UNIVERSITY OF CALGARY

publicité
UNIVERSITY OF CALGARY
Fatty acid synthesis in colorectal cancer: characterization of lipid metabolism in serum, tumour,
and normal host tissues
by
Emily Megan Mackay
A THESIS
SUBMITTED TO THE FACULTY OF GRADUATE STUDIES
IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
DEGREE OF MASTER OF SCIENCE
DEPARTMENT OF MEDICAL SCIENCE
CALGARY, ALBERTA
FEBRUARY 2015
© Emily Mackay 2015
Abstract
Reprogrammed energy metabolism is now listed as one of the central hallmarks of cancer
cells. Aberrant fatty acid metabolism contributes to tumourigenesis through provision of
substrates for membrane synthesis, signalling molecules, and synthesis of complex lipids. In this
thesis, the role of fatty acid metabolism is explored in the context of colorectal cancer.
Metabolomics techniques were employed to characterize fatty acid metabolites in serum, and
lipogenic gene expression was quantified in tumour and normal tissues to investigate host
response to cancer. Fatty acid metabolite abundance was increased in the serum of individuals
with colorectal cancer, and a growth factor signalling axis and lipogenic transcription factor
upstream of the endogenous fatty acid synthesis pathway were increased in colorectal liver
metastases. It was concluded that liver metastases have an effect on growth factor production in
the hepatic microenvironment, leading to increased signalling through a pathway that activates
the lipogenic transcription factor that regulates fatty acid synthesis.
ii
Acknowledgements
I would like to sincerely thank Dr. Oliver Bathe for his guidance and supervision throughout this
degree. He has been an incredible mentor and with his support and encouragement I have been
able to follow my dreams of attending medical school and hope to continue pursuing a career in
the field of oncology.
I would like to thank Dr. Aalim Weljie for his guidance and mentorship and for teaching me
numerous techniques in metabolomics. This project would not have been possible without the
support I received from him. I would like to thank Dr. Weljie for the amazing opportunity to
work in his laboratory at the University of Pennsylvania for several months. This experience was
a highlight of my degree and I sincerely appreciate the opportunity.
I would like to thank Dr. David Schriemer and Dr. Karen Kopciuk for their support throughout
this degree. Their input on mass spectrometry and statistical methodologies was invaluable to the
project and their encouragement and positive feedback over the past several years was a great
support.
I would also like to thank the members of the Bathe and Weljie labs: Kathy Gratton, Farshad
Farshidfar, Nicole Dunse, Omair Sarfaraz, and Ha Dang. Their support over the past several
years has made this an enjoyable experience; each of them has had a positive impact on my
research project and learning process during this degree.
iii
For my parents, who have supported me each step of the way. Without their love and
encouragement I would not be where I am today.
iv
Table of Contents
Abstract ............................................................................................................................... ii Acknowledgements ............................................................................................................ iii List of Tables ................................................................................................................... viii List of Figures and Illustrations ......................................................................................... ix List of Abbreviations .......................................................................................................... x CHAPTER ONE: INTRODUCTION ................................................................................. 1 1.1 Colorectal Cancer ..................................................................................................... 3 1.1.1 Sporadic CRC development ............................................................................. 3 1.1.2 Inherited predisposition to CRC ....................................................................... 4 1.1.3 Lifestyle factors associated with CRC ............................................................. 5 1.2 Cancer cell metabolism ............................................................................................. 6 1.2.1 Metabolic reprogramming: an emerging hallmark of cancer ........................... 6 1.2.2 The Warburg effect........................................................................................... 6 1.2.3 Regulation of cancer cell metabolism .............................................................. 7 1.3 Lipid metabolism ...................................................................................................... 9 1.3.1 Lipid classification system ............................................................................... 9 1.3.2 Fatty acid metabolism..................................................................................... 10 1.3.2.1 Fatty acid synthesis ............................................................................... 11 1.3.2.2 Fatty acid degradation ........................................................................... 11 1.3.3 Sterol regulatory element binding proteins (SREBPs) ................................... 12 1.3.4 Aberrant fatty acid metabolism in cancer ....................................................... 12 1.3.4.1 Expression of FASN in CRC ................................................................ 13 1.3.5 Desaturases and elongases .............................................................................. 14 1.4 Insulin-like growth factor signalling and cancer cell metabolism .......................... 15 1.4.1 IGF-1 and IGF-2 ............................................................................................. 15 1.4.2 IGF-1 receptor ................................................................................................ 16 1.4.3 IGF-1 and lipid synthesis................................................................................ 16 1.5 Techniques in lipidomics ........................................................................................ 16 1.5.1 Gas chromatography-mass spectrometry (GC-MS) ....................................... 17 1.5.2 Liquid chromatography-mass spectrometry (LC-MS) ................................... 17 1.5.3 Mass spectrometry .......................................................................................... 18 1.5.4 Lipidomics as a tool to study lipid dysregulation ........................................... 18 CHAPTER TWO: HYPOTHESIS AND RESEARCH AIMS ......................................... 21 2.1 Hypothesis .............................................................................................................. 22 2.2 Research aims ......................................................................................................... 22 2.2.1 Aim 1: The pattern of abundance of fatty acids in serum will be characterized as a
function of CRC. ............................................................................................. 22 2.2.2 Aim 2: The expression of the fatty acid synthesis pathway will be examined in
colorectal liver metastases and paired non-cancerous liver tissue. ................. 22 2.2.3 Aim 3: The host response to colorectal liver metastases will be determined in
normal host tissues. ......................................................................................... 22 CHAPTER THREE: MATERIALS AND METHODS ................................................... 24 3.1 Human Samples ...................................................................................................... 25 v
3.1.1 Serum samples ................................................................................................ 25 3.1.2 Tissue samples ................................................................................................ 25 3.2 Metabolite extraction .............................................................................................. 25 3.3 Fatty acid metabolite profiling using gas chromatography-mass spectrometry ..... 26 3.3.1 Fatty acid methyl ester derivatization............................................................. 26 3.3.2 Gas chromatography parameters .................................................................... 26 3.3.3 Mass spectrometry parameters ....................................................................... 27 3.3.4 Data transformation and metabolite identification ......................................... 27 3.4 Fatty acid metabolite quantification using ultra pressure liquid chromatography-mass
spectrometry .......................................................................................................... 28 3.4.1 Internal standards............................................................................................ 28 3.4.2 Pentaflurobenzyl (PFB) derivatization ........................................................... 28 3.4.3 UPLC analysis parameters ............................................................................. 29 3.4.4 Mass spectrometry analysis parameters ......................................................... 29 3.4.5 Data processing and statistical analysis .......................................................... 30 3.5 Quantification of lipogenic genes in colorectal liver metastases ............................ 30 3.5.1 Hematoxylin and eosin (H&E) staining ......................................................... 30 3.5.2 RNA extraction ............................................................................................... 30 3.5.3 cDNA synthesis .............................................................................................. 31 3.5.4 Real time reverse transcriptase-polymerase chain reaction (RT-PCR) .......... 31 3.6 Statistical analysis ................................................................................................... 32 3.6.1 Serum fatty acid metabolite abundance .......................................................... 32 3.6.2 Lipogenic gene quantification ........................................................................ 32 3.7 Tables and figures ................................................................................................... 34 CHAPTER FOUR: CHARACTERIZING THE PATTERN OF SERUM FATTY ACID
ABUNDANCE AS A FUNCTION OF CRC .......................................................... 40 4.1 Introduction ............................................................................................................. 41 4.2 Results ..................................................................................................................... 45 4.2.1 Metabolite profiling of serum samples from individuals with CRC using a semiquantitative GC-MS platform ......................................................................... 46 4.2.2 Metabolite profiling of serum samples from individuals with CRC using a fully
quantitative UPLC-MS platform..................................................................... 47 4.3 Discussion ............................................................................................................... 48 4.4 Conclusions ............................................................................................................. 51 4.5 Tables and Figures .................................................................................................. 54 CHAPTER FIVE: QUANTIFYING LIPOGENIC GENE EXPRESSION IN COLORECTAL
LIVER METASTASES ........................................................................................... 60 5.1 Introduction ............................................................................................................. 61 5.1.1 SREBP1c ........................................................................................................ 63 5.1.2 FASN .............................................................................................................. 64 5.1.3 SCD1 .............................................................................................................. 65 5.1.4 ELOVL6 ......................................................................................................... 67 5.1.5 IGF-1, IGF-1R ................................................................................................ 69 5.2 Results ..................................................................................................................... 71 5.2.1 SREBP1c mRNA expression in tumour and liver .......................................... 71 vi
5.2.2 FASN, SCD1, and ELOVL6 mRNA expression in tumour and liver ............ 72 5.2.3 IGF-1 and IGF-1R mRNA expression in tumour and liver ............................ 73 5.2.4 Correlation among mRNA levels of genes in fatty acid synthesis pathway .. 74 5.3 Discussion ............................................................................................................... 74 5.3.1 SREBP1c expression in patient samples ........................................................ 75 5.3.2 FASN expression in patient samples .............................................................. 77 5.3.3 SCD1 and ELOVL expression in patient samples ......................................... 79 5.3.4 Levels of IGF-1, IGF-2, IGF-1R, IGFBPs and CRC risk .............................. 81 5.4 Conclusions ............................................................................................................. 83 5.5 Tables and Figures .................................................................................................. 86 CHAPTER SIX: DETERMINING HOST RESPONSE TO TUMOUR DEVELOPMENT IN
NON-CANCEROUS LIVER AND ADIPOSE TISSUE IN INDIVIDUALS WITH
COLORECTAL LIVER METASTASES................................................................ 92 6.1 Introduction ............................................................................................................. 93 6.1.1 The role of the tumour microenvironment and inflammation ........................ 95 6.1.2 Host response to tumour development in adipose tissue ................................ 96 6.2 Results ..................................................................................................................... 97 6.2.1 The effects of colorectal liver metastases on hepatic expression of genes involved
in fatty acid synthesis ...................................................................................... 98 6.2.2 Fatty acid synthesis in normal host tissues: subcutaneous and omental adipose99 6.3 Discussion ............................................................................................................... 99 6.3.1 Hepatic expression of lipogenic genes ........................................................... 99 6.3.2 Host response in adipose tissue .................................................................... 102 6.4 Conclusions ........................................................................................................... 102 6.5 Tables and Figures ................................................................................................ 104 CHAPTER SEVEN: GENERAL DISCUSSION AND CONCLUSIONS .................... 108 7.1 General discussion ................................................................................................ 109 7.2 Research implications ........................................................................................... 111 7.2.1 Cancer metabolism as a therapeutic target ................................................... 111 7.2.2 Targeting FASN ........................................................................................... 112 7.2.3 Targeting IGF signalling .............................................................................. 114 7.3 Future directions ................................................................................................... 114 REFERENCES ............................................................................................................... 117 vii
List of Tables
Table 1. Internal standard concentrations for UPLC-MS profiling. ............................................. 34 Table 2. C:16:0 and C18:0 internal standard concentrations for UPLC-MS profiling. ................ 35 Table 3. Selected ion recordings for fatty acids under investigation in UPLC-MS profiling. ..... 36 Table 4. Gene-specific primers used in real time RT-PCR assays. .............................................. 38 viii
List of Figures and Illustrations
Figure 1. Lipid classification system. ........................................................................................... 20 Figure 2. Endogenous fatty acid synthesis pathway. .................................................................... 54 Figure 3. Serum fatty acid abundance in individuals with CRC and disease-free controls
determined using GC-MS. .................................................................................................... 56 Figure 4. Serum fatty acid abundance in individuals with CRC and disease-free controls
determined using UPLC-MS. ................................................................................................ 58 Figure 5. Serum arachidonic acid abundance in individuals with CRC and disease-free
controls determined using UPLC-MS. .................................................................................. 59 Figure 6. Quantification of SREBP1c and FASN mRNA expression in colorectal liver
metastases using real-time RT-PCR....................................................................................... 86 Figure 7. Quantification of SCD1 and ELOVL6 mRNA expression in colorectal liver
metastases using real-time RT-PCR....................................................................................... 87 Figure 8. Quantification of IGF-1 and IGF-1R mRNA expression in colorectal liver
metastases using real-time RT-PCR....................................................................................... 88 Figure 9. Correlation between mRNA levels of SREBP1c and FASN. ........................................... 90 Figure 10. Alterations in IGF-1/IGF-1R/ SREBP1c signalling pathway determined by
lipogenic gene quantification using real time RT-PCR. ....................................................... 91 Figure 11. Quantification of lipogenic gene expression in the liver microenvironment in the
setting of colorectal liver metastases. ................................................................................. 106 Figure 12. Quantification of host response in adipose tissue in the setting of colorectal liver
metastases. ........................................................................................................................... 107 ix
List of Abbreviations
Abbreviation
Definition
COX-1/2
Cyclooxygenase-1/2
CRC
Colorectal cancer
B2M
Beta-2-microglobulin
ELISA
Enzyme-linked immunosorbent assay
ELOVL
Elongase
FAP
Familial adenomatous polyposis
FASN
Fatty acid synthase
GAPDH
Glyceraldehyde-3-phosphate dehydrogenase
GC-MS
Gas chromatography-mass spectrometry
H&E
Hematoxylin & Eosin
HNPCC
Hereditary nonpolyposis colorectal cancer
IGF-1
Insulin-like growth factor 1
IGF-1R
Insulin-like growth factor 1 receptor
mRNA
Messenger RNA
mTORC1
Mammalian target of rapamycin complex 1
MUFAs
Monounsaturated fatty acids
NF-kB
Nuclear factor kappa-light-chain-enhancer of B
cells
NSAIDs
Non-steroidal anti-inflammatory drugs
PGE2
Prostaglandin E2
PI3K
Phosphoinositide 3-kinase
PMM1
Phosphomannomutase 1
x
PTEN
Phosphatase and tensin homolog
RNA
Ribonucleic acid
RT-PCR
Reverse transcription-polymerase chain reaction
SCD
Stearyl-CoA desaturase
SFAs
Saturated fatty acids
siRNA
Small interfering RNA
SREBPs
Sterol regulatory element binding proteins
TGF-B
Transforming growth factor beta
UPLC-MS
Ultra pressure liquid chromatography-mass
spectrometry
xi
Chapter One: Introduction
1
Lipids compose a diverse group of molecules with a variety of essential functions
necessary for maintaining cellular homeostasis. Lipids have important roles in energy storage,
cellular membrane synthesis, and signal transduction. The balance between lipid biosynthesis
and degradation is tightly regulated; disruption of this balance can lead to many human diseases,
including cancer. Aberrant lipid metabolism can affect cancer progression through multiple
mechanisms including providing an increased supply of fatty acids for membrane production and
the synthesis of complex lipids; increased synthesis of steroid hormones important in regulating
cell growth; and production of lipid metabolites involved in signalling pathways, such as
arachidonic acid derivatives, which can act as second messengers stimulating proliferation,
resisting apoptosis, and contributing to inflammatory processes1. By characterizing lipid
signalling pathways and expression levels of transcription factors and genes involved in lipid
homeostasis in health and disease states, important information on the underlying biology
associated with disease processes can be gained and lipogenic enzymes involved in aberrant
metabolism may be identified as potential therapeutic targets.
The aims of this thesis are: 1) to characterize the serum fatty acid profile of individuals
with colorectal cancer using gas chromatography-mass spectrometry and liquid chromatographymass spectrometry; 2) to quantify the mRNA expression levels of a main lipogenic transcription
factor, multiple lipogenic enzymes, and an upstream growth factor signalling pathway in
colorectal liver metastases and paired non-cancerous liver; and 3) to investigate host response to
tumour development by examining the effects of colorectal liver metastases on the hepatic
expression of genes involved in fatty acid metabolism, as well as the host response at sites more
distant to the tumour by examining fatty acid metabolism in subcutaneous and omental adipose
tissue in individuals with malignant and benign disease.
2
1.1 Colorectal Cancer
Colorectal cancer (CRC) is the third most common cancer and third leading cause of
cancer related deaths in North America. A person’s lifetime risk of developing CRC is 1/20 and
CRC is responsible for approximately 9% of total cancer deaths. CRC rates are highest in the 7584 year old age group with the median age at diagnosis being 692. CRC can develop from a
sporadic mutation acquired during one’s lifetime or from an inherited germline mutation.
Lifestyle factors including smoking, alcohol intake, diabetes, physical activity, and meat
consumption have been under investigation in recent years for their relationship with risk of
developing CRC.
1.1.1 Sporadic CRC development
Adenomas, localized lesions that arise from the glandular epithelium, are important
precursors to CRC. The incidence of adenomas increases with age, and risk of developing CRC
is higher when adenomas are not removed. Commonly occurring somatic mutations involved in
colon adenoma and carcinoma development have been well characterized at the molecular level.
Inactivating mutations in the adenomatous polyposis coli (APC) gene are seen in approximately
80% of sporadic CRC cases and are thought to be an early, potentially rate-limiting step in
adenoma development3. The APC gene is a tumour suppressor gene with an important role in the
β-catenin/ Wnt signalling pathway as a regulator of β-catenin protein levels. When both alleles
of APC are inactivated, β-catenin is able to accumulate in the cytoplasm, translocate to the
nucleus, and activate the transcription of target genes such as c-Myc, cyclin D, and matrix
metalloprotease 7. Without the regulatory role of APC in moderating degradation and nuclear
localization of β-catenin, Wnt signalling is constitutively activated leading to transcriptional
activation of downstream target genes and deregulation of cell signalling pathways4.
3
Inactivating mutations in several other tumour suppressor genes, TP53, TGF-β, and
PTEN are also commonly seen in sporadic CRC. As well, activating mutations in oncogenes,
such as RAS and BRAF, initiate downstream signalling through the mitogen-activated protein
kinase (MAPK) pathway. Activating mutations in the phosphatidylinositol 3-kinase (PI3K)
pathway have been reported in up to one third of CRCs4. In addition, mutations in genes
involved in inflammation, such as the transcription factors nuclear factor kappa-light-chainenhancer of activated B cells (NF-κB) and signal transducer and activator of transcription 3
(Stat3), along with mutations in cyclooxygenase-2 (COX-2) are frequently observed in CRC,
highlighting the importance of inflammatory processes in the development of this type of
malignancy5.
1.1.2 Inherited predisposition to CRC
While the majority of CRC cases are sporadically occurring, there are a proportion of
cases that arise from inherited gene mutations. Inherited predispositions to CRC can be classified
generally into two groups, hereditary nonpolyposis colorectal cancer (HNPCC) and familial
adenomatous polyposis (FAP).
HNPCC, also known as Lynch syndrome, is the most common type of hereditary CRC
accounting for approximately 3% of CRC cases. HNPCC is an autosomal dominant condition
characterized by inherited mutations in mismatch repair genes leading to microsatellite
instability. Individuals with HNPCC develop cancer at an earlier age than individuals with
sporadic CRC; multiple colon tumours are often seen in patients at 20 to 30 years of age. In
addition to colon tumours, patients with HNPCC also have an increased risk of extra-colonic
cancers including ovarian, uterine, small intestine, stomach, pancreatic, urinary tract, liver,
biliary tract, skin, and brain cancers6.
4
FAP is the second most common form of hereditary CRC, but is a rare condition
accounting for less than 1% of CRC cases. FAP is an autosomal dominant condition that results
from an inherited mutation in the APC gene. This condition is characterized by hundreds to
thousands of colonic adenomas developing in patients at 20 to 30 years of age, resulting in an
almost 100% lifetime risk of developing CRC. Patients with FAP also have an increased risk of
extra-colonic malignancies including adrenal, pancreatic, and biliary tract cancers7,8.
1.1.3 Lifestyle factors associated with CRC
The incidence of CRC is highly variable between different countries suggesting that
lifestyle factors may contribute to the development of this type of cancer. Previous studies have
reported relationships between red or processed meat intake, obesity, and alcohol consumption
and CRC risk. A large study that conducted ten independent meta-analyses on prospective cohort
studies published between 1966 and 2008 investigated the relative risk for CRC development
with several modifiable lifestyle factors. From these meta-analyses, high alcohol consumption,
compared to light or no alcohol consumption, was associated with the greatest increase in CRC
risk out of all the lifestyle factors examined. In addition, high BMI, diabetes, high red or
processed meat consumption, and cigarette smoking were also identified as risk factors for the
development of CRC. A decreased risk of CRC was associated with high levels of physical
activity. Fruit, vegetable, fish and poultry intake were examined but not found to be associated
with CRC risk in these meta-analyses9. Although more research needs to be conducted to define
the specific causal relationships between lifestyle factors and CRC, the available data suggests
that public health initiatives that promote weight loss, decreased alcohol consumption, smoking
cessation and increased physical activity could have a positive impact on the incidence of CRC.
5
1.2 Cancer cell metabolism
1.2.1 Metabolic reprogramming: an emerging hallmark of cancer
The six well known hallmarks of cancer cells defined by Hanahan and Weinberg10
include the following acquired capabilities: self-sufficiency in growth signals, insensitivity to
antigrowth signals, evasion of apoptosis, limitless replicative potential, sustained angiogenesis,
and tissue invasion and metastasis. In addition to these six described alterations in cancer cells,
several enabling and emerging hallmarks have recently been added to the list11. The enabling
characteristics are genomic instability and tumour promoting inflammation, while the emerging
hallmarks are deregulation of cellular energetics and avoiding immune destruction.
Reprogramming of cellular metabolism was added to the list of cancer hallmarks as this
phenomenon has been observed in varied cancer types and appears to be as widespread as many
of the other original hallmarks on the list. In addition, with the increased rates of cell
proliferation related to the self-sufficiency in growth signals and insensitivity to antigrowth
signals, energy metabolism must be reprogrammed in order to sustain the increased metabolic
demands associated with increased cell growth and division.
1.2.2 The Warburg effect
One aspect of metabolic reprogramming has been recognized as a phenotype of most
cancer cells since the 1920’s when Otto Warburg observed that proliferating cancer cells process
glucose molecules differently from normal, differentiated cells. When oxygen is not a limiting
factor, normal cells completely process glucose through oxidative phosphorylation, generating a
maximum amount of ATP. Only in the absence of an oxygen supply do non-malignant cells use
glycolysis as the primary method of metabolizing glucose. In contrast, cancer cells primarily
process glucose through the glycolytic pathway, regardless of oxygen supply. This change in
6
glucose metabolism observed in cancer cells, termed the “Warburg effect”, seems to be an
inefficient method for ATP production producing only 4 molecules of ATP per glucose
molecule, in comparison to the process of mitochondrial oxidative phosphorylation that produces
36 molecules of ATP per glucose molecule12.
There are several hypothesized reasons that support this form of metabolic
reprogramming that is observed in cancer cells. For a proliferating cell population, ATP
production may not be the limiting factor for mitotic cell divisions. Instead, ATP generation must
be balanced with the production of sufficient macromolecules required for a cell to double its
biomass and undergo cellular divisions. For an expanding cell population, this would require
mechanisms to increase nucleotide, protein, and lipid synthesis in order to sustain increased
proliferation. Although ATP is required for biomolecule synthesis, additional metabolic
requirements must be obtained from the catabolism of glucose, such as carbon and reducing
equivalents12,13. Given this, it would not be efficient for a rapidly dividing cancer cell population
to process all of its glucose for the sole purpose of maximizing ATP production through
oxidative phosphorylation. By undergoing a metabolic switch to process glucose primarily
through glycolysis, glycolytic intermediates can be redirected to other biosynthetic pathways for
the production of nucleotides, proteins, and lipids.
1.2.3 Regulation of cancer cell metabolism
Of the numerous mutations that occur during cancer development, it is becoming
apparent that many key mutations in oncogenes and tumour suppressor genes converge to affect
cellular metabolism, in turn supporting both increased ATP production for energy metabolism as
well as macromolecular synthesis of carbohydrates, proteins, lipids, and nucleic acids14. One of
the most commonly activated cell metabolism signalling pathways in human tumours is the
7
PI3K/Akt pathway. This pathway activates cell growth and survival through downstream targets,
most notably the mammalian target of rapamycin complex 1 (mTORC1). mTORC1 is widely
recognized for its role in regulating protein synthesis, but this cell growth regulator also activates
other anabolic processes such as mitochondrial biogenesis and de novo lipogenesis13.
Growth factor signalling commonly functions to activate the PI3K/Akt pathway. In
response to insulin signalling, glucose uptake is stimulated through increased expression of
glucose transporters on cell membranes and activation of glycolytic enzymes. With aberrant
growth factor signalling and subsequent activation of the PI3K/Akt pathway, glycolysis, cell
growth and proliferation can be activated and sustained in cancer cells15.
Activation of the PI3K/Akt pathway, mediated through growth factor signalling, has been
found to increase glucose uptake and the subsequent conversion of glucose into fatty acids. Akt
activation has been determined to activate the transcription factors sterol regulatory element
binding protein (SREBP) 1 and 2, although Akt activation only led to the nuclear accumulation
of SREBP1. The SREBPs are important transcription factors that regulate lipid homeostasis.
SREBP1 is an important transcription factor mediating fatty acid biosynthesis, while SREPB2
mediates cholesterol synthesis. Additionally, activation of Akt has been found to induce the
mRNA expression of fatty acid synthase (FASN) and ATP-citrate lyase (ACYL), and increase
cellular fatty acid concentrations16. The effect of Akt on lipogenesis has been further
characterized and is found to be reliant on mTORC1; nuclear accumulation of SREBP1 and
expression of target fatty acid synthesis genes requires mTORC1 activity as induction of
SREBP1, FASN, and ACYL is blocked by rapamycin, a mTORC1 inhibitor17. This connection
between PI3K/Akt/mTORC1 signalling and SREBP1 activation is important as it provides a key
8
link between growth factor/ nutritional signals and the activation of lipogenic pathways to
support cell proliferation and growth.
1.3 Lipid metabolism
1.3.1 Lipid classification system
Lipid metabolic pathways have critical roles in maintaining cellular homeostasis. Due to
the structural complexity and diversity of lipid species, a standardized classification system was
developed to distinguish similar groupings of lipids from each other (Figure 1). The
classification system consists of the following eight categories: fatty acyls, glycerolipids,
phospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids, and polyketides, with
multiple subgroups within each category18. Below is a brief outline of the main characteristics
and functions of each lipid group.
Fatty acyls consist of a carboxylic acid group with an unbranched carbon chain of
varying lengths and degrees of unsaturation. Fatty acyls are the main building blocks of more
complex lipids such as triglycerides for energy storage or phospholipids for membrane synthesis.
An important class of fatty acyls is the eicosanoids, biologically active molecules derived from
arachidonic acid, a 20-carbon fatty acid. Eicosanoids include prostaglandins and leukotrienes and
are important signalling molecules involved in inflammation; aberrant activity of the eicosanoid
pathway has been implicated in development of various human malignancies19.
Glycerolipids, including triacylglycerols and diacylglycerols, are classified based on the
structural presence of a glycerol backbone and are important in energy storage as well as cell
membrane composition.
Phospholipids are the most abundant membrane lipids and are classified based on their
polar head groups; for example, phosphatidic acids (PA), phosphatidylcholines (PC),
9
phosphatidylethanolamines (PE), phosphatidylinositols (PI), phosphatidylserines (PS), and
cardiolipins (CL) are some of the subgroups that compose the phospholipid category. In addition
to their role as structural components of cell membranes, phospholipids serve as important
binding sites for membrane proteins and are also involved in signal transduction cascades as
second messengers.
Sphingolipids include ceramides, sphinomyelins, cerebrosides, and gangliosides.
Sphingolipids have a range of functions from cell signalling to cell recognition, and
sphingomyelins are an important part of cell membranes.
Sterol lipids represent important hormones in eukaryotes, members of signalling
pathways, and as components of cell membranes regulating membrane fluidity. In mammalian
cells, cholesterol is the most significant sterol and maintaining appropriate levels of this hormone
is critical for proper cellular functioning.
Prenol lipids function as antioxidants and precursors to vitamins required for cell
survival, and are involved in membrane transport.
Saccharolipids include fatty acids linked to a sugar backbone and compose the
lipopolysaccharide wall in Gram-negative bacteria.
The final category of lipids are polyketides, a diverse group of lipids frequently used in
treatments for microbial or parasitic infections20.
1.3.2 Fatty acid metabolism
Fatty acids are important metabolites in health and disease due to their role as the
building blocks of numerous complex lipids, their central role in energy metabolism, and their
involvement in inflammatory processes. Fatty acid synthesis and degradation are separate
processes catalyzed by distinct enzymes in different cellular compartments.
10
1.3.2.1 Fatty acid synthesis
Fatty acid synthesis occurs in the cytoplasm and requires acetyl-CoA as an initial
substrate. Acetyl-CoA must be transferred to the cytoplasm from the mitochondria, and is done
so in the form of citrate. Once citrate is in the cytoplasm it is acted upon by ATP-citrate lyase
which releases acetyl-CoA and oxaloacetate. Acetyl-CoA carboxylase then catalyzes the ratelimiting step of fatty acid synthesis, the ATP-dependent carboxylation of acetyl-CoA to malonylCoA. Finally, FASN catalyzes the condensation of acetyl-CoA and malonyl-CoA to produce
palmitate. Overall, the synthesis of palmitate, a 16-carbon saturated fatty acid, requires 8
molecules of acetyl-CoA, 14 molecules of NADPH, and 7 molecules of ATP21.
Mammalian FASN is a 270 kDa multifunctional enzyme made up of two identical
polypeptides. FASN exists as a homodimer and although each monomer contains the necessary
catalytic domains required for palmitate synthesis, the enzyme is only functional in the dimer
formation. Each polypeptide contains three catalytic domains in the N-terminus, a core region,
and four catalytic domains in the C-terminus22,23.
1.3.2.2 Fatty acid degradation
Fatty acid β-oxidation, a complex process involving many enzymes, is used to break
down fatty acids providing energy for a variety of cellular processes. The following is a brief
summary of fatty acid degradation.
Fatty acids must first be hydrolyzed from triglycerides through the action of lipases. Acyl
CoA synthase then catalyzes the attachment of a coenzyme A group to the fatty acid. Fatty acid
degradation occurs in the mitochondrial matrix; therefore, fatty acids must be transported across
the mitochondrial membrane in order to be oxidized. Carnitine palmitoyltransferase 1, the ratelimiting enzyme in fatty acid oxidation, converts the acyl-CoA molecule to acylcarnitine
11
allowing transport across the inner mitochondrial membrane where the fatty acid molecule can
enter the β-oxidation pathway. The acetyl-CoA produced during each fatty acid β-oxidation
cycle enters the tricarboxylic acid cycle for eventual ATP production through cellular
respiration24.
1.3.3 Sterol regulatory element binding proteins (SREBPs)
Cholesterol, fatty acid, phospholipid, and triglycerol synthesis are regulated by the
SREBP transcription factors. The SREBPs consist of three isoforms, SREBP1a, SREBP1c, and
SREBP2. SREBP1c is highly expressed in the liver, white adipose tissue, skeletal muscle, and
brain and is the most important isoform for regulation of enzymes involved in fatty acid
synthesis25-27, including the three fatty acid synthesis enzymes under investigation in our study.
Physiological signals that nutritional substrates are abundant, such as high levels of glucose and
insulin, act through SREBP1c to induce the expression of fatty acid synthesis genes; induction of
these enzymes increases the activity of the fatty acid synthetic pathway28. The role of SREBPs is
discussed extensively in subsequent chapters. The activity and the mRNA and protein expression
of SREBP1c in the context of human malignancies has not been well described.
1.3.4 Aberrant fatty acid metabolism in cancer
The Warburg effect has been recognized as a phenotype common to cancer cells for
many decades, but more recently another aspect of metabolic reprogramming involving fatty
acid synthesis has become recognized as an additional phenotype of many transformed cells.
Fatty acids have several important roles in cell metabolism including membrane production, cell
signalling, substrates for the synthesis of more complex lipids, and energy storage. Fatty acids
can either be obtained from the diet or endogenously synthesized. Endogenous fatty acid
12
synthesis is catalyzed by FASN, an enzyme that primarily produces palmitate, a 16-carbon,
saturated fatty acid29.
In a healthy individual, dietary fatty acids are preferentially used for necessary cellular
requirements, such as the synthesis of phospholipid membranes, and very low levels of
endogenous fatty acid synthesis occur. Under normal circumstances, FASN acts primarily to
convert carbohydrates into triglycerides for energy storage when an excess amount of
carbohydrates are consumed, and is primarily expressed in hormone sensitive and rapidly
proliferating tissue types such as endometrial cells. In contrast to the minor role of FASN in
normal cell populations, cancer cells exhibit high levels of fatty acid synthesis regardless of the
abundance of dietary fatty acids. FASN is overexpressed in the majority of epithelial cancers,
with the produced fatty acids primarily being used for membrane synthesis, supporting the high
levels of proliferation in cancer cells29,30.
1.3.4.1 Expression of FASN in CRC
FASN was originally known as oncoantigen 519 (OA-519), a poor prognostic marker
identified in breast cancer patients. Tumours positive for this marker were more likely to recur
and metastasize, and patients had a higher chance of death from their disease. In 1994,
sequencing and enzymatic studies identified this prognostic marker as FASN31.
High levels of FASN expression and increased activity of the fatty acid synthesis
pathway are reported early in the development of colorectal neoplasms32, and increased
expression of FASN has been observed in aberrant crypt foci, the earliest precursor lesions of
colon cancer, when compared to matched normal colon mucosa33. Higher levels of FASN have
also been reported in the serum of individuals with CRC, and levels are found to correlate with
13
clinical stage of disease; patients with stage III-IV CRC were found to have significantly higher
levels of serum FASN than patients with stage I-II CRC34.
1.3.5 Desaturases and elongases
In addition to FASN, desaturase and elongase enzymes are also central to endogenous
fatty acid synthesis. Both enzymes are located in the endoplasmic reticulum membrane and
catalyze the desaturation and elongation of the fatty acids generated through the action of FASN.
Fatty acids of varying chain length and unsaturation are necessary to maintain membrane
composition and fluidity, produce signalling molecules, and for the synthesis of other lipid
species26.
The stearyl-CoA desaturase (SCD) enzymes catalyze the introduction of a cis double
bond into saturated fatty acids (SFAs), generating monounsaturated fatty acids (MUFAs). Two
isoforms of SCD exist in humans; SCD1 is ubiquitously expressed with high expression in the
brain, liver, heart, and lung, while expression of SCD5 is more restricted with a lesser known
function35. Palmitate and stearate are the main substrates of SCD1, generating palmitoleate and
oleate, 16 and 18 carbon fatty acids respectively, with one double bond at the delta-9 position of
the carbon backbone. The properties of lipids are altered with degree of unsaturation, affecting
membrane composition, energy storage, and signalling; given this, the activity of the desaturase
enzymes are tightly controlled. MUFAs are also important substrates for the synthesis of other
lipids including phospholipids, triglycerides, cholesterol esters, and diacylglycerols36,37.
The elongation of long-chain fatty acids (ELOVL) enzymes are a group of condensing
enzymes that use malonyl-CoA to add two carbons per reaction to a fatty acid backbone. The
seven elongase subtypes (ELOVL1-7) differ in their substrate specificity in terms of chain length
and degree of unsaturation26. Saturated and monounsaturated fatty acids with 12, 14, or 16
14
carbon backbones are substrates for ELOVL638,39, which made ELOVL6 the elongase enzyme of
focus in our study. The mRNA and protein expression of SCD1 and ELOVL6 in the context of
CRC has not been well described in the literature.
1.4 Insulin-like growth factor signalling and cancer cell metabolism
In recent years, insulin-like growth factor (IGF) signalling has become an area of
heightened interest as a target for cancer therapeutics. IGFs are growth promoting peptide
hormones important in regulating cellular growth, proliferation, and differentiation. This
signalling axis involves the ligands IGF-1 and IGF-2; type 1 and type 2 cell membrane receptors,
IGF-1-R and IGF-2R, and the insulin receptor; and six IGF binding proteins. Evidence
documenting overexpression of growth factors and growth factor receptors in cancer cells,
sustaining the “self-sufficiency in growth signals” hallmark of cancer, supports a key role for
IGF signalling in cancer progression, making IGF signalling an attractive anticancer target40.
1.4.1 IGF-1 and IGF-2
IGF-1 and IGF-2 are encoded by two distinct genes and are regulated separately. Both
peptide hormones are produced mainly in the liver, but can also be made in other tissues. In
circulation, IGFs are primarily bound to one of six insulin-like growth factor binding proteins
(IGFBP1-6) that regulate the bioavailability of IGFs at target sites. Both IGF-1 and IGF-2 bind to
the receptor tyrosine kinase IGF-1R/ IGF-2R to active cell growth and proliferation.
The liver is the primary production site of circulating IGF-1, regulated tightly by growth
hormone levels. An increase in growth hormone from the pituitary gland leads to an increase in
the hepatic synthesis of IGF-1. Although growth hormone is the main regulator, other hormones,
growth factors and nutrition also play a role in IGF-1 production. IGF-2 regulation is achieved
through a different mechanism involving genomic imprinting.
15
The majority of circulating IGF-1 is bound to binding proteins, most frequently IGFBP3.
The different IGFBPs bind to IGF-1 and IGF-2 with varying affinities. IGFBPs transport IGFs in
circulation, prolong the half-life of IGFs, and mediate interactions between IGFs and cell surface
receptors, either increasing or decreasing IGF signalling depending on the physiological
context41,42.
1.4.2 IGF-1 receptor
The IGF-1 receptor (IGF-1R) is the main mediator of IGF signalling. Binding of both
IGF-1 and IGF-2 can activate IGF-1R, although IGF-1 is bound with much higher affinity. This
tyrosine kinase growth factor receptor is a member of the insulin receptor family, and is 70%
homologous to the insulin receptor. IGF-1 or IGF-2 ligand binding activates various signal
transduction cascades, such as the PI3K/Akt/mTOR and Ras/Raf/MEK/ERK pathways,
stimulating proliferative and anti-apoptotic programs43-46.
1.4.3 IGF-1 and lipid synthesis
IGF-1 signalling has been found to activate lipid synthesis through the induction of
SREBP1. Evidence from investigations on lipid production in vitro demonstrated that IGF-1
signalling through IGF-1R increased the expression of SREBP1 mRNA. SREBP1 is an
important transcription factor that regulates the expression of several lipogenic genes including
FASN. Activation of SREBP1 was shown to be directly mediated through the PI3K/ Akt
pathway47,48.
1.5 Techniques in lipidomics
The field of lipidomics, the study of lipids and their biological functions, is rapidly
expanding with new developments in technology facilitating the identification and quantification
of many more lipid species than was previously possible. Given the structural diversity,
16
complexity of functional roles, and the estimated 10,000-100,000 different lipid molecules, it is
still not possible to distinguish every lipid in a biological sample. Despite this, current advances
in mass spectrometry, and the development of standardized protocols and regulated internal
standard mixes by initiatives such as the LIPID Metabolites and Pathways Strategy (LIPID
MAPS) consortium, are greatly advancing the field of lipidomics49,50.
1.5.1 Gas chromatography-mass spectrometry (GC-MS)
GC-MS is a technique used for the separation and analysis of compounds capable of
being volatilized, as the mobile phase used for separation is a carrier gas. Since most lipids are
not volatile, this form of chromatography is not frequently used for the separation of lipids. One
notable exception to this is in the analysis of fatty acids51. Through a transesterification reaction,
volatile derivatives of the original fatty acids, fatty acid methyl esters, can be obtained and used
in subsequent analysis by GC-MS. This method of fatty acid analysis is commonly used to
identify fatty acid profiles specific to certain disease states in human biofluids52,53.
1.5.2 Liquid chromatography-mass spectrometry (LC-MS)
For the analysis of lipids other than fatty acids LC-MS is frequently used. The mobile
phase in this separation technique is a carrier liquid, so compounds under investigation do not
need to be volatile for analysis. Separation can be achieved through either normal-phase
columns, which separate lipids by their polar head groups, or reverse-phase columns which
separate based on the hydrophobicity of the lipid20. While gas chromatography is able to separate
low molecular weight metabolites in the range of 18-350Da, liquid chromatography has the
ability to separate a much wider range of metabolites from 50–1500Da, including glycerolipids,
phospholipids, and sterols54.
17
1.5.3 Mass spectrometry
While adequate separation techniques are important for the study of lipid molecules, it is
the recent advancements in the sensitivity and resolution of mass spectrometers that has allowed
for the characterization of lipids using an untargeted approach. Following chromatographic
separation, charged ions are generated by an ionization method, such as electron ionization (EI),
chemical ionization (CI), or electrospray ionization (ESI). ESI is the most commonly used
ionization method for lipid analysis in combination with liquid chromatography, and can be
operated in both positive and negative ionization modes depending on the specific lipid groups
under analysis. Following the generation of charged particles, ions are accelerated through a
mass analyzer, sorted according to their m/z ratios, and measured and recorded by a detector.
Multiple types of mass spectrometers exist, for example time-of-flight, ion trap, and triple
quadrupole, and the appropriate one must be chosen for the type of analysis being conducted20,55.
1.5.4 Lipidomics as a tool to study lipid dysregulation
Lipidomic methodologies provide an excellent platform for examining lipid metabolites
in health and disease. Lipid dysregulation is seen in many human diseases, and the key roles that
lipids have in energy metabolism, cell signalling, growth, and inflammation highlight that
characterization of the “lipidome” is an important step in understanding the underlying biology
of various disease processes.
Human biofluids from individuals with a specific disease can be characterized using
either a targeted or untargeted lipidomic approach to examine the lipid profile of the disease state
in comparison to the lipid profile of disease-free controls. Lipid metabolites found to be
associated with disease, or specific stage of disease, can be identified and quantified providing
valuable information on the physiological processes of the disease, identifying disease/stage
18
specific biomarkers, and potentially identifying areas of lipid signalling cascades that could be
therapeutically targeted.
19
1.6 Tables and figures
Figure 1. Lipid classification system.
The lipid classification system that is currently used to distinguish lipid species. The lipid species
of relevance to humans include fatty acyls, glycerolipids, phospholipids, sterol lipids, prenol
lipids, and sphingolipids.
20
Chapter Two: Hypothesis and Research Aims
21
2.1 Hypothesis
Individuals with CRC will have multiple levels of aberrant lipid metabolism, due to
pathophysiological changes in cancer cells, as well as alterations originating in normal host
tissues.
2.2 Research aims
2.2.1 Aim 1: The pattern of abundance of fatty acids in serum will be characterized as a
function of CRC.
Aim 1.1 - The serum fatty acid metabolite profile of individuals with CRC (stage I-IV) and
disease-free controls will be characterized using a semi-quantitative gas chromatography-mass
spectrometry platform.
Aim 1.2 – Characterization of serum fatty acid metabolite profile will be repeated using a fully
quantitative liquid chromatography-mass spectrometry platform on a subset of the same patient
and control samples.
2.2.2 Aim 2: The expression of the fatty acid synthesis pathway will be examined in colorectal
liver metastases and paired non-cancerous liver tissue.
Aim 2.1 - The mRNA expression of the main lipogenic transcription factor, SREBP1c, and
downstream fatty acid synthesis genes, FASN, SCD1 and ELOVL6, will be quantified using
real-time RT-PCR in colorectal liver metastases and paired non-cancerous liver tissue.
Aim 2.2 - The expression of an upstream growth factor signalling axis, IGF-1 and IGF-1R, will
be quantified using real-time RT-PCR in colorectal liver metastases and paired non-cancerous
liver tissue.
2.2.3 Aim 3: The host response to colorectal liver metastases will be determined in normal host
tissues.
Aim 3.1 - The mRNA expression of lipogenic growth factors, transcription factors and genes will
be quantified in non-cancerous liver tissue from individuals with benign and malignant disease.
22
Aim 3.2 - The mRNA expression of FASN will be quantified in subcutaneous and omental
adipose tissue from individuals with benign and malignant disease.
23
Chapter Three: Materials and Methods
24
3.1 Human Samples
3.1.1 Serum samples
Blood samples were collected from individuals with CRC (stage I-IV, N=108) and ageand-gender matched disease-free controls (N=108) under standardized, 12-hour fasting
conditions. Samples were allowed to clot for 30 minutes after collection, centrifuged at 2000 rpm
for 15 minutes, and then stored at -80°C until use.
3.1.2 Tissue samples
Tissue samples were obtained from individuals undergoing surgery for colorectal liver
metastases (N=15). These individuals had not received chemotherapy or radiation therapy in a
minimum of three months preceding sample collection. A sample of the tumour tissue, along
with a sample of adjacent non-cancerous liver tissue, was obtained from each individual. Liver
tissue samples were also obtained from individuals undergoing surgery for benign liver disease
(hemangioma, N=2; focal nodular hyperplasia, N=3). In addition, subcutaneous and omental
adipose tissue was obtained, when possible, from individuals with malignant (N=6) and benign
disease (N=2). Immediately after resection, tissue samples were embedded in OCT, frozen, and
stored at -80°C until RNA was extracted.
3.2 Metabolite extraction
Organic metabolites were extracted from 50 μL aliquots of serum using an extraction
protocol based on Bligh and Dryer’s lipid extraction method56. A deproteination step was
performed by addition of 300 μL of cold 2:1 methanol:chloroform to each serum sample,
followed by 15 minutes of sonication. Separation of aqueous and organic metabolites was
achieved by addition of 100 μL of each layer of a 1:1 chloroform:water solution, followed by
centrifugation at 4°C for 7 minutes at 13,300 rpm. The resulting upper and lower fractions were
25
then carefully transferred to new centrifuge tubes and evaporated to dryness. The resulting lower
layer containing the organic metabolites was used for subsequent lipid metabolite analysis.
3.3 Fatty acid metabolite profiling using gas chromatography-mass spectrometry
3.3.1 Fatty acid methyl ester derivatization
After metabolite extracts were evaporated to dryness, chemical derivatization was
performed as follows. The dried organic phase was dissolved in 1 mL of chloroform:methanol
(1:1) under sonication for 15 minutes. 250 μL of the dissolved organic metabolites were
transferred to a new eppendorf tube, evaporated to dryness, and frozen for future metabolite
analysis using LC-MS.
To the remaining 750 μL of dissolved organic metabolites, 100 μL of tridecanoic acidd25 (200 μg/mL, Cambridge Isotopes Laboratories, Tewksbury, MA) was added as an internal
standard to each sample. 125 μL of 14% boron trifluoride in methanol was added, followed by
incubation at 80°C for 90 min. Vials were left to cool for 10 minutes before addition of 300 μL
of H2O and 600 μL of hexane followed by vortexing. The aqueous layer was discarded, and the
derivatized organic layer was evaporated to dryness overnight. Samples were dissolved in 200
μL hexane prior to analysis by GC-MS.
3.3.2 Gas chromatography parameters
Samples were analyzed using an Agilent gas chromatograph coupled with a GCT Premier
time-of-flight mass spectrometer (Waters Corp).
A DB-23 gas chromatography column (Agilent Technologies; 60 m x 0.25 mm x 0.15 um
film thickness) was used. The GC oven temperature program used was: initial hold at 50°C for 2
minutes, increase at 25°C/minute for 7 minutes to175°C, followed by an increase of 4°C/minute
until 250°C where the temperature was held for an additional two minutes, resulting in a total run
26
time of 28 minutes. One microliter of each sample was injected in splitless mode; the port
temperature held at 240°C throughout analysis. Helium was used as the carrier gas with a flow
rate of 1μL/minute.
3.3.3 Mass spectrometry parameters
The time-of-flight mass spectrometer was operated in electron impact (EI) mode with a
source temperature of 225°C and electron energy of 70eV. Data was collected at mass range of
m/z 50-800 with scan duration of 0.9 seconds.
3.3.4 Data transformation and metabolite identification
After converting raw data to netCDF format, data were processed using MET-IDEA
(Metabolomics Ion-based Data Extraction Alogorithm, Version 2.08)57 for peak deconvolution
and chromatographic alignment. Ion/retention time pairs were generated based on the analysis of
a purchased standard mixture of fatty acid methyl esters (37 FAME mix, Sigma-Aldrich), and
this list was used to guide the ion extraction process. The parameters used for the data processing
with MET-IDEA were: average peak width = 0.15; minimum peak width = 0.2 x average peak
width; maximum peak width = 3 x average peak width; peak start/stop slope = 1.5; adjusted
retention time accuracy = 0.9 x average peak width; peak overload factor = 0.9; mass accuracy =
0.01; mass range = +/- 0.4; lower mass limit = 120; and ions per component = 1.
Metabolites were identified based on their match to the corresponding fatty acid methyl
ester in the 37-FAME standard mixture. To perform a semi-quantitative analysis, the ion
abundance of each fatty acid was calculated and normalized to the abundance of the internal
standard, tridecanoic acid-d25. Statistical analysis and figure generation was performed using
GraphPad Prism (Version 6.0b). Data were log transformed to obtain a normal distribution
before analysis. The non-parametric Wilcoxon Mann-Whitney Test was used to compare fatty
27
acid abundance between groups and a p-value < 0.05 was considered significant.
3.4 Fatty acid metabolite quantification using ultra pressure liquid chromatography-mass
spectrometry
Based on the results of the fatty acid profiling using GC-MS, we subsequently decided to
validate our results using UPLC-MS. While the GC-MS profiling method used was semiquantitative, the UPLC-MS methodology we choose to use was fully quantitative with a labelled
internal standard for each fatty acid metabolite under investigation.
3.4.1 Internal standards
Deuterated internal standards were used for quantification of each fatty acid metabolite of
interest. d3-C6:0, d15-C8:0 and d3-C12:0 were purchased from Sigma (Sigma-Aldrich, St.
Louis, MO, USA). d19-C10:0, d3-C14:0, d4-C16:0 and d3-C18:0 were purchased from
Cambridge Isotope Laboratories (Cambridge Isotope Laboratories, Tewksbury, MA, USA). d14C16:1, d17-C18:1, d4-C18:2, d14-C18:3, d8-C20:4, d5-C20:5, and d5-C22:6 were purchased
from Cayman Chemicals (Cayman Chemicals, Ann Arbor, Michigan, USA). An internal
standard mixture was prepared containing 12 of the 14 internal standards, and 1 μL of this
mixture was added to each sample before derivatization. The remaining 2 internal standards were
added separately from the internal standard mixture; 2 μL of d4-C16:0 (50ng/ μL) and 4 μL of
d3-C18:0 (50ng/ μL) were added to each sample. The internal standard concentrations are given
in Table 1 and Table 2.
3.4.2 Pentaflurobenzyl (PFB) derivatization
A portion of the dried organic metabolite extracts (from serum extraction protocol;
approximately 4 µL of the original 50 µL serum sample) were chemically derivatized by
attaching the PFB moiety to the carboxylic acid end of the fatty acid metabolites. After addition
28
of internal standards, 40 µL of 2,3,4,5,6-Pentafluorobenzyl bromide (PFB-Br; Sigma-Aldrich, St.
Louis, MO, USA) and 20 µL of N,N-Diisopropylethylamine (DIPEA; Sigma-Aldrich, St. Louis,
MO) was added and the reaction was incubated for 60 minutes at room temperature. The reaction
mixture was subsequently dried and resuspended in 40 µL of injection solvent (70% MeOH:
30% H2O) before UPLC-MS analysis.
3.4.3 UPLC analysis parameters
Fatty acid PFB-derivatives were analyzed using an ACQUITY UPLC system coupled to
a Xevo TQD triple quadrupole mass spectrometer (Waters, Milford, MA).
Samples were
injected onto a 1.7 µm particle, 100 x 2.1 mm id Waters Acquity BEH C8 column (Waters,
Milford, MA) which was heated to 40°C in the column oven. Mobile phase A consisted of 10%
methanol (HPLC grade, Fisher Optima, Pittsburg, PA) and 90% Milli-Q H2O (Millipore,
Billerica, MA). Mobile phase B consisted of 100% methanol. A linear gradient was used (curve
6) over a total run time of 24 minutes. Initial conditions of 50% A and 50% B were held for 1
minute. The gradient was then ramped in a linear fashion over 2 minutes to 80% B and held for 5
minutes, followed by another linear ramp over 4 minutes to 96% B and held for 4 minutes. The
column was then equilibrated at initial conditions for 8 minutes before the next sample injection.
The flow rate used was 350 µL/ minute and the injection volume was 10 µL.
3.4.4 Mass spectrometry analysis parameters
The triple quadrupole mass spectrometer (Xevo TQD, Waters, Milford, MA) was
operated in negative mode using a multi-mode ionization source, ESCi® (Waters, Milford, MA).
This form of ionization switches rapidly between electrospray ionization (ESI) and atmospheric
pressure chemical ionization (APCI) within the ion source. A capillary voltage of 1.5 kV and
cone voltage of -30 V was used. Additional parameters for the ESCi ionization mode were 8 uA
29
for the corona pin, and 60 V for the cone. The desolvation source conditions were 250 L/hour for
the desolvation gas, nitrogen, held at a desolvation temperature of 400°C.
Selected ion recordings (SIR) were created for each of the 14 fatty acids of interest and
their corresponding labeled internal standards. Specific retention time windows and m/z values
were selected for each SIR (Table 3). 9 points per peak were used.
3.4.5 Data processing and statistical analysis
TargetLynx (Waters, Milford, MA) was used for data processing and quantitative
analysis. Fatty acid ion abundances were normalized to the ion abundances of their
corresponding internal standards. Each sample was analyzed in duplicate; the average of the two
ion abundances was used for statistical analysis. Several samples were removed from analysis
due to large differences in duplicate values and poor chromatogram quality.
3.5 Quantification of lipogenic genes in colorectal liver metastases
3.5.1 Hematoxylin and eosin (H&E) staining
6 μm cryosections were cut using a cryostat maintained at -20°C. H&E staining was
performed to confirm content of the tumour and paired adjacent non-cancerous liver tissue
samples.
3.5.2 RNA extraction
Total RNA was isolated from 5mg of tissue using the RNeasy Plus Micro Kit (Qiagen,
Germantown, MD) following the manufacturer’s instructions. A gDNA eliminator spin column
was used to remove any contaminating genomic DNA. The RNA pellet was re-suspended in
RNase-free water and quantified using absorbance measurements at 260nm. Purity of total RNA
was determined by the A260/A280 ratio and only samples with a value greater than 1.8 were used
for subsequent analysis.
30
3.5.3 cDNA synthesis
100ng of total RNA was used for the reverse transcription reaction using SuperScript II
(Invitrogen, Burlington, ON) according to the manufacturer’s instructions.
3.5.4 Real time reverse transcriptase-polymerase chain reaction (RT-PCR)
Real-time PCRs were performed in 25 μL final volume containing 250ng of cDNA,
SYBR Green master mix (Bio-Rad, Mississauga, Ontario), and primers for SREBP1c, FASN,
SCD1, ELOVL6, IGF-1, IGF-1R, beta-2-microglobulin (B2M), and phosphomannomutase 1
(PMM1). Primers were based on previously published assays or designed using Primer-BLAST
(NCBI, Table 4). Primer efficiency was calculated based on the slope of the standard curve using
the equation: 10[-1/slope] – 1. All reactions were run in triplicate and were analyzed using a Bio-Rad
CFX96 Real-Time PCR Detection System using the following parameters: 95°C for 10 minutes,
followed by 40 cycles of 95°C for 15 seconds, 58°C for 30 seconds and 72°C for 30 seconds.
Melt curve analysis (65-95°C) and gel electrophoresis were used to confirm that there were not
unspecific products.
B2M, an enzyme involved in MHC mediated immunity, and PMM1, an enzyme involved
in monosaccharide metabolism, were selected as internal controls for this study, as expression of
these genes has previously been reported as stable housekeeping gene between colorectal liver
metastases and normal liver tissue58. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), a
commonly used internal control for gene expression normalization, has been shown to vary
significantly in expression between different tissue types and pathological states58,59, so this
housekeeping gene was not an appropriate internal control for relative quantification of gene
expression between tumour and normal liver.
31
3.6 Statistical analysis
Statistical analysis and figures generation was done using GraphPad Prism (Version
6.0b).
3.6.1 Serum fatty acid metabolite abundance
Fatty acid abundances were normalized to the abundance of internal standards in each
sample. For the GC-MS experiments, normalization was performed against the internal standard
tridecanoic acid-25. For the UPLC-MS experiments, normalization was performed against the
matching deuterated internal standard for each metabolite. Abundances were compared between
CRC (stage I-IV) and disease-free controls. Alterations in fatty acid metabolite abundances with
the development of metastatic disease were investigated through comparison of locoregional
CRC (stage I-III) and liver metastatic CRC (stage IV) groups.
For the comparison of CRC and disease-free control groups, the non-parametric MannWhitney Test was used. A p-value < 0.05 was considered significant.
For the comparison of three sample groups, locoregional CRC, liver metastatic CRC, and
disease-free controls, the nonparametric Kruskal-Wallis one-way analysis of variance was used.
Again, the nonparametric test was chosen as this test does not assume a normal distribution. A pvalue < 0.05 was considered significant.
3.6.2 Lipogenic gene quantification
The comparative CT method60 was used to quantify the gene expression of SREBP1c,
FASN, SCD1, ELOVL6, IGF-1, and IGF-1R. Relative quantification was performed and
expression data were normalized by dividing the amount of target mRNA by the amount of
internal control.
32
33
3.7 Tables and figures
Table 1. Internal standard concentrations for UPLC-MS profiling.
1 μL of the internal standard mixture was added to each sample.
Internal Standard
Concentration in prepared mixture
Supplier
d3-C6:0
5ng/uL
Sigma-Aldrich
d15-C8:0
5ng/uL
Sigma-Aldrich
d19-C10:0
5ng/uL
Cambridge Isotope
Laboratories
d3-C12:0
5ng/uL
Sigma-Aldrich
d3-C14:0
5ng/uL
Cambridge Isotope
Laboratories
d4-C18:2
20ng/uL
Cayman Chemicals
d14-C18:3
5ng/uL
Cayman Chemicals
d8-C20:4
5ng/uL
Cayman Chemicals
d5-C20:5
2.5ng/uL
Cayman Chemicals
d5-C22:6
2.5ng/uL
Cayman Chemicals
d14-C16:1
10ng/uL
Cayman Chemicals
d17-C18:1
100ng/uL
Cayman Chemicals
34
Table 2. C:16:0 and C18:0 internal standard concentrations for UPLC-MS profiling.
Internal Standard
Concentration added to sample
Supplier
d4-C16:0
100ng
Cambridge Isotope
Laboratories
d3-C18:0
200ng
Cambridge Isotope
Laboratories
35
Table 3. Selected ion recordings for fatty acids under investigation in UPLC-MS profiling.
Fatty Acid Mass (negative mode) Retention time (minutes) C6:0 115 4.5-­‐5.2 C6:0-­‐d3 118 C8:0 143.1 5.15-­‐6.0 C8:0-­‐d15 158.1 C10:0 171.1 6.4-­‐7.4 C10:0-­‐d19 190.1 C12:0 199.1 9.1-­‐10.2 C12:0-­‐d3 202.1 C14:0 227.2 11.1-­‐12.1 C14:0-­‐d3 230.2 C16:0 255.2 11.3-­‐12.4 C16:0-­‐d4 259.2 C16:1 253.2 12.1-­‐13.15 C16:1-­‐d14 267.2 C18:0 283.2 11.2-­‐12.4 C18:0-­‐d3 286.2 C18:1 281.2 11.9-­‐12.9 C18:1-­‐d17 298.2 C18:2 279.2 12.2-­‐13.15 C18:2-­‐d4 283.2 36
C18:3 277.2 12.6-­‐13.9 C18:3-­‐d14 291.2 C20:4 303.2 11.1-­‐12.1 C20:4-­‐d8 311.2 C20:5 301.2 11.5-­‐12.65 C20:5-­‐d5 306.2 C22:6 327.2 11.5-­‐12.6 C22:6-­‐d5 332.2 37
Table 4. Gene-specific primers used in real time RT-PCR assays.
Gene Name
Accession
Primer sequence
no.
Amplicon
length
(bp)
Fatty acid
NM_004104
Forward:
synthase
AAGGACCTGTCTAGGTTTGATGC
(FASN)
Reverse:
106
TGGCTTCATAGGTGACTTCCA
Stearyl-CoA
NM_005063
desaturase 1
Forward:
117
CACCCAGCTGTCAAAGAGAA
(SCD1)
Reverse:
GATGAAGCACATCATCAGCAA
Elongase 6
NM_024090
(ELOVL6)
Forward:
117
GGCACCTAATGAATAAACGAGC
Reverse:
ACACCATATAAGCACCAGTTCG
Sterol
regulatory
NM_001005
291
Forward:
CTGGCTGCTCAATGGGCT
Reverse:
CCAGAAGTACACGGCGGG
element
binding protein
(SREBP1)
38
103
Insulin like
NM_001111
growth factor 1 283.1
(IGF1)
Insulin-like
NM_000875.
growth factor 1 3
receptor
70
ATCAGCAGTCTTCCAACCCA
Reverse:
TGGTGTGCATCTTCACCTTCA
Forward:
84
CTCCTGTTTCTCTCCGCCG
Reverse:
(IGF1-R)
Beta 2
Forward:
ATAGTCGTTGCGGATGTCGAT
NM_004048
microglobulin
Forward: ACTGAATTCACCCCCAC
114
TGA
(B2M)
Reverse: CCTCCATGATGCTGCTTA
CA
Phosphomanno NM_002676.
mutase I
(PMM1)
2
Forward:
GAACGAGACTAGCCCTGGTG
Reverse:
TGGGAAGAAAATCTCCCGGC
39
118
Chapter Four: Characterizing the pattern of serum fatty acid abundance as a function of
CRC
40
4.1 Introduction
Altered cell metabolism is a central hallmark to the majority of cancer types. A great deal of
research is being conducted examining different metabolic alterations and the oncogenic
mutations that converge to affect metabolic reprogramming. Cancer cell populations exhibit high
rates of cell division, and metabolic reprogramming is a necessary and early step in cellular
transformation and cancer progression. With increased cellular proliferation, additional amounts
of energy and building blocks are needed, and the increased levels of reactive oxygen species
generated from higher rates of metabolism need to be removed. To sustain the energetic and
macromolecular requirements of rapid cell division, metabolic pathways involving lipid, protein,
nucleic acid and carbohydrate synthesis are altered to meet the increased demands for cellular
building blocks and subsequently provide a growth advantage to tumour cells61.
The importance of lipid metabolism and the alterations that occur with respect to the
synthesis, degradation, and storage of lipids has become increasingly recognized as an important
part of the cancer cell’s metabolic phenotype. Endogenous fatty acid synthesis is a central part of
lipid metabolism; fatty acids are necessary for numerous aspects of cellular homeostasis (figure
1). A large portion of endogenously synthesized fatty acids are subsequently esterified into
phospholipids and incorporated into cell membranes, supporting the need for increased
membrane lipids to sustain high rates of cell divisions. Another portion of endogenously
synthesized fatty acids are used as second messengers and signalling molecules. Phosphatidic
acid, diacylglycerol, and lysophosphatidic acid are examples of lipid molecules that have
important roles in proliferative and survival pathways in cancer cells62. In addition to the
importance of fatty acids in cell metabolism, a major difference exists with the way in which
fatty acids are obtained between cancerous and non-cancerous cells. In normal, non-transformed
41
cells, the majority of fatty acids needed for cellular metabolism are obtained from exogenous
dietary sources. In contrast, cancer cells primarily rely on endogenous fatty acid synthesis as a
supply of fatty acids63, even in the presence of sufficient dietary fatty acids. This difference in
the method that fatty acids are obtained for cell metabolism makes the endogenous fatty acid
synthesis pathway an attractive target for cancer therapeutics64. In addition, increased FASN
expression is a phenotype common to many human cancers, and the activity of this enzyme
produces fatty acid metabolites that are important for a variety of cellular functions. The
importance of FASN in the context of cancer will be extensively discussed in subsequent
chapters.
In addition to the important role that endogenous fatty acid synthesis has in cancer cells, a
metabolic profiling study that was recently performed has shown that cancer cells can also
incorporate exogenous palmitate into several complex lipids, suggesting that levels of exogenous
fatty acids also play an important role in tumourigenesis. The incorporation of exogenous
palmitate was mapped across multiple different cancer cell lines, and found to be incorporated
not only into cellular membranes, but also into several oncogenic signalling lipids important for
cell proliferation and survival65. The results of this study suggest that endogenous fatty acid
metabolism is not the sole supplier of fatty acids in some cancer cells, and that exogenous levels
of fatty acids may play a role in the development and progression of cancer. This suggests that
targeting the mechanisms by which fatty acids are imported into cells may represent another
aspect of lipid metabolism that could be targeted for therapeutic purposes.
Supporting the critical roles of fatty acids in cancer cell growth and survival, studies
investigating FASN inhibitors have shown that the effectiveness of the inhibitor is reduced when
exogenous palmitate is added. For example, the mechanism of action of pristimerin in breast
42
cancer cell lines was recently investigated as this compound has been shown to have cytotoxic
effects in cancer cell lines. Pristimerin was found to downregulate FASN levels and reduce the
cell proliferation rate. The anti-proliferative effect of this FASN inhibitor was reduced when
exogenous palmitate, the end product of FASN activity, was added to the cell culture medium66.
Arachidonic acid, an omega-6, polyunsaturated, 20-carbon molecule, is a fatty acid that has
been extensively studied for its role in inflammation and cancer development and progression.
Arachidonic acid is an ingredient in animal fats, and is metabolized to produce a variety of
biologically active lipid molecules called eicosanoids. A cytoplasmic enzyme, phospholipase A2,
cleaves arachidonic acid from cell membranes; arachidonic acid is subsequently metabolized to
produce eicosanoids through the cyclooxygenase (COX), lipooxygenase (LOX), and cytochrome
P450 monooxygenase pathways. Of the numerous eicosanoids involved in inflammation and
cancer, prostaglandin E2 (PGE2), a downstream product of the COX pathway, is one of the most
abundant prostaglandins in human cancers and has been extensively studied in the context of
CRC. There are two isoforms of the COX enzyme, COX1 and COX2. While COX1 is a
constitutive enzyme with relatively constant levels in normal cell populations, COX2 is an
inducible enzyme, with increased levels seen during inflammatory states. Extensive research into
the expression levels of these enzymes in different human malignancies has been conducted.
COX2 upregulation has been documented in CRC compared to normal mucosa67, and high
COX2 expression correlates with tumour recurrence and metastasis in CRC68. As a product of
this COX pathway, PGE2, a pro-inflammatory metabolite, has been demonstrated to promote
tumour growth through multiple mechanisms. These include direct activation of tumour cell
receptors to control proliferation, apoptosis, and invasion; activation of receptors on stromal cells
43
in the microenvironment to create a tumour-promoting environment; and promotion of growth
factor and pro-inflammatory release in the tumour microenvironment19.
Due to the documented upregulation of COX2 and the tumour promoting effects of its
downstream product, PGE2, this pathway represents an attractive therapeutic target in cancer.
Non-steroidal anti-inflammatory drugs (NSAIDs) have been an area of investigation for many
years due to their role in targeting COX1 and COX2. Epidemiological studies and randomized
control trials have identified that long-term NSAID use reduces the risk of CRC development
and the risk of cancer recurrence69. Although the benefits of prophylactic NSAID use for the
prevention of CRC has been appreciated from numerous long-term studies, use in the general
population has not been implemented due to the side effects of NSAIDs70.
In order to study fatty acid metabolism at the metabolite level, various metabolomics based
platforms are available. In addition to the information that can be obtained from genomic and
proteomic investigations, characterization of the metabolite changes that occur with disease
development and progression can provide a valuable snapshot of the metabolic profile
representing the downstream products of signalling pathways. Both untargeted and targeted
approaches are available for investigating the metabolite levels in human biofluids. GC-MS is
one platform commonly employed for cancer metabolomics studies. For GC-MS, compounds
must be volatile, or made volatile through derivatization procedures. For fatty acid analysis, GCMS is often employed with a fatty acid methyl ester derivatization procedure to create volatile
fatty acids that can be analyzed using GC. In contrast, LC-MS does not require compounds to be
volatile for analysis, so derivatization procedures are often not used and molecules can be
directly analyzed71.
44
Due to the importance of fatty acid metabolites in maintaining cellular metabolism and
homeostasis, and the emerging role of reprogrammed energy metabolism in cancer, it is of
interest to gain an increased understanding regarding the metabolic changes that occur with the
development and progression of CRC. In this chapter, the pattern of abundance of serum fatty
acid metabolites as a function of CRC will be discussed. Fatty acid abundance was compared
between individuals with CRC (stage I-IV) and disease-free controls in order to gain an
appreciation of the alterations that occur with the development of malignant disease. In addition,
fatty acid abundances were compared between individuals with locoregional CRC (stage I-III)
and liver metastatic CRC (stage IV) to examine alterations in fatty acid metabolism that occur
specifically with the development of metastatic disease.
4.2 Results
To identify and measure the abundance of fatty acid metabolites in individuals with CRC,
both GC-MS and UPLC-MS metabolomic platforms were employed to analyze the organic phase
of serum samples. Fatty acids present in the serum were derivatized to fatty acid methyl esters
and subsequently analyzed using GC-MS to obtain a semi-quantitative analysis of fatty acid
metabolites. Based on the results obtained from the initial fatty acid profiling with GC-MS,
specific biologically relevant fatty acid metabolites were selected for quantitative analysis using
deuterated internal standards for each fatty acid metabolite of interest. A pentafluorobenzyl
bromide derivatization procedure and UPLC-MS platform was used to quantify metabolite
abundances in a subset of the same sample population. Results obtained from the UPLC-MS
analysis were consistent with the trends in fatty acid abundance observed from GC-MS analysis.
45
4.2.1 Metabolite profiling of serum samples from individuals with CRC using a semiquantitative GC-MS platform
To statistically compare fatty acid abundance between individuals with CRC (stage I-IV)
and age-and-gender matched disease-free controls, the non-parametric Wilcoxon Mann-Whitney
test was used to compare the two sample populations. To compare the three sample groups of
disease-free controls, locoregional CRC (stage I-III), and liver metastatic CRC (stage IV), the
nonparametric Kruskal-Wallis one-way analysis of variance was used. A p-value of <0.05 was
considered significant in all analyses.
Of the fatty acid metabolites identified through metabolite profiling using GC-MS, four
biologically relevant fatty acids central to the endogenous fatty acid synthesis pathway were
found to be statistically relevant when comparing sample groups. These fatty acids were palmitic
acid (C16:0), palmitoleic acid (C16:1), stearic acid (C18:0), and oleic acid (C18:1). From the
analysis, palmitic acid (p-value =0.03, figure 2A), stearic acid (p-value=0.005, figure 2E), and
oleic acid (p-value=0.06, figure 2G) were increased in abundance in individuals with CRC
compared to disease-free controls.
Trends in fatty acid abundance from progression of disease-free controls to locoregional
CRC to liver metastatic CRC are summarized in Figure 2. In addition to differences between
cancer and disease-free controls, we were also interested in determining if fatty acid metabolite
abundance increased with higher stage of disease. In order to investigate this, we compared
serum abundances between individuals with locoregional disease (stage I-III) and individuals
with liver metastatic CRC (stage IV). A significant difference in abundance between
locoregional CRC and liver metastatic CRC was observed for palmitoleic acid (p-value<0.05,
46
figure 2D), with increased levels in the liver metastatic group. No other significant differences
between locoregional and liver metastatic were observed for the other fatty acids examined.
4.2.2 Metabolite profiling of serum samples from individuals with CRC using a fully
quantitative UPLC-MS platform
Based on the results from the GC-MS metabolite profiling experiments, a fully
quantitative UPLC-MS approach was employed to validate the previous results and definitively
quantify metabolite abundances. A randomly selected subset of serum samples from the same
patient and disease-free control population was used. To statistically compare fatty acid
abundance between individuals with CRC (stage I-IV) and age and gender matched disease-free
controls, the non-parametric Wilcoxon Mann-Whitney test was used to compare the two sample
populations. To compare the three sample populations of disease-free controls, locoregional
CRC, and liver metastatic CRC, the nonparametric Kruskal-Wallis one-way analysis of variance
was used. A p-value of <0.05 was considered significant in all analyses.
In keeping with results obtained from GC-MS metabolite profiling, palmitic acid (pvalue=0.0012, figure 3A), stearic acid (p-value=0.023, figure 3E), and oleic acid (pvalue=0.0002, figure 3G) were significantly increased in abundance in serum from individuals
with CRC (stage I-IV) compared to disease-free controls. In addition, palmitoleic acid (pvalue=0.0006, figure 3C) was significantly increased in CRC compared to disease-free controls
in the UPLC-MS profiling experiments.
With the UPLC-MS metabolite profiling platform, an additional fatty acid metabolite of
interest was identified as significantly altered in cancer. Arachidonic acid (C20:4, pvalue=0.0001, figure 4) was elevated in the serum of individuals with CRC compared to diseasefree controls.
47
In addition to differences between cancer and disease-free controls, we were also
interested in determining if fatty acid metabolite abundance increased with higher stage of
disease. Trends in fatty acid metabolite abundance between locoregional CRC, liver metastatic
CRC, and disease-free controls are summarized in Figure 3. A significant difference in
abundance between disease-free controls and locoregional CRC was observed for palmitic acid,
palmitoleic acid, and oleic acid. For these fatty acids, levels were significantly increased with the
progression to locoregional CRC (figure 3B, 3D, 3H). For arachidonic acid, a significant
increase between control and locoregional CRC was observed (figure 4B). No other significant
differences were observed between locoregional and liver metastatic CRC for the fatty acids
examined using UPLC-MS.
4.3 Discussion
From the serum fatty acid profiling experiments using both GC-MS and UPLC-MS
platforms, it was determined that the main fatty acid metabolite products of endogenous fatty
acid synthesis were increased in the serum of individuals with CRC compared to disease-free
controls. Palmitic acid and stearic acid, the main products of FASN, were increased in CRC
patients by both GC-MS and UPLC-MS. Palmitoleic acid and oleic acid, two monounsaturated
fatty acids produced by the action of SCD1, were increased in CRC patients when examined with
the UPLC-MS platform. In addition, arachidonic acid was significantly increased in CRC
patients when the UPLC-MS platform was used for targeted metabolite profiling. This
investigation represents the first investigation into the serum fatty acid profile, confirmed by two
complementary metabolomic platforms, in individuals with varying stages of CRC and age-andgender matched disease-free controls, in which the mRNA expression of members of the fatty
acid synthesis pathway will be subsequently quantified.
48
Several prior investigations in the literature are consistent with the findings of our study,
while other studies have found variable results. In addition to our investigation, another group
has looked specifically at serum fatty acid levels in CRC patients. In this study72, sera from 42
patients and 8 healthy controls were analyzed by GC-MS in order to characterize the fatty acid
profile of these sample groups. In this investigation, C14:0, C15:0, C18:3n-6, and C18:0 were
significantly (p-value < 0.05 according to Student’s t-test) decreased in CRC patients, while
C24:0, C25:0, C26:0, C28:0 and C30:0 were significantly increased in CRC patients. These
results are different than the findings in our study as C18:0 was found to be decreased in CRC,
while our results determined that C18:0 levels were increased in CRC. In addition, this study
focused more on the very-long chain fatty acids which were not examined in our study. In this
study, C18:3n-6 was found to be significantly decreased in the cancer group. This omega-6 fatty
acid is upstream of arachidonic acid (C20:4n-6), which is upstream of PGE2. The authors of this
study suggest that this fatty acid may be decreased in these patients due to the conversion of
C18:3n-6 to C20:4n-6.
Tan et al.73 used both GC-TOFMS and UPLC-QTOFMS to define the serum metabolic
profile of CRC patients (N=101) and healthy controls (N=102). From this profiling study 249
metabolites were found in each sera sample. Of the fatty acid metabolites identified that are
common between our study and this study, both palmitic acid and oleic acid were significantly
increased (p-value < 0.01, Mann-Whitney U test) in the sera from CRC patients compared to the
healthy controls. The results for these two fatty acids are consistent with the results from our
GC-MS and UPLC-MS investigations.
Another study specifically investigated the serum fatty acid profile of individuals with breast
cancer (N=40), benign conditions (N=40, fibroma or chronic fibroadenosis of breast), and
49
healthy controls (N=34)52. Study subjects were carefully selected; all breast cancers were ductal
cancers, no subjects had a family history of breast cancer, there was no use of oral contraceptives
or female hormone drugs by any of the participants, and a three day food record was collected
from the subjects in order to exclude any significant dietary differences that could affect the
serum fatty acid profiles. In addition, age, BMI, smoking, and alcohol consumption were closely
matched between the three groups. From this GC-MS fatty acid profiling investigation, C16:0,
C18:0, and C18:2 were significantly higher (p-value < 0.001) in the breast cancer group than the
control group. In addition, three unsaturated fatty acids, C18:3, C20:5, and C22:5 were
significantly lower (p < 0.05) in the breast cancer patients compared to controls. The increased
levels of C16:0 and C18:0 in the serum of individuals with cancer is consistent with the findings
from our study.
Arachidonic acid levels have been widely studied in the context of cancer with conflicting
results being found within the same cancer type and across different malignancies. A systematic
review was conducted to evaluate 52 studies that had been conducted before 2010 on the
association between arachidonic acid intake levels as well as tissue and serum arachidonic acid
levels and risk of cancer. For CRC, inconsistent results were found. Two studies found a positive
association between dietary arachidonic intake and CRC, while four other studies found a
negative association. In addition, there was no report of an association between tissue levels of
arachidonic acid and CRC. For breast and prostate cancer, a relationship between arachidonic
acid levels and cancer risk was not found in the studies reviewed. The authors propose that
several factors lead to the inconsistencies in results found by the different studies. These include
the fact that a standardized method for measuring arachidonic acid intake is not available, the
assessment methods for measuring arachidonic acid levels in serum and tissue samples were
50
varied, and the selection of subjects to participate in studies was varied and could be affected by
selection biases74. Regardless, the results from this systematic review suggest that there is not a
clear association between arachidonic acid levels and cancer risk, but additional investigations
are needed in standardized, well-designed studies.
A recent UPLC-MS/MS study examined arachidonic acid, in addition to several downstream
metabolites, in serum samples from prostate cancer patients compared to control samples. In
patients with a more advanced stage of prostate cancer, lower levels of arachidonic acid and
higher levels of downstream eicosanoids were detected. In addition, when examining correlation
with prognosis, lower levels of arachidonic acid in the serum of prostate cancer patients was
found to be associated with disease recurrence75.
In the context of CRC, earlier detection methods are continually being sought in order to
diagnose CRC at a localized stage, before metastatic spread, where curative treatment options are
available and disease-free survival rates are higher. Through metabolic profiling, a disease
specific metabolite profile could be generated that assists in early detection and diagnosis of
CRC. In addition, characterizing the fatty acid profile of disease states compared to healthy states
provides valuable information on the pathogenesis of the disease and can provide important
insight into the metabolic alterations that occur with development of a given disease, such as
cancer. The metabolite information can be used as complimentary to the gene and protein data in
order to construct an entire picture of cell metabolism.
4.4 Conclusions
To improve upon the current metabolomic profiling study, several additional steps could
have been taken. Although samples were collected after 12 hours of fasting, obtaining dietary
records from the individuals in the sample groups and correcting for any disparities with respect
51
to lipid intake would have been a valuable step, as dietary differences could affect the serum
fatty acid profiles. In addition, although sample and control groups were matched for age and
gender, matching for other confounding factors such as BMI, medication use, past medical
history, alcohol consumption, and smoking may have improved the current study; factors such as
breakdown of lipid stores and presence of exogenous fatty acids may be important in
tumourigenesis and may be influenced by clinical parameters such as BMI or the presence of
other medical conditions. Along with the abundance of serum fatty acids, it would also be of
interest to quantify tissue fatty acid levels. Enzymes of the fatty acid synthesis pathway were
examined in tissue, and increased levels of tissue enzymes may not directly reflect circulating
serum metabolite levels.
In conclusion, our results suggest that increased levels of endogenous fatty acid
metabolism are occurring in CRC and that this increase in metabolism is reflected in the serum
of individuals with CRC. Elevated levels of the main fatty acid metabolites of endogenous fatty
acid synthesis, palmitic acid, palmitoleic acid, stearic acid, and oleic acid, were found in
individuals with CRC compared to disease-free controls by two different metabolomic platforms,
GC-MS and UPLC-MS. Of the information available in the literature, a common finding appears
to be an increase in serum levels of palmitic acid and stearic acid, the main products of FASN,
which is in keeping with the results of our study. In addition, arachidonic acid was elevated in
the cancer sample group, consistent with other reports stating increased levels of this
polyunsaturated fatty acid and it downstream product, pro-inflammatory PGE2, in association
with CRC.
Due to the metabolite profiling results demonstrating that metabolite levels of fatty acids
central to endogenous fatty acid synthesis are increased in individuals with CRC, we next sought
52
to investigate the mRNA expression levels of the main lipogenic transcription factor and several
downstream fatty acid synthesis genes in tumour tissue and paired non-cancerous tissue from a
subset of the same patient population. The expression of an upstream growth factor signalling
pathway that converges to activate the lipogenic transcription factor and subsequent lipid
synthesis was also investigation.
53
4.5 Tables and Figures
Figure 2. Endogenous fatty acid synthesis pathway.
Fatty acid synthase (FASN) acts upon malonyl-CoA and acetyl-CoA to produce palmitic acid, a
16-carbon saturated fatty acid. Stearyl-CoA desaturase 1 (SCD1) can act upon palmitic acid to
catalyze the introduction of one double bond, producing the monounsaturated fatty acid oleic
acid. Alternatively, elongase 6 (ELOVL6) can act upon palmitic acid to catalyze the elongation
of the carbon backbone producing stearic acid, an 18-carbon saturated fatty acid.
54
55
Figure 3. Serum fatty acid abundance in individuals with CRC and disease-free controls
determined using GC-MS.
Fatty acid abundances in (A, B) palmitic acid, C16:0; (C, D) palmitoleic acid, C16:1; (E, F)
stearic acid, C18:0; and (G,H) oleic acid, C18:1. Abundances compared between disease-free
controls and CRC, and between disease-free controls, locoregional CRC (stage I-III) and liver
metastatic CRC (stage IV). P-value calculated using Mann-Whitney U test for the two group
comparisons, and Kruskal-Wallis one-way analysis of variance for the three group comparisons.
The whiskers indicate the 5th and 95th percentiles; the line in the box is plotted at the median.
56
57
Figure 4. Serum fatty acid abundance in individuals with CRC and disease-free controls
determined using UPLC-MS.
Fatty acid abundances in (A, B) palmitic acid, C16:0; (C, D) palmitoleic acid, C16:1; (E, F)
stearic acid, C18:0; and (G,H) oleic acid, C18:1. Abundances compared between disease-free
controls and CRC, and between disease-free controls, locoregional CRC (stage I-III) and liver
metastatic CRC (stage IV). P-value calculated using Mann-Whitney U test for the two group
comparisons and using Kruskal-Wallis one-way analysis of variance for the three group
comparisons. The whiskers indicate the 5th and 95th percentiles; the line in the box is plotted at
the median.
58
Figure 5. Serum arachidonic acid abundance in individuals with CRC and disease-free
controls determined using UPLC-MS.
Arachidonic acid, C20:4, abundance compared between (A) disease-free controls and CRC
(stage I-IV); and (B) disease-free controls, locoregional CRC (stage I-III), and liver metastatic
CRC (stage IV). P-value calculated using Mann-Whitney U test for the two group comparisons
and using Kruskal-Wallis one-way analysis of variance for the three group comparisons. The
whiskers indicate the 5th and 95th percentiles; the line in the box is plotted at the median.
59
Chapter Five: Quantifying lipogenic gene expression in colorectal liver metastases
60
5.1 Introduction
In previous experiments, we observed that levels of several fatty acid metabolites were
increased in individuals with CRC. Four fatty acids products of endogenous fatty acid
metabolism, palmitic acid, palmitoleic acid, stearic acid and oleic acid, were significantly
increased in CRC. In addition, one metabolite central to inflammatory signalling pathways,
arachidonic acid, was significantly increased in serum from individuals with CRC. Therefore, we
wanted to know whether the expression of lipogenic enzymes responsible for the endogenous
synthesis of these fatty acid metabolites were increased in tumour tissue from a subset of the
same patient population. In order to investigate this, real-time RT-PCR was used to quantify the
expression of a lipogenic transcription factor, SREBP1c; several fatty acid synthesis genes,
FASN, SCD1, and ELOVL6; and members of an upstream signalling pathway, IGF-1 and IGF1R.
In order to sustain the increased rates of proliferation exhibited by cancer cells, metabolic
reprogramming of cellular biosynthetic and energetic pathways is required. Investigation into the
specific alterations in metabolism that occur with cell transformation has become a research area
of heightened interest. Aberrant lipid metabolism is a phenotype common to multiple cancer
types. Specifically, increased activity of the endogenous fatty acid synthesis pathway is accepted
as a common metabolic alteration in multiple cancers. FASN, a main enzyme of endogenous
fatty acid synthesis, has been widely studied with respect to aberrant lipid metabolism in cancer.
Recently, additional lipogenic enzymes have also been investigated for their role in cancer
metabolism, as increased lipid synthesis is necessary to produce building blocks for membrane
synthesis and signalling lipids in rapidly proliferating cell populations. In vitro studies
employing gene silencing techniques and enzyme inhibitors have demonstrated reliance of
61
certain cancer cell lines on fatty acid synthesis genes, highlighting the importance of
dysregulated lipid metabolism in cancer. The importance of lipid metabolic reprogramming in
cancer suggests that lipogenic genes may represent potential biomarkers for disease diagnosis
and prognosis, and modification of lipogenic gene activity may prove to be useful for targeted
cancer therapeutic development.
Fatty acids required for proliferation and maintaining cellular homeostasis are obtained
from exogenous dietary sources or endogenously synthesized. The endogenous fatty acid
synthesis pathway involves transcription factors and multiple enzymes that catalyze the
conversion of citrate into the 16-carbon saturated fatty acid known as palmitic acid. The enzymes
of this pathway include ATP citrate lyase, acetyl-CoA carboxylase, FASN, and acyl-CoA
synthetase. Once palmitic acid is synthesized, desaturase and elongase enzymes catalyze the
introduction of a double bond or elongate the fatty acid carbon chain, respectively.
Transcriptional regulation of the fatty acid synthesis pathway is controlled by the SREBPs64.
In this study, we specifically focused on the SREBP1c isoform, FASN, the desaturase
enzyme SCD1, and the elongase enzyme ELOVL6. These enzymes were selected as they are
required for the endogenous synthesis of the fatty acid metabolites that were determined to be
increased in the serum of individuals with CRC in the previous metabolomic profiling
experiments. As SREBP activity is regulated through insulin and other growth factor signalling
pathways, we also investigated the role of the insulin-like growth factor pathway by examining
IGF-1 and IGF-1R expression. The importance of each of these genes with respect to fatty acid
synthesis and cancer metabolism is reviewed below.
62
5.1.1 SREBP1c
The SREBPs are a family of transcription factors that regulate lipid metabolism; this
family includes three isoforms, SREBP1a, SREBP1c and SREBP2. The SREBP1c isoform
regulates fatty acid synthesis, the SREBP2 isoform regulates cholesterol synthesis, and the
SREBP1a isoform displays overlap between the fatty acid and cholesterol synthesis pathways.
Due to the importance of SREBP1c in the regulation of fatty acid synthesis, and specifically its
role in the induction of FASN and SCD1 mRNA expression, this isoform was the transcription
factor of focus in our study.
SREBP1c is expressed in a wide variety of human tissues with highest levels in liver,
white adipose tissue, adrenal gland, and brain. The expression of SREBP1c is regulated primarily
at the transcriptional level. A high carbohydrate diet induces expression of SREBP1c, while
fasting decreases expression. The induction of fatty acid synthesis genes by SREBP1c is
primarily mediated by insulin’s action through the PI3K pathway27,76.
SREBP1c has been studied for its role in the pathogenesis of metabolic diseases. The
activity of SREBP1c in obesity, diabetes, insulin resistance and hepatic steatosis has been well
characterized. There is also interest surrounding SREBP1c activity in cancer cells. SREBP
activity in cancer cells is of importance because of the increased requirements for lipid synthesis
to meet the demands of high proliferation rates, and due to the fact that SREBP1 is downstream
of the PI3K/Akt/mTORC1 signalling axis17,77, a pathway that is frequently activated in cancer
cells.
In response to signalling through PI3K/Akt, mTORC1 is required for the nuclear
accumulation of SREBP1. SREBP1 is required for lipid synthesis to sustain cell growth as
depletion of SREBP levels in epithelial cells prevented cell growth. The importance of SREBP in
63
relation to the PI3K/Akt/mTORC1 pathway was demonstrated by an investigation that looked at
the viability of multiple human cancer cell lines in response to SREBP1 depletion. Of the cancer
cell lines that showed reduced cell viability in response to SREBP1 depletion, all had mutations
in a component of the PI3K pathway suggesting that SREBP1 activity is of importance in cancer
cells that have a mutation causing activation of this signalling axis. Depletion of SREBP1 levels
in vivo was also shown to reduce tumour growth as measured by tumour volume and weight
when U87 glioblastoma cells with depleted SREBP1 levels were injected into nude mice78.
The current body of research surrounding SREBP1c suggests a role for this transcription
factor in tumourigenesis. Lipid synthesis is increased in numerous cancer types and in order to
sustain the high production of lipids, increased SREBP activity is required. In addition, due to
SREBP’s placement downstream of growth factor signalling and the PI3K/Akt/mTORC1 axis,
and the known importance of these pathways in the pathogenesis of cancer, SREBP may
represent an attractive target for modulating the activity of cancer cells. In this study, we sought
to investigate SREBP1c mRNA levels due to its activity in regulation of fatty acid synthesis.
SREBP1c levels were examined in the context of the IGF-1 signalling axis, and downstream
targets of SREBP1c including FASN, SCD1, and ELOVL6 were examined in order to gain
insight into the activity of this pathway in the context of CRC.
5.1.2 FASN
FASN is the most highly studied enzyme in the fatty acid synthesis pathway with respect
to cancer. FASN catalyzes the production of palmitic acid, a 16-carbon fatty acid, upon which
other enzymes act to produce unsaturated fatty acids and long chain fatty acids needed for
maintaining lipid homeostasis. In a healthy person, the role of endogenous fatty acid synthesis
and the activity of FASN is limited as the majority of fatty acids needed for cellular requirements
64
are obtained from dietary sources. Fatty acids are essential for a variety of functions in the cell
including incorporation into phospholipid molecules for membrane synthesis, for energy
homeostasis, and as important signalling molecules and post-translational protein modifiers. It
has been demonstrated that a large proportion of the fatty acids synthesized through endogenous
fatty acid synthesis in cancer cells are incorporated into growing phospholipid membranes,
potentially supporting the increased demand for lipid substrates in rapidly proliferating cell
populations.
Contrary to the low FASN expression levels in normal adult tissues, the majority of
tumours exhibit upregulation of the endogenous fatty acid synthesis pathway and have high
expression of FASN. Increased FASN expression is seen early in tumour progression as several
types of precursor and preinvasive lesions express FASN at high levels. High FASN protein
levels have been found in breast, CRC, prostate, bladder, ovary, esophagus, lung, thyroid, and
endometrium cancers among others, and expression levels have been studied in the context of
various clinical parameters which will be discussed in subsequent sections29,79.
From our previous metabolomic profiling experiments we found that palmitic acid levels
were increased in the serum of individuals with CRC. Given this result, we wanted to
characterize the expression of FASN in tumour tissue from a subset of the same patient
population. In addition, we wanted to examine FASN expression in the context of the lipogenic
transcription factor SREBP1c by examining the correlation between the expression of SREBP1c
and FASN.
5.1.3 SCD1
Through the fatty acid synthesis pathway, saturated and monounsaturated fatty acids are
produced. These fatty acids serve as building blocks for complex lipids, as important parts of cell
65
membranes, and as signalling molecules. The appropriate balance between SFAs and MUFAs is
necessary to maintain lipid homeostasis. The main enzyme responsible for the balance between
SFA and MUFA levels within a mammalian cell is SCD1. This endoplasmic reticulum enzyme is
central to lipid metabolism and catalyzes the introduction of the first double bond into growing
fatty acid chains. SCD1 can catalyze the synthesis of MUFAs from either endogenously
synthesized SFAs or fatty acids obtained from the diet. SCD1 is the rate-limiting enzyme for
MUFA synthesis. The primary substrates of SCD1 are palmitic acid and stearic acid, catalyzing
the production of palmitoleic acid and oleic acid, respectively. These MUFAs constitute the
major building blocks for phospholipids, triglycerides, and cholesterols80.
Altered expression of SCD1 has previously been described in diabetes, obesity, and
cardiovascular disease. In recent years, the role of SCD1 has been investigated in the context of
cancer. In order to maintain the appropriate balance of SFAs to MUFAs for cellular functioning,
SCD1 activity must adjust accordingly in a rapidly proliferating cancer cell population. SCD1
has been demonstrated to be a target for mitogens and hormones that regulate proliferation. An
increase in SCD1 activity appears to be necessary to maintain proper SFA to MUFA distribution
with the increased amounts of SFAs produced by the activity of FASN in cancer cells. Several
studies in cancer cell lines have reported high levels of SCD1 expression and have demonstrated
that suppression of SCD1 activity through genetic and inhibitor methods results in decreased
proliferation and survival35,80. The role of SCD1 in cancer development and progression has been
further supported in tumour xenograft models in mice. Human lung cancer cells with reduced
SCD1 expression exhibited delayed tumour formation and slower tumour growth rates in
xenograft models81, supporting a key role for SCD1 in tumourigenesis.
66
The mechanism by which SCD1 inhibition causes cancer cell cytotoxicity was studied in
HCT116 colon cancer cells82. Cell viability was decreased by 70% when small interfering RNA
(siRNA) was used to deplete SCD1, and this phenotype was reversible by exogenous addition of
the MUFA, oleic acid, to the culture medium. This suggests that cancer cell viability is impacted
by MUFA depletion and indicates that MUFAs are critical to survival in this cancer cell line.
SCD1 expression is highly regulated, despite the fact that the major products of this
enzyme, palmitoleic acid and oleic acid, are readily available from the diet. Nutrients such as
dietary carbohydrates, and hormones such as insulin, act to induce SCD1 expression through the
action of SREBP1c83,84. The mTOR signalling pathway has been demonstrated to regulate SCD1
expression in breast cancer cell lines, as activators of mTOR increased SCD1 expression, and
rapamycin and other mTOR inhibitors decreased SCD1 expression in three cancer cell lines.
SCD1 activity was decreased by both suppression of SCD1 promoter activity and decreased
SREBP1 protein levels85.
While evidence is available that SCD1 is overexpressed in several cancer cell lines, and
that depletion reduces cancer cell viability and overexpression leads to increased growth rates, a
limited amount of research to date has been conducted on SCD1 expression levels in patient
tumour samples. Given that the serum levels of two MUFAs, palmitoleic acid and oleic acid,
were significantly increased in individuals with CRC in our previous metabolomic profiling
experiments, we sought to characterize the expression of the desaturase enzyme, SCD1,
responsible for catalyzing the endogenous production of these fatty acid metabolites.
5.1.4 ELOVL6
Similar to SCD1, the elongase enzymes have been extensively studied for their role in the
pathogenesis of obesity, type II diabetes, and atherosclerosis. But, unlike SCD1, the elongases
67
have not yet been well studied in the context of cancer in vitro. The elongase enzymes are
encoded for by the Elovl (elongation-of-very-long-chain-fatty-acids) gene family. These
enzymes are located in the endoplasmic reticulum and catalyze the elongation of fatty acids
through addition of two-carbon units using malonyl-CoA as the donor. FASN catalyzes the
production of palmitic acid, a 16-carbon fatty acid, which can then be further elongated through
the action of elongases.
There are seven members in the elongase family (Elovl 1-7) that have varying affinity for
fatty acids of specific chain length and saturation; the elongase enzymes can act on endogenously
synthesized fatty acids or fatty acids obtained from the diet. Saturated and monounsaturated fatty
acids are the primary substrates for Elovl 1, 3, 6, and 7, while polyunsaturated fatty acids are
substrates for Elovl 2, 4 and 586,87. Maintaining appropriate amounts of fatty acids of varying
chain lengths is critical to ensure proper lipid homeostasis, as fatty acids of specific chain lengths
are required for particular functions within the cell.
We specifically focused on the ELOVL6 family member as the main substrates of this
enzyme are saturated and monounsaturated fatty acids with 12, 14, and 16 carbons. Fatty acids of
these chain lengths and degree of unsaturation were relevant from our previous findings in the
fatty acid metabolite profiling experiments. ELOVL6 is expressed in lipogenic tissues such as
the liver and adipose tissue and was initially discovered in mice overexpressing SREBP1.
Subsequent work identified ELOVL6 as a direct target of SREBP1c88, suggesting similar
regulation to other members of the fatty acid synthesis pathway such as FASN and SCD1.
To the best of my knowledge, no studies to date have investigated the consequences of
ELOVL6 deletion or overexpression in cancer cell lines. A different member of the elongase
family, ELOVL7, was found to be overexpressed in prostate cancer tissues in a genome wide
68
gene expression analysis89. Knockdown of ELOVL7 in several prostate cancer cell lines
decreased cell viability, and this enzyme was determined to be regulated by the androgen
pathway through SREBP1 activity. Apart from this investigation of ELOVL7 in prostate cancer,
there is a lack of research investigating other members of the elongase gene family in the context
of tumourigenesis. Due to the role of ELOVL6 in de novo lipogenesis, and the importance of
lipid metabolism in cancer cells, characterization of ELOVL6 activity in the context of cancer is
needed to further understand fatty acid metabolism. From our previous metabolomic profiling
results, serum levels of palmitic acid and stearic acid were increased in individuals with CRC.
Given that these two metabolites are substrates for ELOVL6, we sought to characterize the
expression of this enzyme in tumour tissue from individuals with CRC.
5.1.5 IGF-1, IGF-1R
The activity of IGF-1 has been well characterized as a promoter of proliferation and
survival in multiple in vitro systems. This growth factor has been widely studied in cancer cell
lines for its effect on the PI3K/Akt pathway stimulating cell proliferation, and its effect on
decreasing activity of the tumour suppressor gene PTEN. Mutations in the PI3K/Akt pathway
and PTEN are frequent in cancer; this signalling axis is of importance in regulating cell
proliferation and growth. Growth factors and cytokines commonly signal through the PI3K/Akt
pathway, representing a mechanism by which cancer cells can act to increase cell proliferation
and survival by activation of aberrant growth factor signalling. The effect of IGF-1 on
downstream signalling pathways has been well characterized in several pancreatic cancer cells
lines and is summarized below as an example of IGF-1 activity in cancer cells.
Ma et al.90 studied the effect of exogenous IGF-1 addition to cell medium and found that
IGF-1 increased proliferation and invasiveness in a dose-dependent manner in five pancreatic
69
cell lines. To characterize the effect of IGF-1 on downstream signalling pathways, the activity of
PTEN, PI3K, and Akt kinase activity was determined. IGF-1 inhibited the phosphorylation of
PTEN, decreasing its activity; phosphorylation of PI3K was increased, as was the activity of the
Akt kinase, thereby increasing the activity of this pro-survival pathway. In addition, the effect of
IGF-1 on proliferation and invasion was blocked by the addition of a PI3K inhibitor. In cancer
cells, the constitutive activation of pro-survival pathways either through mutations in the
pathway members themselves, or through aberrant upstream growth factor signalling, can lead to
uncontrolled growth and cell survival. The results from this study suggest that IGF-1 signalling
through PI3K/Akt pathway is important not only for cell proliferation and survival, but also for
the development of the invasive traits of cancer cells.
Due to the importance of aberrant growth factor signalling in various human
malignancies, and the subsequent downstream activation of growth and survival pathways
through the PI3K/Akt/mTORC1 axis, we were interested in investigating the expression of IGF1 and its receptor IGF-1R in colorectal liver metastases. The PI3K/Akt/mTORC1 axis has been
demonstrated to activate SREBP1c, which in turn causes the induction of fatty acid synthesis
genes, thus providing a link from IGF-1 signalling through to fatty acid synthesis. Previous
research has been conducted on the fatty acid synthesis pathway and its role in tumourigenesis,
most notably with a focus on FASN and more recently SCD1 activity. Despite this, there has not
been a comprehensive investigation in the setting of CRC characterizing the expression of
growth factor signalling, lipogenic transcription factor, and fatty acid synthesis gene expression
in individuals with liver metastatic colorectal cancer. By examining this metabolic pathway in a
broader context, we may observe alterations in lipid metabolism at different levels of regulation
throughout the signalling pathway.
70
5.2 Results
To characterize the expression of the fatty acid synthesis pathway in colorectal liver
metastases, real-time RT-PCR was employed to quantify the expression of growth factors, the
main lipogenic transcription factor, and downstream fatty acid synthesis genes. A sample of
tumour tissue, along with a sample of adjacent non-cancerous liver tissue, was obtained from
individuals undergoing surgery for colorectal liver metastases. RNA was extracted from the
tissue samples and real-time RT-PCR was performed to quantify the gene expression of
SREBP1, FASN, SCD1, ELOVL6, IGF-1, and IGF-1R. The housekeeping genes used for data
normalization were B2M and PMM1.
The comparative CT method was used to quantify the gene expression of SREBP1,
FASN, SCD1, ELOVL6, IGF-1, and IGF-1R. Relative quantification was performed and
expression data were normalized by dividing the amount of target mRNA by the amount of
internal control (B2M and PMM1). Data are presented as a comparison between tumour and
non-cancerous liver (p-value < 0.05 considered significant, calculated with t-test using Welch’s
correction), and additionally as a fold change in tumour relative to paired liver in each of the 15
patients (except for IGF-1 which is presented as a fold change value in liver tissue relative to
tumour tissue as this made most sense with the results from this experiment).
5.2.1 SREBP1c mRNA expression in tumour and liver
SREBP1c mRNA expression, a transcription factor important for regulation of genes
involved in fatty acid synthesis, was compared between colorectal liver metastases and paired
non-cancerous liver tissue. SREBP1c had significantly higher expression in tumour tissue
compared to adjacent liver (p-value = 0.0003, figure 5A). When SREBP1c mRNA levels were
compared between tumour tissue and paired liver in individual patients, expression was
71
increased in every tumour tissue sample (figure 5B). There was no individual variation with
respect to increased or decreased expression of SREBP1c. Each patient exhibited increased
SREBP1c mRNA expression in colorectal liver metastases tissue relative to liver tissue.
5.2.2 FASN, SCD1, and ELOVL6 mRNA expression in tumour and liver
The expression of three fatty acid synthesis genes, FASN, SCD1, and ELOVL6, was
characterized in colorectal liver metastases and paired non-cancerous liver. These three genes are
induced by the transcription factor SREBP1c; this transcription factor was determined to have
increased expression in tumour tissue relative to paired liver tissue.
FASN expression demonstrated a trend towards increased expression in tumour tissue,
but this was not statistically significant (p-value = 0.36, figure 5C). When FASN mRNA levels
were compared between tumour tissue and paired liver in individual patients, expression was
variable between different patients (figure 5D). There were substantial inter-individual
differences with respect to expression of FASN as some patients had higher expression in tumour
tissue compared to paired liver, while other patients exhibited lower tumour FASN expression.
The majority of individuals (11/15) had higher FASN mRNA expression in tumour tissue
relative to non-cancerous liver tissue.
SCD1 expression demonstrated a trend towards increased expression in tumour tissue,
but this was not statistically significant (p-value = 0.23, figure 6A). When SCD1 mRNA levels
were compared between tumour tissue and paired liver in individual patients, expression was
variable between different patients (figure 6B). Similar to FASN expression, there were interindividual differences with respect to expression of SCD1. Roughly half the individuals (8/15)
had higher SCD1 mRNA expression in tumour tissue relative to non-cancerous liver tissue. Of
note, one individual had much higher SCD1 expression in tumour tissue relative to the other
72
patients that exhibited increased SCD1 expression. It would be interesting to know if this patient
had any co-existing diseases that have been studied with relation to SCD1, such as obesity or
diabetes, that may offer an explanation for the high expression of this enzyme.
ELOVL6 expression demonstrated a trend towards decreased expression in tumour
tissue, but this was not statistically significant (p-value = 0.04, figure 6C). Again, interindividual differences in ELOVL6 mRNA levels were evident (figure 6D). A minority of
individuals (4/12) had higher ELOVL6 mRNA expression in tumour tissue relative to noncancerous liver tissue. Interestingly, the expression of ELOVL6 mRNA was more consistent
between tumour and liver tissue as many of the fold change values were close to 1 indicating
very similar expression in both the tumour and non-cancerous liver groups.
5.2.3 IGF-1 and IGF-1R mRNA expression in tumour and liver
Significant differences between tumour and liver tissue were found for both IGF-1 and
IGF-1R. In addition, large fold change values were seen for both genes when comparing tumour
and paired liver demonstrating large differences in the expression of IGF-1 and IGF-1R in the
context of colorectal liver metastases. For IGF-1, expression was significantly higher in adjacent
non-cancerous liver tissue (p-value = 0.004, figure 7A) and each individual patient sample
showed higher expression in liver compared to tumour tissue (figure 7B, presented as fold
change value for liver relative to tumour tissue for IGF-1 only).
Conversely, IGF-1R expression was significantly higher in tumour tissue relative to
paired non-cancerous liver tissue (p-value = 0.0001, figure 7C). When looking at patients on an
individual basis, each individual exhibited the trend towards higher IGF-1R expression in tumour
tissue (figure 7D).
73
5.2.4 Correlation among mRNA levels of genes in fatty acid synthesis pathway
In order to investigate if there was coordinated elevation between the lipogenic
transcription factor and enzymes of the fatty acid synthesis pathway, correlation among mRNA
levels of SREBP1c and the lipogenic genes FASN, SCD1, and ELOVL6 was determined.
Significant correlation was seen between SREBP1c mRNA levels and FASN mRNA levels
(figure 8). In contrast, there was no significant correlation between SREBP1c mRNA levels and
SCD1 or ELOVL6 mRNA levels.
5.3 Discussion
From the real-time RT-PCR investigations characterizing mRNA expression of growth
factors, a lipogenic transcription factor, and several fatty acid synthesis genes, it is evident that
the gene expression varies with different levels within the fatty acid metabolic pathway.
Upstream of the fatty acid synthesis pathway, the IGF-1 axis was examined; signalling by this
growth factor can activate the lipogenic transcription factor SREBP1c and subsequent
downstream activity of the fatty acid synthesis pathway. IGF-1 mRNA expression was
significantly increased in non-cancerous liver tissue in comparison to colorectal liver metastases
tissue, while IGF-1R was significantly increased in the liver metastases tissue itself. This may
suggest a mechanism by which, in the setting of liver metastases, IGF-1 expression is
upregulated in surrounding liver tissue to provide a continuous supply of ligand for growth factor
signalling through the upregulated receptor on the tumour cells. SREBP1c mRNA expression
was also significantly increased in tumour tissue suggesting that signalling through the
overexpressed IGF-1R leads to overexpression in the lipogenic transcription factor (figure 9). In
terms of the three fatty acid synthesis genes examined, FASN, SCD1, and ELOVL6, there were
no significant differences in mRNA expression between tumour tissue and paired non-cancerous
74
liver tissue. For these three fatty acid synthesis genes specifically, inter-individual differences in
mRNA levels appear to be very important. Some patients exhibited large fold change increases
in the expression of genes in tumour tissue relative to non-cancerous liver tissue, while others
individuals exhibited decreased expression.
The results from the current investigation on the mRNA expression levels of each
transcription factor and lipogenic genes are discussed in the context of the current literature on
mRNA and protein expression in samples from patients with various human malignancies.
Although there are differences in the underlying biology in different cancer types, several of the
genes examined in this study have not previously been characterized in patient samples from
individuals with CRC which is why other cancer types are additionally discussed.
5.3.1 SREBP1c expression in patient samples
In our study, when comparing SREBP1c mRNA levels in tissue samples from colorectal
liver metastases versus paired liver, SREBP1c mRNA levels were significantly increased in
tumour tissue. In addition, when looking at individual’s paired samples, SREBP1c mRNA levels
were increased in the tumour tissue of all patient samples examined. This suggests that the
increase in SREBP1c mRNA is common to the pathogenesis of colorectal liver metastases, as
SREBP1c is normally expressed in liver tissue, but exhibits an additional increase in expression
in tumour tissue even relative to a background where this gene is normally expressed.
Varying results have been found when examining SREBP1 expression at the mRNA and
protein level in different cancer types. In endometrial cancer, SREBP1 was found to be
expressed, through immunohistochemical analysis, in both normal and cancerous tissues. Despite
this, levels were increased in the poorly differentiated cancer tissues in relation to the normal
tissues. Of the SREBP1 protein that was detected in normal tissues, the majority was found in the
75
cytoplasm. Conversely, in the cancerous tissues SREBP1 protein was mostly detected as the
active form of this transcription factor, namely nuclear SREBP1. In addition, SREBP1
expression levels were higher in precancerous lesions, atypical endometrial hyperplasia, when
compared to normal tissues91.
In a group of 10 patients with hepatocellular carcinoma, mRNA expression of SREBP1a
and SREBP1c along with mRNA expression of several lipogenic genes was examined in tumour
tissue compared to non-cancerous liver tissue. Increased expression of the lipogenic genes
FASN, acetyl-CoA carboxylase, and ATP citrate lyase was seen in cancerous tissue, but
significant differences were not seen between cancerous and non-cancerous liver tissue for
SREBP1 mRNA levels92.
SREBP1 protein expression was examined in prostate cancer specimens and compared
across four clinical groups: benign tissue, tumour tissue from patients who had not received
hormonal therapy, tumour tissue from patients who had received hormonal therapy for varying
lengths of time, and tissue from tumours that had become androgen independent. Benign prostate
tissue samples showed the lowest SREBP1 levels, while expression increased in untreated
prostate cancer samples. Interestingly, in tissue samples taken from patients after up to 3 months
of hormonal treatment, SREBP1 levels decreased to lower than that of untreated samples, but
expression began to increase again in patients who had been on treatment for extended periods of
time93.
SREBP1 activity appears to be a necessary component of cancer metabolism and serves
as a connection point between aberrant growth factor signalling transduced through the
PI3K/Akt/mTORC1 axis and lipid metabolism. In vitro studies have outlined a role for SREBP1
in tumourigenesis and have demonstrated that depletion of SREBP1 levels attenuates cancer cell
76
growth and survival. When examining human cancer specimens, several investigations have
determined increased expression of SREBP1 in cancer compared to non-cancerous tissues, while
others have not found expression differences between cancer and control. It is expected that there
would be some variation in gene expression between different types of human malignancies as
the underlying biology of various cancers is different. The role of SREBP1 in human cancers
requires more characterization as a very limited number of studies are available. In addition,
association between SREBP1 expression and clinical outcomes remains to be investigated. In our
study, SREBP1 mRNA levels were determined to be increased in the tumour tissue of each of
the patients with colorectal liver metastases suggesting an increased role for this transcription
factor in CRC. Whether SREBP1 has a role in CRC in general, or more specifically in colorectal
liver metastases remains to be determined as the expression of this transcription factor has not
been characterized in locoregional colorectal cancer tumour tissue.
5.3.2 FASN expression in patient samples
From our results, it was evident that there were large individual variations in FASN
mRNA expression in tumour tissue. Overall, when comparing FASN mRNA levels in tumour
versus paired liver tissue there was no statistical difference, but when looking at fold changes in
individual patients it was clear that specific individuals had increased expression of this gene at
the mRNA level. These results are consistent with numerous findings in the literature, as FASN
overexpression is often found in a subset of the patient population examined for various human
malignancies. FASN has commonly been examined in the context of various clinical parameters,
suggesting that overexpression is associated with specific patient subsets, which varies with
cancer type. An overview of several FASN expression studies and the association with specific
77
clinical factors is presented below to give a general idea of the types of clinical factors FASN
overexpression is associated with in different cancer types.
One study characterizing FASN expression through immunohistochemistry in 647 colon
cancer patients, determined that FASN was overexpressed in 13% of the cases. Cox proportional
hazards models determined that FASN overexpression was associated with a reduction in
mortality from colon cancer in this patient population. Interestingly, the effect of FASN
expression on mortality appeared to be associated with BMI, as individuals with a normal body
weight had improved survival rates with increased tumour FASN expression, while obese
individuals had worse survival rates with higher tumour expression of FASN94. Another
investigation in CRC patients found that serum FASN levels were significantly higher in the 74
patient samples compared to the 40 healthy control samples as measured by an enzyme-linked
immunosorbent assay (ELISA). For serum FASN levels, 5-year overall survival and disease-free
survival were significantly higher in individuals with lower serum levels of FASN95.
When examining FASN protein expression by immunohistochemistry in 102 men with
prostate cancer, 63% of the specimens were determined to have high FASN expression. High
FASN expression was significantly associated with a Gleason Score >7, a grading system used in
prostate cancer to determine the aggressiveness of the tumour. The authors of this study suggest
that FASN expression may represent a novel method for assessing tumour aggressiveness and
guiding treatment96. Another study determined FASN mRNA and protein levels to be increased
in prostate tissue compared to normal tissue; FASN levels were found to increase with increasing
tumour grade. Interestingly, a significant correlation was found between mRNA and protein
expression levels in 2/3 of the cases, but high protein levels were found in 1/3 of the cases
despite low corresponding FASN mRNA levels97. This suggests that mRNA and protein levels
78
do not always correlate with respect to FASN expression, which is important to consider in the
context of the results from our study as we examined mRNA levels.
Finally, in a large study on gastric cancer, FASN expression was classified as high in
only 34.5% of the 626 gastric cancer tissue samples examined by immunohistochemistry. FASN
expression was significantly associated with well-differentiated tumours, male gender, and age >
51 years. No significant association was found between FASN expression and clinical stage,
depth of invasion, or survival98.
This brief overview of that literature available on FASN expression in individuals with
cancer highlights the large variations in FASN expression that are seen. With different modalities
of measuring FASN, the biofluid in which FASN expression is measured (serum, tissue), and the
level at which expression is examined (mRNA, protein), varying results on FASN are found. In
addition, there appears to be significant variation between different types of malignancies and the
association with various clinical factors, suggesting that the implications of high FASN
expression is highly specific to cancer type.
5.3.3 SCD1 and ELOVL expression in patient samples
In our study, when comparing SCD1 mRNA levels in tissue samples from colorectal liver
metastases and paired non-cancerous liver, SCD1 mRNA was not significantly elevated in
tumour tissue. Although significant differences were not observed when comparing the two
sample groups, differences were observed at an individual level and several patients had higher
levels of SCD1 mRNA in tumour tissue compared to paired liver. Larger sample sizes will be
needed to investigate the role of SCD1 in CRC. In addition, characterization of SCD1 protein
levels would be a useful next step to gain further insight into the expression of this enzyme in
tumour tissue.
79
In addition to the in vitro studies mentioned previously, SCD1 expression has also been
examined in tumour samples from breast cancer patients and has been investigated for its role as
a prognostic marker. In breast cancer tumour samples, immunohistochemical staining showed
higher SCD1 protein content in cancerous areas of the sample, along with high amounts of
MUFAs, the products of the SCD1 enzyme99. In another study investigating SCD1 as a
prognostic marker, high SCD1 protein levels were found to be significantly associated with
shorter relapse-free survival as well as shorter overall survival when examining samples from
250 women with stage I-III breast cancer while correcting for age, tumour type, grade and
clinical stage100.
Elovl6 mRNA levels were determined in patient samples from individuals with colorectal
liver metastases. When comparing tumour tissue and non-cancerous liver tissue, no significant
difference was seen between the two sample groups. Again, individual differences were
observed, but the sample size would need to be increased in order to draw any substantial
conclusions about the role of this enzyme in the context of cancer as no other expression studies
in human tumours have been conducted. In addition, in vitro studies will need to be conducted
investigating the impact of ELOVL6 deletion and overexpression in various cancer cell lines in
order to determine if this enzyme has an impact on cancer cell growth or survival. Once the role
of ELOVL6 has been characterized in cancer cell lines, a larger investigation of ELOVL6
expression in patient samples of various malignancy types should be undertaken in order to gain
more information about this enzyme and its role in fatty acid synthesis in cancer.
In comparison to SREBP1c and FASN, much less research has been conducted on the
expression of SCD1 in human tumour tissue, and no research has been conducted, until now, on
the expression of ELOVL6 in human tumour samples. Our study represents the first investigation
80
into the expression of these enzymes in individuals with colorectal liver metastases. Although no
significant trend was seen towards increased or decreased expression in colorectal liver
metastases, the individual variations suggest that with an increased sample size there may be a
trend towards differential expression and there may be correlation with specific clinical
parameters. SCD1 expression was found to correlate with worse prognosis in breast cancer
patients, and similar findings may be present in CRC but a larger sample size and investigation
into patient factors would be needed. By investigating the expression of these enzymes,
additional insight into the tumour’s reliance on MUFAs and long chain fatty acids can be gained.
Additionally, these enzymes may represent potential prognostic markers or therapeutic targets in
the future with further characterization of the fatty acid synthesis gene phenotype in CRC.
5.3.4 Levels of IGF-1, IGF-2, IGF-1R, IGFBPs and CRC risk
Multiple large-scale studies have been conducted assessing the serum and plasma levels
of IGFs and IGFBPs and the risk of developing CRC over the past decade. The majority of
investigations have focused on circulating levels of these growth factors in serum or plasma,
which differs from the tissue quantification of IGF-1 and IGF-1R undertaken in our study.
Circulating levels of IGF-1 are relevant since, in addition to IGF-1’s local effects at target
tissues, this hormone is secreted into the circulation after its hepatic synthesis. Below a brief
summary of several large-scale prospective studies on the associations between IGF-1 levels and
CRC risk is presented.
Ma et al.101 conducted a prospective case-control study where baseline plasma samples
were obtained from 14,916 men without a diagnosis of cancer. During 14 years of follow-up, 193
men were later diagnosed with CRC. Men in the highest quintile for plasma IGF-I had increased
risk of CRC compared to men in the lowest quintile (relative risk: 2.51), after adjustments were
81
made for IGFBP-3, age, smoking, BMI, and alcohol intake. Conversely, men with higher
IGFBP-3 levels had a lower risk of CRC (relative risk: 0.28). IGF-II was not found to be
associated with CRC risk in this study. A similar prospective study was conducted in women
assessing circulating levels of IGF-1 and IGFBP-3 and risk of CRC102. Plasma was collected
from 32,826 women, and during the following 6 years, 79 cases of CRC, 90 cases of
intermediate/ late stage adenoma, and 107 cases of early stage adenoma were documented. After
controls were matched by age, month of blood draw, and fasting levels, women in the highest
tertile of IGF-1 levels were found to have increased risk of intermediate/ late stage colorectal
adenoma (relative risk: 2.78) compared to women in the lowest tertile.
Probst-Hensch et al.103 collected baseline samples from 18,244 men living in Shanghai,
China. Over 12 years of follow-up 135 men developed CRC, and IGF-I, IGF-II, and IGFBP-3
levels were measured in the serum of these individuals as well as 661 controls matched for
neighbourhood of residence, age, and year and month of sample collection. Serum IGF-I levels
were not found to be associated with CRC risk in this patient population, while IGF-II and
IGFBP-3 were found to have a positive association with CRC risk. Finally, in a prospective
study in Northern Sweden, IFG-I and IGFBP-3 plasma levels were measured in prediagnostic
samples from 168 patients who went on to develop colon and rectal cancer, as well as 336
matched controls. With increasing IGF-I and IGFBP-3 levels, an increase in colon cancer risk
was found, while rectal cancer risk was inversely related to the circulating levels of IGF-I and
IGFBP-3.
Based on these results, from numerous case-control studies with large patient sample sets
with matched controls, and with adjustment for potentially cofounding factors such as BMI, time
82
of sample collection, smoking, and alcohol intake, it is apparent that there is variation in the
findings with respect to circulating levels of IGF signalling molecules and CRC risk.
Our investigations from real time RT-PCR quantification of IGF-1 and IGF-1R mRNA
levels were more focused on the local expression of these growth factors and receptors in the
tumour tissue and surrounding liver tissue itself, rather than circulating levels. From our results,
IGF-1 mRNA expression was significantly increased in non-cancerous liver tissue in comparison
to the colorectal liver metastases. This was an interesting finding in the setting of the result that
IGF-1R was significantly increased in the tumour tissue. The results from this investigation
suggest that, in the presence of colorectal liver metastases, there is an effect on surrounding noncancerous liver tissue in order to upregulate IGF-1 expression. The high levels of IGF-1 could, in
turn, act through the increased levels of IGF-1R on the tumour cells, activating downstream
growth and survival pathways. The effect of liver metastases on the surrounding liver
microenvironment will be further explored in the next chapter.
5.4 Conclusions
In conclusion, the fatty acid synthesis pathway does not seem to be ubiquitously
overexpressed in colorectal liver metastases as demonstrated by the inter-individual variations in
fatty acid synthesis gene expression. While the expression of FASN was increased in tumour
tissue relative to non-cancerous liver in several individuals, it was decreased in others. These
results are consistent with the current literature available on FASN mRNA and protein levels in
various cancer types. FASN expression is commonly associated with clinical parameters such as
prognosis, so increasing the sample size of our study and examining the relationship of FASN
mRNA levels and clinical parameters would be an interesting next step. Similarly for SCD1 and
ELOVL6 mRNA levels, there were individual variations in tumour expression of these genes.
83
Again, examining the correlation between clinical parameters and expression of these genes may
be informative.
While the genes of the fatty acid synthesis pathway, FASN, SCD1, and ELOVL6, did not
demonstrate ubiquitous overexpression in tumour tissue in the patient population studied, the
expression of upstream growth factor receptor and a lipogenic transcription factor did show
significantly higher expression in colorectal liver metastases compared to non-cancerous liver.
Interestingly, IGF-1 mRNA expression was significantly increased in non-cancerous paired liver
tissue, while IGF-1R was significantly increased in the tumour tissue itself. This increase in
ligand and receptor in the liver microenvironment and tumour, respectively, could represent a
mechanism by which the surrounding liver tissue provides a supply of IGF-1 for signalling
through the IGF-1R on the tumour cells. Activation of the IGF-1R signalling axis activates
SREBP-1c activity in tumour cells, which was also significantly increased in tumour tissue.
Results from these experiments indicate that upstream activators of the fatty acid synthesis
pathway are increased in colorectal liver metastases, while there is more individual variation in
the expression of the genes of the fatty acid synthesis pathway themselves. These upstream
activators of lipid synthesis may represent potential therapeutic targets for the treatment or
prevention of colorectal liver metastases.
To improve this study the sample size would need to be increased. In addition, it would
be valuable to characterize the protein expression levels of each of these genes as it is known that
transcript levels do not necessarily correlate with protein levels. As we were looking at
metabolite levels in previous experiments, protein levels would represent a step closer to the
metabolite profile in these patients. Finally, with an increased sample size and corresponding
protein data, correlation with clinical parameters would be very informative. As previous
84
research has found correlation between genes of the fatty acid synthesis pathway and clinical
parameters, it would be interesting to look at individuals with high expression of fatty acid
synthesis genes and investigate if there was any correlation with clinical parameters such as
tumour grade, histological type, prognosis, or response to treatment.
85
5.5 Tables and Figures
Figure 6. Quantification of SREBP1c and FASN mRNA expression in colorectal liver
metastases using real-time RT-PCR.
(A) SREBP1c and (C) FASN mRNA levels in colorectal liver metastases and paired liver tissue in 15
individuals. Expression normalized to mean expression of two housekeeping genes (B2M, PMM1).
Data points presented as linear values using 2^(-ΔCt). P-value calculated using a paired t-test.
Whiskers indicate the 5th and 95th percentiles; the line in the box is plotted at the median. Fold
change values for (B) SREBP1c and (D) FASN in colorectal liver metastases relative to paired liver.
Fold increase above 1 (dotted line) indicates increased expression in tumour relative to paired liver.
86
Figure 7. Quantification of SCD1 and ELOVL6 mRNA expression in colorectal liver
metastases using real-time RT-PCR.
(A) SCD1 and (C) ELOVL6 mRNA levels in colorectal liver metastases and paired liver tissue.
Expression normalized to mean expression of B2M. Data points presented as linear values using 2^(ΔCt). P-value calculated using a paired t-test. Whiskers indicate the 5th and 95th percentiles; the line
in the box is plotted at the median. Fold change values for (B) SCD1 and (D) ELOVL6 in colorectal
liver metastases relative to paired liver. Fold increase above 1 (dotted line) indicates increased
expression in tumour relative to paired liver.
87
Figure 8. Quantification of IGF-1 and IGF-1R mRNA expression in colorectal liver
metastases using real-time RT-PCR.
(A) IGF-1 and (C) IGF-1R mRNA levels in colorectal liver metastases and paired liver tissue in 15
individuals. Expression normalized to mean expression of two housekeeping genes (B2M, PMM1).
Data points presented as linear values using 2^(-ΔCt). P-value calculated using a paired t-test.
Whiskers indicate the 5th and 95th percentiles; the line in the box is plotted at the median. Fold
change values for (B) IGF-1 in liver relative to colorectal liver metastases; fold increase above 1
indicates increased expression in liver. Fold change values for (D) IGF-1R in colorectal liver
88
metastases relative to paired liver. Fold increase above 1 (dotted line) indicates increased expression
in tumour relative to paired liver.
89
Figure 9. Correlation between mRNA levels of SREBP1c and FASN.
A significant correlation was detected (p-value < 0.0001) using Pearson correlation coefficients. No
significant correlation was detected between mRNA levels of SREBP1c and SCD1 or ELOVL6.
90
Figure 10. Alterations in IGF-1/IGF-1R/ SREBP1c signalling pathway determined by
lipogenic gene quantification using real time RT-PCR.
Through gene quantification using real time RT-PCR, non-cancerous liver tissue was determined
to have increased expression of IGF-1 mRNA, while colorectal liver metastases tissue was
determined to have increased mRNA expression of IGF-1R and SREBP1c. This suggests a
mechanism by which the hepatic synthesis of IGF-1 in the tumour microenvironment can signal
through the IGF-1R, which is overexpressed in tumour cells. IGF-1 signalling through the
PI3K/Akt/mTORC1 axis can activate the lipogenic transcription factor SREBP1c, which in turn
induces the expression of fatty acid synthesis genes including FASN, SCD1, and ELOVL6.
91
Chapter Six: Determining host response to tumour development in non-cancerous liver and
adipose tissue in individuals with colorectal liver metastases
92
6.1 Introduction
In previous experiments we observed that IGF-1 levels were increased in the noncancerous liver tissue of individuals with colorectal liver metastases. Therefore, we wanted to
investigate if this change in IGF-1 gene expression in normal liver was a consequence of tumour
development. In order to investigate the host response in liver to the development of cancer
metastases, we quantified the mRNA expression levels of genes of the fatty acid synthesis
pathway in liver tissue from individuals with malignant and benign disease. By examining liver
tissue from individuals with cancer metastases and individuals with benign liver conditions, we
were able to determine the effect that the colorectal liver metastases had on the surrounding liver
microenvironment.
Our prior results on the expression of lipogenic genes in colorectal liver metastases
compared to non-cancerous liver tissue determined that SREBP1c, the master lipogenic
transcription factor, along with the IGF-1R, were significantly increased in tumour tissue. The
growth factor, IGF-1, was significantly increased in paired non-cancerous liver tissue. As for the
lipogenic genes, FASN, SCD1, and ELOVL6, expression levels varied on an individual basis
with only a proportion of the patient samples exhibiting high expression of these genes. These
results are in concordance with the literature available on the expression of fatty acid synthesis
genes in various cancer types; high expression of these genes typically appears in a subset of the
patient population being examined and correlates with varying clinical outcomes depending on
the type of malignancy.
The tumour microenvironment is known to contribute to tumour development and
progression, and crosstalk between tumour cells and stromal cells of the microenvironment has
been implicated in tumourigenesis. By investigating the expression of the growth factor axis
93
IGF-1 and IGF-1R, the lipogenic transcription factor SREBP1c, and the fatty acid synthesis
genes FASN, SCD1, and ELOVL6 in non-cancerous liver from individuals with colorectal liver
metastases and individuals with benign conditions we will gain insight into the metabolic effects
that malignant disease has on surrounding tissue.
In addition to the liver microenvironment and the effect of colorectal liver metastases on
metabolism in the immediately adjacent tissue, we were also interested in determining if the
tumour had any effect on metabolism at sites distant to the tumour. In previous metabolic
profiling experiments, abundances of fatty acid metabolites were increased in individuals with
CRC. The main enzymes responsible for the synthesis of the metabolites, palmitic acid,
palmitoleic acid, stearic acid, and oleic acid, are FASN, SCD1, and ELOLV6. Despite the
increased serum abundances of these metabolites, the overall mRNA expression of these fatty
acid synthesis enzymes was not significantly increased in tumour tissue. Therefore, we wanted to
investigate other host tissues that could be contributing to the circulating levels of serum fatty
acids in these patients. We hypothesized that aberrant fatty acid synthesis occurring at a site
other than the tumour may be contributing to the high levels of circulating fatty acid metabolites
in individuals with CRC. To investigate this, we sought to quantify FASN mRNA expression
levels in subcutaneous and omental adipose tissue in individuals with malignant and benign
disease. For this experiment, we specifically focused on FASN, as this enzyme is central to
endogenous fatty acid synthesis and catalyzes the production of palmitic acid. Palmitic acid was
elevated in the serum of CRC patients in metabolomic profiling by both GC-MS and UPLC-MS
experiments. Palmitic acid serves as a substrate for both desaturase and elongase enzymes, so
appropriate abundance of this metabolite is important for the subsequent synthesis of long chain
fatty acids as well as MUFAs required for maintaining lipid homeostasis. For these reasons, we
94
characterized the mRNA expression levels of FASN in subcutaneous and omental adipose tissue
from individuals with CRC to examine the distant effects of tumour growth on fatty acid
metabolism.
6.1.1 The role of the tumour microenvironment and inflammation
The tumour microenvironment is recognized for its role in cancer initiation and
progression. Non-cancerous cells contribute to cancer progression through the secretion of
growth factors and cytokines that activate signalling pathways in tumour cells. The
microenvironment of the tumour also plays a critical role in the development of metastases. For
cancer cells to metastasize from the primary site to a secondary site in the body, several steps are
necessary. Cancer cells must develop traits that allow local invasion and degradation of tissues,
survival in the circulation, proliferation and survival at the new site, and stimulation of
angiogenesis to provide a new blood supply to the growing tumour. The microenvironment is
thought to be involved, both at the primary site and the site of metastases, in activating
transcription factors necessary to acquire invasive cellular traits needed for metastasis104. With
increased characterization of the interactions between tumour and surrounding non-tumour
stromal cells, a higher level of understanding regarding tumour biology will be gained and may
highlight additional avenues for the development of therapeutics targeting the tumour
microenvironment in combination with targeting the tumour itself.
Inflammation in the tumour microenvironment represents another important factor in
cancer development in several types of malignancies. Chronic states of inflammation have been
implicated in tumourigenesis, and supporting this, the use of certain anti-inflammatory
medications such as NSAIDs has shown association with reduced risk of developing certain
cancers, including CRC. Immune cells commonly infiltrate tumours; CRC is a type of solid
95
tumour that is associated with increased infiltration of immune cells. In addition, the
classification of the type of immune cell infiltrates in CRC patients has been shown to have
prognostic
implications105.
Exploiting
the
inflammatory
component
of
the
tumour
microenvironment represents another potential approach for targeted cancer therapeutics;
specific immune cells or a composite of immune cell infiltrates important for a given tumour
type could be targeted therapeutically to alter the cellular inflammatory programs that provide a
pro-survival environment for tumours to develop within.
For individuals with inflammatory conditions affecting their colon, such as ulcerative
colitis or Crohn’s disease, the risk of developing CRC is increased; this implies a link between
chronic inflammation and CRC. Sustained infiltration of immune cells and chronic inflammation
leads to continual macrophage recruitment and subsequent generation of reactive oxygen species.
The increased production of reactive oxygen species due to inflammation can lead to mutations
in cells of the surrounding environment. This cellular damage is one way in which long-standing
inflammation is thought to contribute to tumourigenesis. In addition, production of cytokines by
inflammatory cells recruited to the site of tumour development may subsequently activate
proliferation and growth pathways in the tumour itself. Of particular importance in the
pathogenesis of CRC is COX-2. This pathway is involved in inflammation and is activated by
pro-inflammatory cytokines. Upon activation, COX-2 acts upon its substrate, arachidonic acid, to
produce various prostaglandins which are involved in downstream inflammatory pathways106.
6.1.2 Host response to tumour development in adipose tissue
In addition to the local tumour microenvironment, metabolic alterations at sites more
distant from the tumour may occur. Adipose tissue represents an important example of a host
tissue in which metabolic responses to cancer development can occur and subsequently affect
96
whole-body metabolism. The role of adipose tissue has been studied extensively in the context of
obesity-associated cancers; with increasing BMI and amount of adipose tissue, the risk of
developing several cancer types increases. Adipose tissue is an endocrine organ that produces
hormones and inflammatory cytokines; given this, with increasing amounts of adipose tissue
seen in obesity, inflammatory and proliferative pathways may be activated with increased
frequency107.
In this study we were not specifically examining the effect of BMI on CRC development,
but instead using subcutaneous and omental adipose tissue to examine the activity of the fatty
acid synthesis pathway at a site distant from the tumour. The expression of FASN in adipose
tissue was of particular interest due to the fact that previous metabolic profiling results
demonstrated that serum levels of palmitic acid, the main product of FASN, as well as
downstream fatty acid metabolites were increased in individuals with CRC. As such, we sought
to quantify the expression of FASN in adipose tissue to investigate whether metabolic changes in
this tissue type were contributing to the high circulating levels of fatty acid metabolites in CRC.
The microenvironment and host response to tumour development is not well
characterized in the setting of colorectal liver metastases. As such, we sought to characterize the
fatty acid metabolic alterations in non-cancerous liver tissue as well as subcutaneous and
omental adipose tissue in individuals with colorectal liver metastases.
6.2 Results
In order to examine the effect of colorectal liver metastases on surrounding noncancerous liver tissue, real time RT-PCR was employed to quantify the mRNA expression of
growth factors, a lipogenic transcription factor, and fatty acid synthesis genes. Gene expression
was compared between liver tissue from individuals with colorectal liver metastases and
97
individuals with benign liver conditions such as hemangioma and focal nodular hyperplasia. The
expression of IGF-1, IGF-1R, SREBP1c, FASN, SCD1, and ELOVL6 was determined and
compared between the malignant and benign sample groups.
In order to investigate host response at a site distant to tumour development,
subcutaneous and omental adipose tissue was obtained from individuals with malignant and
benign disease. Real-time RT-PCR was used to quantify the expression of FASN in adipose
tissue samples.
The comparative CT method was used to quantify gene expression. Relative
quantification was performed and expression data were normalized by dividing the amount of
target mRNA by the amount of house keeping gene mRNA.
6.2.1 The effects of colorectal liver metastases on hepatic expression of genes involved in fatty
acid synthesis
To investigate the local effect of liver metastases on surrounding liver tissue, we
examined gene expression levels in liver tissue from individuals with malignant and benign
disease. No significant difference in the mRNA expression of SREBP1c, FASN, SCD1,
ELOVL6, or IGF-1R was detected when comparing liver tissue samples from individuals with
malignant and benign disease (figure 10). From these results, we can conclude that the liver
metastases are not having an effect on the expression of these specific genes in surrounding noncancerous liver tissue.
Conversely, the presence of liver metastases did appear to have an effect on the
expression of IGF-1 mRNA in surrounding non-cancerous liver tissue. A significant difference
in IGF-1 mRNA expression was detected in individuals with malignant versus benign disease.
Expression of IGF-1 was higher in non-cancerous liver in individuals with malignant disease (p98
value = 0.004, figure 10E) compared to individuals with benign disease. The results of this
experiment suggest that the presence of colorectal liver metastases has an impact on metabolism
in surrounding liver tissue that leads to increased hepatic expression of IGF-1 mRNA.
6.2.2 Fatty acid synthesis in normal host tissues: subcutaneous and omental adipose
To investigate the metabolic response in normal host tissues with respect to fatty acid
synthesis, the expression of FASN was quantified in adipose tissue from individuals with
malignant and benign disease. When FASN mRNA expression in omental adipose tissue (figure
11A) was compared between individuals with colorectal liver metastases and individuals with
benign disease, no significant difference was detected. Similarly, there was no significant
difference in FASN expression when subcutaneous adipose tissue (figure 11B) was examined.
6.3 Discussion
The role of the tumour microenvironment in cancer development is receiving
considerable attention. The various implications that cytokines, mitogens, and inflammatory
mediators released from non-malignant cells of the microenvironment can have on tumour
development and subsequent progression to metastatic disease highlight its importance, both for
further understanding of underlying mechanisms as well as for the development of better
targeted therapeutics. Our study sought to understand the contribution of host hepatic and
extrahepatic tissues to levels of circulating fatty acid metabolites, as previous results determined
increased abundances of fatty acid metabolites in the serum of individuals with CRC.
6.3.1 Hepatic expression of lipogenic genes
From our investigation into the expression of IGF-1, IGF-1R, SREBP1c, FASN, SCD1,
and ELOVL6 in non-cancerous liver from individuals with malignant and benign disease, the
only gene with significantly affected expression was IGF-1. mRNA expression of IGF-1 was
99
significantly increased in non-cancerous liver in individuals with colorectal liver metastases in
comparison to individuals with benign disease. This result was interesting in the context of our
prior mRNA expression studies that determined that levels of the receptor, IGF-1R, were
significantly increased in colorectal liver metastases. In summary, previous results demonstrate
increased expression of the receptor IGF-1R in the tumour itself, and the current results
demonstrate that the presence of liver metastases significantly increases the expression of the
ligand, IGF-1, in the non-cancerous liver microenvironment. These results, taken together,
suggest a mechanism by which the tumour cells of the liver metastases upregulate the expression
of IGF-1R, while also affecting the microenvironment in the liver causing increased expression
of IGF-1. With increased IGF-1 levels in the liver, and increased receptor levels in the tumour,
proliferative and pro-survival pathways could be sustained through the IGF-1 signalling axis in
tumour cells.
Another investigation into the role of IGF-1 in the microenvironment of colorectal liver
metastases has been conducted in obese mice108. The role of IGF-1 in the tumour
microenvironment was examined specifically in the setting of obesity in this study, but the
results demonstrated that IGF-1 is critical for the inflammatory state in the liver that is necessary
for the development of colorectal liver metastases. The incidence of liver metastases was
significantly decreased in mice models with a chronic IGF-1 deficiency compared to control
models. Chronic IGF-1 deficiency was determined to reduce multiple components of an
inflammatory response, as well as macrophage number and activity. The decrease in colorectal
liver metastases in the IGF-1 deplete mice and the corresponding decrease in inflammatory
mediators suggests that IGF-1 signalling is necessary to sustain the inflammatory
microenvironment needed for the development of colorectal liver metastases.
100
In addition to the large amount of prospective studies that have examined IGF-1 levels in
CRC, the IGF-1R has also been examined for its role in tumour metastases. This is relevant with
the results of our study as IGF-1 was increased in non-cancerous liver tissue and IGF-1R was
increased in liver metastases. The role IGF-1R in tumour metastases is summarized thoroughly
by Bahr et al109, but briefly, several of the ways in which IGF-1 signalling has been
demonstrated to modulate the metastatic potential of cancer cell lines is presented below. The
IGF-1 signalling axis has been shown to interact with integrin signalling pathways, a family of
molecules that mediate changes in cell adhesion to the extracellular matrix. IGF-1R signalling
has also been shown to decrease cell adhesion through down-regulation of E-cadherin
expression. Another necessary step in metastasis is breakdown of the extracellular matrix, a
process that is mediated my matrix metalloproteinases (MMPs), most notably MMP2. IGF-1R
has previously been identified as a positive regulator of MMP2 in lung cancer cells, enhancing
invasion and metastasis.
The liver is the most common site for CRC to metastasize to, and is also the organ with
the highest levels of IGF-1 production. In vitro studies have demonstrated that colon cancer cells
frequently overexpress the receptor, IGF-1R, presenting an incentive for colon cancer cells to
metastasize to a site that overexpresses the IGF-1 ligand110. The findings from our current study
correspond with the in vitro results from colon cancer cell lines, as we demonstrate
overexpression of IGF-1R in tumour tissue. In addition, IGF-1 mRNA levels are higher in noncancerous liver from individuals with liver metastases compared to individuals with benign liver
diseases. This suggests that liver metastases have an effect on surrounding tissue, upregulating
the production of IGF-1 in order to provide high amounts of growth factor for signalling through
the corresponding overexpressed receptor on tumour cells.
101
6.3.2 Host response in adipose tissue
From our investigation into the expression of FASN mRNA in subcutaneous and omental
adipose tissue from individuals with malignant and benign disease, no significant difference was
observed between these groups. For these experiments, the sample sizes were very small as only
a limited number of adipose tissue samples from individuals with malignant or benign disease
were available. In order to gain a full appreciation of the host response to colorectal liver
metastases, the sample size of this study would need to be expanded. In addition, characterizing
the expression of other fatty acid synthesis genes and transcription factors would be of interest.
FASN expression has been demonstrated to have variable expression and large inter-individual
differences. With the small sample size used in this investigation it is difficult to draw
conclusions regarding the activity of this enzyme in adipose tissue. By examining the expression
of the complete fatty acid synthesis pathway in adipose tissue samples, a more comprehensive
picture of the host response to tumour development would be gained. Adipose tissue may
represent an important organ for contribution to the circulating serum metabolome, but further
investigation into the expression of different genes involved in lipid metabolism is needed.
6.4 Conclusions
Taken together, our results indicate that colorectal liver metastases affect IGF-1
expression in the liver microenvironment. Increased hepatic production of IGF-1 can act through
high levels of IGF-1R expression on the tumour cells in liver metastases. IGF-1 in the
microenvironment of liver metastases has been implicated in sustaining an inflammatory state
necessary for the development of liver metastases in mouse models, and a similar mechanism
may be present in humans. Characterization of cross-talk between tumour cells and cells of the
102
surrounding environment is important for the development of therapeutics that can target this
interaction.
Based on the current investigation, colorectal liver metastases do not appear to have an
effect on FASN mRNA expression in adipose tissue, although the sample size of this experiment
needs to be increased in order to draw significant conclusions on this as FASN expression is
known to vary significantly on an individual basis.
103
6.5 Tables and Figures
104
105
Figure 11. Quantification of lipogenic gene expression in the liver microenvironment in the
setting of colorectal liver metastases.
Real-time RT-PCR quantification of FASN, SREBP1c, SCD1, ELOVL6, IGF-1 and IGF-1R in
individuals with malignant (N=15) and benign (N=5) disease. Expression normalized to mean
expression of two housekeeping genes (B2M, PMM1). Data points presented as linear values using
2^(-ΔCt). P-value calculated using an unpaired t-test, using Welch's correction (did not assume equal
SD). The whiskers indicate the 5th and 95th percentiles; the line in the box is plotted at the median.
106
Figure 12. Quantification of host response in adipose tissue in the setting of colorectal liver
metastases.
(A) Omental adipose tissue from benign (N=4) and malignant (N=4) disease and (B) subcutaneous
adipose tissue from benign (N=2) and malignant (N=8) disease. FASN expression normalized to
expression of B2M housekeeping gene. Data points presented as linear values using 2^(-ΔCt). Pvalue calculated using an unpaired t-test, using Welch's correction (did not assume equal SD). The
whiskers indicate the 5th and 95th percentiles; the line in the box is plotted at the median.
107
Chapter Seven: General Discussion and Conclusions
108
7.1 General discussion
When taken together, our results from various investigations suggest that fatty acid
synthesis is affected at multiple levels within cell metabolism. Serum abundances of several key
fatty acid metabolites were determined to be increased in individuals with CRC through multiple
metabolomic profiling platforms. The mechanism for the increased serum abundances was
hypothesized to be from increased activity of the fatty acid synthesis pathway in the tumour.
While a proportion of individuals with colorectal liver metastases had higher expression of fatty
acid synthesis genes in tumour relative to non-cancerous liver, the increased expression was not
ubiquitous; overall, there was no significant difference between the sample groups for FASN,
SCD1 or ELOVL6 expression. Conversely, the lipogenic transcription factor responsible for
regulation of fatty acid synthesis, SREBP1c, was increased in all tumour samples examined. In
addition, IGF-1R was increased in all tumour samples. Signalling through the IGF-1 axis has
been previously demonstrated to activate SREBP1c and activity of downstream fatty acid
synthesis, thereby linking growth factor signalling with lipid metabolism. In our study, IGF-1
levels were significantly increased in the non-cancerous liver in individuals with malignant
disease compared to individuals with benign disease. Altogether, this suggests a mechanism by
which the presence of colorectal liver metastases causes an increase in hepatic synthesis of IGF1. This ligand in turn acts to signal through the overexpressed IGF-1R on tumour cells, activating
the downstream SREBP1 transcription factor through PI3K/Akt/mTORC1 signalling. The
nuclear form of SREBP1c then acts to regulate fatty acid synthesis through induction of FASN,
desaturase and elongase enzymes. Increased activity of the IGF-1/ IGF-1R axis may additionally
act to increase various other proliferative, pro-survival, anti-apoptotic pathways through
PI3K/Akt/mTORC1 signalling, although this was not investigated in the current study.
109
The results of variable fatty acid synthesis gene expression in this study are consistent
with the findings from other investigations into lipogenic gene expression in human tumour
tissues. In particular, FASN expression has been characterized at the mRNA and protein levels in
numerous types of human malignancies and appears to be overexpressed in a subset of each
patient population. This subset generally correlates with a particular clinical parameter such as
histological type or disease-free survival. The proportion of tumours with overexpression of
FASN, and the association with specific clinical parameters, is highly dependent on the type of
cancer under investigation; this suggests that the role of FASN and the activity of endogenous
fatty acid synthesis vary significantly between tumour types.
Preliminary investigations into the host response in normal host tissues was undertaken in
this study, but significant results were not determined. Whole-body metabolism contributes to
the circulating levels of serum metabolites. Given that fatty acid metabolite abundances were
determined to be increased in the serum of individuals with CRC, and the fact that fatty acid
synthesis gene expression was not significantly increased in tumour tissue, it was of interest to
look at other tissues that could be contributing to these increased serum metabolite levels.
Adipose tissue was investigated for its host response to liver metastases development. In terms of
FASN, there did not appear to be altered mRNA expression in adipose tissue in individuals with
malignant versus benign disease, but further investigations are needed as there were a limited
number of adipose tissue samples available and as such the sample size for these experiments
was small.
Overall, from our characterization of lipid metabolism in serum, tumour, and normal host
tissues, it is evident that the processes regulating fatty acid synthesis in the context of CRC are
very complex. While serum fatty acid metabolite abundances were altered with the development
110
of CRC, the expression of fatty acid synthesis genes were not as consistently affected. The
development of colorectal liver metastases appears to have an effect on the metabolism of the
surrounding hepatic microenvironment thus providing a mechanism by which proliferation and
growth pathways can be activated in nearby tumour cells. Through activation of the IGF-1 axis,
SREBP1c is activated in turn regulating fatty acid synthesis.
This study represents the first investigation in which the fatty acid synthesis pathway is
comprehensively characterized in individuals with CRC. The pathway was examined starting
from a potential growth factor signalling axis IGF-1/IGF-1R, to lipogenic transcription factor
SREBP1c, and through to subsequent downstream fatty acid genes FASN, SCD1, and ELOVL6.
In addition, the end products of cellular metabolism, the fatty acid metabolite levels, were
quantified providing a broad overview of lipid synthesis from growth factor activation to
metabolite levels. With this comprehensive picture of lipid metabolism in individuals with CRC,
an increased understanding of the pathway as a whole is gained. In addition, SCD1 and ELOVL6
levels had not been characterized in colorectal liver metastases until now. This provides the first
glimpse into the expression of these genes in the context of liver metastases in individuals with
CRC. With further characterization of these genes in tumour tissues, it may be realized that one
of these enzymes represents an attractive target for cancer therapeutics or as an indicator of
prognosis or response to treatment.
7.2 Research implications
7.2.1 Cancer metabolism as a therapeutic target
With increased research into the metabolic reprogramming that occurs in cancer cells,
interest into developing therapeutics that target metabolic enzymes and cell metabolism has
intensified. The increased processing of glucose through the glycolytic pathway regardless of
111
oxygen supply, and the demand for ATP production that sustains cell growth, are two examples
of aspects of cancer cell metabolism that can be targeted therapeutically. In addition, the high
demand for the biosynthesis of nucleic acids, proteins, carbohydrates, and lipids to support cell
growth and proliferation in a rapidly dividing cancer cell population represents another area of
cell metabolism that could be exploited in targeted cancer therapeutics. The numerous upstream
signalling pathways that converge to affect cellular metabolism represent yet another aspect of
metabolic reprogramming in cancer that could be targeted111. Of the genes investigated in this
study, FASN and IGF-1/ IGF-1R are the ones that targeted cancer therapeutics have been
developed for to date. A brief summary of the targeted therapies currently available for these
genes is presented below. Discussion around the development of targeted therapeutics for
SREBP1c and SCD1 has been initiated and with increased characterization of the expression of
these enzymes in tumour tissues, more information regarding the attractiveness and feasibility of
targeting these components of the fatty acid synthesis pathway will be gained.
7.2.2 Targeting FASN
FASN represents an attractive therapeutic target as overexpression of FASN is observed
in many human malignancies and FASN is differentially expressed in cancerous and noncancerous tissues. FASN expression is typically low in normal adult tissues as the majority of
required fatty acids are obtained from the diet. Conversely, endogenous fatty acid synthesis is
upregulated in many cancers, partially through increased expression of FASN, making this
enzyme an appealing target for cancer therapeutics. FASN knockdown experiments have
demonstrated that multiple cancer cell lines require FASN for survival, and several FASN
inhibitors have been shown to induce apoptosis in cancer cell lines and delay tumour growth in
animal models.
112
FASN inhibitors include cerulenin, C75, orlistat, C93, and naturally occurring
polyphenols. One of the first compounds found to inhibit FASN was cerulenin, but this
compound had little clinical relevance because of its chemical instability. C75 was developed as
a cerulenin-derived semi-synthetic FASN inhibitor to improve the stability of this inhibitor. In
mice, both of these compounds have been found to affect food intake and induce weight loss,
which is hindering their development as cancer therapeutics for use in humans. Orlistat was
developed as an anti-obesity drug, and subsequently found to have FASN inhibitory properties.
Although this drug has antiproliferative effects in several cancer cell lines and animal models,
orlistat has poor solubility and bioavailability, which is limiting its development as a targeted
cancer therapeutic. C93 is another synthetically developed FASN inhibitor that has been found to
have anti-tumour activity in cell lines and animal models, and does not appear to have the weight
loss side effects that C75 has. Several naturally occurring polyphenols have also been found to
inhibit FASN, including a natural component of green tea, epigallocatechin-3-gallate (ECGC)112.
A review conducted in 2012113 summarizes all the currently available FASN inhibitors. Several
inhibitors have already been patented but require testing in clinical trials to assess potential
toxicities.
As development of a specific, potent, safe FASN inhibitor remains an active area of
investigation, more large-scale screening methods are being employed to identify novel FASN
inhibitors. In addition, the previously identified naturally occurring and synthetic FASN
inhibitors are being further developed upon to create more potent, selective inhibitors with fewer
side effects.
113
7.2.3 Targeting IGF signalling
The majority of compounds that target the IGF axis focus on the IGF-1 receptor. Both
neutralizing antibodies and receptor tyrosine kinase inhibitors have been developed to target
IGF-1R. Antibodies that target IGF-1R are currently the most clinically developed. Both classes
of compounds inhibit IGF signalling; antibody binding causes internalization and degradation of
the receptor, while tyrosine kinase inhibitors inhibit the kinase activity of the receptor. Because
the tyrosine kinase domains of IGF-1R and insulin receptor are highly homologous, the tyrosine
kinase inhibitors can inhibit the activity of insulin signalling in addition to IGF-1 signalling,
leading to impaired glucose homeostasis. Finally, although not as common, there are antibodies
in development that target the IGF ligands specifically114.
Multiple neutralizing IGF-1R antibodies and small molecule inhibitors are currently in
clinical trials for evaluation of their potential use as cancer therapeutics. In addition, some
investigations are attempting to target both IGF-1 and insulin signalling simultaneously due to
the crosstalk between these pathways. IGFBPs are yet another target of research pursuits looking
for new ways to inhibit the IGF signalling axis, as this pathway has such a critical role in
numerous human malignancies115,116.
7.3 Future directions
Due to the importance of fatty acid metabolism in the development of cancer and the
progression to metastatic disease, there exists a need for increased characterization of the various
parts of this pathway. Further understanding of lipid metabolism will allow the development of
therapeutics targeting the enzymatic activity of the fatty acid synthesis genes that cancer cells
rely on. The next reasonable step for this study would be to examine the protein expression
levels of SREBP1c, FASN, SCD1, ELOVL6, IGF-1 and IGF-1R in colorectal liver metastases
114
and paired non-cancerous liver. As the expression of several of these genes was altered in tumour
tissue at the mRNA level, quantifying the protein expression levels of these genes would be a
useful follow-up. From protein expression levels, a more accurate idea of the biological activity
of these enzymes within tumour metabolism would be obtained as mRNA levels do not
necessarily correlate with protein levels.
Secondly, increasing the sample size of the lipogenic gene expression studies would be
useful. Fatty acid metabolite abundance data was available for over 100 patients with CRC, so
increasing the number of tumour samples profiled for mRNA expression of lipogenic genes
would increase the confidence of the current results and potentially uncover additional findings.
In addition to examining lipogenic gene expression in colorectal liver metastases, it would also
be interesting to examine expression in locoregional colorectal cancer tissue to investigate
whether the fatty acid synthesis pathway has a different role in local versus metastatic disease.
Such information regarding locoregional versus metastatic gene profiles may guide the selection
of targeted treatments in patients based on cancer stage in the future.
With an increased sample size, it would additionally be interesting to collect clinical
parameters on the patient population. Patient factors such as BMI, age, gender, tumour grade,
primary location (colon, rectum), previous history of cancer, comorbidities, medication use,
prognosis, and survival outcomes could then be investigated for association with expression of
specific lipogenic factors in tumour tissue.
Finally, expanding upon the investigation into host response to tumour development
would be of value. Adipose tissue represents an important organ in which to study host response
due to the increasing prevalence of obesity-associated cancers, clinical phenomenon such as
cancer cachexia, and the wide range of regulatory functions that adipose serves through the
115
production of various hormones and cytokines. By learning more about host response to tumour
development, methods may be developed to modulate detrimental host responses while
encouraging beneficial ones, therefore improving patient prognosis. In addition, early changes in
host metabolism as a result of tumour development may aid in early diagnosis of disease before
conventional methods are able to detect the presence of a tumour.
116
References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
Santos, C. R. & Schulze, A. Lipid metabolism in cancer. FEBS J. 279, 2610–2623
(2012).
Siegel, R., Naishadham, D. & Jemal, A. Cancer statistics, 2012. CA: A Cancer Journal
for Clinicians 62, 10–29 (2012).
Fearon, E. R. Molecular Genetics of Colorectal Cancer - Annual Review of Pathology:
Mechanisms of Disease, 6(1):479. Annual Review of Pathology: Mechanisms of …
(2011).
Markowitz, S. Molecular basis of colorectal cancer. New England Journal of … (2009).
Terzić, J., Grivennikov, S., Karin, E. & Karin, M. Inflammation and colon cancer.
Gastroenterology 138, 2101–2114.e5 (2010).
Lynch, H. T. & la Chapelle, de, A. Hereditary colorectal cancer. N. Engl. J. Med. (2003).
Galiatsatos, P. & Foulkes, W. D. Familial Adenomatous Polyposis. Am. J.
Gastroenterol. 101, 385–398 (2006).
Patel, S. G. & Ahnen, D. J. Familial colon cancer syndromes: an update of a rapidly
evolving field. Curr Gastroenterol Rep 14, 428–438 (2012).
Huxley, R. R. et al. The impact of dietary and lifestyle risk factors on risk of colorectal
cancer: a quantitative overview of the epidemiological evidence. Int. J. Cancer 125,
171–180 (2009).
Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).
Hanahan, D. & Weinberg, R. A. Hallmarks of Cancer: The Next Generation. Cell 144,
646–674 (2011).
Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg
effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).
Ward, P. S. & Thompson, C. B. Metabolic reprogramming: a cancer hallmark even
warburg did not anticipate. Cancer Cell 21, 297–308 (2012).
Cairns, R. A., Harris, I. S. & Mak, T. W. Regulation of cancer cell metabolism. Nat Rev
Cancer 11, 85–95 (2011).
Ward, P. S. & Thompson, C. B. Signaling in control of cell growth and metabolism.
Cold Spring Harb Perspect Biol 4, (2012).
Porstmann, T. et al. PKB/Akt induces transcription of enzymes involved in cholesterol
and fatty acid biosynthesis via activation of SREBP. Oncogene 24, 6465–6481 (2005).
Porstmann, T. et al. SREBP Activity Is Regulated by mTORC1 and Contributes to AktDependent Cell Growth. Cell Metabolism 8, 224–236 (2008).
Fahy, E. et al. A comprehensive classification system for lipids. J. Lipid Res. 46, 839–
861 (2005).
Wang, D. & Dubois, R. N. Eicosanoids and cancer. Nat Rev Cancer 10, 181–193 (2010).
Khalil, M. B., Hou, W., Zhou, H. & Elisma, F. Lipidomics era: Accomplishments and
challenges. Mass Spectrometry … (2010).
Berg, J. M., Tymoczko, J. L. & Stryer, L. Fatty Acids Are Synthesized and Degraded by
Different Pathways. (2002).
Chirala, S. S. & Wakil, S. J. Structure and function of animal fatty acid synthase. Lipids
39, 1045–1053 (2004).
Asturias, F. J. et al. Structure and molecular organization of mammalian fatty acid
117
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
synthase. Nat. Struct. Mol. Biol. 12, 225–232 (2005).
Serra, D., Mera, P., Malandrino, M. I., Mir, J. F. & Herrero, L. Mitochondrial fatty acid
oxidation in obesity. Antioxidants & Redox Signaling 19, 269–284 (2013).
Li, J. N., Mahmoud, M. A., Han, W. F., Ripple, M. & Pizer, E. S. Sterol regulatory
element-binding protein-1 participates in the regulation of fatty acid synthase expression
in colorectal neoplasia. Exp. Cell Res. 261, 159–165 (2000).
Wang, Y. et al. Regulation of hepatic fatty acid elongase and desaturase expression in
diabetes and obesity. J. Lipid Res. 47, 2028–2041 (2006).
Eberlé, D., Hegarty, B., Bossard, P., Ferré, P. & Foufelle, F. SREBP transcription
factors: master regulators of lipid homeostasis. Biochimie 86, 839–848 (2004).
Shimano, H. Sterol regulatory element-binding proteins (SREBPs): transcriptional
regulators of lipid synthetic genes. Prog. Lipid Res. (2001).
Menendez, J. & Lupu, R. Fatty acid synthase and the lipogenic phenotype in cancer
pathogenesis. Nat Rev Cancer (2007).
Kuhajda, F. Fatty acid synthase and cancer: new application of an old pathway. Cancer
Res (2006).
Kuhajda, F. P. et al. Fatty acid synthesis: a potential selective target for antineoplastic
therapy. Proc. Natl. Acad. Sci. U.S.A. 91, 6379–6383 (1994).
Rashid, A., Pizer, E. & Moga, M. Elevated expression of fatty acid synthase and fatty
acid synthetic activity in colorectal neoplasia. The American journal … (1997).
Kearney, K. E., Pretlow, T. G. & Pretlow, T. P. Increased expression of fatty acid
synthase in human aberrant crypt foci: possible target for colorectal cancer prevention.
Int. J. Cancer 125, 249–252 (2009).
Notarnicola, M. et al. Serum Levels of Fatty Acid Synthase in Colorectal Cancer
Patients Are Associated with Tumor Stage. J Gastrointest Cancer (2011).
doi:10.1007/s12029-011-9300-2
Igal, R. A. Stearoyl-CoA desaturase-1: a novel key player in the mechanisms of cell
proliferation, programmed cell death and transformation to cancer. Carcinogenesis 31,
1509–1515 (2010).
Sampath, H. & Ntambi, J. M. The role of stearoyl-CoA desaturase in obesity, insulin
resistance, and inflammation. Annals of the New York Academy of Sciences 1243, 47–53
(2011).
Roongta, U., Pabalan, J., Wang, X. & Ryseck, R. Cancer cell dependence on unsaturated
fatty acids implicates stearoyl-coA desaturase as a target for cancer therapy. Mol.
Cancer (2011).
Matsuzaka, T., Shimano, H., Yahagi, N. & Kato, T. Crucial role of a long-chain fatty
acid elongase, Elovl6, in obesity-induced insulin resistance. Nature medicine (2007).
Matsuzaka, T. & Shimano, H. Elovl6: a new player in fatty acid metabolism and insulin
sensitivity. J Mol Med 87, 379–384 (2009).
Samani, A. A., Yakar, S., LeRoith, D. & Brodt, P. The role of the IGF system in cancer
growth and metastasis: overview and recent insights. Endocr. Rev. 28, 20–47 (2007).
Sandhu, M. S. Insulin, Insulin-Like Growth Factor-I (IGF-I), IGF Binding Proteins,
Their Biologic Interactions, and Colorectal Cancer. CancerSpectrum Knowledge
Environment 94, 972–980 (2002).
Pollak, M. N. & Schernhammer, E. S. Insulin-like growth factors and neoplasia. Nat Rev
118
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
Cancer (2004).
Yakar, S., LeRoith, D. & Brodt, P. The role of the growth hormone/insulin-like growth
factor axis in tumor growth and progression: Lessons from animal models. Cytokine &
Growth Factor Reviews 16, 407–420 (2005).
Adams, T. E., Epa, V. C., Garrett, T. P. J. & Ward, C. W. Structure and function of the
type 1 insulin-like growth factor receptor. CMLS, Cell. Mol. Life Sci. 57, 1050–1093
(2000).
Larsson, O., Girnita, A. & Girnita, L. Role of insulin-like growth factor 1 receptor
signalling in cancer. Br. J. Cancer 92, 2097–2101 (2005).
Baserga, R., Peruzzi, F. & Reiss, K. The IGF-1 receptor in cancer biology. Int. J. Cancer
107, 873–877 (2003).
Smith, T. M., Cong, Z., Gilliland, K. L., Clawson, G. A. & Thiboutot, D. M. Insulin-like
growth factor-1 induces lipid production in human SEB-1 sebocytes via sterol response
element-binding protein-1. J. Invest. Dermatol. 126, 1226–1232 (2006).
Smith, T. M., Gilliland, K., Clawson, G. A. & Thiboutot, D. IGF-1 Induces SREBP-1
Expression and Lipogenesis in SEB-1 Sebocytes via Activation of the Phosphoinositide
3-Kinase/Akt Pathway. Journal of Investigative Dermatology 128, 1286–1293 (2007).
Wenk, M. R. Lipidomics: new tools and applications. Cell 143, 888–895 (2010).
Shevchenko, A. & Simons, K. Lipidomics: coming to grips with lipid diversity. Nat.
Rev. Mol. Cell Biol. 11, 593–598 (2010).
Begley, P. et al. Development and performance of a gas chromatography-time-of-flight
mass spectrometry analysis for large-scale nontargeted metabolomic studies of human
serum. Anal. Chem. 81, 7038–7046 (2009).
Lv, W. & Yang, T. Identification of possible biomarkers for breast cancer from free fatty
acid profiles determined by GC-MS and multivariate statistical analysis. Clin. Biochem.
45, 127–133 (2012).
Ecker, J., Scherer, M., Schmitz, G. & Liebisch, G. A rapid GC-MS method for
quantification of positional and geometric isomers of fatty acid methyl esters. J.
Chromatogr. B Analyt. Technol. Biomed. Life Sci. 897, 98–104 (2012).
Dunn, W. B. et al. Procedures for large-scale metabolic profiling of serum and plasma
using gas chromatography and liquid chromatography coupled to mass spectrometry.
Nat Protoc 6, 1060–1083 (2011).
Murphy, R. C. & Axelsen, P. H. Mass spectrometric analysis of long‐chain lipids. Mass
spectrometry reviews (2011).
Bligh, E. G. & Dyer, W. J. A rapid method of total lipid extraction and purification.
Canadian journal of biochemistry and … (1959).
Broeckling, C. D., Reddy, I. R., Duran, A. L., Zhao, X. & Sumner, L. W. METIDEA: Data Extraction Tool for Mass Spectrometry-Based Metabolomics. Anal. Chem.
78, 4334–4341 (2006).
Rubie, C. et al. Housekeeping gene variability in normal and cancerous colorectal,
pancreatic, esophageal, gastric and hepatic tissues. Mol. Cell. Probes 19, 101–109
(2005).
Barber, R. D., Harmer, D. W., Coleman, R. A. & Clark, B. J. GAPDH as a housekeeping
gene: analysis of GAPDH mRNA expression in a panel of 72 human tissues. Physiol.
Genomics 21, 389–395 (2005).
119
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
Schmittgen, T. D. & Livak, K. J. Analyzing real-time PCR data by the comparative CT
method. Nat Protoc 3, 1101–1108 (2008).
Cairns, R. A., Harris, I., McCracken, S. & Mak, T. W. Cancer cell metabolism. Cold
Spring Harb. Symp. Quant. Biol. 76, 299–311 (2011).
Zhang, F. Dysregulated lipid metabolism in cancer. WJBC 3, 167 (2012).
Ookhtens, M., Kannan, R., Lyon, I. & Baker, N. Liver and adipose tissue contributions
to newly formed fatty acids in an ascites tumor. Am. J. Physiol. 247, R146–53 (1984).
Currie, E., Schulze, A., Zechner, R. & Walther, T. C. Cellular fatty acid metabolism and
cancer. Cell Metabolism (2013).
Louie, S. M., Roberts, L. S., Mulvihill, M. M., Luo, K. & Nomura, D. K. Cancer cells
incorporate and remodel exogenous palmitate into structural and oncogenic signaling
lipids. Biochim. Biophys. Acta (2013). doi:10.1016/j.bbalip.2013.07.008
Lee, J. S. et al. Anticancer activity of pristimerin in epidermal growth factor receptor 2positive SKBR3 human breast cancer cells. Biol. Pharm. Bull. 36, 316–325 (2013).
Eberhart, C. E. et al. Up-regulation of cyclooxygenase 2 gene expression in human
colorectal adenomas and adenocarcinomas. Gastroenterology 107, 1183–1188 (1994).
Tomozawa, S. et al. Cyclooxygenase-2 overexpression correlates with tumour
recurrence, especially haematogenous metastasis, of colorectal cancer. Br. J. Cancer 83,
324–328 (2000).
Wang, D. & Dubois, R. N. An inflammatory mediator, prostaglandin E2, in colorectal
cancer. Cancer J 19, 502–510 (2013).
Park, J. H., McMillan, D. C., Horgan, P. G. & Roxburgh, C. S. The impact of antiinflammatory agents on the outcome of patients with colorectal cancer. Cancer Treat.
Rev. 40, 68–77 (2014).
Williams, M. D., Reeves, R., Resar, L. S. & Hill, H. H. Metabolomics of colorectal
cancer: past and current analytical platforms. Anal Bioanal Chem 405, 5013–5030
(2013).
Kondo, Y. et al. Serum fatty acid profiling of colorectal cancer by gas
chromatography/mass spectrometry. Biomark Med 5, 451–460 (2011).
Tan, B. et al. Metabonomics identifies serum metabolite markers of colorectal cancer. J.
Proteome Res. 12, 3000–3009 (2013).
Sakai, M. et al. Arachidonic acid and cancer risk: a systematic review of observational
studies. BMC Cancer 12, 606 (2012).
Rodríguez-Blanco, G. et al. Serum levels of arachidonic acid metabolites change during
prostate cancer progression. Prostate 74, 618–627 (2014).
Ferré, P. & Foufelle, F. SREBP-1c transcription factor and lipid homeostasis: clinical
perspective. Horm. Res. 68, 72–82 (2007).
Düvel, K. et al. Activation of a metabolic gene regulatory network downstream of
mTOR complex 1. Mol. Cell 39, 171–183 (2010).
Griffiths, B. et al. Sterol regulatory element binding protein-dependent regulation of
lipid synthesis supports cell survival and tumor growth. Cancer Metab 1, 3 (2013).
Menendez, J. A. & Lupu, R. Oncogenic properties of the endogenous fatty acid
metabolism: molecular pathology of fatty acid synthase in cancer cells. Curr Opin Clin
Nutr Metab Care 9, 346–357 (2006).
Igal, R. A. Roles of StearoylCoA Desaturase-1 in the Regulation of Cancer Cell Growth,
120
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
Survival and Tumorigenesis. Cancers (Basel) 3, 2462–2477 (2011).
Scaglia, N. & Igal, R. A. Inhibition of Stearoyl-CoA Desaturase 1 expression in human
lung adenocarcinoma cells impairs tumorigenesis. Int. J. Oncol. 33, 839–850 (2008).
Mason, P. et al. SCD1 inhibition causes cancer cell death by depleting mono-unsaturated
fatty acids. PLoS ONE 7, e33823 (2012).
Ntambi, J. M., Miyazaki, M. & Dobrzyn, A. Regulation of stearoyl-CoA desaturase
expression. Lipids 39, 1061–1065 (2004).
Flowers, M. T. & Ntambi, J. M. Role of stearoyl-coenzyme A desaturase in regulating
lipid metabolism. Current Opinion in Lipidology 19, 248–256 (2008).
Luyimbazi, D. et al. Rapamycin Regulates Stearoyl CoA Desaturase 1 Expression in
Breast Cancer. Molecular Cancer Therapeutics 9, 2770–2784 (2010).
Guillou, H., Zadravec, D. & Martin, P. The key roles of elongases and desaturases in
mammalian fatty acid metabolism: Insights from transgenic mice. Prog. Lipid Res.
(2010).
Jakobsson, A., Westerberg, R. & Jacobsson, A. Fatty acid elongases in mammals: Their
regulation and roles in metabolism. Prog. Lipid Res. 45, 237–249 (2006).
Kumadaki, S., Matsuzaka, T., Kato, T. & Yahagi, N. Mouse Elovl-6 promoter is an
SREBP target. Biochemical and … (2008).
Tamura, K. et al. Novel lipogenic enzyme ELOVL7 is involved in prostate cancer
growth through saturated long-chain fatty acid metabolism. Cancer Res 69, 8133–8140
(2009).
Ma, J. et al. IGF-1 mediates PTEN suppression and enhances cell invasion and
proliferation via activation of the IGF-1/PI3K/Akt signaling pathway in pancreatic
cancer cells. J. Surg. Res. 160, 90–101 (2010).
Li, W. et al. Repression of endometrial tumor growth by targeting SREBP1 and
lipogenesis. Cell Cycle 11, 2348–2358 (2012).
Yahagi, N. et al. Co-ordinate activation of lipogenic enzymes in hepatocellular
carcinoma. Eur. J. Cancer 41, 1316–1322 (2005).
Ettinger, S. L. et al. Dysregulation of sterol response element-binding proteins and
downstream effectors in prostate cancer during progression to androgen independence.
Cancer Res 64, 2212–2221 (2004).
Ogino, S. et al. Cohort study of fatty acid synthase expression and patient survival in
colon cancer. J. Clin. Oncol. 26, 5713–5720 (2008).
Long, Q.-Q., Yi, Y.-X., Qiu, J., Xu, C.-J. & Huang, P.-L. Fatty acid synthase (FASN)
levels in serum of colorectal cancer patients: correlation with clinical outcomes. Tumour
Biol. (2014). doi:10.1007/s13277-013-1510-8
Hamada, S. et al. Increased fatty acid synthase expression in prostate biopsy cores
predicts higher Gleason score in radical prostatectomy specimen. BMC Clin Pathol 14, 3
(2014).
Rossi, S. et al. Fatty acid synthase expression defines distinct molecular signatures in
prostate cancer. Mol. Cancer Res. 1, 707–715 (2003).
Kusakabe, T., Nashimoto, A., Honma, K. & Suzuki, T. Fatty acid synthase is highly
expressed in carcinoma, adenoma and in regenerative epithelium and intestinal
metaplasia of the stomach. Histopathology 40, 71–79 (2002).
Ide, Y. et al. Human breast cancer tissues contain abundant phosphatidylcholine(36∶1)
121
100.
101.
102.
103.
104.
105.
106.
107.
108.
109.
110.
111.
112.
113.
114.
115.
116.
with high stearoyl-CoA desaturase-1 expression. PLoS ONE 8, e61204 (2013).
Holder, A. M. et al. High stearoyl-CoA desaturase 1 expression is associated with
shorter survival in breast cancer patients. Breast Cancer Res. Treat. 137, 319–327
(2013).
Ma, J. et al. Prospective study of colorectal cancer risk in men and plasma levels of
insulin-like growth factor (IGF)-I and IGF-binding protein-3. J Natl Cancer Inst 91,
620–625 (1999).
Giovannucci, E. et al. A prospective study of plasma insulin-like growth factor-1 and
binding protein-3 and risk of colorectal neoplasia in women. Cancer Epidemiol.
Biomarkers Prev. 9, 345–349 (2000).
Probst-Hensch, N. M. et al. IGF-1, IGF-2 and IGFBP-3 in prediagnostic serum:
association with colorectal cancer in a cohort of Chinese men in Shanghai. Br. J. Cancer
85, 1695–1699 (2001).
Mbeunkui, F. & Johann, D. J. Cancer and the tumor microenvironment: a review of an
essential relationship. Cancer Chemother. Pharmacol. 63, 571–582 (2009).
Galon, J., Fridman, W.-H. & Pagès, F. The Adaptive Immunologic Microenvironment in
Colorectal Cancer: A Novel Perspective. Cancer Res (2007).
Lu, H. Inflammation, a Key Event in Cancer Development. Molecular Cancer Research
4, 221–233 (2006).
van Kruijsdijk, R. C. M., van der Wall, E. & Visseren, F. L. J. Obesity and cancer: the
role of dysfunctional adipose tissue. Cancer Epidemiol. Biomarkers Prev. 18, 2569–
2578 (2009).
Wu, Y. et al. Insulin-Like Growth Factor-I Regulates the Liver Microenvironment in
Obese Mice and Promotes Liver Metastasis. Cancer Res 70, 57–67 (2010).
Bähr, C. & Groner, B. The IGF-1 receptor and its contributions to metastatic tumor
growth-novel approaches to the inhibition of IGF-1R function. Growth Factors 23, 1–14
(2005).
Reinmuth, N. et al. Blockade of insulin-like growth factor I receptor function inhibits
growth and angiogenesis of colon cancer. (2002).
Vander Heiden, M. G. Targeting cancer metabolism: a therapeutic window opens.
Nature Reviews Drug Discovery 10, 671–684 (2011).
Flavin, R., Peluso, S., Nguyen, P. L. & Loda, M. Fatty acid synthase as a potential
therapeutic target in cancer. Future Oncol 6, 551–562 (2010).
Pandey, P. R., Liu, W., Xing, F., Fukuda, K. & Watabe, K. Anti-cancer drugs targeting
fatty acid synthase (FAS). Recent Pat Anticancer Drug Discov 7, 185–197 (2012).
Gao, J., Chang, Y. S., Jallal, B. & Viner, J. Targeting the insulin-like growth factor axis
for the development of novel therapeutics in oncology. Cancer Res 72, 3–12 (2012).
Buck, E. & Mulvihill, M. Small molecule inhibitors of the IGF-1R/IR axis for the
treatment of cancer. Expert Opin Investig Drugs 20, 605–621 (2011).
Tognon, C. E. & Sorensen, P. H. B. Targeting the insulin-like growth factor 1 receptor
(IGF1R) signaling pathway for cancer therapy. Expert Opin. Ther. Targets 16, 33–48
(2012).
122
Téléchargement
Explore flashcards