NICOTINE-MODULATED GENE EXPRESSION PROFILES IN MCF7

BREAST CANCER CELL LINE AND INVOLVEMENT OF ESTROGEN IN

CHRNA5 EXPRESSION

A THESIS SUBMITTED TO THE DEPARTMENT OF MOLECULAR BIOLOGY

AND GENETICS AND THE INSTITUTE OF ENGINEERING AND SCIENCE OF

BILKENT UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

BY RÜMEYSA BIYIK

AUGUST 2009

I certify that I have read this thesis and that in my opinion it is fully adequate, in scope,

and in quality, as a thesis for the degree of Master of Science

__________________________________

Assist. Prof. Dr. Ozlen Konu

I certify that I have read this thesis and that in my opinion it is fully adequate, in scope,

and in quality, as a thesis for the degree of Master of Science

__________________________________

Assist. Prof. Dr. Sreeparna Banerjee

I certify that I have read this thesis and that in my opinion it is fully adequate, in scope,

and in quality, as a thesis for the degree of Master of Science

__________________________________

Assist. Prof. Dr. Uygar Tazebay

I certify that I have read this thesis and that in my opinion it is fully adequate, in scope,

and in quality, as a thesis for the degree of Master of Science

__________________________________

Prof. Dr. Mehmet Baray

Director of the Institute of Engineering and Science

ii

TO MY FAMILY, TO KONU LAB LABORTORY

AND TO MY PRIMARY SCHOOL TEACHER ( AYŞE YETİŞMİŞ)

iii

ABSTRACT

NICOTINE-MODULATED GENE EXPRESSION PROFILES IN MCF7 BREAST

CANCER CELL LINE AND INVOLVEMENT OF ESTROGEN IN CHRNA5

EXPRESSION

Rümeysa BIYIK

M.Sc. in Molecular Biology and Genetics

Advisor: Asst. Prof. Özlen KONU

August 2009, 152 pages

Breast cancer, highly heterogeneous in nature, has been classified into multiple molecular

subtypes based on hormone receptor status and also possess variable genetic and environmental

etiologies. Prognosis and therapy of breast cancer depends on the presence or absence of these

molecular markers. Nicotine, the major psychoactive addictive component in tobacco smoke,

has been associated with multiple cancers because of its ability to increase cell proliferation,

migration, and angiogenesis, and to decrease apoptosis. Nicotine binds to cholinergic receptors

made up of multiple subunits, one of which, the alpha 5 (CHRNA5) whose polymorphisms have

recently been implicated in nicotine addiction and lung cancer as functional. Microarray

datasets provide genomic and functional information on the whole transcriptome when exposed

to a certain treatment or under different pathological conditions allowing for molecular

classification. The association between nicotine use and breast cancer has been controversial

and to our knowledge no high-throughput expression profiling of breast cancer cells exposed to

nicotine exists in the literature. In the present study, we determined that 1 uM nicotine affects

cell proliferation in MCF7 cells measured by MTT assay only under serum starvation (0.1%

Serum) condition at days 5 or 7 but not earlier. Similarly, effects on the protein levels of

selected molecular markers with roles in proliferation and/or apoptosis, i.e., CyclinE, bcl-xl, and

p53 were affected under serum starved conditions in more pronounced ways. Effects of nicotine

iv

at the transcriptome level were studied in MCF7 cells when exposed to 1uM Nicotine using

Affymetrix Human HGU133 plus 2 arrays under the 10% serum levels. Our findings indicated

that nicotine affects multiple pathways including MAPK, focal adhesion, and apoptosis

although the magnitude of changes was mild. Preliminary analyses performed under serum-

starvation indicated that starvation resulted in drastic changes in MCF7 transcriptome, some of

which can be reversed, by 1uM nicotine. CHRNA5 expression was highly modulated by serum

levels. Multiple microarray datasets on breast cancer cell lines and primary tumors in GEO were

re-analyzed to assess the dependency of CHRNA5 expression on estrogen. Our findings were:

1) CHRNA5 expression increased in the presence of estrogen in a dose- and time-dependent

fashion; 2) CHRNA5 was found to be a likely secondary target of estrogen; 3) CHRNA5

expression was higher in ER negative and/or Grade 3 breast cancer patients, implicating

CHRNA5 with prognosis; 4) DNA replication and cell cycle genes seemed to be highly

correlated with CHRNA5 expression; 5) the coexpressed genes could predict ER and Grade

status of primary tumors with high accuracies. In conclusion, cholinergic signaling as

modulated by nicotine through acetylcholine receptors might have a role in breast cancer

etiology. CHRNA5 represents a novel candidate for breast cancer diagnostic and prognostic

studies.

v

ÖZET

MCF7 MEME KANSER HÜCRE HATTINDA NİKOTİNDEN ETKİLENMİŞ GEN

İFADE PROFİLLERİ VE CHRNA5 İFADESİNDE ESTROJENİN ROLÜ

Rümeysa BIYIK

Master Tezi, Moleküler Biyoloji ve Genetik Bölümü

Tez Yöneticisi: Yrd. Doç. Özlen Konu

Ağustos, 2009 152 sayfa

Heterojen bir yapısı olan meme kanseri bir çok moleküler alttipe ayrılmış olup birbirinden

farklı genetik ve çevresel nedenlerden kaynaklanabilir. Meme kanserinin prognozu ve tedavisi

sözü edilen moleküler belirteçlerin varlığına bağlıdır. Tütünün en belirgin fizyoaktif ve bağlılık

yapıcı maddesi olan nikotin, hücre bölünmesi, hareketi ve anjiogenezi arttırması ve apoptozu

önlemesi dolayısı ile birden fazla kanser ile ilintilendirilmiştir. Nikotin bir çok altüniteden

oluşan kolinerjik reseptörlere bağlanır; bunlardan biri olan alfa 5 (CHRNA5) fonksiyonel

polimorfizmleri dolayısı ile nikotin bağımlılığı ve akciğer kanseri ile ilintilendirilmiştir.

Mikrodizin verisetleri genomik ve işlevsel bilgiyi bir ajana maruz kalma ya da farklı patolojik

durumlarda tüm transkriptom düzeyinde moleküler bir gruplamaya olanak verecek şekilde

sağlarlar. Nikotin kullanımı ve meme kanseri arasındaki ilişki tartışmalı olup bilgimiz dahilinde

nikotine maruz kalan hücrelerde şimdiye kadar yüksek ölçekli ifade profillemesi yapılmamıştır.

Bu çalışmada, MTT assay ile ölçüldüğü üzere, 1 uM nikotin, 0.1% serum açlığına maruz kalan

MCF7 hücrelerinin bölünmesini 5. ve 7. günlerden itibaren etkilemiştir. Benzer bir şekilde,

bölünme ve apoptozda görevli moleküler belirteçlerin, örneğin, CyclinE, bcl-xl, p53, protein

miktarları da serum açlığı halinde daha belirgin bir şekilde gözlenmiştir. 10% serum altında

büyütülen MCF7 hücrelerinin 1 uM nicotine verdikleri yanıt Affymetrix İnsan HGU133 plus 2

mikrodizinleri kullanarak çalışılmıştır. Bulgularımız, nikotinin MAPK, apoptoziz ve fokal

adhezyon gibi sinyal yolaklarını etkilediğini göstermekle birlikte değişikliklerin miktarı

düşüktür. Serum açlığı uygulanarak yapılan ön çalışmalar serum eksikliğinin ifade profilinde

oldukça etkili olduğunu gösterdi; bu değişikliklerin bazıları 1 uM nikotin ile geri çevrilebilir

vi

görünmektedir. CHRNA5 ifadesi serum seviyesinden etkilenmiştir. CHRNA5 ifadesinin

estrogene bağımlığını test etmek üzere GEO veritabanından çok sayıda meme kanser hücre hattı

ve primer tümör verisi incelenmiştir. Bulgularımız şöyle özetlenebilir: 1) CHRNA5 ifadesi

estrojenin varlığında doza ve zamana bağlı olarak yükselmiştir; 2) CHRNA5 estrojenin ikincil

bir hedefi olabilir; 3) CHRNA5 ifadesi ER negatif ve Grade 3 hastalarında daha yüksek olup bu

durum CHRNA5’i prognoz ile ilişkilendirir; 4) DNA replikasyon ve hücre döngüsü genleri

CHRNA5 ifadesi ile yüksek derecede korelasyon gösterir; 5) CHRNA5 ile birlikte ifade olan

genler primer tümörlerin ER ve Grade statüsünü yüksek bir doğrulukla tahmin edebilirler.

Sonuç olarak, nikotinin asetilkolin reseptörleri yoluyla değiştirebildiği kolinerjik sinyaller

meme kanser etiolojisinde yer taşıyabilir. CHRNA5 meme kanser prognostik ve diagnostik

çalışmalarında yeni bir hedef teşkil etmektedir.

vii

ACKNOWLEDGEMENTS

First of all, I would like to express my gratitude for my thesis advisor Assist. Prof. Dr. Özlen

Konu for her guidance throughout this study. Being her student was a true privilege since she

was always accessible for questions and discussions and very supportive during hard times.

I would like to thank Assoc. Prof. Dr. Işık Yuluğ and Bilge Kılıç for perfoming the microarray

experiments. I would also like to thank Öztürk’s group and Akçalı’s group for providing me

antibodies.

I would like to express my deepest thanks to Ceren Sucularlı who has provided me with

experimental support right from the beginning of my study until the very end, always being

there when I needed to ask something. I would like to thank Onur Kaya for sharing ideas about

projects. I would also thank all Konu Lab members for friendly and comfortable atmosphere in

the lab.

I would like to thank the entire MBG faculty and lab members for creating a friendly,

comfortable and helpful atmosphere to do research in.

Of course, my deepest gratitude goes to my family for their patience, unconditional love and

support throughout my life.

I would thank TÜBİTAK for supporting me with scholarship 2210 during my M.Sc. research.

This thesis in part was supported by a grant from TUBITAK (TBAG-106T0548 to O.K).

viii

TABLE OF CONTENTS

NUMBER/NAME PAGE

ABSTRACT iii

ÖZET v

ACKNOWLEDGEMENTS vii

TABLE OF CONTENTS viii

LIST OF FIGURES x

LIST OF TABLES xv

ABBREVIATIONS xvii

CHAPTER 1: INTRODUCTION 1

1.1. Cholinergic Signaling 1

1.1.1. Tobacco use, cancer and nicotine 1

1.1.2. The structure and function of nicotinic acetylcholine receptors (nAChRs) 2

1.1.3. CHRNA5 (Nicotinic acetylcholine receptor alpha 5) 6

1.1.1.1. Function of CHRNA5 6

1.1.1.2. Polymorphism studies of CHRNA5 in connection with nicotine

dependence 7

1.1.1.3. Polymorphism and expression studies of CHRNA5 in regard to cancer 9

1.1.4. Effect of nicotine on cell proliferation 10

1.1.5. Effects of nicotine on apoptosis 12

1.1.6. Effects of nicotine on angiogenesis 14

1.1.7. The association between smoking and breast cancer 15

ix

1.2. Breast Cancer 16

1.2.1. Histological Grade in Breast Cancer 19

1.2.2. Estrogen and Estrogen Receptor in Breast Cancer 21

1.2.3. Hormone Therapy in Breast Cancer 24

1.2.4. Cell Culture Cancer Models 25

1.1.1.4. MCF7 Cells as a Model For The Estrogen Studies: 26

1.1.1.5. Effects of serum starvation on cells 26

1.3. Microarray Technology 27

1.3.1. Microarray studies on nicotine-mediated signaling: 29

CHAPTER 2: MATERIALS AND METHODS 31

2.1. Materials 31

2.2. Solutions and media 32

2.2.1. General solutions 32

2.2.2. Tissue culture solution 32

2.2.3. Protein Extraction and Western blotting solutions 33

2.3. Laboratory Methods 34

2.3.1. Tissue Culture techniques 34

2.3.2. Nicotine treatment protocol 34

2.3.3. RNA experiments 36

2.3.4. Protein experiments 36

2.3.5. Microarray analysis 39

2.4. Microarray Meta-analysis 41

2.4.1. Preparation of expression data 41

2.4.2. Features of the breast cancer microarray experiments 42

2.4.3. Experimental features of the in vitro public microarray experiment 44

x

2.4.4. Microarray analysis 46

2.4.5. Analysis of Data 47

2.4.6. Regression Analysis 47

2.4.7. Analysis of Co-expressed Genes 48

2.5. Statistical Analysis of Cell Proliferation 48

CHAPTER 3. AIMS AND STRATEGY 46

CHAPTER 4: RESULTS 51

4.1. Nicotine-mediated Signaling Pathways via Microarray Experiments in MCF7 Cell

Line 51

4.1.1. The dose-and time-dependent effect of nicotine on breast cancer cell

proliferation 51

4.1.2. Nicotine modulates Cyclin E protein expression in a serum-dependent

manner 53

4.1.3. Nicotine modulates p53 and Bcl-xl protein levels in a serum-dependent

manner 54

4.1.4. Genome-wide profiling of nicotine exposure in MCF7 cells 55

4.1.5. RNA and Microarray data quality assessment 56

4.1.6. Nicotine’s effect on MCF7 transcriptome under the physiological serum

conditions 61

4.1.7. Preliminary analyses of MCF7 transcriptome under serum starvation and

nicotine treatment 69

4.1.7.1. Nicotine treatment under starvation might lead to recovery of cell

expression in a subset of probesets 77

4.1.7.2. Nicotine modulates expression independent of serum starvation for a

subset of probesets 84

4.1.8. Cholinergic nAChR receptor expression analysis 89

xi

4.2. Involvement of Estrogen in CHRNA5 Signaling 95

4.2.1. CHRNA5 Expression Analysis in Breast Cancer Cell Lines 95

4.2.1.1. Estrogen treatment increases CHRNA5 expression 96

4.2.1.2. Estrogen induces the expression of CHRNA5 in a dose- and time-

dependent manner 99

4.2.1.3. CHRNA5 is the secondary target of estrogen signaling 104

4.2.1.4. Estrogen-independent MCF7 sublines have higher CHRNA5

expression 107

4.2.2. CHRNA5 Expression Analysis in Breast Cancer studies 110

4.2.2.1. ER status 111

4.2.2.2. Histological Grade 112

4.2.2.3. Hormone therapy 115

4.2.3. Analysis of Correlated Genes with CHRNA5 117

CHAPTER 5: DISCUSSION 122

5.1. Nicotine-mediated Signaling in MCF-7 Cells 122

5.2. Involvement of Estrogen in CHRNA5 Expression 128

5.3. Future Perspectives 130

CHAPTER 6: APPENDICES 133

REFERENCES 136

xii

LIST OF FIGURES

NUMBER/NAME PAGE

Figure 1.1.1. Structure of nicotine 2

Figure 1.1.2. Schematic organization of a single subunit and the pentameric nAChR 3

Figure 1.1.3. “The diverse functions of the homomeric α7 nAChR and of the heteromeric

α4β2nAChRs” 6

Figure 1.1.4. Schematic representation of the proliferative activity of nicotine via α7nAChR in

cells 11

Figure 1.1.5. Model signaling pathways summarizing the anti-apoptotic activity of nicotine 14

Figure 1.2.1. “Prognostic factors of breast cancer” 17

Figure 1.2.2. The anatomy of human breast 20

Figure 1.2.3. “Model representing the mechanistically distinct molecular pathways used in the

regulatory actions of ERs” 23

Figure 2.3.1. The schematic representation of semi-dry transfer 38

Figure 2.4.1. Establishment of OHT-resistant, ICI-resistant MCF7 cells and maintenance of

wild-type MCF7 cells 46

Figure 4.1.1. Time- and dose-dependent effects of nicotine treatment on MCF7 cell

proliferation/growth at 10% serum 52

Figure 4.1.2. Time- and dose-dependent effects of nicotine treatment on MCF7 cell

proliferation/growth at 0.1% serum 53

Figure 4.1.3.Cyclin E expression in response to nicotine treatment under different serum

conditions 54

Figure 4.1.4. The anti-apoptotic effects of nicotine under different serum conditions 55

xiii

Figure 4.1.5. RNA integrity graphs of microarray samples 56

Figure 4.1.6. Quality control plots of microarray samples 59

Figure 4.1.7. RNA degradation plots of microarray experiments 60

Figure 4.1.8. Scatter plot of probesets with or without nicotine treatment in 10% serum

conditions 62

Figure 4.1.9. GO analysis of nicotine treatment in 10% serum 67

Figure 4.1.10. Scatter plot of probesets with or without nicotine treatment in 0.1% serum

conditions 70

Figure 4.1.11. GO analysis of nicotine treatment in 0.1% serum 75

Figure 4.1.12. GO analysis of genes which nicotine treated cells in 0.1% serum had similar

response to untreated cells in 10% serum 81

Figure 4.1.13. Cluster analysis of genes which nicotine treated cells in 0.1% serum had similar

response to untreated cells in 10% serum 83

Figure 4.1.14. GO analysis of genes inversely correlated between 0.1% serum-nicotine and 10%

serum samples 87

Figure 4.1.15. Cluster analysis of probesets inversely correlated between 0.1% serum-nicotine

and 10% serum samples 88

Figure 4.1.16. Cluster analysis of nAChRs in respose to nicotine treatment at different serum

conditions 90

Figure 4.2.1. Estrogen effect on CHRNA5 expression in the data set GSE3529 (Rae, Johnson et

al. 2005) using BT474, T47D and MCF7 cell lines 97

Figure 4.2.2. CHRNA5 expression in response to E2 98

Figure 4.2.3: CHRNA5 expression to estrogen dose-dependent treatment 100

xiv

Figure 4.2.4: CHRNA5 expression in response to hormone starvation 101

Figure 4.2.5. CHRNA5 expression in response to time-dependent E2 exposure 103

Figure 4.2.6. CHRNA5 expression in response to E2 exposure in the presence or absence of

translation inhibitor cycloheximide 105

Figure 4.2.7. CHRNA5 expression in response to short-time E2 exposure 106

Figure 4.2.8. CHRNA5 expression in response to E2 in different MCF7 sublines 108

Figure 4.2.9. CHRNA5 expression in estrogen-independent MCF7 clones 109

Figure 4.2.10. CHRNA5 expression based on ER status 111

Figure 4.2.11. CHRNA5 expression based on histological grade level 113

Figure 4.2.12. CHRNA5 expression based on histological grade with ER status 114

Figure 4.2.13. CHRNA5 expression based on therapy status with grade 116

Figure 4.2.14. GO analysis of correlated genes 119

Figure 6.1. pS2 expression 133

Figure 6.2. MYC and E2F1 expression in response to E2 in the presence or absence of

cycloheximide 134

Figure 6.3. MYC and E2F1 expression in response to E2 for 2 h 135

xv

LIST OF TABLES

NUMBER/NAME PAGE

Table 1.2.1. Morphological features of breast cancer 20

Table 2.4.1. Publicly available gene expression data from estrogen studies on cell line 41

Table 2.4.2. Publicly available gene expression data from primary breast cancer studies 42

Table 2.4.3. The distribution of data according to ER, grade and treatment status 43

Table 4.1.1. Description table of the microarray samples and RIN values 57

Table 4.1.2. Data associated with Figure 4.1.6 59

Table 4.1.3. Pathway analysis of nicotine treatment in 10% serum 68

Table 4.1.4. Pathway analysis of nicotine treatment in 0.1% serum 76

Table 4.1.5. Pathway analysis of genes which nicotine treated cells in 0.1% serum had similar

response to untreated cells in 10% serum 78

Table 4.1.6. Pathway analysis of genes inversely correlated between 0.1% serum-nicotine and

10% serum samples 85

Table 4.1.7. Results of class comparison analysis of nAChRs under 10 % serum conditions 91

Table 4.1.8. Results of fold-change of nAChRs under serum starvation 92

Table 4.1.9. Results of fold-change of nAChRs between serum starvation and physiological

serum concentrations 94

Table 4.2.1. Class comparison results of in vitro studies 95

Table 4.2.2. Regression analysis of dose-dependent and time-dependent data 102

Table 4.2.3. Class comparison results of primary breast tumor studies 110

Table 4.2.4. Correlated genes with CHRNA5 117

xvi

Table 4.2.5. Pathway analysis of correlated genes with CHRNA5 118

Table 4.2.6. Prediction analysis results for the correlated genes according to ER status 120

Table 4.2.7. Prediction analysis results for the correlated genes according to histological grade

121

xvii

ABBREVIATIONS

Ach Acetylcholine

APS Ammonium Persulphate

Bcl-xl Bcl-2-like protein 1

CHRNA5 Cholinergic Receptor, nicotinic, alpha 5

E2 Estrogen, estradiol

ER Estrogen Receptor

GEO Gene Expression Omnibus

GFR Growth Factor Receptor

GO Gene Ontology

ICI Fulvestrand

MAPK Mitogen-activated Protein Kinase

nAChRs Nicotinic Acetylcholine Receptor

OHT Tamoxifen

RIN RNA Integrity

SNP Single Nucleotide Polymorphism

TEMED Tetramethylethylenediamine

1

CHAPTER 1: INTRODUCTION

1.1. Cholinergic Signaling

1.1.1. Tobacco use, cancer and nicotine:

Smoking is one of the leading causes of cancer–related deaths worldwide. Recent

studies suggest that 25% of all cancers in men and 4% in women are related to smoking

(Stewart and Kleihues 2003). Studies carried out in Europe, Japan and North America also

demonstrate that 91% of all lung cancers in men and 69% in women are related to cigarette

smoking (Stewart and Kleihues 2003). In addition, smoking is one of the main risks for the

oesophagus, larynx, oral cavity and stomach cancers for males and females (Sasco, Secretan

et al. 2004).

4000 different chemicals are found in cigarette smoke and more than 60 are identified

as carcinogens according to the study of International Agency for Research on Cancer (IARC

2004). Nicotine, first isolated from the tobacco plant Nicotiana tabacum (Fig. 1.1.1) by

German chemists Posselt and Riemannin in 1828, is the principal psychoactive and addictive

component in tobacco; and it is highly addictive (Catassi, Servent et al. 2008). Consequently,

smokers are exposed to a diverse array of carcinogens and other bioactive compounds in

tobacco, making tobacco use the leading cause of premature deaths in developed countries

(Peto and Doll 1992). The common idea is that the carcinogens in cigarette smoke are

converted into the bulky compounds and these compounds react with DNA, thereby leading

to DNA adducts and genetic alterations in cancer (Catassi, Servent et al. 2008). However,

recent studies challenge this conception and suggest that components such as nicotine act as

signaling molecules modulating cellular homeostasis (Dasgupta and Chellappan 2006;

Dasgupta, Kinkade et al. 2006; Dasgupta, Rastogi et al. 2006). On the other hand, multiple

studies about the therapeutic effects of nicotine for Parkinson’s disease, Alzheimer’s disease,

and ulcerative colitis are present (Jani and Regueiro 2002; Quik and Kulak 2002; Sabbagh,

Lukas et al. 2002).

2

Figure 1.1.1. Structure of nicotine. A) Tobacco plant. B) Chemical structure of nicotine.

Modified from (Catassi, Servent et al. 2008).

Nicotine absorption takes place through the oral cavity, skin, lung, urinary bladder, and

gastrointestinal tract (Schevelbein, Eberhardt et al. 1973). Nicotine levels in blood increase

rapidly during the completion of cigarette smoking because nicotine is readily absorbed once

it reaches the lungs (Catassi, Servent et al. 2008). Other routes for absorption include skin

and stomach; skin allows for diffusion at different rates while stomach is acidic thus does not

enable rapid absorption. However, nicotine is well absorbed in the small intestine due to the

higher pH and a large surface area (Hukkanen, Jacob et al. 2005).

1.1.2. The structure and function of nicotinic acetylcholine receptors (nAChRs):

Nicotinic acetylcholine receptors (nAChR/CHRNs) are ion channels located on the

plasma membrane of neuronal and non-neuronal cells and tissues (Sobel and Changeux 1977;

Lindstrom, Anand et al. 1996; Lukas, Changeux et al. 1999; Sharma and Vijayaraghavan

2002; Gotti and Clementi 2004; Wessler and Kirkpatrick 2008). 10α subunits (α1-α10) and

3

4β subunits (β1-β4) are identified (Lukas, Changeux et al. 1999). A functional receptor

complex consists of homo- or heteropentamer of α1–α10 and β1–β4 subunits that are

arranged symmetrically around an axis perpendicular to the membrane. The composition and

stoichiometry of the subunits determine the cation selectivity, desensitization kinetics and

spatial distribution (Catassi, Servent et al. 2008). Although nAChRs were first identified in

the neuronal system, recent studies indicate the presence of nAChRs in a variety of non-

neuronal cells (Egleton, Brown et al. 2008).

All nAChR subunits possess a large extracellular domain, four transmembrane regions

(M1–M4) with an α-helix structure, a large cytoplasmic domain between M3 and M4 and

finally a short extracellular C-terminal tail (Fig. 1.1.2). The loops present on the extracellular

regions of nAChR maintain the ACh binding site while transmembrane segment of M2

contributes to the lining of the pore (Corringer, Le Novere et al. 2000). The structural

coupling between these domains plays key roles in signal transduction between the natural

ligand acetylcholine (Ach) binding and the ion channel gating, the depolarization of the post-

synaptic membrane, and the propagation of an action potential. Aside from the

agonistic/antagonist binding site, diverse interaction sites are present on nAChRs for both

non-competitive inhibitors and allosteric modulators (Hogg, Buisson et al. 2005).

Figure 1.1.2. Schematic organization of a single subunit and the pentameric nAChRs (Russo,

Catassi et al. 2006).

4

In recent years, crystallographic structure studies about the extracellular domain of

nAChRs, electronic microscopy images and several biochemical studies on the ligand

binding sites have increased the understanding of the interactions of different nAChRs with

diverse ligands as well as the molecular level of these interactions and the primary motion

leading to the receptor activation. These findings have become important for the drug-design

related to the therapy of neurological disorders such as Alzheimer’s or Parkinson’s disease,

schizophrenia, anxiety, pain or epileptic disorders (Newhouse, Singh et al. 2004; Singh,

Potter et al. 2004).

Acetylcholine is the physiological agonist for all nAChRs (Wessler and Kirkpatrick

2008). Rapid conformational alterations from resting basal state to active or open–channel

state may occur in nAChRs, similar to allosteric receptors. Long-exposure to acetylcholine

leads to stabilization of the receptor in a closed desensitized state (Corringer, Le Novere et al.

2000). Similar to acetylcholine, nicotine binds as an agonist to α subunits of nAChRs. These

interactions lead to conformational changes in the receptor and open the gate on the

intracellular side of the ion channel in the plasma membrane. Consequently, ions flow into

the cells (Lindstrom, Anand et al. 1996). Increased level of cations in the cells leads to

membrane depolarization via decreasing the negative charges on the intracellular side of the

plasma membrane. Then, membrane depolarization opens gates on the intracellular side of

the plasma membrane of voltage-activated Ca++ channels, thereby resulting in an additional

influx of Ca++. Finally, all these events activate many diverse responses such as the release

of neurotransmitters, growth, angiogenic and neurotrophic factors, and stimulation of

different signaling cascades involved in the regulation of cell proliferation, apoptosis,

migration and differentiation (Kunzelmann 2005; Kunzelmann, Sun et al. 2005; Roderick and

Cook 2008).

α7nAChR and α4β2nAChRs are the best studied receptors with respect to the

biological effects of nicotine; and they are found most abundantly in the mammalian brain.

Although prolonged exposure to agonists leads to down-regulation of other types of cellular

receptors, prolonged nicotine exposure upregulates the expression of nAChRs (Kunzelmann

2005). Like acetylcholine, nicotine interacts with higher affinity with the heteromeric

nAChRs, particularly the α4β2nAChR, rather than with the α7nAChR7 (Barik and

5

Wonnacott 2006). While the chronic exposure to nicotine desensitizes α4β2nAChRs the

sensitivity of α7nAChR does not change (Kawai and Berg 2001). Therefore, increased

α7nAChR activity might be seen in smokers while α4β2nAChR may not be functional. Since

desensitization does not occur permanently, removal of nicotine re-establishes receptor

activity. Thus, cell lines rather than samples from tumors or tissues from smokers are more

frequently used for studying effects of chronic exposure on desensitized heteromeric nAChRs

(Schuller 2009).

α7nAChR and α4β2nAChRs stimulate various neurotransmitters which act on brain

and also may play important roles in cancer (Fig. 1.1.3). Nicotine modulates the amount of

dopamine whose activity is essential in cognition and memory. In addition, dopamine is

known to induce the proliferation of cancer cells in the prostate and mammary gland (Lang,

Drell et al. 2004). On the other hand, the α4β2nAChR stimulates the release of the

neurotransmitter γ-aminobutyric acid (GABA) that has an inhibitory activity on the brain.

Moreover, GABA functions as a tumor suppressor for adenocarcinoma of the lung, pancreas,

breast and colon (Schuller, Al-Wadei et al. 2008). Glutamate, serotonin, the stress

neurotransmitters adrenaline and noradrenaline, and the neuropeptide mammalian bombesin

are also stimulated by the α7nAChRs (Jull, Plummer et al. 2001). Apart from neurological

functions, these agents also promote the growth of different types of cancers either via

activation of the intracellular signaling pathways or regulation of the release of EGF, VEGF,

or arachidonic acid. On the other hand, α7nAChR regulates the cholinergic control of

immune cells and anti-inflammatory actions of the cholinergic system (Schuller 2009).

Among the CHRN subunits, CHRNA5-CHRNA3-CHRNB4 cluster that exhibits

conserved synteny (Boulter, O'Shea-Greenfield et al. 1990; Eng, Kozak et al. 1991) has

gained recent attention due to association of particularly the CHRNA5 subunit with nicotine

dependency (Greenbaum, Kanyas et al. 2006) as well as lung cancer (Falvella, Galvan et al.

2009; Wang, Cruchaga et al. 2009).

6

Figure 1.1.3. “The diverse functions of the homomeric α7 nAChR and of the heteromeric

α4β2nAChRs” (Schuller 2009).

1.1.3. CHRNA5 (Nicotinic acetylcholine receptor alpha 5)

1.1.3.1. Function of CHRNA5:

A functional CHRN receptor is an ion channel that can either be homomeric or

heteromeric pentamer of a given set of subunits (Lukas, Changeux et al. 1999). Auxial

7

CHRNA5 can form funtional pentamers with α3β4, α3β2 (Conroy, Vernallis et al. 1992) or

with α4β2 CHRNs (Ramirez-Latorre, Yu et al. 1996). The presence of CHRNA5 subunit in

α3β4 or α3β2 receptors may lead to functional changes including increased Ca++

permeability and desensitization (Gerzanich, Wang et al. 1998). Similarly, CHRNA5 subunit

forming a pentamer with α4β2, which is the most abundant CHNR in the brain, increases

conductance of these receptors and leads to a higher rate of desensitization (Ramirez-Latorre,

Yu et al. 1996). Interestingly, CHRNA5 knock-out mice show no anatomical abnormalities in

brain or other tissues; and these mice have normal level of mRNAs encoding other nAChR

subunits (Wang, Orr-Urtreger et al. 2002). On the other hand, CHRNA5 knockout mice

become less sensitive to nicotine-induced seizures and more resistant to the nicotine-induced

hypo-locomotion in specific mouse strains although these mice demonstrate unchanged

epibatidine and α-bungarotoxin binding ability in the brain (Salas, Orr-Urtreger et al. 2003).

The nicotinic acetylcholine receptor subunit alpha 5 mediates short-term effects of nicotine in

vivo (Salas, Orr-Urtreger et al. 2003). Another study implicates cholinergic signaling through

CHRNA5 in modulation of the severity of colitis symptoms since CHRNA5 knockout mice

exhibit severe experimental colitis when compared with wild type controls (Orr-Urtreger,

Kedmi et al. 2005).

1.1.3.2. Polymorphism studies of CHRNA5 in connection with nicotine

dependence:

Recent studies related to the nicotine dependence and lung cancer risks have focused on

the cluster of CHRNA3-CHRNA5-CHRNB4. One of the first studies that revealed an

association of single nucleotide polymorphisms (SNPs) in cholinergic receptors with

smoking phenotypes in women was by Greenbaum et al. (Greenbaum, 2006 #65). The

authors demonstrated that multiple SNPs from a set of cholinergic receptor subunits might be

associated with smoking initiation, nicotine dependence, neuroticism, or novelty seeking

behaviors, and in particular, implicated SNPs in CHRNA7 and CHRNA5 with the severity of

nicotine dependence (Greenbaum, Kanyas et al. 2006). Rigbi et al. analyzed a set of 39 SNPs

8

in 11 different CHRN subunits on a group of female college students identifying CHRNA5

along with others in association with response inhibition, selective-sustained attention. More

recently small or large scale genome wide studies have taken over the scene to study

cholinergic signaling component driven genomic associations in populations that vary in

gender and ethnicity (Rigbi, Kanyas et al. 2008). A non-synonymous SNP of CHRNA5, i.e.,

rs16969968 (D398N), was first identified for being a significant risk variant in developing

nicotine dependence upon exposure to smoking in a large scale yet focused genome-wide

study (Saccone, Hinrichs et al. 2007). Later, Heitjan et al. also identified a SNP (rs871058)

on CHRNA5, located near to the rs16969968, while screening for a pharmocogenetic marker

in the clinical trial of bupropion versus placebo for smoking cessation (Heitjan, Guo et al.

2008). Grucza et al. found that rs16969968 was associated with a protective effect in cocaine

dependence while it was a risk variant in nicotine dependence (Grucza, Wang et al. 2008).

SNPs of CHRNA3, CHRNA5, and CHRNB4 cluster also were associated with early age of

the initiation of both alcohol and tobacco use in young adults of Caucasian and Hispanic

origin (Schlaepfer, Hoft et al. 2008).

A common haplotype in CHRNA5/CHRNA3 from a large scale study investigating

6000 SNPs on close to 2000 genes was associated with predisposition to nicotine dependence

based on the number of cigarettes smoked per day (Berrettini, Yuan et al. 2008). Stevens et

al. compared heavy smokers with light smokers to find out two different groups in the

CHRNA5-CHRNA3-CHRNB4 cluster related to heavy smoking while rs16969968 was

found to be the most likely candidate for the polymorphism responsible for increased risk of

heavy smoking (Stevens, Bierut et al. 2008). Caporaso et al. recently provided further

evidence that intensity of smoking was associated with rs16969968 while Saccone et al.

extended their studies and showed that CHRNA5-CHRNA3-CHRNB4 together with others

were associated with initiation of nicotine dependence (Caporaso, Gu et al. 2009; Saccone,

Saccone et al. 2009). Weiss et al. investigated the relationship between age dependent-

addiction and the CHRNA5-CHRNA3-CHRNB4 cluster in three different populations of

European origins (Weiss, Baker et al. 2008). They showed that specific haplotypes in this

cluster were significantly associated with severity of nicotine dependence in populations

started smoking before 16 but not in those started after 16. Taken together, these studies

9

implicated the CHRNA5-CHRNA3-CHRNAB4, and especially CHRNA5 in establishment

of smoking behavior and addiction while demonstrating a strong genetic component.

1.1.3.3. Polymorphism and expression studies of CHRNA5 in regard to

cancer

Apart from these studies implicating the CHRNA3-CHRNA5-CHNRB4 cluster with

smoking parameters and behaviors, independent studies mapped these loci on 15q25 in

association with lung cancer (Amos, Wu et al. 2008; Hung, McKay et al. 2008; Thorgeirsson,

Geller et al. 2008).

According to Hung et al., rs16969968 was associated with the lung cancers but this

association was independent of nicotine addiction (Hung, McKay et al. 2008). On the other

hand, Amos et al. found out that variation at rs8034191 was associated with both lung cancer

and smoking behavior (Amos, Wu et al. 2008). Evidently, CHRN variants were more directly

related to the lung cancer than the other smoking related cancers since in non-smoking

populations no significant risk was associated with renal and bladder cancers (Spitz, Amos et

al. 2008).

CHRNA5 strongly being implicated as a risk factor for lung cancer development also

accelerated research on its functional attributes using different systems. Earlier studies

focused on the antisense regulation of CHRNA5 and CHRNA3 since these two genes shared

evolutionarily conserved overlapping 3’UTR sequences that might allow for

CHRNA3/CHRNA5 duplex formation as shown in human neuroblastoma SY5Y cells (Solda,

Boi et al. 2005). In support of such a potential regulation, a 30-fold increase in CHRNA5

expression and a decrease in expression of CHRNA3 were found in a group of lung

adenocarcinoma patients and controls from Italy (Falvella, Galvan et al. 2009). In addition,

they found a direct relationship between mRNA expression and allelic composition implying

D398N (rs16969968) SNP with a functional role in lung cancer (Falvella, Galvan et al.

10

2009). In another study, Wang et al. studied with rs16969968 and rs588765 SNPs and found

that D398N amino acid variant in CHRNA5 (rs16969968) led to altered receptor function

rather than the level of mRNA expression; and both rs16969968 and rs588765 were

associated with the nicotine dependence and lung cancers (Wang, Cruchaga et al. 2009).

Bierut et al. investigated the functional role of D398N variant in D398 or N398 transfected

HEK293T cells. Accordingly, N398 variant demonstrated a lower response to nicotine than

D398 nevertheless N398 did not affect the receptor expression (Bierut, Madden et al. 2007).

The association of lung cancer risk variants in CHRNA3 and CHRNA5 with intensity of

smoking and exposure to carcinogenic nitrosamine (NNK) in cigarettes in regard to nicotine

metabolism also have been well established: carriers with a T variant of rs1051730

(CHRNA3) and/or A variant of rs16969968 (CHRNA5) were exposed to higher NNK levels

than noncarriers even if they smoked the same amount (Le Marchand, Derby et al. 2008).

On the other hand, not many studies focused on the role of CHRNA5 expression in

other cancer types. Among them, nicotine exposure to keratinocytes led to up-regulation of

CHRNA5 via the PKC pathway while this up-regulation was transient and followed by

CHRNA7 up-regulation. Since inclusion of CHRNA5 in α3β2 and α3β4 CHRNs increased

the Ca++ permeability, this suggested that up-regulation of CHRNA5 expression may lead to

the intracellular accumulation of Ca++ and up-regulation of CHRNA7 via of CaMKII and

p38 MAPK pathway. This study further demonstrated that the normal differentiation pattern

of kerotinocytes involved the sequential expression of cholinergic receptors with differential

Ca+ permeability (Arredondo, Chernyavsky et al. 2008). In another study, Song et al. found

that CHRNA5 expression was higher in tumor samples from squamous cell lung cancer

(SCC) patients and higher CHRNA5 expression was associated with lesser differentiated

state in SCC cell lines (Song, Sekhon et al. 2008).

1.1.4. Effect of nicotine on cell proliferation:

The proliferative activity of nicotine has been shown in various normal and cancer cells

(Gotti and Clementi 2004; Schuller 2007). Conti-Fine et al. and Zeidler et al. demonstrate

11

that nicotine causes increase in level of growth factors such as BDNF, VEGF, HGF, VEGF-

C, TGF-β, PDGF and TGF-α as well as growth factor receptors such as VEGFR-2, PDGFR,

HGFR and EGFR, thereby, promoting cell proliferation (Conti-Fine, Navaneetham et al.

2000; Zeidler, Albermann et al. 2007). In addition, studies using selective antagonists such as

α –bungarotoxin, hexamethonium and RNAi show that α7nAChR plays a key role in the

proliferative activity of nicotine (Heeschen, Weis et al. 2002; Trombino, Cesario et al. 2004;

Dasgupta, Rastogi et al. 2006).

Figure 1.1.4. Schematic representation of the proliferative activity of nicotine via α7nAChR

in cells. α-BT: α -bungarotoxin; Hex: hexamethonium (Egleton, Brown et al. 2008).

Nicotine’s interaction with α7-nAChR promotes the formation of an oligomeric

complex, including β-arrestin, Src and α7-nAChR, which in turn leads to the activation of Src

(Fig. 1.1.4). Activated Src initiates the MAP kinase pathway and induces Raf-1 interaction

12

with RB. Then, E2F, Rb and Raf-1 come together and make complexes to bind the

proliferative promoters. Established mitogenic signaling causes the removal of Raf-1 and Rb,

leaving free E2F1 on the proliferative promoters. Finally, all these events promote the cells to

enter into the S-phase. In another nicotine-induced mitogenic pathway by α7-nAChR, influx

of Ca++

into the cells initiates the ERK and MEKK1 activation. Consequently, MEKK1 leads

to the activation of NF-kB, which forces cells toward an S-phase entry (Egleton, Brown et al.

2008).

1.1.5. Effects of nicotine on apoptosis:

Several studies show that nicotine leads to resistance against apoptosis induced by

extracellular stress stimuli such as opioids, UV radiation, Ca2+ ionophores, neurotoxins, oxi-

dative stress and anticancer drugs; and such resistance might help induce survival in lung

cancer, oral cancer, and head and neck cancers (Dasgupta, Kinkade et al. 2006; Zeidler,

Albermann et al. 2007). In addition to cancer cells, nicotine can also protect normal cells

such as NHBE cells, airway epithelial cells, endothelial cells, human gingival fibroblasts and

renal epithelial cells against apoptosis induction (Zeidler, Albermann et al. 2007).

The molecular mechanisms of anti-apoptotic activity of nicotine have been investigated

by several groups. Figure 1.1.5 shows one of the best models: nicotine exerts its anti-

apoptotic activity via binding to nAChRs or β-adrenergic receptors (Schuller, Tithof et al.

1999; Trombino, Cesario et al. 2004; Dasgupta, Rastogi et al. 2006). In one route, nicotine

induces the activity of PKC/MAPK pathway and initiates the phosphorylation of Bcl2 and

Bad, thereby inhibiting cell death (Mai, May et al. 2003; Xin and Deng 2005; Xin, Gao et al.

2007). Alternatively, nicotine triggers the activity of PI3K-AKT pathway leading to

phosphorylation of Bax and Bad and upregulation of XIAP and survivin; so it protects the

cell against apoptosis (Dasgupta, Kinkade et al. 2006). In addition to these pathways, nicotine

also stimulates the PKA pathway to protect cells from apoptotic induction (Jin, Gao et al.

2004).

13

Aside from the anti-apoptotic activity, some studies demonstrate the pro-apoptotic

activity of nicotine. Berger et al. show the pro-apoptotic effects of nicotine on

undifferentiated rat hippocampal progenitor cells via induction of p21 and p53 expression

(Berger, Gage et al. 1998). However, nicotine does not induce apoptosis in cells commenced

to differentiate. In another study, after treatment of embryonic rat brain with 1, 10 or 100 μM

nicotine for 48 h, apoptosis takes place (Roy, Andrews et al. 1998). Yamamura et al. find that

nicotine triggers the influx of Ca++

into cells causing cell death in glioblastoma cell lines

(Yamamura, Amano et al. 1998). According to these studies, nicotine may induce apoptosis

in less differentiated embryonic cells especially when cell are treated with higher doses.

14

Figure 1.1.5. Model signaling pathways summarizing the anti-apoptotic activity of nicotine

(Egleton, Brown et al. 2008).

1.1.6. Effects of nicotine on angiogenesis:

Cucina et al., Conklin et al. and Cooke demonstrate that nicotine induces the expression

of endothelial growth factors like bFGF, PDGF-BB and VEGF, and endothelial nitric oxide

15

synthase (e-NOS) in endothelial cells. These findings encourage researchers to investigate the

pro-angiogenic activity of nicotine (Cucina, Sapienza et al. 2000; Conklin, Zhao et al. 2002;

Cooke 2007). Heeschen et al. provide direct evidence for the angiogenic activity of nicotine

in both the cell culture and animal models. They find that nicotine induces the angiogenic

tubule formation in collagen gels, upregulates capillary and collateral growth and leads to

tissue perfusion in ischemic hind-limbs in mouse models. In addition to these, they show that

nicotine promotes angiogenesis in the Lewis lung carcinoma tumors injected to wild-type

mice and increase the neovascularization in the sinus of Apo-E-deficient mice (Heeschen,

Jang et al. 2001; Heeschen, Weis et al. 2002; Heeschen, Weis et al. 2003).

Recent studies implicate that nicotine promotes angiogenesis and tumor invasion in

lung and gastric cancers (Shin and Cho 2005; Shin, Wu et al. 2005; Shin, Wu et al. 2007).

Valenca et al. find that nicotine leads to morphologic alterations in the lungs of Wistar rats

associated with the increased angiogenesis, infiltration of mononuclear cells and irregular

collapse (Valenca, de Souza da Fonseca et al. 2004). Morimoto et al. use a murine excisional

wound model and show that nicotine promotes wound healing in addition to its pro-

angiogenic activity (Morimoto, Takemoto et al. 2008).

Besides the pro-angiogenic activity, several studies suggest that cigarette smoking

might play key role in metastatic spread of cancer (Murin and Inciardi 2001; Murin,

Pinkerton et al. 2004). Alteration of cell adhesion to ECM proteins and disruption of cell–cell

junctions are the important processes of invasion and metastasis. Since the decrease in E-

cadherin and β-catenin with a parallel increase in fibronectin and vimentin is thought to be

one of the indicators of epithelial to mesenchymal transition, findings of Dasgupta et al.

reveal that chronic treatment with nicotine leads to the downregulation of ECM proteins, E-

cadherin and β-catenin, and upregulation of fibronectin and vimentin in MCF7 and MDA-

MB-468 breast cancer cells (Dasgupta, Rizwani et al. 2009). In another study, PKC and

cdc42 have been associated with the nicotine-induced cell migration in MCF7 and MCF10A

breast cancer cells (Guo, Ibaragi et al. 2008).

1.1.7. The association between smoking and breast cancer:

16

The link between smoking and breast cancer risk is a controversial issue. Most studies

find that smoking is not associated with an increased risk for breast cancer whereas some

studies suggest that smoking increases the risk of breast cancer. According to the 2006 US

Surgeon General’s report, The Health Consequences of Involuntary Exposure To Tobacco

Smoke, there are some supporting evidence that smoking increases breast cancer risk yet not

conclusively (ACS 2008).

However, some of the recent studies have indicated that there is a statistically

significant association between the breast cancer risk and smoking. For example, relative risk

(RR) of breast cancer is higher in postmenopausal woman based on a meta-analysis (RR =

1.21, CI = 1.08-1.36) (Khuder, Mutgi et al. 2001). Furthermore, breast cancer risk increases

in women who have continued smoking within 5 years of menarche and smoked at least 20

cigarettes per day (Band, Le et al. 2002). Long-term smoking, estrogen positivity and steroid

receptor coactivator gene AIB1 genotype also effectively increase breast cancer risk (Colilla,

Kantoff et al. 2006). For women who continued smoking while having the disease,

radiotherapy has increased the risk of breast cancer after 10 years (RR = 3.08; 95% CI, 1.61

to 5.91) (Prochazka, Hall et al. 2005).

Nicotine, a nicotinic acetylcholine receptor agonist, modulates multiple cellular

signaling pathways. Although nicotine is the most potent addictive and signal modulatory

component in cigarette smoke, it has not been studied in detail in the context of breast

cancers.

1.2. Breast Cancer:

Breast cancer is one of the most common types of cancer, after the lung cancer, and is

the second leading cause of cancer death among women in the United States. According to

the American Cancer Society 2009 report, breast cancer is the most frequently diagnosed

cancer in women except the skin cancer (ACS 2009). Because of hormone dependency in

17

breast cancer, hormonal therapies together with options that include surgery, radio- and

chemo-therapy have been widely used to treat breast cancer patients (Bai and Gust 2009).

Again, according to American Cancer Society Report, 192,370 new cases of invasive

breast cancers are estimated among women and 1,910 new cases are expected in men during

2009. Incidence of breast cancer has decreased 2.2% per year from 1995 to 2005 (ACS

2009). This decrease implies the success of improved treatment and earlier diagnosis. To

date, the 5-year survival of breast cancer patients is 89% while in 1960s it was 63%. The

survival rate is higher in women diagnosed with localized breast cancer than women

diagnosed with cancer spreading to the other organs (ACS 2009).

Understanding of histological features and molecular alterations in breast cancer is

essential to find novel therapeutic approaches with higher success in curing the disease.

Although breast cancers are extensively heterogeneous, prognostic factors can be classified

based on three main factors according to Tsuda: (1) the extent of (macro- and

microscopically visible) tumor spread, (2) biological properties of the cancer cells, and (3)

host–tumor relationship (Fig. 1.2.1) (Tsuda 2008).

Figure 1.2.1. “Prognostic factors of breast cancer. These can be largely categorized into

factors of: 1 the extent of tumor spread, 2 biological properties if cancer cells, 3 host-tumor

relationship” (Tsuda 2008).

18

Breast cancer incidence rates are affected by many risk factors. Age, gender, genetics,

reproductive history, radiation, socio-economic status, place of residence, and ethnicity are

the best known risk factors in breast cancer (McPherson, Steel et al. 2000; Key, Verkasalo et

al. 2001; ACS 2008). For example, age of menarche is effective; higher risks of developing

breast cancer are observed in women having more menstrual cycles due to the higher

exposure to estrogen and progesterone hormones (McPherson, Steel et al. 2000). Having no

child or first child at a late age increases the risk of breast cancer (Kvale 1992; Albrektsen,

Heuch et al. 1995; Lambe, Hsieh et al. 1996). About 5% to 10% of breast cancer is associated

with the genetic predisposition. The most common inherited mutations in breast cancer

include BRCA1 and BRCA2 (Easton, Ford et al. 1993; Futreal, Liu et al. 1994; Easton, Ford

et al. 1995; Gayther, Mangion et al. 1997) while mutations in ATM, CHEK2, p53 and PTEN

genes also increase the risk for breast cancer development (Starink, van der Veen et al. 1986;

Swift, Reitnauer et al. 1987; Swift, Morrell et al. 1991). Use of alcohol is associated with

increased risk of breast cancer, and this risk is correlated with the amount of consumption

(Key, Verkasalo et al. 2001).

Breast cancer originates from the epithelial cells of the breast. Mostly, it is seen in

women, but in rare cases, men can develop breast cancers, as well (Beyrouti, Beyrouti et al.

2007). Mammary glands are responsible for production of the breast milk; ducts play a role in

carrying milk from the lobules to the nipple; and the fatty and connective tissue, blood

vessels, and lymph vessels are the essential parts of the breast (Fig. 1.2.2). In most cases,

breast cancers start in the ducts (ductal cancer), but sometimes they can be lobular. Several

types of breast cancer exist but ductal carcinoma in situ (DCIS), lobular carcinoma in situ

(LCIS), infiltrating ductal carcinoma (IDC) and infiltrating lobular carcinoma (ILC) are the

most common (Figure 1.2.2). Ductal carcinoma in situ is diagnosed in 25% of the

mammographic screening programs (Bircan, Kapucuoglu et al. 2006). It is a non-invasive

type of cancer and women diagnosed with DCIS can be cured (ACS 2008). Similarly, lobular

carcinoma in situ, also called lobular neoplasia, is classified as a type of non-invasive breast

cancer; women with LCIS may have increased risk of breast cancer (ACS 2008). Invasive (or

infiltrating) ductal carcinoma is the most common type of invasive breast cancer and is

highly heterogeneous.

19

Although factors related the extent of tumor spread and host-tumor relationship are

important to understand mechanisms underlying breast cancer development and prognosis, in

this study, we will focus on biological factors used to classify breast cancers and assign

prognosis, namely, the histological grade and estrogen receptor status.

1.2.1. Histological Grade in Breast Cancer:

Histological grade has been considered as a prognostic factor in breast cancer. The

common histological grading system has been modified from the Bloom and Richardson

grading system by Elston and Ellis, and now it is known as Nottingham combined

histological grade (Elston and Ellis 1990). These grading systems are based on three

morphologic features including percentage of tubule formation, degree of nuclear

pleomorphism and accurate mitotic count in a defined field area (Ignatiadis and Sotiriou

2008). Each morphological feature is then scored between 1-3 (Table 1.2.1) and the total

score determines the grade such that the total scores of 3- 5, 6-7 and 8-9 are considered as

Grade 1 tumor (well-differentiated), Grade 2 tumor (moderately-differentiated), Grade 3

tumor (poorly-differentiated), respectively. In terms of grading system, Grade 1 of IDC is

characterized by lower nuclear grade, and usually hormone receptor–positivity. On the other

hand, Grade III of IDC shows the higher nuclear grade and hormone receptor negativity and

genomic instability (Sims, Howell et al. 2007). Similar to IDC, invasive (or infiltrating)

lobular carcinoma (ILC) can metastasize to other parts of the body (ACS 2008). In addition,

similar Grading from I to III indicates the degree of nuclear grade and receptor positivity for

ILC, as well (Sims, Howell et al. 2007).

20

Figure 1.2.2. The anatomy of human breast (ACS 2008).

Table 1.2.1. Morphological features of breast cancer (imaginis.com)

21

In general, histological grading is a crucial criterion for estimating the risk of

recurrence of invasive ductal carcinoma and the choice of adjuvant therapies (Goldhirsch,

Glick et al. 2005; Goldhirsch, Wood et al. 2007). Since higher grade reflects mitotic activity,

both the histological grade and nuclear grade defined by Tsuda et al. have a common ground

(Tsuda, Sakamaki et al. 1998). Although independent of tumor size or lymph nodal status,

histological grade is a prognostic marker that significantly correlates with HER2

amplification, activation of p53, negativity of estrogen and progesterone receptors, and

accumulation of chromosome alterations (Tsuda 2008).

Gene expression profiling of breast cancer has been extensively studied to identify

prognostic signatures. However, a common prognostic signature is limited by the number of

individual studies that significantly support an mRNA or protein marker or a gene expression

signature, as more recently available through public data resources. In this respect, Sotiriou et

al. performed a large meta-analysis from the publicly available gene expression and clinical

data from 2,833 patients and defined co-expression modules for biological processes

underlying breast cancer development; these processes included proliferation, ER and HER2

signaling (Sotiriou, Wirapati et al. 2006). Accordingly, it is now possible to classify the

breast tumors into prognostically significant 3 main groups, namely ER–/HER–, HER2+ and

ER+/HER2–. In these groupings, proliferation remained the most significant feature of

histological grading in addition to being a powerful prognostic factor. Indeed, ER- breast

cancers usually are characterized with high proliferation indices (Ignatiadis and Sotiriou

2008). Therefore, these findings confirm the correlation of high grade tumor with ER

negativity (Tsuda 2008).

1.3. Estrogen and Estrogen Receptor in Breast Cancer:

Estrogens, a group of steroid compounds, have important roles during the development

and maintenance of normal sexual and reproductive system. They also act on cardiovascular,

musculoskeletal, immune, and central nervous systems in both men and women (Gustafsson

22

2003). 17β-estradiol (E2) is the most potent estrogen produced in the body. Estrogen exerts

its activity via binding estrogen receptors. Estrogen receptor (ER), a ligand-inducible

transcription factor, belongs to the nuclear receptor family and has to dimerize for becoming

active (Heldring, Pike et al. 2007). There are two types of estrogen receptor such as ER-α and

ER-β although many isoforms of these receptors also are present (Heldring, Pike et al. 2007).

Aside from being important in multiple biological processes, estrogens have a key role

in the growth and development of breast cancer. In general, estrogen leads to breast cancer

development via binding to ER-α rather than ER-β (Bai and Gust 2009). Therefore,

determining the ER status is the most essential factor to identify patients eligible for

preoperative or postoperative endocrine therapies. Hormone therapy is generally advised to

treat the ER+/PgR+ patients (Tsuda 2008). Immunohistochemistry (IHC) is mainly used to

evaluate the presence of ER whereas it is also possible to classify cell lines and/or tumors

based on profiling of a cluster of genes primarily or secondarily related to estrogen receptor

mRNA expression (West, Blanchette et al. 2001).

Estrogens and ERs regulate the biological processes in several distinct pathways

(Fig.1.2.3) (Hall, Couse et al. 2001). There are two possibilities, ER acts upon binding of a

ligand, or ligand-independently (Heldring, Pike et al. 2007). When the estrogen bound ER

directly binds to DNA to regulate the gene expression, the mechanism is called classical

(direct) pathway, which is the most known (Kushner, Agard et al. 2000). Besides the classical

pathway, the tethered pathway is where the estrogen binds to ER and then interacts with other

transcription factors; thereby regulation is performed by indirect DNA binding (Saville,

Wormke et al. 2000). The third pathway known as the nongenomic pathway where estrogen

activates a membrane associated receptor, either ER located closer to the membrane, or to a

different receptor, which in turn this binding initiates signaling cascades via second

messengers such as cAMP. These messengers activate ion channels and produce a rapid

physiological response without involving gene regulation (Saville, Wormke et al. 2000;

Simoncini, Mannella et al. 2004; Song, Fan et al. 2006; Song and Santen 2006). On the other

hand, estrogen receptor also can function in ligand-independent manner (the forth pathway in

Fig. 1.2.3). In such cases, activated kinases may activate ER through phosphorylation and

23

through growth factor signaling; then activated ERs dimerize, bind DNA and regulate gene

transcription (Kato, Endoh et al. 1995). This kind of activation of ERs is a novel idea and

may contribute to hormone-independent growth in some tumors (Coutts and Murphy 1998;

Heldring, Pike et al. 2007).

Figure 1.2.3. “Model representing the mechanistically distinct molecular pathways used in

the regulatory actions of ERs” (Heldring, Pike et al. 2007).

Apart from these extensive studies and therapeutic options for the ER-positive breast

cancer, the mechanisms involved in the biology of ER negative breast cancer are unclear. The

common knowledge is that ER negative breast cancer is more aggressive than ER positive

breast cancer; in ER- cancers, growth is independent of estrogen thus the cancer cell is

resistant to hormone therapy (Sheikh, Garcia et al. 1994; Massarweh and Schiff 2006). There

are multiple mechanisms defined for the down-regulation or the loss of ER expression and

ER negative breast cancer development. One of the suggestions is the loss of heterozygosity

or accumulation of mutations in the ER gene locus but LOH or mutations might not always

lead to ER expression loss (Iwase, Greenman et al. 1995; Herynk and Fuqua 2004; Holst,

Stahl et al. 2007). On the other hand, a well-documented possibility is the hypermethylation

24

of CpG islands within the ER promoter. Epigenetic silencing has been found in 25% of ER-

negative breast cancers (Yang, Phillips et al. 2001; Giacinti, Claudio et al. 2006).Other

possibilities include the altered activity of transcription factors acting on the ER promoter

(Castles, Oesterreich et al. 1997; Reid, Denger et al. 2002), mRNA degradation via miRNAs

(Reid, Denger et al. 2002; Adams, Furneaux et al. 2007), postranscritional modification

leading to degradation (Stoner, Saville et al. 2002), and finally silencing at the mRNA and

protein levels via hyperactivity of growth factor receptor (GFR) signaling (Holloway, Murthy

et al. 2004; Creighton, Hilger et al. 2006; Bayliss, Hilger et al. 2007). In addition,

hyperactivity of GFR signaling and its intermediates can lead to resistance to endocrine

therapy (Lopez-Tarruella and Schiff 2007).

Restoring the ER function is one of the most powerful therapeutic approaches to cure

the ER-negative breast cancer patients. One of them is the treatment of ER-negative breast

cancer with histone deacetylase inhibitors or agents inducing DNA demethylation to restore

ER-positivity (Yang, Phillips et al. 2001; Sharma, Smith et al. 2006). Another approach is

using a MAPK blockade to decrease the GFR/MAPK signaling activity (Bayliss, Hilger et al.

2007). After restored ER functions, hormonal therapy might be used to treat the patients.

1.3.1. Hormone Therapy in Breast Cancer:

Anti-estrogens are used to inhibit the physiological activities of E2 to prevent the

development of cancer in hormone therapy. Tamoxifen is the most common anti-estrogen

used in hormone therapy (Clarke, Liu et al. 2003). Although it functions as anti-estrogen in

breast cancer, it might act also as an estrogen agonist in some other tissues including the

bone, uterus, liver, and the cardiovascular system (McDonald and Stewart 1991; Rutqvist and

Mattsson 1993; Nuttall, Stroup et al. 2000; Chlebowski 2005). These agonistic activities

present advantages for the woman taking tamoxifen to enhance the bone maintenance while

protecting the blood-lipid profile and decreasing the coronary risk (McDonald and Stewart

1991; Dewar, Horobin et al. 1992). On the other hand, its agonistic activity stimulates the

endometrial hyperplesia in uterus and may function as a carcinogen in liver (Gal, Kopel et al.

25

1991; Ahotupa, Hirsimaki et al. 1994; Barakat 1996). The mechanism by which Tamoxifen

works is through competition with estrogen to bind the ER. Upon binding, it alters the

conformation of ER and thereby, blocks the ER dimerization and binding to DNA or to co-

activators, or makes the ER favorable to bind co-repressors (Huang, Norris et al. 2002; Shang

and Brown 2002). Therefore, Tamoxifen inhibits the estrogen-induced growth of breast

cancer. Another anti-estrogen used in breast cancer is RAL. Its activity is very similar to

tamoxifen but it blocks the endometrial cancer growth (Swaby, Sharma et al. 2007). Recent

studies indicate that aromatase inhibitors produce better results than tamoxifen treatment (Bai

and Gust 2009).

Despite estrogen-like activity of tamoxifen, pure anti-estrogens, such as fulvestrants, do

not have any agonistic effects. Fulvestrants interact with newly synthesized ER in the

cytoplasm and inhibit their nuclear transportation; the inhibited receptor is then degraded

(Dauvois, Danielian et al. 1992). Despite the initial response to hormone therapy, the tumor

may exhibit recurrence upon long-term therapy where patients become resistant to multiple

therapies. There are two main pathways leading to resistance to therapy: intrinsic (de novo)

and acquired (Hurvitz and Pietras 2008). Genetic polymorphism is one of the reasons for the

intrinsic resistance to tamoxifen however the intrinsic resistance mechanisms need further

study (Beverage, Sissung et al. 2007; Schroth, Antoniadou et al. 2007). On the other hand,

many studies focus on the acquired resistance. Activation of ER-regulated growth factor

pathways independent of steroid control, hyperactivity of GFR signaling and its components,

constitutively active ER mutants or variants are some of the mechanisms to elucidate the

acquired resistance (Fuqua, Wiltschke et al. 2000; Schiff, Massarweh et al. 2003; Schiff,

Massarweh et al. 2004; Normanno, Di Maio et al. 2005). Recent studies focus on the

understanding of the resistance mechanisms and improvement of the endocrine therapy

which is still one of the best therapies for the breast cancer patients (Hurvitz and Pietras

2008).

1.3.2. Cell Culture Cancer Models

26

1.3.2.1. MCF7 Cells as a Model For The Estrogen Studies:

MCF7 is an ER-α positive breast cancer cell line and its growth depends on the

presence of estrogen. In addition, distinct types of clones can be obtained under certain

conditions that alter the estrogen-sensitivity. Highly-sensitive MCF7/ BUS cells, estrogen-

independent MCF7:2A and MCF7:5C cells, and drug resistant MCF7 cells are some of the

examples (Pink, Jiang et al. 1995; Villalobos, Olea et al. 1995; Coser, Chesnes et al. 2003;

Fan, Yan et al. 2006). These modified types of clones help researchers understand the

estrogen signaling and hormone resistance mechanisms.

1.3.2.2. Effects of serum starvation on cells:

Serum starvation is widely used to obtain synchronized cells. Since serum starvation

prevents TOR protein activity and decreases the levels of cyclin D1, it leads to cell cycle

arrest in the G0/G1 phase (Kues, Anger et al. 2000; Cooper 2003; Demidenko and

Blagosklonny 2008). Under serum starved conditions, the entry into S phase does not occur.

If the cell cycle arrest is induced by serum starvation, the DNA synthesis stops (Cooper

2003). Kim et al. suggest that under serum withdrawal, MRPL41, mitochondrial ribosomal

protein L41, leads to cell cycle arrest via increasing the p21 (WAF1/CIP1) and p27 (Kip1)

levels (Kim, Yoo et al. 2005). Shin et al. use SK-OV-3 ovarian cancer cells. They show that

serum starvation induces the G1 cell cycle arrest without causing cell death and decreases

Skp2, CDK2 and CDK4 in protein level. Since Skp2 suppresses the p27 activity, Skp2

indirectly enhances the CDK2 activity (Shin, Hong et al. 2008). Therefore, they suggest that

serum starvation leads to cell cycle arrest via decreasing Skp2, CDK2 and CDK4 proteins

(Shin, Hong et al. 2008). In another study, serum starvation suppresses Cdc6 at both the

mRNA and protein levels in human diploid fibroblasts, and the addition of serum re-

establishes the expression (Williams, Shohet et al. 1997). Since PKCη interacts with cyclin E

in the absence of serum, serum starvation may inhibit the kinase activity of cyclin E/Cdk2

complex via PKCη (Shtutman, Hershko et al. 2003).

27

Besides the cell cycle arrest, serum starvation can trigger apoptosis. Hasan et al indicate

that serum starvation of V79 cells activates the p53, whose activity depends on PKC-α

(Hasan, Adams et al. 1999). On the other hand, Ming et al. use the p53 mutant cell lines to

examine apoptotic effect of serum starvation (Ming, Sakaida et al. 2008). They find that

serum starvation causes the transcription factors SP1 and p73 to bind the promoter of PUMA

and induce the expression through a p53-independent mechanism. In another study, Raf-1 has

a protective role in response to serum starvation in fibroblasts (Mikula, Schreiber et al. 2001).

Thiel et al. suggest that the signaling cascade BDNF → TrkB stimulation → PI3 kinase

activation → activation of AKT is essential to rescue the cell from serum withdrawal induced

apoptosis (Thiel, Ekici et al. 2009). eIF4E, eukaryotic initiation factor 4E, mediates the gene

expression to activate AKT; and AKT activation protects cells from apoptosis while eIF4E

prevents apoptosis induced by serum starvation (Culjkovic, Tan et al. 2008). Lu et al. use

HaCat cells and MEF cells in their studies showing that serum starvation leads to

phosphorylation of H2AX, a variant of the histone H2A family, and p38 (Lu, Shi et al. 2008).

Since blocking p38 abrogates the H2AX phosphorylation and apoptosis, they conclude that

serum starvation induces apoptosis via signaling pathway, p38/H2AX. The role of caspases is

unclear in serum starvation induced apoptosis; and the outcome depends on the cell culture

model system. Haviv et al. identifies caspase-2 as the main activator in PC-12 cells whereas

caspase-12 has an essential role in mouse AKR2-B fibroblasts (Zhu, Cowie et al. 1996;

Haviv, Lindenboim et al. 1998). On the other hand, Schamberger et al. find that caspase-8

and caspase-3 initiate apoptosis in rat 423 cells (Schamberger, Gerner et al. 2004). Taken

together, the role of caspases in apoptosis in response to serum starvation depends on the cell

culture model.

1.4. Microarray Technology:

The discovery of microarray technology is one of the biggest developments in biology

(Pease, Solas et al. 1994; Schena, Shalon et al. 1995). DNA microarrays provide an

opportunity for researchers to carry out large-scale quantitative experiments simultaneously.

28

Alterations in the expression rate of nearly all the genes in a genome, occuring in a particular

tissue or cell type, can be measured in disease states, during development, and in response to

gene disruptions or drug treatments. Profiling of gene expression patterns allow discovering

the mechanisms of disease and identifying disease subphenotypes, predicting disease

progression, providing functional information for the unannotated genes, grouping genes into

functional pathways, and estimating the activities of new compounds (Hughes, Roberts et al.

2000; Waring, Ciurlionis et al. 2001; Chuaqui, Bonner et al. 2002; Lock, Hermans et al.

2002; Ntzani and Ioannidis 2003; McMillian, Nie et al. 2004; Miklos and Maleszka 2004). In

addition to expression profiling, microarrays are designed from genome sequence itself to

discover novel genes, and binding sites of transcription factors, identify the alterations in

DNA copy number, and variations from a baseline sequence, such as in emerging strains of

pathogens or complex mutations in disease-causing human genes (Stoughton 2005). Multiple

microarray studies can also be compared using a relatively relaxed p value and higher fold

changes (Fan, Shi et al. 2009).

Besides the increased use of gene expression profiling, the analysis of such data has

serious problems (Michiels, Koscielny et al. 2005). Statistical knowledge is essential to

perform a valid analysis of microarray data, whereas there are a few statisticians familiar

with the microarray data despite the use of microarrays in many laboratories. Therefore,

biostatisticians of the Biometric Research Branch of the National Cancer Institute develop the

BRB-ArrayTools, which is non-commercial and user-friendly microarray analysis tool. In

addition to normalization of the microarray data, it has many objectives to perform further

analysis. Class comparison is one such methodology and provides the users with ability to

identify the differentially expressed genes among groups of specimens collected from

different types of tissues or under different experimental conditions. Another important

method is the class prediction. It is used to predict the class of a sample based on its

expression profile. Survival risk group prediction is the other important objectives for

standard clinical and pathological prognostic factors. In addition to these objectives, it has

many statistical tools such as cluster analysis, ANOVA, time-series analysis (Simon, Lam et

al. 2007).

29

The development of high-throughput technologies, especially microarray technologies,

provides the researchers to produce the large-scale expression datasets. This development

requires the development of bioinformatics tool to technologies to analyze tens of thousands

of genes simultaneously. WebGestalt, web-based integrated datamining system, developed by

Zhang groups is one of the essential tools to explore large gene sets. It is a user-friendly

bioinformatics tools to help the users for the management, information retrieval,

organization, visualization and statistical analysis of gene sets. It has different types of

methods such as Gene Ontology analysis, pathway analysis, and protein domain analysis

(Zhang, Kirov et al. 2005).

1.4.1. Microarray studies on nicotine-mediated signaling:

There exist a number of microarray studies focusing on different aspects of nicotine

mediated signaling both in a neuronal and non-neuronal context. For example, human

bronchial epithelial cells go through a large-scale change in their transcriptional profiles upon

exposure to 5 μM nicotine (4-10 hours) (Tsai, Chong et al. 2006). Cellular signaling

pathways responsive to nicotine include ERK and interleukin-8 signaling (Tsai, Chong et al.

2006). Saito et al. based on their study of nicotine-induced changes in brain revealed that

multiple MAPK signaling components, GABA receptors and protein phosphatases were

involved. Nicotine also acts differentially on rat brain tissues; genes affected by nicotine play

important roles in PKC, MAPK, NF-kappaB, and ubiquitin/proteosome signaling pathways

(Konu, Kane et al. 2001; Li, Konu et al. 2002; Kane, Konu et al. 2004; Saito, O'Brien et al.

2005). On the other hand, microarray studies performed using PC12 indicate that nicotine

might lead to modulation of several growth factors, heat-shock proteins, ribosomal and

proteosomal subunit expression together with mRNAs involved in mTOR signaling (Konu,

Xu et al. 2004). These studies are supportive of the findings that nicotine modulates

PI3K/AKT/PTEN/TOR signaling pathway elements. Although nicotine’s cellular effects

were studied in neuronal cell lines and tissues, no such study exists for breast cancers.

Considering that nicotine’s effects might be dependent on the state of the cells, i.e., actively

30

proliferating or growth-arrested may provide important insight into the regulation of cell

proliferation/growth related signaling pathways.

31

CHAPTER 2: MATERIALS AND METHODS

2. Materials and Methods

2.1. Materials:

General Reagents: The general laboratory chemicals were supplied from Sigma

Chemical Co. (St. Louis, USA), Merck (Darmstadt, Germany), Stratagene (Heidelberg,

Germany) and AppliChem (Darmstadt, Germany).

Proteins: Protein size marker was from MBI Fermantas (PageRuler Prestained,

SM0671).

Protein transfer materials: Immobilen P transfer (PVDF) membrane was from Roche

(Germany), and 3MM filter paper was from Whatman International Ltd. (Madison, USA).

Photography and autoradiography: The films used for autoradiography were from

KODAK (Rochester, USA) and the development of the films was performed with

Hyperprocessor (Amsderdam, UK).

Tissue reagents and cell lines: Dulbecco’s Modified Eagle’s Medium (DMEM), and

trypsin were obtained from Biochrom (UK); fetal bovine serum, penicillin/streptomycin, and

non-essential amino acid were supplied from Hyclone (Logan, USA). MCF7 is a human

breast carcinoma cell line was obtained from ATCC (HTB-22) and kindly provided by I.

Yulug, at Bilkent University, Dept. Molecular Biology and Genetics.

Kits: The RNA isolation kit was from Promega (Madison, USA; Z3100). ECL plus

western blotting reagent was from Amersham (UK; RPN2132) and SuperSignal West Femto

reagents were obtained from Perbio (UK; 34095). Vybrant MTT cell proliferation kit was

obtained from Invitrogen (USA; V13154).

Microarrays: HG-U133 plus 2 microarrays and reagents were supplied from Affymetrix

(USA).

Treatment reagents: Nicotine was supplied from Sigma (USA; 54-11-5).

32

Antibodies: p53 (BD Pharmingen, USA; 554290), calnexin (Sigma, USA; C4731)

antibodies, and anti-mouse HRP conjugated secondary antibody (Sigma, USA; A0168) were

kindly provided by M. Ozturk at Bilkent University, Dept. Molecular Biology and Genetics.

Bcl-xl (Santa Cruz, USA; sc-8396) and cyclinE (Santa Cruz, USA; sc-481) antibodies were

kindly provided by K. Can Akcali, at Bilkent University, Department of Molecular Biology

and Genetics. Anti-rabbit HRP conjugated secondary antibodies was obtained from Santa

Cruz (USA; sc-2004).

2.2. Solutions and media

2.2.1. General solutions:

10X PBS: 40 gr NaCl, 1 gr KCl, 7.2 gr Na2HPO4 and 1.2 gr KH2PO4 were dissolved in

400 ml dH2O and pH was adjusted to 7.4 with HCl. ddH2O was added to complete the final

volume 500 ml.

2 M NaCl: 58.44 gr NaCl in 500 ml dd . ddH2O.

10% SDS: 100gr SDS was dissolved n 900 ml ddH2O and heated to 68 0

C to assist

dissolution. Then, pH was adjusted to 7.2 and the final volume was completed to 1lt by

adding ddH2O.

2.2.2. Tissue culture solutions:

Growth medium: DMEM was supplemented with 10% fetal bovine serum, 1%

penicillin/streptomycin and 1% non-essential amino acids.

Starvation medium: DMEM was supplemented with 0.1% fetal bovine serum, 1%

penicillin/streptomycin and 1% non-essential amino acids.

Freezing solution: 5% DMSO and 95% growth media were mixed.

33

1XPBS: 1X PBS was prepared from 10X PBS stock solutions and pH was adjusted pH

at 7.4 followed by autoclaving and filtering before use.

Nicotine treatment solutions: 2 μl of nicotine was dissolved in 1248 μl of starvation

medium to make a 10 mM nicotine stock solution. For dilutions, a 10 μM nicotine solution

was prepared from the 10 mM Nicotine stock solution. All studies were performed in the

dark. For each experiment, 10 mM Nicotine stock solution was prepared fresh.

2.2.3. Protein Extraction and Western blotting solutions:

NP40 lysis buffer: 150 mM NaCl, 50 mM Tris.Cl at pH 8.0, 1% NP40 and 1X protease

inhibitor mix were mixed in dd H2O.

Bradford working solution: 10 mg Coomassie brilliant blue was dissolved in 5 ml

95% ethanol and 10 ml 85% phosphoric acid. ddH2O was added to complete the final volume

at 1 lt. Then, the solutions were filtered by using Whatman paper and stored at 4+ 0C in dark.

30% Acrylamide-bisacrylamide solutions: 29 gr acrylamide and 1 gr biacrylamide

were dissolved in 100 ml ddH2O and stored at 4+ 0C in the dark.

10% APS: 0.15 gr ammonium per sulfate was dissolved in 1 ml ddH2O.

1.5 Tris, pH 8.8: 54.45 gr Tris-base was dissolved in 150 ml dH2O and pH was

adjusted with 1 N HCl to 300 dH2O. It was stored at 4+ 0C.

1M Tris, pH 6.8: 12 gr Tris-base was dissolved in 60 ml dH2O and pH was adjusted

with 1 N HCl to 100 dH2O. It was stored in at 4+ 0C.

2X protein loading buffer: 50 mM Tris. HCl at pH 6.8, 2 mM EDTA at pH 6.8, 1%

SDS, 10% glycerol and 0.02% Bromophenol blue solutions.

5X Running Buffer: 15 gr Tris.base, 72 gr Glycine and 5 gr SDS were dissolved in

ddH2O and completed the final volume to 1 lt.

Semi-dry transfer buffer: 2.5 gr Glycine, 5.8 gr Tris base, 3.7 ml 10% SDS, 200 ml

MeOH were dissolved in ddH2O and completed the final volume to 1 lt.

10X TBS: 12.19 gr Tris-base and 87.76 gr NaCl were dissolved in 1 lt of ddH2O.

TBS-T: 0.1% Tween 20 was added into a 1XTBS solution and the pH was adjusted to

7.4.

34

Blocking Solution: 2.5 gr milk powder was dissolved in 50 ml 1X TBS-T (0.1%).

2.3. Laboratory Methods:

2.3.1. Tissue Culture techniques:

Growth conditions of MCF7 cell line: MCF7 breast carcinoma cell line was used as

the cell line model system. MCF7 cell line was derived from 69 year-old Caucasion female

and is a p53 positive and ER+ cell line. MCF7 cell line was maintained in Dubecco’s

Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 1%

non-essential amino acids and 1% penicillin/streptomycin. The cells were incubated at a 370C

incubator with an atmosphere of 5% CO2 in air. The cells were passaged every 3-4 days

before reaching confluence. The growth medium was aspirated and cells were washed with

1X PBS twice; trypsinized; and were dispersed by pipetting with fresh medium before cells

were transferred to a new flask using a given set of dilutions depending on requirements.

DMEM and PBS were kept at 4 + 0C, trypsin was kept at - 20

0C. All solutions were warmed

to 370C before use.

Cryopreservation of cell lines: Exponentially growing cells were harvested by using

trypsin and neutralized by adding fresh growth medium. The cells were precipitated at 1200

rpm for 3 minutes. The cells were then re-suspended with freezing media, i.e., 5% DMSO in

growth medium. Then, cells in the freezing media were transferred into two cyrotubes and

stored at -800C overnight and then transferred to the liquid nitrogen tank for long-term

storage. To re-culture the frozen cells, they were thawed rapidly at 370C and mixed with 3 ml

growth medium; and centrifuged at 1200 rpm for 3 minutes. After removal of the

supernatant, the cells were re-suspended with fresh growth medium. Cells were grown in 75

cm2 flask in the incubator.

2.3.2. Nicotine treatment protocol:

35

Time- and dose-dependent nicotine treatment experiments: Cells were harvested by

trypsinization; neutralized by adding fresh media; and were counted. For 2 days treatment,

5000 cells/ well; for 3 days, 3000 cells/well; for 5 and 7 days, 1000 cells/well were seeded to

the 96 well-plates. There were four conditions: control (no treatment), 0.1, 1 and 10 μM

nicotine treatment and each condition consisted of 4 replicates. The cells were incubated for

24 h to attach the surface of the plate. After 24 h, the cell media were removed and cells were

incubated in the starvation media for 24 h to obtain quiescent cells. For the 10% serum

treatment, the treatment media (0.1, 1 and 10 μM nicotine in media) were prepared with

growth media and for the 0.1% serum treatment, media with 0.1% FBS were used for all

experimental groups (0.1, 1 and 10 μM nicotine in media). After obtaining quiescent cells, all

media were removed and replaced with freshly prepared treatment media for the 2 days and 3

days experiments. For 5 days and 7 days experiment, first, cells were incubated with the

treatment media for 3 days and then the media were replaced with freshly prepared treatment

media. The cells were then incubated in fresh media for 2 days (5 days experiment), or 4 days

(7 days experiment).

MTT proliferation assay: To measure the cell proliferation, Vybrant MTT cell

proliferation kit were used. 1 ml of 1X PBS was added to the each tube and mixed well. The

treatment media was removed; 100 μl of starvation medium was added to cells in each well

followed by addition of 10 μl of MTT solution. The cells were incubated for 4 h; the media

with MTT solution was removed and 100 μl DMSO was added. After 10 min of incubation,

the plates were read in Elisa-reader (Company, City) at 540 nm wavelengths. During

analysis, blank wells were used as background and an averaged blank reading value was

subtracted from the values obtained from treatment wells.

Nicotine treatment for RNA and protein experiments: Cells were harvested upon

trypsin treatment and neutralized by adding fresh media; and then counted. For the 10 %

serum conditions, 1.2 X10^6 cells were seeded to the 75 cm2 flask in growth media and were

incubated for 24 h for attachment. Then, the cells were incubated in the starvation medium

(0.1% FBS) for another 24 h. After synchronization, the treatment media were added (growth

36

media + 10% FBS for control cells or growth media + 10% FBS + 1μM nicotine) and cells

were incubated in the treatment medium for another 48 h. On the other hand, for the 0.1%

serum treatment, 2X10^6 cell were seeded to the 75 cm2 flasks and incubated in the growth

media for 24 h for attachment followed by 24 h serum starvation for obtaining quiescent

cells. The 48 h treatment medium contained growth media supplemented with 0.1% FBS, and

0 or 1 μM nicotine. Upon completion of the experiments, cells were scraped in ice-cold 1X

PBS; precipitated at 1200 rpm for 3 min; and the cell pellets were stored at -800C for further

experiments. Each experiment included 3 biological replicates for the RNA and protein

extractions.

2.3.3. RNA experiments:

Total RNA isolation: Total RNA isolation was performed using Promega SV Total

RNA isolation kit according to the manufacturer’s instructions. The RNA was eluted in a

total volume of 50 μl. The concentration of the isolated RNA and the ratio of absorbance at

260 nm to 280 nm were measured with the Nanodrop ND-1000 spectrophotometer

(NanoDrop Technologies, Montchanin, USA). If the concentration of RNA was found less

than 625 ng/μl the samples were vacuum dried to concentrate the RNA since 5 μg RNA in 8

μl was needed for the microarray experiments. The integrity of isolated RNA samples was

measured with the Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Isolated RNA

samples were stored at -800C.

2.3.4. Protein experiments:

Protein extraction: The cell pellets were lysed in the NP40 lysis buffers by vortexing

every 5 min for half an hour on ice. Then, they were centrifuged at 13000 rpm for 30 min at

4+ 0C; the supernatant was collected and stored at -80

0C for Western blotting experiments.

37

Quantification of protein concentrations: The concentration of a cell lysate was

measured by Bradford assay. 2 μl of each sample or lysis buffer (for the blank) was mixed

with 98 μl of ddH2O and then 900 μl of Bradford working solution was added to the sample

and mixed well. After 5 min of incubation, the protein concentrations were measured at

OD595 nm versus the blank reagent. For the standard curve, 1 mg of BSA was measured and

dissolved in 1 ml of ddH2O. Then, 0, 1, 2, 4, 8, 16, and 32 μl of BSA solution was mixed

with 100, 99, 98, 96, 92, 84 and 68 μl of ddH2O, respectively and 900 μl of Bradford working

solution was added to the sample and mixed well. After 5 min of incubation, the OD595 nm

values were measured; and the standard curve was plotted with these values. The unknown

concentrations of the samples were calculated from the standard curve.

Western blotting:

SDS polyacrylamide gel electrophoresis: Resolving gel (12% polyacrylamide gel; 15

ml) was prepared as below:

dH2O 4.9 ml

30% Acrylamide mix 6.0 ml

1.5 M Tris, pH=8.8 3.8 ml

10% SDS 15O μl

10% APS 150 μl

TEMED 6 μl

Stacking gel (5% polyacrylamide gel; 8 ml) was prepared as below:

dH2O 5.5 ml

30% Acrylamide mix 1.3 ml

1.0 M Tris, pH=6.8 1 ml

10% SDS 80 μl

10% APS 80 μl

TEMED 8 μl

Equal amount of proteins were mixed with the 2X loading buffer containing 1% β-

mercaptoethanol and boiled for 5 min to denature the proteins. Then, the samples and the

Fermentas PageRuler Protein size marker were loaded on gel. Gels were run at 80 V for the

stacking gel and 120 V for the resolving gel in running buffer.

38

Protein transfer to solid support: Since all proteins were smaller than 120 kda, semi-

dry transfer was performed. Whatman paper and the PVDF membrane were cut at suitable

sizes. The membranes were incubated in 100% methanol for 30 seconds and then, incubated

with ddH2O for 2 min. After that, Whatman papers and membrane were incubated in the

semi-dry buffer for 5 min. Then the following arrangement shown in Figure 2.3.1 was

applied to transfer proteins.

Figure 2.3.1. The schematic representation of semi-dry transfer (Fermentas).

Immunological detection of immobilized proteins: After transfer, the membrane was

immersed in the 5% blocking solution for an hour on a slowly rotating platform to inhibit

non-specific binding. Upon blocking, the membrane was washed with TBS-T (0.1%) three

times, each for 10 min. Each antibody was diluted in the blocking solution at different

concentrations: calnexin: 1/500, Bcl-xl: 1/500, cyclin-E: 1/200 and p53: 1/1000. The

membrane was incubated with the primary antibody solutions for overnight at 4+ 0C on a

slowly rotating platform. The membrane was washed with TBS-T (0.1%) three times, each

for 10 min. The appropriate HRP-conjugated secondary antibody was diluted in blocking

solution (1:5000, as recommended by supplier). The membrane was incubated with the

secondary antibody solutions for an hour at room temperature again on a slowly rotating

39

platform; washed with TBS-T (0.1%) three times, each wash for 10 min. The membrane was

treated with ECL plus reagent (or Femto reagents depending on the protein signal) according

to the manufacturer’s instructions and then wrapped with stretch film. The autoradiography

was performed for various exposure times.

2.3.5. Microarray analysis:

Data normalization: All microarray experiments were carried out on Affymetrix U133

plus 2 GeneChips. 5 μg RNA in 8 μl was used for each sample. Amplification, labeling and

hybridizations were done under the supervision of Asssociate Prof. Işık Yuluğ by the

Genomics Core Facility assistant according to the manufacturer’s protocols. Affymetrix

analysis software (GCOS) was used to perform the preliminary probe-level quantification of

the microarray data for initial analysis including quality control. These data were further

normalized with the RMA normalization method using the BRB-Array tools, 3.7 Version.

The default option of RMA (with background correction, quantile normalization, and log

transformation) was used to generate the normalized intensity for each probeset. Quality

control of the arrays were performed using an option in BRB-Array tools, which provide

measurements on the RNA degradation parameters, and factors such as scaling factor and

background.

Selection of differentially expressed genes: Differentially expressed genes were

identified at each time point separately using the untreated 0.1% serum or 10% serum

controls against 0.1% serum 1 μM nicotine-treated samples or 10% serum 1 μM nicotine-

treated samples, respectively. To find the differentially expressed genes, BRB-Array Tools

were used for all analysis. A 1.2 fold change was used as the cut-off for 10% serum

experiment analysis. Unpaired-class comparison analysis with/without blocking batch effect

(i.e., different days of hybridization) were performed at the p<0.05. After that, the common

probe ID’s to both paired and unpaired class comparisons were used for further analysis.

40

Due to presence of a single replica of each condition in the 0.1% serum treatment

experiment, a 1.5 fold change value was used as the cut-off to determine a potential list of

differentially expressed genes. On the other hand, when comparing the gene expression of

10% serum control samples to the 0.1% serum control or to the 1 μM nicotine-treated

samples, this probeset list from the 0.1% serum analysis was used as a focused gene-set.

Focused gene sets have been frequently used in microarray meta-analysis to test the change

of expression for a given set of genes, which may be unique to a biological process or altered

under a particular treatment. Then, these probesets were separated into two groups: 1) probes

whose expression in response to 0.1% serum 1 μM nicotine-treatment with similarities to

those from 10% serum control samples rather to the 0.1% serum control samples; and 2)

probes whose expression in response to 0.1% serum 1 μM nicotin-treatment behaved unlike

of the 10% serum control samples.

Gene Ontology (GO) Analysis: After determining the differentially expressed

probesets, the GO analysis was performed to find the biological processes that were altered

via nicotine treatment and to assign potential function to the probesets differentially

expressed. To perform the GO analysis, Webgstalt (WEB-based GEne SeT AnaLysis

Toolkit; http://bioinfo.vanderbilt.edu/webgestalt) was used. When performing the GO

analysis, hyper-geometric tests were used at p<0.05 and presence of two genes in each

category was selected as a minimum requirement. GO levels 4, 5 or 6 were chosen for the

biological level and molecular functions graphics. If the groups were significant according to

selected criteria, the significant categories were shown in red color. In addition to GO

analysis, Biocarta and KEGG pathway analyses were performed using similar protocols.

Cluster analysis: Cluster analysis is commonly used to identify the degree of

association between objects. If the degree of association is high between the objects, they are

put into the same group, and if the degree of association is low between the objects, they are

categorized separately. During the cluster analysis, median gene centering was used before

hierarchical clustering: Genes and arrays were clustered; un-centered correlation coefficient

was calculated to find the similarity matrix; and finally, average linkage method was chosen

for the analysis. Cluster analysis was performed using the Cluster 3.0 software program and

the image analysis was done by using Java TreeView software (Saldanha 2004).

41

2.4. Microarray Meta-analysis

2.4.1. Preparation of expression data:

Publicly available datasets were collected from Gene Expression Omnibus (GEO)

(Tables 2.4.1 and 2.4.2). To eliminate the heterogeneity of different microarray platforms, we

used only Affymetrix HG-U133A and HG-U133 plus 2 platforms because they both have a

single probeset for CHRNA5, 206533_at.

Table 2.4.1. Publicly available gene expression data from estrogen studies on cell lines

.

42

Table 2.4.2. Publicly available gene expression data from primary breast cancer studies.

2.4.2. Features of the breast cancer microarray experiments:

Clinical information of breast cancer data sets used in our analysis was presented in

Table 2.4.3.

43

Table 2.4.3. The distribution of data according to ER, grade and treatment status.

Some datasets from GEO or arrays from the datasets were not analyzed herein for the

following reasons indicated below:

GSE1456 performed by Pawitan et al. was eliminated because it did not have

information about the samples either from untreated or treated patients (Pawitan, Bjohle et al.

2004).

GSE6532 Loi et al. had 741 arrays in three different platforms: HG-U133A, B and

plus 2. Due to the absence of probeset for CHRNA5, data performed in HG-U133B was not

analyzed. Dataset performed in HG-U133 plus2 was from ER+ Tamoxifen treated patients.

Data was analyzed but the average absolute expression value of CHRNA5 was lower than 16,

which is not detectable by PCR, so it was not included in our analysis (Loi, Haibe-Kains et

al. 2007; Loi, Haibe-Kains et al. 2008).

44

GSE4922 Ivshina et al. had 578 arrays performed in two different platforms: HG-

U133A and HG-U133B. Similar to GSE6532, the dataset in HG-U133B was not analyzed.

On the other hand, NUH-sample series were eliminated due to the absence of information

about ER status and treatment (Ivshina, George et al. 2006).

GSE5460 Darkee et al. had 129 arrays but 35 arrays could not read from BRB-

ArrayTool. Therefore, the analysis was performed with only 94 arrays (Dairkee, Seok et al.

2008).

2.4.3. Experimental features of the in vitro public microarray experiments:

GSE3529: Three different ER-α positive estrogen dependent MCF7, T47D and BT-474

breast cancer cell lines were used. Before the estrogen treatment, cells were washed and

grown in steroid depleted phenol red-free IMEM media supplemented with 10% charcoal

stripped FBS for three days. After that, cells were treated with E2 for 24 hours (Rae, Johnson

et al. 2005).

GSE11791: MCF7 cells were grown in media supplemented with 10 nM ICI 182780

for 48 hours before the treatment. Then, cells were treated for 6h with either 100 nM E2 or

ethanol vehicle. As in the GSE11467, this data set contains different treatments in addition to

E2, which were eliminated from further analysis in the present study (Musgrove, Sergio et al.

2008).

GSE4668: MCF7/BUS cells were maintained in DMEM supplemented with 5%FBS.

Before the treatment, cells were washed three times with phenol red-free DMEM

supplemented with 5% charcoal-dextran stripped FBS and then grown in the presence of

varying concentrations of E2 (0 pM, 10 pM, 30 pM, 60 pM, and 100 pM) for 48 hours. In

another experiment, MCF7/BUS cells were subjected to hormone starvation for 0, 1 or 2 days

(Coser, Chesnes et al. 2003).

GSE11324: MCF7 cells were grown in a hormone-deprived media for 24 hours before

the treatment. Then, cells were stimulated with 100 nM estrogen for 0, 3, 6 or 12 hours

(Carroll, Meyer et al. 2006).

45

GSE11352: MCF-7 cells were grown to 80% confluence in D-MEM/F-12

supplemented with 10% FBS. Cells were switched to phenol red-free D-MEM/F-12 medium

supplemented with 0.5% charcoal-dextran stripped FBS for 24 h before the 10nM 17β-

estradiol (E2) was provided for 12, 24 and 48 hours in a time-course treatment (Lin, Vega et

al. 2007).

GSE8597: MCF-7 breast carcinoma cells were maintained in a-minimal Eagle’s

medium (a-MEM) supplemented with 10% fetal bovine serum (FBS). Before the treatment,

cells were grown in phenol red-free Dulbecco’s modified Eagle’s medium (DMEM)

containing 10% charcoal-treated FBS, 1% sodium pyruvate, 1% penicillin/streptomycin and

1%L-glutamine for 3 days. The day before hormonal stimulation, the medium was switched

to phenol red-free DMEM supplemented with 0.5% charcoal-treated FBS. Pretreatments

cycloheximide (10 mg/ml, Sigma) were started 1 h before hormonal treatment with/without

17b-estradiol (E2, 25 nM, Sigma), or vehicle (0.1% ethanol). Treatments were done for 24

hours (Bourdeau, Deschenes et al. 2008).

GSE11467: MCF-7 cells were treated with 10 nM E2, 10 ng/ml TNFa, or both for 2h.

Before analysis, the TNF-α with/without E2 treated samples were eliminated. E2 treated and

control samples were used (Frasor 2008).

GSE5840: Establishment of tamoxifen-(OHT-resistant-MCF7) and fulvestrant-(ICI-

resistant MCF7) resistant sublines: Three different media were used: a growth medium, a

hormone-free medium, and a basal medium. Figure 2.4.1 shows how to establish these

sublines. Wild-type MCF7 cells maintain were in the growth medium. OHT- resistant MCF7

cells were maintained in hormone-free medium supplemented with 10-7

mol/L OHT to

establish the tamoxifen-resistant subline, designated as MCF7-T. Finally, ICI-resistant cells

were maintained in the hormone-free medium supplemented with 10-7

mol/L fulvestrant to

establish the resistant subline, namely MCF7-F. Cells were continuously cultured under these

conditions for 12 months (Fan, Yan et al. 2006).

46

Figure 2.4.1. Establishment of OHT-resistant, ICI-resistant MCF7 cells and maintenance of

wild-type MCF7 cells (Fan, Yan et al. 2006).

GSE10879: Wild-type MCF7 cell and long-term estrogen deprived MCF-7:5C and

MCF-7:2A breast cancer cells lines (which are resistant to estrogen-deprivation; aromatase

inhibitor resistant) were used. These cells were grown in estrogen-free media (phenol red-

free RPMI medium supplemented with 10% 4X dextran-coated charcoal-treated fetal bovine

serum) until they were 70-80% confluent. In the result part, more information about

MCF7:5C and MCF7:2A will be given (Lewis-Wambi, Cunliffe et al. 2008).

2.4.4. Microarray analysis:

GEO datasets to be used in the study were divided into two groups, one dealing with

cell line studies, the other with primary human breast tumors (Tables above, Table 2.4.1.1

and 2.4.1.2). Furthermore, the datasets were normalized with RMA using BRB-tool which

47

uses Bioconductor R packages when raw .cel files were available, if unavailable, the series

matrix files were used and global median normalization was performed over the normalized

data for each GSM, separately.

2.4.5. Analysis of Data:

After the normalization step, class comparison analysis with random variance was

performed by using the BRB-tool (Simon, Lam et al. 2007) and the expression value of

CHRNA5 was exported from BRB Array Tools. In in vitro studies, student’s t tests were

used to calculate the level of differential expression at each treatment relative to the control.

In addition, one-way ANOVA and Fisher’s exact test was applied to suitable datasets

(Minitab 15). On the other hand, in breast cancer tumor studies, first, box-plots of samples

were performed to determine the outliers with expression values greater than 1.5 times the

interquartile range (IQR) away from the top or bottom of the box. In general, the fold-change

was not higher than 1.5 but outliers were 2-4 fold higher expression than the control

conditions. Therefore, the outliers were eliminated. Then, a t-test was used to calculate the

level of differential expression at each condition relative to the control condition. In ER

status studies (GSE3494, GSE5460, GSE2034, GSE6532 and GSE4922), the mean value of

ER+ samples was assumed as control samples, mean value was equalized to one and the fold-

change difference was calculated. In grade, studies involving grade with ER status, and the

therapy status, grade 1, ER+ grade 1 and untreated (ER+) grade 1 were assumed as the

control samples and a similar method in ER+ study was used to find the fold changes.

2.4.6. Regression Analysis:

Regression analysis is a commonly used statistical tool to explore the relationships

between variables. In our analysis, to determine the dose and time effects of estrogen,

regression analysis of GSE4668, GSE11324 and GSE11352 was performed. GSE4668 was

48

divided into two groups i.e., dose-dependent and time-dependent hormone starvation.

Polynomial regression analysis was performed via Minitab 15 for these data analyses. On the

other hand, linear regression analysis was performed for GSE11324 and GSE11352 datasets.

2.4.7. Analysis of Co-expressed Genes:

Probesets correlated positively or negatively with CHRNA5 at r = 0.7 were identified

from GSE8597, GSE11352, GSE11324, GSE11791, GSE4668 and GSE5840, individually.

GSE3529 and GSE11467 were not involved in these analysis because GSE3529 consisted of

three different cell lines and CHRNA5 was not altered in GSE11467 due to the short-time

estrogen exposure. After correlation analysis, the common probes were determined from

these data via BRB ArrayTool intersect genelist objectives. After determining the correlated

genes, GO analysis and pathway analysis were performed using Webgstalt. On the other

hand, Binary Tree class prediction analysis was performed via K-nearest neighbours (k= 3)

method, under p<0.05 to investigate the prediction ability of this genelist in terms of ER

status and grade status.

2.5. Statistical Analysis of Cell Proliferation:

In MTT assay, the significance of each treatment compared with the control was

calculated with ANOVA followed by Fisher’s Multiple Comparisons (Minitab 15).

49

CHAPTER 3: AIMS AND STRATEGY

Breast cancers commonly occur in women from multiple genetic and sporadic

backgrounds. Recent high-throughput studies revealed that the disease is heterogeneous, and

is associated with multiple risk factors one of which is smoking. However, the association

between smoking and breast cancer is not investigated in detail; furthermore it is

controversial. On the other hand, recent studies implicated nicotine, an agonist of nicotinic

acetylcholine receptors, with increased proliferation, anti-apoptosis, angiogenesis, and

metastasis, yet mostly in lung cancers. Recent studies also focus on gene expression profiling

of these cholinergic receptors, as well as on mechanisms through with the receptors act on

signaling pathways, including PI, MAPK, RAF1, and others. Most recently, CHRNA5, the

nicotinic acetylcholine subunit alpha 5 have been associated with nicotine addiction as well

as cancer development and became the focus in the field. However, the molecular effects of

nicotine on breast cancer cells under starvation or the expression of cholinergic receptors

and/or association of these with breast cancer are not clear or well studied to our knowledge.

Microarray technologies allow for characterization of the whole transcriptome under specific

treatments and conditions. Nicotine treatment has been studied in the context of neuronal cell

line and bronchial epithelial cells, however, breast cancer cell lines have not been studied in

this context; and we do not have any insight into the global regulation of transcription in

breast cancer cells when exposed to physiological concentrations of nicotine (in the range of

90nM-1μM).

Accordingly, our aim was to determine the proliferation and apoptotic response to

different doses of nicotine at different exposure times using cellular assays and molecular

markers. We also induced serum starvation stress to assess whether nicotine exposure was

able to rescue cells from growth arrest/apoptosis. Furthermore, we used microarrays to

profile expression under 1 uM nicotine treatment with or without serum to identify a set of

differentially expressed genes and to characterize generalized pathways involved in

nicotine’s effects on MCF7 breast cancer cells. Furthermore, we performed a large scale

meta-analysis of existing publicly available microarray datasets (in vitro and in vivo)

obtained from GEO, to assess the involvement of estrogen, estrogen receptor status, and

50

estrogen signaling on expression of CHRNA5, a gene recently identified as important in

nicotine addiction and lung cancer. Our aim was to predict the estrogen receptor and grade

status of primary breast tumors using CHRNA5 and its correlated partners, as the candidate

gene-list.

In summary, in the present thesis, I have tried to answer the following questions using

cell culture, microarray technology, statistics, and meta-analysis methods:

1) Does nicotine dose- and time-dependently affect MCF7 cell line proliferative and

apoptotic status?

2) What is the extent of transcriptional modulation by 1 μM nicotine for 2 days at

10% and 0.1% serum conditions?

3) What signaling pathways and Gene Ontologies, the differentially expressed genes

belong to for giving insights into further research with nicotine on breast cancers?

4) Is CHRNA5 expressed in ER+ and ER- breast cancer cell lines and tumors

differentially?

5) Is tamoxifen effective on CHRNA5 expression?

6) Is CHRNA5 expressed in Grade I, Grade II, and Grade III breast tumors,

differentially?

7) Do CHRNA5 and its correlated gene set module obtained from in vitro microarray

studies accurately predict primary tumor ER and Grade status?

51

CHAPTER 4: RESULTS

4.1. Nicotine-mediated Signaling Pathways via Microarray Experiments in MCF7 Cell

Line

4.1.1. The dose-and time-dependent effect of nicotine on breast cancer cell

proliferation:

Nicotine promotes the cell growth in different cancers such as lung, gastric and colon

cancer (Heeschen, Jang et al. 2001; Trombino, Cesario et al. 2004; Ye, Liu et al. 2004; Shin

and Cho 2005). To investigate the effect of nicotine on breast cancer cells, we treated the

MCF7 cells with different doses of nicotine for different periods of exposure times: 0.1, 1

and 10 μM for 2, 3, 5 and 7 days. Each MTT assay was performed once or twice and a

representative set was shown in Figure 4.1.1 for each condition. We showed that nicotine did

not promote the MCF7 cells proliferation/growth in a either dose-dependent or time-

dependent manner in the presence of 10% serum although 3 days treatment exhibited a

slightly significant decrease.

52

Figure 4.1.1. Time- and dose-dependent effects of nicotine treatment on MCF7 cell

proliferation/growth at 10% serum. MCF7 cells were treated with different dose of nicotine:

0.1, 1 and 10 μM for 2, 3, 5 or 7 days. A) 2 days; B) 3 days; C) 5 days; D) 7 days. * indicates

p<0.05 determined by one-way ANOVA.

In complete media condition, nicotine may not assert its growth promotion. Therefore,

to further examine growth promotion effect, MCF7 cells were treated with nicotine under

serum-starved conditions (0.1% serum in medium) in same doses and for the same periods.

Each MTT assay was performed once or twice and results in the Figure 4.1.2.A, B and C

included a representative set. On the other hand, due to the high variability in one of the day

7 experiment sets, the set demonstrating significance at 1μM nicotine treatment was shown

(Figure 4.1.2.D). Like before, as in the Figure 4.1.2 A and B, either 2 days or 3 days

treatment with nicotine did not induce cell growth. On the other hand, at day 5 (Fig. 4.1.2 C),

nicotine significantly increased the cell growth at all doses, similarly. At day 7, nicotine

significantly increased cell growth at 1 μM concentration (Fig. 4.1.2 D). These results

53

indicated that under serum starvation conditions, nicotine may induce the cell

proliferation/growth at 5 days and to a lesser degree at 7 days.

Figure 4.1.2. Time- and dose-dependent effects of nicotine treatment on MCF7 cell

proliferation/growth at 0.1% serum. MCF7 cells were treated with different dose of nicotine:

0.1, 1 and 10 μM for 2, 3, 5 or 7 days. A) 2 days; B) 3 days; C) 5 days; D) 7 days. * indicates

p<0.05 determined one-way ANOVA.

4.1.2. Nicotine modulates Cyclin E protein expression in a serum-dependent

manner:

Serum starvation leads to cell cycle arrest whereas nicotine is known to promote the

cell cycle leading to cell proliferation (Dasgupta, Rastogi et al. 2006). Therefore, cells were

treated with 1 μM nicotine under 10% serum or 0.1% serum conditions and the protein levels

of cyclin E levels, an important regulator of cell cycle progression, were measured using

Western blotting.

54

Figure 4.1.3.Cyclin E expression in response to nicotine treatment under different serum

conditions. MCF7 cells were treated with 1 μM nicotine treatment under different serum

conditions for 2 days and calnexin was used as a loading control.

Nicotine reduced the cyclin E levels at 10% serum treatment but led to an increase at

0.1% serum levels (Fig. 4.1.3).

4.1.3. Nicotine modulates p53 and Bcl-xl protein levels in a serum-dependent

manner:

In addition to nicotine’s proliferative effect, nicotine also is known to lead to resistance

to apoptotic stimuli (Dasgupta, Kinkade et al. 2006). Serum starvation on the other hand, may

result in apoptosis. Dasgupta et al. showed that nicotine inhibited the induction of p53 upon

chemotherapeutic drug stimulation (Dasgupta, Kinkade et al. 2006). In addition, Bcl-xl, a

known member of the anti-apoptotic Bcl-2 family, might indicate the status of apoptosis in

serum starved nicotine treated cells. Therefore, to examine the effect of nicotine upon serum

withdrawal induced apoptosis in MCF7 cells, cells were treated with 1 μM nicotine in 10%

serum or 0.1% serum conditions for 2 days; and the protein level of p53 and Bcl-xl were

measured. Serum starvation led to p53 induction and nicotine slightly reduced the protein

level of p53 (Fig. 4.1.4A). On the other hand, nicotine induced Bcl-xl at both serum

conditions even though the induction was slightly higher than untreated cell grown under

0.1% serum (Figure 4.1.4B). Taken together, these results indicated that nicotine might

inhibit the serum withdrawal induced apoptosis.

55

Figure 4.1.4. The anti-apoptotic effects of nicotine under different serum conditions. A) p53

B) Bcl-xl. MCF7 cells were treated with 1 μM nicotine treatment under different serum

conditions for 2 days and calnexin was used as a loading control.

4.1.4. Genome-wide profiling of nicotine exposure in MCF7 cells:

It was observed that 1 μM nicotine treatment was statistically significant at both the 5

days and 7 days treatment for recovery of the cell growth (Fig 4.1.2 C and D). Furthermore,

reports indicated that 1μM nicotine closely mimics the physiological concentration of

nicotine in the blood of smokers (Chu, Guo et al. 2005). Accordingly, 1 μM nicotine was

chosen as the dose to profile expression in MCF7. In addition, we wanted to examine the

relatively early effects of nicotine in breast cancer. Accordingly, medium supplemented with

10% serum as physiological conditions and the media supplemented with 0.1% serum as

serum-starved conditions were studied with or without nicotine exposure for 2 days.

56

4.1.5. RNA and Microarray data quality assessment:

Testing of the purity and integrity of the RNA is the most crucial step to obtain high

quality microarray datasets. Therefore, RIN (RNA integrity number) values of samples were

measured using BioAnalyzer before microarrays were hybridized (Fig. 4.1.5).

Figure 4.1.5. RNA integrity graphs of microarray samples. A and B represent samples

grown in media with 0.1% serum. C, D, E, and F represent samples grown in media

supplemented with 10% serum. A, C and E are the control samples whereas B, D and F are

the 1μM nicotine treated samples.

57

Table 4.1.1. Description table of the microarray samples and RIN values.

According to the graphs, RIN values obtained ranged between 9.0 and 9.6 indicating

high quality RNA samples (Table 4.1.1). Apart from the pre-hybridization RNA quality,

post-hybridization quality also is important. BRB-Array tool allows for assessment of the

correspondence between the samples after hybridization. The RNA samples described in

Table 4.1.1 all had comparable RNA degradation profiles, and other parameters that help

identify sample outliers, such as background, scaling factor, housekeeping 3’/5’ ratios.

The blue horizontal lines in Figure 4.1.6 represented the scale factor. If the scaled

factors were out of range, horizontal lines became red. The blue circles and blue triangle

points represented GAPDH and B actin 5’/3’ ratios, respectively. Like the scale factor, if

these values fell out of range, they were marked with red color. Our findings indicated that

GAPDH and B-actin ratios and scale factors of the RNA samples were all within range, and

microarray data were good quality (Table 4.1.2).

58

59

Figure 4.1.6. Quality control plots of microarray samples. The order of the samples from top

to bottom. A) R6-Konu and R5-Konu B) R8-Konu, R7-Konu C) R9-Konu, R10-Konu (see

Table 4.1.1 for descriptions).

Table 4.1.2. Data associated with Figure 4.1.6.

60

Figure 4.1.7. RNA degradation plots of microarray experiments. A) R5-R6 B) R7-8

C) R9-R10

61

RNA degradation graphs indicated that all samples had similar slopes (Fig. 4.1.7)

indicating they were comparable. Accordingly, all these data demonstrated that microarray

experiments were highly qualified for further data analysis.

4.1.6. Nicotine’s effect on MCF7 transcriptome under the physiological serum

conditions:

To examine the acute effects of nicotine in 10% serum treated MCF7 cells, we

performed microarray experiment with RNA samples obtained from MCF7 cells treated with

1 μM nicotine for 2 days. Before determining the differentially expressed genes, whole

genome expression was visualized using a scatter plot between control and nicotine-treated

samples (2 replicates, each) in order to visualize the expression changes at the global level

with a threshold of 2-fold change (Figure 4.1.8). Our findings indicated that there were very

few number of probesets with altered expressions greater than 2 fold suggesting that

nicotine’s effects at the given concentration is mild.

62

Figure 4.1.8. Scatter plot of probesets with or without nicotine treatment in 10% serum

conditions.

A 1.2 fold change cut-off was used to determine the differentially expressed genes since

nicotine altered gene expression to a limited degree. To associate probesets above the

threshold with significance between group means, a class comparison analysis was performed

yielding 1345 probesets. GO (Gene Ontology) analysis performed using the WebsGestalt

bioinformatics tools helped characterize the functional effect of nicotine effect on whole

genome. In Figure 4.1.9, overrepresented biological processes or molecular functions that

were shown in red color were obtained based on hypergeometric tests implicating functions

potentially altered by nicotine treatment. Accordingly, nicotine altered different biological

functions such as cell adhesion, cell growth, cell death, cell proliferation and differentiation

which are also important processes for cancer development. In addition, Figure 4.1.9B

indicated that nicotine regulated DNA binding, enzyme binding, correpressor activity and

protein dimerization activities.

63

Apart from the functional analysis, KEGG and Biocarta pathway analyses were

performed to detect nicotine’s effect on signaling pathways. Again, WebGestalt

bioinformatics tools and hypergeometric tests were used to find the significant pathways. The

significant pathways were represented in Table 4.1.3.

64

A

65

66

B

67

Figure 4.1.9. GO analysis of nicotine treatment in 10% serum. A) Biological process analysis;

B) Molecular function analysis. Functions shown in red color indicated significant at p<0.05 in

terms of hypergeometric test.

68

Table 4.1.3. Pathway analysis of nicotine treatment in 10% serum

69

.

Phosphatases in MAPKinase pathway and caspase cascade activity in apoptosis from

the Biocarta pathway analysis, and the focal adhesion and colorectal cancer pathway in the

KEGG pathway analysis were the most interesting functional groups. These pathways play

key roles in cancer, and some of which have been previously been associated with nicotine

exposure. Thus, these results demonstrated that nicotine might alter the gene expression,

although through mild changes in genes that have a role in cancer development.

4.1.7. Preliminary analyses of MCF7 transcriptome under serum starvation and

nicotine treatment:

MTT assays indicated that nicotine promoted the growth of MCF7 cells under serum-

starved conditions. Therefore, we decided to assess the acute effect of nicotine on MCF7

cells in serum-starved conditions, i.e., 0.1% FBS with or without 1 μM nicotine treated for 2

days.

Scatter plot analysis of nicotine treated samples in comparison with control samples

under 0.1% serum starvation conditions provided a generalized information on the extent of

modulation under the treatment conditions. Similar to experiments in 10% serum condition, a

small number of probesets exhibited more than 2-fold changes; yet the magnitude of

modulation was greater than the experiments performed with 10% serum (Figure 4.1.10).

Since there was only one replica for each experiment in 0.1% serum control and nicotine

groups, a 1.5 fold-change was used to find a potential set of differentially expressed

probesets in response to nicotine treatment under serum starvation. Based on this filtering

criterion, there were 630 probesets that were altered in response to 1 μM nicotine. GO

analysis via WebGestalt bioinformatics tools was performed to identify biological processes

altered in response to treatment (Figure 4.1.11).

70

Figure 4.1.10. Scatter plot of probesets with or without nicotine treatment in 0.1% serum

conditions.

Figure 4.1.11A indicated that probesets functioning in notch signaling, protein kinase

pathway, stress activated protein kinase signaling pathway, positive regulation of signal

transduction, regulation of IκB kinase/ NFκB cascade, apoptosis, regulation of program cell

death, negative regulation of progression through cell cycle and others were significantly

altered by nicotine treatment under serum starvation. These genes had different molecular

functions such as magnesium binding, tubulin binding, phosphatase binding, SH3 domain

binding, phosphotransferase activity, amino-acid polyamine transpoter activity and others

(Fig. 4.1.11B).

71

A

72

73

74

B

75

Figure 4.1.11. GO analysis of nicotine treatment in 0.1% serum. A) Biological processes; B)

Molecular function. Functions shown in red color indicated significance at p<0.05 in terms of

hypergeometric test

76

Table 4.1.4. Pathway analysis of nicotine treatment in 0.1% serum

.

Biocarta and KEGG pathway analyses indicated that nicotine influenced the expression

of genes functioning particularly in the apoptosis pathway. In addition to apoptosis, focal

adhesion, tight junction, MAPK signaling pathway, insulin signaling pathway, Wnt signaling

pathway, chronic myeoid leukemia, glioma and colorectal cancer pathways were found

significant (Table 4.1.4). Thus, these results indicated that nicotine altered the gene

expression related to cancer also under serum starvation.

77

4.1.7.1. Nicotine treatment under starvation might lead to recovery of cell

expression in a subset of probesets:

Many studies showed the proliferative and anti-apoptotic effect of nicotine in different

cancers (Egleton, Brown et al. 2008). Cells grown under serum starvation were known to be

under stress and might induce cell-stress mechanisms such as apoptosis. Since nicotine had

anti-apoptotic effects, we expected that nicotine treated cells grown under serum limiting

conditions might behave as cells grown in physiological serum conditions. Therefore,

nicotine treated and untreated cells grown in 0.1% serum were compared to untreated cells

grown in 10% serum.

630 probes were found in the 0.1% serum treatment data that changed 1.5 fold with

respect to control cells grown under the same conditions. These probes were divided into two

groups according to the relationships between 0.1% nicotine and 10% serum control

conditions, as it was mentioned in materials and methods. Accordingly, 404 probes were

identified as probesets behaving similar to probesets from the 10% serum. Genes functioning

in notch signaling, protein kinase pathway, stress activated protein kinase signaling pathway,

positive regulation of signal transduction, regulation of IκB kinase/ NFκB cascade, apoptosis,

regulation of program cell death, negative regulation of progression through cell cycle and

others were identified as significantly modulated (Figure 4.1.12). These genes had diverse

molecular functions such as magnesium ion binding, insulin-like growth factor binding,

kinase activity, phosphotransferase activity, nucleotide receptor activity and so on (Figure

4.1.12).

78

Table 4.1.5. Pathway analysis of genes which nicotine treated cells in 0.1% serum had

similar response to untreated cells in 10% serum.

Biocarta analyses showed nicotine’s effect on caspase cascade in apoptosis, role of

mitochondria in apoptotic signaling, regulation of BAD phosphorylation and Erk1/Erk2

MAPK signaling. In addition to MAPK signaling pathway, KEGG pathway analysis

indicated the effect of nicotine in focal adhesion, Wnt signaling pathway, insulin signaling

pathway, colorectal cancer and others (Table 4.1.5).

79

A

80

81

Figure 4.1.12. GO analysis of genes which nicotine treated cells in 0.1% serum had similar

response to untreated cells in 10% serum. A) Biological processes B) Molecular function.

Functions shown in red color indicated significance at p<0.05 in terms of hypergeometric

test.

B

82

Besides GO analysis and pathway analysis, cluster analysis was performed whether

nicotine treated cells in 0.1% serum and untreated cells in 10% serum were clustered

together. Since untreated cells in 10% serum had two replicas and others had only one, the

mean value of untreated cells in 10% serum was calculated and used for cluster analysis.

Nicotine treated cells in 0.1% serum and untreated cells in 10% serum were clustered

together (Figure 4.1.13).

83

Figure 4.1.13. Cluster analysis of genes which nicotine treated cells in 0.1% serum had

similar response to untreated cells in 10% serum A) Up-regulated genes in nicotine

treatment. B) Down-regulated genes in nicotine treatment. Partial representations.

84

All together 404 probesets behaved similarly as they were exposed to physiological

serum conditions. In conclusion, nicotine treatment regulated genes functioning in apoptosis

suggesting nicotine might help rescue the cells from serum-starvation stress. In addition,

nicotine altered genes functioning in regulation of progression through cell cycle and MAPK

signaling pathway may lead to cell cycle progression under serum starvation.

4.1.7.2. Nicotine modulates expression independent of serum starvation for a

subset of probesets:

Probesets behaving inverse to those of untreated 10% serum grown MCF7 cells were

226; when compared with 404 probesets behaving similarly this is much less. GO analysis

showed that genes with roles in carboxylic acid transport, organic acid transport and protein

complex assembly. Furthermore, these genes had translation initiation factor activity and

kinase activity as significantly altered molecular functions (Figure 4.1.14). On the other

hand, Biocarta and KEGG pathway analysis showed that regulation of transcriptional

activity, HIV-1Nef: negative factor of FAS and TNF, FAS signaling pathway, tight junction,

VEGF signaling pathway, Histine metabolism, glioma, pancreatic cancer and chronic

myeloid leukemia and others were modulated by nicotine treatment (Table 4.1.6).

85

Table 4.1.6. Pathway analysis of genes inversely correlated between 0.1% serum-nicotine

and 10% serum samples.

Cluster analysis showed that serum-starved samples were clustered together and

separated from untreated cells in 10% serum (Figure 4.1.15). Although these probesets

clustered together with 0.1% serum control treatment, the effect of nicotine was more

prominent.

86

A

87

Figure 4.1.14. GO analysis of probesets inversely correlated between 0.1% serum-nicotine

and 10% serum samples. A) Biological processes B) Molecular function. Functions shown in

red color indicated significance at p<0.05 in terms of hypergeometric test

B

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Figure 4.1.15. Cluster analysis of probesets inversely correlated between 0.1% serum-

nicotine and 10% serum samples. A) Up-regulated genes in nicotine treatment. B) Down-

regulated genes in nicotine treatment.

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4.1.8. Cholinergic nAChR receptor expression analysis:

Nicotine asserts its function via binding to nicotinic acetylcholine receptors. This led us

to examine the effects of nicotine on cholinergic receptor expression. After normalization,

the cholinergic receptor expressions were extracted. Cluster analysis was performed and the

results were visualized via Java Treeview program. Nicotine did not alter the cholinergic

receptor expression at the concentrations and time-courses examined in the present study

(Figure 4.1.16).

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Figure 4.1.16. Cluster analysis of nAChRs in respose to nicotine treatment at different

serum conditions.

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Table 4.1.7. Results of class comparison analysis of nAChRs under 10 % serum conditions.

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Table 4.1.8. Results of fold-change of nAChRs under serum starvation

.

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To further understand the effects of nicotine on receptor expression, samples were

divided into two groups with respect to serum conditions and class comparison analysis was

performed for 10% serum conditions (Tables 4.1.7 and 4.1.8, for 0.1% and 10% serum,

respectively). As in the Table 4.1.7, only the expression of CHRNA4 genes was significantly

altered by nicotine and nicotine decreased the expression according to class comparison

result. However, the fold change was less than 1.2. On the other hand, expression of

CHRNA6, CHRNA3, and CHRNB1 were altered by nicotine under serum starvation but the

significance of these alterations were unknown due to presence of only one replica and very

slight changes (Table 4.1.8).

On the other hand, our findings suggested that (Figure 4.1.16) serum condition strongly

influenced the clustering of the samples although this finding should be confirmed with

analysis of additional samples. The expression of CHRNA5, CHRNA10 was lower under

serum starvation while CHRNAB1 had higher expression in terms of fold change with

respect to 10% FBS conditions (Table 4.1.9), Specifically, CHRNA5 expression was

decreased about 2 fold under serum starvation.

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Table 4.1.9. Results of fold-change of nAChRs between serum starvation and physiological

serum concentrations.

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4.2. Involvement of Estrogen in CHRNA5 Signaling:

In this section, we have performed a meta-analysis of CHRNA5 expression using

microarray datasets obtained from in vitro and and in vivo breast cancer experiments

deposited in the public expression study database GEO.

4.2.1. CHRNA5 Expression Analysis in Breast Cancer Cell Lines:

Before the detailed analysis, class comparison analysis of in vitro studies was

performed via BRB ArrayTool. The p-values and FDR values were represented in Table

4.2.1, such that CHRNA5 expression was significantly altered in each condition except

GSE11467. The reason for the insignificance of GSE11467 will be mentioned in the future

paragraphs.

Table 4.2.1. Class comparison results of in vitro studies.

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4.2.1.1. Estrogen treatment increases CHRNA5 expression:

It is observed that MCF7 breast carcinoma cells were widely used to study estrogen

signaling pathway because MCF7 cells were characterized as ER-α positive breast cancer

cells and their growth depends on estrogen (Bourdeau, Deschenes et al. 2008). Moreover,

MCF7 cell lines have different sublines exhibiting different responses to estrogen; for

example, estrogen-independent MCF7:2A and highly-sensitive MCF7/BUS cells.

To assess the effects of estrogen treatment on CHRNA5 expression, microarray data

sets GSE3529 and GSE11791were used. In GSE3529 Rea et al. used three different ER-α

positive cell lines: MCF7, T47D, and BT-474 (Rae, Johnson et al. 2005). Biological

duplicates of each cell line were grown in the presence or absence of estrogen for 24 hours.

When the untransformed expression values were compared (Fig. 4.2.1A), we found that

CHRNA5 expression was significantly different in each cells suggesting variability across

individuals. The range of the expression was between 100 and 600 units, T47D exhibiting the

highest expression of all three cell lines. Effect of E2 treatment with respect to control was

calculated as fold change. As shown in the figure 4.2.1B, there was no significant change in

BT-474 cells whereas estrogen-induced increases in CHRNA5 expression were observable in

the T47-D and MCF7 cells although the only the change in MCF7 cells was statistically

significant (p<0.05).

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Figure 4.2.1. Estrogen effect on CHRNA5 expression in the data set GSE3529 (Rae, Johnson

et al. 2005) using BT474, T47D and MCF7 cell lines. A) Absolute expression value of the

probeset 206533_at B) Fold change differences between the control and E2 treated cells. *

indicates p <0.05 determined by student’s t-test.

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To validate the estrogen effect on CHRNA5 expression, GSE11791 performed by

Musgrove et al. also was analyzed. In this study, each experimental group had three

biological replicates while MCF7 cells were treated with 100 nM estrogen for 6 hours

(Musgrove, Sergio et al. 2008). Similarly, our results indicated that estrogen had a

significantly positive effect on CHRNA5 expression, which is likely to be cell-type

dependent (Figure 4.2.2).

Figure 4.2.2. CHRNA5 expression in response to E2. In GSE11791 (Musgrove, Sergio et al.

2008), MCF7 cells were treated with 100 nM estrogen for 6 hours. * indicates the p < 0.05

determined by student’s t-test

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4.2.1.2. Estrogen induces the expression of CHRNA5 in a dose- and time-

dependent manner:

To further examine the effects of estrogen treatment on CHRNA5 expression, we

analyzed three independent microarray datasets designed to determine dose- and time-

dependent effect of estrogen (GSE4668, GSE11324 and GSE11352). Before analyzing the

expression of CHRNA5, pS2 expression analysis performed as a positive control for estrogen

treatment (Appendix A, Figure 6.1).

GSE4668 conducted by Coser et al. was designed to assess the dose-response and time-

course hormone starvation effect using MCF7/BUS. MCF7/BUS cells showed higher

proliferative response to estradiol-17 beta then MCF7 from ATCC and responded to as low as

2 pM estrogen with a reproducible estrogen dose-dependent growth curve (Coser, Chesnes et

al. 2003). In addition, in the absence of estrogen, MCF7/BUS cells blocked proliferation and

became arrested in the G0/G1 phase of the cell cycle (Villalobos, Olea et al. 1995). Pure anti-

estrogen treatment fully blocked the estrogen growth-stimulating affect meaning that estrogen

induced the cells via estrogen receptor (Coser, Chesnes et al. 2003). Furthermore, western

blot experiments showed only the presence of ER-α but not the presence of ER-β in these

cells (Coser, Chesnes et al. 2003). Thus, the MCF7/ BUS breast cancer cells represent one of

the best models to study the estrogen signaling via estrogen -ER-α interaction.

As mentioned above, Coser et al. treated the MCF7/BUS cells with 0, 10, 30, 60, and

100 pM estrogen for 48 hours (Coser, Chesnes et al. 2003). It also was shown that beyond

100pM, these cells did not respond to estrogen. As shown in the Figure 4.2.3, CHRNA5

expression was enhanced with increasing E2 concentrations. Interestingly, the polynomial

regression analysis (Table 4.2.2) indicated that a linear increase in expression up to 30 pM

was apparent whereas greater than 30pM of E2 resulted in an asymptotical response.

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Figure 4.2.3. CHRNA5 expression to estrogen dose-dependent treatment. In GSE4668

(Coser, Chesnes et al. 2003), MCF7/BUS cells was treated with different dose of estrogen: 0,

10, 30, 60 and 100 pM for 48h. * indicates significant in the crease CHRNA5 expression to

these doses and p<0.001 calculated by one-way ANOVA.

To further characterize the estrogen effect, Coser et al. performed hormone-starvation

experiment with an expectation of suppression of the estrogen induced genes in the previous

induction study (Coser, Chesnes et al. 2003). CHRNA5 expression decreased in MCF7/BUS

cells exposed to hormone starvation for 0, 1 or 2 days in a time-dependent manner (Figure

4.2.4), with a similar profile as observed in Figure 4.2.3. These results suggested that

estrogen induced the CHRNA5 expression dose-dependently, and CHRNA5 expression was

repressed by hormone starvation in a time-dependent manner. Polynomial regression analysis

(Table 4.2.2) indicated that hormone starvation reduced the expression of CHRNA5 in a

time-dependent manner. Both studies exhibited a similar leveling off above concentrations

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greater than 30pM suggesting a non-linear response, which may be related with the saturation

of receptor signaling or availability of the CHRNA5 inherent response to estrogen.

Figure 4.2.4. CHRNA5 expression in response to hormone starvation. In GSE4668 (Coser,

Chesnes et al. 2003), MCF7/BUS cells were treated with hormone starved medium for 0, 1,

and 2 days. * indicates significant the reduction of CHRNA5 expression and p<0.001

calculated by one-way ANOVA.

Similarly in another time-series study (GSE11324) (Carroll, Meyer et al. 2006) MCF7

cells were stimulated with 100 nM estrogen for 0, 3, 6, or 12 hours and all experiments were

performed in triplicate. Figure 4.2.5A indicated that CHRNA5 expression was enhanced with

increasing time in the presence of estrogen treatment. 3 hr estrogen treatment did not

significantly induce the CHRNA5 expression whereas 6 hr and 12 hr inductions were

statistically significant. Again, a similar leveling off affect was observed suggesting

saturation of the response.

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Table 4.2.2. Regression analysis of dose-dependent and time-dependent data.

On the other hand, a third time-course studies (GSE11352; Lin, Vega et al. 2007), in

which MCF7 cells were investigated for 12, 24 and 48 hours, in the presence or absence of

E2 treatment. CHRNA5 expression increased at all the time point regardless of treatment

condition (Fig. 4.2.5B). However estrogen treated samples exhibited higher basal expression

levels than the untreated samples. Estrogen treatment induced the CHRNA5 expression

initially at around 20 hours after which leveling off took place almost to the basal levels. This

suggests that although there is an induction of CHNRA5 expression initially, this response

may not be maintained over a longer time of exposure and the extent of maintenance might

vary from experiment to experiment. The different time points and different amount of

estrogen treatment also might have caused this discrepancy. The first two studies, GSE4668

and GSE11324, suggested that CHRNA5 expression was induced by estrogen time-

dependent manner and the last study, GSE11352 implies that this induction might reach a

maximum point and increasing the exposure time may not influence the outcome. Therefore,

it can be concluded that estrogen induces the CHRNA5 expression in time- dependent

manner during the short- time exposure.

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Figure 4.2.5. CHRNA5 expression in response to time-dependent E2 exposure. A) In

GSE11324 (Carroll, Meyer et al. 2006), MCF7 cells were treated with 100nM E2 for 0, 3, 6

and 12 h. B) In GSE11352 (Lin, Vega et al. 2007), MCF7 cells were treated with/without

10nM E2 for 12, 24 and 48 h. * indicates the significant time-dependent response to E2 and

p<0.001 determined by regression analysis (Table 4.2.2).

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4.2.1.3. CHRNA5 is the secondary target of estrogen signaling:

Estrogen regulates gene expression in different ways. Estrogen binding to estrogen

receptor regulates the expression of primary target genes; in addition to direct binding to

DNA sequence, estrogen receptors can be recruited the other factors to the DNA sequence or

regulates the gene expression via non-genomic ways. To further characterize the estrogen

affect on gene regulations, in the study GSE8597, Bourdeau et al. treated MCF7 cells with

estrogen in the presence or absence of the translation inhibitor cycloheximide (Bourdeau,

Deschenes et al. 2008). Then, if estrogen changed the gene expression in the absence of

cycloheximide but did not affect the gene expression in the presence of cycloheximide, this

meant that these genes were regulated by estrogen via non-genomic way. In other words,

such genes needed to have a transcription factor(s) induced by estrogen, translated into

proteins before regulating expression of target genes. Bourdeau et al. called these genes as

the secondary targets of estrogen signaling (Bourdeau, Deschenes et al. 2008). In this respect,

we asked the same question: Is CHRNA5 the primary or secondary target of estrogen

signaling? Bourdeau et al. found Myc as a primary target and E2F1 as a secondary target

(Bourdeau, Deschenes et al. 2008). Therefore, in our analysis, Myc and E2F1 were used as

positive control (Appendix A, Figure 6.2). According to the GSE8597 dataset induction of

expression in CHRNA5 by estrogen was blocked in the presence of translation inhibitor

cycloheximide (Fig. 4.2.6).

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Figure 4.2.6. CHRNA5 expression in response to E2 exposure in the presence or absence of

translation inhibitor cycloheximide. In GSE8597 (Bourdeau, Deschenes et al. 2008), MCF7

cells were treated with 25 nM estrogen either in the presence or absence of (25 μg/ml)

cycloheximide. * indicates p< 10-7

determined by one-way ANOVA.

As it is mentioned before, in the figure 4.2.5A, 3 hours estrogen treatment did not

significantly induce CHRNA5 expression. This supported the idea that CHRNA5 was the

secondary target of estrogen signaling. To further support this notion, we analyzed the

GSE11467 performed by Frasor (Frasor 2008; Frasor, Weaver et al. 2008). In this study, the

authors wanted to examine how E2 and TNF-α together influence gene expression in breast

cancer cells compared to either factor alone so they treated MCF7 cells with 10nM estrogen,

10nM TNF-α or both for two hours. We were only interested the estrogen effect so

eliminated the experiment with 10nM TNF-α or both 10nM TNF-α and estrogen treatment.

Here, the estrogen induction was done only for 2 hours so if the CHRNA5 were a primary

target, we expected to see estrogen-induction but otherwise the findings would have

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supported the theory that CHRNA5 was the secondary target of estrogen signaling. Before

analysis of CHRNA5 expression, the effect of estrogen in Myc as primary target and E2F1 as

a secondary target expression was analyzed as a positive control (Appendix A, Figure 6.3)

(Bourdeau, Deschenes et al. 2008). There was no significant changes in response to estrogen

in short term E2 exposure (Fig. 4.2.7). In addition to these meta-analysis results of

microarray expression, we found no putative estrogen receptor binding site on the CHRNA5

promoter (BrownLab). Thus, all these results indicate that CHRNA5 is the secondary target

of estrogen.

Figure 4.2.7. CHRNA5 expression in response to short-time E2 exposure. In GSE11467

(Frasor 2008), MCF7 cells were treated with 10 nM E2 for 2h. Student’s t-test indicates no

significance.

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4.2.1.4. Estrogen-independent MCF7 sublines have higher CHRNA5

expression:

Hormone therapy is a widely used approach in breast cancer patients. In particular,

anti-estrogen tamoxifen is used to control the ER activity. Unfortunately, prolonged anti-

estrogen therapy leads to drug resistance in breast cancer patients. In recent years, many

studies elucidated the anti-estrogen resistance mechanisms. GSE5840 done by Fan et al. was

the one of the microarray studies to compare the expression pattern of estrogen responsive

genes in wild-type MCF7, OHT (tamoxifen)–resistant MCF7, and ICI (Fulvestrand)-resistant

MCF7 cells. The establishment of OHT (tamoxifen) –resistant MCF7 cells and ICI

(Fulvestrand)-resistant MCF7 cells were mentioned in the material and methods parts (Fan,

Yan et al. 2006). Accordingly, the difference between anti-estrogen tamoxifen and the pure

anti-estrogen fulvestrand, is that tamoxifen competitively binds to the ER and leads to an ER

conformational change, which inhibits ER binding to co-activators and/or make favorable ER

binding to corepressors as well as it alters ER dimerization and interaction with ERE so the

genetic estrogen response is blocked (Huang, Norris et al. 2002; Shang and Brown 2002). On

the other hand, the pure anti-estrogens such as fulvestrand can destroy the ER via binding to

the newly synthesized estrogen receptor in the cytoplasm and preventing the transportation to

the nucleus so it prevents estrogen signaling (MacGregor and Jordan 1998; Fan, Yan et al.

2006).

As repeatedly shown estrogen induced the CHRNA5 expression in MCF7 cells with

treatment of 10 nM estrogen for 4 hours, whereas OHT-resistant MCF7 and ICI-resistant

MCF7 cells were both estrogen-resistant and CHRNA5 expression did not change (Fig.

4.2.8).

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Figure 4.2.8. CHRNA5 expression in response to E2 in different MCF7 sublines. In

GSE5840 (Fan, Yan et al. 2006), MCF7 cells were treated with tamoxifen or fulvestrand for

12 months to establish OHT-resistant and ICI-resistant MCF7 cell sublines. Then, these

sublines and wild-type MCF7 cells were treated with 10 nM E2 for 4 h. *indicates p<0.05

determined by Student’s t-test, each treatment compared with its control.

GSE10879, another study performed by Lewis-Wambi et al. tested whether estrogen-

deprived conditions have an effect on breast cancer cell lines using wild-type MCF-7 cells

and long-term estrogen deprived MCF-7:5C and MCF-7:2A breast cancer cells (Lewis-

Wambi, Cunliffe et al. 2008). MCF7:5C cells which were obtained after long-term culture in

estrogen free media were estrogen independent cells. Estrogen treatment did not induce the

growth of these cells and also anti-estrogen treatment such as tamoxifen and fulvestrand did

not block any growth. In addition, the level of ER was minor and there was no alteration in

the affinity of ER for ligand in the cells (Jiang, Wolf et al. 1992). On the other hand, the other

estrogen-independent cell, the MCF-7:2A grew in the absence of estrogen maximally but

anti-estrogen such as tamoxifen and fulvestrand inhibited the growth of these cells. In

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addition, MCF-7:2A cells had high levels of estrogen receptor expression and constitutively

active ER shown via ER-responsive luciferase reporter construct in the absence of any

estrogens. Furthermore, tamoxifen and fulvestrand inhibited the ER-responsive luciferase

reporter construct activation. Therefore, the MCF-7:2A cell represented a good model to use

in the study of ER action and estrogen-independent growth in human breast cancer cells

(Pink, Jiang et al. 1995). Thus, MCF7:5C and MCF7:2A cells represent good models for the

study of the estrogen-deprivation mechanisms. In GSE10879 study where wild-type MCF7,

MCF7:5C and MCF7:2A cells grown in estrogen-free media CHRNA5 expression was

compared among the cell lines. MCF7:5C and MCF7:2A exhibited statistically significantly

higher expression level than wild-type MCF7 cells (Fig. 4.2.9). Therefore, Figures 4.2.8 and

4.2.9 supported each other and indicated that estrogen-deprivation led to higher CHRNA5

expression in an estrogen-independent manner.

Figure 4.2.9. CHRNA5 expression in estrogen-independent MCF7 clones. In GSE10879

(Lewis-Wambi, Cunliffe et al. 2008), wild-type MCF7 cells are estrogen-dependent;

MCF7:5C and MCF7:2A cells are estrogen-independent. * indicates p<0.01.

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4.2.2. CHRNA5 Expression Analysis in Breast Cancer studies:

To understand further CHRNA5 expression in breast cancer, we collected a set of

primary tumor datasets for breast cancer from GEO database. Again, to prevent the cross

platform and different probe affect, studies performed in Affymetrix HG-U133A and HG-

U133 plus2 chips which contain probeset 206533_at. Similar to in vitro studies (Table 4.2.1);

the class comparison analysis of datasets for each condition was performed as a preliminary

data. As shown in Table 4.2.3, CHRNA5 was significantly differentially expressed in the

following conditions. These results leaded us to do further analysis.

Table 4.2.3. Class comparison results of primary breast tumor studies.

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4.2.2.1. ER status:

Breast cancer cell line studies indicated that estrogen induced the CHRNA5 expression,

and estrogen-independent cell lines had higher expression levels than wild-type. Therefore,

we examined whether there was a differential expression between ER+ and ER- breast

cancers. Since endocrine therapy and tamoxifen targets the ER, we selected breast cancer

microarray studies from which samples were taken from untreated patients with information

about the ER status. Statistically, we compared the CHRNA5 expression between the ER+

patients and ER- patients; our results indicated that (Fig. 4.2.10) ER- patients had higher

CHRNA5 expression than ER+ patients. The fold change difference between the ER- and

ER+ samples was not very high yet was highly significant ( p<0.0005).

Figure 4.2.10. CHRNA5 expression based on ER status. ER+ samples were assumed as

control and equalized to 1. The fold change was calculated relative to ER+. ** and * indicate

p <0.0005 and p <0.05 determined by student’s t-test.

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4.2.2.2. Histological Grade:

In breast cancer, histological grade is an essential prognostic factor and higher grade

tumors with ER- status are correlated with higher malignancies (Porter, Evans et al. 2004). In

addition to this, higher expression of CHRNA5 in ER- breast cancer cells led us to analyze

the primary breast tumor studies according to histological grade status. In this case,

microarray studies were divided in two groups such that untreated and treated breast cancer

samples were separated to prevent heterogeneity caused by therapy. Regardless of therapy,

CHRNA5 expression was higher in Grade 3 (Fig. 4.2.11). Similar to ER- conditions, the fold

change difference was not very high yet was very consistant. Grade 1 and Grade 2 tumors did

not exhibit differences between themselves however Grade 3 CHRNA5 expression was

higher than either Grade 1 or Grade 2.

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Figure 4.2.11. CHRNA5 expression based on histological grade level. A) Primary tumor

from untreated patients B) Primary tumor from treated patients. Grade 1 samples were

assumed as control and equalized to 1. The fold change was calculated relative to grade1. **

and * indicate p<0.005 and p<0.05 determined by student’s t-test, respectively.

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Since there is a correlation between higher maligancies and Grade 3 with ER- patients

we analyzed the expression of CHRNA5 with regard to status combining ER and grade. In

general, ER- patients had higher expression regardless of grade whereas Grade 3 ER- patients

exhibited the highest expression value in all samples except the grade 1 ER- level due to the

insufficient number patients (Fig. 4.2.12).

Figure 4.2.12. CHRNA5 expression based on histological grade with ER status. A) GSE3494

studies B) GSE5460 studies C) GSE4922 studies with untreated samples D) GSE6532 studies

with untreated samples. Grade 1 ER+ samples were assumed as control and equalized to 1.

The fold change was calculated relative to Grade 1 ER+. * indicates p <0.0005 determined by

student’s t-test.

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4.2.2.3. Hormone therapy:

In vitro studies show that estrogen induces the expression. In general, hormone therapy

targets the ER receptors and reduces the estrogen- induced growth. So, tamoxifen treated and

untreated samples from GSE6532 and endocrine therapy applied and not applied samples

ER+ from GSE4922 samples were analyzed to examine this effect. In the Figure 4.2.13A,

samples from GSE4922 datasets were ER+ this represents a homogenous population.

Endocrine therapy decreased the expression in Grade 1 but did not affect the Grade 2 and

Grade 3 patients (Figure 4.2.13A). In figure 4.2.13B, GSE6532 data sets were used. In

GSE6532 dataset, although the ER status of patients was not unknown, ER- Grade 1 patients

were very rare cases and the results in Figure 4.2.13B supported results in the Figure 4.2.13A

that hormone therapy leads to reduction in expression of CHRNA5 in Grade 1 patients.

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Figure 4.2.13. CHRNA5 expression based on therapy status with grade. A) GSE4922, all

patients were ER+ and endocrine therapy was applied. B) GSE6532, the ER status of patients

was unknown + and tamoxifen treatment was applied. * indicate p<0.05.

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4.2.3. Analysis of Correlated Genes with CHRNA5:

Previously, our study showed that CHRNA5 expression was altered by estrogen

induction and was higher in ER negative and/or Grade 3 samples. These results led us to

identify the correlated genes for further analysis. First, r>0.7 correlated probesets were

defined in each in vitro data except GSE3529 and GSE11467. Then, the common probesets

were determined, namely correlated genes (Table 4.2.4).

Table 4.2.4. Correlated genes with CHRNA5.

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To elucidate the function of correlated genes, GO and pathway analysis were

performed via Webgstalt tool. Figure 4.2.14A shows that these genes function in regulation

of progression in cell cycle, regulation of program cell death, DNA metabolism and

regulation of apoptosis. In addition, they have DNA binding, purine nucleotide binding,

enzyme binding and hydrolase activity (Fig. 4.2.14B) and KEGG pathway analysis (Table

4.2.5) finds the cell cycle significant. Taken together, these results indicate that these genes

may have role in cell cycle progression.

Table 4.2.5. Pathway analysis of correlated genes with CHRNA5.

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Figure 4.2.14. GO analysis of correlated genes. A) Biological Process level 6; B) Molecular

Function level 4 .Writings in red color indicates p<0.05 according to Hyper Geometric Test.

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Apart from the GO analysis, binary tree class prediction analysis with these genes was

performed via BRB ArrayTool whether they can predict the different classes. Table 4.2.6

showed the results of prediction according to ER status. ER+ and ER- samples were

classified with an accuracy of about 80%. with these genes. Same analysis was performed to

classify the samples according to grade status. Table 4.2.7 demonstrated that Grade 1 and

Grade 3 samples were classified with accuracy of more than 75% with these genes but the

classification of Grade 2 samples was changed from datasets. All these results suggest that

the correlated genes may have role in cell cycle progression and have high prediction ability

to discriminate the ER+ /ER- and Grade 1/Grade 3 samples.

Table 4.2.6. Prediction analysis results for the correlated genes according to ER status.

Binary Tree Prediction via K-nearest neighbor k=3 method, p<0.05.

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Table 4.2.7. Prediction analysis results for the correlated genes according to histological

grade. Binary Tree Prediction via K-nearest neighbor k=3 method, p<0.05.

• G1:Grade 1; G2: Grade 2; G3: Grade 3.

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CHAPTER 5: CONCLUSIONGS AND DISCUSSION

5.1. Nicotine-mediated Signaling in MCF-7 Cells

Tobacco use is one of the biggest threats to human health. Cigarette smoking is

associated with close to 30% of all cancers; and for the lung, larynx and oral cancers this rate

is much higher. On the other hand, smoking is considered as a risk factor for multiple other

cancers, such as cancers of pancreas, liver, colon, and leukemia. Although nicotine is the

most potent addictive and signal modulatory component in cigarette smoke, it has not been

studied in detail in the context of breast cancers. Although it does not have carcinogenic

activity, recent studies suggest that nicotine interacts with nAChRs and causes an increase in

cell proliferation, tumor invasion and angiogenesis; in addition it has anti-apoptotic effects;

thereby functions as a tumor enhancer (Egleton, Brown et al. 2008). Smoking as a risk factor

for breast cancer is a controversial issue and only a few studies are present assessing the

effects of nicotine in breast cancer cell lines, which have led us to study the effect of nicotine

on breast cancer cells.

In this study, nicotine-modulated gene expression in MCF7 breast carcinoma cell line

was investigated. The subunits of α3, 5, 7, 9 and 10 and β1 and 4 nAChRs were found in

MCF7 cells (data not shown) (Guo, Ibaragi et al. 2008). First, the proliferative activity of

nicotine was examined under low and high serum concentrations. According to the MTT

assay results, 2 days of 1 μM nicotine treatment in the presence or absence of serum was

selected to assess the acute affect of nicotine in MCF7 cells. Second, since serum starvation

potentially leads to cell cycle arrest and/or induces apoptosis, the effect of nicotine in MCF7

cells were explored in terms of cell cycle process and apoptosis processes using molecular

markers at the protein level. Finally, to assess the effect of nicotine on transcriptome in a

genome-wide manner, microarray experiments were performed. Gene Ontology and pathway

analyses were used to identify the functions of these nicotine-modulated genes. In addition,

since nicotine exerts its biological activity via interaction with nAChRs, the effect of nicotine

on nAChR expression also was explored in the context of serum responsiveness and response

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to nicotine. Our findings established that the effects of nicotine on MCF7 cells were

functionally extensive yet the changes in gene expression values were relatively mild. Serum

depletion in the presence or absence of nicotine, on the other hand, resulted in drastic

modulations in gene expression values, some of which could be reserved in the presence of

nicotine after 2 days.

Several studies show that nicotine accelerates the cell proliferation in normal and

cancer cells (Gotti and Clementi 2004; Dasgupta, Rastogi et al. 2006; Schuller 2007). To

explore the proliferative activity of nicotine in MCF7 cells, under physiological serum

conditions, we assessed the dose- and time-dependency of nicotine exposure; cells were

treated with different nicotine dose and for different time points; 0.1, 1 and 10 μM at 2, 3, 5

and 7 days. The MTT results showed that nicotine at the selected doses and time periods did

not induce the cell proliferation under physiological serum conditions. However, same dose

of nicotine effectively rescued the loss of cell proliferation in the absence of serum starting

from day 4 of the exposure. Guo et al. show that nicotine induces the cell proliferation in

0.5% serum after 3 days exposure, thereby supporting our findings (Guo, Ibaragi et al. 2008).

One of the outcomes of serum starvation is inhibition of the cell cycle progression.

Serum starvation is known to decrease the levels of expression of cyclin D1 and cyclin E

(Zerfass, Schulze et al. 1995; Kues, Anger et al. 2000; Cooper 2003; Demidenko and

Blagosklonny 2008). In addition, kinases associated with cyclins D and E cause the

hyperphosphorylation of RB and hyperphosphorylated RB dissociates from E2F, thereby

leading to the activation of E2F-regulated proliferative promoters and facilitating the S-phase

entry (Stevaux, Dimova et al. 2002). Therefore, the presence of cyclins D1 and E are

essential for cell cycle progression. In the present study, we found that serum starvation led

to a reduction in cyclin E levels and expectedly upon exposure to nicotine cyclin E protein

levels increased. However, nicotine reduced the cyclin E levels under physiological serum

conditions due to an unknown reason. Nevertheless, since this experiment was performed

once, it should be repeated to come to a conclusion.

Dasgupta et al. have shown that nicotine leads to resistance to chemotherapeutic drug

induced-apoptosis. In addition, they have demonstrated that nicotine inhibits apoptosis via a

reduction in p53 (Dasgupta, Kinkade et al. 2006). Herein, effects of 1 μM nicotine under 10%

124

serum or 0.1% serum conditions were studied at the level of Bcl-xl and p53. Serum starvation

induced the expression of p53 while nicotine slightly decreased the p53 under serum

starvation. No significant changes were observed under physiological serum conditions.

Besides p53, nicotine induced the Bcl-xl expression at both serum levels though the increase

in serum starvation was slightly higher than that in the untreated cell, as expectedly. These

results suggest that proliferative and anti-apoptotic effects of nicotine was condition-

dependent, such that when the cell was insulted via reduction in growth factors, or increased

stress such as lack of nutrients and/or DNA damage, nicotine-modulated pathways might

likely to participate in restoring the cell’s proliferative capacity.

In connection with the results of MTT assays and Western blots of cyclin E, Bcl-xl, and

p53, the whole genome microarray results suggested that nicotine altered gene expression in

subtle manners. Although a large number of genes became modulated within 2 days of

exposure, the magnitude of alterations was low. This has been previously shown by several

studies performed in the rat brain and PC12 cells, such that most genes with altered

expressions did not go beyond 1.5 fold changes (Konu, Kane et al. 2001; Konu, Xu et al.

2004). However, as shown previously, we have found that nicotine significantly influenced

the diverse biological process such as regulation of cell adhesion, neuron differentiation,

regulation of cell differentiation, vasculogenesis, cell death, cell growth, cell proliferation,

regulation of metabolism, circadian rhytmn, and morphogenesis. In terms of Biocarta

pathway analysis, regulation of MAP kinase pathways through dual specific phosphatases,

IGF-1, EGF, and PDGF signaling pathways, caspase cascade in apoptosis, IL-6 signaling

pathway, also were found to be significantly modulated. Focal adhesion, colorectal cancer,

mTOR signaling pathways and nitrogen metabolism were identified according to the KEGG

pathway analysis. In addition to alterations in cell growth and proliferation, alterations in

growth factor signaling and dual specific phosphotases in the regulation of MAPK pathway

suggested that nicotine may alter the cell proliferation of MCF7 cells. On the other hand,

significant alterations in biological process, such as vasculogenesis, cell death and cell

adhesion as well as caspase cascade activity, focal adhesion and colorectal cancer signaling

pathways suggested that nicotine also might modulate the process of apoptosis, migration,

and angiogenesis. Taken together, all these results support the notion that nicotine alters the

proliferative, angiogenetic, migration, and apoptotic processes of the cells (Egleton, Brown et

125

al. 2008). Future studies are needed to show which of these processes and through what

mechanisms are modulated using studies focusing on phosphorylation status of the effector

proteins.

Several studies showed that nicotine leads to resistance against apoptosis induced by

extracellular stress stimuli such as opioids, UV radiation, Ca++ ionophores, neurotoxins, oxi-

dative stress and anticancer drugs and induce survival in lung cancer, oral cancer, and head

and neck cancer cells (Dasgupta, Kinkade et al. 2006; Zeidler, Albermann et al. 2007).

Therefore, herein, we provide preliminary information that starvation-induced stress (Cooper,

Hutchison et al. 2003) increased nicotine-induced modulation of gene expression when

compared with physiological serum conditions. According to GO analysis, differentially

expressed genes were involved in different kind of biological processes: notch signaling,

protein kinase pathway, stress activated protein kinase signaling pathway, positive regulation

of signal transduction, regulation of IκB kinase/NFκB cascade, apoptosis, regulation of

program cell death and negative regulation of progression through cell cycle. Caspase

cascade in apoptosis, role of mitochondria in apoptotic signaling, regulation of BAD

phosphorylation, ceramide signaling pathway (Shirakabe, Yamaguchi et al. 1997) found by

Biocarta pathway analysis, and apoptosis identified by KEGG pathway analysis

demonstrated that serum starvation might have induced the apoptotic signaling and nicotine

significantly altered a subset of these pathways. On the other hand, findings of the regulation

of I-κ B kinase/NFκB cascade and MAPK pathway identified by KEGG pathway analysis

suggest that nicotine might play key roles in cell proliferation as well as cell death (Santen,

Song et al. 2002). Apart from these pathways, focal adhesion, tight junctions (Sawada,

Murata et al. 2003), and VEGF signaling pathways significantly altered by nicotine suggest

roles for nicotine in cell migration (Yamamura, Amano et al. 1998; Cross and Claesson-

Welsh 2001; Wozniak, Modzelewska et al. 2004). Thus, these results also support the recent

studies (Guo, Ibaragi et al. 2008; Dasgupta, Rizwani et al. 2009) implicating nicotine as an

inducer of cell migration in MCF7 cells.

Since nicotine have restored the level of cell proliferation back to normal levels at day 5

or later when treated with no serum, it was important to examine what serum depletion has

done to MCF7 cells. However our analyses have focused on effects of nicotine and serum at

126

day 2, at which cell proliferation was not seemingly affected either in the absence or presence

of nicotine. It is possible that our results showing transcriptional changes in the direction

closer to 10% serum level transcriptome might represent a small modulation towards the

protective effects of nicotine. In the light of these, GO analysis demonstrated that this gene

set functioned in protein kinase pathway, stress activated protein kinase signaling pathway,

positive regulation of signal transduction, regulation of IκB kinase/ NFκB cascade, apoptosis,

regulation of program cell death, and negative regulation of progression through cell cycle.

Similar to previous analysis, caspase cascade in apoptosis, role of mitochondria in apoptotic

signaling, ceramide pathway and regulation of BAD phosphorylation from biocarta pathway

analysis are found significant. These results suggested that nicotine might alter the apoptotic

pathway and might prepare cells for recovery from serum-starvation induced stress. A greater

number of probesets changed in the direction of 10 % serum than opposite to 10% serum

transcriptome suggesting that nicotine might induce mild changes in the opposite direction of

serum starvation. In other words, for instance, when gene expression was higher under 10%

serum conditions, serum starvation led to a decrease but nicotine treatment under serum

starvation conditions led to either an increase reversing the effect of starvation. However,

these findings depended on a single replicate and thus were represented as fold changes. The

statistical significance of these fold changes should be confirmed using multiple replicates

from the Day2 serum starvation experiments. Furthermore, there might be differences

between Day2 and Day5, at which the effects of nicotine was more pronounced, therefore

expression profiling will be extended to Day5 to better understand the magnitude of

alterations and ability of nicotine to counteract changes taking place during serum starvation.

nAChR genes are likely to act in concert because they exist in pentamer combinations.

One possibility is that these genes also have similar expression patterns because of common

transcriptional regulation, at least for subsets of receptor subunits. Our findings indicated that

indeed they behaved similarly, yet the expression levels may not be significantly altered in

the presence of nicotine, although statistical tests are needed at the point upon increasing

sample size of the experiments. Yet a few of the subunits were highly responsive to lack of

serum. This suggested that serum depletion represented a modulator for the nAChR

signaling; nicotine in a dose dependent manner may or may not change the levels of these

subunits. The time period studied here was 2 days, which may not be adequate for the effects

127

on cell proliferation and apoptosis to completely show up. In fact, we have demonstrated that

at days 5 and 7, nicotine at even low concentrations was able to lead to an increase in cell

proliferation at 0.1% serum. The change in CHRNA4 expression was the most striking

finding although the level of modulation is less than the cut-off value, it was highly

significant. Since the heteromeric α4β2nAChR has a major inhibitory activity in cancer

development and progression (Schuller 2009), this decrease suggested that nicotine may have

role in cancer development via modulating the α4nAChR expression.

Since all changes reported in cholinergic receptors at the 0.1% serum levels represent

fold changes they have to be repeated using other microarray experiments and should be

confirmed using real-time RT-PCR measurements. However, CHRNA5, CHRNA10 and

CHRNB1 genes seemed to be modulated by serum to a greater extent. In addition, the

decrease in CHRNA5 was more than 2 fold. Since estrogen is one of the ingredients of the

fetal bovine serum and have a key role in breast cancer development, it is likely that nicotine-

modulated signaling cross-talks with estrogen modulated pathways. This finding together

with accumulating information on the polymorphism studies performed on CHRNA5

recently have lead us to examine the estrogen treated breast cancer cell microarray datasets

with respect to CHRNA5 expression modulation, as presented in later in the results.

In summary, our data showed that nicotine behaved differently under different serum

conditions and nicotine altered the multiple biological process in MCF7 cells including, cell

growth, apoptosis, cell proliferation, vasculogenesis. The abnormality of these biological

processes lead to the development and progression of cancer so our results supported the idea

that nicotine might carry a tumor-enhancer activity.

128

5.2. Involvement of Estrogen in CHRNA5 mRNA expression:

According to the American Cancer Society 2009 report, breast cancer is the most

frequently diagnosed cancer in women except the skin cancer (ACS 2009). Estrogen

hormone is strongly associated with the development and progression of the breast cancer

(Anderson 2002). Therefore, several studies have been performed to elucidate the estrogen

mechanisms in breast cancer. Since understanding of histological features and molecular

alterations in breast cancer is essential to find novel therapeutic approaches with higher

success in curing the disease, ER and histological grade status are some of factors to classify

the breast cancers. On the other hand, recent studies demonstrate the association of CHRNA5

with nicotine dependence and lung cancer risk (Falvella, Galvan et al. 2009; Wang, Cruchaga

et al. 2009). In addition, our gene expression analysis of nAChRs under different serum

conditions shows that serum starvation might reduce CHRNA5 expression. Estrogen is one

of the ingredients of serum suggesting that CHRNA5 might be regulated in a growth factor

dependent manner. Evidently, all these reasons lead us to re-analyze microarray data related

to estrogen treatment and ER.

In this study, our analysis implicated that estrogen induced the expression of CHRNA5.

This induction was dose-dependent at lower doses and time-dependent in short-time

exposure. In addition, CHRNA5 has been identified as a possible secondary target of

estrogen signaling. Estrogen-independent and anti-estrogen resistant cells had higher level of

CHRNA5 expression. According to primary tumor studies, higher level of CHRNA5

expression was observed in ER- patients. Therefore, estrogen might regulate the CHRNA5

expression and its deprivation eventually may lead to higher expression. Primary tumor

studies showed that Grade 3 tumors have higher CHRNA5 expression and hormone therapy

led to reduction in expression in Grade 1. Correlated genes functioning in cell cycle with high

predictive ability indicated that a high level of CHRNA5 expression may be associated with

Grade 3 since high mitotic activity also correlated with Grade 3 (Baak, Van Dop et al. 1985).

Interestingly, estrogen did not induce the expression of CHRNA5 in BT-474 cell line

(GSE3529). Wang et al. found that BT-474 was ER positive yet tamoxifen resistant cell line

129

(Wang, Yang et al. 2006). Therefore, these supported the results of OHT-resistant MCF7

cells (GSE5840) in which CHRNA5 expression did not change in response to estrogen in

tamoxifen-resistant cells.

Nakazawa and Ohno showed that estrogen and phyto-oestrogens led to desensitization

of α4β2nAChR by unknown mechanisms (Nakazawa and Ohno 2001). Our results show the

increase in the expression of CHRNA5 in response to estrogen. Indeed, the inclusion of

CHRNA5 subunit in α4β2 may lead to desensitization (Ramirez-Latorre, Yu et al. 1996).

Thus, estrogen may lead to desensitization of α4β2nAChR via increasing the CHRNA5

expression.

Hyperactive GFR and MAPK signaling is one of the reasons for the ER loss and

hormone therapy resistance in breast cancer (Holloway, Murthy et al. 2004; Creighton,

Cordero et al. 2006; Bayliss, Hilger et al. 2007; Lopez-Tarruella and Schiff 2007). Our

analysis indicated that CHRNA5 expression was higher in ER- patients and anti-estrogen

resistant MCF7 cells. Since it is known that the inclusion of CHRNA5 subunit in α3β4 or

α3β2 receptors may lead to functional changes including increased Ca++ permeability

(Gerzanich, Wang et al. 1998) it would be likely that higher expression of CHRNA5 may

lead to increased MAPK activity thereby, leading to hormone-resistancy and ER loss.

Our microarray meta-analysis studies provided valuable information about the

association of CHRNA5 with breast cancer; further molecular and functional analyses should

be performed to elucidate the mechanisms of CHNRA5 in breast cancer.

130

5.3. Future Perspectives:

In the present study, we have demonstrated that nicotine at 1uuM concentrations affects

cell proliferation in a dose and time dependent manner using MTT assays, and molecular

markers. Future studies will focus on understanding the nature of these proliferative effects.

We will investigate the change of expression at the protein level using known proliferative

and apoptotic markers. Furthermore, apoptosis assays will be used to detect whether the

reduction in proliferation is due to growth arrest and/or apoptosis. These studies will be

performed in a dose- and time-dependent manner.

The microarray experiments analyzed herein are preliminary since they included 1-2

replicates of each group. Studies are on going to increase the number of replications as well

as to extend the expression profiling to Day 5 of serum starvation, at which the effects of

nicotine were more prominent based on the MTT assay. Pathway analysis tools, such as

Pathway Miner will also be used to identify networks, consistently modulated at Day 2 and

Day 5 of serum starvation. Futhermore, a time course study of nicotine exposure at 10%

serum levels also will be analyzed together with the serum data to understand consistent

patterns of expression changes.

It is well known that nicotine exerts its proliferative activity via Ras and its downstream

effector Raf/MAP kinase activity (Egleton, Brown et al. 2008). Chu et al. indicate that long-

term nicotine exposure in mouse epithelial cells increases the RAS activity and RAF1

translocation to the plasma membrane despite no changes could be observed in expression

(Chu, Guo et al. 2005). In another study, nicotine enhances the kinase activity associated with

cyclins D and E thereby, leading to progression of cell cycle (Dasgupta, Rastogi et al. 2006)

Taken together, nicotine accelerates the cell proliferation via activating on different kinases.

MAPK pathways, found to be significantly modulated in our study might, provide a link to

the kinase activity induced by nicotine. The genes affected by nicotine and role in MAPK

pathway will be studied upon exposure to kinase inhibitors to elucidate better the

proliferative activity of nicotine on MCF7 cells.

131

Apart from the proliferative activity, nicotine induces the PI3K/AKT pathway,

PKC/RAF/MEK/ERK1 pathway or PKA and leads to phosphorylation of anti-apoptotic

protein bcl-2, pro-apoptotic proteins bad and bax and might protect cells from going through

apoptosis. Therefore, performing the experiments with these antibodies may give better

results about the effects of nicotine on apoptosis. On the other hand, GO and pathway

analysis showed that nicotine altered the caspase cascade activity. Thus, caspase assays will

be essential to illuminate the effect of nicotine in MCF7 cells under serum starvation

conditions.

Recent studies show nicotine affects migration in MCF7 cells (Guo, Ibaragi et al. 2008;

Dasgupta, Rizwani et al. 2009). We identified candidate genes involved in vasculogenesis,

focal adhesion, tight junction, and VEGF signaling pathways that are essential for cell

migration and metastatic process. Consequently, the metastatic effect of nicotine in MCF7

might be studied in the future by modulating levels of expression of these genes through

overexpression or siRNA applications.

In both 10% serum and 0.1% serum experiments, nicotine altered the gene expression

no more than 2.0 fold. Cells were treated with nicotine only for 2 days. In addition, our study

and Guo et al. demonstrated that the proliferative effect of nicotine took place after 3 days

(Guo, Ibaragi et al. 2008). Furthermore, MCF7 cells were maintained in DMEM medium

supplemented with 10% serum, 1% non-essential and 1% penicillin/streptomycin. Before

nicotine treatment, the cells were starved for 24 hr to minimize the heterogeneity caused by

the effects of growth factors and to obtain the synchronized cells. However, MCF7 cell line is

ER+ and estrogen leads to cell proliferation in this cell line. In addition, the estrogenic

activity of phenol red medium is well-known. Therefore, using phenol red medium might

have decreased the efficiency of our experiments. To sum up, cells grown in phenol-red free

media and longer exposure times to nicotine may lead to higher expression differences and

might give better resolution with respect to effects of nicotine on MCF7 cell proliferation and

apoptosis mechanisms.

Our study identified CHRNA5 as a target of estrogen signaling based on in vitro and in

vivo microarray studies. These studies can be extended and confirmed with an independent

set of breast tumors of different molecular subtypes using RT-PCR and antibodies.

132

Furthermore, microarray studies can be extended to those with data on prognosis of the

patients.

133

CHAPTER 6. APPENDICES

APPENDIX A: Positive control of estrogen studies

Figure 6.1. pS2 expression A) In GSE4668 (Coser, Chesnes et al. 2003), MCF7/BUS cells

were treated with E2 different doses for 48 h. B) In GSE4668 (Coser, Chesnes et al. 2003),

MCF7/BUS cells were grown in hormone-starved medium for 0, 1 or 2 day. C) In GSE11324

(Carroll, Meyer et al. 2006), MCF7 cells were treated with 100 nM E2 for 0, 3, 6 or 12 h. D)

In GSE11352 (Lin, Vega et al. 2007), MCF7 cells were treated with/without 10 nM for 12,

24 or 48 h. * indicates p<0.05 determined by student’s t-test, compared with untreated

samples. sP2 probeset: 205009_at.

134

Figure 6.2. MYC and E2F1 expression in response to E2 in the presence or absence of

cycloheximide. In GSE8597 (Bourdeau, Deschenes et al. 2008), MCF7 cells were treated

with 25 nM E2 in the presence / absence of cycloheximide for 24 h. A) MYC expression in

response to E2 with/without cycloheximide, MYC is the primary target of E2 (Bourdeau,

Deschenes et al. 2008) B) E2F1 expression in response to E2 with/without cycloheximide,

E2F1 is the secondary target of E2 (Bourdeau, Deschenes et al. 2008). * indicates p<0.005

determined by student’s t-test. MYC probeset: 202431_s_at; E2F1 average of probesets:

2028_s_at and 204947_at.

135

Figure 6.3. MYC and E2F1 expression in response to E2 for 2 h. In GSE11467(Frasor 2008),

MCF7 cells were treated with 10 nM E2 for 2 h. A) MYC expression in response to E2. B)

E2F1 expression in response to E2. * indicates p<0.07 determined by student’s t-test. MYC

probeset: 202431_s_at; E2F1 average of probesets: 2028_s_at and 204947_at.

136

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