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ACETYL COA CARBOXYLASE 1 IN PROSTATE CANCER AS A CHEMOTHERAPEUTIC TARGET
BY
AMANDA L. DAVIS
A Dissertation Submitted to the Graduate Faculty of
WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES
In Partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
Cancer Biology
December, 2017
Winston-Salem, North Carolina
Approved By:
Steven Kridel, Ph.D., Advisor
W. Todd Lowther, Ph.D., Chair
Timothy Pardee, M.D., Ph.D.
Ravi Singh, Ph.D.
Ann Tallant, Ph.D.
ii
DEDICATION
This work is dedicated in loving memory of my grandmother, Peggy Ann Cribbs.
iii
TABLE OF CONTENTS
List of Figures and Tables ......................................................................... vi
List of Abbreviations .................................................................................. ix
Abstract .................................................................................................. xvii
CHAPTER I
GENERAL INTRODUCTION
1.1 Prostate cancer ................................................................................2
1.1.1 Current epidemiology ..............................................................2
1.1.2 Evolution of prostate cancer treatment ...................................3
1.2 Cellular metabolism .........................................................................8
1.2.1 Glycolysis ................................................................................9
1.2.2 Pentose phosphate pathway ................................................. 12
1.2.3 Glutaminolysis....................................................................... 13
1.2.4 Tricarboxylic acid cycle ......................................................... 15
1.3 Fatty acid metabolism .................................................................... 15
1.3.1 Sources of fatty acids ............................................................ 16
1.3.1.1 Dietary derived fatty acids ....................................... 16
1.3.1.2 De novo fatty acid synthesis .................................... 20
1.3.2 Fatty acid oxidation ............................................................... 23
1.3.3 Storage and release of fatty acids......................................... 27
1.4 The acetyl-CoA carboxylases ........................................................ 29
1.4.1 ACC1 and ACC2 ................................................................... 30
1.4.2 Regulation of ACC ................................................................ 32
iv
1.4.3 Biotin dependence ................................................................ 35
1.4.4 Sources of acetyl-CoA .......................................................... 37
1.4.5 Fates of malonyl-CoA ........................................................... 41
1.4.6 Fates of de novo derived fatty acids ..................................... 42
1.4.7 ACC in cancer ....................................................................... 44
1.4.8 ACC inhibitors ....................................................................... 48
1.5 Metabolism and chemoresistance ................................................. 51
1.5.1 Current research ................................................................... 52
1.5.2 Metabolism and chemoresistance in prostate cancer ........... 53
1.6 Overview ........................................................................................ 54
1.7 References .................................................................................... 55
CHAPTER II
ACETYL COA CARBOXYLASE 1 INHIBITION DISRUPTS PROSTATE
CANCER METABOLISM AND REDUCES TUMOR BURDEN IN A MURINE
PROSTATE CANCER MODEL
2.1 Abstract .......................................................................................... 93
2.2 Introduction .................................................................................... 95
2.3 Materials and Methods................................................................... 97
2.4 Results ......................................................................................... 106
2.5 Discussion ................................................................................... 114
2.6 Figures ......................................................................................... 122
2.7 References .................................................................................. 142
v
CHAPTER III
COMBINATION TREATMENTWITH SMALL MOLECULE ACETYL-COA
CARBOXYLASE INHIBITOR CP-640186 AND PACLITAXEL IS SYNERGISTIC
AND DISRUPTS METABOLIC FUNCTION IN TAXANE-RESISTANT
PROSTATE CANCER CELLS
3.1 Abstract ........................................................................................ 151
3.2 Introduction .................................................................................. 152
3.3 Materials and Methods................................................................. 154
3.4 Results ......................................................................................... 159
3.5 Discussion ................................................................................... 164
3.6 Figures ......................................................................................... 167
3.7 References .................................................................................. 179
CHAPTER IV
GENERAL DISCUSSION
4.1 Targeting fatty acid metabolism in cancer .................................... 188
4.1.1 Inhibiting ACC to target fatty acid synthesis ........................ 188
4.1.2 Other methods of targeting fatty acid synthesis .................. 190
4.2 Concluding remarks and future directions.................................... 191
4.3 References .................................................................................. 194
Curriculum Vitae ..................................................................................... 196
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LIST OF FIGURES AND TABLES
Chapter I
Figure 1. Development and treatment of PCa............................................4
Figure 2. Major cellular metabolic pathways ............................................ 10
Table 1. Enzymatic reactions of cellular metabolic pathways .................. 11
Table 2. Names, abbreviations, and structures of select fatty acids ......... 17
Chapter II
Figure 1: Elevated ACACA expression is associated with decreased survival in
several cancers. ..................................................................................... 122
Figure 2: ACACA expression is correlated with advanced PCa and ACC1 is
required for survival and proliferation in PCa cell lines. .......................... 123
Figure 3: Pharmacological inhibition of ACC activity alters fatty acid profile in
PCa cells. ............................................................................................... 125
Figure 4: Mice with prostate-specific genetic inactivation of ACC1 (ACC1L/L) are
phenotypically normal. ............................................................................ 126
Figure 5: Prostate-specific genetic inactivation of ACC1 reduces tumor burden
in 12 week old C57BL/6J mice. .............................................................. 127
Figure 6: Gross anatomy and morphology of 12-week-old murine prostates.
............................................................................................................... 128
Figure 7: Fatty acid profile of the anterior prostate lobes collected from 12-week
old mice. ................................................................................................. 129
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Figure 8: Prostate-specific genetic inactivation of ACC1 does not reduce tumor
burden in 24 week old C57BL/6J mice. .................................................. 130
Figure 9: Gross anatomy and morphology of 24-week-old murine prostates.
............................................................................................................... 131
Figure 10: Fatty acid profile of the anterior prostate lobes collected from 24-
week old mice. ........................................................................................ 132
Figure 11: Pharmacological inhibition of ACC does not reduce glycolytic
function of PC3 cells. .............................................................................. 133
Figure 12: siRNA mediated knockdown of ACC1 impairs mitochondrial function
in PCa cells............................................................................................. 135
Figure 13: Pharmacological inhibition of ACC reduces mitochondrial function in
PCa cells ................................................................................................ 137
Figure 14: PCa cells utilize both endogenous and exogenous fatty acids for
mitochondrial function. ............................................................................ 138
Figure 15: Pharmacological inhibition of ACC prevents PCa cells from utilizing
endogenous and/or exogenous fatty acids for fatty acid oxidation. ........ 140
Chapter III
Figure 1: EC50 concentrations of CP-640186 and paclitaxel in parental and
taxane-resistant PCa cells ...................................................................... 167
Figure 2: Paclitaxel resistant (TxR) PCa cell lines show enhanced sensitivity to
CP-640186 ............................................................................................. 168
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Figure 3: CP-640186 treatment reduces proliferation and increases cell death in
taxane resistant PCa cells ...................................................................... 169
Figure 4: Combination treatment with paclitaxel and CP-640186 induces
apoptosis in taxane resistant PCa cells .................................................. 170
Figure 5: Combination paclitaxel and CP-640186 treatment is synergistic in
taxane-resistant PCa cells ...................................................................... 172
Figure 6: Combination paclitaxel and CP-640186 treatment reduces glycolytic
reserve in DU145-TxR cells .................................................................... 173
Figure 7: Combination paclitaxel and CP-640186 treatment reduces glycolytic
reserve in PC3-TxR cells ........................................................................ 175
Figure 8: Combination paclitaxel and CP-640186 treatment reduces basal
mitochondrial respiration in PC3-TxR cells ............................................. 177
Chapter IV
Figure 1: Potential mechanisms of overcoming ACC1 requirement ..... 192
ix
LIST OF ABBREVIATIONS
2-DG 2-deoxyglucose
3T3-L1 Embryonic murine cell line, adherent, fibroblast, can be
differentiated to have adipocyte-like morphology
AA Antimycin A
ACACA ACC1 gene
ACACB ACC2 gene
ACC Acetyl-CoA carboxylase
ACC1 Acetyl-CoA carboxylase 1
ACC2 Acetyl-CoA carboxylase 2
AceCS Acetyl-CoA synthetase
AceCS1 Acetyl-CoA synthetase (nuclear-cytosolic isoform)
AceCS2 Acetyl-CoA synthetase (mitochondrial isoform)
ACLY ATP citrate lyase
ADP Adenosine 5’-diphosphate
ADT Androgen deprivation therapy
AGPAT Acylglycerolphosphate acyltransferase
AKT Protein kinase B
AML Acute myeloid leukemia
AMP Adenosine monophosphate
AMPK AMP-activated protein kinase
ANOVA Analysis of variance
AR Androgen receptor
x
ASCT2 Alanine, serine, cysteine-preferring transporter 2
ATCC American Type Tissue Collection
ATGL Adipose triglyceride lipase
ATP Adenosine 5’-triphosphate
BC Biotin carboxylase
BCCP Biotin carboxyl carrier protein
BCR Biochemical recurrence
BMI Body mass index
BRCA1 Breast cancer type 1 susceptibility gene
BRCA2 Breast cancer type 2 susceptibility gene
BSA Bovine serum albumin
BTD Biotinidase
CABI Chloroacetylated biotin
ChREBP Carbohydrate-responsive element binding protein
C Celsius
cm Centimeter
CO2 Carbon dioxide
CoA Coenzyme A (also HS-CoA)
cPARP cleaved PARP
CPT1 Carnitine palmitoyltransferase 1
Cre Cre recombinase
CRPC Castration resistant prostate cancer
CT Carboxytransferase
xi
CTEN C-terminal tensin-like
CYP17 Cytochrome P450 17
D Dalton
DGAT Diacylglycerol acyltransferase
diH2O Deionized water
DMEM Dulbecco’s modified eagle medium
DMSO Dimethyl sulfoxide
DNA Deoxyribonucleic acid
dNTP Deoxynucleotide triphosphate
DU145 Human PCa cell line, androgen independent, adherent,
epithelial, derived from brain metastasis
DU145-TxR Taxane-resistant DU145 cells
E4P Erythrose 4-phosphate
EC50 Drug concentration which gives half the maximal response
ECAR Extracellular acidification rate
EDTA Ethylenediaminetetraacetic acid
ETC Electron transport chain
F6P Fructose 6-phosphate
FAD Flavin adenine dinucleotide (oxidized)
FADH2 Flavin adenine dinucleotide (reduced)
FAME Fatty acid methyl ester
FAO Fatty acid oxidation
FASN Fatty acid synthase
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FBS Fetal bovine serum
FCCP Carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone
G3P Glyceraldehyde 3-phosphate
GC-MS Gas chromatography-mass spectrometry
GLUT Glucose transporter
GnRH Gonadotropin-releasing hormone
GPAT Glycerol-3-phosphate acyltransferase
H+ Hydrogen (cation)
H2O Water
H460 Human lung cancer cell line, adherent, epithelial
h hours (also hrs)
H&E Hematoxylin and eosin
HCl Hydrochloric acid
HCS Holocarboxylase synthetase
HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid
HER2 Human epithelial growth factor receptor-2
HOXB13 Homeobox B13 gene
hrs hours (also h)
HSA Highest single agent
HS-CoA Coenzyme A (also CoA)
HSL Hormone sensitive lipase
IDH1 Isocitrate dehydrogenase 1
INPP4B Inositol polyphosphate 4-phosphatase type II
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J Joules
k Kilo
Kact Activation constant
KCl Potassium chloride
KOH Potassium hydroxide
LAT1 L-type amino acid transporter 1
LNCaP Human PCa cell line, androgen sensitive, adherent,
epithelial, derived from lymph node metastasis
LPL Lipoprotein lipase
m milli
M Molar
MAGL Monoacylglycerol lipase
MCC Methylcrotonyl-CoA carboxylase
MCF7 Human breast cancer cell line, positive for estrogen receptor,
human epidermal growth factor receptor 2, and progesterone
receptor , adherent, epithelial, derived from a metastatic site
MCT Mono-carboxylate transporter
MDR1 Multidrug resistance gene
ME1 Malic enzyme 1
MFP-2 Multifunctional protein 2
MgCl2 Magnesium chloride
MgSO4 Magnesium sulfate
mTOR Mammalian target of rapamycin
xiv
MTS 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-
(4-sulfophenyl)-2H-tetrazolium
n nano
Na2HPO4 Sodium phosphate dibasic
NaCl Sodium Chloride
NAD+ Nicotinamide adenine dinucleotide (oxidized)
NADH Nicotinamide adenine dinucleotide (reduced)
NADP+ Nicotinamide adenine dinucleotide phosphate (oxidized)
NADPH Nicotinamide adenine dinucleotide phosphate (reduced)
NaOH Sodium hydroxide
NH3 Ammonia
NH4 Ammonium
NSCLC Non-small-cell lung cancer
OCR Oxygen consumption rate
PAGE Polyacrylamide gel electrophoresis
PAP Phosphatidic acid phosphohydrolase
PARP Poly (ADP-ribose) polymerase
Pb Probasin
PBS Phosphate buffered saline
PCa Prostate cancer
PCR Polymerase chain reaction
Pi Inorganic phosphate
PC Pyruvate carboxylase
xv
PC3 Human PCa cell line, androgen independent, adherent,
epithelial, derived from bone metastasis
PC3-TxR Taxane resistant PC3 cells
PCC Propionyl-CoA carboxylase
PHD3 Prolyl hydroxylase 3
PKM2 Pyruvate kinase (M2 isoform)
PP2A Protein phosphatase 2A
PPAR Peroxisome proliferator-activated receptor
PPP Pentose phosphate pathway
PrEC Normal prostate epithelial cells
PS Penicillin and streptomycin
PSA Prostate-specific antigen
PTEN Phosphate and tensin homolog
PTM Post-translational modification
PUFA Polyunsaturated fatty acid
Q Ubiquinone
QH2 Ubiquinol
R5P Ribose 5-phosphate
RNA Ribonucleic acid
RPMI-1640 Cell culture medium suitable for various mammalian cell
lines, developed at Roswell Park Memorial Institute, contains
biotin, vitamin B12, and p-amino benzoic acid, does not
contain proteins, lipids or growth factors
xvi
ROS Reactive oxygen species
Rtn Rotenone
SDS Sodium docecyl sulfate
siRNA Small interfering RNA
SIRT5 Sirtuin 5
SMVT Sodium-dependent multivitamin transporter
SREBP Sterol regulatory element binding protein
TAE Buffer containing tris base, acetic acid and EDTA
TCA Tricarboxylic acid
TCGA The Cancer Genome Atlas
TG Triacylglycerol
TOFA 5-(tetradecyloxy)-2-furoic acid
µ micro
xvii
ABSTRACT
Deregulation of cellular metabolism has emerged as a hallmark of cancer
yet few therapies targeting cellular bioenergetics are utilized in the clinic. A host
of cancer types are known depend on de novo fatty acid synthesis to support
aberrant proliferation and survival with prostate cancer being a noted example.
The data in this dissertation describes the requirement of acetyl-CoA carboxylase
1 (ACC1), the rate-limiting step of de novo fatty acid synthesis, for the
development and progression of prostate cancer. Prostate specific deletion of
ACC1 activity reduced tumor burden in 12-week-old C57BL/6J mice. However,
prostate tumors are able to bypass the requirement for ACC1 by the time mice
are 24-weeks of age. Metabolic analysis revealed different prostate cancer cell
lines prefer to utilize different sources of fatty acids. Androgen-dependent
LNCaP cells tend to metabolize endogenously supplied fatty acids while
androgen independent DU145 cells preferentially metabolize exogenously
supplied fatty acids. Pharmacological inhibition of ACC by a non-isoform specific
inhibitor reduced the ability of each cell line to use their preferred fatty acid
source. Interestingly, we also found that pharmacological ACC inhibition also
synergizes with paclitaxel in two different taxane-resistant prostate cancer cell
lines and co-treatment results in reduced glycolytic and mitochondrial activity.
Cumulatively, these data provide the first evidence that ACC1 is required for
prostate cancer development and that ACC inhibition can re-sensitize
chemoresistant cells to therapy. Further, these studies pose questions
xviii
concerning potential redundancy between the ACC isoforms and the interplay
between diet and therapeutic strategies, both of which must be carefully
considered when attempting to inhibit ACC1 for chemotherapeutic purposes.
1
CHAPTER I
GENERAL INTRODUCTION
Amanda L. Davis and Steven J. Kridel
A. Davis prepared the text and S. Kridel provided advisory and editorial
assistance.
2
1.1 Prostate cancer
1.1.1 Current epidemiology
The 2017 American Cancer Society estimates indicate that approximately
one out of every eight American men will be diagnosed with prostate cancer
(PCa) during his lifetime and one in 39 will die of the disease [1,2]. With an
estimated 161,360 new cases and 26,730 deaths in 2017, PCa is the most
frequently diagnosed cancer and the third leading cause of cancer-related death
in American men [2,3]. There are several risk factors associated with developing
PCa, each of which can be classified as either a non-modifiable factor, an
external exposure, or a lifestyle factor [4].
There are three non-modifiable risk factors (age, African ancestry, and
family history) which are considered the major and well-established risk factors
for PCa [2,5]. Chief among non-modifiable risk factors for developing PCa is age
as two thirds of PCa diagnoses occur in men age 65 or older [3,4,6]. Ancestry is
also a notable non-modifiable risk factor [2,4,6]. For example, American men
with African ancestry have a projected 73% higher incidence rate (198.4 versus
114.8 out of 100,000) and a 129% higher mortality rate (42.8 versus 18.7 out of
100,000) than non-hispanic white American men [3]. This ancestry-based risk
factor supports observable geographic variation as well with incidence rates
varying considerably within Europe [4]. The final non-modifiable risk factor is
family history and/or genetics which are estimated to account for 5%-10% of PCa
incidences [2,4,6,7]. These cases can include inherited BRCA1, BRCA2, and
HOXB13 mutations, as well as others [2,4,8].
3
The significance of external exposures and lifestyle factors to PCa risk is
not as well understood nor as extensively studied as the non-modifiable risk
factors mentioned previously. However, these factors are critical to consider as
they present potential avenues for modification and risk reduction. External
exposure to ionizing or ultraviolet radiation, a history of urinary tract infections,
and exposure to certain sexually transmitted infections have all been implicated
as potential risk factors for PCa development [9,10,11,12]. Cigarette smoking is
correlated with a modest increase in PCa incidence but shows a much stronger
association with aggressiveness, metastasis and PCa mortality [4,13,14]. The
influence of diet, weight, and physical activity on PCa risk is not as clear. Meta-
analysis consisting of over 88,000 cases from 19 cohort studies and 24 case-
control studies reveals there is evidence to suggest a small inverse relationship
between physical activity and PCa risk, mainly driven by occupation-related
physical activity [15]. A separate meta-analysis consisting of over 19,000 cases
of localized PCa and over 7,000 cases of advanced PCa showed body mass
index (BMI) is simultaneously associated with a decreased risk for localized PCa
and an increased risk for advanced PCa [16]. Several specific foods and
nutrients have been implicated as potential influences to PCa risk, prevention,
and survival, but further research and clarification is needed before any clear
links can be established [4,17].
1.1.2 Evolution of prostate cancer treatment
Prevention, diagnosis, and treatment of PCa has progressed greatly since
PCa was first discovered in 1853 by surgeon J. Adams who described PCa as a
4
“very rare disease” [18]. It has now become the most commonly diagnosed
cancer in American men [2,18]. PCa treatment began in the 1940s with the
discovery that metastatic prostate cancer responds to androgen-ablation
achieved by surgical castration and/or oral oestrogen therapy [18,19]. Since that
time, numerous studies and discoveries have fostered better understanding of
PCa biology and given rise to several therapy regimens.
In general, PCa advances through a series of stages starting with
localized disease then progressing sequentially through metastatic relapse,
castration resistant prostate cancer (CRPC), and terminal disease [20]. By
utilizing various treatments and interventions, these stages are interspersed with
a series of increasingly shorter dormancy periods [20,21]. This pattern of
5
development and recurrence illustrates the need for several PCa therapies, each
tailored to address the unique biological characteristics that arise as the cancer
evolves.
Patients diagnosed early with localized disease often choose to undergo
radical prostatectomy or radiation (external-beam radiation therapy or
brachytherapy) which can achieve a dormancy state lasting over a decade
(Figure 1) [20,22]. However, 20%-40% of patients who undergo surgery and
30%-50% of patients treated with radiation will experience biochemical
recurrence (BCR), defined as a rise in prostate-specific antigen (PSA), within ten
years [22].
Many patients who experience BCR will undergo androgen deprivation
therapy (ADT) as their next line of intervention [22,23]. ADT can be achieved
surgically via orchiectomy or medically through gonadotropin-releasing hormone
(GnRH) agonists, GnRH antagonists, adrenal ablating agents, or androgen
receptor antagonists [24]. Although most men respond to ADT, this period of
dormancy is remarkably shorter than the previous period of dormancy [25].
Several mechanisms including androgen receptor (AR) gene amplification, AR
gene mutations, changes in steroid synthesis, and ligand-independent activation
of AR signaling have been implicated as events that lead to ADT resistance and
cause patients to relapse with CRPC (Figure 1) [25,26].
CRPC cases are treated systemically, often with a taxane [27]. While
taxanes are traditionally known for their ability to stabilize microtubules, induce
cell cycle arrest and eventual apoptosis in cancer cells, these drugs display
6
additional mechanisms in PCa [28]. First, taxanes can downregulate AR
transcription by acting on the AR gene promoter [28,29,30]. Additionally,
because AR can bind to tubulin, taxane-induced microtubule stabilization disrupts
AR localization by keeping it in the cytoplasm and disrupting proper AR signaling
[28,31]. Docetaxel was approved for use in CRPC in 2004 and remained the
only treatment available until 2010 when cabazitaxel was approved for use after
prior docetaxel treatment [27,32,33]. But again, this period of dormancy is
temporary as most patients ultimately develop taxane-resistant CRPC [34,35]. A
handful of mechanisms are thought to drive the development of taxane
resistance. These include increased drug export, inhibition of apoptosis,
activation of survival pathways, and tubulin mutations that disrupt taxane binding
[28,36,37,38,39]. Interestingly, the development of docetaxel resistance is
associated with cells undergoing an epithelial to mesenchymal transition, linking
taxane resistance to the development of metastatic disease [28,40].
Recently other therapies such as abiraterone acetate, the AR signaling
inhibitor enzalutamide, and autologous cellular immunotherapy via sipuleucel-T
have been approved for use in CRPC previously treated with docetaxel
[23,25,27,32,33]. Briefly, sipuleucel T is an autologous cellular immunotherapy
which uses a patient’s own antigen-presenting cells targeted against prostatic
acid phosphatase, an antigen typically expressed in PCa cells [32,41].
Abiraterone acetate is a CYP17 inhibitor and works in PCa by inhibiting androgen
synthesis, decreasing intracellular androgen levels, and inhibiting AR activation
[32,42]. Enzalutamide is an AR signaling inhibitor which prevents AR from
7
translocating to the nucleus and disrupts AR-DNA binding [32,42]. Both
enzalutamide and abiraterone acetate show significant results in clinical trials.
The TERRAIN trial compared enzalutamide treatment to bicalutamide (an AR
antagonist) treatment in men minimally symptomatic with metastatic disease
progressing on hormone therapy and found a significant increase in median
progression free survival with enzalutamide treatment (15.7 versus 5.8 months)
[43]. The STRIVE trial compared enzalutamide to bicalutamide treatment in
American men with castration-resistant nonmetastatic or metastatic disease and
again found a significant increase in median progression free survival with
enzalutamide treatment (19.4 versus 5.7 months) [44]. During the 2017
American Society of Clinical Oncology Plenary Session several results from the
LATITUDE clinical trial were presented [45]. This trial has shown adding
abiraterone acetate and prednisone to hormone therapy in men with metastatic
disease increases median progression free survival time from 14.8 to 33 months
[46,47]. Additionally, the LATITUDE trial reports that treating men just starting
ADT with abiraterone acetate, low-dose prednisone and Lupron (a GnRH
receptor agonist) delayed PCa progression by 18 months [45]. Most
impressively, this trial indicated that abiraterone acetate treatment is remarkably
effective if given early in the treatment process, a finding that is potentially
practice-changing [45]. Yet there is preclinical evidence of cross-resistance
between taxanes and abiraterone acetate and enzalutamide suggesting the
characteristic treatment-dormancy-relapse cycles of PCa will likely continue
regardless of novel therapy usage [48]. Ultimately, this highlights the need to
8
explore other cellular pathways and develop strategies to prevent recurrence,
lengthen dormancy times, and address chemoresistance.
As with other cancer types, there are many factors that can influence PCa
development and progression. In 2000, Drs Hanahan and Weinberg proposed
six hallmarks of cancer: sustaining proliferative signaling, evading growth
suppressors, activating metastasis, enabling replicative immortality, inducing
angiogenesis, and resisting cell death [49]. Later, they amended their hallmark
model to include four more factors: avoiding immune destruction, tumor-
promoting inflammation, genome instability and deregulating cellular metabolism
[50]. Gaining a clear insight into the potential of metabolism-based
chemotherapeutics for PCa is essential. Although cellular metabolism has
emerged as a hallmark of cancer (discussed in the next section), PCa
metabolism is known to be unique among tumor types as PCa is thought to be
less glycolytic in its early stages than other cancers, but reliant on fatty acid
metabolism [51]. While research into PCa metabolism continues, there are
currently no chemotherapeutic agents targeting PCa metabolism available to
patients. The following sections will detail cellular metabolism and the potential
of metabolic pathways as chemotherapeutic targets.
1.2 Cellular metabolism
Metabolism is central to cellular biology in any context as all cells require
energy to perform their intended functions as well as replicate genetic material
and synthesize proteins, fatty acids, and lipids to undergo mitosis [52]. As a
9
defining characteristic of cancer cells is the ability to maintain enhanced
proliferation, it is unsurprising that cancer cells display an increased requirement
for sugars, amino acids, and fatty acids to provide energy and to serve as
building blocks for other molecules [53,54]. The idea that cellular metabolism in
the context of cancer could represent a novel area for discovery gained traction
the 1920’s when Otto Warburg discovered that cancer cells preferentially funnel
glucose into lactate production at the expense of supporting oxidative
phosphorylation, purposefully hindering optimal adenosine 5’-triphosphate (ATP)
production [52,55]. Now, Otto Warburg’s initial description of aerobic glycolysis
is commonly referred to as “The Warburg Effect” and enhanced metabolism is
regarded as a hallmark of cancer biology [50,52]. Interestingly, all major cellular
metabolic pathways support fatty acid synthesis either directly or tangentially.
The following sections will detail the major cellular metabolic pathways and their
relationship to fatty acid synthesis which is illustrated in Figure 2.
1.2.1 Glycolysis
Glycolysis supports fatty acid synthesis by providing the necessary carbon
substrate by breaking down glucose into pyruvate and producing two ATP
molecules. Glycolysis begins with the passive transport of extracellular glucose
into the cell through the use of a membrane-spanning glucose transporter [56].
Even at this first step, cancer cells demonstrate dependence on glucose
metabolism as upregulation of several members of the glucose transporter
(GLUT) protein family, predominantly GLUT1 and GLUT3, have been noted in a
variety of cancers, [53,57,58]. After transport, glucose is phosphorylated by
10
hexokinase, thereby trapping it in the cell, and can proceed to be broken down
through glycolysis or through the pentose phosphate pathway (discussed in the
next section) [53,54,56].
Glycolysis involves several enzymatic reactions and the formation of
intermediate products which are detailed in Table 1 [56,59,60]. This sequence of
reactions and intermediates provides several entry points into the pathway [56].
The intermediates of glycolysis can have roles in other processes. For example,
dihydroxyacetone phosphate, one of the products of aldolase, can be used as a
source of glycerol which, in turn, can be used to form triglycerides (detailed in a
later section) [56]. While the various glycolytic intermediates can participate in
various functions, is it pyruvate which most directly supports fatty acid synthesis.
11
Pyruvate, the end product of glycolysis, has two major fates. First, pyruvate can
be shunted into the mitochondria, converted to acetyl-CoA through pyruvate
12
dehydrogenase, producing CO2 and NADH, and participate in the tricarboxylic
acid (TCA) cycle [56]. Second, pyruvate can be converted to lactate via lactate
dehydrogenase, cycling NADH back to NAD+ to support continued glycolysis,
and transported out of the cell through a mono-carboxylate transporter (MCT)
[52,56,61]. The major difference between converting pyruvate to acetyl-CoA
versus lactate is the potential energy yield [52]. If a molecule of glucose is
metabolized to lactate the cell nets 2 ATP molecules. In contrast, if a molecule of
glucose is metabolized to acetyl-CoA and is further metabolized through
oxidative phosphorylation, the cell yields approximately 36 ATP molecules [52].
This difference in ATP production is the foundation of the Warburg Effect
(described previously). Even when oxidative phosphorylation is an option,
cancer cells overwhelmingly direct approximately 90% of pyruvate to conversion
into lactate, leaving roughly 10% of glucose utilization destined for other glucose-
dependent processes, including fatty acid synthesis [52,62]. Elevated expression
of MCTs has been noted in several cancers thus tumors are able to import
lactate through these transporters for use as a carbon source to support other
metabolic processes [63,64]. Ultimately, enhanced glycolysis in cancer cells
helps provide carbon substrate for anabolic processes, including fatty acid
synthesis.
1.2.2 Pentose phosphate pathway
The pentose phosphate pathway (PPP) generates NADPH and several 5-
carbon sugars from glucose-6-phosphate [56]. The PPP is critical in cancer
cells. First, the PPP supports enhanced ribonucleotide synthesis wich is
13
necessary for cancer cells to maintain a high proliferation rate [65]. Additionally,
the PPP is a major source of NADPH production which is not only necessary for
scavenging reactive oxygen species but is also required for and consumed
during fatty acid synthesis [65].
Like glycolysis, the PPP involves a series of reactions which are detailed
in Table 1 [56,59,60]. The PPP intersects glycolysis at the first committed step
of glycolysis catalyzed by hexokinase [56,65]. The PPP is split into two stages:
the oxidative stage and non-oxidative stage [56,65]. For every molecule of
glucose-6-phosphate that enters the oxidative stage of the PPP, two molecules
of NADPH are generated, making the PPP the major source of NADPH
generation in the cell [56,65,66,67]. The non-oxidative stage of the PPP is
responsible for ribose 5-phosphate synthesis which is necessary for ribonucleic
acid (RNA) and deoxyribonucleic acid (DNA) synthesis [56,67]. Additionally, the
non-oxidative stage of the PPP also produces erythrose 4-phosphate which is
critical in the synthesis of aromatic amino acids like tyrosine, phenylalanine, and
tryptophan [56]. If the cell has sufficient amounts of ribose 5-phosphate or
erythrose 4-phosphate, they are broken down into glyceraldehyde 3-phosphate
or fructose 6-phosphate and re-enter glycolysis [56,65].
1.2.3 Glutaminolysis
Glutaminolysis converts glutamine into glutamate through a series of
reactions which are detailed in Table 1 [56,59,60]. Glutaminolysis supports fatty
acid and lipid synthesis through its interaction with the TCA cycle. Although
glutamine is generally considered a non-essential amino acid, glutamine is
14
necessary during periods of enhanced proliferation, making glutamine a
conditionally-essential amino acid [59,62]. Unsurprisingly, glutamine plays an
important role in the context of cancer. It has even been suggested that cancer
can develop “glutamine addiction” [59]. Just as in the case of glycolysis, cancer
cells demonstrate their reliance on glutamine at the very first step of glutamine
metabolism as cancer cells as are known to upregulate two glutamine
transporters, ASCT2 (alanine, serine, cysteine-preferring transporter 2) and LAT1
(L-type amino acid transporter) thus increasing glutamine uptake [68]. After
transport, glutamine is funneled through glutaminolysis to α-ketoglutarate which
then enters the TCA cycle [56].
As mentioned earlier, cancer cells are known to have an increased need
for fatty acids. To maintain high levels of fatty acid synthesis, citrate is often
diverted away from the TCA cycle and into the cytosol where it is converted into
acetyl CoA and oxaloacetate by ATP citrate lyase (ACLY) [59]. Cytosolic
oxaloacetate can be converted to lactate through a series of enzymes including
malic enzyme 1 (ME1) which produces NADPH [59,62]. Additionally, acetyl-CoA
can be used in fatty acid synthesis (discussed in detail in a future section)
[56,62]. While this process supports fatty acid synthesis by providing the
necessary NADPH and acetyl-CoA, the TCA cycle is depleted due to the
diversion of citrate [59]. However, glutaminolysis serves to replenish TCA cycle
intermediates by providing an additional source of α-ketoglutarate [59].
Ultimately, glutaminolysis allows cancer cells to continue the TCA cycle while
supporting enhanced fatty acid synthesis. The importance of glutaminolysis has
15
been noted in several cancer types [69]. Elevated expression of a key enzyme
involved in glutaminolysis, glutaminase (Table 1), has been noted in colorectal,
breast, and prostate cancer [69]. Glutaminase has even been suggested as an
early diagnostic marker in PCa as expression of the enzyme was found to
correlate with tumor progression [69].
1.2.4 Tricarboxylic acid cycle
The TCA cycle serves to release stored energy through the oxidation of
acetyl-CoA to carbon dioxide and ATP [56]. This cycle involves a series of
reactions and the formation of several intermediate metabolites which are
detailed in Table 1 [56,59,60].The TCA cycle and glycolysis are linked through
the intermediate metabolite pyruvate. Pyruvate can be transported into the
mitochondria and subsequently converted to acetyl-CoA through pyruvate
dehydrogenase and enter the TCA cycle [56].
The relationship between the TCA cycle and fatty acid synthesis has been
discussed in previous sections. When all TCA cycle intermediates are sufficient
to support continued function, or if cells need to generate fatty acids, citrate can
be shunted back into the cytoplasm, converted to acetyl-CoA and oxaloacetate
by ACLY [59]. This acetyl-CoA enters the fatty acid synthesis pathway which will
be discussed in detail in a following section.
1.3 Fatty acid metabolism
Fatty acids have four major roles within the cell. Fatty acids are the
precursors of phospholipids and glycolipids which are critical components of
16
cellular membranes [70,71,72]. Fatty acids are protein modifiers, often targeting
select proteins to membranes [70,71]. Fatty acids are stored as triacylglycerols
(TGs) which can be later oxidized for energy [70,71]. Finally, derivatives of fatty
acids act as hormones and intracellular messengers [56,70,71].
There are also four major nodes of fatty acid metabolism: synthesis,
oxidation, storage, and release [71]. It is interesting to note that recent interest in
targeting fatty acid metabolism for chemotherapeutic purposes has been focused
on limiting the pool of available fatty acids [71,73]. Successful depletion of
available fatty acids would likely require concurrent targeting of multiple nodes of
fatty acid metabolism. Ideally this would be achieved by simultaneously limiting
synthesis, activating oxidation, promoting storage, and preventing release.
Therefore, full understanding of each node of fatty acid metabolism will be vital to
developing novel therapies. Sources of fatty acids as well as each of the four
nodes will be detailed in the following sections, with particular emphasis on the
connection to cancer biology.
1.3.1 Sources of fatty acids
Cells can obtain fatty acids from one of two ways, either by obtaining fatty
acids from dietary supplies or through de novo fatty acid synthesis [71,72]. The
following subsections will detail both fatty acid sources and their influence on
cancer.
1.3.1.1 Dietary derived fatty acids
Dietary derived fatty acids refer specifically to fatty acids obtained through
the diet as opposed to fatty acids synthesized with the cell. Fatty acids are
17
written with specific nomenclature to describe the number of carbons and the
number/position of double bonds. Names, abbreviations, and structures of select
fatty acids are shown in Table 2. Mammalian cells are capable of synthesizing
several types of fatty acids through de novo synthesis (discussed in the next
section) [56]. Polyunsaturated fatty acids (PUFAs) with double bonds at the Δ 12
carbon in the fatty acid chain are essential for life as they serve as precursors for
necessary eicosanoids [56]. However, mammals lack the necessary
desaturases to place a carbon-carbon double bond beyond the Δ 9 carbon in the
fatty acid chain [56,74,75]. Therefore, mammals must obtain certain fatty acids,
referred to as essential fatty acids, from dietary supplies [74,75]. Linoleate and
linolenate (Table 2) are two such fatty acids as they serve as the precursors for
18
the ω-6 and ω-3 series of fatty acids, respectively [74]. Once inside a cell,
mammalian desaturases and elongases transform linoleate and linolenate into
other metabolites which serve a variety of physiological functions [74].
The transport of fatty acids from the extracellular space into the cytosol
can occur through passive transport (for short-chain fatty acids), the low-density
lipoprotein receptor, fatty acid transport proteins, or fatty acid translocase along
with fatty acid binding proteins [72]. Fatty acid binding proteins have been
implicated in the development, progression, and/or metastatic potential of breast,
prostate, bladder, kidney, and endometrial cancers [76,77,78]. The role of fatty
acid translocase CD36, which is considered the main channel for cellular fatty
acid uptake, has also been studied [56,79]. In 2011, Kuemmerle et al. found,
lipoprotein lipase (the enzyme responsible for releasing fatty acids from
triacylglycerols in circulating lipoproteins) and fatty acid translocase CD36 are
present in the majority of breast, liposarcoma, and prostate tissues analyzed via
immunohistochemistry [79]. This work proposed that cancer cells are able to
utilize two different mechanisms to meet their enhanced demand for fatty acids:
de novo fatty acid synthesis and exogenous fatty acid uptake [79]. Of particular
interest are the findings concerning PCa. Kuemmerle and colleagues found that
while PCa is known to have a high capacity for de novo lipogenesis, PCa cell
lines express little LPL [79]. Addition of LPL did not accelerate PCa cell growth
[79]. Yet, in the presence of lipoprotein-containing media, LPL supplementation
rescued PCa cells from cytotoxicity caused by pharmacological inhibition of de
novo fatty acid synthesis, indicating PCa cells are able to use exogenous fatty
19
acids to maintain growth under conditions where de novo synthesized fatty acids
are limited or depleted [79]. These results suggest an inconsistency between in
vitro PCa cell culture and PCa primary tissue samples. This work also
highlighted a discrepancy between in vitro breast cancer cell culture and primary
breast cancer tissue samples as LPL expression is limited to triple-negative
breast cancer cell lines yet LPL expression was noted in all breast tumor tissue
samples, regardless of biomarker signature [79]. Kuemmerle et al. offer several
potential explanations for these differences, including lack of microenvironment
influence on in vitro cell culture and composition of cell culture media, and
suggest careful consideration when analyzing differences in in vitro and in vivo
metabolic phenotypes[79]. Ultimately, this work supports the hypothesis that
cancer cells can potentially utilize both de novo derived and dietary supplied fatty
acids to meet their increased metabolic demand [79]. As such, any attempt to
influence fatty acid metabolism for chemotherapeutic purposes should consider
the interplay between diet and therapy.
The role of PUFAs in cancer biology is difficult to describe as studies
designed to address the interaction between PUFA intake and cancer risk and/or
cancer progression have returned inconsistent results [80]. Genetic differences,
diverse environmental exposures, different dietary habits, limitations of self-
reporting, and the interaction among these factors all contribute to the varying
results. The ω-3 series of fatty acids has been shown to suppress angiogenesis
and proliferation while ω-6 fatty acids have been associated with the promotion
of angiogenesis as well as cell proliferation and metastasis [80,81]. Attention has
20
also been given to the ω-6:ω-3 ratio but again results are inconsistent [80].
Although many studies support the idea that dietary fatty acids play a role in
cancer biology, the significance of dietary derived fatty acids on cancer
development/progression, the practicality of targeting this process, and the
interaction between dietary and de novo derived fatty acids is not completely
understood.
1.3.1.2 De novo fatty acid synthesis
De novo fatty acid synthesis is a vital metabolic pathway which involves a
series of several steps and results in the generation of long chain, saturated fatty
acids. This pathway begins with cellular uptake of glucose and glutamine
(discussed in previous sections) [81]. Ultimately, the end products of glycolysis
(pyruvate) and glutaminolysis (glutamate) are shunted into the mitochondria to
participate in the TCA cycle. Citrate formed as part of the TCA cycle can
continue to fuel the cycle or be transported out of the mitochondria to participate
in de novo fatty acid synthesis. Metabolism and de novo fatty acid synthesis are
tied together by ACLY (discussed previously). Acetyl-CoA carboxylase 1 (ACC1)
governs the rate-limiting step of the pathway as it irreversibly carboxylates acetyl-
CoA to form malonyl-CoA in an ATP and biotin dependent reaction [82,83]. Fatty
acid synthase (FASN), with the help of NADPH, uses one acetyl-CoA molecule
and seven malonyl-CoA molecules to build saturated fatty acids, with the 16-
carbon saturated fatty acid palmitate (Table 2) being the major product
[56,84,85]. Once formed, palmitate can be elongated and altered to form other
fatty acids [74,86,87].
21
Few normal adult human tissues (liver, adipose, lactating breast) are
known to undergo de novo fatty acid synthesis [72,88]. The majority of normal
human tissues prefer to use dietary supplied lipids to meet their requirement for
fatty acids rather than conduct the energy-expensive process of de novo
synthesis, and therefore maintain low cellular concentrations of enzymes
involved in the de novo pathway [89,90]. In contrast, de novo fatty acid synthesis
is absolutely essential during embryogenesis as whole-body knockout of FASN
or ACC1 in mice results in embryonic lethality [91,92]. Further, de novo derived
fatty acids are required for enhanced proliferation and cell signaling as they are
necessary for membrane biogenesis and are a major component of lipid rafts
[81,84,89]. Enhanced lipogenic activity is found in the majority of cancers and
enzymes involved in the de novo fatty acid synthesis pathway are upregulated in
a variety of cancer types [93,94]. In addition to the upregulation of the de novo
pathway, cancer cells are dependent on de novo derived fatty acids as multiple
studies covering several cancer types show inhibition of acetyl-CoA carboxylase
(ACC), FASN, or ACLY results in cancer cell death
[95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112].
Despite this reliance on de novo fatty acid synthesis, some studies have shown
cancer cells are capable of both de novo fatty acid synthesis and extracellular
lipolysis and that the addition of exogenous fatty acids can negate anti-tumor
effects achieved through inhibition of de novo fatty acid synthesis
[79,90,97,108,113,114]. Because of this interaction between de novo fatty acid
synthesis and extracellular lipolysis in cancer, it is crucial to understand both
22
processes as well as the difference between dietary-derived and de novo
synthesized fatty acids.
Although enzymes involved in de novo fatty acid synthesis are
consistently found upregulated in a variety of cancers, FASN has received the
majority of attention concerning the potential targeting of the de novo pathway for
cancer therapy [93,94]. Increased expression of FASN has been noted in a
variety of cancers including breast, prostate, ovary, lung, colon, and others
[88,93]. Furthermore, overexpression of FASN is positively correlated with poor
prognosis in many cancer types including breast, prostate, lung, and ovarian
[115,116,117]. Overexpression of FASN is considered an early event in both
colorectal and prostate cancer but the highest expression of FASN is seen in
metastatic lesions of advanced androgen independent prostate cancer
[115,118,119]. This suggests de novo fatty acid synthesis is required at all
stages of cancer growth. The large body of research concerning the contribution
of FASN to cancer biology has resulted in the attractive idea of targeting the de
novo fatty acid synthesis pathway for cancer therapy [108,120,121]. However,
enhanced lipogenesis requires much more than overexpression of FASN. As
such, the potential exists that other enzymes involved in the de novo fatty acid
synthesis pathway are just as vital to cancer initiation and progression and
represent prospective targets for novel cancer therapies. It is interesting that
FASN has received the bulk of attention in cancer research considering it is not
the rate-limiting enzyme of the pathway. Characteristics of the pace-setting
enzyme, ACC1, will be described in detail in a following section.
23
1.3.2 Fatty acid oxidation
Fatty acid oxidation breaks down fatty acids to yield energy [75]. Fatty
acid oxidation can occur through three pathways, α, β, and ω [122,123,124].
These pathways are named by which carboxyl group is first removed during the
oxidation process [123,124]. During ω-oxidation, a hydroxyl group is first added
to the carbon most distant from the carboxyl group, the ω-carbon [123,124]. The
hydroxyl group is subsequently oxidized to an aldehyde then a carboxylic acid,
producing a fatty acid with a carboxyl group at each end [123,124]. These
dicarboxylic acids can then enter the β-oxidation pathway (discussed below)
[123,124]. For fatty acids like phytanic acid, where a methyl group is on the β
carbon, β oxidation is impossible [122,124]. In this case, α oxidation occurs
[122,124]. This process involves the addition of a hydroxyl group onto the α
carbon, a decarboxylation step to form an aldehyde, and then an oxidation step
to form a carboxylic acid [122,124]. At this point, the fatty acid can be oxidized
through β oxidation [122,124]. Although α and ω oxidation are important
physiological processes, this section will mainly focus on β oxidation as β
oxidation is the major catabolic fate for fatty acids in mammalian cells [124].
In eukaryotes, β oxidation occurs in the mitochondrial matrix [56,75].
Before entering the mitochondrial matrix, fatty acids must be activated through a
series of three steps [56,75,124]. This is achieved by attaching coenzyme A to
the fatty acid with a thioester link which is catalyzed by acyl CoA synthetase
[56,75,124]. Next, fatty acids are transiently attached to the hydroxyl group of
24
carnitine by carnitine acyltransferase I, also known as carnitine
palmitoyltransferase I (CPT1) [71,75,124]. The fatty acyl-carnitine ester then
enters the mitochondrial matrix through the acyl-carnitine/carnitine transporter
[124]. Finally, the fatty acyl group is enzymatically transferred from carnitine to
CoA within the intramitochondrial space by carnitine acyltransferase II [124].
This reaction releases fatty acyl-CoA and free carnitine into the mitochondrial
matrix, which allows breakdown of the fatty acid and the transport of the free
carnitine back into the intermembrane space through the acyl-carnitine/carnitine
transporter [124]. This carnitine-mediated entry process is the rate-limiting step
of β oxidation and a potential targeting point, which will be discussed later.
β oxidation of saturated fatty acids with an even number of carbon atoms
occurs in 4 steps [75,124]. First acetyl-CoA dehydrogenase oxidizes the acyl
CoA to form a trans-Δ2-enoyl-CoA in a flavin adenine dinucleotide (FAD)
dependent reaction [75,124]. The FADH2 produced immediately donates its
electrons to the mitochondrial respiratory chain, producing about 1.5 ATP
molecules per electron pair [124]. Next, water is added to the double bond of the
trans-Δ2-enoyl-CoA in a hydration reaction catalyzed by enoyl-CoA hydratase to
form 3-hydroxylacyl-CoA [75,124]. Third, 3-hydroxylacyl-CoA is oxidized to 3-
ketoacyl-CoA by β-hydroxyacyl-CoA dehydrogenase [75,124]. During this
reaction, NADH is produced and donates its electrons to the mitochondrial
respiratory chain [124]. The final reaction, catalyzed by acyl-CoA
acetyltransferase (commonly called thiolase), combines 3-hydroxylacyl-CoA and
a free CoA molecule then splits the product to form acetyl-CoA and a shorter
25
acyl-CoA [75,124]. These four steps represent one cycle of β-oxidation which
serves to sequentially remove two-carbon units from the fatty acid chain until the
fatty acid is completely oxidized [75]. This process is a major source of energy
generation within the cell. For example, complete degradation of the 16 carbon
saturated fatty acid palmitate (Table 2) consumes two ATP equivalents for
activation then generates 35 ATP molecules from oxidation of NADH and FADH2
and an additional 96 ATP molecules from the breakdown of acetyl-CoA
molecules within the TCA cycle, resulting in a net yield of 129 ATP molecules
[75].
β oxidation of unsaturated fatty acids or fatty acids with an odd number of
carbons differs from the process described above. Additional enzymes are
needed for monounsaturated or polyunsaturated fatty acids to be completely
degraded by β oxidation as enoyl-CoA hydratase cannot act on cis-configured
double bonds [75,124]. Auxiliary enzymes Δ3,Δ2-enoyl-CoA isomerase and 2,4-
dienoyl-CoA reductase act to convert cis-configured bonds to trans-configured
bonds thus allowing the fatty acid to re-enter the standard β oxidation cycle [124].
Complete oxidation of fatty acids with an odd number of carbons requires extra
reactions [124]. When an odd-numbered fatty acid exits the last β-oxidation
cycle, a five-carbon fatty acyl-CoA is produced which, when subsequently
oxidized and cleaved, produces acetyl-CoA and propionyl-CoA [75,124]. Acetyl-
CoA enters the TCA cycle as described previously but the propionyl-CoA is
metabolized differently [75,124]. The sequential actions of propionyl-CoA
carboxylase, methylmalonyl-CoA epimerase and methymalonyl-CoA mutase
26
convert propionyl-CoA to succinyl-CoA, which can then enter the TCA cycle
[75,124].
Fatty acid breakdown through β oxidation is regulated in a handful of
manners. In general, regulation of β oxidation occurs through signals designed
to reflect energy sufficiency within a cell [124]. The mitochondrial form of ACC
(ACC2) produces malonyl-CoA within the mitochondria which inhibits CPT1
[71,124]. In this way, the cell signals sufficient energy to conduct de novo fatty
acid synthesis and prevents simultaneous fatty acid synthesis and oxidation
which would set up a futile cycle [75]. Additionally, a high [NADH]/[NAD+] ratio
inhibits β-hydroxyacyl-CoA dehydrogenase and high levels of acetyl-CoA inhibit
thiolase [124].
Research into targeting β oxidation for cancer therapy has resulted in
some conflicting ideas. This is unsurprising as both increasing and inhibiting β
oxidation would, in theory, yield one beneficial and one detrimental outcome. For
example, increasing β oxidation would limit the pool of available fatty acids
(beneficial) but provide the cell ample energy (detrimental) [71]. In contrast,
inhibiting β oxidation would limit cellular energy (beneficial) but sustain or
enhance the pool of available fatty acids (detrimental) [71]. Current research
suggests that CPT1 activity in the brain is necessary for cancer cells to survive
under conditions of energetic stress and that pharmacological inhibition of CPT1
by etomoxir or ranolazine prevents proliferation in leukemia cells and results in
cell death in glioblastoma cell lines [71,125,126,127]. Activation of peroxisome
proliferator-activated receptor alpha (PPARα), the major transcriptional regulator
27
of fatty acid oxidation, induces β oxidation and has been simultaneously
associated with inducing hepatocellular carcinoma in rodents and inhibition of
ovarian tumor growth [71,128,129]. Likewise, simultaneous pro- and anti-
carcinogenic results have also been seen with PPARβ and PPARγ activation
[128]. Ultimately, the relationship between fatty acid oxidation and cancer
biology is not entirely understood. It is likely cancers will vary in their responses
to clinical targeting of β oxidation based on mutation status, tissue type, and
metabolic profile [71].
Interestingly, it is the acetyl-CoA carboxylases which sit at the unique
intersection of fatty acid synthesis and breakdown and, as such, greatly influence
the size and scope of the available fatty acid pool. It is clear that understanding
the acetyl-CoA carboxylases (discussed later) will be necessary to define the
function and consequences of available fatty acids on cancer development as
well as to elucidate the optimal method of targeting fatty acid metabolism for
cancer therapy.
1.3.3 Storage and release of fatty acids
The oxidation of fatty acids yields over two times the amount of energy
that results from the oxidation of proteins or carbohydrates, approximately 37
kilojoules (kJ) per gram versus 16kJ per gram, respectively [56]. As such, fatty
acids are vital metabolic fuels and it is crucial for cells to be able to store them for
future use and release them when needed. Generally, cells store fatty acids in
the form of TGs (three fatty acyl residues esterified to glycerol) within the
cytosolic lipid droplet [56,71,130]. The main TG synthesis pathway, known as
28
the Kennedy or glycerol-phosphate pathway, condenses fatty acids with glycerol-
3-phosphate through four enzymes: glycerol-3-phosphate acyltransferase
(GPAT), acylglycerolphosphate acyltransferase (AGPAT), phosphatidic acid
phosphohydrolase (PAP), and diacylglycerol acyltransferase (DGAT) [71].
Several facets of fatty acid storage have been connected with cancer
biology. Elevated numbers of lipid droplets have been noted in several cancer
types including colon cancer, hepatocarcinoma, brain cancer and invasive
squamous cervical carcinoma [71,131]. Interestingly, it is unclear whether the
actual accumulation of lipid droplets or the ample pool of available fatty acids is
truly associated with neoplastic behavior [71]. In addition, multiple enzymes in
the Kennedy pathway have been associated with cancer. Of the proposed 11
human AGPAT isoforms, 3 have been shown to be overexpressed in a variety of
cancers [71]. Specifically, AGPAT9 overexpression has been noted in PCa and
colorectal cancer, AGPAT11 overexpression has been seen in breast, cervical
and colorectal cancer, and meta-profiling analysis has linked enhanced AGPAT2
expression with several more cancer types [132]. The relationship between PAP
(also called lipin) and cancer is unclear, but it has been proposed that PAP
contributes to the enhanced lipogenesis noted in several cancer types through
interaction with sterol regulatory element-binding proteins (SREBPs) which are
known to regulate fatty acid and cholesterol synthesis [71,133]. Finally, DGAT
overexpression in transformed human lung fibroblasts results in increased TGs
but decreased phospholipids, impaired proliferation, and reduced anchorage-
29
independent growth, suggesting that increasing lipid storage, and thus
decreasing readily available lipids, hinders cancer progression [71,134].
In order for a cell to utilize fatty acids for energy they must be released
from storage. This process is achieved through lipolysis, the process of breaking
down fats and lipids by hydrolysis to release fatty acids [54]. Full hydrolysis of a
TG within a lipid droplet to three fatty acids requires the action of three enzymes:
adipose triglyceride lipase (ATGL), hormone sensitive lipase (HSL), and
monoacylglycerol lipase (MAGL). Additionally, secreted lipoprotein lipase (LPL)
is responsible for releasing fatty acid from triacylglycerols in circulating
lipoproteins [56,79].
Just as in the case of fatty acid storage, aspects of fatty acid release have
been connected to cancer biology. Increased MAGL expression and activity has
been noted in aggressive melanoma, ovarian and breast cancer cell lines as well
as in primary ovarian and ductal breast cancer samples and inhibition of MAGL
reduced tumorigenicity of melanoma and ovarian cancer cells [71,135].
Interestingly, research into MAGL inhibition has initiated questions about
integrating dietary changes into efforts to target cellular metabolism as mice with
MAGL-inhibited tumors fed a high fat diet did not show the same reduced tumor
growth seen in mice fed a standard diet [71,135].
1.4 The acetyl-CoA carboxylases
The acetyl-CoA carboxylases irreversibly carboxylate acetyl-CoA to form
malonyl-CoA in an ATP and biotin dependent reaction [82,83]. The structure,
30
function, and manipulation of ACC have been studied in a wide variety of
contexts spanning from plant propagation and herbicides to a host of mammalian
diseases including diabetes, obesity, microbial infections, and cancer [136,137].
It is clear ACC has an incredible reach across the field of biology. As such, it is
vital to understand all aspects of ACC and the potential consequences of ACC
overexpression and inhibition. The purpose of this section is to condense what is
known about ACC, illustrate how ACC influences overall cellular metabolism,
summarize current research, and discuss the feasibility of exploiting ACC to
develop novel therapies. Particular emphasis will be placed on the mammalian
isoforms of ACC and their involvement in cancer biology.
1.4.1 ACC1 and ACC2
Mammalian cells express two isoforms of acetyl-CoA carboxylase which
are encoded by two separate genes. The human ACACA gene is located on
chromosome 17 and encodes a 265 kilodalton (kD) protein (ACC1) [82,138,139].
The human ACACB gene is located on chromosome 12 and encodes a 280kD
protein (ACC2) [82,140]. The difference in size is due to mitochondrial targeting
sequence at the N-terminus of ACC2 which the ACC1 isoform lacks [82]. Even
though the two ACC isoforms differ in size and location, the catalytic units of the
proteins are homologous and both perform the same function [82,83]. When
active, ACC is found in a polymerized form [141]. The conversion of acetyl-CoA
to malonyl-CoA begins at the biotin carboxylase (BC) domain of the enzyme as
bicarbonate donates a carboxyl group for the ATP-dependent carboxylation of
biotin [83]. The biotin carboxyl carrier protein (BCCP) domain then facilitates the
31
transfer of the carboxylated biotin to the carboxytransferase (CT) domain [83].
Finally, the carboxytransferase domain transfers the carboxyl group originally
donated to biotin onto acetyl-CoA to form malonyl-CoA [83].
The fact that ACC1 is cytosolic and ACC2 is located in the mitochondria
suggests that the two enzymes participate in different functions and the
corresponding pools of malonyl-CoA are distinct and separate. This idea is
widely accepted as ACC1 is known for its role as the rate limiting step of de novo
fatty acid synthesis (discussed previously) and ACC2 is associated with β
oxidation. The tissue distribution of ACC1 and ACC2 generally supports this
hypothesis as ACC1 is mainly expressed liver and adipose tissue, where
lipogenesis is known to occur, and ACC2 is found in oxidative tissues like muscle
and cardiac tissue [142,143,144]. Knockout studies in mice generally support
this hypothesis as well, though some questions remain. Whole body knockout of
ACC1 in mice results in embryonic lethality while whole body knockout of ACC2
results in mice that have constant fatty acid oxidation and a lower body weight
than wild-type mice, but are otherwise developmentally normal [91,145]. The
Wakil group has shown that deletion of ACC1 function in the mouse liver by Cre-
Lox mediated removal of exon 22 (part of the biotin carboxyl carrier protein
domain) results in a 70% reduction of hepatic malonyl-CoA levels when
compared to wild type mice [146]. In contrast, when ACC1 function is deleted in
the mouse liver by Cre-Lox mediated removal of exon 46 (part of the
carboxytransferase domain), de novo lipogenesis still occurs [147]. Harada et al.
contribute this continuation of de novo lipogenesis to an increase in ACC2
32
activity and similar levels of malonyl-CoA levels between ACC1 knockout and
wild type mice [147]. This suggests that ACC2 activity increases in response to
ACC1 loss and/or mitochondrial malonyl-CoA can participate in de novo fatty
acid synthesis. Taken together, this suggests that if the isoforms of ACC can
compensate for the loss of their counterpart, they may not be able to do so in all
processes or all tissues.
1.4.2 Regulation of ACC
Expression of the ACCs is highly regulated in both normal and oncogenic
settings. The human ACACA gene is subject to control by at least three separate
promoters and ACACB is controlled by two promoters [82,148,149]. Insulin,
glucagon, leptin, PPAR, and epinephrine mediated signalling processes lead to
transcriptional activation of ACACA through the carbohydrate-responsive
element-binding protein (ChREBP) or the sterol regulatory element-binding
protein (SREBP) [148,150,151,152]. Likewise, leptin and PPARα are kown to
influence ACACB expression [148].
Genomic and transcriptional regulation of ACC is achieved through
additional methods in an oncogenic setting, particularly in the cases of prostate
and breast cancer [153,154,155]. Several cancer cell lines show copy number
gain of lipogenic enzymes, including ACACA [156]. Additionally, ACACA and
other lipogenic enzymes are known to be transcriptionally regulated by androgen
levels [157]. Loss or mutation of phosphate and tensin homolog (PTEN) and
subsequent activation of the phosphatidylinositol-3 kinase (PI3K) and protein
33
kinase B (AKT) pathways, which occurs in 50-80% of primary PCa cases and is
associated with aggressive disease, promotes ACACA transcription (and
transcription of other lipogenic enzymes) through upregulation of SREBP
[155,158]. In breast cancer, it was originally hypothesized that human epithelial
growth factor receptor 2 (HER2), which is increased in approximately 30% of
cases, triggers PI3K signaling which, in turn promotes transcription of lipogenic
enzymes including ACACA [154,159]. While this may occur, it has been
demonstrated that the major mechanism of HER2-mediated induction of ACC1 in
breast cancer is achieved through selective translational induction facilitated by
mTOR (mammalian target of rapamycin) signaling [154].
The mammalian ACCs are also subject to allosteric regulation by several
metabolites [149]. First, the TCA cycle intermediate citrate, the precursor of
acetyl-coA (discussed previously), acts as a feed-forward activator of ACC and
prevents binding of inhibitory long-chain fatty acids [149]. Glutamate also acts as
an allosteric activator of ACC [149]. While this might simply be due to
glutamate’s structural similarity to citrate, this relationship is a futher link between
glutaminolysis and fatty acid metabolism (Figure 2) [149]. The direct product of
ACC, malonyl-CoA, serves as an inhibitor of ACC as it competes with acetyl-CoA
and interferes with ACC polymerization [149,160].
Lastly, as ACC is ATP dependent, ACC can respond to energy signals
within the cell and, when appropriate, be activated or inactivated through
phosphorylation events. Adenosine monophosphate (AMP) – activated protein
kinase (AMPK) is the major enzyme responsible for phosphorylating ACC
34
[149,161,162]. In a low-energy state, a high AMP:ATP ratio activates AMPK
which carries out an inactivating phosphorylation on ACC (in humans, serine 79
on ACC1 and serine 212 on ACC2) [161,163]. In this way, the cell indicates
insufficient energy to carry out de novo fatty acid synthesis and subsequently
inactivates ACC1, the rate limiting enzyme of the pathway. In addition, the cell
inactivates ACC2 thus decreasing mitochondrial malonyl-CoA levels, relieving
CPT1 inhibition (discussed earlier), and thereby activating fatty acid β oxidation
and subsequent ATP production. The reverse dephosphorylation reaction is
mainly carried out by protein phosphatase 2A (PP2A), though recent evidence
suggests protein phosphatase 4 can perform the same function [149,164].
Details of PP2A expression are not entirely understood. However, it is believed
expression and activity of PP2A is stimulated by increased ceramide levels,
stabilization via an ATP-dependent chaperone PTPA (PP2A phosphatase
activator), and intracellular glutamate and magnesium levels [165,166,167]. The
influence of insulin signaling on ACC activity is also unclear as insulin appears to
stimulate the dephosphorylation and subsequent activation of ACC but insulin is
known to down-regulate the catalytic subunit of PP2A, suggesting a different
enzyme within the protein phosphatase family could dephosphorylate ACC or
that insulin-mediated downregulation of PP2A is tissue and/or process
dependent [168,169,170]. In cancer, the phosphorylation status of ACC seems
to be an important factor. For example, decreased phosphorylated ACC is seen
in aggressive lung adenocarcinomas and phosphorylated ACC is associated with
better survival rates for gastric cancer patients [171,172]. This is consistent with
35
the hypothesis that inhibiting fatty acid synthesis, via phosphorylation-mediated
inactivation of ACC1 results in impaired cancer growth.
It is important to note that ACC1 and ACC2 are believed to respond
differently to different types of regulation [149]. For example, the activation
constant (Kact) for citrate in tissues were ACC2 is dominant is approximately 2
millimolar (mM) where the Kact for citrate in tissues where ACC1 is dominant is
0.65mM [143]. Other studies indicate ACC1 is more sensitive to
phosphorylation-mediated regulation while ACC2 responds more to substrate
level regulation [173,174]. Additionally, it has been suggested that the different
types of ACC regulation may obey a hierarchy as AMPK-mediated inactivation of
ACC can overturn citrate-mediated allosteric activation [175,176].
1.4.3 Biotin dependence
Biotin is a water-soluble vitamin that serves two important roles in
mammalian biology [177]. First, biotinylated histones are found in repeat regions
of the human genome and are believed to aid in genome stability and
transcriptional repression [177]. Second, biotin is a necessary coenzyme for
several enzymes [177,178,179,180]. The mammalian ACCs are two of only five
biotin-dependent enzymes expressed in mammals [177,178,179]. The others,
pyruvate carboxylase (PC), propionyl-CoA carboxylase (PCC) and
methylcrotonyl-CoA carboxylase (MCC) are involved in gluconeogenesis, odd
chain fatty acid oxidation, and amino acid catabolism, respectively,
demonstrating that biotin homeostasis is necessary for proper metabolism
[177,179].
36
Maintaining biotin homeostasis involves the interactions of several
processes including sufficient dietary intake, intestinal absorption, cellular
transport, and minimizing urinary excretion [177]. Biotin deficiency can occur at
multiple points and can have severe consequences [177,180]. First, biotin
deficiency can result from insufficient dietary intake [177]. To maintain adequate
biotin supplies, it is recommended adults consume approximately 30 micrograms
(µg) per day [177]. However some studies suggest, under certain conditions, this
dose is insufficient. For example, many women in America become marginally
biotin-deficient during pregnancy even while maintaining the recommended daily
dose [177]. Smoking has been linked to accelerated biotin catabolism and
subsequent deficiency [181]. Also, biotin deficiency has been associated with
anticonvulsant treatment, long-term antibiotic use, antiseizure medications, and
excessive alcohol consumption [177]. Biotin deficiency can also result from a
deficiency of one or more of the three proteins involved in biotin metabolism.
Biotinidase (BTD) is vital in releasing free biotin from biotinylated peptides for
absorption, transporting biotin to peripheral tissues, and releasing free biotin from
the breakdown of biotin-dependent carboxylases [177]. Holocarboxylase
synthetase (HCS) facilitates the attachment of biotin to the appropriate
carboxylases and histones though some studies suggest BTD can also
participate in the biotinylation of histones [177,179]. The sodium-dependent
multivitamin transporter (SMVT) facilitates the absorption of free biotin within the
intestines, renal reabsorption of biotin, and biotin transport across cell
membranes [177].
37
Unsurprisingly, a biotin deficiency results in low activity of the five biotin-
dependent enzymes [177,180]. Multiple carboxylase deficiency, defined by
impaired activity of the ACCs, MCC, PC, and PCC, can result in a variety of
biological consequences including severe mental damage, lack of muscle and
motor control, abnormal amino acid and lactate metabolism, and fetal defects
[177,180]. Multiple carboxylase deficiency can present as an early-onset form
(neonatal) or a late-onset form (juvenile, weeks to months of age) [177,180,182].
It is believed that a deficit in BTD is the main cause of both forms of multiple
carboxylase deficiency [177,182]. Mutations in the BTD gene are thought to give
rise to the early-onset form while impaired secretion/production of BTD leads to
late-onset multiple carboxylase deficiency [177]. In general, patients with
multiple carboxylase deficiency respond well to treatment with oral biotin, though
appropriate doses must be individually determined [177,180,182].
1.4.4 Sources of acetyl-CoA
In addition to ATP and biotin, the ACCs also require acetyl-CoA as a
substrate. The purpose of this section is to discuss the common sources of
acetyl-CoA. Common sources of acetate will be discussed as acetate can be
converted to acetyl-CoA by acetyl-CoA synthetase [183].
Humans can obtain acetate from both endogenous and exogenous
sources [184]. The main source of acetate comes from bacterial-mediated
digestion of indigestible carbohydrates within the gut [184]. Other dietary
sources of acetate include dairy products, processed meats, and various vinegar-
containing foods [184]. Additionally, liver catabolism of ethanol produces
38
acetate [185]. On a cellular level, deacetylation of histones and proteins, as well
as hydrolysis of acetylated metabolites, provides more localized supplies of
acetate [184].
Before ACC can use acetate it must be converted to acetyl-CoA. This is
done through the ATP-dependent ligation of acetate to CoA catalyzed by acetyl-
CoA synthetase (AceCS) [183,184]. Because there are two isoforms of ACC
which are believed to function in separate cellular locations, it is unsurprising that
there are two isoforms of acetyl-CoA synthetase [183,184]. The cytosolic isoform
of acetyl-CoA synthetase is commonly referred to as AceCS1 and the
mitochondrial isoform is commonly abbreviated as AceCS2 [183]. These
isoforms of AceCS are believed to help provide the acetyl-CoA for the cytosolic
and mitochondrial isoforms of ACC. As with the ACC isoforms, it is unknown
whether the AceCS isoforms can compensate for one another and/or if the
cytosolic and mitochondrial pools of acetyl-CoA are interchangeable.
Interestingly, AceCS is positioned to allow mitochondrial metabolism to support
de novo fatty acid synthesis. Mitochondrial acetyl-CoA produced by AceCS2 can
either be converted to malonyl-CoA by ACC2 or to citrate by citrate synthase
(Table 1, discussed earlier). Mitochondrial citrate can be shunted into the
cytosol by the tricarboxylate carrier [70]. Cytosolic citrate can then be converted
into oxaloacetate and acetyl-CoA by ACLY (discussed previously). This acetyl-
CoA can subsequently go on to fuel de novo fatty acid synthesis through ACC1,
thus providing an opportunity for mitochondrial metabolism to feed fatty acid
synthesis.
39
Other than being derived from acetate, acetyl-CoA can be produced as a
result of glycolysis, fatty acid oxidation, and amino acid degradation. Previous
discussion of glycolysis and β-oxidation detailed how these pathways yield
acetyl-CoA. Amino acids are generally classified as ketogenic or glucogenic
based on what metabolites they are degraded into, although some amino acids
are considered both ketogenic and glucogenic [70]. Carbon atoms from the
degradation of ketogenic (leucine and lysine) or ketogenic/glucogenic (isoleucine,
phenylalanine, tryptophan, tyrosine) can be incorporated into acetoacetyl-CoA or
acetyl-CoA [70].
It is worth noting that acetate and acetyl-CoA have roles within the cell
other than contributing to fatty acid synthesis and the TCA cycle. Acetyl-CoA is
critical in the regulation and maintenance of histones, proteins, and other
metabolites [184,186]. Post-translational modifications (PTMs) like acetylation
are critical components of cell signaling as PTMs allow cells to react and adjust
to environmental stresses, alter enzymatic activities, influence protein stability
and DNA binding, and manage protein localization and protein-protein
interactions [187,188]. Given the reach of acetylation, it is unsurprising that
malfunctioning or deregulated acetylation can result in severe consequences with
cancer being a noted example [184,187,189,190].
In addition to acetylation, AceCS activity, acetyl-CoA metabolism, and
acetate metabolism have all been subjects of cancer research. AceCS1 is
necessary for PCa and breast cancer cell growth and survival under conditions of
hypoxia and low serum [191]. AceCS1 expression is upregulated in a variety of
40
cancers, including breast, glioblastoma, ovarian, and lung cancer and higher
AceCS1 expression often correlated with higher grade and poor survival
[191,192,193]. AceCS2 was also found to be upregulated and correlated with
cell proliferation in hepatocellular carcinoma tissue [194].
Interestingly, tumor cells commonly display altered acetyl-CoA metabolism
which provides cancer cells with alternative sources of acetyl-CoA. During
hypoxia, which commonly occurs in cancer as proliferation outpaces the creation
of new vasculature, cancer cells are known to employ reductive glutamine
metabolism [195,196,197]. In this process, isocitrate dehydrogenase 1 (IDH1)
reverses part of the TCA cycle by converting α-ketoglutarate back into isocitrate
which can then be isomerized to citrate [196]. Citrate can then be further
metabolized into oxaloacetate and acetyl-CoA (discussed earlier). Similarly,
tumor cells can produce acetyl-CoA through reductive glutamine metabolism to
support continued cell growth despite impaired or defective mitochondrial
function [198].
Finally, acetate metabolism has also been implicated in cancer biology.
Mashimo et al. discovered that, despite enhanced oxidation of glucose-derived
metabolites in the TCA cycle in brain tumors, glucose contributes less than half
of the carbons to the cellular acetyl-CoA pool [193]. Instead, the bulk of the
carbon in the acetyl-CoA pool is provided by oxidation of acetate [191,193].
Perhaps most importantly, this phenomenon is observed both in primary
glioblastoma and brain metastases originating from other tissues [191]. This
apparent dependence on acetate and acetate-derived metabolites is mirrored in
41
clinical imaging studies which reveal enhanced 11C-acetate uptake in several
cancer types including prostate, liver, lung and brain [199,200,201,202,203,204].
Ultimately, the fact that acetate appears to support tumor growth and survival will
be critical to consider alongside any efforts to inhibit the ACCs for
chemotherapeutic purposes as inhibition of ACC activity will inevitably result in a
build-up of acetyl-CoA.
1.4.5 Fates of malonyl-CoA
Malonyl-CoA is the immediate product of the ACCs. Previous discussion
of ACC regulation detailed malonyl-CoA’s role in CPT1 and ACC regulation.
Additionally, previous discussion of FASN detailed how malonyl-CoA is used in
the production of palmitate during fatty acid biosynthesis. Malonyl-CoA serves
an additional role in fatty acid biosynthesis as it donates two-carbon units to fatty
acyl-CoAs during fatty acid elongation [86,205,206].
The relatively recent discovery of malonylation revealed malonyl-CoA can
also be used as a post-translational modifier [207,208,209,210]. In contrast to
acetylation, which neutralizes the positive charge on lysines, malonylation adds
bulky, negatively charged groups to proteins [210]. While the lysine deacetylase
sirtuin 5 (SIRT5) has been shown to catalyze lysine demalonylation, it is
unknown what promotes malonylation [207,210]. While there is still much to
learn about the process, function, and consequences of protein malonylation, it
has been reported that lysine malonylation is enhanced in a murine model of type
2 diabetes and that proteins involved in various metabolic pathways are
particularly susceptible to malonylation [209,210]. Whether malonylation plays a
42
rolle in cancer biology remains to be determined. However, a link seems likely
considering the widely demonstrated role of sirtuins, metabolism, and the ACCs
in cancer [93,94,211,212,213].
1.4.6 Fates of de novo derived fatty acids
As discussed previously, fatty acids have four main roles within the cell.
Fatty acids are used in membrane biogenesis, serve as protein modifiers, can be
stored as a cellular energy source, and are derived into various hormones and
second messengers [56,70,71,72]. While the fates of fatty acids are well
understood, the functional difference between de novo and dietary fatty acids is
unclear. It is tempting to suggest that de novo fatty acids are usually
unnecessary as most normal adult tissues choose to obtain necessary fatty acids
from dietary supplies rather than turn on the energy expensive de novo pathway
[72,88,89,90]. However it is clear de novo derived fatty acids are vital in some
contexts as de novo synthesis is seen in a handful of normal tissues (liver,
adipose, lactating breast) and studies in mice have shown whole-body knockout
of ACC1 and FASN results in embryonic lethality [72,88,91,92]. Whether this is
because dietary supplies are limited or de novo fatty acids are necessary for a
particular process or function is unknown. Ultimately, changing the balance
between de novo synthesis and dietary uptake of fatty acids will be important to
fully understand as dependence on or upregulation of de novo derived fatty acids
increases the ratio of saturated and monounsaturated to polyunsaturated fatty
acids within the cell [214].
43
Studies concerning the role of de novo fatty acids in cancer development
have provided some insight into the potential differences between de novo and
dietary fatty acids. Pharmacological inhibition of FASN in several cancer cell
lines results in impaired DNA replication and prevents cells from entering S-
phase [106,215]. This suggests de novo fatty acid synthesis signals adequate
energy and/or the ability to generate sufficient membrane components to support
successful cell division [215]. Interestingly, studies using the LNCaP PCa cell
line reveal that de novo derived fatty acids are preferentially directed toward lipid
raft portions of cell membranes [93]. This is perhaps unsurprising as lipid rafts
are known for having a high composition of saturated and mono-unsaturated
fatty acids, the two major products of de novo synthesis [93,124]. Given that lipid
rafts are known to play a role in several key cellular processes like signal
transduction, intracellular trafficking, polarization and migration, it is reasonable
to hypothesize that cancer cells have an enhanced need for lipid rafts to support
aberrant proliferation and survival under metabolic and environmental stress and
that inhibiting de novo fatty acid synthesis would disrupt these processes
[93,124]. While these examples support the hypothesis that inhibiting de novo
fatty acid synthesis would, in turn, inhibit cancer growth, some studies suggest
the opposite. Because saturated lipids in the cell membrane are less susceptible
to peroxidation, more saturated membranes provide a cancer cell enhanced
protection against reactive oxygen species [216]. Furthermore, inhibition of fatty
acid desaturation was found to be detrimental to cancer cell survival, even
enhancing sensitivity to certain chemotherapeutic agents [217,218]. This
44
suggests a cell with more saturated fatty acids, perhaps provided through de
novo synthesis, would display increased survival under chemotherapeutic stress
and perhaps even chemoresistance. Altogether, it is clear de novo fatty acids
can participate in a variety of processes and the balance between de novo fatty
acids and dietary-derived fatty acids will be important to understand when
targeting fatty acid metabolism for cancer therapy.
1.4.7 ACC in cancer
Since the majority of cancers exhibit enhanced lipogenesis, it is not
surprising that upregulation of ACC1 has been noted in several cancers including
breast, liver, lung, and prostate [88,96,98,171]. Studies involving in vitro
silencing of ACC1 yields inconsistent results. Some groups report siRNA (small
interfering RNA) mediated knockdown of ACC1 does not induce cell death in
prostate or breast cancer cell lines [219]. In contrast, others have shown siRNA
mediated knockdown of ACC1 leads to growth arrest and/or cell death in colon,
ovarian, prostate, and breast cancer cell lines [96,97,220]. In vitro inhibition of
ACC by non-isoform specific pharmacological inhibitors (to be discussed in detail
in the next section) also yields inconsistent results as some groups report 5-
(tetradecyloxy)-2-furoic acid (TOFA) treatment does not induce cell death in
breast cancer or ovarian cancer cell lines while others show TOFA treatment
induces death in lung, colon, and prostate cancer cell lines [98,99,221,222].
TOFA mediated ACC inhibition also results in a decrease in invasive capacity in
breast, glioblastoma and prostate cancer cell lines [223]. Although
consequences of in vitro siRNA mediated or pharmacological inhibition of ACC1
45
seem to differ among cancer cell lines, many groups do report a marked
reduction in palmitate levels, phospholipid content, and cell proliferation
[95,96,220]. Attempts to rescue the effects of siRNA mediated or
pharmacological inhibition of ACC1 also produce conflicting results. Some
groups have shown that the addition of exogenous fatty acid is able to rescue the
effects of ACC1 knockdown or ACC inhibition [95,96,97,223]. Others have
reported they did not see rescue [220].
Most studies looking at the role of ACC2 in cancer are done alongside
studying the role of ACC1 in cancer. This is unsurprising as the focus on de
novo lipogenesis in cancer naturally directs groups to study ACC1 and
knockdown experiments tend to point to ACC1 as the major driver of cell survival
under various stresses [224]. In addition, given the structural similarity of ACC1
and ACC2, isoform-specific pharmacological inhibitors are rare [82,83].
However, there are a handful of studies which mention ACC2 specifically. For
example, prolyl hydroxylase 3 (PHD3) is able to activate ACC2 through
hydroxylation thus inactivating fatty acid oxidation through malonyl-CoA mediated
inhibition of CPT1 [225]. Interestingly, PHD3 expression is decreased in a
subset of cancers with acute myeloid leukemia (AML) as a noted example [225].
This suggests that AML is dependent on fatty acid oxidation and would show
particular sensitivity to fatty acid oxidation inhibitors. Indeed, re-expressing
PHD3 and, in turn, activating ACC2 and inactivating β-oxidation limits AML
growth both in vitro and in vivo [225].
46
Perhaps the most interesting aspect of studying the ACCs in cancer is the
interplay between the two isoforms and the potential results of isoform specific
versus non-isoform specific inhibition. The mammalian ACCs are designed and
regulated in a manner that ensures the cell responds appropriately to energy
signals. In normal cells with low energy, ACC1 and ACC2 are inactivated which
ensures cells do not turn on the energy-expensive de novo pathway and releases
inhibition on fatty acid oxidation to encourage energy production. In normal cells
with ample energy, ACC1 and ACC2 are activated which directs the cell to
conduct de novo synthesis but inhibit β oxidation. This prevents simultaneous
synthesis and breakdown, thus preventing a futile cycle [75]. Interestingly,
neither scenario is optimal to support cancer growth. Ideally, a cancer cell would
direct metabolic processes to both enhance fatty acid synthesis and boost
energy production. In the context of the ACCs, this would require activated
ACC1 and inactivated ACC2 – a setting which would necessitate isoform-specific
regulation. Recently, it was suggested that cancer cells can indeed achieve
isoform-specific regulation, something normal cells typically cannot perform.
Corbet et al. found under acidic conditions, sirtuin mediated histone deacetylation
leads to transcriptional repression of ACC2 in several cancer cell lines [226].
Because ACC1 and other lipogenic enzymes are commonly upregulated in
cancers, this supports simultaneous ACC1 activation and ACC2 deactivation,
allowing concurrent fatty acid synthesis and oxidation and supplying both the
necessary macromolecules and energy to continue enhanced proliferation.
47
The hypothesis that cancers prefer activation of both de novo synthesis
and β oxidation naturally leads to the idea of exploiting this preference to develop
therapies. As discussed previously, recent interest in targeting fatty acid
metabolism has been focused on limiting the pool of available fatty acids [71,73].
This would likely involve inhibition of both ACC1, to suppress synthesis, and
inhibition of ACC2, to activate oxidation. To this end, several non-isoform
specific small molecule inhibitors have been developed (discussed in the next
section). To date, none of these compounds have proven useful in the clinic.
This is perhaps because non-isoform specific ACC inhibitors only partially
reverse the cancer’s preference. ACC1 inactivation should negatively impact the
cancer cell as inhibiting de novo synthesis is the opposite of the cell’s preference.
However, ACC2 inactivation promotes fatty acid oxidation, thus supporting the
cancer cell’s preference.
In theory, the ideal approach would fully reverse the cancer’s preference
and force the cell into inhibited synthesis and inhibited oxidation. This would
likely require inhibited ACC1 and activated ACC2. Inhibition of fatty acid
oxidation can be achieved without developing an ACC2 activator. For example,
etomoxir is known to irreversibly inhibit CPT1 and decrease β oxidation. In fact,
etomoxir treatment has been shown to cause death in both PCa and
glioblastoma cell lines [126,227]. Preventing de novo synthesis through ACC
would require an ACC1 specific inhibitor. However, the separate-but-similar
structure and function of the ACC isoforms make specific targeting difficult.
48
1.4.8 ACC inhibitors
Several ACC inhibitors have been published and are commonly used for
research and agricultural uses. In fact, several pharmaceutical companies,
including Taisho, Sanofi-Aventis, AstraZeneca, Pfizer, Takeda, Numbus, and
Amgen, have developed and published a variety of ACC inhibitors
[73,228,229,230,231,232,233,234,235,236,237,238]. Several of these ACC
inhibitors are detailed in this section. ACC inhibitors used as herbicides
(haloxyfop, diclofop, tepraloxydim, pinoxaden) are not included [239].
TOFA (5-(tetradecyloxy)-2-furoic acid) is a non-isoform specific allosteric
inhibitor of ACC. TOFA competes with acetyl-CoA at the CT domain of ACC and
is considered a lipophilic fatty acid mimetic as it contains a chain of 14 carbons
similar to myristic acid [136]. TOFA is commonly used in in vitro experiments
exploring the consequences of ACC inhibition in cancer. Briefly, TOFA treatment
has been shown to induce cell death and decrease invasive capacity in a variety
of cancer cell lines [98,99,221,222,223]. The in vivo use of TOFA is limited but
use in diabetes and ovarian cancer research has shown TOFA can impair satiety
signals and reduce ovarian tumor growth [240,241,242]. TOFA has yet to be
used in clinical trials.
Soraphen A is another non-isoform specific allosteric ACC inhibitor. In
contrast to TOFA, soraphen A interacts with ACC near the ATP binding site of
the BC active site and interferes with dimerization of the BC domain [136]. Like
TOFA, soraphen A has been used in vitro to inhibit ACC in various cancer cell
lines, but in vivo use is limited [95,243,244,245]. Briefly, in vitro use of soraphen
49
A has been shown to induce PCa cell death through decreased lipogenesis and
reduce mammosphere formation of MCF7 cells [95,244]. In vivo use of soraphen
A improves insulin sensitivity in mice fed a high fat diet [245]. Soraphen A has
also not been used in clinical trials to date.
CP-640186, developed by Pfizer, is a third non-isoform specific ACC
inhibitor [239]. CP-640186 inhibits the CT domain of the enzyme by interacting
at the active center near the carboxy biotin moiety binding site [136]. Published
uses of CP-640186 in mammalian systems are limited but CP-640186 is known
to decrease cell proliferation in H460 cells and maintain meiotic arrest in mouse
oocytes [246,247,248]. This compound has not been used in clinical trials
Recently, Nimbus has developed a series of isoform non-specific ACC
inhibitors, the most successful of which have been ND-630 and ND-646
[73,239,249]. The ND compounds are unique in that they are allosteric biotin
carboxylase inhibitors that simultaneously promote constitutive
dephosphorylation of ACC and prevent ACC dimerization [73,249]. Of the two,
ND-646 has been shown to suppress fatty acid synthesis and tumor growth in
both in vitro and in vivo models of non-small-cell lung cancer (NSCLC) [73]. The
other compound, ND-630 (also known as NDI-010976), was found to be highly
liver-specific, has progressed to phase I clinical trial, and is in the pipeline to be
tested in some cases of liver disease [73,249,250].
ND-630 is not the first ACC inhibitor to be tested in clinical trials. Pfizer’s
compound PF-05175157 first entered phase I clinical trials in late 2010 [239,251].
From 2010 to 2014, PF-05175157 was used in nine phase I clinical trials and four
50
phase II trials were planned to study the use of PF-05175157 in patients with
either type 2 diabetes or acne vulgaris [239,251]. However, the three phase II
trials focused on PF-05175157 use in patients with type 2 diabetes were
terminated and the phase II trial focused on acne vulgaris was withdrawn [251].
Additionally, PF-05175157 was not included as part of Pfizer’s pipeline as of May
2017 [252]. Eventual commercial availability of PF-05175157 for research
purposes revealed the compound is part of a series of non-isoform specific
spirochromanone ACC inhibitors which are believed to interact at the CT domain
of ACC, similar to CP-640186 [234].
Interestingly, there are two compounds which have been published to
inhibit ACC activity without targeting ACC specifically. Treatment with
chloroacetylated biotin (CABI) was found to inhibit ACC activity without
decreasing protein levels in 3T3-L1 preadipocytes [253]. The authors propose
CABI interacts with endogenous CoA, forming a covalent link between biotin and
CoA and prevents ACC activity at the CT domain [136,253]. Interestingly, Levert
et al. show CABI inhibits cytosolic ACC (ACC1) but comment that CABI “inhibits
the cytosolic and/or mitochondrial isoforms of ACC”, therefore it is unclear
whether CABI could be an ACC1 specific inhibitor [253].
In contrast, chromeceptin has been shown to be an ACC1 specific
inhibitor [254]. Chromeceptin was initially found to inhibit adipogenesis and
reduce growth and viability of human hepatocellular carcinoma cells [254,255].
Early studies using chromeceptin revealed that chromeceptin targets
51
multifunctional protein 2 (MFP-2), a protein typically localized to peroxisomes
[254,255]. However, it was unclear how this resulted in impaired adipogenesis.
Further studies showed that the chromeceptin-MFP-2 complex binds ACC1 and
facilitates the translocation of ACC1 from the cytosol to peroxisomes, thus
inhibiting ACC1 activity [254]. Although the details of the chromeceptin-MFP-2-
ACC1 binding are not completely understood, this interaction appears to be
ACC1 specific [254]. The authors hypothesize that the mitochondrial anchoring
of ACC2 might render the enzyme unable to translocate [254].
1.5 Metabolism and chemoresistance
Chemotherapeutic resistance is a major and common problem faced
during treatment [256,257]. In fact, development of therapy resistance, and
subsequent treatment failure and disease progression, is one of the main causes
of cancer-associated mortality [258]. Through years of research, two strategies
to address chemoresistance have emerged. The first is to utilize multiple
cytotoxic chemotherapeutic agents, each with different mechanisms of action
[256]. However, because tumors are able to develop resistance to multiple
chemotherapeutic agents simultaneously, this strategy is often unsuccessful
[256]. The second strategy involves the use of targeted therapies such as small
molecule inhibitors or antibodies [256]. Yet tumors often develop resistance to
targeted agents as well [256].
The mechanisms tumors utilize to gain drug resistance are generally
classified as either pharmacological or cellular [256]. Pharmacological
52
mechanisms of drug resistance include poor delivery (either through inadequate
infusion or poor vasculature), improper metabolism, altered excretion, and
microenvironment interaction [256]. Cellular mechanisms of drug resistance are
difficult to list as they include various genes and proteins and their interactions
with each other and with the overall cellular environment [256]. Recently, the
influence of cellular metabolism on chemotherapeutic resistance has gained
attention [72,259,260]. This section will briefly review what is currently known
about the role of metabolism in drug resistance with an emphasis on PCa.
1.5.1 Current research
Multiple factors involved in glycolysis have been implicated in the
development of chemotherapeutic resistance, with some conflicting results. For
example, temozolomide resistance in glioblastoma is associated with an increase
in GLUT3 and targeting GLUT3 results in delayed resistance [261]. Several
glycolytic enzymes are associated with chemoresistance as well. For example,
hexokinase (discussed earlier), which is upregulated in a variety of cancers, is
known to inhibit apoptosis and depletion of the enzyme has been shown to re-
sensitize radio- or chemoresistant cancer cells to treatment [262]. An increase in
the M2 isoform of pyruvate kinase (PKM2) has been linked to 5-fluorouracil
resistance in colorectal cancer [263]. In contrast, a decrease in PKM2 has been
associated with resistance to platinum-containing drugs [264,265]. Decreased
pyruvate dehydrogenase kinase, which inactivates pyruvate dehydrogenase
(discussed earlier) and inhibits oxidative phosphorylation, is associated with
sorafenib resistance in hepatocellular carcinoma cells [266]. Finally, increased
53
lactate dehydrogenase (discussed earlier) in breast cancer has been shown to
lead to trastuzumab resistance [267].
In addition to glycolysis, lipid metabolism can also affect drug resistance
[72]. Inhibition of FASN activity has been demonstrated to enhance the
chemosensitivity of breast cancer cells [268,269,270]. Additionally, the disease
progression seen in pre-clinical mouse models post angiogenic treatment is
reduced with FASN inhibition [271,272]. As discussed earlier, saturated lipids in
the cell membrane are less susceptible to peroxidation and inhibition of fatty acid
desaturation was found to be detrimental to cancer cell survival, even enhancing
sensitivity to certain chemotherapeutic agents [72,216,217,218].
1.5.2 Metabolism and chemoresistance in prostate cancer
As discussed previously and illustrated in Figure 1, there is a need to
develop methods to address chemoresistance in PCa. Most research into drug
resistance in PCa has been focused on transporter proteins, yet a reliable
approach to treating chemoresistance has yet to be determined
[34,273,274,275]. To date, very few studies have been conducted examining the
role of metabolism in drug resistant PCa. One of these studies indicates INPP4B
(inositol polyphosphate 4-phosphatase type II), a regulator of hexokinase is
downregulated in docetaxel resistant PCa cell lines [276]. Ultimately, much
research is needed to understand how best to target cellular metabolism to treat
chemoresistant PCa.
54
1.6 Overview
The fact that metabolism is a well-recognized hallmark of cancer but
cellular metabolism is not widely targeted in the clinic suggests the role of
metabolism in cancer biology is not completely understood [50]. The purpose of
this work is to further examine and detail the role of cellular metabolism,
specifically fatty acid metabolism and the acetyl-CoA carboxylases, in cancer
initiation, progression, and development of chemoresistance.
55
1.7 References
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Society.
2. (2017) Cancer Facts & Figures 2017. Atlanta, Georgia: American Cancer
Society.
3. Siegel RL, Miller KD, Jemal A (2017) Cancer Statistics, 2017. CA Cancer J
Clin 67: 7-30.
4. Cuzick J, Thorat MA, Andriole G, Brawley OW, Brown PH, et al. (2014)
Prevention and early detection of prostate cancer. Lancet Oncol 15: e484-
492.
5. Lange EM, Ribado JV, Zuhlke KA, Johnson AM, Keele GR, et al. (2016)
Assessing the Cumulative Contribution of New and Established Common
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CHAPTER II
ACETYL COA CARBOXYLASE 1 INHIBITION DISRUPTS PROSTATE
CANCER METABOLISM AND REDUCES TUMOR BURDEN IN A MURINE
PROSTATE CANCER MODEL
Amanda L. Davis1, Kristen E.N. Scott2, Frances B. Wheeler1, Jiansheng Wu1,
Lutfi Abu-Elheiga3, Salih Wakil3, Yong Q. Chen4, Steven J. Kridel1, 5, *
1Department of Cancer Biology, and 5Wake Forest Comprehensive Cancer
Center, Wake Forest University Health Sciences, Winston-Salem, North Carolina
27157, United States 2Department of Tumor Biology, H. Lee Moffitt Cancer
Center and Research Institute, Tampa, Florida 33612, United States, 3Verna and
Marrs McLean Department of Biochemistry and Molecular Biology, Baylor
College of Medicine, Houston, Texas 77030, 4State Key laboratory of Food
Science and Technology, School of Food Science and Technology, Jiangnan
University, Wuxi, People’s Republic of China
The following chapter is formatted for submission to PLoS One. Stylistic
variations are due to journal requirements.
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2.1 Abstract
The majority of cancers undergo metabolic alterations during initiation and
progression of disease. Increased de novo lipogenesis is recognized as a
metabolic mark of many cancers, with prostate cancer (PCa) being a noted
example. The enzymes responsible for de novo fatty acid synthesis are often
overexpressed in cancer, indicating that these enzymes are potential and
promising targets for novel therapies. Acetyl CoA Carboxylase 1 (ACC1) is a
cytosolic enzyme which catalyzes the rate limiting step of de novo fatty acid
synthesis. Inhibition of ACC1 in tumor cell lines blocks fatty acid synthesis,
resulting in selective toxicity. However, limited work has been done to explore
the in vivo consequences of inhibiting ACC1 for chemotherapeutic purposes. To
address this, a murine PCa model with prostate-specific inactivation of acetyl-
CoA carboxylase 1 was developed. Inactivation of ACC1 does not affect normal
prostate development as survival, prostate weight, histology, and breeding were
similar between ACCL/L Cre+ and ACCL/L Cre- mice. We have found that
homozygous inactivation of ACC1 (ACCL/L; PtenL/L; Cre+) is able to reduce tumor
burden in mice at 12 weeks of age when compared to ACC+/+; PtenL/L; Cre+
littermates. Intriguingly, the reduction in tumor burden at 12 weeks is overcome
by 24 weeks of age. An analysis of the metabolic effects of ACC1 deletion
illustrates that ACC activity is required for full mitochondrial function in PCa.
Interestingly, the data demonstrate that different prostate cancer cell lines
preferentially oxidize different pools of fatty acid and ACC inhibition disrupts fatty
acid oxidation. Together, this suggests a potential for metabolic rewiring to
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bypass the dogmatic requirement for ACC1 in tumor progression. Furthermore,
the data indicate that blockade of ACC1 alone may not be sufficient to prevent
disease progression. Ultimately, these data further the notion that tumors rewire
their metabolic circuits to meet the demands associated with cancer biology.
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2.2 Introduction
Enhanced lipogenic activity is found in the majority of cancers and
enzymes responsible for de novo fatty acid synthesis are upregulated in a variety
of cancer types [1,2]. In contrast, very few normal adult human tissues undergo
de novo fatty acid synthesis as the majority of normal cells prefer to utilize
dietary-supplied lipids to meet their fatty acid requirement rather than turn on the
energy-expensive de novo pathway [3,4]. Because of this, targeting de novo
fatty acid synthesis has emerged as an attractive idea for the development of
novel chemotherapeutic agents.
Most interest in targeting the de novo fatty acid synthesis pathway in
cancer has focused on fatty acid synthase (FASN). FASN is the enzyme that
synthesizes palmitate from one acetyl-CoA molecule and seven malonyl-CoA
molecules. Overexpression of FASN is seen early in prostate cancer (PCa)
development and the highest FASN expression is seen in metastatic lesions of
advanced androgen independent PCa [5,6]. This indicates de novo fatty acid
synthesis is necessary at all stages of cancer growth and further signifies de
novo fatty acid synthesis as a promising chemotherapeutic target. In fact.
multiple studies on several cancer types show inhibition of FASN results in
cancer cell death [7,8,9,10,11,12,13,14].
Although FASN has been the primary focus for therapeutic development, it
is not the rate-limiting enzyme of the de novo fatty acid synthesis pathway. The
pace-setting enzyme, acetyl-CoA carboxylase 1 (ACC1), irreversibly
carboxylates acetyl-CoA to form malonyl-CoA in an ATP and biotin dependent
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manner [15,16]. Inhibition of ACC1 by siRNA mediated knockdown or treatment
with non-isoform specific pharmacological inhibitors soraphen A or 5-
(Tetradecyloxy)-2-furoic acid (TOFA) results in decreased proliferation, induction
of apoptosis, increased reactive oxygen species (ROS) levels, and impaired
mitochondrial function in lung, colon, and prostate cancer cell lines
[17,18,19,20,21].
Mammalian cells express two isoforms of ACC [22]. As mentioned earlier,
ACC1 functions as the rate-limiting step of de novo fatty acid synthesis. In
contrast, active ACC2 produces malonyl-CoA within the mitochondria which
inhibits β-oxidation through interaction with carnitine palmitoyltransferase I
(CPT1) [23]. The presence and function of the two ACC isoforms have
interesting consequences in cancer biology. Specifically, recent interest in
targeting fatty acid metabolism for therapeutic purposes in cancer has focused
on limiting the pool of available fatty acids [23,24]. Depletion of available fatty
acids would involve simultaneous inhibition of de novo fatty acid synthesis and
activation of β-oxidation which would necessitate dual inhibition of both ACC1
and ACC2. This suggests that non-isoform specific ACC inhibitors such as
soraphen A and TOFA would be the most effective at inhibiting cancer
development and/or progression. Yet knockdown experiments tend to point to
ACC1 as the major driver of cell survival under various stresses which would
suggest ACC1 specific inhibition would be the most promising avenue for
developing chemotherapeutic strategies [25].
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Previous research has shown that whole body inactivation of ACC1 in
mice results in embryonic lethality [26]. Additionally, genomic inactivation of
ACC1 in mice by Cre-Lox mediated deletion of exon 22 (part of the biotin
carboxyl carrier protein domain) has been studied in adipose, bone, and liver
tissue [27,28]. However, to our knowledge, no studies have explored the role of
ACC1 in an in vivo cancer model. To this end, we have generated a murine
prostate cancer (PCa) model with prostate-specific inactivation of acetyl-CoA
carboxylase 1 by combining by combining the previously described model of
ACC1 exon 22 deletion with the well-studied murine Pten knockout model of
prostate cancer [26,29]. Here, we describe the consequences of ACC1 deletion
in an in vivo model of prostate cancer as well as explore the bioenergetic effects
of ACC inhibition. These results provide valuable insight and direction for
developing novel strategies to target ACC and fatty acid metabolism in cancer.
2.3 Materials and Methods
Materials: 5 -(Tetradecyloxy)-2-furoic acid (TOFA) was purchased from Cayman
Chemical (Ann Arbor, MI). CP-640186 was purchased from BioVision (Milpitas,
CA). Antibodies against acetyl-CoA carboxylase 1, and cleaved PARP (poly
(ADP-ribose) polymerase) were purchased from Cell Signaling Technologies
(Beverly, MA). Antibody against β-actin was purchased from Sigma-Aldrich (St.
Louis, MO). Secondary antibodies were purchased from Bio-Rad Laboratories
(Hercules, CA). All standard chemicals were purchased from Sigma-Aldrich (St.
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Louis, MO). Cell culture reagents were purchased from Invitrogen (Carlsbad,
CA).
Animal Studies: Female C57BL/6J mice with floxed ACC1 (ACC1L/L) were a
generous gift from Dr. Salih Wakil (Baylor College of Medicine) [26,27]. Male
C57BL/6J with floxed Pten (PtenL/L) and probasin (Pb)-controlled cre-
recombinase (Cre+) were a generous gift from Dr. Yong Chen (Jiangnan
University) and were originally described by Wang et al [29]. All mice were
maintained in an isolated environment in barrier cages with a 12 hour/12hour
light/dark cycle. All animals were given ad libitum access to water and standard
chow diet. The Wake Forest University Institutional Animal Care and Use
Committee approved all mouse experiments.
Genotyping: Tail-snips were collected from each mouse at approximately 2
weeks of age and DNA was isolated by adding 200µL 50mM NaOH and
incubating at 95OC for 45 minutes. After incubation, 30µL 1M Tris-HCl pH 8.0
was added to each sample and centrifuged at 14,000rpm for 5 minutes at room
temperature. Samples were stored at -20OC until use. All mice were genotyped
by PCR. Each PCR reaction was composed of the following components: 10mM
Tris-HCl pH 8.4, 50mM KCl, 0.2mM dNTP mixture (Promega, Madison, WI),
1.5mM MgCl2, 0.5µM each primer (described below), 2.5 U Taq DNA
polymerase, 0.5µL template DNA, and nuclease-free water to 20µL per sample.
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Primers for ACC1, Pten, and Cre were purchased from Integrated DNA
Technologies (Coralville, IA) and are as follows:
ACC1 Forward 5’-AAGTCCTCAAGGAGCTGGACA-3’
ACC1 Reverse 5’-GCTCACTCTGAAGTTGAGGAT-3’
Pten Forward 5’-TCCCAGAGTTCATACCAGGA-3’
Pten Reverse 5’-GCAATGGCCAGTACTAGTGAA-3’
Pten Reverse 2 5’-AATCTGTGCATGAAGGGAAC-3’
Pb-Cre Forward 5’-CATCTGCCACCAGCCAGCTATCAACTC-3’
Pb-Cre Reverse 5’-GACAATATTTACATTGGTCCAGCCACC-3’
PCR reactions were performed in a Biometra TPersonal Thermocycler
(Göttingen, Germany). PCR program was designed as described:
Step Action Temperature Duration Notes
1. Denature 94OC 4 minutes
2. Denature 94OC 45 seconds Steps 2-4
repeated for
34 cycles
3. Anneal 60OC 45 seconds
4. Extend 72OC 30 seconds
5. Extend 72OC 10 minutes
6. Store 4OC ∞
All samples were run on a 2% TAE-agarose gel with 2µL ethidium bromide
added at 90 volts. Gels were imaged on ultraviolet lightbox.
Cell Lines and Culture Conditions: The established cell lines PC3, DU145, and
LNCaP were obtained from the American Type Tissue Collection (ATCC). The
human cell lines were determined to be exempt by the Institutional Review Board
of Wake Forest University. All cells were maintained in RPMI-1640
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supplemented with 10% fetal bovine serum (FBS), 1% penicillin/streptomycin
(PS), and cultured in 5% CO2 at 37oC. Cells routinely tested for mycoplasma.
siRNA Knockdown of ACC1: LNCaP or DU145 cells were plated into 10cm
dishes in antibiotic-free media. After 24 hours, cells were transfected with 100nM
of either of two independent siRNAs specific for ACC1 (siRNA #1:
GAUACAUGAUCACGGAUAU or siRNA #2: GAAGAGAGGUCUACACAUC) or
scrambled siRNA (Dharmacon, Lafayette, CO). Transfection was done using
Lipofectamine 2000 (Invitrogen, Carlsbad, CA). After 72 hours, cells were used
for the indicated assay(s) and protein was harvested for analysis by immunoblot
(see Immunoblot Analysis).
Immunoblot Analysis: Protein was harvested by trypsinizing cells and pelleting
by centrifugation. Cells were then washed with ice cold phosphate buffered
saline (PBS) then lysed in ice cold buffer (20 mM Tris, pH 8.3, 5 mM EDTA, 1%
Triton X-100) supplemented with a protease and phosphatase inhibitors (5 µg/mL
aprotinin, 5 µg/mL leupeptin, 5 µg/mL pepstatin A, 1 mM sodium orthovanadate,
1 mM sodium fluoride, 200 nM okadeic acid, and 200 µM phenylmethylsulfonyl
fluoride). Proteins were separated by SDS-PAGE through 10% or 7.5% gels and
transferred onto nitrocellulose membranes. After incubation with indicated
antibodies, immunoreactive proteins were detected with enhanced
chemiluminescence (Perkin Elmer, Waltham, MA). β-actin was used as loading
control. Immunoreactive bands were quantified by densitometry using ImageJ.
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Fatty Acid Methyl Ester (FAME) Analysis: PC-3 cells were plated at 5x105 cells
per well of a 6-well plate. After 24 hours cells were treated with TOFA (10µg/mL)
or vehicle (DMSO) for 24 hours. Cells were trypsinized, collected and counted.
Cell pellets were washed with ice-cold 0.9% saline and frozen in liquid nitrogen.
Lipids were extracted using the Bligh-Dyer method [30]. Samples were
combined with internal standards (pentadecanoic acid (15:0) and heneicosanoic
acid (12:0)), evaporated under argon gas then resuspended in ethanol. Samples
were incubated for 1 hour at 60OC with 50% KOH. Hexane was used to extract
non-saponifiable lipids. The remaining sample was acidified with 6 M HCl and
lipids were recovered with hexane. After evaporating the hexane, 0.5 M
methanolic NaOH was added to the samples which were then incubated at
100OC for 5 minutes. After cooling, hexane and NaCl were added to each
sample and vortexed. The hexane phase was recovered, evaporated, and the
residue was resuspended in hexane. Samples were then transferred to glass
autosampler tubes for analysis. Fatty acids were separated and quantified using
a DB-Wax column in a TSQ Quantum XLS triple quadrupole mass spectrometer
connected to a TRACE Ultra gas chromatograph (GC-MS). Results normalized
to cell count. For FAME analysis of prostate tissue samples, tissue was snap
frozen after collection and stored at -80OC until analysis. Tissue was minced and
FAME was performed as described. Results normalized to tissue weight.
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MTS Assays: LNCaP were plated at 1000 cells/well in a 96-well plate then
treated with 10µg/mL TOFA or 100µM CP-640186 for the indicated time points. A
20µL portion of CellTiter 96 AQueous One reagent was added to each well of a 96-
well plate holding 100uL of RPMI Complete Media and the appropriate cells.
Plates were then incubated at 37oC and 5% CO2 for 3 hours. Absorbance was
measured at 490nm. CellTiter 96 AQueous One Solution was stored at -200C when
not in use. Results were normalized to vehicle treated cells.
Histology: At the indicated ages (11, 12, or 24 weeks) male mice with specified
genotypes were humanely euthanized by CO2 asphyxiation and cervical
dislocation and prostates were collected. Tissue from each prostate lobe was
fixed in 10% buffered formalin (Leica Biosystems, Buffalo Grove, IL), paraffin
embedded and sectioned at 5µm. Hematoxylin and Eosin (H&E) staining was
performed and representative images of multiple stained sections are described
in the results section.
Agilent Seahorse XF Assays
Glycolysis Stress Assay: All extracellular acidification rates (ECAR) were
measured using the Agilent Seahorse XF-24 Extracellular Flux Analyzer (Agilent,
Santa Clara, CA) per manufacturer’s protocols [31]. Cells were plated at 4 x 104
cells/well in RPMI-1640 with 10% FBS/1% PS. Cells were treated with identical
media containing indicated treatment or vehicle for 24 hours. Before ECAR
103
measurements were taken, cells were washed with assay medium supplemented
with fresh glutamine per manufacturer’s protocols and incubated at 37oC without
CO2 for 1 hour. After equilibration, three 3 minute measurements were recorded
to measure non-glycolytic ECAR. Glucose (10mM), oligomycin (1µM), and 2-
deoxyglucose (2-DG) (100mM) were injected into each well sequentially with
three 3 minute measurements after each injection to measure the amount of
ECAR associated with basal glycolysis and glycolytic capacity. The following
metrics were used in accordance with manufacturer’s protocols to analyze
results:
basal glycolysis = maximum rate after glucose injection
glycolytic capacity = maximum rate after oligomycin injection
Mitochondrial Stress Assay: All cellular oxygen consumption rates (OCR) were
measured using the Agilent Seahorse XF-24 Extracellular Flux Analyzer (Agilent,
Santa Clara, CA) per manufacturer’s protocols [32]. Cells were plated at 4x104
cells/well in RPMI-1640 with 10% FBS/1% PS. When appropriate, cells were
treated with identical media containing indicated treatment or vehicle for 24
hours. Before OCR measurements were taken, cells were washed with assay
medium supplemented with fresh sodium pyruvate and glucose per
manufacturer’s protocols and incubated at 37oC without CO2 for 1 hour. After
equilibration, three 3 minute measurements were recorded to measure the basal
level of oxygen consumption. Oligomycin (1µM), carbonyl cyanide 4-
(trifluoromethoxy)phenylhydrazone (FCCP) (0.5µM), and Rotenone/Antimycin A
(Rtn/AA) (1µM each) were injected into each well sequentially with three 3 minute
104
measurements after each injection to measure the amount of oxygen
consumption for adenosine triphosphate (ATP) production (coupling efficiency),
the level of proton leak (non-ATP-linked oxygen consumption), maximal
respiration capacity, and the level of nonmitochondrial respiration. The following
metrics were used in accordance with manufacturer’s protocols to analyze
results:
basal respiration = 3rd basal measurement
ATP coupler response = basal respiration - minimum rate after oligomycin
injection
ETC accelerator response = maximum rate after FCCP injection
Spare respiratory capacity = ETC accelerator response / basal respiration
Fatty Acid Oxidation (FAO) Assay: All cellular oxygen consumption rates (OCR)
were measured using the Agilent Seahorse XF-24 Extracellular Flux Analyzer
(Agilent, Santa Clara, CA) per manufacturer’s protocols [32,33]. Cells were
plated at 4x104 cells/well in RPMI-1640 with 10% FBS/1% PS. Media was
removed and replaced with substrate-limited medium (0.5mM glucose, 1mM
glutaMAX, 0.5mM carnitine and 1% FBS in glucose-, glutamine- and HEPES-free
DMEM) 24 hours prior to the assay. When indicated, cells were treated with
vehicle (DMSO) or TOFA (10µg/mL) 24 hours prior to the assay as well.
Approximately one hour prior to the assay, cells were washed and media
replaced with FAO assay medium (5mM HEPES, 0.5mM carnitine, 2.5mM
glucose, 111mM NaCl, 4.7mM KCl, 2mM MgSO4, and 1.2mM Na2HPO4 in sterile
diH2O). Cells then incubated at 37oC without CO2 for 1 hour. For assays where
105
cells were treated with etomoxir, cells were treated with either 40µM etomoxir or
vehicle (sterile diH2O) after 45 minutes at 37oC without CO2 and incubated for an
additional 15 minutes. Immediately prior to the assay, cells were treated with
75µM control-BSA or palmitate-BSA. After equilibration, three 3 minute
measurements were recorded to measure the basal level of oxygen consumption
due to fatty acid oxidation under the indicated treatments. Oligomycin (3.16µM),
carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP) (0.8µM), and
Rotenone/Antimycin A (Rtn/AA) (2µM/4µM, respectively) were injected into each
well sequentially with three 3 minute measurements after each injection to
measure the amount of oxygen consumption for adenosine triphosphate (ATP)
production (coupling efficiency), the level of proton leak (non-ATP-linked oxygen
consumption), maximal respiration capacity, and the level of nonmitochondrial
respiration. The following metrics were used in accordance with manufacturer’s
protocols to analyze results:
basal respiration = 3rd basal measurement
ATP coupler response = basal respiration - minimum rate after oligomycin
injection
ETC accelerator response = maximum rate after FCCP injection
Spare respiratory capacity = ETC accelerator response / basal respiration
Statistical Analysis: Significance between two groups was determined by a two-
tailed Student’s t-test. Significance between three or more groups was first
analyzed by one-way ANOVA then post-hoc Tukey’s t-tests. Kaplan-Meier
106
survival curves were analyzed by the Mantel-Cox long-rank test. p≤0.05
considered significant. All statistical analysis were calculated with Prism 5 or
Prism 6 software. Error bars represent standard deviation.
2.4 Results
Elevated ACACA expression is associated with decreased survival in
several cancers.
Because enhanced lipogenic activity is found in the majority of cancers
and enzymes involved in de novo fatty acid synthesis are commonly upregulated,
analysis of the correlation between ACC1 and patient survival was performed
[1,2]. The correlation between ACACA gene expression and patient outcome in
several cancer types was examined utilizing data from The Cancer Genome
Atlas (TCGA) and the freely-available OncoLnc program [34]. High ACACA
expression was found to correlate with poorer patient survival in several cancer
types including breast, liver, skin, kidney, sarcoma, cervical, ovarian, bladder and
lung (Figure 1). The observation that ACACA expression is commonly
correlated with poor patient survival supports further study of ACC1 inhibition for
chemotherapeutic purposes.
ACACA expression is correlated with advanced PCa and ACC1 is required
for survival in PCa cell lines.
ACACA expression in prostate cancer patients was also examined by
utilizing the Taylor dataset from memorial Solan Kettering Cancer Center
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(GSE21034) which reports whole transcript and exon-level expression data for
human primary and metastatic prostate cancer samples with normal adjacent
prostate tissue used as a normal control [35]. This dataset shows high ACACA
expression is significantly associated with poor recurrence free survival (Figure
2A.). Furthermore, ACACA expression correlates with disease progression, as
normal tissue adjacent to a primary prostate tumor displays the lowest ACACA
expression, primary tumor tissue samples show intermediate ACACA expression,
and highest ACACA expression is seen in metastatic PCa lesions (Figure 2B.).
Expression of ACC1 was confirmed by immunoblot in three commonly
used PCa cell lines (Figure 2C.). PCa cell lines DU145, LNCaP, and PC3 all
express ACC1 while little to no detectable signal was seen in normal prostate
epithelial cells (PrEC). Treating PC3 or LNCaP cells with the non-isoform
specific ACC inhibitor TOFA for 48 hours results in apoptosis in both cell lines as
indicated by detection of cleaved PARP by immunoblot (Figure 2D.) [36].
Furthermore, treating LNCaP cells with TOFA or another non-isoform specific
ACC inhibitor, CP-640186, from 24 to 72 hours results in reduced viability as
measured by MTS assay (Figure 2E.) [36]. Pharmacological inhibition of ACC
also alters the fatty acid profile of PCa cells (Figure 3). TOFA treatment
significantly reduces total fatty acid levels in both LNCaP (Figure 3A.) and PC3
(Figure 3C.) cells. This decrease in total fatty acid levels is reflected in the
reduction of several saturated and unsaturated fatty acid species in both cell
lines (Figures 3B. & 3C.).
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Prostate-specific genetic inactivation of ACC1 reduces tumor burden in 12
week old C57BL/6J mice.
Analysis performed from data in the TCGA shows ACC1 is associated
with poor survival in several cancer types (Figure 1), is correlated with advanced
disease in PCa (Figure 2A. & 2B.), and is overexpressed in PCa cell lines
(Figure 2C.). However, because most ACC inhibitors are non-isoform specific, it
is unclear whether the anti-cancer properties seen with pharmacological ACC
inhibition is due to ACC1 inhibition, ACC2 inhibition, or both. To this end, we
made use of a well-studied murine model of prostate cancer [29,37] as well as a
murine ACC1 knockout mouse [26,27] to generate a prostate-specific ACC1 and
Pten knockout murine model of prostate cancer. In these mice, cre-recombinase
(Cre) expression is controlled by the prostate-specific probasin promoter [29].
Loxp sites flank exon 5 of Pten which encodes the phosphatase domain of the
enzyme [37]. In addition, loxp sites flank exon 22 of ACC1 which encodes for the
biotin carboxyl carrier protein domain of the enzyme [27]. When Cre is
expressed, this loxp site placement generates an in-frame deletion and produces
a mutant form of ACC1 [27]. Through the merging of these two models, the
effects of wild-type ACC1 (ACC+/+), heterozygous loss of ACC1 (ACC+/L), and
homozygous loss of ACC1 (ACCL/L) on the development and progression of
Pten-deletion (PtenL/L; Cre+) mediated PCa were able to be observed..
To determine that the impact of ACC1 inactivation did not affect the
development of the prostate under normal conditions (Pten+/+), several
characteristics of 11-week-old ACC1L/L; Pten+/+; Cre+ mice and control age-
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matched ACC1L/L; Pten+/+; Cre- siblings were measured (Figure 4). ACC1
inactivation did not alter body weight (Figure 4A.), total prostate weight (Figure
4B.), or individual prostate lobe weight (anterior lobe shown in Figure 4C.,
dorsal-lateral and ventral lobe weight not shown). When crossed with female
ACCL/L; Pten+/+; Cre- mice, male ACC1L/L; Pten+/+; Cre+ mice sired male and
female (Figure 4D.) and Cre+ and Cre- (Figure 4E.) progeny at expected
frequencies. Gross morphology and H&E staining of each prostate lobe from
control ACCL/L; Pten+/+; Cre- (Figure 4F.) and experimental ACC1L/L; Pten+/+; Cre+
mice (Figure 4G.) further demonstrate that ACC1 deletion alone does not impact
prostate development or function under normal conditions.
When mice reached 12 weeks of age, prostates were collected from
ACC1+/+; PtenL/L; Cre+ (n=7), ACC1+/L; PtenL/L; Cre+ (n=9), ACC1L/L; PtenL/L; Cre+
(n=9), and control Cre- mice (n=9) (Figures 5-7). Tumor burden was significantly
reduced in mice with homozygous inactivation of ACC1 (ACC1L/L) when
compared to ACC1+/+ siblings with average total prostate weights of 90mg versus
120mg, respectively (Figure 5A., p≤0.05). This reduction in tumor burden was
reflected in the anterior lobe of the prostate (Figure 5B., average weights of
42mg versus 74mg, p≤0.01) while no significant reduction was seen in the
dorsal-lateral (Figure 5C.) or ventral lobes (Figure 5D.). Examination of the
gross morphology and histology of each prostate lobe also reflect these
differences as prostates from ACC1L/L; PtenL/L; Cre+ mice resemble prostates
from control Cre- littermates much more than prostates from ACC1+/+; PtenL/L;
Cre+ or ACC1+/L; PtenL/L; Cre+ siblings (Figure 6).
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To determine if genetic inactivation of ACC1 also altered the fatty acid
profile of prostate tumors, similar to what was seen with in vitro TOFA treatment
(Figure 3), FAME analysis was performed on anterior prostate lobes from
ACC1+/+; PtenL/L; Cre+, ACC1+/L; PtenL/L; Cre+, and ACC1L/L; PtenL/L; Cre+ (n=3
from each genotype). No difference in total fatty acid level was seen among the
three experimental genotypes (Figure 7A.). However, homozygous ACC1
genetic inactivation reduced levels of some monounsaturated fatty acids (16:1
and 18:1) and was associated with a slight increase in some polyunsaturated
species (20:2, 20:4, and 20:5) (Figure 7B.).
Prostate-specific genetic inactivation of ACC1 does not reduce tumor
burden in 24 week old C57BL/6J mice.
To determine if the ACC1 inactivation mediated reduction in tumor burden
seen at 12 weeks of age would be reflected at a longer timepoint, prostates from
24 week old mice were also collected (Figures 8-10). At this timepoint, prostates
were collected from ACC1+/+; PtenL/L; Cre+ (n=6), ACC1+/L; PtenL/L; Cre+ (n=8),
ACC1L/L; PtenL/L; Cre+ (n=9), and control Cre- mice (n=14). No difference in
tumor burden among the experimental genotypes was observed (total prostate
weight 144mg, 143mg, and 133mg, respectively, p>0.05) (Figure 8A.). This
pattern was also reflected in the anterior, dorsal-lateral, and ventral prostate
lobes (Figure 8B.-8D.). Examination of the gross morphology and histology of
each prostate lobe also showed evidence of tumor formation in each prostate
lobe (Figure 9). Additionally, FAME analysis performed on the anterior prostate
111
lobes of 24-week-old mice revealed no difference in total fatty acid levels (Figure
10A.) or individual fatty acid species (Figure 10B.).
Ultimately, the reduction of tumor burden at the earlier timepoint (12
weeks) and the lack of difference in tumor burden at the later timepoint (24
weeks) suggest ACC1 is required for PCa initiation. However, over time, PCa
cells are able to bypass this requirement and progress despite ACC1
inactivation. We hypothesized that this could be due to metabolic changes that
conferred the ability to retool fatty acid utilization to overcome the decrease in de
novo fatty acid synthesis facilitated by ACC1 inactivation. To this end, we
examined the metabolic effects of ACC1 specific siRNA-mediated silencing and
non-isoform specific pharmacological inhibition of ACC in vitro.
Inhibition of ACC reduces mitochondrial function in PCa cells.
Seahorse analysis was performed on several PCa cell lines to explore the
metabolic consequences of ACC inhibition. PC3 cells treated with 10µg/mL
TOFA for 24 hours did not change basal glycolytic activity (Figures 11A. & 11B.)
or maximal glycolytic capacity (Figures 11A. & 11C.). This suggests that ACC
inhibition does not affect cellular metabolism through glycolysis.
Next, the effects of specific ACC1 knockdown on mitochondrial function
were measured (Figure 12). Robust ACC1 knockdown was achieved through
siRNA-mediated silencing in both LNCaP (Figure 12A.) and DU145 (Figure
12B.) cells. Mitochondrial function of siACC1 knockdown cells was assessed via
a Seahorse mitochondrial stress assay (Figures 12C. & 12D.) [32]. ACC1
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knockdown reduced basal mitochondrial function by approximately 50% in
LNCaP Cells (Figure 12E., p≤0.05) and by approximately 25% in DU145 cells
(Figure 12H., p≤0.05). Likewise, ACC1 knockdown decreased the ATP coupler
response (the amount of oxygen consumption used for mitochondrial ATP
production) by approximately 25% in both LNCaP and DU145 cells (Figures
12F. & 12I., p≤0.05) and decreased the ETC accelerator response (the amount
of oxygen consumed to support maximal mitochondrial function) by
approximately 50% in LNCaP cells (Figure 12G., p≤0.05) and by approximately
25% in DU145 cells (Figure 12J., p≤0.05).
Next the effects of non-isoform specific pharmacological ACC inhibition on
mitochondrial function were measured (Figure 13). LNCaP and DU145 cells
were treated with TOFA for 24 hours then a Seahorse mitochondrial stress assay
was performed. TOFA treatment decreased basal mitochondrial function by
approximately 40% (p≤0.05) and ATP coupler response by approximately 80%
(p≤0.05) in LNCaP cells (Figures 13A.-13B.). Additionally, TOFA treatment
decreased the ATP coupler response by approximately 40% in DU145 cells
(Figure 13E., p≤0.05).
While the impact of pharmacological ACC inhibition and siRNA-mediated
ACC1 knockdown have some granular differences, overall both treatments
impede full mitochondrial function. These results reveal that, in some cases,
both siRNA mediated knockdown of ACC1 and TOFA treatment reduce
mitochondrial metabolism. In contrast, there are some cases in which siRNA
mediated knockdown of ACC1 reduces mitochondrial function but TOFA
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treatment does not. These differences could potentially be attributed to off-target
effects and/or the result of dual ACC1 and ACC2 inhibition. Due to the
relationship between ACC1 and ACC2 and their function in de novo fatty acid
synthesis and β-oxidation, respectively, an analysis of endogenous and
exogenous fatty acid oxidation in both LNCaP and DU145 PCa cell lines was
performed.
Inhibition of ACC prevents PCa cells from utilizing endogenous and/or
exogenous fatty acids for fatty acid oxidation.
Endogenous and exogenous fatty acid oxidation activity was measured in
in LNCaP (Figure 14A.) and DU145 (Figure 14B.) cells using the Seahorse
mitochondrial stress assay [33]. We found both LNCaP (Figures 14C.-14E.) and
DU145 cells (Figures 14F.-14H.) can utilize endogenous or exogenous fatty
acids for all measures of mitochondrial function (Figure 14C.-14H.). Both
LNCaP and DU145 cells show enhanced spare respiratory capacity when
provided exogenous fatty acids (Figure 14E. & 14H.) suggesting these cells
prefer to oxidize exogenous fatty acids to support maximal mitochondrial function
and the availability of exogenous fatty acids provides a metabolic advantage. In
addition, DU145 cells also show enhanced basal mitochondrial respiration
(Figure 14F.) when provided exogenous fatty acids.
To determine if ACC blockade affects fatty acid oxidation, the same assay
described previously to measure oxidation of endogenous and exogenous fatty
acids was performed on LNCaP (Figure 15A.) and DU145 cells (Figure 15B.)
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treated with or without TOFA for 24 hours and supplied either control BSA or
palmitate. TOFA treatment resulted in an approximate 40% reduction in OCR
levels (p≤0.05) in basal response and ATP coupler response in LNCaP cells
provided either BSA or palmitate, indicating that TOFA disrupts the ability of
LNCaP cells to utilize endogenous or exogenous fatty acids for basal
mitochondrial function or ATP production (Figures 15C. & 15D.). TOFA
treatment also reduces the ability of LNCaPs to use exogenous fatty acids for
maximal mitochondrial function by approximately 40% (p≤0.05) (Figure 15E.).
Interestingly, TOFA treatment did not prevent DU145 cells from utilizing
endogenous fatty acids for any aspect of mitochondrial function (Figures 15F.-
15H.) but did prevent DU145 cells from utilizing exogenous fatty acids for ATP
production and maximal mitochondrial function as measured by an approximate
40% decrease in OCR levels (p≤0.05) (Figures 15G.-15H.).
2.5 Discussion
Prostate cancer is the most frequently diagnosed cancer and the third
leading cause of cancer-related death in American men [38,39]. Current
treatment strategies include surgery, chemotherapy, radiation therapy, and
androgen-deprivation which all have distinct benefits, limitations and side effects.
Commonly, PCa tumors recur and present as more aggressive disease [40].
Men who present with recurrent or distant PCa have a low survival rate and
limited therapeutic options [38]. Together, this demonstrates a need for
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additional and more effective treatment strategies for both primary and recurrent
PCa.
De novo fatty acids are required for membrane biogenesis and are a main
component of lipid rafts and therefore are considered vital to sustain cell
signaling and enhanced proliferation [3,41,42]. Increased expression of enzymes
involved in de novo fatty acid synthesis is found in many cancer types but the de
novo fatty acid synthesis pathway is inactive in most normal adult tissues
[1,2,43,44]. Ultimately, this strongly suggests de novo fatty acid synthesis is a
promising target for development of novel chemotherapeutic strategies.
This study focused on ACC1, the rate limiting enzyme of the de novo fatty
acid synthesis pathway [15,16]. High ACACA expression is associated with
poorer survival in several cancer types (Figure 1) as well as more advanced PCa
disease (Figure 2). In this study and others, pharmacological ACC inhibition
results in reduced cancer cell viability and altered fatty acid profiles (Figures 2 &
3) [17,19,20,45]. Homozygous ACC1 inactivation in a murine model of prostate
cancer reuduced tumor burden at an early timepoint (Figures 5 & 6). However
this reduction in tumor burden was not seen at a later timepoint (Figures 8 & 9).
Potential mechanisms for this unexpected phenotype include possible
redundancy between the acetyl-CoA carboxylase isoforms, ACC1 and ACC2, as
well as the possibility that PCa can utilize dietary derived fatty acids in place of
de novo derived fatty acids. Although it has been previously reported that cancer
cells do not make use of exogenous lipids, other studies and reviews have
indicated otherwise [41,46,47,48]. Our study of the metabolic consequences of
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ACC1 inhibition showed both siRNA-mediated knockdown of ACC1 and TOFA
treatment impaired mitochondrial function in LNCaP and DU145 cells, but in
slightly different patterns (Figures 12 & 13). In both LNCaP and DU145 cells,
siACC1 reduced OCR for each measurement of mitochondrial function,
suggesting β-oxidation of de novo fatty acids is necessary to achieve full
mitochondrial activity (Figure 12). Yet, TOFA treatment reduces OCR
associated with basal mitochondrial respiration and mitochondrial ATP
production in LNCaP cells (Figure 13A. & 13B.) and mitochondrial ATP
production in DU145 cells (Figure 13E.). As a non-isoform ACC inhibitor, TOFA
inhibits both ACC1 and ACC2 while siACC1 only targets the ACC1 isoform [36].
Both TOFA and siACC1 treatment inhibit de novo fatty acid synthesis through
ACC1 inhibition. In addition, TOFA treatment also facilitates continuous β-
oxidation as inhibiting ACC2 prevents the production of mitochondrial malonyl-
CoA which acts as a CPT-1 inhibitor [23]. In cases where either siACC1 or
TOFA treatment reduces mitochondrial function, we hypothezied that β-oxidation
of exogenous fatty acids cannot compensate for loss of endogenous fatty acids.
In contrast, in cases where siACC1 treatment reduces mitochondrial function but
TOFA does not, we hypothesized that β-oxidation of exogenous fatty acids can
compensate for loss of endogenous fatty acids.
Results from our analysis of endogenous and exogenous fatty acid
oxidation were consistent with our hypotheses (Figure 14). Both LNCaP and
DU145 cells utilize de novo derived fatty acids to support full mitochondrial
function, as was suggested in the ACC1 knockdown studies (Figures 12 & 14).
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However, the availability of exogenous fatty acids increased the OCR associated
with maximal mitochondrial function of both LNCaP and DU145 cells (Figures
14E. & 14H.) and basal mitochondrial function in DU145 cells (Figure 14F.)
beyond what is achieved by the availability of endogenous fatty acids alone.
These are the same measurements which were decreased with siACC1
treatment but unaffected by TOFA (Figures 12 & 13). Taken together, this
suggests PCa cells utilize endogenous and exogenous fatty acids differently.
LNCaP cells are only able to oxidize exogenous fatty acids to support maximal
mitochondrial function whereas DU145 cells can utilize exogenous fatty acids to
achieve basal and maximal mitochondrial activity. Further, LNCaP cells seem to
have a preference for endogenous fatty acids and thus rely heavily on de novo
fatty acid synthesis but can utilize other sources of fatty acids under conditions of
stress. In contrast, DU145 cells appear to utilize endogenous fatty acids, and
thus de novo fatty acid synthesis for mitochondrial function, but have a
preference for exogenous fatty acids to enhance basal and maximal
mitochondrial function.
In LNCaP cells, TOFA mediated ACC inhibition reduced β-oxidation of
endogenous/exogenous fatty acids for basal mitochondrial function and ATP
production (Figure 15C. & 15D.). This indicates LNCaP cells depend on de
novo derived fatty acids for basal mitochondrial function and mitochondrial ATP
production and inhibition of ACC disrupts proper fatty acid oxidation even when
exogenous fatty acids are available. While TOFA treatment also reduced the
ability of LNCaPs to utilize exogenous fatty acids for their spare respiratory
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capacity, the spare respiratory capacity of LNCaP cells not provided palmitate
was unaffected by TOFA treatment (Figure 15E.). One potential explanation for
this phenotype may lie in how LNCaP cells respond to the availability of
exogenous fatty acids. When exogenous fatty acids are available, LNCaP cells
are set to use exogenous fatty acids for maximal mitochondrial function.
However, without an adequate supply of endogenous fatty acids, exogenous fatty
acids are diverted for other uses, perhaps for membrane formation, and thus
spare respiratory capacity is reduced. On the other hand, when exogenous fatty
acids are not available, LNCaP cells are able to rewire their metabolic pathways
to support maximal fatty acid oxidation, perhaps by mobilizing fatty acids from
existing membranes. In this scenario, maximal mitochondrial function could
continue regardless of ACC1 inhibition.
In DU145 cells, TOFA mediated ACC inhibition reduced β-oxidation of
exogenous fatty acids for ATP production and spare respiratory capacity (Figure
15G. & 15H.). Similarly to what was observed in LNCaP cells, TOFA treatment
reduced the ability of DU145 cells to utilize endogenous fatty acids for their spare
respiratory capacity but the spare respiratory capacity of DU145 cells not
provided palmitate was unaffected by TOFA treatment (Figure 15H.). Again, this
suggests that when exogenous fatty acids are available PCa cells are
programmed to use exogenous fatty acids for maximal mitochondrial function but
a lack of exogenous fatty acids initiates metabolic reprogramming to allow cells
to support mitochondrial function through other pathways. Perhaps most
interestingly, TOFA treatment had no effect on the ability of DU145 cells to utilize
119
endogenous or exogenous fatty acids (Figure 15F.) which is in contrast to what
is seen in LNCaP cells (Figure 15C.). This suggests that even though DU145
cells can use both endogenous and exogenous fatty acids to maintain
mitochondrial function, DU145 cells rewire their metabolism to sustain
mitochondrial activity without the availability of exogenous fatty acids and even
when de novo fatty acid synthesis is inhibited.
These differences in metabolic phenotype offer a potential mechanism
behind our in vivo data. Of the two cell lines used in these studies, LNCaP cells
more closely align with early PCa as LNCaP cells are well-differentiated and
androgen-dependent with low metastatic potential [49,50]. In contrast, DU145
cells resemble more progressed PCa as DU145 cells are less differentiated and
androgen independent with a higher metastatic potential [49,50]. The well-
differentiated LNCaP cells are more dependent on de novo fatty acid synthesis
than the more advanced DU145 cells (Figures 14 & 15). Our in vivo data reflect
this as the 12-week old PCa model is more susceptible to ACC1 inactivation than
the 24-week old model (Figures 5-10). Taken together, this suggests that as it
progresses, PCa can retool its metabolic profile to compensate for lack of de
novo fatty acids either by utilizing dietary supplied fatty acids or through some
other mechanism, perhaps by mobilizing existing fatty acids from other sources.
The work presented here adds valuable insight into the field of cancer
metabolism. The knowledge that de novo fatty acid synthesis is upregulated in
many cancers and is often associated with advanced disease and poor survival
(Figures 1 & 2) naturally lead to the idea of targeting this pathway for
120
chemotherapeutic purposes [1,2,5,6]. The work presented here indicates that
successful targeting of fatty acid metabolism would likely be tissue, context, and
time-dependent. ACC1 inactivation was sufficient to both disrupt cellular
metabolism in the well-differentiated LNCaP cell line and reduce PCa tumor
burden in vivo at an early timepoint. Although ACC1 inactivation disrupted
cellular metabolism in the poorly-differentiated DU145 cell line, ACC1 inactivation
alone did not reduce PCa tumor burden in vivo at the later timepoint, suggesting
that PCa is can retool cellular metabolic properties to overcome ACC1
dependence. This implies ACC1 inhibition may be sufficient to delay tumor
development but not progression. The role of ACC1 in acetate metabolism,
redox modulation, and compensation for ACC1 activity by ACC2 are all potential
mechanisms by which ACC1-deficient prostate cancer cells are able to survive
and proliferate and warrant further research. Inhibition of both ACC isoforms via
TOFA treatment was able to disrupt β-oxidation of in both LNCaP and DU145
cells suggesting dual inhibition of ACC1 and ACC2 would be the most effective
strategy to targeting fatty acid metabolism in cancer, a theory previously put forth
in a review by Currie et al. and by Svensson et al. in their work with the novel
non-isoform specific ACC inhibitor ND-646 in non-small cell lung cancer [23,24].
Ultimately, the interplay between fatty acid synthesis, fatty acid oxidation, and
cancer biology is not entirely understood. It is likely cancers will vary in their
responses to clinical targeting of de novo fatty acid synthesis and/or β-oxidation
based on mutation status, tissue type, and overall metabolic profile [23]. Further
study of the dynamic nature of cancer metabolism is necessary to gain a
121
comprehensive understanding of how best to exploit cancer’s unique
dependence on enhanced metabolic function for novel chemotherapeutic
approaches.
122
2.6 Figures
Figure 1: Elevated ACACA expression is associated with decreased
survival in several cancers. OncoLnc was used to examine ACACA
expression and patient survival using clinical data from The Cancer Genome
Atlas (TCGA) and generate Kaplan-Meier curves. Top 30 percentile and bottom
70 percentile of expression values were considered as high and low groups for
(A.) breast and (I.) lung data. Top 40 percentile and bottom 60 percentile of
expression values were considered as high and low groups for (B.) kidney, (G.)
ovarian and (H) bladder data. Top and bottom 50 percentile of expression values
were considered as high and low groups for (B.) liver, (C.) skin, (E.) sarcoma,
and (F.) cervical data. Logrank p-values ≤ 0.05 were considered significant.
123
Figure 2: ACACA expression is correlated with advanced PCa and ACC1 is
required for survival and proliferation in PCa cell lines. (A.) Taylor cohort
data was used to examine ACACA (NM_198834) expression and recurrence free
survival. Top 20 percentile and bottom 80 percentile of expression values were
considered as high and low groups. Logrank p-values ≤ 0.05 were considered
significant. (B.) Taylor cohort data was used to examine ACACA (NM_198834)
expression and disease state in patients. Normal n=29, tumor n=131, metastasis
n=19, one-way ANOVA p<0.0001, Tukey’s post-tests *p≤0.05. (C.) ACC1
expression was detected by western blot of whole cell lysates collected from
normal prostate epithelial cells (PrEC) and PCa cell lines (DU145, LNCaP, PC-
124
3). (B.) cPARP was detected by western blot of whole cell lysates collected from
PC-3 & LNCaP cells treated with TOFA (10µg/mL) for 24 or 48 hours. (C.)
Reduced cell viability detected via MTS assay of LNCaP cells treated with TOFA
(10µg/mL) or CP-640186 (100mM) for 24, 48, or 72 hours. Cell viability
expressed as % of vehicle treated cells.
125
Figure 3: Pharmacological inhibition of ACC activity alters fatty acid profile
in PCa cells. Fatty acid methyl-ester analysis of (A. & B.) LNCaP and (C. & D.)
PC3 cells treated with TOFA (10µg/mL) or vehicle for 24 hours. Fatty acid profile
was determined by GC-MS. Total fatty acid levels in LNCaP and PC3 cells
treated with TOFA (10µg/mL) quantified in (A.) and (C.) respectively. Levels of
fatty acid species in LNCaP and PC3 cells treated with TOFA (10µg/mL)
quantified in (B.) and (D.) respectively. Results were normalized to cell number
and represent three independent experiments; error bars represent standard
deviation. P-values determined by a two-tailed Student’s t-test (*p≤ 0.05).
126
Figure 4: Mice with prostate-specific genetic inactivation of ACC1
(ACC1L/L) are phenotypically normal. (A.) Body weight, (B.) total prostate
weight, and (C.) anterior lobe weight from 11-week-old ACC1L/L; Pten+/+; Cre-
(n=2) and ACC1L/L; Pten+/+; Cre+ mice (n=2). No significance differences were
observed. 9 crosses of male ACC1L/L; Pten+/+; Cre+ and female ACC1L/L; Pten+/+;
Cre- produced progeny at expected (D.) gender (χ2(1, N=49)=0.1837; p=0.6682)
and (E.) genetic (χ2(1, N=49)=0.0204; p=0.8864) frequency. (F. & G.)
Haematoxylin and eosin (H&E) staining of prostate tissues with indicated
genotypes.
127
Figure 5: Prostate-specific genetic inactivation of ACC1 reduces tumor
burden in 12 week old C57BL/6J mice. (A.) Total prostate (B.) anterior lobe
(C.) dorsal-lateral lobe and (D.) ventral lobe weight normalized to body weight of
12-week-old Cre- (n=9, blue), ACC+/+; PtenL/L; Cre+ (n=7, red), ACC+/L; PtenL/L;
Cre+ (n=9, green), and ACCL/L; PtenL/L; Cre+ (n=9, orange). One-way ANOVA
p≤0.05, post-ANOVA Tukey adjusted p-value *p≤0.05, **p≤0.01, ***p≤0.001.
128
Figure 6: Gross anatomy and morphology of 12-week-old murine
prostates. Gross anatomy and H&E staining of anterior, dorsal-lateral, and
ventral lobes (left to right) of prostates collected from 12-week old mice of the
indicated genotypes (top to bottom - Cre-, ACC+/+; PtenL/L; Cre+, ACC+/L; PtenL/L;
Cre+, and ACCL/L; PtenL/L; Cre+. Images are representative of H&E stains of n=7
stains per genotype.
129
Figure 7: Fatty acid profile of the anterior prostate lobes collected from 12-
week old mice. Fatty acid methyl-ester analysis of anterior prostate lobes from
12-week-old ACC+/+; PtenL/L; Cre+(black), ACC+/L; PtenL/L; Cre+ (gray), and
ACCL/L; PtenL/L; Cre+ (white), n=3 each. Fatty acid profile determined by GC-MS.
Total fatty acid levels in quantified in (A.). Levels of fatty acid species quantified
in (B.). Results normalized to tissue weight, error bars represent standard
deviation. *One-way ANOVA p≤0.05, Tukey’s post-tests p≤0.05.
130
Figure 8: Prostate-specific genetic inactivation of ACC1 does not reduce
tumor burden in 24 week old C57BL/6J mice. (A.) Total prostate (B.) anterior
lobe (C.) dorsal-lateral lobe and (D.) ventral lobe weight normalized to body
weight of 24-week-old Cre- (n=14, blue), ACC+/+; PtenL/L; Cre+ (n=6, red), ACC+/L;
PtenL/L; Cre+ (n=8, green), and ACCL/L; PtenL/L; Cre+ (n=9, orange). Post-ANOVA
Tukey adjusted p-values between experimental groups were not significant
(p>0.05).
131
Figure 9: Gross anatomy and morphology of 24-week-old murine
prostates. Gross anatomy and H&E staining of anterior, dorsal-lateral, and
ventral lobes (left to right) of prostates collected from 24-week old mice of the
indicated genotypes (top to bottom - Cre-, ACC+/+; PtenL/L; Cre+, ACC+/L; PtenL/L;
Cre+, and ACCL/L; PtenL/L; Cre+. Images are representative of H&E stains of n=6
stains per genotype.
132
Figure 10: Fatty acid profile of the anterior prostate lobes collected from
24-week old mice. Fatty acid methyl-ester analysis of anterior prostate lobes
from 24-week-old ACC+/+; PtenL/L; Cre+(black), ACC+/L; PtenL/L; Cre+ (gray), and
ACCL/L; PtenL/L; Cre+ (white), n=3 each. Fatty acid profile determined by GC-mS.
Total fatty acid levels in quantified in (A.). Levels of fatty acid species quantified
in (B.). Results were normalized to tissue weight, error bars represent standard
deviation. *One-way ANOVA p≤0.05, Tukey’s post-tests p≤0.05.
133
Figure 11: Pharmacological inhibition of ACC does not reduce glycolytic
function of PC3 cells. (A.) PC3 cells plated at 40,000 cells per well in an XF-24
well plate were treated with TOFA (10µg/mL) or vehicle (DMSO) for 12 hours.
After treatment, cells were incubated in base medium supplemented with
glutamine followed by incubation at 37oC in a non CO2-incubator for 1 hour.
Three subsequent injections followed as listed: 10mM glucose, 1µM oligomycin,
100mM 2-deoxyglucose. ECAR values were automatically recorded and
calculated by the Seahorse XF-24 software. (B.) Glycolysis and (C.) glycolytic
capacity results were quantified. Error bars represent the mean ± standard
134
deviation resulting from 3 independent experiments of 5 replicates. P-values
determined by two-tailed Student’s t-test, no significance observed.
135
Figure 12: siRNA mediated knockdown of ACC1 impairs mitochondrial
function in PCa cells. (A.) LNCaP and (B.) DU145 cells were transfected with
siACC1 for 72 hours then plated at 40,000 cells per well in an XF-24 cell plate.
136
After transfection (C.) LNCaP cells and (D.) DU145 cells were incubated in base
medium supplemented with 25mM glucose and 1mM sodium pyruvate followed
by incubation at 37oC in a non CO2-incubator for 1h. Three subsequent
injections followed as listed - 1µM oligomycin, 0.5µM FCCP, 1µM
rotenone/antimycin. OCR values were automatically recorded and calculated by
the Seahorse XF-24 software. (E. & H.) Basal respiration, (F. & I.) ATP coupler
response, and (G. & J.) ETC accelerator response results were quantified for
LNCaP and DU145 cells, respectively. Error bars represent the mean ± standard
deviation resulting from 3 independent experiments of 5 replicates. One-way
ANOVA p≤0.05, Tukey’s post-tests p≤0.05.
137
Figure 13: Pharmacological inhibition of ACC reduces mitochondrial
function in PCa cells. (A.-C.) LNCaP and (D.-F.) DU145 cells plated at 40,000
cells per well in an XF-24 well plate then were treated with TOFA (10µg/mL) or
vehicle (DMSO) for 12 hours. After treatment, cells were incubated in base
medium supplemented with 25mM glucose and 1mM sodium pyruvate followed
by incubation at 37oC in a non CO2-incubator for 1 hour. Three subsequent
injections followed as listed - 1µM oligomycin, 0.5µM FCCP, 1µM
rotenone/antimycin. OCR values were automatically recorded and calculated by
the Seahorse XF-24 software. (A. & D.) Basal respiration, (B. & I.) ATP coupler
response, and (C. & F.) spare respiratory capacity were quantified for LNCaP
and DU145 cells, respectively. All results were normalized to vehicle; error bars
represent the mean ± standard deviation resulting from 3 independent
experiments of 5 replicates. P-values determined by two-tailed Student’s t-test,
*p≤0.05.
138
Figure 14: PCa cells utilize both endogenous and exogenous fatty acids
for mitochondrial function. (A.) LNCaP and (B.) DU145 cells were plated in an
XF-24 well plate at 40,000 cells per well. Cells were cultured in substrate-limited
medium 24 hours prior to the assay. Cells were then incubated in FAO assay
medium followed by incubation at 37oC in a non CO2-incubator for 45 minutes.
Etomoxir (40µM) or vehicle (H2O) was added and the plate was incubated for a
further 15 minutes in a non CO2-incubator. Immediately prior to the assay, 75µM
139
palmitate or BSA was added to appropriate wells. Three subsequent injections
followed as listed - 1µM oligomycin, 0.5µM FCCP, 1µM rotenone/antimycin.
OCR values were automatically recorded and calculated by the Seahorse XF-24
software. (C. & F.) Basal respiration, (D. & G.) ATP coupler response, and (E. &
H.) spare respiratory capacity were quantified for LNCaP and DU145 cells,
respectively. All results were normalized to etomoxir treated cells. Error bars
represent the mean ± standard deviation resulting from 3 independent
experiments of 5 replicates. P-values determined by two-tailed Student’s t-test,
*p≤0.05.
140
Figure 15: Pharmacological inhibition of ACC prevents PCa cells from
utilizing endogenous and/or exogenous fatty acids for fatty acid oxidation.
(A.) LNCaP and (B.) DU145 cells were plated in an XF-24 well plate at 40,000
cells per well. Cells were cultured in substrate limited medium and TOFA
(10µg/mL) or vehicle (DMSO) was added 24 hours prior to the assay. Cells were
then incubated in FAO assay medium followed by incubation at 37oC in a non
CO2-incubator for 1 hour. Immediately prior to the assay, 75µM palmitate or BSA
141
was added to appropriate wells. Three subsequent injections followed as listed -
1µM oligomycin, 0.5µM FCCP, 1µM rotenone/antimycin. OCR values
automatically recorded and calculated by the Seahorse XF-24 software. (C. &
F.) Basal respiration, (D. & G.) ATP coupler response, and (E. & H.) spare
respiratory capacity were quantified for LNCaP and DU145 cells, respectively.
All results were normalized to vehicle treated cells. Error bars represent the
mean ± standard deviation resulting from 3 independent experiments of 5
replicates. P-values determined by two-tailed Student’s t-test, *p≤0.05.
142
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CHAPTER III
COMBINATION TREATMENTWITH SMALL MOLECULE ACETYL-COA
CARBOXYLASE INHIBITOR CP-640186 AND PACLITAXEL IS SYNERGISTIC
AND DISRUPTS METABOLIC FUNCTION IN TAXANE-RESISTANT
PROSTATE CANCER CELLS
Amanda L. Davis1, Tomi Ladipo2, Joshua J. Souchek3, Aaron M. Mohs3, 4, 5,
Steven J. Kridel1, 6, *
1Department of Cancer Biology, and 6Wake Forest Comprehensive Cancer
Center, Wake Forest University Health Sciences, Winston-Salem, North Carolina
27157, United States 2Winston-Salem State University, Winston-Salem, North
Carolina 27110, 3Department of Pharmaceutical Sciences, 4Department of
Biochemistry and Molecular Biology, and the 5Fred and Pamela Buffett Cancer
Center, University of Nebraska Medical Center, Omaha, Nebraska 68198, United
States
The following chapter is formatted for submission to PLoS One. Stylistic
variations are due to journal requirements.
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3.1 Abstract
Taxanes are commonly used to treat patients with castration-resistant
prostate cancer. However, patients often experience disease progression
despite treatment due to the development of taxane-resistance. A lack of
therapies to address taxane-resistance necessitates the development of novel
therapeutic strategies to delay resistance and/or improve patient outcome. Here,
we explore the use of CP-640186, a non-isoform specific acetyl-CoA carboxylase
(ACC) inhibitor, in two taxane resistant (TxR) prostate cancer cell lines. The
cytosolic isoform of ACC, ACC1, is the rate-limiting step of de novo fatty acid
synthesis, a pathway overexpressed in a variety of cancer types including
prostate cancer. We found that co-treatment with CP-640186 and paclitaxel
inhibited cell proliferation and viability in both DU145-TxR and PC3-TxR cell
lines. We also observed robust synergy between CP-640186 and paclitaxel in
both cell lines. Furthermore, concurrent use of CP-640186 and paclitaxel
reduced glycolytic capacity and basal mitochondrial function only in the TxR cell
lines. The mechanism underlying the observed synergy is at least in part due to
changes in metabolic activity. These results provide a strong rationale for further
study into the use of ACC inhibitors to address taxane resistance in PCa, and
potentially other cancers.
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3.2 Introduction
Prostate cancer (PCa) is the most frequently diagnosed cancer and third
leading cause of cancer-related death in American men [1,2]. PCa will recur in
approximately 20-50% of patients who receive surgery and/or radiation for
localized disease within 10 years of initial therapy [3]. These cases typically
respond to androgen deprivation therapy (ADT) but patients typically experience
disease progression within 2 years [4]. Disease that progresses despite ADT is
referred to as castration-resistant PCa (CRPC). CRPC represents a spectrum of
disease characteristics ranging from an asymptomatic rise in prostate-specific
antigen (PSA) levels to PCa metastasis with severe symptoms [4].
Patients with CRPC are typically treated with a taxane [5]. Taxanes such
as paclitaxel, docetaxel, and cabazitaxel are known to stabilize microtubules,
induce cell cycle arrest and lead to apoptosis in multiple types of cancer cells [6].
In fact, taxanes are approved for use in a variety of cancers including lung,
pancreatic and breast [7,8,9]. In PCa, taxanes act through additional
mechanisms [6]. Taxanes are known to downregulate androgen receptor (AR)
transcription and disrupt AR localization [6,10,11,12]. In 2004, docetaxel was
approved to treat CRPC and remained the only available treatment until
cabazitaxel was approved in 2010 to treat patients who progress after docetaxel
therapy [5,13,14]. However, the median time to progression on cabazitaxel is 2.8
months [15]. Ultimately, despite initial response to taxane therapy, patients
eventually develop taxane resistance.
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Several mechanisms are thought to drive the development of taxane
resistance. These include increased drug export, inhibition of apoptosis,
activation of survival pathways and tubulin mutations that disrupt taxane binding
[6,16,17,18,19]. To study taxane-resistance in PCa, several groups have
developed taxane resistant (TxR) cell lines. Here, we make use of two PCa TxR
cell lines, DU145-TxR and PC3-TxR, which were developed by Tekeda et al. by
culturing parental DU145 and PC3 cells in increasing concentrations of paclitaxel
[20]. Interestingly, these two cell lines appear to develop taxane resistance
through different mechanisms. Overexpression of the multi-drug resistance gene
(MDR1) was seen in the DU145-TxR cell line [20,21]. In contrast,
downregulation of the C-terminal tensin-like (CTEN) gene and subsequent
enhanced F-actin polymerization was observed to drive taxane resistance in the
PC3-TxR cell line [20,21,22].
Given the variety of mechanisms which contribute to taxane resistance
and the lack of treatment options for patients with taxane resistant CRPC, further
study is necessary to develop strategies to overcome chemoresistance. The
influence of cellular metabolism on chemotherapeutic resistance has emerged as
an interesting area of study [23,24,25,26]. Specifically, fatty acid metabolism has
been linked to chemoresistance [23]. For example, inhibition of fatty acid
synthase (FASN), the enzyme responsible for combining one acetyl-CoA and
seven malonyl-CoA molecules to form the saturated fatty acid palmitate during
de novo fatty acid synthesis, has been shown to enhance the chemosensitivity of
breast cancer cells [27,28]. Recently, we demonstrated that blockade of FASN
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activity with NanoOrl, an experimental nanoparticle formulation of the FDA-
approved FASN inhibitor orlistat, can also enhance the chemosensitivity of
taxane-resistant PCa cells [26,29].
PCa is known to rely on fatty acid metabolism [39]. As such, it is not
surprising that inhibition of FASN and, by extension, inhibition of de novo fatty
acid synthesis displays antitumor properties and increases sensitivity to other
chemotherapeutics. However, FASN is not the only enzyme involved in de novo
fatty acid synthesis. Specifically, acetyl-CoA carboxylase 1 (ACC1) governs the
rate-limiting step of de novo fatty acid synthesis as it irreversibly carboxylates
acetyl-CoA to form malonyl-CoA in an ATP and biotin dependent reaction
[40,41]. Several non-isoform specific small molecule inhibitors of ACC1 have
been shown to induce cell death in a variety of cancer cell lines including lung,
colon and prostate [42,43]. The objective of this study was to investigate the
potential of CP-640186, a non-isoform specific small molecule inhibitor of ACC,
in TxR PCa cells [44]. Here, we demonstrate that ACC inhibition with CP-640186
can synergize with paclitaxel in TxR PCa cells and restore taxane sensitivity.
3.3 Materials and Methods
Materials: CP-640186 was purchased from BioVision (Milpitas, CA). Paclitaxel
was purchased from LC Laboratories (Woburn, MA). Antibodies against cleaved
caspase 3 and cleaved PARP were purchased from Cell Signaling Technologies
(Beverly, MA). Antibody against β-actin was purchased from Sigma-Aldrich (St.
Louis, MO). Secondary antibodies were purchased from Bio-Rad Laboratories
(Hercules, CA). All standard chemicals were purchased from Sigma-Aldrich (St.
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Louis, MO). Cell culture reagents were purchased from Invitrogen (Carlsbad,
CA).
Cell Lines and Culture Conditions: The established cell lines PC3 and DU145
were obtained from the American Type Tissue Collection (ATCC). Taxane
resistant cell lines (PC3-TxR and DU145-TxR) were originally generated by
Tekeda et al. and then provided as a gift from Dr. Aaron Mohs (University of
Nebraska Medical Center) [20]. The human cell lines were determined to be
exempt by the Institutional Review Board of Wake Forest University. All cells
were maintained in RPMI-1640 supplemented with 10% fetal bovine serum, and
1% penicillin/streptomycin, and were cultured in 5% CO2 at 37oC. Taxane-
resistant lines were maintained in 200nM paclitaxel to maintain resistance except
when noted. Cells were routinely tested for mycoplasma.
Immunoblot Analysis: Protein was harvested by trypsinizing cells and pelleting
by centrifugation. Cells were then washed with ice cold PBS and lysed in ice
cold buffer (20 mM Tris, pH 8.3, 5 mM EDTA, 1% Triton X-100) supplemented
with a protease and phosphatase inhibitors (5 µg/mL aprotinin, 5 µg/mL
leupeptin, 5 µg/mL pepstatin A, 1 mM sodium orthovanadate, 1 mM sodium
fluoride, 200 nM okadeic acid, and 200 µM phenylmethylsulfonyl fluoride).
Proteins were separated by SDS-PAGE through 13.5% or 10% gels and
transferred onto nitrocellulose membranes. After incubation with indicated
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antibodies, immunoreactive proteins were detected with enhanced
chemiluminescence (Perkin Elmer, Waltham, MA). β-actin was used as loading
control. Immunoreactive bands were quantified by densitometry using ImageJ.
Cell Viability and Proliferation Assays
MTS Assays: Cells were plated at 1000 cells/well in a 96-well plate and
treated with 10µg/mL TOFA or 100µM CP-640186 for the indicated time points. A
20µL portion of CellTiter 96 AQueous One reagent was added to each well of a 96-
well plate holding 100uL of RPMI Complete Media and the appropriate cells.
Plates were then incubated at 37oC and 5% CO2 for 3 hours. Absorbance was
measured at 490nm and results were normalized to vehicle treated cells.
Trypan Blue Exclusion Assays: Cells were grown in 6-well plates and
treated as indicated for 72 hours. Cells were trypsinized and collected. Cells
were mixed 1:1 with trypan blue and counted using an Invitrogen CountessTM
Automated Cell Counter. Live counts were normalized to vehicle control.
EC50 and Synergy Analysis using Combenefit Software: To examine synergistic
effects of combined taxane and CP-640186 treatment, cell viability was first
assessed via MTS assay (see Cell Viability and Proliferation Assays). Data were
then analyzed using the freely available Combenefit software which analyzes
viability data via the published highest single agent (HSA), Bliss, and Loewe
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models of synergy/antagonism and calculates EC50 values [45]. As the Bliss and
Loewe models are considered more stringent, they were used for analysis [46].
Both models showed similar results. The “contour” and “matrix” views of the
Loewe model are reported in the results section and the Loewe and HSA models
reflect similar results (data not shown).
Agilent Seahorse XF Assays
Glycolysis Stress Assay: All extracellular acidification rates (ECAR) were
measured using the Agilent Seahorse XF-24 Extracellular Flux Analyzer (Agilent,
Santa Clara, CA) per manufacturer’s protocols. Cells were plated at 4 x 104
cells/well in RPMI-1640 with 10% FBS/1% PS. Cells were treated with indicated
treatment or vehicle for 24 hours. Before ECAR measurements were taken, cells
were washed with assay medium supplemented with fresh glutamine per
manufacturer’s protocols and incubated at 37oC without CO2 for 1 hour. After
equilibration, three 3 minute measurements were recorded to measure non-
glycolytic ECAR. Glucose (10mM), oligomycin (1µM), and 2-deoxyglucose (2-
DG) (100mM) were injected into each well sequentially with three 3 minute
measurements after each injection to measure the amount of ECAR associated
with basal glycolysis and glycolytic capacity. The following metrics were used in
accordance with manufacturer’s protocols to analyze results:
basal glycolysis = maximum rate after glucose injection
maximal glycolysis = maximum rate after oligomycin injection
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glycolytic reserve = maximal glycolysis/basal glycolysis
Mitochondrial Stress Assay: All cellular oxygen consumption rates (OCR)
were measured using the Agilent Seahorse XF-24 Extracellular Flux Analyzer
(Agilent, Santa Clara, CA) per manufacturer’s protocols. Cells were plated at 4 x
104 cells/well in RPMI-1640 with 10% FBS/1% PS. Cells were treated with
indicated treatment or vehicle for 24 hours. Before OCR measurements were
taken, cells were washed with assay medium supplemented with fresh sodium
pyruvate and glucose per manufacturer’s protocols and incubated at 37oC
without CO2 for 1 hour. After equilibration, three 3 minute measurements were
recorded to measure the basal level of oxygen consumption. Oligomycin (1µM),
carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP) (0.5µM), and
Rotenone/Antimycin A (Rtn/AA) (1µM each) were injected into each well
sequentially with three 3 minute measurements after each injection to measure
the amount of oxygen consumption for adenosine triphosphate (ATP) production
(coupling efficiency), the level of proton leak (non-ATP-linked oxygen
consumption), maximal respiration capacity, and the level of nonmitochondrial
respiration. The following metrics were used in accordance with manufacturer’s
protocols to analyze results:
basal respiration = 3rd basal measurement
ETC accelerator response = maximum rate after FCCP injection
ATP coupler response = basal respiration - minimum rate after oligomycin
injection
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spare respiratory capacity = ETC accelerator response/basal respiration
coupling efficiency = 1-(ATP coupler response/basal respiration)
Statistical Analysis: Significance between two groups was determined by a two-
tailed Student’s t-test. Significance between three or more groups was first
analyzed by one-way ANOVA then post-hoc Tukey’s t-tests. p≤0.05 considered
significant. Statistical analysis calculated with Prism 6 or Combenefit software.
Error bars represent standard deviation.
3.4 Results
The ACC inhibitor CP-640186 shows enhanced anti-tumor effects in taxane-
resistant prostate cancer cell lines.
To assess the sensitivity of parental and TxR DU145 and PC3 cells to
paclitaxel and CP-640186, cells were treated with varying concentrations of CP-
640186 along with varying concentrations of paclitaxel. Viability was assessed
via MTS assay and normalized to vehicle controls. The freely-available
Combenefit software was used to calculate EC50 values. The DU145-TxR and
PC3-TxR cells show enhanced sensitivity to CP-640186 when compared the
parental lines (Figure 1A.-1D.). Parental DU145 cells were not susceptible to
CP-640186 as an absolute EC50 value could not be calculated (Figure 1A.).
However, DU145-TxR cells were sensitive to CP-640186 treatment as the EC50
was calculated at 75.8µM (Figure 1B.). Similarly, the CP-640186 value for the
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parental PC3 line was determined to be greater than the highest concentration
tested (Figure 1C.) but the PC3-TxR cell line has an EC50 value of 46.2µM
(Figure 1D.). This experiment also confirmed that the TxR cell lines are resistant
to paclitaxel (Figure 1E.-1H.). DU145-TxR cells are over 500-fold more resistant
to paclitaxel when compared to parental DU145 cells with EC50 values of 2968nM
and 5.09nM, respectively (Figure 1E. & 1F.). Similarly, PC3-TxR cells are over
100-fold more resistant to paclitaxel when compared to parental PC3 cells with
EC50 values of 1060nM and 9.65nM, respectively (Figure 1G. & 1H.).
To further assess the sensitivity of DU145-TxR and PC3-TxR cell lines to
CP-640186, we treated both parental and TxR cell lines with increasing
concentrations of CP-640186 (Figure 2.). To maintain taxane, TxR cell lines
were also cultured with a low concentration (200nM) of paclitaxel which does not
affect viability (Figure 1F. & 1H). Viability was again assessed by MTS assay
and results normalized to the vehicle treated groups for each cell line. Treatment
with 100µM CP-640186 reduced DU145 viability to approximately 85% of control
treated cells (Figure 2A.). In contrast, viability of DU145-TxR cells treated at the
same concentration was reduced by 65% compared to vehicle treated cells
(Figure 2A.). PC3-TxR cells showed enhanced sensitivity to CP-640186
treatment at both 50µM and 100µM (Figure 2B.). Treatment with 50µM CP-
640186 reduced PC3 viability to approximately 85% of control while the same
treatment reduced PC3-TxR viability to 25% of control (Figure 2B.). Likewise,
treatment with 100µM CP-640186 reduced PC3 viability to 65% of control but
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further reduced PC3-TxR viability to less than 10% of control treated cells
(Figure 2B.).
Proliferation and cell death due to CP-640186 treatment were evaluated
by trypan blue assays (Figure 3.). Treatment of both parental and DU145-TxR
with 100µM CP-640186 reduces the number of live cells compared to vehicle
treated controls. However, CP-640186 treatment reduces the number of live
cells in DU145-TxR cells to a greater degree compared to the parental line
(Figure 3A.). A similar pattern is seen in the PC3 and PC3-TxR cell lines
(Figure 3B.). Treatment of parental PC3 or DU145 cells with 100µM CP-
640186 did not induce cell death as no significant increase in the percentage of
trypan blue positive cells was observed (Figure 3C. & 3D.). In contrast, 100µM
CP-640186 treatment increased the percentage of trypan blue positive cells
approximately 6 fold in both DU145-TxR cells and PC3-TxR cells (Figure 3C. &
3D).
Evidence of apoptosis was also seen in the TxR cells treated with CP-
640186 (Figure 4). In Figure 4A. & 4B. parental and TxR DU145 and PC3 cells
were treated with 100µM CP-640186 for 72 hours. To maintain taxane, TxR cell
lines were also cultured with a low concentration (200nM) of paclitaxel. Cleaved
caspase 3 was only detectable in the TxR cell lines treated with CP-640186
(Figure 4A. & B.). In Figure 4C. & 4D, parental DU145 and PC3 cells were
treated alone and in combination with 1nM paclitaxel or 100µM CP-640186 while
DU145-TxR and PC3-TxR were treated alone and in combination with 200nM
paclitaxel or 100µM CP-640186 for 72 hours. An increase in cleaved PARP
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signal was only seen in the DU145-TxR treated with both 200nM paclitaxel and
100µM (Figure 4C.). An increase in cleaved PARP signal was seen in parental
PC3 cells treated in combination with 1nM paclitaxel and 100µM CP-640186 but
a much larger increase in cleaved PARP signal was seen in PC3-TxR cells
treated in combination with 200nM paclitaxel and 100µM CP-640186 (Figure
4D.).
CP-640186 synergizes with paclitaxel in TxR prostate cancer cells.
Because we observed evidence of reduced proliferation and cell death in
TxR cells treated with both CP-640186 and a low dose of paclitaxel (200nM)
which does not affect viability (Figure 1F. & 1H.), we hypothesized that CP-
640186 would synergize with paclitaxel. Synergy was analyzed using the freely-
available Combenefit software [45]. The contour and matrix models of the Loewe
model of synergy/antagonism are shown in Figure 5. The Loewe model defines
the “expected” effect as if a drug was combined with itself [45]. The Combenefit
software reports synergy scores which refers to the difference between the
observed effect and expected effect with positive values indicating synergy
[26,45]. Some synergy between paclitaxel and CP-640186 was seen in the
parental DU145 (Figure 5A. & 5E.) and PC3 (Figure 5C. & 5G.) cell lines,
mostly around the paclitaxel EC50 values. However robust synergy was reported
in the DU145-TxR (Figure 5B. & 5F.) and PC3-TxR (Figure 5D. & 5H.) with
synergy scores over 50 at concentrations of paclitaxel and CP-640186 well below
the respective EC50 values (Figure 1).
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Combination paclitaxel and CP-640186 treatment disrupts metabolism in
taxane-resistant prostate cancer cell lines.
Because CP-640186 inhibits ACC, a critical metabolic enzyme, we next
hypothesized that the synergy observed between paclitaxel and CP-640186
might be due to metabolic alterations in the TxR cells. To this end, we tested the
effects of combination paclitaxel and CP-640186 treatment on glycolytic activity
in parental and TxR DU145 (Figure 6) and PC3 (Figure 7). Analysis of glycolytic
activity was done using an Agilent Seahorse XF24 Analyzer. Treatment with
25µM CP-640186 or paclitaxel (1nM in parental cell line, 200nM in TxR cell line)
alone or in combination did not affect basal glycolysis levels in the parental
DU145 or DU145-TxR cell lines (Figure 6A. & 6B.,quantification not shown).
Treatment with 25µM CP-640186 or 1nM paclitaxel alone or in combination also
did not affect glycolytic capacity in the parental DU145 cell line (Figure 6A. &
6C.). In contrast, combination treatment with 25µM CP-640186 and 200nM
paclitaxel significantly reduced glycolytic capacity in DU145-TxR cells (Figure
6B. & 6D.). Similar results were seen with PC3 and PC3-TxR cells (Figure 7).
Single or combination treatment did not affect basal glycolysis in the parental
PC3 or PC3-TxR (Figure 7A. & 7B., quantification not shown). However,
combination treatment was able to significantly reduce the relative glycolytic
capacity of the PC3-TxR cells (Figure 7B. & 7D.).
We also used the Agilent Seahorse XF24 Analyzer to analyze
mitochondrial metabolism in cells treated with paclitaxel and CP-640186 alone or
164
in combination (Figure 8). Treating parental PC3 cells with 25µM CP-640186 or
1nM paclitaxel alone or in combination did not affect mitochondrial function
(Figure 8A.). In contrast, treating PC3-TxR cells with 25µM CP-640186 and
200nM paclitaxel in combination significantly reduced basal response (Figure
8B. & 8D.). Interestingly, this effect seems to be considerably driven by CP-
640186 treatment as CP-640186 treatment results in a considerable but non-
significant decrease of basal mitochondrial function in PC3-TxR cells (Figure
8D., vehicle versus 25µM CP-640186 tukey adjusted p-value = 0.1714, 200nM
paclitaxel versus 25µM CP-640186 tukey adjusted p-value = 0.0747).
3.5 Discussion
Several FASN inhibitors including cerulenin, C75, orlistat, and a novel
nanoparticle formulation of orlistat (NanoOrl) have been shown to enhance
taxane sensitivity in chemoresistant cell lines [26,28,38]. In this study, treatment
with paclitaxel and CP-640186, a non-isoform specific ACC inhibitor, showed
strong synergy in DU145-TxR and PC3-TxR cell lines. To our knowledge, this is
the first study to show a non-isoform specific ACC inhibitor synergizes with a
taxane in TxR prostate cancer cell lines.
In their study using NanoOrl in DU145-TxR and PC3-TxR cells, Souchek
et al. attribute the reported synergy between NanoOrl and several taxanes
(paclitaxel, docetaxel, and cabazitaxel) to increased microtubule stability. While
the effects of CP-640186 and paclitaxel co-treatment on microtubule stability
were not directly explored in this study, we found that CP-640186 and paclitaxel
165
co-treatment reduced glycolytic and mitochondrial metabolism only in the TxR
cell lines (Figures 6-8), suggesting that the observed synergy is associated with
decreased metabolic function.
Interestingly, microtubule formation/stability and de novo fatty acid
synthesis have been found to be related in several cancer models including lung,
ovarian, pancreatic, and prostate [47]. Specifically, Heuer et al. found that FASN
inhibition reduced tubulin palmitoylation, disrupted microtubule organization, and,
when combined with taxane treatment, resulted in in vivo tumor growth inhibition
[47]. Beyond this, the exact reason underlying the synergistic effects of
paclitaxel-mediated microtubule stabilization and inhibition of de novo fatty acid
synthesis are not fully understood. This study indicates that the synergy
between taxane treatment and de novo fatty acid synthesis inhibition is not
dependent on FASN inhibition as ACC inhibition can achieve similar results.
Importantly, this suggests that more than one opportunity exists to target de novo
fatty acid synthesis to enhance taxane sensitivity and anti-tumor results.
Future study is needed to explore the possibilities of co-treating
chemoresistant cancers with taxanes and de novo fatty acid synthesis inhibitors.
First, replication studies should be performed to determine whether the synergy
observed between CP-640186 and paclitaxel is also seen with docetaxel and
cabazitaxel, the taxanes more commonly utilized to treat PCa patients.
Furthermore, CP-640186 is one of several commercially available small molecule
non-isoform specific ACC inhibitors. CP-640186 inhibits ACC by interfering with
the carboxytransferase domain of the enzyme [48]. In contrast, soraphen A
166
inhibits ACC by interacting with the ATP binding site of the biotin carboxylase
domain and TOFA (5-(tetradecyloxy)-2-furoic acid) allosterically interacts at the
ACC carboxytransferase domain [48]. Recently, ND-646 (developed by Nimbus)
was reported to act as an allosteric biotin carboxylase inhibitor that prevents ACC
dimerization and which shows anti-tumor activity in an in vitro and in vivo model
of non-small-cell lung cancer [49]. Determining if all or a subset of ACC inhibitors
synergize with taxanes in the same manner CP-640186 does could provide
insight into the mechanism of synergy. Ultimately, our data showing synergy
between CP-640186 and paclitaxel justifies futher study into the use of ACC, and
other de novo fatty acid synthesis inhibitors, to address taxane resistance.
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3.6 Figures
Figure 1: EC50 concentrations of CP-640186 and paclitaxel in parental and
taxane-resistant PCa cells. Parental and taxane-resistant DU145 and PC3
were treated with varying concentrations of paclitaxel (A.-D.) and CP-640186 (E.-
H.) for 72 hours. Cell viability was assessed via MTS assay and data were
normalized to vehicle control wells on each plate. EC50 results analyzed using
Combenefit software. “X” marks represent means resulting from 2 independent
experiments of 6 replicates.
168
Figure 2: Paclitaxel resistant (TxR) PCa cell lines show enhanced
sensitivity to CP-640186. (A.) DU145 and DU145-TxR or (B.) PC3 and PC3-
TxR cells were treated with vehicle (DMSO), 25µM, 50µM, or 100µM CP-640186
for 72 hours. TxR cells cultured in 200nM paclitaxel to maintain resistance. Cell
viability determined by MTS assay and expressed as % of control DMSO treated
cells. Error bars represent standard deviation. P-values determined by a two-
tailed Student’s t-test. *p≤0.05, **p≤0.01. Results represent 3 independent
experiments of 6 replicates.
169
Figure 3: CP-640186 treatment reduces proliferation and increases cell
death in taxane resistant PCa cells. (A. & C.) DU145 and DU145-TxR or (B. &
D.) PC3 and PC3-TxR cell were treated with vehicle (DMSO) or 100µM CP-
640186 for 72 hours. TxR cells were cultured in 200nM paclitaxel to maintain
resistance. Cells were counted by trypan blue exclusion using an Invitrogen
CountessTM Automated Cell Counter. Error bars represent standard deviation.
Live cell count (A. & B.) results were normalized to vehicle control. P-values
determined by a two-tailed Student’s t-test. *p≤0.05, **p≤0.01. Results
represent three independent experiments.
170
Figure 4: Combination treatment with paclitaxel and CP-640186 induces
apoptosis in taxane resistant PCa cells. (A. & B.) DU145 and PC3 parental
and TxR cells were treated with 100µM CP-640186 or vehicle (DMSO) for 72
hours. TxR cells were cultured in 200nM paclitaxel to maintain resistance. (C. &
D.) DU145 and PC3 parental cells were treated with 100µM CP-640186 or
vehicle (DMSO) and 1nM paclitaxel or vehicle (DMSO) for 72 hours. DU145 and
PC3 taxane resistant cells were treated with 100µM CP-640186 or vehicle
(DMSO) and 200nM paclitaxel or vehicle (DMSO) for 72 hours. Whole cell
171
lysates were collected from (A. & C.) DU145 and DU145-TxR or (B. & D.) PC3
and PC3-TxR cells treated as indicated and described above. Cleaved caspase
3 and cleaved PARP levels determined by western blot analysis. β-actin used as
loading control. Densitometry was performed by ImageJ.
172
Figure 5: Combination paclitaxel and CP-640186 treatment is synergistic in
taxane-resistant PCa cells. Parental and taxane-resistant DU145 and PC3
cells plated at 1,000 cells/well in a 96-well plate and treated with the indicated
concentrations of paclitaxel and CP-640186 for 72 hours. Cell viability data was
normalized to vehicle control wells on each plate. Synergy analyzed using
Combenefit software. Results show the (A.-D.) “contour” and (E.-H.) “matrix”
views of synergy/antagonism calculations for the Loewe model and represent 2
independent experiments of 6 replicates. Significant synergy is indicated by dark
blue areas. (E.-H.) Matrix view reports synergy score, standard deviation, and
significance level. *p≤0.05, **p≤0.01, ***p≤0.001.
173
Figure 6: Combination paclitaxel and CP-640186 treatment reduces
glycolytic reserve in DU145-TxR cells. (A.) DU145 or (B.) DU145-TxR cells
plated at 40,000 cells per well in an XF-24 well plate were treated with 25µM CP-
640186, 1nM paclitaxel (DU145) or 200nM paclitaxel (DU145-TxR), or both CP-
640186 and paclitaxel for 24 hours. After treatment, cells were incubated in base
medium supplemented with glutamine followed by incubation at 37oC in a non
CO2-incubator for 1 hour. Three subsequent injections followed as listed: 10mM
glucose, 1µM oligomycin, 100mM 2-deoxyglucose. ECAR values were
automatically recorded and calculated by the Seahorse XF-24 software. (C. &
D.) Glycolytic reserve quantified for (C.) DU145 and (D.) DU145-TxR cells and
presented as % of basal glycolysis for each treatment group. Error bars
represent the mean ± standard deviation resulting from 2 independent
174
experiments of 5 replicates. (C.) One-way ANOVA not significant. (D.) One-way
ANOVA p<0.05, Tukey’s post-tests *p≤0.05.
175
Figure 7: Combination paclitaxel and CP-640186 treatment reduces
glycolytic reserve in PC3-TxR cells. (A.) PC3 or (B.) PC3-TxR cells plated at
40,000 cells per well in an XF-24 well plate were treated with 25µM CP-640186,
1nM paclitaxel (PC3) or 200nM paclitaxel (PC3-TxR), or both CP-640186 and
paclitaxel. After treatment, cells were incubated in base medium supplemented
with glutamine followed by incubation at 37oC in a non CO2-incubator for 1 hour.
Three subsequent injections followed as listed: 10mM glucose, 1µM oligomycin,
100mM 2-deoxyglucose. ECAR values were automatically recorded and
calculated by the Seahorse XF-24 software. (C. & D.) Glycolytic reserve was
quantified for (C.) PC3 and (D.) PC3-TxR cells and presented as % of basal
glycolysis for each treatment group. Error bars represent the mean ± standard
deviation resulting from 2 independent experiments of 5 replicates. (C.) One-way
176
ANOVA not significant. (D.) One-way ANOVA p<0.05, Tukey’s post-tests
*p≤0.05.
177
Figure 8: Combination paclitaxel and CP-640186 treatment reduces basal
mitochondrial respiration in PC3-TxR cells. (A.) PC3 or (B.) PC3-TxR cells
plated at 40,000 cells per well in an XF-24 well plate were treated with 100µM
CP-640186, 1nM paclitaxel (PC3) or 200nM paclitaxel (PC3-TxR), or both CP-
640186 and paclitaxel. After treatment, cells were incubated in base medium
supplemented with 25mM glucose and 1mM sodium pyruvate followed by
incubation at 37oC in a non CO2-incubator for 1h. Three subsequent injections
followed as listed - 1µM oligomycin, 0.5µM FCCP, 1µM rotenone/antimycin.
OCR values automatically recorded and calculated by the Seahorse XF-24
software. (C. & D.) Basal mitochondrial response was quantified for (C.) PC3
and (D.) PC3-TxR cells and presented as % of basal glycolysis for each
treatment relative to vehicle. Error bars represent the mean ± standard deviation
178
resulting from 2 independent experiments of 5 replicates. (C.) One-way ANOVA
not significant. (D.) One-way ANOVA p<0.05, Tukey’s post-tests *p≤0.05.
179
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187
CHAPTER IV
GENERAL DISCUSSION
Amanda L. Davis and Steven J. Kridel
A. Davis prepared the text and S. Kridel provided advisory and editorial
assistance.
188
4.1 Targeting fatty acid metabolism in cancer
The initiation and progression of any cancer type depends upon a host of
factors and processes – enhanced bioenergetics, membrane synthesis, aberrant
signaling, migration, angiogenesis, immunosuppression, and the development of
drug resistance. Strikingly, lipid and fatty acid metabolism has been found to be
a key player in each of these pathways [1]. Given the importance of fatty acids in
cancer biology, it is unsurprising that the major pathways that support fatty acid
metabolism, (de novo fatty acid synthesis, β-oxidation, fatty acid storage and
release) are the subject of continued research [2]. Yet strategies to inhibit or
target these pathways have not progressed into clinical practice. This is likely
due to several factors including the dynamic nature of cellular metabolism, the
influence of tumor microenvironment, and an incomplete comprehensive
understanding of cancer metabolism. The work presented in this dissertation is
the first to our knowledge to explore the consequences of ACC1 specific
inhibition in an in vivo PCa model (Chapter 2). Additionally, this body of work
provides critical insight into the functions of ACC isoforms (Chapter 2) and
explores the possibility of targeting ACC to overcome chemoresistance (Chapter
3).
4.1.1 Inhibiting ACC in cancer
The mammalian ACCs have three functional domains and are tightly
regulated by several other pathways (discussed in Chapter 1). Together, this
presents several potential avenues to target ACC and multiple opportunities to
develop pharmacological inhibitors. It is likely the method of targeting ACC will
189
have significant influence on the success and properties of ACC inhibition. This
has been demonstrated in vivo as two murine ACC1 liver knockout models (one
targeting exon 22 of the BCCP domain, one targeting exon 46 of the CT domain)
yield different results (discussed in Chapter 1) [3,4]. Properties of currently
available ACC inhibitors were reviewed and discussed in Chapter 1 (see 1.4.8
ACC inhibitors). Of the current ACC inhibitors previously discussed, ND-646
shows the most promise for translation into clinical use. This compound interacts
at the BC domain of ACC to simultaneously prevent ACC dimerization and
maintain dephosphorylation [5]. Interestingly, ND-646 seems to be more
promising than Soraphen A which also prevents ACC dimerization by acting at
the BC domain but does not interfere with phosphorylation [6]. Taken together,
this implies that more work is needed to determine the optimal method of
targeting ACC so that effective compounds may be developed and applied in the
appropriate contexts.
The work presented in this dissertation suggests that, in addition to the
mechanism of ACC inhibition, the timing of ACC inhibition is also important.
Although studies have indicated that ACC1 is the major driver of cancer cell
survival, the work presented in Chapter 2 indicates that an ACC1 specific
inhibitor might not be appropriate in all contexts [7]. However, our data also
suggests that ACC1 specific inhibition would be beneficial if given early in the
development of a cancer (Chapter 2) and that dual ACC1 and ACC2 inhibition
would be beneficial in more advanced cancers, including cases of
chemoresistance (Chapters 2 & 3).
190
Ultimately, additional work is needed to completely understand the
interplay between the ACC isoforms, how a cancer cell reacts to ACC1 inhibition,
and how to properly leverage this for chemotherapeutic purposes. Our data
suggest that in PCa, cells bypass their requirement for ACC1 by shifting their
preferred source of fatty acid from de novo fatty acids to exogenous fatty acids.
However, we acknowledge other possibilities may exist. To this end, Figure 1
details other potential mechanisms to explain this phenotype. It will be important
to fully understand this phenomenon as whatever path a cancer cell uses to
retool its metabolism presents another therapeutic opportunity.
4.1.2 Other methods of targeting fatty acid synthesis
While this work has focused heavily on ACC, it is worth keeping in mind
other methods of targeting fatty acid synthesis in cancer. As discussed in
Chapter 1, FASN receives the bulk of attention when it comes to targeting de
novo fatty acid synthesis for cancer therapy [8,9]. In August of 2015, 3-V
Biosciences started the first-in-human study of a novel orally-available FASN
inhibitor TVB-2640 [10]. Further study confirmed that TVB-2640 inhibits
lipogenesis in patients and, when combined with paclitaxel, achieves partial
responses or stable disease in the majority of heavily pretreated breast cancer
(varying hormone status) patients studied while showing favorable tolerability
[11,12]. TVB-2640 is currently listed in Phase I clinical trial for colon cancer and
in Phase II clinical trial for both breast cancer and astrocytoma [13]. This further
191
supports the hypothesis that targeting fatty acid metabolism is a promising
avenue for chemotherapeutic development.
4.2 Concluding remarks and future directions
The notion of targeting cellular metabolism, particularly fatty acid
metabolism, for chemotherapeutic purposes has become increasingly popular.
The work presented in this dissertation had added valuable insight into the roles
of both mammalian ACC isoforms in cancer biology as well as the consequences
of isoform-specific versus isoform-nonspecific targeting. Chapter II provided the
first evidence that ACC1 is required for cancer development in an in vivo model
of prostate cancer and indicated that as PCa progresses, cells rewire their
metabolism to utilize dietary fatty acids in lieu of de novo derived fatty acids.
This chapter also revealed that non-isoform specific ACC inhibition can disrupt
cancer metabolism, even as the disease progresses. The work presented in
Chapter III adds much to the small body of literature discussing the use of the
non-isoform specific ACC inhibitor CP-640186 and shows that this compound
can re-sensitize chemoresistant cells to previously used chemotherapeutic
agents.
Overall, this body of work not only contributes valuable knowledge to the
field of cancer metabolism, it also provides several platforms for further study.
The role of ACC1 in PCa in mice fed a low-fat diet as well as the role of ACC1 in
an in vivo model of CRPC are two important areas that warrant future study.
Furthermore, the extent that ACC inhibition can reverse chemoresistance is not
192
fully understood. As such, additional ACC inhibitors should be tested in multiple
scenarios of chemoresistance. Ultimately, utilization of the studies presented in
this dissertation will lead to an increased understanding of the field of cancer
metabolism, will help direct development of novel and creative chemotherapeutic
strategies, and perhaps lead to improved patient outcomes.
Figure 1: Potential mechanisms of overcoming ACC1 requirement. Our
data suggest that cancers can progress despite ACC1 inactivation. Potential
mechanisms to explain this phenotype include ACC2 compensation (top), a
193
change in fatty acid utilization (middle), and/or a metabolic shift toward an
increase in energy metabolites such as ATP, NADPH, and acetate (bottom).
194
4.3 References
1. Rohrig F, Schulze A (2016) The multifaceted roles of fatty acid synthesis in
cancer. Nat Rev Cancer 16: 732-749.
2. Currie E, Schulze A, Zechner R, Walther TC, Farese RV, Jr. (2013) Cellular
fatty acid metabolism and cancer. Cell Metab 18: 153-161.
3. Harada N, Oda Z, Hara Y, Fujinami K, Okawa M, et al. (2007) Hepatic de novo
lipogenesis is present in liver-specific ACC1-deficient mice. Mol Cell Biol
27: 1881-1888.
4. Mao J, DeMayo FJ, Li H, Abu-Elheiga L, Gu Z, et al. (2006) Liver-specific
deletion of acetyl-CoA carboxylase 1 reduces hepatic triglyceride
accumulation without affecting glucose homeostasis. Proc Natl Acad Sci U
S A 103: 8552-8557.
5. Svensson RU, Parker SJ, Eichner LJ, Kolar MJ, Wallace M, et al. (2016)
Inhibition of acetyl-CoA carboxylase suppresses fatty acid synthesis and
tumor growth of non-small-cell lung cancer in preclinical models. Nat Med
22: 1108-1119.
6. Tong L, Harwood HJ, Jr. (2006) Acetyl-coenzyme A carboxylases: versatile
targets for drug discovery. J Cell Biochem 99: 1476-1488.
7. Luo J, Hong Y, Lu Y, Qiu S, Chaganty BK, et al. (2017) Acetyl-CoA
carboxylase rewires cancer metabolism to allow cancer cells to survive
inhibition of the Warburg effect by cetuximab. Cancer Lett 384: 39-49.
8. Swinnen JV, Van Veldhoven PP, Timmermans L, De Schrijver E, Brusselmans
K, et al. (2003) Fatty acid synthase drives the synthesis of phospholipids
195
partitioning into detergent-resistant membrane microdomains. Biochem
Biophys Res Commun 302: 898-903.
9. Swinnen JV, Brusselmans K, Verhoeven G (2006) Increased lipogenesis in
cancer cells: new players, novel targets. Curr Opin Clin Nutr Metab Care
9: 358-365.
10. Patel M, Infante J, Von Hoff D, Jones S, Burris H, et al. (2015) Report of a
First-In-Human Study of the First-In-Class Fatty Acid Synthase (FASN)
Inhibitor, TVB-2640.
11. Brenner A, Falchook G, Patel M, Infante J, Arkenau HT, et al. (2016) Heavily
Pre-Treated Breast Cancer Patients show Promising Responses in the
First in Human Study of the First-In-Class Fatty Acid Synthase (FASN)
Inhibitor, TVB-2640 in Combination with Paclitaxel.
12. Jones SF, Infante JR (2015) Molecular Pathways: Fatty Acid Synthase. Clin
Cancer Res 21: 5434-5438.
13. (2017) 3-V Biosciences Pipeline. http://www.3vbio.com/programs/pipeline/.
Amanda LeighAnne Davis [email protected]
EDUCATION
Wake Forest School of Medicine, Winston Salem, NC August 2010-August 2017
Ph.D., Department of Cancer Biology
AACR Women in Cancer Research Scholar Award 2013
Wake Forest Graduate School Alumni Student Travel Award 2012
Wake Forest Cancer Biology Guzenhauser Award 2010
Wake Forest University, Winston-Salem, NC August 2014-August 2016
Masters of Business Administration
Beta Gamma Sigma Honor Society 2016
Wake Forest University PhD/MBA Academic Scholarship 2014
North Carolina State University, Raleigh, NC August 2006-May 2010
B.S. Animal Science, Magna Cum Laude
Phi Sigma Pi National Honor Fraternity, Beta Delta Chapter 2008-2010
Vice President (2009-2010), Leadership Co-Chair (2009)
Zach and Mary Ladd Scholarship 2008-2010
Dean’s List 2007-2010
University Scholars Program 2006-2010
PROFESSIONAL SUMMARY
Experienced researcher and educated to perform at the unique intersection of science
and business. Highly organized and detail-conscious with proven history of academic
accomplishment and institutional service. Able to communicate and present complex
concepts clearly, correctly interpret data, comprehend intricate requirements, effectively
train other researchers, and encourage productive relationships.
Experimental Design
Data Presentation
Public Speaking
Detailed Documentation
Statistical Analysis
Compound Formulation
New Employee Training
Microsoft Office
Mammalian Cell Culture
RESEARCH EXPERIENCE
Wake Forest School of Medicine, Winston Salem, NC August 2010-August 2017
Analyze role of lipid metabolism in prostate cancer biology with particular focus on small
molecule inhibitors of enzymes responsible for de novo fatty acid synthesis and their
potential as chemotherapeutic agents. Responsible for all aspects of research including
small rodent handling and breeding, metabolic analysis using Seahorse Bioscience
XF24, cell culture, western blotting, and metabolic radiolabeling assays.
Piedmont Research Center, Morrisville, NC June 2009-August 2010
Trained in pre-clinical testing of chemotherapeutics. Responsible for all aspects of multiple in-house xenograft studies, compound formulation, cell culture, weekly data presentations, proficient in PO, IP, IV, and SC dosing of mice. Center for Integrated Fungal Research, Raleigh, NC April 2008-August 2009 Responsible for stock fungal cultures and tobacco plants, assisted with various experiments as directed by other researchers to explore interactions between fungal pathogens and their host plants.
INSTITUTIONAL SERVICE & VOLUNTEER WORK
Wake Forest School of Business Evening MBA Honor Council 2014-2016
Wake Forest University Healthcare Strategy Conference & 2014-2015
Case Competition Leadership Team
Wake Forest Cancer Biology Department Curriculum Committee 2012-2017
Student Representative
Wake Forest Security Advisory Committee 2012-2017
Student Representative
Volunteer Science Fair Judge at Sherwood Forest Elementary School 2012-2013
PUBLICATIONS
Davis AL, Scott KEN, Wheeler FB, Wu J, Abu-Elheiga L, Wakil S, Chen YQ, Kridel SJ. Acetyl-coA carboxylase 1 inhibition impairs in vitro prostate cancer metabolism and reduces tumor burden in an in vivo murine model of prostate cancer. (in preparation) Davis AL, Ladipo T, Souchek JJ, Mohs AM, Kridel SJ. Combination treatment with small molecule acetyl-coA carboxylase inhibitor CP-640186 and paclitaxel is synergistic and disrupts metabolic function in taxane-resistant prostate cancer cells. (in preparation) Souchek JJ, Davis AL, Hill TK, Holmes MB, Qi B, Singh PK, Kridel SJ, Mohs AM. (2016) Combination Treatment with Orlistat-Containing Nanoparticles and Taxanes is Synergistic and Enhances Microtubule Stability in Taxane-Resistant Prostate Cancer Cells. (accepted to Molecular Cancer Therapeutics 5/18/17) Davis AL, Kridel SJ. (2016) Trimming the fat in non-small cell lung cancer: A new small molecule inhibitor of Acetyl-CoA Carboxylase to target fatty acid synthesis” [Commentary on Svensson, RU, et al. 2016]. Translational Cancer Research. doi: 10.21037/tcr.2016.12.21 Hill TK, Davis AL, Wheeler FB, Kelkar SS, Freund EC, Lowther WT, Kridel SJ, Mohs AM. (2016) Development of a Self-Assembled Nanoparticle Formulation of Orlistat, Nano-ORL, with Increased Cytotoxicity against Human Tumor Cell Lines. Molecular pharmaceutics. 13(3):720-728. doi:10.1021/acs.molpharmaceut.5b00447. Scott KEN, Wheeler FB, Davis AL, Thomas MJ, Ntambi JM, Seals DF, Kridel SJ. (2012) Metabolic Regulation of Invadopodia and Invasion by Acetyl-CoA Carboxylase 1 and De novo Lipogenesis. PLoS ONE 7(1): e29761. doi:10.1371/journal.pone.0029761
Burger KL, Davis AL, Isom S, Mishra N, Seals DF. (2011), The podosome marker protein Tks5 regulates macrophage invasive behavior. Cytoskeleton, 68: 694–711. doi: 10.1002/cm.20545
PRESENTATIONS
Amanda L. Davis, Kristen E.N. Scott, Jiansheng Wu, Lutfi Abu-Elheiga, Salih Wakil, Yong Q. Chen, Steven J. Kridel. ACC1 is required for prostate cancer initiation, but not progression? Poster presented at: American Association for Cancer Research Metabolism and Cancer Conference; June 8, 2015; Bellevue, WA “ACC 1 Required for Initiation of Prostate Cancer, Not Progression?” Seminar presented at: 2014 Wake Forest Department of Lipid Science Symposium; 2014 September 24, 2014; Winston-Salem, NC. Amanda L. Davis, Kristen E.N. Scott, Jiansheng Wu, Lutfi Abu-Elheiga, Salih Wakil, Yong Q. Chen, Steven J. Kridel. Acetyl CoA Carboxylase 1 and its role in Prostate Cancer Initiation and Progression. Poster presented at: 2013 Annual Meeting of the American Association for Cancer Research; 2013 April 6-10; Washington D. C. Amanda L. Davis, Kristen E.N. Scott, Jiansheng Wu, Lutfi Abu-Elheiga, Salih Wakil, Yong Q. Chen, Steven J. Kridel. Acetyl CoA Carboxylase 1 and its role in Prostate Cancer Initiation and Progression. Poster presented at: Thirteenth Annual Wake Forest University Graduate Student & Postdoc Research Day; 2013 March 21; Winston-Salem, NC. “Acetyl-CoA Carboxylase 1 and its Role in Prostate Cancer Progression” Seminar
presented yearly at: Wake Forest Department of Cancer Biology Research Progress
Report 2010-2015; Winston-Salem, NC.