the search for cancer biomarkers is currently conducted. Early
cancer biomarker discoveries were mainly based on empirical observations, such as
the overexpression of CEA. The modern technologies are capable of performing
parallel rather than serial analyses, and can help to identify distinguishing patterns
and multiple markers rather than a single marker; such strategies represent a central
component and a paradigm shift in the search for novel biomarkers.
These breakthroughs have since paved the way for countless new avenues
for biomarker identification. Very few serum tumor markers, however, have been
introduced to the clinic over the past 15 years56. The next two sections will highlight
some mechanisms behind biomarker elevation in biological fluids and outline
strategies for novel marker identification. These strategies should facilitate delivery
of potential candidate molecules for cancer diagnosis, prognosis and prediction of
therapy. These projected discoveries may be instrumental in substantially reducing
the burden of cancer by providing prevention, individualized therapies and improved
monitoring post-treatment.
1.3 Mechanisms of biomarker elevation in biological fluids
Some of the major mechanisms by which molecules can be elevated in
biological fluids during cancer initiation and progression are discussed below. Such
molecules could serve as effective cancer biomarkers.
Chapter 1: Introduction 23
1.3.1 Gene over-expression
The protein encoded by a gene can be expressed in increased quantities due
to increases in gene or chromosome copy number (i.e. gene amplification) or
through increased transcriptional activity. The latter could be the result of
imbalances between gene repressors and activators. Epigenetic changes, such as
DNA methylation, are also known to affect gene expression. On a larger scale,
chromosomal translocations can result in gene regulation by promoters that are
sometimes enhanced by steroid hormones;57 transposons can also serve a similar
role.
An example of a putative biomarker is the protein human epididymis protein 4
(HE4), which is overexpressed in ovarian carcinoma. Using cDNA microarrays to
identify overexpressed genes in ovarian carcinoma, 101 transcripts were shown to
be overexpressed in ovarian cancers compared with normal tissues58,59. Real-time
polymerase-chain reaction (PCR) of an independent set of benign and malignant
tissues confirmed that 12 of the transcripts were indeed overexpressed in ovarian
cancers. Two of them, WDFC2 (also known as HE4) and MSLN, seemed to have
the highest selectivity. Quantification of HE4 protein levels in serum revealed that it
can be a potential biomarker for ovarian cancer60; though, clinical evaluation is
pending. Gene and protein expression of HE4 in a large series of normal and
malignant adult tissues, however, showed that HE4 is present in pulmonary,
endometrial and breast adenocarcinomas, in addition to positive staining in ovarian
carcinoma61.
Chapter 1: Introduction 24
1.3.2 Increased protein secretion and shedding
Given that 20–25% of all proteins are secreted, aberrant secretion or
shedding of membrane-bound proteins with an extracellular domain (ECD) is
another means by which molecules can be elevated in biological fluids. Alterations in
the signal peptide of proteins caused by single nucleotide polymorphisms may result
in atypical secretion patterns62. Moreover, elevation of molecules in biological fluids
can be the result of a change in the polarity of the cancer cells, which could result in
the release of cancer-associated glycoproteins into the circulation. Increased
expression of proteases that cleave the ECD portion of membrane proteins
represents another possibility for increased circulating levels.
Many proteins are secreted into the circulation such as AFP, which is rapidly
released from both normal and cancer cells63. A classic example of shedding of
membrane proteins into fluids (and thus serving as a cancer biomarker) is HER-2
(also known as ERBB2). HER-2 is a cell membrane surface-bound tyrosine kinase
involved in cell growth and differentiation64. Its overexpression is associated with a
high risk of breast and ovarian cancer relapse and death, and HER-2 is the target of
the therapeutic monoclonal antibody trastuzumab (Herceptin, Genetech)65. The
HER-2 protein consists of a cysteine-rich extracellular ligand-binding domain, a short
transmembrane domain, and a cytoplasmic protein tyrosine kinase domain. The
ECD of HER-2 can be released by proteolytic cleavage from the full-length receptor
protein and can be detected in serum. High levels of HER-2 in serum correlate with
poor prognosis in patients with breast cancer66. In 2000, the Food & Drug
Chapter 1: Introduction 25
Administration (FDA) approved the serum HER-2 test, which is the first blood test for
measuring circulating levels of HER-2 to be approved for the follow-up and
monitoring of patients with metastatic breast cancer.
1.3.3 Angiogenesis, invasion and destruction of tissue architecture
Tissue invasion by the tumor might permit direct release of molecules into the
interstitial fluid and subsequent delivery by the lymphatics into the blood. For
epithelial cancer types, the proteins must break through the basement membrane of
the invading tumor before they appear in blood. For example, PSA is abundantly
expressed by prostatic columnar epithelial cells and secreted into the glandular
lumen, comprising a major component of seminal plasma (0.5–3.0 g/l) upon
ejaculation. In healthy men, low levels of PSA enter the circulation by diffusing
through a number of anatomic barriers, including the basement membrane, stromal
layer and the walls of blood and lymphatic capillaries. This process gives rise to a
normal serum PSA range of 0.5–2.0 g/l.
Prostatic carcinomas most often arise in the glandular epithelium of the
peripheral prostate. Although PSA gene transcription is down-regulated in prostate
cancer, PSA protein levels in the circulation of prostate cancer patients increase due
to disruption of the anatomic barriers between the glandular lumen and capillaries.
Concomitant to early-stage prostate cancer is the loss of basal cells, disruption of
cell attachment, degradation of the basement membrane, initiation of
lymphangiogenesis67 and loss of the polarized structure and luminal secretion by
tumor cells. Consequently, PSA levels in the serum can rise to 4–10 g/l. Late-stage
Chapter 1: Introduction 26
prostate cancer is characterized by invasion of tumor cells into the stromal layers
and the circulation, and total loss of glandular organization. This situation allows for
considerable amounts of PSA to leak into the bloodstream, where levels typically
range from 10 to 1000 g/l. It should be mentioned that while PSA is one of the best
biomarkers currently used in the clinic, increased levels of PSA may be observed in
the blood of men with benign prostate conditions, such as prostatitis and benign
prostatic hyperplasia, or with a malignant growth in the prostate, highlighting its lack
of specificity and consequently its associated high false-positive rate.
1.4 Strategies for discovering novel cancer biomarkers
With the introduction of technologies that enabled simultaneous examination
of thousands of proteins and genes in single experiments (such as mass
spectrometry and protein and DNA arrays), renewed interest emerged to discover
novel cancer biomarkers. The new advances include completion of human genome
project, advances in bioinformatics, array analysis (e.g. DNA, RNA, protein), mass
spectrometry-based profiling and identification, laser-capture microdissection, single
nucleotide polymorphisms, comparative genomic hybridization and high-throughput
sequencing. These modern technologies are capable of performing parallel rather
than serial analyses, therefore, they provide opportunities to identify distinguishing
patterns (signatures; portraits) for cancer diagnosis, classification and prediction of
therapeutic response (individualized treatments). Furthermore, they provide the
means by which new, individual tumor markers could be discovered by using
reasonable hypotheses and novel analytical strategies. Given that the tumor-host
Chapter 1: Introduction 27
interface can generate enzymatic cleavage and shedding, and sharing of growth
factors, it is conceivable that either the tumor itself or its microenvironment could be
sources for biomarkers that would ultimately be shed into the serum proteome,
allowing for early disease detection and for monitoring therapeutic efficacy.
Certainly, genomic and proteomic technologies have significantly increased
the number of potential DNA, RNA and protein biomarkers under investigation. A
paradigm shift has recently been realized, whereby single biomarker analysis is
being replaced by multiparametric analysis of genes or proteins. A multiparametric
assay refers to the concept that a panel of markers may provide better clinical
information than the performance of the individual markers that make up the panel.
This has triggered the question of whether cancer has a unique fingerprint (i.e.
genomic, proteomic, metabolomic). A number of strategies for cancer biomarker
discovery that utilize emerging technologies are outlined below, included in it are
discussions regarding their merits and limitations.
1.4.1 Gene-expression profiling
Genomic microarrays represent a highly powerful technology for gene-
expression studies. Microarray experiments are usually performed with DNA or RNA
isolated from tissues, which are then labeled with a detectable marker and allowed
to hybridize to the arrays that are comprised of gene-specific probes representing
thousands of individual genes68. The greater the degree of hybridization, the more
intense the signal, thus implying a higher relative level of expression. Due to the
massive data per experiment, the molecular markers and their expression patterns
Chapter 1: Introduction 28
need to be analyzed by elaborate computational tools, which add an additional layer
of statistical complexity. Two basic analyses are unsupervised and supervised
hierarchical clustering algorithms69; the latter identify gene-expression patterns that
discriminate tumors on the basis of pre-defined clinical information70. In addition,
quantitative real-time PCR is generally considered the ‘gold standard’ against which
other methods are validated. The cancer sub-classification hypothesis states that
gene-expression patterns identified using DNA microarrays can predict the clinical
behavior of tumors71. The proof-of-principle for the cancer sub-classification
hypothesis has been provided for various malignancies, such as leukemias, breast
cancers and many other tumor types72-78. For example, results from gene-array
technologies have enabled breast cancers to be classified into prognostic categories
depending on the expression of certain genes. The 70-gene-panel microarray study
of survival prediction led to development of MammaPrint,79 which in February 2007
became the first multi-gene panel test to be approved by the US FDA for predicting
breast cancer relapse. Another gene-expression profile, Oncotype DX, based on
quantitative RT-PCR, has been commercially available for the same use since 2004.
National Surgical Adjuvant Breast and Bowel Project (NSABP) clinical trials initiated
in the 1980s were retrospectively analyzed with a median follow-up of 14 years, to
validate the gene signature identified by Oncotype DX for predicting the recurrence
of tamoxifen-treated, node-negative breast cancer80. Oncotype DX and MammaPrint
use different analytical platforms and despite their similar clinical indication, they
have only a single gene overlap in their panels. Nevertheless, over the past decade,
a tremendous growth in the application of gene-expression profiling has been
Chapter 1: Introduction 29
witnessed. It has contributed to the cancer subclassification theory,71 insights into
cancer pathogenesis and to the discovery of a large number of diagnostic markers81.
Michiels et al. recently performed a meta-analysis of seven of the most
prominent studies on cancer prognosis that used microarray-based expression
profiling82. Surprisingly, in five of the seven studies on cancer prognosis, the original
data could not be reproduced83. The other two studies showed much weaker
prognostic information than the original data. The meta-analysis also indicated that
the list of genes identified as predictors of prognosis was highly unreliable and that
the molecular signatures were strongly dependent on the selection of patients in the
training sets. This meta-analysis suggests that the results of the aforementioned
studies are over-optimistic and that they need careful validation and larger sample
sizes before conclusions for their clinical utility can be drawn. Despite promising
proof-of-principle data, discovery of novel subtypes of various carcinomas using
gene arrays and the use of these technologies for discovery of diagnostic markers,
these new tools are not yet recommended for widespread clinical use by either
organizations issuing clinical guidelines or by expert panels84. However, it has been
recommended that in newly diagnosed patients with node-negative, estrogen-
receptor positive breast cancer, the Oncotype DX assay can be used to predict the
risk of recurrence in patients treated with tamoxifen. The precise clinical utility and
appropriate application for other multiparameter assays, such as the MammaPrint
assay (a 70-gene panel for predicting the likelihood that cancer will recur), the
"Rotterdam Signature," (a 76-gene panel for predicting low or high risk of developing
metastatic disease) and the Breast Cancer Gene Expression Ratio (based on the
Chapter 1: Introduction 30
ratio of the expression of two genes: the homeobox gene-B13 (HOXB13) and the
interleukin-17B receptor gene (IL17BR) where in breast cancers that are more likely
to recur, the HOXB13 gene tends to be over-expressed, while the IL-17BR gene
tends to be under-expressed) are under investigation34.
1.4.2 Mass spectrometry-based profiling
Proteomic-pattern profiling is a recent approach to biomarker discovery.
Given that mRNA information does not best reflect the function of proteins, which
are the functional components within organisms, the utilization of tumor diagnosis or
subclassification via proteomic patterns seems promising. The rationale is that
proteins produced by cancer cells or their microenvironment may eventually enter
the circulation and that the patterns of expression of these proteins could be
assessed by mass spectrometry and used for diagnostic purposes, in combination
with a mathematical algorithm. Mass spectrometry-based methods for proteomic
analysis have improved and include more-advanced technology that allows for
higher mass accuracy, higher detection capability, and shorter cycling times, thereby
enabling increased throughput and more-reliable data85. Technologies such as
differential in-gel electrophoresis, two-dimensional polyacrylamide gel
electrophoresis and multidimensional protein-identification technology can be used
for high-throughput protein profiling. The technology that has received considerable
attention over the past involves the use of a minute amount of unfractionated serum
sample added to a “protein-chip”, which is subsequently analyzed by surface-
enhanced laser-desorption ionization time-of-flight mass spectrometry (SELDI-TOF-
Chapter 1: Introduction 31
MS) to generate a proteomic signature of serum86. These patterns reflect part of the
blood proteome, but without knowledge of the actual identity of the proteins. The
potential of proteomic pattern analysis was first demonstrated in the diagnosis of
ovarian cancer87. In this study, exceptional results were seen with a sensitivity of
100% (even for early-stage disease) and 95% specificity. These numbers are far
superior to the sensitivities and specificities obtained with current serologic cancer
biomarkers. Since then, this approach has been extended to a number of other
cancer types, such as breast, prostate, colon, liver, renal, pancreatic, head and neck
cancers88-94.
In spite of the optimism for this approach, a number of important limitations
were subsequently identified95. The limitations included bias from artifacts related to
the clinical sample collection and storage, the inherent qualitative nature of mass
spectrometers, failure to identify well-established cancer biomarkers, bias in
identifying high-abundance molecules within the serum and disagreement between
peaks generated by different research labs96-98. Another limitation includes possible
bioinformatic artefacts. Baggerly et al. showed that background/matrix peaks can
achieve a high level of discrimination between normal and cancer patients99. Despite
a 5-year lapse since the first report, no product has reached the clinic and no
independent validation studies have been published. Guideline-developing
organizations and expert panels do not currently recommend serum proteomic
profiling (identification of discriminating peaks) for clinical use34,100.
Chapter 1: Introduction 32
1.4.3 Peptidomics
The low-molecular-weight plasma or serum proteome has been the focus of
recent attempts to find novel biomarkers101. Peptides are essential for many
physiological processes such as blood pressure (angiotensin II) and blood glucose
(insulin) regulation. It has been suggested that “the low molecular-weight region of
the blood proteome is a treasure trove of diagnostic information ready to be
harvested by nanotechnology”102. The low-molecular-weight serum proteome has
been characterized by ultrafiltration, enzymatic digestion, and liquid chromatography
coupled to tandem mass spectrometry103,104 or via a top-down proteomics approach
(intact peptide is distinguished directly by its fragment ions)105 or by means of
pattern profiling106. Informative diagnostic peptides that are generated after
proteolysis of high abundance proteins by the coagulation and complement
enzymatic cascades can be identified by mass spectrometry. These proteomic
patterns were claimed to distinguish not only controls from cancer patients107 but
also between various types of cancers106.
One major consideration is that these peptides present in the serum are
derived from a low number of high abundance proteins. Koomen et al. studied
peptides in serum and concluded that sample collection is of immense importance,
and could give rise of artifacts, and that serum is not ideal for proteomic experiments
as it contains significant endoproteolytic and exoproteolytic enzymatic activity108.
This finding raises concerns regarding peptidomics data generated by profiling
technologies. Peptidomic profiling might represent nothing more than peptides
cleaved during coagulation or functions inherent to plasma or serum, including
Chapter 1: Introduction 33
immune modulation, inflammatory response and protease inhibition109. Many of the
aforementioned caveats associated with mass spectrometry-based protein profiling
technologies also apply to peptidomics.
1.4.4 Cancer biomarker family approach
The premise for the ’cancer biomarker family’ approach is that if a member of
a protein family is already an established biomarker, then, other members of that
family might also be good cancer biomarkers. For example, PSA is a member of the
human tissue kallikrein family. Kallikreins are secreted enzymes with trypsin-like or
chymotrypsin-like serine protease activity. They consist of a family of 15 genes
clustered in tandem on chromosome 19q13.4110. PSA (or kallikrein 3) and human
kallikrein 2 currently have important clinical applications as prostate cancer
biomarkers111. Other members of the human kallikrein family have been implicated
in the process of carcinogenesis and are currently being investigated as biomarkers
for diagnosis and prognosis. For example, human kallikrein 6 has been studied as a
novel ovarian cancer biomarker112. It was found that elevated serum levels of this
protein were associated with late-stage tumor, high grade and serous histotype and
with resistance to chemotherapy112. In general, levels of kallikrein 6 were linked to
decreased disease-free and overall survival, thus serving as an independent and
unfavorable prognostic indicator. Similarly, kallikreins 3, 5 and 14 have been shown
to be increased in the serum of breast cancer patients, thus potentially serving as
diagnostic markers. Being serine proteases, these proteins could be implicated in
tumor progression through extracellular matrix degradation.
Chapter 1: Introduction 34
1.4.5 Secreted protein approach
In theory, a candidate serological tumor marker should be a secreted protein,
because it has the highest likelihood of entering the circulation. Examination of
tissues or biological fluids near to the tumor site of origin may facilitate identification
of candidate molecules for further investigation. The increasing evidence that tumor
growth and progression is dependent on the malignant potential of the tumor cells as
well as on the microenvironment surrounding the tumor (e.g. stroma, endothelial
cells, immune and inflammatory cells), further supports this approach113,114. A
number of technologies can be utilized, but for systematic characterization of
proteins in complex mixtures, mass spectrometry is the preferred technology. In the
case of breast cancer, breast tissue, nipple aspirate fluid, breast cyst fluid, tumor
interstitial fluid and breast cancer cell lines can all be explored. The tumor interstitial
fluid that perfuse the tumor microenvironment in invasive ductal carcinomas of the
breast was examined by proteomic approaches115. Over 250 proteins were identified,
many of which were relevant to processes such as cell proliferation and invasion.
It should be noted that some of the widely used cancer biomarkers such as CEA, CA
125 and HER-2 are actually membrane-bound proteins, which are shed into the
circulation. The identification of secreted proteins in tissues or other biological fluids
does not necessarily imply that the proteins will be detectable in the sera of cancer
patients. Serum-based diagnostic tests depend on the stability of the protein, its
clearance, its association with other serum proteins and the extent of post-
translational modifications.
Chapter 1: Introduction 35
1.4.6 Other prominent strategies
A number of other cancer biomarker strategies exist. One approach that is
gaining popularity is based on protein arrays. Chinnaiyan and colleagues recently
published data suggesting that autoantibody signatures might improve the early
detection of prostate cancer116. Using a combination of phage-display technology
and protein microarrays, they identified new autoantibody-binding peptides derived
from prostate cancer tissue. Another prevailing view is that tumor-associated
antigens could serve as biosensors for cancer because tumors naturally elicit an
immune response in the host. Moreover, breaking the cancer genetics dogma that
hematologic malignancies result from chromosomal translocations117 and that
mutations underlie epithelial solid tumors, gene-fusions as a result of translocations
in prostate cancer have been reported using gene-expression datasets57. This
translocation seems to be frequent (occurring in 40–50% of cases), may have
prognostic value and it may be an early event in carcinogenesis. In addition, mass
spectrometry-based imaging of fresh-frozen tissue sections has yielded a number of
potential candidate molecules118,119. Besides proteomic profiling of serum, attempts
have been made to decipher the serum proteome via numerous fractionation
schemes to simplify and reduce the dynamic range of molecules present in serum120.
Finally, the use of animal models involving human tumor xenograft experiments
have also shown promise for biomarker discovery121,122.
Chapter 1: Introduction 36
1.5 Emergence of proteomics and relevance to breast cancer
Recently, we have witnessed the emergence of the “omics” era with
proteomics, peptidomics, degradomics, metabolomics, and so forth. However, the
only “omics” other than genomics to become a “buzzword” is proteomics, a term
used for studying the proteome of an organism. Most of the proteomic technology
platforms for biomarker discovery are centered on the implementation of mass
spectrometric techniques in conjunction with several other analytical techniques
such as gel electrophoresis, isoelectric focusing and chromatography. Furthermore,
these technologies have matured over the past few years and hence are capable of
identifying thousands of proteins simultaneously.
1.5.1 Basic components of a mass spectrometer
Mass spectrometry (MS) is an analytical technique that measures the masses
of individual molecules and atoms. Ultrahigh detection sensitivity and high molecular
specificity are hallmarks of MS. The advantages of MS are its ability to provide
molecular mass with high specificity, provide high detection sensitivity (detects a
single molecule), determine structures of most classes of unknown compounds, its
application to all kinds of samples (volatile, non-volatile, polar, nonpolar, solid, liquid,
gaseous materials) and its ability to analyze complex mixtures123. All information
gathered from a mass spectrometer comes from the analysis of gas-phase ions.
Therefore, the first step is to convert the analyte molecules into gas-phase ionic
species (performed by an ionization source) because once gaseous, its motion can
be manipulated (this cannot be done with neutral species)124. Then a mass analyzer
Chapter 1: Introduction 37
separates the molecular ions and their charged fragments according to mass-to-
charge (m/z) ratio. The ion current due to these mass-separated ions are detected
by a detector and displayed as a mass spectrum. All of these steps are carried out
under high vacuum to enable ions not to collide with other species. The generated
spectra are then analyzed by various algorithms125.
A variety of ionization sources exist, such as electron ionization, chemical
ionization, fast atom bombardment (FAB), field ionization, matrix-assisted laser
desorption ionization (MALDI) and electrospray ionization (ESI)123. The choice of
method of ionization depends on the nature of samples used. Electron ionization
was the most popular method for organic compounds since it was useful for
thermally stabile and relatively volatile compounds. Its upper mass limit of
compounds was 1kDa. Desorption ionization was for non-volatile and thermally
unstable compounds. MALDI was developed to study masses of greater than
200kDa. In MALDI, the sample is mixed with a matrix which is then irradiated with a
laser beam of short pulses123. The matrix would absorb energy at the wavelength of
the laser radiation and the energy is then transferred to the sample as laser beam
cause evaporation of the matrix (most applications use UV lasers (N2 lasers) or IR
lasers). Finally ESI encompasses three different processes: droplet formation,
droplet shrinkage and gaseous ion formation. A solution-based sample is passed
through an electrostatic field (3-4kV) generating an aerosol of fine mist of charged
droplets124. Nitrogen gas is normally added to assist in evaporation of solvent from
those charged droplets.
Chapter 1: Introduction 38
Following ionization, the ions enter the mass analyzer which separates gas-
phase ions generated from the ionization source based on their m/z ratio. Ion motion
in the mass analyzer can be manipulated by electric or magnetic fields, to direct ions
to a detector in an m/z-dependent manner. The commonly used mass analyzers can
be broadly grouped into beam (time-of-flight (TOF) and quadrupole) and trapping
(ion-trap and Fourier-transform ion-cyclotron resonance (FT-ICR)) analyzers.
Specifically, an ion-trap MS stores and manipulates ions in time rather than in space.
Quadrupole ion-trap instruments use an oscillating electric field for storage and
mass analysis of ions. The performance of a mass analyzer is based on mass range
(maximum allowable mass that can be analyzed), resolution (ability to separate 2
neighboring mass ions), and scan speed detection sensitivity (smallest amount of an
analyte that can be detected at a certain confidence level).
Finally, a detector measures the electric current in proportion with the number
of ions striking it to generate a mass spectrum which is used to search databases
using pattern matching algorithms such as MASCOT, SEQUEST and X!Tandem, to
generate a list of identified proteins per experiment. Tandem mass spectrometry
refers to mass selection, fragmentation and mass analysis. In this instance, in MS1,
a specified ion from a mixture of ions that are produced in the ion source is selected.
This ion undergoes fragmentation via collisions with neutral gas atoms and in MS2,
the products are analyzed.
Chapter 1: Introduction 39
1.5.2 Breast cancer proteomics: Sources to mine for biomarkers
In 1974, sera from normal volunteers, patients with a variety of non-
neoplastic diseases and patients with malignant or benign tumors were examined by
two-dimensional poly-acrylamide gel electrophoresis126. The authors did not identify
specific proteins, but rather, observed a differential expression pattern discriminating
the groups studied. However, despite the early searches for cancer biomarkers and
despite the rapidly advancing proteomic techniques with superior sensitivity, none of
the potential biomarkers identified in proteomic experiments has found a niche for
the management of breast cancer at the clinical level127.
Nevertheless, proteomics and in particular mass spectrometry, has been
employed to identify novel breast cancer biomarkers. Such studies have
predominantly examined breast tumor tissues and biological fluids including serum,
plasma, nipple aspirate or ductal lavage as well as cancer cell lines128. The intent of
examining these different sources include obtaining a better understanding of
mammary oncogenesis129 and potentially leading to improvements in screening,
diagnosis as well as prognosis and/or prediction of therapeutic response. Since
breast cancer is a complex and heterogenous disease, no single model or biological
source is expected to mimic all aspects of the disease130. For this reason, an
approach to biomarker development should be well conceived and play to the
strengths of current technologies while acknowledging and addressing the
limitations127.
One of the sources to mine for potential biomarkers is serum or plasma of
breast cancer patients, compared to serum of healthy controls. Exploring biological
Chapter 1: Introduction 40
fluids is an attractive way to look at secreted proteins. The analysis of plasma for
breast cancer biomarkers (and other cancer markers) is currently ongoing56,131. It
has been estimated that blood contains more than 100,000 different protein forms
with abundances that span 10-12 orders of magnitude56. Unfortunately, the
discovery of tumor-derived biomarkers by analyzing plasma is challenging because
the 20 most abundant plasma proteins (concentration ranges in the mg/mL range)
account for 99% of the total protein mass and impede detection of lower abundance
tumor antigens56. Potential tumor markers are expected to exist in the low ng-pg/mL
concentration range. Currently, without up-front fractionation techniques, the
presence of major proteins in blood represents a technological challenge for the
detection of the less abundant ones. The main concern is suppression of ionization
of low abundance proteins by high abundance proteins such as albumin and
immunoglobulins.
Fortunately for breast cancer, the mammary gland offers the possibility to
access local fluids, which could be potential sources for breast cancer biomarker
discovery. Fluid found within the ductal and lobular system of the breast can be
extracted through the nipple using an aspiration device to obtain nipple aspirate fluid
(NAF)132. Non-pregnant and non-lactating women continuously secrete and reabsorb
this fluid133. Consequently, NAF is a viable source to mine since it surrounds the
ducts and breast epithelial cells134-136. Despite this, only a limited number of proteins
have been identified in NAF, predominantly owing to the presence of high
abundance plasma proteins135,136.
Chapter 1: Introduction 41
Alternatively, another source to mine for potential biomarkers is at the tissue
level – examining normal mammary gland tissues and breast tumors137.
Nevertheless, these structures are complex, incorporating different cell types with
different proportions such as epithelial cells, adipocytes, myoepithelial cells and
fibroblasts. Due to this multifaceted population, breast tumor cells comprise a minor
fraction of this whole population of cells. Furthermore, tumor biopsies also contain
blood components; therefore proteomic analysis of breast tumor tissues also
identifies proteins from circulating cells and from plasma138. For tissue proteomics,
the hypothesis is that certain proteins originating in the tissue could subsequently
appear and be monitored in the bloodstream. Leaky capillary beds, local production
of proteases, and the high rates of cell death within the tumor mass are expected to
facilitate shedding or secretion of tumor proteins into the bloodstream. But given the
complexity of analyzing tissues, microdissection can be regarded as a reasonable
alternative for selectively isolating individual cell types. The limitations with this
approach though are the low amounts of material obtained, the large amount of
sample that is needed to perform an experiment and the quality of the dissected
material which interfere with proteomic experiments139.
Interestingly, there has also recently been an effort to take advantage of
animal models in breast cancer research and their examiniation by proteomics140.
For example, a conditional HER-2/neu-driven mouse model of breast cancer was
used to examine the proteome of tumor and normal mammary tissue122. The authors
identified over 700 proteins. A caveat to using an animal model to study human
disease is whether the same genetic alterations transform both mouse and human
Chapter 1: Introduction 42
epithelial cells130. Furthermore, some important aspects of breast cancer, particularly
steroid hormone dependence, are not well modeled in mice141. Regardless of the
species differences, examining the tissues and/or biological fluids in the rodent
model has the same limitations as examining them in humans.
1.5.3 Tissue culture based biomarker discovery platform
Despite optimistic views that many more protein cancer biomarkers will be
discovered through various high-throughput techniques, very few, if any, serum
cancer biomarkers have been introduced at the clinic. These molecules have not yet
been identified presumably because their concentration in serum and/or biological
fluids are too low and therefore cannot be measured or purified, unless specific
immunological reagents and highly sensitive ELISA methods are available.
Therefore, in the initial discovery phase for novel cancer biomarkers, a less complex
sample (elimination of high abundance proteins) is essential. Although clinical
validation of biomarkers must address variability arising from genetic, environmental,
and behavioral differences among humans, optimization of the discovery and
candidate verification processes involves controlling as many biological variables as
possible so that the current technologies being employed can be directly evaluated.
Moreover, given that a secretome in a tumor microenvironment contains the
extracellular matrix, constituted by proteins, receptors and adhesion molecules, as
well as a whole host of secreted proteins such as cytokines, chemokines, growth
factors and proteases – all of which can be potential biomarkers142, a cell culture
based model for sampling the secretome associated with breast cancer appears
Chapter 1: Introduction 43
promising. Secreted proteins play important roles in physiology and pathophysiology
and they can act locally and systemically in the body. The secretome reflects the
functionality of a cell in a given environment143. For cell culture-based proteomic
studies, the hypothesis is that proteins or their fragments originating from cancer
cells (hence present in the conditioned media) may eventually enter the circulation.
Conditioned media (CM) as a source to mine for biomarkers is increasingly gaining
popularity, as illustrated by the rise in publications over the past few years.
Breast cancer cell lines have been the most widely used models to
investigate how proliferation, apoptosis and migration become deregulated during
the progression of breast cancer144. A number of studies have used a cell culture
model system where the cells were grown in serum-free media to perform proteomic
analysis145-150. The clinical relevance of using a cell culture model to understand
biological processes and functions has been examined. Using DNA microarrays, the
molecular subtypes of 31 breast cell lines yielded two discriminating clusters
corresponding to luminal cell lines and basal/mesenchymal cell lines151. The basal
subtype was further subdivided into Basal A and Basal B; this subdivision was not
observed in primary tumors. In primary tumors, gene expression patterns have been
used to classify breast tumors into five clinically relevant subgroups (luminal A,
luminal B, basal, ERBB2-overexpressing and normal-like)152,153. In general, the
luminal subtypes are estrogen receptor (ER) positive and grow slowly whereas
basal-type lack ER and are usually high-grade cancers that grow rapidly. Recently,
the molecular taxonomy has been confirmed by protein expression profiling154,155.
Also recently, it was found that cell lines display the same heterogeneity in copy
Chapter 1: Introduction 44
number and expression abnormalities as the primary tumors156. Indeed, cancer cell
lines that are invasive in culture do form tumors in immune deficient mice. This is
primarily because the cancer cells in culture represent the tumor-forming cells in vivo.
While no single cell line is truly representative, a panel of cell lines show the
heterogeneity that is observed in primary breast cancers156. Table 1.2 outlines some
of the advantages and disadvantages to using a cell culture-based approach to
discover biomarkers using proteomics.
Chapter 1: Introduction 45
Table 1.2: Advantages and disadvantages of a cell culture-based model for biomarker discovery ADVANTAGES
Cell lines are readily available Cost-effective High-throughput Easily modified, versatile Easily propagated Enables secretome analysis Permits detection of low abundance proteins (do not represent the dynamic
range problem associated with plasma; less complex mixture) Allows for reproducibility (under well-defined experimental conditions, it yields
reproducible and quantifiable results), growth standardized The proteome of cancer cells should reflect the genetic alterations they
harbor Cancer cells can be grown as xenografts
DISADVANTAGES
No single cell line will reflect the heterogeneity of cancer Multiple variants of the same cell line exist Host stromal environment influencing tumor development and progression is
absent A reductionist approach; cannot mimic complexity of mammary gland; does
not take into account the complex interplay between cell types and the tissue microenvironment
Does not provide insight into the evolution of breast cancer from benign lesions and normal breast epithelial cells
Chapter 1: Introduction 46
1.6 Purpose and aims of the present study
1.6.1 Rationale
Proteins are more diverse than DNA or RNA and therefore carry more
information than nucleic acids, since alternative splicing and post-translational
modifications result in far more species of proteins from the same gene. Proteins are
also more dynamic and reflective of cellular physiology. However, despite optimistic
views that many more protein cancer biomarkers will be discovered through various
high-throughput techniques, very few, if any, serum cancer biomarkers have been
introduced at the clinic over the last 15 years.
The classical tumor markers carcinoembryonic antigen (CEA) and alpha-feto
protein (AFP) were discovered in the ‘60s mainly due to the introduction of novel and
relatively sensitive immunological techniques (such as radial immuno-diffusion),
which allowed for the detection of these antigens in cancer tissues with high
specificity and reasonable sensitivity. The most contemporary cancer biomarkers
used at the clinic today (such as carbohydrate antigen CA 125, CA 15.3, CA 19.9
and PSA) were mainly developed due to the emergence, in the late ‘70s, of the
monoclonal antibody technology. Most of these tumor markers were discovered by
using cell lines or tumor extracts as immunogens and then selecting specific
hybridoma clones which recognized these tumour antigens157. Therefore, it is
conceivable that novel tumor markers may be identified in the conditioned media of
cancer cell lines using newly emerging technologies such as mass spectrometry.
The assumption that cancer biomarkers to be discovered will be secreted or shed
proteins is reasonable, since it is expected that secreted or membrane-bound
Chapter 1: Introduction 47
proteins, the latter having the potential to be cleaved, have a high chance of
reaching the circulation and can be found in serum, where they can be measured
with immunological techniques. All currently known cancer biomarkers are indeed
secreted or shed proteins.
Therefore, in the initial discovery phase for novel cancer biomarkers, a less
complex sample (elimination of high abundance proteins) is essential. Using a cell
culture based model, a proteomic platform for biomarker discovery can be utilized,
which consists of three major phases. The first phase involves the identification of
markers using multi-dimensional protein identification technology (discovery phase).
Following identification, the proteins must be prioritized to select a subset of marker
candidates based on several criteria such as availability of reagent set for assay
development and literature association to disease biology. The second phase
consists of developing preliminary assays to measure the levels of the selected
proteins in a relevant biological fluid comprising cancer and normal patients
(verification phase). The final phase requires expanding the number of samples
used to evaluate only the candidates that continued to show promise in
discriminating cancer from normal from the verification phase (validation phase).
This step involves the development of a robust analytical immunoassay to measure
the proteins accurately in clinical samples.
Unforunately, the set of differentially expressed proteins identified in
proteomic studies thus far are different from one study to another, owing in part to a
lack of experimental standardization and to problems of heterogeneity between
biological materials used. This creates a tremendous challenge to finding statistically
Chapter 1: Introduction 48
relevant biomarkers. Because of these issues, an approach to biomarker
development should be well conceived and play to the strengths of current
technologies while acknowledging and addressing the limitations127.
Chapter 1: Introduction 49
1.6.2 Hypothesis
The evolution to a malignant phenotype involves mutations that often alter the
expression pattern of a variety of genes controlling cell proliferation, differentiation
and cell death. In this respect, cancer patients will likely display a distorted
expression pattern of various proteins early in the course of the disease.
Approximately 20-25% of all cellular proteins are secreted. Therefore, proteins or
their fragments originating from cancer cells or their microenvironment may
eventually enter the circulation. We hypothesize that novel candidate tumor markers
for breast cancer may be secreted or shed proteins and can be harnessed from
tissue culture supernatants of human breast cancer cell lines using mass
spectrometry.
Chapter 1: Introduction 50
1.6.3 Objectives
1. Utilize emerging proteomic technologies such as mass spectrometry to
develop procedures/methodologies to sample the secretome of three human
breast cell lines (discovery phase).
a. Demonstrate that human breast cancer cell lines (MCF-10A, BT474
and MDA-MB-468) can be cultured in large volumes in serum-free
media.
b. Optimize cell culture techniques to minimize cell death by measuring
intracellular protein lactate dehydrogenase (LDH) levels and to
maximize secreted protein concentration.
c. Monitor, whenever possible, the levels of internal positive control
secreted kallikrein proteins (eg. KLK5, KLK6, KLK10) over time in
culture.
d. Positively identify the internal control proteins and other proteins
present in the CM, via a “bottom-up” proteomic approach involving
fractionation and mass spectrometry.
2. Employ various bioinformatic analyses to select the top 10 most promising
candidate molecules to investigate further as serological breast cancer
biomarkers.
a. Focus on extracellular and membrane proteins, differentially expressed
proteins across the cell lines with relevance to disease based on
literature searches and reagent availability.
Chapter 1: Introduction 51
3. Evaluate the discriminatory ability of the 10 molecules in serum of normal and
breast cancer patients using an ELISA (commercially purchased or developed
in-house) (verification phase).
4. Select the top candidate from step 3 for further in-depth analysis using larger
sample size, in combination with currently used breast cancer biomarkers CA
15-3 and CEA (validation phase).
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 52
CHAPTER 2:
ANALYSIS OF THE CONDITIONED MEDIA OF THREE BREAST CELL LINES
The work presented in this chapter was published in Molecular & Cellular Proteomics:
Kulasingam, V. and Diamandis, E.P. Proteomic analysis of conditioned media from three breast cancer cell lines: A mine for
biomarkers and therapeutic targets. Mol Cell Proteomics 2007 6: 1997-2011
Copyright permission has been granted.
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 53
2.1 Introduction
Aberrant secretion or shedding of proteins is commonly associated with
disease, including cancer. The pathogenic signalling pathways involved during the
process of cancer initiation and progression are not confined to the cancer cell itself
but are rather extended to the tumor-host interface113. It is a dynamic environment in
which fluctuating information flows between the tumor cells and the normal host
tissue. Therefore, it is conceivable that either the tumor itself or its microenvironment
could be sources for biomarkers that would ultimately be shed into the serum
proteome, allowing for early disease detection24, monitoring therapeutic efficacy or
understanding the biology of the disease158. Given that approximately 20-25% of all
cellular proteins are secreted, it is reasonable to hypothesize that proteins or their
fragments originating from cancer cells or their microenvironment may eventually
enter the circulation102.
Accordingly, one of the best ways to diagnose cancer early, or to predict
therapeutic response, is to use serum or tissue biomarkers. Carcinoembryonic
antigen (CEA) and carbohydrate antigen 15.3 (CA 15.3) are the most commonly
used tumor markers for breast cancer. Their levels in serum are related to tumor
size and nodal involvement and are recommended for monitoring therapy of
advanced breast cancer or recurrence but are not suitable for population screening
(due to low diagnostic sensitivity and specificity)32,35,36. Currently, mammography
remains the cornerstone of breast cancer screening, despite its disadvantages such
as high false positive and negative rates, hazardous exposure and patient
discomfort16,17. In addition, for women under the age of 40, mammographic
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 54
screening yields a poor sensitivity of 33%18,159. Recent technological advances in
proteomics have opened up new and exciting avenues for the discovery of
biomarkers or for characterization of molecules involved in cancer initiation and
progression.
A number of different proteomic-based approaches have been utilized to
discover and characterize disease-specific molecules. Fluid found within the ductal
and lobular system of the breast can be extracted through the nipple using an
aspiration device to obtain nipple aspirate fluid (NAF)132. Non-pregnant and non-
lactating women continuously secrete and reabsorb this fluid133. Because of the
complex nature of biological fluids relevant to breast cancer, only a handful of high
abundance proteins have been identified in NAF, which illustrate the need to find
another source to mine for the initial biomarker discovery 134,160,161.
A number of studies have used a cell culture model system where the cells
were grown in serum-free media to perform proteomic analysis145-150. Typically,
proteomic analysis of conditioned media (CM) involves culturing the cells in serum-
free media (SFM) to ensure that the collected CM contain no other extraneous
proteins, except for the secreted or shed proteins from the cancer cells, thereby
facilitating their identification through MS. Furthermore, given that the cell lines to be
used are specific to epithelial breast cancer cells, the proteins present in the CM
must originate from the cancer cell and not from the surrounding stroma, thereby
avoiding unnecessary complications in the analyses. Seeding density, incubation
time in SFM, volume of media used, type of SFM, type of tissue culture flasks are all
variables that need to be explored thoroughly to select the most optimal conditions
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 55
for growth. Culture conditions also need to determine the amount of cell death and
autolysis occurring in SFM. Measurements of lactate dehydrogenase (LDH), an
intracellular protein which if measured in the CM of cell lines is an indicator of cell
death, can be utilized. Alternatively, the amount of major cytosolic proteins such as
beta-actin or beta-tubulin can be used to optimize incubation times. Also, when
performing a proteomic analysis of conditioned media from cell lines, it is important
that the cells are extensively washed to remove protein components arising from the
fetal bovine serum (FBS) used in culture. Therefore, an essential experiment to
perform is to incubate tissue culture flasks with no cells added, but treated with the
same wash conditions and incubation times to serve as negative controls. The
proteins identified in the negative controls, predominantly FBS-derived proteins, can
be deleted from the list of proteins identified in the conditioned media as arising from
incomplete washing of the flasks. Moreover, it is known that cell growth is slower in
SFM and that cells are prone to autolysis resulting in non-specific release of
intracellular proteins into the culture supernatant. Hence, it is imporant to obtain a
proteome of the whole cell lysate derived from the same cell samples that were the
sources of CM to consider only proteins found uniquely in the CM versus those that
were found in the cell lysate. Finally, given the selective ionization process of mass
spectrometry, it is important to have at least biological triplicates (same cell line
prepared independently) in the analysis.
In this study, we report a shot-gun proteomics approach to sample the
conditioned media of three human breast cell lines (MCF-10A, BT474 and MDA-MB-
468). MCF-10A, a basal B subtype, with intact p53, was derived by spontaneous
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 56
immortalization of breast epithelial cells from a patient with fibrocystic disease and it
has been used extensively as a normal control in breast cancer studies162. These
cells do not survive when implanted subcutaneously into immunodeficient mice162.
BT474, a luminal subtype, obtained from a stage II localized solid tumor, is positive
for ER and progesterone receptor (PgR), which represent 50-60% of all breast
cancer cases163. This cell line also displays amplification of HER-2/neu or erbB-2 –
which represents 30% of all breast cancer cases64. HER-2/neu is a cell membrane
surface-bound tyrosine kinase involved in signal transduction, leading to cell growth
and differentiation. Its over-expression is associated with a high risk of relapse and
death64 and is the target of the therapeutic monoclonal antibody Herceptin65. Finally,
MDA-MB-468, a basal A-like subtype, obtained from a pleural effusion of a stage IV
patient164, is ER and PgR negative (15-25% of breast cancer) and PTEN negative
(30% of breast cancer)165,166.
These cell lines were cultured in serum-free media (SFM) to ensure that the
collected conditioned media (CM) contain no other extraneous proteins, except for
the secreted or shed proteins from the cancer cells. By collecting and concentrating
large volumes of CM produced from cell lines representing semi-normal (MCF-10A),
non-invasive (BT474) and metastatic origins (MDA-MB-468), the secreted and shed
proteins accumulated in the CM, thereby facilitating their identification through MS.
Our comparative proteomic analysis of the CM of MCF-10A, BT474 and MDA-MB-
468 identified over 600, 500 and 700 proteins, respectively. A large portion of the
proteins was present in all 3 cell lines however, a significant portion contained
proteins that were unique to each of the lines. Among these were the internal control
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 57
proteins, human kallikreins 5, 6 and 10 being identified by MS and ELISA in MDA-
MB-468 cells, at a concentration ranging from 2-50 µg/L. Members of the human
kallikrein family (KLKs) have been implicated in the process of carcinogenesis and
the application of kallikreins as biomarkers for diagnosis and prognosis are currently
being investigated. Kallikreins are secreted enzymes that encode for trypsin-like or
chymotrypsin-like serine proteases110. Prostate-specific antigen (PSA; KLK3),
belonging to the family of human tissue kallikreins, and human kallikrein 2 (KLK2)
currently have important clinical applications as prostate cancer biomarkers111. In
addition to the control proteins, various proteases, receptors, protease inhibitors,
cytokines and growth factors were identified. Finally, spectral counting analysis
revealed promising molecules to investigate further for both understanding the
disease and as potential biomarkers for breast cancer.
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 58
2.2 Materials and Methods
2.2.1 Cell lines
The breast epithelial cell line MCF-10A, and the breast cancer cell lines BT-
474 and MDA-MB-468 were purchased from the American Type Culture Collection
(ATCC), Rockville, MD. MCF-10A was maintained in Dulbecco’s modified Eagle’s
medium and F12 medium (DMEM/F12) supplemented with 8% fetal bovine serum
(FBS), epidermal growth factor (20ng/mL), hydrocortisone (0.5µg/mL), cholera toxin
(100ng/mL) and insulin (10µg/mL). BT-474 and MDA-MB-468 were maintained in
phenol-red-free RPMI 1640 culture medium (Gibco) supplemented with 8% FBS. All
cells were cultured in a humidified incubator at 37oC and 5% CO2 in tissue culture T-
75cm2 flasks.
2.2.2 Cell culture
Approximately 30x106 cells were seeded individually into six 175cm2 tissue
culture flasks per cell line. After 2 days, the RPMI or DMEM/F12 media were
discarded and the cells rinsed twice with 1X phosphate buffered saline (PBS).
Following this, 30mL of Chemically Defined Chinese Hamster Ovary (CDCHO)
serum-free medium (Gibco), supplemented with glutamine (8mM) (Gibco) was
added and the flasks were incubated for an additional 24 hours. The conditioned
media (CM) were collected and spun down to remove cellular debris. CM were then
frozen at -80oC until further use. A 1mL aliquot was taken at the time of harvest to
measure for total protein (Bradford assay), lactate dehydrogenase (LDH) and human
kallikreins 5, 6 and 10 (KLK5, KLK6, KLK10) via ELISA. The adhered cells were
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 59
trypsinized and counted using a hemocytometer. This procedure was repeated
several times for reproducibility. In addition, 30mL of the culture media (RPMI 1640
and DMEM/F12) were subjected to the same conditions as above, with no cells
added, and used for comparison. For the MDA-MB-468 cell lysate experiment, at the
end of 24 hours in SFM, the adhered cells were lyzed using a French Press (Thermo
Electron), where the cells are sheared by forcing them through a narrow space.
Total protein was measured and 400µg of protein from the lysate was added to
60mL of CDCHO medium and processed in the same manner as the CM. The cell
lysate experiment was performed in duplicate.
2.2.3 Sample preparation
Two 30mL CM were combined (60mL) for each cell line, creating 3 biological
replicates per cell line, and dialyzed using a molecular weight cut-off membrane of
3.5kDa. The CM was dialyzed in 5L of 1mM ammonium bicarbonate solution
overnight, at 4oC with two buffer changes. The dialyzed CM was poured equally into
two 50mL conical tubes. The CM was frozen and lyophilized to dryness. The
lyophilized sample was denatured using 8M urea and reduced with dithiothreitol
(DTT, final concentration 13mM; Sigma). Following reduction, the sample was
alkylated with 500mM iodoacetamide (Sigma) and desalted using a NAP5 column
(GE Healthcare). The sample was lyophilized and trypsin (Promega) digested (1:50,
trypsin:protein concentration) overnight in a 37oC waterbath. Following this, the
peptides were lyophilized to dryness.
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 60
2.2.4 Strong cation exchange liquid chromatography
The trypsin-digested dry sample was resuspended in 120µL of mobile phase
A (0.26M formic acid in 10% acetonitrile [ACN]). The sample was directly loaded
onto a PolySULFOETHYL ATM column (The Nest Group, Inc.) containing a
hydrophilic, anionic polymer (poly-2-sulfoethyl aspartamide). A 200Å pore size
column with a diameter of 5µm was used. A one hour fractionation procedure was
performed using a high performance liquid chromatography (HPLC) system (Agilent
1100). A linear gradient of 0.26M formic acid in 10% acetonitrile as the running
buffer and 1M ammonium formate added as the elution buffer was used. The eluent
was monitored at a wavelength of 280nm. Forty fractions, 200µL each, were
collected every minute after the start of the elution gradient. These 40 fractions were
pooled into 8 combined fractions (each pool consisting of 5 fractions) and lyophilized
to ~200µL.
2.2.5 Tandem mass spectrometry (LC-MS/MS)
The 8 pooled fractions per replicate per cell line were C18-extracted using a
ZipTipC18 pipette tip (Millipore; catalogue # ZTC18S096) and eluted in 4µL of 68%
ACN, made up of Buffer A and Buffer B (90% ACN, 0.1% formic acid, 10% water,
0.02% trifluoroacetic acid [TFA]). 80µL of Buffer A (95% water, 0.1% formic acid, 5%
ACN, 0.02% TFA) was added and 40µL were injected onto a 2 cm C18 trap column
(inner diameter 200 µm). The peptides were eluted from the trap column onto a
resolving 5 cm analytical C18 column (inner diameter 75 µm) with an 8 micron tip
(New Objective). The LC set-up was coupled online to a 2-D Linear Ion Trap (LTQ,
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 61
Thermo Inc) mass spectrometer using a nanoelectrospray ionization source (ESI) in
data-dependent mode. Each pooled fraction was run on a 120 minute gradient. The
eluted peptides were subjected to tandem mass spectrometry (MS/MS). DTAs were
created using the Mascot Daemon (v2.16) and extract_msn. The 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 a min. scans per group
of 1.
2.2.6 Data analysis
The resulting raw mass spectra from each pooled fraction were analyzed
using Mascot (Matrix Science, London, UK; version 2.1.03) and X!Tandem (GPM
Manager, version 2.0.0.4) search engines on the non-redundant IPI Human
database V3.16 (62000+ entries). Up to one missed cleave was allowed and
searches were performed with fixed carbamidomethylation of cysteines and variable
oxidation of methionine residues. A fragment tolerance of 0.4 Da and a parent
tolerance of 3.0 Da were used for both search engines, with trypsin as the digestion
enzyme. This operation resulted in 8 DAT files (Mascot) and 8 XML files (X!Tandem)
for each replicate sample per cell line. Scaffold (version Scaffold-01_05_19,
Proteome Software Inc., Portland, OR) was used to validate MS/MS based peptide
and protein identifications. Peptide identifications were accepted if they could be
established at greater than 95.0% probability as specified by the PeptideProphet
algorithm167. Protein identifications were accepted if they could be established at
greater than 80.0% probability and contained at least 1 identified peptide. Protein
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 62
probabilities were assigned by the ProteinProphet algorithm168. Proteins that
contained similar peptides and could not be differentiated based on MS/MS analysis
alone were grouped to satisfy the principles of parsimony. The DAT and XML files
for each cell line plus their respective negative control files (RPMI or DMEM culture
media only) were inputted into Scaffold to cross-validate Mascot and X!Tandem data
files. Each replicate sample was designated as one biological sample containing
both DAT and XML files in Scaffold and searched with MudPit option clicked. The
results obtained from Scaffold were processed using an in-house developed
program that generated the protein overlaps between samples. Protein
identifications were assigned a cellular localization based on information available
from Swiss-Prot, Genome Ontology (GO), Human Protein Reference Database
(HPRD) and other publicly available databases. To calculate the false-positive error
rate, the individual fractions were analyzed using the “sequence-reversed” decoy IPI
Human V3.16 database by Mascot and X!Tandem and data analysis was performed
as mentioned above.
2.2.7 Spectral counting
Using the number of total spectra output from Scaffold, we identified the
differentially expressed proteins using spectral counting. Common peptides among
proteins were grouped and proteins containing more than 10% of their total spectra
from negative control samples were removed and one excel file containing total
proteins identified and their presence (defined by spectral counts) in the 3 cell lines
were generated. A normalization criterion was applied to normalize the spectral
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 63
counts so that the values of the total spectral counts per sample were similar. An
average of the spectral counts was generated for each cell line (based on the
triplicate samples). The sum of the 3 variances for the cell lines, an indicator of the
variance within each cell line, was calculated. The variance of the average spectral
counts for each cell line revealed the variability between the cell lines. ANOVA
(Fisher test) was performed to obtain the ratio of the “between sample variance” to
the “within sample variance”. Apparent fold-changes were calculated when possible.
2.2.8 Total protein and lactate dehydrogenase assay
Total protein was quantitated in the CM using a Coomassie (Bradford) protein
assay reagent (Pierce). All samples were loaded in triplicates on a microtiter plate
and protein concentrations were estimated by reference to absorbances obtained for
a series of bovine serum albumin (BSA) standard protein dilutions. Lactate
dehydrogenase (LDH), an intracellular enzyme which if found in the CM is an
indicator of cell death, was measured using an enzymatic assay based on lactate to
pyruvate conversion and parallel production of NADH from NAD. The production of
NADH was measured by spectrophotometry at 340mm using an automated method
(Roche Modular system).
2.2.9 Quantification of KLK5, KLK6 and KLK10
The concentration of KLK5, 6 and 10 was quantified with KLK5, 6 or 10-
specific non-competitive immunoassays developed in our laboratory 112,169,170. For
more details, see the cited literature.
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 64
2.3 Results
2.3.1 Optimization of cell culture
MCF-10A, BT474 and MDA-MB-468 cells were grown in serum-free media
(SFM) to ensure that the conditioned media contained no other exogenous proteins.
Seeding density, incubation time in SFM, volume of media used, type of SFM, type
of tissue culture flasks were all variables that were explored thoroughly to select the
most optimal conditions for growth. Refer to the Materials and Methods section for
details on the optimal conditions selected for the cell lines. Approximately, 20, 33
and 40x106 cells were found to be attached and alive at the end of the experiment
with 25, 10 and 15 µg of total protein per mL in MCF-10A, BT474 and MDA-MB-468,
respectively (Figure 2.1A and B). Furthermore, to minimize cell death and maximize
secreted protein concentration in the CM, LDH levels, which represent the amount of
cell death occurring in cell culture, were also measured (Figure 2.1C). Known
amounts of all 3 types of cells were lyzed individually and their corresponding LDH
levels were graphed to create a LDH standard curve (Figure 2.1D). Using the LDH
standard graph, the value of LDH found in the 24 hour CM and the total number of
cells alive at the end of harvest, it was estimated that approximately 6-7% cell death
was occurring in the CM of the cells. Finally, to demonstrate the accumulation of
extracellular proteins in the optimized cell culture model system, the internal control
proteins KLK5, KLK6 and KLK10 were quantified in MDA-MB-468, using ELISAs
(Figure 2.1E). Kallikrein 5 was the most abundant kallikrein expressed in this cell line
(50µg/L) followed by KLK10 (~ 3.5µg/L) and KLK6 (~ 2µg/L).
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 65
Figure 2.1
Figure 2.1: Cell number, total protein, LDH and kallikrein levels in 24 hour conditioned media of cell lines. (A) Total number of cells alive during harvest of the CM; (B) Total protein concentration of CM (C) LDH levels, indicating approximately 6-7% cell death; (D) Standard LDH curve (n = 2); (E) KLK5, KLK6 and KLK10 levels in MDA-MB-468. This procedure was repeated several times for reproducibility.
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 66
2.3.2 Identification of proteins by MS
The workflow and experimental design performed in this study is shown in
Figure 2.2. Approximately 35-40 proteins were identified in the negative control
samples, which was made up of the culture media only (the list of proteins can be
found online in the supplementary data171). Many of the proteins in this list originated
from fetal bovine serum used to initially culture the cells. These proteins were
deleted from the list of total proteins identified in the CM and were not considered
further. In MCF-10A, we identified 632 proteins (Figure 2.3A). Of these, 459 were
identified in all 3 replicates, yielding a protein identification reproducibility of 73%.
Furthermore, a total of 505 proteins were identified in BT474 (Figure 2.3B). For this
cell line, 380 proteins were common to all 3 replicates (75% reproducibility). Finally,
723 proteins were identified in MDA-MB-468 (Figure 2.3C), of which 553 were
identified in all 3 replicates (76% reproducibility). In general, using the workflow
presented here, we achieved technical reproducibility (same sample injected twice
into the LTQ) of ~90% (data not shown). The total number of proteins identified per
number of total peptides per cell line is shown in Table 2.1. Many of the proteins
identified contained two or more peptide hits. A table containing detailed information
on all of the proteins identified for each of the cell lines, including number of unique
peptides identified per protein, peptide sequences, precursor ion mass and charge
states can be found online171. In addition, 8, 3 and 5 proteins were identified in MCF-
10A, BT474 and MDA-MB-468 cells respectively using a non-sense database,
yielding a false positive rate of ~ 1%172. The list of proteins found in the non-sense
database can be found online in the supplementary data171.
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 67
Figure 2.2
CELL CULTURE SAMPLE PREPARATION HPLC FRACTIONATION (SCX)
LC-MS/MS (LTQ)
MascotX!TandemMascot
X!Tandem
DATABASE SEARCHINGIDENTIFICATION PROBABILITY
Reproducibility Overlap Cellular Localization Spectral Counting
DATA ANALYSIS
Figure 2.2: Outline of experimental workflow
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 68
Figure 2.3
Rep 1
Rep 2Rep 3
21
7716
459
36167
Rep 1
Rep 2Rep 3
6
2322
380
401618
Rep 1
Rep 2Rep 3
6
3849
553
45725
A) MCF-10A (632 proteins)
B) BT474 (505 proteins)
C) MDA-MB-468 (723 proteins)
Figure 2.3: Number of proteins identified in CM by LC-MS/MS for the 3 cell lines. (A) MCF-10A, (B) BT474, (C) MDA-MB-468. Three independent replicates (rep) were generated and processed in the same manner for each cell line. Good reproducibility is shown as 73-76% overlap is observed among the cell lines.
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 69
Table 2.1: Total number of proteins identified per number of peptides
# of Peptides Identified MCF-10A BT474 MDA-MB-468
1 120 124 155 2 172 137 211 (KLK6) 3 95 77 111 (KLK5, 10)4 63 41 (HER-2/neu) 67
>/=5 182 126 179 Total: 632 505 723
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 70
2.3.3 Identification of internal control proteins in CM by MS
One of the advantages of our approach to biomarker discovery was the
presence of endogenous internal control proteins in the CM of MDA-MB-468. We
identified KLK5, 6 and 10 in the CM of MDA-MB-468 in all 3 replicates for this cell
line by MS. Three unique peptides were identified for KLK5, 2 unique peptides for
KLK6 and 3 unique peptides for KLK10. Furthermore, the BT474 cell line is well-
characterized to exhibit amplification of HER-2/neu (also known as ErbB-2). The
HER-2/neu protein consists of a cysteine-rich extracellular ligand binding domain
(ECD), a short transmembrane domain, and a cytoplasmic protein tyrosine kinase
domain173. ECD/HER-2 can be released by proteolytic cleavage from the full-length
HER-2 receptor and detected in serum. In the CM of BT474, we successfully
identified receptor tyrosine-protein kinase erbB-2 precursor with 4 unique peptides.
All of the peptides identified fall into the ECD portion (23-652 amino acid) of this
1255 amino acid protein. However, we did not identify CA 15-3 in the conditioned
media of the three cell lines by mass spectrometry. To validate this observation,
using a commercially available ELISA kit (Elecsys CA 15-3 Immunoassay; Roche
Diagnostics GmbH), we measured the levels of this glycoprotein in the same CM of
the cell lines. Very little (<3 units/mL) to no CA 15-3 was measured by ELISA in the
CM (data not shown).
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 71
2.3.4 Cellular localization of identified proteins
Figure 2.4 display the cellular distribution of proteins identified in the
conditioned media of MCF-10A (A), BT474 (B) and MDA-MB-468 (C). Twenty-two
percent of the proteins identified in MCF-10A CM were classified as being
extracellular and membrane-bound. For BT474 and MDA-MB-468, the percentages
were 25 and 28, respectively. A large portion of proteins identified were classified as
intracellular (> 50%), while 4-5% remained unclassified.
2.3.5 Overlap of proteins between the three cell lines
The proteins identified among the 3 cell lines were analyzed for overlapping
members (Figure 2.5). A significant portion (234 or 20%) of the 1,139 proteins was
identified in all 3 cell lines (Figure 2.5A). Figure 2.5B and 2.5C show the overlap
among the 175 extracellular proteins and the 211 membrane-bound proteins,
respectively. Combined together, extracellular and membrane proteins accounted for
34% of all proteins identified. MDA-MB-468 displayed the greatest number of
extracellular and membrane proteins, presumably illustrating that cancer cells
secrete and/or express an increased amount of these proteins. In accordance with
this postulation, cellular localization analysis of the overlap between BT474 and
MDA-MB-468, yielded the greatest percentage (40%) of secreted and membrane
proteins (Figure 2.5D).
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 72
Figure 2.4
Cytoplasm26%
Nucleus27%
Intracellular Organelles12%
Unclassified5%
Extracellular9%
Membrane13%
Cytoskeleton8%
Cytoplasm25%
Nucleus27%
Intracellular Organelles11%
Unclassified4%
Extracellular12%
Membrane13%
Cytoskeleton8%
Cytoplasm27%
Nucleus18%
Intracellular Organelles14%
Unclassified4%
Extracellular13%
Membrane15%
Cytoskeleton9%
A)
B)
C)
Figure 2.4: Cellular localization for the 3 cell lines. (A) MCF-10A, (B) BT474, (C) MDA-MB-468. A non-redundant list of proteins was created after Mascot and X!Tandem searching, and each protein were classified by its cellular location.
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 73
Figure 2.5
MCF-10A
BT
474
MD
A-M
B-4
68
234
62102
234
89120298
A) Total Proteins (1,139) B) Secreted Proteins (175)
C) Membrane Proteins (211)
MCF-10A
BT
474
MD
A-M
B-4
68
29
51126
282155
MCF-10AB
T4
74
MD
A-M
B-4
68
40
41835
222567
D) Overlap Between BT474 and MDA-MB-468 (89)
Figure 2.5: Overlap of proteins in CM. (A), (B), (C) Overlap of proteins (total number in brackets) between the 3 cell lines. (D) Pie chart showing the subcellular localization of 89 proteins uniquely identified in BT474 and MDA-MB-468, two cancer cell lines (not including MCF-10A proteins). Forty percent of the proteins are classified as extracellular and membrane-bound.
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 74
2.3.6 Cell lysate proteome
One of the major challenges in the analysis of secreted proteins is
distinguishing between proteins that are targeted to the extracellular space versus
those that arise as low-level contaminants due to cell death during routine cell
culture. To address this, we performed a cell lysate proteome experiment to examine
if our approach was enriching for secreted proteins. A total of 716 proteins were
identified in MDA-MB-468 cell lysate after removal of the negative control proteins
(culture media only). Eighty-seven percent protein identification reproducibility was
observed among the two replicates (Figure 2.6A). Five percent of the total proteome
was classified as extracellular/secreted (Figure 2.6B). In the CM of MDA-MB-468,
13% of the total proteins identified were classified as being secreted (Figure 2.4C).
The internal control secreted proteins, kallikreins 5, 6 and 10, were not identified in
the cell lysate. Of the secreted proteins identified in the cell lysate, 30 were also
identified in the CM for this cell line, while 19 proteins were unique to the lysate. A
table containing all of the lysate proteins identified, as well as the secreted proteins
that were found in both the MDA-MB-468 lysate and CM can be found online171.
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 75
Figure 2.6
A) MDA-MB-468 Lysate (716)
72 626 18 Rep 2Rep 1
B) Lysate Cellular Localization
Figure 2.6: Proteome of MDA-MB-468 cell lysate by LC-MS/MS. (A) Two independent replicates (rep) for the lysate were generated and processed in the same manner. Good reproducibility is shown (87% overlap). (B) GO cellular localization for the 716 proteins identified in the lysate. Five percent is classified as being extracellular.
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 76
2.3.7 Spectral counting and identification of differentially expressed proteins
The peptides that a mass spectrometer fragments are selected from a parent
scan (also called MS, full scan, survey scan, precursor scan). The resulting daughter
scans (also called MS-MS, fragment ion, MS2) are therefore dependant on the
parent scan. In our methodology, we sequence the six most abundant ions in the full
scan. If we continued to do this for every full scan, then we would repeatedly
sequence only the most abundant ions. Thus, to enable sequencing of the low
abundance ions, a criterion called dynamic exclusion is selected. In dynamic
exclusion, the mass spectrometer remembers the ions that it sequences so that it
does not re-sequence them in the future. This allows sequencing of the less
abundance ions. In this instance, the ions that are sequenced are put into a list that
has a fixed size (n=100) and this list constantly refreshes with every MS scan. As
can be imagined, high abundance peptides are sequenced more then low
abundance peptides (i.e. high abundance peptides are displaced from the list and
then are ‘redetected’ and this process repeats itself). This allows for a form of label-
free quantification – spectral counting – to take place. The premise is that counting
the number of spectra acquired per peptide is an indicator of relative protein
abundance in a mixture.
An alternative way to decipher protein abundance is to perform
multidimensional scaling for all 9 experiments (each cell line in triplicate) using
spectral counts. Refer to the Materials and Methods section for details on how the
analysis was conducted. The venn diagram in Figure 2.7 displays the overlaps of
proteins among the cell lines based on spectral count analysis (A) and their cellular
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 77
localization (B). The top ~100 extracellular and membrane-bound proteins obtained
from spectral counting analysis are shown in Table 2.2. The variability within the
replicates (within variance) and between the three cell lines (between variance) are
highlighted along with the F ratio. Apparent fold-changes were calculated where
possible. A numerical value is indicated in places where both cell lines/conditions
being examined contain a normalized spectral count greater than zero. In the event
of a comparison where one of the condition/cell line had a spectral count of zero, an
expression is given (i.e BT>>MCF; indicating that the spectral count for BT474 was
greater than MCF10A). Cells that are grey indicate a negative fold change whereas
cells that are white indicate a positive (numerical value indicated) or no fold change
(cells are blank). In addition, in the first column displaying the fold change between
BT474/MCF-10A, cells in white highlight the proteins that have a higher spectral
count in BT474 compared to MCF-10A whereas cells in black highlight proteins that
have a lower spectral count in BT474 compared to MCF-10A. A similar color coding
scheme applies to the other two columns comparing the different cell
lines/conditions within each column. Known breast cancer biomarkers such as HER-
2/neu is among the top 5 proteins identified by this unbiased method of analysis.
Furthermore, 23 proteins, previously associated with cancer (as determined by
Ingenuity Biomarkers Comparison Analysis software) were found among the top ~
100 extracellular and membrane proteins including epidermal growth factor receptor
(EGFR) and various insulin-like growth factor binding proteins (IGFBP-2, 3, 5 and 6).
A table containing all of the 1,062 proteins that this analysis was performed on can
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 78
be found online, in addition to a table that contains the overlaps of the proteins
among the cell lines171.
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 79
Figure 2.7
MCF-10A
BT
474
MD
A-M
B-4
68
211
5491248
8699273
Intracellular Organelles
15%
Nucleus21%
Cytoplasm25%
Cytoskeleton7%
Unclassified5%
Membrane14%
Extracellular13%
A)
B)
1,062 proteins
Figure 2.7: Spectral counting analysis. Overlap of proteins between cell lines (A) and cellular localization (B) using label-free quantification (spectral counting).
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 80
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 81
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 82
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 83
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 84
2.4 Discussion
In this study, a shot-gun proteomics strategy was utilized to sample the
conditioned media of 3 breast cell lines, MCF-10A, BT474 and MDA-MB-468. By
searching with both Mascot and X!Tandem, we successfully identified over 1,100
proteins in the CM of all 3 cell lines combined, which, to our knowledge, is one of the
largest repositories of proteins identified for breast cancer. Studies have shown that
by combining results from multiple search engines, a better, more confident protein
identification is made since different search engines are based on different
algorithms and scoring174,175. Two different methods of determining relative
abundance were used: protein identification and spectral counts. While spectral
counting as an index of protein abundance is appealing, there are a number of
different ways to analyze a dataset such as counting the spectra and adjusting by
the length of the protein (NSAF)176, counting peptides (not individual spectra) and
adjusting for number of tryptic peptides in the protein (PAI)177, calculating a function
of PAI called emPAI178, counting 1 if any spectra matched a peptide and assigning 0
otherwise (SASPECT) or merely counting the spectra. Currently, there is no
consensus as to which approach to use. In this study, we used both protein
identification and spectral counts to determine protein abundance.
We specifically examined MCF-10A, BT474 and MDA-MB-468 because they
represent a variety of breast cancer cases. We observed higher cell death in MCF-
10A as exhibited by elevated LDH levels and less viable cell counts at the end of the
culture, which was expected, as MCF-10A is considered to be a “normal” breast
epithelial cell line that does not have the advantage of growing uncontrollably as the
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 85
cancer cell lines in SFM (Figure 2.1). It was our aim that examining the proteins that
are unique to each of the cell lines, might shed light into both the pathways leading
to breast cancer development and to the discovery of biomarkers for breast cancer.
One of the advantages of our high-throughput qualitative comparative
proteomic analysis was the presence of internal controls in the CM of MDA-MB-468.
KLK5 expression, as assessed by quantitative RT-PCR, has been implicated as an
independent and unfavorable prognostic marker for breast carcinoma179. The clinical
utility of KLK6 as a breast cancer maker has not been determined, while KLK10
levels have been shown to predict response to tamoxifen therapy180. Based on
experience with other currently used biomarkers, the expected concentration of new
cancer markers in serum should be in the low µg/L range. Through our strategy, we
successfully identified all three control proteins by MS, thus supporting the notion
that we are enriching for, and surveying deep enough, into the low abundance
proteins, to mine for other candidate biomarkers for breast cancer. Furthermore, the
fact that we successfully identified the ECD portion of HER-2/neu in BT474 further
supports our hypothesis that candidate biomarkers can be discovered using the CM
of breast cancer cell lines. High levels of HER-2 in serum correlate with poor
prognosis in patients with breast cancer181. In 2000, the FDA cleared the serum
HER-2/neu test; the first FDA-cleared blood test for measuring circulating levels of
HER-2 in the follow-up and monitoring of patients with metastatic breast cancer. The
assay, ADVIA Centaur HER-2/neu Assay (www.oncogene.com), is a sandwich
immunoassay that uses two monoclonal antibodies that are specific for unique
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 86
epitopes on the extracellular domain of the HER-2 oncoprotein. The assay has a
reference limit 15 µg/L with a sensitivity of 0.1 µg/L182.
Particular emphasis was placed on extracellular, membrane-bound and
unclassified proteins since these proteins have the highest chance of being found in
the circulation and thus serving as cancer biomarkers or as important molecules
involved in cancer progression. More than 34% of these proteins were classified as
being extracellular and membrane-bound. Among the known and novel proteins
released by breast cancer cells, we identified various proteases, receptors, protease
inhibitors and cytokines. All experiments were performed in triplicates with excellent
reproducibility between runs. Due to the inherent nature of mass spectrometers, not
all peptides were ionized in each run and consequently, different peptides were
selected for ionization and detected. This selective ionization can account for the
75% reproducibility in our biological triplicates. As well, the various steps during
sample preparation, including C18 extraction of the fractions cannot be dismissed as
an important contributing factor to the variations observed.
Although the objective of this study was to identify secreted and membrane-
bound proteins that have the potential to be cleaved and thus found in circulation,
the identified proteins included many intracellular proteins, including ones classified
by GO as nuclear and cytoplasmic. During the cell culture process, a portion of the
cell population will die, resulting in the release of intracellular proteins into the media.
Despite optimizing cell culture conditions to minimize cell death, the identification of
intracellular proteins in the CM is inevitable because of the high sensitivity of MS-
based techniques utilized in this study. Martin et al examined the CM of a prostate
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 87
cancer cell line and found a very similar GO distribution to the one we present here -
that more than 50% of the proteins identified were intracellular145. Therefore, one of
the major challenges in the analysis of secreted proteins is distinguishing between
proteins that are targeted to the extracellular space versus those that arise as low-
level contaminants due to normal cell death in routine cell culture. To address this,
we performed a cell lysate proteome experiment to demonstrate conclusively that
through our approach, we are significantly enriching for secreted proteins.
Furthermore, a recent study examining the cell lysate proteome of human mammary
epithelial cell line, HMEC, found that 2% of the entire proteome was classified as
extracellular, which was consistent with our findings183.
Our group has previously published data on the conditioned media of a
prostate cancer cell line, PC3(AR)6 using a roller bottle cell culture method184.
Through this approach, the authors identified 262 proteins in the CM. The workflow
presented in the current study has significant differences and improvements
compared to our previous work. At the tissue culture step, we cultured three cell
lines in triplicate versus only one cell line studied. We also optimized our cell culture
conditions (seeding density, incubation time, volume of media used) to minimize cell
death and maximize secreted protein content. Different methods of fractionation of
the peptides versus protein fractionation and a more robust and sensitive mass
spectrometer was utilized in this study compared to our previous work. Finally, the
bioinformatic analysis has also been significantly improved upon from before as we
used two different search engines and incorporated protein and peptide probability
Chapter 2: Analysis of the Conditioned Media of Three Breast Cell Lines 88
calculations into our final list of proteins. As a result, in the current study, we
identified over 1,000 proteins using the conditioned media approach.
Current therapies for advanced cancers are elusive. Novel breast cancer
biomarkers that can be effective early in the course of the disease have the potential
to reduce morbidity and mortality, as well as receive a higher compliance rate by
patients for undergoing screening. However, there is a growing consensus that
panels of markers may be able to supply the specificity and sensitivity that individual
markers lack. A number of studies have demonstrated that this is indeed true. While
a biomarker should be detected in serum using an immunoassay (ELISA),
developing such an assay for multiple potential novel biomarkers is very labor
intensive185. The vast majority of the proteins identified in this study (extracellular,
membrane and unclassified) do not have commercially available ELISA kits.
Alternatively, to decipher whether the candidates are present in serum, multiple
reaction monitoring (MRM) mass spectrometry technology can be performed. Using
the latter technology, it is possible, in a single experiment, to detect and quantify
specific peptides (representing specific proteins) in biological fluids of patients with
breast cancer to determine if the protein has biomarker potential. A number of
studies have shown the feasibility of such an approach186-188. Nevertheless, before
proceeding to analyzing biological fluids, the potential candidate biomarkers must
first be selected from the > 1,000 proteins identified.
Chapter 3: Bioinformatics and Candidate Selection 89
CHAPTER 3:
BIOINFORMATICS and CANDIDATE SELECTION
The work presented in this chapter is published, in part, in Molecular & Cellular Proteomics:
Kulasingam, V. and Diamandis, E.P. Proteomic analysis of conditioned media from three breast cancer cell lines: A mine for
biomarkers and therapeutic targets. Mol Cell Proteomics 2007 6: 1997-2011
Copyright permission has been granted.
Chapter 3: Bioinformatics and Candidate Selection 90
3.1 Introduction
Rapid advances in proteomic and genomic technologies have created
optimistic views that many more biomarkers will be discovered through various high-
throughput techniques. However, these predictions have yet to come true127. While
numerous strategies exist for biomarker discovery, the bottleneck to product
development and routine clinical use is in the verification phase of candidate
biomarkers. A verification and validation platform is more costly, labor-intensive and
requires more time than a discovery program127,185. There is no doubt that
meticulous verification steps are essential and the impact of the end product on the
future of diagnostics would be enormous.
Our discovery strategy involving analysis of conditioned media from breast
cancer cell lines yielded over 1,100 proteins. Given our hypothesis that novel breast
cancer biomarkers will be secreted or shed proteins, we chose to focus on the 402
extracellular, membrane and unclassified proteins for further analysis. To select the
most promising molecules into the next step of the platform for biomarker
identification, a number of bioinformatic and data analyses were performed including
examining tissue-specificity and biological functions. The degree of overlap between
the proteins identified in this study using a cell culture model and other studies using
relevant biological fluids such as NAF and tumor interstitial fluid (TIF) were
evaluated. Finally, the filtering criteria for candidate selection were performed.
Chapter 3: Bioinformatics and Candidate Selection 91
3.2 Materials and Methods
3.2.1 Tissue-specific expression
To identify genes in our CM that were relatively breast-specific, Unigene
analysis on the 402 extracellular, membrane-bound and unclassified proteins, using
the EST ProfileViewer, was performed. In addition, the proteins identified in our CM
analysis were cross-compared to the proteome of breast tumors identified by gel
electrophoresis and mass spectrometry137.
3.2.2 Biological functions analysis
Extracellular, membrane-bound and unclassified proteins were evaluated by
the Ingenuity Pathways Analysis software to identify global functions of the proteins.
This software uses a knowledge base derived from the literature to relate gene
products to each other, based on their interaction and function. The list of proteins
and their corresponding IPI human identification numbers were uploaded as an
Excel spreadsheet file onto the Ingenuity software (www.ingenuity.com). Ingenuity
then uses these proteins and their identifiers to navigate the curated literature
database. The biological functions assigned to each network were ranked according
to the significance of that biological function to the network. A Fischer’s exact test
was used to calculate a P-value. For a detailed description of IPA, visit
www.ingenuity.com
Chapter 3: Bioinformatics and Candidate Selection 92
3.2.3 Comparison of proteins identified from CM with other publications
The proteins identified in this study were compared to two nipple aspirate fluid
(NAF) proteomes, one tissue culture-based proteome of a well-studied breast cancer
cell line (MCF-7) and one proteome of breast tumor interstitial fluid (TIF)115,135,136,147.
3.2.4 Single nucleotide polymorphisms (SNPs) and human Plasma Proteome
database
The extracellular proteins identified in our conditioned media analysis were
searched using a well-curated SNPs database62.
Furthermore, the 402 extracellular, membrane and unclassified proteins from
the conditioned media analysis were searched against the human Plasma Proteome
database to identify proteins previously found in human plasma. This is a database,
available online, that contains manual annotation of proteins via exhaustive literature
research. The database includes information pertaining to isoform specific
expression, disease, localization, potential post translational modifications and SNPs.
3.2.5 Selection of candidates
Since over 1,000 proteins were identified in our proteomic analysis (discovery
phase), we used several methods of protein filtering to create a more limited set of
proteins for future exploration as potential breast cancer biomarkers. Literature
searches were performed on the top 100 differentially expressed secreted and
membrane proteins, to identify molecules that have not been previously studied as
serological markers for breast cancer. The proteins that met this criterion were again
Chapter 3: Bioinformatics and Candidate Selection 93
filtered by selecting proteins that were expressed exclusively or preferentially in the
early or advanced stages of cancer (expressed in BT474 and MDA-MB-468 and not
in MCF-10A). More information on the selection criteria is provided in the results
section.
Chapter 3: Bioinformatics and Candidate Selection 94
3.3 Results
3.3.1 Tissue-specific expression
Unigene analysis revealed 5 genes that were relatively specific to normal
and/or cancerous breast tissue (SCGB1D2, SBEM, TFF1, DCD and CALML5).
Literature mining on these proteins showed that one of the proteins had previously
been evaluated in serum of breast cancer patients (trefoil factor 1). Two of the
molecules were not considered further since SCGB1D2 (Lipophilin-B) differential
expression levels in the CM were minimal with an F ratio <1 and DCD (Dermcidin)
was identified in the media only culture. From the remaining two molecules, SBEM
(small breast epithelial mucin) had previously been examined by RT-PCR as a
potential marker for breast cancer189 but reagents were not available to perform an
immunoassay to evaluate its protein levels in serum of cancer patients. Finally,
CALML5 (Calmodulin-like protein 5) was also a protein that needed to be evaluated
further for its ability to be a circulating breast cancer biomarker.
Recently, the proteome of breast tumors were deciphered using gel
electrophoresis and mass spectrometry yielding over 2,000 proteins137. The authors
examined the proteomes of several breast tumors, tissues peripheral to the tumors
and also samples from patients undergoing non-cancer surgery. Using label-free
quantification, differentially expressed proteins between the normal breast tissue and
cancerous breast tissue was identified. Specifically, 54 proteins were found to be
strongly over-expressed in breast tumors compared to their corresponding
peripheral tissue samples and healthy mammary gland tissue samples. Cross-
comparison of these 54 breast cancer tissue-specific proteins with our conditioned
Chapter 3: Bioinformatics and Candidate Selection 95
media analysis revealed that 25 of these proteins (46%) were also identified in our
study using a tissue culture approach (Table 3.1).
3.3.2 Biological functions analysis
As well, using the Ingenuity Pathways Analysis (IPA) software, we classified
the proteins by biological function. The top 15 functions are displayed in Figure 3.1.
The top functions were cellular movement followed by cell-to-cell signalling and
interaction.
Also using the Ingenuity Pathways Analysis, the 402 proteins were filtered by
extracting only those genes that were identified in literature to be present in human
and associated with cancer. Approximately 100/402 extracellular and membrane
genes were identified to meet this criterion. The top 14 canonical pathways that
demonstrated a relationship to the 100 genes filtered as well as known genes linked
to breast cancer are highlighted in Figure 3.2.
Chapter 3: Bioinformatics and Candidate Selection 96
Table 3.1: Proteins elevated in breast tumor tissue proteome1 and found in CM analysis
1 Alldridge,L., Metodieva,G., Greenwood,C., Al Janabi,K., Thwaites,L., Sauven,P., & Metodiev,M. Proteome Profiling of Breast Tumors by Gel Electrophoresis and Nanoscale Electrospray Ionization Mass Spectrometry. J. Proteome Res. (2008).
Chapter 3: Bioinformatics and Candidate Selection 97
Figure 3.1
Figure 3.1: Biological functions analyses. The top 15 functions for the 422 extracellular, membrane-bound and unclassified proteins are shown as determined by Ingenuity Pathways Analysis (IPA). The y-axis shows the negative log of P-value.
Chapter 3: Bioinformatics and Candidate Selection 98
Figure 3.2
Figure 3.2: Canonical pathways and known genes linked to breast cancer. Using the Ingenuity Pathways Analysis, ~ 100 genes were identified in the literature as being reported in humans and cancer from the shortened list of candidates. The top 14 canonical pathways have been mapped onto the 100 genes along with genes linked to breast cancer including IFGBP5, VEGF and ERBB2.
Chapter 3: Bioinformatics and Candidate Selection 99
3.3.3 Comparison of proteins identified from CM with other publications
Using the isotope-coded affinity tags (ICAT) technology, Pawlik et al
quantified tumor-specific proteins in NAF135. In total, of the 39 proteins that were
differentially expressed in tumor-bearing versus disease-free breasts, 6 were also
found among the 1,139 proteins in our CM. In addition, Varnum et al identified 64
proteins in NAF using MS, of which 15 were previously reported to be altered in
patients with breast cancer136. From the 64 proteins, 21 were also found in our
proteomic study. More importantly, of the 15 proteins previously reported to be
altered in serum or tumor from women with breast cancer, 10 were found in our CM
proteome. A proteomic study involving MCF-7 targeted membrane-associated breast
cancer proteins as potential biomarkers147. Using two-dimensional gel
electrophoresis (2-DE) and MS analysis of the protein spots, Canelle et al identified
98 proteins. Among the 98 proteins, 42 of them were also found in our study. In
addition, Celis et al mined another source of biomarkers through the investigation of
tumor interstitial fluid (TIF) that perfuse the breast tumor microenvironment115. Given
the importance of the tumor-host interface and the increasing appreciation of the role
that the microenvironment plays in cancer initiation and progression, we compared
our list of proteins identified through a cell culture model system to the proteins
excreted by the cells that are within their native cancer microenvironment. The
authors identified approximately 260 proteins using 2-D gel electrophoresis,
immunoblotting and mass spectrometry, of which 112 were also identified in our
study (43%). Figure 3.3 summarizes the overlaps observed among the other
publications and the data presented here. A table containing all of the proteins
Chapter 3: Bioinformatics and Candidate Selection 100
identified by our group and the four previous studies can be found online171. Finally,
the lung, along with the bone, is one of the most frequent sites of breast cancer
metastasis. A set of 54 genes that mediate breast cancer metastasis to the lungs
have been identified190. Given that MDA-MB-468 cells were collected from pleural
effusion, we compared the list of proteins identified from MDA-MB-468 conditioned
media to the 54 candidate lung metastasis genes. Seven genes were found in the
CM of our cell line (KYNU, TNC, ROBO1, FSCN1, MAN1A1, LTBP1 and GSN).
Interestingly, none of the genes that overlapped between the lung and bone
metastasis signatures were identified in MDA-MB-468.
Another interesting study used tandem mass spectrometry to sample the CM
of four isogenic breast cancer cell lines differing in aggressiveness191. Three
independent secreted proteome preparations (biological replicates) for each of the
cancer cell lines were performed. Using a protein fractionation strategy involving C2
columns previously reported to enrich for secreted proteins in the CM146, the authors
identified over 250 proteins per cell line. From the 37 most significant secreted
proteins across the isogenic cell lines, 31 (87%) were also observed in our breast
cancer conditioned media analysis of 3 cell lines. More recently, another group
analyzed the conditioned media of human mammary epithelial cells (HMEC) by LC-
MS/MS and identified ~900 proteins192. To specifically focus on proteins that were
secreted or shed, the authors compared their conditioned media data with their
previous work on analyzing the proteome of whole HMEC lysates. Approximately
150 proteins were identified to be enriched in the extracellular compartment of
Chapter 3: Bioinformatics and Candidate Selection 101
HMEC. Eighty-three of these proteins were also identified in our conditioned media
analysis (55% overlap).
Chapter 3: Bioinformatics and Candidate Selection 102
Figure 3.3
Figure 3.3: Overlap among other publications. The overlap of proteins identified in the current conditioned media cell culture study and 4 other proteomic studies.
Kulasingam1,139
Celis
Pawlik
VarnumCanelle
148
112 336
43
2156
42
Chapter 3: Bioinformatics and Candidate Selection 103
3.3.4 SNPs and human Plasma Proteome database
A potential mechanism for elevated levels of secreted proteins in circulation is
the presence of a SNP in the signal peptide region of a protein resulting in aberrant
secretion. Nine proteins were identified (CFB, CST1, DAG1, FAM3B, FN1, IGFBP7,
IL22RA2, PI3 and SERPINE1) as potentially containing SNPs in signal peptide
region. CFB (Complement factor B) and FN1 (Fibronectin) were not considered
further as potential targets for the verification phase as they are of high abundance
in circulation and are non-specific to breast cancer. CST1 (Cystatin SN) was not
previously examined as a serological breast cancer marker however no reagents
were available to test its diagnostic potential in serum using an immunoassay. For
DAG1 (Dystroglycan) and IGFBP7 (Insulin-like growth factor-binding protein 7),
there were existing patents with respect to breast cancer and thus these proteins
were not considered further due to conflict of interest. FAM3B (Protein FAM3B) and
IL22RA2 (Interleukin-22 receptor alpha-2 chain) was not considered further as its
differential expression levels in CM were minimal with an F ratio <1. As well, PI3
(elafin) appeared to be a promising molecule to investigate further while SERPINE1
(Plasminogen activator inhibitor 1) had already been examined in breast cancer
patients as a potential biomarker.
Finally, these proteins were searched with the Human Plasma Proteome
database to decipher whether they have been identified in plasma. 104 of 402
proteins were identified in human plasma.
Chapter 3: Bioinformatics and Candidate Selection 104
3.3.5 Selection of candidates
A major rate-limiting step for a biomarker discovery platform thus far has been
in the selection of candidate molecules to investigate further as breast cancer
biomarkers from the over 100s to 1000s of proteins identified in the discovery phase.
Given the different experimental questions being asked, a standard procedure for
candidate selection from a discovery platform could not be established. However, a
number of filtering criteria exists that can be applied to a dataset. In our proteomic
analysis of breast cancer cell lines, we chose to focus on only the extracellular and
membrane proteins identified since it is these proteins that have the highest chance
of entering the circulation and hence serving as serological markers. As well, one of
the properties of an ideal tumor marker is that it should be tissue specific. Currently,
one of the few markers used in the clinic that are tissue-specific is prostate-specific
antigen (PSA) for prostate cancer. Therefore, it may be important to examine tissue
specificity of the narrowed list of proteins from the discovery phase using the
Unigene database or in silico analysis193 or comparing the proteome of breast
cancer tissues to the proteome of CM and focussing only on the overlapping
members. Another criterion to select a more limited set of proteins for future
exploration as potential breast cancer biomarkers is to compare the dataset with
mRNA microarray databases194 as well as to examine overlapping proteins with
proteomes of relevant biological fluids such as NAF or tumor interstitial fluid (TIF)115.
Additionally, it is also equally important to perform literature searches to identify
molecules that have not been previously studied as serological markers for breast
cancer.
Chapter 3: Bioinformatics and Candidate Selection 105
Some of the secondary filtering criteria that may assist in selection of
candidate molecules include examining factors such as reagent availability
(recombinant protein, antibodies and ELISA) and focusing on proteins known to
participate in pathways or signaling cascades relevant to cancer progression. If
different cell lines were used in the discovery phase differing in aggressiveness, then,
proteins can be selected that are expressed only in the early or advanced stages of
cancer and not in the normal cell line. Table 3.2 lists some of the criteria that can be
applied to investigate further only the proteins that have the highest chance of being
successful in the verification and validation phases.
Chapter 3: Bioinformatics and Candidate Selection 106
Table 3.2: Proposed filtering criteria for candidate selection
1. Focus on extracellular and membrane proteins 2. Examine tissue specificity (Unigene database, SAGE*) 3. Compare to mRNA microarray databases 4. Compare to proteins identified in other biological fluids 5. Examine molecules not previously examined as serological markers for the
cancer type (literature searches). 6. Perform tissue proteomics on relevant tissues to the cancer type and
compare with cell line data to select only candidates that overlap between the two
7. Reagent availability such as ELISA, antibodies etc 8. Focus on proteins known to participate in pathways/signaling related to
cancer 9. Focus on differentially expressed proteins (based on label-free or quantitative
proteomics) 10. Exclude high-abundance (µg/mL) serum proteins, especially liver products
and acute-phase reactants
*SAGE: Serial analysis of gene expression
Chapter 3: Bioinformatics and Candidate Selection 107
In the following, we summarize how the candidates were narrowed to a more
manageable number to investigate further. Based on literature searches, from the
top 100 differentially expressed proteins (as defined by spectral counting), 46 of the
molecules had been previously examined as a serological breast cancer marker.
Patent searching of the remaining 54 proteins revealed 11 molecules that had a
previous patent with respect to breast cancer. The remaining 43 proteins were
scrutinized to select proteins for further investigation that was found only in the CM
of breast cancer cell lines and absent in MCF-10A (normal breast epithelial cell line).
This filtering criterion resulted in 30 candidates for further analysis (Table 3.3). Of
these 30 candidates, 11 of them had reagents available to develop an immunoassay
to measure the levels of these proteins in biological fluids. This list included: elafin,
kallikrein 5, 6 and 10, cystatin C, lipocalin-2, transforming growth factor beta-2,
activated leukocyte-cell adhesion molecule (ALCAM), B-cell adhesion molecule
(BCAM), neuronal cell adhesion molecule (NrCAM) and fractalkine. Table 3.4
highlights our candidate selection criteria discussed above and table 3.5
summarizes some of the properties of the 11 candidates selected for further analysis.
Chapter 3: Bioinformatics and Candidate Selection 108
Chapter 3: Bioinformatics and Candidate Selection 109
Table 3.4: Our candidate selection criteria
Selection criteria # of proteins 1) Total proteins identified in conditioned media of all 3 cell lines
1,139
2) Extracellular and membrane proteins identified 402
3) Differentially expressed proteins based on label-free quantification
100
4) Literature searches to focus on novel, serological breast cancer markers
54
5) Patent searches to focus on proteins without conflict of interest 43
6) Proteins present in breast cancer cell lines (BT474, MDA-MB-468); absent in normal cell line (MCF-10A)
30
7) Reagent availability (ELISA, antibodies) 11
Chapter 3: Bioinformatics and Candidate Selection 110
Table 3.5: Top 11 candidates selected for verification phase Protein Name Gene Localization Plasma? NAF?135,136 TIF?115 CM?191,192 Activated leukocyte cell adhesion molecule ALCAM membrane √
B-cell adhesion molecule BCAM membrane Cystatin C CST3 extracellular √ √ √ Elafin PI3 extracellular √ √ Fractalkine CX3CL1 membrane √ Kallikrein 5 KLK5 extracellular √ Kallikrein 6 KLK6 extracellular √ Kallikrein 10 KLK10 extracellular √ Lipocalin-2 LCN2 extracellular √ √ Neuronal cell adhesion molecule NRCAM membrane Transforming growth factor beta-2 TGFB2 extracellular √ √
NAF: nipple aspirate fluid; TIF: tumor interstitial fluid; CM: conditioned media
Chapter 3: Bioinformatics and Candidate Selection 111
3.4 Discussion
One of the properties of an ideal tumor marker is that it should be tissue
specific and elevated in cancer and not in healthy or benign conditions. Currently,
the only marker used in the clinic that is tissue-specific is PSA for prostate cancer.
Examining the proteome of breast cancer tissues and comparing it to the peripheral
normal tissue proteome appears promising to identify potential candidates to
evaluate further. However, examining tissues, a complex source for biomarkers,
gives rise predominantly to intracellular proteins. In fact, among the 54 proteins
identified by Alldridge et al as being strongly over-expressed in breast tumors
compared to healthy control tissues, only a handful of proteins were classified by
localization as extracellular or membrane proteins, which would have the highest
chance of entering into circulation137. Nevertheless, the conditioned media analysis
did identify approximately half of the differentially expressed breast tissue-specific
proteins. The most relevant proteins included Ras GTPase-activating-like protein
IQGAP1, polymeric-immunoglobulin receptor and moesin.
Interestingly, the major biological functions of the top 100 proteins identified in
our study were cellular movement and cell-to-cell signaling. In addition,
approximately 25% of these proteins have previously been associated with other
cancer types in the literature. These proteins, when mapped onto various canonical
pathways reveal an intricate connection among themselves. Given that
dysregulation of signalling pathways play an important role in cancer initiation and
progression, the proteins identified in this study may be useful for further
investigation.
Chapter 3: Bioinformatics and Candidate Selection 112
Recently, the analysis of thousands of genes in breast and colorectal cancers
have shown that individual tumors can accumulate an average of 90 mutant
genes195. The authors identified 189 previously unknown genes that were mutated at
a high frequency. From these genes, six were found in this study (filamin-B, spectrin
alpha chain, gelsolin, extracellular sulfatase Sulf-2, neuronal cell adhesion molecule
and polypeptide N-acetylgalactosaminyltransferase 5). Furthermore, it is particularly
important that many of the proteins identified by other groups using relevant
biological fluids such as NAF and TIF were also present in our analysis (Figure 3.3).
Many of the proteins identified by Pawlik et al were highly abundance serum proteins
such as albumin, transferrin and various immunoglobulins – all of which were not
identified in our serum-free media culture. Finally, a large portion of the shortened
list of proteins identified was also found in the human Plasma Proteome database.
This finding was not unexpected as it served to highlight the fact that many of the
proteins identified in our study had previously been found in plasma. But it also
demonstrated that many more proteins have yet to be identified in plasma. There
can be a number of reasons why they have not been identified – one of which is the
fact that their concentration in plasma is too low to measure by current technologies
and thus other means of initially identifying them and then developing a specific and
sensitive immunoassay are critical.
Finally, based on all of the bioinformatic and data analyses, the selection of
the top candidates to investigate in the verification phase of the proteomic platform
for biomarker discovery yielded 11 proteins. This included 7 extracellular proteins
and 4 membrane proteins.
Chapter 4: Verification Phase 113
CHAPTER 4:
VERIFICATION PHASE
The work presented in this chapter is published, in part, in Molecular & Cellular Proteomics:
Kulasingam, V. and Diamandis, E.P. Proteomic analysis of conditioned media from three breast cancer cell lines: A mine for
biomarkers and therapeutic targets. Mol Cell Proteomics 2007 6: 1997-2011
Copyright permission has been granted.
Chapter 4: Verification Phase 114
4.1 Introduction
Eleven filtered molecules were selected to evaluate their potential to be
circulating breast cancer biomarkers in serum, using an immunoassay specific for
the molecules. Seven of these molecules were secreted proteins and included in this
list was elafin. Elafin, a secreted epithelial proteinase inhibitor, also referred to as
skin-derived anti-leukoproteinase (SKALP) or elastase-specific inhibitor (ESI)
belongs to the Trappin gene family196. Proteases and their inhibitors play an
important role in cancer metastasis and angiogenesis197. Previous studies have
shown that elafin is expressed in normal mammary epithelial cells, but it is down-
regulated in most breast tumor cell lines198.
Another family of molecules that was included in the extracellular filtered
molecules was kallikreins 5, 6 and 10. Kallikreins are secreted enzymes that encode
for trypsin-like or chymotrypsin-like serine proteases199. Accumulating evidence
indicates that the KLK family is dysregulated in cancer. Although many KLKs are
over-expressed in cancerous tissues, it is not currently known whether this also
reflects an increase in proteolytic activity. Clinical data linking an increase in KLK
expression with patient prognosis strongly suggests that KLKs are implicated in
tumor progression. Among all biomarkers to date, KLK3/PSA has had the greatest
impact in clinical medicine, for the screening and monitoring of prostate cancer200.
The fifth molecule to be evaluated was cystatin C, an extracellular cysteine
protease inhibitor that belongs to the cystatin superfamily. It has been reported to be
involved in many disease processes, such as inflammation and tumor metastasis201.
In fact, cystatin C has also been found to have utility as a biomarker for renal
Chapter 4: Verification Phase 115
function assessment. Specifically, cystatin C serum concentration correlates closely
to the glomerular clearance rate202.
Adding to this list of candidates was lipocalin-2 (neutrophil gelatinase-
associated lipocalin) whose expression has been observed in most tissues, and its
synthesis is induced in epithelial cells during inflammation203. Lipocalin-2 has been
implicated in a variety of cellular processes including the innate immune response,
differentiation, tumorigenesis, and cell survival204,205. Its association with MMP-9 may
modulate protease activity by protecting MMP-9 from degradation206. It has been
associated with several tumor types as well, including breast, ovarian, colorectal,
and pancreatic cancers207-209. Its function in cancer is unclear, although the invasive
and metastatic behavior of tumor cells is suppressed by lipocalin-2 in models of
breast and colon cancer210,211.
The final extracellular protein to be included in the top 11 candidates to
investigate further was transforming growth factor beta-2 (TGF-β2). It has four
fundamental activities. TGF-β2 can act as a growth inhibitor for most types of cells,
serve to enhance the deposition of extracellular matrix, be immunosuppressive and
play a role during fetal development. It is expressed in discrete areas, such as
epithelium, myocardium, cartilage and bone of extremities and in the nervous
system, suggesting specific functions189,212,213.
Furthermore, tumor metastasis involves invasive growth into neighboring
tissue, survival in circulation, extravasation and colonization of distant organs.
Therefore, movement through tissue barriers is a pivotal step in metastasis. For this
step to occur, proteolysis of extracellular matrix, remodeling of the actin cytoskeleton
Chapter 4: Verification Phase 116
and selective cell adhesion interactions are all important factors. Cell adhesion
molecules (CAMs) are involved in cell-cell and cell-matrix interactions214. They serve
to maintain tissue architecture and are involved in neurogenesis, hematopoiesis,
immune responses and in tumor progression. Changes in expression of CAMs may
accompany neoplastic progression. In this respect, the first membrane protein to be
evaluated as a potential breast cancer marker was activated leukocyte cell adhesion
molecule (ALCAM). ALCAM is a member of the family of cell adhesion molecules
and is one of the members of a small subgroup of transmembrane glycoproteins of
the immunoglobulin superfamily (IgSF). In addition, ALCAM has been implicated in
cell migration and is a marker for the identification of pluripotent mesenchymal stem
cells. In vitro studies have suggested that ALCAM may favor interactions between
tumor and endothelial cells. One study has suggested that strong cytoplasmic
ALCAM expression in primary breast cancer, as measured by immunohistochemistry,
might be a marker for aggressive breast cancer215.
Besides ALCAM, two additional members of this family are CD146/MUC18
and BCAM/Lutheran blood group glycoprotein (basal cell adhesion molecule;
BCAM)216. BCAM was the first laminin receptor to be identified that is a member of
the Ig superfamily. Laminins are a family of extracellular proteins that are an integral
part of all basement membranes and of the extracellular matrix proteins. Only α5
chain-containing laminins are known ligands for BCAM. Very limited information is
available about the expression of BCAM in tumors and therefore the roles of BCAM
in tumor progression remain unclear. Without wishing to be bound to theory, it may
be that, because BCAM can interact with laminin α5, which is widely expressed in
Chapter 4: Verification Phase 117
basement membranes, BCAM may mediate the involvement of tumor cells during
invasive processes216. Yet another adhesion molecule that was evaluated is
neuronal cell adhesion molecule (NrCAM). Like the other two adhesion molecules,
NrCAM functions in cell adhesion and cell movement.
The last candidate to be explored was fractalkine, also called neurotactin or
CX3C membrane-anchored chemokine. It is a single-pass type I membrane.
Fractalkine has been associated with cell movement and adhesion. The soluble form
of fractalkine is chemotactic for T-cells and monocytes. The membrane-bound form
promotes adhesion of those leukocytes to endothelial cells and hence it may play a
role in regulating leukocyte adhesion and migration processes at the endothelium.
Chapter 4: Verification Phase 118
4.2 Materials and Methods
The first 4 candidates to be analyzed were elafin and kallikreins 5, 6 and 10.
Their levels in serum, various biological fluids and tissues were examined.
4.2.1 Quantification of Elafin and Kallikrein 5, 6 and 10
Elafin sandwich ELISA kit, purchased from Hycult biotechnology, was used to
measure levels of human elafin in serum, pooled biological samples and pooled
tissue lysates. The assay was performed according to the manufacturers’
instructions. The concentration of KLK5, 6 and 10 was quantified with KLK5, 6 or 10-
specific non-competitive immunoassays developed in our laboratory 112,170,217. For
more details, see the cited literature.
Serum from apparently healthy females and from patients with varying levels
of CA 15-3 was used. The following 10 biological fluids were examined: amniotic
fluid, ascites, breast cyst fluid, cerebral spinal fluid (CSF), follicular fluid, milk, NAF,
urine, saliva and seminal plasma. Ten samples were combined for each fluid to
generate a pooled sample. The following 27 human tissues were examined: adrenal
(5), aorta (3), bladder (3), bone (4), colon (5), esophagus (4), heart (5), kidney (5),
liver (5), lung (4), lymph node (3), muscles (3), pancreas (5), skin (4), small intestine
(4), spinal cord (3), spleen (5), stomach (4), thyroid (3), trachea (4), ureter (4),
prostate (4), testis (4), breast (4), fallopian tube (4), uterus (4) and ovary (2). The
number of samples used to pool is indicated in brackets. Tissue extracts were
prepared by pulverizing 0.2 g of each tissue in liquid nitrogen followed by addition of
an extraction buffer (2 mL of 50 mmol/L Tris-HCl buffer, pH 8.0, containing 150
Chapter 4: Verification Phase 119
mmol/L NaCl, 5 mmol/L EDTA, and 10 mL/L NP-40 surfactant). After incubation of
the mixture on ice for 30 min, it was centrifuged at 14 000g at 4°C for 30 min. The
resulting supernatants were collected and stored at -20°C. Our procedures have
been approved by the institutional review boards of Mount Sinai Hospital and the
University Health Network, Toronto, Canada.
4.2.2 Verification strategy
For the remaining 7 candidates, the approach was to examine only serum of
cancer patients since the goal was to obtain a serological marker for breast cancer.
In the first screen, pooled serum samples were used, with each pool containing 3
samples. Normal female, normal male and breast cancer serum from women with <
30 units/mL (each one of these groups containing 3 samples per pool with 3 pools in
total) and from women > 30 units/mL were used (3 samples per pool and 9 pools in
total). To determine specificity of the molecules, 1 pooled sample containing 5 serum
samples for each of the following cancer types was also analyzed: prostate, colon,
lung, ovarian and pancreatic cancers. Candidates that showed potential to
discriminate controls from cases were screened in the second step by breaking
down the pooled samples into individual serum samples.
4.2.3 Quantification of Cystatin C, Lipocalin-2 and Transforming growth factor
beta-2 using commercial ELISA kits
Human cystatin C ELISA kit, purchased from BioVendor, LLC, was used to
measure levels of cystatin C in serum. The assay was performed according to the
Chapter 4: Verification Phase 120
manufacturers’ instructions. Briefly, 96-well plates were coated with polyclonal anti-
human cystatin C specific antibody. Once the samples were added, unbound
proteins were washed. Horseradish peroxidase (HRP) conjugated polyclonal anti-
human cystatin C antibody was added to the wells and incubated. Following another
washing step, to remove unbound antibody-HRP conjugate, a substrate solution
(H2O2 and TMB) was added. The enzymatic reaction yielded a blue product that
turned yellow when acidic stop solution was added. The intensity of the color,
measured spectrophotochemically at 450 nm, was directly proportional to the
amount of the human cystatin C bound in the initial step.
Human lipocalin-2/NGAL Quantikine ELISA kit, purchased from R&D Systems
(Minneapolis, MN), was used to measure its levels in serum. Similar in principle to
cystatin C, a monoclonal antibody specific for lipocalin-2 was coated onto a
microplate. After sample loading, any lipocalin-2 present was bound by the
immobilized antibody. After washing away unbound substances, an enzyme-linked
monoclonal antibody specific for lipocalin-2 was added to the wells. Following a
wash to remove any unbound antibody-enzyme reagent, a substrate solution was
added to the wells and color developed in proportion to the amount of lipocalin-2
bound in the initial step. The color development was stopped and the intensity of the
color was measured spectrophotochemically at 450 nm.
Human TGF-β2 Quantikine ELISA kit, purchased from R&D Systems
(Minneapolis, MN), was used to measure its levels in serum. The assay is similar in
principle to lipocalin-2.
Chapter 4: Verification Phase 121
4.2.4 Quantification of ALCAM, BCAM, NrCAM and Fractalkine using in-house
developed ELISAs
ELISA immunoassays were developed in-house for the following candidates:
ALCAM, BCAM, NrCAM and fractalkine. In general, the assays were based on
mouse monoclonal antibody capture (ALCAM, BCAM, NrCAM: 250ng/well and
fractalkine: 500ng/well) and biotinylated goat anti-human detection antibody
(ALCAM: 5ng/well; BCAM: 2ng/well; NrCAM: 4ng/well and fractalkine: 25ng/well)
(both obtained from R&D Systems, Minneapolis, MN). The assay had a detection
limit of 0.05 µg/L and a dynamic range of up to 10 µg/L. Briefly 96-well polystyrene
plates were first coated with a capture antibody specific for the molecule. After
overnight incubation, the plates were washed and loaded with 50 μL of serum or
standards and 50 µL of an assay buffer for 1.5 hours. After washing the plate, 100
µL of another biotinylated antibody (R&D) was added, creating a sandwich-type
assay, and the plates were incubated for an additional 1 hour with gentle shaking.
After washing, alkaline phosphatase-conjugated streptavidin was added and
incubated for 15 min and washed. Finally, diflunisal phosphate (DFP) and terbium-
based detection was performed, essentially as described by Christopoulos and
Diamandis.218.
Chapter 4: Verification Phase 122
4.3 Results
4.3.1 Elafin
We identified elafin in BT474 and MDA-MB-468 with the latter identifying
more unique peptides for the protein. Using a commercially available sandwich
immunoassay for elafin, we examined a number of different biological samples for its
expression. Serum from women with different levels of CA 15-3 was measured.
Typically, women with CA 15-3 levels of <30 units/mL are considered in the “normal”
range32. Examining sera from women with <30 units/mL, >30<100 units/mL and
>100 units/mL CA15-3 for elafin showed no significant difference among the groups
(Kruskal-Wallis test, P = 0.64) (Figure 4.1A). In addition, using the Mann Whitney
test, we observed no significant difference between normal and tumor breast
cytosols (P = 0.55) (Figure 4.1B). The levels of elafin in a variety of pooled biological
samples showed that milk contained 18 µg/L of this protein, followed by urine at 7
µg/L, follicular fluid and seminal plasma (Figure 4.1C). These were biological
samples from normal individuals and hence do not correlate to diseased phenotype.
Finally, we checked the levels of elafin in normal pooled tissue lysates. After
correction of total protein content in the samples, colon, small intestine and ureter
contained the highest levels. Among the top 10 expressing tissues, breast was
ranked as seventh (Figure 4.1D).
Chapter 4: Verification Phase 123
Figure 4.1
< 30 > 30 <100 >1000
10000
20000
30000
40000
50000
CA 15-3 (Units/mL)
Ela
fin
(n
g/L
)Elafin in Breast Cytosols
Normal Tumor0
2500
50005000
15000
25000
Ela
fin
Co
rrec
ted
fo
r T
ota
l P
rote
in(n
g/g
)
Milk
(10)
Urine
(10)
Follicu
lar F
luid
(10)
Semin
al P
lasm
a (1
0)0
5000
10000
15000
20000
Ela
fin
(n
g/L
)
Colon (5
)
Smal
l Inte
stin
e (4
)
Urete
r (4)
Spinal
Cord
(3)
Skin (4
)
Bone (4
)
Breas
t (4)
Pancr
eas
(5)
Esophag
us (4
)
Heart
(5)
Spleen
(5)
0
5000
10000
15000
20000
25000
30000
35000
Ela
fin
(n
g/
g)
A) B)
C) D)
Figure 4.1: Levels of elafin in biological samples. (A) Serum from women with known CA 15-3 levels were analyzed for elafin expression by ELISA (median values shown). (B) Normal and tumor breast cytosols as well as various pooled biological samples (C) and pooled tissues (D) were assayed. The number of samples used to generate a pooled sample is indicated in brackets. No expression was observed in other biological samples and tissues examined (See Materials and Methods section for a complete list of all samples surveyed).
Chapter 4: Verification Phase 124
4.3.2 Kallikrein 5, 6, 10
In addition, using the same sample sets as elafin, we measured the levels of
kallikreins 5, 6 and 10. In serum of control and breast cancer cases, for KLK5 using
the non-parametric Mann Whitney test, we obtained no significant difference (P =
0.83). However, for both KLK6 and KLK10, there was a weak significance of P =
0.01 and 0.04, respectively using the Mann Whitney test (data not shown).
Furthermore, examining the breast cytosols (normal and tumor) for KLK expression
yielded no significant difference. Interestingly, in pooled biological samples, KLK5
was found expressed in all of the relevant breast fluids (milk, breast cyst fluid and
NAF) (Figure 4.2). Finally, in the various tissues screened for KLK expression, there
was a weak expression in skin and breast for KLK5, weak expression in ovary and
spinal cord for KLK6 and a weak expression in esophagus for KLK10 (data not
shown).
Chapter 4: Verification Phase 125
Figure 4.2
A)
0
20
40
60
80
100
120
brea
st cy
st flu
id (3
)
saliv
a (3
)
norm
al urin
e (1
0)
NAF (10)
amnio
tic fl
uid (1
0)
sem
inal plas
ma
(10)
ascit
es (1
0)m
ilk
follic
ular f
luid
(10)
CSF (7)
KL
K5
(µg
/L)
B)
0
50
100
150
200
250
brea
st cy
st flu
id (3
)
saliv
a (3
)
norm
al urin
e (1
0)
NAF (10)
amnio
tic fl
uid (1
0)
sem
inal plas
ma
(10)
ascit
es (1
0)m
ilk
follic
ular f
luid
(10)
CSF (7)
KL
K6
(µg
/L)
C)
010
2030
4050
6070
8090
100
brea
st cy
st flu
id (3
)
saliv
a (3
)
norm
al urin
e (1
0)
NAF (10)
amnio
tic fl
uid (1
0)
sem
inal plas
ma
(10)
ascit
es (1
0)m
ilk
follic
ular f
luid
(10)
CSF (7)
KL
K10
(µ
g/L
)
Figure 4.2: Levels of KLK5, KLK6 and KLK10 in biological samples. Various pooled biological samples were assayed for kallikrein expression by ELISA. The number of samples used to generate a pooled sample is indicated in brackets.
Chapter 4: Verification Phase 126
4.3.3 Cystatin C
Cystatin C was found in the CM of MCF-10A and BT474 with a 3-fold higher
expression in the latter. It was also identified in the NAF proteome and in the human
plasma proteome. Its levels, based on the immunoassay, indicate that it is a high
abundance protein with values in the low mg/L range. In the first screen, analyzing
the pooled serum samples using the immunoassay showed that breast, prostate and
pancreatic cancers discriminated cases and controls (Figure 4.3A). In the second
phase of analysis, the pools were broken into its respective individual samples and
analyzed for breast, prostate and pancreatic cancers (Figure 4.3B). Individual
analysis revealed a small incremental change between the groups.
Chapter 4: Verification Phase 127
Figure 4.3
A)
B)
Normal
Fem
ale
Normal
Mal
e
Low CA15
-3
Breas
t
Prost
ate
Ovaria
n
Colon
Lung
Pancr
eatic
0
1
2
3
Cys
tati
n C
(m
g/L
)
Normal
Fem
ale
Normal
Mal
e
Low CA 1
5-3
Breas
t
Prost
ate
Pancr
eatic
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Cys
tati
n C
(m
g/L
)
Figure 4.3: Levels of cystatin C in serum. (A) Pooled serum samples representing various cancer types and (B) Individual serum samples from healthy individuals and patients with breast, prostate and pancreatic cancers.
Chapter 4: Verification Phase 128
4.3.4 Lipocalin-2
Lipocalin-2 was found in the CM of BT474 and MDA-MB-468, and absent in
the normal breast epithelial cell line MCF-10A. It was also identified in human
plasma proteome. In the first verification phase of analysis, lipocalin-2 was highly
elevated in pancreatic cancer compared to all other cancer types examined (Figure
4.4A). In the second phase of verification, 91 serum samples were examined
including 6 normal females, 6 normal males, 10 chronic pancreatitis, 35 resected
pancreatic ductal adenocarcinoma (PDAC) and 34 unresectable pancreatic
adenocarcinoma (Figure 4.4B). At 90% specificity (cut-off value 52 µg/L), 51%
sensitivity (18/35) for PDAC patients was observed.
Chapter 4: Verification Phase 129
Figure 4.4
Normal
Fem
ale
Normal
Mal
e
Low CA15
-3
Breas
t
Prost
ate
Ovaria
n
Colon
Lung
Pancr
eatic
0
25
50
75
100
125
150
175
Lip
oca
lin
(
g/L
)
Normal
Fem
ale
Normal
Mal
e
Chronic
Pan
crea
titis
PDAC
Unrese
ctab
le
0
50
100
150
200200
400
Lip
oca
lin
(
g/L
)
A)
B)
Figure 4.4: Levels of lipocalin-2 in serum. (A) Pooled serum samples representing various cancer types and (B) Individual serum samples from healthy individuals, patients with chronic pancreatitis and patients with pancreatic cancer. PDAC: resected pancreatic ductal adenocarcinoma. Dotted horizontal line represents the cut-off at 90% specificity.
Chapter 4: Verification Phase 130
4.3.5 Transforming growth factor beta-2
TGF-β2 was found only in the metastatic breast cancer cell line, MDA-MB-
468. It was previously identified in TIF proteome. Analyzing the pooled serum
samples revealed that TGF- β2 was not good at discriminating between the different
cancer types and between controls and cases (Figure 4.5). This molecule was not
analyzed further.
Chapter 4: Verification Phase 131
Figure 4.5
Normal
Fem
ale
Normal
Mal
e
Low CA15
-3
Breas
t
Prost
ate
Ovaria
n
Colon
Lung
Pancr
eatic
0
50
100
150
200
250
TG
F-
2 (n
g/L
)
Figure 4.5: Levels of transforming growth factor beta-2 (TGF-β2) in serum. Pooled serum samples representing various cancer types were examined.
Chapter 4: Verification Phase 132
4.3.6 B-cell adhesion molecule (BCAM)
BCAM was initially identified in the CM of the cancer cell lines. In the pooled
samples analysis, BCAM levels were increased in breast and prostate cancers
compared to their respective normal groups (Figure 4.6A). In the individual samples
screening process, at 90% specificity (cut-off point of 32 µg/L) the sensitivity for
breast cancer diagnosis was 34% (Figure 4.6B). Examining the values between
normal women (n = 9) and patients with breast cancer (n = 35) by the non-
parametric Mann Whitney test (two-tailed), demonstrated that the medians were not
significantly different (median normals = 27 µg/L; median cancer = 26 µg/L; P <
0.84). However, the Spearman correlation coefficient between BCAM and CA 15-3
was 0.56 for 35 samples with a P-value of <0.0004 (data not shown). Finally, in the
second phase of the screening process, serum samples from prostate cancer
patients continued to be elevated compared to normal males.
Chapter 4: Verification Phase 133
Figure 4.6
Normal
Fem
ale
Normal
Mal
e
Low CA15
-3
Breas
t
Prost
ate
Ovaria
n
Colon
Lung
Pancr
eatic
0
25
50
75
100
BC
AM
(
g/L
)
Normal
Fem
ale
Normal
Mal
e
Low CA 1
5-3
Breas
t
Prost
ate
Ovaria
nLung
Colon
Pancr
eatic
0
25
50
75
100
BC
AM
(
g/L
)
A)
B)
Figure 4.6: Levels of B-cell cell adhesion molecule (BCAM) in serum. (A) Pooled serum samples representing various cancer types and (B) Individual serum samples that made the pooled samples were analyzed individually. Dotted horizontal line represents the cut-off at 90% specificity.
Chapter 4: Verification Phase 134
4.3.7 Neuronal cell adhesion molecule (NrCAM)
NrCAM was found expressed only in the localized breast cancer cell line
(BT474). Pooled breast cancer serum was elevated in the first phase of the
screening process compared to healthy controls (Figure 4.7A). This observation did
not hold true when the pools were broken down into their individual samples (Figure
4.7B). NrCAM was ineffective at discriminating between the different cancer types
and between controls and cases. This molecule was not analyzed further.
Chapter 4: Verification Phase 135
Figure 4.7
Normal
Fem
ale
Normal
Mal
e
Low CA15
-3
Breas
t
Prost
ate
Ovaria
n
Colon
Lung
Pancr
eatic
0
5
10
15
20
25
NrC
AM
(
g/L
)
Normal
Fem
ale
Normal
Mal
e
Low CA 1
5-3
Breas
t
Prost
ate
Ovaria
nLung
Colon
Pancr
eatic
0
5
10
15
20
25
NrC
AM
(
g/L
)
A)
B)
Figure 4.7: Levels of neuronal cell adhesion molecule (NrCAM) in serum. (A) Pooled serum samples representing various cancer types and (B) Individual serum samples that made the pooled samples were analyzed individually. Dotted horizontal line represents the cut-off at 90% specificity.
Chapter 4: Verification Phase 136
4.3.8 Fractalkine
Like many of the other candidates selected for analysis in the verification
phase of this study, fractalkine was initially found expressed in the CM of the breast
cancer cell lines, BT474 and MDA-MB-468 and absent in the semi-normal breast
epithelial cell line, MCF-10A. Fractalkine was elevated in the lung cancer pool
compared to all other cancer types (Figure 4.8A). In the second phase of evaluation,
58 serum samples were examined including 6 normal females, 6 normal males, 22
adenocarcinoma, 1 large cell carcinoma, 17 non-small cell lung carcinoma (NSCLC)
and 6 squamous cell lung carcinoma (Figure 4.8B). No statistically significant
difference was observed between the groups examined (Kruskal-Wallis test; P =
0.1135).
Chapter 4: Verification Phase 137
Figure 4.8
Normal
Fem
ale
Normal
Mal
e
Low CA15
-3
Breas
t
Prost
ate
Ovaria
n
Colon
Lung
Pancr
eatic
0
2
4
6
8
10
2545
Fra
ctal
kin
e (
g/L
)
Normal
Fem
ale
Normal
Mal
e
Adenoca
rcin
oma
Large
Cell
NSCLC
Squamous
0
5
10
15
202060
100
Fra
ctal
kin
e (
g/L
)
A)
B)
Figure 4.8: Levels of fractalkine in serum. (A) Pooled serum samples representing various cancer types and (B) Individual serum samples from healthy individuals and patients with different types of lung cancer. NSCLC: Non-small cell lung cancer. Dotted horizontal line represents the cut-off at 90% specificity.
Chapter 4: Verification Phase 138
4.3.9 Activated leukocyte cell adhesion molecule (ALCAM)
ALCAM was initially identified in the CM of cancer cell lines, BT474 and MDA-
MB-468. Analyzing the pooled samples by an ALCAM-specific immunoassay (Figure
4.9A) showed elevated levels of ALCAM in breast cancer and prostate cancer
compared to their control groups of normal female and male, respectively. In the
second phase of the screening process, the pools were broken down into individual
serum samples (Figure 4.9B) and ALCAM levels continued to be increased in breast
and prostate cancers compared to their controls. When comparing the values
between normal women (n = 9) and patients with breast cancer (n = 35) by the non-
parametric Mann Whitney test (two-tailed), the medians were significantly different
(median normals = 56 µg/L; median cancer = 84 µg/L; P < 0.0002). For ALCAM, at
90% specificity (cut-off point of 62 µg/L) the sensitivity for breast cancer diagnosis
was 91%. CA 15-3 levels were measured in the serum of breast cancer patients and
the Spearman correlation coefficient between ALCAM and CA 15-3 was 0.63 for 35
samples with a P-value of <0.0001 (data not shown). Finally, the sensitivity of the
test for breast cancer diagnosis in patients where CA 15-3 was normal (<30 U/mL)
was 78% (data not shown).
Chapter 4: Verification Phase 139
Figure 4.9
Normal
Fem
ale
Normal
Mal
e
Low CA15
-3
Breas
t
Prost
ate
Ovaria
n
Colon
Lung
Pancr
eatic
0
50
100
150
200
250
AL
CA
M (
g/L
)
Normal
Fem
ale
Normal
Mal
e
Low CA 1
5-3
Breas
t
Prost
ate
Ovaria
nLung
Colon
Pancr
eatic
0
50
100
150
200
250
AL
CA
M (
g/L
)
A)
B)
Figure 4.9: Levels of activated leukocyte cell adhesion molecule (ALCAM) in serum. (A) Pooled serum samples representing various cancer types and (B) Individual serum samples that made the pooled samples were analyzed individually. Dotted horizontal line represents the cut-off at 90% specificity.
Chapter 4: Verification Phase 140
4.4 Discussion
Analysis of serum from healthy individuals and patients with breast cancer, as
well as other cancer types, resulted in one promising candidate molecule: activated
leukocyte cell adhesion molecule (ALCAM). Our results provide strong evidence that
ALCAM may be a novel breast cancer diagnostic marker, either alone, or combined
with existing breast cancer biomarkers such as CA 15-3 and CEA. Importantly,
ALCAM seemed to be superior to CA 15-3 since it was elevated in 78% of patients
with breast cancer who had normal levels of CA 15-3. This means that ALCAM can
identify a considerable number of patients (78%) who will all be missed by CA 15-3
testing. Furthermore, for ALCAM, at 90% specificity, the sensitivity for breast cancer
diagnosis (all stages) was 91%.
Interestingly, ALCAM and BCAM levels were elevated in serum of prostate
cancer patients. It continued to be elevated in the second phase of the screening
process where the pooled samples were broken down into their individual serum
samples. Given that both breast and prostate cancers are partly hormone-dependent,
this may suggest a hormonal regulation of both ALCAM and BCAM in breast and
prostate cancer. To date, no hormone response elements have been identified for
ALCAM. Further studies are needed to examine this observation in more detail.
In addition, while we did not observe circulating biomarker potential in elafin
to discriminate normal from diseased individuals, to our knowledge, we are the first
to report on the expression of this protein in breast cytosols, biological fluids and
tissue lysates using an immunoassay. Elafin was found to be expressed in normal
breast tissue. Lastly, the verification phase revealed a potential biomarker for
Chapter 4: Verification Phase 141
pancreatic cancer, lipocalin-2. Certainly, a larger serum sample size with known
clinicopathologic parameters are needed to decipher whether lipocalin-2 can be a
diagnostic marker for pancreatic cancer.
In summary, elafin, kallikreins 5, 6 and 10, cystatin C, TGF-β2, BCAM,
NrCAM and fractalkine did not show breast cancer biomarker potential in the
verification phase of analysis. Lipocalin-2 showed promise to discriminate between a
subset of pancreatic cancer patients but further studies is warranted to determine its
relevance as a serological marker. ALCAM demonstrated excellent preliminary
promise to be a circulating breast cancer biomarker. It is possibly that the
measurement of ALCAM in blood to obtain information on patient prognosis or
patient recurrence or information on good or bad response to chemotherapy
treatment might be beneficial. Using specimens with known clinical information will
facilitate characterizing the utility of ALCAM as a circulating breast cancer biomarker.
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 142
CHAPTER 5:
VALIDATION OF ALCAM AS A SEROLOGICAL BREAST CANCER DIAGNOSTIC MARKER
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 143
5.1 Introduction
Cell adhesion molecules are cell surface receptors that mediate cell-cell and
cell-substrate interactions. These molecules can be grouped into four families:
integrins, cadherins, selectins and the immunoglobulin superfamily (Ig-SF)219.
Alterations in cellular adhesion and communication can contribute to uncontrolled
cell growth. Tumor cells use adhesion molecules to cluster together and they must
maintain their adhesion to each other to invade. Hence, alterations in adhesion
molecules are essential during primary tumor formation and in metastasis 220. In this
respect, activated leukocyte cell adhesion molecule (ALCAM, CD166 or human
melanoma metastasis clone D [MEMD]) is a type 1 transmembrane glycoprotein of
the Ig-SF221. The molecular weight of ALCAM is 65kDa but with N-glycosylation at 8
putative sites, the mature ALCAM molecule has a molecular weight of 110kDa222.
Five extracellular Ig domains, a transmembrane region and a short cytoplasmic tail
make up the ALCAM protein that resembles E-cadherin in motif-arrangement221.
ALCAM mediates both heterophilic (ALCAM-CD6 [lymphocyte cell-surface receptor])
and homophilic (ALCAM-ALCAM) cell-cell interactions223. The extracellular
structures of ALCAM provide two structurally and functionally distinguishable
modules, one involved in ligand binding (to CD6)224 and the other in avidity225
(Figure 5.1). Both modules are required for stable, homophilic ALCAM-ALCAM cell-
cell adhesion223. Its short cytoplasmic tail does not contain any known signaling
motifs. Physiologically, ALCAM is expressed in activated leukocytes and neural,
epithelial and hematopoietic progenitor cells226. Functionally, ALCAM may act as a
cell surface sensor to register local growth saturation and to regulate cellular
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 144
signaling and dynamic responses214. ALCAM-CD6 interaction is required for optimal
activation of T-cells suggesting a possible ALCAM involvement in the immunologic
response to tumor cells227. ALCAM may favor interactions between tumor and
endothelial cells214. In fact, ALCAM expression has been shown to correlate with the
invasiveness of malignant melanoma and has been proposed as a prognostic
marker in this disease228,229.
The aim of our study was to investigate if ALCAM, either alone, or in
combination with the classical breast cancer biomarkers (CA 15-3 and CEA)
represent a new strategy for breast cancer diagnosis with high sensitivity and
specificity in serum, using quantitative methodologies. The association between
serum marker concentrations with various clinicopathologic parameters was also
examined.
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 145
Figure 5.1
Figure 5.1: Model of homophilic ALCAM-ALCAM interactions between cells. The extracellular structures of ALCAM consist of 2 functional modules. Ig domains D1-2 are essential for ligand binding while domains D3-5 are involved in cis-oligomerization at the cell surface.
D1 D2
D3 D4 D5
Extracellular
Cell membrane
Cytoplasm
Oligomerizing module (D3-5)
Ligand binding module (D1-2)
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 146
5.2 Materials and Methods
5.2.1 Patients and specimens
The clinical material used consisted of 150 serum samples from primary
breast cancer patients (ages 34 to 82 years; median, 62 years), 100 serum samples
from normal, apparently healthy women (ages 24 to 56 years; median, 40 years),
and as an additional control, 50 serum samples from normal healthy men (ages 23
to 61 years; median, 48 years). The samples from primary breast cancer patients
were from untreated individuals collected prior to surgery. Histologically, 94 were
classified as invasive ductal carcinoma and/or multifocal invasive ductal carcinoma,
24 as invasive lobular carcinoma and/or multifocal invasive lobular carcinoma and
32 as either invasive ductal carcinoma + invasive lobular carcinoma, invasive ductal
carcinoma with various aspects, lobular carcinoma in situ, medullary carcinoma or
other. Histologic classification was based on the World Health Organization of breast
tumors recommendation. Patients with disease of clinical stages 1 to 3 were
represented in this study. Of the 150 primary breast carcinoma patients, 32 were
stage 1, 57 were stage 2A or 2B, 27 were stage 3A or 3B and stage information was
not available for the remaining 34. Clinical grades 1, 2 and 3, corresponding to 26,
62 and 56 patients, respectively, were included in this study. The characteristics of
the breast cancer patients in terms of tumor diameter, lymph node status,
menopausal status and hormone receptor status are described later. Serum
samples, obtained from Venice, Italy, from all patients were stored at -80oC until
further analysis. Our protocols have been approved by the review boards of the
participating institutions.
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 147
5.2.2 Measurement of ALCAM, CA 15-3 and CEA in serum
The concentration of ALCAM in serum was measured by using a highly
sensitive and specific non-competitive “sandwich-type” ELISA, developed in our
laboratory. The assay is based on mouse monoclonal antibody capture and
biotinylated goat-anti human detection antibody (both obtained from R&D Systems,
Minneapolis, MN). The assay has a detection limit of 0.05 µg/L and a dynamic range
of up to 10 µg/L. Precision was less than 10% within the measurement range. Assay
parameters such as stability, linearity, cross-reactivity, recovery and reproducibility
were examined. Serum samples were analyzed in triplicate with inclusion of two
quality control samples in every run. In addition, CA 15-3 and CEA were measured
using a commercially available automated ELISA kit (Elecsys CA 15-3 and CEA
Immunoassay, respectively; Roche Diagnostics, Indianapolis, IN). The upper limit of
normal for CA 15-3 for this method is 30 U/mL and for CEA is 5 ng/mL.
5.2.3 Data analysis and statistics
The relationships between biomarkers and patient and tumor characteristics
were examined with the Kruskal-Wallis test, a nonparametric method for examining
differences among multiple groups. Spearman’s rank correlation coefficient was
used to assess the correlations among biomarkers. Logistic regression was
performed to calculate the odds ratio (OR) that defines the relation between
biomarkers and case or control status. OR were calculated on log-transformed
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 148
biomarkers and were represented with their 95% confidence interval (95% CI) and
two-sided p-values.
To further evaluate the diagnostic or prognostic usefulness of the markers for
dichotomous classification, we considered receiver operating characteristic (ROC)
curve analysis. If by convention larger values of a biomarker are associated with
adverse outcome, a cut-off point is used to define a positive marker-based test result,
i.e., positive if the marker value exceeds some cut-off point. For a marker measured
on continuous scales, a ROC curve is a plot of true positive fraction versus false
positive fraction, evaluated for all possible cut-off point values. For binary outcome,
i.e., response to chemotherapy, the ROC curve quantifies the discriminatory ability
of a marker for separating cases from controls. The standard deviations of the area
under the curve (AUC) and the differences between AUCs are computed with the U-
statistic of DeLong et al230, or the bootstrap resampling method.
For each ROC curve, we calculated the AUC, which ranges from 0.5 (for a
non-informative marker) to 1 (for a perfect marker) and corresponds to the
probability that a randomly selected case has a higher marker value than a randomly
selected control. Bootstrap method was used to calculate the confidence intervals
for AUC.
The ROC analysis was first conducted on individual markers and then in
combination, to explore the potential that a marker panel can lead to improved
performance. We considered an algorithm that renders a single composite score
using the linear predictor fitted from a binary regression model. This algorithm has
been justified to be optimal under the linearity assumption231 in the sense that ROC
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 149
curve is maximized (i.e., best sensitivity) at every threshold value. Since an
independent validation series was not available for this study, the predictive
accuracy of the composite scores was evaluated based on re-sampling of the
original data. All analyses were performed using Splus 8.0 software (Insightful Corp.,
Seattle WA).
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 150
5.3 Results
5.3.1 ALCAM ELISA assay development
To investigate the diagnostic potential of ALCAM, we developed a robust
sandwich-type ELISA using two antibodies specific for the human molecule (Figure
5.2). To ensure that the immunoassay was suitable for measuring clinical serum
samples, the recovery, reproducibility, linearity, cross-reactivity and serum sample
stability were examined. Recombinant human ALCAM protein was added into the
general diluent (control), normal serum (male and female) and into serum of breast
cancer patients at different concentrations, and measured with the ALCAM
immunoassay. A recovery of 90-100% was observed in these samples. The assay
also showed negligible cross-reactivity to another adhesion molecule of the Ig-SF, B-
cell adhesion molecule223, displayed excellent linearity with serial dilutions and
showed < 10% CV for intra- and inter-assay variability studies. Finally, the design of
the stability study consisted of collecting serum at different time points (2 weeks, 4
weeks and fresh samples) and storing them at 4oC, -20oC and -80oC. ALCAM levels
were measured in these samples using the immunoassay. No difference was
observed among the samples stored at the different temperature conditions and
among the different time point collections, compared to the freshly obtained samples
(data not shown).
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 151
Figure 5.2
Figure 5.2: Schematic of a sandwich ELISA assay. Ab, antibody; SA-ALP, streptavidin alkaline phosphatase.
YY YY YY YY YY YY B
Coat well with 1st 1o Ab (capture)
Add Antigen Add Biotinylated Ab (detection)
Add SA-ALP Add Substrate
YY B YY B
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 152
5.3.2 Association of biomarkers with age
Since cases and controls were not matched for age, we first explored if
marker values differed by age. The comparisons between cases and controls were
based on data from females only. While no change with age was observed for CA
15-3 concentrations, the level of CEA appeared to increase with age for both cases
and controls. With respect to ALCAM, there was a trend for marker level to increase
with age for cases but not for controls (Figure 5.3).
5.3.3 Correlations among biomarkers
Spearman’s rank correlation coefficients were used to assess the correlations
among markers for female controls and cases, respectively, and the results are
listed in Table 5.1. CEA appeared to be weakly correlated with ALCAM in both cases
(Spearman r = 0.371, p < 0.001) and controls (Spearman r = 0.348, p = 0.001),
whereas CA 15-3 was weakly correlated with ALCAM among cases only (Spearman
r = 0.2, p = 0.015).
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 153
Figure 5.3
Age
log(
CA
15-3
)
20 30 40 50 60 70 80
23
45
67
8
cancer: slope = 0.0004 (p = 0.928 )Normal: slope = 0.007 (p = 0.301 )
Age
log(
CE
A)
20 30 40 50 60 70 80
-10
12
34
cancer: slope = 0.02 (p = 0.001 )Normal: slope = 0.028 (p = 0.012 )
Age
log
(Alc
am
)
20 30 40 50 60 70 80
4.0
4.5
5.0
cancer: slope = 0.0053 (p = 0 )Normal: slope = 0.001 (p = 0.821 )
g p
Figure 5.3: Scatter plot of individual markers for cases and female controls versus age. Solid lines and dashed lines are loess fit (weighted polynomial regression) for cancer and normal patients respectively. Slopes are based on the fit of linear regression models of log(marker) with age as the predictor. The cancer and normal slope for CA 15-3 is horizontal stating that there is no change with age and CA 15-3 concentration. The levels of CEA increase as age increases for both cases and controls as illustrated by the steep slope. Finally for ALCAM, there is a trend for ALCAM concentrations to increase with age for cases but not for controls.
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 154
Table 5.1: Spearman's rank correlation coefficients among 3 markers for female controls and cases Female Controls Cases CA15-3 CEA ALCAM CA15-3 CEA ALCAM CA15-3 1 1 CEA -0.091 1 0.161* 1 ALCAM 0.082 0.348* 1 0.2* 0.371* 1
*: p < 0.05
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 155
5.3.4 Association of biomarkers with tumor characteristics for cases
The association of ALCAM, CA 15-3 and CEA with patient and tumor
characteristics such as age, tumor diameter, ER and PgR status, grade, histology,
ratio of lymph node positive (lpos) and total lymph nodes (ltot), menopausal status,
and stage were examined. A significant association was obtained for the following
clinicopathologic variables: age (<=50, 51-60, 61-70 and >70), menopausal status
(pre- and post-menopausal), and stage (I, II, III). The distributions of each marker in
cases for these variables are given in Table 5.2. Post-menopausal women displayed
higher values of CEA and ALCAM (all p < 0.001). As well, levels of ALCAM were not
significantly associated with stage whereas CEA and CA15-3 were. Finally, while a
statistically significant p-value was not obtained for an association between ALCAM
values and tumor grade, a general trend was observed with elevated ALCAM levels
and tumor grade, with ALCAM levels being elevated as early as grade 1 compared
to control individuals (Figure 5.4).
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 156
Table 5.2: Marker distributions by tumor characteristics for cases
# Of Patients CA15-3 CEA
ALCAM Median Q31* Median Q31* Median Q31* Age <=50 36 20.36 5.11 1.59 0.59 66.00 7.0051-60 34 21.78 6.06 1.82 0.99 77.00 11.5061-70 40 18.90 4.34 2.02 0.75 75.00 9.0070+ 40 22.92 6.12 2.62 0.96 82.00 8.25p value** 0.31 0.01 <0.001
Menopausal status pre 30 21.30 5.35 1.03 0.56 66.00 6.00post 103 20.61 6.06 2.14 0.98 78.00 9.50p value** 0.92 <0.001 <0.001
Stage I 32 17.20 5.93 1.48 0.61 72.00 11.25II 57 19.46 4.66 1.75 0.86 74.00 8.00III 27 23.40 10.32 2.47 1.10 72.00 11.50p value** 0.003 0.004 0.88
* Q31, semi-interquartile range: computed as one half the difference between the 75th percentile (Q3) and the 25th percentile (Q1) ** p value: computed from global nonparametric Kruskal-Wallis test for testing the association between a marker and a clinical variable
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 157
Figure 5.4
Normal
Fem
ale
Normal
Mal
e
Grade
1
Grade
2
Grade
3
0
25
50
75
100100
200A
LC
AM
(
g/L
)
Figure 5.4: Scatter plot of ALCAM (y-axis) distribution by tumor grade (x-axis) of the primary breast carcinoma cases examined. The solid horizontal line indicates the median value for each of the groups.
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 158
5.3.5 Association of biomarkers with breast cancer
The distributions of the 3 markers, as measured by immunoassays, in cases
and controls, are shown in Figure 5.5. Distributions of the patients with breast cancer
differed from controls (female or male) for ALCAM, but to a lesser degree for the
other two markers. The median values of males and females were similar for all 3
markers. When comparing the ALCAM values between normal women (n = 100) and
patients with breast cancer (n = 150) by the non-parametric Mann Whitney test (two-
tailed), the medians were significantly different (median normals = 60 µg/L; median
cancer = 74 µg/L; P<0.0001). For CA 15-3, the medians were significantly different
(median normals = 15 units/mL; median cancer = 21 units/mL; P<0.0001). Lastly for
CEA, the medians were different (median normals = 1.3 µg/L; median cancer = 1.9
µg/L; P = 0.0003). The association of the markers with cancer was further
considered with linear regression models of logarithm-transformed marker values as
a function of clinical status (cancer vs non-cancer; females only) and age. Adjusting
for age, the mean levels of log(CA15-3) and log(ALCAM) were significantly higher in
cancer; levels of log(CEA) did not differ between cancer and controls.
We also considered logistic regression models to further characterize the
associations between markers and breast cancer, adjusting for age. Similar to the
results from linear regression, we found that two individual markers, CA 15-3
(OR=1.12, 95% CI [1.04,1.19]) and ALCAM (OR=1.42, 95% CI [1.14,1.77])
univariately predicted breast cancer, but this was not the case for CEA (OR=0.99,
95% CI [0.95,1.05]), whose 95% confidence interval fell into 1.00. In a logistic
regression model, which included age and all three markers, we found that CA15-3
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 159
and ALCAM independently predicted breast cancer. Results from the logistic
regression models are given in Table 5.3.
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 160
Figure 5.5
Normal
Fem
ales
Breas
t Can
cer
0
25
50
75
100100150200
AL
CA
M (
g/L
)
Normal
Fem
ale
Breas
t Can
cer
0
30
60
90100
3300
CA
15-
3 (U
/mL
)
Normal
Fem
ale
Breas
t Can
cer
0
5
1010
60
CE
A (
g/L
)
A)
B)
C)
Figure 5.5: Distribution of markers A) ALCAM, B) CA 15-3 and C) CEA in the three groups (normal female, normal male and breast carcinoma) examined by an immunoassay specific to the molecule. The solid horizontal line indicates the median value for each of the groups. The dotted horizontal line indicates the cut-off values to discriminate cancer from control subjects A) ALCAM: 78 µg/L, 95% specificity cut-off; B) CA 15-3: 30 U/mL and C) CEA: 5 ng/mL.
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 161
Table 5.3: Results from logistic regression models Univariate * Multivariate** OR 95% CI OR 95% CI Marker CA15-3 1.12 (1.04,1.19) 1.09 (1.02,1.18)CEA 0.99 (0.95,1.05) 0.94 (0.89,1.00)ALCAM 1.42 (1.14,1.77) 1.39 (1.09,1.78)
*: logistic model with logarithm of the marker and age as predictors. **: logistic model with logarithm of all three markers and age as predictors. OR, odds ratio; CI, confidence interval.
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 162
5.3.6 The diagnostic values of the three markers
ROC curve analysis (Figure 5.6) was used to quantify the diagnostic value of
the three markers. All three markers have AUC significantly better than 0.5, with
ALCAM having the best performance (AUC=0.78, 95% CI [0.73,0.84]). The
superiority of ALCAM over the other two markers was also evident when we
considered sensitivities at fixed values of 90% and 80% specificities, respectively
(Table 5.4). For example, at specificity of 80%, ALCAM yielded a sensitivity of 60%,
compared with 48% for CA15-3. Likewise, at 90% specificity, ALCAM displayed
higher sensitivity than CA 15-3 and CEA. The incremental values of AUC for ALCAM
over that for CA15-3 are statistically significant (Delong test, p <0.05). Combining
CA15-3 and ALCAM, based on the linear predictors from a logistic regression model,
yielded a ROC curve with an AUC of 0.81 (bootstrap 95% CI [0.75, 0.87]).
Combining CA15-3, ALCAM and CEA did not result in any improvement in ROC
curves compared to CA15-3 and ALCAM. Re-sampling methods which aimed to
adjust for over-fitting232 did not yield substantially different results.
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 163
Figure 5.6
False Positive Fraction
Tru
e P
ositi
ve F
ract
ion
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
False Positive Fraction
Tru
e P
ositi
ve F
ract
ion
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
False Positive Fraction
Tru
e P
ositi
ve F
ract
ion
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
CA15-3, AUC = 0.7 ( 0.63 , 0.76 )CEA, AUC = 0.63 ( 0.56 , 0.7 )ALCAM, AUC = 0.78 ( 0.73 , 0.84 )
Figure 5.6: ROC curves for the three markers (CA 15-3, CEA, ALCAM).
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 164
Table 5.4: ROC analysis for biomarkers Sensitivity --------------------------------------------------------------------
AUC 95% CI 90%
Specificity 95% CI 80%
Specificity 95% CI Marker CA15-3 0.70 (0.64,0.76) 0.32 (0.19,0.44) 0.48 (0.32,0.63)CEA 0.63 (0.56,0.70) 0.22 (0.12,0.31) 0.32 (0.22,0.41)ALCAM 0.78 (0.73,0.84) 0.47 (0.38,0.57) 0.60 (0.48,0.73)Combined* 0.81 (0.75,0.87) 0.52 (0.39,0.64) 0.67 (0.54,0.80)
Combined*: linear combination of CA15-3 and ALCAM The incremental values of AUC for ALCAM over that for CA15-3 are statistically significant (Delong test, p <0.05)
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 165
5.4 Discussion
In general, epithelial cells are less mobile than cells of mesenchymal origin.
Consequently, development of malignant carcinomas that arise from normal
epithelia (such as in breast cancer) affect the intercellular adhesion system233. Most
primary cancers show loss of expression of adhesion molecules to allow for a critical
step in metastasis to occur: detachment of the invading cell from its neighbors 234,235.
Nevertheless, while the long standing hypothesis that loss of adhesion molecules
permit invasion is predominantly true235,236, studies have showed that loss of
adhesion itself is not sufficient to cause invasiveness237. Specifically, there are
examples of poorly differentiated, non-adhesive carcinomas with unchanged
amounts of E-cadherin (adhesion molecule expressed in epithelial cells), indicating
that the invasive phenotype in these tumors is not due to reduced expression of the
molecule but rather, to interference with its cell adhesive function233.
Therefore, a number of potential reasons exist for observing elevated levels
of adhesion molecules such as ALCAM in cancer patients versus normal individuals.
First, increased homotypic intercellular adhesion (due to elevated levels of these
molecules) may favor the metastatic process since cell aggregates, rather than
single cells breaking away from the primary tumor, have a greater chance of survival
in the circulation and of lodging in other organs238. Second, it is known that cell
adhesion is necessary for the metastatic spread of cancer cells to new organs
(secondary tumor establishment)235. As well, overproduction of adhesion molecules
may disrupt the normally operative intercellular adhesion forces, allowing more cell
movement and the adoption of a less ordered tissue architecture239. As an
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 166
illustration, a substance that has been studied extensively as a marker for breast
cancer is CEA. CEA is a member of the immunoglobulin supergene family and is
expressed in a large variety of secretory tissues240,241. Interestingly, expression of
CEA is increased in colon carcinomas and it may be important to processes of
intercellular recognition242,243. It has been suggested that this might either result in
disturbance of normal intercellular adhesion or provide advantages in further steps
of metastasis239 such as conceivably facilitating establishment of a secondary
tumor233,235. These factors may be true for ALCAM but further studies are needed to
confirm these speculations.
Given that ALCAM is a transmembrane protein with an extracellular domain, it
is very likely that membrane shedding may lead to elevated levels of ALCAM in
circulation. MMP-2, a metalloproteinase involved in degrading cell-cell connections,
has been shown to be elevated in serum of breast cancer patients and its levels
correlate with poor prognosis244,245. It is possible that a putative substrate for MMP-2
is ALCAM. Therefore, it is probable that an increase in MMP-2 or other proteases
(such as the kallikreins) may result in increased shedding of ALCAM into the
circulation. Certainly, further studies are warranted to decipher the mechanisms by
which ALCAM is elevated in breast cancer.
ALCAM expression has been explored in a number of different tumor types
displaying a clear upregulation in some tumors and downregulation in others. In
addition, variable levels of ALCAM expression have been found at different stages of
tumor development in the same type of malignancies. In melanoma, ALCAM has
been suggested to exhibit a role in melanoma cell invasion and neoplastic
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 167
progression246. In prostate carcinoma, ALCAM gene was found upregulated in high
Gleason grade prostate cancers compared to benign prostatic hyperplasia cases247.
However, one study observed an upregulation of ALCAM in low-grade tumors and a
downregulation in high-grade prostatic tumors248. Yet, another study on prostate
cancer found ALCAM to predict prostate-specific antigen (PSA) relapse226. In colon
cancer, using IHC, no significant correlation with patient age, tumor grade, stage or
nodal status and ALCAM expression was observed, but membranous ALCAM
expression correlated significantly with shortened patient survival228.
There have been a few studies investigating ALCAM expression in breast
cancer. Low levels of ALCAM mRNA correlated with nodal involvement, high grade
and worse prognosis249. In fact, low levels of ALCAM transcripts in the primary
breast tumor correlated with skeletal metastases and poor prognosis250. At the
protein level, laser scanning cytometry and confocal microscopy showed that high
levels of ALCAM correlated with small tumor diameter, low grade and the presence
of hormone receptors, which supported the view that this adhesion molecule is a
tumor suppressor with prognostic significance 221. However, an IHC analysis showed
that high cytoplasmic ALCAM expression was associated with shortened patient
disease-free survival215. Yet a further study found that ALCAM-ALCAM interactions
between breast cancer cells were important for survival in the primary tumor and that
a loss of ALCAM was associated with programmed cell death251. Finally, Ihnen et al
discovered that patients with high ALCAM mRNA expression who did not receive
chemotherapy tended to have a worse prognosis, suggesting that high ALCAM
expression levels may be a marker for prediction of the response to adjuvant
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 168
chemotherapy in breast cancer252. Indeed, the discordant data between RNA and
protein levels of ALCAM in breast cancer and even discordance among different
protein expression studies suggest the need for additional research to evaluate the
role of ALCAM in breast cancer.
The finding of decreased levels of ALCAM in breast cancer tissue compared
to normal breast tissue is not contradictory to our results of elevated levels of
ALCAM in serum of breast cancer patients. It is possible that ALCAM levels
decrease in tissue but is elevated in serum. For example, although PSA gene
transcription is down-regulated in prostate cancer, PSA protein levels in the
circulation of prostate cancer patients increase due to disruption of the anatomic
barriers between the glandular lumen and capillaries. Healthy men display serum
PSA range of 0.5-2 g/L (low levels of PSA enter the circulation by diffusing through
a number of anatomic barriers). In early stage prostate cancer, PSA levels in the
serum rise to 4-10 g/L (due to destruction of tissue architecture). In late stage
prostate cancer, due to invasion of tumor cells, considerable amounts of PSA leak
into the bloodstream (PSA levels typically range from 10 to 1000 g/L). In addition,
our data of increased ALCAM levels in cancer versus healthy controls is in line with
data about ALCAM expression in prostate and colon cancer and melanoma. It
should also be noted that we are the first to report presence of ALCAM in serum of
breast cancer patients. Until now, all studies regarding ALCAM expression have
been performed at the transcript level or using IHC or confocal microscopy. In this
study, we developed a robust and highly sensitivity immunoassay to measure
ALCAM in biological fluids.
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 169
It is generally agreed upon that no single cancer biomarker will provide all
necessary information for optimal cancer diagnosis. The current trend is to focus on
the identification of multiple biomarkers that can be used in combination. The
present data provides evidence that serum ALCAM represents a novel biomarker for
breast cancer. This biomarker displays higher diagnostic sensitivity for breast cancer
than the currently used tumor markers CA 15-3 and CEA (Table 5.4). Moreover, as a
result of the moderate correlation between ALCAM and CA 15-3 (Table 5.1), there
are patients with normal levels of CA 15-3 (< 30 U/mL) who have elevated ALCAM
levels. In fact, among the 120/150 cancer patients examined who displayed normal
levels of CA 15-3, 48 of them (40%) had elevated levels of ALCAM (values of 78
µg/L or greater; the cut-off for 95% specificity). For this reason, CA 15-3
measurements will benefit from combining ALCAM measurements, to increase the
diagnostic sensitivity of each of the markers alone. As well, assuming a 95%
specificity, the statistical power of our study (n = >100 for both control and cases)
will allow the detection of a 20% difference between mean values of ALCAM levels
in breast cancer patients and controls (data not shown). The difference between the
ALCAM means in this study was >20%, within the power of our study.
Although a statistically significant correlation between ALCAM expression and
tumor grade in breast cancer was not obtained, a general trend towards elevated
ALCAM levels with increasing grade was observed (Figure 5.4). We hypothesize
that the upregulation of ALCAM is an early event in malignant cell transformation in
breast cancer. In addition, a correlation of elevated ALCAM levels with increasing
age was observed in primary breast carcinoma patients (Table 5.2). However, there
Chapter 5: Validation of ALCAM as a Serological Breast Cancer Diagnostic Marker 170
was no correlation between ALCAM levels and age of normal women (data not
shown). This suggests that the difference in age between cases and controls is not a
confounding factor in this study. Furthermore, CEA was a weak marker in this study
and this is consistent to its performance in the clinic for breast cancer.
In conclusion, we show evidence that serum ALCAM concentration
represents a novel biomarker for breast carcinoma, which has potential utility as a
diagnostic tool. The combination of ALCAM with CA 15-3 improved the diagnostic
sensitivity. The availability of a reliable immunoassay, such as the one developed in
this study, for measuring serum ALCAM may facilitate further studies to establish the
clinical usefulness of this marker and to clarify the biological roles of ALCAM in
breast cancer.
Chapter 6: Summary and Future Directions 171
CHAPTER 6:
SUMMARY AND FUTURE DIRECTIONS
Chapter 6: Summary and Future Directions 172
6.1 Summary
A proteomic platform, consisting of discovery, verification and validation
phases, was utilized in this thesis to identify breast cancer biomarkers. Overall, this
thesis has provided initial insights into potential serological breast cancer biomarkers.
Below is a summary of the key findings of this study.
Key Findings
1. Discovery Phase:
a. Two-dimensional liquid chromatography-tandem mass spectrometry
(2D-LC-MS/MS) strategy was utilized to sample the secretome of 3
breast cell lines, MCF-10A, BT474 and MDA-MB-468.
b. Over 1,100 proteins in the conditioned media of all 3 cell lines
combined were identified.
c. Label-free quantification, biological function, cellular localization and
other bioinformatic and data analyses were performed.
d. Various filtering criteria were applied to narrow the list of potential
biomarkers from 400 extracellular and membrane proteins.
e. The top 11 candidates were selected to enter the second phase of the
proteomic platform for biomarker discovery.
2. Verification Phase:
a. Pooled serum samples, other biological fluids and tissue lysates were
used to examine the levels of the following candidates for its ability to
Chapter 6: Summary and Future Directions 173
discriminate between breast cancer patients and controls using an
immunoassay
II. Elafin
III. Kallikreins 5, 6 and 10
IV. Cystatin C, lipocalin-2 and transforming growth factor beta-2
(secreted proteins)
V. ALCAM, BCAM, NrCAM and fractalkine (membrane
proteins; in-house developed ELISA)
b. ALCAM demonstrated the greatest potential to discriminate
between normal and breast cancer serum samples
3. Validation Phase:
a. Examined levels of ALCAM in 300 serum samples with known
clinicopathological parameters
b. ALCAM, with area under the curve (AUC) of 0.78 [95% CI: 0.73,
0.84] outperformed CA15-3 (AUC= 0.70 [95% CI: 0.64, 0.76]) and
CEA (AUC= 0.63 [95% CI: 0.56, 0.70]).
c. 40% of breast cancer patients with normal levels of CA 15-3
displayed elevated ALCAM levels
d. Serum ALCAM concentrations appear to be a new biomarker for
breast cancer and may have value for disease diagnosis
Chapter 6: Summary and Future Directions 174
6.2 Future Directions
The experimental data provided in this thesis has led to the identification
of a potential serological breast cancer biomarker, activated leukocyte cell
adhesion molecule (ALCAM). The availability of a reliable immunoassay, such
as the one developed in this study, for measuring serum ALCAM may facilitate
further studies to establish the clinical usefulness of this marker and to clarify the
biological roles of ALCAM in breast cancer. Examining the levels of ALCAM in
other serum samples such as those obtained from patients pre- and post-surgery
as well as serial serum samples collected from patients undergoing therapy may
be beneficial in evaluating the biomarker potential of ALCAM. Indeed, ALCAM
may be a predictive or prognostic marker for breast cancer, rather than a
diagnostic or screening marker for the general population. Also, ALCAM may be
useful only for a sub-population of patients. A larger sample set may facilitate
identifying this sub-population.
Moreover, the growing consensus given the heterogeneity of breast
cancer is that no single cancer biomarker will provide all necessary information
for optimal cancer diagnosis. The current trend is to focus on the identification of
multiple biomarkers that can be used in combination. Hence, ALCAM in
combination with some of the other potential candidates discovered in this study,
which have not yet been evaluated, can be examined. As well, for many of the
potential candidates discovered, reagents were not available to develop an
immunoassay to measure their levels in biological fluids. A highly sensitive and
high-throughput verification platform is desperately needed to bridge the gap
Chapter 6: Summary and Future Directions 175
between discovery and validation phases of candidate biomarkers. In addition to
its important role as a discovery platform, the use of MS as a quantification tool is
gaining popularity. It has been suggested that multiple reaction monitoring
(MRM) assays will enable fast, high-throughput verification of candidate
biomarkers in blood. Therefore, for many of the potential candidate molecules
discovered in phase 1 of this study, a quantitative technique based on mass
spectrometry may be developed to measure its levels in serum.
With respect to the verification phase, it was discovered that ALCAM and
BCAM levels were elevated in prostate cancer compared to healthy male serum
samples. The elucidation of whether ALCAM and/or BCAM are potential
diagnostic markers for prostate cancer will involve performing a validation study,
such as the one presented in this thesis, utilizing a large sample set with known
clinical parameters. In addition, brief examination of lipocalin-2 serum levels in
various pancreatic cancer patients revealed that this protein may be elevated in a
sub-population of resected pancreatic ductal adenocarcinoma. Further
investigation into this observation via examining a much larger sample set may
yield a potential pancreatic cancer tumor marker.
Lastly, the work presented in this thesis demonstrated that ALCAM
(transmembrane protein) levels were elevated in serum. Studies examining the
mechanism behind ALCAM shedding will be important. A detailed assessment of
various protease activities in cleaving ALCAM along with examining the biological
roles of ALCAM in breast cancer pathogenesis may provide important
information.
References 176
REFERENCES
References 177
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