STUDIES OF IONIC MECHANISMS ASSOCIATED
WITH HUMAN CANCERS
Refika Mine GUZEL
IMPERIAL COLLEGE LONDON
DIVISION OF CELL AND MOLECULAR BIOLOGY
Ph.D. Thesis
This dissertation is the result of my own work unless otherwise acknowledged in the text or by reference(s).
Signed: Refika Mine GUZEL
Date: June, 2012
2
It's This Way
I stand in the advancing light,
my hands hungry, the world beautiful.
My eyes can't get enough of the trees -
they're so hopeful, so green.
A sunny road runs through the mulberries,
I'm at the window of the prison infirmary.
I can't smell the medicines -
carnations must be blooming nearby.
It's this way:
being captured is beside the point,
the point is not to surrender.
NAZIM HIKMET RAN
(Translated by Randy Blasing and Mutlu Konuk)
To my parents…
3 ABSTRACT
The general aim of this thesis was to undertake a series of inter-related studies with a view to improving our understanding of the role of ion channel expression and its regulation in cancer cells with strong metastatic potential. The emphasis was on neonatal Nav1.5 (nNav1.5) subtype of voltage-gated sodium channel (VGSC).
Chapter 1 (General Introduction) gives an account of the relevant literature and states the main aims of the studies. Chapter 2 details the Materials and Methods, ranging from quantitative molecular biology to in vitro assays of metastatic cell behaviour.
Chapter 3 presents experiments on regulation of VGSCs by insulin and insulin-like growth factor1 (IGF1) in strongly metastatic human breast cancer (BCa) MDA-MB-
231 cells. The central strategy was to treat insulin and IGF1 as an integrated signalling system (”IIS”) and suppress it using pharmacological inhibitors and RNAi.
Inhibiting IIS signalling suppressed metastatic cell behaviours (MCBs) and decreased nNav1.5 expression and activity. Chapter 4 describes studies on mRNA expression of a variety of cancer-associated ion channels (CAICs) in peripheral blood of normal human subjects with a view to laying the foundations for subsequent patient-based studies. The following 8 CAICs were studied: nNav1.5, VGSC-β1,
VGSC-β1b, Kv1.3, Kv10.1, Kv11.1, KCa3.1 and TRPM8. Several differences were noted between healthy cases and cancer patients. In particular, nNav1.5 and Kv1.3 mRNA expressions were up- and down-regulated, respectively. Chapter 5 shows that the anti-diabetic drug Metformin suppressed Matrigel invasion and nNav1.5 mRNA expression in MDA-MB-231 cells. Chapter 6 involves studies on the strongly metastatic human colorectal cancer (CRCa) SW-620 cells, which were found to express nNav1.5 mRNA and protein. Silencing nNav1.5 expression had a significant
4 inhibitory effect on Matrigel invasion. The thesis ends with a General Discussion and
Conclusion chapter, integrating the findings in the context of the field at large and pointing to future directions.
5 TABLE OF CONTENTS
Abstract 4
Table of contents 6
List of figures 13
List of tables 17
Abbreviations 18
Chapter 1. General introduction 21
1.1 Pathophysiology of metastatic disease 22
1.1.1 Current problems in clinical management of BCa 22
1.1.2 Metastatic cascade 23
1.2 Cancer markers 26
1.2.1 Circulating tumour cells 27
1.3 Ion channels in cancer 29
1.3.1 Voltage-gated sodium channel expression in cancer 30
1.3.1.1 Structure-function of voltage-gated sodium channels 33
1.3.1.2 Expression of voltage-gated sodium channel in strongly metastatic cells 35
1.3.1.3 CELEX hypothesis 40
1.3.1.4 VGSC β subunits 42
1.3.1.5 Association of VGSC expression with mainstream cancer mechanisms: Regulatory mechanisms 45
1.4 Ion channel expression in blood mononuclear cells 45
1.4.1 Lymphocytes 46
1.4.2 Macrophages 48
1.4.3 Plasticity of ion channel expression in mononuclear cells 48
6 1.5 Insulin and insulin-like growth factor 1 system 50
1.5.1 IIS and cancer 54
1.5.2 IIS and VGSC 56
1.5.3 Metformin 57
1.5.3.1 Metformin and cancer 58
1.5.3.2 Metformin and ion channels 60
1.6 Aims, hypotheses and scope 61
Chapter 2. Materials and methods 64
2.1 Cell culture 65
2.1.1 Cell lines and basic maintenance 65
2.1.2 Pharmacological agents and treatments 65
2.1.3 Cellular toxicity 66
2.2 Functional assays 67
2.2.1 Measurement of cell number 67
2.2.2 Lateral motility 68
2.2.3 Transverse migration 70
2.2.4 Matrigel invasion 72
2.2.5 Crystal violet staining 72
2.3 Molecular biology 73
2.3.1 RNA extraction 73
2.3.2 cDNA synthesis 74
2.3.3 Conventional PCR 76
2.3.4 Quantitative real-time PCR 78
2.4 RNA interference 80
7 2.4.1 siRNA transfection 80
2.5 Western blotting 85
2.5.1 Protein extraction 85
2.5.2 Sodium dodecyl sulphate polyacrylamide gel electrophoresis 87
2.5.3 Antibodies 87
2.6 Immunocytochemistry 88
2.7 Immunohistochemistry 90
2.8 Protocols involving human blood 91
2.8.1 Collection of peripheral blood 91
2.8.2 Enrichment of mononuclear cells 92
2.8.3 RNA extraction 93
2.8.3.1 DNase treatment 93
2.8.4 cDNA synthesis 94
2.8.5 Quantitative real-time PCR 94
2.8.6 Generation of DNA standards 95
2.9 Electrophysiology 97
2.10 Data analysis 97
Chapter 3. (Results 1) Studies on the insulin – insulin-like growth factor system in MDA-MB-231 cells: Possible association with voltage-gated sodium channel 98
3.1 Introduction and rationale 99
3.2 Aims and scope 100
3.3 Results 101
3.3.1 Exploratory experiments 101
3.3.1.1 Expression of insulin and IGF1 receptors in MDA-MB-231 cells 101
8 3.3.1.2 Effects of serum deprivation 101
3.3.1.3 Functional effects of exogenous insulin in serum-free medium 106
3.3.1.4 Lack of functional effects of exogenous insulin in normal medium: “hyperinsulinemia” 106
3.3.1.5 Conclusion of exogenous insulin studies 110
3.3.2 Effects of AG1024 and OSI-906 112
3.3.2.1 Cell viability and cell number 112
3.3.2.2 Matrigel invasion 112
3.3.4 Effects of dual InsR-IGF1R siRNA 116
3.3.4.1 Control transfection experiments 116
3.3.4.2 Specificity of individual siRNAs 116
3.3.4.3 Dual siRNA transfections 118
3.3.4.4 Effects of dual-siRNA transfection on metastatic cell behaviours 123
3.3.4.5 Effects on nNav1.5 expression 123
3.4 Discussion 127
3.4.1 Effect of insulin on MCBs and the role of the cellular environment 129
3.4.2 Functional effects of inhibiting endogenous IIS 132
3.4.2.1 Lack of effect on cell growth 132
3.4.2.2 Effects on MCBs 133
3.4.3 IIS - VGSC (nNav1.5) interaction in control of MCBs 135
3.4.4 Conclusion 136
Chapter 4. (Results 2) Studies of cancer-associated ion channel expression in human blood 139
4.1 Introduction and rationale 140
4.2 Aims and scope 142
9 4.3 Results 142
4.3.1 Ethical aspects 142
4.3.2 Technical aspects 143
4.3.2.1 Validation of nNav1.5 primers 143
4.3.2.2 Primer efficiencies 143
4.3.2.3 Tests of PCR assay variability 146
4.3.3 Normal human blood 146
4.3.3.1 Initial sampling studies 146
4.3.3.2 CAIC mRNA expression patterns: Healthy controls 151
4.3.4 Blood from cancer patients: Comparison with healthy cases 151
4.4 Discussion 156
4.4.1 Technical aspects 156
4.4.2 Kv1.3 as a ‘control’ channel 157
4.4.3 Cancer cases: comparison with healthy controls 157
4.4.5 Circulating tumour cells 159
Chapter 5. (Results 3) Studies ionic mechanisms in relation to diabetes 161
5.1 Introduction and rationale 162
5.2 Aims and scope 163
5.3 Results 164
5.3.1 In vitro effects of metformin 164
5.3.1.1 Cellular viability and morphology 164
5.3.1.2 Cell number 166
5.3.1.3 nNav1.5 mRNA expression 166
5.3.1.4 Lateral motility 166
10 5.3.1.5 Matrigel invasion 169
5.3.2 Studies on human blood samples 169
5.4 Discussion 172
5.4.1 Effects of metformin on cancer cell invasive behaviour 175
5.4.2 Possible role of VGSC (nNav1.5) in the action of metformin 175
5.4.3 Changes in blood CAIC mRNA expression in T2D 176
Chapter 6. (Results 4) Voltage-gated sodium channel expression in human colorectal cancer 180
6.1 Introduction and rationale 181
6.2 Aims and scope 185
6.3 Results 185
6.3.1 nNav1.5 mRNA expression in SW620 cells 185
6.3.2 nNav1.5 mRNA expression in SW620 cells – PCRs of the ‘spliced’ region and sequencing results 187
6.3.3 Immunocytochemistry of VGSC expression in SW620 cells 191
6.3.4 Silencing nNav1.5 by RNAi 191
6.3.4.1 Effects on nNav1.5 expression 191
6.3.4.2 Effects on invasiveness of SW620 cells 198
6.3.5 nNav1.5 is expressed in CRCa biopsy tissue 202
6.4 Discussion 207
6.4.1 nNav1.5 RNA was expressed in SW620 cells 209
6.4.2 nNav1.5 protein was expressed in SW620 cells 210
6.4.3 siRNA transfection reduced nNav1.5 mRNA and functional protein expression in SW620 cells 210
11 6.4.4 siRNA knock-down of nNav1.5 suppressed invasiveness of SW620 cells 212
6.4.5 nNav1.5 protein is expressed in human CRCa biopsy tissues 212
Chapter 7. General discussion and conclusion 214
7.1 Sensitivity of cancer cells to the biochemical content of their environment 216
7.2 Expression and functional role of IIS in cancer 217
7.3 Metformin as an anti-cancer drug 218
7.4 Expression of cancer-associated ion channels in human blood 219
7.5 Expression and pathophysiological role of nNav1.5 in strongly metastatic cells 220 7.6. Concluding remarks: clinical potential of nNav1.5 221
References 222
Appendices 267
12 LIST OF FIGURES
Figure 1.1 Stages of the metastatic cascade 25
Figure 1.2 Control of metastasis by ion channels 32
Figure 1.3 Functional architecture voltage-gated sodium channels 34
Figure 1.4 A voltage-gated Na+ channel (VGSC) macromolecular signalling complex 36
Figure 1.5 Voltage-activated whole-cell membrane currents recorded from human breast epithelial cell lines of varying metastatic potential 37
Figure 1.6 Differences between Nav1.5 ‘adult’ and ‘neonatal’ splice forms 38
Figure 1.7 Network interactions involving Nav1.5 in human colon cancer 41
Figure 1.8 Splice variants of VGSC β1 subunit 44
Figure 1.9 Changes in K+ channel expression in lymphocytes during activation 49
Figure 1.10 Involvement of insulin-IGF family in BCa pathogenesis 52
Figure 1.11 The members of IIS and related signalling molecules 53
Figure 1.12 Metformin action in cancer 59
Figure 1.13 Conceptual framework of the present study 63
Figure 2.1 Example of typical calibration curve of MTT assay 69
Figure 2.2 Assays of metastatic cell behaviours 71
Figure 2.3 The typical electrophoretic pattern of isolated RNAs 75
Figure 2.4 Typical examples of the melting curve analyses from real-time PCRs 79
Figure 2.5 Validation of the 2-ΔΔC(t) method 81
Figure 2.6 Example of typical Protein (BSA) calibration curve 86
Figure 2.7 Effect of protein dilution on band intensity 89
13 Figure 3.1 Expression of IGF1R and InsR in MDA-MB-231 cells 102
Figure 3.2 Effects of serum deprivation on cell viability and number 104
Figure 3.3 Effects of serum deprivation on nNav1.5 mRNA expression 105
Figure 3.4 Effect of insulin on cell viability and number 107
Figure 3.5 Effect of insulin on lateral motility 108
Figure 3.6 Effect of insulin on Matrigel invasion 109
Figure 3.7 Effects of hyperinsulin on cell number and migration 111
Figure 3.8 Effects of IIS inhibitors on cell viability and number – AG1024 113
Figure 3.9 Effects of IIS inhibitors on cell viability and number – OSI-906 114
Figure 3.10 Effects of IIS inhibitors on Matrigel invasion 115
Figure 3.11 Effect of siControl transfection on IGF1R and InsR expression 117
Figure 3.12 Typical effects of single siRNA transfection on IGF1R and InsR mRNA expression 119
Figure 3.13 Typical effects of single siRNA transfection on IGF1R and InsR protein expression 120
Figure 3.14 Effects of given siRNAs (from the dual-siRNA) on ‘target’ and ‘non-target’ receptors 121
Figure 3.15 Effects of dual siRNA transfection on InsR and IGF1R expression 122
Figure 3.16 Effect of dual-siRNA on cell number 124
Figure 3.17 Effect of dual-siRNA on MCBs 125
Figure 3.18 Effect of dual-siRNA on VGSC mRNAs 126
Figure 3.19 Effect of dual-siRNA on nNav1.5 protein expression 128
Figure 3.20 A conceptual scheme of VGSC-dependent and VGSC- independent control of metastatic cell behaviours 138
Figure 4.1 Design of theoretical validation of nNav1.5 primers 144
Figure 4.2 Validation of nNav1.5 primers 145
14 Figure 4.3 Calibration curves for nNav1.5 and PUM1 147
Figure 4.4 Reproducibility of nNav1.5 PCRs 148
Figure 4.5 Reproducibility of PUM1 PCRs 149
Figure 4.6 Typical examples of CAIC real-time PCR products 150
Figure 4.7 Distribution of Kv1.3 mRNA expression in sample populations 152
Figure 4.8 Comparison of CAIC mRNA expression levels in the populations of healthy volunteers and cancer cases 154
Figure 5.1 Effect metformin on cellular viability 165
Figure 5.2 Effects of metformin morphology 165
Figure 5.3 Effects metformin on cell number 167
Figure 5.4 Effect of metformin on nNav1.5 mRNA expression 167
Figure 5.5 Effects metformin on lateral motility 168
Figure 5.6 Effects of metformin on Matrigel invasion 170
Figure 5.7 Distribution of Kv1.3 mRNA expression in sample populations 171
Figure 5.8 Comparison of CAIC mRNA expression levels in the populations of healthy volunteers and T2D cases 174
Figure 5.9 Metformin and anti-metastatic effects 179
Figure 6.1 Pathways associated with sporadic and colitis-associated colon cancer development 183
Figure 6.2 Expression of nNav1.5 mRNA in different cell lines 186
Figure 6.3 PCR amplifications of the ‘spliced’ region of Nav1.5 in SW620 cells 188
Figure 6.4 Alignment of sequencing results 189
Figure 6.5 Immunocytochemical staining of SW620 cells 192
Figure 6.6 Examination of nNav1.5 immunostaining in SW620 cells 193
Figure 6.7 Effect of culturing time on nNav1.5 protein expression in SW620 cells 194
15 Figure 6.8 Effect of RNAi targeting nNav1.5 at mRNA level 195
Figure 6.9 Effect of RNAi targeting nNav1.5 at protein level 196
Figure 6.10 Optimizing Matrigel invasion assay for SW620 cells 199
Figure 6.11 Further optimization of the Matrigel invasion assay for SW620 cells 200
Figure 6.12 Effect of RNAi targeting Nav1.5 on Matrigel invasion of SW620 cells 201
Figure 6.13 Immunohistochemical staining of human colon tissues 203
Figure 6.14 Additional observations on human colon tissues 206
Figure 6.15 Immunohistochemical staining of human breast cancer tissues 208
16 LIST OF TABLES
Table 1.1 Examples of cancer - associated ion channels (CAICs) and their functional role 31
Table 1.2 Blood mononuclear cell (MNC) subtypes : Typical ion channel expression 47
Table 2.1 The primers used for PCR amplifications 77
Table 2.2 The siRNA sequences used for RNAi experiments 84
Table 3.1 Summary of the effects of inhibiting IIS on MCBs and VGSC (nNav1.5) activity in MDA-MB-231 cells 130
Table 4.1 Expression levels of CAIC mRNAs 153
Table 5.1 Expression levels of CAIC mRNAs 173
Table 6.1 nNav1.5 immunoreactivity scores for colon biopsy samples 205
17 ABBREVIATIONS
AMPK 5'AMP-activated protein kinase aNav1.5 ‘adult’ Nav1.5
APC Adenomatous polyposis coli
AU Arbitrary unit
BCa Breast cancer
Bp Base pair
BSA Bovine serum albumin
CAC Colitis-associated cancer
CAIC Cancer-associated ion channel cDNA Complementary DNA
CNS Central nervous system
COX-2 Cyclooxygenase-2
CRCa Colorectal cancer
CSC Cancer stem cell
CTC Circulating tumour cell
DAB 3,3'-diaminobenzidine
DMEM Dulbecco’s modified eagle’s medium
DMSO Dimethyl sulfoxide
DNA Deoxyribonucleic acid
DRG Dorsal root ganglion
DTC Disseminated tumour cell
ECM Extracellular matrix
EDTA Ethylenediaminetetraacetic acid
18 EGF Epidermal growth factor
EMT Epithelial – mesenchymal transition
ENaC Epithelial sodium channel
ER Estrogen receptor
FAP Familial adenomatous polyposis
FBS Foetal bovine serum
HIF Hypoxia-inducible factor
HNPCC Hereditary nonpolyposis colorectal cancer
HRP Horseradish peroxidase
IBD Inflammatory bowel disease
Ig Immunoglobulin
IGF Insulin-like growth factor
IGFR Insulin-like growth factor receptor
InsR Insulin receptor
IIS Insulin – insulin-like growth factor system
MCB Metastatic cell behaviour
MMP Matrix metalloproteinase
MNC Mononuclear cell (in blood)
MoI Motility index mRNA Messenger RNA
MSI Microsatellite instability mTORC1 mammalian target of rapamycin complex 1
MTT 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide nNav1.5 ‘neonatal’ Nav1.5
NESOpAb nNav1.5-specific polyclonal antibody
19 NGF Nerve growth factor
NKC Natural killer cell
PBS Phosphate buffered saline
PCa Prostate cancer
PCR Polymerase chain reaction
PCOS Polycystic ovary syndrome
PFA Paraformaldehyde
RNA Ribose nucleic acid
RNAi RNA interference
RTK Receptor tyrosine kinase
SDS Sodium dodecyl sulphate
SEM Standard error of mean
SFM Serum-free medium siRNA Small-interfering RNA
T2D Type 2 diabetes
TRP Transient receptor potential (ion channel)
TTX Tetrodotoxin
VEGF Vascular endothelial growth factor
VGPC Voltage-gated potassium channel
VGSC Voltage-gated sodium channel
20
Chapter 1
GENERAL INTRODUCTION
21
Metastasis, the spread of cancer cells from primary tumours to distant organs and the subsequent re-growth, accounts for the majority of deaths from cancer, including breast cancer (BCa) and colon/colorectal cancer (CRCa), the main foci of this thesis
(e.g. Eccles & Welch, 2007; Lu et al., 2009; Chaffer & Weinberg, 2011; Jemal et al.,
2011). Although the major mechanisms associated with cancer as a whole have been identified, at least conceptually (Hanahan & Weinberg, 2011), the complex and difficult-to-cure nature of metastatic disease represent a major challenge to oncology research and to clinical management of cancer.
1.1 Pathophysiology of metastatic disease
1.1.1 Current problems in clinical management of BCa
In BCa, as in all cancers, inaccurate detection and prediction of metastasis in individual patients are important clinical barriers which need to be overcome. Occult metastasis is present in a subset of BCa patients who are (mis)diagnosed as having only primary disease and treated accordingly. These ‘under-treated’ individuals can develop overt metastases even after many years, without revealing major intermittent clinical symptoms, and relapse unexpectedly (Alix-Panabières et al., 2007; Jones,
2008; Slade & Coombes, 2007; Roche & Vahdat, 2011). Conversely, although the predictive factors of some other patients may indicate poor prognosis, these women can survive longer than expected and may unnecessarily receive adjuvant chemotherapy with undesirable side effects (Pantel & Brakenhoff, 2004; Weigelt et al., 2005). Importantly, therefore, correctly characterising the metastatic status and potential at the time of initial diagnosis and predicting the future course of a cancer would have crucial consequences for long-term disease-free survival of patients.
22 Central to these issues is the need to identify the cellular and subcellular / molecular mechanisms that exert significant control upon the metastatic processes. It is well established that steroid hormones and growth factors play a pivotal role in both development and progression of BCa (Perona, 2006; Payne et al., 2008; Ali et al., 2011). The presence or absence of these receptors, their mutations and crosstalk are among the key determinants of therapy outcomes in BCa (Reis-Filho & Tutt,
2008; Buzdar, 2009). However, although the treatment strategies designed according to receptor expression are beneficial to some patients, others may still develop drug resistance and metastatic disease. In fact, it is increasingly being recognized that estrogen signaling downregulation, commonly applied against estrogen receptor positive (ER+) BCa, can have both beneficial and harmful effects (Jordan & Ford,
2011). Thus, to be able to design more effective treatments for BCa, it is also essential to reveal the exact mechanisms, and their relationships, behind the associated hormone and growth factor pathways controlling metastatic cell behaviours.
In conclusion, therefore, there are two significant problems in clinical management of BCa, and other carcinomas: 1) Definitive diagnosis of metastatic potential and (2) long-lasting, effective, ideally non-toxic therapy (Gurel et al., 2008;
Ramírez et al., 2008). This calls for significant improvement in our understanding of the metastatic process.
1.1.2 Metastatic cascade
In evaluating cancer markers and mechanisms, it is first necessary to recognise that primary and secondary tumourigenesis (i.e. proliferation vs. invasion) are controlled differently (Hanahan & Weinberg, 2000; Steeg & Theodorescu, 2008; Welch et al.,
23 2000; Welch, 2004 & 2006). Several metastasis-suppressor genes have been identified that do not affect proliferative activity (Berger et al., 2004; Weigelt et al.,
2005; Vaidya and Welch, 2007). Moreover, ability to metastasize can be ‘pre- programmed’ and can be dynamically modulated by interactions with local microenvironment (Schmidt-Kittler et al., 2003; Fidler, 2003; Hunter, 2004; Hunter et al., 2008).
Metastasis in carcinomas is thought to start often by epithelial cells undergoing a process called “epithelial – mesenchymal transition” (EMT) (Thiery et al., 2009), a topic that is still hotly debated (Ledford, 2011). Classically, it has been assumed that metastatic potential arises eventually as cancer cells divide repeatedly, thereby accumulating multiple mutations (e.g. Poste & Fidler, 1980). However, more recent work involving gene array analyses, even on single cells, have shown that metastatic potential can also be ‘pre-programmed’. This is in line with observations where a clinically detectable primary tumour is absent in some patients with metastatic disease (Hunter, 2004; Hunter et al., 2008). Although metastasis is a complex process, in a reductionist approach the metastatic cascade can be dissected into following steps (Bacac & Stamenkovic, 2008; Hunter et al., 2008; Steeg &
Theodorescu, 2008) (Figure 1.1):
1. A subset of primary tumour cells detaches from each other and migrates out of their original position invading the surrounding stroma. This involves reciprocal interactions between tumour cells and stroma (Sung & Johnstone, 2007).
2. Migrating tumour cells enter blood or lymph circulation. This process, called “intravasation”, may be facilitated by angiogenesis/lymphanogenesis, which in turn is promoted by tumour hypoxia. Interestingly, entry into blood or lymph circulation may be controlled differently (He et al., 2004).
24
Figure 1.1 – Stages of the metastatic cascade. A & B. At the primary site, cancer cells proliferate to form primary tumour. C. A subset of cancer cells detach from the primary tumour and invade their surrounding stroma by degrading the basement membrane. During this process, matrix metalloproteinases (MMPs) and urokinase plasminogen activator (uPA) are involved in the degradation of the extracellular matrix (ECM). D. Through interactions between tumour cells and endothelial cells, new blood vessels are formed (angiogenesis). Cancer cells enter the circulation by a process called intravasation. Macrophages also respond to stimuli from cancer cells and infiltrate the tumour. E. The circulating tumour cells are under apoptotic pressure and a large proportion of them do not survive. The surviving cells are transported to distant organs. F. Cancer cells extravasate at distant site(s) and form a secondary tumour. Proliferative, survival and angiogenic processes in metastatic tumours occur similarly to the early steps of primary tumour progression. A subset metastases may retain a state of dormancy and stay clinically asymptomatic for long periods of time. Modified from Bellahcene et al. (2008).
25 3. Tumour cells in circulation are subject to extensive mechanical and metabolic stress. These circulating tumour cells are under apoptotic pressure and a large proportion of them do not survive (Mehes et al., 2001).
4. Surviving circulating tumour cells, with particular adhesive properties, travel to and attach at ‘distant’ organs. After exiting the circulation (“extravasation”), they lodge and proliferate at this new site to form a secondary tumour.
5. As a result of the growing size of secondary tumour, oxygen supply into the tumour mass becomes insufficient, and angiogenesis is triggered (again by hypoxia). This facilitates further tumour growth by providing nutrients and clearing metabolic by-products.
Importantly, progressive response to local microenvironment contributes to metastasis by a “seed and soil” phenomenon (Fidler, 2003; Hunter et al., 2008; Hu &
Polyak, 2008). Indeed, strongly metastatic BCa and prostate cancer (PCa) cells have been shown to be particularly sensitive to the biochemical (including serum) content of their environment (Ding & Djamgoz, 2004; Pan & Djamgoz, 2008).
1.2 Cancer markers
In clinical management of BCa, several diagnostic markers are routinely used in order to identify patients at risk for distant metastases, to categorize them according to their prognosis, to predict their response to treatment and to plan therapy. On the whole, cancer markers may be divided into two main categories: Tissue-based and blood-based. To date, the most widely used prognostic markers for BCa are tissue- based and include tumour size, histological tumour type and grade, lymph node stage, lympho-vascular invasion and ER, progesterone receptor and human epidermal
26 growth factor receptor 2 (HER-2) status and Ki67 expression (Elston et al., 1999;
Payne et al., 2008; Weigel & Dowsett, 2010). However, these markers have some clinical limitations and may not always lead to an accurate assessment of the patient
(e.g. Sinha et al., 2007). An exciting field of development in molecular diagnostics is expression of particular sets of mRNAs detected upon microarrays, laying the grounds for (i) identifying the genes controlling metastasis to specific organs (e.g.
Brogi et al., 2011) and (ii) personalised therapy (e.g. Agarwal & Kaye, 2006). The most common blood-based marker for BCa is the cancer antigen CA15-3, frequently used for monitoring therapeutic efficacy (Duffy et al., 2010). More recently considered blood-based biomarkers include disseminating and circulating tumour cells, as well as circulating cell-free DNAs and microRNAs (miRNAs) (Weigel &
Dowsett, 2010; Aigner, 2011). Several miRNAs have been associated with different aspect of BCa, some with the metastatic process (Shi et al., 2010).
1.2.1 Circulating tumour cells
During metastasis, a subset of ‘isolated’ tumour cells may retain a state of dormancy and form secondary lesions which stay clinically asymptomatic without giving rise to overt metastasis for long periods of time. These cells may persist in a dormant state either as single cells or as micro-metastatic lesions. “Solitary dormancy” refers to single tumour cells which are under cell cycle arrest and may reside in bone marrow as “disseminated tumour cells” (DTCs) or in vasculature as “circulating tumour cells” (CTCs). In the case of micro-metastases, which are formed by small groups of tumour cells, cell proliferation is in balance with apoptosis without net growth and these lesions can remain undetected in secondary organs for a long time (Aguirre-
Ghiso, 2007; Alix-Panabières et al., 2007; Udagawa, 2008). In BCa, metastatic
27 spread is most commonly observed in lungs, bones, liver and brain (Rabbani &
Mazar, 2007). Since metastases are initiated by isolated tumour cells originating from primary lesions and such cells can be identified in peripheral blood, lymph nodes and bone marrow, even at early stages of malignant progression, there is significant interest in the clinical potential of CTCs and DTCs (e.g. Alix-Panabières et al., 2008; Swaby & Cristofanilli, 2011). The first observation of the presence of tumour cells in peripheral blood of cancer patients was documented more than 140 years ago by Ashworth (1869). A growing body of more recent evidence suggests that the quantity of CTCs is indeed correlated with disease progression and that detection and enumeration of CTCs can be important for clinical management of cancer (Sleijfer et al., 2007). Investigations on metastatic BCa patients showed that detecting “5 CTCs per 7.5 ml of blood” would be associated with shorter survival when compared to patients with fewer cells (Cristofanilli et al., 2004; Dawood et al.,
2008).
Although on a daily basis some 106 tumour cells can enter blood circulation from 1 g of tumour tissue, most of these cells do not survive due to apoptosis (Chang et al., 2000; Méhes et al., 2001). Considering that CTCs are rather rare, therefore, detection methods are usually combined with a pre-enrichment step, which helps to enhance the sensitivity and specificity of the subsequent applications by increasing the ratio of target cells to the rest of the cellular content. Such enrichment methods include density gradient centrifugation, filtration or immune-mediated separation, which are based on cellular density, size or antigens, respectively (Paterlini-Brechot
& Benali, 2007). Further investigation of CTCs utilized mainly two approaches: 1)
PCR-based assays or (2) cytometric methods (Mocellin et al., 2006). The most commonly used PCR-based assay is real-time quantitative reverse-transcription PCR
28 (Q-RT-PCR) targeting specific mRNA(s) expressed in CTCs. Cytometric methods rely on immunocytochemical labeling of epithelial and/or tumour-associated antigens
(Gasent Blesa et al., 2008; Iakovlev et al., 2008).
The critical prerequisite for all CTC detection methods is the availability of a reliable and sensitive cancer-specific marker. Despite recent technological improvements, however, lack of such effective markers is an important limitation in
CTC-based investigations and applications. Currently, for all different types of method, two main groups of markers are used: ‘epithelial’ and ‘tumour cell specific’
(Sleijfer et al., 2007). However, although generally all carcinomas are positive for epithelial markers (e.g. epCAM), activated leucocytes may also express such markers giving rise to possible false-positive results (Jung et al., 1998). Moreover, other markers that are considered to be tumour specific might also be expressed by some normal cells (including leucocytes) again leading to possible false-positive interpretations, so additional ‘negative’ or ‘positive’ markers (e.g. CD45-,
CK8,18,19+) may need to be used (Swaby & Cristofanilli, 2011). False-negative results may also occur when focusing on epithelial markers, which may be lost during EMT (Willipinski-Stapelfeldt et al., 2005). Similarly, choosing a tumour- specific marker instead may also underestimate the real amount tumour cells, owing to the fact that tumours are heterogeneous in their cellular make-up and not all of cells in a tumour mass may express a given marker (Shin et al., 2006).
1.3 Ion channels in cancer
A variety of ion channels are expressed in cancer cells and tissues (Prevarskaya et al., 2010). In particular, voltage-sensitive ion channels have opened a new avenue in
29 the understanding of the pathophysiology of cancer. In addition to their well-known association with neuronal and muscular disorders, functional relationships between ion channels and cancer are increasingly been recognised (Schönherr, 2005; Fraser &
Pardo, 2008; Arcangeli et al., 2009; Onkal & Djamgoz, 2009; Prevarskaya et al.,
2010). Thus, at least 10 cancer-associated ion channels (CAICs) have been recognized from work on a range of cancer cells and tissues (Table 1.1). Most work has been done on voltage-gated potassium channels (VGPCs) and voltage-gated sodium channels (VGSCs). Interestingly, on the whole, VGPC activity has been shown to control mainly proliferative activity, whilst VGSC expression has been associated with metastatic cell behaviours (MCBs), especially directional motility and Matrigel invasion (Roger et al., 2003; Fraser et al., 2005; Onkal & Djamgoz,
2009). Another group of promising ion channels are “transient receptor potential”
(TRP) channels (Lehen'kyi & Prevarskaya, 2011). Amongst the latter, the following channels (and pathophysiological role in PCa) have been demonstrated: TRPV2
(migration and androgen resistance; Monet et al., 2010), TRPV6 (response to vitamin
D; Lehen'kyi et al., 2011) and TRPM8 (response to adrenaline and PSA; Bavencoffe et al., 2010; Gkika et al., 2010). Clearly, the different ion channels are involved in different aspects of cancer, even at a particular stage, such as migration / invasion
(Figure 1.2). Of particular interest to the current study is VGSC (nNav1.5) expression (Onkal & Djamgoz, 2009).
1.3.1 Voltage-gated sodium channel expression in cancer
VGSC expression / upregulation has been found in cancer cells (and in some cases, tissues) derived from a variety of human epithelial organs, including breast (Roger et al., 2003; Fraser et al., 2005; Brackenbury et al., 2008; Gao et al., 2009), prostate
30
Table 1.1 – Examples of cancer - associated ion channels (CAICs) and their
functional role
CAIC Associated cancer(s) Functional role Reference(s)
metastatic cell Fraser et al. (2005) Nav1.5 breast, ovary, colon behaviours, Gao et al. (2010) especially invasion House et al (2010)
metastatic cell Laniado et al. (1997) Nav1.7 Prostate behaviours, Diss et al. (2005) especially invasion
Fraser et al. (2000) Kv1.3 Prostate proliferation
several – e.g. acute Agarwal et al. (2010) Kv10.1 myeloid leukaemia, proliferation, Mello de Queiroz et al. (EAG) soft tissue sarcoma, migration (2006) breast Hemmerlein et al. (2006) several – e.g. ovary, Kv11.1 neuroblastoma, proliferation, Asher et al. (2011) (HERG) breast, pancreas, secretion Masi et al. (2005) colon prostate, Valero et al. (2011) TRPM8 breast, proliferation Chodon et al. (2010) pancreas Yee et al. (2010)
KCa3.1 malignant melanoma migration Schmidt et al. (2010)
31
Figure 1.2 – Control of metastasis by ion channels. Altered expression of a number of ion channels is implicated in the enhanced tumour cell motility, migration and invasion, which are crucial for metastasis. The involvement of different ion channels in metastatic behaviours through different mechanisms is outlined as follows: 1) Perturbed intracellular ionic homeostasis, mainly due to increased Na+ + influx (INa↑) caused by the overexpression of Na permeable channels (voltage- gated: Nav1.5 and Nav1.7, and non-voltage-gated: ENaC/ASIC). 2) Dynamic changes in cell shape and volume, which are aided by increased potassium (IK↑) and 2+ chloride (ICl↑) fluxes due to overexpression of Ca -dependent (KCa1.1, KCa3.1 and + − KCa2.3) and G-protein-regulated (GIRK1) K channels together with the Cl channels (ClC-3). 3) Cyclic morphological and adherence changes, due to periodic-type Ca2+ 2+ signalling which is supported by enhanced Ca influx (ICa↑) caused by the overexpression of Cav3 and TRPV1 channels (and maybe some other TRP members) as well as newly identified store-operated channel (SOC) constituents (Orai1/STIM1). Modified from Prevarskaya et al. (2010).
32 (Laniado et al., 1997; Bennett et al., 2004; Nakajima et al., 2009), lung cancer – small-cell (Onganer & Djamgoz, 2005), non-small-cell (Roger et al., 2007), melanoma (Allen et al., 1997), mesothelioma (Fulgenzi et al., 2006), neuroblastoma
(Ou et al., 2005), cervical cancer (Diaz et al., 2007) and colon cancer (House et al.,
2010). In all these cases, VGSC expression is an epigenetic phenomenon. There is only one recent report (on glioblastoma) showing an association of VGSC mutations with patient prognosis (Joshi et al., 2011).
1.3.1.1 Structure-function of voltage-gated sodium channels
Although VGSCs are classically known to be responsible for generation and propagation of action potentials in excitable cells, such as neurons and myocytes, they were later found to be expressed also in non-excitable cells such as lymphocytes and fibroblasts (Diss et al., 2004). Moreover, accumulating evidence suggests a role for VGSCs in the invasive behaviour of several types of metastatic cancers (section
1.3.1.2). VGSCs are large (220 – 250 kD), multimeric, glycosylated transmembrane proteins, which contain a main single pore-forming component: α-subunit (Figure
1.3). There are 10 identified members of the mammalian VGSCα gene family
(Nav1.1-1.9 and Nax), which differ from each other in terms of their sensitivity to specific channel blocker tetrodotoxin (TTX) and tissue distribution (Catteral, 2000;
Diss et al., 2004). The α-subunit is associated with one or two β-subunits (β1-β4).
The latter control the gating of the channel, regulate the expression level of the α- subunit in plasma membrane and take part in cell adhesion via interactions with cytoskeleton, extracellular matrix and cell adhesion molecules (Yu & Catterall, 2003;
Koopmann et al., 2006; Brackenbury et al., 2008; Chioni et al., 2009). Furthermore, soluble intracellular domains of β-subunits, produced by γ-secretase cleavage, are
33
Figure 1.3 – Functional architecture voltage-gated sodium channels. Four homologous domains (DI-DIV) assemble to form the α−subunit, each of them containing six segments (S1-S6) with a membrane-reentrant P-loop between S5 and S6. S4s in each domain are strongly positively charged and control voltage sensitivity (“gating”) together with S1-S3s. The ion pore is formed by the S5-S6s and the P-loops. In addition to forming the pore, the hydrophobic P-loop also contains the ion selectivity filter. Fast inactivation is controlled by the DIII – DIV inter-linker region. The specific channel blocker tetrodotoxin (TTX) binds in the mouth (“pore”) of the channel. Red circles with P indicate phosphorylation sites. Associated β−subunit is also shown. Modified from Clare et al. (2000).
34 thought to function as transcriptional regulators (Brackenbury et al., 2008). The extracellular Ig domains of β-subunits also are substrates for proteases. These domains are cleaved by beta-site amyloid precursor protein-cleaving enzyme
(BACE1) and α-secretase and shed (Brackenbury and Isom, 2011). It is proposed that α- and β-subunits with other signalling molecules may act as macromolecular signalling complexes (Kaczmarek, 2006; Brackenbury et al., 2008) (Figure 1.4).
1.3.1.2 Expression of voltage-gated sodium channel in strongly metastatic cells
As noted in section 1.3.1, VGSCs have been found specifically to be expressed in strongly metastatic cells (Figure 1.5). VGSCs are not expressed in weakly/non- metastatic cells and VGSC activity is not involved in proliferative activity (Fraser et al., 2003; Fraser et al., 2005; Nakajima et al., 2009). This is a clear example of primary and secondary tumourigenesis being controlled differently (e.g. Steeg &
Theodorescu, 2008). The pathophysiological role of VGSC expression/activity has been studied mostly in BCa (Onkal & Djamgoz, 2009).
The main VGSC subtype identified to be dominant in BCa is the ‘cardiac type’ Nav1.5, expressed in its neonatal splice isoform (nNav1.5) (Fraser et al., 2005;
Brackenbury et al., 2007). Interestingly, the corresponding VGSC in PCa is
‘neonatal’ Nav1.7 (Diss et al., 2001). Neonatal Nav1.5 is a case of developmentally- regulated expression involving alternative splicing of exon 6, corresponding to the
D1:S3/4 region (Figure 1.6). Alternative splicing results in either 5' (‘neonatal’) or 3'
(‘adult’) splice forms. The two splice forms differ in 31 out of 92 nucleotides leading to a difference in seven amino acids (Figure 1.6). These differences enabled the generation of a polyclonal antibody – NESOpAb – with significant (~x10) selectivity for nNav1.5 over the adult form (Chioni et al., 2005). The expression of neonatal
35
Figure 1.4 – A voltage-gated Na+ channel (VGSC) macromolecular signalling complex. VGSC macromolecular signalling complex is expected to contain the α- subunit, one or more β subunits and other partners (e.g. contactin, ankyrins, BACE1 etc) which would vary depending on the cell type and/or subcellular localization. The scheme presented here was originally proposed for migrating neurons and shows the VGSC signalling complex promoting a signal cascade through fyn kinase leading to process outgrowth and migration in response to β1 trans-adhesive interactions with adjacent cells. Additionally, γ-secretase cleavage of β1 is proposed to release the intracellular domain (ICD), which in turn may affect VGSC α-subunit transcription. Na+ influx through the VGSC α-subunit may also further modulate this signalling system. Modified from Brackenbury et al. (2008).
36
Figure 1.5 – Voltage-activated whole-cell membrane currents recorded from human breast epithelial cell lines of varying metastatic potential. Recordings from a normal human breast epithelial cell line (MCF-10A) (A) and breast cancer cells with increasing metastatic potential are shown: (B) MDA-MB-453, (C) MCF-7, (D) MDA-MB-435 and (E) MDA-MB-231 cells. The currents were generated by pulsing the membrane potential from a holding voltage of − 100 mV, in 5 mV steps, from − 60 to + 60 mV for 200 ms. Voltage pulses were applied with a repeat interval of 20 s. For clarity, every second current trace generated is displayed. With increasing metastatic potential, voltage-gated outward (K+) currents are reduced, and inward (Na+) currents appear only in the strongly metastatic MDA-MB-231 cells (E). Scale bars: 4 nA / 50 msec (A and B), 1 nA / 50 msec (C and D) and 200 pA / 10 msec (E). Modified from Onkal and Djamgoz (2009); original data from Fraser et al. (2005).
37
Figure 1.6 – Differences between Nav1.5 ‘adult’ and ‘neonatal’ splice forms. A. Alternative splicing of Nav1.5 α-subunit in exon 6 results in either 5' (‘neonatal’) or 3' (‘adult’) splice forms. When compared, these two splice variants differ in 31 out of 92 nucleotides leading to a difference in seven amino acids at positions marked with red dots. B. The location of ‘adult’/‘neonatal’ alternative splicing region on the protein is in segments 3 and 4 of domain 1 (DI:S3/4) including the extracellular linker (region is marked with grey shading). C. The detailed amino acid composition of ‘neonatal’ variant is shown and differences are indicated with red. Modified from Onkal & Djamgoz (2009).
38 isoform is consistent with the previously proposed notion that at least some embryonic genes are re-expressed in cancer cells, so-called “oncofoetal” or
“onconeural” expression (Monk & Holding, 2001; Ben-Porath et al., 2008; Storstein et al., 2011). Initial studies on the involvement of VGSCs in BCa showed that VGSC currents were present only in a subset of strongly but not in weakly or non-metastatic
BCa cell lines (Roger et al., 2003; Fraser et al., 2005; Gao et al., 2009). At mRNA level, nNav1.5 expression was shown to be more than 1800-fold higher in the strongly metastatic MDA-MB-231 compared to the weakly/non-metastatic MCF-7 cells. Experiments with TTX revealed that blockage of VGSCs in MDA-MB-231
BCa cells can suppress a variety of in vitro MCBs (e.g. directional motility, endocytosis and invasion). Importantly, studies on biopsy samples from BCa patients showed (i) that significant expression of nNav1.5 mRNA and protein occurred and
(ii) that there was a strong positive correlation between Nav1.5 mRNA expression and lymph node metastasis (Fraser et al., 2005). A follow-up study involving gene silencing (siRNA) and NESOpAb confirmed that the VGSC-associated invasive behaviour of MDA-MB-231 cells was indeed mainly due to functional nNav1.5 expression / upregulation (Brackenbury et al., 2007). Using the same antibody, another study demonstrated that the expression of nNav1.5 was under developmental control and that in the examined adult tissues there was no expression of nNav1.5 with the only exception of low levels in brain and skeletal muscle (Chioni et al.,
2005). Wu et al. (2002) also found some Nav1.5 mRNA expression in mammalian brain.
When the electrophysiological characteristics of neonatal and adult Nav1.5 isoforms were compared, it was found that the nNav1.5 had much slower activation and inactivation kinetics and, consequently, could cause greater Na+ influx (Onkal et
39 al, 2008). These results are in accordance with previous observations that Na+ concentration is higher (i) in cancer vs. non-cancer tissues of mouse liver (Cameron et al., 1980) and human breast (Ouwerkerk et al., 2007) and (ii) in strongly vs. weakly / non-metastatic human lung cancer cells (Roger et al., 2007). More recent findings further support the functional role of nNav1.5 in metastatic BCa in terms of promoting the cellular invasiveness via pH-dependent release of cathepsin (Gillet et al., 2009; Brisson et al., 2011).
In a particularly significant study, House et al. (2010) used siRNA and microsarray analyses to show that Nav1.5 activity was upstream of a number of cellular metastatic mechanisms, including membrane and protease genes, Wnt signalling, MAPK signalling, Ca2+ signalling and membrane remodelling / secretion
(Figure 1.7). It was thus concluded that “Voltage-gated Na+ channel SCN5A is a key regulator of a gene transcriptional network that controls colon cancer invasion”
(House et al., 2010). Surprisingly, however, House et al. (2010) commented briefly that the Nav1.5 expressed was the ‘adult’ form, unlike the situation in BCa (Fraser et al., 2005). Re-assessment of this potentially important issue is one of the specific
\aims of the study described in Chapter 6.
1.3.1.3 CELEX hypothesis
Comparative whole-cell patch-clamp recordings of strongly versus weakly metastatic cells suggested further that metastatic potential is associated with both upregulation of VGSC activity and downregulation of voltage-gated outward (mainly K+) currents
(Figure 1.5). Examples of such simultaneous changes were observed in studies on human BCa (Fraser et al., 2005), rat and human PCa (Grimes et al., 1995 and
Laniado et al., 1997, respectively) and human lung cancer (Roger et al., 2006). Such
40
Figure 1.7 – Network interactions involving Nav1.5 in human colon cancer. A. The scheme was predicted from changes in expression of colon cancer (E-) genes resulting from Nav1.5 (S-) gene knock-down (inferred S-gene network and frontier). Nodes represent S-genes (ovals), E-genes (gray boxes), and “gene ontology” (“GO”) categories (white boxes). Activation is shown by arrows and repression by tees. Mixed arrow/tee endings indicate GO set enrichment among both activated and inhibited E-genes. For simplicity, only direct interactions are shown. B. Simplified version of (A) showing the basic sub-cellular mechanisms controlled by Nav1.5. Modified from House et al. (2010).
41 reciprocal changes in VGSC:VGPC currents would indicate (i) that strongly metastatic cells do have electrically excitable membranes and (ii) that such
‘excitability’ could be one of the reasons behind the ‘hyperactive’ behaviour of strongly metastatic cells. This phenomenon was described as the “Cellular
Excitability” (CELEX) hypothesis of metastatic disease (Djamgoz & Isbilen, 2006;
Djamgoz, 2011).
Another study comparing Dunning cell model of rat PCa cells (weakly metastatic AT-2 versus strongly metastatic Mat-LyLu cells) revealed more information about the differential involvement of VGPC activity in proliferation versus invasion (Fraser et al., 2003a,b). Whilst Kv1.3 was the predominant VGPC subtype in both of these cell lines, its functional expression was significantly lower in Mat-LyLu cells (Fraser et al., 2003b). Interestingly, blocking Kv1.3 activity with the selective inhibitor margatoxin suppressed proliferation in AT-2 cells, but had no effect on Mat-LyLu cells (Fraser et al., 2003b). The observed inhibition of proliferation in AT-2 cells agrees with the well-known role of VGPCs in cellular proliferation (e.g. Blackiston et al., 2009). In turn, this is consistent with the main functional feature of the weakly (or non-) metastatic cells being proliferation.
Conversely, functional VGPC expression was downregulated and not involved in proliferation in strongly metastatic cells, supporting the notion that VGPC activity gets downregulated in such cells in order to promote excitability (VGSC activity) which, turn, promotes invasiveness.
1.3.1.4 VGSC β subunits
The auxillary β subunits of VGSCs have also been implicated in BCa. A role for β1 subunits in regulating cell-substrate adhesion and nNav1.5 expression/activity was
42 observed. Thus, VGSC β1 expression was much higher in weakly vs. strongly metastatic BCa cells (Chioni et al., 2009), consistent with the more adherent nature of the former (Palmer et al., 2008). However, upregulation of VGSC β1 expression in MDA-MB-231 cells increased adhesion to substrate and upregulated nNav1.5 mRNA and functional expression (Chioni et al., 2009). Importantly, β3 subunit was consistently absent in BCa and PCa cells (Diss et al., 2007; Chioni et al., 2009). This could be related to β3 being one of the genes known to be controlled (upregulated) by the major tumour suppressor gene p53 (Adachi et al., 2004). Thus, the absence of
β3 would ensure resistance to apoptosis. Although there is substantial evidence about the involvement of VGSC subunits in BCa (and PCa), the mechanisms regulating their functional up/down-regulation and the expression of different splice variants need to be investigated in detail.
There is some evidence for limited developmental regulation of VGSC
β−subunits (Figure 1.8). In particular, Kazen-Gillespie et al. (2000) described a truncated form of β1 (β1B) which was expressed selectively in early development in rat central nervous system (CNS); whilst β1B expression in rat CNS decreases soon after birth, β1 becomes the dominant variant in postnatal CNS. Another recent study investigated the possibility of a similar developmental expression in humans and showed that β1B is indeed the dominant splice variant in foetal human brain (Patino et al., 2011). Extension of exon 3 (with an in-frame stop codon) due to intron retention gives rise to β1B via alternative splicing in humans (Qin et al., 2003).
Structurally, although both variants are similar in the Ig loop region, the trans- membrane domain is missing in β1B. Thus, it is a soluble secreted protein unlike the membrane bound β1 (Brackenbury and Isom, 2011).
43
Figure 1.8 – Splice variants of VGSC β1 subunit. (A) Human SCN1B gene spans six exons (~9 kb) on chromosome 19. Retention of part of intron 3 via alternative splicing gives rise to exon 3A. Whilst β1 is encoded by exons 1, 2, 3, 4 and 5 (solid boxes), exons 1, 2 and 3A (solid and shaded boxes) encode β1B. Solid and shaded grey lines indicate the 5’ and 3’ untranslated regions for β1 and β1B, respectively. The interrupted dashed line represents the unidentified 3’ untranslated region of. The stop codons are marked with asterisks. B. Although an extracellular immunoglobulin (Ig) loop is common for both variants, β1 contains a transmembrane domain with an intracellular C-terminal whilst β1B lacks the transmembrane domain and is a soluble secreted protein. Modified from (A) Qin et al. (2003) and (B) Brackenbury and Isom (2011) .
44 1.3.1.5 Association of VGSC expression with mainstream cancer mechanisms:
Regulatory mechanisms
Although there is compelling evidence for the involvement of VGSCs in MCBs in
BCa, the mechanisms behind the observed VGSC upregulation are not clear yet. An inverse association between steroid hormone receptor and VGSC expression is apparent in both BCa and PCa. In strongly metastatic Mat-LyLu rat PCa cells, it was shown that serum had an important effect on VGSC activity, i.e. increasing serum concentrations decreased VGSC activity (Ding & Djamgoz, 2004). Another study, also on Mat-LyLu cells, demonstrated that nerve growth factor (NGF) upregulated functional VGSC expression. However, although NGF also increased cellular migration, this was not related to VGSC activity (Brackenbury & Djamgoz, 2007).
Treatment of Mat-LyLu cells with epidermal growth factor (EGF) increased both the
VGSC current density and enhanced cellular migration mainly through VGSC activity (Ding et al., 2008). In the case of BCa, a study on MDA-MB-231 BCa cells demonstrated the impact of insulin on VGSC expression/activity, which consequently had a profound effect upon VGSC-related MCBs (Pan & Djamgoz,
2008). VGSC (nNav1.5) activity in MDA-MB-231 cells was also shown to be upregulated by beta-estradiol (E2) non-genomically via GPR30 receptors (Fraser et al., 2010).
All these studies would emphasize the importance of several growth factors and hormones on VGSC expression/activity. Among these, is the insulin / insulin- like growth factor 1 (IGF-1) family. A relation between insulin/IGF-1 signalling and
VGSCs has been demonstrated in several studies (section 1.5.2)
1.4 Ion channel expression in blood mononuclear cells
45 Adult human leukocytes (white blood cells) comprise lymphocytes, monocytes
(macrophages), neutrophils, eosinophils and basophils. However, it is the subpopulation of mononuclear cells (MNCs) (primarly, lymphocytes and monocytes) that are most relevant to this Thesis since the Ficoll separation method used routinely to enrich CTCs also collects the MNC subpopulation (Ferrante & Thong, 1980).
Considerable work has been done on ion channel expression in blood cells, including
MNCs (Gardner, 1990; Gallin, 1991; Cahalan & Chandy, 2009; Varga et al., 2010).
The basic functions of MNCs, their relative proportions and associated ion channels are summarised in Table 1.2.
1.4.1 Lymphocytes
Lymphocytes comprise T-cells, B-cells and natural killer (NK) cells, which express a variety of ion channels (Table 1.2). T-cells commonly express Kv1.3 (a VGPC),
KCa3.1 (a Ca2+-activated intermediate-conductance K+ channel), TRPM7 (a Mg2+-
2+ + inhibited Ca permeable cation channel), TASK (a two-pore K channel) and Clswell
(a swelling-activated i.e. volume-regulated Cl- channel) (Cahalan & Chandy, 2009).
In addition, a small fraction (1 – 5 %) of normal human T-cells expresses a VGSC
(Cahalan et al., 1985). B-cells and NK cells generally express similar channels
(additional specific references: Gallin, 1991; Schlichter et al., 1986; Mandler et al.,
1990). In particular, Kv1.3 activity subserves a variety of immunological functions including proliferation, intracellular Ca2+ signalling, gene expression and motility
(Cahalan & Chandy, 2009). Consequently, Kv1.3 has considerable therapeutic potential in autoimmune disorders. Fraser et al. (2004) questioned whether VGSC activity would contribute to lymphocyte migration (e.g. during tumour infiltration),
46
andy, andy,
References Decoursey et al., 1984 al., et Decoursey 1984, al., et Matteson 1993, al., Grissmer et Ch & Cahalan 2009
1. 2. 3. 4.
swell expression TASK, Cl
Kv1.3, KCa, VGSC TRPM7, Kv1.3, KCa3.1, VGSC, Ion channel(s) expressed
Typical ion channel Typical
tion, cytokine cytokine tion, Functional role (basic) role Functional Antibody production, production, Antibody secretion, lymphokine of pathogen elimination and host cells infected cells tumour antigen Phagocytosis, presenta production
33 8 - - 4 (%) 25 leukocytes
Blood mononuclear cell (MNC) subtypes: cell Blood mononuclear
- 77 23
(%) MNCs Proportion (relative to) (relative Proportion
Table 1.2 Table
cells cells - - subtype Leukocyte Leukocyte T B NK cells Lymphocytes: Monocytes (macrophages)
47 similar to the invasive activity of metastatic cancer cells. Using Jurkat cells as a model, Fraser et al. (2004) indeed showed (i) that these cells expressed functional
VGSCs and (ii) that blocking VGSC activity with TTX significantly reduced
Matrigel invasion.
1.4.2 Macrophages
Macrophages are heterogeneous cells that respond to various stimuli from the microenvironment and serve different functions, including during inflammation, and can also infiltrate tumours (Murdoch et al., 2008). Ion channel expression in macrophages shares some similarities with lymphocytes and include the following:
Kv1.3, Kv1.5 (as possible heterotetramers), KCa3.1, HVCN1 (voltage-gated H+ channel) and VGSC (Roselli et al., 2006). In particular, Kv expression has been shown to have a significant involvement in macrophage proliferation and activation
(Villalonga et al., 2007). As regards the VGSC, Carrithers et al. (2007) showed that
Nav1.5 expression is intracellular and channel activity regulates endosomal acidification, in turn, facilitating phagocytosis.
1.4.3 Plasticity of ion channel expression in mononuclear cells
Importantly, ion channel expression in cells of blood is not ‘static’ but changes depending upon functional state (activation, secondary differentiation etc) (Cahalan
& Chandy, 2009) (Figure 1.9). For example, during B-cell differentiation from naïve to memory cells, the KCa channel expression increased 45-fold whilst there was no change in Kv1.3 expression (Wulff et al., 2004), A remarkable case of such change has been reported in human dendritic cells where the relative VGSC : VGPC expression was as follows:
48
Figure 1.9 – Changes in K+ channel expression in lymphocytes during activation. The average number of functional Kv1.3 (red) and KCa3.1 (blue) channels in specific subsets of T or B cells, isolated from human peripheral blood, is presented. Single-cell patch clamp studies were conducted and numbers of functional channels in each specific cell were determined by dividing the total current with the single-channel conductance for each channel type (Kv1.3or KCa3.1). A. Naive, central memory (TCM) and effector memory (TEM) T cells were studied following visualizing them by fluorescence microscopy. Additionally, patch clamp recordings were taken from activated cells belonging to each of these subsets which were stimulated for 48 h with anti-CD3 or PMA (phorbol 12-myristate 13-acetate) + ionomycin. Both types of K+ channel expression patterns were similar in all quiescent cell subsets, whereas following activation naive effector and TCM effector cells upregulated the KCa3.1 channel and TEM effectors upregulated Kv1.3 channel expression. B. Different subsets of B cells studied involved (i) IgD+CD27−: naïve, (ii) IgD+CD27+: early memory, and (iii) IgD−CD27+: class-switched late memory. Single-cell patch clamp experiments revealed differential expression profiles of K+ channels in quiescent and activated subsets of these cells. Modified from Cahalan & Chandy (2009).
49 Immature (pre-inflammatory): Nav1.7 & Kv1.3
Mature (differentiated / post-inflammatory): Nav1.7↓ & Kv1.3↑
(Zsiros et al., 2009). Interestingly, such a switch parallels the CELEX hypothesis which proposes that change in VGSC : VGPC expression is significant to metastatic progression by controlling the membrane ‘excitability’ and ‘hyperactivity’ of strongly metastatic cells (Djamgoz & Isbilen, 2006; Djamgoz, 2011).
1.5 Insulin and insulin-like growth factor 1 system
Insulin is well known for its role in regulating plasma glucose levels. In response to an elevation in circulating glucose, insulin is secreted from pancreatic β-cells and stimulates glucose uptake in target tissues while inhibiting glucose release from liver
(Scheen & Lefebvre, 1996). In terms of structural and functional homology, insulin is closely related to insulin-like growth factors 1 and 2 (IGF1 and IGF2). IGFs along with their receptors (IGF1R and IGF2R) exert mitogenic effects and are involved in many metabolic and growth-related activities throughout the body. IGF1 is produced mainly in liver and also in some other somatic cells, and its production is controlled primarily by growth hormone and insulin. On the other hand, IGF2 production occurs in an autocrine / paracrine manner and its expression is highest during foetal development (Pavelić et al., 2007). Insulin-like growth factor binding proteins
(IGFBP-1 to IGFBP-6), are responsible for modulating the action and controlling the bioavailability of IGFs (Scheen & Lefebvre, 1996; Holly & Perks, 2006).
InsR and IGF1R are closely related and share a high degree of homology at gene and amino acid levels (Ullrich et al., 1986). Both are receptor tyrosine kinases
50 (RTKs) and contain two α and two β chains, the transmembrane β subunits possessing the catalytic kinase activity (De Meyts & Whittaker, 2002). Following ligand binding to an extracellular site on the α subunits, these receptors are trans- phosphorylated, which in turn triggers the tyrosine kinase activity. Subsequently, this initiates an intracellular signalling cascade involving phosphorylation of several docking proteins, including InsR substrates (IRS-1 to 4) and Src homology collagen
(Shc) (Riedemann & Macaulay, 2006; Youngren, 2007). InsR can occur in two alternatively spliced isoforms, InsR-A and InsR-B. InsR-A is the result of exon 11 skipping and is the predominant isoform in foetal life (Frasca et al., 1999). When co- expressed in the same cell, it is possible for InsR and IGF1R to form hybrid receptors
(HRs) as a consequence of random assembly, in which case an IGF1R α/β chain heterodimerizes with an InsR-A or InsR-B α/β chain (HR-A or HR-B, respectively)
(Figure 1.10 and Figure 1.11) (Soos et al., 1990). The final receptor in the family, the
IGF2R (also known as cation-independent mannose-6-phosphate receptor), is significantly different from InsR and IGF1R in terms of both its structure and function. There is no known intracellular signalling activity of IGF2R and it is thought to be responsible for the clearance of IGF2, hence, for the regulation of its bioavailability. Thus, the activities of IGFs are mainly through IGF1R (Pavelić et al.,
2007). Interestingly, IGF2 can also bind to InsR-A (but not InsR-B) and exert IGF- like mitogenic effects (Denley et al., 2005). It is indeed a general observation for all the other members of the insulin and IGF family that they could be co-expressed in cells both in vivo and in vitro, and overlap in their receptor assembly and ligand binding (e.g. Belfiore & Frasca, 2008; Gallagher & LeRoith, 2010). It is one of the assumptions of the present study (Chapter 3) that the insulin and IGF1 receptors function as one integrated system. This is referred throughout the report as the
51
Figure 1.10 – Involvement of insulin-IGF family in BCa pathogenesis. Upregulation of InsR, IGF1R and their ligands, and also changes in InsR expression patterns are likely to contribute together to BCa development and progression. Different receptors have different affinities to ligands as represented in this figure. The present study proposes that the complex of the receptors and their ligands can be considered as one system, namely the “InsR-IGF1R system”, indicated with the dashed red box. Modified from Frasca et al. (2003, 2008).
52
Figure 1.11 – The members of IIS and related signalling molecules. Three ligands of IIS (insulin, IGF-1 and IGF-2) exert their biological effects through IGF1R and InsR, whilst IGF2R does not initiate any downstream signalling. Instead, IGF2R is responsible for the clearance IGF2. IGFBPs are also control the action of IGFs and their bioavailability. The intracellular signalling cascades involve two main pathways: PI3K (left-hand side) and MAPK (right-hand side), giving rise to metabolic and growth effects, respectively. Some overlap in the cascades is possible (dotted lines).
53 “InsR-IGF1R system” or “IIS” which is also applicable to pathological conditions, including BCa (Figure 1.10).
IIS is associated with highly complex and interactive downstream intracellular signalling cascades (Gallagher & LeRoith, 2010). It has been proposed that there are two main functions associated with IIS signalling: “metabolism” and
“growth” (Figure 1.10). The pathway involving PI3K controls “metabolism” (e.g. protein synthesis, transport, glycogen synthesis, lipolysis). On the other hand, the pathway incorporating MAPK is more concerned with “growth” (including proliferation and apoptosis). It is likely, however, that the two pathways are interactive and additional signalling components, such as protein kinase A (PKA), may be involved (e.g. Palacios et al., 2007).
Importantly, the constitution of IIS and its functional association with other secondary signalling mechanisms are not static. Thus, plasticity of IIS can occur at two levels. In primary plasticity, a change in the level of expression / activity of one component of IIS can significantly affect another. For example, downregulating
IGF1R was shown to result in increased sensitivity of BCa cells to insulin (Zhang et al., 2007). Similarly, when InsR-A was knocked-down, IGF1R activation by IGF1 and IGF2 was enhanced due to increased density of IGF1R homodimer expression
(Brierley et al, 2010). Secondary plasticity may involve interactions between the main IIS and a primary signalling mechanism, such as a steroid hormone, e.g. estrogen (Varea et al., 2010).
1.5.1 IIS and cancer
A role for the IIS has been well documented in formation and progression of several malignancies and various studies have proposed it as a target for potential cancer
54 therapy (Pollak et al., 2004; Hartog et al., 2007; Tao et al., 2007; Frasca et al., 2008).
Considering the fact that the specific ligands of InsR and IGF1R can stimulate each others’ receptors at supra-physiological levels, excess availability of ligands and receptors is likely to have magnified effects on tumour biology. It is also expected that the presence of foetal InsR-A isoform, which is associated with enhanced mitogenic signalling, could contribute to the propagation of malignancy (Figure
1.10) (Frasca et al., 2003; Sachdev & Yee, 2007).
There is considerable evidence for involvement of the IIS in breast malignancies (Perks & Holy, 2003; Yee, 2006; Lann & LeRoith, 2008). Thus, in vitro and in vivo experiments and epidemiological studies indicated (i) over- expression of IGF1R, InsR (with a predominance of InsR-A) and HR-A, and (ii) increased expression of IGF1 and IGF2 (Frasca et al., 2003; Sachdev & Yee, 2007).
A growing body of evidence indicates that high insulin levels are associated with increased risk, poor outcome and mortality in BCa (Goodwin, 2008). Moreover, studies focusing on gene expression profiling revealed a relationship between IGF1 signature and more aggressive human breast tumours (Creighton et al., 2008).
It has been suggested that hyperinsulinemia may influence BCa biology through direct effects or indirectly by increasing circulating concentrations of oestrogens and IGFs (Kaaks, 1996; Kazer, 1995). Additionally, experiments on BCa cell lines demonstrated that the downstream signalling molecules of insulin / IGF1 family, IRS-1 and IRS-2, were associated with different malignant phenotypes.
Whilst activation of IRS-1 was linked to cell proliferation and survival, IRS-2 was associated with cell migration and metastasis (Byron et al., 2006; Chan & Lee,
2008).
There is much evidence suggesting strongly that diabetes and cancer are
55 connected (e.g. Giovannucci et al., 2010). Several epidemiological studies, including meta analyses, have shown that type II diabetes can increase BCa risk and can lead to increased mortality (e.g. Peairs et al., 2011). Indeed, both insulin and IGF1 signalling play a significant role in BCa development and progression (e.g. Lann & LeRoith,
2008). However, importantly, in an interesting, recent large-scale population study,
Spurdle et al. (2011) showed that an endometrial cancer susceptibility locus close to
HNF1B is also associated with risk of PCa and is inversely associated with risk of type 2 diabetes. This study is significant for two reasons, at least: First, it demonstrates a link between cancer and diabetes at gene level but, second, the nature of the association is negative. Thus, it would appear that the cancer-diabetes link is more complex than may first appear and would be worthy of further investigation.
1.5.2 IIS and VGSC
There is some evidence for a connection between VGSCs and IIS. One study on
MDA-MB-231 cells demonstrated the impact of insulin on VGSC expression/activity, which consequently had a profound effect upon VGSC-related
MCBs (Pan & Djamgoz, 2008). A relation between InsR-IGF1R signalling and
VGSCs in various systems has also previously been demonstrated. When cultured bovine adrenal chromaffin cells were treated with Insulin, an upregulation in functional VGSC expression at plasma membrane was observed (Yamamoto et al.,
1996). Studies with Xenopus laevis oocytes showed that insulin could facilitate the induction of the Na+ channels in the plasma membrane in a dose-dependent manner
(Charpentier, 2005). Insulin is also recognised to regulate expression of several genes, including VGSCs, which play a role in development and maintenance of the nervous system (de Pablo & de la Rosa, 1995; Yamamoto et al., 1996). Moreover,
56 IGF1 was shown to upregulate the functional expression of VGSCs on cerebellar granule cells where it also acts as a trophic factor (Calissano et al., 1993).
Another characteristic of IIS, which may further broaden its functional dynamics, is the possible reciprocity of the interaction with ion channels. Essentially, as well as IIS regulating ion channel expression/activity, the latter can influence IIS by controlling the release of insulin and/or IGFs. For example, a study on mice demonstrates that a VGSC (β1-subunit) channelopathy decreased glucose-stimulated insulin (and glucagon) secretion from pancreas (Ernst et al., 2009).
Considering the available evidence indicating (i) the functional relationship between VGSCs and BCa, (ii) the involvement of InsR-IGF1R system in BCa and
(iii) the connection between VGSCs and InsR-IGF1R system, the present study aimed to investigate the possible triangular association: IIS – BCa - VGSC (section
1.6).
1.5.3 Metformin
The biguanide derivative metformin (1,1-dimethylbiguanide hydrochloride -
C4H11N5), also referred to as an “insulin sensitizer”, is an oral anti-hyperglycaemic drug and is widely used for the treatment of type 2 diabetes (T2D) (Nathan et al.,
2009). Although the exact mechanism(s) of action is yet to be determined, it is generally accepted that metformin exerts its effects overall mainly by (i) reducing gluconeogenesis in liver and (ii) improving insulin sensitivity in periphery.
Metformin also suppresses lipolysis in adipocytes and increases glucose uptake in muscle. As a result, blood glucose and insulin levels decrease without causing hypoglycemia or major side-effects (Viollet et al., 2012; Jalving et al., 2010). At the molecular level, activation of adenosine monophosphate-activated protein kinase
57 (AMPK) is considered to be the main mediator of metformin effects (Zhou et al.,
2001; Musi et al., 2002). However, the activation of AMPK by metformin is not via direct phosphorylation of the enzyme but through inhibition of complex I of the mitochondrial electron transport chain (El-Mir et al., 2000; Brunmair et al., 2004).
As a result of impaired mitochondrial activity, AMP:ATP ratio increases which in turn activates the AMPK (Cheung et al., 2000). A recent study has shown that metformin-mediated AMPK activation can also occur through inhibition of AMP deaminase (Ouyang et al., 2011).
In addition to being a well-known medication for T2D, efficacy of metformin is being recognised for managing polycystic ovary syndrome (PCOS) and non- alcoholic fatty liver disease (NAFLD) (Diamanti-Kandarakis et al., 2010; Mazza et al., 2012). Finally, studies are increasingly suggesting that metformin could also be beneficial in prevention and/or treatment of various cancers (Pierotti et al., 2012).
Epidemiological studies have indeed shown that metformin reduces (i) risk of developing cancers and (ii) rate of cancer-related mortality. This is supported by accumulating in vivo and in vitro evidence (e.g Bost et al., 2012; Dowling et al.,
2012).
1.5.3.1 Metformin and cancer
As regards its anti-cancer effects, metformin is generally considered to have both
‘direct’ and ‘indirect’ actions (Figure 1.12). Indirect effects are linked mainly to reduction of circulating insulin levels, which consequently results in decreased IIS signalling. Direct effects, on the other hand, are thought to occur mainly through
AMPK activation in cancer cells (Dowling et al., 2011). The main subsequent step is inhibition of the “mammalian target of rapamycin complex 1” (mTORC1) pathway,
58
Figure 1.12 – Metformin action in cancer. There are 2 basic pathways involved in cellular anti-cancer effects of metformin: 1) Indirect: These effects arise from the reduction of circulating insulin levels by metformin, which in turn reduce receptor tyrosine kinase activation and down-stream signalling mechanisms. 2) Direct: This involves activation of AMPK and/or inhibition of mTORC1. Modified from Dowling et al. (2011).
59 which, in turn, leads to suppression of protein synthesis and cell proliferation
(Zakikhani et al., 2006; Dowling et al., 2007). However, metformin can also inhibit the mTORC1 pathway independently of AMPK, through Rag GTPases or REDD1
(Kalender et al., 2010; Ben Sahra et al., 2011). Additionally, several studies show evidence for anti-proliferative effects of metformin through various other pathways, including cyclin D1 downregulation and p53 activation (Ben Sahra et al., 2008;
Zhuang & Miskimins, 2008; Buzzai et al., 2007). Interestingly, cancer stem cells
(CSCs) demonstrate higher sensitivity to metformin-induced cytotoxity compared to non-CSCs (Hirsch et al., 2009; Song et al., 2012).
Importantly, metformin can also have anti-invasive effects. Studies showed that metformin supresses invasion in some cancer cell lines through inhibition of matrix metalloproteinases (MMPs) (Hwang & Jeong, 2010). Additionally, it was demonstrated that NF-κB, Akt and Erk(1/2) pathways could be involved together with MMP-2/9 in metformin induced anti-invasive effects (Tan et al., 2011).
Interestingly, NF-κB and Erk1/2/Erk5 pathways also were shown to mediate suppression of angiogenesis by metformin (Tan et al., 2009).
1.5.3.2 Metformin and ion channels
There is increasing evidence that at least some of the potent anti-cancer effects of metformin may be independent of its hypoglycaemic actions (Kourelis & Siegel,
2011). In this regard, it is of interest that AMPK can affect epithelial cell ion channels. Activation of AMPK by metformin inhibited epithelial sodium channels
(ENaCs) on polarized mouse collecting duct cells (Carattino et al., 2005). Another study on the same cell type showed that KCNQ1 (VGPC) currents also were inhibited by metformin-induced AMPK activation (Alzamora et al., 2010). Both of
60 these effects were thought to be mediated by Nedd4-2-dependent ENaC endocytosis, a pathway known to control functional ENaC expression (Snyder et al., 1994).
Furthermore, downregulation of K+ channel expression / activity in rat ventricular myocytes, isolated from an animal model of hyperinsulinemia and insulin resistance, was shown to be rescued by metformin (Shimoni et al., 2000). Interestingly, in the case of cloned KATP / Kir 6.0 channels expressed in cell lines, phenformin (another biguanide) had a direct inhibitory effect (Aziz et al., 2010). The possible effects of metformin on other ion channels remain to be investigated, in both normal and cancer cells.
1.6 Aims, hypotheses and scope
The general aim of this Thesis was to undertake a series of inter-related studies with a view to improving our understanding of the role of ion channel expression and regulation in cancer cells. The three central topics of the investigations were the following: MCB (BCa, CRCa), VGSC (nNav1.5) and IIS (Figure 1.13). Mainly in vitro approaches have been used but human blood has also been evaluated in the context of “cancer-associated ion channel” (CAIC) expression, in order to gain insights to the in vivo potential of the findings. The scope adopted ranged from quantitative molecular characterization and manipulation to functional assay of cancer cell behaviour. There were two main hypotheses, as follows:
1. VGSC activity potentiates MCBs.
2. Mechanisms or drugs that affect MCBs would also similarly influence
VGSC / CAIC expression/activity (and vice versa).
61 There are 4 chapters of results each stating its own specific aims and rationale.
Chapter 3 (Results-1) proposes to treat IIS as an integrated signalling system and to use two complementary methods to inhibit it in strongly metastatic human
BCa MDA-MB-231 cells.: Pharmacological and siRNA. Two main questions were addressed: First, what are functional consequences of suppressing IIS and, second, does IIS signalling affect VGSC expression / activity?
Chapter 4 (Results-2) is in two parts. The first part describes studies on expression of CAIC mRNAs in blood of normal human subjects with a view to laying the foundations for subsequent patient-based studies that follow in the second part.
Chapter 5 (Results-3), also in two main parts, aims to complement Results-1 and Results-2. The first part aims to determine the in vitro effects of the diabetes drug metformin on MCBs and nNav1.5 expression in MDA-MB-231 cells. The second main part aims to extending the in vitro and the blood-based studies to diabetic patients.
Chapter 6 (Results-4) aims (i) to test whether nNav1.5 (mRNA and protein) expression occurs in human CRCa SW-620 cells, (ii) to determine if channel’s activity contributes to these cells’ invasiveness and (iii) to examine nNav1.5 protein expression in human CRCa biopsy tissues.
62
Figure 1.13 – Conceptual framework of the present study. Three areas of research are integrated: Cancer (BCa and CRCa), ion channels / cancer-associated ion channels (CAICs, mainly VGSC / nNav1.5), and IIS. Within each area, there are other common parts, such as use of the diabetes drug metformin in studies on metastatic cell behaviour.
63
Chapter 2
MATERIALS AND METHODS
64
2.1 Cell culture
2.1.1 Cell lines and basic maintenance
All experiments described in Chapter 3 and 5 were conducted on MDA-MB-231 cells, which are strongly metastatic human breast cancer (BCa) cells derived from a metastatic pleural effusion (Cailleau et al., 1974). In Chapter 6, a human colorectal adenocarcinoma cell line, SW620 was used. The latter is metastatic, derived originally from lymph node tissue of a patient (Leibovitz et al., 1976).
MDA-MB-231 cells were cultured in Dulbecco’s Modified Eagle’s Medium
(DMEM) supplemented with 4 mM L-glutamine and 5 % foetal bovine serum (FBS)
(Gibco). The growth medium for SW620 cells was prepared with 10 % FBS and 4 mM L-glutamine in Roswell Park Memorial Institute formulation 1640 (RPMI 1640) medium (Gibco). All cultured cells were kept in a humidified incubator at 37 oC, 100
% relative humidity and 5 % CO2 (e.g. Fraser et al., 2005). For routine maintenance,
100 mm tissue culture dishes (Falcon) were utilized for plating the cells.
Cells were grown until they reached ~ 80 % confluence and the culture medium was renewed every 2-3 days. Confluent dishes were either used for initiating experiments or passaged further. For all experiments, a maximum passage number of
25 was used. Whilst MDA-MB-231 cells could be passaged by simple trituration, use of Typsin-EDTA (GIBCO) was necessary to detach SW620 cells from the dish. All manipulations with cells were performed in a biological safety cabinet.
2.1.2 Pharmacological agents and treatments
All pharmacological agents were dissolved in the appropriate solvents, stored at -20 oC until needed and freshly diluted with growth medium to desired concentration at
65 the time of treatment. Tetrodotoxin (TTX) (Alomone), the specific blocker of voltage-gated sodium channels (VGSCs) was prepared as a stock solution of 3132
µM in DMEM. For treatments, 5-10 µM final concentrations were mostly used. The insulin receptor / insulin-like growth factor 1 receptor (InsR / IGF1R) tyrosine kinase inhibitor, AG1024 (Sigma), was prepared as a stock solution of 10 mM in dimethyl sulfoxide (DMSO) (Sigma). For treatments, dilutions were prepared in the range 1 –
20 µM. The final concentrations of DMSO were between 0.01 and 0.02 %. A more recently developed InsR / IGF1R tyrosine kinase inhibitor, OSI-906 (Active
Biochemicals Co.) was also used. It was made up in DMSO as a stock solution of
100 mM; working concentrations ranged from 0.1 to 5 µM (the corresponding
DMSO concentrations were between 0.0001 – 0.005 %). The oral anti-diabetic drug metformin (1,1-Dimethylbiguanide hydrochloride; Sigma) was made up in sterile water as a stock solution of 1 M and used mostly at working concentrations up to 2 mM. Serum deprivation experiments were conducted with serum-free medium which consisted of DMEM with 4 mM L-glutamine and no FBS. Insulin (Sigma) was made up as a stock solution of 174.4 µM (10 mg/ml) in acidified water (1 % glacial acetic acid in water). For treatments of cells, further dilutions were prepared with serum- free medium (as above) supplemented with 0.1 % bovine serum albumin (BSA)
(Sigma) (Pan & Djamgoz, 2008).
2.1.3 Cellular toxicity
Trypan blue exclusion assay was used to assess basic cellular viability that may be affected by the various pharmacological treatments (Fraser et al., 1999). Cells were plated into 35 mm dishes (Falcon) at a density of 3 x 104 cells per dish. After allowing the cells to settle and ‘equilibrate’ for 24 hours, medium was replaced with
66
1 ml of fresh medium containing the required concentration of the agent to be tested.
Over the time course of the experiment, the medium in each dish was refreshed every
24 hours. At the end of the treatment period, the medium was carefully removed and the trypan blue solution (250 µl trypan blue and 800 µl medium) was added into each dish. After incubating the dishes at 37 oC for 10 min, the trypan blue solution was replaced with 1 ml of fresh medium. For each dish, dead cells (blue-coloured) and living cells (clear) were counted under the microscope in 30 randomly chosen fields of view. Cell viability (CV) was calculated as the percentage of live cells using the following equation:
CV (%) = (NV / NT) x 100 Equation 2.1 where NV is the total number of viable cells and NT is the total cell number (viable and dead) assessed from a given dish.
2.2 Functional assays
A number of functional assays were carried out on the cell lines of interest. The essentials of these and the appropriate calibrations are described in this section.
2.2.1 Measurement of cell number
Possible change in cell number during treatments was quantified using a colorimetric
3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) assay (Grimes et al., 1995). Cells were seeded in 12-well plates at a density of 3 x 104 cells per well in 1 ml of growth medium or in 24-well plates at a density of 1.5 x 104 cells per well in 0.5 ml of growth medium (treatment plates). Within each repeat one more separate plate was also set-up to measure the original cell number at the time of starting the
67 treatments (control plate). After allowing them to settle for 24 hours, cells were treated with medium containing the required concentration of agent to be tested. At the same time as the treatments started, the control plate was subjected to MTT assay. Over the time course of the experiment, the medium in each well of the treatment plates was refreshed every 24 hours. At the end of the treatment period, the medium was carefully removed and MTT solution (5 mg/ml stock in growth medium) diluted with fresh medium (1:5) was added into each well. After incubating the plates at 37 °C for 3 hours, the solution was carefully removed and replaced with glycine buffer (0.1 M glycine, 0.1 M NaCl, adjusted to pH 10.5) diluted with DMSO
(1:9). After 15 min, the absorbance at 570 nm, which represents the number of viable cells, was measured using a spectrophotometer. The absorbance corresponding to the treatment was normalized to the absorbance of control for data analysis. In order to confirm the quality and linearity of the MTT assay, a calibration curve covering the range of 0 – 12 x 104 cells was generated by serial dilutions (Figure 2.1). All experiments involved cell numbers in the linear range of the calibration.
2.2.2 Lateral motility
Lateral motility was assessed using culture wounding assays (e.g. Fraser et al., 2003), for which 5 x 105 cells were plated into 35 mm dishes. After allowing the cells to settle for 24 hours and to form a homogeneous monolayer, 3 wounds of 0.5 – 0.8 mm were made per dish using sterile 200 µl pipette tips. The dishes were washed three times with fresh serum-free DMEM to get rid of detached cells and then 1 ml of medium containing the required concentration of the agent to be tested was added.
Initial wound widths were immediately measured at 45 points for each dish via a graticule on an inverted microscope. Fixed positions of these points were created by
68
Figure 2.1 – Example of typical calibration curve of MTT assay. The MDA-MB- 231 cells were plated in 12-well plates at different known cells densities (0, 1.5, 3, 6 and 12 x 104) and were let to settle down for 6 hours. After incubation with MTT for 3 hours, absorbance was measured at 570 nm using a spectrophotometer. Data were fitted using linear regression analysis (solid line) and each data point represents mean ± SEM (n = 3).
69 drawing 3 vertical lines crossing 15 horizontal lines under the dishes before plating the cells. Wounds followed the 3 horizontal lines and each inter-section with vertical lines gave a fixed measuring point with a total of 45 points. Dishes were placed into the incubator and same 45 fixed points were re-measured after 24 and 48 hours with a refreshment of the medium at 24 hours. Cellular motility was calculated as the
“motility index” (MoI), using the following equation:
MoI = 1 – (Wt/W0) Equation 2.2 where Wt represents the width of the wound at either 24 or 48 hours and W0 is the initial wound width. MoI of 1 indicated the full closure of the wound, whereas MoI of 0 meant no closure. Representative images of a wound at 0 and 24 hours are shown in Figure 2.2A&B.
2.2.3 Transverse migration
Transverse migration was assessed using Transwell filters with some modifications
(Grimes et al., 1995; Fraser et al., 2005). Cells were seeded onto 8 µm pore
Transwell filters with polyethylene terephthalate track-etched membrane (BD) in 24- well format at an optimal density of 1.5 x 104 cells per insert. A chemotactic gradient was created by adding (i) 1 % FBS containing medium (+ treatments) into the inserts with cells (‘upper chamber’) and (ii) 5 % FBS containing growth medium (+ treatments) into the bottom wells. Then, cells were allowed to migrate overnight.
Assay was stopped by removing the non-migrated cells from the upper side of the filters and staining the migrated cells on the other side with crystal violet (Figure
2.2). The stained cells were easily visualized and counted under a microscope.
70
A B
C
Figure 2.2 – Assays of metastatic cell behaviours. (A) & (B) Representative images of cells in a ‘culture wounding’ assay. Whilst (A) is taken at 0 hour, (B) visualizes the wound at 24 hours. (C) A typical view from an insert stained with crystal violet.
71
2.2.4 Matrigel invasion
Tumour cell invasiveness was measured using Transwell filters with 8 µm pore size in 24-well format as described in section 2.2.3. Different to the transverse migration assay, filters were coated with 50 µl diluted Matrigel (BD). For every experiment,
Matrigel was freshly diluted from a stock solution of 10 mg/ml with serum-free medium. After optimisation for individual cell lines, the working concentration of
Matrigel was determined as (i) 1.25 mg/ml for MDA-MB-231 cells and (ii) 0.21 mg/ml for SW620 cells. Coating was done one day prior to the experiment and coated inserts were left in the incubator over-night. On the day of the experiment, coated inserts were hydrated with serum-free medium for 2 to 3 hours in the incubator before seeding the cells onto them. Similar to the transverse migration assays, a chemotactic gradient was generated by adding media (+ treatments) with lower FBS concentration into the inserts with cells (‘upper chamber’) and with higher FBS concentration into the bottom of the wells. The chemotactic gradient was
(i) 1-5% FBS for MDA-MB-231 cell and (ii) 0.1-10% FBS for SW620 cells. Cells were seeded very carefully, without disturbing the Matrigel layer, onto each filter at a density of (i) 2x104 cells for MDA-MB-231 cells and (ii) 105 cells for SW620 cells.
Whilst MDA-MB-231 cells were allowed to invade for 24 hours, invasion of SW620 cells lasted for 48 hours and the latter were also serum starved for 24 hours prior to seeding. At the end of invasion, inserts were stained with crystal violet and invaded cells were counted under a microscope.
2.2.5 Crystal violet staining
At the end of transverse migration and Matrigel invasion assays, the cells of interest were visualized by crystal violet staining (Figure 2.2C). Initially, non-
72 migrated/invaded cells were removed from the upper side of the filters by swabbing with cotton buds. Then, inserts were placed into clean wells with ice-cold 100 % methanol for 15 min and were thereby fixed. Immediately after fixation, inserts were moved into clean wells with crystal violet (0.5 g/ml in 0.25 % methanol) for further
15 min. Finally, crystal violet was washed away gently with distilled water and inserts were let dry at room temperature before counting. Stained cells from randomly selected 12 fields of view on each insert were counted using an Axiovert
2000 (Zeiss) microscope under x40 magnification.
2.3 Molecular biology
Several procedures of molecular biology were used in the present study. These are described in this section.
2.3.1 RNA extraction
Total RNA was extracted with TRIzol reagent (Invitrogen) directly from a dish of cultured cells (plating density: 2 x 105 cells per 35 mm dish) (Chomczynski &
Sacchi, 2006). The extraction was performed according to the manufacturer's instructions with some modifications. Briefly, 1 ml of TRIzol was added directly to the dish of cells. The mixture was homogenized thoroughly by repetitive pipetting and incubated at room temperature for 5 min. The homogenate was then transferred to an Eppendorf tube and for every 1 ml of TRIzol used 0.2 ml of chloroform was added. The tube was shaken vigorously by hand for approximately 15 seconds and left at room temperature for 2 to 3 min. The mixture was then centrifuged at 10000 g at 4 oC for 15 min, which resulted in separation into three phases: a lower red phenol- chloroform phase, an interphase, and a colourless upper aqueous phase. RNA would 73 reside in the upper aqueous phase, whereas DNA and protein remained in the interphase and the lower phenol-chloroform phase. The upper aqueous phase containing the RNA was transferred to a clean tube carefully using a pipette and mixed with 0.5 ml of isopropyl-alcohol for every 1 ml of TRIzol used, in order to precipitate the RNA. The samples were incubated at -20 oC for one hour. Afterwards, the mixture was centrifuged at 12000 g at 4 oC for 15 min to obtain the RNA pellet.
At the end of the centrifugation, the supernatant was removed carefully and the RNA pellet was washed twice by adding 1 ml of 75 % ethanol and then centrifuging at
7500 g at 4 oC for 5 min. Finally, after removing the supernatant again, the pellet was air-dried for 10 min and dissolved in 30 µl of sterile RNAse/DNAse-free water
(Invitrogen). The RNA quantity and quality were determined by spectrophotometry
(NanoDrop ND-1000 Spectrophotometer, Labtech) at 260 and 230-280 nm, respectively. RNA integrity was also assessed by electrophoresis on a 1 % agarose gel (Figure 2.3) and stored at -40 oC.
2.3.2 cDNA synthesis
For each sample, one tube for cDNA and one tube for (-)RT control were prepared.
In each tube, 2.5 µl of 100 ng/µl R6 pd(N)6 random hexamer mix (Amersham
Biosciences) was mixed with 1 µg RNA in a total reaction volume of 11 µl with sterile RNAse/DNAse-free water (Invitrogen) and the tubes were placed at 70 oC for
10 min. Immediately at the end of the incubation, the tubes were placed on ice for 2 min and then the following mix was added to each sample: 2 µl of 0.1 M dithiothreitol (DTT) (Invitrogen), 4 µl of 5X Superscript II first strand buffer
(Invitrogen), 1 µl of 10 mM dNTP mix (Amersham Biosciences) and 1 µl of 33800
U/ml porcine RNA guard (Amersham Biosciences). After 2 min incubation at room
74
28S
18S
5S
Figure 2.3 – The typical electrophoretic pattern of isolated RNAs. RNA quality was assessed by running 2 µg of RNA sample on 1 % agarose gel stained with SYBR safe (Invitrogen) at 100 mV for 30 min. Under ultraviolet radiation a more intensely illuminating 28S than 18S ribosomal RNA (rRNA) band was visualised. With this isolation procedure it is expected to obtain the whole spectrum of RNA molecules, including small (4S to 5S) RNAs. Besides the prominent 28S, 18S and 5S ribosomal RNAs, mRNAs of different sizes were also resolved on the agarose gel as a faint smear.
75 temperature, 1 µl of Superscript II RT enzyme (200 U/µl) (Invitrogen) was added to the reaction mix in the cDNA tube and allowed to settle again for 10 min at room temperature. At this step, into the (-)RT control tube sterile RNase/DNAse-free water was added instead of Superscript II RT enzyme. Then, the tubes were incubated at 42 oC for 90 min. Finally, the reaction was stopped by incubating the tubes at 70 oC for
15 min. In order to use a more convenient volume for subsequent applications, the reaction mix (performed in a total volume of 20 µl) was made up to 100 µl with sterile RNAse/DNAse-free water once the reverse-transcription reaction was complete. ThecDNA samples were stored at -40 °C and subjected either to conventional or quantitative real-time polymerase chain reaction (PCR).
2.3.3 Conventional PCR
Conventional PCRs were performed using HotStarTaq Plus technology (Qiagen) and the Primus PCR machine (MWG-Biotech). The PCR master mix contained 10 µl of
HotStarTaq Plus master mix (Qiagen), 2 µl of Coral Load (Qiagen), 1 µl of 10 µM forward primer, 1 µl of 10 µM reverse primer, 1 µl of sterile RNAse/DNAse-free water (Invitrogen) and 5 µl of cDNA. In each PCR set, a negative control containing sterile RNase/DNAse-free water instead of cDNA was also included.
All new primers (Invitrogen) were tested initially with conventional PCR before utilizing them for real-time PCR. Primers were specifically chosen or optimised to work at equal annealing temperatures. A typical PCR protocol consisted of an initial activation at 95 oC for 5 min followed by 35-40 cycles of 3 steps including 94 oC for 30 sec, 60 oC for 30 sec and 72 oC for 30 sec. A final extension at
72 °C for 5 min completed the reaction. The resulting PCR products were visualised by running on 1 % agarose gel. The sequences of all primers are given in Table 2.1.
76
Table 2.1 – The primers used for PCR amplifications. The forward and reverse primer sequences and the reference publications are shown in the table.
Primer pair Sequence (5'à 3') Reference
IGF1R (F) ATGCTGTTTGAACTGATGCGCA Narita et al. IGF1R (R) CCGCTCGTTCTTGCGGCCCCCG (2008)
Total-InsR (F) ACCTGCACCACAAATGCAAGA Pandini et al. Total-InsR (R) AGACGTCACCGAGTCGATGGT (2002)
InsR-A/B (F) AACCAGAGTGAGTATGAGGAT Kolb et al. InsR-A/B (R) CCGTTCCAGAGCGAAGTGCTT (2007)
nNav1.5 (F) CTGCACGCGTTCACTTTCCT Diss, J. K. J. nNav1.5 (R) GACAAATTGCCTAGTTTTATATTT (unpublished)
Beta1 (F) GTCGTCAAGAAGATCCACATTGAGGTAGTGG Isom L Beta1 (R) TTCGGCCACCTGGACGCCCGTGCAGTTCTC (unpublished)
Beta1b (F) GTCGTCAAGAAGATCCACATTGAGGTAGTGG Isom L Beta1b (R) AACCACACCCCGAGAAACACATCGGA (unpublished)
Kv1.3 (F) GGGAGCTGGGATTGCTCATCTTCTT Ohya et al. Kv1.3 (R) GATGCTGCTGAAACCTGAAGTGG (2009) modified
Kv10.1 (F) CACGTTTCTGGAGAATATTGTTCG Patt et al. Kv10.1 (R) TATCAGTCAGCTCCCCATACATA (2004)
Kv11.1 (F) AGATGCTGCGGGTGCGG Patt et al. Kv11.1 (R) CGAAGGCAGCCCTTGGTG (2004)
KCa3.1 (F) CGCCAGGTACGGCTGAAAC Mazzuca et al. KCa3.1 (R) GCAGGTCGCACAGGATCAT (2010) TRPM8 (F) ATTCCGTTCGGTCATCTACG Bidaux et al. TRPM8 (R) CACACACAGTGGCTTGGACT (2007)
PUM1 (F) TGAGGTGTGCACCATGAAC Szabo et al. PUM1 (R) CAGAATGTGCTTGCCATAGG (2004)
CYB5R3 (F) TATACACCCATCTCCAGCGA Ding et al. CYB5R3 (R) CATCTCCTCATTCACGAAGC (2008)
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2.3.4 Quantitative real-time PCR
Quantitative real-time PCRs were carried out utilising the SYBR Green technology
(Qiagen) and the DNA Engine Opticon 2 system (MJ Research - BioRad). Duplicate reactions on each sample were carried out simultaneously for the target (e.g. neonatal
Nav1.5) and the reference genes. For the latter, the main reference gene adopted was
PUM1, which was shown previously to be ideal for BCa studies (Lyng et al., 2008).
MJ Research Opticon Monitor Analysis software version 3.1 was used to set the threshold and determine cycle threshold (Ct) values for each sample.
The following reaction mix was prepared for each set of primers: 1 µl of 10
µM sense primer, 1 µl of 10 µM antisense primer (Invitrogen), 10 µl of 2X
QuantiTect SYBR Green PCR mix (Qiagen), 3 µl sterile RNase/DNAse-free water and 5 µl of cDNA. In each PCR set, a negative control containing sterile
RNase/DNAse-free water instead of cDNA and the (-)RT controls of each sample were also included.
The standard PCR protocol comprised an initial activation for 15 min at 95 oC, then repetitive cycles of 30 seconds at 95 oC, 30 seconds at 59.5 oC and 30 seconds at 72 oC. The amplification was carried out for up to 40 cycles. The melting curve analysis at the end of cycling was set between 65 – 95 oC with 0.3 oC steps to verify the singularity of the reaction product (Figure 2.4). The quality of the product was also assessed by electrophoresis on a 1 % agarose gel. The details of the primers are given in Table 2.1.
The level of mRNA expression was measured by relative quantification for cell culture studies using the comparative 2-ΔΔC(t) method (Livak & Schmittgen
2001). In this method, the expression level of the target gene in the each sample was normalised to the sample-matched expression level of the reference gene and data
78
Figure 2.4 – Typical examples of the melting curve analyses from real-time PCRs. Fluorescence from SYBR green incorporation to double stranded DNA was measured after each PCR cycle and at the end, to verify the product composition, dissociation of the DNA double strand during heating was measured by the reduction in fluorescence. Typical plots of the melting curves, together with their derivative plots, are shown for all primers as indicated on figures.
79 were given as the fold-change of gene expression in test sample relative to the control sample using the following equations:
ΔC(t) = C(t)target gene – C(t)reference gene Equation 2.3
y = 2 – (ΔC(t)test sample - ΔC(t)control sample) Equation 2.4
In order to use the 2-ΔΔC(t) method, the amplification efficiencies of the target and reference genes are expected to be approximately equal. This was validated by investigating the variations in ΔC(t) values with serially diluted cDNA templates
(Figure 2.5) (Livak & Schmittgen 2001).
2.4 RNA interference
A dual knock-down approach was used to silence the InsR / IGF1R system (IIS) in
MDA-MB-231 cells with the aid of specific small interfering RNAs (siRNAs) targeting IGF1R and InsR. Two different siRNA sequences for each of InsR and
IGF1R were used in combination. This enabled successful silencing of both receptors transiently and simultaneously. The RNAi technique was also utilized for silencing the nNav1.5 in SW620 cells using three different siRNA sequences.
2.4.1 siRNA transfection
All non-targeting control and targeting siRNAs were purchased as desalted duplexes and resuspended in buffers provided by the manufacturers upon receipt (Qiagen and
Eurofins). The stock concentrations were adjusted to 10 µM for IGF1R and InsR siRNAs and to 20 µM for nNav1.5 siRNAs and non-targeting controls. All duplexes were stored at -20oC as 50 µl aliquots. The control duplexes and the siRNAs targeting IGF1R and InsR were commercially available, pre-validated and well
80
Figure 2.5 – Validation of the 2-ΔΔC(t) method (legend is overleaf)
81
Figure 2.5 – Validation of the 2-ΔΔC(t) method – continued. cDNA templates were synthesized from 1 µg RNA extracts of MDA-MB-231 and SW620 cells. After serially diluting the cDNA, real-time PCRs were performed for each target gene together with the reference gene PUM1. ΔC(t) values were calculated by subtracting C(t) of reference gene from that of target gene at each dilution sample. Data are presented as means ± SEM (n = 3) and were fitted using linear regression analysis (solid line). The fact that the absolute values of the slopes are close to zero indicates that the efficiencies of the target and reference genes are similar and therefore 2-ΔΔCт method may be used (Livak and Schmittgen, 2001). The fluorescence increase over cycle range for each serially diluted target is shown in the insets on the right (coloured lines indicate: — x1, — x4, — x16, — x64 and — x256 dilutions).
82 characterised duplexes, whereas two of the siRNAs against nNav1.5 (siNeo1 and siNeo2) and siRNA targeting aNav1.5 (siAdult1) were newly designed in our laboratory. These new siRNA sequences were designed using Dharmacon’s online target design algorithm and were then checked with BLAST-search for having homology and similarity only to their target nNav1.5. The final nNav1.5-targeting siRNA (siNESO) was designed and successfully used in our laboratory previously.
All siRNA sequences used in this study are given in Table 2.2.
Transfection of non-targeting control and targeting siRNAs were performed in parallel and a final concentration of 40 nM siRNA was achieved for each condition.
Lipofectamine 2000 reagent (Invitrogen) was used to deliver siRNAs into the cells and transfection protocol was applied according to manufacturer’s instructions.
Briefly, 15x104 cells in 2 ml standard growth medium were plated into 35 mm dishes exactly 24 hours before transfection. Cells were approximately 30-50 % confluent at the time of transfection as recommended by the manufacturer. Lipofectamine 2000 reagent was stored at 4oC and mixed gently before every use. Oligomer-
Lipofectamine 2000 complexes were prepared in 4 consecutive steps: (i) siRNA was diluted in 250 µl Opti-MEM I Reduced Serum Medium without serum (Invitrogen) and mixed gently. (ii) In a separate tube, 5 µl Lipofectamine 2000 was diluted in 250
µl Opti-MEM I Reduced Serum Medium without serum and mixed gently. (iii)
Mixtures were incubated at room temperature for 5 min. (iv) The individually diluted siRNA and Lipofectamine 2000 were combined by mixing them gently and the resulting mixtures were incubated for further 20 min at room temperature. Finally, these oligomer-Lipofectamine 2000 complexes were directly added to the cells in little drops without changing the culture medium. The dishes were also rocked gently for couple of times to equally distribute the complexes and cells were placed into
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Table 2.2 – The siRNA sequences used for RNAi experiments. The sequences of siRNAs used in this study and the relevant references are shown in the table.
Target Name Sequence (5'à 3') Reference
siControl commercial AGGUAGUGUAAUCGCCUUG (1) (Eurofins) Non- targeting siControl commercial UAAUGUAUUGGAACGCAUA (2) (Eurofins)
commercial si1 GGAGAAUAAUCCAGUCCUA (QIAGEN) IGF1R commercial si7 GAAGAAUCGCAUCAUCAUA (QIAGEN)
commercial si2 GAACGAUGUUGGACUCAUA (QIAGEN) InsR commercial si3 ACGGGAGUCUGAUCAUCAA (QIAGEN)
Guzel, R.M. siNeo1 CUAGGCAAUUUGUCGGCUC (unpublished)
Guzel, R.M. nNav1.5 siNeo2 UAUCAUGGCGUAUGUAUCA (unpublished)
Brackenbury siNESO GAGUCCUGAGAGCUCUAAA et al., 2007
Guzel, R.M. aNav1.5 siAdult1 GUCUCAGCCUUACGCACCU (unpublished)
84 the incubator. After approximately 18 hours of incubation, the medium was changed and 2 ml of fresh standard growth medium was added into each dish. The cells were then kept in the incubator without further manipulation until needed for experiments.
Some more controls were added to the siRNA set whenever necessary, including untreated and mock-treated (only Lipofectamine 2000) cells.
2.5 Western blotting
2.5.1 Protein extraction
Whole cell lysates were prepared from 80 - 90 % confluent dishes. After washing the cells with ice-cold PBS, ice-cold lysis buffer with protease inhibitors was added directly into to culture dish. The following protease inhibitors were dissolved freshly per one ml of 1X RIPA lysis buffer (Millipore): 2 µg/ml aprotinin, 1 µ g/ml leupeptin, 1 µg/ml pepstatin, 10 mM Na fluoride and 2 mM phenylmethanesulfonyl fluoride. All protease inhibitors were purchased from Sigma and stock solutions were stored at -20 oC. Lysate was collected from the dish and homogenized by passing several times through a 26 G needle using a sterile syringe. The homogenate was then left on ice for 30 min with agitation and centrifuged at 13000 g, 4oC for further
30 min. The resulting supernatant was collected and protein yield was determined using a Bradford dye binding assay (Bio-Rad) according to manufacturer’s guidelines. Briefly, 20 µl of lysate was thoroughly mixed with 1 ml of diluted
Bradford dye (1:5 with distilled deionized water) and the mixture was incubated at room temperature for 5 min. Spectrophotometric absorbance was then measured at
595 nm and protein yield was determined by comparing the measurement with a
BSA calibration curve (Figure 2.6).
85
Figure 2.6 – Example of typical Protein (BSA) calibration curve. Protein standards were prepared with a range of concentrations of BSA in lysis buffer for Bradford dye binding assay (Bio-Rad). Absorbances were measured at 595 nm. Lysis buffer itself served as a blank and measurements were corrected against it. Data were fitted using linear regression analysis (solid line). The curve was used to calculate protein concentration of a total protein extract for Western blot.
86
2.5.2 Sodium dodecyl sulphate polyacrylamide gel electrophoresis
In order to separate proteins according to their molecular mass, sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) was conducted. The gel was assembled by preparing 2 different layers of acrylamide between glass plates.
The lower layer ‘resolving gel’ was prepared with 9.36 ml of acrylamide (30 %),
13.1 ml of 1 M Tris (pH 8.8), 175 µl of SDS (20 %), 130 µl APS (10 %), 30 µl of
TEMED and 12.3 ml deionized distilled water. The upper layer ‘stacking gel’ (5 %) consisted of 1.8 ml of acrylamide (30 %), 1.25 ml of 1 M Tris (pH 6.8), 50 µl of SDS
(20 %), 50 µl of ammonium persulphate (APS) (10 %), 20 µl of
Tetramethylethylenediamine (TEMED) and 7.0 ml deionized distilled water. First the lower resolving gel was poured into the gel cast and left to polymerize, after which the stacking gel was loaded and combs were inserted to create wells. After preparing the complete gel assembly, protein lysate was mixed with an appropriate volume of
6X Laemmli buffer (to a final concentration of 1X) and heated at 95oC for 10 min.
Appropriate volume (maximum 50 µl) of lysate to obtain 80 µg of protein was loaded into the wells. The gel was run at 100 V until the samples reached the resolving gel, and the power was increased to 150 V thereafter. Proteins were then transferred to nitrocellulose membrane at 4oC, 20 V for 20 hours. At the end of transfer, the membrane was blocked first with 5 % (w/v) non-fat dried milk in PBS-
Tween 20 (0.1 %) for 1 hour and then with 2 % (w/v) BSA in PBS-Tween 20 (0.1 %) for 30 min. Both blocking steps were conducted on a rocker at room temperature.
2.5.3 Antibodies
The following primary antibodies were used in this study, all of which were diluted with 2 % (w/v) BSA in PBS-Tween 20 (0.1 %):
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1- Anti-IGF1Rβ (sc-713). This was a polyclonal antibody raised in rabbit
(Santa Cruz). It was used at a final dilution of 1:1000.
2- Anti-InsRβ (sc-711). This was a polyclonal antibody raised in rabbit (Santa
Cruz). It was used at a final dilution of 1:1000.
3- Anti-actin (A-5060). This was a polyclonal antibody raised in rabbit (Sigma).
It was used at a final dilution of 1:1000.
After incubation with primary antibody, membranes were washed with 0.2 % (v/v)
Tween-20 in PBS for several times and incubated with horseradish peroxidase (HRP) conjugated secondary antibody goat anti-rabbit (A-0545, Sigma) for 1 hour at room temperature. Finally, membrane was re-washed with 0.2 % (v/v) Tween-20 in PBS for several times and were developed into films (Fujifilm) using ECL chemiluminesence technique (Amersham). Densitometric analyses were conducted on the scanned images of the films using Image-J program (Figure 2.7).
2.6 Immunocytochemistry
A sterile coverslip (13 mm) was put into each well of a 24-well culture plate and coated with 10 µg/ml poly-L-lysine (Sigma-Aldrich) (500 µl per well) and incubated for 30 min at 37oC. Afterwards, 3 x 104 cells in 500 µl of growth medium were seeded into each well and allowed to settle for 24 h. The following day, the media were changed with the test / drug solution and incubation continued for another 24 h.
At the end of the treatment period, the medium in each well was discarded and the cells were washed with cold PBS (1X) and fixed with cold 2 % paraformaldehyde
(PFA)/PBS (w/v) (Sigma) for 15 min at room temperature. After 4 x 5 min washes with PBS, cells were blocked with 5 % (w/v) BSA/PBS for 1 h at room temperature.
88
A
B
Figure 2.7 – Effect of protein dilution on band intensity. Antibodies against IGF1R- and InsR β-subunits were tested for their abilities to recognise different protein concentrations. Following the SDS-PAGE gel electrophoresis and immunoblotting of a range of protein concentrations (40-100 µg), densitometric analyses were performed using ImageJ software. The observation of a linear relationship between protein level and band intensity within working range validated densitometric analysis for IGF1R (A) and InsR (B). Data were fitted using linear regression analysis (solid line) and each data point represents mean ± SEM (n = 3).
89
The primary antibody NESOpAb (Chioni et al., 2005) was prepared in 5 % (w/v)
BSA/PBS and used at a final dilution of 1:200. Following 1 h incubation at room temperature with primary antibody, 4 x 5 min washes with PBS were performed. The secondary antibody goat anti rabbit Alexaflour-568 (Invitrogen) was prepared in 5 %
(w/v) BSA/PBS and used at a final dilution of 1:200. Coverslips were kept in dark during incubation with secondary antibody for 1 h at room temperature. Finally, after washing with PBS for 5 x 5 min, the coverslips were mounted in fluorescence preserving medium (Vectashield, Vector Laboratories) and kept at 4 oC in dark until microscopy. Two control experiments were carried out: 1) Autoflourescence – to control for cells’ possible endogenous fluorescence. For this, designated coverslips were treated only with 5 % BSA instead of antibodies. (2) Secondary antibody control - to test for possible ‘off-target’ cross-reactivity of the secondary antibody.
For this, designated coverslips were treated only with the secondary antibody.
Samples were examined under an Axioskop (Zeiss) microscope at magnifications of x10 and x40.
2.7 Immunohistochemistry
Colorectal cancer (CRCa) biopsy tissue sections were kindly provided by Prof.
Annarosa Arcangeli (University of Florence, Italy). Firstly, paraffin embedded tissue sections were de-waxed by 2 x 15 min incubations in Histo-Clear solution (National
Diagnostics), followed by a brief immersion in absolute ethanol. Any possible endogenous peroxidase activity was blocked with 1% (v/v) H2O2 (Sigma) in methanol for 10 min and sections were washed afterwards in running distilled H2O
(dH2O) for 10 min. Antigen retrieval was done by boiling the sections in freshly
90 made 0.01 M citrate buffer (pH 6) in a microwave oven for 2 min, and then samples were allowed to cool for 20 min. Sections were blocked with normal horse serum
(2.5 %) (R.T.U. Vectastain Kit, Vector Laboratories) for 30 min at room temperature. After a brief rinse in PBS, the primary antibody NESOpAb (Chioni et al., 2005) was applied for 1 h at room temperature. NESOpAb was prepared in 5 %
(w/v) BSA/PBS at a final dilution of 1:200. Sections were then washed for 2 x 5 min with 0.1 % (w/v) BSA/PBS. Next, sections were incubated in biotinylated universal anti-rabbit IgG (H+L) secondary antibody (R.T.U. Vectastain Kit, Vector
Laboratories) for 30 min at room temperature, and then washed for 2 x 5 min with
0.1 % (w/v) BSA/PBS. To reveal antigen binding sections were incubated in avidin– biotinylated enzyme complex (Vectastain R.T.U. Elite ABC reagent, Vector
Laboratories) for 30 min, followed by 2 x 5 min washes in 0.1 % (w/v) BSA/PBS.
Then, the diaminobenzidine (DAB) (Peroxidase substrate kit, Vector Laboratories) was added for 5 min and staining was monitored closely. Afterwards, sections were washed in running dH2O for 10 min. Sections were then counterstained with Harris’ haematoxylin (Vector Laboratories) briefly and washed under running dH2O until clear. Sections were dehydrated by incubating them in 70% (for 30 sec), 90% (for 30 sec) and 100% ETOH (for 3 x 30 sec). Finally, sections were immersed in Histo-
Clear for 3 x 5 min and then mounted under coverslips using DPX mountant. As controls, the procedure was repeated (i) without the primary and secondary antibodies and (ii) with secondary antibody only.
2.8 Protocols involving human blood
2.8.1 Collection of peripheral blood
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From every subject, approximately 10 ml of peripheral blood were drawn into sterile
EDTA Vacutainer® tubes (BD). The collected blood samples were transported to the laboratory at room temperature as rapidly as possible within 4 hours for immediate processing.
2.8.2 Enrichment of mononuclear cells
Pre-analytical enrichment of tumour cells was performed by density gradient centrifugation using Ficoll (Bøyum, 1968). Briefly, blood was transferred into a 50 ml Falcon tube and diluted with sterile 1X phosphate buffered saline (PBS) to a total volume of 25 ml. Into another 50 ml Falcon tube, 13 ml of Ficoll was aliquoted and the diluted blood sample was layered very carefully over Ficoll without mixing. The sample was then centrifuged at 500 g for 30 min without applying break. Since the blood mononuclear cells (MNCs) – monocytes and lymphocytes – have a lower buoyant density than the erythrocytes and the granulocytes, this centrifugation step allowed their separation. After centrifugation, erythrocytes and the granulocytes were found in the sediment, while the MNCs remained in the plasma – Ficoll interface with other slowly sedimenting particles (platelets). The layer of MNCs was then collected and transferred into another tube using a sterile Pasteur pipette. The collected MNCs were topped up to 50 ml volume with PBS and centrifuged at 300 g for 10 min. After discarding the supernatant, the cell pellet was loosened by “finger- flicking”, resuspended in 20 ml of fresh PBS and centrifuged at 200 g for 10 min. At this point, a red blood cell (RBC) lysis step was included, where 4 ml of red blood cell lysis buffer (Qiagen) was added onto the loosened cell pellet after removing the supernatant. Incubation in RBC lysis buffer was done for 2 min, and then immediately 20 ml of PBS was added into the tube. After centrifuging at 200 g for 10
92 min, supernatant was discarded and approximately 5-10 ml of PBS was added onto the loosened cell pellet (depending on the size of the pellet). Finally a cell count was carried out and cells were taken immediately for RNA extraction.
2.8.3 RNA extraction
Total RNA was extracted with RNeasy Mini Kit (Qiagen) from the MNC population collected via Ficoll centrifugation. The extraction was performed by following the manufacturer’s instructions. Briefly, cells were lysed with Buffer RLT containing β- mercaptoethanol (Sigma) and homogenized by passing 10 times through a needle using a syringe. Then, 1 volume of 70 % ethanol was added to the homogenate and mixed well by pipetting. The mixture was transferred to an RNeasy spin column placed in a collection tube and centrifuged at 8000 g for 15 s. The flow-through was discarded and the spin column was washed by adding 700 µl Buffer RW1 and centrifuging at 8000 g for 15 s. Two more washes were then performed with 500 µl
Buffer RPE. At the final wash step, centrifuge was done for 2 min at 8000 to dry the spin column membrane. In order to ensure that no Buffer RPE is carried over, spin column was placed in new collection tubes and centrifuged again at full speed for 1 min. Finally, RNA was eluted into clean Eppendorf tubes by adding 40 µl
DNase/RNase-free water directly onto the spin column membrane and centrifuged at
8000 g for 1 min.
2.8.3.1 DNase treatment
Since some of PCRs are prone to genomic DNA contamination, a DNase digestion step was performed before moving onto cDNA synthesis. Turbo DNA-free kit
(Ambion) was used for this purpose and the manufacturer’s instructions were
93 followed. Briefly, 10 µg RNA in 25 µl total volume was gently mixed with 2.5 µl
TURBO DNase buffer and 1 µl TURBO DNase. The mixture was then incubated at
37 oC for 30 min. Afterwards 2.5 µl DNase Inactivation Reagent was added and mixed very well. Sample was incubated for 5 min at room temperature and mixed 2–
3 times during the incubation by flicking the tube to redisperse the DNase
Inactivation Reagent. Finally, tubes were centrifuge at 10,000 g for 1.5 min and the supernatant containing the RNA was transferred to a fresh tube carefully without touching the DNase Inactivation Reagent pellet. The acquired RNA was then used for cDNA synthesis.
2.8.4 cDNA synthesis
Reverse-transcription was performed using SuperScript III RT enzyme. The protocol was slightly different than that of with SuperScript II and involved initially mixing 1
µg of total RNA with 250 ng of random primers and 1 µl 10 mM dNTP mix in a total volume of 13 µl made up with DNase-RNase-free distilled water. After heating the mixture to 65 °C for 5 min, a master mix containing 4 µl 5X First-Strand Buffer, 1 µl
0.1 M DTT and 1 µl Porcine RNA-Guard was added. Then, 1 µl of SuperScript III
RT enzyme (200 units/µl) was mixed gently into the sample. Following 5 min incubation at room temperature, the sample was further incubated at 50 °C for 90 min. The reaction was inactivated by heating the sample at 70 °C for 15 min. The resulting cDNA mixture (20 µl) was diluted with 80 µl of DNase-RNase-free distilled water distilled water and used for PCRs.
2.8.5 Quantitative real-time PCR
94
Quantitative real-time PCRs were carried out as explained in section 2.5.4. The level of mRNA expression in blood samples was analysed by relative quantification using comparative C(T) method. Instead of the 2– ΔΔC(T), the 2–ΔC(T) calculation for each sample was adopted, where ∆C(T) = [C(T)target gene] – [C(T)reference gene] (Schmittgen &
Livak, 2008).
2.8.6 Generation of DNA standards
DNA standards consisting of purified plasmid DNA specific for nNav1.5 (the major cancer-associated ion channel of interest) and PUM1 (as the main reference gene) were constructed and serial dilutions of the standards were prepared to create the calibration curves (Giulietti et al., 2001). First, cDNA from the MDA-MB-231 cells was subjected to PCR amplification using the specific primers for the target genes.
The amplified products were cloned into the pCR 2.1 TOPO vector (Invitrogen) according to manufacturer’s instructions by mixing them with the vector in the presence of a salt solution and incubating them at room temperature for 5 min. Then, the recombinant vector was transformed into the chemically competent TOPO10F' E-
Coli bacteria. The transformation protocol included incubation for 15 min on ice, heat-shocking the cells for 30 seconds at 42 oC and immediate transfer to ice.
Afterwards, 250 µl of SOC medium was added to the tube and the cells were left at
37 oC for one hour to recover. Then, 100 µl from the transformation was spread on a selective plate (amphicilin) in the presence of X-gal and Isopropyl β-D-1- thiogalactopyranoside (IPTG), and the plates were left overnight at 37 oC. The next day, the grown white or light blue colonies were picked from the plate, transferred into liquid cultures containing amphicilin and incubated again overnight at 37 oC.
The following day, each tube was tested for the presence of the plasmid by a PCR
95 with the appropriate primers and then plasmids were isolated using PureYield
Plasmid Miniprep System (Promega) with an additional purification step. The DNA concentration in the sample was determined using the NanoDrop ND-1000
Spectrophotometer.
In order to create a series of standards, serial dilutions of the obtained construct was prepared. First the molecular weight of the obtained construct was calculated. The total number of each nucleotide in the sequence of the pCR 2.1
TOPO vector (provided by the supplier) and in the sequence of the amplicons were determined. Taking the double stranded nature into account, the total number of each nucleotide was multiplied with the molecular weight of the respective nucleotide, giving the molecular weight of the construct (g/mol). Then, the number of moles of
DNA transcripts in 1 ng was calculated, which was further multiplied by the
Avogadro’s Number (6.022 x 1023, the number of molecules in one mole) to find the number of DNA transcripts per ng. Finally, this calculated number of DNA transcripts in 1 ng was multiplied by the previously measured DNA concentration
(ng/µl) to determine the number of DNA transcripts per µl. Afterwards, 10-fold serial dilutions of the DNA transcripts were generated ranging from 101 to 109 using sterile RNAse/DNAse-free water. The dilutions were aliquoted and kept in -20 oC.
The standards spanning five orders of magnitude from 101 to 105 were later used in the quantitative real-time PCR experiments. Using these standards, the intra- and inter-variability of the PCRs were investigated. Efficiency of the standard curves was calculated by the following equation (Pfaffl, 2004):
E (%) = 1 – [10(-1/slope)] Equation 2.5
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2.9 Electrophysiology
Throughout this study, the possible electrophysiological effects of the various treatments on cells were monitored by whole-cell patch-clamp recording (e.g.
Grimes et al., 1995; Fraser et al., 2005). Mainly, voltage-current relationships
(including peak currents) and voltage-dependence of stead-state current activation and inactivation were determined. These experiments were conducted by Dr Scott P.
Fraser and are included as supplementary material in Appendix 1.
2.10 Data analysis
All quantitative data were presented in this report as mean ± standard error of the mean (SEM), unless stated otherwise. Data analyses were performed using Excel
2010 (Microsoft), OriginPro8.5 (Origin Lab) and SigmaStat 2.0 (Systat Software
Inc.). Unpaired student’s t-test and Mann-Whitney rank sum test were used for statistical analysis, as appropriate. Fisher’s exact test was used for the analysis of
CRCa biopsy samples. Differences were considered significant when P<0.05 and marked with (*). P<0.01and P<0.001 indicated higher significance and were marked with (**) and (***), respectively. Further specific details were given in the text when necessary.
97
Chapter 3
(Results 1)
STUDIES ON THE INSULIN – INSULIN-LIKE GROWTH FACTOR
SYSTEM IN MDA-MB-231 CELLS:
POSSIBLE ASSOCIATION WITH VOLTAGE-GATED SODIUM CHANNEL
98
3.1 Introduction and rationale
As reviewed in the General Introduction (section 1.5), the molecular structures of insulin and insulin-like growth factor1 (IGF1) and their receptors (InsR and IGFR, respectively) overlap to considerable extent (e.g. Rowzee et al., 2009; Boucher et al.,
2010). Furthermore, the two receptors can also form hybrids (Soos et al., 1990;
Bailyes et al., 1997). Additionally, importantly, insulin/IGF1 signalling is not ‘static’ and response characteristics can change as a result of activity, depending upon the nature of stimulation. For example, down-regulating IGF1R was shown to result in increased sensitivity of breast cancer (BCa) cells to insulin (Zhang et al., 2007).
Similarly, when InsR-A was knocked-down, IGF1R activation by IGF1 and IGF2 was enhanced due to increased density of IGF1R homodimer expression (Brierley et al, 2010). Accordingly, insulin, IGFs, InsR and IGF1R can best be considered to form an integrated signalling system – “InsR-IGF1R system” (IIS) (e.g. Pandini et al., 1999; Frasca et al., 2003; Belfiore & Frasca, 2008; Guzel & Djamgoz, 2011). In fact, even clinically, it has been suggested that IIS must be targeted as a unit and, consequently, dual inhibitors of InsR and IGFR1 tyrosine kinases are being developed (e.g. Buck et al., 2010; Olmos et al., 2010).
Much work has been done to understand the cellular/molecular bases of action of insulin and IGF1 on BCa (Sachdev & Yee, 2001, 2007; Call et al., 2010;
Rosenaweig & Atreya, 2010). On the whole, upregulation of IIS signalling in BCa cells leads to more aggressive cellular behaviour (e.g. Clemmons, 2007). For example, application of 100 nM insulin to MDA-MB-231 cells increased transverse migration by >40 % (Pan & Djamgoz, 2008). However, it was not clear how insulin might have affected IGF1R activity, and the effects of silencing IIS as a whole on
99 metastatic cell behaviours (MCBs) have not previously been studied. This approach was one of the primary aims of the experiments described in this chapter.
As also reviewed in the General Introduction (section 1.3.1), upregulation of functional voltage-gated sodium channel (VGSC) / neonatal Nav1.5 (nNav1.5) expression is a hallmark of metastatic BCa (Fraser et al.,2005; Onkal & Djamgoz,
2009). Consistent with this, blocking VGSC activity generally with tetrodotoxin
(TTX) or silencing nNav1.5 specifically with siRNA or antibody (NESOpAb) in
BCa cells significantly reduced MCBs (Fraser et al., 2005; Brackenbury et al., 2007;
Chioni et al., 2010). Independent studies have shown that IIS signalling can affect
VGSC expression and activity in a variety of cell types (e.g. Calissano et al., 1993; de Pablo & de la Rosa, 1995; Yamamoto et al., 1996; Charpentier, 2005; Guzel &
Djamgoz, 2011). However, how IIS signalling might affect VGSC (nNav1.5) mRNA, protein expression and function in BCa cells is also not known. A further aim of the present study, therefore, was to address this question in MDA-MB-231 cells.
Taken together, therefore, the available evidence raises the possibility that there is a triangular ‘network’ involving IIS and VGSC (nNav1.5) expression/activity controlling MCBs in BCa (Figure 1.13).
3.2 Aims and scope
The general aim of the present study was to determine the possible functional relationship between the insulin-IGF1 system (IIS) and nNav1.5 expression/activity in BCa. Experiments were performed on the strongly metastatic human BCa MDA-
MB-231 cell line model. The specific aims were as follows:
100
1. To demonstrate the expression of InsR and IGF1R (mRNA and protein) as the bases of IIS;
2. To test the effect of exogenous insulin on MCBs under two different sets of conditions: (i) serum-free and (ii) hyperinsulinemic;
3. To determine the consequences on transverse migration and invasion of inhibiting endogenous IIS signalling (i) pharmacologically and (ii) by siRNA; and
4. To examine the possible effects of the treatments in (3) on VGSC (nNav1.5) mRNA and protein expression.
3.3 Results
3.3.1 Exploratory experiments
Before investigating the effects of ‘silencing’ IIS on MCBs and VGSC expression / activity in the MDA-MB-231 cell model, some essential pilot experiments were carried out, as detailed below.
3.3.1.1 Expression of insulin and IGF1 receptors in MDA-MB-231 cells
Conventional PCRs revealed that InsR and IGF1R mRNAs were present (Figure
3.1A). For InsR, both the short (InsR-A) and the long (InsR-B) isoforms (the former lacking exon 11) were observed. The presence of both receptor proteins, of the expected sizes, was verified by Western blots (Figure 3.1B). These data confirmed that the IIS receptors were expressed in MDA-MB-231cells.
3.3.1.2 Effects of serum deprivation
Since exogenous insulin experiments were to involve treatments in serum-free
101
A IGF1R InsR PUM1
B 1 2
IGF1R-β InsR-β
Actin Actin
Figure 3.1 – Expression of IGF1R and InsR in MDA-MB-231 cells. (A) PCR products of correct sizes for IGF1R, two isoforms of InsR (InsR-A lower band and InsR-B upper band) and the reference gene PUM1 were visualised on 1 % agarose gel. The corresponding product sizes are indicated on the right. (B) IGF1R and InsR proteins (of correct sizes) were detected by western blots using antibodies against β- subunits of the receptors. Actin was used as a loading control and 80 µg of protein from whole cell protein extracts was run on the gels for each target. IGF1R and InsR are shown together with their matching Actin from two different cell extracts in lanes 1 and 2, respectively.
102 medium, effects of serum deprivation on various relevant cellular characteristics were tested as follows:
a. Cell viability. Viability of MDA-MB-231 cells in 0 % FBS was monitored over
72 h (Figure 3.2A). Some change in cell morphology was observed. There was a time-dependent tendency for cell viability to drop after 24 h. However, cell viability was not affected by adding 10 µM tetrodotoxin (TTX) in serum-free medium for 24 h (Figure 3.2A inset).
b. Cell number. Numbers of MDA-MB-231 cells were measured over 72 h (Figure
3.2B). Under control conditions (5 % FBS), the cell number increased steadily
(Figure 3.2B). In 0 % FBS, compared to control, there was a slight decrease in cell number but this was not significant within 24 h. However, this tendency increased over 48 – 72 h and significant decreases in cell numbers were observed (Figure
3.2B). Cell numbers were not affected by adding 10 µM TTX for 24 h in either medium (Figure 3.2B inset).
From these two sets of experiments, it was concluded that application of serum-free medium for 24 h would have no detrimental effect on MDA-MB-231 cells. Accordingly, a maximum of 24 h was adopted as the experimental period in later studies.
c. nNav1.5 mRNA expression. Possible change in nNav1.5 mRNA expression within the 24 h period of serum deprivation was investigated in comparison to respective control values obtained in 5 % FBS (Figure 3.3). The effect was complex.
103
A 105
100 105 NM SFM 95
90
100 (%) Viability 85
80 NM SFM TTX in TTX in 95 NM SFM
90 Viability (%) Viability
85
80 24h 48h 72h
B 2
1.5 4 NM SFM 1
3 0.5
Cell number (relative)number Cell 0 NM SFM TTX in TTX in 2 NM SFM
1
Cell number (relative)number Cell * * 0 24h 48h 72h
Figure 3.2 – Effects of serum deprivation on cell viability and number. (A) The percentage of viable MDA-MB-231 cells under normal medium (NM) and serum- free medium (SFM) conditions were compared using Trypan Blue exclusion assay. A trend for time-dependent decrease in viability was observed in SFM. Histobars indicate averages of 2 repeats (hence, no errors were calculated). (A-inset) Addition of 10 µM TTX did not have any further effect (n = 3). (B) There was also a significant decrease in cell number over time when SFM-grown cells were compared to the cells in NM as measured by the MTT assay (P=0.03 and 0.01 for 48 h and 72 h, respectively; n=3). In the same period of time (24 – 72 h) the cells in NM showed steady growth as expected. (B-inset) 10 µM TTX did not affect cell number in either medium (P>0.05; n=3).
104
4 *
3 *
2
1 ** nNav1.5 mRNA expression (relative) nNav1.5mRNA 0 12h 18h 24h
Figure 3.3 – Effects of serum deprivation on nNav1.5 mRNA expression. When MDA-MB-231 cells were grown in serum-free medium for 24 h, a time-dependent, complex effect on the nNav1.5 mRNA level was observed, compared to the cells grown in normal medium. An initial decrease in mRNA expression (12 h) was followed by an increase (18 h) and finally another decrease (24 h). These changes were significant, as indicated (n=4 for each time point).
105
Initially (at 12 h), there was a decrease followed by increases at 18 and 24 h, respectively. This further indicated that MDA-MB-231 cells were not in steady state in 0 % serum, even within the initial 24 h period.
3.3.1.3 Functional effects of exogenous insulin in serum-free medium
The effects of exogenous insulin on lateral motility and Matrigel invasion were tested next, as examples of MCB in serum-free medium. However, since these assays would take up to 48 h, it was necessary to improve the cell viability. This was achieved by including 0.1 % BSA in the 0 % FBS medium, as shown previously by
Pan & Djamgoz (2008). In this modified medium, 100 nM insulin had no effect on either cell viability or cell growth over 48 h (Figure 3.4). However, 100 nM insulin increased significantly both lateral cell motility over 24 and 48 h (Figure 3.5) and invasiveness over 24h (Figure 3.6). Interestingly, TTX (6 µM) by itself significantly increased invasion in the ‘basal’ (BSA) medium used (Figure 3.6). Also, combined treatments with 100 nM insulin and 6 µM TTX gave surprising results: lateral motility increased further (Figure 3.5), and there was no change in Matrigel invasion
(Figure 3.6). These effects of TTX are unlike what is seen for MDA-MB-231 cells in normal medium, i.e. up to ~50 % inhibition (Roger et al., 2003; Fraser et al., 2005;
Brackenbury et al., 2007).
Complementary patch-clamp experiments showed that 100 nM insulin had no effect on current density over 24 h (Appendix 1.1). Hence, the anomalous effect of
TTX on MCBs in insulin-containing basal could not be due to VGSC activity alone.
3.3.1.4 Lack of functional effects of exogenous insulin in normal medium:
“hyperinsulinemia”
106
A B
105 1.2
100 0.9
95 0.6 90 Viability (%) Viability
0.3
85 (relative)number Cell
80 0 Control Insulin Control Insulin
Figure 3.4 – Effect of insulin on cell viability and number. (A) The effect of 100 nM insulin in serum-free medium (SFM) supplemented with 0.1 % BSA on MDA- MB-231 cell viability was tested for 48 h. No change was observed compared to the control condition (SFM + 0.1 % BSA) (n=3). (B) Similarly, under the same conditions, the cell growth was also not affected (n=6).
107
A 1 24 h *** 0.8 *** ***
0.6
0.4 Motility Index (MoI) (MoI) Index Motility 0.2
0 Control Insulin Insulin + TTX
B 1 48 h *** 0.8 ** ***
0.6
0.4 Motility Index (MoI) (MoI) Index Motility 0.2
0 Control Insulin Insulin + TTX
Figure 3.5 – Effect of insulin on lateral motility. Significant increases were observed in lateral motility of the MDA-MB-231 cells in wound heal assays, when treated with 100 nM insulin for both 24 h (A) and 48 h (B) compared to the control cells in SFM + 0.1 % BSA. For given same time periods, coapplication of TTX (6 µM) increased lateral motility even further (n = 45 wound points from each of 6 dishes).
108
0.8 ***
0.6 **
0.4
0.2 Invasiveness(relative)
0 Control Insulin TTX Insulin+TTX
Figure 3.6 – Effect of insulin on Matrigel invasion. The effects of insulin (Ins) (100 nM) and TTX (6 µM) and their combined application were tested on MDA- MB-231 cells in serum-free medium with 0.1 % BSA. Insulin and TTX individually both increased invasion significantly; their coapplication did not cause any further effect (n = 12 fields of view from each of 6 inserts).
109
Since many insulin-related pathologies in vivo involve high levels of circulating insulin, the effects of such a “hyperinsulinemic” condition was tested next by adding insulin into normal medium. Any possible change in MDA-MB-231 cell number following application of varying doses of insulin in normal medium was assessed in comparison to cells in normal medium. There was no effect of hyperinsulinemia on cell number (P>0.05; n=4 ; Figure 3.7A).
Since none of the insulin concentrations tested had an effect on cell number, similar conditions were then applied to Transwell migration assays. When compared to control condition (normal medium), hyperinsulinemic environment had no effect on migration of MDA-MB-231 cells (P>0.05; n=3; Figure 3.7B).
3.3.1.5 Conclusion of exogenous insulin studies
From these exploratory experiments, it was concluded (i) that the biochemical content of the extracellular medium had a significant impact upon the cells’ viability, growth, nNav1.5 mRNA expression and response to insulin and (ii) in normal medium, insulin signalling was likely to be saturated. In conclusion, a different strategy was adopted for the following set of experiments.
Thus, in order to further study the possible effects of IIS on specific MCBs without disrupting the extracellular medium, endogenous IIS signalling was inhibited as a whole at receptor level. This was achieved in two independent ways:
Pharmacological and RNA interference (RNAi). For pharmacological inhibition, the following two different common blockers of InsR and IGF1R were used: AG1024 and OSI-906. Finally, a dual-siRNA approach was used for simultaneous knock- down of both InsR and IGF1R.
110
A 2.0
1.5
1.0
0.5 Cell number (relative)number Cell
0.0 Control 100 nM 200 nM 500 nM
Insulin
B 1
0.8
0.6
0.4
Migration (relative) Migration 0.2
0 Control 100 nM 200 nM 500 nM
Insulin
Figure 3.7 – Effects of hyperinsulin on cell number and migration. ‘Hyperinsulinemic’ conditions were achieved by adding insulin into normal medium. Increasing doses of insulin were tested to study the effects of ‘hyperinsulinemia’. However these did not affect cell number (A) or transwell migration (B) of MDA- MB-231 cells (P>0.05; n=3-4).
111
3.3.2 Effects of AG1024 and OSI-906
Effects of AG1024 and OSI-906 were tested on Matrigel invasion. Initially, their optimum test concentrations were determined.
3.3.2.1 Cell viability and cell number
At their individual highest concentrations tested (20 and 5 µM), both AG1024 and
OSI-906, respectively, did not cause any toxic effect on MDA-MB-231 cells over the test time period (Figure 3.8A & 3.9A). As regards to cell number, over a treatment period of 24 h, there was a dose-dependent inhibitory effect of AG1024 (P<0.05-
0.01; n=3; Figure 3.8B). No effect was observed in the case of OSI-906 (Figure
3.9B). For both inhibitors, none of the corresponding concentrations of DMSO did affect cell viability or cell number (Figure 3.8 & 3.9). According to these data, 5 and
3 µM were adopted as optimum working concentrations for the subsequent functional assays for AG1024 and OSI-906, respectively.
3.3.2.2 Matrigel invasion
Overnight treatment with 5 µM AG1024 significantly reduced invasiveness of MDA-
MB-231 cells by ~25 %, compared with the corresponding DMSO control (0.05 %).
At this concentration DMSO did not have any affect. When TTX (5 µM) was applied for the same time period, there was a significant reduction in Matrigel invasion (by
~20 %). Combination treatment with AG1024 + TTX (5 µM each) had the same effect as 5 µM AG1024 alone (Figure 3.10A).
OSI-906 (3 µM) reduced the number of MDA-MB-231 cells invading in
Matrigel assays by some ~45 % (Figure 3.10B). This effect was highly significant.
112
A 105
100
95
90
Viability (%) Viability 85
80 Control DMSO AG1024
B
2.5 DMSO (%) AG1024 (uM)
2
1.5
1 *
Cell number (relative)number Cell ** 0.5
0 Control 0.01 % 0.05 % 0.1 % 0.2 % 1 uM 5 uM 10 uM 2 uM
Figure 3.8 – Effects of IIS inhibitors on cell viability and number – AG1024. (A) Any possible toxic effect of the inhibitor and of its solvent (DMSO) on MDA- MB-231 cells was tested over 24 h at 20 µM concentration (the corresponding DMSO concentration was 0.2%) (n=2). No cellular toxicity was observed. (B) At concentrations greater than 10 µM, AG1024 reduced cell number over 24 h. The effect was dose-dependent. The corresponding concentrations of DMSO (0.01 – 0.2 %) had no effect. The effects of DMSO were compared to untreated cells (“control”). ((*) for P<0.05 and (**) for P<0.01; n=3).
113
A 105
100
95
90
Viability (%) Viability 85
80 Control DMSO OSI-906
B
2.5 DMSO (%) OSI-906 (uM)
2
1.5
1
Cell number (relative)number Cell 0.5
0 Control 0.0001% 0.0005% 0.001% 0.005% 0.1uM 0.5uM 1uM 5uM
Figure 3.9 – Effects of IIS inhibitors on cell viability and number – OSI-906. (A) There was no toxic effect of inhibitor at 5 µM concentration on MDA-MB-231 cells treated for 48 h (n=3). (B) Similarly, various doses (between 0.1 – 5 µM) of OSI-906were did not change the cell number after 24 h of treatment compared to respective DMSO concentrations (n=3). All concentrations of DMSO tested did also not affect either cell viability or cell number when compared with untreated cells (“control”).
114
A 1 *
0.8 *** *** x 0.6
0.4
Invasiveness(relative) 0.2
0 Control-1 Control-2 TTX AG1024 AG1024 + TTX
B 1 * *** 0.8 ***
0.6 ***
0.4
Invasiveness(relative) 0.2
0 Control-1 Control-2 TTX OSI-906 OSI-906 + TTX
Figure 3.10 – Effects of IIS inhibitors on Matrigel invasion. (A) AG1024 and (B) OSI-906. (A) MDA-MB-231 were treated with AG1024 (5 µM), TTX (5 µM) or both in combination. Both agents reduced invasion significantly compared to their respective controls; however, their coapplication had no further effect. (B) OSI-906 (3 µM) also showed significant reduction in invasiveness. When it was applied in combination with TTX (5 µM), there was even a bigger reduction and this was significantly different from the effect of OSI-906 alone. (Control-1: normal medium; control-2:corresponding doses of DMSO (0.05 and 0.003 % for AG1024 and OSI- 906, respectively) (n=12 fields of view from 6-8 inserts).
115
Sole application of TTX (5 µM) significantly reduced invasiveness again by some 20
%. Combined treatment of OSI-906 and TTX at the same doses reduced invasion significantly further than for OSI-906 alone (Figure 3.10B).
The possible effects of 24 h treatment with AG1024 (5 µM) and OSI-906 (3 µM) on functional VGSC expression was investigated by whole-cell patch-clamp recording.
There was no effect on current density for AG1024 (Appendix 1.2). Surprisingly, at the concentration tested, OSI-906 significantly increased current density (by some 33
%) (Appendix 1.3).
3.3.4 Effects of dual InsR-IGF1R siRNA
In the experiments described in this section, two individual siRNAs targeting InsR and IGF1R were used in combination to silence IIS as a whole.
3.3.4.1 Control transfection experiments
The non-targeting siRNA sequence (“siControl”) was tested to ensure lack of effect on the two target genes (InsR and IGF1R). Compared with the cells treated with the transfection reagent Lipofectamine 2000 only (“mock-treated”), there was no difference in levels of both InsR and IGF1R mRNAs over 120 h (Figure 3.11A).
Similarly, protein levels of InsR and IGF1R were found not to be affected by the siControl treatment (Figure 3.11B).
3.3.4.2 Specificity of individual siRNAs
In order to choose the optimum siRNA sequences for InsR and IGF1R, in initial pilot experiments, two single siRNAs for each receptor were tested: si-IGF1R(1) and si-
IGF1R(7) (for IGF1R), and si-InsR(2) and si-InsR(3) (for InsR). Forty eight hours
116
A
A
2 IGF1R InsR
1.5
1
0.5 mRNA expression (relative) mRNA
0 48 h 72 h 120 h
B 2 IGF1R
InsR 1.5
1
0.5 Protein expression (relative)Protein
0 48h 72h 120h
Figure 3.11 – Effect of siControl transfection on IGF1R and InsR expression. The mRNA (A) and protein (B) expression levels of both targets were not affected over 120 h post-transfection. The siControl-treated cells were compared to mock- treated (Lipofectamine only) cells (n=1-3 (error bars shown only for n=3)). The horizontal dashed lines indicate the mock treatment level.
117 after transfection with 40 nM siRNA, mRNA and protein levels of each receptor were assessed. Specific suppression was observed for both receptors with the corresponding siRNAs with hardly any cross-silencing (Figure 3.12). Knock-down also occurred at protein level (Figure 3.13). For IGF1R, si-IGF1R(7) gave the best result, causing 75 % and 89 % reduction in mRNA and protein levels, respectively, compared to non-specific control siRNA (Figures 3.12A and 3.13A). In the case of
InsR, si-InsR(3) was best, decreasing mRNA and protein levels by 70 % and 95 %, respectively (Figures 3.12B and 3.13B, respectively). In conclusion, si-IGF1R(7) and si-InsR(3) were chosen for the subsequent dual-siRNA transfections.
3.3.4.3 Dual siRNA transfections
In order to inhibit the InsR-IGF1R system (IIS) as a whole, MDA-MB-231 cells were co-transfected with si-IGF1R(7) and si-InsR(3) siRNAs with a total concentration of 40 nM (20 nM each). First, the effects of 20 nM of each siRNA were tested alone on both receptors (Figure 3.14). The corresponding receptor mRNA levels were reduced significantly by 55 – 75 %. Interestingly, there was a tendency for the ‘non-targeted’ receptor mRNA to increase (also apparent in Figure
3.12). This effect reached significance in the case of InsR (Figure 3.14). These observations were consistent with InsR undergoing ‘compensatory’ increase in response to suppression of IGF1R, and vice versa.
The effect of dual-silencing with 40 nM total (combined) siRNA was tested on InsR and IGF1R mRNA levels over 120 h (Figure 3.15A). Both receptors were suppressed to the same extent (40 - 50 %); both changes were stable during the test period (48 – 120 h) and highly significant. The mRNA changes described above were reflected at protein level (Figure 3.15B). Again, stable reductions of 75 – 95 %
118
A
1.5
1 (relative) 0.5 IGF1R mRNA expression 0 siControl si-IGF1R (1) si-IGF1R (7) si-InsR (2) si-InsR (3)
B
1.5
1 (relative) 0.5 InsR mRNA expression 0 siControl si-IGF1R (1) si-IGF1R (7) si-InsR (2) si-InsR (3)
Figure 3.12 – Typical effects of single siRNA transfection on IGF1R and InsR mRNA expression. After transfecting MDA-MB-231 cells with four different siRNA sequences individually (40 nM each), the change in mRNA expression was tested by real-time PCR 48 hours post-transfection. The tested siRNAs showed specificity for their targets as shown in (A) for IGF1R and in (B) for InsR. The mRNA levels were expressed relative to the siControl level for each siRNA (dotted lines).
119
siControl si-IGF1R(1) si-IGF1R(7) A IGF1R
Actin
1.2
0.8 expression
0.4 (relative)
0 IGF1R protein siControl si-IGF1R (1) si-IGF1R (7)
B siControl si-InsR(2) si-InsR(3)
InsR
Actin
1.2
0.8 expression
(relative) 0.4 R protein protein R ns I 0 siControl si-InsR (2) si-InsR (3)
Figure 3.13 – Typical effects of single siRNA transfection on IGF1R and InsR protein expression. The change in protein levels of IGF1R (A) and InsR (B) were investigated 48 after transfection with single siRNAs (two per each target) and compared to siControl-transfected cells (all at 40 nM concentration). Corresponding western blot images are shown on top of the histograms.
120
1.6 IGF1R mRNA * 1.2 InsR mRNA
0.8 ** 0.4 ** mRNA expression level(relative) mRNA 0 si-IGF1R si-InsR
Figure 3.14 – Effects of given siRNAs (from the dual-siRNA) on ‘target’ and ‘non-target’ receptors. Since individual siRNAs targeting InsR and IGF1R are used at 20 nM concentrations in dual transfections, the sole effect of of each siRNA was tested alone on both receptors. In both cases, after 72 h post-transfection, the level of the ‘target’ receptor mRNA was reduced significantly by 55 – 75 % (compared to siControl) (P=0.004 and 0.002 for InsR and IGF1R, respectively; n=3 for both) whilst the ‘non-target’ did not change or showed an increase (P=0.04 for InsR mRNA targeted with siIGF1R; n=3 for all cases).
121
A 2 IGF1R InsR
1.5
1 (**) (*) (***)
0.5 ** ** ** mRNA expression (relative) mRNA
0 48 h 72 h 120 h
B
2 IGF1R
InsR 1.5
1 ( ) *** *** 0.5 Protein expression (relative)Protein
0 48h 72h 120h
Figure 3.15 – Effects of dual siRNA transfection on InsR and IGF1R expression. (A) Compared to siControl, both receptor mRNAs were significantly reduced when measured at several time points after transfection (P=0.00003–0.04; n=3-4). (B) A higher degree of reduction was observed at protein level, which was significant at 120 h for both receptors (n=1–4). Open asterisks indicate comparisons for IGF1R; asterisks in brackets indicate comparisons for InsR. The horizontal dashed lines indicate the siControl level. Error bars indicate SEM (none for n=1).
122 of both receptor expressions were observed during the 48 – 120 h test period. From the results described in sections 3.3.4.2 and 3.3.4.3, the following conditions were adopted as optimal for the subsequent dual-siRNA experiments: 1) a time window of
48 - 72 hours following siRNA transfections, and (2) a concentration of 20 nM of each siRNA.
3.3.4.4 Effect of dual-siRNA transfection on metastatic cell behaviours
Before evaluating any effect of inhibiting IIS overall on MCBs of MDA-MB-231 cells by dual-siRNA, the possible change in cell number was determined (Figure
3.16). These experiments were carried out in parallel with the MCB assays, under comparable conditions (especially, 1 % FBS was also present). There was no change in cell number during the experimental time window adopted. Two MCBs were assessed (transverse migration and Matrigel invasion) and both were reduced significantly by transfecting the cells with the dual-siRNA (Figure 3.17). Transverse migration was reduced by 30 % (Figure 3.17A). Matrigel invasion was similarly reduced by 38 % (Figure 3.17B).
3.3.4.5 Effects on nNav1.5 expression
In preliminary experiments (n = 1-3), the effects of transfecting MDA-MB-231 cells with the dual-siRNA on nNav1.5 mRNA expression was tested (Figure 3.18). In the dual-siRNA treated cells, nNav1.5 mRNA levels appeared around 50 % lower than the corresponding siControl’s (Figure 3.18A). VGSC-β1 mRNA levels were similarly reduced by around 40 % (Figure 3.18B).
123
2
1.5
1
0.5 Cell number (relative)number Cell
0 siC siR siC+TTX siR+TTX
Figure 3.16 – Effect of dual-siRNA on cell number. Experiments to test any difference in cell number between siControl (siC) and dual-siRNA (siR) cells were carried out in parallel with MCB assays under similar conditions (i.e. cells plated in 1 % FBS containing medium for the same duration). TTX (10 µM) was also added to provide some initial controls for future experiments. None of these conditions showed a significant change in cell number (P>0.05; n=3).
124
A 120 *
100
80
60
Migration (%) Migration 40
20
0 siC siR
B 1 *** 0.8
0.6
0.4
Invasiveness(relative) 0.2
0 siC siR
Figure 3.17 – Effect of dual-siRNA on MCBs. Two different metastatic cell behaviours were studied: Transverse migration (A) and Matrigel invasion (B). In both cases, transfection with the dual-siRNA (siR) reduced the measured parameter significantly compared to siControl (siC) (P<0.05; n=4 inserts for (A) and n=12 fields of view from 4 inserts for (B)).
125
A
2
1.5
1 (relative)
0.5 nNav1.5 mRNA expression nNav1.5 mRNA
0 48 h 72 h 120 h
B 2
1.5
1 mRNA expression mRNA (relative) *** ***
0.5 VGSC- β 1
0 48 h 72 h 120 h
Figure 3.18 – Effect of dual-siRNA on VGSC mRNAs. Two different VGSC characteristics were measured: nNav1.5 mRNA (A) and VGSC-β1 mRNA (B) over 120 h. In both cases, significant decreases were observed, at least at some of the measured time points (n=1-3 (error bars shown only for n=3)). In (A), for 72 h, both measured values are indicated. The horizontal dashed lines indicate the siControl level.
126
Imunocytochemical experiments were carried out with NESOpAb antibody against nNav1.5 (Chioni et al., 2005) (Figure 3.19). With NESOpAb, a minimum amount of poly-L-lysine was used for cell attachment, since adhesion was shown previously to affect VGSC expression (Chioni et al., 2009). Furthermore, cells were not permeabilised in order to focus more on plasma membrane staining. Under these conditions, cells were stained extensively with NESOpAb, whilst no reaction was observed in the negative controls (autofluorescence and secondary antibody alone).
Comparing the dual-siRNA transfectants with the siControls suggested little change in overall nNav1.5 protein expression (Figure 3.19).
Finally, the effect of transfecting MDA-MB-231 cells with the dual-siRNA on
VGSC activity was studied by patch clamp recording. The siRNA treatment reduced current density significantly by ~45 % (Appendix 1.4).
3.4 Discussion
The main results from the studies conducted on these cells in this chapter are as follows:
1. MDA-MB-231 cells expressed both InsR (subtypes A and B) and IGF1R mRNA and protein. This confirmed previous findings (Pandini et al., 1999; Zhang et al., 2007). Thus, IIS was present in the cells.
2. Exogenous insulin (applied under serum-free conditions) significantly enhanced MCBs (lateral motility and Matrigel invasion); however, co-treatment with
TTX produced anomalous effects, further enhancing MCB or having no effect.
3. Treating the cells with AG1024 (in normal medium) reduced both lateral
127
Figure 3.19 – Effect of dual-siRNA on nNav1.5 protein expression. Cells were labelled immunocytochemically with NESOpAb antibody under non-permeabilised conditions. Two different control treatments were also undertaken which showed (i) that autofluorescence from the cells was minimal and (ii) that the secondary antibody alone gave no staining. Phase-contrast (PC) and fluorescent (FL) images of siControl and dual-siRNA transfected cells are shown. Scale applies to all.
128 motility and Matrigel invasion. Co-treatment with TTX produced no further effect.
4. Treating the cells with OSI-906 (in normal medium) reduced Matrigel invasion; co-treatment with TTX reduced it further.
5. Silencing InsR and IGF1R simultaneously with dual-siRNA, suppressed both transverse migration and Matrigel invasion.
6. The results of the co-treatment experiments taken together with the electrophysiological data suggested that the relationship between IIS and VGSC
(nNav1.5) expression / activity in the control of MCBs was complex. The effects of the various treatments aimed at inhibiting endogenous IIS on MCBs and VGSC activity observed are summarised in Table 3.1.
3.4.1 Effect of insulin on MCBs and the role of the cellular environment
Exogenous insulin application significantly increased MCBs, including lateral motility and invasion. This is in agreement with past findings that have demonstrated application of ligands of the IGF1R-InsR pathway to increase motility of BCa cells in vitro, including MDA-MB-231 cells (e.g. Doerr & Jones, 1996; Jackson et al.,
2001; Pan & Djamgoz, 2008). Generally, it is considered that IIS signalling (insulin and IGF1) promote cellular motility and invasion through pathways involving Shc and IRS-1 (e.g. Sachdev & Yee, 2001). However, a recent microarray analysis of
IGF-1-induced migration of BCa cells by Kashyap et al (2011) has also implicated additional genes, including tissue factor (F3) which is generally found in the leading edge of tumours (Ott et al., 1988); ephrin-B2 (EFNB2) which modulates integrin activity, reorganization of actin cytoskeleton and lamellipodia formation (Meyer et al
129
Table 3.1 – Summary of the effects of inhibiting IIS on MCBs and VGSC
(nNav1.5) activity in MDA-MB-231 cells
MCB VGSC (nNav1.5) TREATMENT TTX Matrigel Activity invasion
- ↓ 0 AG1024 + 0 ―
- ↓ ↑ OSI-906 + ↓ ―
- ↓ ↓ dual-siRNA + ― ―
Three different ways of inhibiting IIS were used: AG1024, OSI-906 and dual-siRNA. In some cases, the effect of co-treatment with TTX (5 µM) was also tested. Upward and downward arrows indicate increase and decrease, respectively. 0 indicates no change. Dashes (-’s) indicate not tested.
130
2005); plasminogen activator inhibitor-1 (PAI-1) which increases filipodia formation
(Chazaud et al., 2002) and stimulates migration through mechanisms involving urokinase plasminogen activator receptor (uPAR) (Waltz et al., 1997).
Interestingly, it should be noted that exogenous insulin application has also been shown to inhibit invasion of BCa cells (Bracke et al., 1993). In the present study, it was clear that the biochemical content of the cells’ environment had a significant impact on the control of the cells’ MCBs, especially as regards the role of the VGSC (nNav1.5). Over the years, a variety of papers have reported (i) that human metastatic carcinoma cells express functional VGSCs and (ii) VGSC activity potentiates MCBs, including invasion; accordingly, TTX would normally suppress
MCBs (e.g. Roger et al., 2003; Fraser et al., 2005; Laniado et al., 1997; House et al.,
2010). In contrast, in the presence of insulin, TTX either had no or the opposite effect, increasing MCBs. Comparable results were obtained earlier by Pan &
Djamgoz (2008). This anomalous effect has two implications:
1. General. The cells’ biochemical environment can exerts a significant influence upon cancer cell behaviour. This is in line with the suggested importance of tumour – stroma cellular interactions in cancer progression (Schmidt-Kittler et al.,
2003; Fidler, 2003; Hunter, 2004; Hunter et al., 2008). This is thought to be two-way involving signalling via growth factors and cytokines (Cullig, 2011; Yang et al.,
2011).
2. Specific. The anomalous effect of TTX in the presence of insulin can be explained if the medium changed the mode of the functional association between
VGSC and the cells’ metastatic machinery. As regards the latter, the cytoskeleton is likely to be involved. Some examples and relevant references were given above in this section and this issue is discussed further in section 3.4.4.
131
In the rest of the present study, it was decided to avoid using serum-free medium. Instead, another basic strategy of inhibiting IIS endogenously in normal growth medium was adopted.
3.4.2 Functional effects of inhibiting endogenous IIS
Endogenous IIS signalling in MDA-MB-231 cells was inhibited in 2 independent ways: i) Pharmacological (AG1024 and OSI-906) and (ii) dual-siRNA.
3.4.2.1 Lack of effect on cell growth
Exogenous application of 100 nM insulin or blockage of the InsR-IGF1R system with 5 µM AG1024, 5 µM OSI-906 or the dual siRNA targeted against InsR-IGF1R had no effect on cell growth. This is another example of primary vs secondary tumourigenesis being controlled differently in cancer cells, as reviewed in detailed in the General Introduction. However, concentrations greater than 5 µM AG1024 did reduce cell growth in a dose-dependent manner. This is in agreement with many past findings that have demonstrated that both IGF1R and InsR pathways can control cell growth in cancer cells (e.g. Dupont & LeRoith, 2001). However, at the concentrations relevant here (~5 µM), AG1024 would be expected to inhibit IGF1R, rather than InsR (Parrizas et al., 1997). It should be noted that the present study was interested in the effects of the InsR-IGF1R system primarily on MCBs; cell number was investigated only because any change in cell number would bias the lateral motility and invasion assays used. Thus, the mechanisms behind the reduction in cell growth were not studied in greater detail here but have been demonstrated previously in BCa to include Ras-Raf-MEK-ERK1/2 (Sachdev & Yee, 2001).
132
3.4.2.2 Effects on MCBs
All 3 different ways of inhibiting IIS produced consistent results, i.e. MCBs were suppressed (Table 3.1). This agrees generally with previous findings showing that the
InsR-IGF1R system controls (enhances) the invasive potential of cancer cells (Dunn et al., 1998 & 2000; Guvakova, 2007).
AG1024 has been extensively as an inhibitor of InsR and IGF1R tyrosine kinase (e.g. Perrault et al., 2011; Zhao et al., 2011), including in MDA-MB-231 cells
(Chong et al., 2006). Here, 5 µM AG1024 reduced both lateral motility and Matrigel invasion of MDA-MB-231 cells. Similarly, AG1024 (20 µM) reduced the invasive potential of the glioblastoma multiforme (GBM) cell line, possibly through the PI3-K pathway (Chakravarti et al., 2002). Potential involvement of the PI3-K pathway in invasion of the MDA-MB-231 cell line remains to be determined.
OSI-906 (3 µM) significantly reduced both lateral motility and invasion of the MDA-MB-231 cell line in comparison with the control condition. OSI-906 is a relatively new IGF-IR/IR tyrosine kinase inhibitor (Mulvihill et al., 2009) and has not been extensively studied. Previous work has indicated that OSI-906 also has anti- proliferative effects in several cancer cell lines and in vivo anti-tumour efficacy
(Flanigan et al., 2010; Mulvihill et al., 2009). The present study is the first to report its effects on MCBs and raises the possibility that OSI-906 may be effective as an anti-metastatic agent.
The blocking ability (and specificity) of AG1024 and OSI-906 for the
IGF1R-InsR pathway are very different. AG1024 blocks IGF1R and IR with IC50s of
7 µM and 57 µM, respectively (Párrizas et al., 1997). In contrast, OSI-906 is considered a very specific blocker of the IGF1R and InsR tyrosine kinases, causing complete block of both at 1 µM, whilst blockage of all other kinases showed an IC50
133 of greater than 10 µM (Mulvihill et al., 2009). Since at the concentrations used, OSI-
906 would cause complete inhibition of both IGF1R and InsR tyrosine kinases whilst
AG1024 would not, it is perhaps not surprising that OSI-906 induced a larger reduction in invasion (~50 %) in comparison to AG1024 (~30%).
Since OSI-906 (i) would cause complete inhibition of the IGF1R and InsR tyrosine kinases but (ii) TTX could induce a further significant reduction in invasion, these results are suggestive of the VGSCs and the IGF1R-InsR pathway working independently to reduce invasiveness of the MDA-MB-231 cells. IIS is thought to promote invasion through pathways such as Shc and IRS-1 (e.g. Sachdev & Yee,
2001) and PI3-K (Chakravarti et al., 2002; Mulvihill et al., 2009). At present, however, little is known about the mechanisms through which VGSCs operate to promote invasion. A recent report by Gillet et al. (2009) demonstrated that VGSC activity enhanced invasiveness of the MDA-MB-231 cells by acidifying the peri- membrane area, thereby enhancing the proteolytic activity of membrane-bound or soluble cathepsins B and S. However, the precise mechanism through which VGSC activity would lead to peri-membrane acidification was not determined (Gillet et al.,
2009).
A novel dual-siRNA approach was developed here to inhibit IIS as a whole, in order also to overcome any compensatory change that may result from partial inhibition of IIS. The real-time PCR and western blot data indicated that the siRNAs adopted significantly reduced the mRNA and protein levels of IGF1R and InsR by 65
- 90 %. Matrigel invasion assays also showed a reduction in invasion by about 30-40
% indicating that the ‘knock-down’ was of functional significance. These results are in agreement with the results found when using the pharmacological blockers
(AG1024 and OSI-906) and again indicate that IGF1R and InsR activities are
134 involved in promoting MDA-MB-231 cell invasiveness (also, Dunn et al., 1998 &
2000; Guvakova, 2007).
3.4.3 IIS - VGSC (nNav1.5) interaction in control of MCBs
Previous research has consistently indicated that VGSC activity in MDA-MB-231 cells potentiates MCBs (i.e. directional motility, adhesion and invasion) and that blockage of VGSC activity (either with TTX or siRNA) reduced MCBs (Fraser et al.,
2005; Brackenbury et al., 2007). However, when 6 µM TTX was added to serum- free medium (containing only 0.1% BSA) invasion of the MDA-MB-231 cells was significantly increased. These results would suggest that the lack of FBS (i.e. the growth factors / hormones contained within) altered the functioning/pathways through which the VGSC operated. A similar result had been reported previously for cell motility (both lateral and transverse motility) and adhesion using a commercial serum replacement medium (SR-2), which contains just insulin and BSA only (Pan
& Djamgoz, 2008). Taking the available evidence together, Pan & Djamgoz (2008) concluded that the biochemical constitution of the extracellular medium had a significant impact upon the mode of the functional association between VGSC activity and metastatic machinery of MDA-MB-231 cells.
The quantity and quality (pattern) of VGSC expression following dual-siRNA targeting of the the InsR-IGF1R system was also determined. nNav1.5 is known to be the predominant VGSC subtype expressed in the MDA-MB-231 cells whilst
Nav1.7 is a minor functional channel type present (Fraser et al., 2005). Staining patterns using NESOpAb (to target the nNav1.5 specifically) revealed no obvious change in either the level or distribution of the channel protein. These results are interesting in the light of evidence from other studies (on cultured bovine adrenal
135 chromaffin cells and Xenopus oocytes) showing that the InsR-IGF1R system affects the functional VGSC expression in plasma membrane (e.g. Yamamoto et al., 1996;
Charpentier, 2005).
The apparent anomalous effect of TTX on MCBs under conditions of non- optimal IIS signalling cannot be explained by any effect of IIS on functional expression of VGSC (nNav1.5) alone. Instead, it is necessary to assume that IIS also controls the mode of the VGSC-induced enhancement of MCBs, i.e. IIS controls the
‘link’ between the VGSC and the cells’ metastatic machinery. Two such links are possible: 1) Na+ - H+ exchanger (NHE1) (Gillet et al., 2009; Brisson et al., 2011); and (2) Na+-Ca2+ exchanger (NCX) (Figure 3.20). It is possible that IIS could affect either ‘link’ in such a way as to suppress (or even reverse) the VGSC – MCB functional association. For example, VGSC activity and the resulting Na+ influx may force NCX to operate in reverse-mode, at least locally (Torres et al., 2010).
Interestingly, a recent paper showed that IGF1 would inhibit the outward current of
NCX (corresponding to the reverse-mode) in neurones (Sanchez et al., 2011). If such an effect were to occur in insulin-containing serum-free medium, it could shed some light on the anomaly in question. Thus, inhibition of reverse-mode NCX (RM-NCX) would mean a ‘break’ in the VGSC – MCB connection, leading to loss of effectiveness of TTX, as seen (Figure 3.7). Further work is required to understand how IIS can affect MCBs via the VGSC macro-molecular complex.
3.4.4 Conclusion
In general conclusion, the present study has highlighted the importance of IIS signalling in control of MCBs in BCa and the key role played by the cells’ biochemical environment in such control. The VGSC-dependent component of the
136 control mechanism was particularly sensitive to these conditions. Furthermore, the overall control mechanism is likely to be dynamic, some of the changes seen in nNav1.5 mRNAs for example, becoming functional at later stages and/or under different conditions.
137
Figure 3.20 – A conceptual scheme of VGSC-dependent and VGSC-independent control of metastatic cell behaviours. The two sets of mechanisms can co-occur and summate, as indicated (box “Σ”). The VGSC-dependent component, in turn, may involve HNE1 and NCX as functional ‘linkers’. Extended from the original scheme of Pan & Djamgoz (2008).
138
Chapter 4
(Results 2)
STUDIES OF CANCER-ASSOCIATED ION CHANNEL EXPRESSION
IN HUMAN BLOOD
139
4.1 Introduction and rationale
A novel field of studies developed over the last 15 years have shown that various ion channels, including voltage-gated sodium channels (VGSCs), are expressed in human cancer cells and tissues. These cancer-associated ion channels (CAICs) include the following: neonatal Nav1.5 (nNav1.5), Nav1.7, VGSC-β1 (Beta1),
VGSC-β1B (Beta1b), Kv1.3, Kv10.1, Kv11.1, TRPM8 and KCa3.1 (reviewed in section 1.3; also, Prevarskaya et al., 2010). Two potentially important issues have emerged from these studies:
First, where studied, CAIC, especially VGSC, expression was found to be neonatal, in line with oncofetal gene expression (Fraser et al., 2005; Onkal &
Djamgoz, 2009). The most marked example of this so far is Nav1.5 for which the neonatal splice form differs from the adult by seven amino acids, six of which are in the extracellular region of the D1:S3/S4 loop (Figure 1.6). Neonatal Nav1.5
(nNav1.5) expression / upregulation has been shown to occur in strongly metastatic cells of human breast cancer (BCa) (Fraser et al., 2005) and colon cancer (Chapter
5).
Second, again where studied, upregulation of VGSC expression occurred together with downregulation of voltage-gated potassium channel (VGPC) expression in strongly metastatic cells. Examples of such simultaneous changes were observed in studies comparing weakly and strongly metastatic cells of human BCa
(Fraser et al., 2005), rat and human prostate cancer (PCa) (Grimes et al., 1995 and
Laniado et al., 1997, respectively) and human lung cancer (Roger et al., 2006). These reciprocal changes in VGSC:VGPC currents would indicate (i) that strongly metastatic cells do have electrically excitable membranes and (ii) that such
140 ‘excitability’ could be one of the reasons behind the ‘hyperactive’ behaviour of strongly metastatic cells. This phenomenon was described as the “Cellular
Excitability” (CELEX) hypothesis of metastatic disease (Djamgoz & Isbilen, 2006;
Djamgoz, 2011).
The rationale of the studies to be described in this chapter is based upon these two issues. In the first instance, VGSC (especially nNav1.5) expression has been found in primary tumour biopsies (Fraser et al., 2005) and VGSC expression has been proposed to be “necessary and sufficient” for tumour cell invasion (Bennett et al., 2004). Consequently, expression of VGSC mRNA, especially neonatal splice form, may be expected in circulating tumour cells (CTCs) in blood of adult cancer patients. However, various ion channels, including VGSCs, are known to be expressed by normal blood cells, including lymphocytes and some other cells of the human immune system (e.g. dendritic cells) (DeCoursey et al., 1985; Lai et al., 2000;
Fraser et al., 2004, Roselli et al., 2006; Zsiros et al., 2009). Thus, before evaluating
CAIC expression status in cancer patients, it is necessary to determine the background expression levels in healthy individuals. In particular, detailed analyses of expression patterns of different VGSC splice isoforms and the specific cell types expressing these isoforms in normal human blood has not been previously performed. It is possible that some CAICs will be expressed in healthy subjects. For example, Kv1.3 is well known to be abundant in human lymphocytes (Cahalan &
Chandy, 2009). It is possible, nevertheless, that the relative amounts of CAICs
(rather than the absolute levels) change in cancer compared with normal. Such a situation has been seen in pre- vs post-inflammatory dendritic cells in which, upon maturation, Nav1.7 and Kv1.3 co-expression respond in opposite ways (Zsiros et al.,
2009). Similarly, the KCa3.1:Kv1.3 ratio in T- and B-lymphocytes changed
141 dynamically during activation (Cahalan & Chandy, 2009).
4.2 Aims and scope
This part of the study was aimed at ultimately evaluating the biology and clinical relevance of CAIC mRNA expression in blood of healthy human subjects (first part) with a view to comparing the data from cancer patients (second part). The specific aims were as follows:
1. To adopt a set of PCR primers for selective detection of CAIC mRNAs and where necessary (as in the case of nNav1.5) validate the primers in question;
2. To test whether Kv1.3 mRNA level can serve as a reference for quantitative expression of other CAIC ratios;
3. To determine CAIC : Kv1.3 mRNA ratios for normal human subjects; and
4. To compare these analyses with comparable data from cancer patients.
4.3 Results
4.3.1 Ethical aspects
Prior to the start of the studies on human blood, it was necessary to obtain the necessary ethical permission (Appendix 2). This was in two parts. For studying normal human blood, approval was obtained from National Research Ethics Service,
North London REC 1 (Appendix 2.1). For cancer patients, it proved to be more expedient to collaborate with the Institute of Cancer, Istanbul University Medical
School (Appendix 2.2).
142 4.3.2 Technical aspects
Since, in particular, nNav1.5 mRNA was to be probed in human blood by PCR for the first time, some initial tests were carried out, as follows.
4.3.2.1 Validation of nNav1.5 primers
Primers were designed to amplify the neonatal splice isoform of Nav1.5 with high specificity (Figure 4.1). For this purpose, it was necessary to critically differentiate between nNav1.5 and its ‘nearest neighbour’, the adult Nav1.5. These two isoforms differ by 31 nucleotides. Primers were designed to specifically target this region of difference (Figure 4.1A). The high specificity of the primers for nNav1.5 was confirmed by BLAST analysis (Figure 4.1B).
The specificity of the designed nNav1.5 primers was validated experimentally by using clones containing either neonatal or adult splice isoform of
Nav1.5 as templates (Figure 4.2). These clones were kindly provided by Dr J. K. J.
Diss. Use of the neonatal primers in PCR reaction on both templates gave positive amplification only for the neonatal clones; no product was observed for the adult templates. The integrity of the adult clones was confirmed in PCR using the adult primers. In all cases, the PCR products obtained were of the expected correct sizes
(Figure 4.2).
4.3.2.2 Primer efficiencies
“Standard” curves for the linearity of the PCR reactions were prepared using plasmids for the target of particular interest, nNav1.5, and the normalizing control,
PUM1. Serial dilutions of the plasmids ranged from 10 to 105 transcripts per µl for nNav1.5 and from 1 to 105 transcripts per µl for PUM1. From each dilution, 5 µl
143 A
B
Figure 4.1 – Design of theoretical validation of nNav1.5 primers. (A) Specific primers for nNav1.5 are designed with a reverse primer to recognise a part of the region of difference between the adult and neonatal variants. The different nucleotides on the aligned variants are indicated by dots and the primer sequences are shown in yellow. (B) Specificity was further confirmed by ‘blast’ analysis. NCBI database contains 4 entries with neonatal sequences. All of these were recognised by the primers.
144
L 1 2 3 4 N
Figure 4.2 – Validation of nNav1.5 primers. When conventional PCRs were conducted on two sets of neonatal and adult clones, positive results were only seen with neonatal ones (lanes 1 and 2) whilst adult clones (lanes 4 and 5) were negative within the same reaction. The amplified products were of expected correct sizes when visualised on 1 % agarose gel. Lane numbers indicate the following:
L : DNA ladder 1 : Neonatal Nav1.5 clone 1 2 : Neonatal Nav1.5 clone 2 3 : Adult Nav1.5 clone 1 4 : Adult Nav1.5 clone 2 N : non-template control
145 was used as template for PCR. Standard curves were obtained by plotting the measured threshold cycle numbers [C(T)s] against the logarithm of the corresponding concentrations. From these, the following PCR efficiencies were calculated (Pfaffl, 2004): 99 % (nNav1.5) and 98 % (PUM1) (Figure 4.3).
4.3.2.3 Tests of PCR assay variability
Reproducibility of the PCRs was assessed by testing for possible intra- and inter- assay variability (Figure 4.4 & Figure 4.5). The standards were run either in one
PCR in repeats (intra-assay – Figure 4.4A and Figure 4.5A) or in separate PCRs on different days (inter-assay variability – Figure 4.4B and Figure 4.5B). Both tests showed high degrees of reproducibility for both nNav1.5 and PUM1, with coefficient of variation values between 0.01 and 0.04 %.
4.3.3 Normal human blood
4.3.3.1 Initial sampling studies
Blood was obtained initially from two groups of healthy individuals: Group-1 (living in Istanbul; n = 13) and group-2 (living in London; n = 11). These were analysed separately. Expression of CAIC mRNA levels were to be expressed relative to
Kv1.3, since this was likely to have a robust presence, being present in most mononuclear cell (MNC) types. First, therefore, the pattern of Kv1.3 mRNA expression was determined. Surprisingly, there was a noticeable difference in the mean values and distributions of Kv1.3 mRNA levels in the two populations. It was decided, therefore, to restrict the populations (healthy and cancer) to be studied to one location. Since the cancer cases to be studied were to come from Istanbul, this was decided to be the location of the main study.
146
A 35 nNav1.5 30
25 20 C(T) 15 y = -3.3472x + 36.725 R2 = 0.998 10
5 0 1 2 3 4 5 6 Log (quantity)
B 35 30 PUM1 25 20 C(T) y = -3.3587x + 34.717 15 R2 = 0.996 10 5 0 0 1 2 3 4 5 6 Log (quantity)
Figure 4.3 – Calibration curves for nNav1.5 and PUM1. The calibration curves of nNav1.5 (A) and PUM1 (B) standards were generated by plotting the C(T) values against the corresponding logarithmic concentrations ranging from 5 to 5x106 copies. C(T) values correspond to the intersection between the amplification curve and the threshold line. Slopes and regression coefficients (R2) values are given adjacent to each line.
147
A 105 50 45
100 40 35 30 95 C(T) C(T) (%) 25 20 15 10 5 0 0 50 500 5000 50000 500000 Number of nNav1.5 transcripts
B 105 55 50 45 100 40 35 95 30 C(T) (%) C(T) 25 20 15 10 5 0 0 50 500 5000 50000 500000 Number of nNav1.5 transcripts
Figure 4.4 – Reproducibility of nNav1.5 PCRs. 10-fold serial dilutions of the DNA transcripts were run in repeats either on different days to quantify the error between separate assays (inter-assay variability) (A) or in the same PCR reaction to check error seen within the same assay (intra-assay variability) (B). The calculated “coefficient of variation” (CV) values were less than 0.05 %, and hence, standards proved to be reproducible.
148 A 105 60 55 50 100 45 40 35 C(T) C(T) (%) 95 30
25 20 15 10 5 0 0 5 50 500 5000 50000 Number of PUM1 transcripts
B 105 55 50 45 100 40 35 C(T) (%) 95 30 C(T) 25 20 15 10 5 0 0 5 50 500 5000 50000 Number of PUM1 transcripts
Figure 4.5 – Reproducibility of PUM1 PCRs. Similar to nNav1.5 PCRs, reproducibility of PUM1 PCRs were tested (interassay variability) (A) assay (intraassay variability) (B). The calculated “coefficient of variation” (CV) values were less than 0.05 %, and hence, standards proved to be reproducible.
149
nNav1.5 Beta1 Beta1b Kv1.3 Kv10.1 L + ‒ N L + ‒ N L + ‒ N L + ‒ N L + ‒ N
Kv11.1 KCa3.1 TRPM8 PUM1 L + ‒ N L + ‒ N L + ‒ N L + ‒ N
Figure 4.6 – Typical examples of CAIC real-time PCR products. Real-time PCR products from all CAIC primers were visualized on 1 % agarose gels. The products were all of correct sizes. None of the ‘no reverse transcriptase’ (i.e. (-)RT) sample gave amplification, confirming genomic DNA-free nature of experiments. All ‘no template controls’ were also clean. The lane labels indicate the following:
L : ladder + : cDNA ‒ : (-)RT N : no template control
150 4.3.3.2 CAIC mRNA expression patterns: Healthy controls
Typical gel pictures of CAIC real-time PCR products are shown in Figure 4.6. The following CAIC mRNAs were detected in MNC samples: nNav1.5, Beta1, Beta1b,
Kv1.3, Kv11.1 and KCa3.1; Kv10.1 was expressed at a low level whilst TRPM8 was not detectable. The [2-ΔC(T)] values for all CAICs studied are given in Table 4.1.
Kv1.3 had relatively the most regular distribution and there was only 4 % difference between mean and median (Figure 4.7A; Table 4.1); it was regarded as a ‘control’
CAIC for further experiments.
4.3.4 Blood from cancer patients: Comparison with healthy cases
Data for CAIC mRNAs from cancer patients are summarised in Table 4.1. Again, the values of Kv1.3 mRNA were normally distributed, re-validating its use as a ‘control’
CAIC (Figure 4.7B). Interestingly, however, the range of 2-ΔC(T) values for Kv1.3 appeared noticeably narrower and its mean value was 41 % lower compared with the healthy control. In addition, the following changes were observed (P<0.0001; Table
4.1 and Figure 4.8):
1. The following CAIC mRNAs were significantly increased: nNav1.5 (38- fold) and KCa3.1 (3-fold).
2. The following CAIC mRNAs were significantly reduced: Beta1 (35 %),
Kv1.3 (47 %) and Kv11.1 (41 %).
3. Interestingly, a preliminary analysis of CAIC mRNA expression levels relative to Kv1.3 suggested that the biggest change was for nNav1.5, as a possible reflection of CELEX hypothesis.
151 A 30
25
20
15 Number 10
5
0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
HEALTHY - Kv1.3 mRNA expression (relative)
B 40
35
30
25
20
Number 15
10
5
0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
CANCER - Kv1.3 mRNA expression (relative)
Figure 4.7 – Distribution of Kv1.3 mRNA expression in sample populations. Data is collected from (A) healthy individuals (n = 71) and (B) cancer cases (n = 44). The mean + SD values were 1.66 + 0.52 and 0.94 + 0.22 for healthy and cancer groups, respectively.
152
Table 4.1 – Expression levels of CAIC mRNAs. 2-ΔC(t) data from healthy controls and cancer patients are given for each CAIC.
CAIC Median Range Mean P value Healthy 0 0 – 1.5 x 10-5 1.9 x 10-6 nNav1.5 < 0.0001 Cancer 5.8 x 10-6 0 – 253 x 10-5 71.5 x 10-6
-3 -3 -3 Healthy 9.8 x 10 4.2 – 29.5 x 10 11.2 x 10 Beta1 < 0.0001 Cancer 6.6 x 10-3 1.8 – 18.2 x 10-3 7.3 x 10-3 Healthy 8.2 x 10-4 0.1 – 3.8 x 10-3 9.2 x 10-4 Beta1b 0.545 Cancer 8.2 x 10-4 0.1 – 111 x 10-3 33.4 x 10-4 Healthy 1.6 0.8 – 3.2 1.7 Kv1.3 < 0.0001 Cancer 0.9 0.2 – 1.6 0.9 Healthy 0 0 – 24.6 x 10-6 6.4 x 10-7 Kv10.1 0.576 Cancer 0 0 – 5.3 x 10-6 3.4 x 10-7 Healthy 3.1 x 10-2 1.7 – 8.3 x 10-2 3.2 x 10-2 Kv11.1 < 0.0001 Cancer 1.9 x 10-2 0.5 – 4.6 x 10-2 1.9 x 10-2 Healthy 2.9 x 10-5 0.9 – 33.5 x 10-5 4.7 x 10-5 KCa3.1 < 0.0001 Cancer 12.2 x 10-5 4.0 – 35.9 x 10-5 13.6 x 10-5 Healthy 0 0 0 TRPM8 0 Cancer 0 0 0
153
Figure 4.8 – Comparison of CAIC mRNA expression levels in the populations of healthy volunteers and cancer cases (legend is overleaf).
154
Figure 4.8 – Comparison of CAIC mRNA expression levels in the populations of healthy volunteers and cancer cases. The 2-ΔC(T) values of all cases are shown for each CAIC. Short horizontal lines in each graph indicate median values. Note that y- axis of nNav1.5 and Beta1b graphs are broken.
155 4.4 Discussion
This is the first study describing expression of ion channel mRNAs in blood taken from healthy individuals and cancer patients. The following ion channels that had previously been associated with cancer cells / tissues were investigated (e.g.
Prevarskaya et al., 2010): nNav1.5, Beta1, Beta1b, Kv1.3, Kv10.1, Kv11.1, KCa3.1 and TRPM8.
1. Several cancer-associated ion channel (CAIC) mRNAs were detected in human blood. These included the following: nNav1.5, Beta1, Beta1b, Kv1.3 and
Kv11.1. Kv10.1 was expressed at a low level, whereas TRPM8 was not detectable.
2. Kv1.3 mRNA, in particular, was highly regularly (normally) distributed and abundantly expressed. Thus, this was adopted as a ‘control’ CAIC.
3. Comparison of the healthy individuals with cancer cases revealed a significantly lowered level of Beta1, Kv1.3 and Kv11.1 mRNAs. In contrast, the levels of nNav1.5 and KCa3.1 mRNAs increased.
4.4.1 Technical aspects
From the procedure followed in the present study, blood taken from human volunteers could be processed for real-time PCR and data could be obtained within about 36 h. Although most of the molecular biology procedures followed were basically those used for cell line studies, particular attention had to paid top DNAse treatment. This was because although primers for CAIC PCRs were carefully chosen to avoid amplification of genomic DNA (i.e. spanning intron/exon boundaries), this was not possible for some cases. The difficulty was due to either the nature of the target (e.g. Kv1.3 being an intron-less gene) or the necessity to focus on a specific
156 region of the target (e.g. Beta1b). Therefore, an elimination step of genomic DNA via DNase treatment after RNA extraction was included in the protocol. After testing different options, the best performing commercial DNase enzyme was selected and
PCR results from (-)RT samples confirmed the total elimination genomic DNA
(Figure 4.6).
4.4.2 Kv1.3 as a ‘control’ channel
Kv1.3 is well established as a key voltage-gated K+ channel found in MNCs
(Cahalan & Chandy, 2009). It plays a variety of role in immune cell activities, from activation to immune synapse functioning (Cahalan & Chandy, 2009). Of the CAIC mRNAs studied here, Kv1.3 indeed was the most abundant and had the most regular
(normal) distribution pattern. Previous studies have demonstrated that it is the expression of a given ion channel relative to Kv1.3 that changes during immune cell functioning as in the case of T- and B-cell activation (Cahalan & Chandy, 2009) and dendritic cell maturation (Zsiros et al., 2009). As predicted, in the present study also, use of Kv1.3 proved to be viable as a reference point for expression of CAICs.
4.4.3 Cancer cases: comparison with healthy controls
The cancer cases studied here (n = 44) were all solid cancers, of which two-thirds were BCa. Nevertheless, the CAICs of interest here have been detected in such cancers generally. For example, Kv11.1 (also known as “hERG”) has been found in
BCa, neuroblastoma, various sarcomas, lung cancer, colon cancer, cervical cancer, pituitary tumours, as well as haematological cancers (Bianchi et al., 1998; Asher et al., 2010). On the other hand, nNav1.5 occurs in human BCa (Fraser et al., 2005) and colon cancer (Chapter 6). In contrast, there is more limited evidence for VGSC-β
157 expression in cancers (Chioni et al., 2009). Interestingly, however, there is some evidence for its developmental regulation (giving rise to Beta1b) (Kazen-Gillespie et al., 2000; Patino et al., 2011) which would make it an attractive CAIC candidate.
Both Beta1 and Beta1b were included in the present study.
Of the 8 CAIC mRNAs studied here, the following showed increases: nNav1.5 and KCa3.1 – these changes, if functional, would promote cellular activity
(trafficking, invasiveness etc). In contrast, the following CAIC mRNAs showed significant decreases: Beta1, Kv1.3 and Kv11.1. Again, if functional, such changes would further enhance the cellular activity. For example, the tumour infiltration of
MNCs would be promoted by both sets of changes.
Kv11.1 (hERG). Kv11.1 showed a statistically significant change in mRNA expression, the value falling by some 41 %. Expression of Kv11.1 has not previously been studied in cells of blood. Only one recent paper shows its activity in macrophages (Hunter et al., 2010). If it is assumed that it is specifically macrophages that express Kv11.1, then the reduction observed could be due to macrophages leaving blood to enter the tumours (Qualls et al., 2011). If so, such an effect would have been underestimated here, since it is possible that macrophages would undergo proliferation in response to the cancer which would tend to increase Kv11.1 mRNA expression, the opposite of what was observed.
Kv1.3. Functional expression of Kv1.3 is well known to promote MNC proliferation (e.g. Wulff et al., 2004; Mello de Queiroz et al., 2008). It was surprising, therefore, that a significant (47 %) decrease was observed in Kv1.3 mRNA expression in blood of cancer patients. Assuming that the Kv1.3 mRNA expression leads to functional product, the most likely explanation for the observed decrease in the Kv1.3 mRNA level is that it is involved in promoting VGSC
158 (nNav1.5) activity, rather than increasing proliferation. If so, it would follow that, in cancer, MNC motility (e.g. tumour infiltration) is more important than proliferation.
The same argument can be applied to the decreased Kv11.1 expression.
VGSC (nNav1.5α− and Beta1-subunits). It was of interest to observe that whilst the mRNA expression level of nNav1.5 increased significantly, levels of the other VGSC subunit studied, Beta1, significantly decreased. This, taken together with the fall in voltage-gated potassium channel (VGPC), Kv3.1 and Kv11.1, expression is in line with the CELEX hypothesis (reviewed in the general
Introduction, section 1.3.1.2). Thus, assuming that these changes occurred in the same cell subpopulation, the concomitant changes (VGSC ↑ VGPC ↓) would promote membrane excitability in the cells which, in turn, may facilitate their tumour-infiltrating behaviour. Such a situation has already been proposed to occur in metastatic cancer cells (Djamgoz, 2011). A further parallel has been drawn with angiogenesis (Eccles, 2004), where endothelial cells also express functional VGSCs which enhance their angiogenic behaviour (Andrikopoulos et al., 2011).
We should emphasise that further studies are needed (1) to greatly increase the sizes of populations being characterized (in age-matched sub-groups of males and females); (2) to determine the MNC subpopulation(s) expressing the different
CAICs; (3) to determine the functional consequences of the mRNA changes seen; (4) to elucidate at cell level how expression might change in cancer; and (5) compare different cancer cases with healthy controls for age- and sex-matched populations.
4.4.5 Circulating tumour cells?
At present, it is not clear how any circulating tumour cells (CTCs) in blood would have contributed to the measured CAIC mRNA expression levels in patients. The
159 MNC populations used in the analyses would have included CTCs. Indeed, the procedure used to isolate the MNCs is what is generally used to enrich CTCs in blood samples. Interestingly, the majority of the patients analysed in this study had
BCa which has been associated with nNav1.5 upregulation (Onkal & Djamgoz,
2009). In fact, at cell level, metastatic BCa cells were shown to express ~2000-fold more nNav1.5 mRNA than non-metastatic cells. Although this is a tantalizing possibility, it is unlikely to be realistic since CTC are thought to represent more like
1 in 106 lymphocytes. In future studies, it would be worthwhile to determine in isolated CTCs whether nNav1.5 expression does occur. Additionally, such an evaluation would be an essential test of the VGSC (nNav1.5) hypothesis of BCa metastasis.
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Chapter 5
(Results 3)
STUDIES OF IONIC MECHANISMS IN RELATION TO DIABETES
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5.1 Introduction and rationale
It is now generally agreed that the insulin - insulin-like growth factor-1 signalling system (IIS) is closely associated with a range of human cancers, including breast cancer (BCa) (e.g. McFarland & Cripps, 2010). Furthermore, the anti-diabetes drug metformin may be beneficial to cancer patients (Papanas et al., 2010; Dowling et al.,
2011). Effects of metformin on cancer cells have mostly been investigated in the context of primary tumourigenesis (mainly proliferation) and generally suppressive effects were found (e.g. Feng et al., 2011). There are relatively few studies on metastatic cell behaviours. In one such study, Hwang & Jeong (2010) showed on human fibrosarcoma cells that metformin suppressed migration and invasion by inhibiting MMP-9 activation. As regards BCa, the main focus has been on selective targeting of stem cells (Wysocki & Wierusz-Wysocka, 2010). There are no reported studies on cellular invasiveness in BCa. However, metformin suppressed the expression of metastasis-associated CD24 protein in MDA-MB-468 cells (Vazques-
Martin et al., 2011). The latter is a human BCa cell line that is ‘triple-negative’, similar to MDA-MB-231 cells. In the results presented in Chapter 3, we showed that silencing IIS under conditions where proliferation would not be affected reduced invasion significantly. Taken together, these results would suggest that metformin could inhibit MDA-MB-231 invasiveness. The present study aimed to test this possibility.
Metformin reduces glucose production in liver and increases insulin sensitivity in peripheral tissues (Shaw et al., 2005). Whilst the main mode of action of metformin is thought to be activation of 5'AMP-activated protein kinase (AMPK), there is also increasing evidence that at least some of the potent antitumourigenic
162 effects of metformin may be independent of its hypoglycaemic actions (Kourelis &
Siegel, 2011). Since (i) cellular invasiveness has been shown to be potentiated by voltage-gated sodium channel (VGSC) activity and (ii) that VGSC activity may promote proteolysis, the possibility also arises, therefore, that some of the anti- invasive effects of metformin may involve VGSC expression / activity. Although there is some emerging evidence that metformin does influence ion channel activity
(e.g. Alzamora et al., 2010), there is no reported studies on VGSCs. This is another undertaking of the present study.
Clearly, IIS signalling, cancer, cancer-associated ion channels (CAICs) and diabetes may be associated in different ways. Furthermore, since CAIC mRNAs occur in human blood (Chapter 4), there may be comparable changes in ion channel expression in mononuclear cells of diabetics. The latter have been shown to undergo dynamic changes in their ion channel expression, depending upon functional state
(Cahalan & Chandy, 2009). In an initial PCR-based study, the last part of this chapter aimed to elucidate whether CAIC mRNA expression is different in blood of diabetics, compared with healthy subjects.
5.2 Aims and scope
The main aims of this study were to evaluate possible in vitro effects of the antidiabetic drug metformin on MDA-MB-231 cells, adopted as a model of strongly metastatic BCa, and to make an initial assessment of the status of CAIC mRNA expression in blood of diabetic patients. The specific aims were as follows:
1. To test if metformin could regulate nNav1.5 mRNA levels (and VGSC activity) in MDA-MB-231 cells;
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2. To determine the possible effects of metformin on lateral motility and
Matrigel invasion of MDA-MB-231 cells, as examples of metastatic cell behaviours
(MCBs); and
3. To investigate whether any change can be detected in the mRNA expression levels of CAICs in peripheral blood of diabetic patients compared to healthy individuals.
5.3 Results
Two sets of results are presented: (i) in vitro studies on MDA-MB-231 cells and (ii) measurements on peripheral blood samples from diabetic patients.
5.3.1 In vitro effects of metformin
5.3.1.1 Cellular viability and morphology
Initially, any possible toxic effects of metformin was investigated on MDA-MB-231 cells by using a high dose of the drug (50 mM) over an extended time period (72 h).
Under these conditions metformin did not induce any toxicity and ~99 % of the cells remained viable (Figure 5.1).
Although metformin had no cytotoxic effect, there was an apparent change in cellular morphology. When differing doses of the drug (0.2 – 50 mM) were applied over 24 h, it was observed that the cells became more compact, bearing shorter and thicker processes; these changes appear to be more pronounced with increasing dose of metformin (Figure 5.2).
5.3.1.2 Cell number
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100
99
98
97 Viability (%) Viability
96
95 Control 50 mM Metformin
Figure 5.1 – Effect metformin on cellular viability. When MDA-MB-231 cells were treated with 50 mM metformin for 72 h, the drug caused no toxicity. Viability of treated cells was no different than control cells as measured by Trypan blue dye exclusion assay. Each histobar denotes mean + SEM (P=0.46; n=8).
A B
C D
Figure 5.2 – Effects of metformin morphology. Phase-contrast images of MDA- MB-231 cells illustrating (A) control, (B) 0.2 mM, (C) 2 mM and (D) 50 mM metformin conditions. After 24 h of treatment, cells were more compact and possessed shorter and thicker processes when compared to control cells in standard growth medium. This effect was dose-dependent. Scale bar (47 µm) applies to all.
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Cell number was not affected by up to 2 mM metformin over a time period of 24 h
(P>0.05; n=4-7; Figure 5.3). However, at higher doses (>5 mM), cell number was reduced significantly. Accordingly, use of metformin in subsequent studies was restricted to 2 mM. Application of 20 µM TTX, both alone and in combination with
2 mM metformin, did also not cause any change in cell number (P>0.05; n=4-7;
Figure 5.3)
5.3.1.3 nNav1.5 mRNA expression
Quantitative real-time PCR results revealed that 24 h treatment with 2 mM metformin reduced nNav1.5 mRNA level significantly by ~ 20% when compared to control cells (P<0.001; n=5; Figure 5.4).
5.3.1.4 Lateral motility
Wound heal assays were employed to measure lateral cell motility; wound widths were measured at 24 and 48 h. At both time points, 2 mM metformin caused a significant decrease in motility index by 71 and 75 %, respectively, when compared to control cells in standard growth medium (P<0.001; n=12; Figure 5.5). However, although 2 mM metformin had no effect on proliferative after 24 h, whether its apparent effect on lateral motility over 48 h involved a proliferative component remains to be determined. Nevertheless, over the 48 h time course, MDA-MB-231 cells continued to close the wound significantly in control conditions, indicating a consistent behaviour of these cells (P<0.01; n=12).
5.3.1.5 Matrigel invasion
Effect of metformin on invasiveness of MDA-MB-231 was studied by using Matrigel
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2
1.5
1
0.5 Cell number (relative)number Cell
0 Control 0.2 mM 2 mM TTX TTX + Metformin Metformin (2 mM)
Figure 5.3 – Effects metformin on cell number. There was no change in cell number at 0.2 and 2 mM metformin concentrations over 24 h when compared to control cells in standard growth medium. TTX (20 µM) also had no effect neither when applied alone nor in combination with metformin (2 mM), Repeats were accepted only when control cells showed healthy growth. Each histobar denotes mean + SEM (P>0.05; n=4-7).
100
80
60 ***
(relative) 40
20 nNav1.5 mRNA expression nNav1.5 mRNA
0 Control Metformin
Figure 5.4 – Effect of metformin on nNav1.5 mRNA expression. Treatment with 2 mM metformin over 24 h resulted in a significant decrease of nNav1.5 mRNA expression when compared to MDA-MB-231 cells grown in standard medium. Each histobar denotes mean + SEM (P<0.001; n=5).
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1 ** ***
0.8 Control *** 0.6 MoI MoI 2 mM Metformin 0.4
0.2
0 24 h 48 h
Figure 5.5 – Effects metformin on lateral motility. Whilst control cells in standard growth medium continued to seal the ‘wound’ over the time course of the experiment 2 mM metformin reduced lateral motility of MDA-MB-231 cells at both 24 and 48 h time points. MoI indicates motility index. Each histobar denotes mean + SEM. Significance: (**) P<0.01, (**) P<0.001 (n=12).
168 invasion assays. Similar to lateral motility, 2 mM metformin reduced invasion after
24 h of application and the measured decrease was by 42 % (P<0.01; n>8; Figure
5.6). In order to reveal whether VGSC is involved in this effect, the specific channel blocker tetrodotoxin (TTX) was also included. In accordance with previous findings
(e.g. Fraser et al, 2003), single TTX (20 µM) application reduced Matrigel invasion significantly under the test conditions (P<0.01; n>3; Figure 5.6). After confirming the effect of TTX on current experimental cells, cells were co-treated with 2 mM metformin and 20 µM TTX for 24 h. Invasiveness was reduced further by the co- treatment (P<0.05; n>5-; Figure 5.6).
5.3.2 Studies on human blood samples
In Chapter 4, ‘healthy control’ blood samples were collected from healthy people according to the criteria of (i) not having any type of cancer and (ii) not having any known disease related to channelopathies. Some volunteers in this group had type 2 diabetes (T2D). These samples were excluded from the healthy controls and were then studied separately in this section.
CAIC mRNA levels were investigated by real-time PCR using SYBR Green technology. The following CAICs were studied: nNav1.5, VGSCβ1 (Beta1),
VGSCβ1b (Beta1b), Kv10.1, Kv11.1, KCa3.1 and TRPM8. As in the case of the cancer study (Chapter 4), Kv1.3 was adopted as a control channel due to its robust presence in blood mononuclear cells. In total, 18 T2D samples were examined in an initial study and were compared to same healthy control group studied in Chapter 4.
Kv1.3 showed a normal distribution among the T2D subjects tested with a mean + SD of 1.18 + 0.33 when 2-ΔC(t) values were analysed (n=18; Figure 5.7).
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0.8 ** **
0.6 *** ***
0.4 *
0.2 Invasiveness(relative)
0 Control Metformin TTX Metformin + TTX
Figure 5.6 – Effects of metformin on Matrigel invasion. When MDA-MB-231 cells were treated with 2 mM metformin and 20 µM TTX individually for 24 h, significant decreases were observed in their invasiveness (P<0.01; n>3). Whilst metformin and TTX individually exerted a similar level of effect, co-application of these agents caused even a further reduction (P<0.5 and <0.001; n>5). Each histobar denotes mean + SEM).
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30 A 25
20
15 Number 10
5
0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
HEALTHY - Kv1.3 mRNA expression (relative)
B 30
25
20
15 Number 10
5
0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
DIABETES - Kv1.3 mRNA expression (relative)
Figure 5.7 – Distribution of Kv1.3 mRNA expression in sample populations. Data is collected from (A) healthy individuals (n = 71) and (B) T2D cases (n = 18). The 2-ΔC(t) analysis revealed mean + SD values of 1.66 + 0.52 and 1.18 + 0.33 for healthy and diabetes groups, respectively.
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However, this was statistically different than the control group Kv1.3 mRNA expression level (Table 5.1).
Similar to controls, and also to cancer cases, no expression of TRPM8 was detected in T2D blood samples. Kv10.1 was also absent, which showed only minimal expression in control cases. All the other CAICs were detectable with the methods used in this study and among the following T2D expression levels showed significant differences compared to healthy controls (P<0.01-0.0001; Table 5.1; Figure 5.8):
1. The following CAIC mRNAs were significantly increased: nNav1.5 (8- fold) and KCa3.1 (2-fold).
2. The following CAIC mRNAs were significantly reduced: Beta1 (30 %) and Kv1.3 (29 %).
5.4 Discussion
The main results of the studies described in this chapter are as follows:
1. Metformin (2 mM) significantly reduced Matrigel invasion and lateral motility of MDA-MB-231 cells without affecting the cells’ proliferative activity.
2. Co-application of TTX (20 µM) reduced invasion even further.
3. Same dose of metformin significantly reduced nNav1.5 mRNA expression.
However, there was no effect on the peak density of the VGSC (nNav1.5) current.
3. The following CAIC mRNAs were present in blood mononuclear cells of individuals with T2D: nNav1.5, Beta1, Beta1b, Kv1.3, Kv11.1 and KCa3.1.
Compared with the healthy controls, the following mRNA level changes were observed as significant: nNav1.5 and KCa3.1 (up-regulated); Beta1 and Kv1.3
(down-regulated).
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Table 5.1 – Expression levels of CAIC mRNAs. 2-ΔC(t) data from healthy controls and T2D group are given for each CAIC.
CAIC Median Range Mean P value Healthy 0 0 – 1.5 x 10-5 1.9 x 10-6 nNav1.5 < 0.0001 Diabetes 8.7 x 10-6 0 – 9.2 x 10-5 15.7 x 10-6
-3 -3 -3 Healthy 9.8 x 10 4.2 – 29.5 x 10 11.2 x 10 Beta1 0.0016 Diabetes 6.5 x 10-3 4.4 – 14.7 x 10-3 7.8 x 10-3 Healthy 8.2 x 10-4 0.1 – 3.8 x 10-3 9.2 x 10-4 Beta1b 0.599 Diabetes 7.6 x 10-4 0.4 – 13.3 x 10-3 14.6 x 10-4 Healthy 1.6 0.8 – 3.2 1.7 Kv1.3 < 0.0001 Diabetes 1.2 0.6 – 1.9 1.2 Healthy 0 0 – 24.6 x 10-6 6.4 x 10-7 Kv10.1 0 Diabetes 0 0 0 Healthy 3.1 x 10-2 1.7 – 8.3 x 10-2 3.2 x 10-2 Kv11.1 0.282 Diabetes 2.7 x 10-2 1.2 – 4.3 x 10-2 2.8 x 10-2 Healthy 2.9 x 10-5 0.9 – 33.5 x 10-5 4.7 x 10-5 KCa3.1 < 0.0001 Diabetes 10.7 x 10-5 2.5 – 17.6 x 10-5 9.8 x 10-5 Healthy 0 0 0 TRPM8 0 Diabetes 0 0 0
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Figure 5.8 – Comparison of CAIC mRNA expression levels in the populations of healthy volunteers and T2D cases. The 2-ΔC(T) values of all cases are shown for each CAIC. Short horizontal lines in each graph indicate median values. Note that y- axis of nNav1.5 and Beta1b graphs are broken.
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5.4.1 Effects of metformin on cancer cell invasive behaviour
There is substantial multi-faceted evidence that metformin has anti-cancer effects
(Ben Sahra et al., 2010). At cell level, most work has been done on proliferation.
Only two papers have shown anti-invasive effects of metformin, on fibrosarcoma and endometrial carcinoma cells (Hwang & Jeong, 2010; Tan et al., 2011). Hwang and
Jeong (2010) showed that metformin blocked migration and invasion of human fibrosarcoma HT-1080 cells by inhibition of matrix metalloproteinase-9 activation through in a Ca2+-dependent manner. Tan et al. (2011) showed that metformin treatment produced anti-invasive and anti-metastatic effects in human endometrial carcinoma cells; again, these effects involved matrix metalloproteinase-2/9, at least in part.
The present study adds to these results by showing that metformin has a significant, suppressive effect on human metastatic BCa MDA-MB-231 cell invasiveness. These effects were obtained without any change in cell number, i.e. could be independent of any effect on cellular proliferation. Extrapolating this to the clinic, therefore, it would follow that metformin could be effective against metastatic as well as proliferative disease. This is also suggested from clinical studies on a variety of cancers. In contrast, rather surprisingly, use of metformin was shown recently not to be associated with a decreased risk of colorectal cancer (Bodmer et al., 2011). Clearly, further studies are needed in order to fully evaluate the clinical potential and anti-cancer effects of metformin.
5.4.2 Possible role of VGSC (nNav1.5) in the action of metformin
There is little published data on effects of metformin on ion channels. However,
AMPK has been shown to influence a variety of ion channels, including VGSCs
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(Light et al., 2003). Interestingly, in both cell-based studies on invasiveness matrix metalloproteinase activation was shown to be suppressed by metformin (Hwang &
Jeong, 2010; Tan et al., 2011). Indeed, (de)activation of proteolytic enzymes by
VGSC activity, involving down-stream H+ (peri-cellular pH) and/or intracellular
Ca2+ signalling, indeed, is one of the mechanisms whereby VGSC signalling could control cancer cell invasiveness (e.g. Brisson et al., 2011).
In the present study, pre-treatment of MDA-MB-231 cells with metformin reduced nNav1.5 mRNA expression. However, similar treatment with metformin did not affect the peak current density of nNav1.5, although there was a clear trend for the current to be reduced. It is possible, therefore, that given longer time of treatment and/or even just increased number of recordings that the effect would become significant. Furthermore, the reduced mRNA expression could eventually lead to decreased functional activity. Nevertheless, it would seem that the initial anti- invasive effects of metformin are independent of VGSC activity.
It is possible, therefore, that the effect of metformin in suppressing invasion has both VGSC-independent and VGSC-dependent components. These two components are in dynamic balance so metformin initially can reduce invasion without any functional VGSC involvement. However, the suppression of nNav1.5 manifests itself later at protein and signalling levels, and invasion is reduced further.
Taking the available evidence together, a hypothesis can be presented for time- dependent effects of metformin on cancer progression, as illustrated in Figure 5.8.
5.4.3 Changes in blood CAIC mRNA expression in T2D
Even in the limited number of T2D cases that could be studied, some significant changes in CAIC mRNA expression were found, compared with the healthy cohort.
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There were notable similarities between these changes and those seen in cancer
(Chapter 4), highlighting again, the apparent association between T2D and cancer.
Thus, nNav1.5 and KCa3.1 mRNA levels increased in T2D cases whereas
Beta1 level was reduced. Taken together, these would make the mononuclear cells more motile and less adhesive, possibly as a part of an immune response to diabetes that may involve activation of macrophages (Fernández-Real & Pickup, 2008).
The level of Kv1.3 mRNA also changed (decreased). In the first instance, this is another example of the parallel between T2D and cancer. Although, there is no data yet concerning changes in the voltage-gated potassium channel Kv1.3 in diabetes, importantly, in animal models, knockout of Kv1.3 has been found to reduce body weight, increase metabolic rate and induce resistance to obesity (Xu et al.,
2003). In addition, knockout of Kv1.3 has also been found improve both insulin sensitivity and glucose tolerance (Choi & Hahn, 2010). Although, mechanisms behind these findings are unclear, knockout of Kv1.3 has been shown to induce peripheral insulin sensitivity (by increasing both glucose transporter 4 levels at the plasma membrane and increasing glucose uptake in muscle and adipose tissue; Xu et al., 2004; Li et al.,2006). Interestingly, Tschritter et al. (2006) showed that a variant in the promoter of the Kv1.3 gene is associated with impaired glucose tolerance and lower insulin sensitivity and suggested that Kv1.3 could be a candidate target gene for T2D. However, until more is known about the mechanisms behind Kv1.3 involvement in diabetes (i.e peripheral changes versus pancreatic β–cells), the possibility of blood-based changes in Kv1.3 remain undefined.
In conclusion, there are clear changes in CAIC mRNA expression in mononuclear cells of individuals with T2D. The mechanism(s) underlying these changes and whether they are of functional significance by being translated into
177 functional proteins remain to be elucidated. Nevertheless, the possibility of diagnosing T2D by the changes in CAIC mRNA patterns is an interesting prospect.
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Figure 5.9 – Metformin and anti-metastatic effects. This is a hypothetical scheme for the reported anti-invasive effects of metformin on metastatic cancer cells. This is based upon the data available from MDA-MB-231 and SW-620 cells.
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Chapter 6
(Results 4)
VOLTAGE-GATED SODIUM CHANNEL EXPRESSION IN
HUMAN COLORECTAL CANCER
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6.1 Introduction and rationale
Colon / colorectal cancer (CRCa) is the third most common malignancy and the fourth most common cause of cancer death worldwide (Karsa et al., 2010). In 2008, there were greater than 1.2 million new cases of CRCa and 609,000 estimated deaths.
Importantly, the annual incidence of CRCa is expected to increase to 2.2 million
(~80 % increase) over the next 20 years, with 62 % of cases occurring in the less developed countries (Karsa et al., 2010). Furthermore, there is a recent trend for
CRCa to occur in younger people (You et al., 2011).
CRCa is considered to develop over many years in a complex, multistep process, known as the “adenoma-carcinoma sequence” (Fearon, 2011). Precancerous tumours (polyps) can occur in the colonic epithelium and approximately 5 % of these develop into malignant tumours (Pearson et al., 2009). Most CRCa cases are adenocarcinomas and metastasis is mainly to liver and lymph nodes (Pearson et al.,
2009). Approximately 50 % of all people with CRCa develop local recurrence or distant metastasis with the median survival time being between 4 months to ~2 years.
The standard treatment for both metastatic and recurrent CRCa’s is chemotherapy; surgery or other forms of ‘regional’ treatments may be applicable for a small proportion of patients.
Some 15 - 20 % of all CRCa cases are genetic whereas the remainder are sporadic (Ferlay et al., 2004). The most common inherited conditions are (i) familial adenomatous polyposis (FAP) and (ii) hereditary nonpolyposis colorectal cancer
(HNPCC), both of which are autosomal dominant inherited conditions. Patients with
FAP accumulate many hundreds of benign colorectal polyps or adenomas during their twenties to thirties. With HNPCC, there is an error in the DNA mismatch repair
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genes. The pathological features include (i) poor differentiation, (ii) increased signet cells and (iii) lymphocytes mixed with tumour cells (Pearson et al., 2009).
Sporadic CRCa have been linked to environmental causes rather than heritable genetic changes. Risk factors include environmental and food-borne mutagens, specific intestinal pathogens, and chronic intestinal inflammation. For example, dietary fat intake has long been shown to be a powerful risk factor in the development and progression of CRCa (Berg, 1973). Conversely, diets rich in fibre, vegetables, fruits and garlic are linked to a decreased CRCa incidence (Chao et al.,
2005). In fact, a recent Danish study suggested that adherence to recommendations for five lifestyle factors (physical activity, smoking, waist circumference, alcohol intake and diet) may considerably reduce risk (Kirkegaard, 2010). On the other hand, colitis-associated cancer (CAC) is a CRCa subtype that is associated with inflammatory bowel disease (IBD) which is difficult to treat and has high mortality
(Feagins et al., 2009). Importantly, greater than 20 % of IBD patients develop CRCa within 30 years and ~50 % die from it (Lakatos & Lakatos, 2008). Although immune-mediated mechanisms link IBD and CRCa (Greten et al., 2004; Atreya &
Neurath, 2008), similarities also exist between CAC and other types of CRCa without overt inflammatory disease. Figure 6.1 outlines common pathways associated with CRCa development. Unfortunately, advanced CRCa is very resistant to chemotherapy and novel treatment methods are urgently needed.
As detailed in the Introduction, House et al. (2010) aimed to determine whether (i) human CRCa cells express functional VGSCs and (ii) whether VGSC activity would contribute to cellular invasiveness through transcriptional regulation of target genes involved in the invasion process. This work was prompted, in part, by the findings that several different human carcinomas, including breast cancer (BCa)
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Figure 6.1 – Pathways associated with sporadic and colitis-associated colon cancer development. Although similar events contribute to the pathogenesis of both pathways, they occur at different stages of cancer progression and at different frequencies. Shared similarities include genomic instabilities (aneuploidy and microsatellite instability (MSI)), methylation of DNA, loss of function of tumour suppressor genes (P53, adenomatous polyposis coli (APC) and DCC/DPC4), activation of oncogenes (k-ras) and activation of cyclooxygenase-2 (COX-2). Modified from Ullman & Itzkowitz (2011).
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and prostate cancer (PCa), had previously been shown to express functional VGSC
(Fraser et al., 2005; Laniado et al., 1997). Furthermore, data from both (i) meta- analysis of CRCa patient gene expression profiles and (ii) oncogenic transformation of fibroblasts had suggested that genes from neuronal and/or excitable cells
(including ion channels and intracellular signaling molecules) may be ‘recruited’ during CRCa progression (Malek et al. 2002; Teramoto et al. 2005). The results from the House et al. (2010) study indicated (i) that functional expression of the Nav1.5 subtype of VGSC occurred in a number of metastatic CRCa cell lines, including
SW620 (the cell line derived originally from lymph node metastasis; Leibovitz et al.,
1976), as well as the additional cell lines SW480 and HT29; (ii) that Nav1.5 protein expression was upregulated in vivo, in human CRCa specimens; and (iii) that Nav1.5 was, in fact, a “key regulator” of colon cancer invasion, driving a network of genes involving Wnt signalling, cell migration, ectoderm development, response to biotic stimulus, steroid metabolic process, and cell cycle control.
House et al. (2010) mentioned briefly that only the adult variant was expressed in the CRCa biopsy specimens and the SW620 cell line (from quantitative real-time PCR), although, importantly, no data were shown to support this finding.
This suggestion seemed surprising since previous work on BCa in vitro and in vivo showed convincingly that it is predominantly the neonatal and not the adult splice- variant of Nav1.5 (nNav1.5) that is functionally expressed (Fraser et al., 2005).
Furthermore, it has been established that there is a phenomenon of oncofoetal expression of genes during cancer progression (e.g. Monk & Holding, 2001; Ben-
Porath et al., 2008). Importantly, this phenomenon has been shown to occur also during CRCa progression (e.g. Basora et al., 1998).
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6.2 Aims and scope
The general aim of the present study was to determine whether the adult and/or neonatal splice form of Nav1.5 was expressed in CRCa, using the SW620 cell line as a model. Determination of the specific splice-variant expressed in this cell line (and in vivo) was achieved by employing the following approaches:
1. Conventional PCRs and sequencing studies using specific primers were performed to determine nNav1.5 mRNA expression.
2. Expression at protein level was elucidated by immunocytochemistry using the nNav1.5 specific-antibody, NESOpAb (Chioni et al., 2005).
3. Gene silencing / siRNA ‘knock-down’ was used to determine the possible role of nNav1.5 expression / activity in Matrigel invasion.
4. The approach in (2) was extended to examination of the expression of nNav1.5 protein in human CRCa biopsy tissues.
6.3 Results
6.3.1 nNav1.5 mRNA expression in SW620 cells
Conventional PCR experiments, employing nNav1.5-specific primers, were performed on cDNAs from 3 different SW620 cultures. nNav1.5 mRNA products, of correct sizes (101 bp), were detected in all cases (Figure 6.2A). The strongly metastatic human BCa cell line MDA-MB-231 was used as a positive control (Figure
6.2B) and the weakly/non-metastatic human BCa cell line MCF-7 used as a negative control (Figure 6.2C). These confirmed the specificity of the primers and revealed
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A L 1 2 3 4 5
B C L 1 2 3 4 5 L 1 2 3 4 5
Figure 6.2 – Expression of nNav1.5 mRNA in different cell lines. Conventional PCRs with nNav1.5 specific primers were performed with three sets of cDNAs from (A) SW-620, (B) MDA-MB-231 and (C) MCF-7 cells. The bands of PCR products from SW620 and MDA-MB-231 cells corresponded to the expected size of 101 bp on 1% agarose gel (indicated with red arrows). There was no detectable amplification in MCF-7 cells. In terms of PCR controls, non-template control and (-)RT were also negative.
L : DNA ladder 1 : non-template control 2 : cDNA 1 3 : cDNA 2 4 : cDNA3 5 : (-)RT
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nNav1.5 mRNA expression primarily in the former, as reported before (Fraser et al.,2005).
6.3.2 nNav1.5 mRNA expression in SW620 cells – PCRs of the ‘spliced’ region and sequencing results
In order to confirm further the specific presence of nNav1.5 mRNA in SW620 cells, a further PCR reaction was carried out using ‘general’ Nav1.5 primers bordering the region of difference (DI:S3-S4) between the nNav1.5 and its ‘adult’ variant
(aNav1.5) (Figure 6.3). Since the ‘general’ Nav1.5 primers were newly designed, a gradient PCR was initially run to determine their optional annealing temperature.
Also, lack of any amplification in the (–RT) control confirmed that no genomic DNA was involved (Figure 6.3).
In order to elucidate the content of the initial PCR product obtained using
‘general’ Nav1.5 primers (Figure 6.3A), two further investigations were carried out:
1. nNav1.5- and aNav1.5-specific primers were applied to the original
‘general PCR product’ treated as a template. These PCRs showed that both targets of the correct sizes were present in the ‘pool’ (Figure 6.3B).
2. Three repeats of the gel-purified initial ‘general PCR product’ were sent for sequencing, two of which were sequenced successfully. Aligment of these two results confirmed that their sequences were 99 % similar (Figure 6.4A). Further sequence alignment and homology search using BLAST analysis (NCBI) revealed that these samples were both 99 % similar with the nNav1.5 sequence (Figure 6.4B).
Conversely, same analysis showed only 86 and 88 % similarity with the sequence of aNav1.5 splice variant (Figure 6.4C).
These results, taken together, suggested strongly that nNav1.5 mRNA was
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A B
L 1 2 3 L 4 5 6 7
. .
Figure 6.3 – PCR amplifications of the ‘spliced’ region of Nav1.5 in SW620 cells. (A) After optimising the cycling conditions, conventional PCR was performed with ‘general’ Nav1.5 primers on template cDNA from SW620 cells. The product band corresponded to the expected size (303 bp), and non-template control and (-)RT were negative for amplification. (B) Conventional PCR was conducted using nNav1.5 and aNav1.5 specific primers on purified PCR product generated with ‘general’ Nav1.5 primers from SW620 cDNA. The bands corresponded to the expected sizes (101 and 122 bp for nNav1.5 and aNav1.5, respectively). Non-template controls were clean with no amplification.
L : DNA ladder 1, 4 and 6 : non-template control 2, 5 and 7 : cDNA 3 : (-)RT
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A
B
C
Figure 6.4 – Alignment of sequencing results (legend is overleaf)
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Figure 6.4 – Alignment of sequencing results. (A) When aligned, sequencing results of two repeats (SW1 and SW2) generated via the ‘general’ Nav1.5 PCRs on SW620 cDNAs showed 99 % similarity. (B) BLAST analysis revealed that the sequenced products were 99 % similar with the nNav1.5 splice variant sequence. An example of analysis with one of the repeats (SW1) is shown in figure. (C) Same analysis showed only 88 and 86 % similarities with aNav1.5 sequence for SW1 and SW2 repeats, respectively. An example of analysis with one of the repeats (SW1) is shown in figure and the extensive analysis is presented in Appendix 3.
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expressed in the SW620 CRCa cell line and may even be the dominant splice variant.
6.3.3 Immunocytochemistry of VGSC expression in SW620 cells
NESOpAb antibody, which is specific for nNav1.5 recognizes an extracellular epitope of the channel (Chioni et al., 2005), was tested on the SW620 cell line under non-permeabilized conditions. Strong immunoreactivity was observed in most cells
(Figure 6.5A). A similar pattern of immunoreactivity was observed using a Pan-
VGSC antibody (Figure 6.5B). Examination of NESOpAb at a higher magnification suggested protein expression being both compartmentalized in cytoplasm and occurring in plasma membrane (Figure 6.6). Finally, since patch clamp electrophysiology had indicated a significant reduction in peak VGSC current density 72 h after plating compared to 24 h (Appendix 1.7), it was also investigated whether nNav1.5 protein was down-regulated during this time course. There indeed was some reduction in the staining observed (Figure 6.7).
6.3.4 Silencing nNav1.5 by RNAi
6.3.4.1 Effects on nNav1.5 expression nNav1.5 expression in SW620 cells was suppressed by RNAi. Cells were transfected with 3 different siRNAs targeting different regions of the neonatal sequence. Each siRNA (siNESO, siNeo1 and siNeo2) was used at 40 nM final concentration and mRNA levels were compared to non-targeting siControl (siC) transfected cells 96 h after transfection. Real-time PCR experiments revealed that there was significant decrease in nNav1.5 mRNA levels of 51 ± 12 % and 85 ± 12 % for siNeo1 and siNeo2, respectively, in comparison to siC (Figure 6.8). A similar trend was observed at protein level by immunocytochemistry using NESOpAb (Figure 6.9).
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A B
PC
FL
Figure 6.5 – Immunocytochemical staining of SW620 cells. (A) The nNav1.5- specific antibody, NESOpAb, was used at 1:100 dilution from a 0.7 mg/ml stock. Staining was performed under non-permeabilized conditions. (B) Pan-specific VGSC antibody was used at 1:250 dilution from a 1mg/ml stock. Staining was performed under permeabilized conditions. Top panels represent phase contrast (PC) and bottom panels represent fluorescence (FL) images. Scale bar applies to all.
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Figure 6.6 – Examination of nNav1.5 immunostaining in SW620 cells. The nNav1.5-specific antibody, NESOpAb, was used at 1:100 dilution from a 0.7 mg/ml stock. Staining was performed under non-permeabilized conditions. Scale bar applies to both.
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A
PC
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FL
.
B
ha. PC
FL
30 µm
1
Figure 6.7 – Effect of culturing time on nNav1.5 protein expression in SW620µ cells. The nNav1.5-specific antibody, NESOpAb, was used at 1:250 dilution from a m 0.7 mg/ml stock. Staining was performed under non-permeabilized condition. Cells were cultured for 24 h (A) and 72 h (B) prior to staining. For both (A) and (B), phase contrast (PC) (top panels) and fluorescence (FL) (bottom panels) images are shown. Scale bar applies to all.
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1.5
1
*
0.5 ** nNav1.5 mRNA expression (relative) nNav1.5mRNA 0 siC siNESO siNeo1 siNeo2
Figure 6.8 – Effect of RNAi targeting nNav1.5 at mRNA level. RNA was extracted from SW620 cells four days after transfection. All siRNAs were compared to siControl (siC) (P<0.05-0.01; n=3).
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Figure 6.9 – Effect of RNAi targeting nNav1.5 at protein level (legend is overleaf)
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Figure 6.9 – Effect of RNAi targeting nNav1.5 at protein level. siRNA transfected SW620 cells were non-permeabilized and the nNav1.5-specific antibody, NESOpAb, was used at 1:200 dilution from a 0.7 mg/ml stock. From top: autofluorescence (AF), secondary antibody only (2nd Ab), siControl, siNESO, siNeo1, siNeo2 and siAdult1. Left, middle and right panels represent phase contrast (PC), fluorescence (FL) and merged images, respectively.
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Suppression of functional nNav1.5 activity was demonstrated by complementary patch clamp recordings. Thus, for all 3 siRNAs, there was significant reduction in peak VGSC current density at 96 h, consistent with the real-time PCRs and ICC
(Appendix 1.7 and 1.8). In contrast, a siRNA targeting aNav1.5 (siAdult1) had less apparent effect on nNav1.5 protein expression (Figure 6.9) and VGSC activity
(Appendix 1.9).
6.3.4.2 Effects on invasiveness of SW620 cells
Under conditions optimised for MDA-MB-231 cells (24 h across 62.5 µg Matrigel),
SW620 cells demonstrated only limited invasiveness, even over 48 h (Figure 6.10).
Thus, initially, the Matrigel concentration needed to be optimized in order to effectively study SW620 cell invasiveness; this was found to be 1.95 µg (Figure
6.10). Then, two control experiments were conducted:
1. Comparison of ‘transverse migration’ with Matrigel invasion revealed a significantly greater proportion (~73 %) of cells penetrating in the former (Figure
6.11A). This confirmed that it was invasion that was being measured and not just cell migration, even at the reduced concentration of Matrigel used (1.95 µg).
2. The possibility that the optimised invasiveness of SW620 cells was potentiated by VGSC activity was tested by applying 10 µM tetrodotoxin (TTX)
(Figure 6.11B). Blocking VGSC activity reduced significantly the number of SW620 cells invading over the 48 h time course by ~45 % (Figure 6.11B). This confirmed that under the optimised invasion conditions, the contribution of VGSC activity to invasiveness was comparable to MDA-MB-231 cells (Fraser et al., 2005).
The effect of silencing nNav1.5 expression/activity on invasion was tested. siRNA and siControl transfected cells were plated onto Matrigel-coated invasion
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45
40
35
30
25
20
15 Number of invaded cells invaded Number of 10
5
0 0 1.9 3.9 7.8 15.6 31.3 62.5
Matrigel concentration (µg)
Figure 6.10 – Optimizing Matrigel invasion assay for SW620 cells. Histogram summarizes the effect of Matrigel concentration on the number of SW620 cells invading over 48h in comparison to the control conditions (no Matrigel). The SW620 cells had been serum-starved for 24h prior to plating. 50 µl Matrigel per well was used. (n=12 fields of view for one insert for each, except the control which was repeated twice).
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A 45 *** 40
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15
Number of invaded cells invaded Number of 10
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0 0 1.95
Matrigel concentration (µg)
B 15 ***
12
9
6
Number of invaded cells invaded Number of 3
0 Control TTX
Figure 6.11 – Further optimization of the Matrigel invasion assay for SW620 cells. (A) Effect of 1.95 µg Matrigel on invasiveness of SW620 cells over 48h in comparison to the control conditions (P<0.001; n=12 fields of view for three inserts (control) or six inserts. (B) Effect of 10 µM TTX on SW620 cell invasiveness over 48h in comparison to the control conditions on 1.95 µg Matrigel (P<0.001; n=12 fields of view for each of four inserts). Cells in all experiments were serum-starved for 24h prior to set up.
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(+) TTX
X 0.6 * * X * 0.5 X 0.4 X X 0.3 X
0.2 Invasiveness(relative) 0.1
0.0 siC siNESO siNeo1 siNeo2 siAdult1
Figure 6.12 – Effect of RNAi targeting Nav1.5 on Matrigel invasion of SW620 cells. siRNA transfected cells (siNESO, siNeo1, siNeo2 and siAdult1) were compared to non-targeting siControl transfected cells (siC). Cells were serum-starved for 24 h prior to set-up and invaded over 48h. Whilst siNESO, siNeo1 and siNeo2 were siRNAs against nNav1.5, siAdult1 siRNA was targeting aNav1.5. Except for Adult1, all siRNAs reduced invasion significantly (P<0.05). Co-application of TTX (20 µM) did not cause any further effect with any of these siRNAs (P>0.05) (n>3).
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chambers 48 hours after transfection and were allowed to invade for 48 hours.
Compared with the siControl transfected cells, the numbers of invaded cells in all siRNA conditions were reduced significantly by 35 – 50 %. When siRNA against aNav1.5 was used, there was no reduction in invasion (Figure 6.12). Furthermore, co-application of TTX (20 µM) did not cause any further decrease in all the combinations tested (Figure 6.12).
6.3.5 nNav1.5 is expressed in CRCa biopsy tissue
Expression of nNav1.5 protein in CRCa biopsy tissue was by immunohistochemistry.
House et al. (2010) had shown previously, using a general Nav1.5-specific antibody, that Nav1.5 staining was significantly higher in CRCa tissues compared with normal- matched colon tissues. Using the nNav1.5 specific antibody (NESOpAb), staining in
92 high-grade CRCa biopsy tissues was compared with 49 low-grade / normal colon mucosa tissues. A representative selection of three sections for each is presented in
Figure 6.13. Analysis of the results showed discrete areas of staining in the CRCa biopsy tissues whilst little staining was observed in the normal colon mucosa tissues.
The difference was statistically significant between cancerous and normal samples
(P<0.0001) (Table 6.1).
Following two additional interesting observations were also made during investigation of biopsy samples (Fig.6.14)
1- Observation of crypts showing transitional features confirmed the main conclusion. Thus, within such crypts, ‘normal’ areas were devoid of nNav1.5 immunoreactivity, whilst ‘cancerous’ regions were positive (Figure 6.14A).
2- Some staining was also observed in cells which were ‘presumed inflammatory’ (Prof Annarosa Arcangeli, personal communication). These included
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A
B
Figure 6.13 - Immunohistochemical staining of human colon tissues (legend is overleaf)
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Figure 6.13 - Immunohistochemical staining of human colon tissues. (A) No antibody (No Ab) and secondary antibody only (2nd Ab only) controls on cancer samples indicated specific staining. An example for each control is shown. (B) Examples of three cases of human high-grade colon cancer biopsy tissue sections (left) and low-grade/control mucosa tissues (right) were stained using the nNav1.5 specific antibody (NESOpAb) at a 1:200 dilution from a 0.7 mg/ml stock. Staining was visualized with DAB. Slides were counterstained with haematoxylin. Scale bar applies to all.
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Table 6.1 – nNav1.5 immunoreactivity scores for colon biopsy samples. In total, 92 high-grade CRCa and 49 low-grade / normal colon mucosa biopsy tissues were analysed. The nNav1.5 specific antibody (NESOpAb) showed positive staining (nNav1.5 (+)) in discrete areas of the CRCa biopsy tissues, whilst little staining was observed in the normal colon mucosa tissues. Fisher’s exact test revealed that the difference in the nNav1.5 expression was statistically significant between cancerous and normal samples (P<0.0001).
Cancer Mucosa (n = 92) (n = 49)
nNav1.5 (+) 19 32
nNav1.5 (-) 73 17
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A
B
C
Figure 6.14 – Additional observations on human colon tissues. Staining with the nNav1.5 specific antibody (NESOpAb) at a 1:200 dilution from a 0.7 mg/ml stock revealed some interesting observations: (A) Within ‘transitional’ crypts, ‘normal’ areas were devoid of nNav1.5 immunoreactivity, whereas ‘cancerous’ regions were positive. (B) Examples of putative ‘mast cells’ from a cancer (left) and a mucosa (right) tissue sample with positive staining are marked with red arrows. (C) Some possible ‘immune cells’ were also showed staining on a tissue section from a cancer biopsy sample. Scale bar applies to all.
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possible mast cells (Figure 6-14B) and immune cells (Figure 6-14C). These observations agree broadly with the notion that tumour infiltrating immune cells may employ VGSC part of their invasiveness (Fraser et al., 2004). Importantly, it has been suggested that the extent of tumour infiltration by immune cells has direct prognostic relevance (Bremnes et al., 2011).
The staining of the CRCa biopsy tissue was compared with human BCa tissue sections (Figure 6.15), where nNav1.5 expression has been shown previously to correlate with metastatic status (Fraser et al., 2005). Under identical immunohistochemical processing conditions, the staining of CRCa and BCa tissues appeared comparable.
6.4 Discussion
The main results from the studies on nNav1.5 expression in human CRCa cells and tissues described in this chapter are as follows:
1. nNav1.5 mRNA was expressed at a significant level in SW620 cells. This was confirmed by using specific PCR primers and sequencing the PCR products.
2. nNav1.5 protein was also expressed in SW620 cells. Importantly, the staining produced by NESOpAb was indicative of the protein being at the plasma membrane (i.e. nNav1.5 being the functional channel) since the antibody was used under non-permeabilized conditions.
3. nNav1.5 protein expression at the plasma membrane was shown to be down-regulated over time in culture (i.e. 24 vs. 72 h) which was in agreement with the results from patch clamp electrophysiology.
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Figure 6.15 Immunohistochemical staining of human breast cancer tissues. Cases of human breast cancer biopsy tissue sections (case 97/5549/A1) stained using a NESOpAb at a 1:200 dilution of a 0.7 mg/ml stock. Staining was visualized with DAB. Slides were counterstained with haematoxylin. Scale bar applies to all. Staining protocol was identical to that used for Figure 6.13 and 6-14.
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4. Three different siRNAs specifically targeting nNav1.5 were shown to down-regulate nNav1.5 at both the mRNA and protein / signalling (patch clamp) levels. One siRNA against aNav1.5 was also tested, which had no effect on VGSC currents (patch-clamp)
5. The invasiveness of the SW620 cell line was found to be significantly reduced by the three siRNAs targeting nNav1.5. In contrast, neither siRNA against aNav1.5 nor co-application of TTX (20 µM) with any of the siRNAs caused any reduction in invasion.
6. nNav1.5 protein was found to be expressed in CRCa biopsy tissues; the staining appeared stronger in higher grade tumours. .
6.4.1 nNav1.5 RNA was expressed in SW620 cells
Conventional PCR experiments performed with nNav1.5 specific primers detected nNav1.5 mRNAs, of correct size, in the SW620 cells. Further to this, the experiments using the ‘general’ Nav1.5 primers (which would amplify both ‘adult’ and ‘neonatal’
Nav1.5 splice variants) showed that the nNav1.5 splice variant was amplified. This was confirmed by sequencing the PCR products. This further suggested that the nNav1.5 splice variant was indeed present in the SW620 cells. Although these experiments do not preclude the expression of aNav1.5, the evidence, taken together, would indicate that the nNav1.5 splice variant is likely to be the predominant form of the VGSC expressed. Thus, as in human BCa, VGSC (nNav1.5) expresion in CRCa is an oncofoetal phenomenon (Fraser et al., 2005). At present, it is not clear, especially in the absence of explicit data, why House et al. (2010) suggested that the
Nav1.5 in their cells was the adult variant.
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6.4.2 nNav1.5 protein was expressed in SW620 cells mRNA expression in cells does not guarantee protein synthesis since mRNA can be
‘docked’ intracellularly for synthesis at a later stage. Accordingly, an antibody
(NESOpAb) that specifically recognizes nNav1.5 (Chioni et al., 2005) was used to study nNav1.5 protein expression in SW620 cells. This antibody stained the SW620 cells very effectively. In addition, and as expected, staining was also observed with a pan-VGSC antibody. The staining observed with NESOpAb is indicative of the nNav1.5 splice variant being expressed in SW620 cells. Importantly, since
NESOpAb recognizes an extracellular epitope sequence (Chioni et al., 2005), staining can be performed under non-permeabilized conditions. This would indicate that the nNav1.5 splice variant is at the plasma membrane and is the functional form of the channel expressed. Furthermore, electrophysiology (patch clamp recordings) confirmed that a TTX-resistant VGSC currents were indeed present in SW620 cells, consistent with nNav1.5 expression. Finally, it was observed that there was a reduction in staining of nNav1.5 at 72 h compared to 24 h after plating, in keeping with the parallel significant reduction in peak VGSC current density from the patch clamp electrophysiology (Appendix 1.6). Such reduction in VGSC (nNav1.5) expression could be due to increased cell adhesion with time in culture, which could lower the cells’ metastatic potential. This would agree with previous observations showing that continued culturing of cells lowers functional VGSC expression whilst transient trituration enhanced the expression (Dr Scott P Fraser, personal communication).
6.4.3 siRNA transfection reduced nNav1.5 mRNA and functional protein expression in SW620 cells
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Three different siRNA (siNESO, siNeo1 and siNeo2) transfections all significantly reduced nNav1.5 mRNA levels in comparison to the siControl. The effect of siNESO, also used originally by Brackenbury et al. (2007) was similar to that found previously in down-regulating nNav1.5 mRNA levels in the BCa MDA-MB-231 cells (Brackenbury et al., 2007). However, the reduction in nNav1.5 levels in SW620 cells, especially by siNESO, was rather limited (25 – 80 %). This could be due to the fact the cells had to be maintained for 96 h (partially serum-starved) which could have degraded the siRNAs. Nevertheless, functional effects were apparent (as discussed in the following sections).
siRNA transfections with the siNESO, siNeo1 and siNeo2 also appeared to reduce nNav1.5 protein levels in comparison to the siControl; these analyses could only be performed by immunocytochemistry so quantification was not possible.
More importantly, in terms of quantitative analysis, the patch clamp experiments revealed that siNESO, siNeo1 and siNeo2 all significantly reduced functional nNav1.5 activity at the plasma membrane (Appendices 1.7 and 1.8). Such effects of siNESO are similar to that found previously in down-regulating functional nNav1.5 mRNA protein expression in the BCa MDA-MB-231 cells (Brackenbury et al.,
2007).
In addition, siNESO, siNeo1 and siNeo2 all significantly reduced the proportion of SW620 cells expressing nNav1.5 (Appendices 1.7 and 1.8).
Importantly, where tested (i.e. for the siNESO), there was no change in the following channel characteristics: (i) current-voltage relationship; (ii) steady-state inactivation and (iii) recovery from inactivation (Appendix 1.7). Some change in channel characteristics (kinetics) would be expected if nNav1.5 expression was being knocked-down and leaving a residual functional aNav1.5 population (Onkal et al.,
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2008). Thus, in terms of the results from the protein and functional expression, it would appear that the nNav1.5 splice-variant is the predominant functional VGSC expressed at the plasma membrane. This was further supported by testing the siRNA targeting aNav1.5, which had no effect on VGSC activity (Appendix 1.9).
6.4.4 siRNA knock-down of nNav1.5 suppressed invasiveness of SW620 cells siRNA transfections with siNESO, siNeo1 and siNeo2 all significantly reduced invasiveness of the SW620 cells (Figure 4.17). All three siRNAs reduced invasion by between some 40-50 %, which was in the range of the reduction in invasion of ~40
% induced by 10 µM TTX. Since this concentration of TTX is known to block the nNav1.5 channel by about 80 % (Fraser et al., 2005), which is similar to the level of functional knockdown of the channel found with the siRNA (Appendices 2.2 and
2.3) it would appear that the nNav1.5 splice-variant is the predominant functional
VGSC expressed in the plasma membrane. These results complement the previous findings from siRNA treatments with the siNESO with MDA-MB-231 cells showing that nNav1.5 is involved similarly in promoting cellular motility and invasive potential (Brackenbury et al., 2007). The predominance of nNav1.5 in invasiveness of SW620 cells was further confirmed (i) by testing an aNav1.5 siRNA and (ii) by co-applying TTX (20 µM) with all siRNAs. None of these conditions had an effect on invasion.
6.4.5 nNav1.5 protein is expressed in human CRCa biopsy tissues
The staining of high-grade CRCa biopsy tissues with NESOpAb (which recognizes specifically nNav1.5) revealed discrete areas of staining, including the plasma membrane, whilst little staining was observed in the normal colon tissue. These
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differences were statistically significant. Further analysis by a histopathologist (Prof.
Annarosa Arcangeli, University of Florence, Italy) suggested that nNav1.5 expression correlated with the stage of CRCa. These findings extend those of House et al. (2010) who showed previously, using a general Nav1.5-specific antibody, that Nav1.5 staining was significantly higher in CRCa tissues compared with normal-matched colon tissues. Thus, our results would suggest that it is the nNav1.5 splice variant of
Nav1.5 that is the predominant functional VGSC expressed in vivo, as in vitro.
Comparing CRCa with BCa biopsy staining suggested comparable levels of nNav1.5 protein expression.
Importantly, since House et al. (2010) suggested from system analysis that
(n)Nav1.5 expression was upstream to the CRCa invasive machinery, the possibility arises that nNav1.5 expression may be an early, functional marker for diagnosing and staging CRCa.
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Chapter 7
GENERAL DISCUSSION AND CONCLUSION
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This PhD comprised studies integrating 3 broad areas: 1) the insulin - insulin-like growth factor-1 system (IIS), (2) human cancer, especially breast cancer (BCa) and colon cancer (CRCa) and metastatic disease, and (3) ion channels, especially the neonatal Nav1.5 (nNav1.5) subtype of voltage-gated sodium channel (VGSC).
Experiments were carried out on both human cancer cell lines, mainly the strongly metastatic MDA-MB-231 cells (BCa) and SW-620 cells (CRCa) and blood. The latter was connected to diabetes for which there is increasing evidence for association with cancer. The main results obtained were as follows:
1. IIS was expressed in MDA-MB-231 cells. The VGSC expressed in these cells was very sensitive to the biochemical content of their environment involving
IIS. Thus, when treated with insulin in serum-free medium, the cells’ response to tetrodotoxin (TTX) became anomalous and the contribution of VGSC activity to
MCBs ceased or reversed. It was concluded that insulin also controlled the mode of the functional link between VGSC activity and the cells’ metastatic machinery.
These characteristics broadly highlight the importance of tumour – stroma cellular interactions in cancer progression.
2. Silencing endogenous IIS pharmacologically or using dual-siRNA consistently led to suppression of invasiveness, without any effect on proliferative activity. Thus, IIS can play a significant role in potentiating metastatic progression.
3. The functional relationship between IIS signalling and VGSC (nNav1.5) expression / activity was complex, involving both transcriptional and post- translational effects. However, these were not significant pre-requisites for the observed pro-MCB effects of IIS.
4. Several cancer-associated ion channel (CAIC) mRNAs were detected in mononuclear cells of human blood. These channels included the following: nNav1.5,
215 VGSC-β1 (Beta1), VGSC-β1b (Beta1b), Kv1.3, Kv11.1 and KCa3.1. In contrast,
Kv10.1 was expressed at a low level and TRPM8 was not detectable. The Kv1.3 mRNA expression, in particular, was highly consistent and its level was adopted as a
‘reference’ for the expression of the other CAICs.
5. Comparison of cancer cases with the healthy individuals revealed significant up-regulation of nNav1.5 and KCa3.1. In contrast, Kv1.3, Kv11.1 and
Beta1 mRNA levels were down-regulated.
6. Comparisons, as in (5), of diabetic patients with the healthy individuals revealed generally similar trends. An exception was Kv11.1, which did not show any difference between diabetes and healthy control cases.
7. At concentrations not affecting cell number (‘proliferative activity’), the anti-diabetic Metformin suppressed the invasiveness of MDA-MB-231 cells but had no effect on VGSC (nNav1.5) expression / activity.
8. In line with oncofoetal gene expression, SW-620 cells expressed nNav1.5 mRNA and protein. Furthermore, nNav1.5 activity enhanced the cells’ invasiveness in vitro. nNav1.5 protein expression was detected in human CRCa biopsies.
7.1 Sensitivity of cancer cells to the biochemical content of their environment
There is substantial evidence that cancer progression in vivo involves two-way interactions between the cancer cells and the stroma (Kenny et al., 2007; Ungefroren et al., 2011). The chemical messengers involved include hormones, growth factors
(GFs) and cytokines (Onishi et al., 2010; Takebe et al., 2011). In addition, physical substrate effects are likely (e.g. Margulis et al., 2005). In vitro, such interactions can manifest themselves by the sensitivity of the cancer cells to the biochemical content
216 of the medium. At a ‘gross’ level, Ding & Djamgoz (2004) showed originally that
VGSC expression in rat prostate cancer (PCa) Mat-LyLu cells, indeed, was highly sensitive to serum concentration in medium. More specifically, a number of GFs, e.g. epidermal growth factor (EGF) and nerve growth factor (NGF), have been found to have significant effects on VGSC expression / activity and, at least in some cases, these led to corresponding changes in MCBs in BCa and PCa (Brackenbury &
Djamgoz, 2007; Ding et al., 2008). Autocrine effects are also possible, whereby a GF released from a cancer cells may upregulate VGSC expression in that cell which may increase secretory activity further and this may result in a self-sustaining positive feedback loop that could promote cancer progression (Brackenbury, 2006; Mycielska et al., 2003). This is an interesting concept that would be worthy of further investigation.
7.2 Expression and functional role of IIS in cancer
One of the ‘mainstream’ biochemical mechanisms regulating cancer cell activity comprises insulin and insulin-like growth factor 1 (IGF1) and their receptors
(Belfiore & Frasca, 2008; Frasca et al., 2008). In fact, there is increasing evidence (i) that the two receptors can be cross-stimulated (as well as forming hybrids) and (ii) that the signalling cascades associates with these two primary signals can overlap. In line with recent developments in this field, therefore, the strategy was adopted to treat insulin and IGF1 as a single system (IIS) and study its role MCBs as a whole.
This is also in line with clinical efforts to target IIS pharmacologically as a whole
(Goodwin, 2008; Mulvihill et al., 2009). This is necessary since blocking one component could still leave the other significantly functional. Approached in this
217 way, use of combined blockers and application, for the first time, of dual-siRNAs to silence IIS as a whole revealed that cellular invasiveness would be blocked under conditions when proliferative activity, studied as “cell number”, would not be affected. Thus, IIS is involved in invasive as well as proliferative behaviour, raising the possibility that IIS signalling is a driving force in metastasis. Also, since BCa and
PCa are strongly sensitive to steroid hormones (SHs), GF – SH interactions regulating cellular invasiveness and/or VGSC expression would be worthy of investigation.
7.3 Metformin as an anti-cancer drug
Metformin is commonly prescribed to patients with type-II diabetes (Cusi &
DeFronzo, 1998). Its main mode of action is to activate AMPK which increases the sensitivity of InsR to insulin (Winder & Hardie, 1999). More recently, Metformin has been found to produce anti-cancer, mainly anti-proliferative effects, presumably by restoring homeostasis in IIS signalling (Ben Sahra et al., 2009). The present study tested, for the first time, the possible effect of Metformin as an anti-invasive agent in metastatic BCa cells. The results showed that, Metformin, at clinical doses, suppressed invasiveness without affecting proliferative activity (studied as cell number). This raises the possibility that Metformin may be effective against secondary tumourigenesis as well as primary tumours. Although the anti-invasive effect observed did not involve any change in VGSC activity, nNav1.5 mRNA levels appeared to be reduced implying that further time- and/or dose-dependent inhibition of VGSC-dependent invasiveness may also occur. At present, the mechanism(s) underlying the inhibition of cellular invasion is not known and this would be worthy
218 of investigation. It would also be interesting to elucidate if other, more recently introduced anti-type-II diabetic drugs (e.g. thiazolidinediones) might also produce anti-invasive effects with or without VGSC involvement (He et al., 2011).
Furthermore, it would be interesting to test Metformin on other carcinomas in relation to ion channel expression. Finally, the question remains whether Metformin can produce synergistic effects when combined with VGSC-blocking drugs, such anticonvulsants (Clare et al., 2000).
7.4 Expression of cancer-associated ion channels in human blood
Mononuclear cells in blood express a number of ion channels in their plasma membranes (Cahalan & Chandy, 2009). These are mainly Kv1.3, KCa3.1 and VGSC
(possibly Nav1.7) (Cahalan & Chandy, 2009; Cahalan et al., 1985). In the present study, a PCR approach was used to determine CAIC mRNA expressions in healthy subjects and compare these with cases of cancer and diabetes. The following CAIC mRNAs were detected: nNav1.5, Beta1, Beta1b, Kv1.3, K11.1 and KCa3.1.
Importantly, in the solid cancers studied, nNav1.5 and KCa3.1 mRNA levels were up-regulated, whilst Kv1.3, K11.1 and Beta1 mRNA levels were down-regulated.
Taken together, this pattern suggested that mononuclear cells changed their CAIC expression as a part of their response to the biochemistry of cancer. For example, the changed pattern is precisely what would be expected to facilitate motility and tumour infiltration, known to serve as a prognostic marker (e.g. Naukkarinen & Syrjanen,
1990; Georgiannos et al., 2003). A similar change was seen in post-inflammatory dendritic cells (Zsiros et al., 2009). Furthermore, the limited number of diabetes cases studied also revealed a broadly parallel trend. It should be emphasised,
219 however, the present study is restricted to mRNA analyses and protein-level and signalling (e.g. electrophysiological) studies are necessary to fully evaluate the functional consequences of the pattern of the CAIC mRNA changes. Nevertheless, such a pattern may be useful clinically as the basis of a novel PCR method for blood- based diagnosis of cancer, especially following application of this approach to specific cancers and cancer stages.
7.5 Expression and pathophysiological role of nNav1.5 in strongly metastatic cells
Fraser et al. (2005) and Brackenbury et al. (2007) provided compelling evidence that human metastatic BCa cells and tissues express nNav1.5 and that channel activity promotes MCBs, especially invasiveness. House et al. (2010) found similarly that
VGSC (Nav1.5) expression occurs in a range of human CRCa cells and tissues and the ‘culprit’ VGSC was said to be the adult variant. However, it was not clear if the channel was also expressed in neonatal splice form. Also, Nav1.5 expression was found in human ovarian cancer (OvCa) but, again, it was not clear whether this is a neonatal splice variant (Gao et al., 2010). Here, we have shown that the functional
VGSC expressed in CRCa is, at least partially, nNav1.5 and it is involved in potentiating invasion. Thus, the balance of evidence is consistent with VGSC expression in human metastatic disease being an epigenetic oncofoetal phenomenon.
Several genes are known to be expressed in cancer cells / tissues as embryonic splice variants, presumably as a manifestation of dedifferentiation that is a fundamental feature of the cancer process.
220 7.6 Concluding remarks: Clinical potential of nNav1.5
Onkal and Djamgoz (2009) summarised the evidence for the clinical potential of nNav1.5 as an anti-metastatic target in BCa. The present study would extend the case to CRCa. Thus, an antibody (Ab) that can selectively bind to nNav1.5 over its
‘nearest neighbour’, adult Nav1.5 (aNav1.5) could ultimately serve as a biological agent also against metastatic CRCa. A polyclonal Ab that blocked nNav1.5 with
~x100 selectivity over aNav1.5 was produced and validated by Chioni et al. (2005).
Such a biological agent would also be extremely useful as a diagnostic tool, since in line with the results of House et al. (2010) showing that channel expression is upstream to some of the key promoters of invasiveness, the pathobiology of nNav1.5 expression is indeed such as to make it an early marker of metastatic disease (CRCa).
The possibility of producing a ‘humanised’ monoclonal Ab to nNav1.5 as an anti- metastatic biological agent to detect early and block metastatic BCa, CRCa and possibly OvCa, therefore, is a tantalizing prospect.
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APPENDICES
267
Appendix 1
Supplementary electrophysiological data
(provided by Dr Scott P Fraser)
268
Appendix 1.1 – Effect of 24 h incubation with 100 nM insulin on the voltage- gated Na+ channel activity in MDA-MB-231 cells. (Top) Current-voltage relationship for control cells and following incubation with 100 nM insulin (n=15 and n=21, respectively). (Table) Summary of the peak current density data.
269
Appendix 1.2 – Effect of 24 h incubation with 5 µM AG1024 on the voltage- gated Na+ channel activity in MDA-MB-231 cells. (Top) Current-voltage relationship for control cells and following incubation with 5 µM AG 1024 (n=19 and n=20, respectively). (Table) Summary of the peak current density data.
270
Appendix 1.3 – Effect of 24 h incubation with 3 µM OSI-906 on the voltage- gated Na+ channel activity in MDA-MB-231 cells. (Top) Current-voltage relationship for control cells and following incubation with 3 µM OSI-906 (n=18 and n=20, respectively). (Table) Summary of the peak current density data.
271
Appendix 1.4 – Effect of dual-siRNA targeting IGF1R and InsR on the voltage- gated Na+ channel activity in MDA-MB-231 cells. (Top) Current-voltage relationship for control cells and following dual-siRNA treatment (n=11 and n=10, respectively). (Table) Summary of the peak current density data.
272
Appendix 1.5 – Effect of 24 h incubation with 2 mM metformin on the voltage- gated Na+ channel activity in MDA-MB-231 cells. (Top) Current-voltage relationship for control cells and following incubation with 2 mM metformin (n=11 and n=19, respectively). (Table) Summary of the peak current density data.
273
Appendix 1.6 – Effect of culturing time on voltage-gated Na+ channel peak current density in SW620 cells. Cells were plated for either 24 h or 72 h prior to patch-clamp recording (n = 30 and 17, respectively). *** indicates P<0.0001.
274
Appendix 1.7 – Effect of the siRNA targeting the neonatal splice variant of the Nav1.5 voltage-gated Na+ channel in SW620 cells. (A) The siRNA (siNESO) significantly reduced (i) the current density in comparison to the control cells (scrambled siRNA) (n=37 and n=28, respectively; Mann Whitney U-test) and (ii) the proportion of cells with functional VGSC (Fisher’s Exact test: one-tailed). (B) Current-voltage relationship for control and siNESO-transfected cells (n=13 and n=9, respectively). (C) Steady-state inactivation for control and siNESO-transfected cells (n=11 and n=9, respectively). (D) Recovery from inactivation for control and siNESO-transfected cells (n=11 and n=9, respectively). Cells were patched 96 h after transfection and had been serum-starved for 24 h prior to patching.
275
Appendix 1.8 – Effect of additional siRNAs targeting the neonatal splice variant of the Nav1.5 voltage-gated Na+ channel in SW620 cells. (A) siNeo1 and (B) siNeo2 are siRNA sequences targeting the neonatal Nav1.5. In both cases, the siRNA significantly reduced (i) the peak current density in comparison to the respective control (scrambled siRNA) (Mann Whitney U-test) and (ii) the proportion of cells with functional VGSC (Fisher’s Exact test: two-tailed). Cells were patched 96 h after transfection and had been serum-starved for 24 h prior to patching.
276
Appendix 1.9 – Effect of the siRNA targeting the adult splice variant of the Nav1.5 voltage-gated Na+ channel in SW620 cells. (A) The siRNA (siAdult1) did not reduce the current density in comparison to the control (non-targetting siRNA) (Student’s t-test). siAdult1 also had no effect on the proportion of cells with functional VGSC (Fisher’s Exact test: two-tailed). (B) Current-voltage relationship for control and siAdult1-transfected cells (n=6 and n=4, respectively). (C) Steady- state inactivation for control and siAdult1-transfected cells (n=5 and n=4, respectively). (D) Recovery from inactivation for control and siAdult1-transfected cells (n=4 and n=4, respectively). Cells were patched 96 h after transfection and had been serum-starved for 24 h prior to patching.
277
Appendix 2
Ethics documents for blood work
2.1 London
2.2 Istanbul
278
Appendix 2
Ethics documents for blood work
2.1 London
279
RESEARCH PROTOCOL
Ion Channel Expression in Normal Human Blood
ICE-NORB
th Version 1, 28 April 2010
MAIN SPONSOR: Imperial College London FUNDERS: Imperial College London STUDY COORDINATION CENTRE: Neuroscience Solutions to Cancer Research Group, Imperial College London
NRES reference: N/A
Protocol authorised by:
Name & Role Date Signature
Prof. M. B. A. Djamgoz (Chief Investigator)
Study Management Group
Chief Investigator: Prof. M. B. A. Djamgoz
Co-investigators: Ms Refika M Guzel
Statistician: Mr Rustem Onkal
Study Management: Prof. M. B. A. Djamgoz
Study Coordination Centre N / A
Clinical Queries N / A
Sponsor
Imperial College is the main research sponsor for this study. For further information regarding the sponsorship conditions, please contact the Research Governance Manager at:
Joint Research Office Sir Alexander Fleming Building Imperial College Exhibition Road London SW7 2AZ Tel: 020 7594 1188 Fax: 020 7594 1792 http://www3.imperial.ac.uk/clinicalresearchgovernanceoffice
Funder
Imperial College London.
This protocol describes the ICE-NORB study and provides information about procedures for entering participants. Every care was taken in its drafting, but corrections or amendments may be necessary. These will be circulated to investigators in the study. Problems relating to this study should be referred, in the first instance, to the Chief Investigator.
This study will adhere to the principles outlined in the NHS Research Governance Framework for Health and Social Care (2nd edition). It will be conducted in compliance with the protocol, the Data Protection Act and other regulatory requirements as appropriate.
RESEARCH PROTOCOL (ICE-NORB) Page 2 of 12 Version 1; 28.04.2010 Table of Contents
1. INTRODUCTION 6 1.1 BACKGROUND 6 2. STUDY OBJECTIVES 6 3. STUDY DESIGN 7 3.1 STUDY OUTCOME MEASURES 7 4. PARTICIPANT ENTRY 7 4.1 PRE-REGISTRATION EVALUATIONS 7 4.2 INCLUSION CRITERIA 8 4.3 EXCLUSION CRITERIA 8 4.4 WITHDRAWAL CRITERIA 8 5. ADVERSE EVENTS 8 5.1 DEFINITIONS 8 5.3 REPORTING PROCEDURES 8 6. ASSESSMENT AND FOLLOW-UP 9 7. STATISTICS AND DATA ANALYSIS 9 8. REGULATORY ISSUES 10 8.1 ETHICS APPROVAL 10 8.2 CONSENT 10 8.3 CONFIDENTIALITY 10 8.4 INDEMNITY 10 8.5 SPONSOR 11 8.6 FUNDING 11 8.7 AUDITS 11 9. STUDY MANAGEMENT 11 10. PUBLICATION POLICY 11 11. REFERENCES 11 LIST OF APPENDICES 12
RESEARCH PROTOCOL (ICE-NORB) Page 3 of 12 Version 1; 28.04.2010 GLOSSARY OF ABBREVIATIONS BCa Breast cancer PCa Prostate cancer CTC Circulating tumour cell MNC Mononuclear cell NKC Natural killer cell CAIC Cancer-associated ion channel Na Sodium K Potassium Ca Calcium Nav Voltage-gated sodium channel nNav Voltage-gated sodium channel neonatal splice variant Kv Voltage-gated potassium channel KCa Calcium sensitive potassium channel TRP Transient receptor potential channel
KEYWORDS
Ion channel, sodium, potassium, beta-subunit, splice variant, breast cancer, prostate cancer, blood
RESEARCH PROTOCOL (ICE-NORB) Page 4 of 12 Version 1; 28.04.2010 STUDY SUMMARY
TITLE Ion Channel Expression in Normal Human Blood DESIGN Relative expression levels and profiles of cancer-associated ion channel subunits (CAICs) will be determined systematically (from gene to protein levels) in whole and specific blood cell populations. There are 10 different CAICs with evidence for involvement in invasiveness: Nav1.5, Nav1.7, Kv1.3, Kv10.1, Kv11.1, KCa1.1, KCa2.3, KCa3.1, TRPM8 and TRPV2. Of particular interest are developmentally regulated splice variants of these CAICs. AIMS To study cellular expression of CAIC subunits in normal human blood. OUTCOME MEASURES The basal status of CAIC expression in healthy human blood (total and specific cell populations) will be determined in terms of the following: 1) types (including splice variants) of CAICs, and 2) relative level of expression of each CAIC. It is possible that a secondary outcome may result from correlation of CAIC expression with age or hormonal status. POPULATION Healthy human (male and female) subjects between the ages of 20-60. ELIGIBILITY The principal inclusion criterion for the participants is to be healthy (disease- free) individuals. “Healthy” status is defined as not having any record of either of the following: a. Disease for which there is evidence for ion channel involvement (“channelopathies”). Such diseases are mainly cancer, cystic fibrosis, epilepsy, cardiac arrhythmia, chronic migraine, diabetes, neurodegenerative disease (e.g. motor neurone disease, dementia) and immunological disorders (e.g. multiple sclerosis, asthma). b. Blood-related disorders (e.g. anaemia, haemophilia, HIV, hepatitis B & C). DURATION 18 months
REFERENCE DIAGRAM
RESEARCH PROTOCOL (ICE-NORB) Page 5 of 12 Version 1; 28.04.2010 1. INTRODUCTION
1.1 BACKGROUND
Carcinomas of breast (BCa) and prostate (PCa) are among the most common types of cancer world- wide. In the western world USA alone, BCa and PCa constitute 25-30 % of all newly diagnosed cancer cases and are the second leading causes of cancer-related deaths after lung cancer in both populations [1]. In BCa and PCa, as with other cancers, the main cause of death is metastasis, a process defined as the spread of cancer cells from primary tumours to distant organs and subsequent re-growth. Although several options, such as surgery, radiation, hormone therapy and chemotherapy, are utilized to cure patients with BCa and PCa at different stages, treatment of metastatic disease is mostly palliative [2, 3]. Importantly, therefore, metastasis presents a major challenge to the clinical management of BCa and PCa. Consequently, intensive efforts are focused on understanding, accurately detecting and treating the metastatic process and its constituent cellular behaviours [4].
Metastases originate from isolated tumour cells of primary lesions and such cells may be identified in peripheral blood as “circulating tumour cells” (CTCs) even at early stages of malignant progression. Thus, there is significant interest in clinical potential of CTCs [5]. Although investigation of CTCs represents an important tool for clinical oncology, their potential is limited by the lack of sensitive and specific markers. Recent evidence suggests that a number of ion channels (proteins that allow simple chemicals like sodium and potassium to go in-out of cells) could provide such promising candidate markers [6]. Among those ion channels, VGSCs were recently shown to be involved in the invasive behaviour of several types of metastatic cancers, including BCa and PCa [7]. A detailed investigation into BCa and PCa revealed that the Nav1.5 and Nav1.7 subtypes of VGSCs respectively, in their newly characterized ‘neonatal’ splice forms, may play an important role in the metastatic process [8- 10]. Other candidate ion channels are Kv1.3, Kv10.1, Kv11.1, KCa1.1, KCa2.3, KCa3.1, TRPM8 and TRPV2 [6]. Thus, there are 10 main cancer-associated ion channels (CAICs) involved in invasive cell behaviour [6].
In order to ascertain the clinical potential of CAICs as markers for metastatic BCa and PCa, more detailed studies are needed. An important question to be answered is to what extent these channels occur in normal blood, a major route for the spreading of cancer via CTCs. Since ion channels are involved in several physiological cellular activities, it is expected to observe their expression in normal cells. However, a detailed profiling may reveal differences in relative levels and/or variants of expression between healthy and diseased people. In turn, such information may facilitate development of novel diagnostic and/or therapeutic strategies.
1.2 RATIONALE FOR CURRENT STUDY
The essential rationale is the necessity to determine CAIC expression in normal human blood. The key research question is at what levels and profiles CAICs occur in basal conditions. The main CAICs of interest are the following: Nav1.5, Nav1.7, Kv1.3, Kv10.1, Kv11.1, KCa1.1, KCa2.3, KCa3.1, TRPM8 and TRPV2.
Hypothesis: Specific ratios of CAICs are expressed in normal human blood cells.
Under pathophysiological conditions, including cancer, the quality and quantity of the CAIC expression will change.
2. STUDY OBJECTIVES
Primary: To determine the basal expression levels and profiles of CAICs in peripheral blood – total mononuclear cell (MNC) and individual cell populations – of healthy individuals, using mRNA- and protein-based assays.
RESEARCH PROTOCOL (ICE-NORB) Page 6 of 12 Version 1; 28.04.2010 Secondary: To evaluate the selectivity and sensitivity of the CAIC assays for detecting tumour cells introduced artificially ('spiked') into normal blood.
3. STUDY DESIGN
The design considerations and stages for the study are as follows: a. Tissue collection
Peripheral blood samples will be collected from healthy volunteers. In total, 80 age-matched female and male participants will be included. The study is expected to last for approximately 18 months. b. Studies on whole mononuclear blood cell population
Initially, the total MNC population will be isolated from healthy blood samples and CAIC mRNA and protein expression levels will be determined. This will reveal the expression profile of CAICs in basal conditions. c. Studies on individual blood cell populations
Afterwards, MNCs will be separated into the 3 main lymphocyte subpopulations: T-, B-lymphocytes and natural killer cells (NKCs). Each sub-group will be analysed separately for CAIC mRNA and protein expression, as in (b). The molecular nature (sequence) of each CAIC expressed in these cells will also be determined in order to reveal which form (e.g. neonatal or adult) is present. Data obtained from these studies will enable determination of the possible source(s) of background/basal CAIC expression. This information is essential since the background CAIC expression in normal cells would interfere with the results when cancer patients are analysed in future studies. d. ‘Spiking’ experiments
Finally, human metastatic (CAIC-expressing) ‘model’ BCa and PCa cell lines will be used to ‘spike’ healthy blood samples. In these ‘spiking’ experiments, known numbers of cancer cells (from serial dilutions) will be mixed into normal blood and the routine mRNA and protein detection assays will be performed. This will mimic the situation in cancer patients’ blood. As a result, the lowest possible number of cancer cells that can be detected with the methodologies will be revealed. Establishing the sensitivity of the assays is essential for the possible use of the techniques in future more clinical applications.
3.1 STUDY OUTCOME MEASURES
There will be a series of endpoints to the study as follows:
1) The basal expression levels of the candidate CAICs (and their splice variants) in the total MNC population will be determined, initially relative to Kv1.3.
2) For those CAIC(s) present in total MNCs, similar data as in (1) will be obtained for B-, T- lymphocytes and NKCs.
3) The ‘spiking’ experiments, using CAICs as markers, will determine the minimum tumour cell number that can be detected in given volume of blood.
4. PARTICIPANT ENTRY
4.1 PRE-REGISTRATION EVALUATIONS
RESEARCH PROTOCOL (ICE-NORB) Page 7 of 12 Version 1; 28.04.2010 There is no requirement for any tests to be done before a participant may register for the study.
4.2 INCLUSION CRITERIA
1) The principal inclusion criterion for the participants is to be healthy (disease-free) individuals. “Healthy” status is defined as not having any record of either of the following: a. Disease for which there is evidence for ion channel involvement (“channelopathies”). Such diseases are mainly cancer, cystic fibrosis, epilepsy, cardiac arrhythmia, chronic migraine, diabetes, neurodegenerative disease (e.g. motor neurone disease, dementia) and immunological disorders (e.g. multiple sclerosis, asthma). b. Blood disorders (e.g. anaemia, haemophilia, HIV, hepatitis B & C).
2) Participants need to be adults between 20-60 years of age.
3) Participants may be of any race.
4) Males and females will be recruited in equal numbers.
4.3 EXCLUSION CRITERIA
The principal exclusion criterion for the participants is to be diseased, i.e. to have a channelopathy (as explained in 4.2) or any blood disorder.
4.4 WITHDRAWAL CRITERIA
We do not foresee any event leading to withdrawal of participants / samples.
5. ADVERSE EVENTS
5.1 DEFINITIONS Adverse Event (AE): any untoward medical occurrence in a patient or clinical study subject.
Serious Adverse Event (SAE): any untoward and unexpected medical occurrence or effect that: Results in death Is life-threatening – refers to an event in which the subject was at risk of death at the time of the event; it does not refer to an event which hypothetically might have caused death if it were more severe Requires hospitalisation, or prolongation of existing inpatients’ hospitalisation Results in persistent or significant disability or incapacity Is a congenital anomaly or birth defect
Medical judgement should be exercised in deciding whether an AE is serious in other situations. Important AEs that are not immediately life-threatening or do not result in death or hospitalisation but may jeopardise the subject or may require intervention to prevent one of the other outcomes listed in the definition above, should also be considered serious.
5.3 REPORTING PROCEDURES All adverse events should be reported. Depending on the nature of the event the reporting procedures below should be followed. Any questions concerning adverse event reporting should be directed to the Chief Investigator in the first instance.
5.3.1 Non serious AEs All such events, whether expected or not, should be recorded.
5.3.2 Serious AEs
RESEARCH PROTOCOL (ICE-NORB) Page 8 of 12 Version 1; 28.04.2010 An SAE form should be completed and faxed to the Chief Investigator within 24 hours. However, relapse, death or hospitalisations for elective treatment of a pre-existing condition do not need reporting as SAEs.
All SAEs should be reported to the North London REC 1 where in the opinion of the Chief Investigator, the event was: ‘related’, ie resulted from the administration of any of the research procedures; and ‘unexpected’, ie an event that is not listed in the protocol as an expected occurrence
Reports of related and unexpected SAEs should be submitted within 15 days of the Chief Investigator becoming aware of the event, using the NRES SAE form for non-IMP studies.
Local investigators should report any SAEs as required by their Local Research Ethics Committee and/or Research & Development Office.
Contact details for reporting SAEs Fax: 0207 584 2056, attention: Ms Refika M Guzel Please send SAE forms to: Prof Mustafa B A Djamgoz Tel: 0207 594 5370 (Mon to Fri 09.00 – 17.00)
6. ASSESSMENT AND FOLLOW-UP
There will be no follow up. The volunteers will take part in this study only once and will not be contacted again. They also will not be asked to take any medication or treatment.
The study will end when the processing of the samples and analyses of the data are completed.
7. STATISTICS AND DATA ANALYSIS a. Sample size calculation
The total number of participants will be 80, between the ages of 20 to 60. The sample size will be divided equally between males and females. In turn, each sub-population will be divided into 4 age groups (10 years each). From our earlier, extensive experience on cell lines, at least 5 independent molecular reactions must be conducted for each cell type in order to get a statistically viable result. Translating this to the in vivo situation (i.e. human blood) would necessitate a significantly higher number for which we feel doubling would be minimum, i.e. 10 participants will be needed for each age group. Accordingly, the following equation has been used: Total number of participants = 10 (number in each age group) x 4 (number of age groups) x 2 (number of sexes) = 80. b. Data analysis
A variety of methods of analysis will be used, depending on the nature of the data, including the following: 1) The experiments based on real-time PCR will be analysed by the 2^[-DeltaDeltaC(T)] method [Livak & Schmittgen - Methods (2001) 25(4):402-8].
2) Protein levels will be analysed by quantitative imaging programs (e.g. ImageJ).
RESEARCH PROTOCOL (ICE-NORB) Page 9 of 12 Version 1; 28.04.2010 3) Statistical analyses will be performed using appropriate Student's T-Test or ANOVA followed by a post hoc test e.g. Mann-Whitney (Program: SigmaStat, Origin 6.1).
4) Depending on primer efficiencies etc, it may also be necessary to use a relatively new statistical package ("REST2009") being developed for analyses of real-time PCR data.
8. REGULATORY ISSUES
8.1 ETHICS APPROVAL
The Chief Investigator has obtained approval from the North London REC 1. The study will be conducted in accordance with the recommendations for physicians involved in research on human subjects adopted by the 18th World Medical Assembly, Helsinki 1964 and later revisions.
8.2 CONSENT
Consent to enter the study will be sought from each participant only after a full explanation has been given, a detailed information leaflet offered and time allowed for consideration and, if necessary, discussion. Signed participant consent will be obtained. No potential participant will be subject to undue influence to take part in the study. The right of the participant to refuse to participate without giving reasons will be respected. All participants are free to withdraw at any time from the procedure without giving reasons.
8.3 CONFIDENTIALITY
The Chief Investigator will preserve the confidentiality of participants taking part in the study and is registered under the Data Protection Act. Confidentiality of the personal data will be ensured at all stages. In particular, the following principles will be followed:
1) Each participant will be given a unique code by the CI during the recruitment process while the participant is filling the Informed Consent Forms A and B.
2) Informed Consent Form A will contain both the name and the code of the participant, and will be kept confidential in a safe place known only to the CI.
3) Informed Consent Form B will contain only the code and some scientifically relevant details of the participant. The researcher will have access only to this form.
4) No information about the participants will be kept in personal or public computers. Only a dedicated university computer will be used for data analysis.
5) Sample tubes will be labelled with the code and stored in a freezer used by the research group only.
6) Any experimental result from the samples will be identifiable only by the code.
7) Samples will not be shared with other research teams or be used for other studies within the group.
8) Samples will be stored only for 6-12 months after the study and then will be discarded.
8.4 INDEMNITY
Imperial College London holds negligent harm and non-negligent harm insurance policies which apply to this study.
RESEARCH PROTOCOL (ICE-NORB) Page 10 of 12 Version 1; 28.04.2010 8.5 SPONSOR
Imperial College London will act as the main sponsor for this study.
8.6 FUNDING
The study will be funded through Imperial College London. Research participants will not receive any payments, reimbursement of expenses or any other benefits or incentives for taking part in this research. Similarly, individual researchers will not receive any personal payment over and above normal salary, or any other benefits or incentives, for taking part in this research. The Chief Investigator or any other investigator/collaborator does not have any direct personal involvement (e.g. financial, share holding, personal relationship etc.) in the organisations sponsoring or funding the research that may give rise to a possible conflict of interest.
8.7 AUDITS
The study may be subject, at any time, to inspection and audit by Imperial College London under their remit as sponsor and other regulatory bodies to ensure adherence to GCP and the NHS Research Governance Framework for Health and Social Care (2nd edition).
9. STUDY MANAGEMENT
The day-to-day management of the study will be co-ordinated through Prof. Mustafa B. A. Djamgoz.
10. PUBLICATION POLICY
It is the ultimate aim of the study to publish the results in a peer reviewed scientific journal, which can be accessed by the public via subscription to publication databases (e.g. PubMed, ScienceDirect). The results of this research will be disseminated as an internal report or a conference presentation. No personal information will be given in such publications.
11. REFERENCES
1) Jemal A, Siegel R, Ward E, Hao Y, Xu J & Thun MJ (2009) Cancer Statistics, 2009. CA Cancer J Clin. 59: 225-249.
2) Higa GM (2009) Breast cancer: beyond the cutting edge. Expert Opin. Pharmacother. 10: 2479- 2498.
3) Meulenbeld HJ, Hamberg P & deWit R (2009) Chemotherapy in patients with castration-resistant prostate cancer. European Journal of Cancer. 45: 161-171.
4) Bacac M & Stamenkovic I (2008) Metastatic cancer cell. Annual Review of Pathology Mechanisms of Disease, 3: 221-247.
5) Alix-Panabières C, Riethdorf S & Pantel K (2008) Circulating tumor cells and bone marrow micrometastasis. Clinical Cancer Research, 14: 5013-5021.
6) Prevarskaya N, Skryma R & Shuba Y (2010) Ion channels and the hallmarks of cancer. Trends Mol Med. 16:107-121.
7) Onkal R & Djamgoz MB (2009) Molecular pharmacology of voltage-gated sodium channel expression in metastatic disease: clinical potential of neonatal Nav1.5 in breast cancer. Eur J Pharmacol. 625: 206-219.
RESEARCH PROTOCOL (ICE-NORB) Page 11 of 12 Version 1; 28.04.2010 8) Fraser SP, Diss JK, Chioni AM, Mycielska ME, Pan H, Yamaci RF, Pani F, Siwy Z, Krasowska M, Grzywna Z, Brackenbury WJ, Theodorou D, Koyutürk M, Kaya H, Battaloglu E, De Bella MT, Slade MJ, Tolhurst R, Palmieri C, Jiang J, Latchman DS, Coombes RC & Djamgoz MB (2005) Voltage- gated sodium channel expression and potentiation of human breast cancer metastasis. Clin Cancer Res. 11: 5381-5389.
9) Brackenbury WJ, Chioni AM, Diss JK & Djamgoz MB (2007) The neonatal splice variant of Nav1.5 potentiates in vitro invasive behaviour of MDA-MB-231 human breast cancer cells. Breast Cancer Res Treat. 101: 149-160.
10) Diss JK, Stewart D, Pani F, Foster CS, Walker MM, Patel A & Djamgoz MB (2005) A potential novel marker for human prostate cancer: voltage-gated sodium channel expression in vivo. Prostate Cancer Prostatic Dis. 8: 266-273.
LIST OF APPENDICES
The following Appendices that have relevance to the study have been attached as separate documents:
1) Poster for recruitment of participants
2) Participant information sheet
3) Informed consent form A
4) Informed consent form B
RESEARCH PROTOCOL (ICE-NORB) Page 12 of 12 Version 1; 28.04.2010
Appendix 2
Ethics documents for blood work
2.2 Istanbul
292
Appendix 3
Sequencing results
297
Query : SW1 sequencing result Sbjct : Homo sapiens sodium channel, voltage-gated, type V, alpha subunit (SCN5A), transcript variant 3, mRNA (NM_001099404.1)
Query 6 GGACCAAGTATGTCGAGTACACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGA 65 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 661 GGACCAAGTATGTCGAGTACACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGA 720
Query 66 TTCTGGCTCGAGGCTTCTGCCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGC 125 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 721 TTCTGGCTCGAGGCTTCTGCCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGC 780
Query 126 TGGACTTTAGTGTGATTATCATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGT 185 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 781 TGGACTTTAGTGTGATTATCATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGT 840
Query 186 CGGCTCTTCGAACTTTCAGAGTCCTGAGAGCTCTAAAAACTATTTCTAGTTATCCCAGGG 245 |||||||||||||||||||||||||||||||||||||||||||||| ||||||||||||| Sbjct 841 CGGCTCTTCGAACTTTCAGAGTCCTGAGAGCTCTAAAAACTATTTC-AGTTATCCCAGGG 899
Query 246 CTGAAGACCATCGTGGGG 263 |||||||||||||||||| Sbjct 900 CTGAAGACCATCGTGGGG 917
Query : SW1 sequencing result Sbjct : Homo sapiens sodium channel, voltage-gated, type V, alpha subunit (SCN5A), transcript variant 4, mRNA (NM_001099405.1)
Query 6 GGACCAAGTATGTCGAGTACACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGA 65 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 661 GGACCAAGTATGTCGAGTACACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGA 720
Query 66 TTCTGGCTCGAGGCTTCTGCCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGC 125 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 721 TTCTGGCTCGAGGCTTCTGCCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGC 780
Query 126 TGGACTTTAGTGTGATTATCATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGT 185 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 781 TGGACTTTAGTGTGATTATCATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGT 840
Query 186 CGGCTCTTCGAACTTTCAGAGTCCTGAGAGCTCTAAAAACTATTTCTAGTTATCCCAGGG 245 |||||||||||||||||||||||||||||||||||||||||||||| ||||||||||||| Sbjct 841 CGGCTCTTCGAACTTTCAGAGTCCTGAGAGCTCTAAAAACTATTTC-AGTTATCCCAGGG 899
Query 246 CTGAAGACCATCGTGGGG 263 |||||||||||||||||| Sbjct 900 CTGAAGACCATCGTGGGG 917
Query : SW1 sequencing result Sbjct : Homo sapiens sodium channel, voltage-gated, type V, alpha subunit (SCN5A), transcript variant 5, mRNA (NM_001160160.1)
Query 6 GGACCAAGTATGTCGAGTACACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGA 65 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 662 GGACCAAGTATGTCGAGTACACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGA 721
Query 66 TTCTGGCTCGAGGCTTCTGCCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGC 125 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 722 TTCTGGCTCGAGGCTTCTGCCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGC 781
Query 126 TGGACTTTAGTGTGATTATCATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGT 185 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 782 TGGACTTTAGTGTGATTATCATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGT 841
Query 186 CGGCTCTTCGAACTTTCAGAGTCCTGAGAGCTCTAAAAACTATTTCTAGTTATCCCAGGG 245 |||||||||||||||||||||||||||||||||||||||||||||| ||||||||||||| Sbjct 842 CGGCTCTTCGAACTTTCAGAGTCCTGAGAGCTCTAAAAACTATTTC-AGTTATCCCAGGG 900
Query 246 CTGAAGACCATCGTGGGG 263 |||||||||||||||||| Sbjct 901 CTGAAGACCATCGTGGGG 918
298
Query : SW1 sequencing result Sbjct : Homo sapiens sodium channel, voltage-gated, type V, alpha subunit (SCN5A), transcript variant 6, mRNA (NM_001160161.1)
Query 6 GGACCAAGTATGTCGAGTACACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGA 65 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 662 GGACCAAGTATGTCGAGTACACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGA 721
Query 66 TTCTGGCTCGAGGCTTCTGCCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGC 125 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 722 TTCTGGCTCGAGGCTTCTGCCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGC 781
Query 126 TGGACTTTAGTGTGATTATCATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGT 185 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 782 TGGACTTTAGTGTGATTATCATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGT 841
Query 186 CGGCTCTTCGAACTTTCAGAGTCCTGAGAGCTCTAAAAACTATTTCTAGTTATCCCAGGG 245 |||||||||||||||||||||||||||||||||||||||||||||| ||||||||||||| Sbjct 842 CGGCTCTTCGAACTTTCAGAGTCCTGAGAGCTCTAAAAACTATTTC-AGTTATCCCAGGG 900
Query 246 CTGAAGACCATCGTGGGG 263 |||||||||||||||||| Sbjct 901 CTGAAGACCATCGTGGGG 918
Query : SW1 sequencing result Sbjct : Homo sapiens sodium channel, voltage-gated, type V, alpha subunit (SCN5A), transcript variant 1, mRNA (NM_198056.2)
Query 6 GGACCAAGTATGTCGAGTACACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGA 65 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 661 GGACCAAGTATGTCGAGTACACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGA 720
Query 66 TTCTGGCTCGAGGCTTCTGCCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGC 125 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 721 TTCTGGCTCGAGGCTTCTGCCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGC 780
Query 126 TGGACTTTAGTGTGATTATCATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGT 185 ||||||||||||||||||||||||| || | | ||| | | | || |||||| | | Sbjct 781 TGGACTTTAGTGTGATTATCATGGCATACACAACTGAATTTGTGGACCTGGGCAATGTCT 840
Query 186 CGGCTCTT-CGAACTTTCAGAGTCCTGAGAGCTCTAAAAACTATTTCTAGTTATCCCAGG 244 | || ||| || || ||| ||||||| | || || |||||||| || ||| || |||| Sbjct 841 CAGC-CTTACGCACCTTCCGAGTCCTCCGGGCCCTGAAAACTATATC-AGTCATTTCAGG 898
Query 245 GCTGAAGACCATCGTGGGG 263 ||||||||||||||||||| Sbjct 899 GCTGAAGACCATCGTGGGG 917
Query : SW1 sequencing result Sbjct : Homo sapiens sodium channel, voltage-gated, type V, alpha subunit (SCN5A), transcript variant 2, mRNA (NM_000335.4)
Query 6 GGACCAAGTATGTCGAGTACACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGA 65 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 661 GGACCAAGTATGTCGAGTACACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGA 720
Query 66 TTCTGGCTCGAGGCTTCTGCCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGC 125 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 721 TTCTGGCTCGAGGCTTCTGCCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGC 780
Query 126 TGGACTTTAGTGTGATTATCATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGT 185 ||||||||||||||||||||||||| || | | ||| | | | || |||||| | | Sbjct 781 TGGACTTTAGTGTGATTATCATGGCATACACAACTGAATTTGTGGACCTGGGCAATGTCT 840
Query 186 CGGCTCTT-CGAACTTTCAGAGTCCTGAGAGCTCTAAAAACTATTTCTAGTTATCCCAGG 244 | || ||| || || ||| ||||||| | || || |||||||| || ||| || |||| Sbjct 841 CAGC-CTTACGCACCTTCCGAGTCCTCCGGGCCCTGAAAACTATATC-AGTCATTTCAGG 898
Query 245 GCTGAAGACCATCGTGGGG 263 ||||||||||||||||||| Sbjct 899 GCTGAAGACCATCGTGGGG 917
299
Query : SW2 sequencing result Sbjct : Homo sapiens sodium channel, voltage-gated, type V, alpha subunit (SCN5A), transcript variant 3, mRNA (NM_001099404.1)
Query 5 CACCTTCCCCACCATTTACACCTTTGAGTCTCTGGTCAAGATTCTGGCTCGAGGCTTCTG 64 ||||||| || ||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 680 CACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGATTCTGGCTCGAGGCTTCTG 739
Query 65 CCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGCTGGACTTTAGTGTGATTAT 124 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 740 CCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGCTGGACTTTAGTGTGATTAT 799
Query 125 CATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGTCGGCTCTTCGAACTTTCAG 184 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 800 CATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGTCGGCTCTTCGAACTTTCAG 859
Query 185 AGTCCTGAGAGCTCTAAAAACTATTTCTAGTTATCCCAGGGCTGAAGACCATCGTGGGG 243 ||||||||||||||||||||||||||| ||||||||||||||||||||||||||||||| Sbjct 860 AGTCCTGAGAGCTCTAAAAACTATTTC-AGTTATCCCAGGGCTGAAGACCATCGTGGGG 917
Query : SW2 sequencing result Sbjct : Homo sapiens sodium channel, voltage-gated, type V, alpha subunit (SCN5A), transcript variant 4, mRNA (NM_001099405.1)
Query 5 CACCTTCCCCACCATTTACACCTTTGAGTCTCTGGTCAAGATTCTGGCTCGAGGCTTCTG 64 ||||||| || ||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 680 CACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGATTCTGGCTCGAGGCTTCTG 739
Query 65 CCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGCTGGACTTTAGTGTGATTAT 124 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 740 CCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGCTGGACTTTAGTGTGATTAT 799
Query 125 CATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGTCGGCTCTTCGAACTTTCAG 184 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 800 CATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGTCGGCTCTTCGAACTTTCAG 859
Query 185 AGTCCTGAGAGCTCTAAAAACTATTTCTAGTTATCCCAGGGCTGAAGACCATCGTGGGG 243 ||||||||||||||||||||||||||| ||||||||||||||||||||||||||||||| Sbjct 860 AGTCCTGAGAGCTCTAAAAACTATTTC-AGTTATCCCAGGGCTGAAGACCATCGTGGGG 917
Query : SW2 sequencing result Sbjct : Homo sapiens sodium channel, voltage-gated, type V, alpha subunit (SCN5A), transcript variant 5, mRNA (NM_001160160.1)
Query 5 CACCTTCCCCACCATTTACACCTTTGAGTCTCTGGTCAAGATTCTGGCTCGAGGCTTCTG 64 ||||||| || ||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 681 CACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGATTCTGGCTCGAGGCTTCTG 740
Query 65 CCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGCTGGACTTTAGTGTGATTAT 124 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 741 CCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGCTGGACTTTAGTGTGATTAT 800
Query 125 CATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGTCGGCTCTTCGAACTTTCAG 184 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 801 CATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGTCGGCTCTTCGAACTTTCAG 860
Query 185 AGTCCTGAGAGCTCTAAAAACTATTTCTAGTTATCCCAGGGCTGAAGACCATCGTGGGG 243 ||||||||||||||||||||||||||| ||||||||||||||||||||||||||||||| Sbjct 861 AGTCCTGAGAGCTCTAAAAACTATTTC-AGTTATCCCAGGGCTGAAGACCATCGTGGGG 918
300
Query : SW2 sequencing result Sbjct : Homo sapiens sodium channel, voltage-gated, type V, alpha subunit (SCN5A), transcript variant 6, mRNA (NM_001160161.1)
Query 5 CACCTTCCCCACCATTTACACCTTTGAGTCTCTGGTCAAGATTCTGGCTCGAGGCTTCTG 64 ||||||| || ||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 681 CACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGATTCTGGCTCGAGGCTTCTG 740
Query 65 CCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGCTGGACTTTAGTGTGATTAT 124 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 741 CCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGCTGGACTTTAGTGTGATTAT 800
Query 125 CATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGTCGGCTCTTCGAACTTTCAG 184 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 801 CATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGTCGGCTCTTCGAACTTTCAG 860
Query 185 AGTCCTGAGAGCTCTAAAAACTATTTCTAGTTATCCCAGGGCTGAAGACCATCGTGGGG 243 ||||||||||||||||||||||||||| ||||||||||||||||||||||||||||||| Sbjct 861 AGTCCTGAGAGCTCTAAAAACTATTTC-AGTTATCCCAGGGCTGAAGACCATCGTGGGG 918
Query : SW2 sequencing result Sbjct : Homo sapiens sodium channel, voltage-gated, type V, alpha subunit (SCN5A), transcript variant 1, mRNA (NM_198056.2)
Query 5 CACCTTCCCCACCATTTACACCTTTGAGTCTCTGGTCAAGATTCTGGCTCGAGGCTTCTG 64 ||||||| || ||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 680 CACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGATTCTGGCTCGAGGCTTCTG 739
Query 65 CCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGCTGGACTTTAGTGTGATTAT 124 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 740 CCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGCTGGACTTTAGTGTGATTAT 799
Query 125 CATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGTCGGCTCTT-CGAACTTTCA 183 |||||| || | | ||| | | | || |||||| | || || ||| || || ||| Sbjct 800 CATGGCATACACAACTGAATTTGTGGACCTGGGCAATGTCTCAGC-CTTACGCACCTTCC 858
Query 184 GAGTCCTGAGAGCTCTAAAAACTATTTCTAGTTATCCCAGGGCTGAAGACCATCGTGGGG 243 ||||||| | || || |||||||| || ||| || ||||||||||||||||||||||| Sbjct 859 GAGTCCTCCGGGCCCTGAAAACTATATC-AGTCATTTCAGGGCTGAAGACCATCGTGGGG 917
Query : SW2 sequencing result Sbjct : Homo sapiens sodium channel, voltage-gated, type V, alpha subunit (SCN5A), transcript variant 2, mRNA (NM_000335.4)
Query 5 CACCTTCCCCACCATTTACACCTTTGAGTCTCTGGTCAAGATTCTGGCTCGAGGCTTCTG 64 ||||||| || ||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 680 CACCTTCACCGCCATTTACACCTTTGAGTCTCTGGTCAAGATTCTGGCTCGAGGCTTCTG 739
Query 65 CCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGCTGGACTTTAGTGTGATTAT 124 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 740 CCTGCACGCGTTCACTTTCCTTCGGGACCCATGGAACTGGCTGGACTTTAGTGTGATTAT 799
Query 125 CATGGCGTATGTATCAGAAAATATAAAACTAGGCAATTTGTCGGCTCTT-CGAACTTTCA 183 |||||| || | | ||| | | | || |||||| | || || ||| || || ||| Sbjct 800 CATGGCATACACAACTGAATTTGTGGACCTGGGCAATGTCTCAGC-CTTACGCACCTTCC 858
Query 184 GAGTCCTGAGAGCTCTAAAAACTATTTCTAGTTATCCCAGGGCTGAAGACCATCGTGGGG 243 ||||||| | || || |||||||| || ||| || ||||||||||||||||||||||| Sbjct 859 GAGTCCTCCGGGCCCTGAAAACTATATC-AGTCATTTCAGGGCTGAAGACCATCGTGGGG 917
301
Appendix 3 – Sequencing results. Two repeats (SW1 and SW2) of the PCR products generated with ‘general’ Nav1.5 primers on SW620 cDNAs were sequenced. Sequence alignment and homology search using BLAST analysis (NCBI) revealed that these samples were both 99 % similar with the neonatal Nav1.5 sequence (NM_001099404.1, NM_001099405.1, NM_001160160.1 and NM_001160161.1). Conversely, the same analysis showed only 88 and 86 % similarity for SW1 and SW2, respectively, with the sequence of adult Nav1.5 splice variant (NM_198056.2 and NM_000335.4).
302