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TELOMERES AND PEROXISOME PROLIFERATOR-ACTIVATED RECEPTOR GAMMA IN THE DEVELOPMENT OF HUMAN BREAST CANCER

By

Fariborz Rashid-Kolvear

A Thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Laboratory Medicine and Pathobiology Faculty of Medicine University of Toronto

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While these forms may be included Bien que ces formulaires in the document page count, aient inclus dans la pagination, their removal does not represent il n'y aura aucun contenu manquant. any loss of content from the thesis. Canada Telomeres and peroxisome proliferator-activated receptor gamma in the development of human breast cancer

A Ph.D. thesis by Fariborz Rashid-Kolvear submitted in conformity with the requirements for the degree of Doctor of Philosophy, Graduate Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, 2008

Abstract

Breast cancer is the most common internal malignancy afflicting North American women. The development of breast cancer involves a sequential progression through defined clinical and pathological stages beginning with normal epithelium and progressing to hyperplasia, ductal carcinoma in situ (DCIS), invasive ductal carcinoma

(IDC), and culminating in metastatic disease. This progression into metastatic disease is characterized by many molecular alterations, including the shortening of total telomere length. Cancer cells containing critically short telomeres activate telomerase to avoid cell death and it has been demonstrated to occur in 90% of breast carcinoma. Furthermore, telomerase activity has been shown to be modulated by ligands for peroxisome proliferator-activated receptor gamma (PPARy). The integral role of telomeres in cancer progression has motivated extensive research into targeting telomeres for diagnosis and treatment of breast cancer.

In the present study, the telomere shortening on chromosome 17q was analyzed in relation to average telomere length in normal epithelium, DCIS and IDC to determine if

ii the shortening of specific telomeres can be used as a molecular marker for breast cancer progression. Furthermore, the effect of the PPARy ligand on telomerase

activity and gene expression was examined in MDA-MB-231 breast cancer cells to determine if PPARy ligands have an anti-telomerase effect.

Results indicated that telomere shortening on chromosome 17q, harboring several genes involved in breast cancer development, is higher than the average shortening of all telomeres. Furthermore, troglitazone reduced telomerase activity in MDA-MB-231 cells independently of PPARy activation. In addition, troglitazone inhibited cell proliferation and the cell cycle signaling pathway on MDA-MB-231 and MCF-7 breast cancer cell lines through different mechanisms. Results from microarray comparative genomic hybridization also indicated that the PPARy gene is lost or deleted in 58% of clinical breast cancers.

In conclusion, the high shortening of telomere on chromosome 17q suggests that telomere shortening is not a linear process and may serve as a marker for breast cancer progression. Furthermore, troglitazone demonstrated anti-telomerase and anti­ proliferative activity in breast cancer cells. However, these effects vary depending on the cell type and experimental models employed. Finally, the loss of the PPARy gene in over

50% of breast cancers indicates a potential tumor suppressor role for PPARy in the development of breast cancer.

m ACKNOWLEDGMENTS

I think this is the best time to express my deepest gratitude to all my teachers and

mentors from the time I began learning to now having a Ph.D. degree. I started learning

from a small family in a low middle neighborhood in Tehran and end up at one of the

most prestigious universities in the world: the University of Toronto. I owe this amazing

progression to many people. As a first teacher, my mother taught me the essence of life,

love, hope, humanity, and forgiveness. My wife was my excellent teacher as well. During

many years of marriage, she showed me true love in real life. Her lessons were part of my

success. And finally, my PhD supervisor, Dr. Susan Done, wrapped up my years of

training by teaching me how to think scientifically. I have been fortunate to have Susan

as a supervisor. Susan's outstanding energy, enthusiasm, and her excellent supports made

my learning experiences in her research laboratory very rewarding. I also wish to thank

my supervisory committee, Dr. Jeremy Squire and Dr. Ming-Sound Tsao for their

outstanding advice and input during my time as a graduate student. It was an excellent

opportunity for me to have Dr. Jeremy Squire's supports during my study. His great

supports opened a big door for my future. I will always remember this.

I would like to thank all the collaborators I have worked with, especially Michael

Taboski. I would also like to thank Johnny Nguyen. My experience as a graduate student would not have been the same without his outstanding assistance. I wish to express my gratitude to Melania Pintilie for her patience and continuous assistance with statistics. I also wish to acknowledge the members of Dr. Done's laboratory for their help and friendship: Dr. Dongyu, Dr. Danh Tran-thanh Wang, Vietty Wong, Dr. Chunjie Wang,

Yanxia Wen, Keisha Warren, and especially Dr. Vladimir Iakovlev. It was a pleasure to

IV have been friends and collaborated with Dr. Vladimir Iakovlev during my graduate studies. This acknowledgment would not be completed without special thanks to the coordinator of the Department of Laboratory Medicine and Pathobiology, Dr. Harry

Elsholts. I leaned a lot from his advices. I would also like to take this opportunity and thank all my friends as well as all people in the Department of Laboratory Medicine and

Pathobiology, the Ontario Cancer Institute, Princess Margaret Hospital, the Faculty of

Medicine, and the University of Toronto for their direct and indirect help and support.

Last but not least, I owe a little boy a lot. My son, Matin, will always be remembered in my heart for his amazing patience, support and unbelievable advice at the time that I was hopeless and lonely. I wish him my best for ever.

v To-

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Summer 2008 UvtiverbCty of Toronto- TABLE OF CONTENTS CHAPTER 1

Literature review 2

1.1 Breast cancer 3

1.1.1 Anatomy of human breast 3

1.1.2 Carcinoma of the breast 3

1.1.3 Duct carcinoma in situ 4

1.1.4 Invasive and metastatic breast carcinoma 5

1.1.5 Breast cancer treatment 5

1.2 Telomere 6

1.2.1 Telomere Structure 6

1.2.2 Telomere-associated proteins 7

1.2.3 Telomere Function 8

1.2.4 The length of telomere and chronic diseases 8

1.2.5 The length of telomeres in cancers 9

1.2.6 The length of telomeres in breast cancer 9

1.2.7 The length of telomeres in other types of cancer 12

1.2.8 Telomere maintenance 12

1.3 Telomerase 13

1.3.1 Regulation of hTERT 14

1.3.2 Regulation of hTR 14

1.3.3 Telomerase associated proteins 15

1.3.4. Human telomerase-associated protein 1 15

vii 1.3.5 Heat shock protein 90 chaperone complex 15

1.3.6 Dyskerin 16

1.3.7 hStau 17

1.3.8 L22 17

1.3.9 La autoantigen 17

1.3.10 Heterogeneous nuclear ribonucleoproteins (hnRNPs) 18

1.3.11 Telomerase structure 18

1.3.12 Telomerase in cancer 18

1.3.13 Alternative Lengthening of Telomeres (ALT) 19

1.3.14 ALT in cancer 19

1.3.15 Telomerase as a target for cancer treatment 20

1.4 Nuclear receptors 20

1.4.1 Peroxisome proliferator-activated receptors 21

1.4.2 Peroxisome proliferator-activated receptor a 21

1.4.3 Peroxisome proliferator-activated receptor (3/8 22

1.4.4 Peroxisome proliferator-activated receptor y 22

1.4.5 PPARy structure 23

1.4.6 Co-factors 24

1.4.7 Co-activators 24

1.4.8 Co-repressors 25

1.4.9 PPARy ligands 25

1.4.10 Natural ligands 25

1.4.11 Dual agonists 26 1.4.12 Synthetic ligands 26

1.4.13 PPARy function 27

1.4.14 The Role of PPARy in lipid metabolism 27

1.4.15 The Role of PPARy in 27

1.4.16 The Role of PPARy in cancers 29

1.4.17 PPARy and breast cancer 30

1.4.18 The effects of PPARy ligands on cell cycle regulation 31

1.4.19 The effects of PPARy ligands on apoptosis 33

1.5 Rationale 33

1.6 Hypothesis 37

1.7 Specific aims 37

CHAPTER 2

Telomere length on chromosome 17q shortens more than global telomere length in the development of breast cancer 39

2.1 Abstract 40

2.2 Introduction 41

2.3 Materials and Methods 43

2.3.1 Sample collection 43

2.3.2 Quantitative fluorescence in situ hybridization (Q-FISH) 43

2.3.3 Image capturing and analyzing 44

2.3.4 Immunohistochemical staining of paraffin sections for HER2 45

2.3.5 Data analysis 46

2.3.6 Statistical analysis 46

ix 2.4 Results 47

2.5 Discussion 48

CHAPTER 3

Troglitazone suppresses telomerase activity independently of PPARy transcriptional activity in breast cancer cells 61

3.1 Abstract 62

3.2 Introduction 63

3.3 Materials and Methods .65

3.3.1 Materials 65

3.3.2 Cell Culture 65

3.3.3 Cell toxicity and cell viability assay 66

3.3.4 Western blot analyzing 66

3.3.5 Real-time RT-PCR 68

3.3.6 Stable shRNA Mediated Repression of PPARyin MDA-MB-231 Cells 68

3.3.7 Telomeric repeat amplification protocol (TRAP) assay 69

3.3.8 The NKI microarray dataset analysis 70

3.3.9 Statistical analysis 70

3.4 Results 71

3.4.1 Evaluating the Expression of PPARy in different breast cancer cell lines 71

3.4.2 Determining the expression level of hTERT and telomerase activity in

MDA-MB-231 ells 71

3.4.3 The cell toxicity effect of troglitazone 71

3.4.4 Troglitazone reduces telomerase activity in a time and dose dependent

x manner 72

3.4.5 Troglitazone suppresses hTERT transcription 72

3.4.6 Reduction in telomerase activity is independent from the transcriptional role of

PPARy 73

3.4.7 Troglitazone does not induce apoptosis 74

3.4.8 Troglitazone does not induce the differentiation of MDA-MB-231 74

3.4.9 The expression of hTERT is not correlated with the expression of PPARy in

clinical samples 75

3.4.10 The level of hTERT expression is negatively correlated with ER status in

breast cancer samples 76

3.5 Discussion 76

CHAPTER 4

Genome-wide analysis of differential expression of troglitazone-mediated genes in the estrogen receptor negative breast cancer cell line, MDA-MB-231, and the role of

PPARy in the progression of breast carcinoma 103

4.1 Abstract 104

4.2 Introduction 105

4.3 Materials and Methods 106

4.3.1 Materials 106

4.3.2 Cell culture 106

4.3.3 Flow Cytometry analysis 107

4.3.4 Microarray analysis 108

4.3.5 Real-time RT-PCR 109

xi 4.3.6 Nuclear Extraction 109

4.3.7 Netherlands Cancer Institute (NKI) dataset microarray analysis 110

4.3.8 Array comparative genomic hybridization 110

4.3.9 Sample collection Ill

4.3.10 Statistical analysis Ill

4.4 Results Ill

4.4.1 Troglitazone reduces the number of MDA-MB-231 breast cancer cells by

suppressing Gl—>S cell cycle transition Ill

4.4.2 Troglitazone reduces the phosphorylation status of Rb 113

4.4.3 Troglitazone does not change the protein expression of cyclin Dl and D3 in the

MDA-MB-231 cells 113

4.4.4 mRNA expression profile of MDA-MB-231 cells in response to troglitazone

114

4.4.5 Troglitazone reduces the protein expression of cyclin E2 and CKD2 in MDA-

MB-231 cells 116

4.4.6 Real time RT-PCR confirmation of differentially up-regulated genes from

microarray analysis 117

4.4.7 The localization of PPARy remains unchanged in response to troglitazone in

breast cancer cell lines 117

4.4.8 DNA copy number of PPARy gene is altered in breast cancer patients 118

4.4.9 The level of PPARy expression correlates with good clinical prognostic

parameters in breast cancer patients 118

4.5 Discussion 119

xii CHAPTER 5

Discussion and future directions 157

5.1 Discussion 158

5.2 Future directions 167

REFERENCES LIST 171

Xlll LIST OF TABLES

Table 2.1 Comparing telomere shortening of chrl7q in DCIS and normal tissue with

total telomere shortening in the same tissues 56

Table 2.2 Association between telomere length and HER2 expression 59

Table 4.1 Down-regulated genes according to microarray analysis 146

Table 4.2 Up-regulated genes according to microarray analysis 150

Table 4.3 List of functional group from GO analysis 152

Table 4.4 List of functional group from KEGG analysis 153

Table 4.5 Genes in cell cycle group acquired from functional GO analysis 154

Table 4.6 Genes in cell cycle group acquired from functional KEGG analysis 155

xiv LIST OF FIGURES

Figure 2.1 Labeling strategy used to flag pantelomeres and subtelomeric region of

chromosome 17q 51

Figure 2.2 FISH analysis of breast FFPE sections 52

Figure 2.3 Comparison of the normalized intensities of telomere length between normal

breast epithelial tissue, DCIS and IDC 54

Figure 2.4 Distribution of telomere length between DCIS and DCIS associated with

IDC 55

Figure 2.5 FFPE tissue sections were used for immunohistochemistry using HER2

antibody 57

Figure 3.1 The expression of PAPRy mRNA, the level of PAPRy protein in various

breast cancer cell lines 82

Figure 3.2 The expression level of hTERT and telomerase activity in three different

breast cancer cell lines 83

Figure 3.3 IC50 of troglitazone was measured by MTS assay 85

Figure 3.4 Troglitazone suppresses the activity of telomerase in a dose dependent

manner 86

Figure 3.5 The effect of troglitazone on telomerase activity and hTERT expression is

time dependent 88

Figure 3.6 The suppression role of troglitazone on hTERT is independent from

PPARy 90

Figure 3.7 Suppression of the expression of PPARy by shRNA 91

xv Figure 3.8 Evaluation of hTERT mRNA expression and telomerase activity in PPARy

knocked-down cells 92

Figure 3.9 The effect of troglitazone on telomerase activity was measured in the

absence of PPARy 93

Figure 3.10 Troglitazone does not induce apoptosis in MDA-MB -231 95

Figure 3.11 Cell differentiation was not induced by troglitazone and troglitazone had no

effect on apoptosis 97

Figure 3.12 The expression of hTERT is independent from PPARy in clinical breast

cancer samples 98

Figure 3.13 Investigating the correlation between hTERT mRNA expression and clinical

prognostic parameters 100

Figure 4.1. Troglitazone reduces the number of cells 126

Figure 4.2. Troglitazone suppresses cell proliferation 128

Figure 4.3. Troglitazone inhibits Gl—>S cell cycle transition 129

Figure 4.4. Time course inhibitory effect of troglitazone on S phase cell cycle transition

131

Figure 4.5. The effect of troglitazone on phosphorylation status of Rb protein 132

Figure 4.6. The response to troglitazone is different in ER-negative and -positive breast

cancer cells 133

Figure 4.7. The effect of troglitazone on gene profiling of MDA-MB-231 cells 134

Figure 4.8. Common list of differentially expressed in cell cycle group identified by both

GO and KEGG functional annotation 135

Figure 4.9. Real time RT-PCR was used to analyze the expression of common genes

xvi identified by GO and KEGG annotation 136

Figure 4.10. The response to troglitazone is different in ER-negative and -positive breast

cancer cells 137

Figure 4.11. Real time RT-PCR confirmation of selected genes from the differentially up-

regulated list obtained from microarray analysis 138

Figure 4.12. Troglitazone does not change the localization of PPARy protein in MDA-

MB-231 and MCF-7 cell lines 140

Figure 4.13. Evaluating DNA copy number of PPARy gene in breast cancer patients... 141

Figure 4.14. Analyzing the expression of PPARy mRNA in NKI dataset 142

Figure 4.15. Investigating the correlation between PPARy mRNA expression and clinical

prognostic parameters 143

Figure 4.16.Troglitazone shows different effects on MDA-MB-132 and MCF-7 cell lines

145

xvii ABBREVATIONS

15d-PGJ2 15-deoxy-A ' prostaglandin J2

70GS 70-gene prognosis signature

aCGH Array comparative genomic hybridization

ACS acetyl-CoA synthetase

AF activation function

ALT alternative lengthening of telomeres

aP2 fat-specific adipocyte P2

APB ALT associated promyelocytic leukemia (PML) body

AR androgen receptor

BADGE Bisphenol A diglycidyl ether

CaP prostate cancer

CAP c-CBL-associated protein

CDK cyclin-dependent kinase

CDKI CDK inhibitor

CIN chromosomal instability

DBD DNA binding domain

DCIS duct carcinoma in situ

DKC dyskeratosis congenita

D-loop DNA displacement loop

DSBs double-stranded DNA breaks

ER estrogen receptor

FATP fatty-acid transport protein FBS fetal bovine serum

FCS fetal calf serum

FFA free fatty acid

FFPE formalin-fixed paraffin-embedded

FISH fluorescence in situ hybridization

FLOW-FISH flow cytometric fluorescence in situ hybridization assay

GR glucocorticoid receptor

GSK3p glycogen synthase kinase 3-beta

H&E hematoxylin and eosin

HAT histone acetyltransferase

HDAC histone deacetylase

HDL high density lipoprotein hnRNPs heterogeneous nuclear ribonucleoproteins

HPIN high-grade prostatic intraepithelial neoplasia

HRE hormone response elements hsp90 heat shock protein 90 hStau human staufen hTERT human telomerase reverse transcriptase hTR human telomerase RNA

IDC invasive carcinoma

EN intraepithelial neoplasia

IR -stimulated receptor

IRS insulin receptor substrate

xix K19 keratin 19

LBD ligand binding domain

LN lymph node involvement

LPL lipoprotein lipase

MMTV mouse mammary tumor virus

MR mineralocorticoid receptor

MTS 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-

sulfophenyl)-2H-tetrazolium, inner salt

Muc-1 mucin-1

NHR nuclear hormone receptor

NIH National Institutes of Health

NIMH National Institute of Mental Health

NKI Netherlands Cancer Institute

ONRs orphan nuclear receptors

PARP poly (ADP-ribose) polymerase

PML promyelocytic leukemia

PNA peptide nucleic acid

Pol RNA polymerase

POT1 protection of telomere

PPAR peroxisome proliferator-activated receptor

PPARy peroxisome proliferator-activated receptor gamma

PPRE peroxisome proliferator response element

PR progesterone receptor

xx pRb hyperphosphorylated Rb

FTP phosphotyrosine phosphatase

Q-FISH quantitative fluorescence in situ hybridization

RAP1 repressor activator protein 1

RAR all-trans retinoic acid receptor

Rb retinoblastoma

RNP ribonucleoprotein

R.XR 9-cis retinoic acid receptor

snoRNA classical small nucleolar RNA

TANK1 tankyrase 1

TC telomere DNA content

TDLU terminal duct lobular unit

TEP1 telomerase-associated protein 1

TIN TRF 1-interacting protein

T-loop telomere loop

TNFa tumor necrosis factor a

TR thyroid hormone receptor

TRAP telomeric repeat amplification protocol

TRF telomere repeat factor

TRF telomeric repeat fragment

TZD

UCP uncoupling proteins

VDR vitamin D3 receptor VSMC vascular smooth muscle cell

WS wound signature

xxu Chapter 1

i Literature Review

2 1.1 Breast cancer

Breast cancer is the second most common type of cancer after lung cancer and the most common malignancy affecting women worldwide, increasing significantly since early 1970s (1). It is estimated that there will be 22,400 new cases of breast cancer and more than 5,300 female deaths from this disease in 2008 in Canada (2). One in nine

Canadian women develop breast cancer during their lifetimes, and 1 in 28 women are expected to die from it (2) causing a big challenge in medicine. Despite numerous studies on breast cancer, the exact mechanisms underlying the initiation, development, and progression of this disease are not fully understood.

1.1.1 Anatomy of human breast

The mature female breast is composed primarily of glandular tissue, fat, and connective tissue centered over the pectoralis major muscle of the chest wall. The glandular tissue consists of ducts and lobules and is surrounded by connective tissue.

Each lobule is a cluster of several acini and terminal ducts called a terminal duct lobular unit (TDLU). In the TDLU, milk is produced in the acini and the terminal ducts transport milk from the acini to the nipple. Both the acini and ducts are made of two layers: the outer layer (basal membrane side) is composed of myoepithelial cells and the inner layer

(luminal side) lined with epithelial cells. The epithelial cells are responsible for synthesis and production of milk in the mammary glands. More importantly, carcinoma appears to arise from these epithelial cells (3).

1.1.2 Carcinoma of the breast

3 Familial predisposition to breast cancer has been the focus of intense studies

resulting in the identification of two susceptibility genes; BRCA1 and BRCA2 (4-6).

However, the majority of breast cancer cases is sporadic and occurs in women without a

strong family history. This implies that other genes must be involved. Despite the

heterogeneity, the natural history of breast cancer is thought to involve a sequential

progression through defined clinical and pathological stages beginning probably as early

as normal epithelium, progressing to hyperplasia, duct carcinoma in situ (DCIS) followed

by invasive carcinoma (IDC) and culminating in metastatic disease (7) . Carcinomas are

the most common breast malignancy and can be classified as in situ or invasive

carcinomas.

1.1.3 Duct carcinoma in situ

Duct carcinoma in situ is characterized by a proliferation of presumably malignant epithelial cells within the mammary ductal-lobular system without light- microscopic evidence of invasion into the surrounding stroma. However, DCIS has been found not to be a single disease. Rather, it is a heterogeneous group of lesions (8). While

DCIS itself presents no risk of metastasis, a proportion of patients with DCIS will develop invasive recurrences. Accurate identification of this latter group would allow increased surveillance and more aggressive treatment. Certain pathological features have already been associated with an increased risk of invasive breast cancer recurrence. These include size, the presence of comedo necrosis, and the distance between the DCIS lesion and the periphery of the surgical specimen (width of resection margin) (9-17). However, the development of histological grading schemes for DCIS has been hampered by poor

4 reproducibility of pathologic reporting of DCIS and limited follow-up data (18).

1.1.4 Invasive and metastatic breast carcinoma

Although the majority of early-stage breast cancers (lymph node negative) are not

life threatening, a small proportion of cases will invade adjacent tissue and progress to

metastatic breast cancer. Invasive carcinoma is defined as the extension of cancer cells

beyond the basement membrane into the adjacent tissue. On microscopic examination, it

is frequently observed to extend directly from ducts containing DCIS. It seems that the

invasive behavior of primary breast tumor cells and their potential to metastasize to other

organs such as bones, liver, brain, and lungs is the major cause of death in patients with

breast cancer.

1.1.5 Breast cancer treatment

Many standard chemotherapeutic agents currently used to treat breast cancer are

relatively non-specific and act on all rapidly dividing cells. There is a desperate need for

new therapies to treat breast cancer as many cases fail to respond to current

chemotherapeutic agents. Specifically targeted therapies such as Trastuzumab

(Herceptin) and tamoxifen in HER2 and estrogen receptor (ER)-positive breast cancers respectively have been developed (19). However, despite the introduction of these specifically targeted therapies, special agents have not yet been developed for the treatment of ER-negative breast cancers. This has motivated considerable effort toward finding novel therapeutic approaches for the treatment of this group of breast cancers.

Since cell immortalization is a critical and rate-limiting step in cancer progression

5 (20), it has been shown that agents inhibiting cell immortalization can be considered as a

novel therapeutic approach for cancers. It is known that telomerase, the enzyme that

protects the ends of chromosomes, is active in many type of cancers including breast

cancers but inactive in adjacent normal tissues (21-23)). If it were possible to target cells

with active telomerase then this strategy could be used to treat breast cancer.

1.2 Telomere

The ends of chromosomes are capped by specialized nucleoprotein complexes

called telomeres. A telomere is a specialized structure, which enables cells to recognize

the ends of chromosomes from intrachromosomal double-stranded DNA breaks (DSBs)

that result from radiation, mechanical damage or chemical mutagens. By preventing

fusion of chromosomal ends and degradation of DNA, telomeres inhibit a DNA damage

cellular response and provide genomic stability (24). Telomeres consist of double-

stranded DNA containing a highly conserved G-rich repetition of a (TTAGGG)n

sequence, ending in a 150 to 200 nucleotide single-stranded overhang at the 3' end (G-

strand overhang) (25, 26). The length of telomeres varies between chromosomes and species with an average of 10 to 15 kb and 25 to 40 kb in human and mice respectively

(27-29).

1.2.1 Telomere Structure

Using electron microscopy analysis, Griffith et al. found that the ends of telomeres form a lasso structure by invasion of the 3' telomeric overhang into the duplex telomeric repeat array (29). This invasion creates a telomere loop (T-loop) and a small

6 three-stranded DNA displacement loop (D-loop) (29, 30). This structure is dynamic and

depends on a number of proteins associated with the telomere (31). This model can

explain the general mechanism to protect the chromosome ends from recognition by the

cellular DNA repair processes. However, there are other models such as recruiting

specialized proteins or a loop structure in which the 3' overhang is sequestered in the

interior of the loop but not embedded in the adjacent duplex that can also explain this

protection (32).

1.2.2 Telomere-associated proteins

The lasso structure of the telomere end critically depends on the recruitment of

telomere-associated proteins (known as sheltering) (33). These proteins play important roles in regulating telomere length, integrity, and function that can be classified into three

groups. In the first group, proteins directly bind to the single-stranded 3' G-rich overhang.

This includes the protein Protection of Telomere (POT1) (34, 35). The second group is implicated in double-stranded DNA. This group includes Telomere Repeats Factor 1 and

2 (TRF1, TRF2) (36). TRF1 is a negative regulator of telomere length homeostasis (37) and TRF2 is involved in the t-loop establishment (29). The third group consists of interconnecting proteins including TRF1-interacting protein 2 (TIN2) (38), Repressor

Activator Protein 1 (hRAPl) (39), tankyrase 1 (TANK1) (40) and 2 (TANK2) (41), and

TPP1 (42). In addition, there are other proteins recruited by TRF1 and TRF2 to the telomere that are known to participate in DNA repair including the RAD50-MRE11-

NBS1 (RMN) complex, ATM (43), Ku (DNA end binding subunit) (44) and DNA-PKcs

(catalytic subunit) (45).

7 1.2.3 Telomere Function

In a remarkable publication, Hayflick and Moorhead found that normal human

fibroblasts have finite life-spans in vitro (46). The mechanism underlying this observation

was not clear until Olovnikov et al. suggested that the cause of cellular senescence is the

gradual shortening of telomeres (47, 48). This shortening is mainly due to the inability of

DNA polymerase to replicate the end of the chromosome during lagging strand synthesis

and is known as the "end-replication problem" (49). During each cell division, telomeres

shorten by 50-100 bp until they become critically short inducing senescence (50). It has

been found that critically short telomeres make the ends of chromsomes unprotected

inducing DNA damage responses (51-54).

In addition, telomere shortening leads to the disruption of telomere structure

because of dysfunction or loss of telomere associated protein activity such as TRF2

inducing cell cycle arrest and activation of the ATM-p53 DNA damage-response

pathway (55-57).

1.2.4 The length of telomeres and chronic diseases

In humans, telomeres play a crucial role in the life of the cells by providing a

protective cap on chromosome ends and their shortening is associated with aging (58). A

correlation between telomere length in peripheral blood cells and increased mortality has

been documented in people over 60 years old, suggesting that telomere length could represent a prognostic marker of human aging and age related diseases such as an ATM- like protein deficiency in ataxia telangiectasia (58). However, no correlation has been found with survival in the older individuals (humans over 85 years), indicating that other

8 factors become dominant at very late age (59). In addition, accelerated telomere

shortening has been seen in a variety of chronic diseases that elevate the rates of cell

turnover (60), atherosclerosis and associated cardiovascular disease (61), chronic liver

diseases (62), chronic inflammatory bowel disease (63), chronic HIV infection (64),

different forms of anemia (65, 66), and Alzheimer's disease (67). Some studies have

shown that telomere shortening is linked to disease progression and the development of

chromosomal instability. This often complicates the end-stage of chronic disease and

possibly contributes to the increased rate of malignancies in old age (63, 68-72).

1.2.5 The length of telomeres in cancers

Genomic instability is widely accepted as a hallmark of cancer cells, which

contributes to the evolution of cancers. A line of evidence suggests a significant relationship between telomere maintenance and genomic instability (73). The role of

telomere alteration in tumorigenesis and cancer development has been studied and

established at the molecular level (73). It has been shown that excessively shortened or

dysfunctional telomeres result in end fusions and the onset of chromosomal instability

(CIN) through break-fusion-bridge cycles.

The length of telomeres are generally shorter in human tumors compared to their surrounding normal tissue (74-80). However, a few reports found elongation of telomeres in tumors compared to the adjacent normal tissues in a subset of patients (81-83).

1.2.6 The length of telomeres in breast cancer

The length of telomeres in breast cancer has been studied by several investigators.

9 They found that the length of telomeres in breast carcinomas are shorter than their length

in normal breast epithelial tissues (76, 79, 81, 84-87). However, the role of telomere

length as a prognostic factor and its correlation with clinical parameters is controversial.

For the first time, telomere length in breast cancer was published by Odigari et al.

in 1994 (84). Using Southern blot analysis to measure the length of telomeres, they

observed significant erosion in telomeric length in all breast tissues compared to placental

DNA. They also found a significant correlation between telomere shortening and

histologic grade in breast carcinoma. However, no correlation was found between the

length of telomere and HER2 gene amplification, tumor size, clinical stage, and steroid

receptors, as prognostic parameters in this study. The length of telomeres in breast cancer

was also investigated by other groups (85, 86). Rogalla et al. studied the length of

telomeres in 85 breast cancer samples (85). They noticed that the mean of telomeric

repeat fragment (TRF) length varied between samples. They also found no correlation between TRF length and age of patients, tumor size, lymph node status, and steroid receptor status. This group concluded that TRF size can not be considered as a limiting or a promoting factor for growth of breast cancer (85). In agreement with this finding,

Takubo et al. found that the degree of telomere reduction is not associated with breast cancer grade or the age of patients (86). The same observation was also reported by Rha et al. (88). Interestingly, Griffith et al. found a significant correlation between the content of telomere DNA, metastasis, and chromosomal aneuploidy in human breast cancer using slot-blot titration to measure telomere DNA content (87). This group suggested that telomere DNA content may have prognostic value, although they found no correlation between telomere DNA content and tumor size, grade, stage, fraction of cells in S-phase,

10 and the age of patients. It is important to note that all these investigators studied the

length of telomeres in bulk tissue, which is contaminated with variable amounts of

normal breast epithelial tissue. To avoid this problem, Meeker et al. used fluorescence in

situ hybridization (FISH) to measure the length of telomeres directly in ducts and lobules

of breast intraepithelial neoplasia (JEN) (77). They used a specific peptide nucleic acid

(PNA) probe to label telomeres and a second centromere specific PNA probe as a control.

Interestingly, unlike other investigators instead of applying both probes on the same

tissue section (74, 75, 79), they hybridized the tissue sections in parallel with either the

centromere or the telomere PNA probe in their study. Using this approach, they found

that the majority of DCIS samples contain markedly or moderately shortened telomeres.

More recently, Iwasaki et al. examined the role of telomere length of lymphocytes

in breast cancer susceptibility and radiosensitivity (89). They determined the length of

telomeres using a flow cytometric fluorescence in situ hybridization assay (FLOW-

FISH). Using this approach, no significant reduction in telomere length in cases was

found compared to controls. This observation led the authors to conclude that telomere

length does not appear to be predictive of acute skin reactions to radiotherapy (89).

More importantly, all these groups measured the average length of all telomeres in their studies. Whereas, it has been suggested that the length of the shortest telomere rather than average telomere length is the critical factor determining cell fate because the loss of telomere function may occur preferentially on chromosomes with critically short telomeres (90). However, little is known about the length of the shortest telomere and its role in the progression of cancers.

11 1.2.7 The length of telomeres in other types of cancer

It has been shown that the length of telomeres is significantly shorter in most

types of cancer (74, 75, 78, 82, 83, 91-100). However, the role of telomere length as a

prognostic factor has been found to be controversial. It has been shown that telomere

shortening is correlated with death and disease recurrence in patients with prostate cancer

(96, 97). However, no significant association was found between telomere shortening and nodal status, pathological grades, and the age of patients at diagnosis (96, 97). Similarly, other groups noticed a correlation between telomere shortening and poor prognosis in several other cancer types (75, 77, 78, 95).

In contrast, there is some evidence indicating that the length of telomeres is negatively correlated to poor prognosis. Using the terminal restriction fragment method,

Shirotani et al. discovered that the length of telomeres is reduced in the majority of small cell lung cancer samples, although a few cases showed elongated telomeres compared to control normal lungs (83). Interestingly, they found that longer telomeres are correlated to poor prognosis in colorectal carcinoma patients. This observation was supported by other groups finding that telomere length negatively correlated to clinical prognosis in various types of cancers (82, 98-100).

In general, these studies indicate that telomere shortening is a general observation in tumors compared to normal tissues. However, as it has been stated by these studies, the role of telomere length as a prognostic factor in solid cancers is controversial and the mechanisms underlying these differences remained to be understood.

1.2.8 Telomere maintenance

12 Progressive shortening of telomeres predisposes cancer cells to chromosomal

instability and genomic alteration early in cancer development (32). When telomeres

shorten to a certain threshold, an irreversible growth arrest known as replicative

senescence occurs through activation of p53 and Rb as DNA damage response pathways

(101, 102). In the absence of active DNA damage-response pathways, cells can

temporarily bypass this growth arrest and continue cell division and further telomere

shortening causing multiple chromosome end fusions and severe chromosomal

instability. These cells eventually reach a second proliferation block referred to as

"crisis", which is characterized by telomere dysfunction and cell death (103-107). To

escape from this "crisis" and to continue cell proliferation, cancer cells need to stabilize

their telomere length during cancer progression. To overcome this problem, most human

cancer cells stabilize their telomeres through activation of telomerase (108). However, it

has been shown that a small fraction of malignant cells maintain their telomere length

using a mechanism independent from telomerase activity known as alternative

lengthening of telomeres (ALT) (109). Using either of these two mechanisms leads to

cellular immortalization as a hallmark of human cancer.

1.3 Telomerase

Telomerase is a very large and complex ribonucleoprotein enzyme with an estimated mass over 600 kDa (110). In vitro, two components are essential for telomerase activity: human telomerase reverse transcriptase (hTERT) and the RNA template known as human telomerase RNA (hTR) (111). In humans, hTERT acts as the catalytic subunit of telomerase and tightly regulates the activity of this enzyme. However, many other

13 proteins are present in the telomerase complex to regulate the activity of telomerase (112-

119).

1.3.1 Regulation of hTERT

The mRNA expression level of hTERT has been shown to be the rate-limiting

step in telomerase activation. Because of its important role in telomerase activity,

extensive studies have been done to investigate the regulation of hTERT expression. The

results show that many factors can be involved in the regulation of hTERT expression

including MYC (120-123), estrogen receptor (124, 125), Ets (126), USF1/2 (127-129),

Spl(122, 130), p53 (131), Madl(132, 133), Rb (134, 135), andE2Fl (135). However, the

exact mechanism underlying the regulation of hTERT expression is not fully understood.

1.3.2 Regulation of hTR

hTR was the first telomerase component cloned in 1995 (136). hTR acts as a

template for the addition of telomeric repeat sequences (111). In its 5' exterimty, hTR contains a template region of 11 nucleotides 5'-CUAACCCUAAC-3' (136), whereas its 3' end, hTR has a classical small nucleolar RNA (snoRNA) structure called H/ACA motif

(137). H/ACA motif contains a special structure including a hairpin stem, a hinge sequence, a second hairpin stem, and an ACA sequence required for activity of hTR

(138). Mitcell et al. showed that two motifs within the independently stable H/ACA domain of hTR are required for the accumulation of the mature RNA in vivo (138).

The expression of hTR is tightly regulated at multiple levels by various mechanisms. The transcriptional regulation seems to be the main mechanism controlling

14 the expression of the hTR gene. A number of transcription factors including glucocorticoid, progesterone and androgen steroid hormone receptors, API, Ets, Spl,

Sp3, and Nuclear Factor-Y (NF-Y) have been found to regulate hTR expression. In addition, it has been demonstrated that pRb, MDM2, MAPK proteins also affect the expression of hTR (139).

1.3.3 Telomerase associated proteins

Telomerase associated proteins interact with the telomerase holoenzyme and are required for telomerase accumulation, activity, and function. These proteins include:

1.3.4 Human telomerase-associated protein 1

Telomerase-associated protein 1 (TEP1) is a component of the telomerase ribonucleoprotein complex (112). TEP1 was shown to interact specifically with mammalian telomerase RNA and catalyzes the addition of new telomeres on the chromosome ends. Northern blot analysis of both mouse and human tissues showed widespread expression of the gene, indicating that this component is unlikely to be linked with telomerase re-activation (140,141).

1.3.5 Heat shock protein 90 chaperone complex

The heat shock protein 90 (hsp90) chaperone complex is composed of at least hsp90, p23, hsp70, hsp40, and p60 (142). These proteins act as a foldosome mediating the assembly of a biologically active telomerase complex (143). It has been suggested that for the proper assembly of hTERT and hTR components in active telomerase, all five

15 chaperone proteins (hsp90, p23, hsp70, p60, and hsp40) are needed (113). Holt et al.

showed that hsp90 and p23 interact specifically with hTERT and influence its proper

assembly with hTR (113). They demonstrated that the dissociation of hsp90 and p23 with

hTERT blocks the assembly of functional telomerase in vitro and in vivo. In addition to

hsp90 and p23, it has been found that hsp70 chaperone also associates with hTERT in the

absence of hTR (144). In contrast to hsp90 and p23, which remain associated with

functional telomerase, hsp70 transiently associates with hTERT and dissociates from

hTERT when telomerase is folded into its active state. Similar to hsp70, hsp40 has been

found to interact with hTERT and supports the correct assembly of hTERT protein to

hTR resulting in active telomerase complex (144).

1.3.6 Dyskerin

Identified by Heiss et al. (145), it has been shown that dyskerin (DKC1) causes the X-linked recessive condition dyskeratosis congenita (DKC) and it has been predicted to be involved in the cell cycle and nucleolar function. In addition, primary dermal fibroblasts cultured from a DKC patient showed premature senescence with the presence of short telomeres (114). Further experiments also confirmed that defects in DKC patient cells arise solely from reduced accumulation of hTR (114). Accumulating data indicates that mutations in components of telomerase impair telomere maintenance and cause related human diseases such as the autosomal dominant form of dyskeratosis congenita.

As a consequence of hTR mutation, these patients show accelerated telomere shortening and a reduced lifespan (115, 146). These studies showed that changes in telomerase expression could influence human aging and disease. Furthermore, high turnover organs

16 such as the hematopoietic system or the intestinal epithelium can be easily affected by

telomerase dysfunction (147).

1.3.7 hStau

Human Staufen (hStau) is a double-stranded RNA-binding protein with multiple

double-stranded RNA-binding domains (148), which play a role in RNA transportation

and localization (149, 150). Le et al. showed that hSatu interacts with hTR in vivo and

can be considered as a telomerase-associated protein (116).

1.3.8 L22

L22 is another RNA-binding protein (151, 152), which has been found to interact

with hTR as a telomerase-associated protein (116).

1.3.9 La autoantigen

La protein has been found to interact directly with hTR and telomerase

ribonucleoprotein (RNP) and its expression level affects telomere length in vivo (117). As

a conserved RNA-binding phosphoprotein, La autoantigen is implicated in several

cellular and viral RNA-associated processes. The most ubiquitous function of La is its interaction with newly synthesized RNA polymerase (Pol) III transcripts. However, this protein is involved in other cellular processes such as RNP maturation, transcription termination, mRNA stabilization, and internal ribosome entry site-mediated translation in mammalian cells (153).

17 1.3.10 Heterogeneous nuclear ribonucleoproteins (hnRNPs)

hnRNPs were initially found to be associated with nuclear pre-mRNA. The

mammalian hnRNP proteins Al, CI, and C2 have been proposed to link the telomerase

holoenzyme to telomeres, potentially by bridging hTR and single-stranded DNA (118,

119).

1.3.11 Telomerase structure

At least 32 distinct proteins have been proposed to interact with human

telomerase. Interestingly, the size of the human telomerase complex was found to be

-650 to 670 kD (110), which is bigger than one hTERT (127 kD) and one hTR (153 kD)

and much smaller than the complex of all proposed associated proteins (-2.6 MD) (154,

155). Using mass spectrometric sequencing, Cohen et al. demonstrated that active

telomerase is composed of two copies each of hTERT, hTR and dyskerin proteins (110).

They concluded that other proteins are not required for nucleotide addition, nor do they

constitute integral components of the catalytically active enzyme complex. Rather, they may be involved in telomerase synthesis, trafficking, and its recruitment to the telomere.

1.3.12 Telomerase in cancer

Although telomerase has been found to be expressed in embryonic cells and in adult male germline cells (25, 156), its expression in normal somatic cells is not detectable except in proliferating cells in renewal tissues (157-159). In contrast to normal somatic cells, the vast majority of tumor cells express high levels of telomerase activity.

It has been found that telomerase is active in over 85% of human tumors and more than

18 90% of breast carcinomas in contrast to normal tissues where telomerase is inactive (21-

23). However, the role of telomerase in tumorigenesis is paradoxical (160). Although telomere shortening is an early event, telomerase activation has been found to be a late event in cancer development (161). Based on these finding and other observations, it has been suggested that although telomerase is not oncogenic (161), its activity is necessary for tumor progression (162). However, not all of the cancer cells maintain their telomere length by telomerase activity (163)

1.3.13 Alternative Lengthening of Telomeres

Approximately 10-15% of human tumors maintain their telomere length using a recombination-mediated alternative lengthening of telomeres (ALT) mechanism (163).

There is some evidence indicating that the ALT mechanism relies on the DNA recombination machinery (164). The length of telomeres in ALT positive cells is highly heterogeneous from very small to very long, suggesting that ALT telomeres are maintained by the DNA recombination machinery (165). This finding has been supported by the observation that tagged telomeres move onto other telomeres in an ALT cell line

(109). Furthermore, ALT cells contain ALT associated promyelocytic leukemia (PML) bodies (APBs). The APB is a unique nuclear structure containing telomeric DNA, telomere- associated proteins (166), and proteins involved in recombination and repair.

1.3.14 ALT in cancer

The ALT mechanism has been found commonly activated in tumors of neural

(astrocytomas) or mesenchymal origin (osteosarcomas and liposarcomas). However, this

19 mechanism is not common in epithelial tumors (167, 168). Although there is some

speculation, the mechanism underlying these differences remains to be understood (164).

1.3.15 Telomerase as a target for cancer treatment

Telomerase activation in the great majority of cancers (~ 85%) and its association

with cell immortalization has made it a promising target for anti-cancer therapy (296).

Targeting telomerase activity in cancerous cells, several successful methods have been

introduce including oligonucleotide-based therapeutics, nucleoside analogs and non-

nucleoside small molecules (169), immunotherapy (vaccines targeting telomerase) (170),

gene therapy (telomerase oncolytic virus), chemoprevention using pharmacological

agents, combination therapy, and agents that interact with quadruplex DNA (171, 172).

More recently, some data indicates that nuclear receptors such as estrogen receptor, Retinoid acid receptor, and peroxisome proliferator-activated receptor gamma

(PPARy) inhibit the activity of telomerase in some in vivo and in vitro models (173-175)

and may potentially be used for treating cancer patients.

1.4 Nuclear receptors

The nuclear receptor (NR) superfamily contains ligand-inducible transcription factors that are structurally related but functionally diverse. This family contains both nuclear hormone receptors (NHRs) and orphan nuclear receptors (ONRs) (176). NHRs are those receptors with identified hormonal ligands, whereas ONRs do not have known ligands, at least at the time the receptor is identified. The members of this superfamily are involved in metabolism, development, and reproduction by regulating the expression of

20 target genes.

More than 100 nuclear receptors are known to exist including estrogen receptor

(ER), progesterone receptor (PR), androgen receptor (AR), glucocorticoid receptor (GR),

mineralocorticoid receptor (MR), thyroid hormone receptor (TR), all-trans retinoic acid

receptor (RAR), 9-cis retinoic acid receptor (RXR), vitamin D3 receptor (VDR), and

peroxisome proliferator-activated receptors (PPARs) (177). Together, these proteins

comprise the single largest family of metazoan transcription factors, the nuclear receptor

superfamily.

1.4.1 Peroxisome proliferator-activated receptors

Peroxisome proliferator-activated receptors are members of the nuclear hormone

receptor superfamily that includes several ligand-activated transcription factors involved

in a variety of physiological and pathological processes (178). PPARs were originally

named for their ability to induce hepatic peroxisome proliferation in mice in response to xenobiotic stimuli (179). There are three known subtypes of PPARs, PPARa (180),

PPARp/5 (181), and PPARy (182). The subfamily members of PPARs are encoded by separate genes located on different chromosomes and share 60%-80% homology in their ligand-and DNA-binding domains (183).

1.4.2 Peroxisome proliferator-activated receptor a

Murine PPARa was the first member of this nuclear receptor subclass to be cloned (184) followed by cloning of human PPARa in 1993 (180). Human PPARa was found to be highly expressed in organs that carry out significant catabolism of fatty acids

21 such as liver, kidney, heart, skeletal muscle, brown adipose tissue, and intestine (185-

187), in addition to its expression in monocytic (188), vascular endothelial (189), and

vascular smooth muscle cells (190). Activation of PPARa has been reported to improve

levels of triglycerides, high density lipoprotein (HDL), and the overall atherogenic

plasma lipid profile (191-198).

1.4.3 Peroxisome proliferator-activated receptor (3/8

Human PPAR.p/5 was identified by cDNA cloning from an oesteosarcoma cell

library (181) and it is widely and often abundantly expressed in brain, adipose tissue, and

skin (186, 187, 199, 200). PPARp78 has an important role in adipose tissue metabolism

and weight control by enhancing fatty acid catabolism and energy uncoupling in adipose

tissue and muscle. It also suppresses macrophage-derived inflammation (201). Compared

to the other subfamily members of PPARs, PPARJ3/8 has been studied the least mainly because of its wide tissue expression pattern, which raised early speculation that it may

serve for general housekeeping (202).

1.4.4 Peroxisome proliferator-activated receptor y

PPARy was cloned independently as a new member of the PPAR subgroup of nuclear hormone receptors and as a transcriptional regulator of fat-specific gene expression (182, 192, 203). The PPARy gene can be translated into four different PPARy proteins, PPARyl, PPARy2, PPARy3, and PPARy4 by alternative promoters and RNA splicing (185, 204). The PPARy gene spans more than 100 kb containing 9 exons (exon

Al, exon A2, exon B, and exons 1-6). PPARyl includes exon Al, A2, and exon 1-6.

22 PPARy2 contains exon B plus exon 1-6, whereas PPARy3 consists of exon A2 and exon

1-6. PPARy4 contains only exon 1-6. Since exons Al and A2 are untranslated, therefore

PPARyl, PPARy3, and PPARy4 genes code the same protein. In contrat, PPARy2 has an

additional 28 amino adics at its amino terminus as the result of exon B translation (182).

1.4.5 PPARy structure

As with other NRs, all four PPARys have similar structures consisting of the

(A/B) N-terminal region, the DNA binding domain (DBD), the flexible hinge region, the

ligand binding domain (LBD), the transactivation domain, and the C-terminal domain

(205). The DBD is the most conserved domain between all NRs and facilitates the interaction between PPARy protein and DNA. This domain contains two zinc finger motifs that allow NRs to bind to specific sequences of DNA known as hormone response elements (HRE) within the promoters of target genes. It has been shown that the precise sequence of the HRE, its orientation (direct repeat, palindromic, or inverted palindromic sequences), and the spacing between core recognition motifs are the features that regulate the specificity of DNA recognition by a particular set of NRs (206). Based on these observations and the interaction modes with HREs, NRs can be classified in three groups as monomer, homodimer, and heterodimer receptors. Monomer receptors such as NGFIb contain a single receptor motif. Homodimers such as ER and heterodimers such as TR,

VDR, RAR, and PPARs typically consist of two core recognition motifs within the promoters of target genes (207-214). PPARs have been found to form a heterodimer with the retinoic X receptor a (RXRa) as the DNA-binding partner and preferentially recognize the core of two direct hexanucleotide repeats (AGGTCA)2 with 1 bp spacing

(DR1). In addition, the transcriptional activation of PPARy depends on the activation

23 function (AF) domain, located at the N-terminal of PPARy. AF-1 can be phosphorylated

by kinases and activates PPARy in the absence of ligands. In contrast, AF-2 is a ligand-

dependent transcription domain located at the C-terminal of PPARy. AF-2 is a short

conserved helical sequence, which recruits co-activators to induce the transcriptional

activity of the ligand-bound PPARy/RXRoc heterodimer (215).

1.4.6 Co-factors

The rate of gene transcription is dependent on chromatin remodeling and hi stone

acetylation in eukaryotes. Histone acetyltransferases (HATs) give rise to hyperacetylated

regions with a loose chromatin structure that can be highly transcribed. In contrast, the

regions, which are hypoacetylated by histone deacetylases (HDACs) are highly

condensed and cannot be transcribed. It has been demonstrated that PPARy regulates the

expression of target genes by recruiting a large number of co-factors containing either

HAT or HDAC activity (216). These co-factors can be divided into two groups called co-

activators and co-repressors.

1.4.7 Co-activators

A diverse group of proteins have emerged as potential co-activators for PPARy such as PGC-la, the SWI/SNF chromatin remodeling complex,

TRAP220/DRIP205/PBP, PRIP/NRC/RAP250/TRBP, CBP/p300, and the members of the pl60 family (RC-1/NCoAl, TIF2/GRIP1/NCOA2/SRC-2, and pCIP/ACTR/AIBl/SRC-3) (216). These co-activators have different roles in induction of transcriptional activity of PPARy. Some of the well known co-activators of PPARy such

24 as CBP/p300, and SRC-1 have HAT activity, whereas the other group forms a bridge

between PPARy and the transcription initiation machinery. This group includes members

of the DRIP/TRAP complex such as PPAR binding protein (PBP)/TRAP220, (216).

1.4.8 Co-repressors

Published data suggests that PPARy can act as a repressor by recruiting co-

repressors such as RIP140 (217), SMRT, and NCoR (218) in the absence of ligand or in

the presence of certain antagonists. Compared to co-activators, little is known about co-

reppressors and their roles in the regulation of PPARy transcriptional activity due to the

weak repressor activity of PPARy (216).

1.4.9 PPARy ligands

PPARy receptor can be activated by several types of ligands. These ligands can be

divided in three classes; natural ligands, dual agonists, and synthetic ligands.

1.4.10 Natural ligands

Natural ligands are lipophilic agents that activate PPARy at physiologic levels.

This group of ligands include fatty acids and eicosanoids (219), components of oxidized low-density lipoproteins (220), and oxidized alkyl phospholipids including lysophosphatidic acid (221), and nitrolinoleic acid (222). Among natural ligands, cyclopentone prostaglandin (15-deoxy-A 12, 14-prostaglandin J2) is the most potent and commonly used endogenous PPARy agonist (223).

25 1.4.11 Dual agonists

Dual functional agonists are a new class of ligands that activate both PPARy and

PPARa simultaneously. Having synergetic effects, dual PPARy/PPARa ligands have

been designed to improve anti-diabetic and cardio-protective effects by reducing

triglycerides and raising HDL as the cardioprotective factor. This group includes

naveglitazar, , , ragaglitazar, tesaglitazar, and imiglitazar (224).

1.4.12 Synthetic ligands

The synthetic ligands are the members of the thiazolidinedione (TZD) family

including troglitazone, , , (225). TZDs are also

known as insulin sensitizers used in the treatment of type 2 diabetes. This group was

originally developed without knowledge of their mechanisms of action. It is now

recognized that TZDs are pharmacological PPARy ligands (226). It also has been found

that TZDs promote the differentiation of various cell lines (227-231). Furthermore, it has

been shown that some TZDs, especially troglitazone and ciglitazone, demonstrate

antiproliferative activities in several cancer models including breast cancer (232).

However, there is some evidence indicating an increase in the frequency of tumors in mice treated with synthetic PPARy agonists (233). Interestingly, accumulating data

suggests that many biological effects of TZDs on cell processes are independent from

PPARy transcriptional activity (234-237). Therefore, it becomes very important to understand the clinical and/or biological roles of PPARy and its agonists in the progression or suppression of malignancies in order to assess a potential therapy for cancer patients.

26 1.4.13 PPARy function

PPARy plays an important role in regulation of dyslipidemia, type 2 diabetes,

inflammation, and various cancers.

1.4.14 The Role of PPARy in lipid metabolism

PPARy has been identified as a necessary and sufficient transcription factor for

the differentiation of adipocytes. Tontonoz et al. showed that the introduction of PPARy

into fibroblasts in the presence of PPARy ligands induces the differentiation of the cells

into adipocytes (238). Furthermore, it has been shown that ectopic expression of

dominant-negative PPARy mutants can inhibit the differentiation of 3T3-L1 cells into

adipocytes even in the presence of ligands (239, 240). Using animal models, the role of

PPARy in lipid metabolism has been further investigated. A line of evidence showed that

PPARy knocked out mice were extremely lipodystrophic (241), whereas PPARy

heterozygous null mice exhibited reduced amounts of adipose tissue (241-243). As a lipid

regulator, PPARy controls the expression of numerous genes involved in lipid

metabolism such as fat-specific adipocyte P2 (aP2), lipoprotein lipase (LPL), fatty-acid transport protein (FATP), acetyl-CoA synthetase (ACS), phosphoenolpyruvate carboxykinase, glycerol kinase, glycerol transporter aquaporin 7, and oxidized low density lipoprotein (LDL) receptor 1. In addition, PPARy plays an important role in the regulation of energy homeostasis by inducing the expression of uncoupling proteins 1, 2 and 3 (UCP1, UCP2 and UCP 3) genes as mediators of thermogenesis (244, 245).

1.4.15 The Role of PPARy in type 2 diabetes

27 PPARy is involved in the regulation of cellular glucose uptake by increasing

insulin sensitivity and decreasing insulin resistance in adipose tissue, skeletal muscle, and

liver. PPARy activation by TZDs improves insulin sensitivity and effectively reduces

hyperglycemia in patients with diabetes. TZDs predominantly decrease hemoglobin Aic

and fasting glucose concentrations by inhibiting lipotoxicity-induced insulin resistance.

This effect of PPARy especially in pancreatic P cells inhibits the apoptosis of p cells and

increases the mass of (3 cells, which in turn results in both diabetes prevention and

treatment (246, 247).

Using troglitazone in animal models, Miles et al. found a significant reduction of

free fatty acid (EFA) concentration in rat blood (248). They showed that stimulated

insulin receptor autophosphorylation in the animal muscles resulted in the elevation of

glucose uptake (248). In addition, they discovered that PPARy suppresses the expression

of tumor necrosis factor a (TNFa), a pro-inflammatory cytokine, in TNFoc-induced

insulin resistance (248). Activation of PPARy also stimulates the expression of c-CBL-

associated protein (CAP) in cultured adipocytes (249). CAP contains a functional PPRE within the 5' regulatory region of its gene and appears to play a positive role in the insulin

signaling pathway (250). Expression of insulin receptor substrate-2 (IRS-2), a protein with a proven role in insulin signal transduction in insulin-sensitive tissue, was also increased in differentiated 3T3-L1 adipocytes and cultured human adipose tissue treated with pioglitazone (251). Furthermore, it has been found that pioglitazone had no effects on IRS-1, PKB/Akt, or glucose transporter-4 (GLUT4) gene expression (251). In contrast, Zhang et al. observed a significant reduction in insulin sensitivity with dramatically decreased expression of IRS-1 in skeletal muscle, liver, and white adipose

28 tissue (WAT) and a significant decrease of GLUT4 in the skeletal muscle in male

PPARy2~'~ mice (252). Surprisingly, they found that the impairment of insulin sensitivity

was dramatically improved in PPARy2~'~ mice treated with rosiglitazone (252).

Recent data also showed that ciglitazone increased insulin-stimulated glucose

uptake in the cardiomyocytes of rats (253). This finding was associated with an increase

in Akt phosphorylation, an important protein in downstream insulin signaling regulating

glucose transportation. In agreement with these findings, it has been shown that PPARy

inhibition by siRNA in rat vascular smooth muscle cells (VSMCs) significantly

suppresses the phosphorylation of insulin-stimulated receptor (IR)-beta, Akt, and

glycogen synthase kinase 3-beta (GSK3|3) with an increase in the expression of

phosphotyrosine phosphatase (PTP)-IB (254).

1.4.16 The Role of PPARy in cancers

PPARy has been suggested to be a tumor suppressor gene based on several

observations (255). PPARy was mapped to chromosome 3p25 (256), a region with

detected abnormalities in a variety of human cancers (257-259), suggesting the genes

located in this region may function as tumor suppressor genes. In agreement with this

suggestion, Mueller et al. found that of 21 patients with prostate cancer, 8 (40%) of them had hemizygous deletions of the PPARy gene (260). Furthermore, it has been shown that colon cancer in humans is associated with loss-of-function mutations in PPARy (261).

In contrast, other studies identified PPARy as an oncogene. To study the role of

PPARy in breast cancer development, Saez et al. constructed a constitutively active

PPARy by fusing the activation domain of the herpes simplex virus Vpl6 protein to the

29 PPARyl isoform (262). Using this construct, they generated new transgenic mice

expressing the active form of PPARY under the control of the mouse mammary tumor

virus (MMTV) promoter. When these transgenic mice were bred to the MMTV-PyV

strain (a strain prone to mammary gland cancer), bigenic animals developed tumors with

greatly accelerated kinetics compared to their control matched groups. Based on these

observations, the authors concluded that increased activity of PPARy serves as a tumor

promoter in the mammary gland (262). In addition, it has been found that the treatment of

APC deficient mice with various TZDs unexpectedly increase the number of colon

tumors in these animals (263, 264).

Finally, there is some evidence indicating neither a tumor suppressor nor an

oncogenic role for PPARy in the progression of cancers. Using mutational analysis,

Ikezoe et at. showed that PPARy is expressed in most cancers, and its mutation is a very rare event in a variety of human cancer samples and human cancer cell lines (326 clinical cancer samples and 71 cancer cell lines) (265).

Taken together, these results indicate that the exact role of PPARy in cancer development is not clearly understood and needs more investigation.

1.4.17 PPARy and breast cancer

Human primary and metastatic breast adenocarcinomas express high levels of

PPARy (228, 265, 266). It is also found that many breast cancer cell lines including

MDA-MB-231, MDA-MB-436, MCF-7, and T47D express PPARy protein (265, 267).

However, no PPARy mutations or deletions were detected in clinical breast cancer samples and cell lines (265). For the first time in 1998, Mueller et al. showed that the

30 21PT breast cancer cell line can undergo significant morphologic changes following exposure to various types of PPARy ligands (228). They also found that these morphological changes are associated with the increased expression of maspin (a tumor suppressor gene and a marker of normal breast development). Furthermore, they showed that PPARy ligands suppress the expression of mucin-1 (Muc-1) and keratin 19 (K19) genes, two markers associated with a more malignant state of breast epithelial cells. It was also revealed that PPARy ligands arrest the growth of 21PT cells after long-term treatment. Based on these observations, the authors concluded that the cell growth arrest seems to be the consequence of cellular differentiation that occurs over several days.

However, not all of the breast cancer cell lines responded to the PPARy ligands in their study. Despite its high level expression of PPARy mRNA, 21MT breast cancer cells were not differentiated in response to ligand treatment (228). Further investigations by other groups showed that PPARy ligands especially TZD family members suppress the proliferation of not only breast cancer but also many other type of cancers (230, 231,

268-276).

Interestingly, it has been shown that that TZDs target genes involved in cell cycle regulation, apoptosis, and DNA damage response more than those regulating lipid metabolism and differentiation (268).

1.4.18 The effects of PPARy ligands on cell cycle regulation

The mammalian cell cycle consists of Gl, S, G2, and M phases. Cell cycle progression is regulated by members of the cyclin-dependent kinase (CDK) and cyclin families. Activated cyclin/CDK complexes phosphorylate and inactivate retinoblastoma

31 protein (Rb). Hyperphosphorylated Rb (pRb) induces the expression of E2F-regulated

genes such as cyclin E, and cyclin A leading to induction of cell proliferation (277, 278).

The activity of cyclin/CDK complexes can be inhibited by two families of CDK

inhibitors (CDKIs); the cyclin inhibitor protein/kinase inhibitor protein (CIP/KIP) family

and INK4 inhibitors (279). CIP/KIP family induces p21CIF1/WA]F\ p27Kn>1, and p57KIP2

inhibiting both Gi- and S-phase transitions (280). Whereas, the INK4 inhibitors suppress

the Gi-phase cyclin/Cdk complexes by induction of pie11™*, plS™ plS^40, and

pl9iNK4dproteins(281)

It has been shown that troglitazone inhibits MCF-7 cell proliferation by blocking

events critical for Gl—>S progression (282). Using the same breast cancer cell line, MCF-

7, Wang et al. found that progression of the cell cycle can be inhibited by 15d-PGJ2

through direct repression of cyclin Dl gene expression (283). To determine whether the regulation of cyclin Dl was PPARy receptor dependent, this group also showed that both

15d-PGJ2 natural and synthetic ligands (troglitazone and BRL49653) inhibit the cyclin

Dl promoter in PPARy-deficient HeLa cells expressing ectopic PPARy protein.

However, the involvement of PPARy in the inhibition of cyclin Dl was not shown directly in MCF-7 cells in this study (283).

Conversely, Huang et al. found that ablation of cyclin Dl in the MCF-7 cell line by troglitazone and ciglitazone is independent of PPARy transcriptional activity (234).

Interestingly, another group found that high doses of troglitazone (50/^M) selectively suppress cyclin D3 expression by a PPARy-independent mechanism in MDA-MB-231 cells without affecting other D-type cyclins and cell cycle regulators (cyclin E, p27, and p21) (284). In contrast, treatment of MCF-7 cells with rosiglitazone was found to induce

32 UM/WA1 1 mRNA and protein levels of the tumor suppressor p53 and p2i ' in a PPARy-

dependent mechanism (285). Taken together, this evidence indicates that the biological

effects of PPARy ligands on cell cycle regulation are controversial and can be dependent

or independent of PPARy activity.

1.4.19 The effects of PPARy ligands on apoptosis

TZDs induce apoptosis in various cancer cell lines by different mechanisms (286-

290). It was shown that high concentrations of troglitazone decrease the level of anti-

apoptotic bcl-2 protein in MCF-7 cells (290). Bae et al. found that troglitazone increases

the level of pro-apoptotic proteins such as Bad and Bax in HepG2 hepatoma cell lines

(291). It was also revealed that troglitazone is able to induce changes in Bcl-2 members in the prostate cancer PC3 cell line (288). Using human thyroid carcinoma cell line

BHP18-21, Ohta et al. demonstrated that 10/xM troglitazone can induce DNA fragmentation in BHP18-21 cultured in 0.1% fetal calf serum (FCS) (292). However, this group failed to detect any changes in the expression level of bcl-2 and bax genes in

BHP18-21 cells in response to troglitazone treatment.

In general, published data indicates an apoptosis response to high concentrations of troglitazone. However, this response was not observed at low concentrations of troglitazone. More importantly, regardless of the concentration of troglitazone used, all groups showed that induction of apoptosis was independent from PPARy activity (286-

290, 292).

1.5 Rationale

33 Breast cancer is one the highest causes of female death worldwide (1). The

development of breast cancer involves a sequential progression through defined clinical

and pathological stages beginning with normal epithelium and progressing to hyperplasia,

DCIS, IDC, and culminating in metastatic disease (7).

In humans, telomeres play a crucial role in the life of the cells by providing a

protective cap on chromosome ends (24). Furthermore, telomere alteration in

tumorigenesis and cancer development has been studied and established at the molecular

level (73). A line of evidence suggests a significant correlation between telomere length

and genomic instability in cancers (293). Excessively shortened or dysfunctional

telomeres have been shown to cause end fusions and onset of chromosomal instability

through break-fusion-bridge cycles (293). It has been shown that telomere lengths are

shorter in invasive breast cancer compared to normal breast epithelial tissue (76-79). In

addition, several other studies also identified telomere shortening as a common

observation in various types of cancers compared to their matched normal tissues (74, 75,

80, 91-94, 96, 97). However, the role of telomere length as a prognostic factor and its

correlation with clinical prognostic parameters is controversial. This can be partially

explained by the fact that all research groups measured the average length of total

telomeres in their studies, and no studies were conducted to investigate the lengths of individual telomeres and their correlation with prognostic parameters in cancer samples.

It has been shown that genetic alterations on certain chromosomes are more frequent compared to others (294-303). This suggests that the length of telomeres on these specific chromosomes may play an important role in the progression of cancers.

Furthermore, it has been suggested that the length of the shortest telomere is the critical

34 factor determining cell fate rather than the average of all telomere length (90), supporting

the idea that some individual telomere lengths may have specific roles in the

development of cancers. Numerous studies have demonstrated that genetic abnormalities

such as loss of heterozygosity (LOH) on chromosome 17 in DCIS is as high as 60% to

80% in some studies (297-303), suggesting that genes located on this chromosome may

be particularly important in the development of DCIS. In agreement with this idea, it has

been shown that a frequently amplified gene in IDC and DCIS is HER2 located on

chromosome 17q (19, 304). Additionally, since DCIS is believed to be an intermediate

stage between normal and invasive breast cancer, we were interested to study the length

of telomere on chromosome 17q in normal breast epithelium, DCIS and IDC samples.

Our aim was to determine whether shorter chromosome 17q telomeric lengths are

associated with an increased risk of invasive recurrence. The results of the present

investigation may allow us to predict which DCIS patients have a risk of future invasion.

Furthermore, the progressive shortening of telomeres with each cell division has been shown to limit replicative life span of human cells in culture (305). Most human cancer cells maintain their telomeres through activation of telomerase (108). In over 90% of breast carcinomas and 85% of other types of human tumors telomerase is active (21,

306, 307). In contrast, normal tissues have low or undetectable levels of telomerase activity. Telomerase is a very large complex ribonucleoprotein enzyme that maintains the length of the telomeres (110). It has been shown that inhibition of telomerase limits the growth of human cancer cells (108). Consequently, various anti-telomerase strategies are currently under investigation to limit tumor proliferation in cancer patients. This is even more important in breast cancer, since the current available therapy options are not

35 effective in all breast cancer patients. Standard chemotherapeutic agents currently used to

treat breast cancer are relatively non-specific and act on all rapidly dividing cells. In

recent years, more specifically targeted therapies have been introduced. While tamoxifen

and Herceptin have been successfully used to treat ER-positive and HER2 over-

expressing breast cancers respectively, few special cancer prevention and treatment

strategies are available for ER-negative breast carcinomas (308). This has motivated

considerable efforts toward finding novel therapeutic approaches for the treatment of ER-

negative breast cancer.

Resent evidence showed that PPARy can inhibit telomerase activity in some

primary and cancer cells (173-175). However, whether PPARy exerts anti-telomerase

activity in breast cancer models has not yet been studied. PPARy is a member of the

nuclear hormone receptor superfamily that includes several ligand-activated transcription

factors involved in a variety of physiological and pathological processes (178). Upon

activation by its ligands, PPARy regulates gene expression by binding to specific peroxisome proliferator response elements as a heterodimer with retinoid X receptor in the enhancer sites of target genes (309). Several studies confirmed an anti-tumor role for

PPARy in various cancers (228, 230, 231, 260, 270, 276, 310). It has been shown that synthetic PPARy ligands, especially troglitazone and ciglitazone, demonstrate antiproliferative activities, in addition to their role in the induction of cell differentiation, in several cancer models including breast cancer (227, 228, 230, 231, 311, 312).

However, the molecular mechanism(s) responsible for this antiproliferative effect is not well understood.

Because of the growing potential roles of PPARy ligands as novel anti-cancer

36 therapies, we investigated the effect of the classical PPARy ligand troglitazone on

telomerase activity and cell cycle regulation in an ER-negative breast cancer model. To

our knowledge, this is the first time that the effect of troglitazone on telomerase activity

in ER-negative breast cancer has been investigated. We believe that the PPARy ligand

plays a critical role in this process. The objective of our study is to investigate the effect

of drugs (which interact with PPARy) particularly on telomerase but also by using

microarray analysis to identify and better understand the other genes involved in this

pathway. These results may lead to novel therapeutic mechanisms that could be used to

treat ER-negative breast cancer.

1.6 Hypothesis

This study is based on the hypothesis that telomere length and its control by the nuclear hormone receptor PPARy plays an important role in the development and progression of breast cancer.

1.7 Specific aims

Specific amis are:

1. To compare the length of telomeres on chromosome 17q in pure DCIS with DCIS

associated with IDC.

2. To investigate the role of troglitazone in regulating telomerase activity in an in

vitro model.

3. To assess the gene expression profile of established cell lines in response to

troglitazone treatment using RNA expression microarray analysis.

37 Chapter 2

This chapter has been published as "Telomere length on chromosome 17q shortens more

than global telomere length in the development of breast cancer"

Rashid-Kolvear F, Pintilie M, and Done SJ., Neoplasia. 2007 Apr;9(4): 265-70.

Statistical analysis was performed Pintilie M.

38 Telomere length on chromosome 17q shortens

more than global telomere length in the

development of breast cancer

39 2.1 Abstract

It is known that total telomere length is shorter in invasive breast cancer than in

normal breast tissue but the status of individual telomere lengths has not been studied.

Part of the difficulty is that usually telomere length in interphase cells is measured on all

chromosomes together. In this study, we compared normal breast epithelium, duct

carcinoma in situ (DCIS), and invasive duct carcinoma (IDC) from 18 patients. Telomere

length was specifically measured on chromosome 17q and in DCIS and IDC was found to

be shorter than in normal breast epithelial cells with more heterogeneity in telomere

length in DCIS associated with IDC than in DCIS alone. More importantly, we found that

the shortening of the telomere on chromosome 17q is greater than the average shortening

of all telomeres. This finding indicates that telomere shortening is not simply the result of

the end replication problem; otherwise all telomeres should be subjected to the same rate

of telomere shortening. It seems there are mechanisms that preferentially erode some

telomeres more than others or preferentially protect some chromosome ends. Our results

suggest that the increased level of telomere shortening on chromosome 17q may be involved in chromosome instability and the progression of DCIS.

40 2.2 Introduction

Breast cancer is one of the most common cancers afflicting North American

women. Although the majority of early-stage breast cancers are not life threatening, a

small proportion of cases will progress to metastatic breast cancer. Invasive duct cancer is

frequently observed to extend directly from ducts containing duct carcinoma in situ.

However, not all cases of DCIS develop into invasive tumors. Molecular markers hold

the promise of becoming clinically useful diagnostic tools, particularly markers which

can be studied in formalin-fixed paraffin-embedded (FFPE) tissue samples as virtually all

DCIS is processed in this way for routine pathological diagnosis. As telomeres are

involved in maintenance of chromosomal stability they represent one group of markers of

particular interest.

The telomere is a specialized structure at the end of chromosomes consisting of a

highly conserved repetitive DNA sequence, (TTAGGG)n (25). Telomeres form caps on

the ends of chromosomes that prevent fusion of chromosomal ends and provide genomic

stability. In normal somatic cells, telomeres are progressively shortened with every cell

division. This shortening in normal human cells limits the number of cell divisions. For

human cells to proliferate beyond the senescence checkpoint, they need to stabilize

telomere length. This is accomplished mainly by reactivation of telomerase (313).

Telomerase expression is under the control of many factors (121, 122, 128, 314-319).

Expression of telomerase can lead to cell immortalization and it is activated during tumorigenesis (21).

Using various approaches, it has been shown that telomere length in DCIS is generally shorter than in normal breast epithelial cells with inconsistent results with

41 respect to associations between tumor telomere length and clinicopathological features

(76, 81, 84, 85). However, in other cancer types a correlation between longer telomeres

and more aggressive behavior of cancer cells has been found (82, 320). While all these

groups were measured the average length of pantelomeres in their studies, it has been

reported that it may not be the average but rather the shortest telomeres that constitute

telomere dysfunction and limit cellular survival in the absence of telomerase (90). It has

been suggested that loss of telomere function occurs preferentially on chromosomes with

critically short telomeres (90). This suggestion indicates that certain telomeres may have

important roles in the development of cancers.

Numerous studies have demonstrated genetic abnormalities on chromosome 17 in

DCIS with rates approaching 60 to 80% (85, 298, 303), suggesting that oncogenes and

tumor suppressor genes in these regions may be particularly important in the

development of DCIS. A frequently amplified gene in IDC and DCIS is the HER2

(human epidermal growth factor receptor-2) oncogene located on chromosome 17q.

HER2 is a member of a family of transmembrane receptor tyrosine kinases with no identified ligand. HER2 is overexpressed and amplified in 20-30% of IDC and up to 80% of DCIS. Many groups have reported a correlation between HER2 amplification and poor prognosis (19, 304). Despite much interest, the mechanism of HER2 amplification/overexpression in many cases is unclear. We hypothesized that telomere erosion on chromosome 17q could lead to instability of chromosome 17q and this may be associated with HER2 amplification/overexpression.

In this study, we developed a technique to allow measurement of specific telomeres in routinely processed clinical samples. This allowed us to measure telomere

42 length specifically on chromosome 17q in formalin-fixed paraffin-embedded samples.

We also investigated the relationship between HER2 expression and telomere shortening

on chromosome 17q.

2.3 Materials and Methods

2.3.1 Sample collection

Breast tissue was obtained from archival formalin-fixed paraffin-embedded

(FFPE) blocks that were stored in the Pathology Department of University Health

Network and had been obtained initially for diagnosis. Institutional Research Ethics

Board approved was obtained for the study.

2.3.2 Quantitative fluorescence in situ hybridization (Q-FISH)

To measure the relative changes in telomere length on chromosome 17q in breast

epithelium, Q-FISH was used. Metaphase slides prepared from normal peripheral blood

lymphocytes (Vysis Inc., Downers Grove, IL, USA) were used to optimize Q-FISH

conditions for two different probes, a PNA (CCCTAA)3 pan-telomere probe-FITC from

Applied Biosystems (Bedford, MA, USA) and a specific subtelomeric DNA probe for

chromosome 17q (TelVysion 17q SpectrumOrange) from Vysis Inc. (Downers Grove, IL,

USA) (Fig. 2.1 A). Since the (CCCTAA) 3 PNA probe is not able to distinguish the

parental telomeres from each other, all the telomere signals are the average of two

homologous chromosomes in our report. We also used the specific subtelomeric DNA probe for chromosome 17q (TelVysion 17q SpectrumOrange) as a guide to locate

43 chromosome 17q telomeres. Using these two probes, we were able to detect strong

signals from telomeres and the chromosome 17q subtelomeric region simultaneously

(Fig. 2.1B-D).

To measure the telomere length in our study, five-urn thick FFPE breast cancer

tissue samples on positively charged glass slides were deparaffinized with xylene,

washed with 100% ethanol and air dried followed by RNase A treatment. Slides were

then placed in 1M NaSCN at 80°C and washed in water at room temperature. After

pepsin digestion, slides were thoroughly washed with water followed by dehydration in

serial ethanol dilutions (70%, 90%, and 100%) at room temperature and air dried. The

sections and the PNA pan-telomere probe-FITC were then co-denatured at 80°C for 5 minutes in a Hybrite programmable heating block (Vysis Inc., Downers Grove, IL) followed by incubation in a dark humidified chamber for 90 minutes at 25°C. Then slides were hybridized with a denatured specific chromosome 17q probe mixture (TelVysion

17q SpectrumOrange) and incubated overnight at 33°C. Slides were washed in

2xSSC/0.3%NP-40 at 73°C, 2xSSC/0.3%NP-40, and 2xSSC at room temperature followed by dehydration in serial ethanol dilutions and air dried. Finally, the slides were counterstained with 4', 6 diamidino-2-phenylindole (DAPI) from Vector Laboratories,

Inc. (Burlingame, CA) and viewed with a fluorescence microscope (Leica DMRA2).

2.3.3 Image capturing and analyzing

Using Hematoxylin and Eosin (H&E) staining, the normal breast epithelium,

DCIS and invasive areas in each tissue section were identified (Fig. 2.2A). Then parallel sections were studied for the Q-FISH analysis (Fig. 2.2B). The telomeres (Fig. 2.2C) and

44 sub-telomeric chromosome 17q (Fig. 2.2D) signals were visualized as gray in the

outlined nuclei. In this study, we measured the length of telomere in normal breast

epithelium (n= 8), DCIS (n= 14), and IDC (n= 12) from 18 patients. For each type of

tissue (normal breast epithelium, DCIS and IDC) a minimum of 20 nuclei in five

different areas were scored for the following variables: the total telomere length

(expressed as intensity), the telomere length at the end of chromosome 17q (expressed as

intensity) and subtelomeric chromosome 17q (expressed as intensity).

Images were captured using Openlab 4.0.3d8 software (Improvision, Viscount

Centre II, University of Warwick Science Park, Coventry, England,

http://www.improvision.com). Finally the images were analyzed using Image J software

(developed at the Research Services Branch (RSB) of the National Institute of Mental

Health (NIMH), National Institutes of Health (NIH), USA, http://rsb.info.nih.gov/nih- image).

2.3.4 Immunohistochemical staining of paraffin sections for HER2

Adjacent FFPE tissue sections from the same blocks were dewaxed in xylene and blocked for endogenous peroxidase in 3% hydrogen peroxide for 10 minutes. Following microwave antigen retrieval, the sections were blocked for endogenous biotin using the

Vector Laboratories, Inc. (Burlingame, CA, USA) biotin blocking kit. The sections were incubated with 1:300 diluted primary antibody HER2 rabbit antibody from Dako

(Carpinteria, CA, USA) for 1 hour at room temperature and rinsed in PBS followed by incubation with biotin conjugated anti-rabbit IgG from Signet Laboratories, Inc.

(Dedham, MA, USA) for 30 minutes. After incubation with Streptavidin labeled

45 horseradish peroxidase (HRP) for 30 minutes (Signet Kit) and rinsing in PBS, the

sections were incubated in NovaRed substrate from Vector Laboratories, Inc.

(Burlingame, CA, USA) for another 5 minutes. Finally the nuclei were counterstained in

Mayer's Haematoxylin for 10 seconds and the sections were dehydrated, cleared and

permounted for microscopic evaluation. Samples having more than 10% cancer cells with

complete cell membrane staining of moderate to strong intensity were considered as

HER2 positive.

2.3.5 Data analysis

In this study, the following variables were used in the analysis: telomere length

(TelGen) = average of the telomere length intensity (per patient, per type of tissue, over

the minimum 20 nuclei), normalized telomere length (N-TelGen)= average telomere

length divided by the average chromosome 17q intensity, chromosome 17q telomere

length (TelChl7q) = average of the telomere length intensity of the chromosome 17q

PNA probe-FITC, normalized chromosome 17q telomere length (N-TelChl7q) =

chromosome 17q telomere length divided by the average chromosome 17q intensity, and immunohistochemical percentage positivity for HER2 overexpression. The intensity of the subtelomeric chromosome 17q signals was used to normalize the values for telomere length. Cells without 17q signals were not scored.

2.3.6 Statistical analysis

The exact Wilcoxon signed-rank test and the exact Wilcoxon rank-sum test were used to compare the telomere length between normal breast epithelium, DCIS and IDC

46 samples.

2.4 Results

In the present study, the variables (N-TelGen and N-TelChl7q) were compared

between normal, DCIS, and IDC tissues. Using the exact Wilcoxon signed-rank test, we

found that total telomere length in DCIS was significantly shorter than that measured in

normal breast epithelium (p= 0.023) (Fig. 2.3A). Likewise, the telomere length on

chromosome 17q was notably shorter than its counterpart in DCIS compared with normal

tissues (p= 0.0078) (Fig. 2.3B). In contrast, the differences in the length of total telomere

and telomere on chromosome 17q were minimal between DCIS versus IDC (Fig. 2.3A

and B). Upon investigating the variables, N-TelGen and N-TelChl7q, amongst DCIS

alone (n=6) and DCIS+IDC (n=8) no significant changes were apparent between these

two groups (Fig. 2.4A and B).

To compare the telomere shortening on chromosome 17q with total telomeres in

DCIS and normal tissue, we defined the telomere shortening as the fold decreases in

DCIS as compared to normal tissue for both chromosome 17q and for total telomeres in each sample. We found that the median telomere length on chromosome 17q in DCIS decreased by 89.8% as compared with normal tissue, while the median of telomere length on all the rest of the chromosomes decreased by 49% (Table 2.1).

To study the relationship between telomere shortening on chromosome 17q and

FTER2 expression, all samples were stained with HER2 antibody. We found that 18 samples including 8 normal breast epithelium (Fig. 2.5A), 4, DCIS (Fig. 2.5B), and 6

IDC (Fig. 2.5C) were negative and 16 samples containing 10 DCIS (Fig. 2.5D) and 6

47 IDC (Fig. 2.5E) were positive for HER2. We compared the intensities for TelGen, N-

TelGen, TelChl7q and N-TelChl7q between HER2 negative and HER2 positive

samples. Using the exact Wilcoxon rank-sum test, we found that total telomere length in

the HER2 positive group is not significantly different from telomere length in the HER2

negative group (Table 2.2). Likewise, no significant differences were found between the

telomere length on chromosome 17q in both HER2 positive and negative (Table 2.2),

indicating that the expression of HER2 is independent from telomere shortening for both

total chromosomes and chromosome 17q.

2.5 Discussion

Invasive breast cancer is frequently observed to extend directly from ducts

containing DCIS; however, not all cases of DCIS develop into invasive tumors. The

challenge is to identify molecular markers, which will allow prediction of which cases of

DCIS will progress to IDC. Telomere length is reduced in IDC. Hence, we chose to

investigate telomere length as a potential marker.

It has been reported that it may not be the average telomere length but rather the

shortest telomeres that constitute telomere dysfunction as loss of telomere function has

been shown to occur preferentially on chromosomes with critically short telomeres (245).

However, although the p arm of chromosome 17 has been reported to be the shortest telomere (321), it is the q arm of chromosome 17 not chromosome 17p that shows a signal-free end with high frequency in senescent cells (322). This means that telomere shortening does not occur at the same rate for all telomeres, otherwise chromosome 17p should be one of the signal-free end chromosomes. In agreement with these findings, our

48 data shows that telomere shortening on chromosome 17q is significantly greater than the

average erosion of total telomeres in DCIS compared to normal breast epithelium

supporting the idea that the erosion of telomeres can be varied for different

chromosomes.

While we observed this event in malignant cells, other groups have shown similar

results in different cell types (322, 323). Using BJ fibroblasts, Zou etal. found that there

are no signal-free ends in young BJ cells, whereas in near senescent BJ cells chromosome

17q exhibited one of the highest frequencies of signal-free ends (322). Their report

showed that chromosome 17p has no signal-free ends (0%), although this arm of

chromosome 17 has been reported to have the shortest telomere among human

chromosomes. Similarly, Martens et al. showed that out of 13 samples from 10 different

donors, three of them had significantly short chromosome 17q telomere lengths compared

to the median for each individual donor (323). Interestingly, all these three samples were

from haematopoietic bone marrow (BM) cells, which have a high proliferation capacity.

Based on these observations, it can be suggested that cells with higher proliferation rates

show greater telomere shortening on chromosome 17q compared to chromosome 17p and

the average of total telomeres. This suggests that telomere shortening may not be simply the result of the end replication problem and there are potential mechanisms that preferentially erode or protect specific telomeres more than others. Therefore, it is necessary to evaluate the length of each individual telomere and to study its role in the development of cancers. Based on these findings, we conclude that telomere length on chromosome 17q plays a role in the progression of DCIS, possibly via chromosome instability. However, further studies are required to validate the mechanism behind the

49 differential telomere shortening for different chromosomes as well as the role of this mechanism in chromosome instability and cancer.

50 Figure 2.1. Labeling strategy used to flag pan-telomeres and sub-telomeric region of

chromosome 17q.

A) A schematic figure of a pair of chromosome 17. In this study we used a pantelomeric probe to label all telomeres (green color), chromosome 17q specific subtelomeric DNA probe (orange color), and DAPI to stain whole DNA (blue color). B)

Metaphase slides prepared from normal peripheral blood lymphocytes were used to optimize Q-FISH conditions for two different probes, a PNA (CCCTAA)3 pan-telomere probe-FITC (green color) and a specific sub-telomeric DNA probe for chromosome 17q,

TelVysion 17q SpectrumOrange, (orange color). C-D) magnified figures of chromosome

17.

51 Figure 2.2. FISH analysis on breast FFPE tissue sections.

Comparable H&E sections shown in (A) were used to determine regions of interest. (B-D): FISH with subtelomeric DNA probe to label chromosome 17q (orange signals) and pantelomeric PNA probe (green signals) on FFPE sections with xlOO ((B-D) objective. Representative images of normal breast epithelial tissue, DCIS and IDC are shown in each row. DAPI was used as a counterstain. Identical images in black and white shown in (C-D) with nuclei outlined in gray and (C) telomere and (D) sub-telomeric chromosome 17q signals represented by the dark spots (indicated by arrows). Note reduction in telomere signal number, intensity and size in DCIS and IDC compared to normal tissue.

52 * .-si *"i •-.. . - M 1 t •*"" rji *«. *•> *«

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53 A \ Normal vs. DCIS: p=0.023 R \ Normal vs. DCIS: p=0.0078 1.5 - DCIS vs. IDC: p=0.25 cr DCIS vs. IDC: p=0.58 0.5 • .•#—c• D) C 0) <$ O -^ 0.4 •• C •D 1.0 - CD N o 0.3 • -ti CO omer e rmaliz e %'\ N N v (1) O N. ^<"'\ "* \ —$ = i 02 - 0.5 - ~ z «<" V\"» \-' J!> o CD ,s\\ 0.1 - To t \.—;—-=--^_„, E _o 0 0.0 - o 0.0 -

Normal DCIS IDC Normal DCIS IDC

cr r^ G Normal vs. DCIS: p=0.016 ts - DCIS vs. IDC: p=0.31 sz CO c T5 CI) 1.0 i \ N *-,. o **^<\ K . *s CD

E rm a "^§^\... ""w^x _o v o •^J^^^cr^^^- B *N ~t CD 0.5 • ,*-—-*•-""*** ^N ^*Si *-""*-...*. ^^ _____—• •*-> ^ o

0,0 ' •

Normal DCIS IDC

Figure 2.3. Comparison of the normalized intensities of telomere length between

normal breast epithelial tissue, DCIS and IDC.

A) normalized total telomere length, B) normalized chromosome 17q telomere

length. C) normalized total telomere length excluding chromosome 17q telomere length.

Note significant reduction in telomere intensity in DCIS and EDC in comparison with normal breast epithelial tissue. • A 0.8 - I • • -3 0.6 - Ks • • XS • 8 0.4 - •

er e 1 * • •

1•*3— "3 0.2 - » X •

0.0 - • OnlyDCIS DCIS+IDC

o.oo H OnlyDCIS DCIS+IDC

Figure 2.4. Distribution of telomere length between DCIS and DCIS associated with

IDC.

A) normalized total telomere lengths, B) normalized chromosome 17q telomere lengths. There are no significant differences in telomere length between DCIS and DCIS associated with IDC.

55 Table 2.1 Comparing telomere shortening of chromosome 17q in DCIS and normal

tissue with total telomere shortening in the same tissues.

Samples Group A* Group B**

1 82.517 59.4769

2 100.000 71.4190

3 97.081 77.3483

4 90.364 38.5657

5 89.248 64.1070

6 88.889 38.6999

7 72.074 -47.4690

8 100.000 39.3684

Median 89.8063 49.4227

StdDev 9.4823 39.5324

The telomere shortening in DCIS as compared to normal breast epithelium for both chromosome 17q (Group A), and for total telomeres (Group B). Samples 2 and 8 had a

100% erosion which indicated that there was no detection of telomere signal in these two samples.

* Group A: fold decreased of N-TelChl7q in DCIS as compared to normal tissue

** Group B: fold decreased of N-TelGen in DCIS as compared to normal tissue

56 Figure 2.5. FFPE tissue sections were used for immunohistochemistry using HER2 antibody.

Samples having more than 10% of complete cell membrane staining of moderate to strong intensity were considered as HER2 positive. A) negative HER2 normal epithelial breast, B) negative HER2 DCIS, C) negative HER2 IDC, D) positive HER2

DCIS, and E) positive HER2 IDC.

57 ""V *#>• - -'

o LU '1V1J ^ «.,

CQ

.••*«'>.

58 Table 2.2 Association between telomere length and HER2 expression.

HER2 negative HER2 positive Wilcoxon sum-rank (n=18) (n=16) (p-value) Median Median Total telomere lengths (Normalized) 0.34 0.45 0.82

Telomere length on Chl7q* (Normalized) 0.058 0.029 019

Wilcoxon rank-sum test was used to compare the intensities for the total telomere lengths and teleomere length on Chl7q between HER2 negative samples (n=18) and HER2 positive samples (n=16). * Chl7q= chromosome 17q Chapter 3

60 Troglitazone suppresses telomerase activity independently of PPARy transcriptional activity

in breast cancer cells

61 3.1 Abstract

Many standard chemotherapeutic agents currently used to treat breast cancer are

relatively non-specific and act on all rapidly dividing cells. In recent years, more

specifically targeted therapies have been introduced. It is known that telomerase, the

enzyme that protects the ends of chromosomes, is active in over 90% of breast cancers

but inactive in adjacent normal tissues. If it were possible to target cells with active

telomerase then this strategy could be used to treat breast cancer. Recent evidence

suggests that telomerase activity can be suppressed by peroxisome proliferator activated

receptor gamma (PPARy) ligands in primary cells. PPARy is a nuclear hormone receptor

that stimulates the terminal differentiation of a variety of cancers, including breast

carcinoma. In the present study, we investigated the effect of the PPARy ligand,

troglitazone, on telomerase activity in breast cancer cells. To our knowledge, this is the

first time that the effect of troglitazone on telomerase activity in breast cancer has been

investigated. We demonstrated that troglitazone reduced the mRNA expression of hTERT and telomerase activity in the MDA-MB-231 breast cancer cell line. Using

different approaches, we showed that troglitazone reduced telomerase activity even in the

absence of PPARy. In agreement with this result, we also found no correlation between

PPARy and hTERT transcript levels in breast cancer patients. In summary, given the important role of telomerase in breast cancer progression, our data suggest that troglitazone can be used as an anti-telomerase agent; however, the mechanism underlying this inhibitory effect remains to be determined.

62 3.2 Introduction

Breast cancer is the most common malignancy of North American women.

During their lifetime, 1 in 9 women are expected to develop breast cancer, and 1 in 28

women are expected to die from it. In Canada, it is estimated that there will be 22,400

new cases of breast cancer, and more than 5,300 women will die from this disease in

2008 (2). Current therapy for primary breast cancer includes surgical resection with or

without radiation and/or chemotherapy. Nevertheless, a large percentage of women with

early-stage disease will experience a distant relapse leading to death from recurrence-

related complications and many women will experience significant chemotherapy-

induced toxicity. This has motivated considerable efforts toward finding novel

therapeutic approaches for the treatment of breast cancer.

Immortalization is a necessary step toward malignant transformation of normal

human somatic cells, which have intrinsic mechanisms that monitor cell divisions and

limit their life span. Progressive shortening of telomeres with each cell division has been found to limit replicative life span of human cells in culture (305). Most human cancer cells maintain their telomeres through activation of telomerase (108). In over 85% of human tumors, and more than 90% of breast carcinomas, telomerase is active in contrast to normal tissues where telomerase is active at low levels or is undetectable (21, 306,

307). Telomerase is a very large complex ribonucleoprotein enzyme with an estimated mass of -650 to 670 kDa (110). In vitro, two components are minimally required for telomerase activity: human Telomerase Reverse Transcriptase (hTERT), the catalytic subunit of telomerase, and human telomerase RNA (hTR), the RNA template (111). It has been shown that inhibition of telomerase limits the growth of human cancer cells, and

63 as a consequence various anti-telomerase strategies are currently under investigation to limit tumor proliferation in cancer patients (108).

Peroxisome proliferator activated receptors are members of the nuclear hormone receptor super-family, regulating gene expression via their ligand-activated transcriptional activity. In addition to controlling the expression of many genes involved in lipid metabolism, and insulin sensitization, it has been revealed that PPARy functions as a tumor suppressor in a variety of malignancies such as breast cancer, colon cancer, liposarcoma, ovarian cancer, and prostate cancer (244).

There are three classes of ligands for PPARy; natural ligands, dual ligands, and synthetic ligands. Synthetic ligands are members of the thiazolidinedione (TZD) family including troglitazone, rosiglitazone, pioglitazone, and ciglitazone (268). TZDs are known as insulin sensitizers and are used in the treatment of type 2 diabetes. In addition, it has been shown that TZDs promote the differentiation of various cell lines (227, 228,

230, 231, 311), and some of them, especially troglitazone and ciglitazone, demonstrate antiproliferative activities in several cancer models including breast cancer (232).

PPARy regulates gene expression by forming a heterodimer with retinoid X receptor (RXR) and binding to peroxisome proliferator response elements on the target genes. Using the RXR ligand, Choi et al. demonstrated inhibition of cell growth and telomerase activity of breast cancer cells in vitro (324). Interestingly, the PPARy/RXR heterodimers can be activated by ligands for either PPARy or RXR (325). Interestingly, resent evidence showed that PPARy can inhibit telomerase activity in some experimental models (173-175), however, the same role for PPARy in breast cancer has not been studied. To determine if PPARy regulates telomerase activity in breast cancer, we tested

64 the effect of a PPARy ligand, troglitazone, on telomerase activity in breast cancer cell

lines.

3.3 Materials and Methods

3.3.1 Materials

The MDA-MB-231, MCF-7, and T47D cell lines were obtained from American

Type Culture Collection (ATCC) (Manassas, VA, USA). The PPARy agonist troglitazone

and its antagonist GW9662 were purchased from Sigma (Sigma-Aldrich, St Louis, MO,

USA). Bisphenol A diglycidyl ether (BADGE) was from Cayman (Cayman, Ann Arbor,

MI, USA). Antibodies against Maspin and PPARy were purchased from BD Biosciences

(BD Biosciences, Mississauga, ON, CA) and Santa Cruz Biotechnology (Santa Cruz

Biotechnology, Santa Cruz, CA, USA) respectively. TaqMan specific primers for PPARy, hTERT, K19, Muc-1, and GAPDH were obtained from Applied Biosystems (Branchburg,

New Jersey, USA). Cell culture media was from the Ontario Cancer Institute (OCI)

(Princess Margaret Hospital, Toronto, ON, CA).

3.3.2 Cell Culture

MDA-MB 231 cells were cultured in alpha MEM medium (aMEM) (Princess

Margaret Hospital, ON, CA) supplemented with 10% v/v fetal bovine serum (HyClone,

Logan, UT, USA) at 37°C in a humidified atmosphere with 5% C02 for 24 hours, then treated with either troglitazone or the same amount of DMSO (Sigma-Aldrich Life

Science, Saint Louis, MO, USA) and incubated for different periods of time as indicated

65 in the figure legends.

3.3.3 Cell toxicity and cell viability assay

The cell toxicity assay was measured using CellTiter96 nonradioactive proliferation assay kit (Promega, Madison, WI, USA). This assay can measure cell viability as well as cell toxicity using the dose-response curve to determine IC50 (50% inhibitory concentration at 50%), the concentration of the test substance required to reduce the light absorbance capacity of exposed cell cultures by 50%. To measure cell toxicity, cells were seeded in 96-well plates at a density of 7xl03 cell/well and treated with indicated concentrations of troglitazone. At the end of each time point, cells were incubated with 20 u,l MTS/PMS solution for a further 3 hours in a humidified environment. Finally, the toxicity effect of troglitazone was determined by measuring the formazan produced by proliferating cells at 490nm on a Tecan SpectraFluor Plus Plate

Reader (MTX Lab Systems, Inc, Vienna, VA, USA).

For the cell viability assay, cells were cultured in 6-well culture plates. The next day, cells were exposed to troglitazone or vehicle. After 24 hours inculabation in cell culture incubator, cells were trypsinized and washed with PBS and then resuspended in growth media. The cell viability was measured by automated Vi-CELL, which uses the trypan blue dye exclusion method (Beckman Coulter, Brussels, Belgium).

3.3.4 Western blot analysis

Total protein was extracted from cells using CytoBuster™ Protein Extraction

Reagent (Novagen, Darmstadt, Germany) containing an appropriate concentration of

66 complete protease inhibitor cocktail tablets (Roche Diagnostic GmH, Mannheim,

Germany) and Phosphatase Inhibitor Set II (Calbiochem, La Jolla, CA, USA). Protein concentrations were measured using the Bradford assay (Bio-Rad Laboratories, Inc.,

Hercules, CA, USA) according to the manufacturer's protocol. Proteins were mixed with

IX laemmli buffer (10% SDS, 0.5 M Tris, pH 6.8, (3-mercaptoethanol, glycerol, 0.1%

Bromophenol Blue) and boiled for 5 minutes, followed by separation on 4-12% gradient

NuPAGE® Novex Bis-Tris Gels (Invitrogen, Carlsbad, CA, USA). After electrophoresis, the proteins were transferred to Hybond™-C Extra nitrocellulose membrane (Amersham

BioSciences, Little Chalfont, UK). The transblotted membranes were blocked in 5% skim milk-TBST [lOx Tris Buffered Saline (TBS); 24.2 g Tris base, 80 g NaCl in one liter distilled H20; at pH to 7.6 (use at lx plus 0.1% Tween-20)] for 1 hour at room temperature and washed 3 times each time for 5 minutes with TBST. Blots were then incubated with the appropriate primary antibody overnight at 4°C. After washing three times in TBST, membranes were incubated with the appropriate secondary antibody conjugated to horseradish peroxidase (Amersham BioSciences, Buckinghamshire, UK)

(1:10,000 dilution in 5% skim milk) for 1 hour at room temperature and then washed three times in TBST each time for 10 minutes. The immunoblots were visualized using

ECL Advance™ Western Blotting Detection Kit (GE Healthcare, Buckinghamshire, UK) as described by the manufacturer. The resultant films were analyzed and quantified using

Image J software [Research Services Branch (RSB) of the National Institute of Mental

Health (NIMH), National Institutes of Health (NIH), USA, http://rsb.info.nih.gov/nih- imagel.

67 3.3.5 Real-time RT-PCR

Total RNA was extracted using RNeasy® plus kit (Qiagen, Mississauga, ON,

CA), and used for real-time reverse transcription (RT) in a two-step procedure. In the first step, an aliquot of 2 fig total RNA from each sample was reverse transcribed to cDNA using TaqMan® Reverse Transcription Reagents (Applied Biosystems, Branchburg, New

Jersey, USA). In the second step, 100 ng cDNA was used for PCR using TaqMan®

Universal PCR Master Mix (Applied Biosystems, Branchburg, New Jersey, USA) in a

396-well plate according to the manufacturer's instructions. We used TaqMan specific primers for hTERT, PPARy, Mucl, K19, and GAPDH in our experiments (Branchburg,

New Jersey, USA). The real-time quantitative PCR and analysis were carried out using the ABI Prism 7900HT Sequence Detection system (Foster City, CA, USA).

3.3.6 Stable shRNA mediated repression of PPARy in MDA-MB-231 cells

Human PPARy expression in wild type MDA-MB-231 breast cancer cells was silenced by the shRNA method. For this purpose, four lentiviral gene transfer vectors expressing shRNA against PPARy (NM_138712) were purchased from Open Biosystems

(Huntsville, AL, USA); shPPARy-71 (ClonelD: TRCN0000001671), shPPARy-72

(CloneE): TRCN0000001672), shPPARy-73 (ClonelD: TRCN0000001673), and shPPARy-74 (ClonelD: TRCN0000001674). An adopted Qiagen non-silencing control siRNA sequence (TTCTCCGAACGTGTCACGT) that was not complementary to any human gene was used as a control shRNA (generous gift from Dr. M.S. Tsao, University of Toronto). Lentiviruses were prepared by transfecting three plasmids (pMDLg/pRRE, the vesicular stomatitis virus (VSV-G) envelope plasmid pCMV-VSG, rev expressing

68 plasmid pRSV-Rev and gene transfer vectors containing the self-inactivating LTR) into

293T cells as described (326, 327). MDA-Mb-231 cells were transduced as previously

described (326, 327). Transduced cells were then selected for two weeks in growth media

supplemented with 0.5 u.g/ml puromycin. Selected cells were subjected to real-time RT-

PCR to test the expression level of PPARy in the transduced MDA-MB-231 cells.

3.3.7 Telomeric repeat amplification protocol (TRAP) assay

MDA-MB-231 cells were grown in 60-mm dishes in the presence and absence of

troglitazone at 37°C in a humid incubator. After 24 hours incubation, the medium was removed from each dish and the cells were washed two times with 2ml of ice-cold IX

PBS. A final 2 ml of PBS was added and the cells were detached using a cell scraper and transferred to a 2 ml Eppendorf tube. The cells were then centrifuged at 1 500 g for 5 min at 4°C. CHAPS lysis buffer [lOmM Tris HC1, pH7.5, 1 mM MgCl2, 5 mM beta- mercaptoethanol, 0.5% W/V CHAPS (Sigma-Aldrich, St Louis, MO, USA), and 10% v/v glycerol with 400 U RNase Inhibitor (Roche, Basel, Switzerland) and one tablet of complete (mini) EDTA free Protease Inhibitor Cocktail (Roche, Basel, Switzerland)] at a volume of 4-5 times the volume of the cell pellet was used to lyse the cells on ice for 30 minutes. The cells were then centrifuged at 13,000 x g for 30 mins to pellet the lysed cells. After centrifugation, the protein lysate was transferred to a new 1.5 ml Eppendorf tube. The concentration of the cleared whole cell lysate was determined by the Bradford assay (Bio-Rad Laboratories, Inc., Hercules, CA, USA). TRAP assay reactions were performed, using the radioactive method of the TRAPeze kit (Millipore, Billerica,

MA,USA) with some modifications which follow. The telomerase extension step was

69 performed in the absence of Taq Polymerase in the reactions. After the telomerase

extension, the reactions were heated to 94°C for 2 minutes, followed by the addition of 2

units of Taq polymerase (NEB, Ipswich, MA, USA) and PCR amplification. PCR

conditions were 25 cycles of 94°C for 30 seconds, 50°C for 30 seconds, and 72°C for 90

seconds. Half of the 50/xl reaction was loaded on a 10% non-denaturing PAGE gel in

0.6X TBE buffer. The gel was dried and exposed to a phosphor-imager screen and

scanned using a Typhoon Trio Imager (GE Healthcare, UK). Protein titrations were performed to ensure TRAP reaction products exhibited a semi-linear response.

3.3.8 The NKI microarray dataset analysis

The Netherlands Cancer Institute (NKI) dataset was used to compare the expression of PPARy and hTERT (328). The NKI dataset is published genome-wide gene expression microarray data from 295 breast cancer samples collected between 1984 and

1995. Samples were selected from patients younger than 53 years with primary invasive breast cancers. All patients had stage I and II breast cancer and were either lymph-node- negative (151 patients) or lymph-node-positive (144 patients). These patients received modified radical mastectomy or breast conserving surgery as their treatments. The published expression dataset of PPARy (Probe ID: 17022) and hTERT (Probe ID: 1809) for the 295 patients were analyzed by unpaired Student's t test.

3.3.9 Statistical analysis

All numerical data were expressed as median values ± SD. Statistical significance was determined by performing a paired Student's t test.

70 3.4 Results

3.4.1 Evaluating the expression of PPARy in different breast cancer cell lines

Expression levels of PPARy gene and protein were evaluated in several breast

cancer cell lines by real-time RT-PCR and western blot analysis. Three different breast

cancer cell lines; MDA-MB-231, MCF-7, and T47D were tested for this purpose. Real­

time RT-PCR showed that mRNA expression of PPARy is higher in MDA-MB-231 cells

compared to the other two cell lines (Fig. 3.1 A). In agreement with this result, we found

that although all three cell lines expressed PPARy protein, the expression of PPARy

protein was higher in MDA-MB-231 compared to the two other cell lines (Fig. 3.IB).

3.4.2 Determining the expression level of hTERT and telomerase activity in MDA-

MB-231 cells

We examined the expression level of hTERT mRNA, as well as telomerase

activity in these cell lines. Results from real-time RT-PCR indicated that all three cell lines analyzed expressed hTERT mRNA (Fig. 3.2A) and the TRAP assay confirmed that telomerase was active in these cells (Fig. 3.2B).

3.4.3 The cell toxicity effect of troglitazone

To determine the non-toxic concentration of troglitazone for cell treatment in our study, we measured IC50 for troglitazone using the CellTiter96 non-radioactive proliferation assay kit. This assay measures the activity of dehydrogenase found in metabolically active cells. Dehydrogenase, a mitochondrial enzyme, reduces MTS (3-

71 (4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-

tetrazolium, inner salt) chemically into formazan (329). Since the production of formazan

is proportional to the activation of this enzyme in viable cells, the intensity of the

produced color is an indicator of cell viability. The MTS result showed that the IC50 of

troglitazone after 24 (Fig. 3.3A) and 48 (Fig. 3.3B) hours exposure was 190 /iM.

3.4.4 Troglitazone reduces telomerase activity in a time and dose dependent manner

In order to examine whether troglitazone regulates telomerase activity, MDA-

MB-231 cells were cultured in growth medium enriched with 10% fetal bovine serum

(FBS) in the presence and absence of troglitazone. In the cells exposed to troglitazone, a

decrease in telomerase activity was observed at 24 hours in a dose dependent manner

(Fig. 3.4). MDA-MB-231 cells were also treated with 20 fiM troglitazone or the equal

volume of DMSO. Cells were then harvested at indicated time points and their telomerase

activity was measured by the TRAP assay. Our results showed that the reduction of telomerase activity by troglitazone is time dependent (Fig. 3.5A).

3.4.5 Troglitazone suppresses hTERT transcription

To examine whether the suppression of telomerase by troglitazone is at the mRNA level of hTERT, MDA-MB-231 cells were treated with different concentrations of troglitazone for 24 hours and the mRNA expression of hTERT was determined by real-time RT-PCR. Troglitazone exhibited a significant dose dependent reduction in the expression of hTERT mRNA compared to control (P < 0.05) (Fig. 3.5B), indicating that troglitazone suppressed telomerase activity by decreasing the expression of hTERT

72 mRNA. Time-chase experiments showed that this effect can be observed in 24 hours of

treatment starting with a minimum of 20uM troglitazone, whereas treating the cells with

the same concentration of troglitazone for 12 hours did not show significant changes in

hTERT mRNA compared to controls (Fig. 3.5C). Our results show that the minimum

concentration for the inhibitory effect of troglitazone is 20\iM for a minimum of 24

hours. Based on these observations, the effect of 20uM of troglitazone at 24 hours was

selected for further studies.

3.4.6 Reduction in telomerase activity is independent from the transcriptional role

ofPPARy

To assess the involvement of PPARy in the reduction of telomerase activity,

MDA-MB-231 cells were exposed to two different PPARy antagonists, 10 uM GW9662

(330) and lOOuM of BADGE (331), for 24 hours prior to troglitazone treatment. Results from real-time RT-PCR showed that addition of either GW9662 or BADGE did not recover the suppressive effect of troglitazone on hTERT gene expression (Fig. 3.6).

To confirm these results, we knocked down the expression of PPARy using lentivirus. Results from real-time RT-PCR showed that the expression of PPARy mRNA

(Fig. 3.7A) and its protein level (Fig. 3.7B) in knock-down cells has been significantly decreased compared to wild type and scrambled shRNA cells. Next, we studied the telomerase activity in the knocked-down PPARy cells. Using TRAP assay, we did not find a significant change in telomerase activity in knocked-down versus wild type MDA-

MB-231 cells (Fig. 3.8). Furthermore, to examine the effect of troglitazone in the absence of PPARy, wild type and knocked-down MDA-MB-231 cells were treated with

73 troglitazone for 24 hours and the activity of telomerase was measured. Our data show that

troglitazone was able to suppress telomerase activity in the absence of PPARy (Fig. 3.9).

These results indicate that the effect of troglitazone on telomerase activity does not rely

on PPARy transcriptional activity.

3.4.7 Troglitazone does not induce apoptosis

It has been shown that PPARy ligands induce programmed cell death (apoptosis)

(for review see (332)). To examine the effect of troglitazone on cell viability and

apoptosis in our study, MDA-MB-231 cells were treated with control or troglitazone for

24 hours and cell viability was measured using the trypan blue dye exclusion method.

Cells treated with troglitazone showed a reduction in cell viability compared to controls, however; this reduction was not statistically significant (Fig. 3.10A). Troglitazone treated cells were also evaluated for caspase-3, one of the key executioners of apoptosis, as an apoptosis marker. Caspase-3 did not show any differences in treated cells compared to control DMSO (Fig. 3.1 OB). Furthermore, we examined the protein levels of poly (ADP- ribose) polymerase (PARP), a protein which undergoes caspase-3 mediated cleavage during apoptosis and produces an 89 kDa fragment. In agreement with the caspase-3 result, we did not observe a reduction of PARP protein levels in treated cells compared to control cells (Fig. 3.10B). These results confirm that troglitazone at 20/xM does not induce apoptosis and is not toxic to the cells.

3.4.8 Troglitazone does not induce the differentiation of MDA-MB-231

Accumulating evidence indicates that PPARy promotes cell differentiation

74 following activation by its ligand (227, 228, 230, 231, 311) and since telomerase is not

active in differentiated cells (333, 334), we were interested to determine if the effect of

troglitazone in the reduction of telomerase activity is the result of cell differentiation.

MDA-MB-231 cells were treated with various concentrations of troglitazone for

different periods of time. We measured the expression of maspin as a marker for

differentiated breast epithelial cells (335). As shown in Fig. 3.11A, troglitazone did not

increase the protein level of maspin, indicating that treated cells are not more

differentiated. We also measured the expression of two genes associated with breast malignancy, Keratin 19 (K19) and mucin-1 (Muc-1) as described by Mueller et al. (228).

Real-time RT-PCR showed the expression of Muc-1 (Fig. 3.11B) and K19 (Fig- 3.11C) genes remained unchanged in troglitazone treated cells vs. vehicle treated cells.

3.4.9 The expression of hTERT is not correlated with the expression of PPARY in clinical samples

Published NKI data from 295 young patients with primary invasive breast cancers was used to compare the expression of PPARy and hTERT (328, 336). We found that the expression of PPARy and hTERT in this set of samples was 44% and 62% respectively.

Our results showed that there is no correlation between the level of PPARy expression and hTERT expression (R= -0.152) in these samples (Fig. 3.12A). Since MDA-MB-231 cells are estrogen receptor negative, we compared the expression of PPARy and hTERT genes in estrogen receptor-negative (N= 69) and estrogen receptor-positive (N= 225) tumor samples. We found no significant correlation between the two genes in estrogen receptor-positive (R=-0.156) (Fig. 3.12B) and estrogen receptor-negative (R = -0.08)

75 (Fig. 3.12C) tumors.

3.4.10 The level of hTERT expression is negatively correlated with ER status in

breast cancer samples

To assess the strength of the expression status of hTERT gene as a predictor of

the outcome of disease, we examined the correlation of hTERT gene expression with

different clinical prognostic parameters; tumor ER content, tumor differentiation (grade),

tumor size (Size), 70-gene prognosis signature (70GS) (337), and wound signature (WS)

(336) (Fig. 3.13A). Our results showed that in ER negative patients the expression of

hTERT is significantly higher than in ER positive patients (p<0.02) (Fig. 3.13B).

However, no significant correlation was found between hTERT expression and tumor

differentiation (Fig. 3.13C), WS (Fig. 3.13D), 70GS (Fig. 3.13E), and size (Fig. 3.13F).

3.5 Discussion

It has been shown that inhibition of telomerase limits the growth of human cancer cells (107), and as a consequence various anti-telomerase strategies are currently under investigation to limit tumor proliferation in cancer patients (108). There is some evidence indicating that PPARy may play a critical role in this process (173-175). The objective of this study was to investigate the effect of troglitazone on telomerase as a potential novel therapeutic approach that could be used to treat breast cancer. We studied three human breast cancer cell lines; MDA-MB-231, MCF-7, and T47D. MDA-MB-231 is estrogen receptor negative (338), EGF responsive and IGF-I non-responsive. MCF7 is estrogen receptor positive (338), EGF non-responsive and IGF-I responsive and T47D is estrogen

76 receptor positive, EGF responsive and IGF-I non-responsive (339). Real-time RT-PCR

data showed the expression of PPARy mRNA was higher in MDA-MB-231 cells

compared to MCF-7 and T47D. In agreement with this result, western blot analysis

confirmed that MDA-MB-231 cells produced more PPARy protein than the other two cell

lines. We also validated the expression of hTERT and telomerase activity in these cell

lines. Real-time RT-PCR showed that all three cell lines express hTERT mRNA. This

data has been confirmed by the TRAP assay, which showed that telomerase is active in

all three cell lines. To study the effect of PPARy on telomerase activity, the MDA-MB-

231 cell line was chosen as an ER-negative breast cancer in vitro model as this cell line

contains active telomerase and high PPARy protein. Using the TRAP assay, we showed

that troglitazone suppresses telomerase activity. The presence of a strong internal control

in the troglitazone treated samples indicates that the suppression of telomerase is not due

to PCR inhibition in the TRAP assay. We also found that this suppression is at the level

of gene transcription. We found that hTERT mRNA levels were significantly reduced by

troglitazone. Our results showed that 20jiiM of troglitazone is the minimum concentration

that can suppress telomerase activity in a minimum time of 24 hours. We also found that

troglitazone at this concentration is not toxic to the cells indicating that the inhibition of telomerase by troglitazone is not a consequence of cell toxicity.

There is accumulating published data indicating that troglitazone can act independently from PPARy (268). To study the role of PPARy in modulating the expression of hTERT and telomerase activity, we studied the effects of troglitazone on hTERT expression in the absence and presence of PPARy antagonists, BADGE and

GW9662. We found that neither BADGE nor GW9662 were able to prevent the

77 suppressive effect of troglitazone on hTERT expression. This finding was confirmed

using shRNA silencing of PPARy. We showed that troglitazone suppresses telomerase

even in the absence of PPARy expression. This result indicates that the effect of

troglitazone on telomerase activity is independent from PPARy. There is some evidence

showing that activated PPARy abolishes telomerase activity (173-175). It is noteworthy

that none of these groups used MDA-MB-231 cells as their models. Moreover, none of them used troglitazone as the ligand for PPARy. Recently, using pancreatic cancer cell

12 14 lines, Kondoh et al. showed that 15-deoxy-A ' prostaglandin J2 (15d-PGJ2), a natural ligand for PPARy, suppresses the expression of hTERT by blocking ER functions (340).

However, this group did not demonstrate whether this suppression is through PPARy activation. Although the underlying mechanism for these differences remains to be discovered, it may reflect differences in experimental models and approaches such as cell type, particular ligand, duration of treatment, and dosage.

A line of evidence indicates that PPARy ligands induce cell apoptosis (286-290).

To examine whether the suppression of telomerase is the result of apoptosis induction, we investigated the effect of troglitazone on cell viability and the protein level of caspase-3 and PARP as apoptosis markers. Although it has been shown that troglitazone induces apoptosis in different cancer cell lines by different mechanisms (286-289), our result showed that troglitazone does not induce apoptosis in MDA-MB-231 cells. It is noteworthy that these groups used a higher concentration of troglitazone. However, published data from other groups indicates that a low concentration of troglitazone cannot promote apoptosis. Elstner et al. showed that 10/xM troglitazone does not induce apoptosis in the MCF-7 cell line after 4 days incubation (290). In agreement with this

78 group, Ohta et al. demonstrated that 10/iM troglitazone was not able to change the

expression level of bcl-2 and box genes in BHP18-21, a thyroid papillary carcinoma cell

line (292).

More importantly, all groups showed that regardless of the concentration of

troglitazone used, the induction of apoptosis was independent from PPARy activity (286-

290, 292). Since the promotion of apoptosis was not observed in our cell model, it can be

postulated that the suppression of hTERT and its telomerase activity by troglitazone is not due to apoptosis activation.

It has been also shown that PPARy promotes cell differentiation (227, 228, 230,

231, 311). Since differentiated cells do not show telomerase activity (333, 334), we investigated whether telomerase suppression in our study is the result of cell differentiation. MDA-MB-231 cells were exposed to various concentrations of troglitazone for different time periods. We examined the expression of three different markers associated with breast cancer. It has been shown that the mRNA expression of maspin is decreased in malignant breast cells compared to normal epithelial breast cells

(335). In contrast, K19 and Muc-1 are associated with more malignant breast epithelial cells (228). Our results showed that troglitazone was not able to change the expression of these genes, indicating that treated cells with troglitazone had not been differentiated.

Furthermore, by increasing the concentration of troglitazone, we did not observe any signs of cell differentiation but we observed cell toxicity and apoptosis. This suggests that troglitazone inhibits telomerase activity independently from cell differentiation at low concentration, where no cell toxicity and apoptosis were observed.

To compare our findings with clinical samples, we analyzed the expression of

79 PPARy and hTERT from genome-wide gene expression microarray data for 295 patients

(328, 336). We found no correlation between these two genes (R= -0.152). Furthermore,

since the MDA-MB-231 cell line is an estrogen receptor negative cell line, we compared the expression of hTERT and PPARy in estrogen receptor (ER) negative patients. No significant correlation was found between the expression of hTERT and PPARy (R= -

0.08) in this group of patients. In agreement with our result from in vitro model, this finding suggests that telomerase inhibition by PPARy ligands is independent from PPARy transcriptional activity and conducted by an unknown mechanism.

Using the NKI dataset, we also analyzed the correlation of hTERT expression with clinical prognostic parameters. It has been hypothesized that reactivation of telomerase in breast epithelial tissue may be an important event in the progression to breast cancer (341). Published data reveal that the prognostic significance of telomerase activity in malignant disease is controversial. There is evidence indicating no correlation between telomerase activity and established prognostic factors such as tumor size and lymph node status (307, 342-345). Mokbel et al. showed that despite a significant association between telomerase activity with advanced histopathological grade and tumor type (ductal vs. lobular), there is no correlation between telomerase activity and nodal status or disease outcome in human breast cancer (346). However, the same group found an association between telomerase activity and nodal metastasis in another published report (347). Moreover, others found an association between telomerase activity and prognostic factors (348-350). Analyzing the NKI published dataset, we found no correlation between hTERT expression and tumor size, lymph-node status, 70-gene signature, wound signature, and proliferation status of tumors. However, the incidence of

80 hTERT expression was found to be negatively associated with ER content (p=0.019).

hTERT expression is higher in ER negative patients compared to ER positive patients.

This is in agreement with other published data (349) but in contradiction to the results

reported by Carey et al. (307). We also found that troglitazone significantly suppresses

telomerase activity at low concentration without involving PPARy transcriptional

activity, apoptosis, and cell differentiation. Furthermore, this result was confirmed by

comparing with clinical data, showing that there is no correlation between PPARy and hTERT gene expression in the NKI dataset.

To our knowledge this is the first time that the effect of troglitazone on telomerase

activity has been studied in human breast cancer. We showed that the expression of hTERT and PPARy are two independent events and troglitazone reduces the activity of telomerase by recruiting other pathway(s) rather than PPARy activity. Our study shows although the mechanism underlying this suppression remains unclear, and may be indirect, troglitazone can be considered as an anti-telomerase agent in estrogen-receptor negative breast cancer cells. In addition, based on data from our studies as well as others, we suggest that the role of troglitazone, and probably the other members of TZD family, should be revisited beyond their original role as PPARy ligands.

81 A

^ s 5 O 0.8

PL, >

P* 0.0 MDA-MB-231 MCF-7 T47D

B MDA-MB-231 MCF-7 T47D

MHH PPARY (protein) 1 L i •', i. >

GAPDH (protein)

Figure 3.1. The expression of PPARy mRNA and protein in various breast cancer cell lines.

A) Total RNA was prepared from MDA-MB-231, MCF-7, and T47D cells. Using real-time PCR, the mRNA expression of PPARy was measured. MDA-MB-231 cells express higher PPARy mRNA compared to the other two cell lines. B) Protein lysates were prepared from three different breast cancer cell lines and then analyzed via immunoblotting to examine the level of PPARy protein in these cell lines. Representative immunoblot suggests that MDA-MB-231 cells express more PPARy protein compared to

MCF-7 and T47D.

82 Figure 3.2. The expression level of hTERT and telomerase activity in three different breast cancer cell lines.

A) Using a Taqman probe, total RNA was used to examine the expression level of hTERT in MDA-MB-231, MCF-7, and T47D cells by real-time PCR. Values express the relative quantity of the genes to the level of mRNA expression of GAPDH. B)

Telomerase activity was measured in MDA-MB-231, MCF-7, and T47D cell lines. For each sample two different protein concentrations, 500 ng and 250 ng, were used for

TRAP assay. CHAPS buffer and protein lysate from HeLa cells were used as negative and positive controls respectively. The arrow head indicates a PCR product that serves as positive internal controls (IC) for the PCR reaction itself. TRAP assay showed that telomerase is active in all three cell lines.

83 A

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84 A

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o o B

•os CO 3 0 12 3 4 Logarithmic concentration of troglitazone (/JM) B

0 12 3 4 Logarithmic concentration of troglitazone ((iM)

Figure 3.3. IC50 of troglitazone was measured by MTS assay.

MDA-MB-231 cells were treated with various concentrations of troglitazone (0,

10, 20, 50, 100, 250, 500, 1000 /iM) for A) 24 or B) 48 hours and troglitazone IC50 was determined by MTS assay as described in Materials and Methods. All data were normalized against the control group (DMSO) in the respective treatment condition.

85 Figure 3.4. Troglitazone suppresses the activity of telomerase in a dose dependent manner.

Lysates from MDA-MB-231 cells with no treatment (wt), treated with various concentrations of troglitazone (Tro) or the equal volume of DMSO were examined for telomerase activity by TRAP assay. 500 ng and 250 ng of total protein were used for each sample, with a 25-cycle amplification step (see Materials and Methods). The arrow head indicates a PCR product that serves as positive internal controls (IC) for the PCR reaction itself. Triangles for each sample represent higher (500 ng) and lower amounts of protein

(250 ng).

86 %. <%>.

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87 Figure 3.5. The effect of troglitazone on telomerase activity and hTERT expression is time dependent.

A) MDA-MB-231 cell lysate with no treatment (wt), treated with 20/iM troglitazone (Tro 20/xM) or the equal volume of DMSO for 24 hours or 48 hours were examined for telomerase by TRAP assay. 500 and 250 ng of total protein was used for each sample. The arrow head indicates a PCR product that serves as positive internal controls (IC) for the PCR reaction itself. Triangles for each sample represent higher (500 ng) and lower amounts of protein (250 ng). B-C) MDA-MB cells were treated with with

DMSO or 20 piM of troglitazone (Tro 2(fytM). Using real time RT-PCR, the expression of level of hTERT was analyzed after B) 24 hours or C) 12 hours of treatment. Values express the relative quantity of hTERT to GAPDH. The volume of DMSO was not more than 0.1%. Results shown are as mean ± SD and are representative of at least three independent experiments. * p < 0.05.

[HE DMSO (equal volume to 10/iM troglitazone) HD 10/xM troglitazone

ED DMSO (equal volume to IOJJM troglitazone) H 15/xM troglitazone

• DMSO (equal volume to 10/xM troglitazone) • 20/LiM troglitazone

88 mRNA expresson of hTERT DO (Relative to GAPDH) o o p o bo

r f ^ \ \ i • • -m - * X o i • ?L % I f % II 7 ** t M :•'-.'. -. . mRNA expresson of hTERT <•• i V (Relative to GAPDH) o o o p b bo t .t 'i t-Mfct-ttlt *.:•'> - 100

80 -^ Q 60 o U w O w *-> 40 ^

s P4 20

Tro20 GW9662 BADGE +Tro20 +Tro20

Figure 3.6. The suppressive role of troglitazone on hTERT is independent from

PPARy.

MDA-MB-231 cells were exposed to either 10 [iM GW9662 or lOOjuM BADGE for 24 hours. Cells then were treated with 20 /xM troglitazone in the presence of GW9662 or BADGE for another 24 hours. At the end of the incubation time, the expression level of hTERT was determined by real time PCR. Values represent the relative quantity of hTERT to GAPDH. The volume of DMSO was not more than 0.1%. Results shown are as mean ± SD and are representative of three independent experiments.

90 A

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_^_ wt scrambled 71 72 73 74

B

wt scrambled 71 72 73 74

PPARy •tm>

GAPDH -'«&•* - nimiiir 4MHNNIW' 1 | Tfl^Wr ^^PF( **HUIP"i~ «.,

Figure 3.7. Suppression of the expression of PPARy by shRNA.

Using shRNA protocol, the expression of PPARy was blocked with four different

shRNA oligos (71, 72, 73, and 74) in the MDA-MB-231 cell line. To assess the

specificity of shRNA oligos against PPARy , MDA-MB-231 cells were transfected with

scrambled oligo and the extracted protein was used as a control for further experiments.

A) The mRNA level of PPARy was examined by real-time PCR. B) The protein level of

PPARy was subjected to western blot analysis.

91 o& * ^ ^

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Figure 3.8. Evaluation of hTERT mRNA expression and telomerase activity in

PPARy knocked-down cells.

TRAP assay was used to examine telomerase activity in MDA-MB-231 cells in the absence of PPARy (01igo71, 72, 73, 74) compared to non-transfected MDA-MB-231 cells (wt) and cells transfected with a scrambled oligo. IC represents positive internal controls.

92 Figure 3.9. The effect of troglitazone on telomerase activity was measured in the absence of PPARy.

MDA-MB-231 cells carrying silenced PPARy by shRNA were treated with 20 fjM troglitazone or the equal volume of DMSO for 24 hours. Two different concentrations of protein, 500 ng and 250 ng, from each sample were subjected to TRAP assay. CHAPS buffer and the extracted protein from HeLa cells were used as negative and positive controls respectively. The arrow head indicates a PCR product that serves as an internal positive control for the PCR reaction itself. Result shown is representative of two independent experiments.

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94 Figure 3.10. Troglitazone does not induce apoptosis in MDA-MB-231.

A) Cells were exposed to 20jiiM troglitazone for different numbers of days as indicated and cell viability was measured using automated Vi-CELL. Values expressed as percent survival of vehicle-treated controls (given 100%). Data were collected from three experiments performed in triplicate. B) Equal amounts of MDA-MB-231 cell lysate from

DMSO and treated cells with 20 /xM troglitazone (Tro 20/xM) were subjected to western blot analysis to determine the protein levels of caspase-3 and PARP. No differences were found between control and troglitazone treated cells. Representative immunoblots correspond to three separate experiments.

95 A B 102 DMSO Tro20 100

3 Caspase-3 1 o trt 98 (Full length) I T—1 o i—i l—H O 'D O (D 96 ffii bl , 4-1 PARP • TH o > 4-» (full length) O 94

92 GAPDH 90 Day Day1 Day2 Day3 of treatment

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m o ^^ 4— •!-• if S@ 25 o '-^ _ c .2 u 12 SO %a 15 OS lfl •- > 8 .fc £2-- — 0.4 - OJ1 < cc 00 z: ~ DMSO Tro20 cr DMSO Tro20 e

Figure 3.11. Cell differentiation was not induced by troglitazone and troglitazone

had no effect on apoptosis.

MDA-MB-231 cells were treated with 20 juM of troglitazone (Tro 20/iM) or the

equal volume of DMSO for 24 hours. A) Representative western blot showing the effect

of 20/iM of troglitazone on the protein level of maspin. Western blots of total protein

were probed with anti-maspin antibodies and then reprobed with anti-GAPDH to confirm

equal loading. B-C) Using a Taqman probe, total RNA from treated and control cells was

used to examine the expression level of B) MUC-1 and C) K19 by real-time RT-PCR.

Values express the relative quantity of the genes to the level of mRNA expression of

GAPDH. Data shown is representative of at least three separate experiments.

97 Figure 3.12. The expression of hTERT is independent from PPARY in clinical breast cancer samples.

Genome-wide gene expression microarray data for 295 patients with early breast cancers from NKI published dataset was used to measure the correlation between hTERT and

PPARy expression in A) all 295 tumor samples with early breast cancers (R=-0.1522), B)

225 estrogen receptor-positive tumors (R=0.1563), and C) 69 estrogen receptor negative tumors (R=-0.0807). No significant correlation was found between hTERT and PPARy expression in either of these groups (A, B, and C).

98 A

0.8 All R =-0.1522 N= 294

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-0.2 A

-0.4 —i 1— -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 hTERT

0.4 ER(-) ... * R = -0.0807 0.3 . • N= 69 • 0.2 • • • • • £0.1 • < •• • • • •• a. o-o « • -0.1 . • .. • • -0.2 • -0.3 —i— -0.4 -0.2 0.0 0.2 0.4 0.6 hTERT

99 Figure 3.13. Investigating the correlation between hTERT mRNA expression and clinical prognostic parameters.

mRNA expression of hTERT in the NKI dataset was used to examine its correlation with different clinical prognostic parameters; ER (estrogen receptor) content, tumor differentiation (grade), size (tumor size), LN (lymph node involvement), 70GS

(70-gene prognosis signature), WS (wound signature) profile. B) A negative correlation was found between hTERT expression and ER content in tumors. The mRNA level of hTERT was higher in ER negative patients compared to ER positive tumors (p<0.02). No significant correlation was observed between C) tumor differentiation, D) WS, E) 70GS, and F) tumor size

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101 Chapter 4

102 Genome-wide analysis of differential expression of troglitazone-mediated genes in the estrogen receptor negative breast cancer cell line, MDA-MB-231, and the role of PPARy

in the progression of breast carcinoma 4.1 Abstract

Estrogen receptor (ER) status in breast cancer is a prognostic indicator of tumor

responsiveness to hormone therapies. ER-positive breast cancer patients have a number

of available anti-estrogen treatment options, including Tamoxifen in pre-menopausal

women and aromatase inhibitors in post-menopausal women. However, ER-negative

breast carcinomas are more aggressive and do not respond to anti-estrogen therapies.

Current treatment relies heavily on systemic chemotherapy and has motivated extensive research into finding more specifically targeted therapeutic treatments.

Several studies in the past decade have identified many promising molecular targets in breast cancer other than ERs, including peroxisome proliferator-activated receptor gamma (PPARy). Activation of PPARy has been shown to inhibit cell growth and induce differentiation of several cancer cells including breast cancer. In the present investigation, we examined the anti-tumor effects of the classical PPARy ligand, troglitazone, on the ER-negative breast cancer cell line, MDA-MB-231. We studied the genome-wide expression profile of MDA-MB-231 cells in response to troglitazone treatment. Our microarray analysis and subsequent observations showed that troglitazone inhibits Gl—>S progression in MDA-MB-231 cells independently from PPARy activity.

Furthermore, we observed genomic loss of the region containing the PPARy gene on chromosome 3 in 58% of breast cancer tissues, suggesting a potential tumor suppressor role for PPARy in breast cancers. In agreement with this observation, we also found that

PPARy expression is associated with good prognostic factors in breast cancer patients.

104 4.2 Introduction

Human breast carcinoma is a very heterogeneous group of tumors with diverse behaviors and responsiveness to therapy. Determination of ER status has been found to be an important predictive and prognostic factor in the management of breast cancer

(351). ER-positive breast cancer patients have a number of available anti-estrogen treatment options, however, few effective cancer prevention and treatment strategies are available for ER-negative breast carcinomas (308). This has motivated considerable efforts toward finding novel therapeutic approaches for the treatment of ER-negative breast cancer.

In the past decade, several studies have reported that PPARy may be a potential treatment target in cancers including breast cancer (228, 230, 231, 260, 270, 276, 310).

PPARy is a member of the nuclear hormone receptor superfamily that includes several ligand-activated transcription factors involved in a variety of physiological and pathological processes (178). Upon activation by its ligands, PPARy regulates gene expression by binding to specific peroxisome proliferator response elements (PPREs) as a heterodimer with a retinoid X receptor (RXR) in enhancer sites of regulated genes (309).

The PPARy ligands exist in three classes: natural, dual, and synthetic. The synthetic ligands are the members of the thiazolidinedione (TZD) family including troglitazone, rosiglitazone, pioglitazone, and ciglitazone (225). It has been shown that

TZDs promote the differentiation of various cell lines (227-231), and some of them, especially troglitazone and ciglitazone, demonstrate antiproliferative activities in several cancer models including breast cancer (232). However, accumulating evidence suggests that many biological effects of TZDs on cell processes are independent from PPARy

105 transcriptional activity (234-237). In light of these findings, understanding how PPARy ligands exert their biological effects may lead to novel pathways that can be used in the treatment of breast cancer.

In the present investigation, we examined the anti-tumor effects of troglitazone on the ER-negative breast cancer cell line, MDA-MB-231. Our aim was to determine the role of PPARy in the progression of breast cancers.

4.3 Materials and Methods

4.3.1 Materials

MDA-MB-231 and MCF-7 cell lines were obtained from the American Type Culture

Collection (ATCC) (Manassas, VA, USA). Troglitazone and GW9662 were purchased from Sigma (St Louis, MO, USA) and BADGE was obtained from Cayman (Ann Arbor,

MI, USA). All antibodies were purchased from Cell Signaling Technology (Danvers,

MA, USA), except PPARy antibody from Santa Cruz Biotechnology (Santa Cruz, CA,

USA) and Rb nuclear (RbNuc) antibody from Abeam Inc. (Cambridge, MA, USA). All

TaqMan specific primers for real-time RT-PCR were obtained from Applied Biosystems

(Branchburg, NJ, USA). Cell culture media was from the Ontario Cancer Institute (OCI)

(Princess Margaret Hospital, Toronto, ON, CA).

4.3.2 Cell culture

MDA-MB 231 and MCF-7 cells were cultured in alpha Minimum Essential

Medium (a-MEM) supplemented with 10% fetal bovine serum (HyClone, Logan, UT,

106 USA) at 37°C in a humidified cell culture incubator for 24 hours. The cells were then

treated with either troglitazone or an equal volume of DMSO (Sigma-Aldrich Life

Science, Saint Louis, MO, USA) at different concentrations and were incubated for

various time periods as indicated in the figure legends. This cell culture was performed multiple times in triplicate for the subsequent experiments.

For cell count and cell viability assay, cultured MDA-MB-231 cells were trypsinized, washed with PBS, and resuspended in the growth media. The cell viability was measured by automated Vi-CELL (Beckman Coulter, Brussels, Belgium). Cell counts were calculated from the average of 3 measurements of duplicated wells by the

Vi-CELL counter.

In addition, cell proliferation was measured using a CellTiter96 nonradioactive proliferation assay kit (Promega, Madison, WI, USA). MDA-MB-231 cells were cultured in 96-well plates at a density of 7xl03 cells/well and treated with various concentrations of troglitazone and incubated at 37°C for different periods of time. At the end of each time point, cells were exposed to 20 u,l MTS/PMS solution and incubated for 3 hours in a humidified incubator. At the end, the intensity of color produced by the cells was measured at 490nm on a Tecan Spectrafluor Plus Plate Reader (MTX Lab Systems, Inc,

Vienna, VA, USA).

4.3.3 Flow cytometry analysis

Cultured cells were collected, counted, and washed with PBS followed by 60 min fixation in ice-cold 80% ethanol at the end of each time point. Fixed cells were then washed three times with PBS and permeabilized with 0.2% Triton X-100 for 5 min and

107 then stained with 1 mL propidium iodide/RNase staining buffer (BD Biosciences,

Mississauga, ON, Canada) for 60 min. Cells were acquired with a FACScan flow cytometer (Becton Dickinson , San Jose, CLA, USA) using CellQuest software (Becton

Dickinson, San Jose, CLA, USA). Finally, the distribution of nuclear DNA content of the cells was analyzed with ModFit JJ LT software (Verity Software House, Inc.) to evaluate the cell cycle status of treated cells versus controls.

4.3.4 Microarray analysis

RNA was prepared using an RNeasy® plus kit (Qiagen, Mississauga, ON, CA).

Microarray experiments were performed at The Centre for Applied Genomics (The

Hospital for Sick Children, Toronto, CA). RNA quality was assessed using a Bioanalyzer

(Agilent Technologies, USA) and 5ug of RNA was hybridized to Affymetrix chips according to the manufacturer's instructions. Six chips (three replicates for each untreated control and treated experimental) of Human Genome U133 Plus 2.0 (HG_U133 plus 2.0) arrays were used. Each chip contains 47,000 probesets and 38,000 well annotated genes.

We used GeneSpring v. 7.3 software to analyze GC RMA (GC content of probes in normalization with Robust Multi-Array) as a normalization method. Signals below 50 were filtered out, and using the Student's t test and a false detection rate (FDR) of 0.025, we were able to identify significantly altered genes. In order to identify the type of functional changes at the transcription level, we used the Database for Annotation,

Visualization and Integrated Discovery (DAVID) as a functional analysis tool

(http://david.abcc.ncifcrf.gov). Significantly up and down regulated genes were separately uploaded into the web based algorithm, and the most significant gene ontology

108 (GO) biological processes groups and Kyoto Encyclopedia of Genes and Genomes

(KEGG) pathways were identified. In addition to traditional GO analysis, we used functional annotation clustering based on the function and cellular localization of the expressed genes. This method reduces redundancy and clusters similar annotations together to make the biology clearer.

4.3.5 Real-time RT-PCR

Total RNA was extracted by the RNeasy® plus kit (Qiagen, Mississauga, ON,

CA). An aliquot of 2 /ig total RNA from each sample was reverse transcribed to cDNA using TaqMan® Reverse Transcription Reagents (Applied Biosystems, Branchburg, New

Jersey, USA). 100 ng of cDNA from each sample was then PCR amplified using

TaqMan® Universal PCR Master Mix (Applied Biosystems, Branchburg, New Jersey,

USA) in a 396-well plate according to the manufacturer's instructions. We used TaqMan specific primers for PPARy, MAD2L1, CCNE2, PCNA, CCNA2, CDC2, CDC25A, AQP3,

TPP1, NCoA3, and GAPDH in our experiments and the RT-PCR was performed using the ABI Prism 7900HT Sequence Detection system (Foster City, CA).

4.3.6 Nuclear extraction

Cells were cultured in 60 mm dishes in the presence or absence of troglitazone for

24 hours. Using the Nuclear Extraction Kit (Panomics, Inc., Fremont, CA, USA), cell nuclei were extracted according to the manufacturer's instructions. The Protein concentrations were then measured using the Bio-Rad Protein Assay Reagent (Bio-Rad

Laboratories, Inc., Hercules, CA, USA) according to the manufacturer's protocol. All

109 samples were stored at -80°C for further experiments.

4.3.7 Netherlands Cancer Institute (NKI) dataset microarray analysis

Publicly available genome-wide gene expression microarray data for 295 patients with early breast cancer from the NKI (328, 336) were used to study the expression of

PPARy and its correlation to clinical prognostic parameters. Samples were selected from patients younger than 53 years with primary invasive breast cancers (see Chapter 3 for more details). The published expression dataset of PPARy (Probe ID: 17022) for the 295 patients was analyzed by the Student's t test.

4.3.8 Array comparative genomic hybridization

Paraffin blocks and H&E slides of 16 breast specimens containing ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) as well as 6 specimens containing pure DCIS were acquired. 5um thick sections were deparaffinized, stained in hematoxylin for 30 min and needle micro-dissected to selectively collect DCIS and IDC samples. DNA was extracted using QIAGEN DNA Mini Kit (QUIAGEN Inc., Canada) according to the manufacturer's instructions after a 72 hr period in lysis buffer at 56°C.

Samples were amplified according to the single cell comparative genomic hybridization protocol (SCOMP) (352, 353). DNA from the samples and normal lymph nodes was labeled and hybridized to 19K human cDNA arrays according to standard protocols

(University Health Network Microarray Centre, Toronto, Canada, www.microarrays.ca).

The slides were scanned in a GenePix 4000B slide scanner and digitized by GenePix Pro

6.0 software (Molecular Devices, California, USA). The data was visualized by a moving

110 average with a sliding window of 20 data-points (median width 1.5Mb).

4.3.9 Sample collection

Breast tissue was obtained from archival formalin-fixed paraffin-embedded

(FFPE) blocks that were stored in the Pathology Department of University Health

Network (Toronto, ON, Canada) and had been obtained initially for diagnosis.

Institutional Research Ethics Board approval was obtained for the study.

4.3.10 Statistical analysis

All numerical data were expressed as median values ± SD. Statistical significance was determined by performing a paired Student's t test.

4.4 Results

4.4.1 Troglitazone reduces the number of MDA-MB-231 breast cancer cells by suppressing Gl—>S cell cycle transition

It has been shown that TZD family members suppress the cell growth of many types of cancer (230, 231, 268-276). We examined the effect of troglitazone on the cell growth of MDA-MB-231 cells. In agreement, we found that troglitazone reduces the number of MDA-MB-231 cells compared to controls (Fig. 4.1A-C). This reduction was significant in the presence of 20juM troglitazone after 24 hours of treatment (p < 0.05)

(Fig. 4. ID). The inhibitory effect of troglitazone on cell numbers was also significant for

10 and 15jiiM of troglitazone after 48 hours treatment and continued throughout the time

111 course of the experiment (Fig. 4. ID).

The reduction in cell numbers could be due to the induction of apoptosis or/and cell growth arrest. To address this question, we studied the effect of troglitazone on cell apoptosis and proliferation in MDE-MB-231 cells. As previously observed in Chapter 3, troglitazone did not induce apoptosis after 24 hours treatment. Next, we investigated the effect of troglitazone on the proliferation of MDA-MB-231 cells using the MTS assay.

The assay measures dehydrogenase enzyme activity found in metabolically active cells and it can be used to study the cell proliferation. The MTS assay showed that cells treated with 15 and 20/xM troglitazone have a reduction in their proliferation compared to controls after 24 hours of treatment (Fig. 4.2) (p < 0.05). This reduction was observed throughout the time course of the experiment (72 hours) (Fig. 4.2). This result suggests that the previously observed reduction in cell numbers could be due to the suppression of cell proliferation but not induction of apoptosis (Chapter 3). To investigate this further, flow cytometry was used to study the cell cycle status of MDA-MB-231 cells in the presence and absence of troglitazone (Fig. 4.3A-D). In agreement with the MTS assay, flow cytometry result showed that both 15 and 20/xM of troglitazone have significant effect on cell cycle status (Fig 4.3E-G). We found that 20/xM of troglitazone reduced the number of cells entering into S phase by 31% after 24 hours of treatment (p < 0.05) (Fig.

4.3F). This was accompanied by a 20% increase in G0/G1 phase cells (p < 0.05) (Fig.

4.3E). However, the number of cells in G2-M phase remained unchanged in the presence and absence of troglitazone (Fig. 4.3G). We also found that the inhibitory effects of troglitazone on Gl—>S transition remained significant after 48 hours of treatment (Fig.

4.4).

112 4.4.2 Troglitazone reduces the phosphorylation status of Rb

Rb protein is a central component of the transcriptional repression complex that inhibits the expression of many genes, whose products are necessary for the Gl—>S transition and S phase. Upon Rb phosphorylation, the E2F transcription factors activate the expression of S-phase genes and thereby induce cell cycle progression.

To elucidate the mechanism underlying the suppressive effects of troglitazone on

Gi—>S progression, we examined its effect on retinoblastoma protein (Rb) in MDA-MB-

231 cells cultured in cc-MEM containing 10% FBS. Our result showed that 20/iM troglitazone significantly reduced the phosphorylation of Rb at serines 807 and 811(282,

354, 355) compared to the DMSO control after 24 hours of treatment in non synchronized MDA-MB-231 cells (Fig. 4.5A and B). In addition, to investigate the involvement of PPARy in this process, we studied the effect of troglitazone on phospho-

Rb in the presence and absence of the PPARy antagonists, BADGE and GW9662. Our results showed that GW9662 and particularly BADGE can partially reverse the suppressive effect of troglitazone on Rb phosphorylation, however, this effect failed to reach statistical significance (Fig. 4.5B).

4.4.3 Troglitazone does not change the protein expression of cyclin Dl and D3 in the

MDA-MB-231 cells

It has been shown that phosphorylation of Rb at serines 807 and 811 is principally mediated through D-type cyclins (282, 354, 355). D-type cyclins are rate limiting and essential for the progression through the Gl—>S transition of the cell cycle. These cyclins comprise of three closely related proteins: cyclin Dl, D2 and D3. Cyclin Dl and D3 bind

113 to and activate CDK4 and CDK6, which in turn phosphorylate Rb protein. We studied the effect of troglitazone on the protein expression of cyclin Dl and D3 in MDA-MB-231 cells. We found that the level of cyclin Dl and D3 remained unchanged in response to

20/iM of troglitazone after 24 hours treatment (Fig. 4.6A). In contrast to our results, it has been shown that PPARy ligands suppress the protein expression of cyclin D-type cyclins in some cancerous cell lines including breast cancer cell line, MCF-7 (234, 283, 284). To address this discrepancy, we studied the effect of troglitazone on D-type cyclins in MCF-

7 cells. Interestingly, we found that troglitazone reduces the protein levels of cyclin Dl and cyclin D3 in this cell line (Fig. 4.6B). In addition, this effect was associated with the hypophosphorylation of Rb at serines 807 and 811 in the MCF-7 cells (Fig. 4.6B). More interestingly, our result showed that both BADGE and GW9662 antagonize the effect of troglitazone on phospho-Rb. Furthermore, the suppressive effects of troglitazone on cyclin Dl and D3 were antagonized by GW9662 (Fig. 4.6B). In agreement with our observations, it has been found that progression of the cell cycle can be inhibited by

PPARy ligands;15d-PGJ2and troglitazone through repression of cyclin Dl (234, 283), even though, the involvement of PPARy remains controversial in these studies. These observations suggest that the effects of PPAR on cell biology are not universal and dependent mostly on the ligands and cell type used in each study.

4.4.4 mRNA expression profile of MDA-MB-231 cells in response to troglitazone

Results from our studies as well as others indicate that the effects of PPARy agonists are cell type specific. This suggests that the response of each specific cell line to each individual agonist needs to be characterized separately for further studies. To

114 investigate the genome-wide effects of troglitazone on MDA-MB-231 cells, the Human

Genome U133 (HG_U133) plus 2.0 gene chip (Affymetrix) was employed to perform microarray analysis. Using the false detection rate (FDR) less than 0.025, we found 222 differentially expressed genes in response to troglitazone in MDA-MB-231 cells compared to controls. Our analysis showed that out of 222 genes, 171 genes were down- regulated (Table 4.1) and 51 genes were up-regulated (Table 4.2) (Fig. 4.7). Using the

DAVID algorithm, GO and KEGG functional analysis was performed. Functional GO annotation found 51 groups (Table 4.3), whereas KEGG annotated 3 functional groups

(Table 4.4). Interestingly, both GO and KEGG functional analyses identified genes in the cell cycle group as the most significant differentially expressed genes in troglitazone treated cells compared to controls (p <1.79E-08). Furthermore, in the cell cycle group,

GO clustered 30 genes (Table 4.5), whereas KEGG clustered 10 genes (Table 4.6).

Genes from the GO cell cycle group were compared with the genes identified in the KEGG cell cycle group to find the genes common to both analyses (Fig. 4.8). From the common gene list, we selected the following genes for confirmation of mRNA expression: CDC25A, MAD2L1, CCNA2, CCNE2, CDC2, and PCNA. Using real time

RT-PCR, we found that the expression levels of all of these genes were significantly (p

<0.05) decreased compared to the controls by 20/iM troglitazone after 24 hours treatment

(Fig. 4.9). Furthermore, to study the involvement of PPARy in the anti-proliferative effects in our study, we used PPARy antagonists, lOuM of GW9662 (330) and lOOuM of

BADGE (331) 24 hours in the presence and absence of troglitazone. RT-PCR results showed that neither BADGE nor GW9662 inhibited the effect of troglitazone on the expression of the selected cell cycle genes (Fig. 4.9).

115 4.4.5 Troglitazone reduces the protein expression of cyclin E2 and CKD2 in MDA-

MB-231 cells

As a member of the cyclin family, cyclin E binds to CDK2, which is required for

the transition from Gi to S phase. From microarray analysis, we found that the reduction

of cyclin E2 in response to troglitazone treatment in significant. To study this further, the

protein expression of cyclin E2 was examined in MDA-MB-231 cells treated with troglitazone. Our results showed that 20[iM of troglitazone reduced the expression of cyclin E2 protein after 24 hours treatment (Fig. 4.10A). We also investigated the protein status of CDK2. It was found that CDK2 expression is reduced in troglitazone treated samples compared to controls (Fig. 4.10A). Furthermore, our result showed that BADGE was able to inhibit the effect of troglitazone on CDK2 protein, whereas GW9662 failed to hinder this effect (Fig. 4.10A). However, neither BADGE nor GW9662 reversed the effect of troglitazone on cyclin E2 protein expression (Fig. 4.10A).

Using MCF-7, we studied the effect of troglitazone on the protein expression of cyclin E and CDK2. In contrast to the effect of troglitazone on MDA-MB-231 cells, the protein levels of cyclin E2 and CDK2 remained unchanged in the presence of 20/iM of troglitazone in MCF-7 cells (Fig. 4.10B). Surprisingly, we also found that BADGE and troglitazone co-treatment reduced the protein expression of cyclin E2 in the MCF-7 cells

(Fig. 4.10B), even though this was not observed in MCF-7 cells treated with troglitazone alone. Our observations indicate that troglitazone affects genes regulating Gl—^S phase transition in both MDA-MB-231 and MCF-7 cells. However, the mechanism underlying this inhibition is different in these two breast cancer cell lines.

116 4.4.6 Real time RT-PCR confirmation of differentially up-regulated genes from microarray analysis

For further confirmation of the results obtained from microarray analysis, the effect of troglitazone on differentially up-regulated genes was examined by real time RT-

PCR. We selected AQP3, TPP1, and NCoA3 as representative of up-regulated genes from the Table 4.2 gene list. Real time RT-PCR confirmed that troglitazone significantly increased the mRNA level of these genes (p < 0.05) (Fig. 4.11). We also found that both

BADGE and GW9662 inhibit the induction effect of troglitazone on the AQP3 gene (Fig.

4.11 A), whereas for the TPP1 gene only BADGE was able to suppress the effect of troglitazone (Fig. 4.1 IB). However, neither BADGE nor GW9662 inhibited the effect of troglitazone on the NcoA3 gene (Fig. 4.11C).

4.4.7 The localization of PPARy remains unchanged in response to troglitazone in breast cancer cell lines

Our results showed that PPARy responded to troglitazone treatment in MDA-MB-

231 cells, although its involvement was not significant at the level of Rb phosphorylation as the key regulator of cell cycle progression. To investigate whether this response to troglitazone was accompanied with its translocation, the compartmental localization of

PPARy was studied in the presence and absence of troglitazone. Cytosolic and nuclear fractions from troglitazone treated and control MDA-MB-231 cells were prepared and subjected to western blot analysis using RbNuc antibody as a nuclear marker and PPARy antibodies. Western blot analysis showed that PPARy was localized in the nuclear fraction regardless of the presence or absence of troglitazone (Fig. 4.12A). Using MCF-7

117 cells with the same conditions, we found PPARy protein remained unchanged in the nuclei of MCF-7 in response to troglitazone treatment (Fig. 4.12B).

4.4.8 DNA copy number of PPARy gene is altered in breast cancer patients

To better understand the role of PPARy in the development of breast cancer, a group of 16 breast specimens containing DCIS and IDC as well as specimens containing pure DCIS was examined to compare the DNA copy number of PPARy on chromosome

3. aCGH analysis showed that the distal part of 3p shows genomic gain except for a short segment from 8.7 to 12.9Mb with genomic loss using median data of all the samples (Fig.

4.13). This segment includes the location of PPARy (12.3-12.45Mb) and borders RAF1

(12.6-12.68Mb). We found that 58% of cases (22 out of 38) show genomic loss in the region between 8.7-12.9Mb (3p25) of chromosome 3. There was a non-significant trend for a higher rate of loss detection in pure DCIS without an invasive component (5 out of

6, 83%). There was also a similar detection rate in DCIS (9/16, 56%) and invasive duct carcinoma (8/16, 50%) components of the tumors composed of both in situ and invasive cancer. The remaining samples showed DNA copy number close to that of reference normal DNA.

4.4.9 The level of PPARy expression correlates with good clinical prognostic parameters in breast cancer patients

The role of PPARy in the progression of breast cancer was further examined analyzing the NKI dataset. The NKI data from 295 young patients with early breast cancers (328, 336) was used to study the mRNA expression of PPARy. We found that

118 44% of samples expressed PPARy, whereas 56% of samples were PPARy negative (Fig.

4.14A and B) in accordance with our aCGH result. To assess the strength of the expression status of PPARy as a predictor of the outcome of disease, we examined the correlation of PPARy gene expression with different clinical prognosis parameters (Fig.

4.15A). Our results showed a significant (p < 0.05) correlation between PPARy expression and ER content (Fig. 4.15B), differentiation status of tumors (Fig. 4.15C), tumor size (Fig. 4.15D), 70-gene prognosis signature (Fig. 4.15E), and wound signature

(WS) profile (Fig. 4.15F), suggesting that increased PPARy is associated with a favorable prognosis in breast cancer patients.

4.5 Discussion

Breast cancer is the second most common type of cancer after lung cancer and the most common malignancy affecting women worldwide (1). A published report by the

World Health Organization (WHO) indicates that the number of cases worldwide has significantly increased since early 1970s (1). In Canada, breast cancer was predicted to affect 22,400 with more than 5,300 deaths in 2008 (2). This has motivated many researchers to focus their efforts toward finding novel therapeutic approaches especially for ER-negative breast cancer, since a few effective cancer prevention and treatment strategies are available for ER-negative breast carcinomas compared to ER-positive breast carcinomas (308). Several studies have identified PPARy as a target for cancer therapy. It has been shown that ligand activated PPARy inhibits cell growth and induces differentiation of various cancer cells (228, 230, 231, 260, 270, 276, 310). In the present study, we investigated the effects of troglitazone as a classical PPARy agonist on the ER-

119 negative breast cancer cell line MDA-MB-231.

Our results showed that 20/xM of troglitazone reduced the cell number through cell growth arrest without inducing apoptosis and cell viability (Chapter 3). In agreement with this finding, flow cytometry revealed that troglitazone suppresses cell cycle progression by inhibiting the Gl—>S transition in a dose dependent manner. Further studies indicated the suppressive effect of troglitazone on Rb phosphorylation as the key regulator of Gl—>S cell cycle transition.

Since Rb is as a key regulator protein in Gl—>S transition, we examined Rb status in response to troglitazone exposure. It has been shown that hypophosphorylated Rb inhibits cell cycle progression, whereas hyperphosphorylated Rb induces cell cycle progression. We found that troglitazone reduces the phosphorylation of Rb in agreement with published results from other groups (272, 282). This effect was found to be independent from PPARy contribution as BADGE and GW9662 did not protect phosphorylation status of Rb in the presence of troglitazone in our study. Furthermore, it is well documented that Rb is tightly regulated by several groups of proteins including cyclin-dependent kinases (CDKs), cyclin subunit(s), and CDK inhibitors (CKIs) in mammalians. Published data demonstrates that PPARy ligands reduce cyclin Dl (271,

272, 282, 356-359), CDK2, CDK4, CDK6 (282, 357, 358) and increase p21Kipl (357, 358,

WAF/0 i INK4a /iV c 360-363), p21 > (272, 356, 359, 361, 364, 365), V16 {111 ), and pl8 ^ (361) proteins. Using western blot analysis, the protein levels of cyclin Dl and cyclin D3 was found unchanged in response to troglitazone treatment. Similar to our finding, it has been reported that troglitazone does not reduce the protein level of cyclin Dl in MDA-MB-231 cells (232). We also found that troglitazone decreases the protein level of CDK2 and

120 cyclin E2 in MDA-MB-231 cells. Interestingly, we found that troglitazone reduces the protein levels of cyclin Dl, cyclin D3, and Rb phosphorylation in MCF-7 cells, which was in agreement with other reports using the same cell line and the same concentration of troglitazone (282). These observations indicate that the effects of troglitazone on cell cycle regulation in ER-negative breast cancer cells are different to its effects on ER- positive cells.

Furthermore, our results showed that GW9662 can reverse the suppressive effect of troglitazone on these proteins in MCF-7 but not in the MDA-MB-231 cells. Our findings suggest that troglitazone suppresses cell proliferation selectively through reduction of CDC25A, cyclin E, CDK2, Rb phosphorylation and independently from

PPARy involvement in MDA-MB-231 cells as was created by Biocarta software, whereas in MCF-7 cells, troglitazone arrests cell growth through suppression of cyclin Dl, cyclin

D3, and Rb phosphorylation through PPARy activity (Fig. 4.16).

We also found that PPARy is located in the nucleus of MDA-MB-231 and MCF-7 cells and its location remains unchanged in response to troglitazone treatment. In agreement with some of our observations, Strakova et al. showed that PPARy does not translocate in presence of its ligands in glioblastoma cell lines (175). However, they detected PPARy protein in the cytoplasm of glioblastoma cell lines and not in the cell nuclei as we have observed in this study. In contrast to these observations, it has been reported that PPARy translocates into the nucleus of pituitary tumor cells after ligand exposure (366). Like its translocation, PPARy localization also remains controversial.

While some groups showed that PPARy is located in the nucleus of various cancer tissues including breast cancer (186, 239, 367-372), there is evidence indicating significant

121 cytosolic PPARy localization in other studies (373-375).

These observations show that troglitazone may have different effects in ER- negative and ER-positive cells leading to suppression of cell proliferation. This indicates that the effects of PPARy agonist are not universal and differ according the experimental design and approaches. Interestingly, we found that not only the PPARy agonist troglitazone but also its antagonists can have different effects in different experimental models. This may explain the reason why different research groups observe controversial effects of PPARy agonists and antagonists in their studies.

To investigate the global consequence of troglitazone treatment, microarray experiments have been performed reflecting the complex effects of troglitazone on

MDA-MB-231 cells. Our microarray analysis revealed 222 differentially expressed genes in MDA-MB-231 cells treated with troglitazone for 24 hours. Although PPARy is known to stimulate the expression of target genes, our result showed that out of 222 differentially expressed genes, the majority of the genes were down-regulated (171 down- regulated) compared to 51 up-regulated genes in response to troglitazone treatment. GO and KEGG analysis clearly identified cell cycle regulator genes as the major targets of troglitazone in the current study (Tables 4.3 and 4.4). Comparing both GO and KEGG gene lists, it was found that the GO list contains all of cell cycle regulator genes identified in the KEGG list. Real-time RT-PCR confirmed that the mRNA expression of all selected cell cycle genes (CDC25A, MAD2L1, CCNA, CCNE2, CDC2, and PCNA) had significantly lower expression in response to troglitazone treatment compared to controls. Using BADGE (331) and GW9662 (330) to elucidate the role of PPARy in cell growth arrest, we found that these antagonists cannot inhibit the effect of troglitazone on

122 the expression of the selected genes. This result suggests that troglitazone influences the

cell cycle regulatory system partially independent from PPARy activity in MDA-MB-231

cells.

In addition, we found that troglitazone induces mRNA expression of AQP3,

TPP1, and NCoA3 genes. Furthermore, PPARy antagonists reverse the effect of

troglitazone on the mRNA expression of AQP3 and TPP1 genes but not NCoA3 gene.

Interestingly, we found that both AQP3 and TPP1 genes, but not NCoA3 gene, contain

predicted PPRE in their promoters (376). Using integrated computational genomics,

Lemay et al. published a list of whole human genes containing potential PPRE in their promoters. This observation may explain why AQP3 and TPP1 mRNA responded to

PPARy antagonists in contrast to other genes such as NCoA3 in this study.

To investigate the role of PPARy in the development of breast cancer, we used aCGH to study the copy number of PPARy in breast cancer tissue. Interestingly, we found that breast tumors are associated with PPARy loss/deletion in 58% (22 out of 38) of breast cancer tissues. To study this further, genome-wide gene expression microarray data for 295 young breast cancer patients (NKI samples) (328, 336) was analyzed in this study. In accordance with our aCGH result, we found that 56% of these samples are

PPARy negative in the NKI dataset.

We also analyzed the correlation of PPARy expression with common clinical prognostic parameters. Interestingly, we found a positive correlation with ER, 70GS

(337), WS (336), and differentiation status of tumors, whereas its expression was negatively correlated with tumor size in the NKI samples. Based on our observations and

123 others (260, 261, 367), it can be concluded that PPARy play an important role as a

suppressor gene in the development of cancers.

Based on our observations, we believe that low copy number of the PPARy gene

plays a role in the development or progression of breast cancer suggesting a tumor

suppressor role for PPARy in breast cancer. This has been further supported by other

groups. Using mRNA analysis and immunohistochemistry, Watkins et al. found lower levels of PPARy in breast tumor tissue compared to normal breast epithelium (367). More importantly, it has been shown that loss of function mutations in PPARy are associated with human colon cancer (261), although Ikezoe et al. did not find any PPARy mutation in various type of cancers including colon cancer (265). Furthermore, Mueller et al. found hemizygous deletion of PPAR gene in 40% of informative prostate tumors (260).

However, other groups oppose the role of PPARy as a tumor suppressor (262-264). It has been reported that PPARy is expressed in a variety of cancers and that PPARy mutation is a very rare event in human malignancies (265). Furthermore, using a constitutively active

PPARy, Saez et al generated new transgenic mice expressing the active form of PPARy under the control of the mouse mammary tumor virus (MMTV) promoter (262). Breeding these transgenic mice to the MMTV-PyV strain (a strain prone to mammary gland cancer), they found that bigenic animals developed tumors with greatly accelerated kinetics compared to their control matched groups. Based on these observations, the authors concluded that increased activity of PPARy serves as a tumor promoter in the mammary gland (262). In addition, it has been found that the treatment of APC deficient mice with various TZDs unexpectedly increase the number of colon tumors in these animals (263, 264). Taken together, these results indicate that the exact role of PPARy in

124 cancer development is not clearly understood and needs more investigation.

In conclusion, although PPARy agonists have demonstrated anti-tumor activity in many studies, their effects are generally different depending on the experimental models used. This study showed that the classical PPARy agonist troglitazone plays an anti­ proliferative role in both ER-negative and ER-positive breast cancer cell lines using different mechanisms. However, the exact mechanisms underlying these differences remain to be discovered. This is the first time that the genomic profile of an ER-negative breast cell line has been studied in response to troglitazone treatment. These data can be used for future studies to explore the role of PPARy and its ligands in the development of breast cancer.

Our observations also suggest the importance of further studies to specify the effects of each PPARy agonist for each type of cancer. More importantly, we found that

PPARy deletion may play a role in the development and progression of breast cancer, suggesting a potential tumor suppressor role for this gene. However, to understand the exact role of PPARy in the development of breast cancer requires more studies.

125 Figure 4.1. Troglitazone reduces the number of cells.

Cultured MDA-MB-231 cells were exposed to 10, 15, and 20 jaM of troglitazone for A) 24 hours, B) 48 hours, and C) 72 hours and photographed at each indicated time point. D) To count the number of cells, MDA-MB-231 cells were cultured in 6-well culture plates. Next day, cells were exposed to troglitazone or an equal amount of DMSO for another 24 hours in a cell culture incubator then cells were trypsinized and washed with PBS. Cells were resuspended in the growth media and the number of cells was measured by automated Vi-CELL. Cell counts were calculated from the average of 3 measurements of duplicated wells on the Coulter counter. * Significant difference between 20 /iM troglitazone and non treated cells (p<0.05).

126 A Not treated cells 10/tM troglitazone 15jttM troglitazone 20;iiM troglitazone i i f i r o . ' i

•• "' 1 • V . t . I 1 >, * • •> '• ... * I • ' • i

• ! ». • * • B * 1 • - s-; „ i* ' ta 1 i , < '% * S-l ,1 i P * 1 i O • ' .*. •,-- > ' ;, « , » . -. • •- - . . * . ( » XI , ',..«,«- .*••«' S tT 1 -, .-"".* -'.. ?>.}• ' "W * C"' *.» \ * ' '

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24hrs 48hrs 72 hrs

_e Not treated MDA-MB-231 cells

a MDA-MB-231 cells treated with 10/t/M troglitazone

-A— MDA-MB-231 cells treated with 15jtM troglitazone

-% MDA-MB-231 cells treated with 20jiM troglitazone

127 48hrs 72hrs

0 Not treated MDA-MB-231 cells

_H MDA-MB-231 cells treated with 1O^M troglitazone

-±— MDA-MB-231 cells treated with 15ftM troglitazone

-$K MDA-MB-231 cells treated with 20fiM troglitazone

Figure 4.2. Troglitazone suppresses cell proliferation.

The proliferation of cells was measured by MTS assay. MDA-MB-231 cells were cultured in oc-MEM. After 24 hours (0 hrs), cells were exposed to either troglitazone (10,

15, and 20 /*M) or the equal volume of DMSO for different periods of time. At the end of each time point, the proliferation rate of control and treated cells were tested by MTS assay. The proliferation rate of treated cells was normalized against that of the control group (DMSO) in the respective treatment conditions. The proliferation rate of cells was the median of three independent measurements. * Significant difference between 20 juM troglitazone and non treated cells (p<0.05).

128 Figure 4.3. Troglitazone inhibits G1-+S cell cycle transition.

Overnight cultured MDA-MB-231 cells in alpha MEM medium were treated with

various concentrations of troglitazone or equal amounts of DMSO as controls for 24

hours then, cells were prepared according to the manufacturer's instructions (see

Materials and Methods) and subjected to a flow cytometer. At the end, the distribution of

nuclear DNA content of the cells was analyzed to evaluate the cell cycle status of treated

cells versus controls. Representative figures correspond to the median of three separate

experiments in duplicate. A) non treated cells, B) 10/xM troglitazone, C) 15/iM troglitazone, and D) 20/JM troglitazone. Results are shown as the percentage of control

group (DMSO) for treatment condition. E) Percentage of cells in S phase, F) percentage of cells G0-G1 phase, G) percentage of cells in G2 phase. * p< 0.05.

129 Source: N 1 DIPLOID: 100.00% Dip G0-G1: 56.85 Dip 02-M: 19.46% J': Dip S: 23.7 %

i

JM*jk - 'i•• >• I • • ••I••• • i A" • 103 160 KO 250

B

Source: T10 DIPLOID: 100.00 % to 30 V T Dip GO-G 1:57.7% 5 25 > Dip G2-M: 20.7 % 1 a« 10 u L 83 5 H P*

, .... 3 120 160 MO

Source: T15 DIPLOID: 100.00 % Dip G0-G1: 60.4 % Dip G2-M: 21.4% lj Dip S: 18.16%

| | Not treated MDA-MB-231

Cells treated with DMSO (equal volume to 10 pM. troglitazone) Source: T20 Cells treated with 10 jiM troglitazone DIPLOID: 100.00% Dip G0-G.1: 62.8% Dip G2-M: 21.3 % Cells treated with DMSO (equal volume to 15 fiM troglitazone) Dip S: 16 % Cells treated with 15 /*M troglitazone

[ J Cells treated with DMSO (equal volume to 20 jM. troglitazone)

Wo Cells treated with 20 ftM troglitazone o 120 (-1 -4—» o o 100 -| i o

60 -

6 40

-a ex 20 CO a -i—• ' • ' -"—•• i U Hours 12 24 48 12 24 48 12 24 48

10//M troglitazone 15//Mtroglitazone 20//M troglitazone

Figure 4. 4. Time course inhibitory effect of troglitazone on S phase cell cycle transition.

MDA-MB-231 cells were cultured in 6-well plates overnight. The day following, the cells were exposed to various concentrations of troglitazone in /xM or equal amounts of DMSO for each group (controls). At the end of each time point (12, 24, and 48 hours), cells were prepared according to the manufacturer's instructions (see Materials and

Methods) for flow cytometry to analyze the distribution of nuclear DNA content of the cells. The data represent the median percentage of cells in S phase of three separate experiments in triplicate as the percentage of control group (DMSO) for each treatment condition. * p < 0.05.

131 BADGE GW9662 DMSO Tro20 + Tro20 + Tro20 Phospho -Rb (Ser807/811) ««

GAPDH

B

5 Q 1-6 I-aIs i-»2 «£• 3> as 0.6 0.4 Wb II 0.2 o 0 4 a DMSO Tro20 BADGE GW9662 + Tro20 + Tro20

Figure 4.5. The effect of troglitazone on phosphorylation status of Rb protein.

A) MDA-MB-231 cells were treated with DMSO, 20 /xM of troglitazone alone

(Tro20) or in combination with either 10 jiM GW9662 (GW9662) or 100/xM BADGE

(BADGE) for 24 hours. Cells then were treated with 20 fiM troglitazone in the presence of GW9662 or BADGE for another 24 hours and the protein lysate was examined for the status of Rb phosphorylation (Ser807/811) by western blotting. B) the intensity of each bands was quantified using Image J software and normalized against respective GAPDH intensities. Figures are representative of three independent experiments.

132 A DMSO Tro20 BADGE GW9662 + Tro20 + Tro20

CyclinDl <**«* ***» $£$ «M*

CyclinD3 flrtfe SBKBt 4flH 4H^ ^jjMfifffF TUgW^F TWIBP^ imnmw"

B DMSO Tro20 GW9662 BADGE + Tro20 +Tro20

Figure 4.6. The response to troglitazone is different in ER-negative and -positive breast cancer cells.

MDA-MB-231 and MCF-7 cells exposed to either 10/xM GW9662 or 100/xM

BADGE for 24 hours and then treated with 20 jiiM of troglitazone (Tro20) for another 24 hours. Protein lysates from A) MDA-MB-231 or B) MCF-7 cells were used to examine the level of selected proteins involved in cell regulation. Representative immunoblots correspond to three separate experiments.

133 DMSO 20/iM troglitazone

B 51 up-regulated genes 171 down-regulated genes (23%) (77%)

Figure 4.7. The effect of troglitazone on gene profiling of MDA-MB-231 cells.

A) Hierarchical clustering of microarray data obtained from controls and troglitazone treated samples. MDA-MB-231 cells were cultured in the presence of

DMSO (controls) or 20/iM of troglitazone for 24 hours. Three separate microarray experiments were performed for each DMSO and troglitazone (Tro20) treated group. The red (up-regulated) and green (down-regulated) color intensities represent log values of fold-changes of treated samples versus controls. B) Gene_spring v. 7.3 was used to analyze GC RMA as the normalization method. Using t-test and a false detection rate

(FDR) of 0.025, a total of 222 differentially expressed genes (171down-regulated and 51 up-regulated genes) were identified.

134 30 genes in GO analysis

10 genes in KEGG analysis

CYCLINE2 CYCLINA2 CELL DIVISION CYCLE 25A MAD2 MITOTIC ARREST DEFICIENT-LIKE 1 CELL DIVISION CYCLE 2, Gl TO S AND G2 TO M MCM4 MINICHROMOSOME MAINTENANCE DEFICIENT 4 fS. CEREVISIAE) BUB3 BUDDING UNINHIBITED BYBENZIMIDAZOLES 3 HpMOLOG (YEAST) PROLIFERATING CELL NUCLEAR ANTIGEN POLO-LIKE KINASE 1 (DROSOPHILA) STRATIFIN

Figure 4.8. Common list of differentially expressed in cell cycle group identified by both GO and KEGG functional annotation.

Both GO and KEGG identified CCNE2, CCNA2, CDC25A, MAD2L1, CDC2,

MCM4, BUB3, PCNA, POLO-LIKE KINASE 1, and STRATIFIN genes as differentially expressed genes regulating cell cycle in response to troglitazone treatment.

135 A B CDC25A MAD2L1 t: f > 0.4 \ d o — , r i , mftn & ^ , nffim DMSO Tro20 BADGE GW9662 DMSO Tro20 BADGE GW9662 +Tro 20 + Tro 20 + Tro 20 + Tro 20

D

Cyclin A CyclinE2 t: I 0, «! JUL « o m-hri DMSO Tro20 BADGE GW9662 DMSO Tro20 BADGE GW9662 + Tro 20 + Tro 20 + Tro20 + Tro20

CDC2 PCNA & 1.2 - T •£» g. °-8 Relativ e o P l m JBDL m o DMSO Tro20 BADGE GW9662 DMSO Tro20 BADGE GW9662 + Tro20 + Tro20 + Tro20 + Tro20

Figure 4. 9 Real time RT-PCR was used to analyze the expression of common genes identified by GO and KEGG annotation.

MDA-MB-231 cells were cultured in the presence or absence of PPARy antagonists, BADGE or GW9662 for 24 hours. The day after, cells were exposed to

20/xM of troglitazone (Tro20) or an equal volume of DMSO for another 24 hours. Finally the effect of troglitazone on the mRNA level of A) CDC25A, B) MAD2L1, C) cyclin A,

D) cyclin E2, E) CDC2, and F) PCNA genes was measured. Data show the relative quantity of selected genes to mRNA expression of GAPDH and represent the median of three separate experiments in duplicate.* p < 0.05.

136 A BADGE GW9662 DMSO Tro20 + Tro20 + Tro20

CyclinE2

CDK2 1 ' • .-i

GAPDH

B GW9662 BADGE DMSO Tro20 + Tro20 + Tro20

CyclinE2 . '^gife-/ v .*;•!•

CDK2 W&& IGMMM '*w*aiw '^PSWH

GAPDH

Figure 4.10 The response to troglitazone is different in ER-negative and -positive breast cancer cells.

MDA-MB-231 and MCF-7 cells exposed to either IOJAM GW9662 or 100/xM

BADGE for 24 hours and then treated with 20 ptM of troglitazone (Tro20) for another 24 hours. Protein lysates from A) MDA-MB-231 or B) MCF-7 cells were used to investigate the level of cyclin E2 and CDK2 proteins. Representative immunoblots correspond to three separate experiments.

137 Figure 4.11. Real time RT-PCR confirmation of selected genes from the

differentially up-regulated list obtained from microarray analysis.

Cultured MDA-MB-231 cells were exposed to either 10 /xM GW9662 or 100)UM

BADGE for 24 hours. Cells were then treated with 20 ^M troglitazone (Tro20) in the

presence or absence of GW9662 or BADGE for another 24 hours. At the end of the

incubation time, the expression level of selected genes A) AQP3, B) TPP1, and C)

NCoA3 was determined by real time RT-PCR. Data represents the relative quantities genes to GAPDH from three independent experiments in duplicate. DMSO=cell exposed to DMSO only, Tro20= cells treated with 20/xM of troglitazone only, GW9662= cells treated with 20[iM of troglitazone in presence of 10 [M of GW9662, and BADGE= cells treated with 20/JM of troglitazone in presence of 100/xM of BADGE. * p < 0.05

138 Relative quantity to Relative quantity to Relative quantity to GAPDH expression o GAPDH expression CD GAPDH expression

o Ol -*• en DMS O

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PPARy

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Figure 4.12. Troglitazone does not change the localization of PPARy protein in

MDA-MB-231 and MCF-7 cell lines.

MDA-MB-231 and MCF-7 cells were cultured in the presence or absence of

20JUM of troglitazone (Tro20) for 24 hours. After incubation, cell cytoplasm and nuclei were extracted and kept in separate tubes at -80°C for further experiments. Extracted nuclei (N) and cytoplasm (C) from untreated (wt), DMSO, and troglitazone treated samples were subjected to western blotting using RbNuc (as a marker for nuclei), PPARy, and GAPDH antibodies. A) MDA-MB-132 cells, B) MCF-7 cells.

140 Figure 4.13. Evaluating DNA copy number of PPARy gene in breast cancer patients.

aCGH analysis to compare the DNA copy number of chromosome 3 in 17 breast

specimens containing DCIS and IDC and 6 specimens with pure DCIS samples. Log2 ratios were averaged between duplicate runs and arranged in sequence and centered by medians. The distal part of 3p shows genomic gain except a short segment from 9.5 to

12.6Mb which includes the location of PPARy (12.3-12.45Mb).

141 A

PPARy in NKI (Total samples = 295)

Figure 4.14. Analyzing the expression of PPARy mRNA in NKI dataset.

A) PPARy mRNA expression in 295 young patients with early breast cancer was analyzed from the NKI dataset. Green color represents samples expressing no PPARy and red color indicates samples expressing PPARy. B) Percentage of up- and down-regulation of PPARy obtained from the NKI analysis. 166 patients (56%) showed no PPARy expression, whereas 129 samples (44%) express PPARy

142 Figure 4.15. Investigating the correlation between PPARy mRNA expression and clinical prognostic parameters.

A) The NKI dataset was used to examine the correlation between PPARy expression with different clinical prognostic parameters, B) differentiation status of tumors, C) tumor size, D) estrogen receptor (ESR) content, E) 70-gene prognosis signature (70GS), and F) wound signature (WS) profile.

143 a o +3 (U oo *-< o 00 < O a o PL, W P H r- S i CM II V

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144 Figure 4.16 Troglitazone shows different effects on MDA-MB-132 and MCF-7 cell lines.

In MDA-MB-231 cells, troglitazone suppresses cell proliferation selectively through reduction of CDC25A, cyclin E, CDK2, Rb phosphorylation independent from

PPARy. However, in MCF-7 cells, troglitazone arrests cell growth by suppressing cyclin

Dl, cyclin D3, and Rb phosphorylation through PPARy activity.

145 Table 4.1 List of differentially down-regulated genes according to microarray analysis

Gene Fold Symbol Description Change p-value FDR CCNE2 cyclin E2 -16.10 0.0001127 0.021 TXNIP thioredoxin interacting protein -11.09 0.0000026 0.011 ZNF367 zinc finger protein 367 -10.49 0.0002732 0.024 TXNIP thioredoxin interacting protein -10.01 0.0000873 0.020 DTL denticleless homolog (Drosophila) -6.57 0.0000003 0.006 TXNIP thioredoxin interacting protein -6.53 0.0000534 0.018 PSF1 DNA replication complex GINS protein PSF1 -5.04 0.0000840 0.020 KIAA1199 KIAA1199 -4.99 0.0000319 0.016 CTGF connective tissue growth factor -4.93 0.0001902 0.024 KIF23 kinesin family member 23 -4.90 0.0000124 0.014 CDCA8 cell division cycle associated 8 -4.80 0.0000216 0.016 FST follistatin -4.77 0.0002163 0.024 FEN1 flap structure-specific endonuclease 1 -4.48 0.0000071 0.014 DEPDC1 DEP domain containing 1 -4.05 0.0000627 0.019 MCM4 MCM4 minichromosome maintenance deficient 4 (S. cerevisiae) -4.00 0.0002436 0.024 Homo sapiens, clone IMAGE:4246712, mRNA -3.87 0.0000409 0.017 CTPS CTP synthase -3.76 0.0001042 0.020 FEN1 flap structure-specific endonuclease 1 -3.75 0.0000986 0.020 CENPA centromere protein A, 17kDa -3.54 0.0000196 0.015 SPBC25 spindle pole body component 25 homolog (S. cerevisiae) -3.51 0.0000460 0.018 BM039 uncharacterized bone marrow protein BM039 -3.45 0.0002379 0.024 MLF1IP MLF1 interacting protein -3.43 0.0001768 0.024 MAC30 hypothetical protein MAC30 -3.39 0.0000175 0.015 MKI67 antigen identified by monoclonal antibody Ki-67 -3.39 0.0000030 0.011 LOC494143 similar to RIKEN cDNA 2510006C20 gene -3.29 0.0002605 0.024 SHCBP1 SHC SH2-domain binding protein 1 -3.28 0.0001217 0.021 CDC25A cell division cycle 25A -3.21 0.0002421 0.024 ATAD2 ATPase family, AAA domain containing 2 -3.20 0.0000881 0.020 MAC30 hypothetical protein MAC30 -3.19 0.0001299 0.021 H2AFX H2A histone family, member X -3.09 0.0000024 0.011 DEPDC1 DEP domain containing 1 -3.06 0.0000132 0.014 SIL TAL1 (SCL) interrupting locus -2.83 0.0001987 0.024 MYBL1 v-myb myeloblastosis viral oncogene homolog (avian)-like 1 -2.76 0.0001380 0.022 FLJ20364 hypothetical protein FLJ20364 -2.68 0.0000146 0.014 DEPDC1 DEP domain containing 1 -2.64 0.0002797 0.024 CDC2 cell division cycle 2, G1 to S and G2 to M -2.56 0.0000765 0.019 PLK1 polo-like kinase 1 (Drosophila) -2.52 0.0000646 0.019 LOC146909 hypothetical protein LOC146909 -2.49 0.0001281 0.021 MCM4 MCM4 minichromosome maintenance deficient 4 (S. cerevisiae) -2.43 0.0001600 0.023 MAD2L1 MAD2 mitotic arrest deficient-like 1 (yeast) -2.41 0.0001618 0.023 CCNA2 Cyclin A2 -2.39 0.0000082 0.014 C10orf3 chromosome 10 open reading frame 3 -2.35 0.0001497 0.022 NEIL3 nei endonuclease Vlll-like 3 (E. coli) -2.33 0.0000567 0.018 TMPC- thymopoietin -2.33 0.0001366 0.022 CDC2 Cell division cycle 2, G1 to S and G2 to M -2.32 0.0002349 0.024

146 Table 4.1 Cont'd

Gene Fold Symbol Description Change p-value FDR INSIG1 insulin induced gene 1 -2.29 0.0002347 0.024 OIP5 Opa interacting protein 5 -2.29 0.0001402 0.022 DIAPH3 Diaphanous homolog 3 (Drosophila) -2.27 0.0000008 0.007 SMC4L1 SMC4 structural maintenance of chromosomes 4-like 1 (yeast) -2.25 0.0001019 0.020 HMGB2 high-mobility group box 2 -2.23 0.0001762 0.024 FAM72A family with sequence similarity 72, member A -2.19 0.0000292 0.016 TTK TTK protein kinase -2.19 0.0000591 0.018 ANLN anillin, actin binding protein (scraps homolog, Drosophila) -2.18 0.0001909 0.024 TMEM48 transmembrane protein 48 -2.16 0.0000905 0.020 KNTC1 kinetochore associated 1 -2.10 0.0002237 0.024 CDCA4 cell division cycle associated 4 -2.07 0.0002492 0.024 FLJ20516 timeless-interacting protein -2.05 0.0002037 0.024 PCNA proliferating cell nuclear antigen -2.00 0.0002079 0.024 MAD2L1 MAD2 mitotic arrest deficient-like 1 (yeast) -1.93 0.0000291 0.016 SERS2 splicing factor, arginine/serine-rich 2 -1.87 0.0000372 0.017 KPNA2 karyopherin alpha 2 -1.85 0.0000045 0.014 ZNF92 zinc finger protein 92 (HTF12) -1.84 0.0000798 0.019 Transcribed locus -1.81 0.0002731 0.024 LMNB2 lamin B2 -1.75 0.0002123 0.024 C9orf40 chromosome 9 open reading frame 40 -1.75 0.0002146 0.024 PME-1 protein phosphatase methylesterase-1 -1.74 0.0001209 0.021 STIP1 stress-induced-phosphoprotein 1 (Hsp70/Hsp90-organizing protein) -1.73 0.0001568 0.023 UBE2S ubiquitin-conjugating enzyme E2S -1.64 0.0000535 0.018 TPX2 TPX2, microtubule-associated, homolog (Xenopus laevis) -1.55 0.0000109 0.014 SNRPD1 small nuclear ribonucleoprotein Dl polypeptide 16kDa -1.52 0.0002866 0.024 SNRPD1 small nuclear ribonucleoprotein Dl polypeptide 16kDa -1.52 0.0001615 0.023 FDPS farnesyl diphosphate synthase -1.51 0.0000183 0.015 BM039 uncharacterized bone marrow protein BM039 -1.51 0.0000532 0.018 THBS1 thrombospondin 1 -1.49 0.0002588 0.024 LUZP5 leucine zipper protein 5 -1.49 0.0002118 0.024 SFRS3 splicing factor, arginine/serine-rich 3 -1.46 0.0000843 0.020 RBMX RNA binding motif protein, X-linked -1.45 0.0000745 0.019 CKS1B CDC28 protein kinase regulatory subunit IB -1.44 0.0001781 0.024 EXOSC9 exosome component 9 -1.43 0.0000865 0.020 RRM1 ribonucleotide reductase Ml polypeptide -1.41 0.0002732 0.024 DHFR dihydrofolate reductase -1.36 0.0002345 0.024 USP1 ubiquitin specific peptidase 1 -1.36 0.0002432 0.024 CKAP2 cytoskeleton associated protein 2 -1.34 0.0002569 0.024 HNRPAB heterogeneous nuclear ribonucleoprotein A/B -1.32 0.0000728 0.019 FAM64A family with sequence similarity 64, member A -1.32 0.0002456 0.024 SPAG1 sperm associated antigen 1 -1.32 0.0000630 0.019 RBBP8 retinoblastoma binding protein 8 -1.30 0.0001326 0.021 CP110 CP110 protein -1.29 0.0001180 0.021 CDCA7L cell division cycle associated 7-like -1.29 0.0002274 0.024 GLRB glycine receptor, beta -1.26 0.0000315 0.016 SFN stratifin -1.23 0.0001303 0.021 Table 4.1 Cont'd

Gene Fold Symbol Description Change p-value FDR STS-1 Cbl-interacting protein Sts-1 -1.23 0.0000725 0.019 SAA1 serum amyloid Al -1.20 0.0001418 0.022 KIAA0101 KIAA0101 -1.17 0.0002319 0.024 ODC1 ornithine decarboxylase 1 -1.16 0.0001417 0.022 MSH6 mutS homolog 6 (E. coli) -1.13 0.0000990 0.020 FLJ12442 hypothetical protein FLJ12442 -1.09 0.0000561 0.018 RDH11 retinol dehydrogenase 11 (all-trans and 9-cis) -1.08 0.0002524 0.024 POLH polymerase (DNA directed), eta -1.08 0.0002262 0.024 PAICS phosphoribosylaminoimidazole carboxylase -1.05 0.0000638 0.019 RCC2 regulator of chromosome condensation 2 -1.04 0.0000152 0.014 PARP2 poly (ADP-ribose) polymerase family, member 2 -1.04 0.0000703 0.019 CYR61 cysteine-rich, angiogenic inducer, 61 -1.04 0.0002460 0.024 FLJ35779 Hypothetical protein FLJ35779 -1.03 0.0000285 0.016 HN1 hematological and neurological expressed 1 -1.01 0.0000813 0.019 CDNA FLJ14241 fis, clone OVARC1000533 -1.00 0.0000803 0.019 GPRC5A G protein-coupled receptor, family C, group 5, member A -0.97 0.0000775 0.019 SFPQ splicing factor proline/glutamine-rich -0.95 0.0002681 0.024 CYCS cytochrome c, somatic -0.93 0.0000932 0.020 C3orfl4 chromosome 3 open reading frame 14 -0.92 0.0000329 0.016 EFEMP1 EGF-containing fibulin-like extracellular matrix protein 1 -0.92 0.0002025 0.024 PSME3 proteasome (prosome, macropain) activator subunit 3 (PA28 gamma; Ki) -0.92 0.0001262 0.021 PTDSS1 phosphatidylserine synthase 1 -0.92 0.0001411 0.022 TNFRSF12A tumor necrosis factor receptor superfamily, member 12A -0.89 0.0001814 0.024 HNRPA3 heterogeneous nuclear ribonucleoprotein A3 -0.88 0.0000954 0.020 DKC1 dyskeratosis congenita 1, dyskerin -0.88 0.0000478 0.018 RAN RAN, member RAS oncogene family -0.87 0.0000511 0.018 DTYMK deoxythymidylate kinase (thymidylate kinase) -0.87 0.0001744 0.024 FH fumarate hydratase -0.86 0.0002560 0.024 NUP188 nucleoporin 188kDa -0.85 0.0000393 0.017 BAG2 BCL2-associated athanogene 2 -0.84 0.0002881 0.024 PRPS1 phosphoribosyl pyrophosphate synthetase 1 -0.83 0.0001483 0.022 MACF1 microtubule-actin crosslinking factor 1 -0.82 0.0002812 0.024 BCAT1 branched chain aminotransferase 1, cytosolic -0.82 0.0002358 0.024 TDP1 tyrosyl-DNA phosphodiesterase 1 -0.78 0.0001270 0.021 PKP4 plakophilin 4 -0.76 0.0001059 0.020 ABCF2 ATP-binding cassette, sub-family F (GCN20), member 2 -0.75 0.0002022 0.024 ALG1 asparagine-linked glycosylation 1 homolog (yeast, beta-1,4-mannosyltransferase) -0.68 0.0002489 0.024 RBM4 RNA binding motif protein 4 -0.67 0.0002227 0.024 SLC7A1 solute carrier family 7 (cationic amino acid transporter, y+ system), member 1 -0.66 0.0000469 0.018 SYNCR1P synaptotagmin binding, cytoplasmic RNA interacting protein -0.66 0.0002722 0.024 THOC3 THO complex 3 -0.66 0.0002578 0.024 GLS glutaminase -0.65 0.0001590 0.023 PEA15 phosphoprotein enriched in astrocytes 15 -0.64 0.0002129 0.024 LOC205251 hypothetical protein LOC205251 -0.63 0.0000382 0.017 integrin, alpha E (antigen CD103, human mucosal lymphocyte antigen 1; alpha ITGAE polypeptide) -0.63 0.0001989 0.024 COROIC coronin, actin binding protein, 1C -0.63 0.0002367 0.024

148 Table 4.1 Cont'd

Gene Fold Symbol Description Change p-value FDR BUB3 BUB3 budding uninhibited by benzimidazoles 3 homolog (yeast) -0.63 0.0000569 0.018 HSPD1 heat shock 60kDa protein 1 (chaperonin) -0.61 0.0002244 0.024 PAWR PRKC, apoptosis, WT1, regulator -0.58 0.0000326 0.016 PTBP1 polypyrimidine tract binding protein 1 -0.58 0.0001183 0.021 UCK2 uridine-cytidine kinase 2 -0.58 0.0000068 0.014 NT5E 5'-nucleotidase, ecto (CD73) -0.57 0.0001505 0.022 HNRPU heterogeneous nuclear ribonucleoprotein U (scaffold attachment factor A) -0.57 0.0000454 0.018 NOC3L nucleolar complex associated 3 homolog (S. cerevisiae) -0.56 0.0000462 0.018 HSPA8 heat shock 70kDa protein 8 -0.56 0.0000686 0.019 TMEM33 transmembrane protein 33 -0.55 0.0000891 0.020 G3BP Ras-GTPase-activating protein SH3-domain-binding protein -0.54 0.0001958 0.024 RBMX RNA binding motif protein, X-linked -0.53 0.0002804 0.024 MGC22793 hypothetical protein MGC22793 -0.53 0.0001827 0.024 FTSJ2 FtsJ homolog 2 (E. coli) -0.51 0.0002574 0.024 TXNRD3 thioredoxin reductase 3 -0.46 0.0002364 0.024 TXNDC thioredoxin domain containing; thioredoxin domain containing -0.45 0.0002682 0.024 HSPA14 heat shock 70kDa protein 14 -0.45 0.0002971 0.025 BTN2A2 butyrophilin, subfamily 2, member A2 -0.44 0.0000923 0.020 TUBB2 tubulin, beta, 2 -0.41 0.0001853 0.024 EIF3S8 eukaryotic translation initiation factor 3, subunit 8,1 lOkDa -0.39 0.0000557 0.018 PRPF4 PRP4 pre-mRNA processing factor 4 homolog (yeast) -0.39 0.0002529 0.024 C14orfl chromosome 14 open reading frame 1 -0.38 0.0000297 0.016 ATP1B3 ATPase, Na+/K+ transporting, beta 3 polypeptide -0.35 0.0001112 0.021 HIST1H2BK histone 1, H2bk -0.35 0.0002276 0.024 PSAT1 phosphoserine aminotransferase 1 -0.34 0.0001663 0.023 ZNF271 zinc finger protein 271 -0.31 0.0000195 0.015 PSMB2 proteasome (prosome, macropain) subunit, beta type, 2 -0.30 0.0001620 0.023 HSPCA heat shock 90kDa protein 1, alpha -0.30 0.0001929 0.024 NGRN neugrin, neurite outgrowth associated -0.29 0.0000965 0.020 KPNB1 karyopherin (importin) beta 1 -0.27 0.0002254 0.024 THUMPD3 THUMP domain containing 3 -0.24 0.0000959 0.020 GRSF1 G-rich RNA sequence binding factor 1 -0.24 0.0002244 0.024 PTMA prothymosin, alpha (gene sequence 28) -0.19 0.0001125 0.021 HDHD1A haloacid dehalogenase-like hydrolase domain containing 1A -0.18 0.0002831 0.024

149 Table 4.2 List of differentially up-regulated genes according to microarray analysis

Gene Fold Symbol Description Change p-value FDR AQP3 aquaporin 3 4.82 0.000022 0.016 SQSTM1 sequestosome 1 3.21 0.000047 0.018 IMPA2 inositol(myo)-1 (or 4)-monophosphatase 2 2.92 0.000028 0.016 GDF15 growth differentiation factor 15 2.92 0.000014 0.014 PKIB protein kinase (cAMP-dependent, catalytic) inhibitor beta 2.90 0.000153 0.023 FIJI 1000 hypothetical protein FIJI 1000 2.81 0.000015 0.014 FAM83A family with sequence similarity 83, member A 2.42 0.000115 0.021 Clorf38 chromosome 1 open reading frame 38 1.98 0.000076 0.019 CST1 cystatin SN 1.89 0.000238 0.024 RNASE4 ribonuclease, RNase A family, 4 1.86 0.000194 0.024 SQSTM1 sequestosome 1 1.82 0.000122 0.021 GFPT2 glutamine-fructose-6-phosphate transaminase 2 1.56 0.000101 0.020 ZNF467 zinc finger protein 467 1.52 0.000008 0.014 NOX5 NADPH oxidase, EF-hand calcium binding domain 5 1.50 0.000013 0.014 GCLC glutamate-cysteine ligase, catalytic subunit 1.45 0.000038 0.017 AKR1B10 aldo-keto reductase family 1, member B10 (aldose reductase) 1.33 0.000274 0.024 KLF4 Kruppel-like factor 4 (gut) 1.32 0.000009 0.014 KLF4 Kruppel-like factor 4 (gut) 1.28 0.000253 0.024 ATP2B4 ATPase, Ca++ transporting, plasma membrane 4 1.22 0.000163 0.023 STK17B Serine/threonine kinase 17b (apoptosis-inducing) 1.00 0.000080 0.019 RAB7L1 RAB7, member RAS oncogene family-like 1 0.98 0.000245 0.024 FAM83A family with sequence similarity 83, member A 0.89 0.000096 0.020 RAB 31 RAB 31, member RAS oncogene family 0.84 0.000058 0.018 GSN gelsolin (amyloidosis, Finnish type) 0.75 0.000269 0.024 ARHGEF2 rho/rac guanine nucleotide exchange factor (GEF) 2 0.73 0.000248 0.024 KPNA5 Karyopherin alpha 5 (importin alpha 6) 0.68 0.000144 0.022 Transcribed locus 0.67 0.000009 0.014 PUS3 pseudouridylate synthase 3 ; pseudouridylate synthase 3 0.65 0.000172 0.023 KLHDC2 kelch domain containing 2 0.63 0.000285 0.024 PDCD4 programmed cell death 4 (neoplastic transformation inhibitor) 0.62 0.000200 0.024 TPP1 tripeptidyl peptidase I 0.58 0.000035 0.017 FTH1 ferritin, heavy polypeptide 1 0.57 0.000272 0.024 PACSIN2 protein kinase C and casein kinase substrate in neurons 2 0.57 0.000222 0.024 CYP1B1 cytochrome P450, family 1, subfamily B, polypeptide 1 0.54 0.000057 0.018 SUV420H1 suppressor of variegation 4-20 homolog 1 (Drosophila) 0.51 0.000229 0.024 MGST3 microsomal glutathione S-transferase 3 0.47 0.000121 0.021 ANXA4 annexin A4 0.47 0.000034 0.017 AKAP13 A kinase (PRKA) anchor protein 13 0.46 0.000282 0.024 ITCH Itchy homolog E3 ubiquitin protein ligase (mouse) 0.45 0.000225 0.024 NEK6 NDVIA (never in mitosis gene a)-related kinase 6 0.45 0.000128 0.021 NCOA3 nuclear receptor coactivator 3 0.44 0.000127 0.021 FAM45B & 0.41 0.000165 0.023 A family with sequence similarity 45, member B & A 0.40 0.000218 0.024 MGST1 microsomal glutathione S-transferase 1 0.36 0.000027 0.016 TOB1 transducer of ERBB2, 1 0.35 0.000212 0.024 MAGE membrane associated guanylate kinase, WW and PDZ domain containing 2 0.021 C21orf5 chromosome 21 open reading frame 5 0.35 0.000123 0.024 EVC Ellis van Creveld syndrome 0.34 0.000180

150 Table 4.2 Cont'd

Gene Fold Symbol Description Change p-value FDR FN1 fibronectinl 0.26 0.000251 0.024 C9orf89 chromosome 9 open reading frame 89 0.24 0.000068 0.019 GPX4 glutathione peroxidase 4 (phospholipid hydroperoxidase) 0.24 0.000261 0.024 SERF2 small EDRK-rich factor 2 0.18 0.000096 0.020

151 Table 4.3 List of functional group from GO analysis

Term Count % PValue cell cycle 30 14.1% 1.79E-08 mitotic cell cycle 16 7.5% 6.04E-08 Mphase 15 7.0% 1.78E-07 cell division 14 6.6% 3.52E-07 mitosis 13 6.1% 4.47E-07 M phase of mitotic cell cycle 13 6.1% 5.17E-07 DNA metabolism 25 11.7% 6.10E-07 regulation of progression through cell cycle 20 9.4% 6.57E-06 regulation of cell cycle 20 9.4% 6.77E-06 response to DNA damage stimulus 14 6.6% 1.29E-05 cell cycle checkpoint 7 3.3% 1.49E-05 RNA processing 17 8.0% 1.52E-05 DNA repair 13 6.1% 2.07E-05 mitotic checkpoint 5 2.4% 2.40E-05 response to endogenous stimulus 14 6.6% 2.56E-05 nuclear mRNA splicing, via spliceosome 10 4.7% 4.03E-05 RNA splicing, via transesterification reactions with bulged adenosine as nucleophile 10 4.7% 4.03E-05 RNA splicing, via transesterification reactions 10 4.7% 4.03E-05 mRNA metabolism 13 6.1% 4.05E-05 response to stress 30 14.1% 5.62E-05 RNA metabolism 18 8.5% 6.03E-05 mRNA processing 12 5.6% 6.04E-05 cell proliferation 20 9.4% 6.14E-05 DNA replication 11 5.2% 6.89E-05 regulation of mitosis 6 2.8% 1.93E-04 RNA splicing 10 4.7% 1.95E-04 biopolymer metabolism 54 25.4% 2.54E-04 nucleobase, nucleoside, nucleotide and nucleic acid metabolism 57 26.8% 0.0043 NLS-bearing substrate import into nucleus 3 1.4% 0.0100 mitotic spindle organization and biogenesis 3 1.4% 0.0117 acute-phase response 4 1.9% 0.0127 microtubule-based process 7 3.3% 0.0140 response to stimulus 38 17.8% 0.0142 glutamine family amino acid metabolism 4 1.9% 0.0146 glutamine metabolism 3 1.4% 0.0155 macromolecule metabolism 67 31.5% 0.0176 regulation of cellular process 55 25.8% 0.0176 cell organization and biogenesis 29 13.6% 0.0179 regulation of physiological process 54 25.4% 0.0190 cellular physiological process 135 63.4% 0.0233 organelle organization and biogenesis 18 8.5% 0.0240 response to unfolded protein 4 1.9% 0.0265 response to protein stimulus 4 1.9% 0.0265 spindle organization and biogenesis 3 1.4% 0.0295 regulation of biological process 57 26.8% 0.0295 amino acid and derivative metabolism 9 4.2% 0.0297 microtubule cytoskeleton organization and biogenesis 4 1.9% 0.0323 Table 4.3 Cont'd

Term Count PValue regulation of cellular physiological process 51 23.9% 0.0364 amino acid metabolism 8 3.8% 0.0365 establishment of cellular localization 14 6.6% 0.0391 cellular localization 14 6.6% 0.0414

Table 4.4 List of functional group from KEGG analysis

Term Count PValue CELL CYCLE 10 4.69% 2.64E-05 GLUTATHIONE METABOLISM 4 1.88% 0.0184 PYRMEDINE METABOLISM 5 2.35% 0.0453 Table 4.5 List of genes in cell cycle group acquired from functional GO analysis

DAVID Gene Name RAN, MEMBER RAS ONCOGENE FAMILY STRATMN DEOXYTHYMIDYLATE KINASE (THYMEDYLATE KINASE) MAD2 MITOTIC ARREST DEFICIENT-LIKE 1 (YEAST) REGULATOR OF CHROMOSOME CONDENSATION 2 RETINOBLASTOMA BINDING PROTEIN 8 KINESIN FAMILY MEMBER 23 PROTHYMOSIN, ALPHA (GENE SEQUENCE 28) CELL DIVISION CYCLE 25A TPX2, MICROTUBULE-ASSOCIATED, HOMOLOG (XENOPUS LAEVIS) PROLIFERATING CELL NUCLEAR ANTIGEN SMC4 STRUCTURAL MAINTENANCE OF CHROMOSOMES 4-LIKE 1 (YEAST) TTK PROTEIN KINASE CYCLIN E2 V-MYB MYELOBLASTOSIS VIRAL ONCOGENE HOMOLOG (AVIAN)-LIKE 1 ANTIGEN IDENTIFIED BY MONOCLONAL ANTIBODY KI-67 MICROTUBULE-ACTIN CROSSLINKING FACTOR 1 CELL DIVISION CYCLE 2, Gl TO S AND G2 TO M CYCLIN A2 PROTEIN PHOSPHATASE 1, CATALYTIC SUBUNIT, BETA ISOFORM KINETOCHORE ASSOCIATED 1 DYSKERATOSIS CONGENITA 1, DYSKERESf FUMARATE HYDRATASE POLO-LIKE KINASE 1 (DROSOPHILA) CDC28 PROTEIN KINASE REGULATORY SUBUNIT IB H2A HISTONE FAMILY, MEMBER X BRANCHED CHAIN AMINOTRANSFERASE 1, CYTOSOLIC KARYOPHERIN ALPHA 2 (RAG COHORT 1, MPORTIN ALPHA 1) ANILLIN, ACTIN BINDING PROTEIN (SCRAPS HOMOLOG, DROSOPHILA) BUB3 BUDDING UNINHIBITED BY BENZMIDAZOLES 3 HOMOLOG (YEAST) Table 4.6 List of genes in cell cycle group acquired from functional KEGG analysis

DAVID Gene Name MCM4 MINICHROMOSOME MAINTENANCE DEFICIENT 4 (S. CEREVISIAE) CYCLIN E2 POLO-LIKE KINASE 1 (DROSOPHILA) STRATMN CELL DIVISION CYCLE 25A CELL DIVISION CYCLE 2, Gl TO S AND G2 TO M CYCLIN A2 PROLIFERATING CELL NUCLEAR ANTIGEN MAD2 MITOTIC ARREST DEFICIENT-LIKE 1 (YEAST) BUB3 BUDDING UNINHIBITED BY BENZIMIDAZOLES 3 HOMOLOG (YEAST) Chapter 5

156 Discussion and future directions

157 5.1 Discussion

Breast cancer is the most common internal malignancy afflicting North American

women. During their lifetimes, 1 in 9 Canadian women are expected to develop breast

cancer, and 1 in 28 women are expected to die from it. It was estimated that there would

be 22,400 new cases of breast cancer and 5,300 women would die from this disease in

2008 in Canada (2).

Most breast tumors arise from the duct epithelium, with infiltrating duct carcinomas, accounting for over 70% of breast cancers (377). There has been a dramatic rise in reported cases of duct carcinoma in situ (DCIS) of the breast over the past 20 years. In the United States, the age-adjusted incidence rate has increased from 2.4 per

100,000 in 1973 to 15.8 per 100,000 in 1992 for women of all races (378). Between 1983 and 1993, there was a 314% increase in age-adjusted incidence rates of DCIS compared with a 15.7% increase in invasive cancer (379).

Invasive carcinoma is defined as the extension of cancer cells beyond the basement membrane into the adjacent tissue. On microscopic examination, it is frequently observed to extend directly from DCIS. DCIS is characterized by a proliferation of presumably malignant epithelial cells within the mammary ductal-lobular system without light-microscopic evidence of invasion into the surrounding stroma.

Despite having phenotypic characteristics of invasive cancer such as sustained cell proliferation, resistance to apoptosis, and induction of angiogenesis (380), not all cases of

DCIS develop into invasive tumors. This is likely because they have not yet acquired additional genetic alterations that lead to invasiveness. Furthermore, it seems as if DCIS is not a single disease and is instead a heterogeneous group of lesions. Some DCIS is

158 thought to rapidly progress to invasive cancer, while other cases will take more than 5

years (8). The current challenge is to reliably determine which ones will recur, and of

those that recur, which will recur as DCIS and which will recur as invasive carcinoma.

Hence, the detection of certain molecular markers based on underlying genetic

abnormalities in DCIS has the potential to provide a useful diagnostic tool and help

clinical management of breast cancer. Of particular interest are markers, which can be

studied in formalin-fixed paraffin-embedded tissue samples, since virtually all DCIS is

processed in this way for routine pathological diagnosis. One such potential marker is

telomere length, as it is known that alterations in telomere length lead to genetic

instability and cancer progression. Normally, telomere length decreases with each

successive cell division in somatic cells due to the absence of telomerase, an enzyme responsible for adding telomeric sequences to the ends of chromosomes (50).

A line of evidence indicates that the average telomere length in cancer cells is

significantly shorter than the telomere length in their normal counterparts (75-80).

However, the role of telomere shortening in the development of cancer remains a poorly understood subject. We believe this is in part due to the study of global telomere length by most groups looking at clinical samples (245).

In our study, we investigated telomere length specifically on chromosome 17q since this chromosomal arm harbours several genes that are involved in breast cancer development. In order to do this, we developed an assay that enabled us to measure telomere length on a specific chromosomal arm (17q) in interphase cells from archival formalin-fixed paraffin-embedded material for the first time. This novel technique led us to the discovery that there is more heterogeneity in telomere length in preinvasive breast

159 cancer adjacent to invasive beast cancer than in preinvasive breast cancer alone. We

found that the telomere shortening on chromosome 17q is higher than the average

shortening of all telomeres in our study. In agreement with our results, other groups have

reported that the end of chromosome 17q is signal free in highly proliferative cell types

(322, 323). These results suggest that telomere shortening does not occur at the same rate

for all telomeres as it was suggested by Martens et al. (323). Based on our observations

and others, we speculate that telomere shortening is not simply the result of the end

replication problem and there may be other mechanisms that preferentially erode or

protect certain telomeres against others. Otherwise, chromosome 17p should be one the

chromosomes with the signal-free end, since this telomere on this arm of chromosome 17 has been reported as the shortest one (323).

There is evidence which shows different telomere erosion on chromosomes indirectly (322, 323, 381),. Moreover, in their review, Djojosubroto et al. hypothesized that the kinetics of telomere shortening during aging is not linear and there must be factors other than cell division modulating the attrition of telomeres during aging (60). In addition, it seems that cells can measure and modulate the length of the telomeric repeat array at individual chromosome ends, implying a regulatory mechanism (102). It is difficult to explain the mechanism underlying this event since there is no published data available in this regard. However, it can be speculated that telomere length, its protecting proteins, and chromosome structure may play roles in this event. The length of mammalian telomeres is controlled by a mechanism that involves telomerase, the negative regulators of telomere length (102), the rate at which the t-loop can be folded after DNA synthesis (30) and the structure of the chromosomes (381). It has been shown

160 that telomere length can be extended when new telomeres are generated by transfection

of short stretches of telomeric DNA into cultured cells. It has been found that the

transfected telomeric tracts are elongated until their length matches the other telomeres in

transfected cells (102). Overexpression of TRF1 and TRF2 results in the progressive

shortening of telomere length. Conversely, the expression of the dominant negative allele

of TRF1, which removes endogenous TRF1 from telomeres, leads to telomere elongation.

Therefore, the short telomere has less negative regulators and its folding is slow which

facilitates the accessibility of telomerase to the telomere for its elongation. In addition,

epigenetic factors such as chromatin structure modulated telomere attrition has been

shown to accelerate the rate of telomere shortening in the inactive X-chromosome

compared to that of its active counterpart during aging in vivo (381). These authors

suggested that the inactive X telomeres are part of the highly condensed Barr body, and

infers that the inactive X telomere is. less accessible to the telomere-maintenance

mechanisms such as telomerase or recombination (381). However the mechanisms

underlying this discrimination in telomere attrition by cells is not clear and remains to be

investigated.

The results presented in this thesis provide important information on the role of

telomere length in the development of breast cancer and help resolve some of the existing

conflicts in the literature. However, further studies are required to validate the mechanism behind the differential telomere shortening for different chromosomes as well as the role of this mechanism in chromosome instability and cancer.

As it has been described earlier, human breast carcinomas are a very heterogeneous group of tumors with a diverse behavior and response to therapy. Many

161 standard chemotherapeutic agents currently used to treat breast cancer are relatively non­ specific and act on all rapidly dividing cells. In recent years, more specifically targeted therapies have been introduced. Determination of ER status has been found to be an important predictive and prognostic factor in the management of breast cancer (351).

While the ER has been proven to be a successful target for the treatment of ER- expressing breast cancers, few specific cancer prevention or treatment strategies are available for ER-negative breast carcinomas (308). This has motivated considerable efforts toward finding novel targeted therapeutic approaches for the treatment of ER- negative breast cancer.

It is known that telomerase is active in over 90% of breast cancers but inactive in adjacent normal tissues (21, 306, 307). Therefore, targeting telomerase activity in these cells may be used to treat breast cancer. Recent evidence suggests that telomerase activity can be suppressed by PPARy ligands in some studies (173-175). PPARy is a nuclear hormone receptor that stimulates the terminal differentiation of a variety of cancers including breast carcinoma (228, 230, 231, 260, 270, 276, 310). There is substantial published data indicating an anti-tumor role for PPARy ligands in human malignancies

(227, 228, 230, 231, 312). However, some experiments showed a tumor-promoting role of PPARy ligands in certain colon cancers in animal models (263, 264). There are three classes of ligands for PPARy: natural ligands, dual ligands, and synthetic ligands. It has been shown that synthetic ligands, known as TZDs, promote the differentiation of various cell lines. Some of these TZDs, especially troglitazone and ciglitazone, demonstrate antiproliferative activities in several cancer models including breast cancer (227, 228,

230, 231, 311, 312). However, the molecular mechanism(s) responsible for this

162 antiproliferative effect is not well understood.

A line of evidence indicates that there is cross-talk between members of the NHR

superfamily at several levels of the signal transduction cascades (210, 382-386).

Signaling cross-talk has been observed between PPARy and estrogen receptor (ER) (384,

387, 388). The PPAR/RXR heterodimer has been shown to negatively regulate the effect

of estrogen by binding to estrogen response element (ERE)-related palindromic

sequences, although it cannot transactivate due to a non-permissive natural promoter

structure (384). Furthermore, ERs have been found to negatively interfere with PPRE- mediated transcriptional activity (388). However, the molecular mechanism involved still remains to be understood.

Estrogen receptor is an important and a well characterized member of the NHR superfamily. In response to its ligands, ERs form dimers and bind to the corresponding

ERE in the promoter region of target genes to regulate gene transcription (389). ERs can regulate biological processes through several distinct pathways (390). In a classical model, ligand-activated ERs bind specifically to DNA at EREs through their DNA binding domains and bring co-regulators to the transcription start site. ERs also modulate gene expression by cross-talking with other transcription factors, such as activating protein-1 (AP-1) and stimulating protein 1 (Spl) (391). Furthermore, ERs may respond to estrogen activation through non-genomic mechanisms. Estrogen can bind to ERs localized on the plasma membrane of target cells (392), leading to activation of MAPK and ERK signaling (393) or releasing intracellular calcium (394). Finally, ERs can be activated through ligand-independent pathways. It has been shown that activation of kinases by growth factor signaling or stimulation of other signaling leads to

163 phosphorylation followed by activation of ERs or associated co-regulators in the absence

of ligand (395). Using these mechanisms separately or in combination, ERs were found

to function as key regulators of growth and differentiation in a broad range of target

tissues, including the reproductive tract, mammary gland, central nervous and skeletal

systems. ER is also known to be involved in many pathological processes such as breast

and endometrial cancer. In the breast, 17(3-estradiol (E2) stimulates growth and the

estrogen receptor antagonist tamoxifen has been the most effective treatment for ERa-

positive breast cancer. In line with ER functions, it has been shown that E2 up-regulates

telomerase activity in ER-positive breast cancer MCF-7 cells. This activation was

accompanied by up-regulation of the telomerase catalytic subunit, hTERT mRNA. It has

been found that estrogen activates hTERT directly by binding to EREs found in the

promoter of hTERT and indirectly through activation of c-Myc expression in MCF-7

cells (124, 130). However, a similar result in ER-negative cells was not observed,

suggesting a regulatory role for estrogen that is limited to ER-positive cells (124, 130).

To eliminate the potential cross-talk between ER and PPARy and the effect of ER

on telomerase activity, we investigated the effect of the classical PPARy ligand troglitazone on telomerase activity in an ER-negative breast cancer cell line, MDA-MB-

231. To our knowledge, this is the first time that the effect of troglitazone on telomerase activity in breast cancer has been studied.

We demonstrated that troglitazone reduced the mRNA expression of hTERT and telomerase activity in the MDA-MB-231 breast cancer cell line. The suppressive effect of troglitazone on telomerase activity was found to be independent of PPARy involvement.

In agreement with this result, we also found no correlation between PPARy and hTERT

164 transcript levels in breast cancer patients.

In order to determine the role of PPARy in the progression of breast cancer, we

examined the anti-tumor effects of troglitazone on the MDA-MB-231 breast cancer cell

line. The MDA-MB-231 cell line was used as an in vitro ER-negative breast cancer

model in this study. We found that troglitazone reduces the number of MDA-MB-231

cells in a dose and time dependent manner. Furthermore, we showed that this effect is

due to the suppression of cell proliferation but not apoptosis induction. Using expression

microarray analysis, we observed the complexity of troglitazone's effects on MBA-MB-

231 cells. Despite this, microarray analysis indicated that the most significant targets of

troglitazone are genes regulating the cell cycle including CDC25A, MAD2L1, cyclin A2,

cyclin E2, CDC2, and PCNA. Our results also showed that troglitazone reduces the

phosphorylation status of Rb, which in turn leads to cell growth arrest. Furthermore, we

found that the effect of troglitazone on cell cycle regulators was independent from

PPARy activity.

Our results showed that the response of telomerase and cell cycle regulators

specially Rb to troglitazone follows the same patterns, and since hypophosphorylated Rb

has been found to suppresses telomerase activity (134, 135), it can be speculated that

troglitazone may suppress the activity of telomerase through Rb directly or indirectly through the involvement of other cell cycle regulators. However, more studies need to understand the mechanism underlying the cross talk between troglitazone, telomerase activity and cell cycle regulators in this model.

In addition, we also studied the effect of troglitazone on cell cycle regulators in

MCF-7 cells as an ER-positive cell model. Our results show that troglitazone activates

165 different signaling pathways in MCF-7 breast cancer cells compared to the MDA-MB-

231 cell line. Our observations in agreement with others indicate that the effects of

PPARy agonists and antagonists are controversial and rely on the experimental

approaches.

This can be explained by the possibility that different agonists or antagonists for a

given nuclear hormone receptor could exert diverse effects depending on the

environmental context of a given tissue, cell, or specific promoter. In fact, it is well

understood that nuclear hormone receptors form multi-component assemblies with the

regulatory proteins and these complexes can serve as co-activators or co-repressors. In

general, agonist and antagonists binding to nuclear hormone receptors cause an exchange

between co-activators and co-repressors to facilitate or suppress their transcriptional

activity (396). Therefore, given the tissue specificity of some co-regulators, their ability

to be part of the transcriptional complexes can be affected by agonists and antagonists.

The most, perhaps, well defined nuclear receptor system from the point of view of

biological responses and clinical implication is the estrogen receptor. Hall et al. (390)

discuss of the selective estrogen receptor modification concept, in which different ligands

form specific three-dimensional structures with receptors that lead to tissue- and perhaps

cell-specific biologic effects. This concept has already had ramifications on the clinical

front, where it has been shown that different selective anti estrogen receptor compounds

(Raloxifene and tamoxifen) exert unique estrogenic effects in a tissue-specific manner as follow:

166 Bone Breast Cardiovascular Uterus

Tamoxifen ER agonist ER antagonist ER agonist ER agonist

Raloxifene ER agonist ER antagonist ER agonist ER antagonist

More interestingly, we found that the PPARy gene is lost or deleted in 58% of

clinical breast cancers (22 samples out of 38 breast tumors). To the best of our

knowledge, this is the first study reporting the deletion of PPARy in breast cancer

samples. Moreover, our analysis of the NKI dataset shows that 56% and 44% of samples

are PPARy negative and positive respectively, although it has been reported that PPARy

is expressed in a variety of human cancers and its mutation is a very rare event (265).

This observation suggests that the reduced expression of PPARy shown in our NKI

analysis is due to PPARy deletion in breast cancer.

We also found a positive correlation between the expression of PPARy and

certain clinical prognostic parameters. Based on these observations, we suggest that low

copy number of the PPARy gene may play a role in the development or progression of breast cancer suggesting a tumor suppressor function for PPARy in breast cancer similar to the previously proposed role for PPARy in colon cancer (229). Our results demonstrated that troglitazone can be used as an anti-telomerase and anti-proliferative agent in ER-negative breast cancer cell lines. However, the mechanism underlying these effects remains to be discovered.

5.2 Future directions

This work suggests several directions for future studies. We found that telomere

167 shortening can be different between chromosomes suggesting the important role of each

individual telomere length in the development of breast cancer. As it has been suggested

that chromosome 17p has the shortest telomere, and since the tumor suppressor gene p53

is located on chromosome 17p (397), it is important to study the correlation of telomere

shortening on chromosome 17p and p53 status in the development of breast cancer

development. It has been shown that critically short telomeres activate p53, leading to

cellular senescence or apoptosis, which suppresses tumorigenesis. However, in the

absence of p53 that occurs in 50% of cancers, cells with critically short telomeres

continue their replication and thereby acquire more chromosomal instability. Therefore, it has been suggested that in the absence of active p53, telomere dysfunction plays an important role to generate chromosomal instability as a common event observed in human carcinomas (398). Meanwhile the protocol developed in this study allows the investigation of telomere length on a specific chromosome in FFPE samples.

Furthermore, the observation of PPARy loss/deletion in 58% of breast cancer samples is in agreement with the negative expression of PPARy identified in our NKI analysis. This suggests that low copy number of the PPARy gene may play a role in the development of breast cancer. Based on this finding, we speculate a potential tumor suppressor role for the PPARy gene. However, this result should be confirmed by real time PCR followed by immunohistochemical staining for PPARy to evaluate its protein status in breast cancer compared to normal breast epithelium in a larger number of clinical samples.

In addition, in light of the suppressive effect of troglitazone on telomerase, further studies are needed to investigate its effect on telomerase in breast cancers. Our results

168 show that troglitazone decreases the expression of hTERT at its mRNA level. This

suggests that troglitazone may target transcriptional factors regulating hTERT expression

through its promoter. In addition, it has been shown that the promoter of the hTERT gene

is a target for many transcriptional factors including oncogenes and tumor suppressors.

For example, estrogen has been found to be a positive regulator of the hTERT gene.

There are two specific binding sites for estrogen receptors within the hTERT promoter that enable estrogen to stimulate the endogenous hTERT gene and concomitantly induce telomerase activity (124, 125). The hTERT promoter also contains two E-boxes which mediate Myc/Max binding and their transactivation (120-123). As expected, these two E- boxes also mediate repression of hTERT transcription by Mad (132, 133). In addition to

Myc/Max/Mad, upstream stimulatory factor (USF) can bind to these sites and promote hTERT expression (127-129). Furthermore, five binding sites for the transcription factor

Spl are localized within the hTERT promoter that enable Spl to increase hTERT transcription (122, 130). It has been shown that c-Myc co-operates with Spl to induce the hTERT promoter, and both transcription factors may be overexpressed once cells pass the stage of replicative senescence (130). In addition, Xu et al. found that p53 suppresses hTERT expression by forming a complex with Spl and preventing Spl from binding to and activating the hTERT promoter (131). Interestingly, recent experiments demonstrated that the PPARy ligand, rosiglitazone, up-regulates mRNA and protein levels of p53 by recruiting PPARy to the p53 promoter sequence in the MCF-7 cell line (285). The hTERT promoter was also found to contain two Ets-1 motifs conferring transcriptional activation of the hTERT promoter in response to mitogenic stimulation (126). More interestingly,

Ogawa et al. found that PPARy suppress the expression of TERT by inhibiting Ets-1

169 binding to the TERT promoter in animal models (174).

Another transcription factor, E2F-1, may also contribute to hTERT repression as

E2F-1 binds to the hTERT promoter at two sites and reduces hTERT promoter activity in

human squamous carcinoma cells (135). Similarly, its relatives E2F-2 and E2F-3 repressed hTERT transcription in tumor cells, whereas E2F-4 and E2F-5 did not.

Recruitment of Rb by E2F-1 to the hTERT promoter might account for the fact that Rb downregulates telomerase activity (134, 399). In agreement with this finding, Crowe et al. showed that CDK2 overexpression rescue the suppressive effect of Rb on telomerase activity (134). Conversely, overexpression of cyclin Dl and E induce telomerase activity.

(400). Interestingly, in our study we found that troglitazone reduces the mRNA expression of cyclin E, CDK2, and the phosphorylation of Rb. This effect of troglitazone is associated with reduction of the hTERT mRNA level and telomerase activity. Based on our findings and those of others, it can be speculated that troglitazone may inhibit the activity of telomerase through hypophosphorylation of Rb directly or indireclty.

However, this needs to be confirmed by studying the effect of troglitazone on telomerase in the absence of cyclin E, CD2 and/or Rb. More importantly, it would be interesting to investigate the mechanisms underlying the reduction of genes involved in cell cycle regulation by troglitazone, since we found that PPARy was not involved in our study.

One possibility would be the contribution of an unknown NR targeted by troglitazone.

Further investigations may result in a new NHR or discovery of an orphan NHR interacting with troglitazone or other members of the TZD family. This will in turn help to find a new pathway(s) and potential target proteins to develop novel therapies for breast cancer.

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