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The Impact of Chromosomal Aberrations on the Regulation of Kallikrein 6 Expression in Serous Ovarian Carcinoma by Jane Marie Bayani A thesis submitted in conformity with the requirements for the degree of Doctorate of Philosophy Department of Laboratory Medicine and Pathobiology University of Toronto © Copyright by Jane Marie Bayani 2013

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Page 1: The Impact of Chromosomal Aberrations on the Regulation … · The Impact of Chromosomal Aberrations on the Regulation of Kallikrein 6 Expression in Serous Ovarian Carcinoma ... 27

The Impact of Chromosomal Aberrations on the

Regulation of Kallikrein 6 Expression in Serous Ovarian

Carcinoma

by

Jane Marie Bayani

A thesis submitted in conformity with the requirements for the degree of Doctorate of Philosophy

Department of Laboratory Medicine and Pathobiology University of Toronto

© Copyright by Jane Marie Bayani 2013

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The Impact of Chromosomal Aberrations on the Regulation of

Kallikrein 6 Expression in Serous Ovarian Carcinoma

Jane Marie Bayani

Doctorate of Philosophy

Department of Laboratory Medicine and Pathobiology University of Toronto

2013

Abstract

Ovarian cancer (OCa) remains the leading cause of death due to a gynecologic malignancy in

North American women, and the pathogenesis of this disease is a consequence of the interplay

between DNA, RNA and proteins. The genomes of these cancers are characterized by numerical

and structural aberrations, resulting in copy-number changes of the affected regions. The serine

protease, Kallikrein 6 (KLK6), is a promising biomarker and is over-expressed in OCa.

However, the mechanisms leading to the observed KLK6 overexpression are poorly understood;

and to date, no study examining the chromosomal contributions to the overexpression have been

conducted. Utilization of multi-colour Fluorescence in situ Hybridization (FISH)-based

technologies to untreated primary serous OCa samples and cancer cell lines, showed that the

KLK locus, on 19q13.3/4, is involved in both numerical and structural aberrations; was subject to

high-level copy-number heterogeneity (p<0.001); and structural rearrangements of 19q were

significantly co-related to grade (p<0.001). Patients with a loss of the KLK locus, or no

structural rearrangement on 19q, experienced a trend towards longer disease free survival (DFS)

and better overall survival (OS), over those with a gain or amplification, or with breakage events

on 19q. KLK6-specific immunohistochemistry (IHC) showed weak correlation with KLK6 copy-

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number, suggesting other mechanisms together with copy-number, drives its over-expression.

Among these mechanisms are microRNAs (miRNAs), also shown to be affected by the copy-

number changes in OCas. Therefore, we investigated the role of miRNAs in OCa and their role

in KLK6 regulation. Specifically, we examined the copy-number status and miRNA expression

in a representative OCa cell line, OVCAR-3. miRNA expression profiling of OCa cell lines and

primary tumours showed their differential expression, including the decrease in expression of the

let-7 family members, which are predicted to target KLK6. Indeed, when hsa-let-7a was

transiently transfected into OVCAR-3, a reduction of secreted KLK6 protein was detected. Thus,

the contribution of numerical and structural aberrations of the OCa genome can directly affect

the expression KLK6 through copy number, but is also aided post-transcriptionally by miRNAs.

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Dedication

This body of work is dedicated with love,

to my parents, Jean and Earl Bayani;

to my husband, Ash;

and our children, Leila and Ryan.

I am everything because of you, and nothing without you.

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Acknowledgments

The completion of this work not only symbolizes the period of time spent in the PhD program, but a long journey that has taken me from being a disillusioned university graduate, through to a career in molecular cytogenetics. Therefore, this work not only represents the contributions of many to my scientific life, but also to my personal life. Thank you to Dr. Eleftherios Diamandis for taking the chance on a staunch molecular cytogeneticist and introducing her to the world of proteomics. Through you, I have seen the rewards that come with taking chances; and the lessons to be learned from the failures. Because I believe everything happens for a reason and in its own time, it is perhaps no accident that an innocuous collaboration would become the basis of my doctoral work. You once said that everyone comes to the lab with a story – indeed we do. To Dr. Jeremy Squire and Dr. Maria Zielenska, who started me on this journey, I am grateful for your unending support and friendship; and to have been part of your research family. Thank you for nurturing my desire for discovery and helping me find my way. This work could not have been accomplished without the expertise of my research families. Thank you to all the members of the Advanced Centre for Detection of Cancer (ACDC) laboratory for sharing with me your knowledge and your friendship. Moreover, I am grateful to all the members of the Squire and Zielenska laboratories for your many years of continued support and friendship. To my thesis committee members, Dr. Ivan Blasutig and Dr. David Irwin, I appreciate your contributions; and to Dr. Harry Elsholtz for your guidance. My special thanks to Dr. Sabine Mai for acting as my external examiner. I also thank my dear friends Ajay Pandita and Paula Marrano, who have been with me since the beginning. This journey could not have been completed without the support of my family. My love and thanks to my parents, who taught us that we should never stop learning; and to my sisters, Jen and Jamie, and their families. I also give my sincere thanks to my mother- and father-in-law and extended family. Finally, to my husband and my children, thank you for always being there for me – I love you with all my heart.

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Table of Contents

Dedication ...................................................................................................................................... iv 

Acknowledgments ........................................................................................................................... v 

Table of Contents ........................................................................................................................... vi 

List of Abbreviations ..................................................................................................................... xi 

List of Tables ............................................................................................................................. xviii 

List of Figures .............................................................................................................................. xix 

Chapter 1 ......................................................................................................................................... 1 

1  Ovarian Carcinoma .................................................................................................................... 2 

1.1  Canadian Statistics .............................................................................................................. 2 

1.2  Etiology ............................................................................................................................... 2 

1.3  Diagnosis and Treatment .................................................................................................... 3 

1.4  Pathology ............................................................................................................................ 5 

1.5  Molecular Pathology ........................................................................................................... 6 

1.5.1  Hereditary and Sporadic Ovarian Cancers .............................................................. 6 

1.5.2  Type I and Type II Ovarian Cancers ....................................................................... 7 

1.5.3  Ovarian Cancer Genomics and Transcriptomics .................................................. 10 

1.6  Chromosomal Instability ................................................................................................... 11 

1.7  Defining Chromosomal Instability in Ovarian Carcinoma ............................................... 13 

1.8  The Current Status of Ovarian Cancer Biomarkers: Kallikrein 6 (KLK6) a Promising Ovarian Cancer Biomarker ............................................................................................... 16 

1.8.1  The Kallikreins (KLK) and Kallkrein 6 (KLK6): A Promising Biomarker for Ovarian Cancer ..................................................................................................... 18 

1.8.2  Kallikrein 6: Historical Perspectives and Genetics ............................................... 19 

1.8.3  KLK6 mRNA ........................................................................................................ 20 

1.8.4  KLK6 Protein ........................................................................................................ 22 

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1.8.5  Role of KLK6 in Disease ...................................................................................... 26 

1.8.5.1  Inflammation, Immunity and Skin Pathophysiology .............................. 27 

1.8.5.2  Neurodegeneration.................................................................................. 27 

1.8.5.3  Tumourigenesis ...................................................................................... 28 

1.8.6  Clinical Utility of KLK6 in Ovarian Cancer ......................................................... 30 

1.8.7  Regulation of KLK6 Expression and Activity ...................................................... 32 

1.9  Rationale and Hypothesis ................................................................................................. 36 

1.10 Objectives ......................................................................................................................... 36 

Chapter 2 ....................................................................................................................................... 38 

2  Distinct Patterns of Structural and Numerical Chromosomal Instability Characterize Sporadic Ovarian Cancer ......................................................................................................... 39 

2.1  Introduction ....................................................................................................................... 39 

2.2  Materials and Methods ...................................................................................................... 40 

2.2.1  Patient Specimens and Cell Lines ......................................................................... 40 

2.2.2  Peptide Nucleic Acid (PNA) Fluorescence in situ Hybridization (FISH) and Spectral Karyotyping Analysis ............................................................................. 41 

2.2.3  Centrosome Immunostaining ................................................................................ 42 

2.3  Results ............................................................................................................................... 43 

2.3.1  Numerical Chromosomal Instability: N-CIN ........................................................ 43 

2.3.2  Structural Chromosomal Instability: S-CIN ......................................................... 44 

2.3.3  Multicentric Chromosomes ................................................................................... 45 

2.3.4  Centrosome Aberrations and Association with Increasing N-CIN ....................... 47 

2.4  Discussion ......................................................................................................................... 48 

Chapter 3 ....................................................................................................................................... 55 

3  Impact of Cytogenetic and Genomic Aberrations of the Kallikrein Locus in Ovarian Cancer ...................................................................................................................................... 56 

3.1  Introduction ....................................................................................................................... 56 

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3.2  Materials and Methods ...................................................................................................... 57 

3.2.1  Patient Specimens and Cell Lines ......................................................................... 57 

3.2.2  Fluorescence in situ Hybridization (FISH) ........................................................... 58 

3.2.3  Spectral Karyotyping ............................................................................................ 59 

3.2.4  Array Comparative Genomic Hybridization (aCGH) ........................................... 59 

3.2.5  Measurement of KLKs by ELISA ........................................................................ 60 

3.3  Results ............................................................................................................................... 63 

3.3.1  Fine Structural Analysis of Chromosomal Rearrangements Affecting 19q13 in Cancer Cell Lines .................................................................................................. 63 

3.3.2  Chromosomal Rearrangements of 19q13 in Ovarian Cancer Patient Samples ..... 66 

3.3.3  High-Resolution aCGH and KLK-specific FISH Analysis Confirms Copy-Number Imbalances .............................................................................................. 69 

3.3.4  Protein Expression and Relationship to Copy-Number and Structural Rearrangements in Cancer Cell Lines and Ovarian Specimens ............................ 70 

3.4  Discussion ......................................................................................................................... 73 

Chapter 4 ....................................................................................................................................... 76 

4  Genomic Instability and Copy-Number Heterogeneity of Chromosome 19q, Including the Kallikrein Locus, In Ovarian Carcinomas ............................................................................... 77 

4.1  Introduction ....................................................................................................................... 77 

4.2  Materials and Methods ...................................................................................................... 79 

4.2.1  Patient Specimens ................................................................................................. 79 

4.2.2  Fluorescence in situ Hybridization (FISH) ........................................................... 79 

4.2.3  Measurement of Chromosomal Instability ............................................................ 80 

4.2.4  Immunohistochemistry (IHC) ............................................................................... 81 

4.2.5  Statistical Methods ................................................................................................ 82 

4.3  Results ............................................................................................................................... 82 

4.3.1  Patient Cohort ....................................................................................................... 82 

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4.3.2  The KLK Locus (19q13.3/13.4) is Subject to Frequent Copy-Number Alterations and Chromosomal Instability ............................................................. 82 

4.3.3  Structural Alterations of Chromosome 19q are Associated with Age and Grade ..................................................................................................................... 86 

4.3.4  Serous Ovarian Carcinomas Show a Range of KLK6 and p53 Expression ......... 87 

4.3.5  Univariate and Multivariate Analyses .................................................................. 87 

4.4  Discussion ......................................................................................................................... 91 

Chapter 5 ....................................................................................................................................... 98 

5  Modulation of KLK6 Protein Expression in the Ovarian Cancer Cell Line OVCAR-3 by miRNA Let-7a .......................................................................................................................... 99 

5.1  Introduction ....................................................................................................................... 99 

5.2  Materials and Methods .................................................................................................... 100 

5.2.1  Fluorescence in situ Hybridization (FISH), Spectral Karyotyping (SKY) and Array Comparative Genomic Hybridization (aCGH) ......................................... 100 

5.2.2  microRNA Profiling ............................................................................................ 101 

5.2.3  Identification of miRNAs Predicted to Target KLK6 ......................................... 102 

5.2.4  Transient Transfection of miRNAs to OVCAR-3 .............................................. 102 

5.2.5  Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) ... 103 

5.2.6  Measurement of KLK6 and KLK10 by ELISA .................................................. 103 

5.3  Results ............................................................................................................................. 104 

5.3.1  Identification of Predicted miRNAs Regulating KLK6 and miRNA Profiling .. 104 

5.3.2  OVCAR-3 Cytogenomics ................................................................................... 109 

5.4  Discussion ....................................................................................................................... 115 

Chapter 6 ..................................................................................................................................... 120 

6  Summary and Future Directions ............................................................................................ 121 

6.1  Summary ......................................................................................................................... 121 

6.2  Future Directions ............................................................................................................ 126 

References ................................................................................................................................... 129 

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Summary of Publications ............................................................................................................ 156 

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List of Abbreviations

5-aza 5-Aza-2′-deoxycytidine

aCGH Array Comparative Genomic Hybridization

ACTION Adjuvant Chemotherapy Trial in Ovarian Neoplasia

ACTN4 Alpha-actinin-4

AD Alzheimer's Disease

AKT v-akt murine thymoma viral oncogene homolog 1

AKT2 v-akt murine thymoma viral oncogene homolog 2

Ala Alanine

APC Adenomatous polyposis coli

Apo A-1 Apolipoprotein A-I

APP Amyloid precursor protein

ARID1A AT-rich interactive domain-containing protein 1A

Asp Aspartic acid

ATCC American Type Culture Collection

AUC Area Under the Curve

β2M β2 microglobulin

BAC Bacterial Artificial Chromosome

Bcl-2 B-cell lymphoma 2

Bcl-XL B-cell lymphoma-extra large

BFB Breakage Fusion Bridge

Bim BCL2-like 11

bp base pair

BRAF v-Raf murine sarcoma viral oncogene homolog B1

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BRCA1 Breast Cancer 1, early onset

BRCA2 Breast Cancer 2, early onset

CA-125 Carbohydrate Antigen 125

CCND2 Cyclin D2

CCNE1 Cyclin E1

CGH Comparative Genomic Hybridization

CIN Chromosomal Instability

CNS Central Nervous System

COSMIC Catalogue Of Somatic Mutations In Cancer

CpG Cytosine phosphate Guanine

CT Computed Tomography

CTNNB1 Beta-catenin

DAPI 4',6-diamidino-2-phenylindole

dATP 2'-deoxyadenosine triphosphate

dCTP 2'-deoxycytidine triphosphate

DEAC 7- diethylaminocoumarin-3-carboxylic acid

DFS Disease Free Survival

dGTP 2'-deoxyguanosine triphosphate

DNA Deoxyribonucleic Acid

DSB Double Strand Break

dTTP 2'-deoxythymidine triphosphate

EDRN Early Detection Research Network

EGFR Epidermal Growth Factor Receptor

ELF-2 E74-like factor 2 (ets domain transcription factor)

ELISA Enzyme-linked immunosorbent assay

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ELK-1 E twenty-six (ETS)-like transcription factor 1

EMT Epithelial to Mesenchymal Transition

FDA Food and Drug Administration

FFPE Formalin Fixed Paraffin Embedded

FIGO International Federation of Gynecology and Obstetrics

FISH Fluorescence in situ Hybridization

FITC Fluorescein isothiocyanate

Glu Glutamic acid

hsa-let-7 human-lethal-7

HE4 Human Epididymis Protein 4

HER2/neu Human Epidermal Growth Factor Receptor

HGSC High-Grade Serous Carcinoma

His Histidine

HMGA2 High-mobility group AT-hook 2

HNPCC Hereditary nonpolyposis colorectal cancer

HR Hazard Ratio

HR Homologous Recombination

HRE Hormone Response Element

HSR Homogeneous Staining Region

ICGC International Cancer Genome Consortium

ICON1 International Collaborative Ovarian Neoplasm Trial 1

ID4 Inhibitor of DNA binding 4

IGF2 Insulin-like Growth Factor 2

IHC Immunohistochemistry

IRF2BP2 Interferon Regulatory Factor 2 Binding Protein 2

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ISCN International System for Human Cytogenetic Nomenclature

KLK Kallikrein

KLK1 Kallikrein 1

KLK10 Kallikrein-related peptidase 10

KLK11 Kallikrein-related peptidase 11

KLK12 Kallikrein-related peptidase 12

KLK13 Kallikrein-related peptidase 13

KLK14 Kallikrein-related peptidase 14

KLK15 Kallikrein-related peptidase 15

KLK2 Kallikrein-related peptidase 2

KLK3 Kallikrein-related peptidase 3

KLK4 Kallikrein-related peptidase 4

KLK5 Kallikrein-related peptidase 5

KLK6 Kallikrein-related peptidase 6

KLK7 Kallikrein-related peptidase 7

KLK8 Kallikrein-related peptidase 8

KLK9 Kallikrein-related peptidase 9

KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog

Leu Leucine

LGSC Low-Grade Serous Cancer

Lys Lysine

mCGH metaphase Comparative Genomic Hybridization

MECOM MDS1 and EVI1 complex locus

MFISH Multi-Colour Fluorescence in situ Hybridization

MIN Microsatellite Instability

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miRNA micro Ribonucleic Acid

MLH1 mutL homolog 1, colon cancer, nonpolyposis type 2 (E. coli)

MMR Mismatch Repair

mRNA messenger Ribonucleic Acid

MS Multiple Sclerosis

MS Mass Spectrometry

MSH2 mutS homolog 2, colon cancer, nonpolyposis type 1 (E. coli)

MSH6 mutS homolog 6 (E. coli)

MSI Microsatellite Instability

MUC16 Mucin 16

MYC/MYCC v-myc myelocytomatosis viral oncogene homolog (avian)

NCBI National Center for Biotechnology Information

N-CIN Numerical Chromosomal Instability

NF1 Neurofibromin 1

NHL Normal Human Lymphocyte

NHR Non-Homologous Repair

OCa Ovarian Carcinoma

OS Overall Survival

OSE Ovarian Surface Epithelium

P1 Promoter 1

P2 Promoter 2

p21 cip Cyclin-dependent kinase inhibitor 1A (p21, Cip1)

PAX8 Paired box 8

PBS Phosphate Buffered Saline

PCR Polymerase Chain Reaction

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PD Parkinson's Disease

PFS Progression Free Survival

PIK3CA Phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha

PMS2 PMS2 postmeiotic segregation increased 2 (S. cerevisiae)

PNA Peptide Nucleic Acid

PPP2R1A Protein Phosphatase 2, Regulatory subunit A, alpha

pre-miRNA Precursor micro Ribonucleic Acid

pri-miRNA Primary micro Ribonucleic Acid

PSA Prostate Specific Antigen/ Kallikrein-related peptidase 3

PTEN Phosphatase and Tensin homolog

qRT-PCR quantitative Real Time-Polymerase Chain Reaction

RAB40C RAB40C, member RAS oncogene family

RB1 Retinoblastoma 1

RGB Red Green Blue

RMI Risk of Malignancy Index

ROMA Risk of Malignancy Algorithm

RT Room Temperature

S-CIN Structural Chromosomal Instability

Ser Serine

SILAC Stable Isotope Labeling by Amino acids in Cell culture

siRNA small interfering Ribonucleic Acid

SKY Spectral Karyotyping

SNP Single Nucleotide Polymorphism

SPORE Specialized Program of Research Excellence

SSC Saline Sodium Citrate

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STAT Signal Transducer and Activator of Transcription

STIC Serous Tubal Intraepithelial Carcinoma

TCGA The Cancer Genome Atlas

TERT telomerase reverse transcriptase

Tfr transferrin receptor 2

TIC Tubal Intraepithelial Cancer

TMA Tissue Microarray

TP53 Tumor Protein p53

TT transthyretin

UTR Untranslated Region

v-MYB v-Myb myeloblastosis viral oncogene homolog (avian)

ZMYND8 Zinc finger, MYND-type containing 8

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List of Tables

Chapter 1

Chapter 2

Table 2.1 Summary of N-CIN Indices, S-CIN Events and Centrosomal Aberrations in Primary Ovarian Carcinomas

Table 2.2 Raw FISH Scoring Values

Table 2.3 Composite SKY Karyotypes of OCA27A/B and OCA714

Chapter 3

Table 3.1 KLK-Specific FISH: Analysis of Cell Lines

Table 3.2 ELISA findings for cancer cell lines and patient specimens

Chapter 4

Table 4.1 Clinical characteristics of patients stratified by KLK copy number:

Table 4.2 Clinical characteristics of patients stratified by 19q-rearrangement

Table 4.3 Hazard ratio (HR) estimated from the Cox regression model for disease-free survival (DFS).

Table 4.4 Hazard ratio (HR) estimated from the Cox regression model for overall survival (OS).

Chapter 5

Table 5.1 Expression values of miRNAs predicted to target KLK6 in OVCAR-3

Table 5.2 Expression values of miRNAs predicted to target KLK10 in OVCAR-3

Table 5.3 Summary of miRNA Expression and Copy-Number Changes of miRNAs Predicted to Target KLK6

Table 5.4 Summary of miRNA Expression and Copy-Number Changes of miRNAs Predicted to Target KLK10

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List of Figures

Chapter 1

Figure 1.1 Pathology of Ovarian Carcinoma

Figure 1.2 Numerical and Structural Chromosomal Aberrations in Ovarian Carcinoma

Figure 1.3 The Human Kallikrein Locus at Chromosome 19q13.3/q13.4.

Figure 1.4 Summary of KLK6 Properties

Figure 1.5 Consequences of Dysregulated KLK 6 Expression in Cancer and Neurodegenerative Diseases

Figure 1.6 Mechanisms of Regulation of KLK6 Expression

Chapter 2

Figure 2.1 Genomic Instability Findings in Primary Ovarian Cancers by Interphase FISH, SKY and Centrosomal Analysis.

Figure 2.2 Increasing Copy-Number Instability is Associated with Changes in Ploidy.

Figure 2.3 Interplay between N-CIN, S-CIN, Centrosomal Aberrations and Ploidy.

Chapter 3

Figure 3.1 Summary of FISH findings of the KLK locus in cancer cell lines and ovarian cancer patients

Figure 3.2. KLK status in cancer cell lines by sequential SKY and FISH.

Figure 3.4 aCGH and FISH validation of copy number imbalances of the KLK locus in BT-474.

Chapter 4

Figure 4.1 KLK Copy-Number Analysis of Serous Ovarian Carcinomas by FISH and Immunohistochemistry.

Figure 4.2 Kaplan Meier Curves for Disease Free Survival (DFS) and Overall Survival (OS

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Chapter 5

Figure 5.1 miRNA Profiling of OVCAR-3 and miRNAs Predicted to Target KLK6

Figure 5.2 Cytogenomic Analysis of OVCAR-3

Figure 5.3 Cytogenomic Analysis of hsa-let-7a Family Members in OVCAR-3

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Chapter 1

Introduction

Chapter 1 includes an abridged version of the published manuscript “Review: The Physiology and Pathobiology of Human Kallikrein-Related Peptidase 6 (KLK6)” by Bayani J and Diamandis EP. Clin Chem Lab Med. 2011. Nov 3;50(2):211-33.

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1 Ovarian Carcinoma

1.1 Canadian Statistics

Ovarian cancer (OCa) is the leading cause of death from a gynecological malignancy and the

fourth leading cause of cancer death among Canadian women. Based on 2011 statistics, there

will be an estimated 2,600 new cases and 1,750 estimated deaths in Canada due to OCa

(http://www.cancer.ca). Though comprising only 3% of new cancer cases and 4.9% of cancer

deaths, it remains a deadly disease with an estimated five year survival of only 42%. The

lifetime probability of Canadian women developing OCa is 1.5%, while the probability of dying

from OCa is 1.1%.

1.2 Etiology

Like many cancers, the exact causes are unclear; however it is widely accepted that a number of

factors contribute to the etiology of OCa. The majority of OCa seen in the clinic arises

sporadically, with approximately 5-10% thought to be hereditary. BRCA1 and BRCA2 gene

mutations are most commonly associated with familial ovarian and breast cancer, comprising the

majority of hereditary OCa (Sogaard et al., 2006). Women with germline mutations of BRCA1

or BRCA2 have a 30%-70% chance of developing OCa by age 70 (Risch et al., 2006). BRCA1

and BRCA2 are critical components of the homologous recombination DNA repair pathway

involved in double-stranded breaks (DSB) (Deng, 2006). However, OCa has also been linked to

the spectrum of cancers associated with hereditary nonpolyposis colorectal cancer syndrome

(HNPCC) (Lynch et al., 2009), and familial cancer syndromes such as Peutz-Jegher and Multiple

Endocrine Neoplasia (MEN) type I (Lynch et al., 2009; Papageorgiou and Stratakis, 2002).

Normal reproductive and hormonal factors also play a role in the etiology of the disease.

Briefly, early-aged menstruation and late-aged menopause have been linked with increase OCa

cancer risk, which is in keeping with the incessant ovulation hypothesis (Fathalla, 1971). It

postulates the extended wound-healing events at the ovarian surface epithelium may lead to

greater cancer risk (Cramer et al., 1983). Thus factors that reduce ovulatory events, such as

pregnancy, appears to confer a protective role, with reports suggesting the risk of OCa among

parous women could be decreased as much as 50% as compared to nulliparous women (Salvador

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et al., 2009). Lactation and use of oral contraceptives may also provide some decreased risk,

though the benefit is generally modest (reviewed by Salvador et al., 2009). The role for

hormone replacement remains less consistent and more controversial (Hinds and Price, 2010),

with the hypothesis that replacement of estrogen could stimulate cancer cells. Indeed clinicians

and the patients must weigh the potential risks and benefits of hormone replacement therapies.

Due to the link between BRCA1/2 carriers and OCa, prophylactic oophorectomy among these

higher risk women greatly reduces the chances of developing the cancer and may include the

prophylactic removal of the fallopian tubes (Miller et al., 2010). Moreover, tubal ligation has

also been shown to reduce cancer risk (Cibula et al., 2011). The link between the fallopian tubes

and OCa has been further strengthened by the revelation of tubal intraepithelial carcinomas in

prophylactically removed fallopian tubes (discussed below). Finally environmental and dietary

factors continue to contribute to the etiology of the disease, with the observation of increased

incidences of OCa among industrialize countries. These observations suggest the exposure to

carcinogens and perhaps intake of more processed foods, animal products, coffee, alcohol and

tobacco among industrialized nations increases OCa risk (Benedet et al., 2000).

1.3 Diagnosis and Treatment

Unfortunately, there are no overt or specific symptoms associated with early OCa. However,

symptoms may include vague abdominal pain or discomfort, menstrual irregularities, dyspepsia

and other digestive problems. Later-staged disease may present with the accumulation of ascites

fluid leading to the distention of the abdomen. For sporadic cancers, early diagnosis continues to

be problematic; however hereditary cancers have the (ironic) benefit of family history that offers

prophylactic options. Indeed, prophylactic bilateral salpingo-oophorectomy among these higher

risk women greatly reduces the chances of developing the cancer (reviewed by Miller et al.,

2010; and Lynch et al., 2009). However, such an invasive procedure has profound physical and

emotional implications. Patient age, particularly between 40 and 69, together with any

suspicious symptoms, are cause of further investigation. The International Federation of

Gynaecologists and Obstetricians (FIGO) recommendations for the treatment for suspicious OCa

are described briefly here (Benedet et al., 2000). A detailed medical history, complete pelvic

exam, together with testing for tumour markers, mark the exploratory investigation, to be

followed by chest radiograms, pelvic ultrasound and computerized tomography (CT).

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Laparoscopic surgery serves to stage the cancer and identify the histological subtype. Based on

the extent of disease dissemination, total hysterectomy and bilateral salpingo-oophorectomy can

be expected with the goal of maximal cytoreduction.

The inclusion of adjuvant treatment for early staged disease appears to be most beneficial for

patients with non-optimally staged early cancers based on the International Collaborative

Ovarian Neoplasm Trial 1 (ICON1) and the Adjuvant Chemotherapy Trial in Ovarian Neoplasia

(ACTION) (Trimbos et al., 2003). Typically such first-line chemotherapeutic agents for both

early and advanced staged disease include Carboplatin alone or in combination with Paxitaxel.

The anticancer activity of platinum-based therapies such as Carboplatin or Cisplatin lies in the

ability of these drugs to create links within DNA strands and inhibiting replication and inducing

apoptosis (Siddik, 2003). Paxitaxel, belonging to a family of taxanes, also serves to inhibit cell

proliferation, but achieves this goal by binding tubulin to prevent microtubule disassembly. This

prevents the completion of mitosis during M-phase (Orr et al., 2003). Recurrent diseases are

more aggressively treated with platinum drugs (Carboplatin or Cisplatin) and Paxitaxel, as well

as the addition of other DNA intercalating drugs such as liposomal Doxorubicin (Caelyx or

Doxil); the topoisomerase I inhibitors (Rasheed and Rubin, 2003) Topotecan (Hycamtin) and

Etoposide; or the nucleoside analog (Ewald et al., 2008) Gemcitabine. For patients that have no

clinical evidence of disease, a second-look laparotomy may be ordered to determine the response

to treatment. Moreover, patients may also undergo a secondary cytoreduction procedure

following the completion of first-line chemotherapy. Management of patients during follow-up

is typically every three months in the first few years after treatment, and three to two additional

times a year after the fifth year; and involves physical examinations, blood work and radiological

tests. The vast majority of patients will relapse, however the success of further treatment of

these patients appears to depend on the disease-free interval of the first line therapy. Patients

with a disease-free interval of six months or greater are considered platinum-sensitive, while

those with a shorter disease-free interval are considered platinum-refractory. Patients deemed

platinum-sensitive may benefit from further combined platinum treatment, whereas refractory

patients may enroll in clinical trials or consider non-platinum therapeutics (Ledermann et al.,

2011; Pfisterer and Ledermann, 2006; Pfisterer et al., 2006).

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1.4 Pathology

In recent years, compelling evidence suggests that serous “ovarian” cancers may in fact originate

from the distal fallopian tubes (reviewed by Salvador et al., 2009). These claims have been

largely supported by the fact that the native histology of the fallopian tube is Müllerian in nature,

consistent with observation that OCas exhibit a Müllerian phenotype. This is in contrast to the

characteristics of the normal ovarian surface epithelium (OSE), which is continuous with the

mesothelium of the pelvic organs, derived from the coelomic epithelium, and are generally

poorly differentiated (Auersperg et al., 2001). Thus, there is debate as to the origins of epithelial

OCa (Salvador et al., 2009). The argument for the malignant transformation of the OSE, and

those lining inclusion cysts within the ovary, is consistent with reports identifying lesions only

confined to the ovary. Moreover, unlike other normal adult epithelia, OSEs can convert to either

mesenchymal or epithelial phenotypes in response to different stimuli (Auersperg et al., 2001).

However, until recently, investigators did not systematically examine the fallopian tubes; simply

assuming that any fallopian tube lesions constituted disseminated disease from the ovary. The

identification of tubal intraepithelial carcinomas (TIC) and serous tubal intraepithelial

carcinomas (STIC) (reviewed by Kurman and Shih Ie, 2011) in the fimbria of the fallopian tubes

from prophylactic oophorectomies of BRCA-positive women (Crum et al., 2007; Lee et al.,

2007), offers the possibility that exfoliated TIC/STIC cells implant themselves onto the ovarian

surface epithelium; or to the ovulation site which are then incorporated into cortical inclusion

cysts (Kurman and Shih Ie, 2011) (Figure 1.1A). Indeed, there is speculation that both

hypotheses are correct and may explain the differences between the molecular subtypes

(reviewed by Karst and Drapkin, 2010).

There are now five major histological subtypes to what is classified as epithelial OCa (Figure

1.1B): high-grade serous carcinomas, low-grade serous carcinomas, endometrioid, clear cell and

mucinous tumours (Gilks and Prat, 2009). High-grade serous tumours comprise the majority of

cancers at a frequency of approximately 45% and are described as ciliated columnar cells of

tubal phenotype. Low-grade serous tumours comprise the remaining 15% of the serous

histological subtype, and are molecularly distinct from high-grade serous cancers. Endometrioid

cancers comprise approximately 20% of cancers, are often highly malignant and characterized by

pseduostratified columnar cells with an endometrioid phenotype. These cellular features suggest

that these cancers may represent the malignant transformation of pelvic and ovarian

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endometriotic lesions. Clear cell tumours, which are cytoplasmically vacuolated, are also

believed to originate from transformed endometriosis cells, and account for approximately 10%

of cases seen. Finally mucinous tumours, which are the rarest subtype seen at 3-5%, do not have

Müllerian features like the other subtypes; but may originate from transitional-type epithelium

located at the tubal-peritoneal junction (Kurman and Shih Ie, 2011).

As with other cancers, histological grade generally refers to the overall microscopic appearance

of the cells into well differentiated (G1), moderate (G2) and poorly differentiated (G3) (Benedet

et al., 2000). OCa staging reflects the surgical observations at the time of diagnosis, and for

OCa, the FIGO criteria has been adopted (Benedet et al., 2000). Generally, stage I disease is

limited to the ovaries, stage II cancers are limited to the pelvis, stage III cancers spread beyond

the pelvis, and stage IV disease represents distant metastasis to the chest, liver and other organs

1.5 Molecular Pathology

1.5.1 Hereditary and Sporadic Ovarian Cancers

Approximately 10% of all epithelial OCas are hereditary, with the vast majority of these due to

germline mutations in BRCA1 or BRCA2 (Lynch et al., 2009; Risch et al., 2006; Sogaard et al.,

2006). However, mutations in at least four mismatch repair (MMR) genes (MLH1, MSH2, MSH6

and PMS2), associated with Lynch syndrome account, for up to 15% of hereditary cancers

(reviewed by Lynch et al. 2009). Lynch et al., 2009, provides a comprehensive review regarding

hereditary OCa, and the salient points will be briefly summarized as follows. Numerous studies

have confirmed the increase risk of OCa among specific BRCA1 and BRCA2 mutation carriers,

not only among the general population (Lynch et al., 2009), but also among those in closed

populations such as the Ashkenazi Jews (King et al., 2003; Tobias et al., 2000), and Icelanders

(Gudmundsson et al., 1996; Mikaelsdottir et al., 2004; Rafnar et al., 2004). Lynch syndrome,

known more commonly as HNPCC, predisposes affected individuals to colon cancer and other

malignancies including OCa. The consensus of studies suggests that MMR genes play a role in

the pathogenesis of the specific histological subtypes, particularly non-serous subtypes. Though

genetic predisposition confers greater risk, several studies have noted the relatively improved

prognosis and response to standard chemotherapy among the hereditary OCas (Cass et al., 2003;

Lynch et al., 2009; Pal et al., 2007). Serous carcinomas comprise the majority of OCas seen

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among BRCA1/2 patients (~80%), while Lynch syndrome patients predominantly present with

endometrioid, mixed-endometrioid, mixed-mucinous and mixed- clear cell phenotypes, with

serous tumour comprising 25% (Lynch et al., 2009). In addition to mutations of the BRCA1/2

and MMR genes, a number of other genes are associated with hereditary OCa, and include the

oncogenes EGFR, HER2-neu, MYCC, KRAS, and β-catenin (CTNNB1) and tumour suppressor

genes TP53 and PTEN. In the case of oncogenes, the observed overexpression of these genes

appears to be correlated to activating mutations, or gene amplification. Similarly, mutations,

deletions or epigenetic silencing of TP53 and PTEN results in loss of function.

Sporadic OCa, those cancers not associated with hereditary factors, account for the majority of

cases. The median age at diagnosis of sporadic cancers is 63, in contrast to inherited cancers at

age 54, however there is no difference between the average age of diagnosis between BRCA2

carriers and sporadics (Boyd et al., 2000). Unlike hereditary cancers, sporadic cancers are the

most frequently seen subtype in the clinic and, frequently present at late stage and high grade.

Mutations in several oncogenes include KRAS, BRAF, PIK3CA and CTNNB1 (Landen et al.,

2008). Mutations in TP53 are frequently seen in the high-grade serous cancers and endometrioid

cancers, though less in the other subtypes.

1.5.2 Type I and Type II Ovarian Cancers

Ovarian cancer is increasingly recognized as a heterogeneous disease, not only by histological

subtype and tissues of origin, but in the recognition that they possess distinctive

clinicopathological and molecular features (reviewed by Kurman and Shih Ie, 2011). Kurman

and Shih have proposed two molecular subtypes, irrespective of histological classification: Type

I and Type II OCas. Type I tumours are defined broadly as being low-grade and include low-

grade serous carcinomas (LGSC), low-grade endometrioid, clear cell and mucinous cancers.

They develop from their putative precursor lesions and typically are large masses that are

confined to the ovary (stage I). Moreover, these tumours are relatively indolent with a good

prognosis. Genomically, these tumours have greater stability, possessing somatic sequence

mutations in KRAS, BRAF, PTEN, PIK3CA, CTNNB1, ARID1A and PPP2R1A. Type I tumours

have been hypothesized to arise from the malignant transformation of the OSE (Karst and

Drapkin, 2010). Type II tumours, however, comprise the majority of cancers which are high-

grade; including high-grade serous carcinomas (HGSC), high-grade endometrioid carcinomas,

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carcinosarcomas and undifferentiated sarcomas. Type II cancers are often present in the

advanced stages (II-IV), and are highly proliferative and aggressive and recently hypothesized to

arise from TIC/STIC lesions in the fallopian tube (Karst and Drapkin, 2010). The majority of

studies focus on HGSC; which comprise over 75% of the cases, are prototypical Type II

tumours, exhibit unstable genomes and frequently possess TP53 mutations. Moreover,

inactivation of the BRCA pathways, either by mutation or methylation occurs in up to 50% of

HGSC. This is in contrast to Type I cancers, which do not possess mutations in TP53, or have

perturbations of the BRCA pathway (Kurman and Shih Ie, 2011) (Figure 1.1C).

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Figure 1.1. Pathology of Ovarian Carcinoma. A) Aside from the malignant transformation of the ovarian surface epithelium, OCa is hypothesized to originate from lesions in the distal fallopian tube and exfoliating onto the surface of the ovary or cortical inclusion cysts. Shown is a histological section showing STIC lesions within the fallopian tube positive for mutant p53, with regions of normal p53 expression. B) Five histological subtypes of ovarian carcinoma. Shown are representative histological sections illustrating the morphologic characteristics of the major histological subtypes of epithelial OCa. C) Molecular classification of Type I and Type II ovarian carcinomas. Molecular classification based on grade, stage, immunohistological testing, genomics and response the chemotherapeutics.

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1.5.3 Ovarian Cancer Genomics and Transcriptomics

The technical advances of the past decade has resulted in the (continued) amassing of high-

throughput data; including, and not limited to, the sequencing of whole OCa genomes, the

expression profiling of these genomes and, increasingly, the proteomes of these genomes

(Cancer Genome Atlas Research Network, 2011; Gorringe and Campbell, 2009). A common

feature of OCas, are their abnormal karyotypes (Bayani et al., 2002; Shridhar et al., 2001; Taetle

et al., 1999a; Taetle et al., 1999b). Indeed, traditional cytogenetics (Taetle et al., 1999a; Taetle et

al., 1999b) have revealed the karyotypes of OCas to possess both numerical and structural

rearrangements, which were further elucidated with the advent of simultaneous whole

chromosomal Fluorescence in situ Hybridization (FISH)-painting methods such as Spectral

Karyotyping (SKY) (Macville et al., 1997; Schrock et al., 1997) or Multi-colour FISH (MFISH)

(Liehr et al., 2004). Moreover, Comparative Genomic Hybridization (CGH) to both metaphase

and to microarrays delineated a recurrent pattern of chromosomal gains, losses and

amplifications, which permitted the identification of putative oncogenes and tumour suppressor

genes (reviewed by Gorringe and Campbell, 2009). With the advent of higher-resolution Single

Nucleotide Polymorphic (SNP) arrays as well as lower-resolution array platforms, we now have

the ability to distinguish regions of copy-number alteration and allelic imbalances. This has

culminated in the recent release by The Cancer Genome Atlas Research Network (TCGA)

(Cancer Genome Atlas Research Network, 2011) of a catalog of molecular aberrations relevant

to OCa. In this ambitious endeavor, 489 clinically annotated stage II to stage IV high-grade

serous ovarian cancers and matched normal DNAs were analysed for DNA copy-number, DNA

promoter methylation, mRNA expression, microRNA (miRNA) expression, and whole genome

and exome DNA sequencing (in a subset of tumours). Not surprisingly, mutational analysis

confirmed previous findings that mutations in TP53 were frequent events (303 of 316), and that

BRCA1 and BRCA2 germline mutations occurred in 9% and 8% of cases respectively. Other

statistically significant mutated genes included RB1, NF1, FAT3, CSMD3, GABRA6 and CDK12;

while copy-number analysis largely confirmed the findings from previous CGH studies. Perhaps

the most interesting findings of this study were the results of the mRNA, miRNA and DNA

methylation analyses, with the revelation of at least four expression subtypes: immunoreactive,

differentiated, proliferative and mesenchymal. These transcriptional subtypes were largely

correlative to the hierarchical clustering described by Tothill and colleagues (Tothill et al., 2008),

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who described a six-member classification based on the profiling of endometrioid and serous

cancers. A general concordance between DNA methylation and reduced expression was

observed, and these studies confirmed the previous observations that the BRCA1 promoter is

hypermethylated and silenced in approximately 11% of cancers. Correlation to the clinical data

showed no significant differences between the transcriptional subtypes and survival duration;

however a 193-gene transcriptional signature predictive for overall survival was defined. One

hundred and eight genes were correlated with poor survival and 85 were correlated with good

survival, which were then validated on an independent set of 255 samples.

Prior to the TCGA findings, interesting results from the transcriptional profiling a decade ago, by

Jazaeri et al. 2002, found the greatest difference between BRCA1 and BRCA2 cancers. Sixty one

clinically confirmed OCas, comprising sporadic and hereditary cancers, were analysed using

cDNA microarrays. Among the hereditary cancers, tumours segregated according to their BRCA

status. Interestingly, the sporadic cancers segregated as being either BRCA1-like or BRCA2-like,

suggesting that dysregulated BRCA pathways are critical to the pathogenesis of both hereditary

and sporadic OCa. These early observations, were largely confirmed by the TCGA (Cancer

Genome Atlas Research Network, 2011) and more recently by Yang et al. (Yang et al., 2011a),

showing the greatest transcriptional differences occur between BRCA1- and BRCA2-mutated

tumours.

1.6 Chromosomal Instability

The estimated rate of somatic cell mutation in humans is approximately 1.1×10−8 per base pair

per generation (Loeb and Christians, 1996; Roach et al., 2010). Therefore, genetic or genomic

instability refers to the overall process that increases this rate of mutation and more importantly,

results in a phenotypic change. Genomic instability is classically categorized into two main

forms: microsatellite instability (MIN) and chromosomal instability (CIN); with the CIN

phenotype predominantly seen in OCa. The prototypic example for defining genomic instability

comes from studies in colorectal cancer, where distinct genomic phenotypes are seen between

hereditary and sporadic cases (Vilar and Gruber, 2010). The MIN phenotype causes the

shortening or expansion of short tandem repeat units that make up microsatellite sequences

scattered throughout the genome. Such events are indicative of underlying MMR deficiencies

associated with hereditary colorectal cancer (Lynch and de la Chapelle, 2003). Patients affected

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by HNPCC with germline mutations of a variety of MMR genes, previously discussed, possess

cancer genomes that are predominantly near-diploid. In contrast, sporadic cancers comprise the

majority of colorectal cancers which exhibit the CIN phenotype (Lengauer et al., 1997); that is,

genomes with gross chromosomal changes. Among these patients, adenomatous polyps arise

through the bi-allelic loss in function of the adenomatous polyposis coli (APC) gene. The

progression of polyps to greater dysplasia coincides with the acquisition of mutations to activate

oncogenes; or results in the loss of tumor suppressor genes (Fearon and Vogelstein, 1990). The

consequences of these acquired mutations are the perturbations in cell cycle checkpoints and cell

division, leading to the observed chromosomal changes. Just as genomic instability refers to a

rate of genomic change, CIN refers to the excessive rate of chromosomal change (Lengauer et

al., 1997). The term aneuploidy is often interchangeably used with CIN; however it is important

to mention that the presence of aneuploidy alone does not constitute CIN, since a genome may

be stably aneuploid as in the case individuals affected by Downs Syndrome. Therefore, the

extent of clonality; or lack thereof, defines the level of CIN. As such, a tumor genome

composed of many different DNA populations can be characterized as having greater CIN, than

one where the genomes of individual tumor cells are more similar.

Many studies that investigate CIN, focus primarily on whole chromosomal gains or losses, and

fail to recognize the importance of structural rearrangements as an additional measure of

chromosomal instability. Changes at the chromosomal level encompass both numerical CIN (N-

CIN) as well as structural CIN (S-CIN) (Bayani et al., 2007). Generally, N-CIN describes the

on-going gains or losses of whole chromosomes, whole chromosome arms, or changes in DNA

ploidy (ie. multiples of a haploid number – 2n/3n/4n etc). The mechanisms mediating individual

chromosomal gains/losses and ploidy changes are influenced by errors in chromosomal

segregation and spindle attachment as well as supernumerary centrosomes (reviewed by Holland

and Cleveland, 2009; and Gisselsson, 2011). In contrast, S-CIN refers to on-going

chromosomal breakage events, which include chromosomal additions, deletions, translocations

and other structural rearrangements. Like polyploidy, structural changes can affect gene copy

number, but may also facilitate the formation of abnormal gene fusion products. Changes in

chromosome structure are generally influenced by two major DNA repair mechanisms:

homologous recombination (HR) and non-homologous recombination (NHR) (reviewed by

Hastings et al. (Hastings et al., 2009)). Whereas HR describes the normal exchange or repair of

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identical DNA sequences, NHR does not require the identical template for such exchange or

repair. Indeed many cancers show dysregulated DNA repair pathways such as those affected by

BRCA1 (Khanna and Jackson, 2001). However, such promiscuous DNA repair can also be

potentiated through various features associated with genomic sequence architecture including

GC- and AT-content (Abeysinghe et al., 2003; Chuzhanova et al., 2003), Alu motifs (Batzer and

Deininger, 2002), alpha-satellite DNA (Grady et al., 1992), and telomeric sequences (Gisselsson

et al., 2005; Mai, 2010; Murnane, 2006). High-level genomic amplifications, complex structural

changes and deletions have also been associated with the Breakage-Fusion-Bridge (BFB) cycle

(Jones, 2005), characterized by the self-perpetuating cycle involving chromatid breaks and

fusions, triggered by dicentric and ring chromosome rupture during anaphase resolution.

Excessive telomere shortening in cancers has been shown to lead to BFB cycles and, eventually

to generalized genome instability and leading to either to cell death or crisis (Mai, 2010). More

recently, another mechanism for the complex structural rearrangements seen in cancers has been

proposed – chromothripsis (Stephens et al., 2011). Literally translated to mean “chromosome

shattering”, this rare event is believed to result in the shuffling of the genome in one cataclysmic

event whereby the DNA becomes fragmented then reassembled. This event may explain the

pattern of single copy losses, and unique fusions in transcriptional orientations that cannot be

adequately explained based on current DNA repair mechanisms. Though still controversial, deep

sequencing confirms that such a reshuffling event occurs, and is more compelling in tumours

whose genomes remain diploid. Nevertheless, it is evident that through various mechanisms,

genomic regions can experience local copy-number changes (ie. focal amplifications, gains or

deletions) in the absence of ploidy changes. The non-clonality of such changes whether N-CIN

or S-CIN, may also be applicable to specific genes or chromosomal loci. Indeed, these issues are

discussed by Bayani et al., 2007, whereby a distinction between structural chromosomal

instability, S-CIN; and numerical instability N-CIN was proposed.

1.7 Defining Chromosomal Instability in Ovarian Carcinoma

Karyotypic abnormalities are a common feature among epithelial cancers including OCa (Bayani

et al., 2002; Shridhar et al., 2001; Taetle et al., 1999a; Taetle et al., 1999b; Thompson et al.,

1994a; Thompson et al., 1994b; Thompson et al., 1996). The aCGH findings of the TCGA study

(Cancer Genome Atlas Research Network, 2011), not surprisingly, failed to reveal new insight

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with respect to the pattern of recurrent copy-number changes among OCas. Both classical and

molecular cytogenetic methods have identified the recurrent net gains/amplifications of 1q, 3q 6p

7q, 8q, 12p, 20p and 20q; and net losses of 4p, 4q, 5q, 6q, 8p, 9p, 9q, 11p, 11q ,13q, 14q, 15q,

16p, 16q, 17p, 17q, 18p, 18q, 19p, 19q and 22q (Bayani et al., 2002; Cancer Genome Atlas

Research Network, 2011; Shridhar et al., 2001; Taetle et al., 1999a; Taetle et al., 1999b). The

TCGA studies identified focal amplifications of CCNE1, MYC, MECOM, ZMYND8, IRF2BP2,

ID4, PAX8, TERT and deletions of PTEN, RB1 and NF1. Advances in molecular cytogenetics

(reviewed by Bayani et al., 2007) have helped our understanding of the chromosomal

contributions to carcinogenesis. aCGH has proven to be invaluable for identifying genomic loci

that may contain putative oncogenes or tumour suppressor genes (reviewed by Gorringe and

Campbell, 2009) (Figure 1.2A). However, the major limitation of this technique is that it cannot

accurately reflect the level of chromosomal or genomic heterogeneity that exists. Thus,

information regarding the range of copy-number for a given locus or gene is lost due to the

diluting effects of dominant clones or by high rates of heterogeneity (Bayani and Squire, 2007).

Moreover, the inability of aCGH to accurately assess ploidy also limits our understanding of the

mechanisms that influence the observed karyotypic changes (Albertson et al., 2003; Tan et al.,

2007). It has been previously demonstrated by Bayani et al. 2002, that the karyotypes of OCas

are both structurally and numerically abnormal (Figure 1.2B). Despite the comprehensive

karyotypic analyses of OCas (Bayani et al., 2002; Shridhar et al., 2001; Taetle et al., 1999a;

Taetle et al., 1999b; Thompson et al., 1994a; Thompson et al., 1994b; Thompson et al., 1996),

there have been no studies that distinguish between the CIN of these two forms of

chromosomal change in OCa, and the implications for copy-number change in those gene

affected by such CIN.

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Figure 1. 2 Numerical and Structural Chromosomal Aberrations in Ovarian Carcinoma.A) Pattern of chromosomal gains (green) and losses (red) across 711 OCas as determined by the Progenetix CGH database (http://www.progenetix.net) , shows non-random whole and partial chromosomal imbalances. B) Spectral karyotyping (SKY) analysis (Bayani et al., 2002), of a metaphase spread from an untreated primary OCa shows both numerical and complex structural chromosomal aberrations. Shown from left to right are the fluorescent images, where each different chromosome is represented by its raw fluorescence; inverted DAPI image; and example of numerical and structural aberrations involving chromosome 3. In this example, six chromosomes 3 were identified: two normal chromosomes 3 and four involved in duplicate structural rearrangements involving chromosomes 2 and 8. Also shown below is the karytoypic description.

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1.8 The Current Status of Ovarian Cancer Biomarkers: Kallikrein

6 (KLK6) a Promising Ovarian Cancer Biomarker

Undoubtedly, the success of any treatment is dependent on the early detection of the cancer

before the disease can progress and metastasize. The current status of OCa biomarkers is

promising, but still largely inadequate, as there are no biomarkers for detection or screening

(Bast et al., 2009). Therefore, it is imperative to discover cancer biomarkers that are informative

across various clinical parameters. Though the scientific literature regarding putative biomarkers

is voluminous, the past two years has seen the U.S Food and Drug Administration (FDA)

approve very few additional OCa biomarkers to CA-125. These new additions include HE4, the

biomarker panel OVA1™ (Vermillion Inc.), which includes Transthyretin (TT or prealbumin),

Apolipoprotein A-1 (Apo A-1), Beta2-Microglobulin (Beta2M), Transferrin (Tfr) and CA-125

which is integrated with a proprietary algorithm; and the Risk of Ovarian Malignancy Algorithm

(ROMA™) (Fujirebio Diagnostics) which includes CA-125 and HE4 (Moore et al., 2009).

Non-FDA-approved biomarker panels and algorithms have also made their way into clinical

settings and met with promising results, including the Risk of Malignancy Index (RMI) which

improves specificity by combining CA-125 together with menopausal status and imaging scores

(Jacobs et al., 1990); while others, including OvaCheck® (Correlogic Systems Inc.), which

combines CA-125 with seven other markers; and OvaSure™ (LabCorp) (Visintin et al., 2008),

which also includes CA-125 along with leptin, prolactin, osteopontin, IGF2, and macrophage

inhibitory factor, have been met with either continued controversy or have been subsequently

discontinued.

The gold standard serum biomarker for OCa continues to be the carbohydrate antigen, CA-125

(MUC16) a member of the Mucin family of glycoproteins (Kufe, 2009); which is routinely used

to monitor the relative antigen levels following surgery and treatment. This high-molecular

weight transmembrane glycoprotein is expressed by the coelomic- and Müllerian-derived

epithelium and is expressed in cells of the fallopian tube, endometrium and endocervix (Jacobs

and Bast, 1989); while not expressed by normal OSE (Kabawat et al., 1983). Indeed, CA-125,

has no prognostic significance when measured at the time of diagnosis, but appears to have a

high correlation with survival when measured one month after the third course of chemotherapy

for patients with stage III or stage IV disease. As such, CA-125 is valuable in the follow-up and

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restaging of patients who have elevated CA-125 levels from the time of diagnosis (Hogberg and

Kagedal, 1992; Hoskins, 1993; Mogensen, 1992). However, while elevated CA-125 levels

indicate a high probability of epithelial ovarian cancer, negative CA-125 levels cannot be used to

exclude the presence of residual disease (Makar et al., 1992). Moreover, CA-125 levels can also

be elevated in other malignancies or benign gynecologic problems such as endometriosis (Bast et

al., 1998) and poses a problem for those 20% of ovarian cancers that do not exhibit elevated CA-

125 levels. HE4 (human epididymis protein 4), a low molecular weight glycoprotein, was

discovered by transcriptional profiling to be elevated and expressed in 90% of OCa (Hellstrom et

al., 2003). Expressed by the epididymis in the male reproductive tract, HE4 is also secreted by

the Müllerian epithelia of the female reproductive tract, but not by normal OSE (Drapkin et al.,

2005). To date, HE4 has shown greater sensitivity and specificity than CA-125 for detecting

early-staged OCas, as HE4 tends not to be highly associated with benign lesions, making it a

better marker for malignancy (Hellstrom et al., 2003; Moore et al., 2009). Several studies have

shown that the combined use of CA-125 and HE4 (Montagnana et al., 2011; Moore et al., 2009)

can increase the accuracy of OCa diagnosis and discriminate between OCa and ovarian

endometriotic cysts (Drapkin et al., 2005), though much larger-scaled studies are required to

determine whether this combination of markers is suitable for the screening of asymptomatic

women. The OVA1™ panel utilizes the observed decrease of TT, Tfr and Apo A-1, in addition

to the observed increase in and Beta2M; together with CA-125 into an ovarian malignancy risk

index score. This non-screening serum test however, was approved for use only in women for

whom surgery was already scheduled for an ovarian mass, and for whom consultation with an

oncologist has not yet been ordered, or for which independent radiological assessment fails to

indicate malignancy. Finally ROMA™ (Fujirebio Diagnostics), integrates HE4 and CA-125

with menopausal status, in addition to clinical and radiologic evaluation; into a numerical score

intended to aid in assessing whether premenopausal or postmenopausal women presenting with

an ovarian adnexal mass has a high or low likelihood of malignancy. While promising,

prognostic and predictive biomarkers remain elusive and likely requires the integration of serum,

genomic and transcriptomic markers.

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1.8.1 The Kallikreins (KLK) and Kallkrein 6 (KLK6): A Promising Biomarker for Ovarian Cancer

The KLKs, which include the tissue kallikrein gene (KLK1) and kallikrein-related peptidase

genes (KLK2-KLK15), encode for secreted serine proteases with trypsin- or chymotrypsin-like

activities (Diamandis et al., 2000a; Yousef et al., 2000). In normal physiology, the KLK genes

are expressed in various tissues (Shaw and Diamandis, 2007) and are involved in proteolytic

cascades (Eissa et al., 2010; Emami and Diamandis, 2008; Kishibe et al., 2007; Komatsu et al.,

2003; Michael et al., 2006). The KLK proteins also cleave a number of substrates (reviewed by

Borgono and Diamandis, 2004). More recently, the KLKs have also been implicated in

tumourigenic events (Mo et al., 2010; Pampalakis et al., 2009) and postulated to play a role in

the pathogenesis of Alzheimer’s disease (AD) (Ashby et al., 2010; Yousef et al., 2003a). It is

apparent, therefore, that KLKs play diverse roles in physiology and pathobiology. The 15

protein-encoding gene members share common features (Harvey et al., 2000; Yousef et al.,

2000). In addition to maintaining similar exon sizes, the catalytic triad (His, Asp and Ser) is

conserved (Schmidt et al., 2004; Sotiropoulou et al., 2009). Each gene encodes a single-chain

prepro-enzyme, which is subsequently processed to an enzymatically inactive proKLK and later

cleaved into its active form (Sotiropoulou et al., 2009). The entire family maps to an

approximately 350kb region at chromosome 19q13.3-13.4 (Harvey et al., 2000; Yousef et al.,

2000) (Figure 1.3) in humans. In addition to the 15 protein-encoding members, there is at least

one KLK pseudogene located between KLKs 2 and 4 (Gan et al., 2000; Yousef et al., 2004a).

One of the most distinguishing characteristics of the human KLK gene family is the fact that all

members localize to the same chromosomal region in a contiguous manner, unlike other trypsin

and trypsin-like proteases, whose gene-family members map to other genomic loci (Pavlopoulou

et al., 2010; Puente and Lopez-Otin, 2004).

KLK expression can be generally described as restrictive (Borgono and Diamandis, 2004;

Clements, 2008; Clements et al., 2004; Yousef and Diamandis, 2001). In the human KLK

cluster, a specific preponderance for prostate-specific expression is seen among KLK15, KLK3

(PSA), KLK2 and KLK4 (Clements et al., 2004; Shaw and Diamandis, 2007; Yousef and

Diamandis, 2001). Diverse mechanisms influencing gene or protein expression have been

investigated including methylation (Cho et al., 2009; Huang et al., 2007; Li et al., 2001; Lu et al.,

2009; Pampalakis and Sotiropoulou, 2006; Sidiropoulos et al., 2005). However, the vast majority

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of work has shown the influence of hormones on KLK expression (Borgono and Diamandis,

2004; Paliouras and Diamandis, 2007; Zarghami et al., 1997). The advent of high-throughput

sequencing has also enabled the identification of single nucleotide polymorphisms (SNPs) for

each family member (Goard et al., 2007). Post-transcriptionally, microRNAs (miRNAs)

(Barbarotto et al., 2008; Lee et al., 1993) have been shown to be important players in the

regulation of protein synthesis (Chow et al., 2008; Jung et al., 2010; Kojima et al., 2010; Sehic et

al., 2010; White et al., 2010a; White et al., 2010b; Wissmann et al., 2007; Yousef, 2008). Post-

translationally, activated KLK proteins are controlled by endogenous inhibitors, and may also

lose activity through internal cleavage (Borgono and Diamandis, 2004). Amazingly, despite the

similarities between the family members, they are sufficiently unique to encompass the gamut of

normal and abnormal biological processes.

Figure 1.3. The Human Kallikrein Locus at Chromosome 19q13.3/q13.4. Shown is the organization and relative phylogenetic relationship of the human KLK locus, the transcriptional orientations and relative relationship (not drawn to scale) of the KLK gene family members based on the studies by Harvey et al., 2000; Pavlopoulou et al., 2010; and Yousef et al., 2001. Members with similar colours show more relatedness.

1.8.2 Kallikrein 6: Historical Perspectives and Genetics

Kallikrein 6 (KLK6), the HUGO Gene Nomenclature Committee (HGNC) Database – approved

name and symbol, has previously been identified by a number of aliases, including PRSS9, Bssp,

Neurosin, serine protease 9, Protease M, serine protease 19, PRSS18, SP59, and ZYME

(http://www.genenames.org/index.html). Although the classical KLKs (KLK1, 2 and 3), were

already known, KLK6 was the first of the expanded members to be identified. It was originally

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recognized as Protease M, through a cDNA screen of primary and metastatic breast cancer cell

lines (Anisowicz et al., 1996).

With seven exons and six introns, KLK6 spans approximately 11.0 kb (NCBI Reference

Sequence: NG_011825.1), mapping between KLK5 and KLK7 (Harvey et al., 2000; Yousef et

al., 2004b; Yousef and Diamandis, 2001; Yousef et al., 1999) (Figure 1.3 and Figure 1.4A). The

first two exons are untranslated, with the remaining five exons and position of the catalytic triad

consistent with the other family members. No mutations in the coding exons of KLK6 have

been reported within the Sanger’s Catalogue of Somatic Mutations in Cancer (COSMIC)

http://www.sanger.ac.uk/perl/genetics/CGP/cosmic), International Cancer Genome Consortium

(http://www.icgc.org/), nor by sequencing of the coding regions of primary cancers and controls

(Shan et al., 2007). Likely evolving from an ancestral trypsin-like-KLK gene, KLK6, together

with KLK14, KLK5, KLK10 and KLK15, represents one of the earliest KLKs to come on scene

(Pavlopoulou et al., 2010).

Defined as common variations at a single nucleotide position, where the least common allele is

present in at least 1% of a given population (Collins et al., 1998), SNPs occur approximately one

in every 150-300 bp in the human genome (Brookes, 1999; Kruglyak and Nickerson, 2001;

Ladiges et al., 2004). Within the locus previously analysed by Goard et al. 2007 and Yousef et

al., 2004, over 110 SNPs on the positive and negative strands now span the gene (Figure 1.4A),

and continue to change as more genomic sequences become available. Six SNPs (of the positive

strand) have been localized to coding or splice sites and include: rs77760094; rs111672933;

rs1701950; rs113724718; rs61469141; rs111738447. Two synonymous SNPs are found in exon

6, resulting in no change in the amino acid. However, the four remaining SNPs have a missense

functional class, resulting in an amino acid change, whose significance requires investigation.

For many of these SNPs as well as those detected throughout the gene, validation of the

frequency has still yet to be determined.

1.8.3 KLK6 mRNA

KLK6 possesses a number of alternative transcripts encoding for the full-length KLK6 protein

(Kurlender et al., 2005; Yousef et al., 2004b), in addition to those predicted to produce truncated

forms (http://www.ncbi.nlm.nih.gov/genbank/) (Figure 1.4B). The KLK6 “classical transcript”

(also called Isoform A; NM_002774.3; GenBank Accession U62801) was originally detected in

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a normal mammary myoepithelial cell strain (Anisowicz et al., 1996), then from the human colon

adenocarcinoma cell line (COLO 201) (Yamashiro et al., 1997), and later from Alzheimer's

disease brain tissues (Little et al., 1997). The longest reported classic transcript cDNA clone

spans 1,526 nucleotides (Anisowicz et al., 1996), yielding a predicted 244 amino acid protein

(Figure 1.4B and C).

The classical transcript utilizes the promoter (P1) located in exon 1, while transcriptional variants

(AY318867 and AY318869) utilize a second promoter (P2) starting in a 20 bp region within the

first intron of KLK6 (Pampalakis et al., 2004), thus lacking exon 1 (Figure 1.4B). A third

promoter (P3), has also been identified in intron 2 and contains no untranslated exons

(Christophi et al., 2004; Pampalakis et al., 2004). Variant transcript 1 (AY318869) has a size of

1517 bp, while transcript variant 2 (AY318867) has a size of 1503 bp (Christophi et al., 2004;

Pampalakis et al., 2004). The ability to generate multiple mRNAs from a given gene increases

protein diversity, and is a reflection of the inherent biological complexities and specificities of

different cell types (Landry et al., 2003). The significance of these KLK6 variant transcripts has

yet to be determined (Tan et al., 2006), however it is evident that there is the propensity for

some tissues to express more than one transcript (Christophi et al., 2004; Kurlender et al., 2005;

Pampalakis et al., 2004; Yousef et al., 2004b). Pampalakis et al. (Pampalakis et al., 2004),

reported the presence of transcript heterogeneity; with tissues of the CNS overwhelmingly

expressing the classical transcript over the variants AY318869, AY318867 and AY279383. In

contrast, breast, spleen, uterus, and skeletal muscle predominantly expressed the variants

AY318869 and AY318867, with either very little or non-detectable levels of the classical

transcript. It has also been shown that primary ovarian carcinomas exhibit transcript

heterogeneity (Shan et al., 2007). In transcript-specific PCR, ovarian cancers were shown to

express the classic transcript, alternative transcript (AY318869), and in a small number of cases,

the splice variant for AY279383. The alternative transcript, AY318869, comprised the major

species of ovarian cancer-derived KLK6 mRNA (Shan et al., 2007). With the exception of these

studies, the majority of expression analyses do not specify the KLK6 transcript being detected.

Therefore, the significance of variant transcripts has not been fully appreciated.

RNA expression of KLK6 in normal human tissues has been confirmed (Figure 1.4B), with

tissues of the central nervous system (CNS) unequivocally showing the highest expression of

RNA transcripts, followed closely by tissues of the breast, kidney and uterus; salivary gland,

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thymus, spleen, thyroid and testis (Anisowicz et al., 1996; Christophi et al., 2004; Harvey et al.,

2000; Komatsu et al., 2007c; Little et al., 1997; Pampalakis et al., 2004; Shaw and Diamandis,

2007; Yamashiro et al., 1997; Yousef et al., 2004b; Yousef et al., 1999; Yousef et al., 2003b).

Cells of the ovary, lung, liver, bone marrow and pituitary show comparatively the least amount

of KLK6 mRNA. In diseased states, increased KLK6 mRNA expression has been identified in

ovarian carcinomas (Anisowicz et al., 1996; Ni et al., 2004; Rosen et al., 2005; Shan et al., 2007;

Shaw and Diamandis, 2007 ; Tanimoto et al., 2001; White et al., 2009a; White et al., 2009b),

breast (Anisowicz et al., 1996; Wang et al., 2008), uterine (Santin et al., 2005), pancreatic

(Ruckert et al., 2008), colorectal (Henkhaus et al., 2008; Kim et al., 2011; Ogawa et al., 2005),

gastric (Nagahara et al., 2005), skin (Klucky et al., 2007), and urinary bladder cancers (Shinoda

et al., 2007). In contrast to various cancers, the decrease of KLK6 mRNA has been identified in

the brain tissues of patients with AD and PD (Ashby et al., 2010; Ogawa et al., 2000). The

physiological implications of these findings will be discussed later.

1.8.4 KLK6 Protein

KLK6 encodes a single-chain enzymatically inactive prepro-enzyme of 244 amino acids

(Anisowicz et al., 1996; Little et al., 1997; Yamashiro et al., 1997; Yousef et al., 1999)(Figure

1.4C). This prepro-enzyme is subsequently processed to an inactive proKLK through the

removal of the predicted 16 amino acid signal peptide (Ala (A) ↓ Glu (E)) and passed through

the endoplasmic reticulum. The proKLK is secreted to the extracellular space and becomes

converted into its activated mature peptidase by the proteolytic cleavage of the five amino acid,

(Lys (K) ↓ Leu (L)), activation peptide (Debela et al., 2008; Yousef et al., 1999). The catalytic

triad, which characterizes serine proteases, is conserved in KLK6 (ie. His62, Asp106 and Ser197)

(Yousef et al., 1999). Furthermore, the presence of Asp at position 191 suggests that KLK6 has

trypsin-like activity. The activation of KLK6 is likely to involve proteinases endowed with

trypsin-like properties, thus the KLKs may be able to activate themselves or each other (Debela

et al., 2008; Yousef et al., 1999). Critical to understanding the physiological role of KLK6 lies in

discovering its substrates. Magklara et al. (Magklara et al., 2003) demonstrated the degradation

of casein, collagen type I, collagen type IV, fibrinogen as well as synthetic peptides

corresponding to the N-terminal region of amyloid precursor protein (APP) by KLK6; while the

chymotrypsin substrate AAPF-AMC was not cleaved; again confirming the lack of

chymotrypsin-like activity for KLK6 (Ghosh et al., 2004).

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Interrogation of adult and fetal tissues, and various biological fluids by Shaw et al., 2007

reported that the highest expression of KLK6 is found in the cell lysate of adult brain and in the

CSF by ELISA. Adult spinal cord showed the second greatest KLK6 expression, at roughly 50%

that of the brain. All other adult tissues, possessed significantly lower concentrations (<10% of

the brain). In contrast, fetal stomach showed the greatest expression of KLK6 among all fetal

tissues tested, but still less than 50% of the adult brain; followed by fetal kidney and skin.

Though not tested by Shaw et al., 2007; KLK6 has not been detected in fetal brain (Scarisbrick

et al., 2001). By immunohistochemistry (IHC) (Petraki et al., 2001), tissues of the central and

peripheral nervous system showed a range of immunoreactivity; with intense staining in the

peripheral nerves, but weak nerve cell staining within the CNS; strong staining in the epithelium

of the choroid plexus; and moderate expression among Purkinje, stellate and glial cells. Others

have similarly reported the lack or barely detectable levels of KLK6 in the normal epithelium of

the ovary (Ni et al., 2004; Petraki et al., 2003; Petraki et al., 2006). For most normal tissues and

biological fluids analysed, there is general concordance between RNA and protein levels.

However, due to the issues of transcript heterogeneity, it is unclear whether the mRNA detected

represents translatable protein. Nevertheless bulk extracted brain tissue and CNS fluids maintain

the highest KLK6 mRNA and protein expression in the adult; while most other tissues that

expressed KLK6 mRNA, also showed some degree of protein expression. In OCas, increases in

KLK6 protein levels have been identified, not only in tumour cells (Bayani et al., 2011; Bayani

et al., 2008b; Diamandis et al., 2000b; Diamandis et al., 2000c; Hoffman et al., 2002; Koh et al.,

2011; Kountourakis et al., 2008; Luo et al., 2006; Ni et al., 2004; Petraki et al., 2006; Prezas et

al., 2006; Shan et al., 2007; Yousef et al., 1999; Yousef et al., 2003b; Zheng et al., 2007), but

also in the tumour stroma (Seiz et al., 2012); as well as in renal cancers (Gabril et al., 2010),

gliomas (Strojnik et al., 2009), lung carcinomas (Nathalie et al., 2009; Singh et al., 2008),

salivary gland tumours (Darling et al., 2006), pancreatic ductal carcinomas (Ruckert et al., 2008)

and uterine cancers (Santin et al., 2005). In the case of Alzheimer’s and Parkinson’s Disease,

decreased KLK6 has been observed (Ashby et al., 2010; Menendez-Gonzalez et al., 2008;

Scarisbrick, 2008; Zarghooni et al., 2002); while skin disorders such as psoriasis have also

reported changes in KLK6 levels (Komatsu et al., 2007a; Komatsu et al., 2007b).

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Figure 1.4. Summary of KLK6 Properties A) Genomic organization of KLK6. Shown are the seven exons (I-VII) with untranslated exons shaded in grey, and coding exons in green. The sizes of each exon and intron are indicated. The distribution of both simple tandem-, di- and trinucleotide repeats are also shown below. Relative SNP distribution across the gene is shown. Green SNPs indicate those identified as missense or synonymous, near splice sites; while Red SNPs indicate those located in coding exons. Blue SNPs indicate those located in non-coding regions and introns. B) Organization of KLK6 RNA transcripts. Shown are a number of identified RNA transcripts as described in the text. The asterisk(*) indicates the transcriptional start site and the downward arrowhead indicates the stop site. Predicted untranslated exons are shaded in grey, predicted coding exons are shown in green. Right) Shown is the relative expression of KLK6 RNA in normal adult human tissues based on several studies Human adult brain shows the greatest KLK6 transcript expression among all tissues in the human body as compared to ovary, lung and liver which barely shows any expression or expression at the level of detection for PCR or northern analysis. C) Organization of KLK6 protein. Shown is the amino acid sequence of the full-length KLK transcript. Amino acids shaded in: Blue indicates the signal peptide sequence; Green indicates the propeptide sequence; Fusia indicates the autolysis loop; and amino acids coloured in Red

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are those in the catalytic triad. Lower Left) Summary of other protein features including disulfide bonds (red), substrate binding sites (blue) and a glycosylation site (green). The amino acids of the catalytic triad are indicated in triangles. Lower Right) Shown is the relative expression of KLK6 protein detected in both normal adult and fetal tissues as detected by ELISA. Similar to RNA findings, normal adult brain shows the highest KLK6 protein expression. The majority of tissues show low to non-detectable levels when compared to the adult brain. In fetal tissues, stomach shows the greatest expression followed by kidney and skin. Bottom) KLK6-specific immunohistochemical analyses of normal ovarian surface epithelium and ovarian carcinoma. Normal ovarian surface epithelium shows very low/barely detectable levels of KLK6, in contrast to a representative ovarian cancer showing strong immunoreactivity to KLK6 antibodies.

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1.8.5 Role of KLK6 in Disease

The expression of the KLK genes in a variety of tissues (Shaw and Diamandis, 2007), and their

proteolytic abilities enable them to participate in a variety of physiological and pathobiological

processes (Borgono and Diamandis, 2004; Sotiropoulou et al., 2009). The observed

overexpression or underexpression of KLKs, have helped to elucidate their role in these cellular

processes when the normal balance of their expression is shifted. In the following sections, we

present the experimental evidence linking KLK6 to numerous cellular processes and consider the

integrative effect this could have in the context of diseases such as cancer or neurodegeneration,

which are summarized in Figure 1.5.

Figure 1.5. Consequences of Dysregulated KLK 6 Expression in Cancer and Neurodegenerative Diseases. Experimental evidence discussed in the text implicates KLK6 in many diverse molecular pathways. In cancers, the increase of KLK expression can be oncogenic, leading to increases in inflammation, enhanced signaling and evasion of apoptosis; which can drive cancer progression to enhance pathways involved with cellular proliferation, invasion and metastasis. However, studies in breast cancer indicate a tumour suppressor role for KLK6. Loss of KLK6 expression in breast, and possibly renal cancers, results in the loss of this tumour suppressive function, leading to cancer progression. For multiple sclerosis, experimental evidence suggests that increased KLK6 levels are associated with inflammation, which can also enhance signaling and evasion of apoptosis for inflammatory cells with sustained signaling. Loss of KLK6 in the case of AD and PD leads to a shift in the normal turnover of neural proteins, leading to increased accumulation of plaque-forming fragments.

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1.8.5.1 Inflammation, Immunity and Skin Pathophysiology

A number of KLK members have been implicated in the normal physiological functioning of the

skin which is tightly regulated for innate immunity and inflammation (reviewed by Sotiropoulou

and Pampalakis, 2010). A large body of work has shown the role of KLKs in skin desquamation,

thus forming the basis for their study in various skin disorders (Borgono et al., 2007; Komatsu et

al., 2005; Komatsu et al., 2003). Indeed, elevated levels of KLK6, KLK8 and KLK13 have been

identified among psoriasis and atopic dermatitis patients (Kishibe et al., 2007; Komatsu et al.,

2007b; Komatsu et al., 2005; Naldi, 2010). Work by Scarisbrick et al. (Scarisbrick, 2008;

Scarisbrick et al., 2006; Scarisbrick et al., 2008), suggested that KLK6 drives the dysregulated

inflammatory processes in the CNS and can alter immune cell survival by inducing the

upregulation of the pro-survival protein Bcl-XL and inhibition of the pro-apoptotic Bcl-2 family

member protein, Bim (Scarisbrick et al., 2011).

1.8.5.2 Neurodegeneration

The highest expression of KLK6 in normal tissues is in the CNS. Particular interest in the role of

KLK6 in the brain comes from its identification (as Zyme) by Little et al., 1997. As such, its role

in both normal and diseased brains has been actively pursued. Alzheimer’s disease (AD), the

most common form of dementia (Ferri et al., 2005; Kalaria et al., 2008), is characterized by the

accumulation of amyloid β(Aβ), a proteolytic fragment of the amyloid precursor protein (APP)

(Zhang et al., 2011). Little et al. 1997, observed in vitro that co-expression of KLK6 cDNA with

APP cDNA in HEK cells resulted in an abundance of truncated amyloidogenic fragments,

implicating a role for KLK6 in the normal turnover of such proteins in the brain. These

observations are in keeping with observations by IHC in normal and AD brain sections showing

the decreased expression of KLK6 in AD brains over controls (Ashby et al., 2010; Ogawa et al.,

2000). Similarly, KLK6 levels in cerebrospinal fluid, blood and tissues from AD patients

revealed a similar trend with an average two-fold decrease of KLK6 in brain tissue extracts

compared to controls (Menendez-Gonzalez et al., 2008; Mitsui et al., 2002; Zarghooni et al.,

2002). Like AD, Parkinson’s disease (PD) is also a neurodegenerative disorder but is

characterized by the accumulation of insoluble alpha-synuclein (α-synuclein) (Shulman et al.,

2011), whereby KLK6 fails to degrade α-synuclein, contributing to the pathogenesis of PD,

possibly through the disrupted trafficking of KLK6 (Tatebe et al., 2010).

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Unlike AD and PD, KLK6 levels in patients with Multiple Sclerosis (MS) are elevated (Hebb et

al., 2010; Scarisbrick, 2008; Scarisbrick et al., 2006; Scarisbrick et al., 2011; Scarisbrick et al.,

2008) and linked to inflammatory pathways. MS is a neurodegenerative disorder whereby the

myelin sheath, that protects nerve cells, is damaged. Damage to the myelin sheath is driven

primarily by inflammation (Stadelmann et al., 2011). Indeed, several studies (Hebb et al., 2010;

Scarisbrick, 2008; Scarisbrick et al., 2011) have documented significant increases in serum-

KLK6 in patients with MS, as well as those experiencing a secondary progressive disease course.

We have already alluded to the role of KLK6 in inflammation above, and many of the KLK6-

related studies undertaken in MS point to dysregulated inflammatory pathways. In addition to its

contribution to sustained activation of inflammatory signaling, KLK6 also degrades myelin

proteins (Bernett et al., 2002; Scarisbrick et al., 2002) and has been detected in elevated levels in

inflammatory demyelinating lesions of both viral and autoimmune MS models, as well as in MS

pathogenic lesions (Blaber et al., 2002; Scarisbrick et al., 2002).

1.8.5.3 Tumourigenesis

We already mentioned the observed overexpression of KLK6 transcript and protein in a variety

of cancers such as ovarian carcinomas (Anisowicz et al., 1996; Bayani et al., 2011; Bayani et al.,

2008b; Diamandis et al., 2000b; Diamandis et al., 2000c; Hoffman et al., 2002; Koh et al., 2011;

Kountourakis et al., 2008; Luo et al., 2006; Ni et al., 2004; Petraki et al., 2006; Prezas et al.,

2006; Rosen et al., 2005; Shan et al., 2007; Shaw and Diamandis, 2007 ; Tanimoto et al., 2001;

White et al., 2009a; White et al., 2009b; Yousef et al., 1999; Yousef et al., 2003b; Zheng et al.,

2007), breast (Anisowicz et al., 1996; Wang et al., 2008), uterine (Santin et al., 2005),

pancreatic (Ruckert et al., 2008), colorectal (Henkhaus et al., 2008; Kim et al., 2011; Ogawa et

al., 2005), gastric (Nagahara et al., 2005), skin (Klucky et al., 2007), urinary bladder cancers

(Shinoda et al., 2007), gliomas (Strojnik et al., 2009), lung (Nathalie et al., 2009; Singh et al.,

2008) and salivary gland tumours (Darling et al., 2006). Focus is now being diverted to

revealing the details of KLK6’s role in promoting tumourigenesis.

The majority of investigations regarding the consequences of KLK6 overexpression have been

linked to functions supported by in vitro studies demonstrating the ability of KLK6 to degrade

fibrinogen and various collagens, which constitute the basement membrane (Ghosh et al., 2004;

Magklara et al., 2003). These findings have significant implications for tissue remodeling,

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tumour invasion and migration, with several studies showing that the decrease of KLK6 protein,

either through the use of inhibitors or siRNAs (Ghosh et al., 2004; Henkhaus et al., 2008; Kim et

al., 2011; Nagahara et al., 2005; Shinoda et al., 2007), results in the concomitant reduction in

invasive potential and cellular proliferation. The KLK6-siRNA treatment of colon cancer cell

lines (Kim et al., 2011) showed variable decrease in cell proliferation, though some cell lines

remained similar to controls; while Prezas et al., 2006, showed an increase in tumour burden and

invasive potential in ovarian cancers transfected with an expression vector inducing KLKs 4, 5, 6

and 7, though it is unclear what level of contribution to the observed tumourigenicity each

member made. The ectopic Klk6 expression in mouse keratinocyte cell lines by Klucky et al.,

2007 induced spindle-like morphology and enhanced proliferation, migration, and invasive

capacity. Furthermore, a reduction of E-cadherin protein levels at the cell membrane and of β-

catenin nuclear translocation was also detected in Klk6-expressing mouse keratinocytes and

human HEK293 cells transfected with a KLK6 expression plasmid. Cell-adhesion defects were

also observed in the presence of KLK6, which were rescued in the presence of inhibitor of

metalloproteinase (TIMP)-1 and TIMP-3.

Cell adhesion defects not only characterize morphological changes, but mark alterations in other

molecular pathways including apoptosis and angiogenesis. In keeping with findings by

Scarisbrick et al., 2011, describing that the overexpression of KLK6 in hematopoietic cells leads

to the activation of pro-survival pathways; Kim et al. 2011, found that suppression of KLK6 was

associated with the activation of caspase-8 and caspase-3, as well as the up-regulation of p21cip.

Anoikis, defined as the process by which an anchorage-dependent cell undergoes programmed

cell death as a result of detachment from the surrounding extracellular matrix (Frisch and

Screaton, 2001; Nagaprashantha et al., 2011), is another mechanism that cancers alter in order to

progress to metastatic disease. In a study by Kupferman et al. (Kupferman et al., 2007), anoikis-

resistant oral squamous carcinoma cell lines were generated under detached growth conditions.

cDNA expression profiling identified a 6.2-fold increase in KLK6, among others, in anoikis-

resistant cell lines compared to anoikis-sensitive cells under detached conditions. Based on these

findings, this novel association of KLK6 in anti-apoptotic pathways should be further

investigated. Cancer progression also involves the formation of additional vasculature. Aimes et

al. (Aimes et al., 2003) identified the expression of genes, including KLK6, in endothelial cells

which appear to be regulated, in part, by the nature of the substratum associated with these cells.

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The role of KLK6 in intracellular signaling has also been actively studied in the context of

protease-activated receptors (PARs) (Hollenberg et al., 2008; Oikonomopoulou et al., 2006a;

Oikonomopoulou et al., 2006c). PARs 1-4 are members of the G-protein–coupled family of

receptors which are subject to activation through the proteolytic cleavage of the receptor N-

terminal sequence to reveal an activating tethered ligand (Hollenberg and Compton, 2002;

Macfarlane et al., 2001). These receptors have been implicated in various physiological roles,

including platelet activation, the modulation of endothelial and vascular smooth muscle cell

function, inflammatory responses to joint injury and tumour cell growth and metastasis

(Oikonomopoulou et al., 2010). Studies by Oikonomopoulou et al. (Oikonomopoulou et al.,

2006a; Oikonomopoulou et al., 2006b; Oikonomopoulou et al., 2006c), demonstrated the ability

of KLK6, as well as other KLK members, to cleave the synthetic peptides representing the

cleavage-activation sequences of PAR1, PAR2 and PAR4, and either activating or disarming the

receptor. Though the majority of work points to pathways associated with inflammation, cancer

investigators are increasingly aware of the link and high degree of cross-talk with inflammation

in contributing to pathogenesis (Aggarwal et al., 2009; Coussens and Werb, 2002; Gonda et al.,

2009). Thus, activation of PARs by KLK6 in the context of carcinogenesis, would feed into

known oncogenic signaling pathways such as PI3K and RAS (Castellano and Downward, 2010).

Due to the associated overexpression in virtually most epithelial cancers, the vast majority of

evidence supports an oncogenic role for KLK6. However, the original cloning of KLK6

(Anisowicz et al., 1996), implicated KLK6 as a putative class II tumour suppressor gene due to

its loss of expression in metastatic breast cancer. Work performed by Pampalakis et al., 2009,

demonstrated the inactivation of KLK6 in metastatic breast cancer due to epigenetic events. The

functional consequences of such inactivation, was further investigated to show that KLK6 may

have a protective role against breast cancer progression by inhibiting the epithelial-to-

mesenchymal transition (EMT). These findings suggest that a role for KLK6-tissue specificity.

1.8.6 Clinical Utility of KLK6 in Ovarian Cancer

The use of KLK3/PSA as a diagnostic and prognostic marker in prostate cancer has led to the

evaluation of KLK6 as a putative biomarker for many of the neoplasms and diseased states.

Since KLK6 is secreted, it has great potential as a biomarker that can be evaluated in patient

specimens obtained by less invasive means, such as blood or urine. To date, the majority of

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clinical investigations have been performed retrospectively from tissue specimens, with a

handful also looking at KLK6 levels from paired biological fluids.

In cancer, an overwhelming number of studies show the overall increase in KLK6 mRNA and

protein expression when compared to their respective normal tissues, providing the basis for its

evaluation across clinical parameters. The vast majority of KLK6 cancer-related studies

encompass OCa (Bayani et al., 2011; El Sherbini et al., 2011; Hoffman et al., 2002; Koh et al., ;

Rosen et al., 2005; Shan et al., 2007; White et al., 2009a; White et al., 2009b; Zheng et al.,

2007). KLK6 appears to be overexpressed across all subtypes of OCa (Kountourakis et al.,

2008; Ni et al., 2004; White et al., 2009a; White et al., 2009b), with serous and undifferentiated

cancers generally showing greater percentages of cases with KLK6 positivity (Hoffman et al.,

2002; Shan et al., 2007). Because of some of the limitations of CA-125, KLK6 has been assessed

for its ability as a viable OCa biomarker. Rosen et al. (Rosen et al., 2005), who showed that of

the approximately 22% of ovarian cancers which were negative for CA-125 expression, all were

positive for other markers including KLK6. Along these lines, sseveral studies have consistently

shown that the combination of KLK6 with CA-125 enhances their individual diagnostic power

(El Sherbini et al., 2011; Hoffman et al., 2002; Koh et al., 2011; White et al., 2009a). These

findings are consistent with KLK6 RNA expression studies by Shan et al., 2007 who also

showed that KLK6 expression was significantly associated with late stage (stage III/IV) disease,

higher tumour grade, suboptimal debulking, and serous and undifferentiated histotypes.

Furthermore, univariate Cox regression showed that KLK6-positive patients showed an increase

risk of relapse and death, in contrast to KLK6-negative patients. Progression free survival (PFS)

and overall survival (OS) continued to be significant with positive KLK6 expression when

treated as a continuous variable. Kaplan-Meier analysis revealed a significant association

between KLK6 expression status and both progression-free survival (PFS) and overall survival

(OS); which was in contrast to those generated based on CA-125 alone. The value of KLK6

RNA levels to tumour recurrence was also investigated by White et al. 2009 showing that the

expression levels of KLK6 and KLK13 were significantly increased in invasive cancers as

compared to normal ovarian tissues. The high expression was also shown to be a poor prognostic

indicator and indicative of a shorter recurrence-free survival. Based on these observations, KLK6

has been included in large ovarian cancer biomarker validation efforts by the Early Detection

Research Network (EDRN) and Specialized Programs of Research Excellence (SPORE) (Cramer

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et al., 2011; Zhu et al., 2011). KLK6 ranking among the top 10-20 (of 49 markers).

Specifically, it ranked 9th across all cases and general population controls in terms of sensitivity

(95% confidence interval), and 15th in Area Under the Curve (AUC – 95% confidence interval).

However, for early staged cases and general population controls, KLK6 ranked 29th and 28th for

sensitivity (95% confidence interval) and AUC (95% confidence interval), respectively. The

results of these studies clearly indicate the need for identifying novel biomarkers, to be utilized

individually and in panels, for both early detection and monitoring, since none of the biomarkers

tested performed any better than CA-125 alone.

1.8.7 Regulation of KLK6 Expression and Activity

The biology of an organism relies on the regulation of a gene at the DNA, mRNA, and protein

levels, but also relies on the interplay between these factors and tissue-specific

microenvironment. A number of genomic features that influence KLK6 expression have been

alluded to above and shown in Figure 1.6, and include the finding that no overt sequence

mutations in the coding regions of KLK6 have been found to date that results in its aberrant

expression. However, the presence of SNPs within both coding and non-coding regions of the

gene offers the possibility for uncovering genotype-phenotype associations as more genome

sequences become available.

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Figure 1.6. Mechanisms of Regulation of KLK6 Expression. Shown is a summary of the various modes of regulation identified for KLK6 as described in the text.

Several studies have documented the concomitant dose-dependent increase in KLK6 gene by the

in vitro exposure of cell lines to androgens and estrogens (Paliouras and Diamandis, 2007;

Yousef et al., 1999). However, others (Shan et al., 2007) have shown minimal effects on KLK6

expression upon stimulation. Interestingly, significant hormone responses were detected in

breast cancer cell lines (Paliouras and Diamandis, 2007; Yousef et al., 1999), while ovarian

cancer cell lines showed non-significant responses (Shan et al., 2007), suggesting that factors

such as cell-type specificity plays a role in regulation. To date, no estrogen response elements

(EREs) have been identified within 6kb upstream of the proximal KLK6 promoter (Paliouras and

Diamandis, 2007), which is consistent with previous observations that classical hormone

response elements (HREs) have not been identified (Yousef and Diamandis, 2001). This

implicates a more complexed integration of mechanisms.

Christophi et al., 2004 examined a region of 500 bp upstream from the start site of the human

and mouse KLK6 genes and found 10 cis-acting regulatory sequences which were common to

both, and were of interest in terms of inflammatory responses in the CNS. Some of the candidate

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genes associated with these cis-acting regulatory elements include; v-Myb, Ets-family member

ELF-2 (NERF1), sterol regulatory element binding protein 1 and 2, and ELK-1. Later, similar

integrative in silico studies in breast cancer cell lines also identified the putative ELK-1 binding

site, in addition to an E-box and AP-1 binding site and several Sp-1 sites (Pampalakis and

Sotiropoulou, 2006). Finally, KLK6 has also been identified as a target for the vitamin D

analog, EB1089, in studies where KLK6 expression was induced upon treatment with EB1089,

in squamous carcinomas (Lin et al., 2002), colon cancer (Palmer et al., 2003)and the T47D breast

cancer cell line (Pampalakis et al., 2006). Further examination of the KLK6 proximal promoter,

indicated predicted multiple vitamin D response-element consensus sequences (Pampalakis et al.,

2006).

Epigenetic mechanisms have also been extensively examined by Pampalakis and colleagues

(Pampalakis et al., 2006; Pampalakis et al., 2009; Pampalakis and Sotiropoulou, 2006).

Sequence analysis (Pampalakis and Sotiropoulou, 2006) found no CpG islands, defined by the

parameters of a length of more than 200bp, and with a C+G content more than 50%, or with an

observed/expected ratio greater than 0.6; either upstream or within exon-intron sequences of

KLK6. Experimentally, treatment of breast cancer cell lines with 5-aza-2’deoxycytidine (5-aza-

dC), resulted in the induction of KLK6 gene and protein expression offering the possibility that

non-CpG island cytosines are involved in the regulation of transcription. Indeed, these findings

were confirmed (Pampalakis et al., 2008; Pampalakis et al., 2009; Pampalakis and Sotiropoulou,

2006), where specific CpG dinucleotides were subjected to methylation in breast cancer cell

lines, resulting in the loss of KLK6 expression. Interestingly, the in silico comparison of these

CpGs across other mammalian KLK6 orthologs showed conservation of these dinucleotides

(Pampalakis et al., 2008). Fifteen CpG dinucleotides located within the human P1 transcriptional

start site of the classic transcript were analysed. In breast cancer cell lines lacking KLK6

expression, CpG dinucleotides at positions -72, -64, -56, -53, -35, +3 and +14 were completely

methylated. Similarly, those cell lines showing KLK6 expression possessed unmethylated CpG

dinucleotides.

As discussed above, KLK6 possesses several mRNA variants and promoter sites, however the

significance and frequency of such variants has not been thoroughly investigated; nor has it been

determined whether a functional protein results or what physiological role such isoforms may

play (Tan et al., 2006). However, the translation of RNA to protein provides another level for

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gene regulation. In recent years, microRNAs (miRNAs) have been implicated in contributing to

disease and malignancies (Croce, 2009). miRNAs represent a class of non-coding RNAs that

range in size from 19 to 25 nucleotides, originally described in C. elegans (Lee et al., 1993). The

mechanism of regulation is mediated through degrees of complementarity to the target mRNA 3′

untranslated region (UTR). Perfect complementarity results in the cleavage and degradation of

the target mRNA; whereas less than perfect pairing, represses the translation process (Barbarotto

et al., 2008). In silico analyses of publicly accessible databases show that a number of miRNAs

are predicted to target KLK6, among them the members of the hsa-let-7 family of miRNAs

(Chow et al., 2008). Experimentally, Chow et al., 2008, demonstrated the decrease of KLK6

expression upon transient transfection of hsa-let-7f to the breast cancer cell line MDA-MB-468.

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1.9 Rationale and Hypothesis

Kallikrein 6 (KLK6) is a putative biomarker for ovarian cancer whose expression is frequently

elevated in the tumour tissues and fluids derived from these patients. However the mechanisms

influencing the observed overexpression are currently unclear. Ovarian cancers show recurrent

patterns of genomic alteration leading to patterns of genomic instability, reflected by

chromosomal gains and losses resulting in both numerical and structural rearrangements.

Consequently, the expression of genes within these genomic regions may also be affected; either

directly by alteration to the KLK genomic region, or by the expression of other genes regulating

the cluster. Therefore, we hypothesize that the observed overexpression of KLK6 is influenced

by its copy-number in ovarian cancer, and that copy-number changes in other genes influencing

its post-transcriptional mechanisms serve to further modulate its expression. Although whole-

genome copy-number studies have been conducted for OCa, there have been no specific studies

focusing on KLK copy number or genes potentially regulating them.

1.10 Objectives

• To distinguish between numerical and structural chromosomal instability in ovarian

cancers utilizing molecular cytogenetic techniques and the implications for copy-

number change in those affected genomic regions.

• To determine the copy-number status of the KLK locus in ovarian cancers and cell lines

and the chromosomal mode of such copy-number change (ie. numerical or structural

changes) using cytogenomic techniques.

• To identify whether the entire locus experiences such copy-number changes or whether

individual gene members are affected.

• To determine the frequency and range of KLK locus changes in ovarian cancers and its

relationship to protein expression, specifically KLK6.

• To determine whether copy-number changes of the KLK locus are clinically relevant.

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• To investigate other mechanisms affected by chromosomal aberrations which modulate

the observed protein overexpression of KLK6, namely the role of miRNAs in

influencing KLK6 expression.

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Chapter 2

Distinct Patterns of Numerical and Structural Chromosomal Instability Characterize Sporadic Ovarian

Cancer

Chapter 2 includes the published manuscript entitled “Distinct Patterns of Structural and Numerical Chromosomal Instability Characterize Sporadic Ovarian Cancer” Bayani J, Paderova J, Murphy J, Rosen B, Zielenska M, Squire JA. Neoplasia. 2008. 10:1057-65.

J.Bayani contributed to the development and experimental design of the study and manuscript; established primary cultures and cytogenetic preparations; performed all experiments, analyses and interpretation. J.Paderova provided technical support of cytogenetic preparations and confirmation of karyotypic nomenclature. J.Murphy and B.Rosen provided patient material and associated clinical information. M.Zielenska contributed to the development and design of the study and approved the manuscript. JA Squire contributed to the development and design of the study, manuscript and granting support.

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2 Distinct Patterns of Structural and Numerical Chromosomal Instability Characterize Sporadic Ovarian Cancer

2.1 Introduction Ovarian cancer is the fourth leading cause of cancer death among North American women, and

the leading cause of death from a gynecological malignancy. The karyotypes of these tumours

are characterized as aneuploid with complex chromosomal aberrations (Bayani et al., 2002;

Bernardini et al., 2005; Shridhar et al., 2001; Taetle et al., 1999a; Taetle et al., 1999b). The

features of many epithelial tumours, including OCa, are the presence of structural changes and

aneuploidy, arising as a consequence of CIN. CIN has been classically defined as the rate of

whole chromosomal gains and losses (Lengauer et al., 1997; Rajagopalan et al., 2003), and has

been a useful means for assessing genomic heterogeneity. CIN has also been less frequently

used to describe the presence of structural aberrations (Lengauer et al., 1997; Muleris et al.,

2008). More complex genomic changes in tumours arise as a consequence of chromosomal

instability (CIN), which can generate both numerical (N-CIN) and structural chromosomal

instability (S-CIN) (Bayani et al., 2007). The observed high levels of aneuploidy and structural

complexity in these tumours suggests errors in DNA repair, mitotic segregation errors and

dysregulation of cell cycle checkpoints (Cahill et al., 1998; Kops et al., 2005) may generate N-

CIN. However the mechanisms that influence structural changes and gross aneuploidy are

distinct. Structural rearrangements appear to be influenced by abnormal DNA repair pathways,

that result in errors in both homologous and non-homologous end-joining of double stranded

DNA and may contribute to S-CIN (Hoeijmakers, 2001) (and reviewed in Bayani et al. 2007).

The formation of structural rearrangements may also come about through telomere-mediated

events, where critically short telomeres are recognized as DNA breaks, capable of recombining

either homologously or non-homologously when DNA-repair pathways are compromised and

telomerase is activated (Stewart et al., 1999). In contrast to structural changes, aneuploidy arises

through various mechanisms predominantly influenced by the dysregulation of cell cycle

checkpoints and mitotic segregation errors; or some combination of all (Cahill et al., 1998; Nigg,

2002).

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Tetraploidy is a common feature of many carcinomas and sarcomas and is believed to be an

initial stage in the development of aneuploidy, arising either through disruption of chromosome

segregation during mitosis (Stewart et al., 1999) or through failure of cytokinesis (Andreassen et

al., 2001). The tetrapoloidization event not only results in the doubling of genomic content, but

also in the doubling of centrosomes (Andreassen et al., 2001; Skoufias et al., 2001). Aneuploidy

can also arise from mechanisms independent of a tetraploid intermediate, suggesting that the

components of the mitotic machinery possess aberrant function, such as the loss of centrosome

duplication control, leading to abnormal centrosome amplification and creating, multipolar

spindles and unequal genomic segregation (Saunders, 2005).

Cytogenetic analyses of primary OCas have identified tumours with a range of numerical

change, from near-diploid to highly aneuploid abnormal karyotypes. Moreover, within most

reported studies some tumours also have many chromosomes with a high degree of structural

aberration; whilst others, exhibited relatively few, simple rearrangements (Bayani et al., 2002;

Shridhar et al., 2001; Taetle et al., 1999a; Taetle et al., 1999b). Defining these varying

characteristics in greater detail, using primary OCa samples will provide insights concerning the

mechanisms responsible for genomic diversity in these tumours. Recognition of such

mechanisms associated with each class of chromosomal aberration will be beneficial in

understanding the nature of karyotypic progression, as well as avenues for future therapeutic

strategies in ovarian carcinoma. Using integrative molecular cytogenetic analyses including

Spectral Karyotyping (SKY), Comparative Genomic Hybridization (CGH) and interphase

fluorescence in situ hybridization (FISH), ten sporadic OCa specimens derived from seven

patients, previously described by our group (Bayani et al., 2002), were assessed for features of

N-CIN and S-CIN; in addition to centrosome amplification.

2.2 Materials and Methods

2.2.1 Patient Specimens and Cell Lines

Ten tumours from seven sporadic ovarian cancer patients, with informed consent were obtained

from The University Health Network, Toronto, during the years 2000-2001. All tumours, except

for OCA27A/B and OCA714, were previously described by Bayani et al., 2002. These patients

had no previous family history for ovarian or breast cancer, and all specimens were obtained at

the first surgery prior to chemotherapeutic treatment. For three patients, two distinct tumour

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samples were obtained. OCA21A/OCA21B and OCA27A/OCA27B are paired

primary/metastasis samples. For OCA15A/B tumours were obtained from the right and left

ovaries, respectively. A normal female control fibroblast (passage 9) was also maintained in

culture and used as a technical control for hybridization efficiency and normal mitotic

segregation.

2.2.2 Peptide Nucleic Acid (PNA) Fluorescence in situ Hybridization (FISH) and Spectral Karyotyping Analysis

Cell culture and chromosome preparation: Primary tissues were obtained at the time of surgery

and promptly minced and collagenase treated (Invitrogen, Burlington, Canada) for 24 hours at

37°C in a CO2 incubator. The following day, the disaggregated tissue was processed for short-

term culture and then prepared for cytogenetic harvest as previously described (Bayani et al.,

2002). A normal control fibroblast culture was maintained and processed similarly.

Peptide Nucleic Acid (PNA) Fluorescence in situ Hybridization (FISH): For chromosomal

instability studies, interphase FISH was performed using peptide nucleic acid (PNA) probes

directly labeled with either FITC or Rhodamine for centromeres 2, 4, 7 and 8 (Applied

Biosystems, Foster City, CA.). Pan-centromeric PNA probes (Applied Biosystems, Foster City,

CA.) were also used to identify the presence of multi-centromeric chromosomes. The standard

technique for PNA FISH was carried out as previously published by Wan et al., (Wan et al.,

1999). Two hundred nuclei were assessed in a coded fashion by two independent observers.

Generation of Instability Index: Each tumour specimen was assessed using all four PNA

centromere probes. An instability index was generated based on published criteria by Lengauer

et al. 1997. For each specimen, including a normal fibroblast, two hundred nuclei were counted.

The normal fibroblast sample served as a technical control for the overall hybridization

efficiency for each centromere probe used; and was found to be greater than 96%, with the

average background noise across all centromere probes tested to be 3.6% ((2+3.5+4+4)/4)). For

the tumour specimens, the prevalent clones for each centromere probe tested were initially

established by karyotypic (SKY) analysis, and the percentage of cells containing greater or less

than that clonal population were enumerated. In keeping with guidelines of clonality (Shaffer and

Tommerup, 2005), if it was determined that the prevalent signal count for that centromere was

not consistent with the karytoype analysis, the interphase FISH results were used in our analysis.

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The identification of populations greater than 30% were also considered as an additional clone

and excluded from the enumeration. For example in OCA3, the total percentage of cells

possessing greater or less than 4 signals for centromere 2 was 52%; for centromere 4, the total

percentage of cells possessing greater or less than 2 signals per cell was 24.5%; for centromere 7,

the total percentage of cells possessing greater or less than 4 signals per cell was 37%; and for

centromere 8, the total percentage of cells possessing greater or less than 3 signals per cell was

22%. Thus the average CIN index is 33.9 ((52+24.5+37+22)/4).

Spectral Karyotyping (SKY): SKY analysis was performed on metaphase preparations using the

SKY Paints™ according to the manufacturer’s instructions (Applied Spectral Imaging, Carlsbad,

CA) and as described by previously by Bayani et al., 2002. Karyotypes derived by SKY analysis

reflect the clonal changes determined from at least ten metaphase spreads where possible. A gain

of a chromosome was described when identified in at least two metaphase spreads, a loss when

identified in three or more cells and a chromosomal rearrangement when identified in two or

more cells. The karyotype descriptions were assigned according to the guidelines set forth by the

International System for Cytogenetic Nomenclature 2005 (ISCN) (Shaffer and Tommerup,

2005). Breakpoint enumeration was performed on intact metaphase spreads that were

analysable.

2.2.3 Centrosome Immunostaining

For all cases, cells were grown onto chambered glass slides and cultured to approximately 70%

confluency. The cells were fixed with a 4% paraformaldehyde solution and processed for

fluorescence immunohistochemistry with mouse monoclonal anti-γ-tubulin antibodies (Sigma-

Aldrich, Oakville, Canada) as previously described (Al-Romaih et al., 2003). Centrosome

signals were evaluated in 200 non-overlapping cells. For each specimen, the percentage of cells

possessing greater than 2 centrosomes per cell were considered abnormal or if the signal size and

shape were different from the size and shape in control cells (Al-Romaih et al., 2003; Pihan et

al., 1998). The slides were viewed and imaged using a Nikon Labophot-2 fluorescent

microscope, and analyzed using the Vysis Quips SmartCapture™ imaging system (Vysis,

Downers Grove, Il).

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2.3 Results

2.3.1 Numerical Chromosomal Instability: N-CIN

To determine N-CIN levels within the study group, interphase FISH was performed using

centromere–specific probes from four representative chromosomes. Centromere probes for

chromosomes 2, 4, 7 and 8 were chosen for this study based on their propensity for net gain

(chromosomes 7 and 8), loss (chromosome 4) or tendency for little change (chromosome 2) as

determined by previous cytogenetic and CGH studies conducted by this group and others

(Bayani et al., 2002; Bernardini et al., 2005; Shridhar et al., 2001; Taetle et al., 1999a; Taetle et

al., 1999b). Table 2.1 summarizes the results of interphase chromosomal instability findings

using the PNA probes, with representative images from OCA5, OCA 27B and OCA714 in

Figure 2.1. The raw scoring data is detailed in Table 2.2. An N-CIN index was generated for

each tumour as described in the Materials and Methods. Analysis based on the N-CIN indices

revealed two distinct ranges of instability; those with a low N-CIN index ranging from 7.0 to

21.0, and another group of tumours with a higher N-CIN index ranging from 24.9 to 54.9. The

copy number status of these tumours, as determined by previous SKY analysis (Bayani et al.,

2002), revealed those cases with lower instability indices possessed karyotypes in the diploid or

near-diploid range. This was in contrast to the second group where the instability indices were

associated with triploid and tetraploid karyotypes. The tumour that showed the greatest

instability was OCA5 (Figure 2.1A), a near-triploid tumour with an instability index of 54.9.

The results of the individual chromosomes revealed a range of centromere signals from 1 to as

many as 9 signals per cell, for the four chromosomes tested. OCA3 displayed the next greatest

instability index of 33.9. Like OCA5, OCA3 was characterized as near-triploid; however the

range of instability was not as extensive as OCA5. Among the near-tetraploid tumours,

OCA27A/B, paired primary and metastatic tumours, possessed instability indices of 24.9 and

32.7 respectively, for which the FISH and SKY findings for OCA27B are illustrated in Figure

2.1B. OCA15A/B are pair samples showing similar instability indices of 10.7 and 12.7

respectively. Karyotype analysis revealed a hypodiploid karyotype with numerous structural

aberrations similar to both specimens. OCA714 possessed an instability index of 7.0 showing a

diploid karyotype as confirmed by SKY analysis (Figure 2.1C). When the N-CIN indices were

plotted in ascending order (Figure 2.2), the resulting trend revealed that numerical instability

increased in association with transitions in ploidy, specifically there was very little instability

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among the more diploid tumours, while near-tetraploid tumours showed a moderate to high level

of instability and near-triploid tumours showing the greatest level of instability.

2.3.2 Structural Chromosomal Instability: S-CIN

To determine S-CIN levels, the number of clonal breakpoints were enumerated for each case

(Table 2.1) based on previously published SKY analysis (Bayani et al. 2002 Supplementary

Table A), as well as the inclusion of three new specimens from two patients. SKY analysis of

OCA27A/B revealed similar tetraploid karyotypes between the primary and metastatic tumour

with complex aberrations. The composite karyotype for both OCA27A/B is summarized in

Table 2.3. In the case of OCA714, SKY revealed a highly rearranged karyotype, where almost

every chromosome was involved in a complex aberration (Table 2.3 and Figure 2.1C).

Numerous complex translocations involved more than three different chromosomes and showed

an extremely high number of breakpoints (n=83) (Table 2.1, Figure 2.1C) against a diploid

background.

The enumeration of the number of clonal aberrations and breakpoints showed that diploid and

near-diploid tumours possessed elevated DNA breakage events resulting in complex

translocations, inversions and deletions, than whole chromosomal gains and losses (Table 2.1,

Figure 2.3 and see Bayani et al., 2002 Supplementary Table A). Non-diploid tumours, on the

other hand showed fewer incidences of clonal DNA breakage events, but were associated with

copy number changes that were reflected by the wider range of chromosomes identified per cell.

Moreover, the findings of several duplicate structural changes in only the tetraploid and triploid

cases (Table 2.1 and see Bayani et al. 2002 Supplementary Table B), and illustrated by the

karyotypic changes in OCA27A/B including i(8)(q10)X2, i(14)(q10)X2,

der(19)t(19;20)(p11;p11)X2, der(22)t(11;22)(q14;p11)X2, suggests these rearrangements

occurred as an early event during the diploid stage.

Non-clonal rearrangements were a consistent feature for all the tumours, with diploid and triploid

tumours showing a slightly greater frequency of non-clonal aberrations over tetraploid tumours

(data not shown). However due to the differences in mitotic indices of each specimen and the

number of analyzable metaphases, a precise enumeration of the total number of non-clonal

rearrangements could not be accurately assessed.

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2.3.3 Multicentric Chromosomes

The presence of chromosomal aberrations such as multicentric chromosomes, are additional

hallmarks for genomic instability mediated by Bridge-Fusion-Breakage (BFB) events or critical

telomere shortening (Gisselsson and Hoglund, 2005; Murnane, 2006). Thus to determine whether

these structures were present in these tumours, FISH using pan-centromeric PNA probes was

performed and revealed the presence of dicentric chromosomes or ring chromosomes containing

multiple pan-centromeric sequences as both clonal and non-clonal changes. Of the tumours

analyzed, OCA8 was the only tumour that did not show evidence of multi-centric chromosomes.

The configurations of multicentromeric chromosomes varied and were identified as ring

chromosomes as in the case of OCA5, telomeric fusions as detected in OCA714 as well as more

typical dicentric configurations as in OCA21B and OCA27B (Figure 2.1D).

Table 2.1 Summary of N-CIN Indices, S-CIN Events and Centrosomal Aberrations in Primary Ovarian Carcinomas

Case Subtype Ploidy N-CIN Index

Percentage of cells with abnormal

Centrosomes

S-CIN Index

OCA714 Serous 2n- 7.0 20.0 83 OCA15A Endometrioid 2n- 10.7 5.5 58 OCA15B Endometrioid 2n- 12.7 14.0 53

OCA8 Pap.Serous 2n+ 21.0 20.0 21 OCA21A Serous 4n/2n 7.6 15.0 26 OCA21B Serous 2n 7.4 11.5 26 OCA27A Serous 4n- 24.9 16.5 29 OCA27B Serous 4n- 32.7 19.0 29

OCA3 Pap.Serous 3n 33.9 20.5 43 OCA5 Clear Cell 3n+ 54.9 24.0 15

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Figure 2.1. Genomic Instability Findings in Primary Ovarian Cancers by Interphase FISH, SKY and Centrosomal Analysis. A. Genomic Instability findings for OCA5. Shown is the histogram generated by interphase FISH analysis using centromere probes 2, 4, 7 and 8. The X axis represents the number of signals per cell and the Y axis shows the percentage of cells. Interphase analysis identified a large distribution of signals per cell outside the established near triploid genome. An example of the interphase FISH is shown using centromeres 7 (red) and 8 (green) illustrating the variability of centromere signals per cell. Centrosome staining using γ-tubuln (red) illustrate the presence of supernumerary centrosomes present during cell division and interphase. A representative SKY karyotype is also illustrated showing the presence of duplicate rearrangements as well as unique rearrangements. B. Genomic Instability findings for OCA27B. Shown is the histogram OCA27B. A representative interphase FISH image for centromeres 7 and 8 is also shown as well as centrosome staining showing a tripolar metaphase and supernumerary centrosomes. A representative SKY karytoype below illustrates the near tetraploid karyotype and duplicate markers. C. Genomic Instability findings for OCA714. The histogram for the diploid tumour OCA714 shows over 95% of cells with two signals for each centromere tested, with a representative interphase FISH image for centromeres 7 and 8 shown. Supernumerary centrosomes were also detected in a proportion of cells. The SKY karyotype below reveals the structural complexity of the genome, despite its relatively non-aneuploid status. D. Examples of multi-centric chromosomes using pan-centromeric FISH probes. Shown are examples from OCA5, OCA714, OCA21B and OCA27B found to contain multi-centric chromosomes, including the presence of a ring chromosome in OCA5 (inset) and telomere fusions in OCA714 (inset) as well as typical dicentric chromosomes in OCA21B and OCA27B (inset).

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Figure 2.2. Increasing Copy-Number Instability is Associated with Changes in Ploidy. Based on the generated CIN indices, increasing chromosomal instability was associated with changes in ploidy. The X axis represents the cases sorted in ascending order based on N-CIN indices while the Y axis represents the N-CIN indices. * OCA21A was found to possess a tetraploid karyotype, but contained many normal contaminating normal cells by karyotype analysis. OCA21B possessed a diploid karyotype, but also contained many normal contaminating cells by karyotype analysis

2.3.4 Centrosome Aberrations and Association with Increasing N-CIN

The percentage of cells with an aberrant centrosome was evaluated in all the specimens by

fluorescent immunohistochemistry using anti-γ-tubulin (Table 2.1). The most abnormal specimen

was OCA5 with a 24% of cells showing abnormal centrosome configuration or number (Figure

2.1A). The corresponding instability for this case, as previously discussed, was also the highest.

Although mitotic figures were not frequent, abnormal mitoses were detected and shown in Figure

2.1B for OCA 27B. OCA3 and OCA27A/B also exhibited frequent centrosome aberrations

(Table 2.1) with percentages of 20.5, 16.5 and 19.0 respectively.

While the observed range of cells containing centrosomal aberrations was between 5.5% - 24.0%

across all cases, increasing N-CIN indices were generally associated with the increasing

frequency of cells with aberrant centrosomes as summarized in Figure 2.3. One case, which

failed to follow the general trend, was OCA714, where the relative N-CIN was low (7.0),

although the incidence of centrosome abnormalities (20%) was high.

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2.4 Discussion The karyotypes of OCas are characterized by aneuploidy and structurally complex chromosomal

rearrangements (Bayani et al., 2002; Shridhar et al., 2001; Taetle et al., 1999a; Taetle et al.,

1999b). Chromosomal instability (CIN) has traditionally been assessed by the rate of whole

chromosomal copy number changes (Lengauer et al., 1997) typically by interphase FISH

analysis using centromere specific probes. With the exception of a few recent studies (Gorringe

et al., 2005; Grigorova et al., 2004; Muleris et al., 2008), the potential significance and extent of

structural changes have been cursory. In this study we investigated the relationship between the

observed karyotypic complexity and ploidy changes observed by structural instability, S-CIN;

and copy number-driven chromosomal instability, N-CIN; respectively; with features associated

with aberrant mitotic progression, in untreated sporadic OCas.

The interphase FISH study and the establishment of N-CIN indices revealed two distinct groups:

Group 1 possessed a low range of N-CIN scores (7.0 to 21.0), characterized as near-diploid; and

Group 2 which possessed a higher range of N-CIN scores (24.0 to 54.9), which were

characterized by near-triploid and near-tetraploid karyotypes. The trend observed by the N-CIN

indices was also seen, although more subtly, when the enumeration of abnormal centrosomes

were assigned to each case. The increasing frequencies of cells with abnormal centrosomes were

observed to be associated with the progression from a 2n cell complement to 2n±; then to 4n and

4n±, through to 3n± (Figure 2.3). The apparent transition to the tetraploidization event and

subsequent reduction to pseudo-tetraploidy and (near) triploidy, implicates errors in

chromosomal segregation and cytokinesis. This is in keeping with centrosome studies showing

the increased occurrence of mitotic segregation anomalies associated with centrosome

amplification (Doxsey, 2002; Pihan and Doxsey, 1999). However, the presence of centrosome

amplification does not always commit cells to multipolarity (Wang et al., 2004), since

centrosome coalescent functions may still remain intact resulting in normal bi-polar division

(Quintyne et al., 2005; Saunders, 2005). This may explain the findings of increased centrosome

number in some diploid tumours, but showing relatively low-level N-CIN. In this situation, the

presence of aberrant centrosome number or shape/configuration may simply be an early indicator

of a general dysregulation of mitotic pathways. Unlike aneuploid genomes (ie. 3n±, 4n±) which

possess sufficient genomic material to survive the losses of several whole chromosomes through

missegreation and multipolarity; gross missegregation of a diploid cell may yield daughter cells

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lacking the genomic material necessary for adequate survival into the next cell cycle.

Interestingly Rebacz et al. (Rebacz et al., 2007), recently identified griseofulvin as an inhibitor of

centrosomal clustering and demonstrated that induction in tumour cell lines cells containing extra

centrosomes, but maintaining bi-polar division, resulted in loss of centrosomal clustering and

multipolar metaphases leading to cell death. Treatment of normal cells with only two

centrosomes showed no affect on cell viability and centrosomal functioning. This suggests that

tumour cells exhibiting centrosomal amplification and maintaining centrosomal clustering

functions, observed by bi-polar division (or relatively low N-CIN), can be selectively targeted.

When the extent of S-CIN was investigated through the enumeration of clonal breakpoints

(resulting from translocations, inversions, deletions), we found diploid and near-diploid tumours

(OCA 8, OCA 15A/B, OCA718) possessed more occurrences of chromosomal breakage (Table

2.1). In particular, OCA714 displayed the greatest number of chromosomal breakages (n=83

breakages), amidst a diploid background (Table 2.1). These results suggested significant errors

in DNA repair occurred before gross changes in copy number. Although chromosome breakage

events (clonal and non-clonal aberrations) were observed as an ongoing event in the non-diploid

cases, we found many complex rearrangements occurring as duplicates in near-tetraploid or near-

triploid cases, indicating these rearrangements occurred prior to the tetraploidization of the

genome. The presence of non-clonal chromosomal rearrangements across all tumour specimens

indicated that errors in DNA repair was an ongoing event in the evolution of the tumour genome,

however it was noted that the diploid and triploid tumours showed slightly more non-clonal

changes which were generally more structurally complex. The presence of multicentric

chromosomes in all cases, except for OCA8 further supports the notion that impaired DNA

repair is an early event. Studies have shown the presence of such structures are mediated by the

BFB cycle, described as a cycle involving chromatid breaks and fusions triggered by dicentric

and ring chromosome rupture during anaphase resolution (Jones, 2005). This self-perpetuating

process gives rise to amplifications (HSRs, ladder amplifications), complex chromosomal

rearrangements, inverted repeats, interstitial deletions and large duplications (Lim et al., 2005;

Selvarajah et al., 2006). The occurrence of telomere fusions in some cases also implicate

telomere shortening as a mechanism for generating the observed genomic instability (Vukovic et

al., 2007) and has been previously described (Gisselsson and Hoglund, 2005). The most striking

result of the S-CIN study was the finding of numerous DNA breakage events that were

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independent of copy number changes. The limitation of such analyses however is in the ability

of obtaining sufficient metaphases that are analyzable from short-term primary cultures.

Interestingly, the analysis revealed paired (OCA15A/B) and primary/metastatic (OCA21A/B and

OCA27A/B) specimens showed similar N-CIN and S-CIN indices, suggesting the existence of a

putative clonal progenitor. These observations demonstrate that analysis of recurrent and

metastatic samples derived from the same patients, can provide insightful illustrations of the

adaptive potential of tumour genomes during disease progression and in response to treatment.

The molecular cytogenetic analysis of these tumours revealed several aspects of N-CIN and S-

CIN, namely that: 1) sporadic OCas can show a wide range of N-CIN; 2) increasing N-CIN and

changes in ploidy were associated with the increased occurrence of centrosome aberrations; and

3) that S-CIN occurs more frequently in the diploid stage of karyotypic evolution implicating a

more prominent role for impaired DNA repair pathways early in tumour progression. A re-

evaluation of our previous molecular cytogenetic data in primary osteosarcoma and cell lines

(Bayani et al., 2003; Selvarajah et al., 2007; Zielenska et al., 2001) have also shown this

interplay between N-CIN and S-CIN; and are recapitulated in current studies (unpublished

observations). Based on these findings, we suggest that both forms of chromosomal aberrations

can arise dependently and/or independently of each other, with one form more prevalent than the

other at different times within the evolution of the tumour genome (Figure 2.3). Thus a delicate

balance between N-CIN and S-CIN processes will influence the observed levels of structural and

numerical change in a tumour.

BRCA1 is strongly associated with OCa (Prat et al., 2005), and has been shown to act as a

regulator of DNA damage, repair and transcription, and likely has a role in maintaining genomic

stability (Deng, 2006). There is increasing evidence (reviewed by Deng, 2006), suggesting the S-

CIN damage response function of BRCA1, can act through RB and p53. Interestingly, BRCA1

mouse models in breast cancer (Deng and Wang, 2003; Weaver et al., 2002) have demonstrated

patterns of aneuploidy and centrosomal amplification similar to the N-CIN findings presented in

this study. In keeping with these observations, Bae et al. (Bae et al., 2005), demonstrated a role

for BRCA1 in regulating the expression of genes implicated in the mitotic spindle checkpoint,

chromosome segregation, centrosome function, cytokinesis, and the progression into and through

mitosis resulting in multi-nucleated cells and failed cytokinesis.

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The concept of tumour ploidy as an indicator of disease aggression and progression, in ovarian

cancer has been previously studied primarily by flow or image cytometric analyses (Kimmig et

al., 2002; Kristensen et al., 2003; Resnik et al., 1997). The extensive use of high-throughput

microarray analyses have shown the average genomic changes in these tumours include whole

and partial chromosomal gains and losses, as well as focal regions of amplification and deletion

(Bernardini et al., 2005; Prasad et al., 2008; Zhang et al., 2007). More recently, these regions of

genomic imbalance have been linked to the differential expression of microRNAs (miRNAs) -

now emerging as an important regulator of protein expression (Barbarotto et al., 2008; Calin and

Croce, 2007). However, while providing detailed information regarding the net copy-number

imbalances, the drawback of net/bulk genomic-based analysis is the inference of the tumour

ploidy and rearrangement status, in the absence of any parallel interphase or metaphase analyses.

This poses a limitation, since there is increasing interest in the karyotypic heterogeneity of

tumours as a measure of genome (in)stability, which is lost when performing such bulk-based

experiments (reviewed by Bayani et al., 2007). Such bulk-based studies appear to ignore the

potentially significant contributions of poly-clonality, which is often considered the source of

“noise” in array analyses and consequently filtered out statistically. Certainly as an extreme

example, it has been demonstrated that rare cell populations such as cancer stem cells, have a

profound and significant impact on disease recurrence and treatment resistance (Ailles and

Weissman, 2007; Dean et al., 2005), despite its relatively small population.

In summary, the results of our study have demonstrated both aspects of CIN, that is copy number

change and structural change, should be distinguished. Thus we propose to differentiate between

these two aspects of chromosomal instability as numerical chromosomal instability (N-CIN),

characterized by the rate of copy number changes; and structural chromosomal instability (S-

CIN), characterized by the extent of structural change and complexity. Indeed we have shown

the presence of S-CIN can occur independently of gross copy number or ploidy changes, and that

interphase FISH analysis, for copy-number enumeration, alone is not sufficient to classify a

given specimen as chromosomally unstable. The significance of such a distinction lies in the

mechanisms that mediate the observed changes, providing viable avenues for therapeutic

intervention.

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Figure 2.3. Interplay between N-CIN, S-CIN, Centrosomal Aberrations and Ploidy. Illustrated are the N-CIN, S-CIN and Centrosomal Aberrations histograms showing the relationship with CIN indices and ploidy. In this figure, S-CIN and centrosome aberrations were plotted relative to increasing N-CIN indices, which were previously shown in Figure 2.2. As shown here and in Figure 2.2, during the progression from 2n± to 4n± to 3n±, the N-CIN indices of the tumours tested increased (blue). When the S-CIN events were plotted, we observed more predominant S-CIN events in the diploid tumours (green) with lower N-CIN indices. Fewer structural events appear to occur in tetraploid and triploid cancers, while their N-CIN indices increase. A less prominent trend is seen for the frequency of supernumerary and abnormal centrosomes (red); however the cells with more centrosomal alterations were those that were near-tetraploid and near-triploid. The bottom figure illustrates the interplay between numerical and structural changes as it relates to changes in ploidy. At initiation, the diploid genome undergoes DNA-damaging events resulting in the accumulation of structural rearrangements. Such rearrangements result in accompanying changes in gene expression, affecting all pathways including the cell cycle and mitosis. Early indicators of cell cycle and mitotic dysregulation can include centrosomal amplification and low-leveled chromosomal polysomy, while maintaining overall diploidy (bi-polar division) during tumour expansion. At some critical point during tumour progression, the failure of cytokinesis occurs resulting in the doubling of the genome as well as centrosomes. This event provides the genomic content and mitotic machinery to undergo gross numerical changes yielding tumour genomes which can assume near-tetraploid or near-triploid content. While gross numerical changes are occurring, structural events are ongoing and may once again become the predominant genomic event once a stable ploidy level has been obtained. It can be predicted that another failed cytokinetic event can occur and the cycle repeated, since 6n± tumours and 5n± have been observed in many cancers.

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Table 2.2. Raw FISH Scoring Values

                           

  

Cen  0  1  2  3 4 5 6 7 8 >9  Percentage of Non‐Clonal Cells 

CIN Index 

Control  2  0  0  98  1 1 0 0 0 0 0  2      4  0  0.5  96.5 2 1 0 0 0 0 0  3.5  3.6    7  0  2.5  96  1 0.5 0 0 0 0 0  4      8  0  2.5  96  1 0.5 0 0 0 0 0  4   OCA3  2  0  0  25.5 24 48 2.5 0 0 0 0  52      4  0  8  74.5 14 2.5 0 0 0 0 0  24.5  33.9    7  0  0.5  16  20 64 0.5 0 0 0 0  37      8  0  1  19  78 1 0.5 0.5 0 0 0  22   OCA5  2  0  1.5  11.5 13 60 6 7 1 0 0  40      4  0  5  22  24 29 14 1 4 0 1  71  54.9    7  0  0.5  20  3 31 31.5 4.5 1 0 4.5  33.5      8  0  0.5  22  15 25 24 7 1 0.5 5  75   OCA8  2  0  0  15.5 80 4.5 0 0 0 0 0  20      4  0  3.5  43.5 34.5 16 1 0.5 0.5 0.5 0  22  21    7  0  2  92  3 3 0 0 0 0 0  8      8  0  0  21  66 9 0.5 3 0.5 0 0  34   OCA15A  2  0  1  92.5 2.5 3 0.5 0.5 0 0 0  7.5      4  0  3  85.5 2.5 9 0 0 0 0 0  14.5  10.7    7  0  0.5  89.5 3 5.5 0.5 1 0 0 0  10.5      8  0  1  89.5 3 6 0 0.5 0 0 0  10.5   OCA15B  2  0  4.5  89  3 3.5 0 0 0 0 0  11      4  0  3.5  83  4 8.5 1 0 0 0 0  17  12.7    7  0  0.5  90  7.5 2 0 0 0 0 0  10      8  0  0.5  87.5 5 7.5 0 0 0 0 0  13   OCA21A  2  0  0  85.5 1 13 0 0.5 0 0 0  1.5      4  0  0  85.5 5.5 6 0.5 1.5 0.5 0 0.5  8.5  7.6    7  0  4.5  88  1.5 5.5 0 0 0.5 0 0  6.5      8  0  8  80  4.5 6 0.5 0 0.5 0.5 0  14   OCA21B  2  0  4  91  3 2 0 0 0 0 0  7      4  0  3.5  81.5 4 9.5 0.5 0 0.5 0.5 0  9  7.4    7  0  4  82.5 4 8 0.5 0 0.5 0.5 0  9.5      8  0  1  89  2.5 7 0 0 0.5 0 0  4   OCA27A  2  0  0  45* 4 9 35 4.5 0 1 1.5  20      4  0  3  36.5* 22.5 32.5 3 0.5 0 1 1  31  24.9    7  0  0  48* 8 41.5 2 0.5 0 0 0  10.5      8  0  1  47* 9 15 12.5 6.5 6.5 1.5 1  38   OCA27B  2  0  1  10* 13.5 60 11.5 1.5 1 1.5 0  30      4  0  1.5  10* 18.5 60 9.5 0.5 0 0 0  30  32.7    7  0  1  14* 20 62 2.5 0.5 0 0 0  24      8  0  0  13* 12 39 20 10 3.5 0.5 1  47   OCA714  2  0  4  90  4.5 1.5 0 0 0 0 0  10      4  0  5  89  3.5 2 0.5 0 0 0 0  11  7    7  0  1  96  1 1 0 1 0 0 0  4      8  0  0  97  0.5 2 0 0 0 0.5 0  3   

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Shown are the tabulated data for each centromere (Cen) tested in control fibroblasts and primary tumours. Two hundred nuclei were enumerated and the values reflect the percentage of cells containing the specified numbers of signals per cell. Bolded values shaded in grey correspond to the number of signals corresponding to the dominant clone established by karyotype analysis (SKY). In situations where the percentage was equal to or exceeded 30%, this was considered another clonal population. In addition, cells containing a diploid count corresponding to normal contamination (*) as determined by karyotypic analysis were also excluded. All other cell populations were considered as non-clonal which were averaged across all centromere probes used and expressed as the N-CIN Index.

Table 2.3. Composite SKY Karyotypes of OCA27A/B and OCA714

Case SKY Karyotype

OCA27A/B 76-80<4n>,XXXX,der(X)t(X;8)(p22;q22),-1,del(1)(p34),der(2)dup(p11p15), der(2)t(2;17)(p11;11),del(4)(q12),der(5)t(1;5)(p11;q11),-6,i(8)(q10)X2,der(8) t(2;8)(p11;q11),-9,der(9)t(8;9)(q33;q11),der(12)t(12;22)(p11;q11),-13,-13, der(13)t(X;13)(q11;p11),der(13)t(8;13)(p12;p11),-14,i(14)(q10)X2,-16,-16, der(5;16)(q22;?),-17,-17,i(17)(q10),der(19)t(19;20)(p11;p11)X2,-20,-20,-22, -22,der(22)t(11;22)(q14;p11)X2{cp5}.

OCA714 41-46,X,-X,der(1)(13qter→13q21::dup1p21→p22→q11::5p11→5p14),der(2) t(2;3)(q14;?),der(2)(8qter→q21::2p16→q23::20?→20>::3?→3?),der(3)(3qter→3q10→3q10::5?→5?),i13(q10),der(4)(2?→2?::4p15→4q24::2?→2?::?→?),5,der(5) (14qter→14q21::5p12→15q34::8?→8?),der(6)t(6;11)(q10;q13),der(6)(pter→6q10::22?→22?::14q21→14qter),der(7)t(7;19)(q11;q10),der(7)del(p?)del(q?),+der(7) del(p21)del(q31),der(8)(8pter→8q10::13q21→13q34::6?→6?),i(9)(q10),der(9) t(6;9)(?q21:q10),der(10)t(8;10)(?;p14),der(11)(14q?→?::11p12→11q10::2?→2?),der(12)t(12;21)(q14;?),der(13)(3pter→p11::13p13→13q32::15q24→15q26), der(13)t(3;13)(p13;q10),der(14)(14p13→14q24::9?→?::14q24→14qter),-16, der(16)t(16;20)(q22;q13),-17,-18,der(19)(19pter→19q13.3::2q22→2q32),-22.

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Chapter 3

Impact of Cytogenetic and Genomic Aberrations of the Kallikrein Locus in Ovarian Cancer

This Chapter represents the published manuscript entitled “Impact of Cytogenetic and Genomic Aberrations of the Kallikrein Locus in Ovarian Cancer” Bayani J, Paliouras M, Planque C, Shan SJ, Graham C, Squire JA, Diamandis EP. 2008. Mol Oncol. 3:250-60.

J.Bayani contributed to the development and experimental design of the study and manuscript; established primary cultures and maintained cell lines; identified, extracted and labeled BAC clones for FISH; performed SKY analyses and interpretation; performed aCGH experiments and interpretation; and designed primers and performed PCR. M. Paliouras and C.Planque created KLK-specific probes by long-PCR and cloning; performed KLK-specific ELISA and contributed to the manuscript. SJ. Shan provided technical assistance with the KLK-specific ELISA. C.Graham provided technical support for cell line maintenance; BAC extraction, labeling and quality control. JA Squire contributed to the development and design of the study and manuscript; in addition to granting support. EP. Diamandis contributed to the development and design of the study and manuscript; in addition to granting support.

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3 Impact of Cytogenetic and Genomic Aberrations of the Kallikrein Locus in Ovarian Cancer

3.1 Introduction

Chromosomal changes, including copy number and structural rearrangements, are a hallmark of

many cancers, since they may significantly alter gene function. Classical cytogenetic and more

contemporary high-resolution integrative genomic strategies have identified genomic signatures

that are common and specific to many tumour types. These findings facilitated identification of

candidate tumour suppressor genes and oncogenes, as well as specific translocations as genomic

biomarkers. Serum biomarkers, such as prostate-specific antigen (PSA), also known as kallikrein

3 (KLK3), have already been shown to be important for screening and monitoring of prostate

cancer (Borgono and Diamandis, 2004). The overexpression of other members of the kallikrein

family in ovarian, breast and other cancers (Borgono et al., 2004) makes this protease family an

attractive source of new biomarkers. However, little is known about the relationship between

chromosomal and genomic aberrations of this gene family and protein expression. The human

tissue kallikreins (KLKs) are a family of secreted serine proteases. All 15 genes are located in

tandem on chromosome 19q13.4 and share, on average, 30–50% sequence identity (Borgono and

Diamandis, 2004; Borgono et al., 2004; Yousef and Diamandis, 2001). The KLKs are aberrantly

expressed in several cancer types; most strikingly, 12 KLKs are concurrently overexpressed in

ovarian cancer (Borgono and Diamandis, 2004). Accumulating evidence suggests that these

kallikreins are potential prognostic and diagnostic cancer biomarkers (Borgono and Diamandis,

2004; Diamandis and Yousef, 2002; Paliouras and Diamandis, 2007; Yousef and Diamandis,

2001) and that several may also be involved in cancer progression (Anisowicz et al., 1996;

Cohen et al., 1992; Ghosh et al., 2004; Magklara et al., 2003) In order to more thoroughly define

the roles of KLKs in cancer pathophysiology and clinical management, it is important to

investigate the mechanisms that influence their dysregulation in cancer. Somatically acquired

chromosomal aberrations are a major mechanism for gene activation. This is well-known in

hematologic malignancies, where specific and recurrent chromosomal translocations result in

oncogenic fusion transcripts.

Recent data indicate that common epithelial tumours, such as prostate cancer, may also harbor

recurrent translocations (Tomlins et al., 2006; Tomlins et al., 2005). Changes in gene expression

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may be regulated by epigenetic modifications, as well as by gene copy number and chromosomal

translocations (Mitelman et al., 2007). The karyotypes of many carcinomas including ovarian

(Bayani et al., 2002; Rao et al., 2002; Taetle et al., 1999a; Taetle et al., 1999b), breast (Adeyinka

et al., 2000; Pandis et al., 1995; Rummukainen et al., 2001) and prostate cancers (Beheshti et al.,

2000; Wang et al., 2005) are complex and are characterized by numerical and structural

rearrangements. However, it is clear that more advanced molecular cytogenetic and genomic

mapping methods may be required to delineate the precise chromosomal rearrangements that

may influence the expression of specific loci. Chromosome 19 and the 19q13 region, which

harbors the KLK family of genes, are a site of frequent rearrangements in neoplasms

(http://cgap.nci.nih.gov/Chromosomes/Mitelman) and a site for copy-number imbalances in

breast and ovarian cancer, as determined by metaphase (mCGH) and array comparative genomic

hybridization (aCGH) (http://www.progenetix.net/cgi-bin/pgHome.cgi). Due to the known

overexpression of the KLK genes in ovarian, breast and prostate cancers, the objective of this

study was to investigate the copy number and structural rearrangement status of the KLK locus

by fluorescence in situ hybridization (FISH) and/or high-resolution oligonucleotide array

comparative genomic hybridization (aCGH), in ovarian, breast and prostate cancer cell lines and

ovarian cancer tumours.

3.2 Materials and Methods

3.2.1 Patient Specimens and Cell Lines

The ovarian cancer cell line CAOV-3; the breast cancer cell lines T47D, MCF-10A, MDA-MB-

468, MCF-7, BT474; and the prostate cancer cell lines LNCaP and 22RV1 were obtained from

the American Type Culture Collection (ATCC, Manassas, VA) and maintained according to the

product specifications. Excess ascites fluid from ovarian cancer patients, collected for routine

diagnostic testing, was used for short-term cultures. All patient specimens were collected and

handled with protocols approved by the Research Ethics Boards of the University Health

Network and Mount Sinai Hospital (Toronto). The ascites cultures were maintained in alpha-

MEM, supplemented with 15% (v/v) fetal calf serum (Invitrogen Canada, Burlington, ON,

Canada), 1% (v/v) penicillin–streptomycin (Invitrogen) and 1% (v/v) L-glutamine (Invitrogen).

In addition, heparinized whole blood from a normal female donor was cultured with

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phytohemagglutinin for 72 h in a CO2 incubator in RPMI 1640 medium, supplemented with 20%

(v/v) fetal calf serum, 1% (v/v) L-glutamine and 1% (v/v) penicillin–streptomycin.

3.2.2 Fluorescence in situ Hybridization (FISH)

Cell line, patient and control lymphocyte cultures were prepared for cytogenetic analysis with

0.1 mg/mL Colcemid (Invitrogen) for 2–3 h, followed by osmotic swelling in KCl (0.075 M) and

fixed in methanol:acetic acid (3:1), as previously described (Bayani and Squire, 2004c). For

translocation studies, DNA from the BAC clones RP11- 76F7 and RP11-10I11 was extracted by

standard methods. These clones have been previously shown to include the following kallikrein

genes: KLK3, KLK2, KLK4, KLK5, KLK6, KLK7, KLK8, KLK9, KLK10, KLK11, KLK12, KLK13

and KLK14 (Yousef et al., 2000), (Figure 3.1). Other BAC clones used in this study include

RP11-716O8, RP11-451E12 and RP11-46I12. The extracted BAC DNA was directly labeled

with Spectrum Orange (Vysis/Abbott Laboratories, Des Plaines, IL) by nick translation using the

Vysis Nick Translation Kit, according to the manufacturer’s instructions. FISH to normal human

lymphocytes confirmed the genomic location to the 19q13.3/ 19q13.4 region. Approximately 300

ng of labeled probes were precipitated in excess human Cot-1 DNA (Invitrogen) and sonicated

salmon sperm DNA (Roche) and resuspended in a 50% formamide, 10% dextran sulfate and 2X

SSC hybridization buffer (DAKO, Mississauga, ON, Canada). The probes were heat-denatured

and hybridized to pepsin-treated and denatured normal human metaphase chromosomes, as

described previously (Bayani and Squire, 2004b). Following a rapid-wash technique consisting

of one wash in 0.4X SSC and 0.3% NP- 40 at 72oC for 3min, followed by a 5-min wash at room

temperature in 2X SSC and 0.1% NP-40, slides were mounted in DAPI/Antifade (Vector

Laboratories, Burlington, ON, Canada) and visualized with a Zeiss Axioskop fluorescence

microscope (Carl Zeiss Canada). For each cell line and patient specimen, metaphase preparations

were hybridized with both Spectrum Green-labeled, whole chromosome 19 paint (Vysis) and the

Spectrum Orange-labeled BAC probes and processed as described above. In each case, at least

10 metaphase spreads were analyzed. To determine the copy number and mapping status of

specific KLKs in a subset of tumours, PCR products of genomic fragments of KLK2, KLK4,

KLK6 and KLK13 were cloned into TOPO TA cloning vectors (Invitrogen). The DNAs were

extracted, labeled directly by nick translation and hybridized, as described above using either,

Spectrum Green, Spectrum Orange or the aqua-fluorescing-DEAC (Applied Biosystems).

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3.2.3 Spectral Karyotyping

Metaphase preparations (described above) were pre-treated and hybridized with SKY Paints

(Applied Spectral Imaging, Carlsbad, CA) and processed as described elsewhere (Bayani and

Squire, 2002). The images were collected using a Zeiss Axioplan fluorescence microscope (Carl

Zeiss Canada) and processed using the Spectral Karyotyping Image Capturing and Analysis

system (ASI). Karyotypic descriptions were assigned according to the guidelines of the

International System for Human Cytogenetic Nomenclature (Shaffer and Tommerup, 2005). For

sequential SKY to FISH experiments, the coverslip was removed and the previously SKYed

slide washed in 0.3% NP-40/0.1X SSC for 5 minutes and dehydrated in an ethanol series. The

slide was then denatured in 70% formamide/2X SSC at 72 ˚C for 3 minutes and dehydrated.

Heat-denatured probe, as described above, was applied to the slide and allowed to hybridize

overnight at 37 ˚C. The slides were washed as described above.

3.2.4 Array Comparative Genomic Hybridization (aCGH)

The Agilent Human Genome 244K microarray platform was used (Agilent Technologies,

Inc.,Palo Alto, CA, USA cat. G4411B) containing 236,381 unique 60-mer oligonucleotide

features. The array design included a total of 5045 features used as internal controls. Features

were based on the UCSChg17 (NCBI build 35, May 2004) Build. DNA was extracted by

standard phenol:chloroform methods, RNase-treated and resuspended in sterile RNase/ DNase-

free water (Sigma Canada). The direct labeling of DNAs, probe evaluation, clean-up,

hybridization to the array and post-hybridization washes were carried out using reagents and

equipment, according to Agilent protocols (http://www.chem.agilent.com). Briefly, at least 3 μg

of male genomic DNA reference (Promega) and 3 μg of test genomic DNA samples were

digested with AluI (12.5 units) and RsaI (12.5 units) (Promega), then verified using the DNA

1000 LabChip Kit and Agilent 2100 Bioanalyzer, according to the manufacturer’s instructions

(Agilent Technologies). Individual reference and experimental samples were then filtered using

the Qiaquick PCR Cleanup Kit (Qiagen Inc., Hilden, Germany). Labeling reactions were

performed with 2 μg of purified digested DNA using the Invitrogen Bioprime labeling kit,

according to the manufacturer’s directions in a 50-μL volume, consisting of a modified dNTP

pool of 120 mM each of dATP, dGTP, dCTP, 60 mM dTTP, and 60 mM of either Cy5-dUTP for

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the experimental sample or Cy3-dUTP for the 46, XY male reference (Perkin Elmer Life and

Analytical Sciences, Woodbridge, ON, Canada). ‘‘Dye-swaps’’ were carried out for all

experiments. Labeled targets were subsequently filtered using a Centricon YM-30 filter

(Millipore, Bedford, MA, USA). Experimental and reference targets for each hybridization were

pooled, mixed with 50 μg of human Cot-1 DNA (Invitrogen), 50 μL of Agilent 10X blocking

agent and 250 μL of Agilent 2X hybridization buffer. Prior to hybridization, the 500-μL

hybridization mixtures were denatured at 100˚C for 1.5 minutes and incubated at 37˚C for 30

minutes. The sample was applied to the array using an Agilent microarray hybridization chamber

and hybridization was carried out for 40 hours at 65˚C in a rotating oven (Robbins Scientific,

Sunnyvale, CA) at 20 rpm. The arrays were washed for 5 minutes at room temperature in 0.5X

SSPE/0.005% NLS, followed by 3 minutes at 37˚C in 0.1X SSPE/0.005% NLS. Slides were

dried using the Agilent drying solution and scanned using an Agilent 2565AA DNA microarray

scanner (Agilent Technologies). The CGH Analytics software version 3.4 (Agilent

Technologies) was used to analyze the aCGH data. Copy number aberrations were objectively

detected in replicate (dye-swap) experiments using an aberration calling method based on

computing significance scores for all genomic intervals. According to the Agilent CGH

Analytics software, a ratio of 0 represents the presence of two copies within the tumour/ test

genome, and a ratio of 1 represents the net gain of two additional copies (i.e. net gain of four

copies in the tumour/ test at 2n), hence the ratio of 0.5 suggests the net gain of one copy, based

on a normal diploid genome (i.e. three copies at 2n or five copies at 4n).

3.2.5 Measurement of KLKs by ELISA

We have previously described our ELISA methodologies for all KLKs. Briefly, the assays are

based on ‘‘sandwich’’-type ELISA principles with one antibody used for capture and one for

detection. More details on procedures and performance can be found elsewhere (Shaw and

Diamandis, 2007). All assays are highly specific, with no cross-reactivity from other KLKs. For

KLK analysis of cell lines, we used supernatants collected after 7 days of culture. No steroid

hormonal stimulation was used. Ascites fluid was analyzed after centrifugation, to remove cells.

Primary cultures of ascites cells were maintained for 1–7 days and the supernatants were

collected for analysis. Also, cell pellets were lysed and analyzed in a similar fashion. When solid

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tumour was available (one case, OCA19), tumour tissue was homogenized and KLKs extracted

in a lysis buffer containing NP-40 surfactant. All KLK values were expressed as μg/L.

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Figure 3.1 Summary of FISH findings of the KLK locus in cancer cell lines and ovarian cancer patients. (A) Schematic illustrating the BAC clones used in this study. (B) Summary of FISH studies in cell line and tumors. Chromosome 19 was analyzed using the paint probe WCP19 and BACs that cover most of the 325-kb KLK genomic region using dual color FISH. Closed circles indicate the presence of the KLK locus located at the resident chromosome 19q site. Open circles indicate the presence of the KLK locus associated with either an intra-chromosomally or interchromosomal structural rearrangement. Also indicated for each analysis is the ploidy established by chromosomal counts. MCF10A and 22RV1 did not show copy number changes or involvement of translocation. In 70% of cells, T47D also showed a normal diploid pattern, but 30% showed a whole chromosomal gain of 19. LNCAP showed four copies of chromosome 19 with four copies of the KLK locus, showing no net gain over ploidy, but two copies over a normal diploid cell. The remaining cell lines and tumors displayed net gains of the KLK locus by unbalanced translocation

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3.3 Results

3.3.1 Fine Structural Analysis of Chromosomal Rearrangements

Affecting 19q13 in Cancer Cell Lines

Following the confirmatory mapping of the RP11-76F7 and RP11-10I11 BAC clones to normal

human lymphocyte metaphase spreads (data not shown), a dual-color FISH strategy was used to

identify the presence of translocations of the 19q13 region in various cancer cell lines. For each

case, the combined Spectrum Orange-labeled 19q13 BACs (RP11-76F7 and RP11-10I11) were

hybridized with a Spectrum Green-labeled whole chromosome 19 painting probe (WCP19).

Rearrangements were identified when the red KLK FISH signals (and the corresponding WCP19

signal), were associated with chromosomes that did not contiguously hybridize with the whole

chromosome 19 paint. Using this strategy, numerical and structural rearrangements involving the

KLK locus were identified in the ovarian cancer cell line CAOV-3 and the breast cancer cell

lines, MDA-MB-468, MCF-7 and BT474 (Figure 3.1B). No structural changes involving 19q

were identified in the prostate cancer cell lines 22RV1 and LNCaP, consistent with previous

SKY analysis (Beheshti et al., 2000) or the breast cancer cell lines MCF10A and T47D. In

addition, for these cell lines, there was no change in copy number relative to the ploidy of the

genome. Thus, 22RV1 and MCF10A maintained two copies of the KLK locus within the diploid

genome and LNCaP possessed four copies of the KLK locus against a tetraploid genome.

However, T47D, with a pseudo-triploid karyotype, was shown to possess a population of cells

with an additional chromosome 19. Approximately 70% of cells enumerated possessed two

copies of the KLK locus within chromosome 19, with the remaining 30% of cells possessing

three copies of the KLK locus, due to a gain of chromosome 19. Sequential SKY and FISH

analysis was performed on CAOV-3, MDA-MB-468 and MCF7 (Figure 3.2) to determine the

position of rearrangements relative to the KLK genes and to assign copy numbers for the gene

cluster in each cell line. CAOV-3 was shown to be a hypertriploid line showing many complex

structural rearrangements (Figure 3.2A). Two KLK signals were located at their usual 19q

location, whilst another two KLK loci were involved in a duplicated complex, unbalanced

translocation involving chromosomes 19, 2 and 13. These paired rearrangements suggest that this

structural abnormality occurred prior to tetraploidization. Similarly, in MDA-MB-468, two KLK

signals were present at their usual chromosome 19q location, along with an aberrant KLK signal,

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due to an unbalanced translocation with chromosome 20 (Figure 3.2B). MCF-7, a hypotriploid

line, possessed three unbalanced translocations and only one apparently normal chromosome 19.

One of the rearrangements, the der(19)t(17::11::19), appears to be a derivative of the

der(19)t(11;19) and the other rearrangement is an unbalanced translocation with portions of 12q

translocated adjacent to the KLK locus (Figure 3.2C). The hypertetraploid BT-474 cell line

showed two chromosomes 19 containing the KLK locus, and three additional unbalanced

translocations, each containing the KLK locus, consistent with previously published SKY

findings (Kytola et al., 2000). The unbalanced rearrangements included a der(7)t(7:19),

der(10)t(10;19), and der(8)t(8::20::19) (data not shown).

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Figure 3.2. KLK status in cancer cell lines by sequential SKY and FISH. Shown are representative metaphases of CAOV-3 (A), MDA-MB-468 (B) and MCF-7 (C) by SKY and FISH using the KLK containing BACs (red). The affected chromosomes are indicated by arrows and shown in the inset.

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3.3.2 Chromosomal Rearrangements of 19q13 in Ovarian Cancer

Patient Samples

To determine whether the genomic and chromosomal alterations affecting the KLK and the

19q13 region in cancer cell lines were also present in ovarian cancer patient tumours, FISH

analysis was performed on cytogenetic preparations from three representative ascites fluid

samples from ovarian cancer patients. In all three samples, copy number imbalances of the KLK

region were detected (Figure 3.1B). In Patient 1, two copies of the KLK locus were identified at

the resident chromosome 19 site. An additional signal was detected in an unbalanced

rearrangement. For Patient 2, the presence of different cell populations based on chromosome

number (ploidy) were identified, however FISH identified the presence of at least one

chromosome 19 containing the KLK locus at its usual location, and the presence of up to two

additional KLK signals involved in translocations (Figure 3.3A, B). In addition, inserted portions

of chromosome 19 were detected throughout the karyotype. Spectral karyotyping (SKY) of this

patient sample revealed a karyotype possessing complex structural changes, as illustrated in

Figure 3.3C. Aberrations involving chromosome 19 were identified in at least six chromosomes

with one reflecting an intact 19, consistent with initial FISH results. Patient 3 also displayed

genomic heterogeneity, possessing at least two populations of cells, primarily an abnormal

diploid population (~30%) and a tetraploid population (~60%). The tetraploid population was

found to be similar to the diploid clone, suggesting that it arose from duplication of a diploid

progenitor following failed cytokinesis. FISH identified the presence of five KLK signals within

the tetraploid population, and three signals within the diploid population, yielding a net gain of

the KLK locus per cell. Four of the five signals were shown to result from two isochromosomes

19(q) (Figure 3.3D,E arrow) with the remaining signal localized at the resident chromosome 19q

(Figure 3.3F). Other additions and insertions of chromosome 19 material were also seen within

the genome (Figure 3.3F) and confirmed by SKY analysis (Figure 3.3G). In addition, five

primary, untreated ovarian cancers, previously described (Bayani et al., 2002), by SKY and

mCGH were analyzed to determine whether specific alterations leading to KLK imbalances and

structural rearrangements were also present (Figure 3.1B). The BAC RP11-716O8, mapping to

19q11/12, was used to identify affected chromosomes. In all five tumours, copy number

imbalances, either due to whole copy number gains of chromosome 19 or unbalanced

translocations were identified. Moreover, complex structural rearrangements, including a ring

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chromosome in OCA5 (Figure 3.3H, I) were shown to include the KLK locus, as well as the

detection of i(19)(q) in OCA19. In many cases, the heterogeneity of these short-term cultures

was revealed, with some tumours showing low-level copy number variation due to clonal and

non-clonal changes, as shown by the analysis of interphase nuclei (OCA21A; Figure 3.3J).

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Figure 3.3 Status of the KLK locus in patient ascites. In each case, WCP19 (green) was hybridized with BACs for the KLK locus (red) to metaphase preparations. (A–C) Patient ascites 2. (A and (B) show the presence of chromosome 19 material (green) dispersed throughout the genome (A), with one KLK signal mapping to the resident chromosome 19, with the remaining two copies involved in translocations (B) (arrows). (C) Integrative FISH and SKY analysis of affected chromosomes. Inverted DAPI banding in shows the banding pattern of the metaphase chromosomes hybridized with the SKY paints (left) with FISH of corresponding chromosomes showing chromosome 19 paint (green) and the KLK locus (red) from A/B (left). By SKY complex rearrangements involving two or more chromosomal partners can be seen through the change of color along the length of a contiguous chromosome. The RGB (red–green–blue) display color for chromosome 19 is bright green, with a normal chromosome 19 (top–bottom), followed by five abnormal chromosomes involved in structural rearrangements with other chromosomal partners. The corresponding FISH using the KLK and whole chromosome 19 painting probes confirmed that structurally rearranged chromosomes by SKY also included the KLK locus. (D–G) Patient ascites 3 shows the presence of a KLK signal at the resident chromosome 19 and the presence two isochromosomes 19q (i(19q)) (D,E). The net result is the gain of KLK copy number due to these isochromosomes. (F) Integrative SKY and FISH analysis revealed the involvement of chromosome 19 in translocations with chromosome 3, but without involvement of the KLK locus. (G) Representative SKY metaphase of patient ascites 3. (H,I) Sporadic and untreated primary cancer OCA5. Shown in red is the KLK locus and a BAC clone for the 19q12 region, shown in green revealing the net gain of the KLK locus through whole gains of chromosome 19 and the presence of a multi-centric (green) ring chromosome containing the KLK locus (red). (I) Integrative SKY and FISH of OCA5. SKY and FISH confirms that no other chromosomes are involved in structural rearrangements on the three chromosomes 19, but the possibility exists for other chromosomal fragments in the ring chromosome. (J) Interphase nuclei of OCA21A shows variability in copy numbers of the KLK locus from cell to cell.

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3.3.3 High-Resolution aCGH and KLK-specific FISH Analysis Confirms

Copy-Number Imbalances

Since the RP11-76F7 and RP11-10I11 BACs span the entire KLK family locus, aCGH using the

Agilent oligonucleotide platform was performed on the BT474 cell line to determine the copy

number status of each KLK. Array CGH of BT-474 showed a net gain of 19q12-19qter, with a

ratio of approximately 0.5 (Figure 3.4), suggesting an average copy number of five per cell.

These findings are consistent with initial FISH findings using the combined BAC probes (Figure

3.1, Table 3.1). To confirm the aCGH findings from this study and others (Neve et al., 2006), a

multi-color KLK-specific FISH strategy was followed in a subset of lines (Figure 3.4, Table 3.1).

Figure 3.4 illustrates the KLK-specific FISH findings in BT474, which confirm the copy number

gain and the movement of the entire KLK family locus in unbalanced translocations. Based on

these aCGH findings and to those published by others (Shinoda et al., 2007), it was also

determined that among the cell lines analyzed, no specific breakpoint locus within 4 Mb

centromeric to the KLK locus was identified, with most imbalances arising from whole copy

number alterations of 19q. FISH using BAC clones within the 19q13.3 and 19q11/19q12 regions

confirmed these observations (data not shown).

Table 3.1: KLK-Specific FISH: Analysis of Cell Lines

Specimen Number of Copies per Cell

KLK2 KLK4 KLK6 KLK13

NHL (2n) 2 2 2 2

MCF-10A (2n) 2 2 2 2

T47D (2n and pseudo 3n) 2

3

2

3

2

3

2

3

BT474 (4n+) 5 5 5 5

MDA-MB-468 (2n+) 3 3 3 3

NHL= Normal human lymphocytes; 2n= normal diploid genome.

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Figure 3.4 aCGH and FISH validation of copy number imbalances of the KLK locus in BT-474. (A,B) aCGH for chromosome 19. Regions of gain are highlighted in green with regions of loss indicated by red, and no net change in yellow. (B) Magnification of the KLK locus shows an average ratio approaching +0.5 indicating a net gain of the region. Due to the ploidy of the genome, the net gain suggests one copy over ploidy, thus1+ 4n =5 copies of the KLK locus. Side panels: FISH Validation of individual KLKs in BT474. A three-color FISH approach was used for KLK2 (green), KLK4 (red) and KLK6 (aqua) to BT474 metaphase spreads. Bottom panel: KLK13 validation in BT474. In this three-color experiment, KLK2 (green), KLK6 (blue) and KLK13 (red) were co-hybridized and also shown to co-localize and be present in copy numbers consistent with aCGH findings.

3.3.4 Protein Expression and Relationship to Copy-Number and

Structural Rearrangements in Cancer Cell Lines and Ovarian

Specimens

KLK protein levels were measured prospectively in all cell line tissue culture supernatants and

ascites fluid; and retrospectively, in the primary ovarian cancer specimens, either in cultured cell

supernatants and/or cell lysates, or in tumour extracts (Table 3.2). Of the breast cancer lines,

MCF-10A, a near normal breast epithelial cell line produced relatively very low levels of all

KLKs, with highest expression detected for KLK10. Similar findings were found for T47D, with

low protein levels for all KLKs tested, with the exception of KLK9 and KLK10. Neither line

possessed structural rearrangements involving the KLK locus, nor were there any copy number

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changes detected in MCF-10A. T47D, as discussed earlier, did possess a population of cells

(30%) containing the whole gain of chromosome 19. The remaining breast and ovarian lines

showed variable KLK protein expression (higher levels of KLK proteins are shown in brackets).

CAOV-3 (KLKs 5, 6, 7, 8, 9, 10); MDA-MB-468 (KLK 5, 6, 7, 8, 9, 10); BT-474 (KLK9);

MCF-7 (KLKs 6, 7, 9, 11); of which all were associated with copy number changes of the KLK

locus due to unbalanced translocations. ELISA results of the prostate cancer cell lines LNCaP

and 22RV1 revealed significant amounts of only KLKs 2, 3 and 9, consistent with previous

findings (Borgono and Diamandis, 2004; Yousef and Diamandis, 2001). The cytogenetic results

of these lines revealed normal copy number for 22RV1 and no net change of copy number for

LNCaP, based on the ploidy status of the cell. Moreover, no structural change of the KLK locus

was identified among these two lines. Analysis of prospective and retrospective ovarian

specimens showed that patient ascites 1 contained high levels of KLKs 1, 5, 6, 7, 8, 9, 10, 11;

patient ascites 2 contained high levels of KLKs 5, 6, 7, 8, 10, 11; patient ascites 3 contained high

levels of KLKs 5, 6, 7, 8, 10, 11 (italic and bold values indicate levels > 10 μg/L). ELISA results

of retrospective ovarian cancer samples were associated with high levels of the KLKs 5, 6, 7, 8,

9, 10 and 11. In all cases, copy number changes were associated with whole chromosomal gains

of chromosome 19 and/or unbalanced translocations of the KLK locus, suggesting that in breast

and ovarian cancers, copy number gains of the KLK locus, particularly through unbalanced

translocations, may be a mechanism for the high expression of KLKs 5, 6, 7, 8, 9, 10 and 11.

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Table 3.2 ELISA findings for cancer cell lines and patient specimens1

1. Data for KLKs 13, 14 and 15 are not shown, since the values were low in all cases. All values are in µg/L ± standard deviation. KLK12 was not measured; no ELISA assay available; specimen description is given under ‘methods’. 2. Indicated in brackets is the ploidy of the cell, as determined by cytogenetic analysis. 3. sup, supernatant

Specimen  KLK1  KLK2 KLK3 KLK4 KLK5 KLK6  KLK7 KLK8 KLK9 KLK10 

KLK11 

CaOV3(3n+)2  <0.2  <0.005 <0.005 <0.2 227±17 14.5±0.75 

3.9±0.27 12.4±0.47 3.2±0.3 50±16 <0.2

MCF‐10A(2n)  <0.2  <0.005 <0.005 <0.2 0.25±0.04 <0.2  0.4±0.06 <0.2 <0.5 2.8±1.3 <0.2T47D (2n+, pseudo 3n)     <0.2  <0.005 <0.005 <0.2 <0.05 <0.2  <0.2 0.2±0.02 13.8±2.6 0.7±0.04 0.08±0.02 MDA‐MB‐468 (2n+)  <0.2  <0.005 <0.005 <0.2 640±29 103±14  1.3±0.2 2.1±0.06 39±2.4 21±3.5 0.21±0.15 BT‐474 (4n+)  <0.2  <0.005 <0.005 <0.2 <0.05 <0.2  <0.2 <0.2 13±0.2 <0.05 0.11±0.02 MCF‐7 (3n‐)  <0.2  <0.005 <0.005 <0.2 <0.05 0.3±0.0

7 0.44±0.19 <0.2 13±2.5 <0.05 2.3±0.6 

LNCaP (4n)  <0.2  28±7.0 410±23 0.13±0.09 <0.05 <0.2  <0.2 <0.2 16±5.0 <0.05 <0.222RV1 (2n)  <0.2  5±2.5 68±50 <0.2 <0.05 <0.2  <0.2 <0.2 9±4.8 <0.05 <0.2Ascites   Patient 1 (2n)  6.0  <0.005 <0.005 <0.2 3.8 84  0.62 5.0 1.2 17 97Patient 2 (3n/6n)  0.75  <0.005 <0.005 <0.2 41 388  2.5 81 0.65 65 94Patient 3 (2n+/4n+)   1.3  <0.005 <0.005 <0.2 11.9 80  9.4 14 0.2 35 90OCA5 (3n+) ‐1 week culture sup3 

<0.2  <0.005 <0.005 <0.005 <0.05 0.4  0.3 0.2 5.8 0.5 <0.2

OCA8 (2n+) ‐1 week culture sup 

<0.2  <0.005 <0.005 <0.005 2.4 1.3  2.4 0.8 7.2 7.2 2.1

OCA19 (4n)  ‐tumor tissue extract 

<0.2  <0.005 <0.005 <0.005 0.6 0.1  <0.2 0.1 0.8 0.2 0.8

OCA21 (4n)  –pelleted cultured cancer cells 

<0.2  <0.005 <0.005 <0.005 17 16  34 11 2.2 30 64

Patient OCA27 (4n)  ‐1 week culture sup 

<0.2  <0.005 <0.005 <0.005 14 18  4.7 34 2.6 20 40

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3.4 Discussion

The human tissue KLKs are a family of secreted serine proteases which are often co-expressed

within the same tissues and thought to participate in enzymatic cascades (Borgono et al., 2004).

The KLKs are aberrantly expressed in several cancer types and they are potential biomarkers for

diagnosis, prognosis and prediction of therapy response (Borgono et al., 2007; Diamandis and

Yousef, 2002; Paliouras et al., 2007; Yousef and Diamandis, 2001). The regulation of gene

expression appears to be influenced by steroid hormones (Paliouras and Diamandis, 2007) and

epigenetic changes (Pampalakis et al., 2006; Pampalakis and Sotiropoulou, 2006; Sidiropoulos et

al., 2005); however, the mechanisms that mediate aberrant KLK expression in cancer have not

been entirely delineated. For many neoplasms, altered gene expression is linked to gene copy

number, as in the case of PTEN in prostate cancer (Yoshimoto et al., 2006) and EGFR in lung

cancer. To date, there has been no cytogenetic study that has examined the copy number status or

the possible translocation of KLK gene members, in association with their relative protein

expression in ovarian, breast or prostate cancer cell lines or tissues. Recently, Shinoda et al.

reported the overexpression of KLK5 in urinary bladder carcinoma cell lines, which were

associated with changes in gene copy number (Shinoda et al., 2007), and Ni et al. (Ni et al.,

2004) demonstrated by Southern blot and RT-PCR analysis that the overexpression of KLK6 in a

subset of ovarian cancers was associated with copy number gains. In this study, we analyzed the

copy number, positional mapping status and protein level of the KLK genes in breast, ovarian

and prostate cancer cell lines. Additionally, since many KLKs are overexpressed in ovarian

cancers (Borgono et al., 2007; Diamandis and Yousef, 2002; Yousef and Diamandis, 2001), a

cohort of primary tumours and ascites samples was subjected to detailed cytogenetic and

genomic analyses. FISH and SKY analysis of the eight ovarian cancers studied revealed that the

KLK region was frequently involved in copy number gains in the form of unbalanced

translocations. Similarly, FISH analysis of all three patient ascites samples and five ovarian

carcinomas identified the presence of copy number changes of the KLK locus due to unbalanced

rearrangements. Analysis of the KLK cluster by aCGH for the breast cancer cell line BT474

showed that imbalance of a large region of chromosome 19 was associated with KLK copy

number increases. To determine whether rearrangements took place within the KLK gene locus,

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cluster-specific FISH for KLK2, KLK4, KLK6 and KLK13 were used to confirm the contiguous

presence of five copies of each of these genes. These findings suggested that copy number

changes usually affect the entire KLK locus (approx. 300 kb) and that elevated expression is not

associated with genomic disruption of the cluster. Similar findings were revealed for the other

cell lines tested (Table 3.1). A major advantage of aCGH is the ability to ascertain the loci that

are involved in breakage, resulting in copy number change, revealing translocation breakpoints

(Selvarajah et al., 2006). Our aCGH data, as well as those of others (Neve et al., 2006), have

shown that the majority of the copy number transition sites in these cell lines occur centromeric

to the KLK locus at 19q12, a region closely associated with the centromere. This suggests that in

a subset of tumours the copy number and unbalanced translocation events likely occur at the

level of the whole chromosome arm. FISH analysis of this region confirmed these observations,

however, also implicate the presence of more subtle genomic aberrations between 19q12 and a 4-

Mb region centromeric to the KLK locus, in BT474, MDA-MB-468 and CAOV-3 cells. These

data draw attention to the possibility that chromosomal alterations, or gains in the genomic

vicinity of the KLK locus, may indirectly contribute towards elevating the transcript and protein

levels of kallikreins. However, we consider it less likely that a classical oncogenic fusion event

involving the KLK cluster itself is taking place, since no common translocation partner or

recurrent rearrangement was observed in the cell lines and ovarian tumours with 19q13

alterations. KLK protein analysis revealed a relationship between copy number gains of the KLK

locus and KLK gene family members. The most consistent data were seen in the prospective

analysis of ascites fluid, whereby high levels of KLKs 5, 6, 7, 8, 10 and 11 were observed (Table

3.2). Similar elevations were also detected in CAOV-3 and MDA-MB-468 (Table 3.2),

suggesting that copy number changes may influence the expression of genes in this region. The

retrospective analysis of cryopreserved primary ovarian cancers and primary cultures also

revealed elevations in KLK protein, although at lower levels, in comparison to freshly obtained

specimens. Human chromosome 19, although being one of the smallest chromosomes, is the

most gene-rich (Venter et al., 2001) and possesses the highest number of CpG islands (Craig and

Bickmore, 1994). The lack of constitutional rearrangements involving chromosome 19 appears

to correlate to the finding of selection against translocations involving gene-dense chromosomes

(Bickmore and Teague, 2002). Since chromosome 19 possesses a high CpG content,

transcriptional regulation by methylation-dependent mechanisms may be compromised as a

result of the translocation event. Indeed, studies have demonstrated the transcriptional silencing

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of genes due to the introduction of heterochromatin. Changes in the spatial orientation of

chromosomal domains within the nucleus may also influence the extent of gene transcription

(Cremer et al., 2000; Cremer et al., 2004; Sadoni et al., 1999). Furthermore, the extent of

chromatin compaction at the translocation site/region could enhance or hinder the access of

transcription factors or other regulatory binding sites, offering an attractive mechanism for the

aberrant expression of KLKs. The translocation event can also alter the proximity or sensitivity

to hormone responsive elements, which may have important implications for breast, ovarian and

prostate carcinogenesis. The current lack of KLK6 knock-out murine models precludes our

ability to ascertain whether KLK6 has “driver” characteristics (Stratton et al., 2009) or is merely

a “passenger”. In conclusion, in this preliminary analysis of cell lines and patient specimens, we

have demonstrated that there is an association between copy number changes and changes in

protein expression of certain KLK family members, and that these copy number imbalances

primarily result from unbalanced translocations. Moreover, we have shown in this initial survey

that these events are extremely common and may be characteristic of late stage and progressive

ovarian cancer, however additional studies are necessary. The careful examination of early stage

disease and other malignancies will also be required, to determine whether these genomic

changes have prognostic or predictive value.

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Chapter 4

Genomic Instability and Copy-Number Heterogeneity of Chromosome 19q, Including the Kallikrein Locus, in

Ovarian Carcinomas

This Chapter represents the published manuscript entitled “Genomic Instability and Copy-Number Heterogeneity of Chromosome 19q, Including the Kallkrein Locus, in Ovarian Carcinomas” by Bayani J, Marrano P, Graham C, Zheng Y, Li L, Katsaros D, Lassus H, Butzow R, Squire JA, Diamandis EP. 2011. Mol. Oncol. 1:48-60.

J.Bayani contributed to the development and experimental design of the study and manuscript; identified, extracted and labeled BAC clones for FISH; performed FISH to TMAs and paraffin sections; performed and interpreted all IHC. P.Marrano provided technical support for IHC. C.Graham provided technical support for BAC extraction, labeling and quality control. Y.Zheng and L.Li performed statistical analyses. D.Katsaros, H. Lassus and R.Butzow provided patient specimens and clinical information. JA. Squire contributed to the development and design of the study and manuscript. EP. Diamandis contributed to the development and design of the study and manuscript; in addition to granting support.

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4 Genomic Instability and Copy-Number Heterogeneity of Chromosome 19q, Including the Kallikrein Locus, In Ovarian Carcinomas

4.1 Introduction

Ovarian carcinoma (OCa) is a heterogeneous disease, reflected by distinct histopathological

subtypes, and clinically characterized according to stage and grade (reviewed by Bast et al.,

2009). A growing body of evidence suggests distinct molecular pathways may mediate the

development and characterization of low-grade and high-grade serous tumours; among them, the

relative differences in genomic instability and mutations of PTEN and KRAS in low-grade

tumours; and mutations and aberrant expression of TP53 among high-grade tumours (Bast et al.,

2009; Shih Ie and Kurman, 2004). Genomically, OCas exhibit complex numerical and structural

chromosomal alterations (Bayani et al., 2002; Pejovic et al., 1992a; Pejovic et al., 1990; Pejovic

et al., 1992b; Shridhar et al., 2001; Taetle et al., 1999a; Taetle et al., 1999b; Thompson et al.,

1994a; Thompson et al., 1994b), and are prone to chromosomal instability (CIN) (Bayani et al.,

2008b; Gorringe et al., 2005). CIN is mediated by various mechanisms (reviewed by Bayani et

al., 2007), resulting in chromosomal abnormalities which can be broadly classified as numerical

chromosomal instability (N-CIN) or structural chromosomal instability (S-CIN) (Bayani et al.,

2007). The consequences of gross numerical and structural chromosomal rearrangements

include changes in gene sequence, gene/locus-specific dosage (Frohling and Dohner, 2008), as

well as epigenetics (Sadikovic et al., 2008); which all contribute to the pathogenesis of this

disease. Chromosome 19q is a site of frequent rearrangements

(http://cgap.nci.nih.gov/Chromosomes/Mitelman), and copy number imbalances

(http://www.progenetix.net/) in many neoplasms, including OCa. Classical cytogenetic analyses

(Taetle et al., 1999a; Taetle et al., 1999b), and more recent molecular cytogenetic findings

(Micci et al., 2009; Tsao et al., 2001) suggest that the non-random numerical and structural

alterations of chromosome 19 play an important role in ovarian carcinogenesis.

In a small pilot study, we recently investigated the role of copy-number of the kallikrein (KLK)

genes in a series of cancer cell lines and primary ovarian cancers shown to possess KLK6

overexpression (Bayani et al., 2008b). In all primary ovarian cancer specimens, a net gain of the

entire KLK locus (19q13.3/4) was identified, either by the whole gains of chromosome 19 or

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through unbalanced translocations involving breakpoints centromeric to 19q13.3/4, suggesting a

role of copy-number in the observed elevated KLK6 protein levels. The KLKs are a family of 15

genes and 1 pseudogene located in tandem on chromosome 19q13.3/4 (Yousef et al., 2000),

which encode trypsin-like serine proteases. They cleave a variety of substrates including MMPs,

IGFBPs, fibronectins and collagens (Borgono and Diamandis, 2004) and seem to be involved in

many of the pathways deemed the hallmarks of cancer (Hanahan and Weinberg, 2000, 2011).

Due to the widespread and successful use of PSA (also known as KLK3) as a biomarker for the

detection and monitoring of prostate cancer (Lilja et al., 2008), the potential for other KLKs as

biomarkers in ovarian (Kim et al., 2001; Kyriakopoulou et al., 2003; Shan et al., 2007; Yousef et

al., 2003b) and other cancers has been actively pursued (Inoue et al., 2010; Li et al., 2009b;

Mavridis and Scorilas, 2010; Nathalie et al., 2009; Pettus et al., 2009; Planque et al., 2005;

Shinoda et al., 2007). The observed differential expression of the KLK genes has also fueled

numerous studies investigating the mechanism regulating their expression, including hormone

stimulation (Lai et al., 2009; Shan et al., 2007; Shaw and Diamandis, 2007) differential

methylation (Pampalakis et al., 2008; Pampalakis et al., 2009; Pampalakis and Sotiropoulou,

2006; Sidiropoulos et al., 2005) and microRNAs (miRNAs) (Chow et al., 2008; White et al.,

2010a), with few studies investigating copy-number (Bayani et al., 2008b; Ni et al., 2004;

Shinoda et al., 2007). These variable findings suggest that the KLKs may be regulated by

multiple mechanisms.

Among the KLK gene family, KLK6 has been demonstrated to be a promising biomarker for OCa

(Borgono et al., 2006; Hoffman et al., 2002; Obiezu et al., 2001; Shan et al., 2007; White et al.,

2009b; Yousef et al., 2003b). Recent studies by our group (Shan et al., 2007) have shown up to

57-fold higher KLK6 protein expression in cancer specimens compared to normal tissues; with

univariate and multivariate survival analyses showing that patients deemed KLK6-positive, are at

an increased risk of relapse, in comparison to KLK6-negative patients. A significant association

with progression-free survival (PFS) and overall survival (OS) was also demonstrated.

While the assessment of KLK6 as a novel biomarker in OCa continues, the mechanisms leading

to the observed protein overexpression of KLK6 are still unclear (Borgono et al., 2006). Our

previous findings (Bayani et al., 2008b) suggest that copy number may contribute to such

overexpression, and the numerical and structural complexity of OCa karyotypes influences copy

number change. Thus, in this investigation, we have expanded the study to specifically

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investigate the overall frequency of KLK locus-specific copy-number changes, and to correlate

these findings to KLK6 expression in a series of untreated serous ovarian carcinomas.

Furthermore our experimental design enabled us to examine the frequency of structural and

numerical CIN on 19q, and its implications for understanding ovarian cancer pathogenesis.

4.2 Materials and Methods

4.2.1 Patient Specimens

Seventeen formalin-fixed paraffin embedded (FFPE) sections were obtained from the

Department of Gynecology and Obstetrics, University of Turin, Italy; and an ovarian tissue

microarray (TMA), consisting of 102 patients was obtained from the Department of Pathology at

the Helsinki University Central Hospital (C2 block); and were collected with consent according

to the guidelines of research ethics boards from all institutions. All specimens were classified

histologically as serous tumours derived from patients naïve to chemotherapy. The tissues were

fixed according to standard procedures and the TMA was constructed as previously described

(Lassus and Butzow, 2007). FFPE normal ovarian tissues were obtained from US Biomax

(Rockville, MD, catalog number HuFPT076).

4.2.2 Fluorescence in situ Hybridization (FISH)

For copy-number and instability studies, the following bacterial artificial chromosome (BAC)

clones mapping to three regions of chromosome 19q were obtained from The Centre for Applied

Genomics (Toronto, Canada): 19q12 (RP11-888D2, RP11-197B9, RP11-716O8 and RP11-

1107F24, 19q13.2 (RP11-67A5, RP11-264N23, RP11-825A10), and 19q13.3/4 (RP11-288H1,

RP11-10I11, RP11-615L12). The 19q13.3/4 overlapping BACs span the entire KLK locus to

include KLK1 at the most centromeric end to span all KLK gene family members; and including

CD33 at the telomeric end. To confirm that KLK6 was represented in the overlapping BAC

clones RP11-10I11 and RP11-615L12, KLK6- specific PCR was performed using the following

primer set designed using Primer 3 (http://frodo.wi.mit.edu/): KLK6 forward

GGGGTCCTTATCCATCCACT and KLK6 reverse cagtcgcatctgctgttcat, to yield a 90bp band.

Using the Illustra Hot Start Master Mix (GE Healthcare Life Sciences), 100 ng of BAC DNA

were tested, in addition to proper positive and negative controls. DNA extracted from the BAC

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clones was labelled with either Spectrum Green (Vysis/Abbott Laboratories, Des Plaines, IL),

Spectrum Orange (Vysis/Abbott), or blue-fluorescing DEAC (Applied Biosystems) by nick

translation using the Vysis Nick Translation Kit (Vysis/Abbott), according to the manufacturer’s

instructions. FISH to normal human lymphocytes (Bayani and Squire, 2004b) confirmed the

genomic location of all BACs. All 19q12 overlapping clones (RP11-888D2, RP11-197B9,

RP11-716O8 and RP11-1107F24) were labeled with Spectrum Green. Similarly, all 19q13.2

clones were labeled with DEAC; and all 19q13.3/4 clones which span the KLK locus, were

labeled with Spectrum Orange. Approximately 300 ng of each labeled DNA were precipitated in

excess Human Cot-1 DNA (Invitrogen, Canada), sonicated salmon sperm DNA (Roche, Canada)

and resuspended in a 50% formamide/10% dextran sulfate/2X SSC hybridization buffer (DAKO,

Mississauga, ON, Canada) to a final volume of 35μl (Bayani and Squire, 2004b). All tissues

were baked overnight at 56oC. The following day, the slides were de-waxed in xylene and

dehydrated in 100% ethanol and pre-treated prior to co-denaturation and hybridization with the

probe cocktail. The following day, the slides were processed in a wash of 0.3% NP-40/0.4XSSC

for 2 minutes at 72oC and a wash of 0.1%NP-40/2XSSC for 5 minutes at RT. The slides were

rinsed in 1XPBS, mounted in a DAPI/Antifade medium (Vectashield/ Vector Laboratories

Canada), and visualized at 60X with a Zeiss Axioskop fluorescence microscope (Carl Zeiss

Canada).

4.2.3 Measurement of Chromosomal Instability

For each case/core at least 50-100 tumour nuclei were scored for the presence of each signal. A

normal diploid cut-off was established using normal tissues, resulting in at least 65% of cells

with 2 signals for each probe and 35% containing 1 signal (due to tissue sectioning). Greater

than 40% of cells with 1 signal indicated the presence of a population with a net loss. The

presence of additional populations were noted when the frequency of positive cells exceeded

10% per population (or cumulatively exceeding 10% if several populations were detected).

Using a modification of chromosomal instability index previously published (Bayani et al.,

2008a) the designation of low, medium or high chromosomal instability (CIN) was assigned

when 1 or 2 populations of cells were identified (low), 2-3 populations of cells were identified

(medium), or greater than 3 populations of cells or amplification (high) were detected.

Numerical changes of 19q: Whole copy-number changes of 19q were identified when the

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percentage of signals per loci along 19q were similar. Structural changes of 19q: Evidence of

structural rearrangement on 19q was identified when the percentage of signals per cell, for each

of the loci tested, were discordant by greater than 10%.

4.2.4 Immunohistochemistry (IHC)

Immunohistochemical analyses using a mouse monoclonal (1:150), and rabbit poly-clonal

(1:1000) antibody with high specificity for KLK6 was performed as described previously by

Petraki et al. (Petraki et al., 2001; Petraki et al., 2006). IHC for the detection of both wildtype

and mutant p53 protein was performed using a monoclonal antibody (DO-7, 1:25 Dako Canada,

Mississauga, ON.) according to the manufacturer’s instructions. Since wildtype p53 possesses a

short half-life, its presence in minute amounts is below the level of detection for IHC (Vojtesek

et al., 1992). Thus, the accumulation of p53 protein detected by IHC reflects aberrant p53 forms.

Briefly, tissue sections were dewaxed in xylene, dehydrated in 100% ethanol and rinsed in water.

Antigen retrieval was performed by pressure cooking in 0.01M citrate buffer for 30 minutes.

The slides were rinsed in water and treated for 30 minutes in 0.3% hydrogen peroxide/methanol.

The slides were rinsed in water and processed using the ImPRESS Detection Kit (Vector

Laboratories). Following a 20-minute incubation with normal horse serum, both KLK6

antibodies diluted in DAKO diluents, or p53 antibody, were applied to the slides for 1 hour at

room temperature. The slides were incubated with the appropriate secondary anti-rabbit and

anti-mouse antibodies for 30 minutes, washed, treated with the polymer reagent, and detected

with standard DAB solution for 5 minutes. The slides were stained with hematoxylin, and

finally mounted in Permount (Fisher Scientific Canada). No antibody controls were also

performed. The slides were scanned using the Aperio ScanScope (Aperio Technologies Inc, San

Diego, CA) for visualization and image acquisition. Standard hematoxylin and eosin-stained

slides were also scanned for visualization. KLK6 positivity was assessed according to overall

staining intensity (1=low, 2=medium, 3=high) compared to surface epithelial cells from a normal

ovarian tissue control and in “no antibody” controls. Similarly, p53 expression was assessed

against the surface epithelial cells from a normal ovarian tissue control and “no antibody”

controls, and scored according to staining intensity and cell distribution (1=normal; negative/low

staining with <20% cells positive; 2=moderate; moderate staining with >50% cells positive;

3=strong; strong positivity with >50% cells with strong staining).

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4.2.5 Statistical Methods

The associations between outcome measurements: 19q-rearrangement, KLK copy-number,

chromosomal instability and p53 immunohistochemical intensity and clinical parameters were

examined by ANOVA, two-sample t-test and Chi-square test, as deemed appropriate. Cox

survival analysis was applied to disease free survival (DFS) and overall survival outcomes both

univariately and multivariately. In the multivariate survival analyses, only the clinical parameters

that were shown to be independent were included for adjustment. All statistical analyses were

performed using SAS software (version 9.2; SAS Institute). Two-sided P-values for statistical

significance were set at <0.05.

4.3 Results

4.3.1 Patient Cohort

The entire patient cohort consisted of 119 serous ovarian carcinoma tissues from patients naïve

to chemotherapy. The final number of patients available for detailed analysis was 81, due either

to poor tissue quality or lack of sufficient clinical data. The median age at diagnosis was 56.2

years. Tumours were staged according to the International Federation of Gynecology and

Obstetrics (FIGO) criteria and graded according to Day et al. (Day et al., 1975). Thirteen

tumours were classified as stage I, 6 as stage II, 50 as stage III and 16 as stage IV, with one

unknown stage. Four cases were grade 0 and included samples with evidence of malignancy, 22

were grade I, 15 were grade II and 40 cases were grade III. Response was classified as

complete, partial, progressive or stable at follow-up within 6 months. The median follow-up

time was 72 months.

4.3.2 The KLK Locus (19q13.3/13.4) is Subject to Frequent Copy-

Number Alterations and Chromosomal Instability

All 19q bacterial artificial chromosome (BAC) clones used in this study were individually FISH-

mapped to normal human metaphase chromosomes (data not shown) to ensure the proper

mapping location and absence of cross-hybridization to other chromosomes. The multi-colour

FISH strategy is illustrated in Figure 4.1A, with 19q12 probes, near the centromere labeled in

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green; 19q13.2 probes, approximately 11MB telomeric to the 19q12 BACs labeled in blue; and

the KLK locus at 19q13.3/4, a further 10MB telomeric to the 19q13.2 BACs, labeled in red.

KLK6-specific PCR confirmed the presence of KLK6 in BAC clones RP11-10I11 and RP11-

615L12 (Figure 4.1A). Of the 119 specimens available, 81 were successfully analysed by FISH

for copy-number changes of the KLK locus. Across all cases, 14 showed 2 copies of the KLK

locus (17.3%); 24 showed a net loss of the locus (29.6%), 41 (50.6%) showed a net gain of the

KLK locus, and 2 (2.5%) showed high-level amplification (Figure 4.1B, Table 4.1). These

findings indicated that 82.7% of cases exhibited copy-number alterations (ie. gain, loss, or

amplification) at the 19q13.3/4 locus, containing the KLK genes. Moreover, because three

genomic regions on 19q were enumerated (19q12, 19q13.2 and 19q13.3/4), we could assess

whether such copy-number gains or losses were due to whole gains of 19q or through structural

rearrangements along 19q. The presence of structural alterations on 19q were identified when

the percentage of signals per cell, per 19q locus scored, was discordant. In contrast, when there

were similar frequencies of signals per cell across all genomic loci, the 19q arm was considered

intact (Figure 4.1C). Of the 14 cases which showed two copies of the KLK locus per cell, 4

(28.6%) were involved in a structural rearrangement on 19q and the remaining 10 cases (71.4%)

showed no indication of structural rearrangement of 19q. Of the 24 cases showing a net loss of

the KLK locus, 18 (75%) were a consequence of structural rearrangements of 19q, while the

remaining 6 (25%) were due to the entire loss of the 19q arm. Twenty-five of the cases showing

net gains of the KLK locus (61.0%) were a result of structural aberrations of 19q, while 16 of the

cases (39%) showed gains of the KLK locus through whole 19q arm copy-number gains. Finally,

the two cases which possessed amplification of the KLK locus, resulted from structural

aberrations of 19q (100%). Examples of these different classes of genomic changes are

illustrated in Figure 4.1C.

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Figure 4.1. KLK Copy-Number Analysis of Serous Ovarian Carcinomas by FISH and Immunohistochemistry. A. Multi-colour FISH strategy. Shown is a normal chromosome 19 with the mapping location of the BAC clones used and PCR confirmation for the presence of KLK6 in BAC clones RP11-1OI11 and RP11-615L12. Also shown are “+” (positive) and “-” (negative) control lanes. The 19q12 locus is identified by green signals, 19q13.2 by blue signals and 19q13.3/4 (KLK locus) by red signals. B. Left panel: Distribution of copy-number changes of the KLK locus in 81 cases shows that the KLK locus (19q13.3) is subject to frequent copy-number changes which are dominated by gains/amplifications. Right panel: Classification of the presence or absence of 19q rearrangement reveals that 19q is frequently subject to structural aberrations. C. Representative images of IHC for p53 and KLK6 including hematoxylin/eosin (H&E), together with corresponding FISH images depicting each of the 4 classes of copy-number changes of the KLK locus and the two rearrangement classes. The 19q12 locus is identified by green signals, 19q13.2 by blue signals and 19q13.3/4 by red signals. The corresponding histograms show the range in number of signals per cell for each genomic locus. Rearranged cases were identified when the frequency of signals for each of the genomic loci were discordant. Cases were identified as not possessing rearrangements on 19q when the frequency of signals across all genomic loci tested per cell were similar.

It was evident from these results that the copy-number changes were often heterogeneous. In

many cases a predominant clone was present and accompanied with lesser populations, all

contributing to the net gain or loss of the locus. The extent of numerical CIN of the KLK locus

was assessed by enumerating the number of populations (greater than 10% in frequency). In all

cases in which only 2 copies of the KLK locus were identified, low-level CIN was observed; that

is, there was presence of one or two populations of cells. Similarly, cases showing the net loss of

the locus were also characterized primarily as low-level CIN. However, unlike 2-copied and net-

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deleted cases, the observed net gains of the KLK locus were characterized predominantly by

moderate-CIN (up to 3 populations of cells) and high-level CIN (>3 populations), particularly

among those cases where 19q rearrangements were detected. In those net KLK gained cases

resulting from whole 19q arm gains, the level of CIN ranged from low to moderate (Table 4.1).

The correlation of KLK locus copy number to clinical parameters is summarized in Table 4.1.

Across age, stage, grade, response to treatment, KLK6-specific expression and extent of

chromosomal instability, statistical significance was shown only for chromosomal instability.

Low instability was associated with cases showing only 2 copies of the KLK locus, with copy-

number gains and losses associated with increasing instability (p<0.001).

Table 4.1: Clinical characteristics of patients stratified by KLK copy number:

KLK Copy Number 2 Copies (n=14) Net Loss (n=24) †Net Gain (n=43) p-value* Age (years) Mean (SD) 57.1 (16.1) 51.5 (10.9) 58.6 (13.3) 0.113 Stage I/II 2 (14%) 7 (29%) 10 (24%) 0.582

III/IV 12 (86%) 17 (71%) 32 (76%) Grade 0/I 6 (43%) 8 (33%) 12 (28%) 0.575

II/III 8 (57%) 16 (67%) 31 (72%) Response Complete 11 (79%) 16 (67%) 20 (48%) 0.271

Partial/Stable 2 (14%) 6 (25%) 15 (36%) Progression 1 (7%) 2 (8%) 7 (17%)

Instability Low 14 (100%) 18 (75%) 14 (33%) <0.001 Mod/High 0 (0%) 6 (25%) 29 (67%)

p53-IHC Normal 5 (36%) 7 (29%) 10 (24%) 0.672 Mod/Strong 9 (64%) 17 (71%) 32 (76%)

KLK6-IHC Low/Mod 4 (29%) 11 (46%) 12 (28%) 0.301 Strong 10 (71%) 13 (54%) 31 (72%)

* p value from t-test and Chi-square test.

† Net Gain includes amplified cases

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4.3.3 Structural Alterations of Chromosome 19q are Associated with

Age and Grade

When the tumours were classified as either possessing 19q rearrangements or without

rearrangements, 61.7% (50/81) possessed 19q rearrangements and the remaining 38.3% (31/81)

had no apparent gross structural rearrangements (Figure 4.1B). The ranges of copy-number

gains and losses for the genomic loci analysed were primarily low. Losses were typically

characterized by the loss of one copy from the primary clonal populations; and gains were

characterized by one to three extra copies compared to the primary clonal population. As

mentioned previously, the KLK locus was amplified in 2 of the rearranged cases; and 19q13.2

amplification was seen in 9/50 (18%) of rearranged cases (or 9/81 (11.1%) of total cases). In 6

of the 9 cases, 19q13.2 was amplified in 100% of cells scored, whereas the remaining 3 cases

showed amplification of 19q13.2 as an additional population to polysomy for the locus,

indicating genomic heterogeneity. Amplification of 19q12 was also detected in 2/50 (4%) of

rearranged cases, but as additional populations to polysomy for the locus (Figure 4.1C).

When 19q rearrangement status was correlated to the same clinical parameters (Table 4.2), grade

was found to be significant (p=<0.001), with 58% of the low-grade tumours showing no 19q

rearrangements. This was in contrast to the high-grade tumours in which 84% of cases possessed

19q rearrangements. Age was also significant (p=0.003), revealing that 19q rearrangements

occurred more frequently in older patients.

Table 4.2: Clinical characteristics of patients stratified by 19q-rearrangement:

19q-Rearranged No (n=31) Yes (n=50) p-value*

Age (years) Mean (SD) 50.8 (13.3) 59.6 (12.3) 0.003 Stage I/II 10 (33%) 9 (18%) 0.119

III/IV 20 (67%) 41 (82%) Grade 0/I 18 (58%) 8 (16%) <0.001

II/III 13 (42%) 42 (84%) Response Complete 20 (65%) 27 (55%) 0.618

Partial/Stable 7 (23%) 16 (33%) Progression 4 (13%) 6 (12%)

* p value from t-test and Chi-square test.

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4.3.4 Serous Ovarian Carcinomas Show a Range of KLK6 and p53

Expression

Since KLK6 protein overexpression has been observed in ovarian carcinomas,

immunohistochemical analysis was performed, revealing a range of KLK6 overexpression.

Normal ovarian surface epithelium expressed KLK6 at a barely detectable level by IHC,

consistent with previous findings (Ni et al., 2004; Petraki et al., 2001). Positive controls were

derived from formalin fixed paraffin embedded (FFPE) sections with accompanying KLK6

quantification by ELISA from tissue extracts, as previously described (Shan et al., 2007). All 81

patient specimens showed positive cytoplasmic staining for KLK6, ranging from low-level to

very high expression (Figure 4.1C). The comparison of the overall intensity of KLK6 staining to

KLK-locus copy-number showed no clear relationship (Table 4.1, Figure 4.1C). Strong

expression of KLK6 was found in the majority of cases with two copies of the locus (10/14); as

was also the case for specimens with a net gain of the locus (31/43). Cases with a net loss of the

locus showed roughly equal numbers of specimens with low/moderate and strong staining (11/24

and 13/14, respectively). In the two cases which were shown to be amplified, strong cytoplasmic

staining was shown in both cases.

Immunohistochemical analysis of p53 was successful in 80/81 specimens. Normal ovarian tissue

showed negative to very weak reactivity for p53 with less than 20% of cells showing weak

positivity, corresponding to the normally short half-life of wildtype p53 (Vojtesek et al., 1992).

Twenty-two cases (27.5%) were identified as having reactivity consistent with normal p53

protein expression; whereas abnormal p53 was identified in 22 cases (27.5%) showing moderate

staining, and in 36 cases (45%) showing strong reactivity, indicating abnormal p53

accumulation. When p53 IHC was correlated to age, stage, grade, response, KLK copy-number,

chromosomal instability and KLK6 IHC results, only grade was found to be statistically

significant (p=0.040) with higher grade tumours associated with stronger p53 staining.

4.3.5 Univariate and Multivariate Analyses

When hazard ratios (HR) estimated from the Cox regression model for disease-free survival

(DFS) were calculated at both univariate and multivariate levels (Table 4.3), response to

chemotherapy was highly significant (complete response HR=1; partial response/stable disease

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HR=3.19; progression, HR = 6.96, p=<0.001) for both univariate and multivariate analyses.

Similar data were seen for stage and grade (Table 4.3). Age was significant only in univariate

analyses (p=0.026). Although hazard ratios based on KLK copy number (2 copies, HR =1; Net

loss, HR = 0.78; Net gain, HR = 1.2, p=0.441), or KLK6 IHC (low, HR=1; Mod/High, HR 1.9,

p=0.557) were not statistically significant on univariate and multivariate analyses, Kaplan-Meier

curves for DFS displayed a trend for those patients with a net loss of the locus to have a

relatively better DFS over patients with 2 copies or gains of the locus (Figure 4.2A). Hazard

ratios for DFS revealed that 19q rearrangement status was marginally significant (no

rearrangement, HR=1; 19q rearrangement, HR=1.68; p=0.078) in univariate analysis, but not

significant in multivariate analysis. Furthermore, p53 IHC was also not significant on univariate

(p=0.458) or multivariate (p=0.554) for DFS.

For overall survival (OS) (Table 4.4), chemotherapy response was highly significant in both

univariate and multivariate analysis (p<0.001), as was grade (p<0.001 and p=0.002, respectively)

and age (p<0.001 and p=0.021, respectively). Stage was only significant in univariate analysis

(p=0.009). Weak associations based on 19q-rearrangement and p53 protein status were seen

only in univariate analyses (p=0.068 and p=0.077, respectively). Neither KLK copy-number or

KLK6-specific IHC were significant for OS on univariate or multivariate analyses However, as

demonstrated for DFS, Kaplan Meier curves (Figure 4.2B) for 19q-rearrangement, KLK copy-

number and p53 expression, show a trend for better overall survival in patients who had no

rearrangements in 19q; who had a net loss of the KLK locus or with normal p53 expression.

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Figure 4.2. Kaplan Meier Curves for Disease Free Survival (DFS) and Overall Survival (OS). Shown are the Kaplan Meier curves (unadjusted) based on 19q rearrangement status, KLK copy number status, KLK locus chromosomal instability and p53-IHC. For both DFS and OS, patients with no 19q rearrangement had a better outcome than those with disruptions of 19q. Similarly, a trend could be seen for better DFS and OS in patients with a net loss of the KLK locus.

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Table 4.3. Hazard ratio (HR) estimated from the Cox regression model for disease-free survival (DFS).

Univariate Multivariate* HR P HR P

Age (years) 1.02 0.026 1 0.792 Stage I/II 1 0.002 1 0.011

III/IV 3.65 3.13 Grade 0/I 1 0.001 1 0.001

II/III 3.58 4.19 Response Complete 1 <0.001 1 <0.001

Partial/Stable 3.19 3.29 Progression 6.96 6.64

KLK6-IHC Low 1 0.557 1 0.879 Mod/High 1.19 0.95 19q-rearranged No 1 0.078 1 0.756

Yes 1.68 0.9 KLK Copy-Number 2 Copies 1 0.441 1 0.812

Net Loss 0.78 0.78 Net Gain 1.2 0.94

Instability Low 1 0.302 1 0.677 Mod/High 1.33 1.13

p53-IHC Normal 1 0.458 1 0.554 Mod/Strong 1.27 1.22 * Multivariate Cox model for 19q-rearranged, KLK copy-number, chromosomal instability, and p53-IHC includes: stage, grade and chemotherapy response. Mod = moderate .

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Table 4.4. Hazard ratio (HR) estimated from the Cox regression model for overall survival (OS). Univariate Multivariate* HR P HR P

Age 1.04 <0.001 1.03 0.021 Stage I/II 1 0.009 1 0.237

III/IV 2.94 1.75 Grade 0/I 1 <0.001 1 0.002

II/III 5.22 4.03 Response Complete 1 <0.001 1 <0.001

Partial/Stable 2.89 2.83 Progression 13.82 11.92

KLK6-IHC Low 1 0.498 1 0.574 Mod/High 1.23 1.2 19q-rearranged No 1 0.068 1 0.909

Yes 1.75 1.04 KLK Copy-Number 2 Copies 1 0.109 1 0.718

Net Loss 0.59 0.7 Net Gain 1.26 0.93

Instability Low 1 0.163 1 0.512 Mod/High 1.49 1.22

p53-IHC Normal 1 0.077 1 0.545 Mod/Strong 1.88 1.25 * Multivariate Cox model for 19q-rearranged, KLK copy-number, chromosomal instability, and p53-IHC includes: age, grade and chemotherapy response. Mod = moderate.

4.4 Discussion

Ovarian carcinoma is an aggressive disease with a poor outcome. The search for biomarkers, not

only for diagnosis, but also for better prediction of outcome and response to therapy is of

paramount importance. Although CA-125 is used for monitoring disease recurrence following

surgery and chemotherapy, it lacks specificity as an early detection diagnostic biomarker (Bast et

al., 2009). Potential new biomarkers for OCa include members of the kallikrein gene family

(KLK) which maps to 19q13.3/4 (Borgono et al., 2004; Yousef et al., 2000). Among them,

overexpression of KLK6 protein, as detected by tissue extract enzyme-linked immunosorbent

assay (ELISA) has been demonstrated to be an important prognostic marker (Shan et al., 2007).

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The mechanisms leading to the observed KLK6 overexpression are still unclear (Borgono et al.,

2004), though hormone stimulation (Lai et al., 2009; Lawrence et al., 2010; Shan et al., 2007;

Shaw and Diamandis, 2007), differential methylation (Pampalakis et al., 2008; Pampalakis et al.,

2006; Pampalakis et al., 2009; Sidiropoulos et al., 2005) and regulation by microRNAs

(miRNAs) (Chow et al., 2008; White et al., 2010b) have been investigated. Based on our

previous preliminary findings (Bayani et al., 2008b), we have further investigated the role of

KLK copy-number in 81 untreated serous ovarian cancers using a three-colour FISH approach

targeting loci on 19q, including the KLK locus, which harbours KLK6, and performing KLK6-

specific immunohistochemistry (IHC) to investigate the relationship between KLK copy number

and KLK6 protein expression. In this way, we were also able to determine whether such copy-

number imbalances were the result of whole chromosomal gains of 19q, or disruption of 19q; as

alluded to in early and more recent studies implicating abnormalities of chromosome 19

(particularly 19q13), in ovarian cancer pathogenesis (Micci et al., 2009; Taetle et al., 1999a;

Taetle et al., 1999b; Thompson et al., 1994a; Tsao et al., 2001).

Of the 81 tumours, we found that the KLK locus, using BAC clones confirmed to contain KLK6,

was subject to changes in copy number (gain, loss or amplification) in 82.7% of cases, with

gains/amplifications (53.1%) being more common over losses (29.6%). Only 2.5% of cases

showed high-level amplification and 17.3% harboured only 2 copies. Consistent with our

previous cytogenetic data (Bayani et al., 2008b), we found that the gains/amplifications of the

KLK locus resulted primarily from structural rearrangements of 19q. The same observation was

made for tumours showing net loss of the KLK locus, and this was in contrast to tumours where

only two copies of the KLK locus were identified. Many carcinomas (Al-Romaih et al., 2003;

Bayani et al., 2003; Maire et al., 2009; Vukovic et al., 2007) including ovarian, show karyotypic

heterogeneity both numerically and structurally (Bayani et al., 2002; Bayani et al., 2008a;

Gorringe et al., 2005) and such on-going chromosomal instability (CIN) and genomic variability

is believed to drive tumour progression (Li et al., 2009a). Indeed, our analyses revealed CIN and

copy-number heterogeneity of the KLK locus (p<0.001), illustrated by the observed number of

subpopulations of cells with greater or fewer KLK locus signals, in addition to the primary clonal

population (Figure 4.1). Hazard ratio and Cox regression analyses for DFS based on KLK copy-

number status showed no significant correlation in univariate and multivariate analysis (p=0.441

and p=0.812, respectively). However, Kaplan-Meier analysis suggested a trend for increasing

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DFS among those cases with net loss of the KLK locus; whereas, patients with 2 copies, or those

who were shown to possess gains/amplifications had similar DFS.

KLK6-specific IHC analyses revealed overexpression in all cases ranging from weak to intense

immunoreactivity. Although there was no correlation between copy-number and protein

expression (p=0.301), there was a trend suggesting that those tumours with a gain of the locus

were more likely to overexpress KLK6. When only two copies of the locus were identified, the

majority of cases expressed KLK6 levels with equal frequencies of moderate to low expression.

This suggests that copy-number could contribute somewhat to protein expression, but is not the

primary regulatory mechanism, as exemplified by the cases with two copies, or loss of one copy,

but still exhibiting strong KLK6 expression; or cases with gain/amplification of the locus and

relatively moderate expression. No relationship with respect to DFS (p=0.557), was observed

based on KLK6 IHC expression, in keeping with the results of another study (Kountourakis et

al., 2008), but in contrast to Shan et al., 2007, suggesting ELISA-based measurements of protein

levels may be more informative. Thus, the results of the KLK6 immunohistochemical and copy-

number analyses point to additional mechanisms contributing to KLK6 protein overexpression

Interestingly, when tumours were stratified as 19q rearranged or non-19q rearranged, 61.7% of

cases showed structural abnormalities at 19q; which were strongly associated with tumour grade

(p<0.001). Fewer low-grade tumours possessed structural rearrangements of 19q (16%) as

compared to their high-grade counterparts (84%). Moreover, correlation to DFS and OS showed

that patients with 19q rearrangements had a shorter DFS and worse OS, which were of borderline

significance (p=0.078 and p=0.068 respectively) in univariate analyses. These combined

findings provide some credence to the two pathway model for ovarian tumourigenesis, where

type I ovarian tumours are classified as low-grade, slow-growing, with low-level and increasing

CIN; and type II tumours, are classified as higher grade, more rapidly growing, with high CIN,

and with a much lower 5-year survival (Ricciardelli and Oehler, 2009; Shih Ie and Kurman,

2004). Our study demonstrated that tumours classified as low-grade (grade 0/I) had fewer

structural and range of numerical abnormalities of chromosome 19 than high-grade tumours

(grade II/III) (Table 4.2); supporting the notion that changes on chromosome 19q, or the

molecular pathways influencing the observed numerical and structural changes on chromosome

19q, may play a role in OCa pathogenesis. Indeed, Type I tumours more frequently possess

mutations in KRAS and PTEN, and are subject to greater microsatellite instability (MSI), whereas

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Type II tumours frequently possess mutations in p53 and BRCA1/2, and are subject to CIN. Our

p53 IHC findings are consistent with this model, with high-grade tumours more frequently

showing overexpression for p53 over low-grade tumours (p=0.040). The contribution of TP53 to

chromosomal instability has been well-documented (Fukasawa, 2005; Tomasini et al., 2008).

Thus, the concurrent findings of genomic instability and p53 overexpression, in high-grade cases

is consistent with previous reports (Blegen et al., 2000; Ceccaroni et al., 2004; McManus et al.,

1996). Although some studies have shown correlation of p53 expression and overall poor

outcome and survival (Lassus and Butzow, 2007), our analyses showed only a weak association

with poor overall survival (p=0.077), likely due to our smaller sample size; or choice of

antibodies, since the DO-7 clone detects both mutant and wildtype p53.

To our knowledge, this is the first report suggesting that both structural and numerical

abnormalities of chromosome 19 distinguish low-grade and high-grade cancers. Further analyses

on a larger cohort of tumours using the specific markers distinguishing Type I and Type II

tumours (Ricciardelli and Oehler, 2009; Shih Ie and Kurman, 2004) is warranted to determine

whether the preliminary observations seen here are associated. Indeed, early classical

cytogenetic studies have demonstrated OCa karyotypes are numerically and structurally

complex, with a propensity for the non-random gain and loss of specific chromosomes, and the

apparent preferential sites of chromosomal breakage (Taetle et al., 1999a; Taetle et al., 1999b),

including 1p1*, 1q1*, 1p2*, 1q2*, 1p3*, 1q3, 3p1*, 1q4*, 6q1*, 6p2, 6q2, 7p1, 7p2*, 11p1*,

11q2*, 12p1, 12q2*, 13p1, and 19q1 (where the asterisk (*) denotes the major band associated

with the chromosomal rearrangement). These early studies have implicated a role for

chromosome 19 in OCa pathogenesis (Pejovic et al., 1992a; Pejovic et al., 1990; Taetle et al.,

1999a; Taetle et al., 1999b; Thompson et al., 1994a; Thompson et al., 1994b). Improvements in

molecular cytogenetic analyses by way of multi-colour karyotyping (Bayani and Squire, 2004a),

readily revealed the cytogenetic complexity and heterogeneity of OCas (Bayani et al., 2002; Rao

et al., 2002), further refining copy-number changes and preferential sites of chromosome

breakage. Based on bulk high-throughput DNA analyses of OCas (derived largely from aCGH

experiments), chromosome 19 has a relatively equal frequency of copy-number gain and loss of

approximately 10% (reviewed by Gorringe et al., 2009), with allelic imbalances showing more

preferential gain in most of 19p and 19q (Gorringe et al., 2007). Very little information,

however, regarding the nature, frequency and heterogeneity of structural alterations leading to

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local copy-number imbalances for chromosome 19 exists on a per cell basis. Moreover,

deciphering the link between the observed global patterns of ploidy change and chromosomal

instability with clinical factors, are becoming increasingly more relevant (Swanton et al., 2009).

The chromosome 19 landscape was recently revisited by Micci et al., 2009 who undertook an

extensive microdissection and reverse-FISH approach to identify the critical regions and loci on

chromosome 19 that are subject to copy number alterations in ovarian carcinomas; showing the

frequent involvement of chromosome 19q13 in unbalanced translocations both intra- and

interchromosomally. We have extended this observation to examine the 19q13 sub-bands:

19q13.2 and 19q13.3/4, demonstrating genomic instability and copy-number heterogeneity exists

within this major band. As well, Tsao et al. (Tsao et al., 2001) virally immortalized human

ovarian surface epithelial cells to identify early chromosomal events leading to tumourigenesis,

and found that all immortalized cell lines showed gains of 19q13, and the whole gain of

chromosome 19 prior to immortalization, re-enforcing the idea that chromosome 19 plays an

important role in early tumourigenesis and in maintaining tumourigenicity. A number of genes

subject to copy number changes, in addition to the KLK locus, have also been identified on 19q

(Thompson et al., 1996), including ACTN4 (Yamamoto et al., 2009), AKT2 (Bellacosa et al.,

1995; Nakayama et al., 2006) and cyclin E (Etemadmoghadam et al., 2009). The amplification

of AKT2 (19q13.2) has been demonstrated to be an important indicator of poor prognosis

(Bellacosa et al., 1995) and implicated in the pathogenesis of high-grade ovarian cancers

(Nakayama et al., 2007; Nakayama et al., 2006). Nine of our cases (11.1%) showed

amplification of the 19q13.2 region, which we speculate may reflect AKT2 amplification, since

one of the BACs used for the 19q13.2 region (RP11-67A5), includes AKT2. Moreover, our

observations in these chemotherapy-naïve tumours indicate that these changes likely result from

on-going tumourigenic processes rather than induced changes from radiation or

chemotherapeutic agents.

Chromosome 19 is unique, possessing the greatest number of genes, gene families, CpG island

density, and also contains a high level of repeat elements despite being among the smallest

chromosomes (Grimwood et al., 2004). Thus, it is not surprising that defects in DNA repair

pathways associated with OCa (ie. BRCA1/2) may be manifested in chromosomes with greater

potential for damage or epigenetic modification when protective pathways have been

compromised. Additionally, of the over 700 microRNAs (miRNAs) listed in the miRBase

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(http://www.mirbase.org), chromosome 19q also harbours the highest number of miRNAs, with

greater than 60 of the approximately 90 miRNAs located on chromosome 19 mapping to 19q

(specifically at 19q13.4). miRNAs, which have been shown to be important regulators of gene

expression (reviewed by Barbarotto et al., 2008 and implicated in ovarian cancer (Nam et al.,

2008; Zhang et al., 2006; Zhang et al., 2008) can potentially regulate hundreds of gene targets,

thus the consequences of genomic changes at 19q can have far reaching implications than simply

the expression of genes mapping to chromosome 19. In fact, the KLK findings in this study

suggest that other mechanisms, in addition to copy-number, influence the observed

overexpression of KLK6, which may include miRNAs. Recently Chow et al., 2008,

demonstrated KLK6 and KLK10 were regulated by miRNAs in a breast cancer cell line.

Preliminary findings by our group (Bayani et al., In press) show that the expression of a number

of miRNAs predicted to regulate KLK6, are decreased in ovarian cancer cell lines and primary

tumours as compared to normal ovarian miRNAs. Certainly, the role of miRNAs to protein

expression may explain the lack of correlation between KLK copy-number and KLK6 protein

expression. Since the primary mechanism of gene regulation by miRNAs is mediated by the

inhibition of translation, rather than the degradation of RNA (Barbarotto et al., 2008); the

observed increases in KLK6 RNA transcript influenced by copy-number and/or combinations of

the aforementioned mechanisms, may not reflect the actual level of protein ultimately expressed.

This could explain the observation for tumours with gains of the KLK locus and relatively

moderate/low KLK6 IHC overexpression, wherein these tumour cells may continue to express

those miRNAs regulating (and inhibiting) KLK6 translation. Similarly, for tumours with the

observed net loss of the KLK locus and strong KLK6 protein expression, the miRNAs regulating

KLK6 may be diminished in these cells, permitting translation of the up-regulated KLK6

transcript. Interestingly, a few of these miRNAs predicted to regulate KLK6, as well as other

KLKs, are also located on chromosome 19q.

In summary we have demonstrated that the KLK locus at 19q13.3/4 is subject to high genomic

instability and copy number heterogeneity, mediated by structural rearrangements of 19q.

Moreover, structural rearrangements on 19q are associated with tumour grade, and may be

associated with, or a marker of the differential pathogenesis distinguishing low-grade and high-

grade serous cancers. While KLK6 copy number, through the enumeration of the KLK locus,

does not appear to directly regulate the observed KLK6 overexpression per se, it is one

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contributing factor. Lastly, the unique genomic properties of chromosome 19 suggest that the

observed instability of 19q and the genes mapping to this location, including KLK gene members

warrant further investigation.

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Chapter 5

Modulation of KLK6 Protein Expression in the Ovarian Cancer Cell Line OVCAR-3 by miRNA Let-7a

This Chapter represents data from the accepted manuscript entitled “Copy-Number and Expression Alterations of miRNAs in the Ovarian Cancer Cell Line OVCAR-3: Impact on Kallikrein 6 Protein Expression”. Bayani J, Kuzmanov U, Saraon P; Fung, WA, Soosaipillai A, Squire JA and Diamanids EP Clinical Chemistry published November 7, 2012 as doi:10.1373/clinchem.2012.193060 (Publish-Ahead-of-Print).

J.Bayani contributed to the development and experimental design of the study and manuscript; maintained cultures; identified, extracted and labeled BAC clones for FISH; performed, analysed and interpreted all FISH, SKY and aCGH; performed miRNA/mRNA extraction and miRNA profiling; performed transient transfections and KLK-specific-ELISA. U.Kuzmanov contributed to the development and experimental design of the study; assisted with transient transfections and quality control. P.Saraon performed and provided technical support PCR validation of miRNA profiling results. WA.Fung assisted with KLK-specific ELISA and quality control. A.Soosaipillai provided technical support and production of KLK-specific antibodies; JA. Squire contributed to the development and design of the study and manuscript. EP. Diamandis contributed to the development and design of the study and manuscript; in addition to granting support.

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5 Modulation of KLK6 Protein Expression in the Ovarian Cancer Cell Line OVCAR-3 by miRNA Let-7a

5.1 Introduction

Ovarian cancer (OCa) continues to be the leading cause of death from a gynecological

malignancy for North American women. The largely asymptomatic nature of the disease

contributes significantly to its lethality. There currently are no markers for early diagnosis or for

screening (Bast et al., 2009), therefore identifying novel biomarkers to generate meaningful

proteomic signatures is essential. One promising biomarker comes from the Kallikrein (KLK)

gene family of serine proteases (Borgono and Diamandis, 2004). Kallikrein 6 (KLK6) is

frequently overexpressed at the mRNA and protein levels in tissues and fluids derived from OCa

patients; and implicated in a variety of normal physiological processes. These include

extracellular matrix remodeling, maintenance of protein clearance in the brain, and activating

inflammatory pathways and signaling cascades (reviewed by Bayani and Diamandis, 2011). The

overexpression of KLK6 in OCas at the mRNA and protein levels has been associated with poor

prognosis and the experimental evidence shows this overexpression enhances malignancy

(Bayani and Diamandis, 2011). Prognostically, several studies have consistently shown that the

combination of KLK6 with CA-125 enhances their diagnostic power (El Sherbini et al., 2011;

Shan et al., 2007; White et al., 2009a). Recently, we demonstrated that the KLK locus at

19q13.3/4, to which all members of the KLK gene family contiguously map to, are subject to

copy-number changes in OCa (Bayani et al., 2011; Bayani et al., 2008b). Our cytogenetic

studies (Bayani et al., 2008b), revealed that the entire locus was involved in the copy-number

change, rather than individual members. Our initial observations have recently been confirmed

by array studies (Beroukhim et al., 2010), showing a consistent copy-number ratio across the

entire locus in several cancer types, including OCa. The highly aneuploid and structurally

abnormal OCa karyotypes also demonstrated that the locus was subject to copy-number

heterogeneity (Bayani et al., 2002; Bayani et al., 2008a), reflecting genomic and chromosomal

instability often not revealed by the averaging algorithms of these high-throughput genome-wide

technologies (Albertson et al., 2003). Although we demonstrated that copy-number gains of

KLK6 were associated with increased KLK6 protein expression, a number of cancers with either

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two copies and/or a subpopulation containing one copy of the KLK locus (in these predominantly

polyploidy cancers), exhibited protein levels comparable to cases with extra copies or

amplification of the locus. Conversely, a portion of cancers with populations of copy-number

gains or amplifications, exhibited KLK6 protein expression deemed as moderate to low.

Therefore, these data suggest that in addition to copy-number, other mechanisms influence the

observed overexpression of KLK6 protein in OCa, particularly at the post-transcriptional level.

Despite the fact that a number of mechanisms have been studied, it is an area that remains

unclear and in need of further study (reviewed by Bayani and Diamandis, 2011). A growing

body of evidence demonstrates that microRNAs (miRNAs) are key players in regulating and

fine-tuning protein expression in both normal and diseased tissues (reviewed by Schickel et al.,

2008). miRNAs comprise a class of non-coding RNAs. Excised from a 6-10 nucleotide hairpin

precursor RNA (pre-miRNA), miRNAs are transcribed from a larger primary transcript (pri-

miRNA) and processed in a similar fashion as many of the siRNAs to yield a mature miRNA.

Perfect complementarity between the miRNA and the target gene mRNA 3′ untranslated region

(UTR), results in the cleavage and degradation of the target mRNA; whereas less than perfect

pairing represses the translation process. It is in this elegant fashion that miRNAs can target

different mRNAs, increasing the diversity of gene regulation. Alterations in miRNAs have been

analysed in many tumour types including OCa (reviewed by Dahiya and Morin, 2010), and have

also been shown to be affected by copy-number (Croce, 2009; Dahiya and Morin, 2010; Zhang

et al., 2008). Recent bioinfomatic and experimental findings by Chow et al., 2008; and White et

al., 2010, suggest that the KLK genes are also subject to regulation by miRNAs. In this study,

we examined the contribution of copy-number, of both the KLK locus and of miRNAs predicted

to target KLK6, to the observed protein expression of KLK6 in a representative KLK6-

overexpressing ovarian cancer cell line OVCAR-3.

5.2 Materials and Methods

5.2.1 Fluorescence in situ Hybridization (FISH), Spectral Karyotyping

(SKY) and Array Comparative Genomic Hybridization (aCGH)

The OVCAR-3 cell line was obtained from the American Type Culture Collection (ATCC) and

maintained according the manufacturer’s instructions FISH: For copy-number studies, bacterial

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artificial chromosome (BAC) clones mapping to three regions of chromosome 19q (19q12,

19q13.2, the KLK locus at 19q13.3/4) were obtained from The Centre for Applied Genomics

(Toronto, Canada) and differentially labeled as described previously by Bayani et al., 2011.

19q12 probes were labeled with Spectrum Green, all 19q13.2 probes were labeled with DEAC;

and all 19q13.3/4 clones which span the KLK locus, were labeled with Spectrum Orange.

Metaphase spreads from OVCAR-3 were prepared by standard cytogenetic techniques, as

described by Bayani and Squire, 2004, denatured and hybridized with the 19q multi-probe

cocktail (Bayani et al., 2011). The slides were mounted in a DAPI/Antifade medium

(Vectashield/ Vector Laboratories Canada) and visualized at 60X with a Zeiss Axioskop

fluorescence microscope (Carl Zeiss Canada). SKY: Metaphase preparations were pre-treated

and hybridized with SKY Paints (Applied Spectral Imaging, Carlsbad, CA) and processed as

described by Bayani and Squire, 2002. The images were collected using a Zeiss Axioplan

fluorescence microscope (Carl Zeiss Canada) and processed using the Spectral Karyotyping

Image Capturing and Analysis system (ASI). Karyotypic descriptions were assigned according to

the guidelines of the International System for Human Cytogenetic Nomenclature (Shaffer and

Tommerup, 2005). aCGH: The Agilent Human Genome 244K microarray platform was used

(Agilent Technologies, Inc.,Palo Alto, CA, USA cat. G4411B) containing 236,381 unique 60-

mer oligonucleotide features. The array design included a total of 5,045 features used as internal

controls. The features were based on the UCSChg17 (NCBI build 35, May 2004) Build. DNA

was extracted by standard phenol:chloroform methods, RNase-treated and resuspended in sterile

RNase/ DNase-free water (Sigma Canada). The direct labeling of DNAs, probe evaluation,

clean-up, hybridization to the array and post-hybridization washes were carried out using

reagents and equipment, according to Agilent protocols (http://www.chem.agilent.com) and as

previously described by Bayani et al., 2008. The CGH Analytics software version 3.4 (Agilent

Technologies) was used to analyze the aCGH data. Copy number aberrations were objectively

detected in replicate (dye-swap) experiments using an aberration calling method based on

computing significance scores for all genomic intervals.

5.2.2 microRNA Profiling

miRNAs were extracted using the mirVana™ miRNA Isolation Kit (Ambion, Life Technologies)

according to the manufacturer’s instructions and stored at -80˚C until ready for use.

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Quantification of miRNA expression was performed by reverse-transcriptase real-time PCR

using the TaqMan® MicroRNA v1.0 system (Applied Biosystems Inc. (ABI), Life

Technologies) and compared to normal ovarian derived miRNA (Ambion Inc, Life

Technologies). The TaqMan® Low Density Array (TLDA) Human miRNAs Panel and

Multiplex RT pools of eight 48-miRNA sets (ABI) were used for global miRNA expression

analysis by qRT-PCR. Briefly, miRNAs were converted into specific cDNA derived from mature

miRNAs and quantified using the TaqMan® MicroRNA Assay. The TLDA card contains 365

lyophilized human TaqMan® miRNA sequences plus three small nucleolar RNAs (RU6B,

RNU4 and RNU44) as endogenous controls. Data were quantified and analyzed using Sequence

Detection System (version 2.3) (ABI, Life Technologies). miRNAs relative expression was

normalized against endogenous controls and normal ovarian miRNAs according to the

following: 2−ΔCT, where ΔCT=(CTmiRNA−CTsnoRNAs)(Schmittgen et al., 2004).

5.2.3 Identification of miRNAs Predicted to Target KLK6

To identify putative miRNAs predicted to target the 3’untranslated region (UTR) of KLK6, three

public databases were accessed: the miRNA Registry at the Sanger Institute

(http://microrna.sanger.ac.uk/sequences/) (Griffiths-Jones et al., 2008), Memorial Sloan-

Kettering Cancer Center’s miRNA database (http://www.microrna.org/microrna/home.do) (Betel

et al., 2010), and TargetScan (http://www.targetscan.org/) (Grimson et al., 2007). Predicted

miRNAs common to all three databases were considered candidates for further study.

5.2.4 Transient Transfection of miRNAs to OVCAR-3

For transient transfection studies, the Pre-miR™ miRNA Precursor Kit (Ambion, Life

Technologies) was utilized according to the manufacturer’s instructions. Briefly, the hsa-let-7a-

5p precursor miRNA (PM10050, Ambion, Life Technologies), which corresponds to identical

mature miRNAs derived from hsa-let-7a-1, hsa-let-7a-2, and hsa-let-7a-3 loci; and a scrambled

miRNA negative control (Cat. No. 17110, Life Technologies) were prepared at a final

concentration of 6.25 μM. OVCAR-3 cells were trypsinized and adjusted to a final

concentration of 1 x 105 cells/ml. In six-well tissue culture plates, 2.4 ml of OVCAR-3 cells

(performed in duplicate) were combined with the transfection mixture comprised of the siPORT

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NeoFX transfection agent (Ambion, Life Technologies) and Opti-Mem I Medium (Ambion, Life

Technologies), as outlined by the manufacturer. The cultures were allowed to incubate at 37˚C in

a CO2 incubator for 24 hours, after which the culture medium was replaced with fresh normal

growth medium. Biological replicates for each condition were prepared. After 72 hours, the

media was collected and stored at -80˚C, the cells counted by trypan blue staining and the cell

pellet stored for later RNA extraction using the mirVana™ miRNA Isolation Kit (Ambion, Life

Technologies).

5.2.5 Quantitative Reverse Transcription Polymerase Chain Reaction

(qRT-PCR)

Quantification of mature hsa-let-7a, using the TaqMan® miRNA Assays (ABI, ID RT000377

and TM000377), including a control small nuclear RNA, RNU44 (ABI, ID RT001094 and

TM001094) were performed according to the manufacturer’s instructions. Briefly, 10ng of

extracted miRNAs were reverse transcribed using RT-specific primers, then subjected to PCR

using miRNA or control-specific TaqMan® system. As described above, the data were

quantified and analyzed using Sequence Detection System (version 2.3) (Life Technologies).

miRNAs relative expression was normalized against endogenous controls according to the

following: 2−ΔCT, where ΔCT=(CTmiRNA−CTsnoRNAs)(Schmittgen et al., 2004). The assays were

performed in triplicate for each sample.

5.2.6 Measurement of KLK6 and KLK10 by ELISA

We have previously described our ELISA methodologies for all KLKs. Briefly, the assays are

based on ‘‘sandwich’’-type ELISA principles with one antibody used for capture and one for

detection. A monoclonal-monoclonal ELISA configuration for the immunodetection of KLKs 6

and 10 was utilized as follows. White polystyrene microtiter plates were coated with 500-ng/well

monoclonal antibody in 100 μL coating buffer (50 mmol/L Tris- HCl, pH 7.8) overnight. The

plates were washed twice in wash buffer (10 mmol/L Tris-HCl, pH 7.4, containing 150 mmol/L

NaCl and 0.5 mL/L Tween 20), to which 50 μL of recombinant protein calibrators or samples,

diluted in a 6% BSA solution, were added to the wells, along with 50 μL assay buffer (6% BSA

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containing 25 mL/L normal mouse serum, 100 mL/L normal goat serum, and 10 g/L bovine

IgG). The plates were incubated for 2 hours with continuous shaking and washed 6 times as

described above. 100 μL/well of biotinylated monoclonal detection antibody (50 ng) diluted in

assay buffer were added to each well, incubated for 1 hour, and washed the plates 6 times. 100

μL (5 ng) alkaline phosphatase–conjugated streptavidin diluted in 6% BSA was added to each

well, incubated for 15 minutes with continuous shaking, and washed. 100 μL of diflunisal

phosphate solution (0.1 mol/L Tris-HCl, pH 9.1, containing 1 mmol/L diflunisal phosphate, 0.1

mol/L NaCl, and 1 mmol/L MgCl2) were added to each well, and incubated for 10 minutes with

continuous shaking. Finally, 100 μL developing solution (1 mmol/L Tris, 0.4 mol/L NaOH, 2

mmol/L TbCl3, and 3 mmol/L EDTA) was added to each well and mixed for 1 minute.

Fluorescence was measured with a time-resolved fluorometer (Perkin-Elmer); and the calibration

and data reduction analyses were performed automatically, as described (Christopoulos and

Diamandis, 1992).

5.3 Results

5.3.1 Identification of Predicted miRNAs Regulating KLK6 and miRNA

Profiling

To identify the predicted miRNAs targeting the 3’UTR of the KLK6 gene, a survey of three

publically accessible databases: the miRNA Registry at the Sanger Institute

(http://microrna.sanger.ac.uk/sequences/), Memorial Sloan-Kettering Cancer Center’s miRNA

database (http://www.microrna.org/microrna/home.do), and TargetScan

(http://www.targetscan.org/), was conducted. From these three databases, the most commonly

predicted miRNAs for KLK6 were from the members of the hsa-let-7 family of miRNAs,

namely hsa-let-7a, hsa-let-7b, hsa-let-7c, hsa-let-7d and hsa-let-7e (Figure 5.1A). miRNA

profiling of the KLK6-overexpressing OVCAR-3 using the TLDA arrays, showed the

dysregulation of these miRNAs as compared to normal ovarian-derived miRNA, with the

decrease in expression of hsa-let-7 family members. Shown in Table 5.1 are the log10

transformed expression ratios for the miRNAs on the TLDA array that are predicted to target

KLK6 as identified by the database searches. In addition, the miRNA expression of those

predicted by White et al., 2010 to target KLK6 are also included in Table 5.1. Of the 15

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miRNAs predicted to target KLK6 (in all three or in two of three databases, and for which

expression data was available on the TLDA array, as well as those predicted by White et al.,

2010) 10 of these miRNAs showed decreased expression. Expression profiling of the other

predicted mRNAs (ie. identified only in one database), showed a similar trend, with 13 of 24

miRNAs showing decreased expression. Validation of the miRNA findings by mature hsa-let-

7a-specific qPCR confirmed the relative low-leveled expression of hsa-let-7a in OVCAR-3

compared to the normal ovarian miRNA control (Figure 5.1B).

To test whether these candidate miRNAs could affect protein expression, hsa-let-7a-5p was

transiently transfected into OVCAR-3. Mature hsa-let-7a is encoded by three different loci in

humans: hsa-let-7a-1 (9q22.32), hsa-let-7a-2 (11q24.1), and hsa-let-7a-3 (22q.13.3) (Boyerinas

et al., 2010). Following a 72-hour transfection with either hsa-let-7a-5p; or a scrambled

miRNA control, the amount of secreted KLK6 was determined by ELISA in the media.

Additionally, qRT-PCR for mature hsa-let-7a in the hsa-let-7a-5p-transfected cell line confirmed

the 6-fold increase of the mature transcript over the scrambled-transfected control (data not

shown). After adjusting for the final cell number, an average decrease of 25-35% in the level of

KLK6 protein in the hsa-let-7a-5p treated cells was detected, in comparison to the scrambled-

transfected control. Based on previous studies showing the modulation of KLK6 and 10 protein

expression by hsa-let-7f (Chow et al., 2008; White et al., 2010b), we surveyed the databases

from miRNAs predicted to target KLK10 and their expressions (Table 5.2). Of the miRNAs

identified in all three databases, three of four showed decreases in expression, including hsa-let-

7b, hsa-miR-214 and hsa-miR-485-5p (Table 5.2). Of the 20 miRNAs identified in two

databases, 11 also showed a relative decrease in expression. Based on these database algorithms,

among the hsa-let-7 family members, hsa-let-7b is more strongly predicted to target KLK10,

than hsa-let-7a, hsa-let-7c, hsa-let-7d or hsa-let-7e; with hsa-let-7a identified in two databases as

a putative regulating miRNA. Nevertheless, we tested whether hsa-let-7a could also affect

KLK10 protein expression. ELISA for KLK10 in the hsa-let-7a-5p transfected line showed an

8%-15% decrease in secreted KLK10 protein as compared to the control (Figure 5.1C).

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Figure 5.1 . miRNA Profiling of OVCAR-3 and miRNAs Predicted to Target KLK6. A) The Venn diagram depicts the results of a search using the publically accessible databases including the miRNA Registry at the Sanger Institute (http://microrna.sanger.ac.uk/sequences/), Memorial Sloan-Kettering Cancer Center’s miRNA database (http://www.microrna.org/microrna/home.do) and TargetScan (http://www.targetscan.org/). miRNAs common to all databases are shown in red. The expression of those miRNAs present on the TLDA microarray are also depicted and reflect the relative expression to normal ovarian miRNAs (performed in duplicate). B) hsa-let-7a specific RT-PCR confirming the relative decrease in expression of hsa-let-7a OVCAR-3 (performed in duplicate) compared to normal ovary. C) Results of KLK6 and KLK10 ELISA of supernatants from hsa-let-7a- transfected OVCAR-3 compared to the negative control OVCAR-3 showing the decrease in KLK6 and KLK10 expression upon transient transfection. Error bars represent the standard deviation of replicates.

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Table 5.1. Expression values of miRNAs predicted to target KLK6 in OVCAR-3

Shown above are the log10 ratios of duplicate TLDA miRNA profiling for OVCAR-3 when compared to miRNAs derived from normal ovarian tissue. † = those miRNAs previously reported by White et al. (White et al., 2010b) to be predicted to target KLK6. Negative values reflect the relative loss of expression and positive values reflect the relative gain in expression.

Database miRNA Average Expression (Range) Error (±) Target Scan hsa -let-7a† -0.69 (-0.74/-0.64) 0.05 Sanger hsa -let-7b† -1.23 (-1.29/-1.16) 0.06 Sloan Kettering hsa -let-7c -0.27 (-0.27/-0.26) 0.00

hsa -let-7d† -0.23 (-0.37/-0.09) 0.14 hsa -let-7e† -0.43 (-0.54/-0.32) 0.11 hsa-mir-98† 0.32(0.26/0.38) 0.06 hsa-mir-608 0.35 -

Target Scan hsa -let-7f† -0.36 (-0.21/-0.50) 0.15 Sloan Kettering hsa -let-7g -0.76 (-0.78/-0.74) 0.02 Target Scan hsa-mir-575 -1.76 (-2.07/-1.45) 0.31 Sanger hsa-mir-659 -1.66 (-1.68/-1.63) 0.03

hsa-mir-639 1.44 (1.54/1.34) 0.10 hsa-mir-555 0.13 - hsa-mir-296 0.04 (0.05/0.03) 0.01

Sanger hsa-mir-17-3p -0.25 (-0.30/-0.20) 0.05 hsa-mir-17-5p 0.81 (0.73/0.89) 0.08 hsa-mir-551b 1.17 (1.26/1.07) 0.09 hsa-mir-200a 1.67 (1.73/1.61) 0.06 hsa-mir-200b 2.75 (2.74/2.77) 0.02 hsa-mir-193a -1.16 (-1.17/-1.14) 0.01 hsa-mir-193b -0.76 (-0.77/-0.75) 0.01 hsa-mir-18a 1.34 (1.32/1.37) 0.03

hsa-mir-324-3p -0.25 (-0.27/-0.24) 0.02 hsa-mir-324-5p 0.30 (0.30/0.29) 0.01

hsa-mir-132 -2.04 (-2.23/-1.85) 0.19 hsa-mir-554 0.47 -

Target Scan hsa-mir-576 0.39 (0.61/0.17) 0.22 hsa-mir-342 -0.44 (-0.47/-0.42) 0.03 hsa-mir-601 -0.19 (-0.53/0.15) 0.34 hsa-mir-654 -2.61(-2.67/-2.55) 0.06 hsa-mir-140† -1.71(-1.71/-1.72) 0.00 hsa-mir-630 -0.45 (0.05/-0.96) 0.50 hsa-mir-520a -0.49 (-0.68/-0.31) 0.19 hsa-mir-503 -0.89 (-0.77/-1.01) 0.12 hsa-mir-548a 0.56 (0.65/0.47) 0.09 hsa-mir-548b 0.36 - hsa-mir-125a -0.97 (-0.96/-0.96) 0.01 hsa-mir-125b -1.28 (-1.24/-1.32) 0.04

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Table 5.2. Expression values of miRNAs predicted to target KLK10 in OVCAR-3

Database miRNA Average Expression (Range) Error (±) Target Scan hsa-let-7b† -1.23 (-1.29/-1.16) 0.06 Sloan Kettering hsa-miR-214† -4.74 (-4.22/-5.27) 0.52 Sanger hsa-miR-224† 1.11 (1.10/1.13) 0.02

hsa-miR-485-5p -2.61 (-2.67/-2.55) 0.06 Sanger hsa-let-7a† -0.69 (-0.74/-0.64) 0.05 Target Scan hsa-let-7c -0.27 (-0.27/-0.26) 0.00

hsa-let-7d -0.23 (-0.37/-0.09) 0.14 hsa-let-7e -0.43 (-0.54/-0.32) 0.11

hsa-miR-148b 0.54 (0.52/0.55) 0.01 hsa-miR-152 -1.06 (-1.00/-1.12) 0.06 hsa-miR-197 -0.94 (-1.05/-0.83) 0.11 hsa-miR-326 0.38 (0.26/0.50) 0.12 hsa-miR-98† 0.32 (0.26/0.38) 0.06

Target Scan hsa-miR-1 -1.91 (-1.93/-1.90) 0.02 Sloan Kettering hsa-miR-143† -4.42 (-4.48/-4.36) 0.06

hsa-miR-18a 1.34 (1.32/1.37) 0.03 hsa-miR-192 -0.66 (-0.76/-0.56) 0.10 hsa-miR-193b -0.76 (-0.77/-0.75) 0.01 hsa-miR-206† 0.40 (0.33/0.46) 0.07 hsa-miR-215 0.71 (0.65/0.77) 0.06 hsa-miR-510 -0.14 (0.09/-0.38) 0.24

hsa-miR-515-3p -3.00 (-0.14/-5.87) 2.87 hsa-miR-613 0.40 (0.03/0.77) 0.37 hsa-miR-646 0.12 (0.33/-0.08) 0.21

White et al., 2010 hsa-miR-125b -1.28 (-1.24/-1.32) 0.04 hsa-miR-140 -1.71 (-1.71/-1.72) 0.00 hsa-miR-149 0.10 (0.07/0.13) 0.03 hsa-miR-432 -2.92 (-2.96/-2.87) 0.05

†White et al., 2010

Shown above are the log10 ratios of duplicate TLDA miRNA profiling for OVCAR-3 when compared to miRNAs derived from normal ovarian tissue. miRNAs indicated in bold represent those miRNAs also predicted to target KLK6. † = those miRNAs previously reported by White et al., 2010 to be predicted to target KLK10. Negative values reflect the relative loss of expression and positive values reflect the relative gain in expression.

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5.3.2 OVCAR-3 Cytogenomics

Spectral karyotyping (SKY) analysis of OVCAR-3 showed a hyper-triploid cell line (59-70

chromosomes) with numerous simple and complex structural rearrangements consistent with

previous reports (http://www.ncbi.nlm.nih.gov/sky/skyweb.cgi). Structural rearrangements

involving chromosome 19 were identified, in addition to two apparently normal copies of

chromosome 19. A complex chromosome described as a der(22)(16;19;22) was shown to contain

a large homogeneously staining region (hsr) indicative of locus amplification (Figure 5.2A).

aCGH of the cell line revealed a high-level amplification spanning 19q11 to 19q13.2 and focal

amplification at 19q13.4 (Figure 5.2B). These were consistent with other aCGH findings for

this cell line (Beroukhim et al., 2010). Using a multi-colour 19q probe set previously described

(Bayani et al., 2011), we confirmed the copy-number findings identified by aCGH.

Amplification of 19q11 (green) and 19q13.2 (blue) were identified on the der(22), as seen by the

co-localization of FISH signals. Single hybridization signals, representing the KLK locus, were

seen only on the two normal chromosomes 19 (red) (Figure 5.2C). aCGH ratios for the KLK

locus suggested its net loss, reflecting the limitations of CGH to account for ploidy status of the

cell line and the possibility of non-clonal losses and gains of chromosome 19. Thus, at most,

there are two copies of the KLK locus in this triploid cell line.

Since copy-number has been shown to be a contributing mechanism to changes in miRNA

expression (Croce, 2009; Dahiya and Morin, 2010; Zhang et al., 2008), we assessed the copy-

number status of the three hsa-let-7a genes which encode for an identical mature hsa-let-7a

miRNA (Boyerinas et al., 2010): hsa-let-7a-1 (9q22.32), hsa-let-7a-2 (11q24.1), and hsa-let-7a-

3 (22q.13.3) using the aCGH data (Figure 5.3). Both hsa-let-7a-2 and hsa-let-7a-3 mapped to

loci generating ratios indicative of an overall net loss, while hsa-let-7a-1 indicated ratios

indicative of two copies. Since OVCAR-3 possesses a triploid karyotype, with both clonal and

non-clonal changes of these chromosomes; there is the presence of at least two copies of hsa-let-

7a-1; and at least one, but not more than two copies, each of hsa-let-7a-2 and hsa-let-7a-3;

which is consistent with the SKY findings. Indeed, when we analysed the structural

rearrangements of those chromosomes to which these miRNAs mapped, we found that while the

majority of metaphases possessed these structural rearrangements affecting these loci, for some,

only 50% (5/10) of metaphases possessed these derivative chromosomes (Figure 5.3). For hsa-

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let-7a-1 (and hsa-let-7f-1) mapping to 9q22.3, two normal chromosomes 9 were detected in all

cells (10/10), however the der(9)t(8;9) was only detected in 60% of metaphases with the loss of

material telomeric to 9q12. For hsa-let-7a-2, mapping at 11q24.1; no normal chromosomes 11

were identified, however 80% (8/10) of metaphases possessed the der(11)t(11;14) rearrangement

accounting for copies of hsa-let-7a-2 at the telomeric end of the chromosome. However, the

der(11)t(11;16;18) rearrangement, present in only 60% (6/10) of metaphases possesses a deletion

telomeric to 11q23. Together, these account for the net deletion of the locus. Finally, hsa-let-7a-

3 which maps to 22q13.2, showed a net loss, contributed primarily by the combination of losses

of the locus in the following rearrangements: i(22)(p10) (8/10 metaphases); and

der(22)t(11;8;16) (5/10 metaphases) and the presence of the locus in the der(22)t(16;22) (7/10

metaphases) and der(22)t(16;19;22) (9/10 metaphases). Table 5.3 summarizes the net-copy

number changes for the miRNAs predicted to target KLK6, in at least two of the databases and

for those identified by White et al., 2010, by combined aCGH and cytogenetic analysis, and

accounting for the ploidy of the cell line. For each mature miRNA, there is a general

concordance in the association between overepxression of the mature miRNA and net copy-

number gains of their genomic mapping locations; or decreased expression associated with net

copy-number losses of their associated genomic loci. The exceptions to these findings are hsa-

let-7c and hsa-let-7f-2 which exhibited the relative decrease in expression, despite possessing net

gains of their loci; while hsa-miR-639 showed a relative increase in expression though there was

a net loss in copy-number. Similarly, Table 5.4 summarizes the net-copy number changes by

combined aCGH and cytogenetic analysis for the miRNAs predicted to target KLK10 in at least

two of the databases and for those identified by White et al., (2010), showing a similar trend of

positive association between the copy-number status of the miRNA and its expression, with the

exception of seven which showed an inverse association.

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Table 5.3. Summary of miRNA Expression and Copy-Number Changes of miRNAs Predicted to Target KLK6

†White et al. , 2010.

Shown are the combined expression and cytogenomic analyses of miRNAs predicted to target KLK6. miRNAs indicated in bold represent those miRNAs identified in all three publically accessible databases as described in the Methods. † = those miRNAs previously reported by White et al., 2010 to be predicted to target KLK6. Loss=net decrease in copy-number. Gain=net increase in copy number. Balanced=net copy number of 2/no change in copy number.

aCGH miRNA ExpressionDatabase miRNA Locus Net Copy Number Mature miRNA Target Scan Sloan Kettering Sanger

hsa-let-7a-1† hsa-let-7a-2† hsa-let-7a-3†

9q22.32 11q24.1 22q13.3

Balanced Loss Loss

Decreased

hsa-let-7b† 22q13.3 Loss Decreased hsa-let-7c 21q21.21 Gain Decreased hsa-let-7d† 9q22.32 Balanced Decreased hsa-let-7e† 19q13.3 Loss Decreased hsa-miR-98† Xp11.22 Gain Increased Target Scan Sloan Kettering

hsa-let-7f-1† hsa-let-7f-2†

9q22.32 Xp11.22

Balanced Gain

Decreased

hsa-let-7g 3p21.1 Balanced Decreased Target Scan hsa-miR-296 20q13.32 Gain Increased Sanger hsa-miR-575 4q21.22 Balanced Decreased hsa-miR-639 19p13.2 Loss Increased hsa-miR-659 22q13.1 Loss Decreased hsa-miR-140† 16q22.1 Loss Decreased

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Table 5.4 Summary of miRNA Expression and Copy-Number Changes of miRNAs Predicted to Target KLK10 aCGH miRNA ExpressionDatabase miRNA Locus Net Copy Number Mature miRNA Target Scan hsa-let-7b† 22q13.3 Balanced Decreased Sloan Kettering hsa-miR-214† 1q24.3 Gain Decreased Sanger hsa-miR-224† Xq28 Balanced Increased hsa-miR-485-5p 14q32.3 Balanced Decreased Sanger hsa-let-7a-1† 9q22.32 Balanced Decreased Target Scan hsa-let-7a-2† 11q2.1 Loss Decreased hsa-let-7a-3† 22q13.3 Loss Decreased hsa-let-7c 21q21.21 Gain Decreased hsa-let-7d 9q22.32 Balanced Decreased hsa-let-7e 19q13.3 Loss Decreased hsa-miR-148b 12q13.13 Gain Increased hsa-miR-152 7q21.32 Gain Decreased hsa-miR-197 1p13.3 Loss Decreased hsa-miR-326 11q13.4 Amplified Increased hsa-miR-98† Xp11.22 Gain Increased Target Scan hsa-miR-1-1 20q13.33 Gain Decreased Sloan Kettering hsa-miR-1-2 18q11.2 Loss Decreased hsa-miR-143† 5q32 Loss Decreased hsa-miR-18a 13q31.1 Loss Increased hsa-miR-192 11q13.1 Gain Decreased hsa-miR-193b 16p13.2 Loss Decreased hsa-miR-206† 6p12.2 Balanced Increased hsa-miR-215 1q41 Gain Increased hsa-miR-510 Xq27.3 Loss Decreased hsa-miR-515-3p 19q13.42 Amplified Decreased hsa-miR-613 12p13.1 Gain Increased hsa-miR-646 20q13.3 Gain Increased White et al. 2010. hsa-miR-125b-1† 11q24.1 Loss Decreased hsa-miR-125b-2† 21q21.1 Gain Decreased hsa-miR-140† 16q22.1 Loss Decreased hsa-miR-149† 2q37.3 Gain Increased hsa-miR-432† 14q32.2 Balanced Decreased

†White et al,. 2010.

Shown are the combined expression and cytogenomic analyses of miRNAs predicted to target KLK10. miRNAs indicated in bold represent those miRNAs also predicted to target KLK6. † = those miRNAs previously reported by White et al., 2010, to be predicted to target KLK10. Loss=net decrease in copy-number. Gain=net increase in copy number. Balanced=net copy number of 2/no change in copy number.

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Figure 5.2 Cytogenomic Analysis of OVCAR-3. A) Spectral Karyotyping (SKY) shows the presence of two normal chromosomes 19 (green) and a structurally abnormal der(22) comprised of chromosomes 16 (orange), 19 (green) and 22 (pink). B) Array Comparative Genomic Hybridization (aCGH) results of chromosome 19 in OVCAR-3 shows the copy-number amplifications, gains and losses along the chromosome. An enlargement of the KLK locus at 19q13.3/4 shows the net copy-number loss of the entire KLK locus. Green, blue and red circles beside the ideogram depict the location of the multicolour FISH probes used to verify the copy-number status of KLK6 as described in the Methods. C) Locus-specific FISH to OVCAR-3 metaphases confirm the amplification of 19q12 (green signals) on the der(22) and single copies at the normal chromosome 19 position (left panel); amplification of 19q13.3 (blue signals) on the der(22) and single copies at the normal mapping position (middle panel); single copy of the KLK locus (red signals) only on the normal chromosomes 19.

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Figure 5.3. Cytogenomic Analysis of hsa-let-7a Family Members in OVCAR-3. A) hsa-let-7a-1 mapping to chromosome 9q22.32 together in a cluster with hsa-let-7f-1 and hsa-let-7d shows a 1:1 ratio in copy number indicating two copies. Cytogenetic analysis of chromosome 9 confirms the presence of two normal chromosomes 9 and one der(9)t(9;8), with a breakpoint centromeric to the mapping location of the miRNA cluster. B) hsa-let-7a-2 mapping to chromosome 11q24.1 shows the net loss in copy number suggesting that no more than two copies for the locus exists. Cytogenetic analysis for chromosome 11 shows the presence of two abnormal chromosomes 11 as a der(11:14) in 8/10 cells, accounting for the copies of the miRNA; and the presence of a der(11)t(11;16;18) present in 6/10 cells with a deletion of the terminal region of chromosome 11 containing hsa-let-7a-2. C) hsa-let-7a-3 mapping to chromosome 22q13.3 also showing the net loss of the locus, with no more than two copies present. Cytogenetic analyses for chromosome 22 shows the presence of several structural aberrations involving chromosome 22, with such structural aberrations resulting in the loss of the locus.

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5.4 Discussion

In this study, we investigated the role of miRNAs in the regulation of KLK6 protein expression

in a representative ovarian cancer cell line OVCAR-3, and how genomic instability and copy-

number may contribute to this mode of regulation. The serine protease, KLK6, is significantly

overexpressed in OCa tumours and ascetic fluids, and associated with a poor prognosis

(reviewed by Bayani and Diamandis, 2011). We and others, have shown the prognostic utility of

KLK6 to enhance, and in some cases out-perform CA-125 (El Sherbini et al., 2011; Koh et al.,

2011; Shan et al., 2007; White et al., 2009a). Therefore, its potential as an OCa biomarker

continues to be evaluated, ranking among the top 10-20 putative biomarkers in large-scale

multicenter studies (Cramer et al., 2011; Zhu et al., 2011). Increasing evidence suggest that

KLK6 contributes significantly to tumourigenesis beyond the functions extracellular matrix

degradation and tissue remodeling, including the participation in inflammatory processes, altered

immune cell survival and in enhanced intracellular signaling via activation of G-protein–coupled

receptor family members: protease-activated receptors (PARs) (reviewed by Bayani and

Diamandis, 2011). Therefore, the observed increase in KLK6 expression in cancers signifies the

dysregulation of many pathways, especially those deemed the hallmarks of cancer (Hanahan and

Weinberg, 2000, 2011). Although several mechanisms have been investigated including

methylation, and the response to steroid hormones, inconsistencies within and between various

cancers renders our understanding of KLK6 regulation still largely unknown (Bayani and

Diamandis, 2011). Recently, we reported that serous OCas exhibit copy-number heterogeneity

due to genomic instability of the KLK locus and showed a general concordance for

overexpression of KLK6 protein with copy-number (Bayani et al., 2011; Bayani et al., 2008b).

However, a number of cancers with either two copies, or with a combined subpopulation

containing one copy of the KLK locus, showed protein levels comparable to cases with extra

copies or amplification of the locus. Conversely, a portion of cancers with populations of copy-

number gains or amplifications, exhibited KLK6 protein expression deemed as moderate to low.

Therefore, we explored other mechanisms known to modulate protein expression which are also

affected by copy-number, namely microRNAs (miRNAs).

miRNAs form an important class of regulators that participate in diverse biological functions

including development, cell proliferation, differentiation, and apoptosis, and may function as

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oncogenes or tumour suppressors (reviewed by Schickel et al., 2008). Thus, a decrease in the

expression of a miRNA results in the permissive expression of target genes that promote

tumourigenesis; and the overexpression of miRNAs limits the expression of target genes that

could inhibit tumourigenesis (Esquela-Kerscher and Slack, 2006). The complexity in this form

of regulation is demonstrated by the fact that a miRNA can target multiple mRNAs, and each

mRNA may be targeted by different miRNAs (Esquela-Kerscher and Slack, 2006; Griffiths-

Jones et al., 2008). Studies by Chow et al., 2008 and White et al., 2010 have recently shown that

protein levels of various members of the KLK family are modulated by miRNAs in ovarian,

breast and renal carcinomas. Their bioinfomatical approaches (Chow et al., 2008; White et al.,

2010a; White et al., 2010b) have identified candidate miRNAs predicted to target the members

of the KLK family, and our own survey of the publically accessible databases reported here are in

agreement with their findings. The consensus of these results indicates that members of the hsa-

let-7 family of miRNAs are strong candidates for targeting specific KLKs, namely KLK6, and to

a lesser extent, KLK10 (Figure 5.1, Table 5.1, Table 5.2). Our TLDA profiling of the KLK6-

overexpressing OCa cell line, OVCAR-3, showed the majority of miRNAs predicted to target

KLK6 were decreased in expression (Table 5.1). All hsa-let-7 family members present on the

array showed the decreased expression of their mature miRNA compared to miRNAs derived

from normal ovary.

In addition to being KLK6-expressing, OVCAR-3 also satisfied the copy-number criteria for the

KLK locus. Our previous work in primary tumours indicated that when lost, the KLK locus

rarely exist clonally as one copy (Bayani et al., 2011; Bayani et al., 2008b). While a significant

portion of tumour cells could possess one copy, an admixture of tumour cells containing two or

more copies were also seen (Bayani et al., 2011; Bayani et al., 2008b). Other cytogenetic studies

support the observation that chromosome 19 is typically involved in both clonal and non-clonal

numerical and structural aberrations (Bayani et al., 2008b; Micci et al., 2009), and that the vast

majority of OCas are polyploidy rather than diploid (Bayani et al., 2002; Bayani et al., 2008a;

Bayani et al., 2008b; Taetle et al., 1999b). Therefore the averaging algorithms of aCGH studies

that report the frequent net loss of the locus (Cancer Genome Atlas Research Network, 2011;

Gorringe and Campbell, 2009), fail to take into account ploidy and copy-number heterogeneity

(Albertson et al., 2003). This falsely leads to the assumption that one copy of the locus exists

when designated as “lost” or “deleted”. FISH, using our multi-colour probe cocktail, showed

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only two copies of the KLK locus at the normal mapping location (19q13.3/4). Moreover, SKY

analysis of the cell line showed OVCAR-3 to be structurally and numerically abnormal, with a

near triploid genome, consistent with reported karyotypic changes for this cell line at the

National Center for Biotechnology Information (NCBI) SKY/M-FISH and CGH Database

(http://www.ncbi.nlm.nih.gov/sky/). Although chromosomal abnormalities involving

chromosome 19 were identified (Figure 5.2A), the KLK locus was not subject to these structural

rearrangements, residing only at its normal mapping location (Figure 5.2C). Complementing the

karyotypic analyses, aCGH of OVCAR-3 confirmed the net loss of the entire KLK locus,

including KLK6 (Figure 5.2B), consistent with the finding of two copies against a triploid

background and accounting for the random losses of chromosome 19. Therefore, OVCAR-3

represents the KLK6-overexpressing and diploid/net loss copy-number scenario observed in our

primary tumour study.

Because copy-number has been known to play a role in miRNA expression (Croce, 2009; Dahiya

and Morin, 2010; Zhang et al., 2008), we also assessed the copy-number status of the hsa-let-7

family members commonly predicted to target KLK6, as well as other candidate miRNAs (Table

5.3). Indeed for both KLK6 and KLK10, there was good concordance between the copy-number

status and the expression of the miRNA (Table 5.3 and Table 5.4). The hsa-let-7 members map

to regions subjected to frequent copy number changes (Beroukhim et al., 2010; Cancer Genome

Atlas Research Network, 2011), with the net copy number changes exhibited in this cell line

representative of the genomic profiles seen in most OCas. With the exception of hsa-let-7f-2 and

hsa-let-7c, we found that expression of miRNAs were associated with their copy-number status.

The combined aCGH and karyotypic analyses of hsa-let-7a, which are encoded by three distinct

genes and are ultimately processed to result in identical mature hsa-let-7a (hsa-let-7a-1

(9q22.32), hsa-let-7a-2 (11q24.1), and hsa-let-7a-3 (22q.13.3)); showed the overall net loss of

genomic material (Figure 5.3). Because hsa-let-7a has been identified in many OCa miRNA

profiling and functional studies to be an important member of the OCa miRNA signature

(reviewed by Dahiya et al., 2010), and since the mature form results from three distinct copies

within the normal human genome, hsa-let-7a was chosen as a candidate for modulating KLK6

protein expression. Indeed when hsa-let-7a miRNA was transiently transfected into OVCAR-3,

there was a decrease in KLK6 protein in the hsa-let-7a-transfected cell line supernatant, as

compared to the scrambled-transfected control (Figure 5.1C). These findings were consistent

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with those (Chow et al., 2008; White et al., 2010a; White et al., 2010b) showing the decrease in

KLK6 expression by hsa-let-7f , which possesses the identical seed sequence to hsa-let-7a

(Boyerinas et al., 2010 ) and binding site on the KLK6 3’ UTR (Chow et al., 2008; White et al.,

2010a; White et al., 2010b). The biological consequences of lowered KLK6 protein levels in

ovarian tumour cells has been shown experimentally to result in reduced migration, proliferation

and malignant potential (Bayani and Diamandis, 2011). Together with the experimental

evidence of hsa-let-7a’s tumour suppressive functions (Boyerinas et al., 2010), it supports an

important physiological role for KLK6 during pathogenesis. Bioinfomatical analyses by White

et al., 2010, as well as ours (Table 4.2) identified hsa-let-7a as a possible modulator for KLK10,

thus we also performed ELISA for KLK10 and found that hsa-let-7a was able to decrease

KLK10 protein expression but to a lower extent than for KLK6. Like KLK6, KLK10

overexpression has also been reported in OCa, and associated with worse prognosis in late-

staged disease (El Sherbini et al., 2011; Koh et al., 2011).

These findings add to the growing number of mRNA targets for the hsa-let-7 members, including

those genes implicated in many facets of tumourigenesis such as: MYC (Helland et al., 2011;

Nadiminty et al., 2011), RAS (Johnson et al., 2005), HMGA2 (Shell et al., 2007), Dicer

(Tokumaru et al., 2008), RAB40C (Yang et al., 2011b), E2F1, E2F3 (Bueno et al., 2010), E2F2,

CCND2 (Dong et al., 2010), and STAT (Wang et al., 2010). The observed decrease in

expression of the let-7 genes in many cancers (Boyerinas et al., 2010) is consistent with the

observed cellular dedifferentiation and re-expression of oncofetal genes seen in tumourigenesis

(Boyerinas et al., 2010; Shell et al., 2007). An ancient and highly conserved gene family, 13

members have been identified among humans (hsa-let-7a-1, 7a-2, 7a-3, 7b, 7c, 7d, 7e, 7f-1, 7f-2,

7g, 7i, hsa-mir-98, and hsa-mir-202) with identical seed sequences (Boyerinas et al., 2010). It is

intriguing to consider that these specific miRNAs exist in multiple copies because they perform

functions necessary to the survival of the organism. These findings also add to the current

understanding of KLK protein expression, and linking KLK6 to fundamental physiological

pathways mediated by this highly conserved and ancient family of miRNAs. The KLK gene

family also has an extensive phylogenetic history, with KLK6 being an ancestral member

(Pavlopoulou et al., 2010). Moreover, the bioinfomatic predictions performed to date suggest

members of the hsa-let-7 family may preferentially target KLK6 (Chow et al., 2008; White et al.,

2010a; White et al., 2010b), and KLK10 to a lesser extent (Table 5.3). The down regulation of

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hsa-let-7a has been consistently reported among OCas (Dahiya and Morin, 2010), and its relative

expression shown to have an impact on survival outcomes of patients based on their

chemotherapy (Lu et al., 2011). The dysregulation of hsa-let-7a expression has profound

biological consequences that can be expressed through the changes in the levels of target

proteins, making such proteins putative biomarkers. Identifying and verifying the putative targets

of this and other miRNAs can provide the opportunity to discover relevant biomarkers and

protein-based signatures that could enhance genomically and transcriptomically-derived

signatures and delineate important clinical subgroups. Currently limited by the availability of

specific and sensitive antibodies for protein analyses, advances in mass-spectrometric technology

may help overcome this obstacle (Kulasingam et al., 2010).

In this study, we have demonstrated the hsa-let-7 family member, hsa-let-7a, is a contributing

modulator of KLK6 protein expression that is independent of the KLK6 copy-number status.

Additionally, we demonstrated that hsa-let-7a can also weakly affect the protein expression of

KLK10. Our cytogenomic analyses showed the strong contribution of copy-number and miRNA

expression in this representative OCa cell line. Moreover, the data present here and supported by

others (Chow et al., 2008; White et al., 2010a; White et al., 2010b), links the KLKs to a

fundamentally important regulator of gene expression essential to growth and development

(Boyerinas et al., 2010). With the continued validation of other hsa-let-7a targets, it is possible

that a clinically significant proteomic signature, including KLK6, can be developed to improve

the diagnostic and predictive needs so urgently needed in ovarian carcinoma.

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Chapter 6

Summary and Future Directions

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6 Summary and Future Directions

6.1 Summary

Ovarian carcinoma remains the leading cause of death from a gynecological malignancy. The

vague and lack of specific symptoms permits the virtually unnoticed progression of the disease;

such that at the time of diagnosis, the cancer has almost always metastasized. Although the FDA

has recently approved HE4, OVA1™ and the ROMA™ algorithm to the only other approved

marker CA-125, the lack of additional biomarkers for diagnosis contributes to the high mortality

rate. Prostate specific antigen (PSA/KLK3), the most well-known cancer biomarker, belongs to a

family of serine proteases – the Kallikreins (KLKs). This 15-member gene family holds great

potential as biomarkers for other cancers including OCa. Specifically, KLK6 has shown promise

as a biomarker for predicting disease free survival (DSF) and OS (reviewed by Bayani and

Diamandis, 2011), as it has been shown to be over-expressed at the mRNA and protein levels in

serous OCas as well as in the ascites fluids and serum of these patients. Aside from its ability to

degrade components of the extracellular matrix and basement membrane, KLK6 has been linked

to a variety of biological processes deemed the hallmarks of cancer (Hanahan and Weinberg,

2000, 2011). The mechanisms to the observed overexpression of KLK6 have been investigated

but are still poorly understood and should consider the integrative contributions at the level of

DNA, RNA and protein.

This current body of work examined the contributions of the observed karyotypic complexity

that characterizes OCa genomes, to the overexpression of a potentially important biomarker for

OCa – KLK6. It was systematically demonstrated for the first time in the published manuscript

entitled, “Distinct patterns of structural and numerical chromosomal instability characterize

sporadic ovarian cancer”, that the karyotypes of sporadic OCas experience a range of

chromosomal instability; both numerically (N-CIN) and structurally (S-CIN) (Bayani et al.,

2008a). Utilizing both interphase and metaphase FISH-based techniques, it was shown that

diploid tumours showed low-levels of N-CIN compared to triploid and tetraploid cancers. The

relationship of numerical instability and ploidy was highlighted by the finding that

supernumerary centrosomes were similarly identified most frequently among the triploid and

tetrapoloid cancers. This is consistent with the notion that mitotic segregation errors are

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associated with centrosome amplification (Doxsey, 2002). The enumeration of breakpoints,

aided by the use of SKY, revealed that diploid cancers showed a higher number of structural

aberrations than triploid or tetrapoloid cancers. Though the presence of clonal and non-clonal

breakage events were on-going, many of the stable structural changes appeared to occur at the

diploid stage, since duplicate structural rearrangements were often present in the polyploid

cancers. These observations are in keeping with the significant role of the BRCA genes in OCa.

Recalling that expression profiling showed that sporadic cancers segregated into BRCA1- or

BRCA2-like cancers (Jazaeri, 2009; Jazaeri et al., 2002), and that hyper-methylation of BRCA1 is

a frequent event in sporadic cancers (Cancer Genome Atlas Research Network, 2011), the

finding that structural rearrangements appear to precede numerical changes supports the primary

role of the BRCA genes in the repair of DNA double stranded breaks. Secondarily, evidence

suggests that BRCA1 may additionally contribute to numerical and ploidy changes, via RB and

TP53 pathways (Deng, 2006). This initial study demonstrated the ability for OCa genomes to

undergo local copy-number changes independent of ploidy.

Having established the interplay between N-CIN and S-CIN among OCa cancers, the copy-

number status of the KLK locus in a series of cancer cell lines, primary OCa tumours and ascites

was determined and compared to KLK protein levels, in the published manuscript “Impact of

cytogenetic and genomic aberrations of the kallikrein locus in ovarian cancer” (Bayani et al.,

2008b). Utilizing molecular cytogenetic techniques including KLK-specific FISH, aCGH and

SKY, together with ELISA, it was first to demonstrate that the entire locus experiences copy-

number changes rather than individual KLK-family members and, by enlarge, the entire locus is

involved in unbalanced translocations without disruption of the cluster. However, in prostate

cancer there have been two reports describing extremely rare fusion transcripts between ETV4

(17q21) and KLK2 (Hermans et al., 2008), as well as ETV1 (7p21) and KLK2 (Pflueger et al.,

2011). Interestingly, the orientation of KLK2 and ETV4, and the resulting fusion product cannot

be explained by a single chromosome translocation occurrence; however, the recent proposal of

the chromothripsis model (Stephens et al., 2011), may offer some explanation of this rare event.

However, caution should be taken with respect to this model, as there is no concrete evidence to

suggest that the extremely complexed inter- and intra-chromsomal rearrangements detected by

DNA sequencing (Stephens et al., 2011) truly arises from a single catastrophic event (Righolt

and Mai, 2012). Unfortunately, no cytogenetic analysis was performed on these prostate cancer

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patients to elucidate the possible chromosomal events leading to the fusion transcript.

Nevertheless, this initial KLK study also demonstrated that regions on 19q12 and 19q13 were

common sites of chromosomal breakage. In this small cohort of various cancer cell lines and

primary cancers, there was general concordance with the increase of KLK copy-number with

protein expression, however, this was not always the case, suggesting that tissue specificity and

other regulators of gene expression were at play. Interrogation of primary OCas similarly showed

that copy-number increases were also due to unbalanced translocations and more importantly,

was first to show that the KLK locus experiences copy-number heterogeneity.

Following this pilot study, the copy-number status was assessed in a larger and clinically

annotated series of untreated serous OCas in the published manuscript “Genomic instability and

copy-number heterogeneity of chromosome 19q, including the kallikrein locus, in ovarian

carcinomas” (Bayani et al., 2011). The purpose was to determine the range of KLK copy-

number, the extent of copy-number heterogeneity and instability, its relationship to KLK6

expression and whether such copy-number status was clinically relevant. A three-colour FISH

approach to a clinically-annotated serous OCa TMA, was the first comprehensive study to

examine KLK copy-number. Differentially labeled FISH probes for 19q12, 19q13.2 and the KLK

locus at 19q13.3/4 were utilized. Eighty-one patients were analysed to reveal that contrary to the

amassed aCGH findings, the KLK locus frequently experienced more gains/amplifications

(51.3%) than losses (29.6%). This confirms the diluting effects of contaminating normal cells,

normalization algorithms of aCGH experiments and the inability of aCGH to account for ploidy

(Albertson et al., 2003). The KLK locus was also found to be subjected to high copy-number

heterogeneity (p<0.001), particularly in cases with a net gain of the locus, supporting our earlier

observations (Bayani et al., 2008b). Moreover, these KLK locus gains were typically due to

structural rearrangements along 19q, also consistent with the previous findings (Bayani et al.,

2008b). Co-relations to clinical data showed that KLK copy-number status was not associated

with age, stage, grade or response to treatment. KLK6-specfic IHC showed a weak correlation to

copy-number indicating that other mechanisms together with copy-number drives the observed

protein over-expression. Interestingly, when 19q rearrangement status was analysed, older

women (mean 59.6 years) were more likely to have these gross structural rearrangements on 19q

than younger women (mean 50.8 years) (p = 0.003). Furthermore, grade 0/I tumours were more

likely to not have gross structural changes on 19q than higher grade tumours (II/II) (p<0.001),

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suggesting that disruptions of chromosome 19 may confer a more aggressive phenotype. Indeed

several studies have shown that aberrations of chromosome 19 may be important in early OCa

tumorigenesis (Micci et al., 2009; Tsao et al., 2001). Moreover, the unique genomic properties

of chromosome 19, such as the high GC content, high density of genes and gene families, as well

as harbouring the largest number of miRNAs within the human genome (Grimwood et al., 2004),

supports the notion for a more malignant phenotype associated with numerical and structural

disruption of chromosome 19. Although these cancers were not classified as being Type I or

Type II (Kurman and Shih Ie, 2011), perhaps such changes on 19q are an additional

characteristic of Type I and Type II cancers. Kaplan Meier curves for DFS and OS reveal a

trend towards better DFS and OS in patients with a net loss of the KLK locus or in patients

without a 19q rearrangement. This could support the notion that the loss of KLK copy-number

may still contribute, in some way, to the decrease in transcript and protein; and that structural

disruption of 19q affects the expression of genes on chromosome 19q and/or genes associated

with their rearranged chromosomal partners. These results indicate that KLK copy-number

status may be clinically relevant, though further investigation is required.

The results of the aforementioned study demonstrated the modest contributing role of the

chromosomal changes to KLK6 copy number and to KLK6 protein expression. However, the

role of other genes influencing KLK6 protein expression had yet to be investigated. In the

submitted manuscript entitled “Modulation of KLK6 Protein Expression in the Ovarian Cancer

Cell Line OVCAR-3 by miRNA Let-7a” the role of miRNAs in regulating KLK6 protein

expression was explored utilizing an OCa cancer cell line model. Molecular cytogenomic

techniques including FISH, SKY and aCGH defined the karytoypic landscape of OVCAR-3.

With a triploid genome, the KLK locus was present in only two copies, indicating a net loss

relative to ploidy. In fact, this cell line underscores the limitation of aCGH and its interpretation.

In the absence of karyotypic analyses, the observed decrease in aCGH ratio is consistent with a

one-copy loss, leading most to believe that only one copy of the locus exists (ie. relative to a

normal diploid cell); when in reality two copies of the locus was found to be present against a

background of low-level numerical change for chromosome 19 and a triploid genome. Thus, this

cell line is representative of most OCas. The recent studies of the role of miRNAs in cancer

have highlighted the importance of regulation at the post-transcription level (Schickel et al.,

2008). To this end, we investigated the potential role of miRNAs in regulating KLK6 protein

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expression. miRNA profiling of OVCAR-3 showed the increased and decreased expression of

miRNAs consistent with other published reports (reviewed by Dahiya et al. (Dahiya and Morin,

2010)). miRNA prediction databases identified the hsa-let-7 family of miRNAs to be strong

candidates of regulating miRNAs for KLK6; with those hsa-let-7 family members showing

decreased expression in OVCAR-3. Indeed, hsa-let-7 has been shown to be frequently decreased

in expression in OCa (Dahiya and Morin, 2010). Several studies have shown that regions of

copy-number change in OCas house miRNAs that are subjected to concomitant changes in

expression (Croce, 2009; Dahiya and Morin, 2010; Zhang et al., 2008). aCGH and karyotypic

analysis by SKY, confirmed the decrease in expression of these putative regulating miRNAs

were associated with the net loss of copy-number. Previous studies (Chow et al., 2008; White et

al., 2010a; White et al., 2010b) demonstrated the ability for hsa-let-7f to directly target the 3’-

UTR of KLK6. Thus, with hsa-let-7a possessing the identical seed sequence that characterized

this miRNA family, the ability of this hsa-let-7 member to affect KLK6 expression was assessed.

Precursor hsa-let-7a was transiently transfected to OVCAR-3 and shown to reduce KLK6

expression in the culture supernatant as compared to the scrambled control. Moreover, it was

demonstrated that this miRNA could also weakly affect the expression of another KLK member,

KLK10. Therefore, it has been demonstrated that KLK6 expression is affected not only by its

copy-number, but also by the copy-number and subsequent expression of miRNAs predicted to

target it. Moreover, the post-transcriptional regulation by miRNA can act to both greatly affect

protein expression or to fine-tune protein expression, independent of RNA expression.

In conclusion, the studies summarized here support the hypothesis that the observed

overexpression of KLK6 protein in OCa is mediated in part by its copy-number, but also

by miRNAs targeting it post-transcriptionally; and such changes in copy-number of the

KLK locus and regulating miRNAs are subjected to chromosomal instability that are

defined by numerical and structural alterations in the OCa genome. Clinically, patients

with a net loss of the locus experience a trend for better DFS and OS; as do patients

without structural alterations along chromosome 19q.

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6.2 Future Directions

This body of work has demonstrated that KLK6 expression, in serous OCa, is influenced at the

genomic level and post-transcriptionally, by miRNAs; both by the copy-number KLK6 and by

the copy-number of the regulating miRNAs. Moreover, we have demonstrated that these genes

experience copy-number heterogeneity mediated by the underlying genomic instability that

characterizes these cancers. These findings have added to the current understanding of KLK6

protein expression, integrating regulation at the level of DNA and RNA; not only of KLK6, but

of the miRNA(s) regulating it. However, because miRNAs can potentially target hundreds of

mRNAs, there exists the opportunity to identify additional and new biomarkers with clinical

significance. Therefore, elevated KLK6 levels may coincide with the differential modulation of

other proteins and together serve as important biological signatures in OCa.

To this end, improvements in mass spectrometry (MS) now make it possible to identify

differentially expressed proteins (Kulasingam and Diamandis, 2008). It can be envisioned that

these comparative proteomic technologies can identify the differentially expressed proteins

regulated by changes in these miRNAs. One such technique, termed Stable isotope labeling by

amino acids in cell culture (SILAC) (Ong et al., 2002) allows for the detection of differences in

protein abundance between two (or more) samples. In SILAC, the cells are grown in media,

incorporating either “light” or “heavy” essential amino acids (typically lysine and arginine);

analogous to fluorescently labeled “normal” vs. fluorescently labeled “tumour/test” in array

comparative genomic hybridization (aCGH) or expression studies. When proteins from different

conditions are combined and analyzed simultaneously by MS, heavy and light-labeled peptides

of the same proteins will generate spectra that are slightly different due to a shift in mass caused

by the heavy isotopes, enabling relative quantification (Cox et al., 2009).While SILAC has only

been utilized by a handful of groups (Baek et al., 2008; Cox et al., 2009; Ebner and Selbach,

2011; Huang et al., 2012; Lossner et al., 2011; Mestdagh et al., 2010; Ong et al., 2002; Selbach

et al., 2008; Vinther et al., 2006; Yan et al., 2011; Yang et al., 2010); no one to date has

systematically used this method for OCas. Therefore, there is the opportunity to identify these

differentially expressed and putative biomarkers, as related to the currently accepted biomarkers,

CA-125 and HE4; and in addition to promising biomarkers such as KLK6.

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Indeed, preliminary experiments performed by our group (Bayani et al., 2012. unpublished

observations), OVCAR-3 cells that were differentially labeled in culture with either “heavy”

isotopic arginine and lysine, or “light” isotopic arginine and lysine; were transiently transfected

with hsa-let-7a, as described in Chapter 5. ELISA confirmed the expected decrease in KLK6

protein expression in the culture media of the light-labeled-hsa-let-7a-transfected OVCAR-3

cells in comparison to the heavy-labeled-scramble-transfected OVCAR-3 cells. From these

cultures, total protein was extracted from the cell pellets and equal amounts were combined and

processed for MS. The resulting mass spectra can then be analysed using the freely available

MaxQuant Software (htpp://smsquant.sourceforge.net/)(Cox et al., 2009). It is expected that a

number of predicted targets will be modulated by the introduction of hsa-let-7a, and could serve

as putative OCa biomarkers. However, the assay is limited by a number of factors. Firstly, it

should be kept in mind that the miRNA targets listed in the aforementioned databases constitute

in silico predictions, and require independent validation. Therefore, it is conceivable for this

method to identify new miRNA targets while providing some supporting data to exclude others.

Secondly, the reliable identification of proteins by MS, depends heavily on the multi-stepped and

careful sample preparation and handling; as well as operation of the equipment by technical

experts. Thirdly, the variable inefficiencies of the transient transfection methods between

experiments could result in discrepancies during validation, thus a stable transfection approach

would provide a reliable platform for consequent validation experiments. Moreover, the model

presented here should be repeated not only in this cell line, but among other OCa cell lines and

other miRNAs to generate a more comprehensive miRNA-based differential signature. Only

then, and through biomarker candidate selection criteria (Kulasingam and Diamandis, 2008;

Kulasingam et al., 2010), can these putative biomarkers be tested rigorously in clinically-

annotated retrospective specimens. It is in this way that leading-edge proteomic techniques may

be integrated into the now mature and growing (epi)genomics and transcriptomic data presently

available.

Finally, the data linking miRNAs to KLK6 described in this body of work, may also reveal

another biological function for the increased mRNA KLK6; not only in cancers, but during

normal physiological and diseased states where copy-number is not affect - whereby the

increased KLK6 mRNA expression may serve as a natural miRNA “sponge” to sequester

miRNAs from other mRNA targets (Ebert and Sharp, 2010). Therefore, the presence of

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functional, variant and non-coding KLK6 transcripts (Bayani and Diamandis, 2011) could serve

as miRNA decoys to modulate seemingly unrelated biological processes to those of already

associated with KLK6. To date, there is no research examining the role of KLK6 mRNA in this

context.

Despite the fact that the KLK locus was identified over a decade ago, the contributions to both

normal and disease physiology have yet to be fully elucidated. Clearly these future studies could

prove to be very interesting and moving our understanding of the KLK family forward.

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