EFFECT OF α-LINOLENIC ACID ON GROWTH OF BREAST CANCER … · 2013-12-11 · 2.1. Steroid hormone...

EFFECT OF α-LINOLENIC ACID ON GROWTH OF BREAST CANCER CELLS

WITH VARYING RECEPTOR EXPRESSION AND ESTROGEN ENVIRONMENTS

By

Ashleigh Wiggins

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Graduate Department of Nutritional Science

University of Toronto

© Copyright by Ashleigh Wiggins, 2013

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EFFECT OF α-LINOLENIC ACID ON GROWTH OF BREAST CANCER CELLS

WITH VARYING RECEPTOR EXPRESSION AND ESTROGEN ENVIRONMENTS

Ashleigh Wiggins

Master of Science

Graduate Department of Nutritional Sciences

University of Toronto

2013

ABSTRACT

Breast cancer molecular subtypes, based on expression of estrogen, progesterone and

human epidermal growth factor 2 receptors, alter prognosis and treatment options. α-linolenic

acid (ALA) is a complementary therapy, however its effectiveness across breast cancer types and

estrogen environments is unclear. This research determined the effect of ALA on growth,

apoptosis, fatty acid profile, and gene changes in four breast cancer cell lines with varying

receptor expression with or without (±) estradiol (E2). ALA (50-200uM) ± E2 reduced growth in

all cell lines. 75μM ALA +E2 increased phospholipid % ALA in all cell lines and induced

apoptosis in cell lines lacking the three receptors. Cellular % ALA was positively associated with

apoptosis and inversely associated with cell growth. ALA altered expression of cell cycle,

apoptosis and signal transduction genes. In conclusion, ALA incorporates into breast cancer

cells, reduces growth and induces apoptosis regardless of receptor status or E2 level.

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ACKNOWLEDGEMENTS

I would like to first thank my supervisor Dr. Lilian Thompson for the endless support,

guidance, and encouragement throughout my MSc journey, and her contagious drive and

enthusiasm towards research which motivated me through the rough patches. This thesis also

greatly benefited from the advice and insight of my advisory committee members Dr. David Ma

and Dr. Krista Power. To all members of the Thompson lab, thank you so much for all the

training, advice and assistance over the past 2 years, in particular Minghua for my training and

Shikhil for assisting with the fatty acid analysis. And of course Julie- when I started my research

I never imagined gaining such an amazing lifelong friend; you inspired me every day, made the

lab as fun as humanly possible, and provided me with endless guidance. I would not have made

it through without you. I would also like to thank Dr. Richard Bazinet and Dr. Ahmed El

Sohemy for use of their labs and equipment, and the departmental staff for keeping me on track

(and paid!). And to all my pals in the department, you have made my experience unforgettable

and I’m so happy to have met and bonded with you all, in particular the ‘B crew’, the Rogues,

the Nutrilyzers and the NSGSA. A special thanks to Chuck for his hugs and honesty, Kayla for

making me feel like the coolest girl in the world, Katie for her wisdom and calmness, and

Bibiana and Matt for teaching me it’s ok to have a ‘crusty day’.

To all of my friends, thank you so much for making the past 2 years amazing, and for

ensuring I made time for sports, concerts, laughing, and letting loose. I cannot begin to express

my gratitude and appreciation for everyone's support, love and encouragement to get me through

my graduate work with a smile on my face.

And thank you to my family. I have been blessed with the most supportive, loving,

unique, fun, and down to earth family a person could ask for. You encouraged me to pursuit this

research, made me feel proud of what I was doing every day and picked me up when I was

down. To mom, your ‘Wendy voicemails’ made every day special and you will never know how

many times I have replayed them (including for other people, whoops). To dad, your sarcasm

and ability to pick up the phone whenever I needed a reality check got me through a lot of tough

days. To Tristan and Adrienne, thank you so much for being my Toronto family and celebrating

all the small victories with me. To Dave, thank you for knowing exactly what to say even when

you say nothing at all, for my guitar, and for saying yes to Spain and Portugal without hesitation.

To Angelique, Joe and Ashlynn, thank you for providing me with a loving and welcoming family

to escape to. And to my Nana and Papa for teaching me the value of hard work, the importance

of family and friends, and most of all to appreciate everything I have been blessed with and

worked for.

I would like to dedicate this work to my family. I know that without you all I would not

be where I am today.

A special thank you to the Canadian Breast Cancer Foundation Ontario region and the

Natural Sciences and Engineering Research Council for their financial assistance that made this

research possible.

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TABLE OF CONTENTS

PAGE

ABSTRACT ii

ACKNOWLEDGEMENTS iii

LIST OF TABLES vi

LIST OF FIGURES vii

LIST OF ABBREVIATIONS viii

1.0 INTRODUCTION 1

2.0 LITERATURE REVIEW 4

2.1 Breast Cancer 4

2.1.1 Breast Cancer Incidence and Risk Factors 4

2.1.2 Molecular Subtypes 6

2.1.3 Hormone Receptors and Signalling 7

2.1.4 HER2 and the Epidermal Growth Factor Receptor Family 9

2.2 Breast Cancer Therapy 13

2.2.1 Traditional Therapies and Personalized Medicine 13

2.2.2 Complementary and Alternative Medicine in Breast Cancer 14

2.3 Flaxseed, Flaxseed Oil and Breast Cancer 15

2.3.1 Components of Flaxseed and Flaxseed Oil 15

2.3.2 Epidemiological and Clinical Evidence 15

2.3.3 Pre-Clinical Evidence 20

2.3.4 Limitations in Current Understanding of Flaxseed, Flaxseed Oil

and Breast Cancer 22

2.4 n-3 PUFA and Breast Cancer 23

2.4.1 n-3 PUFA Classification 23

2.4.2 Epidemiological and Clinical Evidence 25

2.4.3 Pre-Clinical Evidence 31

2.4.4 Limitations in Current Understanding of n-3 PUFA and Breast Cancer 32

2.5 Potential Mechanisms of ALA on Breast Cancer 33

2.5.1 Alteration of Membrane Fatty Acid Profile and Receptors 33

2.5.2 Transcription Factor Regulation 35

2.5.3 Other Mechanisms 35

2.6 Summary and Questions 36

3.0 OBJECTIVES, HYPOTHESES AND EXPERIMENTAL DESIGN 38

3.1 Objectives 38

3.2 Hypotheses 38

3.3 Experimental Design and Rationale 38

4.0 MATERIALS AND METHODS 41

4.1 Cell Line Selection and Culture 41

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4.2 Treatment Medium 42

4.3 Study 1: Effect of ALA on Cell Growth with and without E2 42

4.4 Study 2: Effect of ALA on Apoptosis 43

4.5 Study 3: Effect of ALA on Phospholipid Fatty Acid Composition 43

4.6 Study 4: Effect of ALA on mRNA Expression of Receptors and Signalling

Biomarkers 44

4.7 Statistical Analysis 45

5.0 RESULTS 47





Biomarkers 58

6.0 DISCUSSION 63





Biomarkers 68

6.5 Summary 73

7.0 CONCLUSIONS 75

8.0 STUDY LIMITATIONS AND FUTURE DIRECTIONS 76

8.1 Study Limitations 76

8.2 Future Directions 78

9.0 IMPLICATIONS 79

10.0 REFERENCES 80

APPENDICES 95

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LIST OF TABLES

Table PAGE

2.1. Breast cancer risk factors 5

2.2. Molecular subtypes of breast cancer 6

2.3. Summary of studies investigating effect of flaxseed and flaxseed oil on breast cancer 16

2.4. Summary of studies investigating effect of n-3 PUFA on breast cancer 26

4.1. Receptor expression of commercial breast cancer cell lines 41

5.1. Three way ANOVA results on effect of E2, cell lines and ALA concentration 48

on cell growth

5.2. Phospholipid fatty acid composition of breast cancer cell lines 54

5.3. Relative gene expression (ΔCt) of tumour classification markers in four untreated

breast cancer cell lines from PCR array 59

5.4. Significant and large changes in gene expression after ALA treatment of four breast

cancer cell lines 60

Appendix Table 1. Relative gene expression (ΔCt) in four breast cancer cell lines from

PCR array 95

Appendix Table 2. Changes in gene expression from ALA treatment in four breast cancer

cell lines 101

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LIST OF FIGURES

Figure PAGE

2.1. Steroid hormone signalling in breast cancer 8

2.2. Growth factor signalling in breast cancer 10

2.3. Cross talk in breast cancer signalling 12

2.4. Conversion of α-linolenic acid to long chain n-3 PUFA 24

2.5. Potential mechanisms for growth reduction in breast cancer cells by ALA 34

3.1. Experimental design 39

4.1 Representative dot plots of annexin V-PR and 7-AAD staining for apoptosis in ALA

treated cells 44

5.1. Effect of ALA with and without E2 on growth of four breast cancer cell lines 49

5.2. Differences between cell lines with increasing ALA concentrations, with and 50

without E2

5.3. Representative dot plots of annexin V-PE and 7-AAD staining for apoptosis in control

and ALA treated cells 52

5.4. Effect of ALA on early, late and total apoptosis between cell lines 53

5.5. Effect of ALA on phospholipid ALA, EPA and DHA and n6:n3 ratio 56

5.6. Relationship between phospholipid % ALA and viability of breast cancer cells 57

5.7. Relationship between phospholipid % ALA and total apoptosis 57

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LIST OF ABBREVIATIONS

Akt protein kinase B

ALA alpha-linolenic acid

ANOVA analysis of variance

BD basal diet

BMI body mass index

CAM complementary and alternative medicine

CS FBS charcoal stripped fetal bovine serum

Ct threshold cycle

DHA docosahexaenoic acid

DMBA 7,12-Dimethylbenz(a) anthracene

E2 17-β estradiol

EGF epidermal growth factor

EGFR epidermal growth factor receptor

EMT epithelial to mesenchymal transition

EPA eicosapentaenoic acid

ER estrogen receptor

ERE estrogen response element

ERK extracellular signal-regulated kinase

FAS fatty acid synthase

FBS fetal bovine serum

FFQ food frequency questionnaire

FS flaxseed

FSO flaxseed oil

HER2 human epidermal growth factor receptor 2

IGF-IR insulin-like growth factor-1 receptor

MAPK mitogen activated protein kinase

mTOR mammalian target of rapamycin

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n-3 PUFA omega-3 polyunsaturated fatty acid

NFkB nuclear factor-kappa B

NMU N-methyl-N-nitrosourea

NRG neuregulins

PBS phosphate buffered saline

PI3K phosphoinositide-3 kinase

PPAR peroxisome proliferator-activated receptor

PR progesterone receptor

PTEN phosphatase and tensin homologue

SDG secoisolariciresinol digluoside

SEM standard error of mean

TAM tamoxifen

TGF-α transforming growth factor-alpha

TNBC triple negative breast cancer

TRAS trastuzumab

VEGF vascular endothelial growth factor

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1.0 INTRODUCTION

Breast cancer is the most highly diagnosed and second deadliest form of cancer in

Canadian women (Canadian Cancer Society's Advisory Committee on Cancer Statistics, 2013).

Determining prognosis and optimal treatment options for breast cancer patients is difficult due to

the diversity of the disease, including variation in expression of tumor cell receptors and 17-β

estradiol (E2) levels of patients, both of which may influence tumor growth. E2 levels in the

body vary greatly throughout a women’s lifetime, but are generally high during pre-menopause

and then decrease after menopause. This is an important factor in breast cancer as E2 can

influence the growth of tumors, and alter effectiveness of treatment options. Receptors present

on tumour cells also influence growth and vary greatly from patient to patient. Three important

breast cancer receptors are the estrogen receptor (ER), progesterone receptor (PR) and human

epidermal growth factor receptor 2 (HER2). Based on the expression of these receptors, breast

cancers can be divided into four molecular subtypes that aid in prognosis and the personalization

of traditional therapies. For example, the drug tamoxifen (TAM) is typically used in ER-positive

breast cancer, while trastuzumab (TRAS) is selective for HER2-overexpressing breast cancers.

Despite advances in traditional therapies, negative side effects, high cost, drug resistance and

ineffectiveness have led to an increased use of complementary and alternative medicine (CAM).

One of the most common CAM therapies in breast cancer patients are nutritional or dietary

agents (Morris et al., 2000), the third most common being flaxseed (FS) (Boon et al., 2007;

Boucher et al., 2012).

FS contains two components that have been shown to have a protective role against

breast cancer - the plant lignan secoisolariciresinol diglycoside (SDG), and flaxseed oil (FSO)

which is rich in the omega-3 polyunsaturated fatty acid (n-3 PUFA) α-linolenic acid (ALA)

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(Power & Thompson, 2007). ALA is an essential fatty acid found primarily in plant sources such

as walnuts, soy, canola, and FS with FSO having the highest concentration. There are two other

main longer chain n-3 PUFA, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA),

obtained primarily from marine sources. In the body, ALA can be converted to EPA and DHA

by desaturase and elongase enzymes, however the conversion rate is very low, reported as low as

<1% (Berquin et al., 2008).

The n-3 PUFA are currently being investigated for the potential prevention and treatment

of breast cancer, but evidence is conflicting. Several in vitro and in vivo studies (Blanckaert et

al., 2010; Chen et al., 2009; Corsetto et al., 2011; Hardman, 2007; Hardman & Ion, 2008; Kim et

al., 2009; Saggar et al., 2010a; Saggar et al., 2010b; Truan et al., 2010) show growth reduction in

breast cancer cells and tumours with ALA, EPA, DHA, and FSO supplementation, but others

show little effect (Chajès et al., 1995; Chamras,et al., 2002; Mason et al., 2010). Epidemiological

data is also conflicting, as highlighted in meta-analyses which have shown that there is no overall

significant effect of n-3 PUFA intake on breast cancer risk; however many individual studies do

show a reduction in risk (MacLean et al., 2006; Zheng et al., 2013). This controversy

surrounding the role of n-3 PUFA in breast cancer may be a result of several factors including

not stratifying studies by (i) breast cancer cell type/molecular subtype, (ii) hormonal

environment/ E2 level, and (iii) type of n-3 PUFA.

Despite being much more prevalent in the North American diet, ALA is understudied in

comparison to EPA and DHA for its role in breast cancer prevention and treatment, as its cell

growth effects are thought to be less potent (Anderson & Ma, 2009; MacLean et al., 2006). One

meta-analysis investigated the effect of fish, marine n-3 PUFA and ALA intake on breast cancer

risk and found that only marine n-3 PUFA significantly reduced risk. There were fewer studies

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of ALA intake compared to fish and marine n-3 PUFA and the difference between ALA quartile

intakes was much smaller (Zheng et al., 2013). A prospective cohort study did separate EPA,

DHA and ALA intake however, and found that ALA was the only n-3 PUFA to significantly

reduce breast cancer risk (Voorrips et al., 2002), highlighting the need for more research

attention devoted to ALA and breast cancer.

This research project will investigate in vitro, the role of ALA in breast cancer treatment

by supplementing ALA (0-200μM) ± 1nM E2 on four different breast cancer cell lines with

varying ER, PR and HER2 expression. The outcomes assessed were (i) cell growth and viability,

(ii) changes in apoptosis, (iii) ALA incorporation into the cell phospholipid membrane, and (iv)

changes in mRNA expression of ER, PR, HER2 and other breast cancer related cell signalling

molecules. Demonstrating an E2 and molecular subtype dependent effect of ALA, as well as the

mechanism of action, may help resolve current controversies and establish ALA as a viable and

personalized complementary treatment option for certain types of breast cancers.

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2.0 LITERATURE REVIEW

2.1 Breast Cancer

2.1.1 Breast Cancer Incidence and Risk Factors

Breast cancer, characterized by uncontrolled cellular growth in the breast, is the most

highly diagnosed and second deadliest form of cancer in Canadian women with an estimated

23,800 new cases and 5,020 deaths in 2013 (Canadian Cancer Society’s Advisory Committee on

Cancer Statistics, 2013). Globally, breast cancer is the number one cancer killer in women with

mortality rates of almost 460,000 worldwide in 2008 (World Health Organization, 2008). Due to

the high prevalence and mortality rate of breast cancer, extensive research continues to explore

the biology, development, diagnostics and treatment options for the disease.

There are a number of factors, both modifiable and non-modifiable, which increase ones

risk of developing breast cancer (Table 2.1). E2 exposure, both natural and synthetic, can play an

important role in breast cancer development and progression (Bernstein et al., 1992; Clemons &

Goss, 2001). Long natural lifetime estrogen exposure has been shown to increase a woman’s risk

of developing breast cancer (Kelsey et al., 1993; Pike et al., 1993). This includes early age of

menarche and late age of menopause, and other factors such as age of first pregnancy and parity

(Bernstein et al., 1992; Kelsey et al., 1993). Exposure to synthetic forms of estrogen, such as use

of hormone replacement therapy and oral contraceptives is also linked to an increased risk in the

development of breast cancer (Colditz et al., 1995; Pike et al., 1993). Further validation of

estrogen increasing breast cancer risk and progression is the relationship between obesity and

breast cancer risk in postmenopausal women (Rose & Vona-Davis, 2010; Rose & Vona-Davis,

2013). After menopause there are drastic declines in plasma E2 levels as a result of decreased E2

production from the ovaries, however E2 can be produced from adipose tissue leading to higher

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levels in obese women. This increased E2 compared to normal weight postmenopausal women

leads to a greater risk and more aggressive breast cancers.

Once established, breast cancer can be categorized by the initiation site (ductal or lobule),

infiltration (invasive or non-invasive) and stage (0-IV). With advances in understanding of breast

cancer biology and improved diagnostics, breast cancer can also be categorized by molecular

subtype, which can assist in determining prognosis as well as speculate on how patients will

respond to various treatments.

Table 2.1. Breast cancer risk factors. Table modified from Canadian Breast Cancer

Foundation, 2010.

Non-Modifiable Risk Factors Modifiable Risk Factors

Genetics (e.g.. BRCA mutations) High Body Mass Index (BMI)/ Weight

Estrogen Exposure (early menarche, late

menopause, parity)

Synthetic Hormone Exposure (Hormone

Replacement Therapy, birth control pill)

Age Physical Activity

High Breast Density Alcohol Consumption

Smoking

Radiation Exposure

Diet

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2.1.2 Molecular Subtypes

The molecular subtypes of breast cancer are based on the level of expression of three

cellular receptors that influence cell growth: ER, PR, and HER2. These make up four subtypes:

luminal A (ER+, PR+, low HER2), luminal B (ER+, PR+, HER2 overexpressing), HER2

overexpressing (ER-, PR-, HER2 overexpressing) and basal, also referred to as triple negative

(ER-, PR-, -/low HER2) (Yang et al., 2007) (Table 2.2). The most prevalent subtype is Luminal

A, comprising approximately 40-50% of breast cancers, followed by luminal B (~20%), basal

(~15-20%) and HER2 overexpressing (~10-15%) (Carey et al., 2006; Vetto et al., 2009).

Luminal A breast cancer typically has the best prognosis and lowest reoccurrence rates, followed

closely by luminal B (Schnitt, 2010). Basal breast cancer, most often diagnosed in young and

African American women, is the most aggressive, and has poor prognosis and few treatment

options (Carey et al., 2006; Schnitt, 2010). HER2 overexpressing tumors also have poor

prognosis and are prone to metastasis and reoccurrence (Carey et al., 2006; Dawood et al., 2011).

ER, PR and HER2 have been used to characterize breast cancer as they can activate growth

signalling pathways in cells and thus regulate progression of the cancer as well as act as targets

for therapy.

Table 2.2. Molecular subtypes of breast cancer.

Molecular Subtype ER Expression PR Expression HER2 Expression

Luminal A + + Low/-

Luminal B + + +

HER2 Overexpressing - - +

Basal - - Low/-

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2.1.3 Hormone Receptors and Signalling

Estrogen and progesterone are two important hormones that alter the growth of breast

cancer cells, and act through the steroid hormone receptors ER and PR. Estrogen is a steroid

hormone that is necessary for the normal development of a variety of tissues including the

mammary gland, but can also lead to growth and proliferation of breast cancer. The highest

circulating and most potent estrogen in premenopausal women is E2, but there are also two other

estrogen forms, estrone and estriol (Björnström & Sjöberg, 2005). The biological effects of

estrogens are mediated through the ER which consists of two forms, ERα and ERβ. ERα was

discovered first and is the more predominant form, particularly in the breast (Dahlman-Wright et

al., 2006; Hall, Couse, & Korach, 2001). ER is a member of the steroid nuclear receptor

superfamily and acts as a ligand-activated transcription factor (Dahlman-Wright et al., 2006).

Traditionally ER was thought to act exclusively through genomic signalling, but it is now known

that there are several pathways leading to ER activation and transcription of ER sensitive genes,

including non-genomic signalling (Björnström & Sjöberg, 2005) (Figure 2.1).

Genomic ER signalling is a result of E2 diffusing into the cytoplasm and binding

ERα/ERβ which releases heat shock protein 90 and causes a conformational change and

translocation of the complex to the cell nucleus (Sommer & Fuqua, 2001). Here it dimerizes and

binds estrogen response elements (ERE) of DNA, leading to the transcription of ER sensitive

genes important in breast cancer growth regulation such as cyclin D1, PR, transforming growth

factor-alpha (TGF-α), and epidermal growth factor (EGF) (Tanos et al., 2012). E2 can also bind

ER and control genes not containing an ERE through protein-protein interaction with

transcription factors, known as ERE-independent genomic signalling (Björnström & Sjöberg,

2005).

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Figure 2.1. Steroid hormone signalling in breast cancer. Representation of estrogen signalling

pathways, which is similar to progesterone signalling with both nuclear and membrane PR.

Estrogen signalling in breast cancer cells can occur through at least four main pathways: (1)

classic genomic signalling (black arrows), (2) genomic non-ERE signalling (red arrows), (3)

ligand-independent signalling (green arrows), and (4) non-genomic signalling (blue arrows).

Genomic signalling: E2 diffuses into the cell, binds ER which causes dimerization and

translocation to the nucleus where the complex binds estrogen response elements (ERE) on DNA

leading to synthesis of ER sensitive genes. ERE-independent genomic signalling: E2 can also

control non-ERE genes through binding ER and interacting with DNA via transcription factors

such as jun and Fos. Ligand-independent signalling: ER can be activated in the absence of E2 by

activation of growth factors and activation of protein kinase cascades which phosphorylates

transcription factors that bind ERE. Non-genomic signalling: ER found in the cell membrane

alone or bound to other receptors or proteins such as caveolin-1 can be activated by E2 and

synthesize ERE and non-ERE gene products.

E2= 17 B estradiol, GF= growth factor, ER= estrogen receptor, ERE= estrogen response

element, TF= transcription factor, PR= progesterone receptor, IGF-IR = insulin like growth

factor 1 receptor

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Even in the absence of E2, ligand-independent ER signalling is possible through growth factor

activation of protein kinase cascades which phosphorylate and activate nuclear ER and regulate

transcription of ERE containing genes (Björnström & Sjöberg, 2005).

Non-genomic ER signalling is a result of E2 binding and activating membrane-associated

ER (Björnström & Sjöberg, 2005). This leads to the activation of protein-kinase cascades and

tyrosine kinase receptors. Membrane ER-E2 complexes have been shown in breast cancer to

activate the transmembrane receptors EGFR, HER2 and insulin-like growth factor-1 receptor

(IGF-IR), resulting in activation of mitogen activated protein kinase (MAPK) and

phosphoinositide-3 kinase (PI3K)/ protein kinase B (Akt) signalling cascades and an increase in

cancer cell growth (Björnström & Sjöberg, 2005; Yu et al., 2012).

2.1.4 HER2 and the Epidermal Growth Factor Receptor Family

Another class of receptors whose expression is often deregulated in breast cancer are the

epidermal growth factor receptor family, consisting of the epidermal growth factor receptor

(EGFR), and human epidermal growth factor receptor 2, 3 and 4 (HER2, HER3, HER4) (Yarden,

2001; Zhang et al., 2008) (Figure 2.2). These receptors have intrinsic tyrosine kinase activity,

and regulate several signalling pathways involved in cell growth (Figure 2.2). HER2 and EGFR

are of particular interest in breast cancer as they are often overexpressed in aggressive cases and

associated with poor clinical outcomes (Zhang et al., 2008).

HER2 is a transmembrane glycoprotein, overexpressed in 25-30% of breast cancers and a

result of amplification of the ErbB2/neu proto-oncogene (Suter & Marcum, 2007; Szöllösi,

Balázs, Feuerstein, Benz, & Waldman, 1995). Unlike ER, HER2 has no direct ligand but rather

becomes activated through homodimerization, or heterodimerization with EGFR, HER3 or

10

Figure 2.2. Growth factor signalling in breast cancer. There are a wide variety of growth

factor receptors present in breast cancer cells, including the family of epidermal growth factor

receptors (EGFR, HER2, HER3) and IGF-IR. These receptors are primarily found in the

phospholipid rich cell membrane and are activated by a number of ligands including EGF, NRG

and IGF-I. These receptors create homo or heterodimers and act through intrinsic tyrosine kinase

activity to induce growth signalling pathways including MAPK and PI3K/Akt. Signalling

through these pathways leads to increased cell proliferation and decreased apoptosis, leading to

an increase in cancer cell growth. PTEN is a negative regulator of the PI3K/Akt cascade and

results in a decrease in cell growth through increasing apoptosis.

EGF= epidermal growth factor, EGFR= epidermal growth factor receptor, HER2= human

epidermal growth factor 2, HER3= human epidermal growth factor 3, NRG= neuregulin, IGF-1=

insulin like growth factor 1, IGF-IR= isulin like growth factor receptor 1,

PIP2=phosphatidylinositol 4,5-bisphosphate, PIP3= phosphatidylinositol (3,4,5)-triphosphate ,

PTEN= phosphatase and tensin homologue , mTOR= mammalian target of rapamycin

11

HER4 (Suter & Marcum, 2007). Dimers involving HER2 are the most potent of the EGFR

family, as HER2 is the most stable at the cell membrane and decreases the rate of dissociation

from EGFR (Yarden, 2001). Upon activation, HER2 can increase cell proliferation and decrease

apoptosis through tyrosine kinase initiation of signalling pathways including MAPK and

PI3K/Akt (Suter & Marcum, 2007). HER2 can also cross-talk and activate estrogen signalling,

even in the absence of E2, leading to an increase in cancer cell growth (Shou et al., 2004). See

Figure 2.3.

Similar to HER2, overexpression of EGFR is a result of amplification of the ErbB1 gene,

and is overexpressed in 20-80% of breast cancers (Zhang et al., 2008). EGFR has a number of

natural ligands, including EGF, TGF-α, and neuregulins (NRG), which cause the formation of

homo or heterodimers with HER2, HER3 or HER4 (Suter & Marcum, 2007; Yarden, 2001).

Once dimerization occurs, signalling cascades such as MAPK and PI3K/Akt discussed above are

activated and cancer cell growth occurs. It has been shown that the expression of EGFR is

inversely proportional to ER expression and typically high in aggressive triple negative breast

cancer (TNBC) and thus a breast cancer receptor of interest (Lehmann et al., 2011; Rakha et al.,

2007).

IGF-IR can also play a significant role in breast cancer growth as it is involved in

signalling for apoptosis, cell proliferation, angiogenesis and metastasis (Fagan & Yee, 2008).

IGF-IR is located in the cell membrane and is activated by insulin-like growth factors resulting

in the downstream activation of both PI3K/Akt and MAPK signalling pathways (Fagan & Yee,

2008). Similar to HER2 and EGFR, IGF-IR activation can also lead to the activation of ER and

result in increased expression of ER sensitive genes and pathways (Figure 2.3) (Fagan & Yee,

2008; Yu et al., 2012). Due to their regulation of genes and signalling cascades intimately

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Figure 2.3. Cross talk in breast cancer cell signalling. Growth of breast cancer cells is

influenced by a number of factors and signalling pathways, and activation of these can further

influence the expression and activation of other receptors and pathways leading to increased cell

growth. Steroid hormone receptors (ER, PR) can regulate the expression and activation of

growth factor receptors including HER2, EGFR and IGR-IR. Similarly, growth factor receptors

can regulate the expression and activation of ER and PR. Activation of MAPK and PI3K/Akt

signalling pathways can also regulate steroid hormone signalling. This cross-talk between

pathways allows for resilience in cancer cell growth, as steroid hormone and growth factor

receptors can be activated even in the absence of their ligands.

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connected to breast cancer cell growth, ER, EGF, HER2 and IGF-IR have become common

targets for breast cancer therapy.

2.2 Breast Cancer Therapy

2.2.1 Traditional Therapies and Personalized Medicine

Therapy for breast cancer is complex and is constantly evolving as new targets, drugs and

approaches are developed. Typical treatment involves surgery, radiation therapy, chemotherapy,

hormone or targeted therapy, or a combination of these (National Cancer Institute, 2012). While

surgery, radiation and chemotherapy are broad treatments, hormone and targeted therapy are

personalized and based on tumor characteristics of patients.

A common hormone therapy for ER-positive breast cancer patients is the selective ER

modulator TAM. By binding and temporarily blocking the ER, TAM inhibits E2 from binding

ER and inducing cell growth. Other hormone therapies include aromatase inhibitors which stop

E2 production in fatty tissue; however this is not effective at reducing E2 production in the

ovaries and therefore ineffective in pre-menopausal women (Sommer & Fuqua, 2001).

A common targeted therapy is TRAS, better known by its trade name Herceptin, used in

HER2-overexpressing breast cancers. TRAS is a recombinant humanized monoclonal antibody,

whose mechanism of action is not fully understood, but likely acts through binding, internalizing

or degrading HER2 leading to a reduction in downstream MAPK and PI3K/Akt signalling

(Baselga et al., 2001; Ripple et al., 2005; Sliwkowski et al., 1999). HER2 has proven to be a

good target for breast cancer therapy as many other drugs also target HER2, including

pertuzumab and Lapatinib.

Despite the vast improvement and personalization of traditional therapies such as TAM

and TRAS, breast cancer mortality remains high due to a number of problems. There are a wide

14

range of negative side effects associated with the drugs, such as fatigue, hot flashes,

gastrointestinal upset, muscle and bone pain, blood clots, and in extreme cases cardiotoxicity and

liver damage (National Cancer Institute, 2007; Vogel et al., 2002). In addition, these drugs are

very costly, with one year of TAM or TRAS costing approximately $45,000 to $75,000 (Drucker

et al., 2008; Imai et al., 2007). Even with these advances, many breast cancers do not respond to

treatment. Basal, or TNBC for example, do not respond to TAM or TRAS as they do not have

ER or HER2 receptors. These cancers are very aggressive and the lack of effective treatment

options is a serious issue. Finally, even in cancers that initially respond well to TAM or TRAS,

drug resistance usually occurs in patients within one year, after which treatment is ineffective

(Baselga et al., 2005; Vogel et al., 2002). Due to these problems, health care providers and

patients are looking to complementary and alternative medicine (CAM) to provide other avenues

for treatment, or assist in the effectiveness of current therapies.

2.2.2. Complementary and Alternative Medicine in Breast Cancer

CAM, medical and health care systems, practices and products that are not considered

conventional medicine (National Center for Complementary and Alternative Medicine, 2013),

are popular amongst patients with a variety of cancers; in particular as high as 81.9% of those

with breast cancer reported CAM use (Boon et al., 2007; Rausch et al., 2011). Strategies include,

but are not limited to, natural products (herbs, vitamins and minerals, probiotics, dietary

supplements) and mind and body practises (acupuncture, massage therapy, mediation, movement

therapies, relaxation techniques, chiropractic, physical therapy, yoga, and tai chi) (Boon et al.,

2000; Tripathy, 2011; National Center for Complementary and Alternative Medicine, 2013).

Studies have shown that North American breast cancer patients use a wide range of dietary

supplements, with two of the most commonly used being ALA-rich FS and FSO, and n-3 PUFA

15

(Anderson & Taylor, 2012; Boon et al., 2007; Boucher et al., 2012; Greenlee et al., 2009; Rausch

et al., 2011). Current literature on breast cancer and FS and FSO, and n-3 PUFA are discussed in

sections 2.3 and 2.4 respectively.

2.3 Flaxseed, Flaxseed Oil and Breast Cancer

2.3.1 Components of Flaxseed and Flaxseed Oil

FS has garnered interest as a dietary supplement due to its high amount of dietary fiber,

protein and phytoestrogen lignans including SDG, as well as its oil being very rich in the n-3

PUFA ALA. The composition of FS varies but generally contains approximately 30% dietary

fiber, 20% protein, 40% oil and 820–1,050 μmol lignan per 100 g of FS (Daun et al., 2003; Liu

et al., 2006; Thompson et al., 2006). FSO is comprised of a variety of neutral (acylglycerols,

fatty acids) and polar (glycolipids, phospholipids) lipids, with approximately 57% of FSO being

ALA, 16% n-6 PUFA linoleic acid (LA), 9% saturated fat and 18 % monounsaturated fat (Daun

et al., 2003).

2.3.2 Epidemiological and Clinical Evidence

Despite their widespread use, very few clinical trials have studied the effectiveness of FS

and FSO as a complementary breast cancer therapy (Table 2.3). One randomized double blind

placebo controlled study showed that a muffin containing 25g of ground FS consumed daily

decreased cell proliferation (34.2%) and HER2 protein expression (71%), and increased

apoptosis (30.7%) compared to baseline while there were no changes in the placebo group

(Thompson et al., 2005). This study was conducted in postmenopausal breast cancer patients and

did not separate tumours based on molecular subtype. One ongoing clinical trial is investigating

the effect of 25g/day ground FS with and without aromatase inhibitor drugs

16

16

Table 2.3. Summary of studies investigating the effect of FS and FSO on breast cancer.

Model Treatments/Measures Results Reference

Clinical and Epidemiological Studies

RCT; 32 postmenopausal

breast cancer patients

25g FS muffin/day or control placebo muffin

biopsy tissue at diagnosis and surgery

↓ cell proliferation 34.2% (p =0.001) in FS group

↑ apoptosis 30.7% (p =0.007) in FS group

↓ HER2 expression 71% (p =.0.003) in FS group

Thompson et al, 2005

RCT; ongoing, 28

postmenopausal ER-

positive breast cancer

patients

25g FS/day with or without 1mg Anastrozole/day

or placebo control

biopsy tissue at diagnosis and surgery

Ongoing NCT00612560

Case-control; 2,999


and 3,370 healthy

controls

FS and flax bread consumption (FFQ) FS↓ breast cancer risk, OR=0.82 (0.69-0.97)

Flax bread ↓ breast cancer risk, OR=0.77 (0.67-

0.89)

Lowcock et al., 2013

Meta-analysis; 11

prospective cohort and

10 case-control

Lignan consumption (FFQ and biomarker

measurements)

No significant association between lignan

exposure and breast cancer risk in all women

Lignan intake ↓ ER-positive breast cancer risk in

postmenopausal women , RR=0.86 (0.78-0.94)

Buck et al., 2010

Meta-analysis; 7

prospective cohort and

casr-control

Enterolactone concentrations in serum, plasma

and urine

Serum enterolactone ↓breast cancer risk in all

women. RR= 0.72 (0.55-0.88)

Serum enterolactone ↓breast cancer risk in

postmenopausal women, RR= 0.66 (0.55-0.77)

Zaineddin et al., 2012

Meta-analysis; 23 studies Lignan consumption (FFQ) No association between plant lignan intake and

breast cancer risk in all women

In postmenopausal women lignan intake ↓ breast

cancer risk; OR=0.85 (0.78-0.93)

Velentzis et al, 2009

In Vivo Animal Studies

OVX athymic mice with

MCF-7 xenografts

BD, FSO (38.5g /kg), SDG (1g/kg) and

FSO+SDG

Low E2

↑ tumor regression rate in all groups vs. control

↓ cell proliferation in all groups compared to

control

No effect on apoptosis

Saggar et al., 2010b


MCF-7 xenografts

BD, 10% FS

Low E2

↓ tumor growth, cell proliferation and ↑ apoptosis

in FS vs. control

Chen et al., 2009

17

17


MCF-7 xenografts

BD, 10% FS diet

Low E2

↓ tumor growth in all treatment groups compared

to control

No difference in tumor area, cell proliferation or

apoptosis in FS vs control

Power et al., 2008;

Saarinen et al., 2006


MCF-7 xenografts

BD, 4% FSO

High E2


in FSO vs. control

Truan et al., 2010


MCF-7 xenografts

BD, ED (15mg/kg), EL (15mg/kg) or 10% FS

High E2

↓ tumor growth and angiogenesis in all treatments

vs. control

Bergman Jungestrom

et al., 2007

Athymic mice with

MDA-MB-435

xenografts

BD, 10% FS, SDG and FSO at levels present in

10% FS or SDG+FSO

High E2


in all treatments except SDG vs. control

Wang et al., 2005b

Athymic mice with

MDA-MB-435

xenografts

BD, 10% FS

High E2

↓ tumor growth and cell proliferation in FS

compared to control

Chen et al., 2002

Sprague-Dawley rats

with DMBA-induced

tumours

(progression and tumour

development stages)

BD, 2.5% or 5% FS diet or FSO or SDG at

levels present in 5% FS

Diet treatment started 13 weeks post DMBA

↓ established tumor growth in 2.5% and 5% FS

and FO compared to control; no effect of SDG

↓ new tumor volume in SDG vs. control; no effect

of 2.5% or 5% FS or FO

No difference in tumor incidence and number

between groups

Thompson et al.,

1996a

Sprague-Dawley rats

with DMBA-induced

tumours

(initiation and early

promotion stages)

BD, 5% FS diet

FS fed at (i) initiation, (ii) early promotion or

(iii) initiation and promotion

↓ tumor size in rats fed FS at promotional stage; no

effect of FS fed at initiation

↑ tumor burden in promotion only vs. initiation and

promotion FS groups

Serraino & Thompson,

1992

Sprague-Dawley rats

with DMBA-induced

(initiation stage)

BD, 5% or 10% FS flour (FF; 1.9-3.8% FO) or

defatted FS meal (FM; 0.14-0.28% FO)

Diets fed for 4 weeks pre DMBA exposure and

rats sacrificed 24h post DMBA

↓ mitotic index in terminal end buds of 5 and 10%

FF groups

↓ cell proliferation in terminal end buds of 5% FF

groups

↓ nuclear aberrations in terminal end buds of 5%

FF, in terminal duct of 5 and 10% FM, in alveolar

buds of 10% FF and 10% FM

Serraino & Thompson,

1991

Sprague-Dawley rats

with NMU-induced

tumours

(Early promotion stage)

BD, 2.5% or 5% FS

Diet treatment started 2 days post NMU

↓ tumor invasiveness and grade in 2.5% and 5%

FS vs. control

No effects on final tumor weight, volume,

multiplicity and incidence

Rickard et al., 1999

18

18

Sprague-Dawley rats

with NMU-induced

(initiation stage)

Diets contained either 15% FSO or 15% palm

oil/sunflower oil

FSO ± vit E and +vit E+oxidant

↑ tumor growth FSO + vit E compared to FSO - vit

E; no difference in tumor area and multiplicity,

latency or incidence

↓ tumor area, multiplicity, incidence and number

in FSO + vit E + oxidant compared to FSO + vit E

Cognault et al.,2000

Tg.NK (MMTV-c-neu)

model

BD, FS diets (0.006%, 0.018%, 0.054%)

starting at day 25

↓tumor incidence, number of tumors per mouse

and number of large tumors in 0.054% FS group

vs control

No effect on the number of tumor bearing mice

and tumor multiplicity

Birkved et al. 2011

Tg.NK (MMTV-c-neu)

model

Gavage of FSO or melatonin in corn oil starting

at 4 weeks of age

Varying dose of FSO

No significant effect of FSO on tumor incidence,

multiplicity

Trend toward ↓number of tumors/mouse in high

dose FSO

↓ weight of tumors/mouse and mean tumor weight

in high dose FSO group

Rao et al., 2000

Athymic mice with 410

and 410.4 xenografts

BD, FSO or 4:1 fish oil (FO):corn oil (CO) fed (i)

before implantation, (ii) before implantation

with removal of primary tumor, (iii) after

implantation

no difference in tumor incidence or tumor size

primary tumors grew faster and were larger in the

FSO group vs CO

primary tumors were smallest in the FSO group vs

FO and lowest metastasis in FSO

Fritsch et al., 1990

In Vivo Animal Studies : Drug-Diet Interaction


BT-474 xenografts

TRAS ± FSO (80 g/kg)

No significant effect of FSO alone on tumour

growth

↓ tumor area, cell proliferation and ↑ apoptosis in

FSO+TRAS2.5 vs. TRAS2.5

Mason et al., 2010


MCF-7 xenografts

BD, FSO (38.5g/kg), SDG (1g/kg) and FO+SDG

± TAM

Low E2


in all treatment groups vs control

FSO and FSO+SDG had the greatest effects

Saggar et al., 2010a


MCF-7 xenografts

BD± TAM, ± 5%, 10% FS

Low E2

↓ tumor regrowth, cell proliferation and ↑

apoptosis in TAM+10% FS vs TAM alone

Chen et al., 2007b


MCF-7 xenografts

BD± TAM, ± 5%, 10% FS

High E2


in all groups vs control

10% FS as effective as TAM alone; TAM+5% FS

more effective than TAM or 5% alone in ↓tumor

growth

Chen et al., 2007a

19

19


MCF-7 xenografts

BD± TAM, ± 10% FS

Low and high E2

Low E2: ↓ tumor growth, cell proliferation and ↑

apoptosis in FS and FS+TAM vs. TAM and

control

High E2: ↓ tumor growth, cell proliferation and ↑

apoptosis in all treatments vs control; ↓ cell

proliferation in FS+TAM vs TAM alone

Chen et al., 2004

Abbreviations: ALA= α-linolenic acid, BD= basal diet, CO= corn oil, DMBA= dimethylbenz(α)anthracene, E2= 17-β estradiol, ED= enterodiol, EL=

enterolactone, FFQ= food frequency questionnaire, FO= fish oil, FS= flaxseed, FSO= flaxseed oil, HER2= human epidermal growth factor receptor 2, NMU= N-

nitrosomethyl-urea, OR= odds ratio, OVX= ovariectomized, RCT= randomized controlled trial; RR= relative risk, SDG= secoisolariciresinol diglucoside, TAM=

tamoxifen, TRAS= trastuzumab.

Table modified from Wiggins, A.K., Mason, J.K., & Thompson, L.U. (2013).Beneficial influence of diets enriched with flaxseed and

flaxseed oil on cancer. In Cancer chemoprevention and treatment by diet therapy. Cho, W.C.S. (1 Ed). pp. 55-90. Dordrecht :

Springer, with kind copyright permission from Springer and Business Media.

20

(anastrozole) in postmenopausal patients with ER+ breast cancers on proliferation, apoptosis and

ER, PR, HER2 and IGF-IR protein expression (NCT00612560). Due to the limited number of

clinical studies, few conclusions can be made regarding the use of FS and FSO as a

complementary breast cancer therapy, but initial studies are promising.

In Ontario women, consumption of both FS and FS bread, measured by food frequency

questionnaires (FFQ), showed a 20-30% reduction in breast cancer risk with odds ratio of 0.82

(0.69-0.97) and 0.77 (0.67-0.89) respectively (Lowcock et al., 2013). This effect was seen

regardless of intake level (monthly or less to weekly and daily) but did depend on menopausal

status, with no significant decrease in risk in premenopausal women. Enterolactone, the product

of FS lignans after intestinal bacteria metabolism, measured in serum, plasma and urine was

associated with a significant reduced breast cancer risk by 28% in all women, however risk was

reduced to a greater extent (34%) in postmenopausal women (Zaineddin et al., 2012). Similarly,

stratifying by menopausal status in another meta-analyses showed a postmenopausal specific 14-

15% reduction in breast cancer risk with lignan intake by FFQ (Buck et al., 2010; Velentzis et

al., 2009) but no similar association with serum lignans. Conflicting evidence is likely a result of

heterogeneity of studies with respect to lignan measurement (diet intake from FFQ versus lignan

biomarkers), recall bias, unrepresentative blood/urine samples, and factors such as menopausal

status and breast cancer subtype.

2.3.3 Pre-Clinical Evidence

Many in vivo rodents models with established mammary tumours have been used to

investigate the antitumorigenic effect of FS and FSO on breast cancer. The research generally

supports the use of FS and FSO as a complementary agent, however there are conflicting

evidence leading to confusion (Table 2.3).

21

Ovariectomized athymic nude mice with MCF-7 (ER+, low HER2) xenografts showed a

decrease in tumour growth from diets supplemented with 10% FS (Chen et al., 2009; Power et

al., 2008; Saarinen et al., 2006) and 4% FSO (Saggar et al., 2010b; Truan et al., 2010). These

studies were done in both high and low E2 environments suggesting that FS and FSO effects are

similar regardless of E2 level in ER+ breast cancers, however effects were more pronounced in

high E2 conditions (Truan et al., 2010). Discrepancies in cell proliferation and apoptosis effects

exist however, with 2 studies (Chen et al., 2009; Truan et al., 2010) showing a decrease in cell

proliferation and increase in apoptosis with FS and FSO respectively, while the other studies

showed no effect on cell proliferation (Power et al., 2008; Saarinen et al., 2006) and apoptosis

(Power et al., 2008; Saarinen et al., 2006; Saggar et al., 2010b). Tumour growth of ER- negative

breast cancer xenografts (MDA MB 435) in high E2 environments also caused a significant

reduction in growth with 10% FS (Chen et al., 2002; Wang et al., 2005) and FSO at a level

present in 10% FS (Wang et al., 2005) diets. In these studies there was also a decrease in cell

proliferation and increase in apoptosis. This cell line has now been identified as melanoma and

not mammary derived so data should be interpreted with caution, although it still supports FS

and FSO effects at reducing carcinoma cell growth (Ellison et al., 2002; Rae et al., 2007). In a

similar model but using BT-474 (ER+, HER2 +) xenografts in a high E2 environment there was

no significant reduction in tumor growth from a 8% FSO diet, however this diet, in conjunction

with 2.5mg/kg TRAS, decreased tumour area and cell proliferation, and increased apoptosis

compared to 2.5 mg/kg of TRAS alone (Mason et al., 2010). This suggests that reduction in

tumour growth may be breast cancer subtype specific, and that FSO may enhance effectiveness

of breast cancer drugs.

22

FS and FSO effects in other rodent models of breast cancer have also been studied

including 7,12-Dimethylbenz(a)anthracene (DMBA) and N-methyl-N-nitrosourea (NMU)

induced tumours and MMTV-c-neu transgenic mice. These models are useful in determining

effects of compounds during the initiation and progression stages of mammary carcinogenesis

and allows for comparisons of the number of tumours initiated and tumour growth. In Sprague-

Dawley rats with DMBA-induced tumours, 2.5-5% FS (Serraino & Thompson, 1992; Thompson,

et al., 1996) and FSO at a level present in 5% FS (Thompson et al., 1996) reduced established

tumour growth but had no effect on the initiation of new tumours. Time of FS exposure has also

been shown to alter tumour growth, as rats fed a 5% FS diet during the promotion stage only had

greater tumour burden than those fed FS during both the initiation and promotion stages

(Serraino & Thompson, 1992). Other models showed a range of tumour effects with FS and FSO

supplementation ranging from decreased tumour incidence and number (Birkved et al., 2011)

and tumour weight (Rao et al., 2000) to no effect on tumour incidence (Rao et al., 2000; Rickard

et al., 1999) or tumour weight (Rickard et al., 1999).

2.3.4 Limitations in Current Understanding of Flaxseed, Flaxseed Oil and Breast Cancer

The limited clinical and epidemiological evidence on FS and FSO makes it difficult to

make conclusions regarding their effects on breast cancer tumour growth and progression in

humans. The positive results to date in pre-clinical studies highlight the need for further clinical

based investigation into FS and FSO antitumorigenic effects. Overall in vivo studies show

promise for the use of FS and FSO as a complementary breast cancer therapy, but several factors

may alter their effectiveness including receptor expression of the tumour as indicated by

differences in effect between MCF-7 and BT-474 xenographs, and E2 levels present. It is also

unclear what FS component is responsible for the antitumorigenic effects (i.e. whole FS, FSO or

23

a specific bioactive component such as ALA). In vitro studies allow for control of these variables

and direct comparison of differences in cancer outcomes, however very few studies have been

done in this regard.

2.4 n-3 PUFA and Breast Cancer

2.4.1 n-3 PUFA Classification and Sources

n-3 PUFA are fatty acids which contain multiple double bonds, with the first one

occurring at the third carbon from the methyl end of the hydrocarbon chain. ALA (18:3n-3) is

the essential n-3 PUFA that must be obtained from dietary sources as the body is incapable of

synthesizing it. FSO is the richest dietary source of ALA, while others include rapeseed,

soybean, perilla and chia seed oils, as well as walnuts (Cunnane, 2003). The other two main n-3

PUFA are EPA (20:5n-3) and DHA (22:6n-3) found primarily in marine sources. ALA can be

converted in the body to EPA and DHA through a series of elongation (addition of 2 carbons)

and desaturation (double bond insertion) reactions, as depicted in Figure 2.4. This conversion

however is thought to be quite low, with reports ranging from less than 1% to 5% for DHA

conversion (Anderson & Ma, 2009; Brenna et al., 2009). n-3 PUFA have been associated with

improved health, including improvements in heart health, diabetes, mental illness and cancer

(Anderson & Ma, 2009; Fetterman Jr & Zdanowicz, 2009; Pelliccia et al., 2013). The vast

majority of n-3 PUFA research has focused on the longer chain EPA and DHA, even though

ALA is much more prevalent in the North American diet, with the average US adult consuming

~1.5 g/d ALA and ~135 mg/d of EPA+DHA in 2006 (United States Department of Agriculture,

2012).

24

Figure 2.4. Conversion of ALA to longer chain n-3 PUFA . ALA (C18:3n-3), the

essential n-3 PUFA required in the diet, can be converted to long chain n-3 PUFA EPA

(20:5n-3) and DHA (22:6n-3) through a series of steps involving elongase and Δ5 and Δ6

desaturase enzymes.

25

2.4.2 Epidemiological and Clinical Evidence

To date, few clinical trials have looked at the relationship between n-3 PUFA and breast

cancer, however there are currently 3 ongoing trials (Table 2.4) studying the effect of a daily n-3

PUFA capsule on breast tumour fatty acid profiles, markers of cancer risk and progression and

tumour cell proliferation and apoptosis (NCT01869764, NCT01282580, NCT00627276).

Many epidemiological studies have investigated the potential role of n-3 PUFA in breast

cancer prevention, as summarized in Table 2.5. Case-control studies have generally shown no

effect of total n-3 PUFA intake on breast cancer risk (Chajès et al., 1999; Chajès et al., 2008;

Pala et al., 2001; Saadatian-Elahi et al., 2002; Shannon et al., 2007; Takata et al., 2009; Vatten,

et al., 1993; Wirfält et al., 2002), however one study did show a decrease in risk (Kuriki et al.,

2007). This lack of significant total n-3 PUFA effect on breast cancer risk is also observed in

prospective cohort studies (Gago-Dominguez et al., 2003; Park et al., 2012; Thiébaut et al., 2009;

Wakai et al., 2005).

Marine n-3 PUFA EPA and DHA have also been specifically studied in both case-control

and prospective cohort models. Similar to total n-3 PUFA intake, the majority of case-control

(Chajès et al., 1999; Chajès et al., 2008; Pala et al., 2001; Saadatian-Elahi et al., 2002; Takata et

al., 2009; Vatten et al., 1993; Voorrips et al., 2002) and cohort studies (Cho et al., 2003; Folsom

& Demissie, 2004; Murff et al., 2011; Park et al., 2012; Thiébaut et al., 2009) showed no

significant reduction in breast cancer risk with EPA and DHA intake. Four prospective cohort

studies (Gago-Dominguez et al., 2003; Patterson et al., 2011; Sczaniecka et al., 2012; Wakai et

al., 2005) did however show a significant reduction in breast cancer risk with marine n-3 PUFA

intake and highlight the need for continued work in this area.

26

26

Table 2.4. Summary of studies investigating effect of n-3 PUFA on breast cancer.

Model Treatments/Measures Results Reference

Clinical and Epidemiological Studies

RCT; 60 newly

diagnosed breast cancer

patients

7-14 day oral supplementation with n-3 PUFA or

placebo

Measure breast and plasma n-3 PUFA

concentrations, and level of cell proliferation

and apoptosis in tumour

Ongoing NCT01869764

RCT; 25 high breast

cancer risk women

Intake of fish versus n-3 PUFA capsule (Lovaza)

for 3 months

Serum and breast fatty acids

Ongoing NCT01282580

RCT; 16 newly

diagnosed breast cancer

patients

8 week n-3 PUFA capsule or placebo

Markers of breast cancer risk and progression

Breast and serum fatty acid profile

Ongoing NCT00627276

Case control; 65 breast

cancer patients, 260

controls

Fatty acid composition of serum phospholipids No association with breast cancer risk and total n-

3 PUFA, EPA, DHA or ALA levels in serum

phospholipids

Vatten et al., 1993



controls

Fatty acid composition of erythrocytes No association with breast cancer risk and total n-

3 PUFA, EPA, DHA or ALA levels in erythrocyte

phospholipids

Pala et al., 2001



controls



phospholipids

Saadatian-Elahi et al.,

2002



controls



phospholipids

Chajes et al., 2008



controls



phospholipids

Takata et al., 2009


cancer patients, 59

controls

Fatty acid composition of breast adipose tissue ↓ breast cancer risk with increasing ALA levels

in breast adipose tissue (p trend= 0.026)

Klein et al., 2000

27

27

Case control ; 365 breast


controls

Questionnaire and FFQ ↑ breast cancer risk with ALA intake, OR=3.8

(1.5-9.4)

De Stefani et al, 1998

Case control; 241

patients and 88 controls

Fatty acid composition of breast adipose tissue ↓ breast cancer risk with ALA breast adipose

levels, adjusted OR =0.39 (0.19-0.78), p trend=

0.01

Maillard et al, 2002

Case control;414 cases,

429 controls

FFQ for ALA intake No association with breast cancer risk and ALA

intake, OR= 1.27 (0.85-1.89), p trend= 0.284

Nkondjock et al., 2003

Case control ; 196 cases,

584 controls



phospholipids

Chajes et al., 1999

Case Control; 322 cases,

1030 controls

Erythrocyte fatty acid concentrations No association with breast cancer risk and total n-

3 PUFA, EPA, DHA or ALA levels of

erythrocytes, OR=0.99 (0.54-1.82), p trend=0.59

Shannon et al, 2007

Case Control; 103 cases,

309 controls

Erythrocyte fatty acid concentrations

Dietary record

No association with breast cancer risk and ALA

intake or erythrocyte composition

↓ breast cancer risk from total n-3 PUFA, EPA and

DHA

Kuriki et al, 2007

Case Control; 237 breast

cancer patients and 910

controls

Total n-3 PUFA intake (FFQ) No association with breast cancer risk and total n-3

PUFA intake

Wirfalt et al., 2002

Prospective cohort;

43,721 postmenopausal

women

Marine n-3 PUFA intake (FFQ) No association with breast cancer risk and marine

n-3 PUFA intake

Folsom et al., 2004

Prospective cohort;

26,420 women

Total and marine n-3 PUFA intake (FFQ) No association with breast cancer risk and total n-3

PUFA intake

Marine n-3 PUFA ↓ breast cancer risk, RR=0.50

(0.30-0.85)

Wakai et al., 2005

Prospective cohort;

91,369 women

Total and marine n-3 PUFA intake (FFQ) No association with breast cancer risk and total

and marine n-3 PUFA intake

Cho et al, 2003

Prospective cohort;

35,612

Total and marine n-3 PUFA, and ALA intake

(FFQ)

No association with breast cancer risk and total n-3

PUFA and ALA intake

↓ breast cancer risk from marine n-3 PUFA intake,

RR=0.72 (0.53-0.98)

Gago-Dominguez et al,

2003

Prospective Cohort; 121


Fatty acid methyl esters of breast adipose tissue ↓ breast cancer metastases when breast adipose

ALA above 0.38% of total fatty acids

Bougnoux et al, 1994

28

28

Prospective cohort in 56,007

French women

ALA, marine and total -3 PUFA intake (FFQ) No association with breast cancer risk and total or

marine n-3 PUFA or ALA intake

↓ breast cancer hazard ratio with ALA intake from

fruits and vegetables, and vegetable oils (p trend

<0.0001, 0.017)

↑ with ALA intake from nut mixes (p trend 0.004)

and processed foods (p trend .068)

Thiebaut et al, 2009

Prospective cohort; 73,303

women

ALA and marine n-3 PUFA intake No association with breast cancer risk and marine

n-3 PUFA or ALA intake

Murff et al., 2011


women

Marine n-3 PUFA intake (24-hour recall) ↓ breast cancer risk with marine n-3 PUFA intake

RR=0.76 (0.61-0.95)

Patterson et al., 2011


postmenopausal women

Total n-3 PUFA, EPA, DHA and ALA intake

(FFQ)

No association with breast cancer risk and total n-3

PUFA, EPA, DHA or ALA

Park et al., 2012


postmenopausal women

ALA, EPA and DHA intake (FFQ) No association with breast cancer and ALA intake

↓ breast cancer risk with EPA and DHA intake

RR=0.70 (0.54-0.90), RR=0.67 (0.52-0.87)

Sczaniecka et al., 2012

Cohort study; 62 573

women

FFQ for EPA, DHA and ALA intake ↓ breast cancer risk with ALA intake RR=0.70

(0.51-0.97), p trend=0.006

No association with breast cancer risk and EPA

intake, RR=0.98 (0.72-1.35)

No association with breast cancer risk and DHA

PUFA intake, RR=1.00 (0.72-1.37)

Voorrips et al, 2002

Meta-analysis; 8 prospective

cohort studies

Fish, ALA, EPA and DHA consumption No association with fish, total n-3 PUFA, EPA or

DHA intake and breast cancer incidence

↓ breast cancer risk in highest ALA intake quintile,

RR=0.70 (0.51-0.97)

MacLean et al, 2006

Meta-analysis; 21

prospective cohort studies

Fish, ALA, EPA and DHA intake or tissue

biomarkers

↓ breast cancer risk with marine n-3 PUFA intake,

RR=0.86 (0.78-0.94)

No association with fish and ALA intake and

breast cancer incidence

Zheng et al, 2013

Meta-analysis; 3 cohort and

7 case-control studies

Fatty acid composition of adipose

tissue/serum

Case control studies: high ALA content ↓ risk of

breast cancer

Cohort Studies: No association between ALA

content and breast cancer risk; in postmenopausal

women ALA content ↑ breast cancer risk,

RR=1.14 (1.03-1.26)

Saadatian-Elahi et al,

2004

29

29

In Vitro Studies

MCF-7 cells 50 μM ALA + 1nM E2 for 5 days ↓ cell proliferation by 33% Traun et al, 2010

MCF-7 cells Up to 100μM ALA for 24, 48, 72 hours ↓ cell growth dose and time dependently

↑ apoptosis dose dependently

Kim et al, 2009

MCF-7, MDA MB 231 cells 50-300μM DHA and EPA, 72 hours ↓ cell viability above 200μM EPA and DHA

↓ EGFR, Bcl2 expression above 200μM EPA and

DHA

Corsetto et al, 2011

MCF-7 cells 100μM EPA and DHA, 5 days ↓ cell growth

No effect on apoptosis

Chamras et al, 2002

MCF-7, MDA MB 231 cells 71.83μM ALA, 66.13μM EPA, 60.89μM

DHA, 5 days

↓ cell growth in MDA MB 231 but not MCF-7

No Effect on cell viability

Chajes et al, 1995

MDA MB 231 10-200μM ALA, 24 hours ↓ cell number Horia et al, 2005

MDA MB 231 20-100μM DHA, 24-72 hours ↓ cell proliferation

↑ apoptosis

Blanckaert et al, 2010

MDA MB 231 100μM DHA, 48 hours ↓ cell growth and proliferation

↓ EGFR expression in lipid rafts

Rogers et al, 2010

SKBr3, BT 474 cells 10-20 μM ALA + TRAS, 48 hours ↓ HER2 expression and dose dependently

↓ cell proliferation when ALA combined with

TRAS

Menendez et al, 2006

Abbreviations: ALA= α-linolenic acid, BD= basal diet, CO= corn oil, DHA= docosahexaenoic acid, DMBA=

dimethylbenz(α)anthracene, E2= 17-β estradiol, ED= enterodiol, EGFR= epidermal growth factor receptor, EL= enterolactone, EPA=

eicosapentaenoic acid, FFQ= food frequency questionnaire, FO= fish oil, FS= flaxseed, FSO= flaxseed oil, HER2= human epidermal

growth factor receptor 2, NMU= N-nitrosomethyl-urea, OR= odds ratio, OVX= ovariectomized, RCT= randomized controlled trial;

RR= relative risk, SDG= secoisolariciresinol diglucoside, TAM= tamoxifen, TRAS= trastuzumab

Table modified from Wiggins, A.K., Mason, J.K., & Thompson, L.U. (2013).Beneficial influence of diets enriched with flaxseed and

flaxseed oil on cancer. In Cancer chemoprevention and treatment by diet therapy. Cho, W.C.S. (1 Ed). pp. 55-90. Dordrecht :

Springer, with kind copyright permission from Springer and Business Media.

30

Focusing specifically on ALA and breast cancer risk, case-control studies also provide

inconclusive evidence. Four case-control studies which measured ALA intake (FFQ) and

erythrocyte content found no association between ALA and breast cancer risk (Chajès et al.,

1999; Kuriki et al., 2007; Nkondjock et al., 2003; Shannon et al., 2007). Two case-control

studies however showed that ALA content in breast adipose tissue was inversely associated with

breast cancer risk (Klein et al., 2000; Maillard et al., 2002), while one Uruguayan case-control

study found ALA intake (FFQ) increased breast cancer risk (De Stefani et al., 1998). These

findings however may be a result of red meat being the major ALA source rather than plant

sources (Bougnoux and Chajes, 2003). The role of ALA in breast cancer as measured by cohort

studies generally shows a protective effect. One study in the Netherlands which measured ALA

intake by FFQ found an inverse association with breast cancer risk (Voorrips et al., 2002), and

another found breast adipose ALA content was inversely associated with risk of metastasis in

patients with non- metastatic breast cancer (Bougnoux et al., 1994). As seen in the Uruguayan

study, the food source of ALA may alter the effectiveness of ALA in reducing breast cancer risk.

A French cohort study found that ALA intake from fruit, vegetables and vegetable oils (by FFQ)

was inversely associated with breast cancer risk while ALA from nuts and processed foods

increased risk (Thiébaut et al., 2009). Another factor which may also alter ALA effects in

menopausal status, highlighted in cohort studies which showed an increase in breast cancer risk

with ALA intake in postmenopausal women only (Saadatian-Elahi et al., 2004). A variety of

factors may contribute to the inconsistent findings for the role of ALA in breast cancer risk

including the biomarkers used for ALA intake, inappropriate FFQs, food source, menopausal

status, cancer subtype, and ALA intake ranges.

31

Meta-analyses highlight the inconsistencies in studies determining the role of n-3 PUFA

in breast cancer (MacLean et al., 2006; Saadatian‐Elahi et al., 2004; Zheng et al., 2013). A recent

meta-analysis of 21 prospective cohort studies investigating the relationship between fish (n=11),

marine n-3 PUFA (n=17) and ALA (n=12) intake and breast cancer found that marine n-3 PUFA

(DHA and EPA) decreased breast cancer risk by 14% (relative risk=0.86, 0.78-0.94) and there

was a significant dose response with a 5% reduction in risk per 0.1g/d (Zheng et al., 2013). This

meta-analysis found no significant reduction in breast cancer risk from ALA intake, however

issues including variation in study length, follow up, level and source of n-3 PUFA, and fewer

studies measuring ALA may contribute to the lack of effect. As well, the majority of included

studies that measured ALA, EPA and DHA had large differences in EPA and DHA intake but

relatively small changes in ALA intake. Contrary to these findings, a small meta-analysis of 5

case-control studies found a significant decrease risk in breast cancer risk with increasing levels

of biomarkers for ALA intake (Saadatian‐Elahi et al., 2004). Similarly, a meta-analysis of 8

prospective cohort studies found that total n-3 PUFA, EPA and DHA intake had no effect on

breast cancer incidence but the one study that separated the individual n-3 PUFA found a

significant reduction in breast cancer risk with ALA intake only (RR=0.70, 0.51-0.97) (MacLean

et al., 2006; Voorrips et al., 2002).

2.4.3 Pre-Clinical Evidence

In vivo studies investigating the relationship between n-3 PUFA and breast cancer are

discussed in section 2.3. In vitro studies have also been inconclusive in regards to the effect of n-

3 PUFA on breast cancer growth (Table 2.4). Several studies have shown a reduction in ER+

breast cancer (MCF-7) cell viability and/or proliferation with 50-300μM EPA and DHA

(Chamras et al., 2002; Corsetto et al., 2011), and ALA (Kim et al., 2009; Truan et al., 2010)

32

supplementation. One study done in MCF-7 cells however found that ALA (71μM), EPA

(66μM) and DHA (60μM) treatment for 5 days did not reduce cell growth (Chajès et al., 1995).

This study did however find a reduction in cell growth from ALA, EPA and DHA in the basal

MDA MB 231 cell line. Other studies found a reduction in MDA MB 231 cell growth and

proliferation from 20-100μM DHA (Blanckaert et al., 2010; Rogers et al., 2010) and 10-100μM

ALA (Horia & Watkins, 2005). In HER2+ breast cancer cell lines BT-474 and SKBR-3 a

reduction in HER2 protein expression was induced from 10-20μM ALA treatments (Menéndez

et al., 2006). Apoptotic effects of n-3 PUFA are also inconsistent as one study found EPA and

DHA (100μM, 5 days) had no effect on apoptosis in MCF-7 cells (Chamras et al., 2002) while

another study found an increase in apoptosis in MDA MB 231 cells from 20-100μM DHA for 24

hours (Blanckaert et al., 2010). A variety of factors may be responsible for these discrepancies

including variations in the concentrations of n-3 PUFA, the type of n-3 PUFA, duration of the

study, cell environment including presence of E2 and receptor expression of the cell lines.

2.4.4 Limitations in Current Understanding of n-3 PUFA and Breast Cancer

Despite an extensive interest in n-3 PUFA and breast cancer, both pre-clinical and

clinical/ epidemiological studies are producing inconsistent results. There are a number of factors

which may be contributing to this and should be considered when comparing and contrasting

data, and when conducting research. In clinical and epidemiological studies, variation in study

length, biomarkers used for quantifying n-3 PUFA intake, inappropriate FFQs, food source of n-

3 PUFA and population/patient characteristics including molecular subtype of breast cancer and

menopausal status likely lead to conflicting data. In vitro work allows for control over a number

of these variables but effectiveness of n-3 PUFA likely depends on receptor expression of cell

lines used, type and concentration of n-3 PUFA, presence of E2 and length of treatment. Another

33

advantage of in vitro work is the ability to explore potential mechanisms of action, but to date

few have investigated this.

2.5 Potential Mechanism of ALA on Breast Cancer

If ALA reduces the growth of breast cancer cells there are several potential mechanisms

of action including regulation of transcription factors, increasing lipid peroxidation, modulation

of eicosanoids and tumor suppressors, and incorporation into and alteration of the cell membrane

fluidity and receptors (Figure 2.5). In this thesis, the focus is on membrane associated changes

and subsequent receptor and growth signalling effects.

2.5.1 Alteration of Membrane Fatty Acid Profile and Receptors

When ALA is available to breast cancer cells, it incorporates into the phospholipid rich

cell membrane, which houses many important growth factor and hormone signalling receptors

including HER2, EGFR, IGR-IR, and membrane associated ER. ALA is thought to alter the

expression, location, and signalling of these receptors which would lead to a potential decrease in

cancer cell growth. Several studies have shown that FS, FSO and ALA decrease protein

expression of HER2 (Menéndez et al., 2006; Saggar et al., 2010a; Thompson et al., 2005; Truan

et al., 2010), IGF-IR (Chen et al., 2007; Saggar et al., 2010a) and EGFR (Chen et al., 2002;

Truan et al., 2010). These alterations may lead to a decrease in the gene and protein expression

of mediators of growth signalling pathways, as seen with Akt and MAPK (Saggar et al., 2010a;

Truan et al., 2010). ALA may also modify membrane-associated ER ( Lee, 2001), leading to

decreased cross talk with growth receptors (Arpino et al., 2008; Fagan & Yee, 2008) and

decreased ER gene products involved in cell proliferation such as cyclin D1 and pS2 ( Lee et al.,

2001; Lin et al., 2004).

34

Figure 2.5. Potential mechanisms for growth reduction in breast cancer cells by ALA.

1. Incorporation of ALA into the cell membrane alters the phospholipid and fatty acid profile,

disrupting protein receptors such as HER2, IGF-IR, EGFR and ER. Reduction in these receptors

activity cause downregulation of both the PI3K/Akt and ERK/MAPK signalling pathways,

leading to reduced cell proliferation and increased apoptosis. 2. ALA is easily oxidized and may

increase lipid peroxidation and increase production of free radicals capable of disrupting cell

growth. 3. ALA may alter the production or action of a variety of transcription factors including

PPAR and NFkB leading to a reduction in cancer growth. 4. ALA may reduce FAS expression, a

positive regulator of HER2, leading to a reduction in HER2 activity and decreased cell growth. 5.

ALA may increase the expression of activity of the tumour suppressor PTEN, interfering with

the PI3K/Akt signalling pathway and increasing apoptosis.

ALA = α-linolenic acid, HER2 = human epidermal growth factor receptor 2, IGF-IR = insulin –

like growth factor receptor 1, EGFR= epidermal growth factor receptor, ER= estrogen receptor,

PPAR= peroxisome proliferator activated receptor, NFκB= nuclear factor-kappa B, FAS= fatty

acid synthase, PTEN= phosphatase and tensin homologue, MAPK= mitogen activated

phosphatase kinase, E2= 17-β estradiol.

35

ALA is also thought to modify lipid rafts, membrane domains that are a hub for these growth

factor receptors (Menendez & Lupu, 2007; Sawyer M, 2010). This may lead to receptors moving

from lipid raft to non-lipid raft domains and decrease dimerization and activation of growth

signalling pathways (Schley et al., 2007; Staubach & Hanisch, 2011).

2.5.2 Transcription Factor Regulation

ALA has been shown to alter the gene expression and activity of transcription factors that

influence cell growth, including Peroxisome Proliferator-Activated Receptors (PPAR) and

Nuclear factor- kappa B (NFκB) (Jump, 2004). There are three types of PPAR which have roles

in regulating cell proliferation, differentiation and inflammation, and ALA has been shown in

leukemia cells to increase PPAR gene expression which may lead to decreased cancer growth

(Larsson et al., 2004; Zhao et al., 2005). NFκB can increase cancer growth through altering

apoptosis, cell proliferation, inflammation and angiogenesis (Ghosh & Karin, 2002; Karin & Lin,

2002; Shibata et al., 2002). Fatty acids, including ALA, can decrease activation of NFκB through

inhibiting phosphorylation, leading to a potential decrease in cancer cell growth (Hassan et al.,

2010; Lee et al., 2003; Oh et al., 2010).

2.5.3 Other Mechanisms

There are several other potential mechanisms by which ALA may decrease breast cancer

cell growth, one of which is increasing lipid peroxidation. ALA is highly unsaturated which

makes it easily oxidized leading to the production of free radicals and reactive oxygen species

which can induce cell death (Chajès et al., 1995; Cognault et al., 2000; Gonzalez et al., 1991;

Larsson et al., 2004). ALA may also increase the activity of the tumor suppressor phosphatase

and tensin homologue (PTEN) which supresses the PI3K/Akt pathway leading to an increase in

36

apoptosis (Ghosh-Choudhury et al., 2009), and decrease fatty acid synthase (FAS) expression

leading to a reduction in HER2 gene expression (Menendez et al., 2004).

ALA likely acts through a number of these and other pathways to decrease breast cancer

cell growth. Few studies have investigated the potential mechanisms of ALA on breast cancer

growth, but would lead to a better understanding of ALA as a complementary breast cancer

therapy and provide insight into other potential therapeutic targets and approaches.

2.6 Summary and Questions

Treatment of breast cancer is difficult due to heterogeneity of the disease, including

variation in patient E2 levels and expression of ER, PR and HER2 which alter tumour growth

and effectiveness of traditional therapies. Breast cancer drugs and treatment options have

advanced, however issues with side effects, cost, drug resistance and ineffectiveness have led to

the increased use of CAM including ALA-rich FSO. Several epidemiological studies have

investigated the effectiveness of ALA as an agent for breast cancer prevention however

outcomes are inconsistent. This may be partially explained by variation in breast cancer

molecular subtypes and E2 environment within and between studies, as in vivo and limited in

vitro studies have indicated that both breast cancer cell line and absence or presence of E2 alter

effectiveness of ALA on cancer growth reduction. For example, in vivo studies have shown that

MCF-7 xenografts (ER+, PR+ low HER2) are more sensitive to tumour growth reduction from

FSO supplementation than BT-474 xenografts (ER+, PR+, HER2+) and in vitro studies have

shown MDA MB 231 (ER-, PR-, low HER2) cell growth was reduced by EPA and DHA

supplementation, but MCF-7 cell growth was not affected.

In vitro studies can control cell receptor expression and E2 level, and can provide insight

into mechanism of action, but no studies have directly compared the growth effect of ALA in

37

breast cancer cell lines with varying ER, PR and HER2 expression, ± E2. Several mechanisms

for the potential effect of ALA in reducing breast cancer growth have been hypothesized

including incorporation of ALA into the cell membrane causing a disruption in cell receptors

such as ER, PR and HER2 and subsequent reduction in downstream growth signalling pathways.

From this, it is suggested that variation in cell receptor expression will alter the degree of growth

reduction from ALA, and may explain discrepancies seen in epidemiological studies not

stratifying by molecular subtype and variation in tumour growth from in vivo studies using

xenographs of different breast cancer cell types. Further, cells expressing ER typically have

greater growth in the presence of E2, and ALA effect may change in those cancer cells

depending on the E2 environment. To further understand and optimize the use of ALA as a

complementary breast cancer treatment, its effectiveness across cancers with varying ER, PR and

HER2 expression and E2 environments, and potential mechanisms of effect need to be

determined.

38

3.0 OBJECTIVES, HYPOTHESES, AND EXPERIMENTAL DESIGN

3.1 Objectives

To determine the effect of ALA in human breast cancer cell lines with varying ER, PR

and HER2 expression levels, ± E2, on (a) cell growth, (b) apoptosis induction, (c) phospholipid

fatty acid profile, and (d) expression of genes involved in common growth signalling pathways

and cell receptors.

3.2 Hypotheses

ALA will reduce the growth of breast cancer in vitro, but to varying degrees depending

on cell receptor expression and presence of E2. Cell growth reduction will be a result of ALA

incorporation into and alteration of the cell phospholipid fatty acid profile. This leads to

alteration of gene expression, localization and or function of membrane receptors causing an

increase in apoptosis and reduction in growth signalling pathway activation downstream of ER,

PR and HER2 including MAPK and PI3K/Akt. Cells with greater ALA incorporation into the

cell membrane will likely have larger reductions in cell growth, and greater apoptosis induction

and gene changes. If ALA decreases cell growth through alteration of membrane receptors, cells

expressing ER, PR and HER2 will be more sensitive to growth reduction by ALA. The presence

of E2 may also alter effectiveness of ALA in cell lines which express ER and are dependent on

E2 for cell growth.

3.3. Experimental Design and Rationale

An overview of the experimental design is shown in Figure 3.1. Four studies were

carried out to address the research objectives described in section 3.1. Study 1 measured the

growth of breast cancer cells with varying receptor expression when incubated with a range of

39

Figure 3.1. Experimental design.

40

ALA doses, with and without E2, using trypan blue exclusion. This was done to determine (a) if

ALA with or without E2 can effectively reduce breast cancer cell growth, (b) if the degree of

growth reduction is dependent on cell receptor expression or presence of E2, and (c) the

concentration of ALA with or without E2 that will reduce the growth of the cells by at least 50%

that can be used in studies 2-4. Study 2 measured the level of apoptosis under conditions

selected from Study 1 in breast cancer cells with varying receptor expression by flow cytometry

detection of annexin V stain. This was done to determine if growth effects of ALA in breast

cancer are a result of increased cell apoptosis, and if this differs between molecular subtypes.

Study 3 measured the changes in phospholipid fatty acids of breast cancer cell with varying

receptor expression by thin layer chromatography (TLC) and gas chromatography (GC). This

was done to determine (a) if ALA is successfully incorporated into breast cancer cell

phospholipids in vitro, (b) if the fatty acid profile and level of ALA incorporation differs

between molecular subtypes, and (c) if greater phospholipid ALA is associated with greater

growth and apoptosis effects. Finally, Study 4 measured changes in mRNA expression of breast

cancer cell receptors and important mediators of cell growth from ALA treatment by PCR array.

This was done to confirm that the commercial cell lines had the expected receptor gene

expression and tumour classification characteristics, and to explore potential mechanisms of

ALA action. An array was used opposed to specific biomarkers to screen a wide range of

potential pathways including apoptosis, signal transduction, angiogenesis, metastasis, and cell

cycle regulation. The results will be used in the further exploration of mechanisms within each

cell line in the future. Hence this study investigated ALA effect within each cell line, not

differences between cell lines.

Study 1 used mean triplicate values. Each experiment was repeated at least three times.

41

4.0 MATERIALS AND METHODS

4.1 Cell Line Selection and Culture

Four commercial human breast cancer cell lines were selected for their varying

expression of ER, PR and HER2 (Table 4.1): MCF-7, BT-474, MDA MB 231, MDA MB 468.

All cell lines were purchased from American Type Culture Collection (ATCC, Manassas, VA,

USA) and all were cultured in DMEM medium (Gibco, Carlsbad, CA, USA) supplemented with

10% fetal bovine serum (FBS; Sigma-Aldrich, St. Louis, MO,USA) and 1% antibiotic-

antimycotic solution containing penicillin, streptomycin and amphotericin B (Gibco). Cells were

maintained in a humidified 37°C 5% CO2 atmosphere incubator. For experimental testing cells

were all under passage number 12, and 70-80% confluent.

Table 4.1. Receptor expression of commercial breast cancer cell lines.

Cell Line ER PR HER2

MCF-7 + + Low

BT-474 + + +

MDA MB 231 - - Low

MDA MB 468 - - -

42

4.2 Treatment Medium

ALA ( >99% pure, Sigma-Aldrich) was dissolved in 100% ethanol and stored at -20°C,

after being flushed with nitrogen gas to limit oxidation. On experimental treatment days, ethanol

was evaporated from the ALA by N2 gas and charcoal stripped FBS (CS FBS; Sigma-Aldrich)

was added to the ALA to a final concentration of 4mM ALA. This was incubated for 1 hour at

37°C. ALA-CS FBS was added to phenol red free DMEM-F12 (Gibco) containing 1%

antibiotics-antimycotics and CS FBS to a final concentration of 5% FBS and 0-200uM ALA.

Treatment medium contained 1nM 17-β estradiol (E2; Sigma-Aldrich) dissolved in ethanol, or

no E2 but with equal volume of ethanol as in 1nM E2. Control medium was phenol red free

DMEM-F12 (Gibco) containing 1% antibiotics-antimycotics and CS FBS to a final concentration

of 5% FBS, with and without 1nM E2, and no ALA. For Studies 2-4, treatment medium

contained 75μM ALA + 1nM E2 and control medium contained 1nM E2 with no ALA.

4.3 Study 1: Effect of ALA on cell growth with and without E2

Cells were plated in 24-well tissue culture plates (Sarstedt, Nümbrecht, Germany) at a

density of 2.4x104 to 3.6x10

4 cells/well, and allowed to adhere for 72 hours. Medium was then

switched to the treatment medium with 0, 50, 75, 100, 125, 150 or 200μM ALA ± 1nM E2, 3

wells per treatment condition. ALA treatment medium was replenished after 48 hours. After 96

hours total treatment cells were collected from each well with 0.25% trypsin-EDTA (Sigma-

Aldrich), centrifuged and resuspended in 50μL medium. A 10μL aliquot was immediately added

to 10μL 0.4 % trypan blue stain (Gibco) and total and viable cells counted using a TC10

automated cell counter (Bio-Rad, Hercules, CA, USA). The average viable cell counts of the 3

wells for each treatment condition was divided by the mean of the control wells to represent % of

control.

43

4.4 Study 2: Effect of ALA on Apoptosis

Cells were plated (9.5x104 to 1.28x10

5 cells/well) in 6-well tissue culture plates (Sarstedt)

and allowed to adhere for 72 hours. Medium was switched to the treatment medium containing

75μM ALA with 1nM E2, or control medium with 1nM E2 and no ALA. After 48 hours,

treatment cells were collected, washed with phosphate buffered saline (PBS) and incubated with

5μL Annexin V-PE and 7-AAD stain (BD Biosciences, Mississauga, ON) for 15 minutes at room

temperature in the dark. Binding buffer (400μL; BD Biosciences) was added and samples were

placed on ice. Samples were immediately analyzed by flow cytometry using a BD LSR Fortessa.

Viable (Annexin V -/ 7-AAD -), early apoptotic (Annexin V +), late apoptotic (Annexin V +/ 7-

AAD +) and dead (7-AAD +) cells were quantified as % of total cells. Unstained, annexin V-PE

only and 7-AAD only stained cells were used for compensation to diminish overlap between the

stains.

4.5 Study 3: Effect of ALA on Total Phospholipid Fatty Acid Composition

Cells were plated in T-75 tissue culture flasks (Sarstedt) at 5x105 – 1x10

6 cells/flask and

allowed to adhere for 72 hours. Medium was then switched to the treatment medium containing

75μM ALA and 1nM E2. Treatment was replenished after 48 hours, and cells were collected by

trypsinization after 96 hours total ALA treatment. Cells were washed with PBS and collected

with 0.25% trypsin-EDTA (Gibco). Cells were resuspended in fatty acid free medium and total

lipids were immediately extracted using methods previously developed (Bligh and Dyer, 1959)

using NaCl, methanol and chloroform (Sigma-Aldrich) in a 1:2:2 ratio. Total lipids were

separated into lipid classes on silica TLC G-plates (EMD Chemical, Gibbstown, NJ, USA)

washed with 2:1 chloroform: methanol (Sigma-Aldrich) and incubated at 100°C for 1 hour to

activate. Lipids were separated on the plate in a solution of heptane: diethyl ether: acetic acid

(60:40:2) for 1 hour. Plates were sprayed with 0.1% 8-anilino-1-naphthalene sulfonic acid

44

(Sigma-Aldrich) and visualized under UV light to identify and collect phospholipids into test

tubes. A known amount of 17:0 (heptadecanoic acid) standard (Avanti, Alabaster, AL) was

added. Samples were methylated by incubating with hexane and boron trifluoride-methanol

(BF3, Sigma-Aldrich) for 1 hour at 100°C. The reaction was stopped with distilled water and

samples centrifuged to separate the hexane and fatty acid methyl esters from the other phases.

The top layer containing the fatty acid methyl esters was transferred to gas chromatography (GC)

vials and measured by GC- flame ionization detection (GC-FID) using a Varian-430 GC (Varian,

Lake Forest, CA, USA). Fatty acids were quantified by comparing chromatograph peaks

(retention time) to the 17:0 standard, in both ALA treated and control cell phospholipids.

4.6 Study 4: Effect of ALA on mRNA expression of receptors and signalling biomarkers

Cells were plated (9.5x104 to 1.28x10

5 cells/well) in 6-well tissue culture plates (Sarstedt)

and allowed to adhere for 72 hours. Medium was switched to the treatment medium containing

75μM ALA with 1nM E2, or control with no ALA and 1nM E2. After 24 hours cells were

collected and washed with PBS. After centrifugation the supernatant was removed and cell

pellets were placed on dry ice and stored at -80°C. After all samples were collected, RNA was

extracted using the RNeasy mini kit with on-column DNase digest using manufacturer’s protocol

(Qiagen, Frederick, MD, USA). RNA concentration and quality was measured using the

NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA ) and

Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA) completed at the

University Health Network Microarray Centre. cDNA was synthesized using 0.5μg RNA with

the RT2 First Strand kit (Qiagen) following manufacturer’s protocol (Qiagen). For gene

expression analysis, SYBR Green Mastermix (Qiagen) was added to the cDNA for each sample

and 25μL was loaded to each well of a customized RT2

Profiler Breast Cancer PCR array

45

(Qiagen) measuring 88 genes of interest, with 1 sample per plate. This array was selected as it

screens for a wide range of breast cancer pathways potentially altered by ALA including markers

for apoptosis, angiogenesis, cell cycle regulation and metastasis. Gene expression was measured

using the ABI PRISM 7000 Sequence detection system (Applied Biosystems, Carlsbad, CA,

USA ). Raw Ct values for each gene on plates were uploaded to the Qiagen/SABiosciences RT2

Profiler PCR Array Data Analysis version 3.5 (Qiagen). Three reference genes were selected

based on lowest standard deviation across all plates. ΔΔCt values were used to generate fold

change (2- ΔΔCt

) and fold regulation (negative inverse of fold change).

4.7 Statistical Analysis

Statistical analysis of data from Studies 1-3 was completed with Sigma Stat 3.5 and

Graph Pad Prism 5 while those from the PCR array (Study 4) was completed with the

Qiagen/SABiosciences RT2 Profiler PCR Array Data Analysis version 3.5. For all testing,

significance was set at p<0.05.

Study 1: Each treatment group was calculated as a % of –E2 control viable cell number.

Differences amongst ALA doses, cell lines and E2 on cell growth were analyzed by three-way

analysis of variance (ANOVA). When significant interactions were observed, differences in

ALA dose and cell line, and ALA dose and E2 were further analyzed by two-way ANOVA with

post-hoc Tukey test.

Study 2: The early, late and total apoptosis of ALA treated cells were calculated as % of

control using the mean % apoptosis in the control runs. Differences between ALA treated cell

and control, as well as cell lines, were analyzed by two-way ANOVA with post-hoc Tukey

testing when interaction was observed.

46

Study 3: Differences between % fatty acid composition in ALA treated cells and

untreated control cells, and between cell lines, were analyzed by two-way ANOVA with post-

hoc Tukey testing when interaction was observed. Linear regression analysis was done to

determine the relationship between phospholipid % ALA and cell growth, and % ALA and total

apoptosis, as well as the relationship between % ALA in control cells and % ALA in treated

cells.

Study 4: Differences in 2- ΔΔCt

(fold change) between ALA treated and control cells

within each cell line for each gene of interest were analyzed by Student’s t-test. Comparison of

control (untreated) cell gene expression (ΔCt) between cell lines was analyzed by one-way

ANOVA with post-hoc Tukey testing.

47

5.0 RESULTS

5.1 Study 1: Effect of ALA on Cell Growth with and without E2

There were significant differences in cell growth between +E2 and – E2 treatments,

between cell lines, and between ALA doses (Table 5.1). Three way ANOVA showed significant

interactions between E2 and ALA, cell line and ALA, and E2, cell line and ALA, so data was

further analyzed by two-way ANOVA keeping cell line constant in one case and E2 constant in

another case.

Keeping cell line constant, two-way ANOVA showed that MCF-7 cells were the only

ones to have a significant E2 effect (Figure 5.1), with the +E2 control having 91% higher viable

cell number compared to –E2 control (p=0.002). In all cell lines there was significantly lower

growth with ALA supplementation, even at the lowest concentration of 50μM ALA (Figure 5.1).

In MCF-7 cells, 50μM ALA completely negated the E2 growth effect. At 75μM ALA with E2,

there was at least a 55% reduction in growth in all cell lines: MCF-7 (55% ±4.72), BT-474

(74%±8.44), MDA MB 231 (80% ±5.58), MDA MB 468 (68% ±4.43). In MCF-7, MDA MB

231 and MDA MB 468, there was no significant difference in growth between 75μM and 100μM

ALA.

Keeping E2 constant, two-way ANOVA showed that there was a significant difference in

growth between ALA doses and between cell lines, both with and without E2 (Figure 5.2). Up to

100μM ALA, MCF-7 cells had significantly higher growth in comparison to the other cell lines.

Beyond this ALA concentration, there were no differences in growth between all cell lines. In

the 75μM +E2 treatment, MDA MB 231 had the lowest cell growth, which is significantly lower

than that of MCF-7.

48

A concentration of 75μM ALA was selected for Studies 2-4 as there was little additional

growth effect at levels greater than 75μM ALA. E2 did not alter ALA effect, and since MCF-7

cells grow best when cultured with E2, Studies 2-4 were conducted in the +E2 condition only.

Table 5.1. Three way ANOVA results on effect of E2, cell line

and ALA concentration on cell growth.

Variable p-value

E2 0.007

Cell Line <0.001

ALA <0.001

E2 x Cell Line 0.144

E2 x ALA 0.002

Cell Line x ALA <0.001

E2 x Cell Line x ALA <0.001

49

Figure 5.1. Effect of ALA with and without E2 on growth of four breast cancer cell lines.

Asterisks (*) indicate differences in growth from 1nM E2 compared to no E2, and different

letters (a-e) indicate differences in growth between ALA concentrations within each cell line

(combined +E2 and – E2) by two-way ANOVA with post-hoc Tukey testing.

50

Figure 5.2. Differences between Cell Lines with increasing ALA concentration, with no E2

(top) and 1nM E2 (bottom). Cell lines with different lower case letters (a-c) are significantly

different; Doses with different upper case letters (A-E) are significantly different by two-way

ANOVA with post-hoc Tukey testing.

51


Representative dot plots of Annexin V-PE and 7-AAD staining for apoptosis in control

and ALA treated cells are shown in Figure 5.3. There was a significant ALA effect in both late

and total apoptosis, with 75μM ALA + 1nM E2 significantly increasing late apoptosis in MDA

MB 231 (100.2% ± 53.46), and total apoptosis in MDA MB 231 (111.2% ± 53.77) and MDA

MB 468 (68.5% ± 16.98) compared to control populations (Figure 5.4). There was no significant

difference between cell lines in early, late or total apoptosis. MCF-7 and BT-474 had no

significant increase in any of the measured forms of apoptosis.

5.3 Study 3: Effect of ALA on Phospholipid Fatty Acid Composition

Analysis of control cells (no ALA treatment) and 75µM ALA treated cells, and between

cell lines, showed significant differences in phospholipid fatty acid profile by two-way ANOVA

(Table 5.2). There was a significant ALA and cell line interactions for composition of 18:0, 22:0,

18:1n-7, 22:1n-9, 18:3n-3 (ALA), 18:2n-6 (LA), 22:4n-6, and 22:5n-6.

Comparison of cell lines within the two-way ANOVA showed that MDA MB 231 had

significantly greater % 18:0, 22:4n-6, 22:5n-6, 22:1n-9, and 20:5n-3 (EPA) than all other cell

lines. MDA MB 231 also had significantly greater % 18:3n-6, 20:2n-6, 20:4n-6 (arachidonic

acid), and significantly lower % 16:0, compared to BT-474 and MCF-7 cells. MDA MB 231 and

MDA MB 468 had significantly greater % 22:0 and 22:6n-3 (DHA) than MCF-7 and BT-474.

MDA MB 468 and MCF-7 cells had significantly greater % 18:1n-9 compared to MDA MB 231

and BT-474 cells. BT-474 cells had significantly lower % 18:2n-6 and 18:1n-7 than all other

cell lines.

52

Figure 5.3. Representative dot plots of Annexin V-PE and 7-AAD staining for apoptosis in

control and ALA treated breast cancer cells. Control MCF-7 (A), BT-474 (B), MDA MB 231

(C) and MDA MB 468 (D), and 75μM ALA + E2 treated MCF-7 (E), BT-474 (F), MDA MB

231 (G) and MDA MB 468 (H)cells, stained with Annexin V-PE (x-axis) and 7-AAD (y-axis)

and detected by flow cytometry to measure early and late apoptosis. Q1 represents dead cells (7-

AAD only), Q2 represents late apoptotic cells (7-AAD and Annexin V-PE), Q3 represents viable

healthy cells (unstained), and Q4 represents early apoptotic cells (Annexin V-PE only).

53

Figure 5.4. Effect of ALA on early, late and total apoptosis between cell lines. Means with

different lower case letters (a-b) represent differences between control and ALA (75μM) treated

cells for % early (A), late (B), and total (C) apoptosis by two-way ANOVA with post-hoc Tukey

testing.

54

54

54

Table 5.2. Phospholipid fatty acids composition of breast cancer cell lines.

p-value

MCF-7 BT-474 MDA MB 231 MDA MB 468 Cell

Line ALA

Cell

line x

ALA CON ALA CON ALA CON ALA CON ALA

Sat

ura

ted

14:0 0.836 1.527 0.468 1.141 0.343 0.422 0.344 0.209 0.300 0.378 0.806

16:0 28.371B 25.046B 36.057B 31.794B 10.052

A 15.167

A 23.299

B 15.137

B <0.001 0.311 0.342

18:0 16.185a 18.620

a 15.468

a 17.016

a 25.111

b 19.501

a 16.702

a 17.579

a 0.002 0.849 0.030

20:0 0.426 0.418 0.443 0.550 0.415 0.256 0.182 0.294 0.338 0.895 0.721

22:0 0.097a 1.331

b 0.081

a 1.248

b 0.307

a 7.354

d 0.158

a 5.771

c <0.001 <0.001 <0.001

MU

FA

14:1n-7 0.843 1.540 0.472 1.151 0.346 0.426 0.347 0.211 0.300 0.378 0.806

16:1n-7 5.302 5.609 3.701 5.605 1.989 1.595 1.749 1.340 0.022 0.716 0.791

18:1n-9 22.888A 13.832

A* 19.051

B 13.855

B 20.422

B 10.167

B 22.783

A 17.401

A 0.013 <0.001 0.145

18:1n-7 5.735c 3.216

e 4.587

d 2.639

e 7.771

b 3.247

e 9.185

a 5.241

f <0.001 <0.001 0.011

20:1n-9 0.220 0.159 0.214 0.148 0.328 0.211 0.223 0.185 0.250 0.058 0.872

22:1n-9 0.442a 0.324

a 0.269

a 0.481

a 1.581

c 0.923

b 0.536

a 0.856

a <0.001 0.604 0.037

n-3

PU

FA

18:3n-3 (ALA) 0.178a 17.645

c 0.139

a 13.094

b 0.540

a 25.080

d 0.441

a 18.396

c <0.001 <0.001 <0.001

20:3n-3 1.407 0.604* 2.503 1.153* 2.160 0.788* 3.123 1.209* 0.114 <0.001 0.704

20:5n-3 (EPA) 0.651B 0.484

B 0.459

B 0.571

B 1.332

A 1.051

A 0.548

B 0.780

B <0.001 0.752 0.152

22:5n-3 (DPA) 1.015 0.317* 1.205 0.762* 1.814 0.659* 1.569 0.708* 0.088 <0.001 0.430

22:6n-3 (DHA) 1.449B 0.654

B* 1.404

B 0.482

B* 2.616

A 1.028

A* 2.227

A 1.707

A* 0.002 <0.001 0.244

n-6

PU

FA

18:2n-6 (LA) 5.878a 4.292

b 4.428

b 3.311

b 7.603

a 3.206

b 7.628

a 3.611

b 0.007 <0.001 0.004

18:3n-6 0.615B 0.942

B 0.431

B 0.651

B 1.521

A 1.111

A 0.850

B 1.044

B 0.016 0.608 0.329

20:2n-6 0.313B 0.20

B* 0.259

B 0.122

B* 0.713

A 0.345

A* 0.487

B 0.317

B* 0.001 0.002 0.267

20:4n-6 (AA) 6.726B 3.072

B* 7.804

B 4.066

B* 11.585

A 6.540

A* 7.260

AB 7.577

AB 0.004 <0.001 0.140

22:4n-6 0.343a 0.072

b 0.436

a 0.145

b 1.283

d 0.555

c 0.322

a 0.187

a <0.001 <0.001 0.027

22:5n-6 0.078ab

0.087ab

0.121a 0.015

b 0.169

a 0.370

d 0.034

a 0.240

c <0.001 0.007 <0.001

n-6:n-3 3.468 0.443* 2.365 0.518* 2.713 0.434* 2.181 0.568* 0.224 <0.001 0.123

Means with different letters (a-d) within a row are significantly different; Significant difference between control and ALA within cell

line (*); Cell lines with different letters (A-B) are significantly different. Represented as % composition of total fatty acids.

55

59

Comparison of control and 75µM ALA treated cells showed significantly higher %

composition of 22:0, 18:3n-3 (ALA), and 22:5n-6, and significantly lower % composition of

18:1n-9, 18:1n-7, 20:3n-3, 22:5n-3, 22:6n-3 (DHA), 18:2n-6 (LA), 20:2n-6, 20:4n-6, 22:4n-6,

and the n6:n3 ratio.

There was a significantly higher phospholipid % ALA in all cell lines treated with 75μM

ALA +E2 compared to control (Figure 5.5, Table 5.2). Comparing cell lines, MDA MB 231 had

the highest % ALA in phospholipids (25.1% ± 2.22), significantly higher than BT-474 (13.1% ±

0.72) and MCF-7 (17.6% ± 1.33). There was no significant change in % EPA in any of the cell

lines, however MDA MB 231 had significantly higher % EPA (1.05% ± 0.25) than the other cell

lines (Figure 5.5). There was significantly lower % DHA in all cell lines except MDA MB 468,

while MDA MB 231 and MDA MB 468 had significantly higher % DHA than MCF-7 and BT-

474 (Figure 5.5). ALA treatment significantly lowered the n6:n3 ratio in all cell lines (0.43 –

0.57), and there was no difference between cell lines (Figure 5.4).

ALA also caused significantly higher composition of the saturated fat 22:0, by

approximately 25 fold across the four cell lines (Table 5.2). Several other fatty acids (18:1n-9,

18:1n-7, 20:3n-3, 22:5n-3, 18:2n-6, 20:4n-6, 22:4n-6 and 20:2n-6) had a significant reduction

(1.6 to 2.7 fold) in % composition after ALA treatment.

Phospholipid % ALA is inversely associated with cell growth by linear regression

(r=0.9535, p<0.001). MDA MB 231 had the highest % ALA and the lowest cell growth at the

75μM ALA +E2 condition (Figure 5.6). Similarly total apoptosis was positively associated with

phospholipid % ALA (r=0.923, p=0.001), with MDA MB 231 having the highest level of total

apoptosis induction and the highest % ALA (Figure 5.7). There was also a near significant

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59

Figure 5.5. Effect of ALA on phospholipid % ALA (A), EPA (B), DHA(C) and n6:n3 ratio

(D). Different lower case letters (a-b) indicate significant difference between control and ALA

(75μM) within a cell line; Cell lines with different upper case letters (A-B) are significantly

different by two-way ANOVA with post-hoc Tukey test.

57

59

Figure 5.6. Relationship between Phospholipid % ALA and viability of breast cancer cells.

Linear regression of mean phospholipid % ALA for each cell line and mean viability of each cell

line for 75μM ALA +1nM E2 and control.

Figure 5.7. Relationship between phospholipid % ALA and total apoptosis. Linear

regression of mean phospholipid % ALA for each cell line and mean total apoptosis of each cell

line for 75μM ALA +1nM E2 and control.

58

59

positive relationship between % ALA composition in control cells and % ALA composition of

75μM ALA +E2 treated cells (r=0.8925, p=0.055).


Quality control reports generated from the PCR array analysis indicated that all samples

were of adequate quality free of DNA contamination. The positive control (plasmid with primer

assay that detects a sequence it produces) had an average Ct of 20±2 for all plates indicating

proper performance of PCR steps, and all plates were within 2 of each other indicating that there

was adequate PCR array reproducibility. The reverse transcription control (built in external RNA

control) indicated efficient reverse transcription in all arrays as the difference between the

average reverse transcription control ΔCt and positive control was <5, indicating no inhibition of

the reverse transcription reaction. The genomic DNA control validated purity of the samples as

all arrays had genomic DNA control Ct > 35 indicating contamination is too low to be detected.

Three reference genes, B2M (β-2 microglobulin), HPRT1 (hypoxanthine-guanine

phosphoribosyltransferase), and RPLP0 (ribosomal protein P0) were used for all analysis as

these had the smallest variation (standard deviation) in ΔCt across all plates.

Gene expression and fold changes in all genes are provided in Appendix Tables 1

(relative expression as ΔCt) and 2 (gene fold changes). Gene expression of tumour classification

genes in untreated controls are provided in Table 5.3 (ΔCt), but only those genes that show

significant and large changes from ALA treatment are provided in Table 5.4 (fold changes).

Differences exist between cell lines in expression of tumour classification genes in

untreated (control) cells (Table 5.3). MCF-7 and BT-474 cells had higher relative gene

expression (lower ΔCt) of luminal classification genes including ESR1 (ERα), FOXA1 (forkhead

59

Table 5.3. Relative gene expression (ΔCt) of tumour classification markers in four untreated breast cancer cell lines from PCR array.

MCF-7 BT-474 MDA MB 231 MDA MB 468

Gene Name ΔCt SEM ΔCt SEM ΔCt SEM ΔCt SEM

Luminal A and B (MCF-7 and BT-474)

ESR1 Estrogen receptor 1 3.3a ±0.4 4.9

b ±0.4 8.7

c ±0.3 8.8

c ±0.5

FOXA1 Forkhead box A1 3.2ab

±0.5 1.7a ±0.2 11.0

c ±1.5 8.1

bc ±4.5

GATA3 GATA binding protein 3 0.6a ±0.3 3.6

b ±0.6 8.1

c ±0.2 7.2

c ±1.7

KRT18 Keratin 18 -3.6a ±0.2 -1.8b ±0.1 1.2c ±0.7 -0.6b ±0.7

KRT8 Keratin 8 -0.4a ±0.2 1.8

b ±0.2 5.1

d ±0.5 3.8

c ±0.8

SLC39A6

Solute carrier family 39 (zinc

transporter), member 6 -1.5a ±0.2 1.7

b ±0.2 4.6

c ±0.2 4.8

c ±0.2

TFF3 Trefoil factor 3 (intestinal) 1.2b ±0.5 -1.0

a ±0.2 8.0

c ±0.2 7.6

c ±0.8

XBP1 X-box binding protein 1 -2.6a ±0.4 -1.6

a ±0.2 3.0

b ±0.4 2.1

b ±0.6

HER2 Overexpressing (BT-474)

ERBB2

V-erb-b2 erythroblastic

leukemia viral oncogene

homolog 2 (HER2) 5.2b ±0.31 -0.5

a ±0.3 7.1

d ±0.1 6.1

c ±0.3

GRB7

Growth factor receptor-

bound protein 7 5.3b ±0.3 0.0

a ±0.1 7.3

c ±0.1 6.3

bc ±1.5

Basal (MDA MB 231 and MDA MB 468)

BIRC5

Baculoviral IAP repeat

containing 5 5.8a ±0.5 5.8

a ±0.3 5.6

a ±0.5 5.3

a ±0.3

EGFR

Epidermal growth factor

receptor 7.4c ±0.5 4.5

b ±0.4 3.3

b ±0.3 0.7

a ±1.9

KRT5 Keratin 5 8.6b ±0.5 8.0

ab ±0.5 9.3

b ±0.4 2.5

a ±4.3

NOTCH1 Notch 1 6.7a ±0.5 6.4

a ±0.3 6.6

a ±0.1 6.7

a ±1.2

Untreated cell line controls with different letters (a-d) within a row are significantly different by two-way ANOVA with post-hoc Tukey test (p<0.05)

60

Table 5.4. Significant and large changes in gene expression after ALA treatment of four breast cancer cell lines.


Gene Function

Fold

Change

p-

value

Fold

Change p-value

Fold

Change

p-

value

Fold

Change

p-

value

Tumour Classification Markers

FOXA1

Luminal A and B; Transcription factor for ER signalling, associated with

growth inhibition and good prognosis 2.1 0.076 -1.3 0.037 -1.1 0.868 -1.5 0.630

GATA3

Luminal A and B; Transcription factor for mammary gland differentiation,

associated with low metastasis and good prognosis -3.0 0.026 1.0 0.953 1.1 0.497 -1.4 0.797

KRT8

Luminal A and B; Structural and signalling roles, associated with less invasive

breast cancer -1.4 0.035 1.3 0.116 -1.2 0.400 -1.0 0.844

ERBB2

HER2 overexpressing; EGFR family receptor, increased growth signalling,

associated with aggressive cancers 1.1 0.753 1.5 0.250 1.4 0.167 -2.0 0.046

ID1 Lung metastasis; Associated with cell growth, EMT and poor prognosis -1.0 0.804 1.3 0.559 -1.1 0.938 -2.1 0.083

Signal Transduction

BRCA1 DNA repair and tumour suppressor, mutations increase breast cancer risk 1.0 0.807 -1.4 0.037 -1.1 0.659 1.1 0.504

KRT19

Steroid receptor-mediated; structural integrity of epithelial cells, tumour

suppressor that inhibits Akt signalling -1.4 0.282 1.1 0.128 -1.6 0.034 1.1 0.896

SNAI2 Hedgehog; Increase EMT and metastasis, prevents apoptosis -1.5 0.030 1.0 0.847 -1.0 0.830 1.0 0.731

ERBB2

PI3K/Akt; EGFR family receptor, increased growth signalling, associated with

aggressive cancers 1.1 0.753 1.5 0.250 1.4 0.167 -2.0 0.046

IGF1 PI3K/Akt; Increases cell growth, ER and growth factor signalling -2.5 0.864 -1.0 0.785 1.1 0.753 1.0 0.871

MAPK1 MAPK; Promotes cell proliferation and increases cell growth -1.7 0.017 1.0 0.976 -1.1 0.761 1.0 0.884

Epithelial to Mesenchymal Transition

SRC

Proto-oncogene; activates EGFR signalling and promotes cell survival and

proliferation -1.7 0.023 -1.0 0.760 1.6 0.081 -1.3 0.443

TWIST1 Oncogene; Induces EMT and metastasis, evades apoptosis -1.2 0.349 -1.2 0.461 3.3 0.346 -1.5 0.362

Angiogenesis

ERBB2 EGFR family receptor, associated with cell growth and aggressive cancers 1.1 0.753 1.5 0.250 1.4 0.167 -2.0 0.046

ID1 Associated with cell growth, EMT and poor prognosis -1.0 0.804 1.3 0.559 -1.1 0.938 -2.1 0.083

SERPINE1 associated with poor prognosis and cancer progression 1.1 0.795 -1.1 0.707 1.9 0.037 1.3 0.629

THBS1 Glycoprotein that inhibits angiogenesis, expression decreased in cancer 1.1 0.901 1.0 0.805 1.2 0.025 1.3 0.457

61


Gene Function

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

Adhesion

ADAM23 Involved in cell to cell adhesion, down regulated in breast cancer 1.4 0.414 5.0 0.142 1.1 0.798 -1.0 0.988

CSF1

Influences macrophage development; increased expression in breast cancer and

associated with poor prognosis -1.1 0.933 1.1 0.593 -1.7 0.008 -1.1 0.779

ERBB2 EGFR family receptor, associated with cell growth and aggressive cancers 1.1 0.753 1.5 0.250 1.4 0.167 -2.0 0.046

THBS1 Glycoprotein that inhibits angiogenesis, expression decreased in cancer 1.1 0.901 1.0 0.805 1.2 0.025 1.3 0.457

Proteolysis

ADAM23 Involved in cell to cell adhesion, down regulated in breast cancer 1.4 0.414 5.0 0.142 1.1 0.798 -1.0 0.988

Apoptosis

IGF1 PI3K/Akt; Reduces cell apoptosis leading to increased cancer growth -2.5 0.864 -1.0 0.785 1.1 0.753 1.0 0.871


Cell Cycle

CCNA1 Cell cycle progression; often amplified in cancer and increase growth -2.9 0.635 -1.0 0.842 -1.7 0.360 1.2 0.742

DNA Damage

BRCA1 DNA repair and tumour suppressor, mutations increase breast cancer risk 1.0 0.807 -1.4 0.037 -1.1 0.659 1.1 0.504

MAPK1

Involved in MAPK signalling, promotes cell proliferation and increases cell

growth -1.7 0.017 1.0 0.976 -1.1 0.761 1.0 0.884

MGMT Tumor suppressor gene, low levels associated with metastasis and cancer risk -1.3 0.204 1.2 0.283 -1.9 0.029 -1.1 0.930

Xenobiotic Transport

ABCB1 Transports substances across cell membrane including drugs, steroids and lipids -2.2 0.389 -1.8 0.276 1.1 0.975 -1.5 0.565

ABCG2 Transports substances across cell membrane including drugs, steroids and lipids -2.0 0.041 1.5 0.185 -1.8 0.169 -1.7 0.537

Transcription Factors

FOXA1

transcription factor for ER signalling, associated with growth inhibition and

good prognosis 2.1 0.076 -1.3 0.037 -1.1 0.868 -1.5 0.630

GATA3

Transcription factor for mammary gland differentiation, associated with low

metastasis and good prognosis -3.0 0.026 1.0 0.953 1.1 0.497 -1.4 0.797

HIC1 Tumour suppressor gene and regulates cancer cell growth -3.7 0.177 1.1 0.584 1.3 0.379 -1.3 0.506

Fold regulation (2-ΔΔCt

) of genes after treatment with 75μM ALA in four breast cancer cell lines by Student t-test. Significant (p<0.05) and large (> 2

fold) differences bolded.

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box A1), GATA3 (GATA binding protein 3), KRT18 (keratin 18), KRT8 (keratin 8), SLC39A6

(solute carrier family 39, zinc transporter), TFF3 (trefoil factor 3), and XBP1 (X-box binding

protein 1) compared to MDA MB 231 and MDA MB 468 cells. BT-474 cells also had higher

gene expression of luminal B/HER2 overexpression genes ERBB2 (HER2) and GRB7 (growth

factor receptor-bound protein 7) compared to other cell lines. MDA MB 468 cells had

significantly higher gene expression of the basal subtype genes EGFR and KRT5 (keratin 5)

compared to other cell lines.

There were significant changes in a variety of genes from treatment with 75μM ALA

(Table 5.4). In MCF-7 cells, ALA caused significant down-regulation of GATA3 (GATA

binding protein 3), KRT8 (keratin 8), SNAI2 (Snail homolog 2), MAPK1 (Mitogen-activated

protein kinase 1), SRC (V-src sarcoma viral oncogene homolog), and ABCG2 (ATP-binding

cassette, sub-family G member 2). There was also large yet statistically insignificant up-

regulation of FOXA1 (Forkhead box A1) and down-regulation of IGF1 (Insulin-like growth

factor 1), CCNA1 (Cyclin A1), ABCB1 (ATP-binding cassette, sub-family B member 1), and

HIC1 (Hypermethylated in cancer 1). In BT-474 cells, 75μM ALA significantly down-regulated

FOXA1 (Forkhead box A1) and BRCA1 (Breast cancer 1, early onset), and caused large yet

statistically insignificant up-regulation of ADAM23 (ADAM metallopeptidase domain 23).

MDA MB 231 cells had a significant reduction in gene expression of KRT19 (keratin 19), CSF1

(Colony stimulating factor 1), and MGMT (O-6-methylguanine-DNA methyltransferase) gene

expression from ALA treatment, and significant up-regulation of SERPINE1 (Serpin peptidase

inhibitor, clade E) and THBS1 (Thrombospondin 1). There was also a large yet insignificant up-

regulation of TWIST1 (Twist homolog 1) gene expression. Finally, MDA MB 468 cells had a

significant reduction in ERBB2 (HER2) gene expression and insignificant reduction in ID1

(inhibitor of DNA binding 1) gene expression from incubation with 75μM ALA for 24 hours.

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6.0 DISCUSSION

6.1. Study 1: Effect of ALA on Cell Growth with and without E2

All concentrations of ALA (50-200μM) applied for 4 days significantly reduced growth

in breast cancer cells with high and low ER, PR and HER2 expression, and ALA effects were not

diminished by the absence or presence of 1nM E2. This proves that ALA reduces breast cancer

cell growth in vitro regardless of receptor expression and E2 environment. MCF-7 cells which

are ER+, PR+, and have low HER2 expression had significantly higher % viable cells compared

to the other 3 cell lines up to 100μM ALA both ± E2, suggesting that ALA is less effective at

reducing growth in this cell line. This cell line was the only one to have a significant increase

(91%) in cell growth from incubation with 1nM E2, which is expected due to the high expression

of ERα and similar findings from others (Falany et al., 2002). BT-474 cells also express ERα

however not to the same degree as MCF-7 cells as seen in the PCR array. They also have

extremely high HER2 expression which is likely the large driver of cell growth. TAM, a drug

which blocks E2 signalling in breast cancer, was found to be effective in MCF-7 cells but not

BT-474 cells suggesting that E2 has less of an impact on BT-474 cell growth (Su et al., 2008).

As MDA MB 231 and MDA MB 468 lack ERα, E2 did not alter cell growth. The increase in

growth from E2 in MCF-7 cells was completely negated with the addition of 50μM ALA. With

all doses of ALA, the +E2 and - E2 treated MCF-7 cells had almost identical cell growth

suggesting that ALA may be working, at least partially, through ER related mechanisms in MCF-

7 cells by decreasing the bioaccessibility of E2 to the cells or altering ER signalling in the cell.

This is further supported by the decrease in ABCG2 gene expression seen from the PCR array.

ABCG2 codes for the ATP-binding cassette sub-family G member 2 protein in human which acts

as a transporter of a variety of substances into cells including steroids such as E2 (Hardwick et

al., 2007). A reduction in expression of this gene by ALA treatment may decrease the amount of

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E2 entering the MCF-7 cells and lead to decreased activation of ERα and its downstream growth

signalling pathways.

MDA MB 231 cells, which have little to no mRNA expression (and possibly protein

expression) of ER, PR and HER2 as confirmed by the PCR array, had the largest reduction in %

viable cell number at both the 75 and 100 μM ALA doses. Other studies have also shown that

basal subtypes may experience greater reduction in cell growth from n-3 PUFA treatment

(Chajès et al., 1995; Corsetto et al., 2011). From the hypothesis that ALA would decrease growth

by incorporating into the cell membrane and altering ER, PR and HER2 expression or activity, it

was thought that MDA MB 231 and MDA MB 468 (basal cell lines) would have lesser growth

reduction from ALA than MCF-7 and BT-474, due to the low expression of these receptors.

Basal cell lines however typically have high EGFR gene expression (Subik et al., 2010), which

was validated in Study 4 by PCR array. Therefore ALA may be decreasing growth in this cell

line through alteration of this receptor leading to down-regulation of growth signalling pathways.

Previous work supports the suppression of EGFR by n-3 PUFA and FSO leading to a reduction

in viable cell number (Chen et al., 2002; Corsetto et al., 2011; Truan et al., 2010) and reduction

in growth signalling biomarkers MAPK and Akt (Saggar et al., 2010a; Truan et al., 2010). The

PCR array analysis showed no significant changes to whole cell EGFR mRNA expression;

however, reduction may be restricted to specific cellular domains such as the lipid rafts as seen

previously (Schley et al., 2007; Staubach & Hanisch, 2011) or protein level changes. These

findings are of particular interest as this cell line represents the basal breast cancer subtype which

is extremely aggressive and currently lacks effective treatment options.

ALA dose-dependently decreased cell growth in all the cell lines, and in all but one cell

line (BT-474) there was no significant additional decrease in cell growth between the 75μM and

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100μM doses. Thus, a concentration of 75μM ALA + E2 was the selected condition for the

subsequent studies as all cell lines under this condition had at least a 50% reduction in growth;

This dose is physiologically relevant as in vivo studies have shown serum concentrations

upwards of 100μM with a 10% FS/ 4%FSO diet (Francis et al., 2013; Mason et al., 2013) and a

6g/d ALA diet in humans raised serum ALA to 197.5μM (Austria et al., 2008). At this dose, the

basal cell line MDA MB 231 had the lowest cell growth, significantly lower than MCF-7 cells.


Measurement of apoptosis with annexin V-PE and 7-AAD stain quantified by flow

cytometry showed that 75μM ALA + E2 significantly increased late apoptosis in MDA MB 231

and MDA MB 468 cells, and total apoptosis in MDA MB 231 cells. This indicates that the

changes in cell growth from ALA treatment can at least be partially explained by an increase in

cell apoptosis. There were no significant changes in early apoptosis with 2 day treatment of cells

with 75μM ALA + E2, however BT-474 cells did experience a large biological but statistically

insignificant increase. This is due to the large variability in the apoptosis measurements, which

may be a result of the trypsinization process to collect the cells, and may be expected to reach

statistical significance with more study power. Differences in the type of apoptosis seen between

cell lines suggests that MDA MB 231 and MDA MB 468 may have more immediate apoptotic

effects from ALA (late apoptosis increases) while MCF-7 and BT-474 may have a slower

reaction to apoptosis induction by ALA. MDA MB 231 had the lowest cell growth from 75μM

ALA +1nM E2 treatment in study 1 and was the only cell line to have a significant increase in

late apoptosis suggesting that ALA effects in this cell line are through induction of apoptosis.

Previous studies also support the induction of apoptosis in breast cancer cells with n-3 PUFA

treatment (Cao et al., 2012; Corsetto et al., 2011). Interestingly, effects may be specific to cell

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type and n-3 PUFA type, as EPA increased apoptosis in MCF-7 cells but not MDA MB 231, and

DHA increased apoptosis in MDA MB 231 cells and not MCF-7 (Corsetto et al., 2011).

6.3 Study 3: Effect of ALA on Phospholipid Fatty Acid Composition

Comparison by two-way ANOVA showed significant differences in phospholipid fatty

acid profile between cell lines. MDA MB 231 cells had the most unique profile, with higher

composition of a variety of saturated (18:0), monounsaturated (22:1n-9), n-6 PUFA (22:4n-6,

22:5n-6), n-9 PUFA (22:1n-9) and the n-3 PUFA EPA compared to the other cell lines. MDA

MB 231 cells also had lower % 16:0 (over 3-fold lower than MCF-7 cells), and greater % 18:3n-

6, 20:2n-6, and 20:4n-6 compared to BT-474 and MCF-7 cells, however there was no difference

in these fatty acids between MDA MB 231 and MDA MB 468 cells. The basal cell lines also had

greater % 22:0 and 22:6n-3 (DHA) than MCF-7 and BT-474 cells. As MDA MB 231 cells had

most unique fatty acid profile and the greatest growth reduction from ALA treatment, there may

be a relationship between phospholipid fatty acid composition and subsequent growth effects.

Incubating breast cancer cells with 75μM ALA + E2 for 4 days caused significantly

higher phospholipid ALA composition in all four cell lines, ranging from 13.1% to 25.1% ALA.

This provides evidence that ALA in the cell medium is effectively incorporated into the cell

phospholipids. MDA MB 231 had the highest % ALA composition, significantly higher than

MCF-7 and BT-474. It is unclear why this cell line incorporates ALA at a greater level, but may

nevertheless help explain its greater sensitivity to growth reduction and apoptosis induction.

Previous research comparing MCF-7 and MDA MB 231 cells also have shown a greater n-3

PUFA (EPA and DHA) uptake in MDA MB 231 cells (Corsetto et al., 2011). ALA treatment

caused no changes to cell EPA levels, but significantly lowered both % DHA and the n6:n3

PUFA ratio of cells. The lower n6:n3 ratio is largely driven by the drastic increase in ALA, an n-

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3 PUFA. The lower % DHA may be expected as a compensation for the large increase in %

ALA, however, DHA concentration of ALA treated cells also decreased. Others have shown in

HepG2 (hepatocyte cell line) cells that DHA accumulation in phospholipids remained stable

when ALA supplementation exceeded 18μM and cells become saturated with ALA (Portolesi,

Powell, & Gibson, 2007). The mechanism for DHA loss in our study is unknown but may be a

result of DHA being oxidized, or being displaced by ALA as the cell becomes saturated with

ALA. The change in % DHA (lowered ~1%) from ALA treatment is a small change in

comparison to the drastic increase in % ALA, indicating that the growth effects are likely more a

result of the higher % ALA than the small change in %DHA. There were also changes to a

variety of other fatty acids as summarized in section 5.3, but the magnitude of change to these

fatty acids was very minimal in comparison to ALA and likely not physiologically relevant. Due

to the drastic increase in % ALA, no change in % EPA, and biologically insignificant lower %

DHA, the growth effects on the breast cancer cells can be attributed to ALA and not the

downstream metabolites.

Regression analysis showed that there is a trend for control cells with high % ALA to

have higher % ALA in the cells treated with 75μM ALA +E2, indicating that differences in ALA

phospholipid fatty acid composition may alter the level of ALA incorporation into the cells, and

this may alter growth reduction. Regression analysis also indicates that the increase in

phospholipid % ALA is inversely associated with breast cancer cell growth, with MDA MB 231

having the highest % ALA composition with 75μM ALA +E2 treatment, and also having the

lowest cell growth. Similarly, phospholipid % ALA was positively associated with total

apoptosis induction, with MDA MB 231 having the highest level of total apoptosis and highest

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% ALA. This is of great importance as it provides causation that growth and apoptosis effects are

directly related to the level of ALA incorporation into breast cancer cells.


With the expectation that the gene expression relates to protein expression, PCR array

confirmed the molecular subtypes of the commercial cell line based on the ERα, PR and HER2

gene expressions, with MCF-7 cells having high ERα and PR and low HER2 gene expression,

BT 474 cells having high ERα, PR and HER2 gene expression, and MDA MB 231 and MDA

MB 468 cells having low ERα, PR and HER2 gene expression. Several other tumour

classification markers further confirmed cell line molecular subtypes with MCF-7 and BT-474

cell lines having high gene expression of luminal tumour classification genes including FOXA1

(forkhead box A1), GATA3 (GATA binding protein 3), KRT18 (keratin 18), KRT8 (keratin 8),

SLC39A6 (solute carrier family 39, zinc transporter), TFF3 (trefoil factor 3), and XBP1 (X-box

binding protein 1). BT-474 cells also had high gene expression of the HER2 overexpressing

subtype biomarker GRB7 (growth factor receptor-bound protein 7). In addition to the low ERα,

PR and HER2 gene expressions, MDA MB 468 also had high gene expression of basal subtype

genes EGFR and KRT5 (keratin 5) compared to other cell lines.

Comparison of fold changes between the control and ALA treated cells in each cell line

provided some unexpected results, with the largest changes occurring in MCF-7 cells. It was

expected that MCF-7 cells would have few gene expression changes since they experienced the

lowest reduction in growth from 75μM ALA in Study 1, and the largest changes would occur in

MDA MB 231 cells. A number of factors may have contributed to this, including only testing at

one time point (24 hours). This treatment time was selected as many others have also tested after

24 hours fatty acid treatment (Park et al., 2012; Reyes et al., 2004; Song et al., 2012), but it may

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have been too long and extensive gene changes in the MDA MB 231, MDA MB 468 and BT-474

cell lines may have been missed. As these cell lines were most sensitive to growth reduction by

ALA it is possible that their gene changes may be occurring faster than those changes in MCF-7

cells. This is supported by literature showing that changes in mRNA expression in breast cancer

cells after incubation with fatty acids are dependent on exposure time, and that increased

treatment time can decrease the fold changes observed (Song et al., 2012). Nevertheless, despite

few changes in mRNA expression, protein expression of biomarkers may still be altered and

influence breast cancer cell growth, as well as phosphorylation and activation of those proteins.

The gene expression changes from ALA treatment in MCF-7 span a wide range of

functional groups including luminal biomarkers (GATA3, KRT8), signalling pathways (SNAI2,

MAPK1), EMT (SRC) and xenobiotic transport (ABCG2). Of particular interest is the ALA

reduction in MAPK1 gene expression in MCF-7 cells, as this gene codes a protein that is part of

the MAPK signalling cascade that leads to increased cell growth, as well as cross talk with other

cell growth pathways discussed in section 2.1. Previous research has also shown a decrease in

MAPK activation from the n-3 PUFA DHA in both breast cancer cell lines (MCF-7 and SKBR3)

and in rodent models (Sun et al., 2011). ALA also caused a large (2.5 fold) yet insignificant

reduction in IGF1 gene expression in MCF-7 cells. Similar to MAPK1, IGF1 is an important

activator of growth signalling pathways, in particular PI3K/Akt, and a reduction in IGF1 from

ALA treatment is likely linked to the reduction in cell growth. Reductions in IGF1 protein

expression in MDA MB 435 xenografts from a 10% FS diet have previously been seen (Chen et

al., 2002). ALA may also reduce MCF-7 cell growth through reduction in EMT and xenobiotic

transport. ALA reduced expression of two genes involved in EMT, metastasis and poor

prognosis; SNAI2 (snail homolog 2) and SRC (v-src sarcoma viral oncogene homolog) (Foubert

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et al., 2010). Although the effect of n-3 PUFA on these genes has not been previously

researched, n-3 PUFA (DHA) has been shown to reduce EMT in prostate cancer cells in vitro

and these genes may have been implicated (Bianchini et al., 2012). There was also a reduction in

ABCG2 (ATP-binding cassette, sub-family G member 2) gene expression, and a large yet

statistically insignificant reduction in ABCG1 (ATP-binding cassette, sub-family B member 1)

gene expression, with ALA treatment in MCF-7 cells, both associated with drug transport and

resistance (Hardwick et al., 2007). Of importance for ER-sensitive MCF-7 cells, ABCG2 has the

ability to not only transport drugs into cancer cells but also sterols including E2 (Hardwick et al.,

2007). Thus a reduction in ABCG2 gene expression from ALA treatment may decrease transport

of E2 into the cell leading to a reduction in ER-mediated cell growth. This is of particular

importance as it was seen that even 50μM ALA completely negated the growth effects of E2 in

MCF-7 cells, potentially through reduction of E2 bioaccessibility into the cell.

BT-474 cells experienced fewer changes in mRNA expression from ALA treatment

compared to MCF-7 cells, but did show significant reductions in both FOXA1 and BRCA1

(breast cancer 1) expression. FOXA1 codes for the forkhead box protein A1 and has been

identified as an inhibitor of cancer cell growth (Badve et al., 2007; Wolf et al., 2007), so reduced

expression from ALA treatment is likely not implicated in the reductions in BT-474 cell growth

observed. BRCA1 encodes a DNA repair protein and low expression or mutations in this gene

are associated with increased risk of breast cancer (Bernardo et al., 2012; Friedenson, 2007).

Previously, DHA and EPA have been shown to increase BRCA1 protein expression in rats prior

to tumourigenesis (Jourdan et al., 2007) and increase BRCA1 and BRCA2 mRNA expression in

MCF-7 and MDA MB 231 cells (Bernard-Gallon et al., 2002). These studies however used very

high DHA diets in vivo (21.4%; Jourdan et al., 2007) and longer exposure times in vitro (96

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hours; Bernard-Gallon et al., 2002) than our study which may have altered results. As well, once

cancer has developed, low BRCA1 expression has been associated with an increase in survival

and better response to chemotherapy in lung cancer as it may make cancer cells more vulnerable

to death (Papadaki et al., 2012; Taron et al., 2004). If this is similar in breast cancer, ALA

decreasing BRCA1gene expression may actually reduce viability and growth of BT 474 cells.

ALA may also reduce BT-474 cell growth through its large yet statistically insignificant increase

in ADAM23 (ADAM metallopeptidase domain 23) gene expression, a cell-cell adhesion

molecule whose expression is inversely associated with cancer development and progression

(Costa et al., 2003).

Due to the large reduction in MDA MB 231 cell growth from study 1, it was expected

that this cell line would have a large number of genes altered from ALA treatment, however few

changes were seen. ALA did down-regulate CSF1 (colony stimulating factor 1) gene expression

which regulates macrophages in the body and its expression is associated with poor prognosis

and cancer growth (Lin et al., 2002), and is regulated by the tumour suppressor PTEN (Mandal et

al., 2012). n-3 PUFA rich fish oil has previously been shown to reduce CSF1 gene expression in

MDA MB 231 and MCF-7 cell lines and support our findings of a reduction in CSF1 gene

expression with ALA treatment (Mandal et al., 2012). Further growth reduction from ALA on

MDA MB 231 cells may be a result of up-regulation of THBS1 (thrombospondin 1), an inhibitor

of angiogenesis. Low level of THBS1 expression has been associated with a variety of cancers

(Li et al., 1999), however the effects of n-3 PUFA on this gene in cancer has not been

investigated.

The only significant change in MDA MB 468 cells from ALA treatment was a reduction

in ERBB (HER2) gene expression. This cell line has a very low expression of HER2 so changes

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were unexpected. ALA down-regulation of HER2 has previously been shown in cell lines over-

expressing HER2 including BT-474 and SK-BR3 (Menéndez et al., 2006) and is associated with

a reduction in cancer growth through regulation of PI3K/Akt and MAPK signalling. ALA also

caused a large and nearly significant (p=0.083) down-regulation of ID1 (inhibitor of DNA

binding 1) gene expression, a gene indicative of EMT and invasiveness (Tobin, Sims, Lundgren,

Lehn, & Landberg, 2011) which may also contribute to the reduction in cell growth and

aggressiveness.

Contrary to our hypothesis that ALA incorporating into cell membranes would decrease

expression of membrane receptors, few changes were seen in the gene expression of ER, PR and

HER2, and other membrane receptors involved in cell growth such as EGFR and IGF-IR. Due to

ALA successfully incorporating into the cell membranes and altering the phospholipid fatty acid

profile it was expected that receptors in the membrane may also be altered. Despite the lack of

changes in mRNA expression, the receptors may still be altered in terms of localization within

the membrane which would potentially contribute to the reduction in growth observed in all cell

lines with ALA treatment. This has previously been seen, as EPA and DHA reduced EGFR

protein expression in lipid rafts, but measurement of whole cell EGFR and MAPK actually

increased (Schley et al., 2007). Similarly, there were few changes, in all cell lines, to genes

involved in apoptosis and growth signalling such as MAPK and PI3K/Akt. In particular changes

were expected in apoptosis markers as there was a significant increase in total and late apoptosis

in both MDA MB 231 and MDA MB 468 cell lines. Apoptosis is a complex cellular event and

can be activated by intrinsic and extrinsic pathways signalling, with the extrinsic pathway being

triggered by death receptors and the intrinsic pathway being activated mainly through Bcl-2

family signalling (Fulda & Debatin, 2006; Ghobrial et al., 2005). These pathways both result in

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activation of various caspases that induce apoptosis (Ghobrial et al., 2005). Common death

receptors and regulators of extrinsic apoptotic signalling include TNFR1 (tumor necrosis factor

receptor 1), CD95 (Fas/APO-1) and TRAIL-R1 (tumour necrosis factor related apoptosis

inducing ligand receptor 1) (Fulda & Debatin, 2006; Ghobrial et al., 2005). Important activators

and regulators of the intrinsic pathway include Bcl-2 family proteins such as Bax and Bad (pro-

apoptotic) and Bcl-2 (anti-apoptotic) which regulate the downstream release of cytochrome-c

(Fulda & Debatin, 2006; Ghobrial et al., 2005). Some of the genes coding for these proteins or

involved in regulation of them were included in the PCR array (Bcl2, Bad, TP53), however

measurement of a wider range of genes, such as TRAIL, CD95 and caspases, may be needed to

detect changes from ALA.

6.5 Summary

This study has shown for the first time that ALA is effective at reducing cell growth

across breast cancer cell lines with varying ER, PR and HER2 expression, both with and without

E2. The basal cell lines MDA MB 231 and MDA MB 468 were especially sensitive to the

growth reducing effects of ALA. The reduction in cancer cell growth is partially explained by

increases in late and total apoptosis in the basal breast cancer cell lines MDA MB 231 and MDA

MB 468. Treating cells with 75μM ALA + E2 caused significantly higher cell phospholipid ALA

% composition, with MDA MB 231 cells having the highest level of ALA incorporation. This

increase in % ALA was inversely associated with cell growth, and positively associated with

total apoptosis. PCR array analysis showed that there was no change in receptor gene expression

from ALA treatment; however there was a significant reduction in the cell signalling molecules

MAPK1 and IGF1 in MCF-7 cells. Several other genes related to epithelial to mesenchymal

transition (EMT), xenobiotic transport and DNA repair were altered in the other cell lines, with

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the largest number of changes in MCF-7 cells. The exact mechanism of ALA for reducing breast

cancer growth still remains unclear, but likely is a result of incorporation and alteration of the

phospholipid rich cell membrane. PCR array analysis indicated that there was very little

similarity in genes changed between the cell lines suggesting that ALA may be altering breast

cancer cell growth in a subtype specific fashion.

This research suggests that ALA shows promise as a potential complementary treatment

option for breast cancer patients regardless of tumour receptor expression and menopausal status,

and may be particularly effective in difficult to treat triple negative subtypes.

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7.0 CONCLUSIONS

In four human breast cancer cell lines with varying ER, PR and HER2 expression:

Study 1. ALA decreases cell growth both with and without E2, regardless of receptor

expression, at doses of 50-200 μM. MCF-7 cells (ER+/PR+, low HER2) were less sensitive to

ALA, while MDA MB 231 cells (ER-, PR-, low HER2) were the most sensitive to growth

reduction at 75μM ALA, the ALA concentration used in subsequent studies.

Study 2. 75μM ALA + 1nM E2 induces total and late apoptosis in cell lines with little or

no ER, PR and HER2 expression (MDA MB 231 and MDA MB 468). Other cell lines had

increases in apoptosis, in particular early apoptosis in BT-474 cells (ER/PR-, HER2 +), but failed

to reach statistical significance.

Study 3. 75μM ALA + 1nM E2 treatment successfully incorporates ALA into cell

phospholipids, increasing phospholipid % ALA while decreasing % DHA and the n6:n3 PUFA

ratio in all cell lines. MDA MB 231 cells had the greatest level of ALA incorporation. The

proportion of ALA in phospholipids is inversely related to cell growth and positively associated

with total apoptosis induction.

Study 4. 75μM ALA + 1nM E2 did not alter whole cell gene expression of membrane

receptors in any of the cell lines, however there was a reduction in the cell signalling biomarkers

MAPK1 and IGF1 in MCF-7 cells. As well, several other genes involved in EMT, xenobiotic

transport and DNA repair were altered in MCF-7 cells and the other cell lines.

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8.0 STUDY LIMITATIONS AND FUTURE DIRECTIONS

8.1 Study Limitations

This research successfully showed that ALA incorporates into and reduces the growth of

breast cancer cells in vitro regardless of ER, PR and HER2 expression or E2 environment.

However several limitations exist that should be addressed in future work.

1. This work is done in vitro and thus caution needs to be taken when extrapolating to

humans. There are many factors that differ between cell based ALA supplementation design and

the action of ingested ALA in humans including fatty acid metabolism, distribution within the

body and tumour, and other factors such as immunological response. In vitro work does however

lay the groundwork to justify future clinical work, provide insight into potential effects in

humans, as well as allow for determination of potential mechanisms of action.

2. There was no cell line representing the HER2 overexpressing molecular subtype

(HER2 overexpressing, ER/PR negative). However, due to the drastic growth reduction across

all other subtypes, including luminal B cells which also overexpress HER2, it is expected that

cell lines representative of the HER2 overexpressing subtype would experience a similar

reduction in growth from ALA supplementation.

3. Lower range doses of ALA (<50μM) were not tested. These doses are likely effective

as well and may show differences in sensitivity across the cell lines and E2 environments. These

doses may also better represent typical human levels of ALA consumption. The doses used in

this study are however physiologically plausible as in vivo work has shown serum ALA at

>100μM (Francis et al., 2013; Mason et al., 2013).

4. In this research ALA (± E2) was used as the sole treatment, whereas in the vast

majority of breast cancer patients there are other concurrent treatments in place. It is unclear if

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the growth reducing effect of ALA would remain significant if other drugs or therapies are also

being used, or if ALA may even interfere with other drug effectiveness. Prior in vivo research in

MCF-7 and BT-474 mouse xenografts however showed that FSO combined with TAM or TRAS

respectively not only did not interfere with drug effectiveness at reducing tumour growth, but

actually enhanced their effects (Mason et al., 2010; Saggar et al., 2010a).

5. These studies, in particular the apoptosis and PCR array work, would benefit from

added power. Larger sample size may decrease variability and provide a better interpretation of

results currently showing trends but lacking statistical significance. For example, the apoptosis

data had large variability and data such as early apoptosis in BT-474 cells shows a large effect

but variability is preventing differences from being significant. The PCR array was only done in

n=3 for each group due to high cost, but more samples would likely lead to more significant

findings.

6. This research lacks a non-tumorigenic breast cell line, and also did not determine if

this growth reducing effect is specific to ALA. Nevertheless, other studies have supplemented

ALA on MCF-10A cells, a commercial non-tumorigenic mammary cell lines, and showed no

effect on cell growth (Bernard-Gallon et al., 2002). Studies have also investigated the effect of

the n-6 PUFA LA on breast cancer cell growth and found no effect or even an increase in growth

(Corsetto et al., 2011; Larsson et al., 2004; Reyes et al., 2004), suggesting that this reduction in

growth is specific to ALA and n-3 PUFA.

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8.2 Future Directions

Several questions still remain and should be addressed including the rationale for why

basal breast cancer cell lines are more sensitive to ALA, whether ALA is preferentially

incorporated into specific phospholipids or regions of the cell membrane (ie. lipid rafts), and

whether ALA alters the location of membrane receptors in the cells. The exact mechanism of

ALA growth reduction also remains unclear and future work in the area of ALA and breast

cancer should further investigate potential mechanisms for altering cell growth including, but not

limited to, oxidative stress, changes to signalling biomarkers and receptors at the protein level,

micro RNA changes, and markers of apoptosis. Future studies should also investigate the effect

of ALA on breast cancer tumour growth when combined with traditional therapies such as TAM

and TRAS, as well as the effect of ALA when combined with other fatty acids as would be seen

in a typical diet to create a more representative human model. Further investigation including the

use of lower doses of ALA and the effect of ALA on the HER2 overexpressing subtype would

also further the knowledge and understanding in the area of ALA, FSO and FS and breast cancer.

Finally, future studies should move towards clinical investigation into the efficacy and safety of

ALA as a complementary therapy in all breast cancer subtypes. In addition to ALA as a

complementary treatment, investigation into its role in breast cancer prevention should continue.

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9.0 IMPLICATIONS

This research provides support for the use of ALA and ALA- rich foods as a

complementary treatment for breast cancer patients, regardless of tumour receptor expression

and menopausal status. This is of importance to patients who may be pursuing complementary

therapies such as FS, FSO and ALA, their physicians and oncologists, as well as the breast

cancer research community. This research also shows that ALA is particularly effective at

reducing growth of basal/TNBC which currently has poor prognosis and few treatment options.

ALA may provide a safe, cost effective treatment option for patients not only with basal breast

cancer, but with a wide range of breast tumor receptor expression profiles. This work provides

insight into some potential mechanisms of ALA effect, showing that ALA effectively

incorporates into cell phospholipids and induces apoptosis, however further investigation is

needed to determine exact mechanisms and determine why ALA is more effective at reducing

growth in basal breast cancer cell lines.

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10.0 REFERENCES

Anderson, B. M., & Ma, D. (2009). Are all n-3 polyunsaturated fatty acids created equal.

Lipids in Health and Disease, 8(33), 1-20.

Anderson, J. G., & Taylor, A. G. (2012). Use of complementary therapies for cancer

symptom management: Results of the 2007 national health interview survey. The Journal of

Alternative and Complementary Medicine, 18(3), 235-241.

Arpino, G., Wiechmann, L., Osborne, C. K., & Schiff, R. (2008). Crosstalk between the

estrogen receptor and the HER tyrosine kinase receptor family: Molecular mechanism and

clinical implications for endocrine therapy resistance. Endocrine Reviews, 29(2), 217-33.

doi: 10.1210/er.2006-0045

Austria, J., Richard, M., Chahine, M., Edel, A., Malcolmson, L., Dupasquier, C., & Pierce,

G. (2008). Bioavailability of alpha-linolenic acid in subjects after ingestion of three different

forms of flaxseed. Journal of the American College of Nutrition, 27(2), 214-221.

Badve, S., Turbin, D., Thorat, M. A., Morimiya, A., Nielsen, T. O., Perou, C. M., Nakshatri,

H. (2007). FOXA1 expression in breast cancer—correlation with luminal subtype A and

survival. Clinical Cancer Research, 13(15), 4415-4421.

Baselga, J., Albanell, J., Molina, M. A., & Arribas, J. (2001). Mechanism of action of

trastuzumab and scientific update. Seminars in Oncology, 28 4-11.

Baselga, J., Carbonell, X., Castañeda-Soto, N., Clemens, M., Green, M., Harvey, V.,

Ghahramani, P. (2005). Phase II study of efficacy, safety, and pharmacokinetics of

trastuzumab monotherapy administered on a 3-weekly schedule. Journal of Clinical

Oncology, 23(10), 2162-2171.

Bernard-Gallon, D. J., Vissac-Sabatier, C., Antoine-Vincent, D., Rio, P. G., Maurizis, J.,

Fustier, P., & Bignon, Y. (2002). Differential effects of n-3 and n-6 polyunsaturated fatty

acids on BRCA1 and BRCA2 gene expression in breast cell lines. British Journal of

Nutrition, 87(04), 281-289.

Bernardo, G., Bebek, G., Ginther, C., Sizemore, S., Lozada, K., Miedler, J., Slamon, D.

(2012). FOXA1 represses the molecular phenotype of basal breast cancer cells. Oncogene,

32(5), 554-563.

Bernstein, L., Ross, R., & Henderson, B. (1992). Relationship of hormone use to cancer risk.

Journal of the National Cancer Institute. Monographs, (12), 137.

Berquin, I. M., Edwards, I. J., & Chen, Y. Q. (2008). Multi-targeted therapy of cancer by

omega-3 fatty acids. Cancer Letters, 269(2), 363-377.

81

61

Bianchini, F., Giannoni, E., Serni, S., Chiarugi, P., & Calorini, L. (2012). 22: 6n-3 DHA

inhibits differentiation of prostate fibroblasts into myofibroblasts and tumorigenesis. British

Journal of Nutrition, 108(12), 2129-2137.

Birkved, F. K., Mortensen, A., Peñalvo, J. L., Lindecrona, R. H., & Sørensen, I. K. (2011).

Investigation into the cancer protective effect of flaxseed in tg. NK (MMTV/c-neu) mice, a

murine mammary tumor model. Genes & Nutrition, 6(4), 403-411.

Björnström, L., & Sjöberg, M. (2005). Mechanisms of estrogen receptor signalling:

Convergence of genomic and nongenomic actions on target genes. Molecular

Endocrinology, 19(4), 833-842.

Blanckaert, V., Ulmann, L., Mimouni, V., Antol, J., Brancquart, L., & Chénais, B. (2010).

Docosahexaenoic acid intake decreases proliferation, increases apoptosis and decreases the

invasive potential of the human breast carcinoma cell line MDA-MB-231. International

Journal of Oncology, 36(3), 737.

Bligh, E.G., Dyer, W.J. (1959). A rapid method of total lipid extraction and purification.

Canadian Journal of Biochemistry and Physiology. 37(8), 911-917.

Boon, H. S., Olatunde, F., & Zick, S. M. (2007). Trends in complementary/alternative

medicine use by breast cancer survivors: Comparing survey data from 1998 and 2005. BMC

Women's Health, 7(4).

Boon, H., Stewart, M., Kennard, M. A., Gray, R., Sawka, C., Brown, J. B., Aaron, D.

(2000). Use of complementary/alternative medicine by breast cancer survivors in ontario:

Prevalence and perceptions. Journal of Clinical Oncology, 18(13), 2515-2521.

Boucher, B. A., Cotterchio, M., Curca, I. A., Kreiger, N., Harris, S. A., Kirsh, V. A., &

Goodwin, P. J. (2012). Intake of phytoestrogen foods and supplements among women

recently diagnosed with breast cancer in ontario, canada. Nutrition and Cancer, 64(5), 695-

703.

Bougnoux, P. and Chajes, V. (2003). Alpha-linolenic acid and cancer. In Flaxseed in human

nutrition. L. U. Thompson and S. C. Cunnane. (2 Ed.) pp. 233-244. Champaign: AOCS

Press.

Bougnoux, P., Koscielny, S., V, C., Descamps, P., Couet, C., & Calais, G. (1994). Alpha-

linolenic acid content of adipose breast tissue: A host determinant of the risk of early

metastasis in breast cancer. British Journal of Cancer, 70(2), 330.

Brenna, J. T., Salem Jr, N., Sinclair, A. J., & Cunnane, S. C. (2009). Α-linolenic acid

supplementation and conversion to n-3 long-chain polyunsaturated fatty acids in humans.

Prostaglandins, Leukotrienes and Essential Fatty Acids, 80(2), 85-91.

82

61

Buck, K., Zaineddin, A. K., Vrieling, A., Linseisen, J., & Chang-Claude, J. (2010). Meta-

analyses of lignans and enterolignans in relation to breast cancer risk. The American Journal

of Clinical Nutrition, 92(1), 141-153.

Canadian Cancer Society's Advisory Committee on Cancer Statistics. (2013). Canadian

cancer statistics 2013. Toronto, ON, Canada. Canadian Cancer Society.

Cao, W., Ma, Z., Rasenick, M. M., Yeh, S., & Yu, J. (2012). N-3 poly-unsaturated fatty

acids shift estrogen signalling to inhibit human breast cancer cell growth. PloS One, 7(12),

e52838.

Carey, L. A., Perou, C. M., Livasy, C. A., Dressler, L. G., Cowan, D., Conway, K.,

Edmiston, S. (2006). Race, breast cancer subtypes, and survival in the carolina breast cancer

study. JAMA: The Journal of the American Medical Association, 295(21), 2492-2502.

Chajès, V., Hultén, K., Van Kappel, A., Winkvist, A., Kaaks, R., Hallmans, G., Riboli, E.

(1999). Fatty‐acid composition in serum phospholipids and risk of breast cancer: An

incident case‐control study in sweden. International Journal of Cancer, 83(5), 585-590.

Chajès, V., Sattler, W., Stranzl, A., & Kostner, G. M. (1995). Influence of n-3 fatty acids on

the growth of human breast cancer cellsin vitro: Relationship to peroxides and vitamin-E.

Breast Cancer Research and Treatment, 34(3), 199-212.

Chajès, V., Thiébaut, A. C., Rotival, M., Gauthier, E., Maillard, V., Boutron-Ruault, M.,

Clavel-Chapelon, F. (2008). Association between serum trans-monounsaturated fatty acids

and breast cancer risk in the E3N-EPIC study. American Journal of Epidemiology, 167(11),

1312-1320.

Chamras, H., Ardashian, A., Heber, D., & Glaspy, J. A. (2002). Fatty acid modulation of

MCF-7 human breast cancer cell proliferation, apoptosis and differentiation. The Journal of

Nutritional Biochemistry, 13(12), 711-716.

Chen, J., Power, K. A., Mann, J., Cheng, A., & Thompson, L. U. (2007). Dietary flaxseed

interaction with tamoxifen induced tumor regression in athymic mice with MCF-7

xenografts by downregulating the expression of estrogen related gene products and signal

transduction pathways. Nutrition and Cancer, 58(2), 162-170.

Chen, J., Saggar, J. K., Corey, P., & Thompson, L. U. (2009). Flaxseed and pure

secoisolariciresinol diglucoside, but not flaxseed hull, reduce human breast tumor growth

(MCF-7) in athymic mice. The Journal of Nutrition, 139(11), 2061-2066.

Chen, J., Stavro, P. M., & Thompson, L. U. (2002). Dietary flaxseed inhibits human breast

cancer growth and metastasis and downregulates expression of insulin-like growth factor

and epidermal growth factor receptor. Nutrition and Cancer, 43(2), 187-192.

83

61

Cho, E., Spiegelman, D., Hunter, D. J., Chen, W. Y., Stampfer, M. J., Colditz, G. A., &

Willett, W. C. (2003). Premenopausal fat intake and risk of breast cancer. Journal of the

National Cancer Institute, 95(14), 1079-1085.

Clemons, M., & Goss, P. (2001). Estrogen and the risk of breast cancer. New England

Journal of Medicine, 344(4), 276-285.

Cognault, S., Jourdan, M. L., Germain, E., Pitavy, R., Morel, E., Durand, G., Lhuillery, C.

(2000). Effect of an a-linolenic acid-rich diet on rat mammary tumor growth depends on the

dietary oxidative status. Nutrition and Cancer, 36(1), 33-41.

Colditz, G. A., Hankinson, S. E., Hunter, D. J., Willett, W. C., Manson, J. E., Stampfer, M.

J., Speizer, F. E. (1995). The use of estrogens and progestins and the risk of breast cancer in

postmenopausal women. New England Journal of Medicine, 332(24), 1589-1593.

Corsetto, P. A., Montorfano, G., Zava, S., Jovenitti, I. E., Cremona, A., Berra, B., & Rizzo,

A. M. (2011). Effects of n-3 PUFAs on breast cancer cells through their incorporation in

plasma membrane. Lipids in Health and Disease, 10(1), 73.

Costa, F. F., Verbisck, N. V., Salim, A. C. M., Ierardi, D. F., Pires, L. C., Sasahara, R. M.,

O'Hare, M. (2003). Epigenetic silencing of the adhesion molecule ADAM23 is highly

frequent in breast tumors. Oncogene, 23(7), 1481-1488.

Cunnane, S. (2003). Dietary sources and metabolism of α-linolenic acid. In Flaxseed in

human nutrition. L. U. Thompson and S. C. Cunnane. (2 Ed.) pp. 63-91. Champaign: AOCS

Press.

Dahlman-Wright, K., Cavailles, V., Fuqua, S. A., Jordan, V. C., Katzenellenbogen, J. A.,

Korach, K. S., Gustafsson, J. (2006). International union of pharmacology. LXIV. estrogen

receptors. Pharmacological Reviews, 58(4), 773-781.

Daun, J. K., Barthet, V. J., Chornick, T.L., Duguid, S. (2003). Structure, composition, and

variety development of flaxseed. In Flaxseed in Human Nutrition. L.U. Thompson and S.C.

Cunnane. (2 Ed.). pp 1-40. Champaign: AOCS Press.

Dawood, S., Hu, R., Homes, M. D., Collins, L. C., Schnitt, S. J., Connolly, J., Tamimi, R.

M. (2011). Defining breast cancer prognosis based on molecular phenotypes: Results from a

large cohort study. Breast Cancer Research and Treatment, 126(1), 185-192.

De Stefani, E., Deneo-Pellegrini, H., Mendilaharsu, M., & Ronco, A. (1998). Essential fatty

acids and breast cancer: A case-control study in uruguay. International Journal of Cancer,

76(4), 491-494.

Drucker, A., Skedgel, C., Virik, K., Rayson, D., Sellon, M., & Younis, T. (2008). The cost

burden of trastuzumab and bevacizumab therapy for solid tumours in Canada. Current

Oncology, 15(3), 136-142.

84

61

Ellison, G., Klinowska, T., Westwood, R., Docter, E., French, T., & Fox, J. (2002). Further

evidence to support the melanocytic origin of MDA-MB-435. Molecular Pathology, 55(5),

294-299.

Fagan, D. H., & Yee, D. (2008). Crosstalk between IGF1R and estrogen receptor signalling

in breast cancer. J Mammary Gland Biol Neoplasia, 13(4), 423-9.

Falany, J. L., Macrina, N., & Falany, C. N. (2002). Regulation of MCF-7 breast cancer cell

growth by β-estradiol sulfation. Breast Cancer Research and Treatment, 74(2), 167-176.

Fetterman , J. W. & Zdanowicz, M. M. (2009). Therapeutic potential of n-3 polyunsaturated

fatty acids in disease. American Journal of Health-System Pharmacy, 66(13), 1169-79.

Folsom, A. R., & Demissie, Z. (2004). Fish intake, marine omega-3 fatty acids, and

mortality in a cohort of postmenopausal women. American Journal of Epidemiology,

160(10), 1005-10.

Foubert, E., De Craene, B., & Berx, G. (2010). Key signalling nodes in mammary gland

development and cancer. the Snail1-Twist1 conspiracy in malignant breast cancer

progression. Breast Cancer Research, 12(206).

Francis, A. A., Deniset, J. F., Austria, J. A., LaValleé, R. K., Maddaford, G. G., Hedley, T.

E., Pierce, G. N. (2013). Effects of dietary flaxseed on atherosclerotic plaque regression.

American Journal of Physiology-Heart and Circulatory Physiology, 304(12), H1743-H1751.

Friedenson, B. (2007). The BRCA1/2 pathway prevents hematologic cancers in addition to

breast and ovarian cancers. BMC Cancer, 7(152).

Fulda, S., & Debatin, K. (2006). Extrinsic versus intrinsic apoptosis pathways in anticancer

chemotherapy. Oncogene, 25(34), 4798-4811.

Gago-Dominguez, M., Yuan, J., Sun, C., Lee, H., & Yu, M. (2003). Opposing effects of

dietary n-3 and n-6 fatty acids on mammary carcinogenesis: The singapore chinese health

study. British Journal of Cancer, 89(9), 1686-1692.

Ghobrial, I. M., Witzig, T. E., & Adjei, A. A. (2005). Targeting apoptosis pathways in

cancer therapy. CA: A Cancer Journal for Clinicians, 55(3), 178-194.

Ghosh, S., & Karin, M. (2002). Missing pieces in the NF-ºB puzzle. Cell, 109(2), S81-S96.

Ghosh-Choudhury, T., Mandal, C. C., Woodruff, K., St Clair, P., Fernandes, G., Choudhury,

G. G., & Ghosh-Choudhury, N. (2009). Fish oil targets PTEN to regulate NFκB for

downregulation of anti-apoptotic genes in breast tumor growth. Breast Cancer Research and

Treatment, 118(1), 213-228.

85

61

Gonzalez, M. J., Schemmel, R. A., Gray, J. I., Dugan, L., Sheffield, L. G., & Welsch, C. W.

(1991). Effect of dietary fat on growth of MCF-7 and MDA-MB231 human breast

carcinomas in athymic nude mice: Relationship between carcinoma growth and lipid

peroxidation product levels. Carcinogenesis, 12(7), 1231-1235.

Greenlee, H., Kwan, M. L., Ergas, I. J., Sherman, K. J., Krathwohl, S. E., Bonnell, C.,

Kushi, L. H. (2009). Complementary and alternative therapy use before and after breast

cancer diagnosis: The pathways study. Breast Cancer Research and Treatment, 117(3), 653-

665.

Hall, J. M., Couse, J. F., & Korach, K. S. (2001). The multifaceted mechanisms of estradiol

and estrogen receptor signalling. Journal of Biological Chemistry, 276(40), 36869-36872.

Hardman, W. E. (2007). Dietary canola oil suppressed growth of implanted MDA-MB 231

human breast tumors in nude mice. Nutrition and Cancer, 57(2), 177-183.

Hardman, W. E., & Ion, G. (2008). Suppression of implanted MDA-MB 231 human breast

cancer growth in nude mice by dietary walnut. Nutrition and Cancer, 60(5), 666-674.

Hardwick, L., Velamakanni, S., & Veen, H. (2007). The emerging pharmacotherapeutic

significance of the breast cancer resistance protein (ABCG2). British Journal of

Pharmacology, 151(2), 163-174.

Hassan, A., Ibrahim, A., Mbodji, K., Coëffier, M., Ziegler, F., Bounoure, F., Déchelotte, P.

(2010). An α-linolenic acid-rich formula reduces oxidative stress and inflammation by

regulating NF-κB in rats with TNBS-induced colitis. The Journal of Nutrition, 140(10),

1714-1721.

Horia, E., & Watkins, B. A. (2005). Comparison of stearidonic acid and α-linolenic acid on

PGE2 production and COX-2 protein levels in MDA-MB-231 breast cancer cell cultures.

The Journal of Nutritional Biochemistry, 16(3), 184-192.

Imai, H., Kuroi, K., Ohsumi, S., Ono, M., & Shimozuma, K. (2007). Economic evaluation of

the prevention and treatment of breast cancer-present status and open issues. Breast Cancer,

14(1), 81-87.

Jourdan, M., Mahéo, K., Barascu, A., Goupille, C., De Latour, M. P., Bougnoux, P., & Rio,

P. G. (2007). Increased BRCA1 protein in mammary tumours of rats fed marine ω-3 fatty

acids. Oncology Reports, 17(4), 713-719.

Jump, D. B. (2004). Fatty acid regulation of gene transcription. Critical Reviews in Clinical

Laboratory Sciences, 41(1), 41-78.

Karin, M., & Lin, A. (2002). NF-κB at the crossroads of life and death. Nature Immunology,

3(3), 221-227.

86

61

Kelsey, J. L., Gammon, M. D., & John, E. M. (1993). Reproductive factors and breast

cancer. Epidemiologic Reviews, 15(1), 36-47.

Kim, J., Park, H. D., Park, E., Chon, J., & Park, Y. K. (2009). Growth‐Inhibitory and

proapoptotic effects of Alpha‐Linolenic acid on Estrogen‐Positive breast cancer cells.

Annals of the New York Academy of Sciences, 1171(1), 190-195.

Klein, V., Chajes, V., Germain, E., Schulgen, G., Pinault, M., Malvy, D., Lhuillery, C.

(2000). Low alpha-linolenic acid content of adipose breast tissue is associated with an

increased risk of breast cancer. European Journal of Cancer, 36(3), 335-340.

Kuriki, K., Hirose, K., Wakai, K., Matsuo, K., Ito, H., Suzuki, T., Tatematsu, M. (2007).

Breast cancer risk and erythrocyte compositions of n‐3 highly unsaturated fatty acids in

japanese. International Journal of Cancer, 121(2), 377-385.

Larsson, S. C., Kumlin, M., Ingelman-Sundberg, M., & Wolk, A. (2004). Dietary long-chain

n− 3 fatty acids for the prevention of cancer: A review of potential mechanisms. The

American Journal of Clinical Nutrition, 79(6), 935-945.

Lee, A. V., Cui, X., & Oesterreich, S. (2001). Cross-talk among estrogen receptor, epidermal

growth factor, and insulin-like growth factor signalling in breast cancer. Clinical Cancer

Research, 7(12 Suppl), 4429s-4435s; discussion 4411s-4412s.

Lee, J. Y., Plakidas, A., Lee, W. H., Heikkinen, A., Chanmugam, P., Bray, G., & Hwang, D.

H. (2003). Differential modulation of toll-like receptors by fatty acids preferential inhibition

by n-3 polyunsaturated fatty acids. Journal of Lipid Research, 44(3), 479-486.

Lehmann, B. D., Bauer, J. A., Chen, X., Sanders, M. E., Chakravarthy, A. B., Shyr, Y., &

Pietenpol, J. A. (2011). Identification of human triple-negative breast cancer subtypes and

preclinical models for selection of targeted therapies. The Journal of Clinical Investigation,

121(7), 2750.

Li, Q., Ahuja, N., Burger, P. C., & Issa, J. (1999). Methylation and silencing of the

thrombospondin-1 promoter in human cancer. Oncogene, 18(21), 3284-3289.

Lin, E., Gouon-Evans, V., Nguyen, A., & Pollard, J. (2002). The macrophage growth factor

CSF-1 in mammary gland development and tumor progression. Journal of Mammary Gland

Biology and Neoplasia, 7(2), 147-162.

Lin, C. Y., Strom, A., Vega, V. B., Kong, S. L., Yeo, A. L., Thomsen, J. S., Liu, E. T.

(2004). Discovery of estrogen receptor alpha target genes and response elements in breast

tumor cells. Genome Biology, 5(9), R66.1-R66.18.

Liu, X., Shi, Y., Giranda, V. L., & Luo, Y. (2006). Inhibition of the phosphatidylinositol 3-

kinase/akt pathway sensitizes MDA-MB468 human breast cancer cells to cerulenin-induced

apoptosis. Molecular Cancer Therapeutics, 5(3), 494-501.

87

61

Lowcock, E. C., Cotterchio, M., & Boucher, B. A. (2013). Consumption of flaxseed, a rich

source of lignans, is associated with reduced breast cancer risk. Cancer Causes & Control,

(24) 813-816.

MacLean, C. H., Newberry, S. J., Mojica, W. A., Khanna, P., Issa, A. M., Suttorp, M. J.,

Garland, R. (2006). Effects of omega-3 fatty acids on cancer risk. JAMA: The Journal of the

American Medical Association, 295(4), 403-415.

Maillard, V., Bougnoux, P., Ferrari, P., Jourdan, M., Pinault, M., Lavillonnière, F., Chajès,

V. (2002). N‐3 and N‐6 fatty acids in breast adipose tissue and relative risk of breast cancer

in a case‐control study in tours, france. International Journal of Cancer, 98(1), 78-83.

Mandal, C. C., Ghosh-Choudhury, T., Dey, N., Choudhury, G. G., & Ghosh-Choudhury, N.

(2012). miR-21 is targeted by omega-3 polyunsaturated fatty acid to regulate breast tumor

CSF-1 expression. Carcinogenesis, 33(10), 1897-1908.

Mason, J. K., Chen, J., & Thompson, L. U. (2010). Flaxseed oil–trastuzumab interaction in

breast cancer. Food and Chemical Toxicology, 48(8), 2223-2226.

Mason, J. K., Kharotia, S., Wiggins, A. K. A., Chen, J., & Thompson, L. U. (2013).

Estrogen increases the conversion of {alpha}-linolenic acid to docosahexaenoic acid in

ovariectomized mice. The FASEB Journal, 27(1_MeetingAbstracts), 1072.5.

Menendez, J. A., & Lupu, R. (2007). Fatty acid synthase and the lipogenic phenotype in

cancer pathogenesis. Nature Reviews Cancer, 7(10), 763-77.

Menendez, J. A., Ropero, S., Mehmi, I., Atlas, E., Colomer, R., & Lupu, R. (2004).

Overexpression and hyperactivity of breast cancer-associated fatty acid synthase (oncogenic

antigen-519) is insensitive to normal arachidonic fatty acid-induced suppression in lipogenic

tissues but it is selectively inhibited by tumoricidal α-linolenic and γ-linolenic fatty acids: A

novel mechanism by which dietary fat can alter mammary tumorigenesis. International

Journal of Oncology, 24(6), 1369.

Menéndez, J. A., Vázquez-Martín, A., Ropero, S., Colomer, R., Lupu, R., & Trueta, J.

(2006). HER2 (erbB-2)-targeted effects of the ϖ-3 polyunsaturated. fatty acid α-linolenic

acid (ALA; 18: 3n-3) in breast cancer cells: The «fat features» of the «Mediterranean diet»

as an «anti-HER2 cocktail». Clinical and Translational Oncology, 8(11), 812-820.

Morris, K. T., Johnson, N., Homer, L., & Walts, D. (2000). A comparison of complementary

therapy use between breast cancer patients and patients with other primary tumor sites. The

American Journal of Surgery, 179(5), 407-411.

Murff, H. J., Shu, X., Li, H., Yang, G., Wu, X., Cai, H., Zheng, W. (2011). Dietary

polyunsaturated fatty acids and breast cancer risk in chinese women: A prospective cohort

study. International Journal of Cancer, 128(6), 1434-1441.

88

61

National Cancer Institute. (2007). Chemotherapy and you. Bethesda, MD: National Cancer

Institute.

National Cancer Institute. (2012). Cancer trends progress report – 2011/2012 update.

Bethesda, MD: National Cancer Institute.

National Center for Complementary and Alternative Medicine. (2013). Complementary,

alternative, or integrative health: What’s in a name?. Bethesda, MD: National Center for

Complementary and Alternative Medicine.

Nkondjock, A., Shatenstein, B., & Ghadirian, P. (2003). A case–control study of breast

cancer and dietary intake of individual fatty acids and antioxidants in Montreal, Canada. The

Breast, 12(2), 128-135.

Oh, D. Y., Talukdar, S., Bae, E. J., Imamura, T., Morinaga, H., Fan, W., Olefsky, J. M.

(2010). GPR120 is an omega-3 fatty acid receptor mediating potent anti-inflammatory and

insulin-sensitizing effects. Cell, 142(5), 687-698.

Pala, V., Krogh, V., Muti, P., Chajès, V., Riboli, E., Micheli, A., Berrino, F. (2001).

Erythrocyte membrane fatty acids and subsequent breast cancer: A prospective italian study.

Journal of the National Cancer Institute, 93(14), 1088-1095.

Papadaki, C., Sfakianaki, M., Ioannidis, G., Lagoudaki, E., Trypaki, M., Tryfonidis, K.,

Souglakos, J. (2012). ERCC1 and BRAC1 mRNA expression levels in the primary tumor

could predict the effectiveness of the second-line cisplatin-based chemotherapy in pretreated

patients with metastatic non-small cell lung cancer. Journal of Thoracic Oncology, 7(4),

663-671.

Park, S., Kolonel, L. N., Henderson, B. E., & Wilkens, L. R. (2012). Dietary fat and breast

cancer in postmenopausal women according to ethnicity and hormone receptor status: The

multiethnic cohort study. Cancer Prevention Research, 5(2), 216-228.

Patterson, R. E., Flatt, S. W., Newman, V. A., Natarajan, L., Rock, C. L., Thomson, C. A.,

Pierce, J. P. (2011). Marine fatty acid intake is associated with breast cancer prognosis. The

Journal of Nutrition, 141(2), 201-206.

Pelliccia, F., Marazzi, G., Greco, C., Franzoni, F., Speziale, G., & Gaudio, C. (2013).

Current evidence and future perspectives on n− 3 PUFAs. International Journal of

Cardiology, In Press.

Pike, M. C., Spicer, D. V., Dahmoush, L., & Press, M. F. (1993). Estrogens, progestogens,

normal breast cell proliferation, and breast cancer risk. Epidemiologic Reviews, 15(1), 17-35.

Portolesi, R., Powell, B. C., & Gibson, R. A. (2007). Competition between 24: 5n-3 and

ALA for Δ6 desaturase may limit the accumulation of DHA in HepG2 cell membranes.

Journal of Lipid Research, 48(7), 1592-1598.

89

61

Power, K. A., Chen, J. M., Saarinen, N. M., & Thompson, L. U. (2008). Changes in

biomarkers of estrogen receptor and growth factor signalling pathways in MCF-7 tumors

after short-and long-term treatment with soy and flaxseed. The Journal of Steroid

Biochemistry and Molecular Biology, 112(1), 13-19.

Power, K. A., & Thompson, L. U. (2007). Can the combination of flaxseed and its lignans

with soy and its isoflavones reduce the growth stimulatory effect of soy and its isoflavones

on established breast cancer? Molecular Nutrition & Food Research, 51(7), 845-856.

Rae, J. M., Creighton, C. J., Meck, J. M., Haddad, B. R., & Johnson, M. D. (2007). MDA-

MB-435 cells are derived from M14 melanoma cells––a loss for breast cancer, but a boon

for melanoma research. Breast Cancer Research and Treatment, 104(1), 13-19.

Rakha, E. A., El‐Sayed, M. E., Green, A. R., Lee, A. H., Robertson, J. F., & Ellis, I. O.

(2007). Prognostic markers in triple‐negative breast cancer. Cancer, 109(1), 25-32.

Rao, G. N., Ney, E., & Herbert, R. A. (2000). Effect of melatonin and linolenic acid on

mammary cancer in transgenic mice with c-neu breast cancer oncogene. Breast Cancer

Research and Treatment, 64(3), 287-296.

Rausch, S. M., Winegardner, F., Kruk, K. M., Phatak, V., Wahner-Roedler, D. L., Bauer, B.,

& Vincent, A. (2011). Complementary and alternative medicine: Use and disclosure in

radiation oncology community practice. Supportive Care in Cancer, 19(4), 521-529.

Reyes, N., Reyes, I., Tiwari, R., & Geliebter, J. (2004). Effect of linoleic acid on

proliferation and gene expression in the breast cancer cell line T47D. Cancer Letters,

209(1), 25-35.

Rickard, S. E., Yuan, Y. V., Chen, J., & Thompson, L. U. (1999). Dose effects of flaxseed

and its lignan on N-methyl-N-nitrosourea-induced mammary tumorigenesis in rats. Nutrition

and Cancer, 35(1), 50-57.

Ripple, M. O., Kalmadi, S., & Eastman, A. (2005). Inhibition of either phosphatidylinositol

3-kinase/akt or the mitogen/extracellular-regulated kinase, MEK/ERK, signalling pathways

suppress growth of breast cancer cell lines, but MEK/ERK signalling is critical for cell

survival. Breast Cancer Research and Treatment, 93(2), 177-188.

Rogers, K. R., Kikawa, K. D., Mouradian, M., Hernandez, K., McKinnon, K. M., Ahwah, S.

M., & Pardini, R. S. (2010). Docosahexaenoic acid alters epidermal growth factor receptor-

related signalling by disrupting its lipid raft association. Carcinogenesis, 31(9), 1523-1530.

Rose, D. P., & Vona‐Davis, L. (2013). Biochemical and molecular mechanisms for the

association between obesity, chronic inflammation, and breast cancer. BioFactors, Advance

online publication. doi: 10.1002/biof.1109.

90

61

Rose, D. P., & Vona-Davis, L. (2010). Interaction between menopausal status and obesity in

affecting breast cancer risk. Maturitas, 66(1), 33-38.

Saadatian‐Elahi, M., Norat, T., Goudable, J., & Riboli, E. (2004). Biomarkers of dietary

fatty acid intake and the risk of breast cancer: A meta‐analysis. International Journal of

Cancer, 111(4), 584-591.

Saadatian-Elahi, M., Toniolo, P., Ferrari, P., Goudable, J., Akhmedkhanov, A., Zeleniuch-

Jacquotte, A., & Riboli, E. (2002). Serum fatty acids and risk of breast cancer in a nested

case-control study of the new york university women’s health study. Cancer Epidemiology

Biomarkers & Prevention, 11(11), 1353-1360.

Saarinen, N. M., Power, K., Chen, J., & Thompson, L. U. (2006). Flaxseed attenuates the

tumor growth stimulating effect of soy protein in ovariectomized athymic mice with MCF‐7

human breast cancer xenografts. International Journal of Cancer, 119(4), 925-931.

Saggar, J. K., Chen, J., Corey, P., & Thompson, L. U. (2010a). Dietary flaxseed lignan or oil

combined with tamoxifen treatment affects MCF‐7 tumor growth through estrogen receptor‐and growth factor‐signalling pathways. Molecular Nutrition & Food Research, 54(3), 415-

425.

Saggar, J. K., Chen, J., Corey, P., & Thompson, L. U. (2010b). The effect of

secoisolariciresinol diglucoside and flaxseed oil, alone and in combination, on MCF-7 tumor

growth and signalling pathways. Nutrition and Cancer, 62(4), 533-542.

Sawyer M, F. C. (2010). Possible mechanisms of n-3 PUFA anti-tumor action. In S. S.

Calviello G (Ed.), Dietary omega-3 polyunsaturated fatty acids and cancer (pp. 3-39).

Dordrecht, NLD: Springer.

Schley, P. D., Brindley, D. N., & Field, C. J. (2007). (N-3) PUFA alter raft lipid composition

and decrease epidermal growth factor receptor levels in lipid rafts of human breast cancer

cells. The Journal of Nutrition, 137(3), 548-553.

Schnitt, S. J. (2010). Classification and prognosis of invasive breast cancer: From

morphology to molecular taxonomy. Modern Pathology, 23, S60-S64.

Sczaniecka, A. K., Brasky, T. M., Lampe, J. W., Patterson, R. E., & White, E. (2012).

Dietary intake of specific fatty acids and breast cancer risk among postmenopausal women

in the VITAL cohort. Nutrition and Cancer, 64(8), 1131-1142.

Serraino, M., & Thompson, L.U. (1991). The effect of flaxseed supplementation on early

risk markers for mammary carcinogenesis. Cancer Letters, 60(2), 135-142.

Serraino, M., & Thompson, L. U. (1992). Flaxseed supplementation and early markers of

colon carcinogenesis. Cancer Letters, 63(2), 159-165.

91

61

Shannon, J., King, I. B., Moshofsky, R., Lampe, J. W., Gao, D. L., Ray, R. M., & Thomas,

D. B. (2007). Erythrocyte fatty acids and breast cancer risk: A case-control study in

shanghai, china. The American Journal of Clinical Nutrition, 85(4), 1090-1097.

Shibata, A., Nagaya, T., Imai, T., Funahashi, H., Nakao, A., & Seo, H. (2002). Inhibition of

NF-κB activity decreases the VEGF mRNA expression in MDA-MB-231 breast cancer

cells. Breast Cancer Research and Treatment, 73(3), 237-243.

Shou, J., Massarweh, S., Osborne, C. K., Wakeling, A. E., Ali, S., Weiss, H., & Schiff, R.

(2004). Mechanisms of tamoxifen resistance: Increased estrogen receptor-HER2/neu cross-

talk in ER/HER2–positive breast cancer. Journal of the National Cancer Institute, 96(12),

926-935.

Sliwkowski, M. X., Lofgren, J. A., Lewis, G. D., Hotaling, T. E., Fendly, B. M., & Fox, J.

A. (1999). Nonclinical studies addressing the mechanism of action of trastuzumab

(herceptin). Seminars in Oncology, 26(4 Suppl 12) 60-70.

Sommer, S., & Fuqua, S. A. (2001). Estrogen receptor and breast cancer. Seminars in

Cancer Biology, 11(5) 339-352.

Song, H. J., Sneddon, A. A., Heys, S. D., & Wahle, K. W. J. (2012). Regulation of fatty acid

synthase (FAS) and apoptosis in estrogen-receptor positive and negative breast cancer cells

by conjugated linoleic acids. Prostaglandins, Leukotrienes, and Essential Fatty Acids, 87(6),

197-203.

Staubach, S., & Hanisch, F. (2011). Lipid rafts: Signalling and sorting platforms of cells and

their roles in cancer. Expert Review of Proteomics, 8(2), 263-277.

Su, Q., Hu, S., Gao, H., Ma, R., Yang, Q., Pan, Z., Li, F. (2008). Role of AIB1 for tamoxifen

resistance in estrogen receptor-positive breast cancer cells. Oncology, 75(3-4), 159-168.

Subik, K., Lee, J., Baxter, L., Strzepek, T., Costello, D., Crowley, P., Hicks, D. G. (2010).

The expression patterns of ER, PR, HER2, CK5/6, EGFR, ki-67 and AR by

immunohistochemical analysis in breast cancer cell lines. Breast Cancer: Basic and Clinical

Research, (4) 35-41.

Sun, H., Hu, Y., Gu, Z., Owens, R., Chen, Y., & Edwards, I. (2011). Omega-3 fatty acids

induce apoptosis in human breast cancer cells and mouse mammary tissue through

syndecan-1 inhibition of the MEK-erk pathway. Carcinogenesis, 32(10), 1518-1524.

Suter, R., & Marcum, J. A. (2007). The molecular genetics of breast cancer and targeted

therapy. Biologics: Targets & Therapy, 1(3), 241-258.

Szöllösi, J., Balázs, M., Feuerstein, B. G., Benz, C. C., & Waldman, F. M. (1995). ERBB-2

(HER2/neu) gene copy number, p185HER-2 overexpression, and intratumor heterogeneity

in human breast cancer. Cancer Research, 55(22), 5400-5407.

92

61

Takata, Y., King, I. B., Neuhouser, M. L., Schaffer, S., Barnett, M., Thornquist, M.,

Goodman, G. E. (2009). Association of serum phospholipid fatty acids with breast cancer

risk among postmenopausal cigarette smokers. Cancer Causes & Control, 20(4), 497-504.

Tanos, T., Rojo, L. J., Echeverria, P., & Brisken, C. (2012). ER and PR signalling nodes

during mammary gland development. Breast Cancer Research, 14(210).

Taron, M., Rosell, R., Felip, E., Mendez, P., Souglakos, J., Ronco, M. S., Sanchez, J. J.

(2004). BRCA1 mRNA expression levels as an indicator of chemoresistance in lung cancer.

Human Molecular Genetics, 13(20), 2443-2449.

Thiébaut, A., Chajès, V., Gerber, M., Boutron‐Ruault, M., Joulin, V., Lenoir, G., Clavel‐Chapelon, F. (2009). Dietary intakes of ω‐6 and ω‐3 polyunsaturated fatty acids and the risk

of breast cancer. International Journal of Cancer, 124(4), 924-931.

Thompson, L. U., Boucher, B. A., Liu, Z., Cotterchio, M., & Kreiger, N. (2006).

Phytoestrogen content of foods consumed in canada, including isoflavones, lignans, and

coumestan. Nutrition and Cancer, 54(2), 184-201.

Thompson, L. U., Chen, J. M., Li, T., Strasser-Weippl, K., & Goss, P. E. (2005). Dietary

flaxseed alters tumor biological markers in postmenopausal breast cancer. Clinical Cancer

Research, 11(10), 3828-3835.

Thompson, L. U., Rickard, S. E., Orcheson, L. J., & Seidl, M. M. (1996). SHORT

COMMUNICATION: Flaxseed and its lignan and oil components reduce mammary tumor

growth at a late stage of carcinogenesis. Carcinogenesis, 17(6), 1373-1376.

Tobin, N., Sims, A., Lundgren, K., Lehn, S., & Landberg, G. (2011). Cyclin D1, Id1 and

EMT in breast cancer. BMC Cancer, 11(417).

Tripathy, D. (2011). Complementary and alternative medicine in breast cancer. In Breast

surgical techniques and interdisciplinary management. Dirbas, F., & Scott-Conner, C. (1

Ed.) pp. 787 -796. New York: Springer.

Truan, J. S., Chen, J., & Thompson, L. U. (2010). Flaxseed oil reduces the growth of human

breast tumors (MCF‐7) at high levels of circulating estrogen. Molecular Nutrition & Food

Research, 54(10), 1414-1421.

United States Department of Agriculture. (2012). Nutrient intakes from food: Mean amounts

consumed per individual by gender and age, what we eat in America, NHANES 2007-2008.

United States Department of Agriculture. Washington, DC.

Vatten, L. J., Bjerve, K. S., Andersen, A., & Jellum, E. (1993). Polyunsaturated fatty acids

in serum phospholipids and risk of breast cancer: A case-control study from the janus serum

bank in norway. European Journal of Cancer, 29(4), 532-538.

93

61

Velentzis, L., Cantwell, M., Cardwell, C., Keshtgar, M., Leathem, A., & Woodside, J.

(2009). Lignans and breast cancer risk in pre-and post-menopausal women: Meta-analyses

of observational studies. British Journal of Cancer, 100(9), 1492-1498.

Vetto, J. T., Luoh, S. W., & Naik, A. (2009). Breast cancer in premenopausal women.

Current Problems in Surgery, 46(12), 944-1004.

Vogel, C. L., Cobleigh, M. A., Tripathy, D., Gutheil, J. C., Harris, L. N., Fehrenbacher, L.,

Burchmore, M. (2002). Efficacy and safety of trastuzumab as a single agent in first-line

treatment of HER2-overexpressing metastatic breast cancer. Journal of Clinical Oncology,

20(3), 719-726.

Voorrips, L. E., Brants, H. A., Kardinaal, A. F., Hiddink, G. J., van den Brandt, P. A., &

Goldbohm, R. A. (2002). Intake of conjugated linoleic acid, fat, and other fatty acids in

relation to postmenopausal breast cancer: The netherlands cohort study on diet and cancer.

The American Journal of Clinical Nutrition, 76(4), 873-882.

Wakai, K., Tamakoshi, K., Fukui, M., Suzuki, S., Lin, Y., Niwa, Y., Tokudome, S. (2005).

Dietary intakes of fat and fatty acids and risk of breast cancer: A prospective study in japan.

Cancer Science, 96(9), 590-599.

Wang, L., Chen, J., & Thompson, L. U. (2005). The inhibitory effect of flaxseed on the

growth and metastasisof estrogen receptor negative human breast cancer xenograftsis

attributed to both its lignan and oil components. International Journal of Cancer, 116(5),

793-798.

Wiggins, A.K., Mason, J.K., & Thompson, L.U. (2013).Beneficial influence of diets

enriched with flaxseed and flaxseed oil on cancer. In Cancer chemoprevention and

treatment by diet therapy. Cho, W.C.S. (1 Ed). pp. 55-90. Dordrecht : Springer.

Wirfält, E., Mattisson, I., Gullberg, B., Johansson, U., Olsson, H., & Berglund, G. (2002).

Postmenopausal breast cancer is associated with high intakes of ω6 fatty acids (sweden).

Cancer Causes & Control, 13(10), 883-893.

Wolf, I., Bose, S., Williamson, E. A., Miller, C. W., Karlan, B. Y., & Koeffler, H. P. (2007).

FOXA1: Growth inhibitor and a favorable prognostic factor in human breast cancer.

International Journal of Cancer, 120(5), 1013-1022.

World Health Organization. (2008). World cancer report 2008. Lyon: World Health

Organization.

Yang, X. R., Sherman, M. E., Rimm, D. L., Lissowska, J., Brinton, L. A., Peplonska, B.,

Bardin-Mikolajczak, A. (2007). Differences in risk factors for breast cancer molecular

subtypes in a population-based study. Cancer Epidemiology Biomarkers & Prevention,

16(3), 439-443.

94

61

Yarden, Y. (2001). The EGFR family and its ligands in human cancer: Signalling

mechanisms and therapeutic opportunities. European Journal of Cancer, 37, 3-8.

Yu, L., Moore, A. B., Castro, L., Gao, X., Huynh, H. C., Klippel, M., Dixon, D. (2012).

Estrogen regulates MAPK-related genes through genomic and nongenomic interactions

between IGF-I receptor tyrosine kinase and estrogen receptor-alpha signalling pathways in

human uterine leiomyoma cells. Journal of Signal Transduction, [Published online Oct 9

2012] doi: 10.1155/2012/204236.

Zaineddin, A. K., Vrieling, A., Buck, K., Becker, S., Linseisen, J., Flesch‐Janys, D., Chang‐Claude, J. (2012). Serum enterolactone and postmenopausal breast cancer risk by estrogen,

progesterone and herceptin 2 receptor status. International Journal of Cancer, 130(6), 1401-

1410.

Zhang, D., Pal, A., Bornmann, W. G., Yamasaki, F., Esteva, F. J., Hortobagyi, G. N., Ueno,

N. T. (2008). Activity of lapatinib is independent of EGFR expression level in HER2-

overexpressing breast cancer cells. Molecular Cancer Therapeutics, 7(7), 1846-1850.

Zhao, G., Etherton, T. D., Martin, K. R., Vanden Heuvel, J. P., Gillies, P. J., West, S. G., &

Kris-Etherton, P. M. (2005). Anti-inflammatory effects of polyunsaturated fatty acids in

THP-1 cells. Biochemical and Biophysical Research Communications, 336(3), 909-917.

Zheng, J., Hu, X., Zhao, Y., Yang, J., & Li, D. (2013). Intake of fish and marine n-3

polyunsaturated fatty acids and risk of breast cancer: Meta-analysis of data from 21

independent prospective cohort studies. BMJ: British Medical Journal, [Published online

ahead of print June 27 2013]. doi: 10.1136/bmj.f3706.

95

APPENDICES

Appendix Table 1. Relative gene expression (ΔCt) in four breast cancer cell lines from PCR array.


Control ALA Control ALA Control ALA Control ALA

Gene Name ΔCt St

Dev ΔCt St

Dev ΔCt St

Dev ΔCt St

Dev ΔCt St

Dev ΔCt St Dev ΔCt St

Dev ΔCt St

Dev

ABCB1

ATP-binding cassette,

sub-family B member 1 13.425 1.091 14.565 1.238 13.284 0.405 14.138 1.077 15.725 0.708 15.651 0.560 11.756 2.913 12.319 2.695

ABCG2

ATP-binding cassette,

sub-family G member 2 6.129 0.264 7.102 0.595 9.136 0.371 8.528 0.466 8.147 0.895 8.967 0.184 8.729 1.371 9.523 1.423

ADAM23

ADAM metallopeptidase

domain 23 11.166 0.937 10.631 0.384 13.690 2.093 11.359 0.228 12.321 1.405 12.178 0.464 10.923 0.377 10.951 0.514

AKT1

V-akt murine thymoma

viral oncogene homolog 1 1.418 0.556 1.212 0.180 0.987 0.078 1.204 0.678 3.091 0.985 2.895 0.033 2.719 0.291 2.943 0.508

APC

Adenomatous polyposis

coli 8.072 0.152 7.468 0.455 7.216 0.141 6.793 0.338 8.844 0.320 9.167 0.804 8.034 0.662 8.426 0.730

AR Androgen receptor 5.607 0.225 6.107 0.226 3.372 0.586 3.302 0.724 12.357 0.670 13.097 1.183 12.088 0.092 12.113 1.036

ATM

Ataxia telangiectasia

mutated 7.408 0.365 7.040 0.488 5.976 0.144 5.967 0.536 6.528 0.298 6.439 0.352 7.029 0.309 7.731 0.296

BAD

BCL2-associated agonist

of cell death 3.785 0.308 4.297 0.322 2.276 0.135 2.261 0.144 3.643 0.877 3.806 0.572 3.632 0.309 4.178 0.703

BCL2 B-cell CLL/lymphoma 2 5.061 0.546 5.601 0.387 4.489 0.409 4.764 0.483 7.595 0.229 7.787 0.510 7.424 0.205 7.341 0.239

BIRC5

Baculoviral IAP repeat

containing 5 5.782 0.528 5.982 0.479 5.791 0.322 6.118 0.225 5.637 0.495 5.605 0.241 5.272 0.308 5.565 1.314

BRCA1

Breast cancer 1, early

onset 4.675 0.219 4.632 0.366 4.419 0.261 4.940 0.067 5.383 0.169 5.559 0.456 5.473 0.229 5.313 0.314

BRCA2

Breast cancer 2, early

onset 4.973 0.287 5.146 0.344 6.051 0.156 6.338 0.516 6.252 0.406 6.710 0.531 6.544 0.672 6.033 0.399

CCNA1 Cyclin A1 11.266 1.111 12.777 2.555 14.553 0.566 14.558 0.934 6.786 0.507 7.581 0.994 8.708 0.893 8.437 0.746

CCND1 Cyclin D1 0.793 0.333 1.109 0.277 1.855 0.389 1.812 0.049 2.795 0.840 2.055 0.046 2.622 0.416 3.134 0.381

CCND2 Cyclin D2 13.362 1.631 13.324 1.786 9.803 0.385 9.873 0.480 8.512 0.406 8.089 0.777 6.607 0.772 6.685 1.134

CCNE1 Cyclin E1 5.253 0.599 5.109 0.090 3.959 0.150 4.458 0.842 5.295 1.013 4.661 0.341 4.060 0.549 4.439 0.734

CDH1

Cadherin 1, type 1, E-

cadherin -0.649 0.426 -0.769 0.614 -0.510 0.439 -0.749 0.428 8.416 0.264 9.321 0.654 5.300 2.530 5.522 3.065

96



Gene Name ΔCt St

Dev ΔCt St

Dev ΔCt St

Dev ΔCt St

Dev ΔCt St


Dev ΔCt St

Dev

CDH13 Cadherin 13, H-cadherin 9.005 0.418 8.544 0.394 7.499 0.532 6.890 0.294 10.231 1.254 10.327 0.585 9.699 0.265 10.367 0.875

CDK2

Cyclin-dependent kinase

2 2.614 0.176 2.648 0.365 2.308 0.123 2.489 0.328 3.540 0.245 3.512 0.444 3.446 0.298 3.686 0.166

CDKN1A


inhibitor 1A 1.855 0.438 2.177 0.862 4.403 0.555 4.565 0.217 3.334 0.456 2.655 0.792 2.840 0.508 2.552 0.290

CDKN1C


inhibitor 1C 5.902 0.340 6.743 0.491 6.007 0.320 6.005 0.539 4.444 0.350 4.802 0.720 5.497 0.664 5.877 0.617

CDKN2A


inhibitor 2A 13.924 1.217 14.641 0.659 6.430 0.294 6.477 0.130 12.293 0.370 12.129 0.149 6.094 4.422 6.203 4.637

CSF1

Colony stimulating factor

1 9.413 0.161 9.511 0.587 8.676 0.404 8.507 0.036 1.771 0.124 2.565 0.290 2.602 0.936 2.777 0.895

CST6 Cystatin E/M 13.927 0.379 14.216 1.041 14.150 0.735 13.884 1.466 6.505 0.294 6.697 1.083 6.037 0.251 5.972 0.599

CTNNB1

Catenin (cadherin-

associated protein) 4.295 0.463 4.467 0.406 3.988 0.274 4.552 0.352 4.694 0.591 5.025 0.263 3.935 0.369 4.700 1.136

CTSD Cathepsin D -0.923 0.435 -0.987 0.133 0.512 0.249 0.331 0.220 0.468 0.343 0.367 0.312 1.350 0.246 1.432 0.440

EGF Epidermal growth factor 8.345 0.478 7.872 0.592 6.267 0.270 5.979 0.527 8.678 0.437 7.993 1.300 7.189 0.304 7.210 0.160

EGFR

Epidermal growth factor

receptor 7.423 0.462 7.186 0.370 4.525 0.361 4.746 1.025 3.291 0.306 3.325 0.643 0.672 1.861 0.729 2.192

ERBB2

V-erb-b2 erythroblastic

leukemia viral oncogene

homolog 2 5.244 0.309 5.159 0.382 -0.549 0.324 -1.139 0.824 7.053 0.087 6.524 0.573 6.067 0.288 7.035 0.611

ESR1 Estrogen receptor 1 3.334 0.447 3.118 0.482 4.934 0.404 4.797 0.696 8.684 0.258 8.574 0.538 8.758 0.468 8.242 0.239

ESR2

Estrogen receptor 2 (ER

beta) 8.760 0.348 8.337 0.621 8.624 0.428 8.155 0.437 9.707 0.411 9.258 0.377 9.126 0.472 9.684 0.352

FOXA1 Forkhead box A1 3.164 0.480 2.119 0.529 1.666 0.152 2.062 0.159 11.009 1.466 11.094 1.146 8.110 4.455 8.687 4.488

GATA3 GATA binding protein 3 0.558 0.277 2.164 0.902 3.568 0.554 3.534 0.231 8.082 0.201 7.935 0.307 7.167 1.656 7.627 2.107

GLI1 GLI family zinc finger 1 9.077 0.927 9.418 0.458 8.154 0.752 8.238 0.620 9.067 0.321 8.867 0.121 8.695 0.393 8.627 0.283

GRB7

Growth factor receptor-

bound protein 7 5.296 0.291 6.023 0.343 0.005 0.056 -0.097 0.361 7.273 0.143 7.354 0.302 6.262 1.456 5.834 1.344

97



Gene Name ΔCt St

Dev ΔCt St

Dev ΔCt St

Dev ΔCt St

Dev ΔCt St


Dev ΔCt St

Dev

GSTP1

Glutathione S-transferase

pi 1 11.342 1.040 12.120 2.860 10.357 0.481 10.303 0.581 0.837 0.082 1.274 0.516 1.106 0.400 1.255 0.379

HIC1

Hypermethylated in

cancer 1 10.904 0.750 12.773 2.274 11.506 0.213 11.341 0.987 10.317 0.466 9.995 0.337 10.072 0.448 10.453 0.620

ID1

Inhibitor of DNA binding

1, dominant negative

helix-loop-helix protein 2.920 0.682 2.931 0.179 4.003 0.740 3.656 1.273 3.259 0.428 3.440 0.960 2.369 0.548 3.419 0.400

IGF1

Insulin-like growth factor

1 10.690 0.390 12.035 2.961 8.811 0.484 8.819 1.085 10.546 0.472 10.418 0.263 10.064 0.079 10.058 0.413

IGF1R


1 receptor 1.362 0.445 1.834 0.187 3.130 0.305 3.287 0.265 6.263 0.424 5.528 0.374 5.920 0.386 6.038 0.368

IGFBP3


binding protein 3 5.448 0.326 5.281 0.412 4.990 0.482 5.024 0.981 4.193 0.766 3.905 0.365 2.044 1.823 1.879 1.902

IL6

Interleukin 6 (interferon,

beta 2) 10.323 0.082 9.919 0.570 11.048 0.765 10.191 0.744 2.492 0.972 2.352 1.048 4.684 1.666 5.169 1.463

JUN Jun proto-oncogene 4.432 0.307 4.521 0.737 3.733 0.896 3.824 0.388 4.356 0.569 3.765 0.483 3.661 0.483 4.130 0.362

KRT18 Keratin 18 -3.561 0.179 -3.308 0.187 -1.773 0.084 -1.579 0.490 1.191 0.689 0.913 0.557 -0.626 0.723 -0.140 0.899

KRT19 Keratin 19 -2.438 0.532 -1.921 0.427 -2.144 0.150 -2.325 0.067 0.757 0.177 1.473 0.425 -0.631 1.542 -0.730 1.063

KRT5 Keratin 5 8.626 0.488 8.579 0.270 8.036 0.463 7.704 0.357 9.339 0.415 9.355 0.245 2.491 4.314 2.072 4.674

KRT8 Keratin 8 -0.412 0.188 0.044 0.147 1.807 0.156 1.388 0.322 5.086 0.481 5.337 0.233 3.778 0.758 3.796 1.201

MAPK1

Mitogen-activated protein

kinase 1 2.300 0.163 3.090 0.374 1.632 0.141 1.630 0.176 3.212 0.674 3.369 0.637 2.877 0.494 2.830 0.617

MAPK3


kinase 3 2.402 0.572 2.445 0.625 3.325 0.099 3.385 0.198 5.960 0.262 5.841 0.494 5.065 0.373 5.437 0.210

MAPK8


kinase 8 4.284 0.375 4.474 0.042 3.435 0.040 3.803 0.707 4.383 0.278 4.195 0.228 4.307 0.159 4.456 0.334

MGMT

O-6-methylguanine-DNA

methyltransferase 3.601 0.383 3.994 0.251 5.354 0.368 5.083 0.099 11.020 0.301 11.934 0.303 7.252 1.952 7.450 2.511

MKI67

Antigen identified by

monoclonal antibody Ki-

67 1.921 0.575 1.610 0.658 2.621 0.296 2.791 0.728 2.031 0.371 1.821 0.668 2.170 0.136 2.321 0.394

98



Gene Name ΔCt St

Dev ΔCt St

Dev ΔCt St

Dev ΔCt St

Dev ΔCt St


Dev ΔCt St

Dev

MLH1

MutL homolog 1, colon

cancer, nonpolyposis type

2 (E. coli) 2.428 0.115 2.379 0.308 2.805 0.124 3.025 0.579 3.616 0.691 3.331 0.035 3.194 0.164 3.328 0.291

MMP2 Matrix metallopeptidase 2 8.248 0.820 8.448 0.526 7.527 0.749 8.327 0.498 8.572 0.439 8.312 0.548 7.596 0.531 7.572 0.873

MMP9 Matrix metallopeptidase 9 6.027 0.416 5.890 0.430 9.533 0.533 9.324 0.465 7.916 0.170 7.803 0.496 7.855 1.099 8.266 0.531

MUC1

Mucin 1, cell surface

associated 0.887 0.494 1.341 0.577 1.690 0.284 1.772 0.255 5.410 0.385 5.945 0.554 3.646 2.255 4.121 1.821

MYC

V-myc myelocytomatosis

viral oncogene homolog

(avian) 1.935 0.914 1.404 0.567 0.692 0.298 0.659 0.464 0.991 0.423 0.899 0.171 1.345 0.489 1.186 0.347

NME1 Non-metastatic cells 1 0.165 0.323 0.626 0.618 0.364 0.112 0.470 0.159 1.710 0.376 1.232 0.227 1.830 0.641 1.306 0.260

NOTCH1 Notch 1 6.691 0.472 6.414 0.294 6.415 0.347 6.520 0.265 6.649 0.085 6.403 0.299 6.691 1.217 6.278 0.416

NR3C1

Nuclear receptor

subfamily 3, group C,

member 1 4.676 0.336 5.255 0.376 5.535 0.118 5.521 0.233 3.473 0.063 4.069 0.453 3.812 0.288 3.974 0.262

PGR Progesterone receptor 2.746 0.311 3.175 0.569 -0.052 0.706 0.763 0.464 13.578 2.266 14.187 0.833 12.444 1.479 12.989 1.353

PLAU

Plasminogen activator,

urokinase 9.400 0.532 9.728 0.608 8.550 0.299 8.159 0.417 1.932 0.593 2.120 0.558 2.160 0.714 2.350 0.414

PRDM2 PR domain containing 2 5.350 0.360 5.446 0.210 5.612 0.114 5.510 0.174 7.000 0.100 7.066 0.464 6.845 0.482 6.686 0.318

PTEN

Phosphatase and tensin

homolog 3.195 0.296 3.172 0.162 1.758 0.253 1.682 0.262 3.070 0.115 3.473 0.510 3.364 0.473 3.809 0.329

PTGS2

Prostaglandin-

endoperoxide synthase 2 6.980 0.845 6.807 0.298 7.361 0.374 7.359 0.391 7.544 0.166 7.916 0.343 7.518 0.552 7.712 0.455

PYCARD

PYD and CARD domain

containing 2.088 0.153 2.183 0.236 3.203 0.252 2.965 0.052 4.540 0.283 4.795 0.244 3.944 1.239 4.616 0.894

RARB

Retinoic acid receptor,

beta 6.984 0.576 6.527 0.871 6.089 0.286 5.889 0.475 8.383 0.290 8.178 0.442 7.774 0.435 7.699 0.665

RASSF1

Ras association

(RalGDS/AF-6) domain

family member 1 5.942 0.239 5.989 0.374 5.303 0.466 5.823 0.692 5.843 0.455 5.538 0.539 5.113 0.411 5.480 0.527

99



Gene Name ΔCt St

Dev ΔCt St

Dev ΔCt St

Dev ΔCt St

Dev ΔCt St


Dev ΔCt St

Dev

RB1 Retinoblastoma 1 2.837 0.166 3.043 0.417 2.706 0.066 2.638 0.258 3.559 0.115 3.748 0.482 4.316 0.603 4.324 0.572

SERPINE1

Serpin peptidase inhibitor,

clade E 7.036 0.415 6.952 0.439 9.101 0.339 9.200 0.281 0.084 0.408 -0.857 0.341 -0.672 0.840 -1.063 0.776

SFN Stratifin 2.908 0.179 3.162 0.302 3.934 0.631 4.435 0.061 3.204 0.218 3.276 0.418 2.240 0.725 2.161 0.848

SFRP1

Secreted frizzled-related

protein 1 13.651 1.037 14.005 2.371 14.030 1.055 13.681 2.124 11.784 0.094 11.908 0.728 7.012 2.866 7.480 2.591

SLC39A6

Solute carrier family 39

(zinc transporter),

member 6 -1.469 0.151 -1.197 0.113 1.720 0.199 2.397 0.457 4.571 0.196 4.379 0.375 4.758 0.221 4.716 0.326

SLIT2

Slit homolog 2

(Drosophila) 9.258 1.272 9.211 0.979 8.211 0.510 8.181 0.645 6.040 0.164 6.094 0.290 6.742 0.884 7.258 1.172

SNAI2

Snail homolog 2

(Drosophila) 10.065 0.208 10.696 0.254 10.288 1.198 10.252 1.425 4.766 0.518 4.828 0.402 5.712 0.302 5.677 0.963

SRC

V-src sarcoma (Schmidt-

Ruppin A-2) viral

oncogene homolog

(avian) 4.997 0.190 5.762 0.351 6.343 0.152 6.384 0.152 5.449 0.428 4.786 0.310 5.168 0.340 5.508 0.545

TFF3

Trefoil factor 3

(intestinal) 1.197 0.535 1.629 0.335 -0.981 0.235 -0.796 0.421 8.025 0.184 8.117 0.449 7.583 0.822 7.578 0.846

TGFB1

Transforming growth

factor, beta 1 2.533 0.478 2.644 0.439 5.624 0.312 5.486 0.302 2.917 0.246 3.195 0.550 3.211 0.710 3.435 0.304

THBS1 Thrombospondin 1 0.324 0.519 0.253 0.398 2.331 0.508 2.302 0.886 0.268 0.168 -0.046 0.027 2.784 0.613 2.389 0.468

TP53 Tumor protein p53 0.896 0.183 1.094 0.038 0.453 0.141 0.501 0.187 2.100 0.325 2.531 0.659 1.888 0.580 2.114 0.805

TP73 Tumor protein p73 6.501 0.649 6.239 0.494 6.498 0.220 6.621 0.190 7.847 0.212 7.961 0.280 7.040 0.633 7.123 0.416

TWIST1

Twist homolog 1

(Drosophila) 10.300 0.296 10.501 0.155 4.241 0.736 4.503 0.273 12.824 2.913 11.097 0.273 9.788 1.257 10.397 0.740

VEGFA

Vascular endothelial

growth factor A 3.729 0.358 3.551 0.537 2.970 0.375 3.207 1.118 2.174 0.269 1.805 0.335 2.733 0.767 2.443 0.388

XBP1 X-box binding protein 1 -2.637 0.376 -2.926 0.421 -1.647 0.191 -1.507 0.579 3.019 0.383 3.414 0.677 2.103 0.623 2.240 0.808

100



Gene Name ΔCt St

Dev ΔCt St

Dev ΔCt St

Dev ΔCt St

Dev ΔCt St


Dev ΔCt St

Dev

ACTB Actin, beta -3.812 0.179 -3.360 0.358 -3.793 0.203 -3.353 0.284 -3.033 0.573 -3.324 0.417 -3.762 0.273 -3.876 0.466

B2M Beta-2-microglobulin 1.265 0.415 0.831 0.199 0.138 0.100 0.043 0.110 -0.609 0.346 -0.497 0.436 -0.241 0.271 -0.583 0.193

GAPDH

Glyceraldehyde-3-

phosphate dehydrogenase -2.886 0.351 -2.946 0.690 -2.219 0.342 -2.563 0.198 -1.126 0.542 -1.143 0.599 -2.129 0.197 -1.712 0.392

HPRT1

Hypoxanthine

phosphoribosyltransferase

1 2.352 0.091 2.734 0.363 3.860 0.117 4.090 0.114 3.444 0.188 3.214 0.104 3.267 0.217 3.375 0.419

RPLP0

Ribosomal protein, large,

P0 -3.617 0.326 -3.566 0.202 -3.999 0.090 -4.133 0.105 -2.835 0.158 -2.717 0.517 -3.026 0.334 -2.792 0.256

NFKB1 Nuclear factor of kappa B 4.985 0.292 5.603 0.592 4.961 0.387 5.060 0.382 5.550 0.250 5.700 0.768 5.234 0.224 5.299 0.480

FAS

Fas (TNF receptor

superfamily, member 6) 6.151 0.256 6.073 0.448 9.487 0.661 9.844 0.736 5.546 0.287 5.923 0.245 5.861 0.575 6.065 0.558

PPARA

Peroxisome proliferator-

activated receptor alpha 6.583 0.320 6.698 0.333 8.001 0.332 7.991 0.230 6.312 0.166 6.419 0.345 5.797 1.155 6.017 1.409

CAV1

Caveolin 1, caveolae

protein 3.591 0.507 4.708 0.561 8.892 0.896 9.198 1.324 -0.640 0.133 0.029 0.499 0.542 0.678 0.343 0.440

101

Appendix Table 2. Fold changes in gene expression after ALA treatment in four breast cancer cell lines.


Gene Function

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

Tumour Classification Markers

ESR1

Luminal A and B; Increase growth through E2 signalling,

associated with good prognosis 1.162 0.590 1.100 0.692 1.079 0.681 1.430 0.170

FOXA1

Luminal A and B; Transcription factor for ER signalling,

associated with growth inhibition and good prognosis 2.063 0.076 -1.316 0.037 -1.061 0.868 -1.491 0.630

GATA3

Luminal A and B; Transcription factor for mammary gland

differentiation, associated with low metastasis and good prognosis -3.044 0.026 1.024 0.953 1.108 0.497 -1.375 0.797

KRT18

Luminal A and B; Structural and signalling roles, associated with

less invasive breast cancer -1.192 0.162 -1.143 0.589 1.213 0.668 -1.401 0.478

KRT8

Luminal A and B; Structural and signalling roles, associated with

less invasive breast cancer -1.372 0.035 1.337 0.116 -1.190 0.400 -1.013 0.844

SLC39A6

Luminal A and B; Zinc transportation, induced by E2, conflicting

role in cancer prognosis and growth -1.207 0.075 -1.599 0.053 1.142 0.421 1.030 0.822

TFF3

Luminal A and B; normal mucosal protection and repair,

associated with good prognosis but also with cancer progression

and invasion -1.349 0.310 -1.138 0.607 -1.066 0.862 1.004 0.952

XBP1

Luminal A and B; coexpressed with ESR1,associated with cell

survival and chemotherapy resistance 1.221 0.405 -1.102 0.838 -1.315 0.432 -1.100 0.871

ERBB2

HER2 overexpressing; EGFR family receptor, increased growth

signalling, associated with aggressive cancers 1.061 0.753 1.505 0.250 1.442 0.167 -1.956 0.046

GRB7

HER2 overexpressing; Phosphrylates HER2 and Akt to increase

growth, associated with agresive cancers -1.655 0.051 1.074 0.556 -1.058 0.761 1.346 0.680

BIRC5 Basal; Inhibits apoptosis and leads to increased cell growth -1.149 0.618 -1.254 0.236 1.022 0.977 -1.225 0.994

EGFR

Basal; Tyrosine kinase receptor associated with increased cell

growth 1.179 0.511 -1.166 0.976 -1.024 0.921 -1.040 0.909

KRT5 Basal; Associated with increased risk of breast cancer 1.033 0.968 1.259 0.364 -1.011 0.889 1.337 0.600

NOTCH1

Basal; Involved in cell signalling and associated with EMT and

metastasis 1.211 0.497 -1.075 0.666 1.186 0.235 1.331 0.824

ID1

Lung metastasis; Associated with cell growth, EMT and poor

prognosis -1.008 0.804 1.272 0.559 -1.133 0.938 -2.070 0.083

102


Gene Function

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

MMP2

Lung metastasis; Degrade membranes and lead to increaed

metastasis and cancer progression -1.149 0.621 -1.742 0.228 1.198 0.529 1.017 0.833

PTGS2

Lung metastasis; part of inflammatory response, associated with

proliferation and growth 1.128 0.978 1.001 0.989 -1.294 0.160 -1.144 0.609

Signal Transduction

AR

Steroid receptor-mediated; Shown to both increase and decrease

cell proliferation and survival -1.414 0.055 1.049 0.836 -1.670 0.435 -1.017 0.750

BRCA1

DNA repair and tumour supressor, mutations increase breast

cancer risk 1.030 0.807 -1.435 0.037 -1.130 0.659 1.117 0.504

CCNE1

Steroid receptor-mediated; cell cycle regulation and tumour

development 1.104 0.886 -1.413 0.381 1.552 0.500 -1.300 0.565

CTNNB1

Steroid receptor-mediated; proto-oncogene that regulates cell

growth and adhesion -1.126 0.650 -1.479 0.101 -1.258 0.362 -1.700 0.305

ESR1

Steroid receptor-mediated; Increase growth through E2 signalling,

associated with good prognosis 1.162 0.590 1.100 0.692 1.079 0.681 1.430 0.170

ESR2

Steroid receptor-mediated; Associated with reduced cell

proliferation and increased survival 1.340 0.359 1.385 0.289 1.365 0.235 -1.472 0.192

IGF1

Steroid receptor-mediated; Increases cell growth, ER and growth

factor signalling -2.541 0.864 -1.006 0.785 1.093 0.753 1.004 0.871

KRT19

Steroid receptor-mediated; structural integrity of epithelial cells,

tumour supressor that inhibits Akt signalling, but Soloustros

showed poor clinical outcomes -1.431 0.282 1.134 0.128 -1.642 0.034 1.072 0.896

PGR

Steroid receptor-mediated; Increased cancer cell growth through

progesterone signalling -1.346 0.370 -1.758 0.154 -1.525 0.304 -1.459 0.584

RB1

Steroid receptor-mediated; Negative regulator of cell cycle,

tumour supressor -1.154 0.521 1.048 0.639 -1.140 0.609 -1.005 0.968

BCL2 Hedgehog; Blocks apoptosis and increases cell growth -1.454 0.209 -1.210 0.480 -1.142 0.662 1.059 0.664

CCND1 Hedgehog; Cell cycle regulation -1.245 0.258 1.031 0.976 1.670 0.208 -1.426 0.211

GLI1 Hedgehog; Increases hedgehog signalling and cancer cell growth -1.267 0.457 -1.060 0.841 1.149 0.368 1.048 0.858

SNAI2 Hedgehog; Increase EMT and metastasis, prevents apoptosis -1.548 0.030 1.025 0.847 -1.044 0.830 1.025 0.731

IGFBP3 Glucocorticoid; Induce apoptosis and arrest cell cycle 1.122 0.600 -1.024 0.849 1.220 0.744 1.121 0.831

103


Gene Function

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

NME1 Glucocorticoid; supresses metastasis -1.377 0.313 -1.076 0.399 1.393 0.123 1.438 0.256

NR3C1

Glucocorticoid; Receptor for cortisol and glococorticoids,

associated with cell survival and apoptosis inhibition -1.495 0.137 1.010 0.885 -1.512 0.059 -1.119 0.502

APC

Classical WNT; tumour supressor gene, controls beta-catenin and

inhibits WNT signalling 1.520 0.128 1.341 0.148 -1.251 0.620 -1.312 0.481

CCND1 Classical WNT; Cell cycle regulation -1.245 0.258 1.031 0.976 1.670 0.208 -1.426 0.211

CTNNB1 Classical WNT; regulate cell growth and adhesion -1.126 0.650 -1.479 0.101 -1.258 0.362 -1.700 0.305

SFRP1 Classical WNT; Tumour supressor gene -1.278 0.674 1.274 0.523 -1.090 0.993 -1.383 0.571

AKT1 PI3K/Akt; Involved in PI3K/Akt signalling and inhibits apoptosis 1.154 0.697 -1.162 0.727 1.146 0.988 -1.168 0.568

ERBB2

PI3K/Akt; EGFR family receptor, increased growth signalling,

associated with aggressive cancers 1.061 0.753 1.505 0.250 1.442 0.167 -1.956 0.046

IGF1 PI3K/Akt; Increases cell growth, ER and growth factor signalling -2.541 0.864 -1.006 0.785 1.093 0.753 1.004 0.871

IGF1R PI3K/Akt -1.387 0.163 -1.115 0.519 1.665 0.100 -1.085 0.705

PTEN

PI3K/Akt; Tumour supresor gene that negatively regulates

PI3K/Akt signalling 1.016 0.970 1.054 0.721 -1.323 0.231 -1.361 0.271

BIRC5 NOTCH; Inhibits apoptosis and leads to increased cell growth -1.149 0.618 -1.254 0.236 1.022 0.977 -1.225 0.994

NOTCH1

NOTCH; Involved in cell signalling and associated with EMT and

metastasis 1.211 0.497 -1.075 0.666 1.186 0.235 1.331 0.824

MAPK1 MAPK; Promotes cell proliferation and increases cell growth -1.730 0.017 1.001 0.976 -1.115 0.761 1.033 0.884

MAPK3 MAPK; Promotes cell proliferation and increases cell growth -1.030 0.952 -1.042 0.693 1.086 0.662 -1.294 0.212

MAPK8 MAPK; Promotes cell proliferation and increases cell growth -1.141 0.391 -1.291 0.451 1.139 0.426 -1.109 0.583

TP73 MAPK; Tumour supressor gene, 1.199 0.626 -1.089 0.511 -1.082 0.640 -1.060 0.762

Epithelial to Mesenchymal Transition

CTNNB1

Proto-oncogene overexpressed in breast cancer, regulate cell


NOTCH1

Involved in cell signalling and associated with EMT and

metastasis 1.211 0.497 -1.075 0.666 1.186 0.235 1.331 0.824

SRC

Proto-oncogene; activates EGFR signalling and promotes cell

survival and proliferation -1.699 0.023 -1.028 0.760 1.584 0.081 -1.266 0.443

104


Gene Function

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

TGFB1

Tumour supressor; decreases cell proliferation and induces

apoptosis -1.080 0.771 1.100 0.622 -1.212 0.491 -1.168 0.520


Angiogenesis

CDH13 Decreases tumour metastasis and invasiveness 1.377 0.230 1.525 0.155 -1.069 0.647 -1.589 0.373

CTNNB1



EGF

Binds EGFR and increases cell growth through MAPK and

PI3K/Akt signalling 1.388 0.320 1.221 0.419 1.609 0.284 -1.015 0.862

ERBB2

EGFR family receptor, associated with cell growth and aggressive

cancers 1.061 0.753 1.505 0.250 1.442 0.167 -1.956 0.046

ID1 Associated with cell growth, EMT and poor prognosis -1.008 0.804 1.272 0.559 -1.133 0.938 -2.070 0.083

IL6 Pro-inflammatory cytokine, induces EMT and poor prognosis 1.323 0.247 1.811 0.263 1.102 0.837 -1.400 0.675

JUN

Interacts with DNA to regulate expression, overexpression

increases cancer aggressiveness -1.064 0.984 -1.065 0.684 1.507 0.238 -1.384 0.224

NOTCH1


metastasis 1.211 0.497 -1.075 0.666 1.186 0.235 1.331 0.824

PLAU Protease associated with tumour proliferation and migration -1.255 0.548 1.311 0.242 -1.139 0.664 -1.141 0.629

PTEN

Tumour supresor gene that negatively regulates PI3K/Akt

signalling 1.016 0.970 1.054 0.721 -1.323 0.231 -1.361 0.271

SERPINE1 associated with poor prognosis and cancer progression 1.060 0.795 -1.072 0.707 1.919 0.037 1.311 0.629

SLIT2 Silencing of this gene associated with breast cancer risk 1.034 0.923 1.021 0.910 -1.038 0.835 -1.430 0.681

THBS1

Glycoprotein that inhibits angiogenesis, expression decreased in

cancer 1.050 0.901 1.021 0.805 1.243 0.025 1.315 0.457

VEGFA Increases angiogenesis and cell growth, inhibits apoptosis 1.131 0.608 -1.178 0.964 1.292 0.229 1.223 0.743

Adhesion

ADAM23 Involved in cell to cell adhesion, downregulated in breast cancer 1.449 0.414 5.030 0.142 1.104 0.798 -1.020 0.988

APC

tumour supressor gene, controls beta-catenin and inhibits WNT

signalling 1.520 0.128 1.341 0.148 -1.251 0.620 -1.312 0.481

BCL2 Blocks apoptosis and increases cell growth -1.454 0.209 -1.210 0.480 -1.142 0.662 1.059 0.664

105


Gene Function

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

CDH1 Tumour supressor gene and inhibits invasion 1.087 0.726 1.181 0.557 -1.872 0.061 -1.167 0.992


CDKN2A Tumour supressor gene and regulates cell cycle -1.643 0.405 -1.033 0.762 1.120 0.542 -1.078 0.985

CSF1

Influences macrophage development; increased expression in

breast cancer and associated with poor prognosis -1.070 0.933 1.125 0.593 -1.734 0.008 -1.129 0.779

CTNNB1



EGFR Tyrosine kinase receptor associated with increased cell growth 1.179 0.511 -1.166 0.976 -1.024 0.921 -1.040 0.909

ERBB2

EGFR family receptor, associated with cell growth and aggressive

cancers 1.061 0.753 1.505 0.250 1.442 0.167 -1.956 0.046

PTEN


signalling 1.016 0.970 1.054 0.721 -1.323 0.231 -1.361 0.271

TGFB1 Regulate proliferation and migration; upregulated in cancer cells -1.080 0.771 1.100 0.622 -1.212 0.491 -1.168 0.520

THBS1

Glycoprotein that inhibits angiogenesis, expression decreased in

cancer 1.050 0.901 1.021 0.805 1.243 0.025 1.315 0.457

Proteolysis

ADAM23 Involved in cell to cell adhesion, downregulated in breast cancer 1.449 0.414 5.030 0.142 1.104 0.798 -1.020 0.988

CST6 Cystatin that is downregulated in metastatic breast cancer -1.222 0.847 1.203 0.597 -1.142 0.932 1.046 0.737

CTSD

Increased expression in breast cancer, increases proliferation and

metastasis 1.045 0.940 1.133 0.420 1.072 0.723 -1.058 0.870

MMP2

Degrade membranes and lead to increaed metastasis and cancer

progression -1.149 0.621 -1.742 0.228 1.198 0.529 1.017 0.833

MMP9

Degrade membranes and lead to increaed metastasis and cancer

progression 1.099 0.715 1.157 0.631 1.082 0.648 -1.330 0.503

PLAU Protease associated with tumour proliferation and migration -1.255 0.548 1.311 0.242 -1.139 0.664 -1.141 0.629

PYCARD Inhibits apoptosis and increases cancer growth -1.068 0.624 1.179 0.163 -1.193 0.296 -1.594 0.352

Apoptosis

AKT1 Involved in PI3K/Akt signalling and inhibits apoptosis 1.154 0.697 -1.162 0.727 1.146 0.988 -1.168 0.568

APC


signalling 1.520 0.128 1.341 0.148 -1.251 0.620 -1.312 0.481

106


Gene Function

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

BAD Induces apoptosis and decreases cancer growth -1.426 0.114 1.010 0.897 -1.120 0.674 -1.460 0.269


CDH1 Tumour supressor gene and inhibits invasion 1.087 0.726 1.181 0.557 -1.872 0.061 -1.167 0.992


CDKN1A Inhibits apoptosis and increased expression in breast cancer -1.250 0.731 -1.119 0.541 1.601 0.232 1.221 0.501

CDKN2A

Tumour supressor, regulates cell cylce and mutated in breast

cancer -1.643 0.405 -1.033 0.762 1.120 0.542 -1.078 0.985

GSTP1

Involved in detoxification and drug metabolism; associated with

drug resistance -1.714 0.900 1.038 0.854 -1.353 0.206 -1.109 0.669

IGF1

PI3K/Akt; Reduces cell apoptosis leading to increased cancer

growth -2.541 0.864 -1.006 0.785 1.093 0.753 1.004 0.871

IL6 Pro-inflammatory cytokine, induces EMT and poor prognosis 1.323 0.247 1.811 0.263 1.102 0.837 -1.400 0.675

JUN



MUC1 Mucin protein present in breast tumours -1.369 0.369 -1.059 0.711 -1.449 0.263 -1.390 0.510

NME1 supresses metastasis -1.377 0.313 -1.076 0.399 1.393 0.123 1.438 0.256

RARB

Regulates cell groth and differentiation; decreased expression in

breast cancer 1.372 0.436 1.149 0.525 1.153 0.517 1.053 0.795

SFN Disrupts MAPK signalling to decrease tumour cell growth -1.193 0.268 -1.415 0.210 -1.051 0.868 1.056 0.856

SFRP1 Tumour supressor gene -1.278 0.674 1.274 0.523 -1.090 0.993 -1.383 0.571

TP53 Tumour supressor gene -1.147 0.138 -1.033 0.763 -1.348 0.376 -1.170 0.746

TP73 Tumour supressor gene, 1.199 0.626 -1.089 0.511 -1.082 0.640 -1.060 0.762


Cell Cycle

APC


signalling 1.520 0.128 1.341 0.148 -1.251 0.620 -1.312 0.481


CCNA1

Cell cycle progression; often apmlified in cancer and increase

growth -2.851 0.635 -1.003 0.842 -1.736 0.360 1.207 0.742

107


Gene Function

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

CCND1


growth -1.245 0.258 1.031 0.976 1.670 0.208 -1.426 0.211

CCND2


growth 1.026 0.974 -1.049 0.879 1.341 0.388 -1.056 0.961

CCNE1


growth 1.104 0.886 -1.413 0.381 1.552 0.500 -1.300 0.565

CDK2


growth -1.024 0.970 -1.134 0.481 1.020 0.851 -1.181 0.268

CDKN1A


growth -1.250 0.731 -1.119 0.541 1.601 0.232 1.221 0.501

CDKN1C Potential tumour supressor; decreases cell proliferation -1.792 0.064 1.002 0.910 -1.282 0.610 -1.302 0.465

CDKN2A

Tumour supressor, regulates cell cylce and mutated in breast

cancer -1.643 0.405 -1.033 0.762 1.120 0.542 -1.078 0.985

JUN



MKI67 Associated with cell proliferation 1.240 0.530 -1.125 0.899 1.156 0.550 -1.110 0.657

MYC

Involved in cell cycle progression and apoptosis; elevated

expression in cancer 1.445 0.504 1.023 0.852 1.066 0.844 1.117 0.735

PTEN


signalling 1.016 0.970 1.054 0.721 -1.323 0.231 -1.361 0.271

RASSF1 Tumour supressor gene; inhibits cell cycle progression -1.033 0.913 -1.434 0.363 1.235 0.453 -1.289 0.444

RB1 Negative regulator of cell cycle, tumour supressor -1.154 0.521 1.048 0.639 -1.140 0.609 -1.005 0.968



DNA Damage

APC


signalling 1.520 0.128 1.341 0.148 -1.251 0.620 -1.312 0.481

ATM

DNA repair and cell cycle supression; mutations increase breast

cancer risk 1.291 0.308 1.006 0.839 1.064 0.745 -1.626 0.059

BRCA1


cancer risk 1.030 0.807 -1.435 0.037 -1.130 0.659 1.117 0.504

108


Gene Function

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

BRCA2


cancer risk -1.127 0.547 -1.220 0.464 -1.373 0.299 1.425 0.325

CCND1

Cell cycle regulation; often apmlified in cancer and contribute to

tumourigenesis -1.245 0.258 1.031 0.976 1.670 0.208 -1.426 0.211

CDKN1A Inhibits apoptosis and increased expression in breast cancer -1.250 0.731 -1.119 0.541 1.601 0.232 1.221 0.501

MAPK1

Involved in MAPK signalling, promotes cell proliferation and

increases cell growth -1.730 0.017 1.001 0.976 -1.115 0.761 1.033 0.884

MGMT

Tumor Supressor gene, low levels associated with metatstasis and

cancer risk -1.313 0.204 1.206 0.283 -1.884 0.029 -1.147 0.930

MLH1 Repairs DNS mismatches; mutations increase cancer risk 1.035 0.747 -1.165 0.640 1.218 0.588 -1.098 0.549



TP73 Tumour supressor gene 1.199 0.626 -1.089 0.511 -1.082 0.640 -1.060 0.762

Xenobiotic Transport

ABCB1

Transports substances across cell membrane including drugs,

steroids and lipids -2.204 0.389 -1.807 0.276 1.053 0.975 -1.477 0.565

ABCG2

Transports substances across cell membrane including drugs,

steroids and lipids -1.963 0.041 1.524 0.185 -1.764 0.169 -1.734 0.537

Transcription Factors

AR

Steroid receptor signalling; Shown to both increase and decrease

cell proliferation and survival -1.414 0.055 1.049 0.836 -1.670 0.435 -1.017 0.750

CTNNB1



ESR1

Increase growth through E2 signalling, associated with good

prognosis 1.162 0.590 1.100 0.692 1.079 0.681 1.430 0.170

ESR2 Associated with reduced cell proliferation and increased survival 1.340 0.359 1.385 0.289 1.365 0.235 -1.472 0.192

FOXA1

transcription factor for ER signalling, associated with growth

inhibition and good prognosis 2.063 0.076 -1.316 0.037 -1.061 0.868 -1.491 0.630

GATA3

Transcription factor for mammary gland differentiation,

associated with low metastasis and good prognosis -3.044 0.026 1.024 0.953 1.108 0.497 -1.375 0.797

109


Gene Function

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

Fold

Change

p-

value

HIC1 Tumour supressor gene and regulates cancer cell growth -3.651 0.177 1.122 0.584 1.250 0.379 -1.302 0.506

JUN



MYC

Involved in cell cycle progression and apoptosis; elevated

expression in cancer 1.445 0.504 1.023 0.852 1.066 0.844 1.117 0.735

NOTCH1


metastasis 1.211 0.497 -1.075 0.666 1.186 0.235 1.331 0.824

NR3C1

Receptor for cortisol and glococorticoids, associated with cell

survival and apoptosis inhibition -1.495 0.137 1.010 0.885 -1.512 0.059 -1.119 0.502

PGR Increased cancer cell growth through progesterone signalling -1.346 0.370 -1.758 0.154 -1.525 0.304 -1.459 0.584

PRDM2 Activates ER signalling, potential tumour supressor role -1.069 0.646 1.073 0.435 -1.046 0.945 1.117 0.693

RARB

Regulates cell groth and differentiation; decreased expression in

breast cancer 1.372 0.436 1.149 0.525 1.153 0.517 1.053 0.795

RB1 Negative regulator of cell cycle, tumour supressor -1.154 0.521 1.048 0.639 -1.140 0.609 -1.005 0.968


TP73 Tumour supressor gene, 1.199 0.626 -1.089 0.511 -1.082 0.640 -1.060 0.762

XBP1

coexpressed with ESR1,associated with cell survival and

chemotherapy resistance 1.221 0.405 -1.102 0.838 -1.315 0.432 -1.100 0.871

Fold regulation (2-ΔΔCt

) of genes after treatment with 75μM ALA in four breast cancer cell lines by Student t-test. Significant (p<0.05) and large (> 2

fold) differences bolded.

EFFECT OF α-LINOLENIC ACID ON GROWTH OF BREAST CANCER … · 2013-12-11 · 2.1. Steroid hormone...

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