The Role of Lipid Metabolism in Melanoma and Identifying Therapeutic Targets in Lipid Metabolic Pathways

The role of lipid metabolism in melanoma and identifying therapeutic targets in lipid metabolic pathways

A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Life Sciences

2015

Hannah Rachel Johnston

I Contents

I Contents ...... 2

II List of Figures ...... 7

III List of Tables ...... 9

IV Abstract ...... 11

V Declaration ...... 12

VI Copyright Statements ...... 13

VII Acknowledgments ...... 14

VIII Abbreviations ...... 15

1 Introduction ...... 20

1.1 Cancer Biology ...... 20

1.2 Structure of the Skin and Melanoma Biology ...... 22

1.3 Genetics of Melanoma ...... 26

1.4 Melanoma Therapeutic Breakthroughs ...... 33

1.5 Modelling Melanoma ...... 36

1.5.1 Cellular Based Assays ...... 37

1.5.2 Mouse Models ...... 38

1.5.3 Zebrafish Models ...... 40

2 General Methods ...... 47

2.1 Zebrafish Techniques ...... 50

2.1.1 Zebrafish husbandry ...... 50

2.1.2 Zebrafish breeding...... 50

2.1.3 Zebrafish anaesthesia and euthanasia ...... 50

2.2 Cloning Techniques ...... 51

2.2.1 PCR ...... 51

2.2.2 Transformation of E.coli ...... 51

2.2.3 MiniPrep ...... 52

2.2.4 Maxiprep ...... 52

2.2.5 Sequencing ...... 52

Chapter 3: Identifying therapeutic pathways using transcriptome and mass spectroscopy techniques ...... 55

3.1 Introduction ...... 55

3.2 Aims ...... 59

3.3 Methods for determination of therapeutic targets ...... 60

3.3.1 Determination of therapeutic targets by transcriptome analysis ...... 60

3.3.2 Lipid extraction for mass spectroscopy analysis of metabolic pathways ...... 61

3.3.3 UPLC-MS analysis of the sample ...... 62

3.3.4 Data pre-processing for analysis ...... 62

3.3.5 Statistical analysis to determine significant metabolite changes and cluster analysis ...... 63

3.3.6 Metabolite identiﬁcation and pathways analysis ...... 63

3.3.7 GCMS analysis of metabolites ...... 64

3.4 Results ...... 65

3.4.1 Transcriptome analysis reveals deregulated lipid metabolic pathways ...... 65 3

3.4.2 Mass spectroscopy analysis shows that plasma membrane and fatty acid metabolism is altered ...... 73

3.5 Discussion ...... 78

3.5.1 Transcriptome analysis of zebrafish progression models...... 78

3.5.2 Identifying therapeutic pathways ...... 78

3.5.3 Plasma membrane metabolism is dramatically altered in VGP melanoma ...... 80

3.5.4 Fatty acid metabolism is also increased in the VGP melanoma model ...... 83

3.5.5 Conclusion ...... 85

Chapter 4: Identifying therapeutic targets in lipid metabolism ...... 86

4.1 Introduction ...... 86

4.1.1 Transcriptional regulation of lipid metabolism ...... 86

4.1.2 Acquisition of fatty acids ...... 87

4.1.3 The role for de novo lipid synthesis ...... 90

4.1.4 The storage and metabolism of fatty acids ...... 91

4.1.5 Plasma Membrane Lipids ...... 92

4.1.6 Therapy in metabolism...... 93

4.3 Aims ...... 95

4.3 Methods for Functional Analysis of Candidate Malignancy Gene Overexpression ...... 96

4.3.1 Constructing MiniCoopR constructs ...... 96

4.3.2 Injection of Embryos with MiniCoopR construct ...... 98

4.3.3 Cryosectioning and staining of zebrafish ...... 102 4

4.3.4 Analysis of LPL in human transcriptome data ...... 104

4.3.5 Immunohistochemistry on human nevi and melanoma samples. .... 104

4.3.6 Cell Culture ...... 105

4.3.7 Western blotting for lipid metabolism genes and autophagy markers ...... 109

4.3.8 Measuring Fatty Acid Uptake ...... 110

4.3.9 Measuring LPL Activity in Cell Media ...... 111

4.4 Results ...... 112

4.4.1 Identifying potential targets ...... 112

4.4.3 Interrogating the presence of LPL in human melanoma samples .... 120

4.4.4 Exploring the mechanism of LPL in melanoma ...... 127

4.5 Discussion ...... 142

4.5.1 LPL is known to play a role in cancer progression ...... 142

4.5.2 LPL appears to be a tumour promoter in this study and inhibition of LPL synergises with FASN inhibitor...... 144

4.5.3 There is a novel mechanism for LPL in melanoma cell lines ...... 146

Chapter 5: Development of a novel PET tracer for fatty acid metabolism and preclinical drug testing with the zebrafish...... 149

5.1 Introduction ...... 149

5.2 Aims ...... 151

5.3 Methods ...... 152

5.3.1 PET/CT scanning of zebrafish ...... 152

5.3.2 Sedation and Tracer Administration ...... 152

5.3.3 Soaking for FTHA administration ...... 153

5.4 Figures and Results ...... 154

5.4.1 Developing an anaesthesia protocol for PET scanning ...... 154

5.4.2 Determination of probe administration for FDG-PET ...... 157

5.4.3 The testing of a novel FA tracer FTHA in the zebrafish ...... 160

5.5 Discussion ...... 164

5.5.1 Anaesthesia by chilling was successful ...... 165

5.5.2 Reliable administration methods were developed for both tracers .. 166

5.5.3 Scans showed that both probes worked in the zebrafish and revealed that FTHA was identifying tumours undetectable by FDG-PET ...... 168

6. General Discussion ...... 172

7 Supplementary ...... 182

8 References...... 198

Word Count: 55,366

II List of Figures

Figure 1.1 The transition from RGP to VGP within the tissues...... 24

Figure 1.2 The MAPK pathway extensively contributes to melanoma development and progression...... 31

Figure 1.3 The typical pathway for screening genes using the MiniCoopR method...... 42

Figure 1.4 Melanoma models of Zebrafish developed using transgenesis...... 45

Figure 3.1 The transcriptome revealed V12VGP to be distinct from V600EBRAF and V12 RAS RGP samples...... 66

Figure 3.2 Metabolism is a large proportion of the significantly deviating genes and lipid metabolism is a significantly enriched pathway...... 68

Figure 3.3 The lipid genes identified in the VGP sample suggest FA catabolism was activated...... 72

Figure 3.4 As the melanoma progressed the number of altered lipids grew. .... 74

Figure 3.5 The resulting heat maps from the LCMS analysis...... 77

Figure 4.1 Lipid staining in zebrafish tumours and melanoma cell lines indicates increases in neutral lipid...... 114

Figure 4.2 A summary of the process for isolating lipid metabolism targets from the microarray...... 115

Figure 4.3 The genes lpl and dgat1a are the most significant up-regulated genes involved in lipid metabolism in the V12 RAS VGP zebrafish model...... 116

Figure 4.4 LPL increases tumour appearance rate and tumour growth...... 119

Figure 4.5 LPL is expressed in benign and cutaneous melanoma tissues, melanoma cell lines and may correlate with survival...... 122

Figure 4.6 LPL protein is upregulated in primary lesions and metastases and located within melanoma cells...... 124

Figure 4.7 Melanoma cell lines have LPL protein expression and high LPL activity compared to melanocytes...... 126

Figure 4.8 Histology and lipid staining reveal LPL-MiniCoopR tumours have highly pigmented and invasive lesions, they also appear to have reduced lipid...... 128

Figure 4.9 LPL knockdown induces a reduction in cell number and reduces proliferation...... 130

Figure 4.10 fasn may correlate with the effectiveness of LPL siRNA...... 132

Figure 4.11 An LPL inhibitor works in synergy with 15 µM of FASN inhibitor in A375P and WM266-4 cells but not with WM852 cells...... 134

Figure 4.12 The most highly correlated genes with LPL expression were mapped using Oncomine with FABP4 and CD36 being prime targets.. ... 136

Figure 4.13 LPL expression may affect CD36 but not FABP4 expression...... 138

Figure 4.14 Loss of LPL increases FA uptake and neutral lipid staining...... 140

Figure 5.1 The injection set-up for tracer administration and the container for scanning...... 156

Figure 5.2 PET scanning of FDG-PET can detect tumours on the zebrafish and provide good resolution on organs...... 159

Figure 5.3 FTHA is easily administered by soaking and organs were resolved from the circulatory signal. Tumours could be detected...... 161

Figure 5.4 GFP MiniCoopR driven tumours in a RAS background have higher FTHA uptake...... 163

Figure 6.1 LPL siRNA induces defects in autophagy, accumulation of autophagic vacuoles/autolysosomes and loss of mitochondrial cristae.. . 177

III List of Tables

Table 1.1: The identification of four melanoma subtypes based on large-scale microarrays undertaken by the TCGA group. T ...... 32

Table 2.1 General reagents used within this thesis...... 47

Table 2.2 Primers sequences used in sequencing ...... 54

Table 3.1 IPA gene enrichment analysis demonstrated that cancer biology was the most enriched molecular cluster...... 70

Table 3.2 The pathway enrichment analysis demonstrated a lipid pathway was significant...... 70

Table 4.1 Primers used for ATT site PCR ...... 97

Table 4.2 Primers used for qPCR ...... 101

Table 4.3 Programme settings for Autostainer XL (Leica) for H&E staining .... 103

Table 4.4 siRNA used in cell culture ...... 105

Table 4.5 A table depicting the Combination Index (C.I.) range and the relevant descriptive characteristics...... 107

Table 4.6 Antibodies used in cell staining ...... 108

Table 4.7 The antibodies used in western blotting...... 110

Supplementary Table 1 There are many genes related to known cancer pathways in the lipid gene dataset...... 182

Supplementary Table 2 The metabolites from the LCMS analysis of VGP tumours...... 186

Supplementary Table 3 The metabolites analysis from the GCMS experiment...... 194

Supplementary Table 4 The shortlist of lipid metabolism genes...... 195

IV Abstract

There have been dramatic advances in melanoma therapy in the last 10 years, yet there is still a demand for effective and affordable therapies. To identify novel therapeutic pathways a transcriptome analysis was performed on zebrafish melanoma models representing the different stages of melanoma progression. Transcriptomic differences between pre-malignant and malignant conditions highlighted lipid metabolism as a potential mediator of progression. A mass spectrometry analysis confirmed multiple changes in lipid composition between wild type fish, pre-malignant and advanced melanoma models. To better investigate metabolism a positron emission tomography (PET) technique was developed in zebrafish. Tumours in the zebrafish were successfully scanned with FDG used to detect human tumours. A novel tracer of unconjugated FA was then developed and, consistent with inferences from the transcriptome and mass spectrometry, was shown to be incorporated into tumours. Demonstrating the feasibility of PET in zebrafish now opens the way to systematic use of this organism in tracer development with potential time-saving and cost benefits. One of the most significantly up-regulated genes exclusive to the malignant state encodes lipoprotein lipase (LPL). LPL is involved in the release and uptake of FA from circulating triglyceride. LPL was found to increase the rate of tumour appearance and tumour growth in a zebrafish tumour assay. LPL was expressed in human tumours and expression correlated with progression. Melanoma cell lines expressed LPL and knocking-down LPL resulted in reduced cell numbers. The effect was most dramatic in WM852 cells. A novel role for LPL in autophagy was identified. WM852 cells treated with LPL siRNA showed a stabilisation of p62/SQSTM and induction of LC3B II. Electron microscopy revealed large autolysosomal vacuoles in the cytoplasm. Additionally many cells showed damaged mitochondria with absent cristae. The dependency of cells on LPL seemed to be modified by the co-expression of fatty acid synthase (FASN) required for de novo FA synthesis, as the magnitude of the effect of LPL-knockdown was dependent on the levels of FASN expressed in melanoma cell lines. Moreover, combining LPL and FASN inhibitors synergised to kill cells previously less sensitive to LPL inhibitor. FASN and LPL co-inhibition could provide a unique combinatorial therapeutic strategy. 11

V Declaration

No portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning.

VI Copyright Statements

The author of this thesis (including any appendices and/or schedules to this thesis) owns certain copyright or related rights in it (the “Copyright”) and s/he has given The University of Manchester certain rights to use such Copyright, including for administrative purposes. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has from time to time. This page must form part of any such copies made.

The ownership of certain Copyright, patents, designs, trade marks and other intellectual property (the “Intellectual Property”) and any reproductions of copyright works in the thesis, for example graphs and tables (“Reproductions”), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions.

Further information on the conditions under which disclosure, publication and commercialisation of this thesis, the Copyright and any Intellectual Property and/or Reproductions described in it may take place is available in the University IP Policy (see http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=487), in any relevant Thesis restriction declarations deposited in the University Library, The University Library’s regulations (see http://www.manchester.ac.uk/library/aboutus/regulations) and in The University’s policy on Presentation of Theses.

VII Acknowledgments

A big thank you to Adam for giving me this opportunity, for all that I have learnt and letting me be part of this wonderful group. I also thank Paul Lorigan for the friendly science discussions and advice at all stages of the PhD. I would also like to thank my advisor Claudia for her help and guidance.

I would like to thank the collaborators who generated data for this thesis.

Thank you to Professor Herman Spaink who collaborated with the Hurlstone Group to generate the transcriptome data analysed and presented in Chapter 3

I would like to thank Dr. Warwick Dunn who ran the mass spectroscopy and performed the initial data analysis also in Chapter 3.

A thank you to Dr. Jivko Kamarachev who stained human melanoma samples with LPL in Chapter 4.

Thanks also to Peter March, Roger Meadows & Steve Mardsen for their help with the microscopy and related training and Peter Walker for training in histology and the Histology Facility at the University of Manchester. Sarah in the BSF, you have kept my little fishies swimming.

To the amazing Hurlstone group: Helen, Laura, Irene and Anna, Rags. Mai, Jorge, Chris, Andy, Marcel, Muchaala, Susann… People make a lab and this is a fantastic one. Plus our neighbours are awesome Taylors and Wellbrocks. Thank you for such a supportive environment.

Now there is my wonderful family. You all provide amazing love and support, laughs and the hugs. I love you all unconditionally. You taught me everything I needed to know to get where I am.

I would also like to thank Alex, for being so understanding and always being able to cheer me up no matter what. I am so ridiculously lucky to have you in my life. Meringue.

A huge thank you to all my friends, you know who you are, you wonderful people. I love you all.

VIII Abbreviations

AA Arachodonic acid

ACAT Acetyl-CoA:Cholesterol acyltransferase

ACC Acetyl CoA Carboxylase

AKT Protein Kinase B

Apo BII Apolipoprotein BII

ATGL Adipose Triglyceride Lipase

BSA Bovine serum albumin

CCND1 Cyclin D1

CD36 Cluster of Differentiation 36

CDK Cyclin Dependent Kinase

CER-1-P Ceramide-1-Phosphate

CTLA4 Cytotoxic T-lymphocyte-associated protein 4

DAG Diacylglycerol (or diglyceride)

DGAT1 Diacylglycerol O-Acyltransferase 1

DNA Deoxyribonucleic acid

EDTA Ethylenediaminetetraacetic acid

ELOVL ELOVL fatty acid elongase

FA Fatty Acid (Free)

FABP4 Fatty Acid Binding Protein 4

FABP7 Fatty Acid Binding Protein 7

FASN Fatty Acid Synthase

FATP Fatty Acid Transporting Protein

FCS Foetal calf serum

GAP GTPase Activating Protein

GEF Guanine Exchange Factor;

GTP Guanine Triphosphate

GDP Guanine Diphosphate

HDL High density lipoprotein

HIF-1α Hypoxia Inducible Factor - 1α hr Hour

IGFBP7 Insulin-like Growth Factor Binding Protein 7

IL-1 Interleukin

IRS2 Insulin Receptor Substrate 2

MAGL Monoglycerol Lipase

MAPK Mitogen Activated Protein Kinase min Minute

MITF Microphthalmia-associated transcription factor mRNA Messenger ribonucleic acid

NAPDH Nicotinamide Adenine Dinucleotide Phosphate

NF1 Neurofibromin 1

NRAS Neuroblastoma RAS viral oncogene homolog

LDL Low Density Lipoprotein

LDLR Low density lipoprotein receptor

LIPF Lipase, gastric

LPC Lysophosphocholine

LPL Lipoprotein Lipase

LYPLA3 Lysophospholipase A2 Group XV (encoded by PLA2G15)

MFSD2AA Major Facilitator Superfamily Domain containing 2aa

P16INK4A Cyclin dependent kinase inhibitor A

PA Phosphatidic Acid

PAF Platelet activating factor

PBS Phosphate buffered saline

PC Phosphatidylcholine

PD1 Programmed cell Death 1 protein

PE Phosphatidylethanolamine pERK Phosphorylated ERK

PI Phosphatidylinositol

PI3K Phosphoinositol-3-kinase

PLA2 Phospholipase A2

PLA2G6 Phospholipase A2 Group VI

PLA2G7 Phospholipase A2 Group VII

PLA2G15 Phospholipase A2 Group XV

PLD1 Phospholipase D1

PPARγ Peroxisome Proliferator-Activated Receptor-γ

PS Phosphatidylserine

PTEN Phosphatase and tensin homolog

PtdCho Phosphatidylcholine qPCR Quantitative polymerase chain reaction

Rb Retinoblastoma protein

RGP Radial Growth Phase

RNA Ribonucleic acid

RT Room temperature

RTK Receptor Tyrosine Kinase

S-1-P Sphingosine-1-Phosphate

SCD Stearoyl-CoA Desaturase

SCD1 Stearoyl-CoA Desaturase 1

SCD5 Stearoyl-CoA Desaturase 5

SD Standard Deviation

SDS Sodium Dodecyl Sulphate siRNA Small interfering RNA

SM Sphingomyelin

SREBP Sterol regulatory element-binding protein

TAG Triacylglycerol (Triglyceride)

TGF Transforming growth factor

TNF Tumour necrosis factor

Treg Regulatory T cell

TYR Tyrosinase

UV Ultra Violet

VGP Vertical Growth Phase

VLDL Very Low Density Lipoprotein

VLDLR Very Low Density Lipoprotein Receptor

WT Wild type

1 Introduction

1.1 Cancer Biology

Around 8.2 million people died globally from cancer in 2012 (Cancer Research, UK). Cancer is the uncontrolled growth of cells within the body. This uncontrolled growth can be lethal in some tissues, for example in glioblastomas or lung cancer, where the changes to the tissue composition prevent the correct functioning of essential organs. In other cancers, such as in breast cancer, the lethality is due to the metastasis of the tumour to key organs and tissues (NCI, 2015).

Cancer is primarily a genetic disease (Shukla et al., 2015). A cell acquires a mutation that allows it to proliferate in the absence of growth signals, or to survive despite activation of a cell death signal. This allows the cell to proliferate and to thrive in the host tissue (Hanahan and Weinberg, 2000). Tumours often acquire more mutations or adapt to prevent detection and destruction by the immune system (Berezhnaya, 2010). Tumours can also encourage angiogenesis to provide nutrients (Carmeliet and Jain, 2011) or subvert the normal metabolic processes of the cell to promote tumour proliferation (Dang, 2012). Cancer cells are readily adaptable to a range of conditions that healthy tissues cannot cope with, including acidified and hypoxic environments, a common consequence of tumours’ accelerated metabolism and proliferation (Hanahan and Weinberg, 2011). Many of the genes found mutated in cancer cells are common to many different tumour types and often one key mutation is a major driver of cancer biology (Chen et al., 2014b). These genes are oncogenes. Oncogenes become activated by mutation and their encoded products stimulate cancer pathways. Cancers require several mutations as cells contain several failsafe mechanisms in an effort to prevent excessive growth (Croce, 2008). The genes whose encoded products block or inhibit cancer pathways are tumour suppressors and they are often deleted or acquire loss of function mutations in cancers (Liu et al., 2015).

Mutations in oncogenes or tumour suppressors can be inherited or acquired but as single mutations or one mutation in one allele they are not able to induce tumours, perhaps due to the action of multiple tumour suppressors (Friend et al., 1986). People inheriting mutations in tumour suppressor genes have a higher risk of—or a familial link to— tumour development (Sijmons and Hofstra, 2015). Individuals carrying mutations in the BRCA1 breast cancer early onset gene have a much higher risk of developing breast and ovarian cancers. BRCA1 mutations are present in 87% of familial breast cancer cases (Antoniou et al., 2003). Individuals with an inherited mutation in an oncogene or tumour suppressor often develop cancer at an earlier age or in more unusual tissues. An example is a mutation in the retinoblastoma (Rb) tumour suppressor that renders it absent or inactive. Patients develop tumours in the retina, an uncommon tumour site, often in infancy (Ortiz and Dunkel, 2015). Even without a genetic background of susceptibility there are several risk factors for cancer present in the environment. Cells can be mutated after attempting to repair DNA damage or mutations can be directly introduced by a mutagen (Jeggo and Löbrich, 2015). Common factors include exposure to ultra violet radiation, UV- light, which can introduce double stranded DNA breaks and increase the risk for various skin cancers (Nishisgori, 2015) . Smoking results in the inhalation of a large number of carcinogenic substances including polycyclic aromatic hydrocarbons and the nicotine-derived nitrosamines that alter DNA bases or cause DNA damage (Pfeifer et al., 2002). Smoking is a leading cause of lung cancer and is implicated in around 80% of lung cancer cases (Leon et al., 2015).

Research into oncogenes, tumour suppressors and environmental effects has led to many new therapies in cancer biology. Several cancers have a reduced incidence rate as the understanding of environmental causes develops. For example, reduced smoking rates are leading to a reduction in lung and oral cancers by around 1 to 2% per year. These rates are expected to continue decreasing as a younger generation age without smoke exposure (Siegel et al., 2013). However several cancers are increasing globally. A key example is melanoma skin cancer which has seen one of the most dramatic rises in the last 10 years (Arnold et al., 2014). 21

1.2 Structure of the Skin and Melanoma Biology Melanoma is a form of skin cancer. It comprises only 4% of diagnosed skin cancers but is the most lethal of all skin neoplasms. Patients with metastatic melanoma have only a 10 to 15% 5-year survival rate with a median survival of 5 months, reflecting a lack of an effective therapy for malignant and metastatic melanomas (Song et al., 2015).

The skin consists of several defined layers. Broadly these are the epidermis, dermis and subcutaneous tissues. The epidermis is the uppermost layer and comprises mainly keratinocytes and the first several layers are a protective waterproof layer of dead keratinocytes. The lower levels are the proliferative keratinocytes that divide horizontally to renew the epidermal layer that is continually sloughed off (Leyva-Mendivil et al., 2015). Beneath this layer is the dermis. The dermis contains sweat glands, hair follicles, small capillaries, sebaceous glands and nerve endings. These provide touch and pain receptors to protect the tissues, nutrients and oxygen, and sebaceous oils to increase the skins flexibility. Beneath these tissues are the deposits of subcutaneous fats that act as an energy store and an insulating layer (Fore, 2006).

Melanoma is a cancer of melanocytes. Melanocytes reside in the basal layer of the epidermis and are recognizable by their branched appearance and pigmentation. They are also identifiable by expression of melanocyte-specific proteins like tyrosinase (TYR) and microphthalmia transcription factor (MITF) (Cichorek et al., 2013). Melanocytes secrete melanin into the epidermis and melanin contributes to both the pigmentation of the skin and provides protection from DNA damage by UV radiation and reactive oxygen species (Brenner and Hearing, 2008). Melanocytes are also found in the uveal tract of the eye (Couturier and Saule, 2012), within the structure of the ear (Tachibana, 1999), within neural tissues and in the mucosal tissues lining the urogenital, gastrointestinal and respiratory tract (Mihajlovic et al., 2012). The role for melanocytes in these tissues is less well understood but it is hypothesised that they may play a role as antigen presenting cells or act as sensory cells 22

(Mihajlovic et al., 2012). Melanomas can arise in any of the tissues containing melanocytes (Zhu et al., 2015). Melanomas in the eye are known as uveal melanomas. Melanomas in the mucosa are mucosal melanoma. Cutaneous melanoma accounts for 90% of all melanomas and occur in the skin (Thoelke et al., 2004). Cutaneous melanoma is the focus of this thesis.

A spectrum of melanocyte neoplasms has been noted in man and other animals, suggesting the possibility of progression from benign to malignant disease (Pellacani et al., 2014, Zalaudek et al., 2009, Welch and Goldberg, 1997). The least abnormal lesions on the spectrum are benign proliferations of melanocytes resulting in nevi (moles). These lesions generally undergo senescence and do not pose any threat to life expectancy (Bennett, 2003, Zalaudek et al., 2009). Initially it was postulated that nevocytes might overcome senescence by acquisition of additional mutations or epigenetic changes, and begin to spread radially in the first stages of melanoma (Palmieri et al., 2009). However, more recent studies have challenged this theory, proposing instead that malignant lesions appear de novo, independent of benign nevi and fail to senesce (Rose et al., 2011). Regardless, melanomas that have spread radially through the epidermis are easily treated with surgery with a 97% success rate (Ciarletta et al., 2011). However when tumour cells acquire the ability to invade vertically through to the dermis and subcutaneous tissues this stage is known as the vertical growth phase (VGP) (Figure 1.1), surgery becomes significantly less effective (effective less than 50% of the time). If the tumour has invaded into the dermis and subcutaneous tissues there is much greater risk of metastasis due to the presence of lymphatic and vascular tissues. Once tumours have metastasised the survival rate drops from 75% to 10% (Voit et al., 2005).

Figure 1.1 The transition from RGP to VGP within the tissues. The RGP phase is more easily ablated as the melanoma is localised to the epidermis. The VGP has begun invading and is nearing vessels and lymphatic tissues, which would promote metastasis.

There are several factors that can increase the risk of developing melanoma. The risk of melanoma is increased in patients that already have a large number of nevi or multiple nevi that are already dysplastic, partially because these patients have more melanocytes that have already proliferated (Gandini et al., 2005). These nevi may only require a further mutation to develop into more aggressive RGP or VGP melanoma. Furthermore, individuals with paler skin, Fitzpatrick skin type I or II, are at higher risk because UV damage is more significant in these individuals (Torrens and Swan, 2009). The protection from melanin is just not as high. Melanin, which produces skin pigmentation, forms a cap over DNA acting as physical shield against UV light and is also involved in protective anti-oxidant signalling pathways (Brenner and Hearing, 2008). In red- headed individuals the type of melanin is different and known as pheomelanin. This red form of melanin differs from the more common and darker eumelanin (Le Pape et al., 2008). Pheomelanin offers less protection from UV light and is linked with oxidative signalling that increases the risk of mutation events after UV light exposure (Panzella et al., 2014). UV light is the best-known environmental factor and UV-A and UV-B light produced by the sun are both contributors to melanoma development (de Vries et al., 2003). Exposure to UV light can introduce DNA breaks (Erb et al., 2008). These DNA breaks result in mutations that can contribute to melanoma development. Melanomas arising due to UV exposure often have a C to T mutation signature, the result of defective repairs to damaged DNA (Mar et al., 2013). The combination can result in uncontrolled proliferation of melanocytes (Nishisgori, 2015). This is why melanoma is often apparent in sun-exposed skin. The earlier the burn is acquired also seems to affect the development of melanoma. Burns in childhood, perhaps due to the earlier initial mutation event, increases the risk of melanoma significantly higher than a burn occurring in adulthood (Greinert et al., 2015). Much is now understood about the environmental risks of melanoma but unfortunately melanoma exhibits the fastest increase of incidence over any other cancer (Arnold et al., 2014). Thus, morbidity and mortality are predicted to continue to rise, especially in young women (de Vries et al., 2003, Ohtsuka and Nagamatsu, 2003). The increases are linked with a rise in indoor tanning and access to foreign holidays. Despite an increased awareness of the risks of UV

exposure many younger people are still prioritising tanning (Kristjánsson et al., 2003, O'Leary et al., 2014). There is also another potential environmental factor that may affect the rates of melanoma in the future. New drugs that stimulate melanocytes for tanning purposes, for example the compound cyclic α- melanocyte-stimulating hormone analogue MT-II, are growing in popularity despite being unlicensed and not fully tested (Hjuler and Lorentzen, 2014). These drugs have led to hyperproliferation of melanocytes in a number of patients and have been associated with melanomas in young patients (Reid et al., 2013, Hjuler and Lorentzen, 2014). Several clinicians believe that use of MT-II may increase the risk of melanoma. Due to an increase in young patients presenting with melanoma the current average loss of life from melanoma is around 15 years (Guy and Ekwueme, 2011). It is therefore essential to develop new therapeutics to tackle this growing problem.

1.3 Genetics of Melanoma There are four distinct subclasses of melanoma mutations (Network, 2015). The most common mutations occur within the mitogen-activated protein kinase (MAPK) pathway (Wellbrock, 2014). To better understand the consequences of the common mutations found in melanoma the MAPK pathway will be detailed. Receptor tyrosine kinases (RTK) activate the major MAPK components. The RAS family proteins are recruited to the cytoplasmic side of the plasma membrane. Here the RAS protein is activated by swapping its inactive GDP for active GTP catalysed by guanine nucleotide exchange factors (GEFs). The presence of GTP activates RAS and it can now phosphorylate the downstream RAF family proteins. These can then phosphorylate and activate mitogen activated kinase kinase (MEK) and MEK activates extracellular regulated kinases (ERK). The RAS protein is a GTPase and eventually cleaves the GTP phosphoryl group to generate GDP; this reaction is promoted by GTPase activating proteins (GAPs) that attenuate the pathway (Fecher et al., 2008). The MAPK pathway regulates cell proliferation, cell survival and growth responses among many others. Several of the MAPK components can directly phosphorylate key proteins in this process (Lei et al., 2014, Fecher et al., 2008). Additionally, ERK is a potent transcription factor, which can increase expression

of several key genes for proliferation for example MITF. MITF is a critical mediator of melanocyte differentiation and also has oncogenic properties and promotes proliferation and invasion (Wellbrock and Arozarena, 2015).

The most common mutation in melanoma affects the serine/threonine-protein kinase B-Raf (BRAF) (Thomas, 2006). BRAF is the most active member of the RAF family in the MAPK pathway. BRAF mutations are predicted to occur in around 66% of melanoma lesions (Inumaru et al., 2014). The mutations in BRAF are not always associated with lesions resulting from UV-light exposure. A single substitution mutation at amino acid 600, valine to glutamine (V600E), is the most common mutation within BRAF occurring in around 90% of cases (Davies et al. 2002). V600E results in a constitutively active form of BRAF. Normally BRAF has to dimerise for full activation, however the mutation allows BRAF to signal as a monomer (Solit and Sawyers, 2010). By allowing BRAF to signal as a monomer the BRAF signal can no longer be switched off by negative feedback, as normally dissociation of the dimer would terminate BRAF signalling and it is now independent of upstream growth signals. This produces constitutive activation of the MAPK pathway and the resulting increase in MEK and ERK signalling (Hall and Kudchadkar, 2014).

BRAF mutations have been associated with the key processes in melanoma development (DeLuca et al., 2008). One example is the now up-regulated MITF transcription factor promotes expression of proteins such as hypoxic initiated factor 1 (HIF1α) whilst MEK can phosphorylate and activate HIF1α. The combination of MEK and HIF1α signalling encourages angiogenesis providing nutrients to the tumour and increased metastatic capabilities (Mills et al., 2009). BRAF mutations also promote immune invasion in melanoma cell lines by increasing expression of anti-inflammatory cytokines IL-6 and IL-10 (Sumimoto et al., 2006).Yet, BRAF mutations alone are unable to promote malignancy (Casula et al., 2004, Patton et al., 2005). This may be surprising given the high mutation rate and wide range of effects resulting from BRAF mutations. However, many benign nevi have BRAF mutations. These nevi can remain senescent for decades (Dhomen et al., 2009). The senescence seen does appear to be independent of the classical cause of senescence, telomere

attrition. Therefore, the BRAF mutation itself seems to induce the changes in cell behaviour (Michaloglou et al., 2005). Several studies have implicated the activation and expression of cyclin-dependent kinase 4 inhibitor A (p16INK4A) in oncogene induced cell cycle arrest and senescence (Michaloglou et al., 2005). The expression of p16INK4A is not universal and it appears other independent mechanisms can also be triggered within the cell. Studies are still investigating the direct role for BRAF in oncogene-induced senescence. A genome wide RNA interference screen identified that secretion of insulin-like growth factor- binding protein 7 (IGFBP7) may also contribute to BRAF induced senescence. Over-expression of IGFBP7 actually led to apoptosis in A375 melanoma cell lines (Wajapeyee et al., 2010).

Another common mutation in melanoma is within the Neuroblastoma RAS viral oncogene homolog (NRAS) protein. Mutations in NRAS are seen in around 20% of tumours (Goel et al., 2006). The melanoma lesions with NRAS mutations are more likely to occur on the extremities of the body and often are more invasive and aggressive (Ellerhorst et al., 2011). The mutation within NRAS commonly occurs at Q61, which reduces the GTP hydrolysis rate of NRAS leading to constitutive activity. Other mutations are seen at codon 12, and again this prevents the GTP from being hydrolysed. A less common mutation is at codon 13 and in this case the NRAS is rendered insensitive to GAPs (Burd et al., 2014, Prior et al., 2012). Sometimes mutations are identified in GAPs, which normally act as tumour suppressors. Mutations in the GAP neurofibromin 1 (NF1) render it inactive or produce early STOP codons. This prevents inactivation of NRAS by the hydrolysis of GTP to GDP (Andersen et al., 1993). NF1 mutations are found in a number of melanoma patients (Network, 2015). All these mutations result in constitutive activation of the MAPK pathway in ways very similar to BRAF mutants. The reason NRAS mutations are often more potent is due to the simultaneous activation of MAPK and phosphoinositol-3-kinase (PI3K) (Jaiswal et al., 2009). Several studies have demonstrated that activation of PI3K is critical to the development of RAS driven melanoma. Deletion of PI3K or downstream proteins such as the oncogene protein kinase B (AKT) can reduce the aggression of RAS melanoma models and in some cases prevent malignant development (Michailidou et al., 28

2009a, Stahl et al., 2004).

The PI3K pathway can be activated by many factors; in melanoma the chief activators are RAS signalling, growth factor signalling and deletion of the PI3K inhibitor phosphatase and tensin homolog (PTEN) (Kim, 2010). The PI3K complex consists of a catalytic and a regulatory subunit. When stimulated the regulatory subunit aids the activation of the catalytic domain. PI3K phosphorylates the inositol ring of phosphatidylinositol (PI). This generates both phosphatidylinositol-(3,4)-P2 (PIP2) and phosphatidylinositol-(3,4,5)-P3 (PIP3). The appearance and accumulation of these lipids acts as a platform recruiting proteins with pleckstrin homology domains. The oncogene AKT is recruited in this manner and the recruitment aids its phosphorylation and activation by phosphoinositide-dependent kinase 1 (PDK1) (Cully et al., 2006); AKT can also auto-phosphorylate itself for activation (Meier et al., 2005). PDK1 also contains a pleckstrin homology domain and is recruited by the PIP2 and PIP3 lipids. Once active AKT can phosphorylate and activate many other proteins and produce proliferative and survival signals (Franke et al., 2003, Falasca and Maffucci, 2012). AKT can phosphorylate and inactivate apoptosis signal-regulating kinase 1 (ASK1) which promotes survival and inhibits apoptosis (Kim et al., 2001). It also phosphorylates and inactivates the apoptosis promoting BCL2-associated agonist of cell death (BAD) (Datta et al., 1997). Many of the effects of PI3K activation promote cell survival. However, AKT and other recruited proteins also promote proliferation, motility and growth. The phosphorylation of cyclin dependent kinase 2 (CDK2) by AKT can promote binding to cyclins and promote cell cycle progression (Maddika et al., 2008). Insulin like growth factor (IGF-1) induces a high cell motility and metastasis in melanoma cell lines. When the MAPK/ERK pathway was inhibited this effect was not attenuated. However, when PI3K was inhibited the cells no longer had increased motility when exposed to IGF-1 (Neudauer and McCarthy, 2003). The activation of PI3K and MAPK seems to overcome the oncogene senescence seen in BRAF mutant nevi (Vredeveld et al., 2012). In fact some studies have shown that RAS mutations in melanoma can also induce senescence if the PI3K component is inactive. This is supported by evidence provided in zebrafish melanoma models. Typically V12 RAS mutant fish develop RGP and spontaneous malignant VGP 29

tumours. When a dominant negative regulatory p85 subunit is co-expressed with V12 RAS, fish do not develop malignant lesions (Michailidou et al., 2009b). The phenotype of the V12 RAS- ∆p85 reverts to that of BRAF mutants. BRAF mutant melanomas can acquire PI3K activation to replicate the NRAS activation. Several loss-of-function mutations in PTEN have been identified in BRAF mutant melanomas. Several other melanomas also have increased copies of AKT (Goel et al., 2006). Figure 1.2 demonstrates the contribution of the MAPK and PI3K pathway to melanoma and Table 1.1 outlines the four mutant subtypes seen in melanoma patients.

Figure 1.2 The MAPK pathway extensively contributes to melanoma development and progression. Melanoma lesions have mutations in RAS, BRAF and associated MAPK components. Often there is a contribution from PI3K in the form of PTEN loss, AKT overexpression or PI3K activation. Green or a green glow indicates activation in melanoma pathways.

Table 1.1: The identification of four melanoma subtypes based on large-scale microarrays undertaken by the TCGA group. Targeting therapies at these mutations has provided a number of breakthroughs. (Adapted from (Network, 2015))

BRAF RAS NF1 Triple WT

Active Active Inhibited

Patients are Increased Patients older Lacks UV younger, MITF MAPK with increased signature, amplifications. activation and tumour burden. increased copy AKT3 UV signature number and overexpression detected. gene rearrangements.

1.4 Melanoma Therapeutic Breakthroughs The first breakthrough that successfully targeted a driver mutation came through the development of BRAF inhibitors in 2009 (Smalley and Flaherty, 2009). The small molecule inhibitor Vemurafenib was licensed globally for use as a melanoma monotherapy in 2012 following a successful clinical trial. Inhibiting BRAF showed strong efficacy with 80% of patients experiencing a rapid reduction in tumour size (McArthur et al., 2014, Chapman et al., 2011, Young et al., 2012). However, this promising therapy does carry caveats. The first is that Vemurafenib is only effective in V600EBRAF mutant melanomas. Other mutations in BRAF or NRAS mutant melanomas do not respond adequately to this therapy and most significantly, patients treated with BRAF inhibitors rapidly relapse within 6 months of initiating therapy (Wellbrock, 2014). Mechanisms of resistance are both inherent and acquired. These mechanisms included reactivation of MEK or ERK (Goetz et al., 2014), up-regulation of RTK and RAS signalling (Nazarian et al., 2010) and signalling via proto-oncogene c- Raf/ Raf-1 (CRAF) among others (Rizos et al., 2014). Research into overcoming acquired and inherent resistance has looked at pan-RAF inhibitors (Chapman et al., 2014b) and several MEK inhibitors, with several MEK inhibitors now in pre-clinical trials (Johnson et al., 2014, Long et al., 2014)

BRAF inhibitors demonstrated the advantage of targeted therapy but also show the difficulty in targeting a single pathway in melanoma (Smalley et al., 2006). The genetic instability of melanoma is thought to be a significant contributing factor to tumour heterogeneity, while also allowing a rapid mutational response to cancer therapeutics, with both mechanisms contributing to resistance to targeted therapies (Boeckmann et al., 2011, Wagle et al., 2011). Shortly after the development of BRAF inhibitors another, perhaps more promising, therapy entered clinical trials, immunotherapy. The interest in immunotherapy stemmed from clinical cases of spontaneous melanoma regression. It was found that patients with spontaneous regression were able to launch a strong and sustained immune response against the melanoma (Kalialis et al., 2009). Typically immunotherapy has been based on overcoming tumour mediated immune suppression. Tumours can express receptors that inhibit or kill T-cells,

the main mediators of immune responses, or express receptors to transform T- cells to suppressive regulatory T-cell (T-reg) types. In both situations T-cells are not able to stimulate the immune system to kill the tumour tissue and Treg cells actively suppress the immune system (Jacobs et al., 2012). In 2010 the cytotoxic T-lymphocyte-associated protein-4 (CTLA4) inhibitor Ipilimumab, a monoclonal antibody, was approved for front line melanoma therapy. In trials it had extended the 2- year survival rate of patients from 14% to 24% with a small percentage of patients achieving complete remission (Hodi et al., 2010, Lebbé et al., 2014). The exact mechanism for anti-CTLA4 therapy is under investigation. Typically CTLA4 is active in the lymphatic tissues and inhibits T- cells to prevent excessive inflammatory responses. A study found that the number of activated CD8 T-cells and CD4 T-cells increased in patients within 4 weeks of Ipilimumab treatment. The patients were more responsive to a vaccine, suggesting enhanced immune responses and the T-cells were also more responsive to melanoma antigens (Weber et al., 2012). The CD8 T-cells are responsible for killing cells that are foreign to the body and CD4 T cells aid in activating and co-ordinating the immune system (Harty et al., 2000, Luckheeram et al., 2012). The enhanced response would aid in destruction of the tumour (Weber et al., 2012). In 2014 the FDA approved a number of programmed cell death protein 1 (PD1) or programmed cell death protein ligand 1 (PDL1) inhibitors after trials had shown that PD1 inhibition provided better responses compared to anti-CTLA4 therapy. One trial found that the PD1 inhibitor Nivolumab achieved objective responses in 72.9% of patients versus 42.1% on Ipilimumab alone. In addition combining anti-CTLA4 and anti- PD1 greatly increased the objective response rates for advanced disease from 11% with Ipilimumab alone to nearly 61% with combined Nivolumab and Ipilimumab. In addition 22% of patients in the combination trial achieved complete responses whilst none did when treated with the Ipilimumab monotherapy (Postow et al., 2015). The PD1 receptor is a critical checkpoint in immune tolerance. In healthy tissues the ligands PDL1 or PDL2 can bind PD1 and trigger inhibition of T-cell activation and also increase apoptosis in T-cells by decreasing expression of inflammatory and survival genes (Keir et al., 2006). The cascade ultimately suppresses the immune system to prevent excessive

inflammation (Keir et al., 2008). Melanoma cells express PD1 to evade T-cell killing and recognition. Inhibition of PD1 prevents the cancer cells from suppressing T-cells and triggering T-cell apoptosis (Taube et al., 2012). Both PD1 and CTLA4 therapy lead to distinct responses in patients. Patients treated with anti-PD1, anti-CTLA4 or a combination treatment were analysed and distinct responses were noted. CTLA4 therapy increased the number of memory T-cells whilst anti-PD1 therapy showed changes in cytokine profiles and changes to natural killer immune cells. The combination with CTLA4 would also likely combine immune stimulation with limiting T-cell inhibition (Das et al., 2015).

Immunotherapy can be effective in a wide range of patients but recent studies have noted that NRAS patients may experience enhanced responses to immunotherapy. Patients were screened for NRAS mutations after being treated with anti-PD1 therapy or Ipilimumab and patients with mutant NRAS responded with a clinical benefit rate of 73% compared with 35% for wild type NRAS (Johnson et al., 2015). The reasons for this response are under investigation. Some studies have indicated that pre-treatment with BRAF inhibitors reduces the success of anti-PD1 or anti-CTLA4 therapy (Ackerman et al., 2014). However, early studies in a mouse V600EBRAF model have shown that immunotherapies, including PD-1 inhibition might be combined successfully with MEK and BRAF inhibitors. The combination reduced growth of tumours in these mice and in some cases the mice entered remission (Hu-Lieskovan et al., 2015). The addition of MEK inhibitors is already in trials in human melanoma to overcome the resistance acquired in BRAF inhibition (Long et al., 2014). The immune system is dynamic and able to evolve once stimulated and a treatment that can respond to the variety of melanoma mutations and then keep up with the rapidly evolving tumours is likely to be effective.

These therapies are exciting and several cases of complete remission demonstrate the power of manipulating the immune system but despite these great advances there are still unresolved issues. The trials cite strong numbers and some patients achieve complete remission but the number of patients surviving beyond 3-years is still relatively low (Jacobs et al., 2012). Several

patients also do not respond to immunotherapy or have limited responses (Lebbé et al., 2014, McDermott et al., 2014). There are also side effects to immunotherapies. Many are small and manageable. However some patients have experienced severe side effects (Chapman et al., 2011). Immunotherapy also precludes treating patients with pre-existing auto-immune conditions (Stahl and Loquai, 2015). Yet, the most significant hurdle for immunotherapy is cost. One estimate is that combination immunotherapies could cost over $290,000 per course for an average increase in progressive free survival of only 11.5 months and this may limit access to these therapies (Johnston et al., 2015). In countries with private insurance systems this could mean many patients are unable to afford the best treatment, whilst countries with public health systems would struggle to cover the costs for a large number of patients (Goldstein and Zeichner, 2015). Therefore interest is still strong in developing novel therapeutics based on cheaper small molecule inhibitors.

Although great progress has been made in the last decade there is still a need for new therapeutics. Issues with BRAF inhibitor resistance and the costs associated with immunotherapies will impact on the use of these drugs in the clinic. Identifying new therapeutic targets is challenging. Several global genomic studies have mapped the melanoma genome thoroughly. The data is extensive and much of it is readily accessible. Identifying targets from these datasets is ongoing (Hodis et al., 2012). Yet identifying critical gene changes from passenger mutations or genetic noise requires functional studies (Liu et al., 2013, Miranda et al., 2014). For these reasons several robust melanoma models are used in screening for therapeutic targets.

1.5 Modelling Melanoma The isolation of potential therapeutic targets is possible due to the variety in models and genetic techniques available to researchers. The models range from cell culture (Nurmenniemi et al., 2009) to complex in vivo models (Cranmer et al., 2005). These models are used in conjunction with each other to discover and analyse potential melanoma drivers.

1.5.1 Cellular Based Assays Melanoma cell lines have been useful in determining the basic behaviour of melanoma cells. They have also been useful in rapidly screening therapeutic compounds and establishing some of the resistance mechanisms occurring in inhibitor resistance.

One such study has been completed by Boussemart and colleagues. They noted that resistance mechanisms to BRAF and MEK inhibitors all converged onto the eIF4F eukaryotic translation initiation complex. This complex is critical to the translation of mRNA transcripts (Boussemart et al., 2014). Another study has seen that epidermal growth factor receptor (EGFR) expression and transforming growth factor-β (TGF-β) are detrimental to untreated melanoma cells, inducing oncogene senescence, but confer a growth advantage to BRAF inhibitor or MEK inhibitor treated cells. The increase in EGFR was driven by a reduction in SOX10 expression by inhibitor treatment. Stopping treatment removed SOX10 suppression and consequently reduced EGFR expression. This study provides a rationale for the use of drug holidays to re-sensitise patients to therapy (Sun et al., 2014).

Cells can be easily mutated and recent developments in small-interfering RNAs have made knockdown studies rapid and inexpensive. Therefore, overexpression of proteins of interest followed by knockdown and rescue can elucidate much about a proteins cellular role (Liu et al., 2011b, Vaughan et al., 2006). The role of PI3K in cell adhesion has been of interest to multiple melanoma research groups. Goundiam et al. showed that Rho A, B and C activation driven by Akt and PI3K was essential for anoikis resistance in a melanoma cell line using siRNA models (Goundiam et al., 2011). Additionally, one group showed that Notch1 signalling drove PI3K and MAPK pathways to increase adhesion and invasion in a melanoma cell line (Liu et al., 2006).

One of the drawbacks of cell lines is that cell lines cannot display the heterogeneity that is present in tumours. For example, within cell lines there are no inflammatory factors, which may dramatically alter findings (MacDonald et al., 2002). The lack of inflammatory input is important as melanoma is considered an inflammatory cancer (Carmi et al., 2011, Umansky and Sevko, 37

2011). Cell lines are also prone to forming monolayers when in culture. It is now known that the shape of a cell mass can alter the cells behaviour and adhesions (Liu et al., 2011a). To overcome some of these issues 3D cell culture has been developed. Cells can be encouraged to form spheres when placed in a collagen matrix. A study by Lucas et al. shows how 2D and 3D culture react differently to the BCL-2 inhibitor ABT-737. It is simply that the drug cannot penetrate the spheroid, a finding more relevant to the clinical setting than simple 2D cell culture (Lucas et al., 2011). Additional assays utilising these ‘melanospheres’ have been useful in studying the invasion and metastatic potential of melanoma cells (Schatton and Frank, 2010). A further step from 3D culture has been the development of organotypic culture or ‘complex culture’ (Alépée et al., 2014). Organotypic culture is an attempt to accurately recreate the human skin tissue within culture by combining a 3D environment, a variety of cell types and typical human skin extracellular matrix. The models often combine fibroblasts, collagen and keratinocytes with melanocytes to produce an epidermis, basement membrane, dermis and correctly localised melanocytes (Li et al., 2011, Arnette et al., 2016). The characteristics of cells within these cultures better recapitulates in vivo findings (Hatina and Ruzicka, 2008). Other groups are attempting to recreate the tumour microenvironment with complex culture, combining cell types that produce mediators or key proteins found in human tissue (Nurmenniemi et al., 2009). However, these models are still being established and still cannot completely replicate the complexity of in vivo models. Therefore, for further analysis in vivo models are essential. There are several species that have melanoma models and here we will briefly look at some in the mouse and zebrafish.

1.5.2 Mouse Models Mouse models are important in drug development and in vivo studies of melanoma. The models for melanoma range from transplant assays , to genetic and chemically induced models (Becker et al., 2010). In transplantation assays the tumour cells are derived from human tissues, cell culture or mouse tumours and placed into immunocompromised SCID mice to avoid rejection, additionally mouse cells can be transplanted into a syngeneic host (Rofstad and Lyng,

1996). A lot of knowledge about metastasis has been determined from mouse studies. Tumour cells can be injected into a site, such as the tail vein, and using fluorescent tags the cancer spread can be followed (Bobek et al., 2011). The common sites of melanoma metastasis are conserved between humans and mouse. This is demonstrated in a study that showed brain metastases follow the same patterning between human cases and murine models. In this study information was gleaned on how different tumour subpopulations preferentially located to differing brain regions and tissue types (Cranmer et al., 2005).

The recent increase in genetically-engineered mouse models has increased knowledge of several key mutations. As mentioned previously, the finding that BRAF senescence was not overcome by loss of the inhibitory protein p16INK4A, was derived from a BRAF mutant mouse model (Dhomen et al., 2009). There are now mouse models with multiple mutations. One such model is a mouse line with a V600E BRAF mutation and a loss of PTEN. These mice have been used to study the role of β-catenin in melanoma metastasis and showed that β- catenin stabilisation promoted metastasis (Damsky et al., 2011). Some models combine oncogenic genes with external carcinogens such as UV to develop an inductive model. One such model has been developed by Contassot et al. The model uses a melanocyte specific oncogenic-Ras expressing mouse in combination with a carcinogen, 7,12- dimethylbenzanthracene (DMBA). The model closely follows the development of human melanoma and is transplantable to other mice (Contassot et al., 2011).

Mouse models are often used for studying the tumour microenvironment. This is due to the similarity between mouse and human tissues (Pasparakis et al., 2014). Much information about the roles of inflammation and angiogenesis in tumours has been derived from mouse models. There are now mouse models that express GFP in their vessels for tracing angiogenesis (Hoffman, 2004, Heo et al., 2011) and other models have been used to study the roles of individual proteins such as E-Cadherin in angiogenesis (Liu et al., 2011b). Multiple models exist that can trace the immune systems role in melanoma and tumour development. They have helped to highlight the complexity of inflammation’s

role in melanoma as one of both suppression and promotion (Umansky and Sevko, 2011, Meyer et al., 2011, Ko et al., 2012). One study showed that interleukin 1 and interleukin-17 both altered lung metastasis. However, both knock-down and over-expression of IL-1 resulted in a poor prognosis in the mouse models (Carmi et al., 2011).

There are some drawbacks to the use of mouse models. One key issue raised has been the condition of the mice. Mouse models are classically obese and hyperglycaemic on ad libitum diets and this can greatly affect cancer development and survival (Martin et al., 2010). Mouse models can take months to develop thus extending the time frame for researchers. They are also more expensive to house and analyse than other models. Mouse models also require complex or invasive methods for detailed tumour observations and although various genetic models exist, they are difficult to develop. The mouse is therefore not ideal for rapid screening of potential oncogenes (Kuzu et al., 2015). A further point is the distribution of melanocytes between humans and mice. Melanocytes within mice are located at the hair shaft and therefore have different characteristics and a different niche to the evenly distributed human melanocytes (Li et al., 2011).

1.5.3 Zebrafish Models

1.5.3.1 Genetic techniques A cheaper and faster method of in vivo assay is using the zebrafish. Fish have been used for modelling melanoma since the 1920’s (Patton et al., 2010). The tumours derived from fish models often show near identical histopathology to human melanoma tumours. This is due to the presence of melanin-pigmented melanophores within fish scales (Rawls et al., 2001) and conservation of skin structure (Rakers et al., 2010). Like human melanocytes, development of melanophores is under the control of the MITF gene allowing human mutations which affect pigmentation to be directly modelled (Widlund and Fisher, 2003). In addition the melanophores are present throughout the skin much like human melanoma. The various mutations present in human melanoma that have been modelled in zebrafish tend to show phenotypes that correlate with the human pathology (Patton et al., 2010, Rakers et al., 2010). There are various genetic 40

models of melanoma in the zebrafish that have been used to study a wide range of melanoma aspects. There are models for BRAF, RAS, PI3K and p53 mutants to name a few (Dovey et al., 2009, Santoriello et al., 2010).

The genetic manipulation of zebrafish has become simple and straightforward over the years. Zebrafish embryos are visible within their chorions (Kimmel et al., 1995). This makes injection of genetic material at various developmental stages simple (Stuart et al., 1988). Integration of genetic material into zebrafish is now very efficient after the introduction of the Tol2 transposon method. The Tol2 gene is a portion of DNA isolated from the fish Oryzias latipes which codes for a transposase. When Tol2 is flanking a gene of interest the transposase allows for effective integration of that gene within the zebrafish genome (Abe et al., 2011). This technique is widely used to study the effects of genes on tumour development and various other conditions within zebrafish. For the study of melanoma a more advanced method has been developed. This method utilizes a vector named MiniCoopR and allows for melanophore specific gene expression (Ceol et al., 2011).

The MiniCoopR method was developed by the Zon laboratory (Bourque and Houvras, 2011, Ceol et al., 2011). A specific construct containing a gene of interest is injected into the zebrafish embryo at the one cell stage. The construct is shown below along with the common screening method:

Figure 1.3 The typical pathway for screening genes using the MiniCoopR method. The MiniCoopR vector contains a candidate oncogene and the MITF gene and promoter flanked by two Tol2 arms. The MiniCoopR construct is then injected into an unpigmented fish breed at the one cell stage. Successful integration is indicated by the development of pigment and the development of tumours can be effectively traced and studied (Adapted from (Bourque and Houvras, 2011)).

The construct is flanked by two Tol2 arms to increase efficiency and contains: a gene of interest (in this case a potential driver of melanoma tumourogenesis), the MITF gene and the MITF promoter. When injected into fish lacking pigment due to MITF knockout (such as the nacre mutant zebrafish) successful integration is represented by rescuing melanocytes within the fish (Lister et al., 1999, Iyengar et al., 2012). Levels of MITF expression should match with the expression of the potential oncogene. If used in fish with a V600Ebraf p53-/- or a V12hras background you can observe how gene integration has affected tumour development. Studies can be made on tumour size, latency time or the number of tumours developing. Multiple groups now use this method to screen selected genes in zebrafish (Iyengar et al., 2012).

1.5.3.2 Xenografts and spontaneous models Xenograft assays are also useful for modelling melanoma lesions in zebrafish. Melanoma cells can be injected into zebrafish embryos and tumours develop rapidly thereafter (Konantz et al., 2012). The cell number required is much lower than for mouse models. The cells can be traced through the fish to look at tumour growth and metastasis (Teng et al., 2013). The zebrafish do not develop an adaptive immune system until several weeks post fertilisation (dpf) and therefore do not reject xenografts (Taylor and Zon, 2009). This prevents the complication of having to suppress the host immune system, which in itself could affect findings. Studies utilising xenografts have been important in the study of metastasis and vascularisation (Zhao et al., 2011). Vascularisation is extensively studied in zebrafish due to the transparency of embryos and the development of non-pigmented zebrafish strains. These strains allow for visualisation of the underlying vasculaculture in situ (Zhao et al., 2011). Additionally, studies with zebrafish xenografts have allowed for detailed study of cell co-operation dynamics in vivo. A study from the Hurlstone laboratory used zebrafish xenografts to study the co-operation between two melanoma cell lines in tissue invasion. The study demonstrated that remodelling of the microenvironment by inherently invasive cells aided the invasion of poorly invasive cells (Chapman et al., 2014a).

Some zebrafish models are available that develop melanoma in response to UV

light, much like human melanoma. The advantage of these models is that the system is inducible and the animals can be maintained for longer periods and therefore studied at a more advanced age (Patton et al., 2010). The models are also much simpler in zebrafish as embryos can also be easily exposed to UV radiation. The basic UV response and repair mechanisms present in human skin are also present in the zebrafish and can therefore be efficiently studied within the zebrafish model (Zeng et al., 2009). Testing therapeutics is also possible in the zebrafish. Many chemical and drug screens have been performed in the zebrafish. Administration is rapid and easy if the compound is stable in water (Stoletov and Klemke, 2008).

1.5.3.3 Key models of oncogenes There are several zebrafish models used to study potential melanoma oncogenes. These models can be mapped onto the staging of melanoma. They are briefly summarised below:

Figure 1.4 Melanoma models of Zebrafish developed using transgenesis. A) An example of wild type zebrafish melanocyte patterning. B) A V12 RAS transgenic fish showing the proliferating melanocytes in the RGP phase. This model is also capable of developing spontaneous VGP. C) The BRAF over-expression mutant shows some proliferation but the patterning remains similar to wild-type re-capitulating findings in human nevi (Taken from Michailidou et al. 2009).

Inserting V600EBRAF mutations in zebrafish produce a proliferative phenotype. The melanocytes proliferate but remain within the skin. This mirrors what is seen in benign nevi; in this model the fish never develop malignant or metastatic melanoma (Michailidou et al., 2009a, Patton et al., 2005). The Zon lab established however that in a p53 deficient background these BRAF tumours could develop into invasive tumours that resembled human melanoma samples. The investigation into p53 revealed some of the required mechanisms to overcome BRAF induced senescence (Patton et al., 2005). However, V12 RAS expressing melanophores proliferate and spread beyond their normal locations. This mirrors the RGP seen in human melanoma. This model can also spontaneously develop VGP malignant melanoma shown most clearly in nodular tumour formation (Michailidou et al., 2009a). The importance of both downstream ras pathways is mirrored in the zebrafish models. When the V12 RAS mutant has an additional dominant negative mutation in PI3K, rendering PI3K inactive, the zebrafish show a restricted RGP phenotype. There is increased proliferation over the V600EBRAF model, but cells remain within their normal positions. The fish additionally do not develop malignant tumours (Michailidou et al., 2009b). Although NRAS is the primary mutant protein in human melanoma the use of human HRAS in zebrafish is unlikely to matter as the phylogeny indicates that, with the clear exception of kras paralogues, zebrafish ras paralogues blend features of hras and nras, which are already highly conserved in mammals (Michailidou et al., 2009b). These models have been used in multiple analyses and allow for the study of discrete stages of melanoma. These benefits mean a large number of disease models have been generated in zebrafish (Ceol et al., 2008). In the field of melanoma research zebrafish models are frequently used to analyse melanoma biology. Combining melanoma models with human genetic data is a powerful tool in therapeutic screens. The models can reduce the noise associated with genetic screens in human tumour samples and allow for functional testing of any novel targets (Kuzu et al., 2015, Patton et al., 2010). Targets identified in the zebrafish, cell lines and human tumour samples are likely to represent fundamental genetic changes in melanoma progression.

2 General Methods

Table 2.1 General reagents used within this thesis. Where appropriate solutions have been filter sterilised or autoclaved

Solution Procedure Components

Chorion water Embryo culture 60 mg Instant Ocean Salts (Tropic Marine®), 5 % Methylene Blue

(Lonza), 1 L dH2O

LB broth Bacteria culture 25 g LB broth (Merck Millipore) in 1 L

ddH20.

4% Fixing adult fish 40 g PFA (Sigma-Aldrich) in 800 ml Paraformaldehyde and cells 1x PBS (Sigma-Aldrich) (PFA)

DEPC H20 Reconstituting 1ml Diethylpyrocarbonate (Sigma-

primers, dissolving Aldrich) in 1L Milli-Q H20 RNA and DNA, qPCR and PCR reactions

MS222 Anesthetizing/ 7.7 mM MS222 (Sigma Aldrich), 1

Euthanasia of fish mM Tris. pH 8.5 in 1 L dH2O

Injection Gels Injection of one- 2% Agarose in chorion water cell stage chorions

Solution I (Alkaline Plasmid Miniprep 100 mM Tris pH 8.0, 50 µg/mL Lysis) RNase A (New England Biosciences)

Solution II Plasmid Miniprep 0.2 M NaOH in 1% SDS (Alkaline Lysis)

Solution III Plasmid Miniprep 4 M KAc, 11.5% Glacial acetic acid (Alkaline Lysis) (Fisher Scientific)

TAE Buffer 50x DNA 50X TAE (1 L), 242g Tris, 57.1 ml

Stock Electrophoresis Glacial Acetic Acid, 37.2g Na2EDTA2,

H2O. pH ~ 8.5

TE Buffer Re-suspension of 10 mM Tris (pH 7.5), 1 mM EDTA DNA/RNA pellets

Nile Red Stock Staining of neutral 0.5 µg Nile Red (Sigma Aldrich), 1 lipid mL Acetone (Fisher Scientific)

Running Buffer Western Blotting 25 mM Tris, 192 mM Glycine, 1% SDS (w/v) (Sigma Aldrich)

Transfer Buffer Western Blotting 25 mM Tris, 192 mM Glycine, 1% SDS (w/v), 20% Methanol

Crystal violet Staining cells for 4% formaldehyde (Fisher Scientific), survival assay 0.5% Crystal violet (Sigma-Aldrich) in 1x PBS

2x SDS blue lysis Protein extraction 125 mM Tris (pH 6.8), 4% SDS, 20% buffer/ Lamelli glycerol (Fisher Scientific), 0.02% Buffer Bromophenol blue (Sigma-Aldrich)

8% SDS resolving SDS-PAGE 10 ml: 4.9 ml ddH20, 2.5 ml 1.5 M gel Tris pH 8.8, 2.6 ml 30% acrylamide (National Diagnostics), 100 µl 10% SDS, 100 µl 10% ammonium persulphate (APS, Sigma-Aldrich), 5 µl TEMED (Bio-Rad Laboratories Ltd)

15% SDS SDS-PAGE 10 ml: 2.3 ml ddH20, 1.3 ml 1.5 M resolving gel Tris pH 8.8, 5 ml 30% acrylamide, 100 µl 10% SDS, 100 µl 10% APS, 5 µl TEMED

Stacking gel SDS-PAGE 5 ml: 3.5 ml ddH20, 650 l 1.5 M Tris

pH 6.8, 850 µl 30% acrylamide, 50 µl 10% SDS, 50 µl 10% APS, 10 µl TEMED

10x SDS running SDS-PAGE 120 g Tris, 576 g Glycine (Fisher buffer Scientific), 40 g SDS, in 4 L ddH20.

Transfer buffer Immunoblotting: 57.7 g Glycine, 12.2 g Tris, 800 ml protein transfer methanol (Fisher Scientific), 1.3 ml

10% SDS, 3.2 L ddH20.

1x TBST Immunoblotting: 8 g NaCl (Fisher Scientific), 3 g Tris, washing and 80 ml HCl (pH 7.5, Fisher Scientific), dilution of reagents 10 ml Tween (Sigma-Aldrich) in 1 L

Milli-Q H20

Blocking buffer Immunoblotting: 1.5% BSA (Melford) in 1x TBST blocking membrane

2.1 Zebrafish Techniques

2.1.1 Zebrafish husbandry All zebrafish were maintained in the Manchester Biological Services Facility aquarium. The fish were fed a live brine and flake food (ZM Systems) diet. The fish were maintained on a 10/14-hour light/dark cycle. The ambient temperature for adult fish and embryos was 28.5 °C. Embryos were maintained in chorion water until 5 days post-fertilisation before they were transferred to the main aquarium system.

All experiments were subject to local ethical review and performed under a Home Office License.

2.1.2 Zebrafish breeding Breeding boxes were assembled after the evening feed and fish were allowed to acclimatise overnight. Pairs of males and groups of three females were placed in a partitioned crossing tank (Aquatic Crossing Tank, Thoren, Aquatics Inc.). Water was allowed to circulate. At the beginning of the light cycle the divider was removed and the fish were allowed to breed. Embryos would fall through the tank for collection. Embryos were collected within 10 minutes of being laid.

2.1.3 Zebrafish anaesthesia and euthanasia An anaesthetic plane was induced by placing the zebrafish in a 0.4% w/v solution of MS222. When fish were non-responsive to caudal fin pressure they were taken for fin clipping or imaging. Following completion of the experiment fish were recovered in fresh system water.

Tissue extraction and fixation only occurred after euthanasia of the zebrafish. Fish were anaesthetised as described and decapitated with subsequent brain destruction.

2.2 Cloning Techniques

2.2.1 PCR The following protocol was used for the amplification and mutagenesis of DNA products. Details and modifications are shown with the specific protocol when suitable.

1 μg cDNA, 1 μL genomic DNA or 100 ng of plasmid DNA, were added to 25 μL of Biomix Red Master Mix (Qiagen). Additionally, 5 μL of forward primer and 5 μL reverse primer were added and water to bring the reaction to 50 μL. The PCR was run with the following conditions on a G-Storm Thermocycler:

Heated Lid 110 °C

Denature 95 °C – 5 minutes

Denature 95 °C – 30 seconds

Anneal 55 °C – 30 seconds 28 Cycles

Extension 74 °C – 45 seconds

Extension 74 °C – 10 minutes

2.2.2 Transformation of E.coli Plasmids and constructs purchased that arrived in glycerol stocks were initially placed in LB broth overnight at 37 °C. All other cloning techniques used E. coli transformation.

Top10 E.coli cells were acquired from Sigma-Aldrich and stored at -80 °C. The E.coli cells were defrosted on ice and then 2 µL of the reaction mix was added to the cells. The E. coli were heat-shocked for 30 seconds at 42 °C then placed back on ice. 100 µL of SOC medium was added to the cells and they were shaken horizontally at 37 °C for one hour. The cells were then streaked onto LB-agar plates that contained the appropriate selection antibiotic. Plates were incubated overnight at 37 °C. 51

Colonies were then picked from these plates and incubated overnight in LB broth, shaking at 37 °C. Volume of LB broth varied depending on required volumes of bacterial culture.

2.2.3 MiniPrep Isolation of plasmid material was initially performed as a miniprep prior to sequencing.

1 mL of bacteria was centrifuged at 13,000 rpm for 1 minute and the supernatant discarded. The remaining bacterial pellet was re-suspended in 100 μL of chilled Solution I. 200 μL Solution II was then added and the sample inverted 5 times.150 μL of Solution III was added and inverted 5 times. 200 μL of chloroform was added prior to 10 seconds of vigorous shaking. The sample was then centrifuged for 5 minutes at 13,000 rpm. The sample will form three layers; a lower chloroform phase, a white DNA phase and an upper RNA phase. The top layer containing the RNA was transferred to a clean 1 mL tube. After addition of 400 μL isopropanol the sample was centrifuged for a further 10 minutes at 13,000 rpm. The supernatant was discarded and the resulting pellet washed in 70% ethanol. The pellet was allowed to air-dry for 5 minutes before resuspension in TE buffer.

2.2.4 Maxiprep The Maxiprep was used to isolate plasmids at a higher purification and higher concentration. The Maxiprep was performed using 50 mL of overnight-cultured transformed E. Coli cells. The Maxiprep was performed using the Qiagen kit following the kit instructions. An additional step was added after the addition of Buffer P3. A 10 mL dose of chloroform was added to the solution, prior to centrifugation.

2.2.5 Sequencing Sequencing was performed after a miniprep, or in cases where greater purity is required after a maxiprep. A 20 μL reaction mix was made up of the following components: 400 ng of plasmid, genomic DNA or cDNA was added to 2 μL Terminator Ready Mix (Big 52

Dye Version 3.1, Thermofisher Scientific), with 3 μL sequencing buffer. To this 3.3 pmol forward or reverse primer was added and the reaction mix was brought up to 20 μL with distilled water. The following cycle conditions were used on a thermocycler (G-storm)

Heated Lid 110 °C

Denature 94 °C – 5 minutes

Denature 96 °C – 30 seconds

Anneal 50 °C – 10 seconds 35 Cycles

Extension 60 °C – 5 minutes

Extension 72 °C – 10 minutes

Post cycling the reaction was stored at 4 °C. The second stage required the precipitation of the DNA. The reaction mix was placed in a 1.5 mL microcentrifuge tube and 2 μL of 3 M sodium acetate was added, followed by 50 μL of 95% ethanol. To this, 1 μL of Glycoblue (Ambion) reagent was added and the reaction mix was vortexed briefly, this aids visualisation of the DNA pellet. The samples were then centrifuged for 20 minutes at 13,000 rpm to pellet the DNA. The supernatant was discarded and the pellet washed in 100 μL of 70% ethanol. The wash was completed with a 15-minute centrifugation at 13,000 rpm. The ethanol was then discarded and the pellet left to dry overnight. The precipitated DNA pellet was then analysed at the Sequencing Facility within the University. All other methods are detailed within the specific chapters.

Table 2.2 Primers sequences used in sequencing

Primer Name Sequence

DEST 5F CTATGACCATGATTACGCCAAGCTA

DEST 5R CTGCTTTTTTGTACAAACTTG

DEST MF CAAGTTTGTACAAAAAAGCAG

DEST MR CCACTTTGTACAAGAAAGCTG

DEST 3F CAGCTTTCTTTGTACAAAGTGG

DEST 3R CAGTGAATTATCAACTATGTA

M13F GTAAAACGACGGCCAG

M13R CAGGAAACAGCTATGAC

Chapter 3: Identifying therapeutic pathways using transcriptome and mass spectroscopy techniques

3.1 Introduction There are many methods for identifying therapeutic compounds and targets in melanoma. Some of these have been mentioned in the introduction. One approach is to use chemical or compound screens. The zebrafish is frequently used in studies of this type. Often a library of chemicals or approved compounds is applied at a set dose and the resulting phenotypes recorded (Veinotte et al., 2014, Cox and Goessling, 2015). In the cell culture setting often a reduction in proliferation is a read out. In the zebrafish more in depth phenotype analysis can occur including observations for toxicity and off target effects. These studies have been used to some success in toxicity screening in particular. One study compared the use of breast cancer cell lines with zebrafish for anti-cytotoxic compound testing (Li et al., 2012). The study noted that the zebrafish model detected more cases of toxicity and the metabolism of the drug could be analysed in more depth. Often drugs from traditional chemical screens have only a 1% chance of reaching human trials but the in vivo step in the previous study removed several more toxic compounds at an earlier stage (Li et al., 2012). Despite the improvements of adding an in vivo analysis identifying compounds is still ineffective in developing therapeutics.

The recent successes in melanoma have been based on identifying targets and then developing therapeutics. The presence of BRAF mutations led to the use of a BRAF inhibitor in the clinic with the successful effect discussed previously. Extensive transcriptome and mutagenesis studies have been performed in melanoma (Scott et al., 2011, Hodis et al., 2012, Harbst et al., 2014). Transcriptome analyses and sequencing experiments have increased the genetic map of melanoma. Some studies have even been able to map the key mutations occurring at different stages of metastasis and therapy resistance as a phylogenic tree (Shi et al., 2014). There is growing understanding that outside

of the major driver mutations there may be a wealth of genetic targets that would aid in melanoma treatment, preventing progression, angiogenesis, metastasis and proliferation. Even within one patient there can be hugely diverse genetic changes, many of which may actually lie on redundant pathways (Harbst et al., 2014).

Genetic analysis is only one way of exploring the biology of melanoma. One area of growing interest is the study of metabolism in melanoma. Raman spectroscopy is a technique that can be applied in a non-invasive manner to study changes in key metabolites (Smith and Macneil, 2011, Lim et al., 2014). Raman spectroscopy has revealed that malignant melanoma lesions show distinctive changes in metabolite profiles during melanoma progression (Gniadecka et al., 2004). The technique can also be used to distinguish between different types of melanoma cell and different types of cell death. It could be used to identify hypoxic or necrotic lesions (Brauchle et al., 2014) . The changes in metabolites are high enough to be used for screening but in depth analysis of the cause and consequences of these metabolite changes is lacking. The sensitivity and throughput are not on par with other techniques such as mass spectroscopy (Schleusener et al., 2015).

Mass spectroscopy (MS) has been rapidly developing to allow for sensitive investigations into protein structure and metabolism in many cancers (Giskeødegård et al., 2015, Beretov et al., 2015). Mass spectroscopy can give high levels of detail, including detecting mutations not previously identified (Davies et al., 2009). The technique could be used to screen patient biopsies to determine responses to therapeutics. There are a diverse range of different molecules that can be studied with mass spectroscopy (Badran et al., 2016, Xiang et al., 2004, Zaia, 2008). One area that is rapidly developing is the study of lipid metabolism. The lipidome encompasses a broad range of metabolites and they are often insoluble making detailed study difficult (Schultz, 2010). There are a range of MS techniques now in use to study the lipidome. The most common are liquid chromatography-MS (LC-MS), also high performance liquid chromatography coupled MS (HPLC-MS) and gas chromatography coupled MS (GC-MS) (Dunn et al., 2011). LC-MS employs chromatography techniques to

separate lipids by retention time before mass spectroscopy analysis. The ability to separate metabolites can be used for more focussed and detailed study of a lipid class or just allow for better discrimination of metabolites. The metabolites identified by LC-MS tend to have higher molecular weights. This technique has been readily applied to a number of cancers, including breast cancer and ovarian cancer (Denkert et al., 2012, Liu et al., 2010). These cancers have all been classically associated with a lipogenic phenotype, due to expression of a wide range of lipid synthesis or uptake genes (Zaidi et al., 2013). The difficulty with LC-MS is that the databases on metabolite identification are not readily comparable between equipment set-ups. This means the identification of metabolites without defined standards cannot be definitive in all cases (Dunn et al., 2011). This does not preclude developing the field as broad shifts in certain lipid classes can provide valuable information this technique can be hypothesis generating (Cravatt et al., 2007, Brown and Hazen, 2014).

GC-MS is another technique employing chromatography. The gas-phase significantly reduces the sample required and several equipment set-ups require as little as 1 μL of sample. GC-MS is limited in that it needs lipids to be volatile, or made volatile, for identification. In addition the metabolites identified by GC-MS tend to be of a lower molecular weight (Dunn et al., 2011). Yet this means the technique identifies a wide range of lipids not identified in LC-MS and often the two techniques are combined in studies. GC-MS develops metabolite profiles that can be shared between equipment set ups and researchers. This has resulted in detailed databases for lipid and far more definitive identification of GC-MS spectra. Interpreting mass spectroscopy data requires access to extensive databases and there are a number available. Most of these are freely accessible and include LipidMaps, PubChem and combined databases like the Manchester Metabolomics Database (MMD) (Pantham et al., 2015).

Combinations of LC-MS and GC-MS have been successfully used in large- scale metabolic studies (Pantham et al., 2015, Luan et al., 2015). One of the more relevant studies developed a detailed map of metabolic shifts occurring in zebrafish development (Huang et al., 2013). The study used a combination of

LC-MS and GC-MS to probe zebrafish at different developmental stages. This highlights a key advantage of metabolic studies. The metabolites are much more highly conserved between species than genetic information. This means techniques do not require species-specific databases (Huang et al., 2013).

Studying the metabolome and the effect on melanoma development also requires detailed understanding of the genetic changes occurring within the metabolic pathways. Genome sequencing studies have greatly advanced understanding of cancer pathways. Melanoma in particular has been studied in extensive detail (Network, 2015). Therapies like BRAF inhibition are based on earlier mutation studies. Transcript microarrays have been critical in defining the molecular changes occurring in patients and have been used to explore many phenomena associated with melanoma. Expression phenotypes defining aggressive or migratory tumour cells have been described (Hoek et al., 2008). The immune system has been explored by performing microarrays on circulating and tumour associated immune cells (Xu et al., 2004, Watson et al., 2013).

Despite the benefits of combining these data types the process is difficult and a common challenge encountered in systems biology (Ritchie et al., 2015). There is currently no defined standard for data integration. Recent efforts have included two large EU driven projects to develop a standardised data integration set (Ritchie et al., 2015). Pinpointing metabolic changes driving cancer development entails comparing cancer cells/tissue to healthy cells/tissue. This comparison is difficult as differences may be due to genetic noise, passenger mutations or as a consequence of more important, but unidentified metabolic processes (McFarland et al., 2014). This is particularly problematic in melanoma where genetic variation is huge and passenger mutations are common. Several groups have proposed starting in model systems to reduce complexity (Yen et al., 2013). Often model species or genetic models have reduced system noise. Changes occurring in these models may represent fundamental shifts relevant to the biology researchers wish to investigate (Peeper and Berns, 2006).

Understanding the role of metabolism in melanoma by investigating gene 58

expression and mass spectroscopy profiles will shed light on therapeutic options. It would also generate a more complete picture of the metabolic profile of melanoma cells. Therapies aimed at inhibiting these pathways may prevent further transformation of radial lesions whilst inhibiting the aggressive nature of established vertical lesions.

3.2 Aims

 One of the aims of this thesis is to use the zebrafish melanoma model to identify pathways and compounds that may reflect changes common to a large range of melanoma patients. A transcriptome analysis had been performed on wild type fish, V12RAS RGP, V12RAS VGP and V600EBRAF melanoma models. I will analyse this data using Ingenuity pathway analysis. A pathway will be selected for further study after a literature review.

 Metabolism was identified and a secondary aim became to analyse lipid metabolism in the wild type, V12RAS RGP and V12RAS VGP with mass spectroscopy. This analysis will be used to develop new hypotheses alongside providing descriptive data and potential functional information to support the transcriptome.

3.3 Methods for determination of therapeutic targets

3.3.1 Determination of therapeutic targets by transcriptome analysis An Agilent 44K Zebrafish transcriptome array had been performed on tails and fins pooled into three biological replicates from wild type, V600EBRAF, V12 HRAS zebrafish and tumours extracted from the tail fin of V12 HRAS zebrafish. The transcriptome data included standard Agilent quality control processes. The spot intensity and quality control data was processed in Rosetta using Agilent databases in house. Probes were definitively identified with a Unigene code and gene name. Genes with a P-Value > 0.0001, and a fold change of at least 2 were selected as significant gene changes. Work was completed in collaboration with Prof. Herman Spaink at Leiden University. This was not performed by the author of this thesis. All subsequent analysis was performed by the author.

Properties of genes were determined using database-to-database searches on BioDBnet (Last accessed July 2015) (Mudunuri et al., 2009). GO terms were assigned for biological function and molecular ontology based on the Unigene code. Genes were verified by literature searches. Genes with ontology relating to lipid were identified as potential targets.

The probes were converted to human homologues and placed in the IPA Ingenuity format. 1576/4438 Unigene codes were successfully identified. The data was analysed with Ingenuity IPA analysis. In Ingenuity terminology a ‘core analysis’ was completed and enriched molecular characteristics and pathways identified. Enriched pathways and lipid metabolism pathways were overlaid by expression data from the probes. The genes with lipid ontology were used to generate a pathway; predicted activity on pathways was calculated based on the VGP expression dataset. Major pathways affected were determined by the coverage of the pathway on the dataset. Pathways affected by at least 30% of the genes were added. Molecules of interest were also analysed by the Search function and pathway overlay. Functions and techniques for IPA are available in the help menu. 60

3.3.2 Lipid extraction for mass spectroscopy analysis of metabolic pathways Techniques were performed in collaboration with Dr. Warwick Dunn. Techniques are adapted from published papers for UPLC-MS (Pantham et al., 2015) and GC-MS (Dunn et al., 2011).

Samples were to be divided in polar (methanol/water) and non-polar (chloroform) fractions. Working solvents were prepared and stored at -20 °C for at least two hours. Solvent B is H20 and solvent A is methanol: CHCl3. Additionally, racks for tissue homogenization (Tissuelyser LT, Qiagen) were stored at -20 °C for two hours.

Zebrafish were euthanized and the skins of WT and V12 RAS RGP fish were pooled to a mass between 25-35 mg. Tumour samples were weighed and in required cases pooled. At least 6 biological pools were gathered for each sample. Tissue was accurately weighed and lysed. The lysation involved addition of 800 μL solvent A and a carbide bead to each sample. Samples were homogenised for 10 minutes at 25 Hz. Samples were incubated on ice for a further 10 minutes. 400 μL of ice-cold solvent B was added. Samples were vortexed for 10s and then centrifuged at 13,000 xg for 15 minutes. Samples were subsequently incubated on ice for 10 minutes.

Initial sample weight was used to calculate the volume of solvent to collect.

Polar Fraction (upper phase): volume taken (µL) = (40 * 700)/(actual sample weight (mg))

Non-polar fraction (lower phase): volume taken (µL) = (40 * 400)/(actual sample weight (mg)).

Polar fractions were dried for 16 hours at 30 °C using an Eppendorf Concentrator (Concentrator Plus, Eppendorf). Non-polar fractions were lyophilised in an Eppendorf centrifugal vacuum evaporator (Model 5301, Eppendorf) for 18 hours and until analysis by HPLC-MS or GC-MS.

3.3.3 UPLC-MS analysis of the sample Samples were analysed on an Accela UHPLC system coupled to an electrospray LTQ-Or-bitrap Velos hybrid mass spectrometer (ThermoFisher Scientiﬁc). Samples were randomized for class structure and sample preparation order applying the ‘RAND’ function in Microsoft Excel and analysed as a single analytical batch in positive and negative ion mode separately, along with quality control (QC) samples. QC samples were mixed aliquots of all biological samples in the study. They are important to provide signal correction data and to assess for potential ‘drift’ in the samples.

Chromatographic separations were performed on a Hypersil GOLD column (100 9 2.1 mm, 1.9 mm; ThermoFisher Scientiﬁc) operating at a column temperature of 50°C. Two solvents were applied (solvent A—0.1% formic acid in water (vol/vol) and solvent B—0.1% formic acid in methanol (vol/vol)) at a ﬂow rate of 400 mL/ min.

Solvent A was held at 100% for 30 seconds followed by a gradual increase to 100% solvent B over 4.5 min, which was then held at 100% solvent B for a further 5.5 min. A change to 100% solvent A was performed at 12.5 min and then held at 100% solvent A to equilibrate for 1.5 min. All column eluent was transferred to the mass spectrometer, and full-scan proﬁling data were acquired in the Orbitrap mass analyser (mass resolution 30,000 at m/z = 400). The source and ion transfer parameters applied were as follows: source heater = 200 °C, sheath gas = 50 (arbitrary units), aux gas = 15 (arbitrary units), capillary temperature = 300 °C, ISpray voltage = 4 kV (positive ion mode) and 3 kV (negative ion mode), slens = 65% and AGC = 5.

3.3.4 Data pre-processing for analysis Data obtained were in a .raw format following UHPLC-MS and were converted to the NetCDF ﬁle format on the FileConverter program available in XCalibur (ThermoFisher Scientiﬁc) and deconvoluted using the XCMS software. The quality control-robust locally estimated scatter plot smoothing (QC-RSLC) method was used to correct for drift in response correlated to analysis order. For quality assurance, only metabolite features that were detected in >60% of

all QC samples and with <20% relative standard deviation were retained for further analysis.

3.3.5 Statistical analysis to determine significant metabolite changes and cluster analysis Processed data were exported followed by univariate and multivariate data analysis in R (version 2.14). Exploratory, unsupervised multivariate analysis was performed to determine the origin of variation among the samples using principal components analysis (PCA), which was performed on data normalized to zero mean and unit variance. Unsupervised PCA was performed to visually describe the distribution of variance in the datasets with two samples close together being metabolically similar and two samples far apart being metabolically different. Principal components (PC) 1 and 2 describe the greatest level of variance, which can be described in a single PC. Univariate statistical analysis was performed using the nonparametric Wilcoxon signed rank tests to calculate whether there were statistically signiﬁcant differences for any given peak between the same biological sample under the three models (Wild type, V12 RAS-RGP and V12 RAS-VGP). Significant metabolites were determined to be significantly different (P<0.05) from each comparison; WT vs RGP, RGP vs VGP and WT vs VGP. Data were collected as a ratio between the samples. For analysis of changes in VGP samples ratios were converted to –Log(Ratio) to present a scale reflecting changes in VGP. Critically significant lipids would be further analysed within Excel (UPLC-MS) or IPA (GC-MS).

The relative difference (fold change) in peak area between sample classes was also calculated as the difference between the median values of two sample classes, and the 95% conﬁdence intervals were calculated.Presentation of the LCMS was done in R (version 2.12.1). The gplots R library was used with the heatmap.2 function to plot the fold change in VGP in ratio to WT.

3.3.6 Metabolite identiﬁcation and pathways analysis Multiple metabolite features can represent the same metabolite, and these features were putatively annotated as metabolites in the processed dataset applying a previously described workﬂow, PUTME-DID_LCMS30 according to

reporting standards deﬁned by the Metabolomics Standards Initiative.

Initial annotation was completed by comparing accurately measured m/z to molecular formula and the subsequent matching of molecular formula to those present in the Manchester Metabolomics Database (using the trimMMD_sortMF.txt ﬁle available when downloading the software). Metabolites that could not be identified were not included in further analysis. Further analysis of UPLC-MS data was not conducted beyond assignation of broad lipid class due to the lack of definitive identification.

3.3.7 GCMS analysis of metabolites Samples were collected and extracted as previously described. The randomization of samples and inclusion of QC samples was performed as previously. For GCMS the equipment was surveyed and components replaced as required, all samples were run within one experiment.

The samples were run on a LECO Pegasus III mass spectrometer (Leco), an ‘Acquisition System Adjust’, ‘Filament Focus’, ‘Ion Optics Focus’ and ‘Mass Calibration’ set of commands were performed to calibrate and tune the equipment. 1 μL of each sample was injected with a helium carrier gas. During sample analysis, chromatographic separations were performed on a Varian VF- 17MS column. Gas saver flow (25 ml/min− 1) was switched on 15 s after sample injection. The temperature program began at 70 °C, was held for 4 minutes and followed with a linear temperature ramp of 20 °C/min-1 to 300 °C before being transferred to the mass spectrometer. The mass spectrometer source was at 250 °C in EI mode with an electron energy of 70 eV.

Determination of peaks was completed by using a ‘peak find’ data processing method. Peaks were analysed with a signal to noise ratio determined from known peaks. Peaks that did not appear in a robust way or showed evidence of peak splitting were removed. The retention index and mass spectrum of the sample was then compared with the MMD in house library to identify the metabolites. A match of 70% by mass spectroscopy and a retention time within +/- 10 can be determined as a definitive match. Further analysis was completed as for UPLC-MS.

3.4 Results

3.4.1 Transcriptome analysis reveals deregulated lipid metabolic pathways This experiment was performed prior to the work in this thesis and was not performed by the author.

Transcriptome profiling was undertaken in collaboration with Prof. Herman Spaink at Leiden University using an Agilent 44K zebrafish transcriptome microarray on zebrafish models representing different stages of melanoma development that had been previously generated in the Hurlstone laboratory. In brief, a transgenic line with forced expression of V600EBRAF resulted in melanocyte hyperplasia while expression of V12 RAS resulted in malignant melanocyte neoplasia that initially grows radially (RGP) and then vertically (VGP) (Figure 3.1A). Total RNA extracted from 3 pools of 10 caudal fins were used for WT, BRAF and RGP samples, whilst for VGP RNA was extracted from 3 pools of 6 caudal fin tumours. Transcriptome profiles were analysed by Rosetta and a heat map of differentially expressed genes was established.

Hierarchical clustering suggested a closer relationship between RGP and VGP profiles than any other two-way comparison (Figure 3.1B). Differentially expressed genes with a P value below 0.0005 and a fold change of at least 2 compared to wild type were considered significant hits. The VGP tumour model had the greatest number of differentially expressed genes totalling 4429 (Figure 3.1C). Venn diagrams were generated to display overlap between gene models. The BRAF model showed very little overlap with the V12 RAS generated models (Figure 3.1D).

R V

S S

A A

0 R R

0 2 2

6 1

A B 1

V V V

Wild Type

BRAF – benign lesion

V12RAS – early malignant neoplasia (RGP)

V12RAS – late malignant neoplasia (VGP)

3000

e p

y 2118

d 2000

o Up-regulated t 1000

e 496 583

l a

e 0

e n

e -1000 683 Down-regulated G

1059

e -2000

b m

u 2311

N -3000 F S P A A G R R V B D

Figure 3.1 The transcriptome revealed V12VGP to be distinct from V600EBRAF and V12 RAS RGP samples. A) The four models from which fin or tumour samples were excised and RNA extracted. The samples were analyzed on an Agilent 44K Zebrafish transcriptome array. B) The derived heat map is shown with hierarchal clustering. Red signified up-regulated genes and green down-regulated genes. C) The statistically significant genes with at least a two-fold difference in expression from wild type (P<0.0005). The VGP sample showed a much greater number of significant genes in both up and down- regulated. D) A Venn diagram demonstrates the lack of overlap in the up-regulated genes between the models. 66

Genes were named by comparison of probe IDs with the most recent 44K Probe Database (2014). Genes were then assigned gene ontology using the database-to-database search on BioDBnet. The ontologies were grouped into broad classes. Metabolism encompassed 9% of differentially expressed genes, equalling developmental genes (Figure 3.2A). Of that subset lipid metabolism formed 20% of metabolism genes.

Figure 3.2 Metabolism is a large proportion of the significantly deviating genes and lipid metabolism is a significantly enriched pathway. A) Metabolism represents 29% of genes differentially expressed in VGP samples. Probes were assigned a gene identity using the Agilent 44K probe ID dataset. BioDBnet assigned gene ontology terms (GO terms) for each gene. Genes were separated into broad categories based on their ontology and genes were further subdivided into specific metabolic pathways. B) Lipid metabolism genes represented 20% of metabolic genes, the largest defined subgroup.

To determine the biological processes enriched in this sample Ingenuity Pathway Analysis was performed. The software initially identified human homologues of the zebrafish genes. The greatest class of gene alterations were, as expected, in cancer biology with 1273 gene products represented. The classes are shown in Table 3.1. To identify more specific pathways involved in the VGP model pathway enrichment analysis was also completed with IPA. The most enriched pathway with the highest score was cellular assembly and organisation, connective tissue disorders and developmental disorders. The second largest group was lipid metabolism, molecular transport and small molecule biochemistry (Table 3.2). All analyses were performed with the standard IPA parameters. All the top pathways identified are known to be implicated in melanoma progression.

Table 3.1 IPA gene enrichment analysis demonstrated that cancer biology was the most enriched molecular cluster. Unigene codes, fold change and P-value were entered into the IPA analysis. 1576/4428 genes were identified as human homologues in proportion to the number of probes with unknown gene ontology. The VGP gene list was entered in a Core Analysis and the most enriched molecular processes determined. Pathways related to cancer were identified.

Name Molecules P-value range

Cancer 1273 5.04E-7 – 1.27E-31 Organismal Injury and 1279 5.04E-7 – 1.27E-31 Abnormalities Developmental Disorder 362 5.10E-7-5.93E-19 Skeletal and Muscular 342 4.96E-07-5.93E-19 Disorders Neurological Disease 328 4.85E-07-4.35E-17

Table 3.2 The pathway enrichment analysis demonstrated a lipid pathway was significant. Data was derived from the same Core Analysis. All pathways are known to cancer biology. The number of positive hits validated the transcriptome data as relevant to human melanoma

Gene Cellular Assembly and Organization, Connective Tissue 47 Disorders and Developmental Disorders Lipid Metabolism, Molecular Transport, Small Molecule 40 Biochemistry Connective Tissue Development and Function, 39 Organismal Development, Skeletal and Muscular System Development and Function Cell-to-Cell Signaling and Interaction, Cellular Assembly 39 and Organization, Cellular Function and Maintenance Developmental Disorder, Hereditary Disorder, Metabolic 37 Disease

The enriched pathways supported the investigation of lipid metabolism. A short- list of genes with gene ontology relating to lipid metabolism was determined. A literature search was performed on Pubmed to determine if any of these genes had previously been implicated in melanoma. Several genes were known to play a role in melanoma and were highlighted in bold (Supplementary Table 1).

All the genes involved in lipid metabolism were mapped onto IPAs Pathway Builder. Predicted activity, modelled on gene expression data was used to define the pathways most affected by a majority (>30%) of these genes. The map demonstrated that these genes would activate FA metabolism. This included FA synthesis, increased lipid uptake, lipid storage and increased phospholipid metabolism. The strongest predicted activation would be in FA metabolism. This suggested that the pathways were remodelling or catabolising FA within the cell (Figure 3.3).

Figure 3.3 The lipid genes identified in the VGP sample suggest FA catabolism was activated. The list of lipid genes from the VGP sample was mapped onto pathways with IPA. Orphan genes that did not relate to the top predicted pathways were not included in the image. Expression data was used to predict activation of the pathways that associated with the most genes. FA metabolism was predicted to be highly activated, with most components of the pathway predicted to be activated. Red =-up-regulated and green = down-regulated. Orange = predicted activation.

The data gleaned from the transcriptome had revealed a number of potentially active pathways and revealed dramatic changes occurring in lipid metabolic genes. Lipid metabolism was one of the most enriched pathways and lipid metabolism formed a large subset of genes differentiated in the VGP dataset. Determining the impact of these changes would require detailed analysis of the lipid metabolites.

3.4.2 Mass spectroscopy analysis shows that plasma membrane and fatty acid metabolism is altered To better understand the changes occurring mass spectroscopy was performed on the V12 RAS zebrafish model and wild type zebrafish. Four samples were generated for each model by pooling 6 fins and skins of wild type fish and RGP, whilst four tumours weighing between 25 mg and 35 mg were also taken. Samples were stored on liquid nitrogen and then underwent chloroform and methanol extraction. The polar and non-polar fractions were dehydrated and run on liquid phase high performance liquid chromatography mass spectroscopy (HPLC-MS) and gas-phase chromatography mass spectroscopy (GC-MS) at the CADET facility by collaborator Warwick Dunn. The data were further processed and labelled with m/z parameters. Identification was assigned based on the MMD database and nomenclature. The UPLC-MS PCA analysis indicated a maximal 34.2% variance between the groups occurring at wild type to VGP, the minimal variance represented 8.4% between RGP and VGP (Figure 3.4A). The GC-MS showed a similar pattern (Figure 3.4B) however fewer metabolites were identified. Each model could be readily resolved from the others revealing a consistent pattern of lipid alterations within models. The samples, when plotted, show a graduated progression from wild type to tumour samples (Figure 3.4C). The different lipid classes were assigned a ratio compared with wild type and a P-value. The number of differential lipids is shown in Table 3.3. The VGP tumour had the greatest number of differentially expressed lipids.

Figure 3.4 As the melanoma progressed the number of altered lipids grew. A) A PCA analysis from UPLC-MS negative and positive modes demonstrates the significant difference between groups. B) The PCA analysis from GC-MS revealed significant differences between models. C) A vector diagram with lines representing the number of significantly differentially regulated metabolites. The changes suggest that the changes in lipids may be progressive and that each sample is metabolically distinct.

Table 3.3: A table showing the number of differentiated lipids in the UPLC-MS analysis at varying significance. As determined by Kruskal–Wallis one-way analysis of variance

Features

p<0.05 (KW) p<0.01 (KW) p<0.005 (KW)

WT vs RGP 854 165 103

WT vs VGP 1183 683 443

RGP vs VGP 1517 262 117

Mass spectroscopy revealed large changes in lipid composition during melanoma progression. The lipid groups were assigned to broad classes and analysed to determine specific areas that could be contributing to tumour pathogenesis. The focus was on the lipid changes occurring in the VGP sample as this represented the more malignant VGP phenotype. The ratio of the lipid was converted to a negative log2 to better represent the changes occurring between the models. The data are represented as a heat map generated in R. The broad trends are a strong consistent decrease in acylglycerides (Figure 3.5B) and lysoglycerophospholipids (Figure 3.5D). The ceramide groups are largely increased (Figure 3.5A), as are nucleotides (Supplementary Table 2). The final groups, the glycerophospholipids (Figure 3.5C), fatty acids (Figure 3.5E) and mixed classification lipids (Supplementary Table 2) show much more variety. When RGP log ratios were plotted the pattern of metabolites was very similar but often the ratio was much smaller (data not shown). It was not possible to accurately determine metabolites although broad classes could be determined.

Specifically, the sphingomyelin levels in the ceramide class (40 and 41 on the heat map) were greatly reduced. As was unmodified ceramide (1-5). The biggest increases were in glycosylated (15-29) or phosphorylated sphingolipids (12, 13 & 42) (Figure 3.5A). In the acylglycerides there was a decrease in long- chain triglycerides (7-13) but a dramatic increase in saturated triglyceride (TG: 36:0), typically comprised of saturated FA chains (6) and and diacylglycerol (1) was also increased (Figure 3.5B). There was no easily determined pattern in the glycerophospholipids, or lysoglycerophospholipids. Consistent changes in FA were seen but they did not appear to fit a particular pattern. The nucleotides were increased, notable UDP and ATP metabolites but few were identified in the analysis. The GC-MS data revealed a smaller number of detectable changes. However, the metabolites were well defined and this allows for accurate interpretation of the results. The metabolites identified are shown in Supplementary Table 3.

Figure 3.5 The resulting heat maps from the LCMS analysis. The major trends are depicted and detailed information is provided in Supplementary Table 2. A) Ceramides and sphingolipids B) Acyl glycerides C) Glycerophospholipids D) Lyso-glycerophospholipids E) Fatty acid and related metabolites Red = reduced and Green = increased. Numbers are for reference to the text and represent lipids. Numbers represent the order of metabolites in each class in Supplementary Table 2.

3.5 Discussion

3.5.1 Transcriptome analysis of zebrafish progression models. The transcriptome array analysis provided high quality data on the differentially regulated genes in the three melanoma models. The number of differentially expressed genes was relatively low in the V12 RAS RGP, with the V600EBRAF model expressing significantly more, and the V12 RAS VGP the highest (Figure 3.1C). There was little overlap between the V12 RAS models and the V600E BRAF model (Figure 3.1D). This may be due to very different developmental pathways. Several groups have shown that malignant neoplasms may develop independently from benign nevi and this would include in genetic profile (Rose et al., 2011). Further, the V600EBRAF model has likely undergone a program of proliferation and senescence that would be diverse from wild type skin and the more malignant progression of the V12 RAS model (Dhomen et al., 2009, Fecher et al., 2008). The number of genes differentially expressed in the V12 RAS VGP model was very high. Many of the genes present in the RGP model were also encompassed by the VGP. Yet many unique genes were expressed in the VGP, absent from the RGP demonstrating potentially step-wise progression. This is to be expected as frequently melanomas increase their genetic diversity as they progress. This is associated with increased genetic instability and also the presence of varying microenvironments. Induction of hypoxia in larger lesions can generate large changes in gene transcription (Chi et al., 2006).

3.5.2 Identifying therapeutic pathways The VGP became the focus as genes up regulated may represent genes that promoted the switch from RGP to VGP or genes important to maintaining this malignant phenotype. Genes over-expressed in the benign BRAF model in particular are unlikely to drive melanoma progression. Further analysis of the differentially expressed VGP genes highlighted many gene classes and pathways known to melanoma, and general cancer development. These included differential expression of developmental pathways. These pathways are hijacked by cancer cells to promote proliferation and resist apoptosis and

are more commonly seen as melanoma lesions dedifferentiate. Often the pathways are associated with melanocyte development (Mort et al., 2015, Liu et al., 2014). The very broad molecular analysis by IPA also highlighted cell morphology and motility. Again, cell motility is thoroughly studied and is likely to be up regulated in a highly invasive melanoma model (Michailidou et al., 2009a, Sadok et al., 2015).

What was unique was an enrichment of a lipid metabolism pathway. Furthermore, lipid genes made up 20% of differentially expressed metabolism genes. Melanoma lipid metabolism is not well described in the literature despite being seen as critical in many cancers. Logically, lipid metabolism would be dramatically changing as the melanoma cells switched to a more malignant phenotype. Plasma membrane lipids are altered as a cell rapidly divides or require new membrane compositions. Lipids are an excellent source of energy for a dividing cell and many cancer signalling pathways utilise lipid intermediates (Santos and Schulze, 2012). The types of lipid genes identified were primarily lipases, these genes breakdown more complex lipids often freeing FA and more fundamental metabolites. This was reflected well in the predicted affected pathways. When lipid genes were looked at in isolation FA metabolism, FA uptake and FA synthesis were the top affected pathways and were also all predicted to be activated. These pathways suggest the cells are acquiring a large amount of FA. This could indicate a strong therapeutic target area, as these pathways are fundamental to many other lipid pathways.

To further study lipid metabolism both HPLC-MS and GC-MS were used on the wild type, V12 RAS RGP and V12 RAS VGP samples. Since the aim was to identify novel therapeutic targets a shotgun mass spectroscopy analysis was preferred. This would prevent us from missing any novel pathways by constraining the metabolites. The study revealed that the number of differentially regulated lipids progressed by stages and mirrored the genetic diversity seen in the transcriptome. The changes were dramatic and affected all lipid metabolite classes investigated. The data for the HPLC-MS provided a large number of metabolites but they could not be specifically identified. This was a known difficulty in LC-MS but the data can still be broadly analysed. The

GC-MS however revealed changes in discrete metabolites. The GC-MS indicated an increase in saturated FA, namely palmitate and stearate, these are the major FA synthesised by the cell or derived from circulating TG. The transcriptome would suggest a FA increase demonstrating the value in combining these studies. One thing to note here is that many of the features identified in the GC-MS and HPLC-MS are being investigated as clinical markers for malignancy in melanoma including the increases in choline and phosphocholine, changes in creatine and aspartate. The changes in various metabolites match many of the findings a study by Fedele et al that was correlating metabolism markers with progression in a mouse xenograft model (Fedele et al., 2013).

3.5.3 Plasma membrane metabolism is dramatically altered in VGP melanoma The changes in FA metabolism will be explored later; however another area of lipid metabolism highlighted by the transcriptome and mass spectroscopy was choline metabolism. To a highly proliferative cancer cell, balancing plasma membrane synthesis with production of signalling molecules is critical. The data here revealed extensive remodelling of the plasma membrane. There was loss of sphingomyelin (SM) alongside large changes to phosphatidyl lipid composition. The significant up regulated lipid genes were primarily involved in choline metabolism. Choline metabolism is critical in both phosphatidylcholine (PtdCho) and SM synthesis and may be a key driver of many of the lipid changes in this tumour model. The overall pathway seems to promote the breakdown of PtdCho for the generation of choline. The tumours appear to import lysophosphocholine (LPC), with mfsd2aa, to sustain this cycle (Segi- Nishida, 2014). The gene mfsd2aa imports LPC metabolites into the brain, in healthy tissues and also maintains the blood brain barrier. Mutation of the gene is embryonic lethal as the provision of LPC is essential to the correct functioning of the brain (Guemez-Gamboa et al., 2015). It is assumed that in the cancer cell it is acting to increase the intermediates for choline metabolism. The transcriptome reveals a large drop in LPC and this could be due to the increase in metabolism. Pla2g15, up regulated in the VGP model, generates

glycerophosphocholine (GPC) from LPC which feeds into the processes described below. LPC loss is seen in cancer patients and is associated with increased inflammation and weight loss (Taylor et al., 2007).

The changes described above have been seen in numerous cancers. Changes can be driven by hypoxia (Glunde et al., 2008) and acidification of the tumour environment (Galons et al., 1995), however growth factors and malignant signalling are the major enhancers of choline metabolism (Aboagye and Bhujwalla, 1999, Mori et al., 2003). Loss of PtdCho by catabolism provides cancer cells with key signalling molecules that promote cell adhesion, proliferation and growth signals. PtdCho can be converted to GPC and subsequently phosphocholine. Phosphocholine can be converted back to choline, by pld1, up regulated in zebrafish VGP melanoma. The by-product, phosphatidic acid (PA), can stabilise mTor to promote cancer cell survival and rapamycin resistance (Fang et al., 2001). PLD1 is overexpressed in nearly all cancers, including melanoma and is also able to influence metastasis, PLD1 inhibitors significantly reduced in vitro breast cancer cell invasion (Su et al., 2009). Phosphocholine can also be converted to platelet activating factor (PAF), by pla2g6 seen in the screen. PAF promotes tumour cell adhesion and metastasis and activates pro-tumourogenic inflammatory pathways. The inflammatory effect comes from the production of arachidonic acid (AA), downstream of PAF. AA is a secondary messenger that activates Phospholipase C enzymes and Phosphokinase D and both aid cancer cell survival and proliferation. AA is also a key vasodilator that can aid flow to the tumour and AA can be converted to prostaglandin E2, known to be involved in tumour migration. The adhesive effect of PAF can be pro-tumourogenic by allowing tumour cells to adhere to tissues during migration. This pathway is known to be present in melanoma cancer cells (Sreevidya et al., 2008, Melnikova and Bar-Eli, 2007). The PLA2 family of genes are well represented in this study and they are a key target in cancer therapeutics. Pla2g15 generates GPC from LPC, whilst pla2g6 generates PAF from phosphocholine, pla2g7 then breaks PAF down further. The PLA2 family of enzymes is currently being investigated as a potential drug target. Several studies have found that melanoma conditioned media is able to induce PLA2 and COX proteins which 81

suppresses macrophages. This triggers a pro-survival and proliferative pathway and inhibition could reverse this effect (Duff et al., 2003). It seems this would be a very valuable site of therapeutic intervention. Investigating this pathway with more detailed mass spectroscopy analysis and testing inhibitors for these proteins in vitro may shed some more light on their role in melanoma.

Further dramatic changes occur in the plasma membrane. The LC-MS indicated that SM was being lost within the sample. Simultaneously the metabolites ceramide, sphingomyelin-1-phosphate (S-1-P), ceramide-1-phosphate (Cer-1- P) and glucosylceramide were increasing. The transcriptome profiling demonstrated that the rate limiting regulator for sphingolipid-biosynthesis, ormdl1, was increased. This gene mediates the negative feedback loop for ceramide de novo synthesis, in response to production of ceramide from SM breakdown and synthesis (Siow and Wattenberg, 2012). This gene has not been described in cancer literature but the up-regulation indicates that the cell is likely limiting de novo ceramide synthesis due to the high levels of ceramide in the cell. The loss of SM is known to be pro-tumorigenic. A major mediator of SM breakdown is acid sphingomyelinase (A-SMASE). In melanoma A-SMASE is important in melanoma progression. It was found to inversely correlate with prognosis and higher A- SMASE is seen in later melanoma stages. It works via extracellular signalling due to changes in plasma membrane composition that leads to proteosomal breakdown of MITF and subsequent increases in invasion. The broad range of effects was surprising but demonstrates the power of manipulating SM metabolism (Bizzozero et al., 2014). The downstream products of SM breakdown are also largely pro-tumorigenic.

The increased ceramide generated by SM breakdown could push a cell to apoptosis, via the formation of a ceramide pore on mitochondria triggering the release of cytochrome c (Verheij et al., 1996). The analysis suggests that the VGP lesion may be maintaining this high level of turnover by funnelling ceramide into glycosylation or phosphorylation. Glucosylceramide is implicated in drug resistance in melanoma and inhibition of glucosylceramide also reduces tumour growth in a mouse xenograft melanoma model (Weiss et al., 2003). Both S-1-P and Cer-1-P are up regulated in the sample. Both are potent

signalling molecules. Cer-1-P is generated by ceramide kinase (CERK) whilst S-1-P is generated by sphingosine kinase (SphK). Cer-1-P is a known mitogen that can trigger cell division and DNA synthesis (Gomez-Muñoz et al., 1997) but has not been fully explored in cancer development. In contrast S-1-P has been widely researched in cancer biology. S-1-P has been implicated in cancer cell growth, migration and neovascularisation as reviewed by Pyne et al (Pyne and Pyne, 2010). SphK is upregulated in a number of melanoma cell lines, particularly cell lines representing the VGP stage of melanoma. There also has been success in inhibiting SphK. The resulting loss of S-1-P increased the sensitivity of LU1205 and UACC 903 melanoma cell lines to apoptosis. Furthermore the growth of the cell lines was significantly inhibited. The effect was exclusive to melanoma cell lines and both melanocytes and fibroblasts were unaffected (Madhunapantula et al., 2012).

This pathway seems to be very involved and offers multiple sites of therapeutic intervention. Several A-SMASE inhibitors have been developed and would reduce the build-up of ceramide. In contrast a reduction in CERK or glucosylerase would reduce the presence of these pro-tumourogenic metabolites and increase ceramide, possibly generating an apoptotic build up. Inhibitors for glucosylceramide have also been tried in melanoma xenograft mouse models and reduced tumour growth (Weiss et al., 2003). The above pathways generate FA and as already mentioned FA metabolism likely is a lynchpin for many of the above pathways.

3.5.4 Fatty acid metabolism is also increased in the VGP melanoma model There is a high demand for FA within cancer cells. FAs are the fundamental building blocks for many other important lipids. Commonly in cancer the demand for FA is met by increasing de novo lipid synthesis. However, the study here appears to demonstrate a scavenging phenotype. The IPA analysis suggested that FA metabolism was increased. The GCMS showed that palmitate and stearate were both decreased within the tumour sample compared to wild type. The transcriptome suggested up regulation of the downstream pathways of the saturated FAs. These include increases in 83

desaturating enzyme SCD1, scd and scdb in the zebrafish. The desaturation of FA is a critical first step for many of the synthesis routes of the cell as well as reducing the risk of toxicity from saturated FA (Ariyama et al., 2010). The increase in dgat1 also suggests that FA is being stored as TAG. DGAT1 mediates the final and rate limiting step in TAG synthesis. Cancer cells acquire a large amount of FA and this can be toxic to the cell. Storing it as TAG would benefit the cell by providing a long-term energy source and reducing the FA within the cell. There is evidence implicating DGAT1 in cancer development. DGAT1 is amplified in around 5-20% of cancer cases. Inhibiting this process could limit a cells energy sources and also induce cell death from lipid toxicity (Bagnato and Igal, 2003). These processes are not directly linked with melanoma and may indicate a more universal mechanism than previously thought. The loss of FA elongases 1a and 7b (elovl1a and elovl7b) indicated that the elongation of FAs was likely reduced. These are usually tissue specific and occur as part of de novo lipid synthesis and generate longer chain FA for synthesis of more complex lipids (Wang et al., 2005). Loss of long chain TG indicated in LC-MS could be due to reduced volumes of long chain FA. This may suggest that these FA are being channelled to another pathway.

The uses of the FA seem to fit well with the literature. Yet, as stated earlier the source of the FA appears to be a scavenging route. The RAS activation of PI3K can regulate a number of lipid uptake pathways (White, 2013). For scavenging pathways a cell requires lipases to liberate FA and then transporters to import the FA. Additionally lipids can be taken up as more complex lipids, as demonstrated by the LPC importer mfsd2aa discussed earlier. Several genes are very highly up regulated. These include LPL, which would lyse TAG rich dietary lipids within the endothelial vessels. The up regulation of LPL in muscle tissues increases the import of FA into the tissues (Merkel et al., 1998). LPL has been identified as a potential target in breast cancer and will be discussed in the next chapter in more detail. The import of FA is also likely mediated by FABP7. The up regulation of fabp7 in the screen is unsurprising. One study has indicated that FABP7 is overexpressed in melanoma. The transporter provided the cell with palmitate (Goto et al., 2010). There is no evidence of changes to de novo lipid synthesis. This is surprising as literature suggests this to be a 84

common mechanism between many different cancers. However, targeting both scavenging and de novo would be possible. The source of the lipid can be circulating TAG or FA, which are both increased in cancer burdened patients. Many patients have adipocyte lipolysis further increasing the amount of FA available to the tumour (Arner and Langin, 2014). Furthermore the microenvironment can provide FA using this scavenging route. A study in 2014 demonstrated that subcutaneous adipocytes supported melanoma cell growth by providing palmitic acid. These circumstances would provide tumours with adequate FA, preventing them from having to utilise glucose or other synthetic pathways (Kwan et al., 2014).

Further testing of the hypotheses here could be tested with genetic silencing, or inhibitors. Many inhibitors are available against a substantial number of the genes listed: A-SMASE, CERK and SPHK inhibitors are all available. The contribution of the different enzymes to saturated FA levels could be tracked. Whether the cell is relying on de novo synthesis or lipolysis of lipid stores could be determined. Additionally, radiolabelled carbon in the forms of glucose or triglyceride could be used to determine the tumours preference for particular energy sources.

3.5.5 Conclusion This study has demonstrated the large alteration occurring in lipid metabolism in melanoma. The transcriptome highlighted the number of lipid genes with altered expression and mapping these revealed they would have strong effects on lipid metabolism as indeed was seen with the mass spectrometry results- revealing a dramatic alteration in lipid metabolites within the RGP to VGP transition in the model. This is the first study to explore the wide range of lipid pathways changing in a late stage melanoma model. Several aspects of lipid metabolism have been covered with these techniques. The study has generated several hypotheses as expected. Many of these pathways would bear further investigation and, where relevant, future experiments have been suggested.

The next stage is to use this data to identify new therapeutic targets in melanoma. The information on lipid genes and pathways can guide the investigation into likely candidates for melanoma disease progression. 85

Chapter 4 Identifying therapeutic targets in lipid metabolism

4.1 Introduction

4.1.1 Transcriptional regulation of lipid metabolism The evidence in the previous chapter established that lipid metabolism plays a key role in progression in the melanoma model. To better understand the context of many of these genes a further review of the literature has been performed.

The master regulator of lipid metabolism is the transcription factor family sterol regulatory element binding protein (SREBP). These transcription factors are controlled by a number of key metabolic regulators such as AKT or AMP- activated protein kinase –a (AMPK). They have been shown to activate in response to growth factors and other mediators of metabolism and regulate lipid metabolism in response to dietary requirements. SREBP transcription factors initiate the transcription of several key lipid transporters, like FA binding proteins (FABP) or lipoprotein lipase (LPL) and FA de novo synthesis genes, such as fatty acid synthase (FASN) and acetyl-coA carboxylase (ACC). These genes will be discussed in detail later. SREBP works in concert with other transcription factors associated with lipid such as the peroxisome proliferator-activated receptor (PPAR) family (Shao and Espenshade, 2012). At the transcriptional level SREBP has been described as an oncogene. In EGFR-driven glioblastoma the SREBP1 pathway was essential to the survival of the cancer cells. It mediated this via up-regulation of low density lipoprotein receptor (LDLR) and FASN. In addition the gene signature generated by SREBP1 is associated with poor prognosis and aids cancer cells survival in lipid and oxygen poor environments (Lewis et al., 2015).

The transcriptional regulation of lipid metabolism ensures that lipid metabolism responds appropriately to nutrient deprivation or excess nutrients. For example, excessive glucose in the bloodstream triggers the release of insulin. Insulin receptors regulate the conversion of excess glucose carbons to triglyceride 86

(TAG). This mechanism stores the carbon for use in starvation and also limits the circulating blood glucose. The expression of SREBP is increased with insulin receptor signalling as is the expression of PPAR transcription factors (Xiao and Song, 2013, Kohjima et al., 2008).

4.1.2 Acquisition of fatty acids Healthy cells, like cancer cells, require a supply of FA for key signalling pathways, production of cellular membranes and for β-oxidation to generate ATP. Most healthy tissues are dependent on lipid uptake, using lipases and receptors. Only lactating tissues, liver cells and occasionally adipocytes utilise de novo lipid synthesis (Mashima et al., 2009). The FA taken up from the circulation are primarily dietary lipids. They are transported throughout the body as lipoproteins; these contain large quantities of lipid, triacylglycerol and cholesterol supported by a number of adaptor proteins (Lusis and Pajukanta, 2008). The image below summarises the major pathway and genes involved in lipoproteins.

Figure 4.0.1 The lipoprotein pathways. This summarises the uptake of fatty acids via lipoproteins. Including several genes identified to be relevant to the uptake of lipoproteins and FA derived from them. Taken from Lusis and Pajukanta, 2008.

The route for exogenous FA starts when dietary lipid enters the gut. Here the lipids are incorporated into chylomicrons, large lipid vesicles that are able to pass through the gut barrier. Once they enter the blood stream the chylomicrons partition their load into high-density lipoproteins (HDL). The HDL lipoproteins become intermediate low-density lipoprotein (IDL), low-density lipoproteins (LDL) and very low-density lipoproteins (VLDL) after transport and breakdown within the liver. The different lipoproteins have varying lipid compositions, for example VLDL particles produced by the liver contain large amounts of triglyceride while HDL particles contain large amounts of cholesterol. The lipoproteins are recognised by receptors like VLDL receptor (VLDLR) and LDL receptor (LDLR) and recruited to tissues. Lipases, including 88

endothelial lipase (LIPG) and lipoprotein lipase (LPL), recognise the lipoprotein by binding adaptor proteins. For example VLDL has an ApoB100 adaptor protein. This allows specific recognition of VLDL particles by LPL, subsequently increasing LPL activity which then lyses TAG to FA. Once the FAs are liberated by the lipases they can bind several receptors and enter the different tissues of the body (Lusis and Pajukanta, 2008). Cancer cells have been seen to overexpress VLDLR, this includes melanoma, as a means to increase uptake of lipoproteins (He et al., 2010). Both of these pathways increase the number of FA and lipids available to the cancer cell. The lipid uptake receptors are typically known as fatty acid binding proteins (FABP), other scavenging receptors are known, for example cluster of differentiation 36 (CD36) which is a major lipid transporter (Abumrad et al., 1999, Zaidi et al., 2013). These receptors do not appear to be channels but accumulate FAs at the plasma membrane to enhance the gradient to allow for diffusion or ‘flipping’ into the membrane. Once taken up the FA is partitioned throughout the cell. Several lipid uptake proteins are up-regulated in tumours. CD36 is up-regulated in over 10% of all tumours tested on the Human Protein Atlas. Additionally CD36 is also known as a cancer stem cell specific antigen in glioblastoma (Hale et al., 2014). Expression of CD36 could distinguish a population of cells with stem cell characteristics. Furthermore, addition of CD36 substrates oxidised lipoproteins resulted in the enhanced proliferation of the CD36 expressing cells, but not healthy or differentiated tissues (Hale et al., 2014). The expression of FABP4 appears to correlate with metastasis, FABP4 overexpression was noted in metastatic breast cancer tissues (Jung et al., 2015). In a mouse model of prostate cancer small molecules inhibiting FABP4 reduced the subcutaneous growth of prostate cancer xenografts and reduced lung metastases (Uehara et al., 2014). In fact FABP4 appears to support the metastasis of many cancers to adipocyte rich tissues. It appears to do this by allowing the cancer cell to take up FAs extracted from the adipocytes (Nieman et al., 2011, Herroon et al., 2013). FABP7 is a transporter of long chain FAs and it was identified as being overexpressed in melanoma primary lesions. FABP7 knock-down inhibited proliferation of melanoma cell lines (Slipicevic et al., 2008). Critically high FABP7 is associated with reduced relapse free survival in a cohort of melanoma

patients (Goto et al., 2010). FA uptake may be important to the proliferation, metastasis and progression of many different cancers.

4.1.3 The role for de novo lipid synthesis Lipid uptake is only one side of the coin, in healthy tissues without an adequate supply of dietary lipid de novo synthesis is available. However, nearly all cancer cells investigated have been shown to preferentially use de novo lipid synthesis (Mashima et al., 2009). This trait is relatively unique to cancer cells and is being actively investigated as an area for therapeutic intervention. Several metabolites from glucose and glutamine metabolism are converted to acetyl-CoA, either by the Krebs cycle or from amino acid catabolism. To pass to the cytoplasm, acetyl-CoA is typically transported as soluble citrate or lactate. FA synthesis occurs in the cytoplasm and several large enzyme complexes mediate the stages of FA synthesis. Alongside acetyl-CoA, NADPH is required as a reducing agent in these reactions. In the first step Acetyl-CoA is carboxylated by Acetyl CoA Carboxylase (ACC) to form malonyl-CoA, and then sent to the Fatty Acid Synthase complex (FASN) for conversion to fatty acid- CoA. This stage comprises elongation with the FA chain length being extended by two carbons each cycle. During synthesis the FA intermediates are held by acetyl-coupling protein. At C16 the FA chain is cleaved as palmitate by palmitate thioesterase (Mashima et al., 2009). Palmitate is a key intermediate and fed into the different cellular pathways. In breast cancer patient samples expression of FASN was high and associated with highly proliferative areas (Jung et al., 2015). Many other cancers, including melanoma show FASN expression (Innocenzi et al., 2003) and in melanoma the expression correlates with more invasive lesions (Kapur et al., 2005) and FASN knock-down in melanoma cells triggered apoptosis (Zecchin et al., 2011). In other cancers the presence of FASN seems to support proliferation and also provide drug resistance. Knockdown of FASN in breast cancer cell lines induced cell cycle arrest at the G1 checkpoint, this was associated with a down-regulation of S-phase kinase-associated protein 2, E3 ubiquitin protein ligase, a key regulator of the G1/S transition (Knowles et al., 2004). Knockdown of FASN in pancreatic cancer mouse models also enhanced drug sensitivity and radiation sensitivity (Yang et al., 2011).

4.1.4 The storage and metabolism of fatty acids Once the cell has acquired FA intermediates, whether by dietary uptake or de novo lipid synthesis, there are several routes it can take. One use is in plasma membrane synthesis. The plasma membrane requires desaturated FA. A critical point of desaturation occurs between C9 and C10 within the fatty acyl chain. The regulator of this step is Stearoyl-CoA-9-desaturase (SCD1) (Enoch et al., 1976). In addition to acting as precursors for membrane phospholipids desaturating FA reduces the toxicity of saturated FA (Ariyama et al., 2010). Another modification is further elongation. Elongation typically occurs within the endoplasmic reticulum and can be used to make carbon chains greater than C16. Another abundant FA, other than palmitate, is the C18 molecule stearate. Stearate also can undergo C9 desaturation to create oleate. SCD1 was identified as being up-regulated in a number of transformed cells (Li et al., 1994) and is now known to play a role in a number of different cancers. The mechanism relates to the provision of monounsaturated FA (Mason et al., 2012). Over-expression of SCD1, like FASN, affects a number of pathways besides those of its canonical pathway. SCD1 is dramatically up regulated when lung cancer stem cells form spheroids. SCD1 inhibitors prevented formation of regulated spheroids, instead cells developed smaller aggregates (Noto et al., 2013). Inhibition of SCD1 in 2D culture inhibits cancer cell proliferation and induces apoptosis in renal cell carcinoma and in mouse breast cancer models SCD1 inhibitors dramatically reduced tumour size. SCD1 inhibitors were also effective in melanoma cell lines and reduced proliferation (von Roemeling et al., 2013).

In healthy tissues a frequent reason for the activation of FA synthesis is excessive consumption of glucose. Once adequate glycogen stores are developed tissues begin turning glucose intermediates to FA and incorporating these FA in TAG for storage within lipid droplets in the cell (DiAngelo and Birnbaum, 2009). Excessive lipid uptake also has this effect. TAG is a neutral lipid and is therefore a stable form for storing FA. There are multiple enzymes involved in the synthesis of TAG. A majority of TAG is synthesised via the Kennedy Pathway but another pathway is the monoacylglycerol pathway.

Figure 4.0.2 Triglyceride can be synthesised from glycerol-3 phosphate in a sequential process or can be synthesised from MAG and FA to form DAG. DGAT1 can then convert DAG to triglyceride. Lipid droplets are broken down for FA when required. Taken from Chen and Farese, 2005 (Chen and Farese, 2005). GPAT, Glycerol-3-phosphate acyltransferase, AGPAT acylglycerol-3-phosphate acyltransferase, MGAT, monoacylglycerol aceyltransferase, PPH-1, phosphatidate phosphohydrolase.

If FA builds up within a cell it can induce toxicity. Cells can prevent this by converting FA to neutral triglyceride and storing it within lipid droplets (Chen and Farese, 2005). They can release FA to the cell when required. Lipases such as adipose triglyceride lipase (ATGL) are present on lipid droplet membranes and directly lyse TAG to form FA for the cell. There may be other enzymes as many of the proteins residing on lipid droplets have not been identified (Morak et al., 2012). Another mechanism for liberating FA is lipophagy. Autophagic membranes form next to lipid droplets and sequester portions. The lipids are then liberated via autophagic breakdown (Singh et al., 2009).

4.1.5 Plasma Membrane Lipids The synthesis of membrane lipids is another critical process particularly in highly proliferative tissues and in cancer cells. The composition of a cell varies in the outer and inner leaflets. In the outer leaflet PtdCho and SM form the majority of lipids, alongside cholesterol. In the inner leaflet phosphatidylserine

(PS), phosphatidylethanolamine (PE) and phsophatidyiinositol (PI) are the major classes. The cytosolic lipids, particularly PI can be phosphorylated or cleaved to produce signals for the cell as demonstrated with PI3K activation. The outer leaflet lipids are more readily glycosylated and help mediate cell motility and the formation of plasma membrane lipid rafts (Pomorski et al., 2001).

Specific manipulation of plasma membrane composition is critical to many cells particularly relating to proliferation and motility. Many tumours show decreased SM and increased PCs as has been discussed in the previous chapter. The lipid ceramide generated by SM breakdown or synthesised de novo can interact with several mediators or directly via a ceramide specific apoptotic pathway. Ceramide can polymerise to form a pore on mitochondria. The pore leads to the release of cytochrome C and the initiation of apoptosis. Breakdown of SM tends to occur as a stress response for example from to oxidative damage or inflammation (Verheij et al., 1996). Several anti-cancer therapies have been attempted, including the addition of excess ceramide to cancer cells. Many groups have had success with manipulating ceramide and inducing cell death. There is promise in manipulating this pathway. C6-ceramide delivered to cells reduced migration of breast and melanoma cell lines (Zhang et al., 2015b).

The next stage is to identify specific targets in lipid metabolism that could be manipulated to halt melanoma progression or be utilised as a therapy.

4.1.6 Therapy in metabolism. Work presented in the previous chapter had predicted that FA metabolism was up regulated in melanoma. Based on the differential expression of several lipases and lysis metabolites a hypothesis based on lipid breakdown was posited. However, this must be matched by compensatory uptake or synthesis regulated on some level to prevent a completely destructive cycle. Indeed, we hypothesized that interfering with lipid uptake or synthesis could precipitate a metabolic crisis in melanoma cells. Our next objective was to screen lipid metabolism enzymes for their effect on melanoma cell survival both in vivo and in vitro.

Current literature shows there are many targets within lipid metabolism that have been investigated in melanoma. Many of these have been covered in the introduction. For targets such as SCD1 and FASN there are readily available inhibitors (von Roemeling et al., 2013, Zecchin et al., 2011) and clinical trials are underway for FASN inhibitors (clinical trials ID: NCT02223247). The wide scale expression of DGAT1 and CD36 are testament to the broad effects of these genes. These examples provide proof-of-principle for the value of manipulating lipid metabolism as a cancer therapy.

The complexity of the lipidome has been highlighted by work presented in the previous chapter. Besides de novo FA synthesis, uptake of dietary FA, lysis of other lipids and even degradation of organelles can all provide a cancer cell with the FA it requires for proliferation and energy production. Targeting a single branch of the pathway such as de novo synthesis may push cells to depend upon another method of sourcing FA and thereby make them susceptible to synthetic lethality with combinations of inhibitors. New targets should therefore be developed in conjunction with the current known processes.

A lipolytic phenotype, defining cancer cells that rely on the lysis of lipid or uptake of extracellular FA is common in RAS driven cancers (Kamphorst et al., 2013, White, 2013). The expression of many lipases in the transcriptome microarray, including LPL and LIPF and a lack of key de novo lipid genes indicates that the melanoma V12 RAS model is likely relying on lipid uptake from extracellular sources. LPL and LIPF are both secreted lipases that bind and lyse circulating lipoproteins. Selecting targets from the transcriptome lipid genes would likely be targeting the lipolytic phenotype and could work in complement with the current treatments targeting SCD1 and FASN.

4.3 Aims

 The aims of this chapter are to identify specific therapeutic targets within the lipid metabolism genes identified in the previous chapter. The presence of internal lipid stores would be predicted by a lipogenic and FA rich phenotype and could explain the preference for lipolytic FA acquisition exposed by our MS analysis. Therefore targets in lipid metabolism will be assessed.

 These targets will be further explored using functional assays tp determine true targets as opposed to genetic noise; this includes in vivo assays like the MiniCoopR assay and in vitro techniques including transient cell knockdown using siRNA. The MiniCoopR method overexpresses potential genes under the melanocyte promoter MITF. This allows you to see the effect of your gene of interest on tumour appearance and growth. Targets that can influence melanoma development in vvio will be taken further in studies.

 Targets that prove to be functional will be tested with inhibitors. This will allow for studies that will elucidate their mechanisms of action. This would further work to validate the target as a good therapeutic target. The ultimate aim would be to develop a potential therapeutic programme to be tested in vivo.

4.3 Methods for Functional Analysis of Candidate Malignancy Gene Overexpression

4.3.1 Constructing MiniCoopR constructs Once a list of genes for analysis had been determined, cDNA for the genes were ordered from Open Biosystems as bacterial glycerol stocks. These stocks were spread on kanamyacin agar plates (50 μg/uL) at 37 °C overnight. Single colonies were removed and placed in 3 mL of 50 μg/uL kanamyacin overnight at 37 °C. DNA plasmids were extracted from bacteria as described.

4.3.1.1 ATT site PCR This method was used for the addition of ATT sites to the cDNA for eventual insertion into the MiniCoopR vector.

The protocol was modified from the Novagen KOD XL PCR (Novagen) Kit. The reaction mix includes 5 μL 10x Buffer, 5 μL dNTPs, 2 μL, 5’ ATT site primer (10 μM) and 2 μL 3’ ATT site primer (10 μM). To this 1 μL of cDNA and 1 μL KOD XL DNA polymerase was added and the reaction brought to 50 μL with 34 μL of distilled H2O.

The cycler settings were as follows:

Hot Lid 110 °C

Denature 94 °C – 5 minutes

Denature 94 °C – 30 seconds 28 Cycles

Anneal 55 °C – 5 seconds

Extension 74 °C – 2 minutes

Extension 74 °C – 10 minutes

Resulting reaction was run on a 2 % TAE agarose gel with 10 μL of loading dye (Qiagen). Bands were extracted using the QIAquick gel extraction kit following the protocol for 2% gels.

Table 4.1 Primers used for ATT site PCR mCherry attB1F GGGGACAAGTTTGTACAAAAAAGCAGGCTCGCCACC ATGGTGAGCAAGGGCGAGGAGG mCherry attB2R GGGGACCACTTTGTACAAGAAAGCTGGGTACCTTAC TTGTACAGCTCGTCCATG attB1 LPL F GGGGACAAGTTTGTACAAAAAAGCAGGCTTCGCCAC CATGATGTTTAATAAGGGGAGAG attB2 LPL R GGGGACCACTTTGTACAAGAAAGCTGGGTACTTTAC TCGTTGTTCTGTTTG

4.3.1.2 BP Reaction The BP reaction was for the insertion of the ATT site cDNA into the entry clone, pDonR 221 (Gateway Cloning Kit, Thermo Scientific), for eventual insertion into the MiniCoopR vector.

The BP reaction was performed using the multisite gateway cloning kit (Invitrogen) and following manufacturer’s instructions. The following calculation was performed to elucidate the correct concentration and volume of PCR product to add.

The attB-PCR product generated previously was added to 1 μL of pDonR 221 donor vector (150 ng/μL) and the reaction was brought to 8 μL with TE buffer. To this 2 μL of the BP clonase II enzyme was added (BP clonase II must be thawed and kept on ice). The reaction mix is incubated at 25 °C for 1 hour. To terminate the reaction 1 μL of Proteinase K solution is added to the reaction mix. Samples were then vortexed briefly and incubated for 10 minutes at 37 °C.

The BP reaction was transformed into TOP10 Competent E. coli as described and the resulting mix spread onto pre-warmed kanamyacin plates and left

overnight at 37 °C. Resulting colonies were purified using the miniprep protocol and a further LR reaction was performed.

4.3.1.3 LR Reaction The final step in the synthesis of the MiniCoopR vector was the integration of the three entry clones; MITF, Poly A tail and the gene of interest, into the MiniCoopR vector.

The LR reaction was performed as described in the Multisite Gateway Cloning manual. The following vectors were used. 2 μL 5’ entry clone MITF (20 fmols), 2 μL 3’ entry clone poly A tail (20 fmols), 2 μL MiniCoopR destination vector (40 fmols) and then 1 μL gene of interest in the entry clone at 20 fmols. Finally 2 μL of LR Clonase II Enzyme was added. The reaction was left for 16 hours at 25 °C. After this time the reaction was quenched by the addition of 1 μL Proteinase K. The resulting reaction was then transformed into E.coli cells.

4.3.2 Injection of Embryos with MiniCoopR construct Embryos were generated as described and collected within 10 minutes of being laid. Embryos were placed in 2% agarose moulds to allow for easier injection. The MiniCoopR constructs were at a concentration 50 ng/µL and were mixed in a 1:1 ratio with the Tol2 transposon (50 ng/µL). The solution was injected via a pulled glass capillary needle held in a M325 manual micromanipulator (World Precision Instruments) and a nitrogen pressured PLI-90 micro-injector (Harvard Apparatus, Medical Systems Research Products). The solution was injected at the cell and yolk interface at the one cell stage. Embryos were then placed in a petri dish of chorion water and left for 5 days, embryos were cleaned during this time period. At 5 days the fish enter the main nursery system and primary care was handed over to the BSF staff.

4.3.2.1 Screening of Embryos and Adult Fish for pigmentation and tumour development At 5 dpf the fish were anaesthetised with MS222. Fish were imaged with a Leica MZ7.5 steriomicroscope. Adult fish were imaged with a Canon Digital Ixus 80 IS camera. Fish were screened as rapidly as possible to reduce any risk to the embryos and immediately after screening were placed in water from the aquaria 98

system to recover. At 4 wpf the fish were then screened weekly for tumour occurrence, using the same method as for the embryos. If a tumour was found it was measured and recorded with a calliper (Expert Dual Reading Ip65 Digital Vernier Caliper 46611, Draper), the fish was then separated and measured at two weeks for measurement of tumour growth.

4.3.2.2 RNA Extraction Fish were euthanized as described. Skin and fins were removed from V12 RAS mutant fish, V600EBRAF mutant fish and wild type AB fish. Tumours generated from MiniCoopR were excised and care was made to avoid excess healthy tissues. Tissue was flash frozen in liquid nitrogen and stored at -80 °C till extraction.

RNA extraction was completed using the RNA-easy lipid tissue kit (Qiagen). The extraction was completed according to the provided protocol. Briefly, 1 mL Qiazol® (Qiagen) was added to each sample pool. Tissue was homogenised for 1 minute at high speed with a Turrax T8 tissue homogenizer (IKA-Werke) and left at room temperature for 5 minutes to dissociate. 200 μL chloroform was added as a purification step. The sample was then agitated for 15 seconds before being incubated for 3 minutes at room temperature. Samples were then added to RNAeasy columns. The column was washed as described by the manufacturer’s instructions. After washing, DNAase treatment was performed on the column using the Qiagen RNAase free DNAase kit (Qiagen). 5 μL of DNAse was added to 40 μL of buffer, then added to each column. Columns were incubated for 15 minutes before the sample was eluted in 30 uL of TE buffer. Sample concentration was quantified by a Nanodrop Spectrophotometer (Thermo Scientific). Samples were stored at -80 °C or immediately proceeded to reverse transcription.

4.3.2.3 Synthesis of cDNA from RNA The reverse transcription was performed with the Omniscript Reverse Transcriptase (Qiagen), the original protocol was followed apart from minor variations in the reaction mix. Briefly 20 μL of 10 X buffer and 20 μL, dNTPs (20 μM), 20 μL random hexamers (50 μM) and 7.5 μL murine protease inhibitor (4 U/μL) (New England Biosystems) were placed in a reaction mix. To this 20 μL 99

Omniscript Reverse Transcriptase was added and 2 μg RNA. Water was used to bring the reaction mix to 200 μL.

Samples were incubated for 1 hour at 37 °C. Samples were then transferred for analysis or stored at -20 °C.

4.3.2.4 RT-PCR to confirm gene integration cDNA extracted from tumours was prepared in PCR reaction. The MiniCoopR plasmid diluted 1:1000 was included as a positive control. A no reverse transcriptase control was also generated for each sample. The reaction mix consisted of 12.5 μL 2x Biomix Red (Qiagen) with 2.5 μL primer 1 (DEST MF) (10 µM) and 2.5 μL primer 2 (Gene of Interest qPCR R). 1 μL of template was added and the reaction mix bought to 25 μL with dH2O. The mix is placed in a thermocycler (G-Storm) at the following cycling settings.

95 °C – 1 minute

95 °C – 15 seconds

58 °C – 15 seconds 35 Cycles

72 °C – 5 seconds

72 °C – 10 minutes

4.3.2.5 qPCR of zebrafish models to confirm gene expression and MiniCoopR expression The qPCR was completed with the SYBRGreen Jumpstart kit (Sigma Aldrich), using 2 µL of cDNA that had been diluted to 2 μg/μL concentration. Each reaction contained the following: 12.5 uL SYBR Green (Jumpstart) Reaction Mix, 2.5 μL primers (forward and reverse mixed at 3 μM) and 2 μL of cDNA brought to 25 μL with DEPC treated H2O.

The qPCR was run on a qPCR machine (Agilent Stratagene MX3000p, Agilent Technologies) with the following settings:

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95 °C – 10 minutes

95 °C – 30 seconds

55 °C – 45 seconds 40 Cycles

72 °C – 45 seconds

All qPCR primers were analysed for efficiency and screened for single product recognition by running the qPCR protocol on serial dilutions of a known concentration of cDNA. All primers were between 98% and 101% efficient.

Genes of interest had expression confirmed using cDNA from zebrafish and MiniCoopR injected fish. RNA was extracted and cDNA generated as described previously.

Table 4.2 Primers used for qPCR

Human LPL F GGGAGAAAGTGTCTCATTTGCAG

Human LPL R TGGGTCCTAGCCTGACTTCT

EF1α F CTTCTCAGGCTGACTGTGC

EF1 α R CCGCTAGCATTACCCTCC

Zebrafish LPL F CCGCAAAAACCAGAGATTGC

Zebrafish LPL R TGGACTTGGGTTGGGTGAAG

Zebrafish β-actin F CGAGCTGTCTTCCCATCCA

Zebrafish β-actin R TCACCAACGTAGCTGTCTTTCTG

Zebrafish CD36 F TGCTGTCGCAGGTGTTTACC

Zebrafish CD36 R GGTCCATCTACGGTGCCATT

Zebrafish FASN F CTGGACAACATAACCGCTGG

Zebrafish FASN R AGACGCTGCACTAGACCTTT

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Zebrafish SCD1 F GCCCCAAGCCTCCTATGAAA

Zebrafish SCD1 R CGAAACACGCAAAAGCCCAG

Human CCND1 F ACCTGGATGCTGGAGGTCT

Human CCND1 R CAGGCGGCTCTTTTTCAC

Zebrafish DGAT1 F TCACAAGTGGTGCCTACGAC

Zebrafish DGAT1 R AGCGGAGAGGAAGAAGACG

4.3.3 Cryosectioning and staining of zebrafish

4.3.3.1 Preparing tumour tissue for cryosectioning Tumour tissue intended for histology was collected from euthanized fish and incorporated a cross-section of the fish, this allowed for correct orientation and analysis of tumour invasion. The samples were placed into a foil mould that contained OCT medium to a depth of 0.5 cm; OCT was then placed over the sample till it was adequately covered. The foil was sealed around the entire block and the sample placed in isopentane that had been suspended above liquid nitrogen. Samples were then stored at -80 °C until sectioning.

4.3.3.2 Cryosectioning of tissue for analysis Sections frozen in OTC were acclimatised to -20 °C for 1 hour prior to slicing. The samples were cut at 10 μm thickness on a Cryostat (3050 S Research Cryostat, Leica Biosystems). The cryostat chamber was placed at -21 °C and the base at -29 °C. Samples were transferred to frozen slides (Superfrost Plus, Thermo Scientific) and immediately stored on dry ice. Slides were stored at -80 °C prior to staining.

4.3.3.3 Haematoxylin & Eosin (H&E) Staining OCT sections were allowed to defrost for 3 minutes at room temperature prior to staining. Staining was performed with an AutoStainer XL (Leica) with a pre-set program outlined below.

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Table 4.3 Programme settings for Autostainer XL (Leica) for H&E staining

Stage Time

90% Ethanol 1 minute

70% Ethanol 1 minute

Distilled Water 2 minutes

Hematoxylin 2 minutes

Distilled Water 1 minute

4% Acetic Acid 30 seconds

Distilled Water 30 seconds

Scott Solutions 1 minute

Water 30 seconds

70% Ethanol 1 minute

Eosin 10 seconds

70% Ethanol 1 minute 30 seconds

90% Ethanol 1 minute 30 seconds

100% Ethanol 1 minute

Xylene 1 minute

Slides were cover-slipped with Depex mounting medium. Dr. Roger Meadows of the Bioimaging Facility imaged slides using a 20x/0.80 Plan Apo objective using the 3D Histech Pannoramic 250 Flash II slide scanner.

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4.3.3.4 Nile Red Staining Nile red stock was at 0.5 μg/mL in acetone. For staining the stock was diluted 1:1000 in 80% glycerol. Cryosections were allowed to defrost for 40 minutes at room temperature. Samples were briefly washed with distilled water before 100 μL of Nile Red was added to the slide. Samples were incubated for 5 minutes in the dark before being cover slipped and immediately imaged. Images were captured on an on a Olympus BX51 upright microscope using a 10x/ 0.30 Plan Fln objective and captured using a Coolsnap ES camera (Photometrics) through MetaVue Software (Molecular Devices). Specific band pass filter sets for FITC and Texas red were used to prevent bleed through from one channel to the next. Images were then processed and analysed using ImageJ (http://rsb.info.nih.gov/ij).

4.3.4 Analysis of LPL in human transcriptome data The relevance to human melanoma was determined by investigating gene expression in human microarray datasets using Oncomine. The Oncomine software was accessed through www.oncomine.org (last accessed August 2015) (Rhodes et al., 2007). Each gene symbol was searched individually within the search function. The settings were maintained as standard including a P- value greater than 0.0001 for expression data. The microarrays of interest were melanoma tumour tissue arrays that, additionally, compared normal skin with melanoma samples. Human homologs of the genes had to be used within the software. Datasets were extracted from the website and analysed in Excel. Determination of survival expression data was confirmed by identifying data sets that had tracked survival for at least 3 years.

4.3.5 Immunohistochemistry on human nevi and melanoma samples. Sectioning and staining was performed by Jivko Kamarachev, Universpitӓl Zrich. Briefly, samples were sectioned to a 5 um thickness. Eosin staining was performed in an automated machine, staining with LPL.A4 (Abcam) antibody occurred after antigen retrieval on the samples. A histopathologist graded sample staining and staging of tumours was determined by a dermohistopathologist. 104

4.3.6 Cell Culture Melanoma cell lines analysed were WM266-4, WM35, DO4, WM1361, WM85-1, WM852, MM485, SKMEL28, WM9, 501mel, A375P and WM98-A. All melanoma lines were grown in DMEM media, supplemented with 1 U ml−1 penicillin, 1 μg ml−1 streptomycin and 10% foetal bovine serum. Melanocyte lines were neonatal human melanocytes, lightly pigmented (NHM-LP) and grown in medium 254CF supplemented with 5 mL human melanocyte growth supplement (HMGS) both Invitrogen Life Sciences. Cells were passaged at 70% confluence and all cells were maintained at 5% CO2 and at 37 °C. Cells were clear of mycoplasma throughout experimentation.

4.3.6.1 LPL siRNA treatment to determine phenotypes The siRNA was purchased from Dharmacon (sequences below). All siRNA was diluted to 20 µM in 5X siRNA resuspension buffer (GE Dharmacon). The cells were plated 24 hours prior to transfection in 6 well plates and at a cell number to allow the cells to be at 60% confluence (200,000 cells per well). Cells were treated with 20 pmol of siRNA, the transfections were performed using the Lipofectamine RNAiMAX transfection reagent. Briefly, cells were washed and 1 mL serum free media was added. Then 2 µL of 20 µM siRNA and 9.5 µL Lipofectamine RNAiMAX was added to 100 µL serum free media and briefly vortexed. Samples were then incubated for 15 minutes at room temperature before being applied dropwise to the treated well. Non-targeting controls were prepared for every experiment. Fresh media was added after 24 hours or cells were collected.

Table 4.4 siRNA used in cell culture siRNA Sequence

LPL siRNA A GGCCUCUGCUUGAGUUGUA

LPL siRNA B CCUACAAAGUCUUCCAUUA

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4.3.6.2 Inhibition of cells with FASN and LPL Inhibitors and autophagy inhibition and induction. An inhibitor of LPL, GSK 264220 A, was purchased from Tocris and a FASN inhibitor, C75, was purchased from Sigma (Sigma Aldrich). Both compounds were diluted to 50 μM in DMSO.

For inhibition curves WM852, A365P and WM266-4 cells were plated in 96 well plates. A 600 μM concentration of GSK 264220 A was added to the cells and serially diluted 1:3 through the plate. The cells were incubated for 72 hours with the treatment. The compound was tested with DMSO control and a 15 μM dose of C75 to establish any synergy. Simultaneously, plates were treated with a gradient of C75 beginning at 600 µM and serially diluted 1:3 with DMSO or 30 µM GSK 264220 A. Survival was determined by the crystal violet assay.

Synergy was calculated by looking at cell survival in Compusyn, using the Chou-Talalay calculation method for determining the combination index. The table below depicts the thresholds for synergy (Chou, 2006).

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Table 4.5 A table depicting the Combination Index (C.I.) range and the relevant descriptive characteristics. Values taken from the Chou-Talalay calculation. Adapted from Chou et al. (Chou, 2006).

<0.1 Very strong synergism

0.1-0.3 Strong synergism

0.3-0.7 Synergism

0.7-0.85 Moderate synergism

0.85-0.9 Slight synergism

0.9-1.1 Nearly additive

1.1-1.2 Slight antagonism

1.2-1.45 Moderate antagonism

1.45-3.3 Antagonism

3.3-10 Strong antagonism

>10 Very strong antagonism

For examination of autophagy 30 µM of autophagy inhibitor chloroquinone (Sigma Aldrich) was added to wells for 24 hours, autophagy was induced by a 4 µM treatment with Rapamycin (Tocris) for 24 hours.

4.3.6.3 96-well Crystal Violet with combination treatments Cells were plated and then treated. On the day of the assay the cell media was removed and the cells were then washed in 1 x PBS for one minute. The PBS was removed and a 1% crystal violet in 4% PFA solution was added to the cells for one hour. After the hour the PFA was washed off in running water and the plates were left to dry for 8 hours. A 10% acetic acid solution or 2% SDS was added to the wells and they were shaken for 30 minutes or till all the crystal violet was dissolved. From each well 50 μL was transferred to a 96 well plate in triplicate. The absorbance of the plates was read at 595 nm on a spectrophotometer.

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4.3.6.4 Cell Fixation for Staining Cells were plated onto UV treated cover slips contained in 6 well plates. 7 x 104 cells were plated per well. After treatment the media was aspirated, the cells were washed with 1x PBS and then placed in a 4% paraformaldehyde and 1X PBS solution for 30 minutes. Cells were then washed again in 1x PBS and stored at -4 °C for a maximum of 5 days.

4.3.6.5 Cell Staining with or without Nile Red lipid stain Fixed cells were initially washed three times for 4 minutes in 1X PBS, on the second wash 200 μL of 1M glycine (Sigma Aldrich) was added to the wash. The slides were then incubated in 0.1% TX100/PBS (Sigma Aldrich) solution for 4 minutes to permeabilise the cells. Samples were then placed onto the primary antibody for 20 minutes, washed three times in PBS and then placed onto the secondary antibody for a further 20 minutes. All antibodies are diluted in 0.5 µg/mL BSA/TBST. The cells were then washed again before being incubated with the Nile Red stain solution for 10 minutes or being directly mounted onto slides. For mounting slides were briefly air-dried before being mounted with Vector Shield with DAPI mounting media (Vectorshield Antifade with DAPI, Vector Laboratories). Slides were sealed with clear polish. Images were collected on a Olympus BX51 upright microscope using a 10x/ 0.30 Plan Fln or 40x/0.75 UPlanFln or 60x/1.40 UPlanApo objective and captured using a Coolsnap ES camera (Photometrics) through MetaVue Software (Molecular Devices). Specific band pass filter sets for DAPI, FITC, Texas Red and Cy3 were used to prevent bleed through from one channel to the next. Images were then processed and analysed using ImageJ (http://rsb.info.nih.gov/ij).

Table 4.6 Antibodies used in cell staining

Antibody Concentration

Rabbit anti-LC3B (Cell Signalling Technologies) 1:200

Mouse anti-LPL.A4 (Abcam) 1:100

Mouse anti-CD36 (FA6-152) (Abcam) 1:200

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4.3.6.6 Cell lysis for western blotting Cells were plated at 2x105 cells per well into a 6-well plate. If cells were being transfected or treated with drugs cells were plated at 7 x 104 cells per well. Once cells were confluent, or post-treatment, 150 μL of Lamelli buffer was added to each well. The buffer remained on the cells for 1 minute, before the well was scraped with a cell scraper and the buffer was placed in a 0.5 mL tube. Samples were sonicated (VibraCell X130PB, Sonics Materials) for 30 seconds at 20% amplitude.

4.3.7 Western blotting for lipid metabolism genes and autophagy markers Samples were boiled for 5 minutes in a 100 °C heat block. Samples were loaded into precast gels in the SureLock© Precast Gel System (Novex). All blots were run at 160 V, 300 mA using pre-made running buffer diluted in distilled water (MES 20x Running Buffer, Novex). Precision Plus Blue protein ladder was loaded with samples (Biorad). Before transfer, sponges, filter paper and nitrocellulose membrane were soaked in 1 x transfer buffer and then loaded into the SureLock© transfer cassette. The transfer was performed for 1 hour at 30 V, 300 mA in 1x transfer buffer. For proteins with an unusually high or low Mw (<25 or >150) 8% or 15% gels were made and run in the BioRad system. The gel was run at 100 V and 70 mA, whilst transfer was completed in the BioRad system at 100 V and 365 mA.

Blocking was performed in 5% BSA in TBST or 5% Skimmed Milk in TBST dependent on the antibody. Details are presented in the table below. Primary antibodies were incubated at 4 °C overnight, the blot was washed 5 times for 5 minute periods in TBST and secondary antibody was added at 1:5000 for 1 hour at room temperature. The blots were then washed again for further 5 washes and then placed on cling film. The blot was briefly air-dried and then 2 mL of the Western Lightning© Plus ECL chemiluminescence was added to the blot for 1 minute. The blot was then rapidly transferred to a cassette and, within a dark room, exposed to X-ray film (Biomax MR Film, Kodak) for varying times. The film was developed using a JP-33 Film Processor (JPI Healthcare).

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Table 4.7 The antibodies used in western blotting.

Antibody Concentration

Goat α h/m polyclonal LPL (R&D Systems) 1:1000 (5% BSA/TBST)

Rabbit α FASN (C20G5) (Cell Signalling) 1:1000 (5% BSA/TBST)

Rabbit α SCD1 (M38) (Cell Signalling) 1:1000 (5% BSA/TBST)

Mouse α CD36 (FA6-152) (Abcam) 1:2000 (5% BSA/TBST)

Rabbit α FABP4 (ab13979) (Abcam) 1:1000 (5% BSA/TBST)

Rabbit α ERK2 (Santa Cruz) 1:2000 (5% Milk/TBST)

Rabbit α β-Tubulin (Santa Cruz) 1:2000 (5% Milk/TBST)

Donkey α Goat Secondary (Santa Cruz) 1:5000 (5% Milk/TBST)

HRP conjugated Donkey α Rabbit Secondary (GE Healthcare) 1:5000 (Mirrors Primary)

HRP conjugated Sheep α Mouse Secondary (GE Healthcare) 1:5000 (Mirrors Primary)

4.3.8 Measuring Fatty Acid Uptake Analysis of FA uptake was performed using a fluorometric FA uptake assay purchased from Abcam following manufacturer’s instructions. In brief, cells were plated in 96-well format. Cells were treated with LPL siRNA or control. On the morning of the assay the media was removed and serum free media applied to the cells for one hour. 20 μL FA dye solution was diluted in 10 mL of assay buffer. The basal fluorescence of the pate was measured at Ex/Em 485/515 using a spectrophotometer with K4.5 software (BioTek). 100 μL FA dye diluted in assay buffer (1:500) was then added to the wells and the fluorescence was read immediately and every minute for one hour to get an initial uptake value and a kinetic measurement.

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4.3.9 Measuring LPL Activity in Cell Media Secreted LPL activity was measured with a fluorometric LPL activity assay purchased from Abcam using a modified protocol. Cells of interest were plated in T75 flasks. Media was incubated on cells for 72 hours, then removed and centrifuged at 13,000 rpm for 4 minutes at 4 °C. Prior to the assay an initial standard curve was generated using serial dilutions (1:2) from 100 ρmols of purified LPL enzyme. For the assay 10 μL of the centrifuged supernatant was added to each well and then 50 μL of assay reaction mix. The plate was incubated at 37 °C in the dark for 30 minutes before being read on a fluorescence spectrophotometer (BioTek) at Ex/Em 425/515 nm. The values were compared to the standard curve generated earlier to calculate the LPL activity within the media.

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4.4 Results

4.4.1 Identifying potential targets The presence of internal lipid stores would be predicted by a lipogenic and FA rich phenotype and could explain the preference for lipolytic FA acquisition exposed by our MS analysis. Tumour nodules from V12 RAS transgenic zebrafish were cryo-sectioned and stained with Nile Red. Sections were chosen with dedifferentiated melanoma cells (as indicated by GFP signal and lack of melanin (GFP image not shown). Melanin bleaching was not possible with Nile red stain as it the staining requirements of the tissue differed substantially. Nile red detects both polar and neutral lipid groups. Neutral lipid was found to be greatly increased in tumour regions when compared with matched areas of healthy muscle (Figure 4.1A) and the fluorescence intensity was quantified (Figure 4.1B). In addition the sizes of the lipid droplets in each section were measured and were significantly smaller in tumour sections than in muscle (Figure 4.1C). To see if this was reflected in human melanoma cells, WM266-4 cells were labelled with a neutral lipid tracer LipidTox, revealing a large number of droplets within cells (Figure 4.1D). When melanocytes were stained and compared with WM266-4 cells there appeared to be an increase in fluorescence in the WM266-4 cells (Figure 4.1E).

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Normal Muscle

Tumour Tissue

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Figure 4.1 Lipid staining in zebrafish tumours and melanoma cell lines indicates increases in neutral lipid. A) A representative image of lipid staining within VGP tumours. V12 RAS zebrafish with VGP lesions were cryo-sectioned and stained with Nile Red. Images from unaffected muscle (A’) and tumour tissue (A’’) were compared within the same samples. B and C) Lipid levels increase and were contained within smaller droplets in tumour tissue (N=4). Students T-test determined significance. **P<0.01 **** P<0.0001 Error bars represent S.D. C) WM266-4 melanoma cells stained with neutral lipid stain LipidTox (green) (N=3) D) Comparison between neonatal human melanocytes and WM266-4 cells stained with LipidTox (green). WM266-4 cells had increased neutral lipid.

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Our next aim was to identify lipid metabolism genes critical to the development of VGP melanoma that could be targeted therapeutically. To this end all VGP up-regulated lipid metabolism genes were filtered using the process shown in Figure 4.2.

Figure 4.2 A summary of the process for isolating lipid metabolism targets from the microarray. The lipid genes had been isolated previously by GO. From this only up regulated genes were selected. Finally, genes present in the benign melanocyte neoplasia model were excluded because they are unlikely to represent malignant processes.

Genes with human homologues were prioritised. Genes that were also up regulated in the benign melanocyte neoplasia (V600EBRAF) model were then removed. This left a manageable short-list of genes (Supplementary Table 4). Promisingly, several known therapeutic targets appeared in the screen including fabp7, scd and lipf. The two hits with the highest up-regulation and the most significant were lpl and dgat1a (Figure 4.3). We decided to further explore the role of these two genes in melanoma development.

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F o ld C h a n g e D G A T 1 4 0 (3 8 .5 )

2 0

L P L (9 .5 )

-1 5 0 -1 0 0 -5 0 0 P -V a lu e (L o g 2 P -V a lu e )

-2 0

Figure 4.3 The genes lpl and dgat1a are the most significant up-regulated genes involved in lipid metabolism in the V12 RAS VGP zebrafish model. The deregulated lipid genes present in the VGP data, as compared to wild type are shown with fold change and the log2 p-value as compared to wild type. Lpl and dgat1a were the genes with the highest up-regulation and most significant p-value. The full list of genes is present in supplementary tables 4 and 5. Genes were isolated as demonstrated in Figure 4.2.

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4.4.2 Functional Analysis of lpl and dgat1 in a Zebrafish Melanoma Model

To address whether targets might affect the initiation or maintenance of VGP melanoma an in vivo assay was utilised entailing the MiniCoopR vector developed in the Zon laboratory (Figure 4.4A). cDNA of lpl, and mCherry were cloned into a middle entry vector and thence into MiniCoopR. Dgat1a– MiniCoopR had been synthesised previously in the group. As a comparator, a ccnd1 MiniCoopR construct was kindly supplied by Dr. Craig Ceol. Other experiments required GFP-MiniCoopR, another kind gift from Dr. Craig Ceol. Constructs were then injected into single cell embryos from V12 RAS+/- Nacre animals (nacre animals carry a null mutation in mitf rendering them unpigmented). The constructs rescued the nacre melanophore defect (Figure 4.4B), by supplying a wild-type mitf minigene, alongside expressing the gene of interest. The embryos were screened for rescued pigment on day 5 and placed into the nursery. From 4 weeks post-fertilisation (wpf), fish were examined weekly for the appearance of tumour nodules. Successful expression of the construct cargo gene was confirmed by RT-PCR performed on RNA extracted from tumours (Figure 4.4C). Previously, the Zon group had used the MiniCoopR assay to identify oncogenes and tumour suppressors interacting with V600EBRAF on a p53-/- and nacre background. Tumour development required several months. In the V12 RAS model tumours appeared within weeks. The qPCR for LPL is shown to demonstrate the level of over-expression in the MiniCoopR lesions (Figure 4.4D). As for ccnd1 and relative to mcherry, lpl overexpression but not dgat1 greatly reduced tumour latency (Figure 4.4E). Tumour nodules arising on fins, where the full mass could be evaluated, were measured using callipers when they first appeared and again two weeks later to determine growth rate. Overexpression of lpl or dgat1a increased tumour growth (Figure 4.4F). Quantifying the individual growth revealed the variability in lpl tumours. From this in vivo analysis, lpl was believed to be a more promising target.

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Figure 4.4 LPL increases tumour appearance rate and tumour growth. A) A schematic of the MiniCoopR assay. Adapted from Ceol et al.. B) The V12 RAS+/nacre fish lack melanophores that are rescued by the construct that additionally over-expresses the gene of interest. Tumour nodules arise from 4 weeks and are tracked and measured. C) An RT-PCR for the genes of interest (GOI) performed on tumours arising from the MiniCoopR experiments. Primers spanned the GOI and exogenous 3’UTR to remove endogenous sources of gene expression. D) A qPCR analysis of lpl revealed over-expression of LPL significantly above the V12 RAS skin sample E) Kaplan-Meier plots displaying tumour appearance from which Hazard ratios were determined. A Log-Rank test comparing tumour appearance in Lpl and mcherry cohorts was significant (P<0.0001). F) The growth rate of fin tumours was determined by measuring change in cross sectional area over two weeks from the first appearance of the tumour. At least 9 independent tumours were measured per construct.

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4.4.3 Interrogating the presence of LPL in human melanoma samples

The next aim was to determine the relevance of LPL in human melanoma. Initially the expression of lpl in the zebrafish models was confirmed with qPCR (Figure 4.5A). This was then compared to human data using the Oncomine platform. The expression of LPL in a number of human microarrays was analysed comparing normal skin, nevi and cutaneous melanoma samples (Figure 4.5B and C). LPL was over-expressed in melanoma primary lesions in the Haqq study but in Talantov there was no significant change. A study by Bittner et. al. contained 3 years survival data for 14 patients and LPL expression levels at diagnosis. It appeared that patients with lower LPL expression had increased survival numbers atat three years post-diagnosis (Figure 4.5D). A large scale DNA analysis of melanoma cell lines was interrogated for LPL expression. Nearly all melanoma cell lines expressed higher LPL than the melanocyte lines tested (Figure 4.5E). Several melanoma cell lines cultured in the lab were then tested by q-PCR for LPL expression (Figure 4.5F). Cell lines had variable LPL expression with RAS lines expressing particularly higher amounts mirroring the findings in microarrays. Most melanoma cell lines had significantly higher expression than the melanocyte line.

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Figure 4.5 LPL is expressed in benign and cutaneous melanoma tissues, melanoma cell lines and may correlate with survival. A) A qPCR was performed on the skin of zebrafish melanoma models and demonstrates that LPL expression is significantly higher in V12 RAS melanoma model than the benign V600EBRAF model or wild type. B) Oncomine was used to interrogate data from Haqq et al (GEO: GSE1 N=37) and C) Talentov et al (GEO GSE3189, N=70) microarray analyses and LPL expression was plotted for normal skin and cutaneous melanoma samples. LPL showed higher expression cutaneous melanoma in Haqq but Talentov indicated a wide range of expression. D) The Bittner et al. (GEO GSE1, N=31) study was used to investigate survival at 3 years, the N number was low as only 14 samples had survival data but the early indication was that low LPL levels were associated with survival. E) The Lin cell data (GEO GSE6779) demonstrates that nearly all melanoma cell lines showed higher LPL expression than melanocytes, the dotted line indicates the highest melanocyte LPL expression. F) A panel of cell lines including neonatal human melanocytes (NHM) and multiple melanoma lines were examined for LPL mRNA expression by qPCR. A majority of the cell lines overexpressed LPL when compared to NHM cells. Significance determined by one way ANOVA * P<0.01 and **** P<0.00001 Error bars = S.D in all graphs.

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A clinically relevant analysis was now required. To address the protein expression of LPL a panel of 47 patient samples were labelled with LPL antibody. The samples included benign nevi, primary lesions and metastases (Figure 4.6A). The protein expression was graded by a dermatopathologist (Figure 4.6B). LPL expression was present in a higher percentage of primary lesions (85%) and metastases (100%) then benign nevi (77%). It also showed that LPL expression in benign nevi was confined to the epithelial/dermal junction and did not appear in the dermis. The staining also showed that LPL protein was found within the tumour cells and not confined to a tumour associated cell type or within the tumour microenvironment (Figure 4.6C).

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Figure 4.6 LPL protein is upregulated in primary lesions and metastases and located within melanoma cells. A) Representative images from a panel of 47 melanoma samples stained with LPL antibody (pink) and counterstained with haematoxylin (blue). A histopathologist scored the LPL staining. B) Quantification of grading with percentage of samples in each grade, - no staining, * some heterogeneous staining, ** strong heterogeneous staining, *** strong homogenous staining. The benign nevi did not show LPL expression in the dermis as it remained at the epidermal junction (indicated by *). Primary lesions and metastases showed high LPL protein levels and primarily heterogeneous expression. C) A melanoma nodule surrounded by subcutaneous adipose. Magnified regions showing expression of LPL as expected in adipocytes and capillaries but also present within melanoma cells themselves.

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To investigate the role of LPL in melanoma cell survival, protein expression of LPL and the activity of LPL were studied in a panel of melanoma cell lines. Several melanoma cell lines showed LPL protein expression (Figure 4.7A), further confirmed by immunofluorescence with an independent antibody (Figure 4.7B). The western blot indicated that A375P and WM852 lines had the highest LPL expression. LPL is secreted and therefore an activity assay was used to screen the media of a select number of cell lines (Figure 4.7C). The WM266-4, WM852 and A375P cells all had detectable LPL activity in the media that was abrogated by treatment with an LPL inhibitor. The NHM cells had no detectable LPL activity. To gain detail of the mechanism these three cell lines were selected for further study.

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Figure 4.7 Melanoma cell lines have LPL protein expression and high LPL activity compared to melanocytes. A) LPL protein expression has been investigated by western blotting on a panel of melanoma cell lines. Several lines show high LPL protein expression with others showing none. B) Immunocytochemistry for LPL (red staining) was performed with an independent antibody on a number of cell lines. It shows LPL protein within the cell and shows similar expression patterns to the western blot. C) An activity assay for LPL. Media was collected from cells after 72 hours incubation and an LPL activity assay performed. The amount of fluorescent LPL lysis product was correlated to activity via a standard curve. All melanoma cell lines showed LPL activity whilst an LPL inhibitor GSK264220-A at 50 μM inhibited all LPL activity. Melanocytes had no detectable LPL activity. (N=18) ** P<0.001. Error bars = S.D.

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4.4.4 Exploring the mechanism of LPL in melanoma To better understand the role of LPL in tumours a preliminary experiment was performed on cryosections of LPL-MiniCoopR and GFP-MiniCoopR tumour burdened zebrafish. The sections were stained with H&E and lipids were stained with nile red. The LPL tumour was highly pigmented and had invaded extensively into the muscle tissue. The lipid staining suggested a reduction in neutral lipids in the tissue compared with GFP-MiniCoopR (Figure 4.8). Higher numbers would be required for quantification.

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Figure 4.8 Histology and lipid staining reveal LPL-MiniCoopR tumours have highly pigmented and invasive lesions, they also appear to have reduced lipid. A) The LPL- MiniCoopR zebrafish sectioned and an example H&E stained section (left). The corresponding nile red stained sections are indicated on the right. There was less lipid staining in the LPL-MiniCoopR fish. B) The GFP-MIniCoopR zebrafish sectioned and an example H&E section. The corresponding nile red staining shows GFP-MiniCoopR tumours contain more lipid (N=2).

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To gain a more detailed understanding of the role LPL is playing in tumours LPL siRNA was used to knockdown LPL in the melanoma cell lines. Cells were treated with single doses of siRNA and assays were performed after 24 hours Loss of LPL resulted in a reduction in cell number, as measured by crystal violet (Figure 4.9A). The loss of LPL resulted in a 20% reduction in A375P cells, whilst WM266-4 cells had a greater loss at around 50%. The effect was most pronounced in WM852 cells that had a nearly 80% reduction in cell number. Knockdown was confirmed by western blotting (Figure 4.9B). When proliferation markers were studied in the WM852 cells it was seen that they had reduced uptake of Clik-EdU (Figure 4.9C).

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Figure 4.9 LPL knockdown induces a reduction in cell number and reduces proliferation. A) A375P, WM266-4 and WM852 cell lines were treated with two independent LPL siRNA after24 hours this led to a significant reduction in cell number for all cell lines. The WM852 experiencing the most dramatic reduction (**** P<0.00001). B) The corresponding western blots demonstrating a loss of LPL after siRNA treatment. C) WM852 had the largest response so a Click-EdU assay was performed on WM852 cells treated with LPL siRNA. There was a significant decrease in EdU uptake in LPL siRNA treated cells demonstrating reduced proliferation. All experiments used non- targeting scrambled siRNA as a control. N=3. 130

Loss of LPL had profoundly affected cell number and proliferation but the variability in response was interesting. The effect may have been due to compensatory de novo lipid synthesis. Therefore several markers for de novo lipid synthesis were studied including FASN, ACC and SCD1 gene expression. Within the fish models, as predicted by the transcriptome, SCD1 was highly over-expressed within the RAS model but FASN showed consistent gene expression (Figure 4.10A). The important factor relative to the mechanism in melanoma cell lines was protein expression of de novo enzymes. Western blotting for FASN, ACC and SCD1 (Figure 4.10B) showed that ACC protein was consistent between the cell lines, whilst SCD1 showed very low expression. However, FASN showed variable expression that appears to correlate with the effect of LPL siRNA on cell number. The A375P cells had the highest FASN expression, WM266-4 had intermediate expression and WM852 cells had very low.

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Figure 4.10 fasn may correlate with the effectiveness of LPL siRNA. A) A qPCR analysis on the zebrafish tumour models revealed that fasn expression did not vary significantly between them (N=4). The scd1 qPCR revealed that VGP tumours contained significantly higher amounts of scd1. * P<0.01 (N=4) determined by students T-test comparing RGP and VGP. Error bars = S.D. B) A representative western blot. Melanoma cell panels were probed for FASN, ACC, SCD1 and ERK2. ACC showed fairly consistent expression. SCD1 showed expression only in adipocytes and 501mel cells. The FASN expression was higher in the BRAF mutant cell lines but did negatively correlate with LPL siRNA effectiveness (N=3).

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To determine if FASN could be providing protection from LPL inhibition the three cell lines were treated an LPL inhibitor (GSK 264220 A), a FASN inhibitor (C75) or a combination of LPL inhibitor with or without 15 μM FASN inhibitor. A further experiment of a FASN gradient with or without 30 µM LPL inhibitor was also performed to calculate synergy (data not shown). The WM852s were not affected by the addition of a FASN inhibitor (IC50 1.586 µM to 0.9147 µM with FASN). The calculated combination index (C.I) showed no significant value for WM852 cells ranging from 0.9 to 4.14 at the different doses of LPL (Figure 4.11A). The WM266-4 cells had dramatically reduced IC50s (IC50 61.29 µM and 0.8052 µM with FASN) when treated with the combination and this was supported by the C.I value (ranging from 0.18-0.88) indicating substantial synergy (Figure 4.11B) The A375P cells also showed greatly increased sensitivity to LPL inhibition with the addition of FASN inhibitor (IC50 for GSK 264220 A from 18.98 µM to 0.7321 µM). The C.I for A375P cells ranged from 0.21-0.89 (Figure 4.11C) again indicating a synergistic relationship.

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Figure 4.11 An LPL inhibitor works in synergy with 15 µM of FASN inhibitor in A375P and WM266-4 cells but not with WM852 cells. All cells were treated with LPL inhibitor GSK 26406 A with or without 15 µM FASN Inhibitor, C75. A) WM852 cells are not significantly affected by the FASN inhibitor as predicted by gene expression, and there was no detected synergy (C.I.= 0.9-4.14). B) WM266-4 cells showed synergy with the LPL and FASN inhibitors (C.I. 0.18-0.88) C) A375P cells also showed synergy with FASN inhibitors (C.I.=0.21-0.89). C.I. values were calculated using Compusyn.

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The effect of LPL knockdown appeared to rely on the provision of FA. However, in the literature LPL works with multiple transporters to promote FA influx. It was critical to assess if these channels may also be contributing to the effect of LPL knockdown. A co-expression coefficient was calculated using datasets from Oncomine showing significantly high LPL expression. The combined co- expression data is presented in Figure 4.12 as a heat map. The two most significantly co-expressed genes were the FA transporters FABP4 and CD36. It may be that the effect of LPL would be related to the regulation of these two transporters.

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Figure 4.12 The most highly correlated genes with LPL expression were mapped using Oncomine with FABP4 and CD36 being prime targets. Various cancers were used to build the correlations and a correlation coefficient was calculated based on Oncomines database.

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The hypothesis developed from the literature had been that the loss of LPL would reduce the presence of CD36 and indeed, knockdown of LPL reduced the CD36 levels within WM266-4 cells and A375P cells (Figure 4.13A), however this was not always consistent and it seems to increase in WM852 cells. The expression of CD36 by qPCR was low in all the zebrafish melanoma models, except when LPL was overexpressed by MiniCoopR. In this situation CD36 was significantly highly up-regulated (Figure 4.13B). FABP4 levels were not consistently expressed or affected reliably by LPL knockdown and even increased in some samples (Figure 4.13A). Cells were stained with an independent CD36 antibody. The immunofluorescence staining expression of CD36 in WM266-4 was heterogeneous (Figure 4.13C), whilst it was much less so in WM852 cells, (Figure 4.13D) it correlated with the western blotting data. The expression of CD36 does not match with the homozygous expression of LPL in the same cell lines.

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Figure 4.13 LPL expression may affect CD36 but not FABP4 expression. A) A western blot for LPL siRNA treated WM266-4, WM852 and A375P cells probed with LPL, FABP4 and CD36. LPL was successfully knocked down. CD36 expression appeared to be affected by LPL knockdown in A375P and WM266-4 cells. WM852 cells seem to show an increase. FABP4 has expression that does not appear to correlate with LPL. B) A qPCR on the tumour progression models reveals CD36 does not vary between them. However, LPL MiniCoopR generated tumours have very large increases in CD36 expression. Significance determined by students T- test comparing VGP with LPL-MiniCoopR. **** P<0.00001. N=6. Error bars = S.D. C) An immunocytochemistry image of WM266-4 cells probed with CD36. Expression of CD36 was heterogeneous through the population of cells. D) WM852 cells showed much less heterogeneous expression of CD36, corresponding with the higher signal in western blot (N=3).

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The loss and increase of CD36 may have altered the uptake of FA from the medium in some cases. When assayed in the responsive cell lines (WM852 and WM266-4) the uptake of FA was significantly increased within WM852 cells whilst increased non-significantly in WM266-4 cells fitting with the CD36 expression data (Figure 4.14A). This increase appeared to contrast with the LPL activity in the cell lines (Figure 4.14B). This increase indicated that the FA uptake increased to compensate for the loss of LPL. To test if the cells were experiencing nutrient stress the lipid droplets of the cell were investigated. If the cell were starving due to loss of FA it may compensate by utilising the triglyceride in lipid droplets for FA. Cells were stained with Nile Red for both neutral and polar lipids (Figure 4.14C) in cells treated with control siRNA or LPL siRNA. The loss of LPL led to a large increase in lipid droplets within the WM852 cell line. The droplets were not being broken down or utilised.

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Figure 4.14 Loss of LPL increases FA uptake and neutral lipid staining. A) FA uptake was measured in a fluorometric assay and normalised to cell number by crystal violet on LPL siRNA treated WM852 and WM266-4 cells. Despite loss of CD36 and LPL the uptake of fatty acids increases in LPL siRNA treated cells. (N=9) Error Bars = S.D. B) An LPL activity assay shows the loss of LPL activity in the media of WM852 and WM266-4 cells treated with LPL siRNA. The drop in activity reflects the severity of cell loss (N=12). Error Bars = S.D. C) Representative images of WM852 cells that were stained with Nile Red, polar lipids are red and neutral lipids are in green. LPL siRNA treatment significantly increases the neutral lipid within the cell (N=6). For fatty acid uptake significance was determined by one-way ANOVA and for LPL activity student’s T-test was used. * P<0.01 ** P<0.001 *** P<0.0001 **** P<0.00001.

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The results here have shown that LPL overexpression increases tumour growth and reduces tumour latency in a zebrafish melanoma model. LPL is expressed in human melanoma and knockdown of LPL reduces cell number. The fact LPL is compensated by FASN suggests that the provision of FA is the cause of the effect of LPL knock down and the synergy between LPL and FASN inhibitors could be a potential therapy for blocking cancer lipid metabolism.

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4.5 Discussion

4.5.1 LPL is known to play a role in cancer progression Transcriptome profiling of zebrafish melanocyte neoplasia has highlighted a number of potential therapeutic targets implicated in lipid metabolism. The most significantly up-regulated genes in the screen, unique to the V12 RAS model were lpl and dgat1. The screen overall had successfully identified a number of known melanoma therapeutic targets. Homologues of fabp7 and scd1 identified in this study have been previously investigated in melanoma (Goto et al., 2010, von Roemeling et al., 2013). This provided confidence in studying lpl and dgat1.

LPL is a member of the lipase superfamily, also including hepatic lipase and endothelial lipase. This family show strong homology and structural characteristics. All three breakdown TAG and are differentiated by their tissue expression and localisation. Alongside this the three family members show substrate specificity. Lipoprotein lipase is selective for chylomicrons and VLDL carrying the co-factor apolipoprotein CII (ApoCII). LPL is typically secreted by muscle and adipose cells to the endothelium. Here the LPL binds passing chylomicrons and VLDL and generates FA for uptake within those tissues. Non- catalytic roles for LPL have also been described, including the uptake of cholesterol and the direct endocytosis of bound FA or lipoproteins (Davies et al., 2012, Krapp et al., 1995, Merkel et al., 1998). DGAT1 mediates a rate limiting step in TAG synthesis. It is responsible for the addition of FA and an acyl-group to diacylglycerol (DAG) to produce TAG. The enzyme DGAT1 is typically highly expressed by adipocytes and is often localised to lipid droplets within cells (Chen and Farese, 2005).

The MiniCoopR experiment revealed that lpl was able to increase the rate of tumour appearance and increase tumour growth. Despite the increased growth identified with DGAT1 it was not considered a strong target. That is because it did not affect tumour latency. LPL on the other hand greatly increased tumour growth and reduced latency. The inability for DGAT1 to induce VGP faster infers that it is not able to contribute to melanoma progression. Yet, it does greatly increase growth and suggests that once established tumours do require 142

DGAT1. In the literature overexpression of DGAT1 seems to have an inhibitory affect. A study in transformed fibroblasts showed that overexpressing DGAT1 reversed the malignant phenotype. The cells were less proliferative and invasive. The overexpression seemed to funnel lipids from other pathways into TAG synthesis (Bagnato and Igal, 2003). DGAT1 increases TAG in a large number of tissue types. In the zebrafish this may have contributed to the rapid growth rate.

The fact LPL was able to increase tumour size and increase the rate of tumour appearance was not unprecedented. Several studies have identified LPL as important to cancer cell survival and proliferation. One prominent study observed that LPL expression was high in breast, sarcoma and prostate tumours and cell lines. The study showed that the knockdown of LPL in a number of cell lines reduced proliferation. It also showed that over-expression of LPL, and supplementation with triglyceride, increased proliferation (Kuemmerle et al., 2011). Here these results have been mirrored in melanoma. A large proportion of melanoma patient samples expressed LPL protein and intensity of staining increased in more advanced lesions and metastases. Knockdown inhibited proliferation in all melanoma cell lines tested. The finding that LPL over-expression may indicate worse prognosis is seen in other cancers. High LPL expression in chronic lymphocytic leukaemia (CLL) is one of the strongest indicators for a poor prognosis. LPL seems to provide the cells with essential FA. Leukaemia cell lines increased proliferation with addition of triglyceride in proportion to their LPL expression (Kaderi et al., 2011, Pallasch et al., 2008). High LPL expression also denotes poor prognosis in small cell lung cancer patients (Cerne et al., 2010). These studies reflect what has been seen in this thesis. LPL expression is high in several melanoma cell lines but relatively heterogeneous and is also found in primary melanomas. Moreover, high LPL expression does appear to suggest a worse prognosis. The caveat here is the reduced sample size. Only certain samples showed data tracking patients for prolonged periods. It would be worth identifying a larger subset of patients and investigating LPL expression to develop a true hazard ratio. Additionally overexpression studies could be valuable comparing LPL expression to the lysis of TAG and the levels of FA within the cell. 143

4.5.2 LPL appears to be a tumour promoter in this study and inhibition of LPL synergises with FASN inhibitor. An interesting finding was the variability in cell response to siRNA. The effect of LPL siRNA was dramatic in the WM852 cells. The cells had reduced proliferation markers, appeared to induce compensatory FA uptake pathways and experienced profound changes in autophagy markers. The effect in WM266-4 cells was less dramatic and not as statistically significant but still resulted in a reduction in cell number and inhibition of autophagy. Finally, the A375P cells also reduced in number to a much lesser degree. There appeared to be compensation in the cell lines, increasing in A375P cells. The investigations into de novo lipid synthesis showed that the reduced efficacy of LPL siRNA correlated with FASN protein levels and it also demonstrated that cells high in FASN expression could be rendered sensitive to LPL inhibition by using a FASN inhibitor. This is expected, the two pathways can compensate for each other and FASN has been described in a number of other lipogenic cancers (Flavin et al., 2010). Due to the interest in FASN as a therapeutic target in cancer there are very effective inhibitors available and one of these is being tested in a Phase I clinical trial. It would seem that loss of the de novo pathway increased reliance on LPL. This combination could be tested now in vivo in the zebrafish or mouse. The provision of dietary lipids in the animal may increase the potency of LPL inhibitors. Another interest is that FASN inhibitors in the clinic could be combined with LPL inhibitors to increase efficiency and reduce the risk of resistance through lipid uptake pathways.

Although many tumour types rely on de novo lipid synthesis, RAS driven tumours tend to promote a lipid scavenging or lipolytic phenotype (Kamphorst et al., 2013). This may be due to the contribution of PI3K signalling which mediates many of the lipid uptake pathways (White, 2013). In this thesis it does appear that RAS cell lines express higher levels of LPL, whilst de novo FASN is expressed in BRAF cancer cells. Therapies that benefit the RAS group are needed but LPL has effects broader than that. Within the tumours stained for LPL protein a large number of them expressed LPL. It is likely these tumours reflect the proportions found in human patients and carry a mix of BRAF, RAS,

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NF1 and WT mutations. Additionally, all the cell lines tested, including a range of genetic backgrounds, all had LPL activity and could be stained for LPL protein by immunohistochemistry or western blot. The microarrays available also suggest that RAS mutations and BRAF mutations do not dramatically affect LPL expression. Other studies have identified LPL within melanoma mouse models. One group generated melanoma models in mice carrying retinoid X receptor α (RXRα) null mutations and over-expression of cyclin D kinase 4 (CDK4). Within this model LPL was found to be up regulated (Coleman et al., 2015). Yet, any therapy that provides benefits to NRAS mutant melanomas is welcome due to the increased aggression and reduced therapeutic options for these patients.

Although a majority of the literature and the results here suggest LPL to be promoting tumours several research papers studying melanoma have concluded that LPL is a potential tumour suppressor. One study found that LPL was methylated or deleted in a number of melanoma patients. This group also suggested that over-expression of LPL inhibited the growth in four melanoma cell lines (Mithani et al., 2011). However, studies performed by the TCGA on over 300 melanoma lesions do not find similar loss of gene function and set the rate at under 2% loss of function. The mutation and over-expression rates combined were higher (>3%) and several mutations identified increased LPL activity. Furthermore, studies from multiple groups are shown here that demonstrate the high expression of LPL in many cell lines and tumours. It is also important to note that in this paper LPL overexpression is not combined with TAG supplementation, which in a number of studies was critical in demonstrating the role of LPL.

The results thus far fit with a simple role of FA provision. Being compensated by FASN certainly supported this. However, when the cells affected by LPL siRNA, WM852 and WM226-4 cells, were analysed in more depth there appeared to be a novel mechanism for LPL, one that is perhaps independent of direct FA provision.

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4.5.3 There is a novel mechanism for LPL in melanoma cell lines When the WM852 cells were analysed for FA uptake it was found that there was a significant increase. Not only that, the cells had increased neutral lipid staining, suggesting an increase in lipid. It is likely that a compensatory pathway had been activated. As several other FA transporters are known to be expressed in melanoma it is likely they have been up regulated. LPL is associated with CD36 in particular (Goldberg et al., 2009) and the co- expression analysis also highlighted FABP4, downstream of PPARγ like LPL and CD36 (Strand et al., 2012). The co-expression was seen in several different cancer types. Here it was demonstrated that FABP4 expression in melanoma cell lines was highly variable and was not consistently affected by LPL expression. This transporter may not be relevant in melanoma, particularly as the analysis incorporated a number of cancers. We see that loss of LPL reduced CD36 protein levels in WM266-4 and A375P cells, but CD36 increased in WM852 cells. Perhaps it plays a role in the increased FA uptake in WM852 cells. Over-expression of LPL in the MiniCoopR model also increases CD36 levels. CD36 does not arise in the transcriptome screen or in the qPCR on the zebrafish melanoma models. The expression of CD36 in cell lines was interesting. The WM266-4 cells had heterogeneous expression of CD36, whilst WM852 cells had much more homozygous expression and higher CD36 expression by western blot. The proportion of CD36 positive cells may predict the vulnerability of cells to LPL inhibition, partially because cells expressing CD36 are likely to be more reliant on FA uptake or at least able to benefit from it. A way of testing this would be to separate the CD36 population in the WM266-4 cells by flow cytometry and seeing if these cells responded more strongly to LPL siRNA or inhibitor. Overall the results suggest that CD36 is unlikely to be the driver for the effects of LPL but may be co-operating. Other candidates for the compensation include FABP7, known in melanoma and identified in the transcriptome. It may be interesting to expand the panel of transporters tested to better understand this compensatory reaction.

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suggested that the cells might be utilising their lipid stores to compensate for the loss of FA provided by LPL. However, when studied it was found that LPL siRNA actually increased the number and volume of lipid droplets. This increased lipid droplet formation may be due to a defect in mitochondria. The appearance of lipid droplets is seen in many pre-apoptotic cells and is proposed to be a result of damage to mitochondria. The damage results in reduced FA oxidation and the FA are pushed to form triglyceride to prevent lipotoxocity (Boren and Brindle, 2012). LPL is a read out for mitochondrial number in several studies. LPL knock out in mouse muscle decreases the number of mitochondria within the tissues (Morino et al., 2012). This could mean LPL plays an important role in mitochondrial development or health. Another potential explanation is an inhibition of lipid droplet autophagy, known as lipophagy. This is a relatively new discovery, that autophagy of a cells lipid droplets could be used to provide FA to a cell in times of stress. The exact mechanism is not known and intracellular LPL could be involved in this process. It is known that LPL active within the cytoplasm of the cell (Ben-Zeev et al., 2002) and mediates lipid droplet formation (Trent et al., 2014). It also could be loss of LPL in the WM852 cells could negatively affect lipophagy. This would result in increased lipid droplet formation even in the face of reduced FA supply. Increased TAG is toxic to the cell. A study by Aflaki et al demonstrated that loss of ATGL induced a loss of mitochondrial cristae and function. Furthermore there was a significant increase in lipid droplets. The study showed that loading the cell with TAG, in the form of VLDL phenocopied this effect (Aflaki et al., 2011). The loss of LPL shows similar results and could indicate a build-up of triglyceride that inhibits cristae and damages mitochondria. As the effect of LPL is dependent on FASN it may simply be that the loss of FA triggers an uncontrolled increase in FA uptake that is toxic to the cell that coincides with the loss of LPL preventing use of the FA.

The mechanism is interesting. Looking at the mitochondria at an earlier time point may be important. It may also be beneficial to see if LPL is directly interacting with mitochondria. Tracing the FA with labels may also show if the lipid droplets are arising from FA taken up by the cell or if it is simply stabilising and no longer being broken down. 147

Inhibiting LPL may provide a therapeutic benefit to melanoma patients. At this point it is prudent to mention the LPL inhibitors in development or found in vivo. One of the most potent inhibitors of LPL in vivo is angiopoietin-like- 4 (ANGPTL4). ANGPTL4 deactivates LPL by encouraging the dimer to split and maintaining LPL in a monomer state (Dijk and Kersten, 2014). Using ANGPTL4 as a therapy against LPL would be possible. Preliminary work within the group, not shown here, has shown that ANGPTL4 is able to significantly delay tumour onset and greatly reduce the growth rate of tumours. It is now a matter of determining the contribution of LPL to this effect. Small molecule inhibitors are available for LPL. They are demonstrated here to have efficacy against melanoma cell lines. The use of LPL as a therapy must take into consideration the compensation by FASN. However, the presence of inhibitors means that the two may be easily combined in a therapy. The identification of an autophagy pathway also suggests that compounds known to induce autophagy may work to selectively kill melanoma cells. Examples include rapamycin, shown to synergise with lipin-1 and already used within patients. Several chemotherapeutic agents also induce autophagy and may be combined with LPL inhibition.

Using LPL as a therapeutic treatment would likely be well tolerated. The strongest evidence for LPL inhibition tolerance is in patients born with inactive or absent LPL. LPL deficient patients do not present with a higher risk of cancer. They do show an increased propensity for pancreatitis but this occurs much later in life and is a result of hyperlipidaemia. LPL inhibitors may not pose a high risk (Foubert et al., 1994).

Data in other cancers and the data here suggest that LPL may be a strong therapeutic target. Further testing and work in vivo on inhibiting LPL could provide more insight into its role in melanoma and the therapeutic potential.

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Chapter 5: Development of a novel PET tracer for fatty acid metabolism and preclinical drug testing with the zebrafish.

5.1 Introduction The importance of lipid metabolism within melanoma is hopefully better understood and multiple targets have been identified within these pathways that may have broad influences on cell behaviour and work in combination. Part of utilising any lipid therapy will be looking for patients with a lipolytic or lipogenic phenotype. A non-invasive tracer would allow clinicians to identify these patients. Additionally, a lipid-based tracer would aid in understanding lipid metabolism within melanoma. One critical technique for the study of metabolism that could be adapted is positron emission topography (PET). PET is widely used clinically for the diagnosis of cancers and for tracking patient progression in a non-invasive manner (Zhu et al., 2011). Radioactive tracers are injected into patients and after a period the patient is scanned and tissues enriched with tracer are visible. The most common PET tracer is fluorodeoxy-glucose (FDG), a modified form of glucose that is phosphorylated after cells take it up, but cannot proceed further on the glycolytic pathway and is retained in the cell until radioactive decay. FDG is preferentially enriched in tissues utilising a high amount of glucose and therefore is useful in detecting actively proliferating tumours. Within the context of melanoma PET is widely used to non-invasively follow patient disease progress, identify metastases and trace therapeutics (McIvor et al., 2014, Strobel et al., 2007). Lipid tracers are also in existence. One that has been trialled is known as 14(R,S)-[18F]fluro-6-thia-heptadecanoic acid (FTHA) and is an unconjugated fatty acid taken up by tissues (REF). Similar to FDG the FTHA cannot progress beyond the initial stages of metabolism (REF).

PET can be combined with multiple other imaging techniques, such as CT scans and MRI scans to develop detailed clinical pictures with anatomical

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information. Scans can be compared as the scans generate a standard uptake value (SUV). Healthy tissues have reproducible SUVs allowing for normalisation between patients, in humans accurate circulating tracer levels can be determined using blood sampling during scans (Kinahan and Fletcher, 2010).

Many molecules can be combined with short-lived radionuclides and there are a vast number of potential tracers and applications. These tracers could provide increased sensitivity in tumour detection or provide new information about tumour metabolism. There are even groups moving to test novel therapeutics by adding small amounts of radionuclides to track the compounds metabolism and circulation (Bergström et al., 2003). Novel tracers may also tackle some of the existing issues with PET scanning. Currently PET scanning cannot be used after surgical resection as the remaining tumour microenvironment will have adapted to take on glucose to feed the tumour and the tumour will not have ‘disappeared’ at scan before a 6-week rest (Zhang et al., 2015a). Another tracer may not have this issue. Additionally, some therapies reduce the glucose flux within a tumour cell. BRAF inhibitors reduce glucose metabolism and uptake as a side effect and although treatment is very effective, it may also mask tumours (Theodosakis et al., 2015). This may explain why tumours disappear at PET scan but rapidly re-emerge at such great sizes. Novel tracers may be able to circumvent some of these problems or be used in concert to develop a more detailed map of patient metabolism.

Tracers are typically developed in mice. Scanners have become sensitive enough that small animal scanners are now available. Mice can be anaesthetised for adequate periods to allow for the prolonged scanning and also show similar metabolic profiles to humans to allow comparable data. However using mice can be slow and expensive. Mice can only be scanned one at a time and the scans required for the body mass can be lengthy. Within one day only a handful of mice can be screened. To address this issue other groups have looked at fish species in FDG PET/CT scanning. Browning et al. demonstrated that many fish species showed similar glucose uptake and glucose profiles. In fact the uptake SUVs were shown to be more similar to human SUVs than rodents. The group also scanned several fish simultaneously

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and demonstrated the faster throughput available. The group does show the limitations of the technology at the time. They were unable to scan zebrafish or medaka, the most widely used fish species in research. They did predict that technology would evolve soon making it a possibility (Browning et al., 2013).

Zebrafish would be a strong model for utilisation with PET scanning. The zebrafish would allow for high throughput tracer testing in a clinically relevant manner. The zebrafish has been used widely in research to model a number of conditions and diseases and could therefore be used to test the benefits of PET tracers in a number of disease models.

5.2 Aims

 The overall aim is to look at melanoma lipid metabolism in vivo in a live zebrafish. Our secondary aim was to validate the zebrafish as a tool for screening novel PET tracers. A number of issues make PET scanning fish difficult for many groups. One aim is to develop a method for scanning zebrafish that does not require modification of the PET scanner. To do this we will perform scans on cadavers to determine the water-tightness of our experimental system in the scanner. Further, we will be attempting to develop anaesthesia that reduces stress to the animals and allows for anaesthesia lasting 40 minutes. This will be done by attempting different anaesthetic doses and time courses. We will also be attempting anaesthesia by chilling the animals.

 The next issues relate to introducing the tracer in a safe and reproducible way. We will optimise administration of the tracer (dose, site and incubation time) in a systematic way in wild type zebrafish before moving into tumour burdened zebrafish. Once optimised we will attempt scans with a novel fatty acid tracer. This should give us information on the suitability of zebrafish for PET scanning. Once these issues have been overcome, the zebrafish may become a key tool in developing PET scanning tracers. This has led to us to pioneer developing the zebrafish as a pre-clinical PET model.

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5.3 Methods

5.3.1 PET/CT scanning of zebrafish Phantom scans were performed on Bequerel tubes containing system water, the sponge beds and 250 μL Eppendorf tubes containing radiotracer in order to optimise the system for scanning multiple fish at once. Animals underwent scanning one hour after administration of FDG-PET or FTHA soaking. Animals were euthanized with administration of tris-buffered 6 mg/L-1 MS222 (Tricaine- S, Western Chemical). Zebrafish were then placed in sponge and loaded into 5 mL Bequerel tubes. Fish were weighed and then transferred to the Siemens bedplate and secured with tape to prevent rolling. Zebrafish were then transferred to an Inveon preclinical PET/CT scanner (Siemens). An initial CT scan was performed to provide attenuation correction during reconstruction, followed by a PET acquisition. Listmode data were collected for 30 minutes. Before image reconstruction, the listmode data were histogrammed into 5 minute time frames (6 x 5 min time frames). Images were reconstructed using the 3d-OSEM/MAP algorithm (4 OSEM3D iterations and no MAP iterations, with a requested resolution of 1mm). Regions of interest (ROIs) were drawn manually over selected areas (tumour, heart and brain) using Inveon Research Workplace software (Siemens, Germany). Normalisation was performed using injected dose and animal weight to give standardised uptake values.

SUVmax was calculated from the maximum voxel value within the ROI and

SUVmean as the average over all voxels, normalized uptake values were calculated by dividing SUVmax or SUVmean from the tumour by that from the brain contents. Whole fish and organ radioactivity from treated fish were measured with an IsoMed2000 Dose Calibrator (Nuklear-Medizintechnik, Dresden). Organs were dissected from euthanised fish after tracer administration and incubation for one hour.

5.3.2 Sedation and Tracer Administration Zebrafish were housed at 28 °C prior to PET scanning. Animals were not fed an evening feed prior to scanning to limit background signal in the gut. For sedation animals were transferred to a litre tank containing system water at 14

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°C. Addition of ice to an outer chamber allowed for gradual cooling at a rate of 2 °C/min-1. Cooling was halted at 10 °C for males and 8 °C for females. Upon cessation of swimming, fin pressure was used to determine anaesthesia. Animals were transferred to a bed soaked in 10 °C water and viewed under a microscope. A 36-gauge needle linked to a micro-injector (Micro4 micro syringe pump with UMP3 holder, WPI) was used to inject 4.5 µL of FDG or FTHM tracer at 200 nL/s-1. The animals were injected in the dorsal muscle ~3 mm anterior to the dorsal fin. Animals were then transferred to a 14 °C recovery tank and aerated with a Pasteur pipette. After recovery animals were housed individually in 200 mL of system water at 26 °C whilst incubating. Blockages in the injection equipment did occur and were treated by attachment of a syring (Nanofil, WPI) and pressure being used to dislodge the blockage. A higher gauge needle (35 gauge) could also be used to increase pressure.

5.3.3 Soaking for FTHA administration Animals were placed in 300 mL containers containing 200 mL of system water. 600 MBq of tracer was added to the water in a volume less than 100 µL.

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5.4 Figures and Results

5.4.1 Developing an anaesthesia protocol for PET scanning The enclosed system requires the use of an anaesthetic technique with no flowing water requirements. Two small pilot studies were launched to investigate the use of MS222 and chilling as long-term anaesthesia techniques. Wild type fish were treated with MS222 at 4% w/v for either 10 or 15 minutes. The fish were monitored throughout and then placed in recovery and subsequently monitored. It was found that the two fish maintained for 10 minutes rapidly recovered from anaesthesia: within 1 minute of returning to fresh water they were moving. Any movement would be detrimental to the scan. The 15 minute incubation led to a cessation of movement for 45 minutes after the transfer to fresh water and the presence of a heartbeat indicated they were still alive but unconscious. However, the fish did not recover and were euthanized. The study indicated that MS222 would not be ideal for the induction of long-term anaesthesia with a view to longitudinal studies. After the initial studies it became apparent through the literature that MS222 could also affect glucose metabolism. An alternative method of anaesthesia was sought, for both tracer administration and potential long-term anaesthesia.

Cooling of the water to 8-10 °C can induce an anaesthetic plane in the zebrafish. They can be maintained at this temperature for prolonged periods. Six wild type zebrafish were gradually cooled in water till they reached a maintenance temperature of 10 °C (for males) and 8 °C (for females). The fish were maintained at this temperature throughout the experiment. The fish remained immobile whilst in containers for 40 minutes. Once returned to 17 °C water the fish recovered. This appeared to be of limited stress to the fish. The gradual cooling did not produce any indication of distress. Swimming and mobility were normal. They were not responsive to pressure or touch throughout the cooling. However, when the experiment was tried on V12 RAS fish bearing tumours the fish only had a 40% survival after the cooling period. The fish burdened with tumours were unable to cope, likely due to the disease state

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affecting body temperature regulation. Furthermore, although unresponsive the injection of tracer may contribute to stressing the fish and reduce recovery.

To prevent unnecessary stress to the zebrafish the cooling technique would be used for injection of the FDG tracer, but after an incubation period to allow uptake of the tracer, the fish would be culled and scanned.

The equipment for injection and scanning had also been fully tested. A microinjector allowed for accurate quantification of tracer for injections. The set- up also had to include the means to cool zebrafish without direct contact with the ice. The final set-up is shown in Figure 5.1.

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Figure 5.1 The injection set-up for tracer administration and the container for scanning. A) Ice water was used to chill the outer chamber to avoid contacting the fish directly with ice. The surgical table was soaked in 10 °C water. A recovery tank was available to the zebrafish. The micro-injector can also be seen. B) Zebrafish were placed in 5 mL containers, they were supported by sponge to give consistent shape to fins and tails. These tubes were placed within a larger container containing 8 °C water in sequential studies (Figure 4B).

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5.4.2 Determination of probe administration for FDG-PET To determine if the zebrafish would utilise FDG glucose and to determine an ideal injection site three trials were performed, one looking at a peritoneal injection site, one in the musculature of the tail and the third in the dorsal musculature. Injections within the peritoneal space did not provide consistent results for uptake or imaging. Initial studies imaged fish injected with 400 nL of FDG and the dose was increased to 2 μL improve resolution and subsequently 4.5 μL. The presence of reproductive tissues in females made administration difficult. Furthermore, injections at this site appeared to result in distress in some animals with restricted swimming and bleeding. The bleeding may have also contributed to variable uptake results. The injections into the muscle of the tail also did not provide consistent results. The width of the tail varied between fish and tails were a common site for internal tumours in V12 RAS fish that could interfere with data collection. The dorsal musculature injections provided very reliable data. The scans were consistent and the uptake in the fish was high. This is likely due to the lack of variability between fish in this region. This injection site also appeared to be less stressful or aversive to the fish. Fish showed normal movement and minimal bleeding. In addition the scans showed strong uptake of the FDG into the predicted organs (Figure 5.2A). The organs were consistent with those visualised in human scans.

The determination of a consistent injection site provided the means to test incubation periods with the tracer. Wild type fish were injected with FDG-PET and recovered for 5, 10, 20, 30, 40 and 60 minutes. Fish were then scanned and the SUV of the organs measured and compared with the injection site. This was to determine the optimum ratio of signal to potential artefacts or background. The ratio improved as time went on and an hour provided a strong ratio of injection site to organ uptake. Longer periods may have impacted on the radioactivity remaining in the zebrafish (Figure 5.2C).

The administration of FDG-PET was optimised and zebrafish could now be reliably and consistently scanned. The next stage was to determine if tumours could be detected by FDG-PET. V12 RAS zebrafish with tumours were injected with FDG-PET and scanned. PET scanning readily resolved the presence of fin 157

tumours whilst tumours on the body and tail were not visible (Figure 5.2B).

The tumour was readily resolved from the body of the fish but signal was not significantly higher than in normal tissues. In fact several organs showed much higher FDG uptake. Organs from 6 wild type fish and 6 V12 RAS tumour burdened fish were pooled, along with wild type muscle tissue and 6 fin tumours. The samples were measured for radioactivity and normalised to weight. It was found that despite strong visual resolution of tumours the counts per milligram of tissue were far lower than for the organs (Figure 5.2D).

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D Mean NUV Injection Site Vs Brain C 2.00 1.80 1.60 1.40 1.20 1.00 0.80 0.60 0.40 0.20 0.00 5 10 20 30 45 60 Time (minutes)

Figure 5.2 PET scanning of FDG-PET can detect tumours on the zebrafish and provide good resolution on organs. A) Wild Type zebrafish were injected intramuscularly with 4.5 µL of FDG-PET tracer. Fish were recovered for an hour before being euthanised and scanned. Detection of the eyes, brain, heart and intestine could be resolved in the image. B) A V12 RAS zebrafish with a fin tumour with FDG-PET. The lesion is clearly differentiated from the background tissue signal. C) Quantification of the radioactivity detected in a pooled sample of organs from wild type and tumours from V12 RAS. Despite the ready resolution of the tumour the uptake of FDG was much lower than the brain or heart. D) The mean SUV of the injection site in ratio to the brain revealed that a 1-hour incubation had reduced noise from the injection site (Error bars=S.D.) N=6.

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5.4.3 The testing of a novel FA tracer FTHA in the zebrafish The tracer administration and scanning conditions had now been optimised and testing a novel tracer was now possible. The FTHA had been developed as a FA based probe (DeGrado et al., 1991) and therefore was dissolved in a solvent. Initially the tracer was injected with the dorsal musculature. The initial scans revealed that the tracer had accumulated at the injection site. Incubation after injection demonstrated that this signal was long lasting and proved a source of noise (Figure 5.3A and quantified in Figure 5.3D). Despite this, testing with a V12 RAS tumour burdened fish revealed that tumours on the tail could take up the tracer. The FTHA tracer was dissolved in a solvent and for this reason it was hypothesised that it may be possible to administer the tracer to the zebrafish by soaking. The tracer would be taken up in the water via the gills or mouth. A preliminary test revealed that soaking produced clearer images with no noise (Figure 5.3B). The fish were soaked in a range of radioactive concentrations for one hour. After an hour the fish were euthanized and scanned. The radioactivity of the fish post scan was also quantified (Figure 5.3C). The technique provided clean signal, with organs easily resolved. The noise provided by the injection site was absent. Additional benefits included reduced stress to the fish and reliable dosing of the radioactive signal.

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Figure 5.3 FTHA is easily administered by soaking and organs were resolved from the circulatory signal. Tumours could be detected. A) V12 RAS zebrafish with fin tumours were injected with 4.5 µL of FTHA. Fish were incubated for two hours before euthanasia and scanning. The injection site has a high signal but the tumour is detectable and shows quite high intensity of uptake. B) A comparison of a wild type zebrafish injected with FTHA and one soaked in water dosed with FTHA. Fish were euthanized after 2 hours and scanned. The soaked fish does not show an artifact from injection and the signal in organs is clearer. A higher signal is seen in the circulatory system of the fish. C) Quantification of the radioactivity of fish post-scan after injection with FTHA or soaking with FTHA. Soaking with FTHA resulted in much higher and consistent activity post-scan. A dose that was scalable to starting activity administration. D) Showing normalised uptake values at injection site, normalised with brain uptake at different time points post- injection. Left hand graph shows average values from regions of interest. Right hand graph shows maximum voxel intensities from regions of interest. Overall it can be seen that injected tracer takes considerable time to be fully transported away from the injection site. All error bars depicted represent S.D, except C, which is S.E.M, N=6 for all.

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The detection of V12 RAS tumours in different locations was a promising finding. The V12 RAS model develops tumours at a relatively slow rate. The tumours also do not grow rapidly. To test novel tracers a more rapid tumour model may be beneficial. Zebrafish injected as embryos with the MiniCoopR construct develop tumours in a V12 RAS+/- background with reduced latency. The tumours also grow faster and represent aggressive malignancies. This would mean rapid tumour development for novel tracer testing but also could be used with the FTHA to look at FA uptake in more rapidly growing lesions.

GFP-MiniCoopR was injected into fish with a V12 RAS+/- background. The tumours appeared within 8 weeks. The fish were soaked for one hour in 200 mL water containing 100 MBq of FTHA. Fish were then euthanized and scanned. The tumours were readily resolved by FTHA and the signal within the fish was very high (Figure 5.4A and B). Tumours in all locations were detectable. The tumour signal varied between fish but was always detectable.

The organs of these fish were collected and taken for analysis. The development of FTHA soaking allowed for an attempt at sequential studies. Six V12 RAS zebrafish with tumours were soaked in FTHA and anaesthetised with cooling. The fish were scanned with a 5-minute scan and 20 minute CT. The fish were then successfully recovered. The fish were treated with 10 µM LPL inhibitor (GSK 264220 A) for 7 days. After this fish were soaked again and then culled before scanning. The results revealed a small but non-significant reduction in lipid uptake (Figure 5.4C). However, it also acted as a proof of principle and demonstrated that longitudinal studies were possible with the correct settings.

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Figure 5.4 GFP MiniCoopR driven tumours in a RAS background have higher FTHA uptake. With the new methodology for FTHA sequential scans are possible. No significant difference was noted after a trial treatment with LPL inhibitor GSK264220A. A+B) GFP- MiniCoopR derived tumours were soaked for 1 hour in 600 MBq FTHA and scanned. FTHA detected all visible tumours. Tumours are indicated with white arrows. Several fish had tumour tissues within the tail that were not detectable visually. C) The same zebrafish were recovered and treated for 7 days with the LPL inhibitor GSK264220A, they were again soaked for 1 hour in 600 MBq FTHA and scanned. No significant difference was detected in FTHA uptake within the tumours. The images of the zebrafish are not shown due to a lack of detectable difference. Error bars = S.D, N=6.

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5.5 Discussion The study here has established the use of zebrafish as a preclinical PET screening model. It has also been demonstrated that the zebrafish could be used in studies with pre-existing probes. Furthermore this study demonstrates the use of a FA based PET probe. Finally, initial early studies with a longitudinal approach have been developed. All of the techniques listed here do not require modification of existing small animal PET scanners and could be applicable to a number of groups.

Use of small animals in pre-clinical studies has been established for many years. Small animal PET scanners have been used in studies exploring diabetes, cancer and in preclinical drug testing among others (Iwamoto et al., 2014, Hueper et al., 2012). These studies have resulted in many developments in these fields but they are often expensive and lengthy. Attempts have been made to increase throughput by use of multiple mouse scanning (Yagi et al., 2014) but using the zebrafish to replace the early stages of PET pre-clinical testing could speed up screening, dramatically reduce costs and supporting the three R’s of animal testing (replacement, reduction and refinement). Several other groups have been interested in utilising non-mammalian species for PET scanning. A paper previously mentioned screened a wide range of fish species successfully with FDG-PET. A footnote mentioned that the inability to scan zebrafish was due to poor resolution of scanners (Browning et al., 2013). This study proves that the technology is now sensitive enough to make this possible.

The scope for these studies is large. Using the zebrafish can give information about the rate of excretion, the uptake in particular organs and the time of incubation for a tracer. Screening can also provide valuable information about administration. The FTHA tracer required a longer incubation period with injection, over 2 hours, as the tracer remained at injection site. Bath administration provided better distribution. Longer incubation periods or circulatory administration is therefore suggested for mammalian studies. Finally the potential toxicity of a probe, perhaps one that terminates at a certain metabolic point, could be identified and prevent the use of this probe in mammals. 164

5.5.1 Anaesthesia by chilling was successful The development of a PET tracer required the optimisation of anaesthesia and injection site. Sadly, for injected tracers the anaesthesia methods tested were not suitable. Long-term anaesthesia requires water flow over the gills to provide oxygen. With both tricaine and chilling the lack of water flow and the resulting stress would have impacted on the survival of the zebrafish during the scans. The alternative would be sequential anaesthesia for injection and prior to scanning but this would also greatly stress the fish. Therefore the decision was made to euthanize the zebrafish for all scans that required injection or prolonged scan times (post-injection and incubation). Long-term anaesthesia of zebrafish has been performed for surgical experiments. The anaesthesia was achieved by directing fluid over the fish gills through the mouth of the zebrafish. The anaesthetic plane was maintained for over 40 minutes (Collymore et al., 2013). This would be suitable for PET scanning. If a modified setup could be developed for the zebrafish that encompasses a water flow system it would allow for sequential scanning. If the zebrafish became a more readily used preclinical model the modifications required may be developed. Testing a water circulation system would require careful development. Introduction of flowing water into PET scanning systems carries risks. Safe modifications would be a priority if the zebrafish were to replace mammals in the full range of tests.

Despite the caveats noted for injected tracers, the use of chilled water as an anaesthetic was successful if unusual. Studies have shown that Tricaine can interfere with glucose metabolism in similar teleost fish species (Chavin and Young, 1970) and a personal communication confirmed this may be the case in the zebrafish. The revelation prompted the exploration for another form of anaesthesia. Chilling has recently been identified as a non-aversive form of anaesthesia. The graduated chilling and maintenance of a chilled environment allowed for induction of anaesthesia suitable for injection (Chen et al., 2014a, Collymore et al., 2014). When the fish recovered they were not indicating discomfort. Swimming was rapidly resumed and respiration of these fish was normal. The combination of chilling with the FTHA soaking was ideal for reducing stress to the fish and improving survival.

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5.5.2 Reliable administration methods were developed for both tracers The development of a reliable injection protocol was critical and there was unexpected diversity between injection sites. The peritoneal injections into the stomach cavity presented some issues. The volume required for a strong signal, 4.5 µL, was large and appeared to show distension of the peritoneal cavity. The variation in radioactivity after injection at this site suggested some loss of the tracer. The variability may be due to loss of the tracer by retrograde flow at the injection site, possibly caused by the high pressure. Variation may have also come from the presence of reproductive tissues in the zebrafish. Furthermore the injection site did seem to lead to discomfort in some animals. The muscle would provide a firmer injection site and the tail was initially trialled however the primary trial indicated that the tail was not ideal. Several zebrafish in the V12 RAS model have been found with tumour tissue in the tail. They also can show signs of muscle wastage (Michailidou et al., 2009a). This would complicate the use of this injection site in tumour tests. This may have contributed to unreliability at this site. Regarding these issues the dorsal muscle was considered. This tissue rarely showed tumour development and was also consistent between all zebrafish tested. The site was determined to be optimal. It also produced less signs of discomfort or injury to the zebrafish. The 4.5 µL volume here did not distend the tissue and tracer uptake was very consistent. There is a dorsal cavity at this site that is likely the cause of the lack of pain or tissue distension in the zebrafish.

Injection of FTHA generated large signals at the injection site. The FTHA probe was dissolved in a solvent. This or the nature of the FA itself may have caused the high signal at the injection site. Administration of glucose to this site would have been readily metabolised by tissues, an excess of FA may be perceived as toxic and the immune system may have been recruited to mop up the excess FA. Macrophages are able to phagocytose fats within tissues. The production of foam cells in atherosclerotic plaques is testament to this (Schrijvers et al., 2007). Equally the musculature may have taken up the FA at the site of injection. A further explanation is the retention of FA by the lymphatic system.

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The lymphatic tissues trap oils used as adjuvants in vaccines (Gnjatic and Bhardwaj, 2013). Further tests would be required to determine why the injection site retained such high activity. Examples would be testing a vehicle only fluorescent solution or looking at the metabolism of the FTHA probe.

The soaking was an idea due to the non-dissipation issue, the water the fish were swimming in could be dosed and it was hypothesised the FTHA would enter through the gills and gut of the zebrafish. The tracer did not appear aversive to the fish. The strength of the probe ensured that only small amounts of FTHA were required in the water, which prevented any dramatic changes in composition. The methodology of soaking reduces the exposure to the tracer for researchers, as equipment does not need to be handled and there is reduced stress for the fish as there is no anaesthesia or injection. The tracer soaking also increased the amount of radioactivity in the fish. It was found that a 5-minute PET scan now provided enough detail to be used in measuring uptake. This reduced the scan time to 25 minutes, increasing the number of fish scanned. FDG-PET was also trialled by soaking but the resulting scans only showed very weak uptake as predicted by its composition. The composition of future tracers will likely ensure both injection and soaking methodologies are required so it is useful that the protocols for both have been partially established.

With FTHA sequential scanning was possible. The high radioactivity present in the fish meant that the PET scanning time could be reduced to 5 minutes. Chilling was used as longer anaesthesia could be maintained and the fish still recover. The fish were anaesthetised and maintained at 10 °C throughout the scan. After fish recovered they were given an LPL inhibitor to see if lipid uptake could be affected in the tumour or surrounding tissues. After a week of drug administration the fish were again soaked in FTHA. This time they were euthanized with MS222 prior to scanning. This proof of principle experiment demonstrated the potential use of sequential scanning for drug testing or other interventions. Time lapses could be taken to investigate tumour metabolism over time. If longer-term anaesthesia can be developed fish stress would be greatly reduced and sequential scans could become a valuable addition to

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zebrafish PET.

For both tracers incubating the zebrafish with the tracer was a further stage of optimisation. A simple time course indicated those longer incubation periods, extending to an hour, for both tracers was optimal. This fits with the protocol for patient scanning. Patients are administered with FDG for PET scanning and an hour is standard. It could be a reflection of the similarity of metabolism and metabolic uptake between species.

5.5.3 Scans showed that both probes worked in the zebrafish and revealed that FTHA was identifying tumours undetectable by FDG-PET The zebrafish PET scans fit with the findings of another PET study in several fish species. The organ uptake was also similar to human uptake localisation and the resulting SUVs. The brain and heart provided the strongest signal (Engel et al., 1996). The similarity and conservation between species with SUVs is known. Mouse studies have shown extensive similarities despite differences in size and in some metabolic functions (Hutchins et al., 2008). The scans showed that FDG-PET could be used to identify tumours in the zebrafish. The uptake in tumours was weaker than in the brain and heart but could still be identified as the surrounding tissues exhibited little uptake. The finding again mirrors the experience in PET scanning where tumours often exhibit weaker uptake than in the brain and heart. The scans equality with that of human PET scanning supports the use of the zebrafish as a preclinical PET model.

Several tumour sites were not detected by FDG-PET in the zebrafish. In humans prostate cancer is harder to detect with FDG (Salminen et al., 2002). Several other tumour types and stages are also not detectable. These cancers sometimes present with different metabolic profiles, prioritising FA or glutamic acids to provide metabolites. The FTHA, unlike FDG, was able to detect lesions on the body and tail in the spontaneous RAS background. These lesions appeared to be preferentially consuming FA. Much of the work in this thesis has mentioned FA uptake as a method exploited by tumours and this study further supports that this is the case in this model. To investigate why these tumours

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are not taking up FDG but FTHA, tumours with no apparent glucose uptake could be sectioned and probed for proliferation markers and glucose transporters. Antibodies looking for (Glucose Transporter 1 or 4) GLUT1 or GLUT4, or similar transporters versus FA transporters, could provide this information.

The MiniCoopR derived tumours showed very high uptake with FTHA. These tumours grow very rapidly. Within the V12 RAS mutant fish line tumours often arise in adulthood and develop relatively slowly, growing over the course of months. This is compared to the MiniCoopR lesions, where tumours can appear as early as three weeks and grow very rapidly, between as much as 2mm3 to 7mm3 a week. Literature suggests that very proliferative lesions use FA uptake and the high FTHA levels in these tumours could be a result of their higher proliferation. FDG may also have increased uptake and that experiment would be valuable and may shed light on the metabolism of these rapidly proliferating lesions.

The protocol has been well optimised and both FDG and FTHA scanning are now possible in the zebrafish. It would be prudent to talk in more detail about the novel FTHA tracer used in these studies. The results here have shown some critical findings about the use of the tracer going forward. The FTHA probe was synthesised from a FA backbone. The probe was designed to address questions about lipid metabolism in tumours. The critical information after a tracer is found to be taken up by tissues is to establish the metabolism of the tracer. Organs from FTHA soaked fish were dissected and analysed. The results are still pending on these experiments but preliminary assays suggest the FTHA is metabolised rapidly by the tissues, the parental probe is reduced after one hour. Changes in mass of the tracer signal suggest that the FTHA may be incorporated into larger lipids within the cell. Mass spectroscopy on specific metabolites would indicate the time course for FTHA in the tissues. Understanding the metabolism of the probe is of importance. Excretion of FDG- PET by patients and rapid clearing ensure a reduced exposure to radioactivity and a clean background to visualise tumours.

The studies here show that the FTHA tracer may have to be administered in the 169

circulation. The tracer did not appear to highlight adipocytes in the zebrafish. These tissues are located around the gut and under the skin. Adipocytes would also be seen near the gills (Imrie and Sadler, 2010). However, mammalian adipocytes are more frequent in number and it would be interesting to see if these are identified. There are also considerations about diet. The zebrafish have a highly regulated diet and if the tracer was administered to humans then the variability in diet may alter lipid uptake. However, tumours will take up lipid independent of the whole metabolism. The uptake of FA by adipose can be limited by influencing diet. Studies in mice may show the effect of adipose on the tracer as they have adipose deposits similar to humans like the mammary fat pads and adipose present around the stomach. Humans have the added complication of obesity. Several patients will have large subcutaneous or visceral fat stores that prevent identification of lesions. If the tracer is still able to function with increased adipose then the possibilities increase. The tracer could be used in a wider range of applications, looking to identify atherosclerotic plaques, studying the development of therapies targeting adipose. A vast number of patients experience metabolic diseases related to lipids and a tracer may be beneficial in studying the effect of anti-obesity medication on FA uptake. A further application could be in the study of cachexia. Cachexia is a condition present at the end stages of a number of diseases. The wasting of muscle is combined with lipolysis of adipose stores and a high volume of free FA in the blood stream (Arner and Langin, 2014). This condition is not fully understood but a probe able to readily track FA uptake may indicate why the body is undergoing this metabolic shift. Further conditions, which require FA investigation, include diabetes. High levels of FA in the muscles of diabetic patients can lead to muscular dystrophies and damage to tissues. Identifying patients at risk may help the condition be treated (Goodpaster et al., 2000).

The scope of testing is also wider in other ways. There are a large number of zebrafish models representing many different diseases. The ease of genetic manipulation ensures a growing number of models. This context means the possibility of studying glucose uptake in a zebrafish diabetes model after screening with chemical compounds. As multiple fish can be simultaneously scanned there is also the aspect of high throughput tracer studies. This could 170

be incorporated into drug screening, even potential toxicity testing, or investigating particular mutations and the effect on metabolism.

Principally the zebrafish appears suitable for the early studies of tracers. Costs for early stage testing are much reduced. Tissue samples from the zebrafish revealed information on the localisation of the tracer, processing of the tracer and information about administration of the tracer. Information gathered without the use of mice or rat models. The work is also high throughput with over 18 fish scanned in one 4-hour session. A panel of probes could be screened to establish the best tracers for a particular metabolite. Or the tracer could be screened for administration methods in one day. The work here also highlights that modifying a PET scanner is not required to perform zebrafish studies making zebrafish PET scanning available to all groups with access to a small animal PET scanner with high enough sensitivity.

The only limitation on longitudinal studies is on fish survival. However, simple devices that allow for the incorporation of flowing water systems with PET are available. The trials shown here have allowed for the consideration of adding these systems to existing scanners that would overcome this hurdle. The ability to test the effects of drugs on metabolism would be an amazing addition. The range of experiments available is exponential. Different metabolic models, combined with different compounds and different tracers. The work shown here shows it is possible. Establishing a reliable PET tracing protocol has allowed us to explore lipid uptake and glucose uptake in the zebrafish model. Confirming FA uptake in the tumours alongside a novel method for testing PET tracers.

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6. General Discussion

Melanoma rates are rising still and despite exciting developments in the field of targeted therapy and immunotherapy there is still a lack of cheap and effective long-term therapeutic options (Arnold et al., 2014). One method for developing new therapeutics is to identify critical pathways to melanoma progression. Here, transcriptome analysis, mass spectroscopy analysis and PET scanning have all demonstrated the contribution of lipid metabolism, specifically FA uptake, to melanoma development. The study has also investigated LPL in further detail and established that LPL inhibition may provide therapeutic benefit, particularly in combination with FASN inhibitors, which are already in clinical trials. Finally, the development of a simple PET scanning technique in the zebrafish may open up a whole new range of techniques for studying metabolism in melanoma and in many other zebrafish models.

The transcriptome analysis and mass spectroscopy analysis demonstrated that the V600EBRAF model likely developed by an independent mechanism to the V12 RAS model, there was very little overlap between the transcriptomes. The V12 RAS RGP and VGP represent step-wise progression, in each case the VGP sample encompassed many of the features of the RGP model but had many unique genes or metabolites. The mass spectroscopy PCA analysis also reflected this stepwise progression. The transcriptome analysis first highlighted lipid metabolism as an enriched pathway in the V12 RAS VGP melanoma model. There were also a large proportion of lipid metabolism genes in the differentially regulated gene list. A study that recently set out to identify lipid metabolic genes associated with malignant melanoma in human microarray dataset identified over 33 genes involved in lipid metabolism that were over 10- fold overexpressed (Sumantran et al., 2015). This indicates large changes in lipid metabolism in melanoma similar to that seen in the zebrafish transcriptome. To this end a short-list of genes was made from the lipid metabolic genes up regulated in the study. Promisingly several of the genes in the short-list had been investigated in melanoma or other cancers. These included scd and fabp7. Other genes included the insulin receptor-2 (irs2) gene IRS2 exhibits its effect on melanoma via increasing the expression of lipid

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metabolism genes like FASN (Reuveni et al., 2013, Manchenkov et al., 2015). The transcriptome and mass spectroscopy also revealed how nearly every aspect of lipid metabolism was altered in the tumour. Many of the changes are likely to promote malignancy, particularly the changes in plasma membrane metabolism, loss of SM and changes to choline metabolism are known tumour drivers (Fedele et al., 2013, Bizzozero et al., 2014). The screen generated many hypotheses and testing these could provide valuable therapeutic insights. It may be valuable to probe for some of the key regulatory proteins in plasma membrane metabolism. In addition a more focussed mass spectroscopy analysis containing controls may aid in more detailed analysis of the plasma membrane alterations.

The two most significant genes in the microarray were dgat1 and lpl. An analysis of the lipid metabolism genes revealed that FA metabolism was a central pathway with all aspects being highlighted as potentially activated. FA metabolism is essential to nearly every lipid metabolic pathway, however there is a surprising lack of studies investigating FA metabolism in melanoma. Many of the changes detected in the mass spectroscopy analysis, including the extensive changes in plasma membrane composition would rely on the provision of FA and FA derivatives. The PET scanning data gathered with FTHM showed that V12 RAS tumours took up unconjugated FA, in addition faster growing HRAS driven lesions took up more FA. For this reason it was fitting that the two highest expressed V12 RAS exclusive genes would be LPL and DGAT1. LPL mediates the liberation of FA from TG and DGAT1 drives the rate-limiting step in synthesis of TG from DG and FA. DGAT1 is under investigation, as it appears to contribute to tumour cell proliferation. In this study DGAT1 did promote the growth of tumours in a MiniCoopR assay yet was unable to increase the rate of tumour appearance agreeing with studies (Bagnato and Igal, 2003). However, LPL was able to increase growth and increase the rate of tumour appearance. LPL may be able to contribute to the initiation of tumour nodules. LPL was expressed in human melanoma samples, both in expression and in staining of patient samples. The studies here support that the provision of FA is key to LPLs mechanism of action. That is because loss of LPL could be compensated by FASN expression in cell lines and LPL 173

and FASN inhibitors worked in synergy in FASN expressing cell lines. Studies in breast cancer have noted that both FASN and LPL are expressed in highly proliferative tissues suggesting both pathways could be active within tumours (Jung et al., 2015, Kuemmerle et al., 2011). This is one of the reasons it is important to study melanoma lipid metabolism. Many studies of LPL have referenced CD36 and CD36 was expressed in melanoma cell lines but heterogeneously. Knockdown of LPL had a variable effect on CD36 levels, reducing them in A375P cells but increasing in WM852. Furthermore LPL overexpression in the zebrafish led to large increases in CD36 expression when measured by qPCR. This suggests LPL regulates CD36 expression as described in the literature (Augustus et al., 2004). How LPL mediates gene expression is not currently known but LPL is internalized and can be found in the nucleus of cells (Gong et al., 2013). As CD36 was not identified in the transcriptome or by qPCR in the V12 RAS model it is unlikely to be an independent factor for melanoma progression in this model. It would seem that CD36 and LPL may work in synergy to increase FA uptake for tumours. The increase in FA uptake in WM852 cells may be a consequence of increased CD36 expression but it is also important to establish whether other lipid metabolic pathways could compensate for LPL inhibition, prime targets include transporters like fabp7. Western blotting for a larger panel of lipid transporters would help elucidate this mechanism.

The role of LPL is still to be fully explored and could encompass proliferation, survival and migration (Carter et al., 2012, Kamphorst et al., 2013). Yet, the effect varied between cells dependent on their expression of FASN. The cell assays showed that FASN and LPL inhibition could work in synergy in cell lines. The expression of FASN appeared to correlate with BRAF expression. RAS driven cancers do have a tendency towards lipid scavenging and it could be that BRAF driven lesions are more likely to use de novo FA synthesis (Kamphorst et al., 2013). It would be interesting to see if, like the cell lines, FASN expression is present in BRAF driven tumours. Staining human samples and probing publically available databases may show if this is a significant pattern. An indicator like this may aid in stratifying treatments for patients. LPL inhibition may work well in RAS driven lesions but BRAF driven lesions may require a 174

combination FASN and LPL inhibition.

The work in this thesis has focused on LPL in terms of proliferation and survival. However, studies have demonstrated a role for LPL in migration. In cervical carcinoma LPL is often overexpressed or sometimes translocated. The translocation is a PEX5-LPL or LPL-PEX5 translocation that overexpresses the non-catalytic C-terminus of LPL and increases migration in proportion to overexpression of LPL. The proliferative effect was absent, supporting the role of FA liberation for proliferation. The theory was that the LPL was aiding binding to heparin in the extracellular matrix and increasing migration via both signalling and bridging molecules (Carter et al., 2012). One experiment could be to knockdown LPL and see if this affects the migration of melanoma cells in a migration assay. An in vivo invasion assay using zebrafish xenografts and LPL knockdown may show how the extracellular matrix is contributing to this effect, if indeed the effect is present in melanoma. Another experiment is to overexpress LPL in human melanoma cell lines. It will be interesting to see if LPL has an effect on cell proliferation. Supplementing the media with TAG was the only way to increase the effectiveness of LPL overexpression in a study by Kummerele et al. Therefore it would be important to express LPL and provide a substrate to the cell to fully activate LPL and to allow it to generate FA for the cell to consume.

Another interesting area to consider is autophagy. Autophagy is a common response in cell lines stressed by nutrient deprivation and if the LPL siRNA cells are not breaking down their lipid stores the presence of autophagy may indicate if they are starving and unable to use lipid stores or if they do not use them as they have compensated and have an alternative lipid supply. For preliminary data autophagy was investigated by western blot and electron microscopy. WM266-4 cells and WM852 cells were treated with LPL siRNA and autophagy markers, LC3B and p62/SQSTM, were probed by western blot. Chloroquine was used as a negative control to block autophagy, whilst rapamycin was used to induce it. Currently the rapamycin and chloroquine give unexpected and conflicting results and this must be addressed in further experiments. However, LPL siRNA resulted in an accumulation of the lower molecular weight LC3BII

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isoform. In the WM852 cells p62/SQSTM also accumulated, it accumulated to the same degree as the CQ treatment (Figure 6.1B). This data infers that LPL siRNA might be leading to an inhibition of autophagy occurring after autophagy induction. To assess the potential point of autophagic inhibition WM852 cells were treated with LPL siRNA and chemically fixed and fractured before viewing with electron microscopy (Figure 6.1C). The microscopy revealed that WM852 cells treated with LPL siRNA had large autolysosomes occurring in the cytoplasm suggesting a defect in the late stages of autophagy. Further to this many LPL siRNA treated cells had mitochondria with absent cristae (Figure 6.1D).

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Figure 6.15 LPL siRNA induces defects in autophagy, accumulation of autophagic vacuoles/autolysosomes and loss of mitochondrial cristae. A) WM852 cells were treated with LPL siRNA and DMSO, CQ or rapamycin and lysed at 10 hours. Lysates were probed with p62, LC3B and ERK2 antibodies. Loss of LPL resulted in induction of LC3B with stabilisation of p62. N=3 B) WM266-4 cells were treated as with WM852 cells and did not show any significant difference. N=3 C) WM852 cells were treated with scrambled siRNA, chemically fixed and imaged with electron microscopy. No abnormal phenotype was detected. D) WM852 cells treated with LPL siRNA and imaged with electron microscopy showed large autolysosome vacuoles (upper panel indicated with black arrows) and several cells also no mitochondrial cristae (lower panels, white arrows).

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The identification of a potentially novel LPL mediated autophagy pathway was exciting. LPL loss was able to inhibit autophagic flux, as indicated by the increase in LCB3II and stabilisation of p62. There was a large increase in autolysosomes when cells were viewed with TEM. These experiments provide some evidence of autophagy inhibition. However, the experimental controls need to be optimised to increase reliability. If it is a true effect it would appear that it may be due to provision of FA as WM266-4 cells, with greater FASN and de novo lipid, did not show the same inhibition of autophagy. The link between autophagy and lipid genes has been explored by a number of researchers. Neutral lipid lipases have been associated with autophagy and these include lipin-1 and PNPLA5. Lipin-1 mediates a number of de novo lipid pathways and is known to be up-regulated in prostate and breast cancer samples. Inhibition of lipin-1 leads to cancer cell specific reductions in proliferation. The loss of lipin-1 also sensitised the cells to autophagy inducer rapamycin (Brohée et al., 2015, Ravikumar et al., 2002), although this may have been the result of lipin-1’s interaction with AKT. In the case of lipin-1 the presence of DAG was the critical mediator for its effects on autophagy. The loss of Lipin-1 reduced lipid to the cell, inducing autophagy. Co-currently the subsequent reduction in DAG prevented the activation of phosphokinase D, resulting in inhibition of autophagic lysosomal fusion (Zhang et al., 2014). The PNPLA5 lipase worked at an earlier time point in autophagy. Research had shown that loss of lipid droplets was seen on autophagy induction due to starvation and that PNPLA5 was critical in using these lipids to form membranes for autophagosomes. Loss of PNPLA5 reduced the effectiveness of autophagy initiation (Dupont et al., 2014). Further work on this pathway is required. Studying the DAG levels within cells with LPL siRNA would be beneficial and looking at the activation of PKD. Seeing if LPL directly interacts with autophagosome machinery may elucidate a better role for LPL. It is also important to mention here that the difference in effect between cell lines of LPL siRNA may be due to the cells inherent ability to undergo autophagy. WM852 cells may have higher rates of autophagy, shown when compared with WM266-4, so would be susceptible to autophagy inhibition. This may be independent of FASN. Autophagy is still not fully understood the presence of FA may also help in the synthesis and fusion of the

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various autophagy membranes. The mass spectroscopy does indicate very big changes in membranes, both for lipids on the extracellular and intracellular surface. The size of the autophagosomes suggests they are unable to resolve. Perhaps this is a defect in autophagosome and lysosome fusion. Without this stage the aggregation of autophagosomes will begin to destroy the cell. This would make the mechanism similar to that of other neutral lipases like lipin-1 (Brohée et al., 2015). Seeing with immunocytochemistry if LPL associates with autophagosome machinery will be important. As will studies probing the levels of DAG in the cell in LPL knockdown lines. Overall this will be an area of intense future investigation. The next phase of experiments for LPL will be looking at human melanoma cell xenografts in zebrafish treating the cells with inhibitors and siRNA. A stable knockdown cell line would also be useful. The system could be made inducible to allow knockdown studies of WM852 cells in vivo. This experiment may be a good example of the contribution of LPL to growth and migration. Another experiment is to inhibit LPL in the zebrafish melanoma model to see if this affects tumour latency or growth. There are endogenous inhibitors of LPL and one of the most prominent is ANGPTL-4 (Dijk and Kersten, 2014). Over-expressing ANGPTL4 may reduce the proliferation of tumour cells in zebrafish or delay the rate of tumour appearance. Preliminary studies conducted in the lab seem to demonstrate that ANGPTL4 overexpression does reduce tumour appearance rate and tumour growth. Further analysis is required but it would be interesting to see if LPL activity is affected and see if loss of LPL activity is responsible for this effect.

LPL is a classic readout for mitochondrial health, although this has never been fully explored, the effects on WM852 cells may be based on changes to autophagy or changes in mitochondrial health. The loss of LPL is clearly impacting on a range of cell processes. The loss of LPL mirrors the phenotype for ATGL knockdown and overloading with VLDL. It seems loss of LPL may lead to excessive TAG levels, demonstrated by the neutral lipid stain. This would cause the loss of cristae and damage the mitochondria (Aflaki et al., 2011). It suggests LPL helps decrease the TAG within the cell perhaps lysing or contributing to the lipophagy of lipid droplets to generate FA. This would explain why loss of LPL leads to reflexive increases in FA uptake whilst lipid droplets 179

are simultaneously increasing. It is critical to investigate this further. Overexpression in the zebrafish does seem to reduce lipid droplet number in the tissues. LPL is known to co-localise to lipid droplets but high-powered microscopy with labelled LPL may demonstrate if LPL is breaking down lipid droplets. Inhibitors of ATGL for example may be rescued by high LPL expression. It would be interesting to see if the two enzymes support each other.

The PET scanning was a great success. The only caveat for the experiment was a need for circulating water for long-term anaesthesia. This is a relatively simple issue to amend with engineering and it is predicted to be solved soon. This would allow for longitudinal studies on the zebrafish and would make it a complete model for early tracer testing. The FDG-PET scans mimicked many of the features in human melanoma and the analysis of the zebrafish with FDG and FTHA revealed some metabolite preferences for different tumours. FTHA uptake demonstrates that large tumours on the body and tail, or rapidly growing tumours take up large volumes on unconjugated FA. Studying lipid uptake in melanoma with this tracer, again, demonstrates the value of lipid scavenging in melanoma and the targets in this pathway. There is another benefit to studying lipid metabolism.

The model revealed important information about FTHM. Tissue administration would be unlikely to work as the tracer does not dissipate. Circulatory or oral consumption may be best going forward into mammalian studies. Data was also gathered on the toxicity and metabolism of the tracer before the use of mammalian models. One of the next stages is to analyse the metabolism of the tracer in more detail. Preliminary data has been gathered and now that is complete more in depth studies on the zebrafish are to be performed. This includes mass spectroscopy analysis in a range of time points after FTHA administration. Sectioning tissues and analysing their composition for signs of the tracer are also in the future for a more detailed picture of the tracer’s uptake (Joshi et al., 2014). The zebrafish is a valuable initial screening step. The next stage is testing in mouse models. It is important to see how larger amounts of adipose affect the FTHA tracer. The FA tracer FTHM itself was could prove

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valuable in investigating FA metabolism in animal models and in eventually in a clinical setting. Treatments that target metabolism may require tracers that track FA uptake. This could monitor patient progression. This tracer in particular may be applicable to several other research fields. Obesity, metabolic syndromes and lipid-based conditions such as atherosclerosis are all under intense investigation. Independent of the FTHM benefits, this demonstrates the value in using the zebrafish to screen novel therapeutics. Using zebrafish in PET scanning could be a powerful investigative and screening tool for disease models and novel PET tracers. It will be exciting to test another novel tracer or to see if FTHA is successful in mouse studies.

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7 Supplementary

Supplementary Table 1 There are many genes related to known cancer pathways in the lipid gene dataset. All genes from the VGP dataset with GO terms relating to lipid metabolism are presented with fold change. Genes were cross-referenced with literature searches performed on NCBI Pubmed. Genes identified in studies as relating to melanoma are in bold. References provided.

Unigene Fold Reference Gene Code Change slc27a2a Dr.34746 40.54 dgat1a Dr.79871 38.47 ndufa4 Dr.75972 32.50 yjefn3 Dr.89654 28.77 zdhhc2 Dr.39077 25.51

(Slipicevic et al., 2008, Goto et al., 2010, fabp7a Dr.20850 24.52 Liu et al., 2012) lpl Dr.75099 9.54 si:rp71-40n14.1 Dr.80845 9.51

(Proikas-Cezanne et al., 2015, Müller wdr45 Dr.79570 7.83 and Proikas-Cezanne, 2015)

(Reuveni et al., 2013, Manchenkov et al., irs2a Dr.113626 6.67 2015)

(Sumantran et al., 2015, von Roemeling scd Dr.105241 6.53 et al., 2013) zgc:92162 Dr.87011 6.04

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wu:fi17f06 Dr.76153 5.84 pla2g15 Dr.360 5.32 abca1a; wu:fa03d03 Dr.35358 4.65

(Sumantran et al., 2015, von Roemeling scdb Dr.81181 4.14 et al., 2013) zgc:101553 Dr.37073 4.13 pisd Dr.22015 3.68 mfsd2aa Dr.86869 3.65 sigmar1 Dr.84840 3.08 lipf Dr.77330 2.97 pnpla7b Dr.75248 2.96 pla2g7 Dr.81839 2.62 hsd17b12b Dr.20571 2.52 lycat Dr.80904 2.34 tamm41 Dr.85587 2.20 ormdl1 Dr.27136 2.16 alg6 Dr.34150 2.16 pld1a Dr.86329 2.07 pla2g6 Dr.78310 2.01 (Liu et al., 2013, Falchi et al., 2009)

Unigene Fold Reference Gene Code Change

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gde1 Dr.4501 -2.05 slmo2 Dr.115452 -2.14 zgc:153704 Dr.87016 -2.31 atp11c Dr.77497 -2.34 wu:fd57b02 Dr.80070 -2.42 sult1st3 Dr.81478 -2.84

(Vogel et al., 1994, Pencheva et al., apoeb Dr.23502 -2.99 2012) acsf2 Dr.79130 -2.99 b4galnt1a Dr.42820 -3.33 cptp Dr.77249 -3.40 msmo1 Dr.12110 -3.48 lpcat2 Dr.14414 -3.92 gdpd3a Dr.107569 -4.37 slc27a2b Dr.83184 -4.71 si:rp71-1g18.13 Dr.86885 -4.76 plce1 Dr.80689 -4.79 elovl1a Dr.82281 -4.86 dgat1b Dr.31557 -5.09] hsd17b12a Dr.29406 -5.21 ch25hl1.1 Dr.76197 -5.45

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lipg Dr.77883 -5.52 lipia Dr.132253 -5.60 abca12 Dr.75341 -6.20 srd5a2a Dr.82025 -6.41 ch25h Dr.82220 -7.96 osbpl3a Dr.82956 -8.23 acer1 Dr.80506 -8.31 plcd1b Dr.22968 -8.79 ptgdsb Dr.75704 -9.98 osbpl7 Dr.81634 -10.87 si:ch73-21k16.5 Dr.121648 -12.77 elovl7b Dr.78825 -12.87 cyp8b1 Dr.105274 -16.81 zgc:158621 Dr.17074 -32.75

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Supplementary Table 2 The metabolites from the LCMS analysis of VGP tumours. The fold change is compared to wild type samples. Several metabolite classes had shifts in metabolites. Fold change is given with blue indicating reduced abundance and red indicating increased abundance. Significance was determined by Kruskal Wallis one-way analysis of variance.

Fold Metabolite Change CERAMIDES AND SPHINGOLIPIDS Cer(d16:1/17:0) -2.22033 Cer(d16:1/17:0) -0.9927684 Cer(d16:1/17:0) -2.9183862 Cer(d16:1/17:0) -2.295723 Cer(d18:0/h17:0) -4.0470148 Ceramide (d18:1/22:0) 1.28630419 Ceramide (d18:1/22:0) 1.28630419 Ceramide (d18:1/24:1) 2.32192809 Ceramide (d18:1/24:1) 2.39592868 Ceramide (d18:1/24:1) 2.25153877 Ceramide (d18:1/24:1) 2.39592868 CerP(d18:1/22:0) 3.64385619 CerP(d18:1/22:0) 3.05889369 GlcCer(d18:1/23:0);GalCer(d18:1/23:0) 2.18442457 Glucosylceramide (d18:1/16:0);Galactosylceramide (d18:1/16:0);GlcCer(d18:1/16:0) 3.64385619 Glucosylceramide (d18:1/16:0);Galactosylceramide (d18:1/16:0);GlcCer(d18:1/16:0) 3.05889369 Glucosylceramide (d18:1/16:0);Galactosylceramide (d18:1/16:0);GlcCer(d18:1/16:0) 3.05889369 Glucosylceramide (d18:1/16:0);Galactosylceramide (d18:1/16:0);GlcCer(d18:1/16:0) 2.94341647 Glucosylceramide (d18:1/16:0);Galactosylceramide (d18:1/16:0);GlcCer(d18:1/16:0) 2.12029423 Glucosylceramide (d18:1/20:0);Galactosylceramide (d18:1/20:0) 2.39592868 Glucosylceramide (d18:1/22:0);Galactosylceramide (d18:1/22:0) 2.94341647 Glucosylceramide (d18:1/22:0);Galactosylceramide (d18:1/22:0) 3.32192809 Glucosylceramide (d18:1/24:0);GlcCer(d18:0/24:1) 3.05889369 Glucosylceramide (d18:1/24:0);GlcCer(d18:0/24:1) 3.18442457 Glucosylceramide (d18:1/24:0);GlcCer(d18:0/24:1) 3.05889369 Glucosylceramide (d18:1/24:0);GlcCer(d18:0/24:1) 3.18442457 Glucosylceramide (d18:1/24:1);Galactosylceramide (d18:1/24:1) 4.64385619 Glucosylceramide (d18:1/24:1);Galactosylceramide (d18:1/24:1) 4.05889369 Glucosylsphingosine;Galactosylsphingosine -2.5310695 N-Palmitoylsphingosine 1.88896869 N-Palmitoylsphingosine 1 N-Palmitoylsphingosine 1.02914635 N-Palmitoylsphingosine 0.97143085 N-Palmitoylsphingosine 0.97143085 N-(2-hydroxyhexadecanoyl)-phytosphingosine -2.641546 N-(2-hydroxyhexadecanoyl)-phytosphingosine 3.83650127 N-(2-hydroxyhexadecanoyl)-phytosphingosine 3.64385619 N-(2-hydroxyoctadecanoyl)-phytosphingosine -2.0179219 N-(2-hydroxyoctadecanoyl)-phytosphingosine -2.0107798 SM(d18:1/20:0) -1.6229304 186

SM(d18:2/22:1) -1.5410192 Sphingosine 1-phosphate;;Cer(d18:1/2:0);N-stearoyl glycine 2

ACYL GLYCERIDES

DAGe(34:1) 1.28630419 DG(35:1);Cer(d18:1/19:0) -2.3391374 MG(18:0) -1.4276062 MG(18:0) -1.5410192 TG(18:3(9Z,12Z,15Z)/14:0/18:3(9Z,12Z,15Z))[iso3] -1.7990873 TG(36:0) 2.47393119 TG(52:4);TG(50:1) -3.1969217 TG(52:4);TG(50:1) -3.1906149 TG(52:6);TG(50:2) -3.5335633 TG(54:5);TG(52:2) -3.864929 TG(54:6);TG(52:3) -3.7676548 TG(54:7);TG(52:4) -4.1366836 TG(54:7);TG(52:4) -3.9382858

LYSO -GLYCEROPHOSPHOLIPIDS

LysoPA(20:4) -1.5410192 LysoPA(20:4) -1.6644828 LysoPC(14:0);PC(O-12:0/2:0) -3.1779178 LysoPC(14:0);PC(O-12:0/2:0) -2.9030383 LysoPC(15:0);LysoPE(18:0);PC(14:0/O-1:0);PC(7:0/O-8:0);PC(15:0/0:0) -2.4168397 LysoPC(15:0);LysoPE(18:0);PC(14:0/O-1:0);PC(7:0/O-8:0);PC(15:0/0:0) -2.4356286 LysoPC(17:1);LysoPA(22:2) -1.9183862 LysoPC(17:1);LysoPA(22:2) -1.929791 LysoPC(18:0) -1.1763228 LysoPC(18:0) -1.1043367 LysoPC(18:0) -1.0976108 LysoPC(18:1);PC(O-16:1/2:0);PC(O-16:1/2:0);PC(P-16:0/2:0) -1.4436067 LysoPC(18:1);PC(O-16:1/2:0);PC(O-16:1/2:0);PC(P-16:0/2:0) -1.4382929 LysoPC(18:1);PC(O-16:1/2:0);PC(O-16:1/2:0);PC(P-16:0/2:0) -1.8237494 LysoPC(18:1);PC(O-16:1/2:0);PC(O-16:1/2:0);PC(P-16:0/2:0) -1.304511 LysoPC(18:1);PC(O-16:1/2:0);PC(O-16:1/2:0);PC(P-16:0/2:0) -1.2326608 LysoPC(18:1);PC(O-16:1/2:0);PC(O-16:1/2:0);PC(P-16:0/2:0) -2.0071955 LysoPC(18:2) -1.989139 LysoPC(18:2) -1.9927684 LysoPC(18:2) -2.0321008 LysoPC(20:1);PC(O-18:1/2:0);PC(P-18:0/2:0);PC(20:1/0:0) -3.8011587 LysoPC(20:1);PC(O-18:1/2:0);PC(P-18:0/2:0);PC(20:1/0:0) -3.5655972 LysoPC(20:3) -2.448901 LysoPC(20:3) -2.1667154 LysoPC(22:4) -1.3729521 LysoPC(22:6) -2.3419857 LysoPC(22:6) -2.3729521 LysoPC(22:6) -2.2418402 LysoPC(dm18:1);PC(O-18:2/0:0) 1.47393119 LysoPC(dm18:1);PC(O-18:2/0:0) 1.51457317

187

LysoPE(16:0) -2.3923174 LysoPE(16:0) -2.3391374 LysoPE(16:0) -2.0107798 LysoPE(20:3) -2.4329594 LysoPE(20:3) -1.6825733 LysoPE(20:3) -2.6016965 LysoPS(14:0);LysoPE(14:1) -1.7527486 LysoPS(20:0);LysoPG(20:2);LysoPC(17:1) -1.7824086 LysoPS(20:1);LysoPG(20:3);LysoPE(20:2) -1.4594316 LysoPS(20:2) -2.6690268 LysoPS(20:2) -2.077243 LysoPS(20:2) -2.9726927 LysoPS(21:0) -1.5109619 LysoPS(21:0) -1.4436067 LysoPS(21:0);LysoPC(18:1);PC(O-16:1/2:0);PC(P-16:0/2:0);LysoPC(20:4) -2.4646683 LysoPS(21:0);LysoPC(18:1);PC(O-16:1/2:0);PC(P-16:0/2:0);LysoPC(20:4) -2.3617684 LysoPS(22:0);LysoPG(22:2);PC(O-16:0/3:1) -2.6276068

GLYCEROPHOSPHOL IPIDS

PA(38:5) 1.47393119 PA(40:4) 1.94341647 PA(43:0) -1.9107327 PA(O-18:0/18:3);PA(O-16:0/20:3);PA(P-16:0/20:2);PA(P-18:0/18:2);DG(40:8) 0.55639335 PA(O-18:0/18:4);PA(O-16:0/20:4);PA(P-16:0/20:3);PA(P-18:0/18:3);PA(O- 20:0/14:1);PA(O-18:0/16:1);PA(O-16:0/18:1);PA(P-16:0/18:0);PA(P- 18:0/16:0);PA(P-20:0/14:0);DG(38:4) -1.4750849 PA(O-20:0/22:2);PA(P-20:0/22:1);DG(44:5);CE(20:0) -2.4568061 PC(14:0/dm18:1);PC(16:1/dm16:0);PC(O-14:0/18:2);PC(P-14:0/18:1) -1.304511 PC(14:1/dm18:1);PC(O-14:0/18:3) 1.32192809 PC(15:0/dm18:1);PE(18:0/dm18:1);PE(18:1/dm18:0);PE(20:1/dm16:0);PE(P- 18:0/18:1);PA(O-18:0/20:3);PA(O-20:0/18:3);PA(P-16:0/22:2);PA(P- 18:0/20:2);PA(P-20:0/18:2);2-Octaprenyl-3-methyl-5-hydroxy-6-methoxy-1,4- benzoquinone;3-demethylubiquinone-8 -1.5058909 PC(15:0/dm18:1);PE(18:0/dm18:1);PE(18:1/dm18:0);PE(20:1/dm16:0);PE(P- 18:0/18:1);PA(O-18:0/20:3);PA(O-20:0/18:3);PA(P-16:0/22:2);PA(P- 18:0/20:2);PA(P-20:0/18:2);2-Octaprenyl-3-methyl-5-hydroxy-6-methoxy-1,4- benzoquinone;3-demethylubiquinone-9 -1.4802651 PC(18:0/dm18:0);PC(20:0/dm16:0);PC(O-16:0/20:1) -2.6182387 PC(20:1/dm18:1);PC(20:2/dm18:0);PC(22:2/dm16:0) 1.55639335 PC(28:0);PE(31:0) 2.83650127 PC(31:1);PE(34:1);DG(40:2) -1.827819 PC(32:0);PE(35:0) 1 PC(32:0);PE(35:0) 0.97143085 PC(32:0);PE(35:0) 1 PC(32:3);PE(35:3);PC(30:0);PE(33:0) 2.73696559 PC(32:3);PE(35:3);PC(30:0);PE(33:0) 1.94341647 PC(32:3);PE(35:3);PC(30:0);PE(33:0) 2.18442457 PC(33:1);PE(36:1) 1.55639335 PC(33:1);PE(36:1);PA(38:2);PC(O-14:0/O-16:0);PC(O-16:0/O-14:0);PE- NMe(O-16:0/O-16:0) -3.6229304 PC(33:4);PE(36:4);PE(34:1);PA(38:5) 1.12029423 PC(34:0);PE(37:0) 1.25153877 188

PC(34:0);PE(37:0) 1.21759144 PC(34:0);PE(37:0) 1.32192809 PC(35:3);PE(18:0/20:3);PC(16:0/19:3) -1.3276874 PC(36:1) 0.86249648 PC(36:1) 0.88896869 PC(36:1) 0.83650127 PC(36:6) 1.08926734 PC(37:1);PE(40:1);DG(44:2);SM(d16:1/23:0);SM(d17:1/22:0);SM(d18:1/21:0);S M(d19:1/20:0);SM(d20:1/19:0) -2.3923174 PC(37:2);PE(40:2) -2.0565835 PC(37:6);PE(40:6) -1.2986583 PC(40:8) -1.8875253 PC(O-1:0/16:0);PC(O-15:0/2:0);PC(O-16:0/1:0);PC(16:0/O- 1:0);lysoPC(17:0);lysoPA(22:1) -2.9164766 PC(O-1:0/16:0);PC(O-15:0/2:0);PC(O-16:0/1:0);PC(16:0/O- 1:0);lysoPC(17:0);lysoPA(22:1) -2.1826923 PC(O-10:0/O-8:0);PC(O-16:0/O-2:0);PC(O-18:0/0:0) -2.6299394 PC(O-10:0/O-8:0);PC(O-16:0/O-2:0);PC(O-18:0/0:0) -3.0857646 PC(O-10:0/O-8:0);PC(O-16:0/O-2:0);PC(O-18:0/0:0) -3.2971914 PC(O-16:0/3:1) -3.4541759 PC(P-15:0/0:0);PE(P-18:0/0:0) -1.8875253 PC(P-15:0/0:0);PE(P-18:0/0:0) -1.8836208 PC(P-15:0/0:0);PE(P-18:0/0:0) -1.9486008 PE(18:4/dm18:1);PE(20:5/dm16:0);PC(30:0);PE(33:0);PC(30:0) 2.12029423 PE(20:4/dm18:1);PE(20:5/dm18:0);PE(22:5/dm16:0);PC(32:0);PE-NMe(34:0) 1.88896869 PE(20:4/dm18:1);PE(20:5/dm18:0);PE(22:5/dm16:0);PC(32:0);PE-NMe(34:0) 1.83650127 PE(22:6/dm18:1);PE(O- 18:1/20:4);PE(20:3/dm18:1);PE(20:4/dm18:0);PE(22:4/dm16:0);PE(O- 16:0/22:5);PE(O- 18:0/20:5);PC(15:0/dm18:1);PE(18:0/dm18:1);PE(18:0/dm18:1);PE(18:1/dm18: 0);PE(20:1/dm16:0);PE(P-18:0/18:1) 1.78587519 PE(22:6/dm18:1);PE(O- 18:1/20:4);PE(20:3/dm18:1);PE(20:4/dm18:0);PE(22:4/dm16:0);PE(O- 16:0/22:5);PE(O- 18:0/20:5);PC(15:0/dm18:1);PE(18:0/dm18:1);PE(18:0/dm18:1);PE(18:1/dm18: 0);PE(20:1/dm16:0);PE(P-18:0/18:1) 1.78587519 PE(36:5);PE(34:2) 1.68965988 PE(36:5);PE(34:2) 1.47393119 PG(20:1/0:0);PC(O-1:0/16:0);PC(O-15:0/2:0);PC(O-16:0/1:0);PC(16:0/O- 1:0);PC(17:0/0:0) -2.797013 PG(20:1/0:0);PC(O-1:0/16:0);PC(O-15:0/2:0);PC(O-16:0/1:0);PC(16:0/O- 1:0);PC(17:0/0:0) -2.827819 PG(22:1/0:0);PC(O-16:0/3:0);PC(O-17:0/2:0);PC(O-18:0/1:0);PC(3:0/O- 16:0);PC(19:0/0:0) -2.7634116 PG(34:3);SM(d18:2/15:0);CE(20:5) 2.18442457 PG(40:6);PC(35:2);PE(38:2);PE(40:5) -1.1309309 PG(40:6);PC(35:2);PE(38:2);PE(40:5) -1.3161457 PG(O-18:0/14:0);PG(O-20:0/12:0);PG(O-16:0/16:0);DG(34:0);DG(36:0) 1.02914635 PG(O-18:0/17:0);PG(O-20:0/15:0);PG(O-16:0/19:0) 0.97143085 Phenylbutyric acid;Benzenebutanoic acid 1.47393119 PI(34:2) 1.83650127 PI(36:0) -2.1210154 PI(38:6);PI(36:3) 1.68965988 PI(38:6);PI(36:3) 1.32192809 189

PI(39:3);PI(37:0) -2.077243 PS(18:2/0:0) -1.6553518 PS(18:2/0:0) -2.0143553 PS(19:0/0:0) -1.9183862 PS(35:0) 1.51457317 PS(35:0) 1.51457317 PS(35:0) 1.28630419 PS(35:0) 1.28630419 PS(38:0);PG(40:5);PC(35:1);PE(38:1);PE(40:4) -1.4646683 PS(39:3);PS(37:0) -0.9634741 PS(39:3);PS(37:0) -0.8953026 PS(40:7);PE(40:8) 0.88896869 PS(40:8) 1.18442457 PS(41:3);PS(39:0) -0.9030383 PS(41:4) -1.8718436 PS(41:5);PS(39:2);PC(38:6);PC(36:3);PC(40:9) -1.7570232 PS(41:5);PS(39:2);PC(38:6);PC(36:3);PC(40:9) -1.6915342 PS(41:5);PS(39:2/17:1);PC(36:3);PC(38:6) -1.275007 PS(41:5);PS(39:2/17:1);PC(36:3);PC(38:6) -1.2809563 PS(41:6);PC(38:7) -1.2630344 PS(41:6);PC(38:7) -1.2690331 PS(43:6);PC(38:4);PC(36:1);PC(40:7) -1.7484612 PS(43:6);PC(40:7);PC(38:4);PC(42:10) -2.4956952 PS(43:6);PC(40:7);PC(38:4);PC(42:10) -2.4724878 PS(44:6);PG(42:8);PE(40:4);PE(38:1);PE(42:7) -1.0976108 PS(O-16:0/21:0);PS(O-18:0/19:0);PS(O-20:0/17:0);PG(O-20:0/17:2);PG(P- 18:0/19:1);PG(P-20:0/17:1);PC(16:0/dm18:0);PC(18:0/dm16:0);PC(O- 16:0/18:1);PC(O-18:0/16:1);PC(O-18:1/16:0) -0.9107327 PS(O-18:0/15:0);PS(O-20:0/13:0);PS(O-16:0/17:0);PG(O-16:0/17:2);PG(P- 16:0/17:1);PG(P-18:0/15:1);PC(14:0/dm16:0);PE(15:0/dm18:0);PC(O- 14:0/16:1) 1.39592868 PS(O-18:0/15:0);PS(O-20:0/13:0);PS(O-16:0/17:0);PG(O-16:0/17:2);PG(P- 16:0/17:1);PG(P-18:0/15:1);PC(14:0/dm16:0);PE(15:0/dm18:0);PC(O- 14:0/16:1) 2.12029423 PS(O-18:0/22:4);PS(O-20:0/20:4);PS(P-20:0/20:3);PS(O-16:0/22:1);PS(O- 18:0/20:1);PS(O-20:0/18:1);PS(P-16:0/22:0);PS(P-18:0/20:0);PS(P- 20:0/18:0);PG(O-18:0/22:6);PG(P- 20:0/20:5);GlcCer(d18:2/21:0);GalCer(d18:2/21:0);PE(20:0/dm18:1);PE(20:1/d m18:0);PE(22:1/dm16:0);PE(22:4/dm18:0) -1.8237494 PS(P-20:0/19:1);PC(18:1/dm18:1);PC(18:2/dm18:0);PC(20:2/dm16:0);PC(O- 16:0/20:3);PC(O-16:0/18:0);PC(O-17:0/17:0);PC(O- 18:0/16:0);PC(20:4/dm18:1);PC(20:5/dm18:0);PC(22:5/dm16:0);PC(O- 16:0/22:6) -1.6040713 PS(P-20:0/19:1);PC(O-16:0/21:0);PC(O-17:0/20:0);PE(O-18:0/22:0) -2.7092906

NUCLEOTIDES, NUCLEOSIDES AND ASSOCIATED METABOLITES

1 -(5-Phospho-D-ribosyl)-ATP;phosphoribosyl-ATP 1.83650127 UDP-N-acetyl-D-galactosamine 4-sulfate 1.51457317 UDP-N-acetylmuramate 2.25153877 Inosine;Arabinosylhypoxanthine -3.6848187

FATTY ACIDS AND RELATED METABOLITES

190

12,15-epoxy-13,14-dimethyl-12,14-eicosadienoic acid -1.6135317 12,15-epoxy-13,14-dimethyl-12,14-eicosadienoic acid 1.08926734 1-eicosanol 1.78587519 dihydroxy-pentadecanoic acid;Trimethylundecanoic acid;dimethyl-dodecanoic acid;methyl-tridecanoic acid;ethyl-dodecanoic acid 0.39592868 Hexacosenoic acid 0.59946207 Hexacosenoic acid 0.66657627 Octadecenoic acid -0.7311832 oxo-tricosanoic acid -1.3895668 oxo-tricosanoic acid -1.2509616 Quisqualic acid 1.88896869

OTHERS OR MIXED CLASSIFICATION

PC(30:1);PE(33:1);SM(d18:1/14:0) -2.1921942 PA(O-14:0/O-14:0);2-Octaprenyl-3-methyl-6-methoxy-1,4-benzoquinol;N- Palmitoylsphingosine 1.55639335 PC(O-12:0/O-1:0);PE(O-16:0/0:0);N-Oleoyl dopamine;1alpha,25-dihydroxy-23- azavitamin D3;N-oleoyl isoleucine;N-oleoyl leucine;N-palmitoyl histidine -3.2156786 PC(O-14:1(1E)/0:0);PC(P-14:0/0:0);Heptadecanoyl carnitine -2.4141355 PC(O-14:1/0:0);PC(P-14:0/0:0);Heptadecanoyl carnitine;Gamma-linolenyl carnitine;Alpha-linolenyl carnitine -2.4568061 PG(19:1(9Z)/0:0);Docosapentaenoyl carnitine;Clupanodonyl carnitine;LysoPC(16:0);PC(O-14:0/2:0) -1.803227 PG(19:1(9Z)/0:0);Docosapentaenoyl carnitine;Clupanodonyl carnitine;LysoPC(16:0);PC(O-14:0/2:0) -1.7949357 PC(O-18:1/O-1:0);PC(O-18:1/O-1:0);tetracosapentaenoyl carnitine -1.077243 Prostaglandin H2;Prostaglandin E2;(5Z,13E)-(15S)-9alpha,15-Dihydroxy-11- oxoprosta-5,13-dienoate;Prostaglandin I2;(13E)-11alpha-Hydroxy-9,15- dioxoprost-13-enoate;(5Z)-(15S)-11alpha-Hydroxy-9,15-dioxoprostanoate;20- OH-Leukotriene B4;15-Keto-prostaglandin F2alpha;Lipoxin A4;Lipoxin B4;Prostaglandin F3alpha;Narbonolide;Levuglandin E2;Levuglandin D2;8-iso- PGF3a;8-iso-15-keto-PGF2a;8-isoprostaglandin E2;PGE2;prostaglandin- H2;(5z,13e)-(15s)-6,9-alpha-epoxy-11-alpha,15-dihydroxyprosta-5,13- dienoate;(6Z,8E,10E,14Z)-(5S,12R)-5,12,20-Trihydroxyicosa-6,8,10,14- tetraenoate;(5Z,13E)-(15S)-11-alpha,15-dihydroxy-9-oxoprosta-5,13- dienoate;(5Z,13E)-(15S)-9alpha,11alpha-epoxy-15-hydroxythromboxa-5,13- dienoate;5,14,15-trihydroxy-6,8,10,12-Eicosatetraenoic acid;13,14-dihydro-15- keto-PGD2;11beta-PGE2;15R-PGE2;delta-12-PGD2;15R-PGD2;D17 PGE1;5- trans-PGE2;PGK1;5S,6S-Lipoxin A4;5,12,20-TriHETE;(5e,13e)-9,15- Dihydroxy-11-Oxoprosta-5,13-Dien-1-Oicacid;Thromboxane A2;20-OH- Leukotriene B4;15-epi-lipoxin A4;15-epi-lipoxin B4;13,14-dihydro-15-oxo-lipoxin A4;20-OH-hepoxilin A3;15-oxo-PGE1;w-hydroxyl leukotriene B4 2.05889369 PS(O-18:0/0:0);Stearoylcarnitine;PC(P-15:0/0:0);PE(P-18:0/0:0) -2.2234225 PS(O-16:0/21:0);PS(O-18:0/19:0);PS(O-20:0/17:0);Glucosylceramide (d18:1/20:0);Galactosylceramide (d18:1/20:0);PC(16:0/dm18:0);PC(18:0/dm16:0);PC(O-16:0/18:1);PC(O- 18:0/16:1);PC(O- 18:1(9Z)/16:0);PC(18:2/dm18:1);PC(18:3/dm18:0);PC(20:3/dm16:0);PC(O- 16:0/20:4) -1.7570232 (24R,24'R)-Fucosterol epoxide;Cholesteryl acetate;(22alpha)-hydroxy- isofucosterol;4alpha-formyl-4beta-methyl-5alpha-cholesta-8-en-3beta- ol;4alpha-hydroxymethyl-4beta-methyl-5alpha-cholesta-8,24-dien-3beta- ol;1alpha-hydroxy-26,27-dimethylvitamin D3;25-hydroxy-26,27-dimethylvitamin D3;(6S)-6,19-ethano-25-hydroxy-6,19-dihydrovitamin D3;(6R)-6,19-ethano-25- hydroxy-6,19-dihydrovitamin D3;25-Hydroxy-6,19-dihydro-6,19-ethanovitamin D3;1-Hydroxyvitamin D5 1.18442457 191

(25S)-5alpha-cholestan-3beta,6alpha,7beta,8beta,15alpha,16beta,26- heptol;(25S)-5alpha-cholestan-3beta,4beta,6alpha,8beta,15alpha,16beta,26- heptol;;1-O-alpha-D-glucopyranosyl-1,2-nonadecandiol;;27-nor-5b-cholestane- 3a,7a,12a,24,25-pentol -2.929791 1-(1Z-hexadecenyl)-sn-glycero-3-phosphoethanolamine;Palmitoylcarnitine -2.2868811 1-(O-alpha-D-glucopyranosyl)-29-keto-(1,3R,31R)-dotriacontanetriol;1-(O- alpha-D-mannopyranosyl)-29-keto-(1,3R,31R)-dotriacontanetriol -1.9708537 11-(3-acetoxy-1-propynyl)-1alpha,25-dihydroxy-9,11-didehydrovitamin D3 -1.0565835 1alpha,25-dihydroxy-11alpha-phenylvitamin D3;1alpha,25-dihydroxy-11beta- phenylvitamin D3;;2-hydroxyoleanolate;26,27-diethyl-1alpha,25-dihydroxy- 20,21-didehydro-23-oxavitamin D3 -0.9855004 2-Aminoethylphosphocholate;LysoPC(18:4) -2.0179219 2-Dehydropantoate;2-Aceto-2-hydroxybutanoate;3-Hydroxy-3-methyl-2- oxopentanoic acid;(5-L-Glutamyl)-L-glutamate;Adipate;Methylglutaric acid;Dimethylsuccinic acid 0.88896869 2-Methyl-4-amino-5-hydroxymethylpyrimidine diphosphate;;Monoisopropylphosphorylserine 1.88896869 3,5-Dinitro-L-tyrosine 1.83650127 3alpha,7alpha-Dihydroxy-5beta-cholestan-26-al;7alpha,12alpha-Dihydroxy- 5beta-cholestan-3-one;7alpha,12alpha-Dihydroxy-5alpha-cholestan-3- one;17alpha,20alpha-Dihydroxycholesterol;20alpha,22beta- Dihydroxycholesterol;7alpha,27-Dihydroxycholesterol;(24S)-Cholest-5-ene- 3beta,7alpha,24-triol;Cholest-5-ene-3beta,7alpha,25-triol;7-a,25- Dihydroxycholesterol;20a,22b-Dihydroxycholesterol;17a,20a- Dihydroxycholesterol;7a,12a-Dihydroxy-5a-cholestan-3-one;7a,12a-Dihydroxy- 5b-cholestan-3-one;3a,7a-Dihydroxy-5b-cholestan-26-al;cholest-5-ene- 3beta,7alpha,27-triol;cholest-5-en-3beta,7alpha,12alpha-triol;Dormatinol;1,25- Dihydroxycholesterol;3,5-dihydroxy-B-norcholestane-6- carboxaldehyde;cholestan-6-oxo-3,5-diol;3beta,5-oxo-5,6-secocholestan-6- al;24S,25-dihydroxycholesterol;24S,27-dihydroxycholesterol;7alpha,25- dihydroxycholesterol;25,27-dihydroxycholesterol;6,24S-dihydroxycholesterol;1- methyl-1,25-dihydroxy-4-nor-2,3-secovitamin D3;1alpha,25-dihydroxy-2alpha- methyl-19-norvitamin D3;1alpha,25-dihydroxy-2alpha-methyl-19-nor-20- epivitamin D3;5beta-Cholest-25-ene-3alpha,7alpha,12alpha-triol;3beta- Hydroxy-5beta-cholestan-26-oic acid;3alpha-Hydroxy-5beta-cholestan-26-oic acid;5alpha-Cholest-25-ene-3alpha,7alpha,12alpha-triol;7alpha,25-Dihydroxy- 5beta-cholestan-3-one;3alpha,12alpha-Dihydroxy-5beta-cholestan-7- one;Cholest-4-ene-3alpha,7alpha,12alpha-triol;;Hydroxynervonic acid;;Pithecolobine;;A-Nor-5alpha-cholestan-2-one;B-Norcholesterol;;1,2- docasanediol 1.35845397 3beta-Hydroxy-4beta-methyl-5alpha-cholest-7-ene-4alpha-carboxylate;4alpha- carboxy-4beta-methyl-5alpha-cholesta-8-en-3beta-ol;11alpha-ethyl-1alpha,25- dihydroxyvitamin D3;1alpha,25-dihydroxy-26,27-dimethylvitamin D3;1alpha,25- dihydroxy-24a,24b-dihomovitamin D3;1alpha,25-dihydroxy-24a,24b-dihomo-20- epivitamin D3 1.08926734 3-Deoxyvitamin D3 1.51457317 3-Deoxyvitamin D3 1.51457317 3-Deoxyvitamin D3 1.55639335 5-Hydroxy-2-polyprenylphenol;Geranylhydroquinone -2.9690123 5-Phosphomevalonate;Dehydropantoate;3-Hydroxy-3-methyl-2-oxopentanoic acid;(5-L-Glutamyl)-L-glutamate;Adipate;Methylglutaric acid;Dimethylsuccinic acid -1.9221978 5-Thio-a/B-D-Mannopyranosylamine;Methionine 1.51457317 6(R)-hydroxy-tetradeca-2E,4E,8Z-trienoate -2.7333543 Acetylspermidine -2.6182387 Arachidonyl carnitine;LysoPC(20:2);PC(O-18:2/2:0) -2.587365 Coproporphyrin I;Coproporphyrin III;Coproporphyrin II;Coproporphyrin -1.6825733 192

IV;Isocoproporphyrin;Uroporphyrin IV;;LysoPC(20:3);LysoPC(20:3) Docosapentaenoyl carnitine;Clupanodonyl carnitine -1.5310695 Glycerylphosphorylethanolamine 2.12029423 Hippurate;N-Acetylanthranilate;2,5,6-Trihydroxy-5,6-dihydroquinoline;3- succinoylpyridine;adrenochrome 1.59946207 LysoPC(16:0);PC(O- 14:0/2:0);Palmitoylglycerophosphocholine;Docosapentaenoyl carnitine;Clupanodonyl carnitine -2.1505597 LysoPC(16:0);PC(O- 14:0/2:0);Palmitoylglycerophosphocholine;Docosapentaenoyl carnitine;Clupanodonyl carnitine -2.5210507 LysoPC(16:0);PC(O- 14:0/2:0);Palmitoylglycerophosphocholine;Docosapentaenoyl carnitine;Clupanodonyl carnitine -1.5753123 LysoPC(16:0);PC(O- 14:0/2:0);Palmitoylglycerophosphocholine;Docosapentaenoyl carnitine;Clupanodonyl carnitine -1.6088092 LysoPE(16:1);3,4-Di-N-Hexanoyloxybutyl-1-Phosphocholine;;N-stearoyl glutamic acid;;Oleoylglycerone phosphate;LPA(18:2) -0.9560567 LysoPE(18:1);LysoPA(20:2/0:0);Cyclopamine;;N-oleoyl isoleucine;N-oleoyl leucine -1.0143553 LysoPE(18:1);PA(20:2/0:0);Cyclopamine;;N-oleoyl isoleucine;N-oleoyl leucine -1.2986583 LysoPE(18:1);PA(20:2/0:0);Cyclopamine;;N-oleoyl isoleucine;N-oleoyl leucine -1.3103401 LysoPE(18:2);15-HETE-DA;15-HETE-VA;;PA(20:3/0:0) -1.790772 LysoPC(18:0);PC(O-16:0/2:0);tetracosapentaenoyl carnitine;LysoPC(20:3) -2.2898345 LysoPC(18:0);PC(O-16:0/2:0);tetracosapentaenoyl carnitine;LysoPC(20:3) -2.3334237 LysoPC(18:3)PC(O-14:0/2:0);PC(16:0/0:0);Heptadecanoyl carnitine -1.6322682 Methoxytyrosine;3-O-methyldopa 1.68965988 Methyluric acid -0.8155754 N-Acetyl-leu-leu-leu-leu-tyr-amide 1.08926734 N-arachidonoyl tyrosine;;PA(21:4/0:0) -2.5459684 N-Methylnicotinium 1.39592868 N-palmitoyl valine;N-stearoyl alanine 2.64385619 Taurocholic acid 3-sulfate -1.7355222 Taurolithocholate;Lithocholyltaurine -0.8073549 Valinol;Choline -0.9486008

193

Metabolite Fold Change (Log2 Ratio)

Hexadecanoic acid (Palmitic Acid) 0.58

Ethylbis(trimethylsilyl)amine -1.37

Pyroglutamic acid 1.64

Glutamic acid 1.47

Lactic acid 0.89

Creatinine -0.72

Octadecanoic acid (Stearic Acid) 1.32

Xylose methoxyamine 1.89

Aspartic acid -1.66

Glycine -1.01

Uracil -0.40

Supplementary Table 3 The metabolites analysis from the GCMS experiment. Fold change is presented and was calculated as a log of the ratio from wild type to VGP tumour.

194

Supplementary Table 4 The shortlist of lipid metabolism genes. These genes were originally identified previously from the 20% of lipid metabolic genes. The genes were further stratified by V12 RAS VGP exclusivity and up regulation

Unigene Fold Gene Name Code GO Terms Change

GO:0019432 [Name: triglyceride biosynthetic dgat1a Dr.79871 process] 38.47

GO:0006629 [Name: lipid metabolic process]; lpl Dr.75099 GO:0016042 [Name: lipid catabolic process] 9.55

GO:0034497 [Name: protein localization to pre-autophagosomal structure]; GO:0006497 [Name: protein lipidation]; GO:0000045 [Name: wdr45 Dr.79570 autophagic vacuole assembly]; GO:0000422 7.83

GO:0019216 [Name: regulation of lipid metabolic process]; GO:0008286 [Name: irs2a Dr.113626 insulin receptor signaling pathway] 6.67

GO:0006633 [Name: fatty acid biosynthetic process]; GO:0006629 [Name: lipid metabolic process]; GO:0006631 [Name: fatty acid metabolic process]; GO:0055114 [Name: scd Dr.105241 oxidation-reduction process] 6.54

GO:0010874 [Name: regulation of cholesterol efflux]; GO:0033344 [Name: cholesterol efflux]; GO: GO:0033700 [Name: phospholipid efflux]; abca1a; GO:0043691 [Name: reverse cholesterol wu:fa03d03 Dr.35358 transport] 4.65

GO:0006631 [Name: fatty acid metabolic process]; GO:0055114 [Name: oxidation- scdb Dr.81181 4.14 reduction process]; GO:0006633 [Name: fatty 195

acid biosynthetic process];

GO:0008654 [Name: phospholipid biosynthetic pisd Dr.22015 process] 3.68

GO:0006869 [Name: lipid transport]; sigmar1 Dr.84840 GO:0006810 [Name: transport] 3.08

GO:0016042 [Name: lipid catabolic process]; GO:0001666 [Name: response to hypoxia]; lipf Dr.77330 GO:0006629 [Name: lipid metabolic process] 2.97

GO:0032502 [Name: developmental process]; GO:0006629 [Name: lipid metabolic process]; pnpla7b Dr.75248 GO:0008152 [Name: metabolic process] 2.96 pla2g7 Dr.81839 GO:0016042 [Name: lipid catabolic process] 2.62

GO:0055114 [Name: oxidation-reduction process]; GO:0006694 [Name: steroid biosynthetic process]; GO:0006629 [Name: lipid metabolic process]; GO:0006633 [Name: fatty acid biosynthetic process]; GO:0006703 hsd17b12b Dr.20571 [Name: estrogen biosynthetic process] 2.52

GO:0006629 [Name: lipid metabolic process]; GO:0001885 [Name: endothelial cell development]; GO:0008152 [Name: metabolic process];GO:0008654 [Name: phospholipid biosynthetic process]; GO:0016024 [Name: lycat Dr.80904 CDP-diacylglycerol biosynthetic process] 2.34

GO:0016024 [Name: CDP-diacylglycerol biosynthetic process]; GO:0008654 [Name: phospholipid biosynthetic process]; tamm41 Dr.85587 2.20 GO:0032049 [Name: cardiolipin biosynthetic

196

process]; GO:0006629 [Name: lipid metabolic process]

GO:0090155 [Name: negative regulation of sphingolipid biosynthetic process]; GO:0006672 [Name: ceramide metabolic ormdl1 Dr.27136 process] 2.16

GO:0006629 [Name: lipid metabolic process]; pla2g6 Dr.78310 GO:0008152 [Name: metabolic process] 2.01

197

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