The Effects of Metformin on Pancreatic Cancer Metabolism

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Authors Cantoria, Mary Jo Castro

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THE EFFECTS OF METFORMIN ON PANCREATIC CANCER METABOLISM

Mary Jo Cantoria

______

A Dissertation Submitted to the Faculty of the

DEPARTMENT OF NUTRITIONAL SCIENCES

In Partial Fulfillment of the Requirements For the Degree of

DOCTOR OF PHILOSOPHY

In the Graduate College

THE UNIVERSITY OF ARIZONA

2014

THE UNIVERSITY OF ARIZONA

GRADUATE COLLEGE

As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Mary Jo Cantoria, titled The Effects of Metformin on Pancreatic Cancer Metabolism and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy.

______Date: May 1, 2014

Emmanuelle J. Meuillet, PhD

______Date: May 1, 2014

Craig S. Stump, MD PhD

______Date: May 1, 2014

Jennifer A. Teske, PhD

______Date: May 1, 2014

Tsu-Shuen Tsao, PhD

Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College.

I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement.

______Date: May 1, 2014

Dissertation Director: Emmanuelle J. Meuillet, PhD

STATEMENT BY AUTHOR

This dissertation has been submitted in partial fulfillment of the requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.

Brief quotations from this dissertation are allowable without special permission, provided that an accurate acknowledgement of the source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the copyright holder.

SIGNED: Mary Jo Cantoria

ACKNOWLEDGEMENTS

I would like to express my deepest gratitude to my mentors, family and friends who have helped me and seen me through graduate school.

I would like to thank my adviser, Dr. Emmanuelle J. Meuillet, for her guidance and support. As a young investigator embarking on her first steps in cancer research, this area can be very overwhelming. Emmanuelle encouraged me along my journey and more importantly, she believed in me when I didn’t believe in myself.

My committee members, Drs. Tsu-Shuen Tsao, Craig S. Stump and Jennifer Teske have also been very supportive and helpful as co-mentors. They have provided invaluable ideas during our committee meetings which have significantly contributed to my dissertation project.

I would also like to thank Dr. Laszlo G. Boros (UCLA/SiDMAP) for his mentorship. His patience and willingness to share his knowledge of metabolism, scientific thinking and scientific creativity has opened a new research world for me.

I would like to express my gratitude to the former and current members of the Meuillet Lab – Drs. Sylvestor A. Moses, Hui-Hua Chang, Weixi Kong; Jessica Norberg, Earlphia Sells and Tracey E. Beyer. We have had great discussions about science and our graduate school experiences. Thank you for keeping me company throughout my PhD.

Besides my committee, I am thankful to the people who have helped me with methods and with whom I have had lively scientific discussions with. I would like to thank Dr. Jana Jandova for her help with RT-PCR and our scientific conversations. I would also like to thank Dr. Jennifer H. Stern and Dina V. Hingorani for being both science friends and “normal” friends.

I would like to thank the AZ Cancer Center EMSS and TACMASS core facilities for their immense help with the in vivo experiments.

I would not have done my PhD without the support of the Nutritional Sciences Department, its faculty and staff. Of course, I am grateful to the AZ Cancer Center for being my science home during my PhD, and its staff and faculty for being good neighbors.

My PhD was funded by the USDA National Needs Fellowship (Grant 2010-38420- 20369) to which I am thankful for.

Last but definitely not the least, I would like to thank my family and friends for their support and patience. My mom, Auntie Tess, Uncle Sefer, Lolo Gold, Lola Egg, Sharon, Charles, Matt and my family in the Philippines who have been there for me to support me in my education.

TABLE OF CONTENTS

LIST OF FIGURES………………………………………………………………….10

LIST OF TABLES…………………………………………………………………...13

LIST OF ABBREVIATIONS……………………………………………………….14

ABSTRACT…………………………………………………………………………..19

CHAPTER I: INTRODUCTION…………………………………………………....21

I. Pancreatic Cancer: Epidemiology, Biology and Therapeutics………....21

II. The Hallmarks of Cancer: Focus on Deregulating Cellular Energetics.31

A. Introduction……………………………………………………………31 B. Self-sufficiency in Growth Signals…………………………………….31 C. Insensitivity to Anti-growth Signals…………………………………...33 D. Evading Programmed Cell Death……………………………………...33 E. Limitless Replicative Potential…………………….…………………..35 F. Sustained Angiogenesis………………………………………………..36 G. Tissue Invasion and Metastasis………………………………………..36 H. Emerging Hallmarks of Cancer………………………………………..37 1. Isocitrate dehydrogenases…………………………………….…....40 2. Glutamine Addiction………………………………………….…...43 3. Pyruvate Kinase……………………………………………….…...44 4. Lipogenic Phenotype……………………………………………....47 5. Alternative ATP-Producing Pathway……………………………...48

III. Metformin as a Chemopreventive and Chemotherapeutic Agent…….50

A. The History Behind MET……………………………………………...50 B. Synthesis, Structure and Pharmacology of MET……………………....51 C. Physiological Effects of MET Against Hyperglycemia………………..53 D. Chemopreventive Properties of MET………………………………….54 E. Chemotherapeutic Properties of Metformin…………………………...60

F. MET as an Anti-lipogenic Drug………………………………………64 1. Revisiting Aberrant Metabolism of Cancer Cells…………………64 2. MET Inhibits ETC Complex I…………………………………….66 3. MET Inhibits Expression of Lipogenic Enzymes………………....67 4. MET Inhibits Oxidation of Fatty Acids…………………………...68 5. MET Inhibits SREBP Transcription Factors……………………....68 6. MET Inhibits SCD1……………………………………………….69 7. MET’s Anti-Lipogenic Effect is Context-Dependent………….....71 G. Ongoing Clinical Trials on Metformin as a Chemotherapeutic Drug for Pancreatic Cancer…………………………………….……...72

IV. Research Problem, Hypotheses and Specific Aims………….…………74

CHAPTER II: IN VITRO EVIDENCE OF THE ANTI-CANCER EFFECTS OF METFORMIN USING STABLE ISOTOPE-BASED DYNAMIC METABOLIC PROFILING (SIDMAP) TECHNOLOGY………………………………………..76

I. Introduction……………………………………………………………………76

A. MET’s Anti-cancer Properties……………………………………………..77 B. The SiDMAP Platform…………………………………………………….79

II. Materials and Methods……………………………………………………….82

A. Cell Culture and Proliferation……………………………………………..82 1. Cell Culture Conditions………………………………………….……82 2. In vitro Model of Insulin Resistance via CHS Supplementation of the Media………………………………………………………………...... 82 3. Cell Proliferation Measurement by Counting…………………….…....84 4. MTT Assay to Assess Cell Viability……………………………….…..84 B. Stable Glucose Isotope……………………………………………………..85 C. Product Extraction and Derivatization……………………………………..85 D. Statistics…………………………………………………………………….87 E. Visual System Wide Association Interface………………………………...88 F. Practical Note to Multiple SWAS Entry Interpretations…………..….……88

III. Results………………………………………………………………………..89

A. MET Does Not Change PDAC Cell Viability and Proliferation…………..89 13 B. Heavy [1,2- C2]-D-glucose Enrichment and Cholesteryl Hemisuccinate (CHS) Media Preparation……….………….....91 C. Complete Glucose Oxidation…………………………………………….....95 D. Lactate Synthesis……………………………………………………….…...96 E. Fatty Acid Palmitate Synthesis………………………………….…….…....98 F. Sterol Ring Synthesis…………………………………………….….……...98 G. System Wide Associations……………………………………….…….…...99

IV. Discussion……………………………………………………………..………102

V. Concluding Remarks………………………………………………..………..108

CHAPTER III: METABOLIC EFFECTS OF CHRONIC METFORMIN TREATMENT IN VITRO…………………………….………………………..…….109

I. Introduction……………………………………………………………..……....110

II. Materials and Methods…………………………………………………..……112

A. Cell Culture…………………………………………………………..…….112 B. Cell Viability (MTT Assay)…………………………………………..……112 C. Gene expression as Determined by Reverse Transcription Polymerase Chain Reaction (RT-PCR)……………………………..……...113 D. Protein Expression Analysis by Western Blotting……………..…………..114 E. Cholesterol and Triglyceride Measurement…………………..…………….114 F. Data Analysis……………………………………………………..………...115

III. Results………………………………………………………………….……...116

A. Chronic MET Treatment Does not Inhibit PDAC Cell Proliferation……....114 B. Intracellular Cholesterol is Regulated in PDAC Cells…………………..….128 C. MET Decreases Intracellular Triglyceride in PDAC Cells…………..……..129

D. MET has No Effect on HMGCR and FASN Gene Expression………..…...131 E. MET Decreases FAS Protein Expression in MIA PaCa-2-CHS Treated Cells……………………………………………………..……..…....133

IV. Discussion………………………………………………………………..…….136

V. Concluding Remarks…………………………………………………...... 145

CHAPTER IV: EVIDENCE OF THE ANTI-CANCER EFFECTS OF METFORMIN IN VIVO………………………………………………….……..…....146

I. Introduction……………………………………………………………..……...147

A. The KPC Mouse Model………………………………………………….…147 B. Similarities with the Human Disease……………………………….……….150 C. Importance of the Microenvironment…………………………….…………151 D. Rationale and Hypothesis of Our Animal Studies………………….………153

II. Materials and Methods……………………………………….……………….154

A. Animal Treatment………………………………………….………………154 B. Immunohistochemistry…………………………………………….……….155 C. GC/MS and SiDMAP……………………………………………….……..155 D. Data Analysis……………………………………………………….……...156

III. Results…………………………………………………………………………156

A. MET Modulates the Warburg Effect in KC and KPC Mice……………….157 B. MET Decreases the Expression of FAS, Ki67 and HMGA2 in the Pancreas of KPC Mice……………………………………….………165

IV. Discussion……………………………………………………………………...167

V. Concluding Remarks…...…………………………………………………….171

CHAPTER V. FUTURE DIRECTIONS AND OVERALL CONCLUSION...... 173

I. Exploring Deregulating Cellular Energetics in PDAC…………………………..173 II. Addressing the Dose-Dependence of MET Against PDAC…………………...174 III. Studies on KPC Mice………………………………………………………...... 175 IV. Overall Conclusion……………………………………………………………..178

REFERENCES………………………………………………………………………..179

LIST OF FIGURES

FIGURE 1 The pancreas is functionally divided into the exocrine

and endocrine compartments…………………………………………….22

FIGURE 2 Histology of normal and abnormal pancreas…………..………………...24

FIGURE 3 Development of PanIN lesions precede pancreatic cancer………………26

FIGURE 4 Gemcitabine combined with nab-paclitaxel is

standard of care for metastatic PDAC…………………………………..28

FIGURE 5 The PI3K/AKT/mTOR pathway is constitutively activated in cancer…..32

FIGURE 6 Cellular apoptosis is governed by intrinsic and extrinsic signaling……...34

FIGURE 7 Cancer metastasis is a multi-step process………………………………..37

FIGURE 8 Metabolic pathways downstream of glucose metabolism

provide substrates and reducing equivalents for biosynthetic

reactions………………………………………………………..………...39

FIGURE 9 Isocitrate dehydrogenase (IDH) 1 and 2 catalyze

the oxidative decarboxylation of isocitrate to

α-ketoglutarate (α-KG)…………………………………………………..40

FIGURE 10 Mutated isocitrate dehydrogenase (IDH) 1 and 2 catalyze

the oxidative decarboxylation of isocitrate

to 2-hydroxyglutarate (2-HG)……………………………………………41

FIGURE 11 The TCA cycle is comprised of cataplerotic (red)

and anaplerotic reactions (green)………………………………………...44

FIGURE 12 Glutamine-derived aspartate is exported to the cytosol

to maintain redox status in PDAC……………………………………….46

FIGURE 13 The cancer lipogenic phenotype is characterized by

up-regulated expression and activity of fatty acid synthesis enzymes…..48

FIGURE 14 The serine, one-carbon, glycine (SOG) pathway is a significant

source of ATP in NCI60 cell line panel………………………………….49

FIGURE 15 Structures of guanidine and biguanides………………………………....51

FIGURE 16 Synthesis of N,N’-dimethyl biguanide…………………………………..52

FIGURE 17 MET impairs signalling molecules for cancer survival………………….61

FIGURE 18 MET inhibits key metabolic steps in lipogenesis………………………..65

FIGURE 19 Outline of approaches followed by the targeted (A)

and non- targeted (B) metabolomics approaches………………………...81

FIGURE 20 Structures of cholesterol (A) and cholesteryl hemisuccinate (B)………..83

FIGURE 21 No change in cell viability and proliferation were observed

after MET treatment……………………………………………………...90

FIGURE 22 EZTopolome (K-RAS); isotopolome-wide association study (IWAS) array showing heat map [percent changes to untreated control (100 %)] of flux responses associated with CHS and MET treatment in BxPC-3 and the mutant K-ras (MIA PaCa-2) PDAC cell lines……………………….94

FIGURE 23 Complete glucose oxidation of BxPC-3 and MIA PaCa-2 pancreatic adenocarcinoma cells in response to 100 μM MET after 24 h of culture with and without CHS pretreatment for 2 weeks……………………….95

FIGURE 24 m2 glutamate (A) and m4 glutamate (B) are surrogate markers for pyruvate dehydrogenase (PDH) and pyruvate carboxylase (PC), respectively………………………………………………………...97

FIGURE 25 EZTopolome(CHS-Met); isotopolome-wide association study (IWAS) array showing heat map [percent changes to CHS treated control (100 %)] of flux responses associated with MET treatment in BxPC-3 and the mutant K- RAS (MIA PaCa-2) PDAC cell lines……………………….100

FIGURE 26 Metabolic profile changes associated with CHS and MET treatment in mutant K- RAS (MIA PaCa-2) PDAC cell lines…………………….....101

FIGURE 27 Chronic MET treatment shows no significant effect on PDAC viability…………………………………………………………………117

FIGURE 28 Acute MET treatment shows no significant effect on BxPC-3 + CHS viability…………………………………………………………………127

FIGURE 29 Total intracellular cholesterol is decreased in BxPC-3 cells treated with CHS and MET…………………………………………………………..129

FIGURE 30 MET decreases intracellular triglyceride in PDAC cells……………….130

FIGURE 31 FASN gene expression is up-regulated with CHS treatment…………...132

FIGURE 32 Chronic CHS and MET treatment do not affect HMGCR protein expression in PDAC cells………………………………………………134

FIGURE 33 Chronic CHS and MET treatment decrease FAS protein expression in MIA PaCa-2-CHS cells…………………………………………………135

FIGURE 34 SREBP-mediated regulation of lipid synthesis during low cholesterol concentrations in the cell……………………………………………….139

FIGURE 35 SREBP-mediated regulation of lipid synthesis during high cholesterol concentrations in the cell……………………………………………….140

FIGURE 36 K-ras and Tp53 gene expression in the KPC mouse…………………...149

FIGURE 37 Similarities in PanIN lesions between humans and the KPC mouse model of PDAC………………………………………………………………..150

FIGURE 38 A simplified diagram showing metabolic pathways investigated in the in vivo study……………………………………………………………….157

FIGURE 39 MET decreases pancreatic lactate 13C labeled fraction via all pathways production in KPC and KC mice pancreas……………………………..158

FIGURE 40 Hepatic glucose production is decreased in KC and KPC mice treated with MET…………………………………………………………………….159

FIGURE 41 KPC mice oxidize glucose one half of that of KC and C57BL/6 mice pancreas…………………………………………………………………161

FIGURE 42 MET restores TCA anaplerosis in the KPC mice pancreas…………….162

FIGURE 43 MET inhibits palmitate synthesis in K-ras WT and K-ras

mutant mice……………………………………………………………..164

FIGURE 44 MET decreases the protein expression of Ki67, FAS, and HMGA2 in KPC mice……………………………………………………………………..166

LIST OF TABLES

TABLE 1 Different Types of Pancreatic Malignancies……………… ………………..23

TABLE 2 Gemcitabine and combinatorial chemotherapies for PDAC………………..30

TABLE 3 Human studies on pancreatic cancer risk and mortality with MET use among diabetics……………………………………………………………..56

TABLE 4 Human studies on overall cancer risk and mortality with MET use among diabetics……………………………………………………………..59 TABLE 5 Ongoing clinical trials on MET and pancreatic cancer……………………..73 TABLE 6 Summary of metabolic profiles of BxPC-3 (columns 3–6) and MIA PaCa-2 (columns 7–10) pancreatic adenocarcinoma cells (PDAC)………………...93

TABLE 7 Viability of BxPC-3 and MIA PaCa-2 + CHS relative to vehicle control (VC) with acute (72 h) increasing doses of MET……………………………….119

TABLE 8 Viability of of BxPC-3 and MIA PaCa-2 + CHS chronically treated with MET and then acutely treated with MET……………………………122

TABLE 9 Effect of CHS on the viability of BxPC-3 and MIA PaCa-2…………..…125

LIST OF ABBREVIATIONS

α-KG α-ketoglutarate

2-HG 2-hydroxyglutarate

ACAT acyl coenzyme A:cholesterol acyltransferase

ACC acetyl-CoA carboxylase

ACLY ATP citrate lyase

AKT protein kinase B

ALT alternative lengthening of telomeres

ANOVA analysis of variance

APAF1 apoptotic protease-activating factor 1

ATGL adipose triglyeride lipase

BAX BCL-2 associated X protein

BCL-2 B-cell lymphoma-2

BCL-XL B-cell lymphoma extra-large bHLH-Zip basic helix-loop-helix leucine zipper

BIM B-cell lymphoma 2 interacting mediator of cell death

BPTES bis-2-(5-phenylacetamido-1,2,4-thiadiazol-2-yl)ethyl sulfide

BRAC2 breast cancer 2, early onset

BSA bovine serum albumin

CDKN2A cyclin-dependent kinase inhibitor 2A

CHS cholesteryl hemisuccinate

CK-19 cytokeratin-19

ERRB2 v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2

LIST OF ABBREVIATIONS - Continued

EMT epithelial-to-mesenchymal transition

ENO1 enolase 1, gene

ERBB2 v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2

FABP4 foxo1-mediated fatty acid binding protein 4

FAS fatty acid synthase

FASL Fas ligand

FBS fetal bovine serum

Foxo1 forkhead Box O1

FSP-1 fibroblast specific protein-1

GAPDH glyceraldehyde-3-P-dehydrogenase, gene

GC/MS gas chromatography/mass spectrometry

GLUT1 glucose transporter 1, gene

GOT1 aspartate aminotransferase, cytosolic

GOT2 aspartate aminotransferase, mitochondrial

HIF1-α hypoxia inducible factor1-alpha

HK hexokinase, gene

HMGA2 high mobility group A2

HMGCR 3-hydroxy-3-methyl-glutaryl-CoA reductase

HSL hormone sensitive lipase

IDH isocitrate dehydrogenase

IGF insulin-like growth factor

IL interleukin

LIST OF ABBREVIATIONS - Continued

IP intraperitoneal

IPMN intraductal papillary mucinous neoplasm

IPGTT intraperitoneal glucose tolerance test

K-RAS Kirsten rat sarcoma viral oncogene homolog

LDHA lactate dehydrogenase, gene

Lipa lysosomal acid lipase

LKB1 liver kinase B1

LSL Lox-stop-lox

MAGL monoacyl glycerol lipase

MAPK mitogen activated protein kinase

MCN mucinous cystic neoplasm

MDM double minute protein

Met mesenchymal-to-epithelial transition

MET Metformin

MOMP mitochondrial outer membrane permeabilization mTOR mammalian target of rapamycin

P53 protein p53

PanIN pancreatic intraepithelial neoplasia

PC pyruvate carboxylase

PDAC pancreatic ductal adenocarcinoma

PDH pyruvate dehydrogenase

Pdx1 pancreatic and duodenal homeobox 1

LIST OF ABBREVIATIONS – Continued

PFKBF3 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase-3, gene

PFKL phosphofructokinase L, gene

PFS progression-free survival

PGK1 phosphoglycerate kinase 1, gene

PHD prolyl hydroxylase domain protein

PKM pyruvate kinase M, gene

P13K phosphatidylinositol-4,5-bisphosphate 3-kinase

PPP pentose phosphate pathway

PTEN phosphatase and tensin homolog

PUMA p53 up-regulated modulator of apoptosis

RAF rapidly accelerated fibrosarcoma

RB retinoblastoma protein

ROS reactive oxygen species

SCAP SREBP cleavage activating protein

SCD steroyl-CoA desaturase

SiDMAP stable isotope-based dynamic metabolic profiling

SMAD4 SMAD family member 4

SOG serine, one-carbon, glycine

SRE sterol regulatory element

SREBP sterol regulatory element-binding protein

S1P site-1 protease

S2P site-2 protease

LIST OF ABBREVIATIONS - Continued

SMAD4 SMAD family member 4

TGF-β transforming growth factor-beta

TNF tumor necrosis factor

TPI triosephosphate isomerase, gene

TRAIL TNF-related apoptosis-inducing ligand

TRP53 protein 53

TSC tuberous sclerosis

VEGF vascular endothelial growth factor

VHL von Hippel-Lindau

XIAP X-linked inhibitor of apoptosis protein

ABSTRACT

Metformin (MET) is a widely used drug indicated for type-2 diabetes management.

Interestingly, numerous epidemiological studies show that MET may confer protective benefits from overall cancer risk and cancer-related mortality. Various pre-clinical studies show that MET also exerts chemotherapeutic properties using doses that far exceed those for glycemic control. Currently, there are numerous ongoing clinical trials testing the chemotherapeutic advantages of MET treatment in various cancer types using doses that are within the therapeutic range for diabetes management.

We sought out to determine whether therapeutic doses of MET for diabetes management is chemopreventive or chemotherapeutic. We have shown that such doses are chemopreventive at best since they were unable to decrease cell viability in BxPC-3

(wild type K-RAS) and MIA PaCa-2 (mutant K-RAS) pancreatic cancer (PDAC) cells.

13 Through a targeted metabolomics approach using 1,2- C2-D-glucose as the sole tracer, we have shown that a therapeutic dose (100 μM) of MET that is prescribed for diabetes management inhibits glucose-derived new palmitate synthesis when acetyl-CoA is dedicated towards de novo fatty acid synthesis. This occurs when a) K-RAS mutation is present (in MIA PaCa-2 cells) and b) cholesterol in the form of the more water-soluble derivative cholesteryl hemisuccinate (CHS) is supplemented in the media to prevent acetyl-CoA from being directed towards cholesterol synthesis. Immnunoblot analyses showed that this phenomenon is regulated by decreased protein expression of the fatty acid synthase (FAS) enzyme.

We also showed that chronic (every two days in 30 days) CHS and MET treatment decreased triglyceride intracellular concentrations in BxPC-3 and MIA PaCa-2 cells.

These treatments exerted no effect on FASN gene expression, indicating that the lipid- inhibitory effects of MET in PDAC is regulated at the metabolic flux and protein expression levels.

Finally, we interrogated the metabolic effects of MET treatment using uniformly-

13 labeled glucose tracer ( C6-D-glucose) and gas chromatography/mass spectrometry

(GC/MS) using a LSL-K-rasG12D/+;LSL-Trp53R172H/+;Pdx-1-Cre (KPC) mouse model of

PDAC. We showed that acute (5 days), high-dose treatment (250 mg/kg body weight administered intraperitoneally) of MET reversed the glycolytic metabolism of pancreatic tumor into an oxidative one in the presence of K-ras and Tp53 mutation. MET, regardless of K-ras status, inhibited glucose-derived acetate enrichment towards palmitate synthesis. Immunohistochemistry analysis of KPC pancreases revealed that a decrease in the tumor cell proliferation marker Ki67 and in the FAS protein was observed in KPC mice treated with MET.

In summary, we have identified a mechanism of action of the popular anti- hyperglycemic drug MET against PDAC. It is an inhibitor of de novo fatty acid synthesis that is dependent on the K-RAS and metabolic status of the tumor. Future pre-clinical and clinical studies should take this into consideration when performing mechanistic studies on MET and when investigating the potential chemotherapeutic effects of this drug. It is also important to determine what dose of MET is chemotherapeutic in the clinic. As shown by other pre-clinical studies and ours as well, this dose exceeds the range for diabetes management.

CHAPTER I: INTRODUCTION

I. Pancreatic Cancer: Epidemiology, Biology and Therapeutics

Pancreatic cancer is the fourth leading cause of cancer-related deaths in the United

States, with a five-year survival rate estimated to be six percent (1, 2). The annual cancer incidence (46,420) is almost equal to the annual number of deaths (39,590) (2), making this cancer the deadliest malignancy known to man. The risk factors are smoking, chronic pancreatitis, age, diabetes mellitus, obesity, occupational exposures, non-O blood group, African-American race, high-fat diet and Helicobacter pylori infection (3-8).

Pancreatic ductal adenocarcinoma (PDAC) is cancer of the exocrine pancreas occurring primarily in the head (60-70%) and the body or tail of the organ (9). The exocrine pancreas, composed of acinar cells, zymogen granules, pancreatic stellate cells, centroacinar cells and ductal cells, is responsible for secreting digestive enzymes (Fig 1)

(10). The endocrine portion of the pancreas is comprised of the Islets of Langerhans that secrete the hormones insulin and glucagon for glucose and lipid homeostasis (10).

There are many types of pancreatic malignancies but PDAC comprises over 85% of cases in adults (11). As shown in Table 1 and Figure 2, PDAC is a type of pancreatic cancer that is morphologically like ductal epithelia characterized by mucin secretion (9).

FIGURE 1 The pancreas is functionally divided into the exocrine and endocrine compartments. The exocrine part releases zymogens or proenzymes

(chymotrypsinogen, trypsinogen, procarboxypeptidase and proelastase) into the pancreatic duct for digestion while the endocrine pancreas releases hormones for glucose and lipid metabolism (insulin and glucagon). Image obtained from Omary et al (10).

TABLE 1 Different types of pancreatic malignancies

Type of Malignancy Description Histopathology

Pancreatic ductal Phenotypically similar to pancreatic Well-developed glandular structures adenocarcinoma duct epithelia with mucin production surrounded by fibrotic stroma

and cytokeratin expression pattern

Serous Cystic pancreatic tumors with Sponge-like pattern of cysts adenocarcinoma glycogen-rich, ductal-type epithelial

cells that produce watery fluid

Mucinous cystic Columnar, mucin-producing Two compartments are: inner epithelial neoplasms epithelium supported by ovarian-type layer and outerovarian-type stroma

stroma with no communication with

the pancreatic ductal system

Intraductal papillary- Mucin-producing neoplasm arising Tall, columnar, mucin-producing mucinous neosplasms from the main pancreatic duct or its epithelial cells that line dilated ducts

branches

Acinar cell carcinoma Uniform neoplastic cells arranged in Large cellular nodules separated by

solid and acinar patterns fibrous bands; usually absent

desmoplastic reaction (increased

reactive fibrosis in surrounding stroma)

Pancreatoblastoma Epithelial tumor with well-defined Cellular, well-defined islands separated

solid nests of acinar formations and by stromal bands

squamous corpuscles separated by

stromal bands

Solid-pseudopapillary Monomorphic cells forming solid and Pseudopapillary pattern that usually neoplasm pseudopapillary structures with merge into one another

hemorrhagic cystic features

Table constructed from information published in International Agency for Research on Cancer’s

“Pathology and genetics of tumours of the digestive system” (9).

FIGURE 2 Histology of normal and abnormal pancreas. Image obtained from Pan et al (12).

The molecular pathogenesis of PDAC is characterized by the development of pre- maligant lesions. These lesions can be mucinous cystic neoplasm (MCN), intraductal papillary mucinous neoplasm (IPMN) and pancreatic intraepithelial neoplasia (PanIN)

(13, 14). MCNs are mucin and carcinoembyonic antigen-producing lesions that are surrounded by ovarian-like stroma while IPMNs present like PanINs but form enlarged cysts (15). Majority of precursor lesions are PanINs. These lesions are characterized by morphological changes that progress towards the atypia found in cancer and genetic mutations in tumor suppressors and oncogenes (Fig 3).

FIGURE 3 Development of PanIN lesions precede pancreatic cancer. The progressive development of pancreatic intraepithelial neoplasia (PanIN) lesions are accompanied by changes in epithelial morphology and various genetic mutations (15).

PanIN-1A (flat) and IB (micropapillary) possess mutations in oncogenes ERBB2 and

KRAS but without nuclear atypia. PanIN-2 shows mutations in the tumor suppressor

CDKN2A and mild to moderate atypia with papillary formation. PanIN-3 presents with mutations in the tumor suppressors TP53, SMAD4 and BRCA2 with cribriform appearance and may be described as carcinoma in situ (no basement membrane invasion). v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2 (ERBB2), Kirsten rat sarcoma viral oncogene homolog (KRAS), cyclin-dependent kinase inhibitor 2A

(CDKN2A), protein 53 (TP53), SMAD family member 4 (SMAD4), breast cancer 2, early onset (BRAC2). Image was obtained from Vincent et al (16).

PDAC is a very aggressive disease and commonly metastasizes to the other parts of the pancreas, spleen, stomach, colon, liver, lungs, adrenals, kidneys, bones, brain and skin (9). Only about 1/5th of patients present with resectable tumors upon diagnosis and even after resection, five-year survival rate is only 20-25% (16). At its early stages,

PDAC is asymptomatic leading to its poor early detection and PDAC being diagnosed at an advanced stage where metastases are already present (17). PDAC is also highly resistant to chemotherapy (18). Another reason for the poor survival rate in PDAC patients is the lack of effective treatment regimens for this cancer type. Currently, the standard of care for treating PDAC is surgical resection, chemotherapy with gemcitabine- nab Paclitaxel (19) that may be combined with radiation (16). The use of the nucleoside analog gemcitabine as an adjuvant therapy was approved in 1997, when it showed improvement in median survival when compared to the anti-metabolite 5-fluorouracil

(Fig 4 A) (20). Docetaxel and gemcitabine combination showed improvement with median survival between four to eight months but no phase III trials were pursued (21,

22).

FIGURE 4 Gemcitabine combined with nab-paclitaxel is standard of care for metastatic PDAC. In 1997, the FDA approved the use of gemcitabine over 5-FU after it has been shown by the group of Von Hoff that gemcitabine extends median survival by

1.24 mo (A). In 2013, the FDA approved the combined use of gemcitabine and nab- paclitaxel for first line of treatment for metastatic PDAC after it improved median survival by 1.8 mo when compared to gemcitabine alone. Images obtained from Burris

III et al (A) (20) and Von Hoff et al (B) (19).

Since gemcitabine’s approval as the standard of care for metastatic PDAC, numerous clinical trials on combinatory modalities have been performed (23) (Table 2). Drugs such as cetuximab (monoclonal antibody epidermal growth factor receptor inhibitor), bevacizumab (monoclonal antibody angiogenesis inhibitor), erlotinib (tyrosine kinase epidermal growth factor receptor inhibitor) and aflibercept (vascular endothelial growth factor inhibitor) have been combined with gemcitabine, compared with gemcitabine only treatment using progression-free survival (PFS) and overall survival (OS) as outcomes.

Other modalities besides those listed in Table I combined with gemcitabine such as the monoclonal antibody inhibitors to the insulin-like growth factor receptor cixutumumab, ganitumab, conatumab extended overall survival by only several weeks (24). The mTOR inhibitor everolimus was unsuccessful in the clinic i.e., zero partial and complete response (25). The farnesyl inhibitor, which prohibits K-RAS farnesylation and signaling in the membrane, failed to improve median overall survival and progress-free survival in the clinic (26). Recently, a combination of gemcitabine and Nab-paclitaxel (albumin- bound form of the mitotic inhibitor paclitaxel) showed improved median survival (8.5 mo) when compared to gemcitabine alone (6.7 mo; Fig 4 B) (19). The combinatorial therapy was accompanied by greater side effects (19). However, these side effects were well-tolerated and did not cause adverse-event related deaths (19, 27).

Overall, the combination chemotherapeutic regimens either failed or extended overall survival by only a few weeks. Hence, finding an effective chemotherapeutic drug against pancreatic cancer remains to be the big goal in PDAC research.

TABLE 2 Gemcitabine and combinatorial chemotherapies for PDAC

Table obtained from Ghosn et al (23).

In more advanced stages, PDAC patients experience back pain, abdominal pain, anorexia, cachexia, diarrhea and jaundice (28). Because PDAC is a cancer of the exocrine pancreas (10), the consequent inability of the pancreas to produce digestive proenzymes leads to diarrhea and nutritional deficiencies (28).

II. The Hallmarks of Cancer: Focus on Deregulating Cellular Energetics

A. Introduction

In 2000, Weinberg and Hanahan published the seminal review paper entitled “The

Hallmarks of Cancer,” which outlined key features shared by various types of human cancers. These 6 characteristics are: 1) self-sufficiency in growth signals, 2) insensitivity to anti-growth signals, 3) evasion of programmed cell death, 4) limitless replicative potential, 5) sustained angiogenesis and 6) tissue invasion and metastasis (29). A decade after, they published additional emerging cancer hallmarks and incorporated the most recent research advances in the six established hallmarks. The emerging hallmarks of cancer are the following: 1) avoiding immune destruction, 2) tumor-promoting inflammation, 3) genome mutation and instability and 4) deregulating cellular energetics

(30).

B. Self-sufficiency in Growth Signals

Self-sufficiency in growth signals refers to the ability of cancer cells to proliferate independently of growth factors (29). Cancer cells produce growth factor ligands and receptors, the surrounding stroma also produces signaling factors that communicate with cancer cells, and the signaling pathway downstream of receptors are hyper-activated in cancer (30). As an example, the PI3K/AKT/mTOR pathway (Fig 5) is commonly mutated in cancer and inhibitors for signaling proteins in this pathway are being currently tested in the clinic to treat breast, lung, endometrial, brain, lymph, blood, stomach and pancreatic cancers (31). Signaling proteins that activate the pathway are highly

31 expressed with up-regulated activities while proteins that inhibit the pathway are down- regulated resulting in constitutive pathway activation, increased cell survival, proliferation and increased protein translation in cancer cells (31).

FIGURE 5 The PI3K/AKT/mTOR pathway is constitutively activated in cancer.

Proteins that activate the pathway- Kirsten rat sarcoma viral oncogene homolog (K-

RAS), phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K), protein kinase B (AKT), mammalian target of rapamycin (mTOR) have increased expression and activity while the opposite is true for proteins that inhibit the pathway-phosphatase and tensin homolog

(PTEN), tuberous sclerosis 2 (TSC2), liver kinase B1 (LKB1). The end result is increased cell survival, proliferation and protein translation in cancer cells. Image obtained from Polivka Jr et al. (31).

C. Insensitivity to Anti-growth Signals

Cancer cells’ insensitivity to anti-growth signals allows them to escape the anti- proliferative actions of tumor suppressors (30). The tumor suppressor p53 is primarily activated by DNA damage, hypoxia or oncogenic signaling (32). Under these cellular stresses, p53 is released from double minute 2 protein (MDM2) and double minute 4 protein (MDM4) inhibition, allowing it to activate pathways of cell cycle arrest, senescence and apoptosis (33). This prevents further DNA damage which is a pre- requisite to carcinogenesis; hence, this tumor suppressor is called the “guardian of the genome (34).” Other tumor suppressors that exhibit loss of function in cancer include retinoblastoma protein (RB), transforming growth factor-beta (TGF-β) and LKB1 (30).

D. Evading Programmed Cell Death

Cancer cells are able to evade programmed cell death. Programmed cell death or apoptosis can be mediated by either extrinsic or intrinsic responses (Fig 6). Cancer cells resist apoptosis through p53 mutations, overactivation of the PI3K/AKT pathway, production of extracellular factors insulin-like growth factor 1/2 (IGF-1/2) or interleukin-

3 (IL-3), Ras intracellular signaling, loss-of-function of PTEN, increased expression of anti-apoptotic regulators B-cell lymphoma-2 (BCL-2) and B-cell lymphoma extra-large

(BCL-XL), by decreasing the expression of pro-apoptotic factors BCL-2 associated X protein (BAX), B-cell lymphoma 2 interacting mediator of cell death (BIM) and p53 upregulated modulator of apoptosis (PUMA) (29, 30).

FIGURE 6 Cellular apoptosis is governed by intrinsic and extrinsic signaling.

Programmed cell death is mediated by an intrinsic response to DNA damage or cellular stress. This activates BAX and mitochondrial outer membrane permeabilization

(MOMP). Cytochrome c is released from the mitochondrion and it binds to apoptotic protease-activating factor 1 (APAF-1). The oligomerization of APAF1 forms the apoptosome which recruits the initiator caspase, caspase 9. Caspase 9 signals the executioner caspases, caspase 3 and caspase 7 to carry out apoptosis. The extrinsic pathway is comprised of a death ligand to which the Fas ligand (FASL), TNF-related apoptosis-inducing ligand (TRAIL) or tumor necrosis factor (TNF) binds to. This is

34 followed by the recruitment of adaptor molecules, Fas-associated death domain protein

(FADD) and caspase 8. These then activate the executioner caspases 3 and 7 to execute apoptosis. Inhibitors of apoptosis include X-linked inhibitor of apoptosis protein (XIAP) and BCL-2. Image obtained from Tait et al (35).

E. Limitless Replicative Potential

Next, cancer cells possess limitless replicative potential or are immortalized, meaning they can divide indefinitely given the proper culture conditions (29). This is due to the ability of cancer cells to maintain telomeres, 6 base pair repeats that cap DNAs, which serve to protect the ends of chromosomal DNA (29). The telomeres also provide the distinctive mark of healthy DNA from damaged DNA and adds “buffer” to replication ends to prevent loss of genetic material during replication (36). In non-malignant cells, telomeres become eroded during cell replication and at a certain number of cell division

(termed the Hayflick limit), telomeres become incapable of protecting DNA ends (29).

When telomeres become too short, it triggers a p53-mediated cell cycle arrest and eventually senesce permanently (37). Cancer cells evade telomere erosion, cell cycle arrest and apoptosis by a) activating the enzyme telomerase which results in continued addition of short base pairs to the DNA ends and b) alternative lengthening of telomeres

(ALT) by DNA recombination (37, 38).

F. Sustained Angiogenesis

Sustained angiogenesis in cancer cells allow delivery of oxygen and nutrients to cells and a means to remove cellular waste (30). This is mediated by the up-regulation of pro- angiogenic factors that include vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF) with down-regulation of anti-angiogenic thrombospondin and β- interferon (29). The neovascularization observed in carcinogenesis seems to be an early event (30) because some advanced tumors such as PDAC have hypovascularized stroma

(39).

G. Tissue Invasion and Metastasis

The last hallmark, tissue invasion and metastasis, is illustrated in Figure 7 and has been reviewed extensively by Talmadge and Filder (40). Transformed cells undergo neovascularization through a process called angiogenesis. They then invade new blood vessels (intravasation) through which they travel into local or a distal site/s. Intravasation is aided by epithelial-to-mesenchymal transition (EMT), where cancer cells express characteristics of the mesenchymal migratory type such as loss of polarity, vimentin, fibronectin, N-Cadherin, alphavbeta3-integrins and fibroblast specific protein-1 (FSP-1)- features common during embryonic development (41). Cancer cells then extravasate, to a local site or to a distal organ relative to the primary tumor. Cancer cells home in to their new environment and show mesenchymal-to-epithelial (Met) transition and finally invade the new site or organ. The tumor becomes well-established and becomes clinically detectable as metastatic tumor/s.

FIGURE 7 Cancer metastasis is a multi-step process. Metastatic tumors stem from a primary tumor that has gone through multiple rate-limiting steps towards a final clinical presentation of a tumor that has invaded a local or distal organ. Image obtained from

Talmadge and Fidler (40).

H. Emerging Hallmarks of Cancer

Hanahan and Weinberg (30) added four new emerging hallmarks to their original six features. These are avoiding immune destruction, tumor-promoting inflammation, genome mutation and instability and deregulating cellular energetics. While these features are still not firmly established as well as the previous six hallmarks, they are

37 gaining significant attention in cancer research. This next section will be dedicated to entirely on the abnormal metabolism of cancer cells.

I. Emerging Hallmark: Deregulating Cellular Energetics

The German biochemist, Otto Warburg, was first to report that lactic acid production is greater in tumors. These findings were based on lactic acid measurements in venous blood (blood that has passed from the tumor) as well as in the tumor cavity of a Jensen sarcoma murine model (42). In the presence of sufficient levels of oxygen, non- cancerous differentiated tissues perform oxidative phosphorylation of glucose resulting in the production of CO2 and ATP. During oxygen shortage, differentiated tissues resort to glycolysis, a non-oxygen requiring metabolic pathway that yields 2 mol ATP/mol glucose compared to 36 mol ATP/mol glucose via oxidative phosphorylation (43). Tumors, on the other hand, prefer to utilize glycolysis regardless of oxygen levels, a phenomenon termed the Warburg Effect or aerobic glycolysis (the term aerobic does not relate to glycolysis but to the presence of oxygen in the environment) (43). The current dogma is that glycolysis provides enough ATP and that the cancer cells’ priority is to synthesize biomolecules such as RNA, DNA, lipids, amino acids and proteins to support proliferation requirements, metabolic pathways that are downstream of glucose metabolism (Fig 8) (43).

FIGURE 8 Metabolic pathways downstream of glucose metabolism provide substrates and reducing equivalents for biosynthetic reactions. The glycolytic product glucose-6-phosphate (Glucose-6-P) can enter the oxidative arm of the pentose phosphate pathway (PPP) to yield NADPH (which is required for lipid synthesis, fatty acid chain elongation and reduced glutathione generation) and eventually, the non- oxidative arm of the PPP to yield ribose-5-phosphate which serves as backbone for nucleotide synthesis. The TCA cycle produces citrate, which can be used to fuel lipid synthesis via its lysis into oxaloacetate and acetyl-CoA. Transamination reactions in the

TCA yield the non-essential amino acids alanine, aspartate, glutamine, glutamate,

+ arginine, ornithine and citrulline. The TCA cycle yields FADH2 and NADH + H as well as ATP. TCA-related reactions also produce NADPH cholesterol and fatty acid

39 synthesis, fatty acid elongation and for the reduction of glutathione. Image is obtained from Vander Heiden et al. (43).

1. Isocitrate Dehydrogenases

Mutations in isocitrate dehydrogenase 1 and 2 (IDH1/2), enzymes that catalyze the decarboxylation of isocitrate to α-ketoglutarate (α-KG), are observed in colorectal cancer

(44), brain cancer (45), leukemia (46), chondrosarcoma, cholangiocarcinoma, and angioimmunoblastic T-cell lymphoma (47). The IDH 1 (cytosolic and peroxisomal) and

2 (mitochondrial) isoforms catalyze the conversion of isocitrate to α-ketoglutarate, in an

NADP+- dependent reaction (Fig 9) (47). The mutated IDH1/2 show decreased enzymatic activity, leading to decreased levels of α-KG (48) or NADPH (49) and the production of 2-hydroxyglutarate (2-HG) instead of α-KG (Fig 10) (47).

FIGURE 9 Isocitrate dehydrogenase (IDH) 1 and 2 catalyze the oxidative decarboxylation of isocitrate to α-ketoglutarate (α-KG). IDH1 catalyzes the reaction

40 in the cytosol and in peroxisomes whereas IDH2 is located in the mitochondrion as the third step in the TCA cycle. Both isoforms reduce NADP+ to NADPH. Image obtained from Cairns et al (47).

FIGURE 10 Mutated isocitrate dehydrogenase (IDH) 1 and 2 catalyze the oxidative decarboxylation of isocitrate to 2-hydroxyglutarate (2-HG). Mutated

IDH1/2 in tumors leads to the consumption of α-KG instead of its production. The product of mutated IDH1/2 is 2-HG instead of α-KG. The accompanying reaction is the oxidation of NADPH to NADP+. Image obtained from Cairns et al (47).

The mutation in IDH1/2 has the following consequences: 1) the generation of 2-HG as product instead of 2-KG, 2) the oxidation of NADPH and 3) decrease in glucose oxidation. The production of 2-HG (Fig 10) reinforces the Warburg phenotype by increasing the activity and stability of hypoxia inducible factor-1α (HIF1-α), a transcription factor induced by hypoxic conditions (50). PHD requires 2-KG in order to

41 hydroxylate HIF-1α (51). During normoxia, proline hydroxylase-domain proteins 1-3

(PHD1-3) modify HIF1α through hydroxylation at Pro-402 and -564, leading to its binding to its suppressor, the von Hippel-Lindau (VHL) protein, which allows it to be targeted for proteosomal degradation (52-54). During hypoxia, the absence of hydroxylation by PHD proteins removes the inhibition by VHL allowing HIF1-α to accumulate and translocate to the nucleus to induce transcription of a multitude of genes involved in glucose metabolism including hexokinase (HK), enolase 1 (ENO1), glucose transporter 1 (GLUT1), glyceraldehyde-3-P-dehydrogenase (GAPDH), lactate dehydrogenase (LDHA), 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase-3

(PFKBF3), phosphofructokinase L (PFKL), phosphoglycerate kinase 1 (PGK1), pyruvate kinase M; (PKM), triosephosphate isomerase (TPI) (55).

Instead of generating NADPH which occurs with wild type (WT) IDH, mutant

IDH1/2 mutation oxidizes NADPH to NADP+ (Fig 10). The depletion of reduced NADP

(NADPH) results in increased susceptibility of NIH3T3 fibroblasts to H2O2-induced oxidative damage (56) and decreased concentrations of the antioxidant reduced glutathione (GSH) in U87 glioblastoma cells (57). The decreased protection of cancer cells from oxidative damage due to IDH mutation may confer some survival benefits in brain cancer patients (58, 59).

Next, the decrease in glucose oxidation and α-KG consumption as a consequence of

IDH1/2 mutation (Fig 10) prompts the cancer cell to use an alternative pathway for the generation of α-KG. This pathway is glutamine-dependent (60). In this context, D54 glioblastoma cells carrying an IDH1 mutation exhibit significant decrease in cell proliferation when compared to vector control and with a greater decrease in cell

42 proliferation observed when glutaminase is inhibited by bis-2-(5-phenylacetamido-1,2,4- thiadiazol-2-yl)ethyl sulfide (BPTES). The inhibition of cell proliferation is ameliorated upon treatment with α-KG (60). These data point out that IDH1/2 mutation disrupts glucose oxidation and impairs antioxidant regeneration.

2. Glutamine Addiction

The glutamine “addiction” of cancer cells is a recognized phenomenon. Glutamine up-regulates glucose uptake and fuels the TCA cycle (61). Glutamine becomes an important substrate for TCA cycle anaplerosis when glucose is limited and this is accomplished through activation of glutaminase and glutamate dehydrogenase (62).

TCA cycle anaplerosis, or the replenishment of TCA cycle intermediates lost during the loss of four- and five-carbon intermediates during cataplerosis is important to ensure

TCA cycling (63). Figure 11 presents anaplerotic reactions boxed in green and cataplerotic reactions in red.

As shown in Fig 11, maintaining a functional TCA cycle is critical in supplying not only ATP but also reducing equivalents and substrates for anabolic reactions for the synthesis of amino acids, nucleotides and lipids. In addition, the TCA cycle plays an important part in redox regulation. In PDAC, glutamine is used to generate aspartate, which is then exported from the mitochondrion into the cytosol to eventually yield pyruvate and NADPH, a reducing equivalent that is necessary for the generation of the antioxidant reduced glutathione (Fig 12) (64).

3. Pyruvate Kinase

Pyruvate kinase catalyzes the phosphoenolpyruvate to pyruvate reaction yielding

ATP (Fig 8). There are four isoforms of PK – PKM1, PKM2, PKL, and PKR with

PKM1 being predominantly expressed in adult tissues while PKM2 being the major isoform in developing tissues (65). In adulthood, PKL (liver isoform) and PKR (red blood cell isoform) replace PKM2 (66). In tumor cells, the less enzymatically active pyruvate kinase isoform 2 (PKM2), dominates (66). This appears counterintuitive given the high glycolytic rates in cancer cells and one would expect that the rate-limiting step catalyzed by PK should be uninterrupted. However, reactions upstream of the PK step are used for nucleotide synthesis and NADPH generation, a reducing equivalent required for fatty acid synthesis and defense against oxidative damage (67) (Fig 8).

FIGURE 11 The TCA cycle is comprised of cataplerotic (red) and anaplerotic reactions (green). The TCA cycle is composed of reactions that remove four- to five- carbon intermediates via cataplerotic reactions. These reactions are the following: 1)

ATP citrate lyase catalyzes the cleavage of citrate to acetyl-CoA and oxaloacetate as for fatty acid synthesis, 2) α-KG is a substrate for the synthesis of glutamate in a reversible reaction by glutamate dehydrogenase, 3) oxaloacetate is converted to aspartate (and vice versa) via aspartate aminotransferase, and 4) oxaloacetate is decarboxylated and phosphorylated to form phosphoenolpyruvate via phosphoenolpyruvate carboxykinase to eventually generate glucose. Anaplerotic reactions replenish four- and five-carbon intermediates in the TCA cycle and include: 1) pyruvate carboxylase-catalyzed carboxylation of pyruvate to oxaloacetate, 2) the reversible glutamate dehydrogenase conversion of glutamate to α-KG, 3) numerous amino acids can be converted into the

TCA cycle intermediates succinyl-CoA, fumarate and oxaloacetate. This research was originally published in J Biol Chem. Owen et al. The key role of anaplerosis and cataplerosis for citric acid cycle function. J Biol Chem. 2002; 277:30409-12. © the

American Society for Biochemistry and Molecular Biology. Image obtained from Owen et al (63).

FIGURE 12 Glutamine-derived aspartate is exported to the cytosol to maintain redox status in PDAC. Glutamine is a substrate for the synthesis of mitochondrial aspartate via mitochondrial aspartate transaminase (GOT2). The oncogene KRAS up- regulates cytosolic aspartate transaminase (GOT1)-dependent metabolism of aspartate to oxaloacetate. Malate dehydrogenase catalyzes the formation of malate from oxaloacetate in an NADH + H+ consuming reaction. Malic enzyme catalyzes the decarboxylation of malate to pyruvate coupled with the reduction of NADP+ to NADPH, which is used to maintain a pool of reduced glutathione needed to quench the oxidant hydrogen peroxide

(H2O2). Image obtained from Son et al (64).

4. Lipogenic Phenotype

Another consequence of the Warburg effect for cancer cells is the presentation of a lipogenic phenotype, where enzymes for lipid synthesis are highly expressed and activated (68). This ensures sufficient supply of lipids dedicated to membrane formation, signaling protein post-translational modifications, storage and as source of ATP (69). Fig

13 provides an overview of the Warburg effect with the lipogenic phenotype.

There are numerous enzymes involved in the maintenance of the lipogenic phenotype

(Fig 13). Glucose-derived de novo fatty acid synthesis starts with the generation of acetyl-CoA and oxaloacetate from citrate catalyzed by ATP citrate lyase (ACLY). The resulting acetyl-CoA is first shuttled from the mitochondrion into the cytosol through tricarboxylate translocase (not shown) and becomes a substrate for carboxylation to malonyl-CoA via acetyl-CoA carboxylase (ACC). Malonyl-CoA and acetyl-CoA are used to generate the saturated fatty acid palmitate in a series of reactions catalyzed by fatty acid synthase (FAS). Acyl-CoA synthetase (ACS) catalyze the generation of acyl-

CoA for protein palmitoylation (70). Note that substrates for fatty acid synthesis, malonyl-CoA and fatty acyl-CoA, inhibit fatty acid β-oxidation. Finally, triglycerides are formed through a series of reactions catalyzed by glycerol phosphate acyltransferase (GPAT), 1-acylglycerol-3-phosphate-O-acyltransferase (AGPAT), lipin or phosphatidic acid phosphatase (PAP) and diacylglycerol acyltransferase (DGAT).

Lipolysis is carried out by cancer cells to generate signaling lipids through the actions of adipose triglyeride lipase (ATGL), hormone sensitive lipase (HSL) and monoacyl glycerol lipase (MAGL) (71).

FIGURE 13 The cancer lipogenic phenotype is characterized by up-regulated expression and activity of fatty acid synthesis enzymes. Glucose is utilized by tumor cells for de novo lipid synthesis involving numerous enzymatic reactions. See text for explanation of individual enzymes found to be active in cancer for fatty acid synthesis.

Image obtained from Currie et al (69).

5. Alternative ATP-Producing Pathway

We have discussed glucose-derived biomass production in the tumor. However, it remains unclear how cancer cells generate ATP to supply their biosynthetic requirements.

Where, then, does the cancer cell obtain ATP? Recently, the serine, one-carbon, glycine

(SOG) metabolism was reported to be a significant source of ATP, while integrating lipid, nucleotide protein and NADPH synthesis (72). The study from Tedeschi and others have identified the SOG pathway as a significant contributor to the cells’ ATP,

NADPH and purine requirements such that its inhibition by methotrexate resulted in ATP shortage in a panel of NCI60 cells (Fig 14) (73).

FIGURE 14 The serine, one-carbon, glycine (SOG) pathway is a significant source of ATP in NCI60 cell line panel. The SOG pathway is a significant source of ATP in cells and presents a pathway by which cancer cells supply energy for biosynthesis.

Image obtained from Tedeschi et al (73).

Overall, the Warburg effect is not merely about increased glycolysis. It is an adaptation by transformed and malignant cells to support the requirements of cell proliferation. It is characterized by up-regulated glucose utilization for reactions that synthesize biomolecules through complex integration of glycolysis and the TCA cycle that branch out towards anabolic metabolic pathways. We discussed the direct relationship of glucose and lipid metabolism. The potential anti-cancer properties of

MET may be explained by a thorough investigation of how it affects cancer metabolism.

In order to provide a big picture of how MET alters PDAC metabolism, we combined metabolomic and molecular biology approaches to demystify MET’s anti-cancer effects.

III. Metformin as a Chemopreventive and Chemotherapeutic Agent

A. The History Behind MET

Galega officinalis (also known as French lilac or Goat’s Rue) is a plant that has been used for the treatment of diabetes mellitus in traditional medicine for centuries. At the end of the 19th century, guanidine compounds were discovered in Galega extracts

(Fig 15). Shortly after, in 1918, animal studies showed that these compounds lowered blood glucose levels (74). However, guanidines are fairly toxic. Following this discovery, some less toxic derivatives, named synthalin A and synthalin B (Fig 15), were synthesized based on the structure of galegine and used for diabetes treatment under the marketed name of Synthalin in the 1940s. The discovery of insulin overshadowed the use and further development of synthalin compounds and they were forgotten for the next several decades. When chemists found that they could also make the guanidine

50 compounds more tolerable by bonding two guanidines together, forming a biguanide, interest in these molecules was regained and attention was focused on MET, phenformin and buformin (Fig 15). Finally, interest in MET, synthesized by K. Slotta, was further renewed in the late 1950s after several reports that it could reduce blood sugar levels in people. The French physician Jean Sterne published the first clinical trial of MET as a treatment for diabetes in 1957 (75).

B. Synthesis, Structure and Pharmacology of MET

Synthesis: MET is synthesized by equimolar fusion of hydrochloric dimethylamine and dicyandiamide at 130-150°C for 0.5 to 2 hours (76).

FIGURE 15 Structures of guanidine and biguanides.

FIGURE 16 Synthesis of N,N’-dimethyl biguanide.

Structure: MET is globally charged positively and does not permeate or poorly permeates the plasma membrane. The structure was represented by an incorrect tautomeric form until it was corrected in 2005 by a group of chemists from India (77). MET is given to patients in hydrochloride form. Several studies have demonstrated an affinity of biguanides for phospholipids at the plasma membrane (78) as well as some protein binding (79). The interaction of the biguanide with the polar head group of phospholipids induces a diminution of the plasma membrane fluidity, leading to the rigidification of the plasma membrane (78). However, this reduction of fluidity was not reproduced by

Wiernsperger and collaborators (79), who discovered an increase in the fluidity of red blood cell membranes.

MET has an oral bioavailability of 50-60% under fasting conditions and the peak plasma concentration is reached within a couple of hours. The plasma protein binding is negligible. MET is not metabolized but it is accumulated in tissues such as the liver, the kidneys, the salivary glands and the gastrointestinal tract (80). Eighty percent of the elimination of MET occurs by the urinary tract. The average elimination half-life in plasma is 6.2 hours. The half-life of biguanides is approximately 2 hours (80).

Interestingly, MET is distributed to (and appears to accumulate in) red blood cells with a much longer elimination half-life: 17.6 hours.

C. Physiological Effects of MET Against Hyperglycemia

Contrary to sulfonylureas, which act at the level of the pancreatic secretion of insulin, biguanides act at the level of sensitivity of the target tissues for insulin.

Moreover, the biguanides can reduce the hyperglycaemia without leading to incidental hypoglycaemia. Hence, the term “anti-glycaemic” agent was coined for MET.

MET works by decreasing hepatic gluconeogenesis (81), activating insulin receptor tyrosine phosphorylation (82), decreasing intestinal glucose absorption and increasing skeletal and adipose tissue glucose uptake (82). One mechanistic study conducted in mice demonstrates that MET (250 mg/kg/day for three consecutive days) increases the association of the glycolytic enzymes hexokinase to the mitochondria and phosphofuctokinase to F-actin in mice hearts (83). These associations result in their activation and up-regulation in glycolysis, increasing cardiac glucose utilization, which may partly explain the cardio-protective effects of the drug (84, 85). Since 1995, MET has been a widely prescribed glucose-lowering agent in the United States for type 2 diabetic and polycystic ovary syndrome patients. It is a well-tolerated drug with lactic acidosis as a reported serious side effect (86). However, the link between lactic acidosis and MET use has recently been questioned (87) .

D. Chemopreventive Properties of MET

Numerous observational studies show that MET use, when compared against other diabetic agents such as insulin and sulfonylureas, decreases cancer risk and overall cancer mortality among the diabetic population. These protective associations have been reported across different cancer types and among various diabetic populations.

Table 3 summarizes epidemiological studies that show pancreatic cancer risk and cancer mortality associated with MET use, while Table 4 presents observational studies and clinical trials on overall cancer risk and mortality in relation to MET use. While some studies show a reduction in pancreatic cancer (7, 88-91) and overall cancer risk (89, 91-

97) among diabetic MET users, there are also studies that report no significant difference in cancer risk among diabetics who take MET compared to patients who take other antidiabetic treatments (88, 98-103). These conflicting results may be explained by differences in the study population, the confounding factors accounted for during statistical analysis and the selected study design (e.g., cohort versus case-control). For example, patients prescribed MET may generally have better glucose control compared to those prescribed insulin; hence the risk of future diseases such as cancer may be lower at baseline for MET users versus diabetics treated with other modalities. As shown in

Tables 3 and 4, different studies account for different confounding factors, which can substantially influence the results of disease risk calculations. There is no standardized procedure for selecting which potential confounding factors are to be included in a statistical model; this can lead to large variations among observational study outcomes.

Overall, although these epidemiological studies are correlative in nature and hence cannot establish causality between MET use and cancer risk and mortality, they provide a

54 biological basis to further explore whether MET possesses chemopreventive and/or chemotherapeutic properties.

TABLE 3 Human studies on pancreatic cancer risk and mortality with MET use among diabetics

First author/ Year Location Design Outcome Comparison Risk Confounding (reference) (95% CI) factors

Oliveria et al., 2007 United States Cohort Pancreatic cancer MET vs. no Met RR: 1.26 Age, gender, (98) risk (0.80-1.99) gastrectomy, chronic pancreatitis, deep venous thrombosis, dermatomyositi s/polymyositis, alcoholism, hepatitis B/C, history of polyps

Currie et al., 2009 United Cohort Progression to Sulfonylureas vs. HR: 4.95 Age, sex, (99) Kingdom pancreatic cancer MET (2.74-8.96) smoking status, diagnosis of a MET + sulfonylureas HR: 0.38 previous cancer vs. Met (0.13-1.12) Insulin-based HR: 4.63 therapies (2.64-8.10)

Li et al., 2009 (7) United States Case-control Pancreatic cancer MET OR: 0.38 Age, sex, race, risk (0.22-0.69) smoking, alcohol, BMI, family history of cancer, diabetes duration, use of insulin

Bodmer et al., 2011 United Case-control Pancreatic cancer MET (both sexes) OR: 0.87 BMI, smoking, (88) Kingdom risk MET (females only) (0.59-1.29) Sulfonylureas OR: 0.43 alcohol Insulin (0.23-0.80) consumption, OR: 1.90 diabetes (1.32-2.74) OR: 2.29 duration (1.34-3.92)

Ferrara et al., 2011 United States Cohort Pancreatic cancer MET and HR: 1.2 (1.0- Age, ever use (100) risk pioglitazone 1.5) of other diabetes medications, year of cohort entry, sex, race/ethnicity, income, current smoking, baseline HbA1c, diabetes duration, new diabetes diagnosis, creatinine, and congestive heart failure

Liao et al., 2011 Taiwan Cohort Pancreatic cancer Met HR: 0.85 Crude/ (101) risk (0.39-1.89) unadjusted

Lee et al., 2011 (89) Taiwan Cohort Pancreatic cancer MET vs. potential use HR: 0.15 Age, gender, risk of other oral anti- (0.03-0.79) hyperglycaemic other oral anti- medications hyperglycaemic medication, Charlson comorbidity index score, time-dependent MET use

Morden et al., 2011 United States Cohort Pancreatic cancer Met HR: 1.25 Age category, (102) risk (0.89-1.75) race/ethnicity, diabetes complications, obesity diagnosis, oral oestrogen use, Part D low income subsidy (a poverty indicator), 14 Charlson comorbidities, and tobacco exposure diagnosis

Ruiter et al., 2012 Netherlands Cohort Pancreatic cancer MET vs. HR: 0.73 Age at first oral (90) risk sulfonylureas (0.66-0.80) glucose- lowering drug (OGLD) prescription, sex, year in which the first OGLD prescription was dispensed, number of unique drugs used in the year, number of hospitalizations in the year before the start of OGLD

Sadeghi et al., 2012 United States Cohort Median survival MET vs. non-Met 15.2 months No significant (104) in pancreatic vs. 11.1 cancer, prognostic Univariate analysis: months (P = differences in factors of overall Met 0.009) BMI, age, sex, survival in Multivariate analysis: HR: 0.68 race, diabetes pancreatic cancer Met (0.52-0.89) duration, HR: 0.64 disease stage, (0.48-0.86) tumour size, performance status, serum

CA-19-9 between MET and non-MET group

Nakai et al., 2013 Japan Cohort Prognostic factors Univariate analysis: (103) of overall survival Biguanide Age (G65 or in pancreatic Q65 years old), cancer Sulfonylureas HR: 0.61 sex (male or (0.19-1.44) female), Insulin HR: 0.60 Thiazolidine-dione (0.39-0.88) performance HR: 0.83 status (PS; 0Y1 (0.59-1.15) or 2Y3), HR: 0.19 primary tumour (0.01-0.84) size (G30 or Q30 mm), distant metastasis (yes or no), body mass index (G22 or Q22 kg/m2), chemotherapy (combination therapy with gemcitabine and S-1 vs. others), DM (yes or no), insulin (yes or no), sulfonylurea (yes or no), biguanide (yes or no), thiazolidine (yes or no), hypertension (yes or no), ACEI or ARB (yes or no), Ca- blocker (yes or no), A-blocker (yes or no), and statin (yes or no)

Singh et al., 2013 Various Meta-analysis Pancreatic cancer MET use OR: (105) risk 0.76 (0.57- 1.03)

Zhang et al., 2013 Various Meta-analysis Cancer incidence MET (including in SRR: (91) and mortality combination with Incidence other drugs) vs. non- 0.54 (0.35- users 0.83)

Mortality 0.64 (0.48- 0.86

Bold type under “Risk” column indicates statistical significance using 95% confidence interval. HR, hazard ratio; OR, odds ratio, SRR, summary relative risk. Table obtained from Cantoria et al (106).

TABLE 4 Human studies on overall cancer risk and mortality with MET use among diabetics

First Location Design Outcome Comparison Relative risk Confounding author/ (95% CI) factors accounted Year for

Evans et al., Scotland Case-control Cancer MET vs. no MET OR: 0.77 Smoking, body 2005 (93) incidence (0.64-0.92) mass index, blood pressure, and postcode rank for material deprivation

Libby et al., Scotland Nested case- Cancer deaths MET vs. no Met HR: 0.63 Sex, age, BMI, 2009 (92) control (0.53-0.75) A1C, deprivation, smoking, other drug use

Monami et Italy Case-control Cancer 36 mo MET vs. no OR: 0.28 Concomitant al., 2009 incidence MET (0.13-0.57) therapies, (94) exposure to MET and gliclazide

Bowker et Canada Cohort Cancer death MET vs. HR: 0.80 Age, sex and al., 2010 sulfonylurea (0.65-0.98) chronic disease (96) score

Home et al., Various Randomized Cancer MET vs. HR: 0.92 (0.63- Not reported 2010 (107) control trials incidence rosiglitazone 1.35) MET vs. HR: 0.78 (0.53- 1.14) glibenclamide MET + HR: 1.22 (0.86- sulfonylurea vs. 1.74) Rosiglitazone + sulfonylurea

Landman et Netherlands Cohort Cancer MET vs. no Met HR: 0.43 Smoking (yes or al., 2010 mortality (0.23-0.80) no), age, sex, (97) diabetes duration, A1C, serum creatinine, BMI, blood pressure, total cholesterol- to-HDL ratio, albuminuria, insulin use, sulfonylurea use, and macrovascular complications (yes or no)

Lee et al., Taiwan Cohort Cancer MET vs. no Met HR: 0.12 Age, gender, other 2011 (89) incidence (0.08-0.19) oral antihyperglycaemic medication usage, CCI score and dose and duration of MET exposure

Monami et Italy Case-control Cancer MET vs. no MET OR: 0.46 Charlson al., 2011 incidence in patients under (0.25-0.85) comorbidity score (95) insulin treatment (CCS), glargine

mean daily dose

(MDD), and total MDD of insulin

Zhang et al., Various Meta-analysis Cancer MET (including in SRR: Incidence 2013 (91) incidence and combination with 0.73 (0.64- mortality other drugs) vs. 0.83)

non-users Mortality 0.82 (0.76-0.89

E. Chemotherapeutic Properties of Metformin

Experimental studies show that MET possesses anti-cancer effects in various cancer types. As the metabolic effects of MET are discussed in the section “MET as an Anti- lipogenic Drug”, Figure 17 provides a summary of MET’s effects exclusively on cancer signalling pathways. Overall, we present MET’s effects on cancer cells as AMPK- dependent (pathways 2, 4, 5, 6 and 10) and independent (pathways 1, 3, 7-9 and 11).

Although there is an overlap between cell signalling and metabolic alterations due to

MET treatment (e.g., via ETC complex I and ATP production, AMPK/mTORC1 axis and metabolic control), MET’s anti-cancer effects can be grouped into: (a) inhibition of ATP and ROS production, (b) inhibition of IRS-1/AKT/mTORC1 axis, (c) anti-inflammatory effects, (d) cell cycle arrest and (e) inhibition of general transcription factors.

FIGURE 17 MET impairs signalling molecules for cancer survival. 1) MET is a known inhibitor of complex I of the electron transport chain (ETC) (108, 109). 2) The resulting decrease in adenosine triphosphate (ATP) production and increase in adenosine monophosphate (AMP) levels activate the kinase AMP-activated protein kinase (AMPK), a regulator of cellular energy status. Besides inhibition of energy-consuming biosynthetic processes (e.g., lipid synthesis and gluconeogenesis) and up-regulation of energy- generating catabolic metabolic pathways (e.g., β-oxidation of fatty acids and glycolysis),

AMPK also signals to numerous proteins involved in cell survival, senescence, autophagy and cell death. For example, both high AMP and adenosine diphosphate

(ADP) levels (from ETC complex I inhibition) are permissive for AMPK activation.

AMP promotes AMPK phosphorylation at its catalytic α-subunit (Thr-172) by its upstream kinases liver kinase B1 (LKB1) and calcium/calmodulin-dependent protein kinase kinase-beta (CaMKKβ). AMP promotes its allosteric activation and prevents

AMPK dephosphorylation by protein phosphatases type 2a (PP2a) and protein

61 phosphatase type 2c (PP2c) (110-112). ADP also protects AMPK from dephosphorylation (113). 3) The MET-induced decline in endogenous reactive oxygen species (ROS) levels has been implicated to be involved in cancer risk reduction owing to its ability to reduce ROS-induced DNA damage (114). 4) AMPK has also been shown to activate the tumour suppressor protein 53 (p53) (Ser-15) and induce cancer cell cycle arrest and senescence (115). The reversible arrows between p53 and pAMPK indicate that p53 has been shown to increase AMPK activity, which ultimately leads to the mammalian target of rapamycin (mTOR) inhibition in vitro (116). 5) MET has been shown to cause a G0/G1 cell cycle arrest by decreasing the expression of cyclin D1 and preventing the phosphorylation and inactivation of pRb (117). 6) MET-induced AMPK activation has been shown to phosphorylate insulin receptor substrate-1 (IRS-1) at Ser-

794, which results in decreased recruitment of the p85 subunit of phosphoinositide-3- kinase (PI3K), thus impairing the insulin-like growth factor (IGF)-stimulated

PI3K/protein kinase B/ mammalian target of rapamycin complex 1

(PI3K/AKT/mTORC1) signalling pathway (118). MET has also been shown to inhibit the crosstalk between the insulin/IGF receptor and G protein-coupled receptor (GPCR) signalling, resulting in the inhibition of mTORC1 (119, 120). 7) Biguanides are implicated in the inhibition of the Rag-dependent mTORC1 signalling (121), preventing the co-localization of mTORC1 with its activator Ras homologue enriched in brain

(RHEB). Rags are GTPases composed of four proteins RagA, RagB, RagC and RagD that heterodimerize to activate mTORC1 upon amino acid stimulation (122). Rags bind to the Ragulator complex made up of mitogen-activated protein kinase scaffold protein 1

(MP1), p14 and p18 trimeric proteins, localizing mTORC1 from the perinuclear

62 compartment (where RHEB is located) into the cytoplasm, preventing Rheb activation of mTORC1 (123). 8) MET also increases the expression of the mTOR inhibitor, regulated in development and DNA damage responses (REDD1), consequently down-regulating mTOR signalling (124). 9) In human monocytes, MET prevents lipopolysaccharide

(LPS) and oxidized low density lipoprotein (LDL)-induced tumour necrosis factor (TNF) production at micromolar concentrations (125). 10) Activation and phosphorylation of

AMPK is dependent on the serine-threonine kinase, ataxia telangiectasia mutated (ATM), a checkpoint that responds to double-strand breaks and oxidative stress by activating the

DNA damage response involving numerous downstream targets such as p53, checkpoint kinase 2 (CHK2), breast cancer 1 (BRCA1), Fanconi anaemia, complementation group 2

(FANCD2), Nijmegen breakage syndrome 1 (NBS1), p53 up-regulated modulator of apoptosis (PUMA) - Phorbol-12-myristate-13-acetate-induced protein 1 (NOXA) and

BCL2-associated X protein (BAX) (126, 127), thus preventing further DNA insult. 11)

MET induces nuclear degradation and decreased expression of Sp proteins, transcription factors for genes involved in cell proliferation (cyclin D1), metabolism (FAS), apoptosis

((B-cell lymphoma 2 (bcl-2) and survivin)) and angiogenesis ((vascular endothelial growth factor (VEGF) and its receptor VEGFR1)) (128). Figure obtained from Cantoria et al (106).

F. MET as an Anti-lipogenic Drug

1. Revisiting Aberrant Metabolism of Cancer Cells

Figure 18 is an integrated visual of cancer metabolism showing which enzymes and transcription factors are inhibited by MET. The reliance of cancer cells on glucose metabolism stems from their need to generate metabolites and reducing equivalents that are used to support crucial biosynthetic reactions that make lipids, nucleotides and amino acids. These biomolecules are rate-limiting for cell proliferation and survival.

Glycolysis yields glucose-6-phosphate that enters the oxidative arm of the Pentose

Phosphate Pathway (PPP). The oxidative PPP produces NADPH, which, together with acetyl-CoA, fuels lipid synthesis in the cytosol. The non-oxidative branch of the PPP yields ribose-5-phosphate, the precursor for nucleotides. As early as 1998, it has been argued that both PPP branches (but primarily the non-oxidative branch) serve to produce ribose to sustain the increased needs of the cancer cell for DNA and RNA (129).

Fructose-6-phosphate and glyceraldehyde-3-phosphate are by-products of the glycolytic and the non-oxidative pentose phosphate pathways, providing an intimate link between glucose metabolism and nucleotide generation. Acetyl-CoA produced from the pyruvate dehydrogenase reaction enters the tricarboxylic acid (TCA) cycle in the mitochondria.

Citrate can be exported from the mitochondria into the cytosol and converted back to acetyl-CoA (catalysed by ATP citrate lyase) for lipid synthesis. Malate, an intermediate in the TCA cycle, can be converted into pyruvate by malic enzyme with the production of NADPH, a reducing equivalent that is used to generate reduced glutathione, allowing cancer cells greater tolerance to free-radical-induced damage (64). Glutaminase catalyses the hydrolysis of the amine group of glutamine to form glutamate and

64 ammonia. Glutamate equilibrates with α-ketoglutarate via glutamate dehydrogenase. In a process termed reductive carboxylation, glutamine-derived citrate provides the acetyl-

CoA for lipid synthesis and TCA cycle intermediates (130). Hence, the glutamine addiction of cancer cells is another mechanism by which the metabolism is rewired to support biosynthesis (64, 131).

FIGURE 18 MET inhibits key metabolic steps in lipogenesis. MET inhibits the gene expression of carnitine palmitoyltransferase 1 (CPT1), a mitochondrial enzyme that is the rate-limiting step in long-chain fatty acid β-oxidation. CPT1 catalyses the transfer of acyl-CoA to the carnitine hydroxyl group, forming acyl-carnitine, which is then transported into the mitochondrial matrix via translocase. Carnitine palmitoyltransferase

2 (CPT2) catalyses the formation of acyl-CoA from acyl-carnitine. Acyl-CoA then undergoes β-oxidation. MET prevents the nuclear activation of sterol-regulatory element- binding protein 1c isoform (SREBP-1c) and sterol-regulatory element-binding protein 2

65 isoform (SREBP-2), transcription factors that induce the expression of enzymatic genes involved in fatty acid and cholesterol synthesis, respectively. MET decreases the activities of 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGCR) and acyl coenzyme

A:cholesterol acyltransferase (ACAT). HMGCR is the rate-limiting enzyme of the mevalonate pathway, which catalyses the reduction of 3-hydroxy-3-methylglutaryl- coenzyme A (HMGCoA) to mevalonate. The mevalonate pathway synthesizes isoprenoids and cholesterol. ACAT is an endoplasmic reticulum protein that catalyses the formation of cholesterol esters from acyl-CoA and cholesterol. MET decreases the gene expression of steroyl-CoA desaturase 1 (SCD1), the enzyme responsible for desaturation of stearic acid (18:0) into oleic acid (18:1 n-9) and of palmitic acid (16:0) to palmitoleic acid (16:1 n-7). MET decreases the protein expression of FAS, acetyl-CoA carboxylase

(ACC) and ATP citrate lyase (ACLY), which are enzymes involved in fatty acid synthesis. Figure obtained from Cantoria et al (106).

2. MET Inhibits ETC Complex I

MET is an inhibitor of complex I (108, 109) of the ETC. In 2000, two research groups independently showed that dimethylbiguanide selectively blocks complex I of the ETC (108, 109). In intact isolated hepatocytes, dimethylbiguanide has been reported to dose-dependently (0.1 to 10 mM) inhibit oxygen consumption maximally at 20–30 min (108). The inhibition of respiration only occurred when glutamate-malate (complex

I substrates) were used as substrates versus when succinate (complex II substrate)- rotenone or N, N, N’, N’-tetramethyl-1,4-phenylenediamine dihydrochloride (TMPD)- ascorbate (complex IV substrates) were added during the assessment of oxygen uptake.

It is interesting to note that El-Mir and colleagues did not observe these changes when oxygen uptake experiments were performed in digitonin-permeabilized hepatocytes or in isolated liver mitochondria (108). This is in contradiction to what Owen and colleagues published when they showed that lower MET concentrations of 50 and 100 μM were able to significantly decrease state 3 respiration rate in digitonin-permeabilized rat hepatoma cells (109). MET has slow permeation properties across the inner mitochondrial membrane (109), and longer incubation periods (30 min in the El-Mir group versus 24–60 hours in the Owen group) may have eventually yielded comparable results. In support of the previous findings, recent reports confirm that MET is a specific inhibitor of the ETC complex I, which leads to some impairment in mitochondrial function in human-derived non-malignant and in cancer cells (132-136). This potentially limits the intact oxidative respiration capabilities of the cancer cell (135).

3. MET Inhibits Expression of Lipogenic Enzymes

One of the most notable effects of MET is inhibition of lipogenesis, a metabolic pathway that is critical for a cancer cell’s survival advantage. The effects of MET on energy homeostasis in normal hepatocytes and breast and colon cancer cells have been characterized by the blocked activation or expression of key lipid biosynthesis enzymes such as ACC, FAS, HMGCR and enhanced expression of regulators of mitochondrial biogenesis, peroxisome proliferator-activated receptor-gamma co-activator 1 (PGC-1)

(137-140).

4. MET Inhibits Oxidation of Fatty Acids

The normoglycaemic effects of MET have also been attributed to its ability to prevent fatty acid oxidation, which decreases acetyl-CoA, ATP, the availability of reducing equivalents for hepatic gluconeogenesis (141), an effect likely mediated by a reduction in the expression of the carnitine palmitoyltransferase I gene (142), and eventually a decrease in protein expression and activity of the enzyme, resulting in impairment of long fatty acid chain transport from the mitochondrial outer membrane into the matrix where

β-oxidation takes place.

5. MET Inhibits SREBP Transcription Factors

MET (0.2 to 1.0 mM for 16 h) has been shown to activate AMPK and decrease the mRNA, nuclear translocation and consequent activation via cleavage of the nuclear portion and the promoter activity of SREBP-1c in rat hepatoma McA-RH7777 cells (143,

144). The mRNA and nuclear protein levels of SREBP-2 were also reduced after MET treatment. This AMPK-mediated suppression of SREBP-1c has also been reported to prevent lipogenesis in an insulin-resistant mouse model (145) and is consistent with a decrease in hepatic SREBP-1 expression in mice fed a high fat (60% lipids) diet for 10 weeks and then MET (0.48 mg% of the diet) for another six weeks (146). Since SREBP-

1c and SREBP-2 are transcription factors that promote the expression of enzymatic genes involved in fatty acid and cholesterol synthesis, respectively (147), we would expect diminished lipid synthesis as a biological endpoint of their down-regulation. In accordance, MRC5 human foetal lung fibroblasts incubated for 72 h with MET (5 x 10-5 to 5 x 10-4 M) decreased 1-14acetate incorporation into sterols, fatty acids and

68 triglycerides compared to control, accompanied by a reduction in the activities of

HMGCR and ACAT, enzymes that catalyse the formation of mevalonic acid from

HMGCoA and the esterification of cholesterol, respectively (148).

6. MET Inhibits SCD1

Also, MET has been shown to induce the phosphorylation (Ser-351) of the nuclear receptor TR4 via AMPK, leading to decreased TR4 transactivation and a decrease in the gene expression of its target, steroyl-CoA desaturase 1 (SCD1) gene expression. SCD is an enzyme that catalyses the synthesis of the monounsaturated fatty acids palmitoleic acid (16:1 n-7) and oleic acid (18:1 n-9) from saturated fatty acids obtained from de novo lipogenesis or from the diet (149). SCD1 has been shown to be associated with numerous diseases including but not limited to obesity, hepatic steatosis, hypertriglyceridaemia, insulin resistance, low grade inflammation and bone fractures (149).

The role of SCD1 in cancer has been gaining more attention as a potential pharmacological target in cancer interventions (150). SCD is an endoplasmic reticulum- bound protein encoded by the SCD1 and SCD5 genes in humans (151). They are highly expressed in liver and adipose tissue (SCD1) and in the brain and the pancreas (SCD5)

(149). Observational studies have reported a positive association between saturation index (18:0 to 18:1 n-9 ratio used by investigators as a marker for SCD activity) with cancer risk (152-156). The first cDNA of human SCD, published in 1994, revealed that the mRNA levels of this enzyme were elevated in tissues derived from oesophageal carcinoma, colorectal cancer and hepatocellular adenoma (157). Its protein levels are highly expressed in SV40-transformed fibroblasts compared to their wild-type

69 counterpart (158). However, decreased transcript expression was reported in prostate cancer when compared to normal epithelium (159). These seemingly conflicting results may reflect variation in expression depending on the tissue type or some malignancy- induced metabolic changes in lipid synthesis and/or lipid profile which would overall guarantee cancer cell survival advantage. Indeed, Moore and colleagues speculated that the down-regulation of SCD cDNA in prostate carcinoma may be due to: (a) the need of the cancer cell to increase the levels of palmitate, which can be done by decreasing SCD activity; (b) the need to eliminate the SCD-induced down-regulation of lipid membrane rafts; or (c) the need to down-regulate susceptibility of tumour cells containing more unsaturated fatty acids to TNF-induced free radical attack (159). Nevertheless, knockdown of human SCD in transformed human fibroblasts resulted in decreased oleic acid synthesis, lowered desaturation index profiles of the main polar lipids phosphatidylcholine and phosphatidylethanolamine, and decreased de novo synthesis of

14C-labelled phospholipids, cholesterol and cholesterol esters, free fatty acids and triacylglycerols (160). Interestingly, there was an inhibition in cellular proliferation and anchorage-independent growth in the SCD knockdown cells (160). Other studies confirm that SCD inhibition by chemical or genetic manipulations result in inhibition of cancer cell proliferation and/or death (161-164). Thus, as SCD appears to be involved in modulating lipid metabolism and signalling processes crucial for cancer cell replication and anchorage-independent growth, effects are likely influenced by the effects of SCD loss on membrane integrity (160).

7. MET’s Anti-Lipogenic Effect is Context-Dependent

We recently found that the in vitro response to MET depends on the level of intracellular cholesterol synthesis of the tumour (165). We were the first to demonstrate that a therapeutically relevant dose of MET for type 2 diabetes management impairs glucose utilization in pancreatic cancer by inhibiting FAS when cholesterol synthesis is limited. Specifically, we found that pancreatic cells that have a K-RAS mutation, and that require de novo fatty acid (FA) synthesis for lipids (‘lipogenic cells’), were unable to synthesize FA from acetyl-CoA in the presence of an inhibitor of cholesterol synthesis and MET. Our in vitro model shows that a therapeutically relevant dose of

MET (100 μM) using an acute treatment of 24 h decreases de novo lipid synthesis via the FAS pathway in pancreatic adenocarcinoma only when: (a) the glucose-derived acetyl-CoA is made available for fatty acid synthesis by inhibition of cholesterol synthesis (addition of exogenous cholesterol) and (b) K-RAS mutation is present (166).

As the fatty acid and cholesterol synthesis pathways utilize acetyl-CoA as a common substrate (167), the addition of cholesterol (in the form of the more water-soluble cholesteryl hemisuccinate) inhibits the thiolase-catalysed cholesterol pathway and shifts the glucose-derived acetyl CoA- towards the acetyl-CoA carboxylase-catalysed fatty acid synthesis pathway. These effects are observed only in MIA PaCa-2-cells, which harbour the GGT(Gly) to TGT(Cys) codon 12 K-ras mutation. Further, non-lipogenic cancer cells harbouring a K-RASG12C mutation (168) with suppressed cholesterol synthesis were significantly more sensitive to the growth-inhibiting effects of MET than tumour cells containing wild-type K-RAS with normal cholesterol synthesis.

G. Ongoing Clinical Trials on Metformin as a Chemotherapeutic Drug for

Pancreatic Cancer

There is considerable interest in the anti-tumour action of the commonly used antidiabetic drug MET for the treatment and management of patients with pancreatic cancer.

Enthusiasm for MET has been significantly strengthened by in vitro and in vivo experimental findings of potent anti-tumour activity of MET at therapeutically safe doses.

As a result, a number of early-phase trials are now being conducted to assess the efficacy of MET in combination with standard and experimental therapeutics in pancreatic cancer patients. Although there are numerous studies that show the cancer-preventive and cancer-therapeutic actions of MET in preclinical models, there is a need to conduct adequately powered clinical trials on the therapeutic effects of MET that include prognosis and survival markers. At the time of writing, there are six ongoing clinical trials specifically on pancreatic cancer and MET, according to the ClinicalTrials.gov website (Table 5).

TABLE 5 Ongoing clinical trials on MET and pancreatic cancer

Title Recruitment Results Conditions Interventions MET hydrochloride in Not yet No Pancreatic cancer: Drug: MET hydrochloride treating patients with recruiting results Stage IA Other: pharmacological pancreatic cancer that can available Stage IB study be removed by surgery Stage IIA Stage (Trial Number IIB NCT01954732) MET combined with Recruiting No Locally advanced Drug: gemcitabine, chemotherapy for results pancreatic cancer/ erlotinib, Met, placebo pancreatic cancer available metastatic (Trial Number pancreatic cancer NCT01210911) MET plus modified Recruiting No Acinar cell Drug: MET FOLFOX 6 in metastatic results adenocarcinoma of hydrochloride, oxaliplatin, pancreatic cancer available the pancreas, leucovorin calcium, (Trial Number duct cell, fluorouracil NCT01666730) adenocarcinoma of Other: the pancreas, laboratory biomarker recurrent analysis pancreatic cancer, stage IV pancreatic cancer Combination Recruiting No Pancreatic cancer Drug: capecitabine, chemotherapy with or results cisplatin, epirubicin, without MET available gemcitabine, Met hydrochloride in treating patients with metastatic pancreatic cancer (Trial Number NCT01167738) Treatment of patients with Recruiting No Pancreatic Drug: paclitaxel, Met advanced pancreatic results adenocarcinoma cancer after gemcitabine available advanced or failure metastatic (Trial Number NCT01971034) Gemcitabine+Nab- Recruiting No Stage IV Drug: Gemcitabine, nab- paclitaxel and results pancreatic cancer paclitaxel, FOLFIRINOX, FOLFIRINOX and available Met molecular profiling for Genetic: patients with advanced Immunohistochemistry pancreatic cancer (IHC) Analysis (Trial Number NCT01488552) Table obtained from Cantoria et al (106).

IV. Research Problem, Hypotheses and Specific Aims

We now know that the Warburg Effect in cancer cells exists to supply substrates and reducing equivalents that support anabolic reactions downstream of glucose metabolism.

The anti-hyperglycemic effects of MET is based on its abilities to improve glucose metabolism by restoring the integrity of the insulin signalling pathway, by improving glucose uptake by peripheral tissues and by decreasing hepatic gluconeogenesis (82,

169). With the surmounting evidences pointing to the preventive and therapeutic actions of MET against cancer, there is a lack of definition of the context of these anti-cancer effects.

The dosage of MET for diabetes management (500 mg three to four times a day or

850 mg two to three times a day) produces plasma concentrations of one to two μg/mL

(170), which is equal to about 6 - 12 μM. Modification of the ratio of free to bound insulin was observed in human sera treated with therapeutically relevant MET dosages of up to 120 μM (171). In various preclinical models, the concentrations used to test the chemotherapeutic doses of MET range from the micromolar to the millimolar range.

However, in clinical trials, less than 2000 mg MET /day are being tested, which is still within the prescription dosage indicated for diabetes management (170). Clearly, there is a gap in translating the chemotherapeutic dose of MET from cellular and animal studies into clinical trials. This begs the question of whether the dose used to manage diabetes is chemopreventive or chemotherapeutic against pancreatic cancer.

As shown in Fig 18, MET inhibits lipid synthesis in non-malignant and malignant pre-clinical models. Yet, we know that de novo lipid production is downstream of

74 glucose metabolism. How does MET regulate lipid metabolism and other metabolic pathways? Are these regulatory mechanisms also true in a clinically relevant mouse model of pancreatic cancer?

Our central hypothesis is that a therapeutically relevant dose of MET (100μM) for type 2 diabetes management is a preventive drug against pancreatic cancer through regulation of cancer cell metabolism. To address our hypothesis, we sought to achieve the following aims:

1) to determine the metabolic context by which MET exerts its anti-tumor properties through metabolic profiling of pancreatic cancer cell lines

2) to identify how MET regulates metabolism of PDAC cells and

3) to identify how MET regulates metabolism in a PDAC in vivo model.

CHAPTER II: IN VITRO EVIDENCE OF THE ANTI-CANCER EFFECTS OF

METFORMIN USING STABLE ISOTOPE-BASED DYNAMIC METABOLIC

PROFILING (SIDMAP) TECHNOLOGY

This work was published by Cantoria et al. in the journal Metabolomics. 2014. 10:91-

104. Mary Jo Cantoria and Emmanuelle J. Meuillet designed the experiment. Mary Jo

Cantoria performed the experiments and F. Tracy Lagunero helped with sample extraction, sample derivatization and GC/MS. Mary Jo Cantoria, Emmanuelle J. Meuillet and Laszlo G. Boros analyzed the data and wrote the manuscript.

CHAPTER II: IN VITRO EVIDENCE OF THE ANTI-CANCER EFFECTS OF

METFORMIN USING STABLE ISOTOPE-BASED DYNAMIC METABOLIC

PROFILING (SIDMAP) TECHNOLOGY

I. Introduction

A. MET’s Anti-cancer Properties

MET (1,1-dimethylbiguanide) is the first-line oral therapy prescribed for type 2 diabetes (81). It is a potent anti-hyperglycemic and insulin-sensitizing drug that works by decreasing hepatic gluconeogenesis, activating insulin receptor tyrosine phosphorylation

(81), decreasing intestinal glucose absorption, and increasing skeletal muscle and adipose tissue glucose uptake (82). Moreover, MET increases the more active mitochondria- bound hexokinase and actin-bound phosphofructokinase in streptozotocin-induced diabetic male Swiss mice hearts, enhancing glucose sensitivity of those organs (83).

Interestingly, numerous studies have reported a lower risk of cancer (90, 92, 93, 95) and a reduced risk of cancer-related mortality in diabetics (96, 172) treated with MET compared to diabetics that were prescribed other glucose-lowering therapies. Recently, improved survival was observed in diabetic pancreatic cancer patients who were taking

MET (104). Published treatment protocols suggest that lactic acidosis is potentially a very serious (86) but a rare side effect of MET, although the link with MET has been questioned (87).

Various mechanisms of action for MET’s anti-cancer properties have been published, such as its ability to inhibit the mammalian target of rapamycin complex I (mTORC1) in an AMP activated protein kinase (AMPK)-mediated manner (173). Other reported mechanisms are the AMPK-independent suppression of mTORC1 activation via

77 inhibition of the Regulator complex (121, 122, 174) and the up-regulation of the mTORC1 inhibitor REDD1 (regulated in development and DNA damage responses)

(124). MET has also been shown to prevent insulin/IGF1 crosstalk with G protein coupled receptor (GPCR) signaling (120) and to induce p53-dependent cell cycle arrest and apoptosis (175).

Metabolic downstream targets of MET involve the electron transport chain (ETC) complex I (135, 136, 176), which results in energy depletion in cancer cells. The addition of MET with 2DG induces cell death and promotes ATP depletion, underscoring the importance of oxidative phosphorylation as a cancer therapeutic target (177). In addition, it is demonstrated that MET inhibits glycolytic flux by suppressing the translocation of glucokinase from the nucleus into the cytosol in rat hepatocytes, possibly due to its ATP-depleting properties (134).

In vivo, MET decreases the expression of acetyl CoA carboxylase, fatty acid synthase and citrate lyase, which are involved in hepatic fatty acid synthesis (140, 178). Kim et al.

(179) demonstrated that MET hinders the AMPK-dependent transactivation of nuclear receptor TR4, which then fails to bind to TR4RE on the SCD1 5’ promoter impairing

SCD1 gene expression. This results in the inhibition of lipogenesis and up-regulation of

β-oxidation in hepatocytes (179).

Metabolic adaptation of transformed mammalian cells to codon K12K-ras mutation is identical in fibroblasts (168) and MIA PaCa-2 cells, the latter harboring the GGT→TGT mutation (180). The mutant phenotype exhibits greatly increased glycolysis with a low flux along pathways that produce lipid synthesis precursors via the oxidative branch of the pentose cycle, pyruvate dehydrogenase and citrate synthase. The K-ras oncogene also

78 mediates a metabolic phenotype that readily trades glucose-derived acetyl-CoA between cholesterol synthesis, controlled by biosynthetic thiolases, and the fatty acid synthase precursor malonyl-CoA, controlled by acetyl-CoA carboxylase. In the presence of either synthetic (C75) or natural (luteolin) FAS inhibitors, cholesterol synthesis readily serves as the alternate route for glucose-derived acetyl-CoA use in MIA PaCa-2 cells (167).

This channeling of acetyl-CoA between palmitate and cholesterol syntheses serves as the marker of drug efficacies inhibiting metabolic enzymes that compete for the glucose- derived acetyl-CoA substrate.

B. The SiDMAP Platform

Stable Isotope-Based Dynamic Metabolic Profiling (SiDMAP) uses targeted metabolomics via 13C-glucose, 13C-glutamine or 13C-palmitate tracer to interrogate specific metabolic pathways via gas chromatography/mass spectrometry (181). This method is combined with powerful bioinformatics to measure metabolic fluxes in various model systems (cells, animals and humans). The tracers are non-radiating, non-toxic to animals and humans and are metabolized similar to regular (non-heavy) molecules. Data sent to the investigator are presented in user-friendly formats i.e., raw data are included with descriptive statistics, graphical representations of the control and treatment groups are included along with calculations of the p values. In publications, a reader-friendly format is used to represent changes in the treated groups relative to control using the

EZTopolome matrix following a color-coded “heatmap” scheme (Fig 22). SiDMAP has been used as a research method in multiple academic and private industry-initiated publications as well as for pharmaceutical applications.

The advantage of the targeted metabolomics approach is its ability to identify which reactions and enzymatic activities are being affected by a treatment or condition (such as a genomic mutation) (182). This is done through the determination of the quantity and positional labeling of the tracer carbon into various metabolites (183). Hence, causality can be established between a treatment (e.g., anti-cancer drug) and the model system’s metabolic response. In the non-targeted approach, a large pool of metabolites from non- treated (control) versus treated groups are identified and their levels are qualitatively expressed as being up- or down-regulated using statistical methods (184). Because no internal standards are used (184) and metabolite levels can change using multiple metabolic pathways, the non-targeted approach is only able to generate associative relationships (182). Figure 19 summarizes the approaches of targeted versus non- targeted metabolomics (184). In the non-targeted approach, the investigator must be careful in classifying metabolites as biomarkers that could have appeared by chance

(185). However, the non-targeted approach is a widely used technique for hypothesis generation. The vast array of metabolites measured using non-targeted metabolomics eliminates the limiting characteristic of the targeted approach. Because the targeted approach is confined to select metabolic pathways, other reactions and enzymes that are excluded from the pre-defined pathways of interest may be unknowingly important players during treatment (185). In our study, we opted to use the targeted metabolomics approach because we were interested in the Warburg effect and the metabolic pathways of glycolysis, TCA cycle, pentose phosphate pathway, and lipid synthesis.

FIGURE 19 Outline of approaches followed by the targeted (A) and non- targeted

(B) metabolomics approaches. Image obtained from Bain et al (184).

In the present study, we evaluated the metabolic effects of a therapeutically relevant dosage of MET on two pancreatic cancer cell lines. We show MET, in the context of available acetyl-CoA and cholesterol, limits fatty acid synthesis in pancreatic tumor cells with mutated K-RAS. This explains how MET controls K-RAS induced malignant cell growth via limiting new fatty acid production necessary for cancer cell formation in patients with insulin resistance and the metabolic syndrome. The results of our report provide metabolic explanations for studies showing an anti-cancer effect of MET in animals fed with a high energy (39.8 % lard) diet (178, 186).

II. Materials and Methods

A. Cell Culture and Proliferation

1. Cell Culture Conditions

G12C BxPC-3 (WT K-RAS) and MIA PaCa-2 (mutant K-RAS ) pancreatic cancer cells were purchased from American Type Culture Collection (Manassas, VA, USA). Cell culture media, penicillin–streptomycin (P/S) and trypsin–EDTA were purchased from

Mediatech (Manassas, VA, USA). BxPC-3 cells were cultured in RPMI media and MIA

PaCa-2 cells were grown in DMEM. Both media were supplemented with 10 % FBS from PAA Laboratories, Inc., (Pasching, Austria) and 1 % P/S. The cells were incubated at 37⁰C, 5% CO2 and 95% humidity and passaged with 0.25 % trypsin–EDTA once the cells reached 75–80 % confluence. Cells treated with cholesteryl hemisuccinate (CHS;

Sigma-Aldrich, St. Louis, MO), from now on referred to as BxPC3-CHS and MIA PaCa-

2-CHS, were incubated in media supplemented with 1 mM CHS complexed to 1 % BSA for 2 weeks prior to metabolomics analysis.

2. In vitro Model of Insulin Resistance via CHS Supplementation of the

Media

CHS is a more soluble derivative of cholesterol (187, 188) (Fig 20).

FIGURE 20 Structures of cholesterol (A) and cholesteryl hemisuccinate (B).

Cholesteryl hemisuccinate has a hemisuccinate group attached to the hydroxyl group at the end of the steroid ring. It is the more-water soluble form of cholesterol.

In the late 90s, amongst many studies published on the cellular effects of MET,

Meuillet and others showed that MET is able to modulate the insulin receptor (IR) in cholesterol (chol)-treated human hepatoma cells, HepG2 (187). In that study, they used a cellular model in which insulin sensitivity was altered by supplementing the culture medium of HepG2 cells with a derivative of CHOL, cholesteryl hemisuccinate (CHS)

(189, 190). IR and IRS phosphorylation are defective in insulin resistance (191).

Activities of downstream effectors of the pathway such as the PI3K and MAPK are also decreased in insulin resistance (187). Indeed, CHS-treated HepG2 cells showed decreased IR and IRS phosphorylation and PI3K and MAPK activities (187, 190). We

83 now use this in vitro model of insulin resistance in PDAC cell lines BxPC-3 and MIA

PaCa-2.

3. Cell Proliferation Measurement by Counting

Cell proliferation was assessed by plating 1 x 105 cells into T-25 cm2 flasks. Cells were immediately treated with 100 μM MET for 72 h as appropriate. The doubling times of BxPC-3 cells and MIA PaCa-2 are 48–60 and 40 h, respectively (192). Based on these reported doubling times, we decided to use 72 h for cell proliferation measurements to ensure that the cells have undergone one round of doubling before counting. Cells were then counted using trypan blue exclusion.

4. MTT Assay to Assess Cell Viability

BxPC-3 and MIA PaCa-2 cells were plated at 2,000 and 500 cells, respectively in 96- well plates and incubated for 24 h in complete RPMI or DMEM media (+1 mM CHS).

The following day (day 1), cells were treated with either vehicle (PBS) or 100 μM MET and incubated for 4 days. On day 5, 50 μL of 3-(4,5-dimethylthiazol-2-yl)-2,5- diphenyltetrazolium bromide (MTT) was added to the wells. After 4 h of incubation, the resulting precipitates were dissolved in 100 μL DMSO. Plates were read at 540 nm using the Synergy 2 Microplate Reader (Winooski, VT

USA).

B. Stable Glucose Isotope

All reagents were purchased from Sigma-Aldrich (St. Louis, MO) unless otherwise stated. All experiments were conducted in triplicate. Twenty-four hours prior to MET treatment and metabolomics study, 2 x 106 cells were grown in T-75 cm2 culture flasks with glucose and sodium pyruvate-free RPMI and DMEM containing 10% FBS, 1% P/S,

4.5 g glucose/L, of which 23–40 % of total final glucose was derived from the [1,2-13C]-

D-glucose tracer (Isotec, Miamisburg, OH, USA) after media preparation, as measured by GC–MS and reported in Table 6. The tracer was added to the media for all cells along with 100 μM MET in half of the non-CHS and CHS-treated cells and allowed to incubate for 24 h. Media and trypsinized cell lysates were collected and frozen at -80 ⁰C until analysis.

C. Product Extraction and Derivatization

Extraction and derivatization procedures for glucose, cholesterol, fatty acids, lactate,

CO2 and glutamate were previously published (167, 193). Sterols and fatty acids were extracted by saponification of Trizol (500 μL, Invitrogen, Carlsbad, CA) cell extract after removal of the upper glycogen- and RNA-containing supernatant using 30 % KOH and

70 % ethanol (300 μL each) for 2 h. Sterol extraction was performed using 5 mL petroleum ether (EMD, Gibbstown, NJ) with repeated shaking for 20 s three times. The molecular ion of cholesterol was monitored at the m/z 386 ion cluster. Fatty acids were extracted by further acidification using 6 N hydrochloric acid to pH below 2.0 and repeated vortexing with 5 mL petroleum ether. Fatty acids (palmitate) were monitored at m/z 270 using canola oil as positive control. The enrichment of acetyl units in media and

85 cell pellet palmitate in response to CHS and MET treatments was determined using the mass isotopomer distribution analysis (MIDA) approach. Acetyl-CoA and fractions of new synthesis were calculated from the m4/m2 ratio using the formula m4/m2 = (n-

1)/2*(p/q), where n is the number of acetyl units, p is the 13C labeled precursor acetate fraction and q is the 12C labeled natural acetate fraction (p + q = 1) (194). Additional details of mathematical approaches are described in by Lee et al. (195) for spectra processing and 13C positional distribution diagnostics.

For glucose extraction, 500 μL each of 0.3 N barium hydroxide and 0.3 N zinc sulfate were added to 100 μL media. Samples were vortexed and centrifuged for 15 min at

10,000 rpm. Supernatant was dried on air over heat and were derivatized by adding 150

μL hydroxylamine solution and incubated for 2 h at 100 ⁰C followed by addition of 100

μL of acetic anhydride. Samples were incubated at 100 ⁰C for 1 h and dried under nitrogen over heat as previously described in the fatty acids derivatization section. Ethyl acetate (200 ⁰L) was added. Peak glucose ion was detected at m/z 187 cluster.

Lactate was extracted from media through acidification of 100 μL media with HCl and addition of 1 mL of ethyl acetate. The resulting aqueous layer was dried under nitrogen over heat and derivatized using lactate standard solution as positive control.

Two hundred microlitre of 2,2-dimethoxypropane was added followed by 50 μL of 0.5 N methanolic HCl. Samples were incubated at 75 ⁰C for an hour. Sixty microlitre of n- propylamine was added and samples were heated for 100 ⁰C for an hour followed by addition of 200 μL dichloromethane. Heptafluorobutyric anhydride (15 μL) was added followed by 150 μL of dichloromethane and samples were subjected to GC/MS. M1 and m2 lactate were differentiated to distinguish the pentose phosphate flux from anaerobic

86 glycolysis (194, 196) and the ion cluster at m/z 328 was examined.

Media glutamate was converted into its n-trifluoroacteyl-n-butyl derivative and monitored at ion clusters at m/z 152 and m/z 198.

13 CO2 Assay for CO2 was generated by adding equal volumes (50 μL) of 0.1 N

12 13 NaHCO3 and 1 N HCl to spent media and CO2/ CO2 ion currents were monitored and

13 13 calculated from the m/z 44 and m/z 45 peak intensities, respectively, using CO2/ CO2

13 of cell culture cabinet’s CO2 tank as the reference ratio for CO2 D calculations.

D. Statistics

Mass spectral analyses were obtained by consecutive and independent injections of 1

μL sample using an autosampler with optimal split ratios for column loading (106 > abundance > 104 abundance). Data was accepted if the standard sample deviation was below 10% of the normalized peak intensity (integrated peak area of ion currents; 100%) among repeated injections. Data download was performed in triplicate manual peak integrations using modified (background subtracted) spectra under the overlapping isotopomer peaks of the total ion chromatogram (TIC) window displayed by the

Chemstation (Agilent, Palo Alto, CA) software. A two-tailed independent sample t-test was used to test for significance (P < 0.05, P < 0.01) between control and treated groups

(*, **) or between cell lines (#).

E. Visual System Wide Association Interface

Rapid system-wide association study (SWAS) evaluation of both cell lines was performed by the color assisted visual isotopolome data matrix screening tool (193), to diagnose phenotypic differences and response to drug treatment.

F. Practical Note to Multiple SWAS Entry Interpretations

Please note that there is a distinct functional relevance of each value in Table 6, which is the source matrix for the SWAS interface. For example, there are four table entries for palmitate, which show close to equilibrium non-treatment responsive chain elongation of shorter (C14:0) acyl chain by a single acetyl unit from glucose to form 13C m2 palmitate (101). On the other hand, there are significant differences in new palmitate synthesis, which results in altered 13C labeled fractions (98), as well as its synthesis from scratch (FNS; 102) with varying glucose derived acetyl-CoA enrichments (103). For

System level interpretations we take into account that a significant inhibitory effect of

MET in net new palmitate synthesis from glucose may be considered more rate limiting on new membrane synthesis and cell proliferation, while its effect on elongating a previously existing shorter acyl chain is not affected. Therefore, multiple SWAS interface entries for the same product clarify the potential biological impact(s) of MET treatment on important precursor-product relationships in a complex biological system.

III. Results

A. MET Does Not Change PDAC Cell Viability and Proliferation

The ability of MET (MET, 100 μM) to affect cell viability of various PDAC cell lines with and without CHS pre-treatment for 2 weeks was examined using MTT assay (Fig

21 A). MET alone was unable to decrease cancer cell viability after 4 days of drug treatment. Hence, the metabolic impacts of CHS and MET in this study cannot be attributed to cell death-inducing properties.

The ability of MET to affect cell proliferation for 72 h in all groups was assessed by counting using the trypan blue exclusion method. MET treatment did not significantly alter cell proliferation in control or CHS-treated cells (Fig 21 B). As expected, MIA

PaCa-2 cells showed shorter doubling times than BxPC-3 cells did.

50 50

# #

) )

4 # 40 4 40 # 30 30

20 20

Cell Number (x10 Number Cell 10 Cell Number (x10 Number Cell 0 0

BxPC-3 VC BxPC-3 Met

BxPC-3 CHS VC BxPC-3 CHS Met MIA PaCa-2 VCMIA PaCa-2 Met MIA PaCa-2 CHS VC MIA PaCa-2 CHS Met

FIGURE 21 No change in cell viability and proliferation were observed after MET treatment. A) Cell survival of various pancreatic adenocarcinoma cell lines treated with

MET. MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay was performed to measure cell viability in BxPC-3 and MIA PaCa-2 cells after treatment with

MET (100 μM) in the absence or presence of cholesteryl hemisuccinate (CHS) pre-

90 treatment for 2 weeks. Blue bars are control and red bars are MET-treated cells. All data are mean ± SD (n = 3 per group). B) Cell proliferation of various pancreatic adenocarcinoma cell lines treated with MET. Cell proliferation was assessed by plating 1 x 105 cells into T-25 cm2 flasks in triplicate. Cells were immediately treated with 100

μM MET for 72 h as appropriate. Cells were then counted using trypan blue exclusion.

We used a relatively short (72 h) incubation time for MET treatment, which showed a slowing trend in MIA PaCa-2 proliferation with no (yet) significant differences but decreasing NS P values [Fig 21 B; (P = 0.293-Met; 0.139-CHS; 0.089-CHS + Met)].

BxPC-3 cells fell short of showing initial response to MET (P = 0.425-Met; 0.118-CHS;

0.127-CHS + Met)

13 B. Heavy [1,2- C2]-D-glucose Enrichment and Cholesteryl Hemisuccinate

(CHS) Media Preparation

There is a uniform decrease in 13C-glucose labeled fraction in the media with

13 identical tracer carbon substitutions [1,2- C2]-D-glucose of non-CHS-treated BxPC-3 and MIA PaCa-2 cells in comparison with their cell-specific controls (Fig 22-

EZTopolome (K-RAS) ID 280 and 283; Table 1, media 13C glucose panel 280 and 283).

This difference is consistent with the increased natural 13C labeled glucose ratio of the excess media that was replaced with CHS in bovine albumin for CHS treatment. Instead

13 of providing calculated values, we report, as determined by GC-MS, exact [1,2- C2]-D- glucose enrichment after preparing all FBS, albumin and CHS supplemented DMEM and

RPMI in Table 6. More specifically, there were a relative 18.8 (±0.15) and 29.4 %

(±0.01 %) differences in CHS solution treated RPMI (BxPC-3) and DMEM (MIA PaCa-

13 2) in [1,2- C2]-D-glucose enrichment (please note that glucose consumption between cell lines and among treatments remains unaffected), which are shown in Fig 22-

EZTopolome (K-RAS ID 6 and 7; Table 6, media glucose panel 6 and 7, before and after

13 the 24-h culturing period. Due to the expected and observed differences in [1,2- C2]-D- glucose in the CHS containing media, below we report either 13C isotope ratios in

13 glucose-derived isotopomer products as positional C enrichment (mn/mk) or divide

13 isotopomer extracted ion chromatogram (EIC) by the C labeled fraction (mn/Ʃm).

13 These isotopomer markers of glucose to product flux show [1,2- C2]-D-glucose tracer distribution and thus readily reflect changes in cells’ phenotypes after MET treatment. In other words, normalized isotopomer distribution patterns are independent of the amount of tracer uptake, while product concentrations are reported as total ion currents that include unlabeled and labeled fractions, alike. In simple words mn/Ʃm reflects how cells use a single glucose molecule as surrogate markers of flux. This is consistent with the use

13 of [1,2- C2]-D-glucose as a true tracer for investigating MET’s effect on cultured tumor cell metabolism and its branching routes. To this end, for example, the identical ~97 % media glucose labeled specifically on the 1,2 carbon positions of the 13C glucose fraction

(m2/Ʃm) indicates that there was truly negligible glucose release by cultured cells via gluconeogenesis, necrosis and glucose production to scramble the glucose tracer (Fig 22-

EZTopolome (K-RAS ID 283; Table 6, media 13C glucose panel 283; please note the small SD values characteristic of the mn/Ʃm mathematics in isotopomer analysis methods).

TABLE 6 Summary of metabolic profiles of BxPC-3 (columns 3–6) and MIA PaCa-

2 (columns 7–10) pancreatic adenocarcinoma cells (PDAC)

The metabolic profiles of BxPC-3 and MIA PaCa-2 cells in response to 100 μM MET after 24 h of culture with and without CHS pre-treatment for 2 weeks were obtained via SiDMAP analysis

13 using [1,2- C2]-D-glucose tracer and are shown as Ave ± SD.

Source-data-matrix-file-log: source of metabolite, i.e.: culture media or pellets with raw data locator file number.

13 Mn/Ʃm: Isotopomer/ C labeled fraction as SUM(m1 + m2 + ... + mn). Ʃmn: Molar Enrichment

13 (ME) C content as SUM (1 x m1 + 2 x m2 + ... + n x mn) (195). Number of observations per group: n = 3 (± SD).

CAS Chemical abstracts service registry number

* P < 0.05 versus control

** P < 0.01 versus control

a P < 0.05 versus BxPC-3 (treatment matching comparison between cell lines; where cell lines have been cultured in different media as described in Sect. Methods). Due to the poor resolution of this Table, the reader is encouraged to refer to the published paper in Metabolomics, 2014,

10:91;104.

RAS (MIA PaCa-2) PDAC cell lines. EZTopolome(K-RAS) contains group averages from Table 6 as percent of control values in an identical, coherent matrix format [please note control 100 % values are omitted for EZTopolome (K- RAS)]. Visual system-wide

94 association study (SWAS) evaluations show the significant phenotypic differences as well as effects of CHS and MET for a rapid overview of Results. *P < 0.05 versus control; **P < 0.01 versus control; †P < 0.05 versus BxPC-3 (treatment matching comparison between cell lines).

C. Complete Glucose Oxidation

13 The decrease in complete glucose oxidation (Fig 23) into CO2 observed in the MIA

PaCa-2 cells after the combined CHS and MET treatment indicates that MET decreases direct and indirect glucose oxidation relative to that of amino- and fatty acids (unlabeled substrates) for ATP synthesis. Thus, K-RAS -mutated MIA PaCa-2 cells, pre-treated with CHS, respond with a decrease in TCA cycle glucose-derived oxaloacetate and citrate turnover, anaplerosis and oxidation.

)

2 2

CO in

CO in   20 20

15 15

10 10

5 5

0 Complete Glucose Oxidation (13C Oxidation Glucose Complete

(13C Oxidation Glucose Complete BxPC-3 VC BxPC-3 Met MIA PaCa-2 VCMIA PaCa-2 Met

BxPC-3 CHS VCBxPC-3 CHS Met MIA PaCa-2 CHSMIA VC PaCa-2 CHS Met

MET in MIA PaCa-2 cells showed a significant inhibition of the TCA cycle measured by a decrease in glucose oxidation. VC = cells grown in media, MET = cells treated with

MET (100 μM) for 24 h, CHS = cells pre-treated with 1 mM CHS for 2 weeks, CHS +

MET = cells pre-incubated with 1 mM CHS for 2 weeks then treated with MET (100 μM) for 24 h. All data are Mean + SEM ( n = 3 per group). **P < 0.01; #P <0.05 between cell lines.

D. Lactate Synthesis

We observed an expected over 75 % 13C m2 lactate via glycolysis in the glucose derived (labeled) lactate species in media (Fig 22- EZTopolome (K-RAS) ID 20 and

13 22B; Table 6, media lactate panel 22 and 22B). On the other hand, C m2 glutamate positional labeling, which is a surrogate of pyruvate dehydrogenase activity for pyruvate’s entry into the TCA cycle, increased in CHS-MET MIA PaCa-2 cells, supporting MET’s ability to increase TCA cycle cataplerosis at the expense of anaplerosis (anabolic use of pyruvate for new net oxaloacetate and citrate production, also confirmed with increasing m2/m1) in this group (Fig 22—EZTopolome (K-RAS) ID

79 and 81; Table 6, media glutamate panel 79 and 81). Please refer to figure 24 which differentiates m2 from m4 glutamate. Extracellular glutamate concentration TIC surrogates shown as GC/MS peak areas decreased in both cell lines after CHS and MET treatments, which also indicates a uniform decrease in ketoglutarate and glutamate output of TCA cycle (Fig 22-EZTopolome (K-RAS) ID 87B; Table 6, media glutamate panel

13 87B). While glutamate’s C m4 fractions are small in wild type K-RAS BxPC-3 cells

13 (<1 %), there is a prominent C m4 glutamate fraction in K-RAS mutated MIA PaCa-2

96 cells (Table 6, media glutamate panel 81). In MIA cells CHS and CHS + MET prominently inhibits oxaloacetate’s replenishment from glucose for new citrate synthesis via pyruvate carboxylase and by repeated cycling. Due to decreased m1 (Table 6, 78) pyruvate carboxylase is also a potential target of the CHS + MET treatment.

FIGURE 24 m2 glutamate (A) and m4 glutamate (B) are surrogate markers for pyruvate dehydrogenase (PDH) and pyruvate carboxylase (PC), respectively. A) m2

97 glutamate is produced from citrate labeled at two carbons, which is metabolized in the

TCA cycle to produce glutamate labeled at two carbons (m2 glutamate). The labeled acetyl-CoA eventually comes off as CO2 and does not contribute to the labeling of m2 glutamate but is required for TCA cycling. Thus, m2 glutamate is a surrogate marker for

TCA cycling from pyruvate to acetyl-CoA reaction catalyzed by PDH. M4 glutamate is produced from oxaloacetate labeled at two carbon positions. Oxaloacetate regeneration in the TCA cycle is catalyzed by PC. Therefore, in order to produce m4 glutamate, labeled oxaloacetate must be present. Pyruvate to oxaloacetate is catalyzed by PC and indicated by m4 glutamate. Figures were taken from previous SiDMAP website (site removed).

E. Fatty Acid Palmitate Synthesis

Significant phenotypic differences between BxPC-3 and MIA PaCa-2 cells continue in terms of de novo fatty acid synthesis deriving from the tracer glucose. There is an 8.95

% (±0.24 %) of glucose-derived palmitate labeled in BxPC-3 cells, while only 4.61 %

(±0.20 %) (~half) in MIAPaCa-2 (Table 6, pellet palmitate panel 98). This shows that at baseline, MIA PaCa-2 cells are less lipogenic from glucose in comparison with control

BxPC-3. Both cell types reach equilibrium in palmitate’s acetyl-CoA enrichment from glucose after 4 h of culturing (data not shown).

F. Sterol Ring Synthesis

As cholesterol and de novo fatty acid syntheses compete for acetyl-CoA, external cholesterol (CHS) administration blocked new sterol synthesis shown by the severely

98 decreased 13C labeled cholesterol fractions with severely increased concentrations (total ion current) values (Table 6, pellet cholesterol panel 235, 236, 238H). However, in K-

RAS transformed cells the addition of cholesterol in the form of CHS increased the glucose derived acetyl-CoA enrichment and the fraction of newly synthesized (FNS) palmitate from the tracer glucose derived acetyl-CoA. Cholesterol supplementation had no effect on BxPC-3’s already high glucose-derived acetyl-CoA enrichment in palmitate.

Hence, addition of CHS did not increase de novo palmitate synthesis in BxPC-3 cells, yet, there was an up-regulation, close to double, in glucose-derived synthesis of new palmitate in CHS-supplemented MIA PaCa-2 cells (Fig 22-EZTopolome(K-RAS) ID

102, 103; Table 6, pellet palmitate panel 102, 103). CHS + MET treatment significantly decreased de novo palmitate synthesis both BxPC-3 versus control and MIA PaCa-2 versus CHS. This suggests that MET clearly is able to inhibit glucose-derived acetyl-

CoA flux via fatty acid synthase in the context of acetyl-CoA availability and its consumption by acetyl-CoA carboxylase when sterol synthesis is blocked.

G. System Wide Associations

The rapid system-wide association study (SWAS) evaluation of both cell lines, using the color assisted visual isotopolome data matrix screening tool (193), confirmed phenotypic differences by increased lactate production in treated MIA PaCa-2 cells [Fig

22-EZTo-polome (K-RAS) media 22B; square labeled as 1], the ready uptake of cholesteryl hemisuccinate by both cell lines [Fig 22-EZTopolome (K-RAS) pellets 238H; squares labeled as 2], acetyl-CoA shuttling towards newly synthesized palmitate [Fig 22-

EZTopolome (K-RAS) pellets 102 and 103; squares labeled as 3] in the presence of CHS.

On the other hand, rapid system-wide association study (SWAS) evaluation of MET effect in addition to CHS treatment (100 %) showed a significant decrease in newly synthesized palmitate fraction via FAS (m4/m2) [Fig 25-EZTopolome (CHS-MET) media 102; square labeled as 4], the re-labeling of cholesterol in both cell lines [Fig 25-

EZTopolome (CHS-MET) pellets 235; squares labeled as 5], consistent with less acetyl-

CoA used for palmitate synthesis, as well as further lactate disposal from glucose in the

K- RAS positive cells (Fig 25-EZTopolome (CHS-MET) pellets 235; squares labeled as

6) in the presence of CHS.

100

PaCa-2) PDAC cell lines. EZTopolome (CHS-MET) contains group averages from

Table 25 as percent of CHS values in an identical, coherent matrix format [please note

CHS 100 % values are omitted for EZTopolome (CHS-Met)]. Visual system-wide association study (SWAS) evaluations show significant phenotypic differences after CHS treatment, as well as effects of MET for a rapid overview of Results. (@, P < 0.05 in comparison with CHS treated control (100 %); cholesterol 13C content 236 is not shown for comparison due to low values after external CHS treatment).

FIGURE 26 Metabolic profile changes associated with CHS and MET treatment in mutant K-RAS (MIA PaCa-2) PDAC cell lines. At baseline, the mutant K-RAS cancer cells exhibit less efficient glucose oxidation and low fatty acid synthase flux with cholesterol readily synthesized. CHS treatment (green) blocks cholesterol synthesis, by

101 which glucose-deriving acetyl-CoA is diverted towards fatty acid synthase, instead of new cholesterol synthesis. This is when addition of MET (red) gains a functional fatty acid synthase inhibitory effect. This demonstrates the contextual System effects of mutated K- RAS, cholesterol and MET in the metabolic syndrome to inhibit potentially membrane production and cancer growth. Please note that hypotheses for further testing are suggested as (1) the effect of CHS on glut-aminotransferase, (2) further evidence for

MET inhibition of the citrate arm of the TCA cycle and (3) pyruvate carboxylase, which is only significant in the presence of CHS.

IV. Discussion

Various studies have implicated MET as a potential anti-cancer agent. However,

MET’s mechanism of action against cancer remains to be determined (197). Because

MET affects critical metabolic pathways to ameliorate diabetic symptoms, and because cancer cell proliferation is dependent upon altered metabolism, we investigated how this drug controls metabolic flux in two PDAC cell lines, BxPC-3 and MIA PaCa-2, using

13 [1,2- C2]-D-glucose as the tracer and GC/MS. We used the stable isotope-labeled dynamic metabolic profiling (SiDMAP) (182) approach as 13C tracers provide the most comprehensive means of characterizing cellular metabolism and uniquely labeled 13C substrates offer probes of specific reactions within complex networks. The choice of tracer largely determines the precision available to estimate metabolic fluxes in complex

13 mammalian systems, with [1,2- C2]-D-glucose providing the most precise estimates for glycolysis, the pentose phosphate pathway, and the overall metabolic network (198). A challenging metabolite to measure is ribose, due to low abundance in cells. Future in

102 vitro studies should address this by increasing cell concentrations for SiDMAP analysis.

In this study, there may be indication that MET controls PDAC cell metabolism by inhibiting TCA cycle anaplerosis and de novo fatty acid palmitate synthesis from glucose-derived acetyl-CoA. For an overview, please see Fig 26. These effects were only observed in MIA PaCa-2 cells that were pre-treated with 1 mM CHS for 2 weeks.

Although previous studies (187, 190) implicated that cholesterol supplementation causes a reduction in plasma membrane fluidity, we herein show that cholesterol also alters cellular metabolism by redirecting glucose-derived acetyl-CoA towards fatty acid palmitate synthesis, a change through which MET gains its contextual efficacy to inhibit

FAS, an important target to control cancer cell proliferation (68, 199). Whether this is due to the ability of CHS to decrease fluidity of the PDAC plasma membrane remains to be determined. This can be addressed by performing anisotropy in the control and CHS- treated groups. Nevertheless, these results show that MET may also control pancreatic cancer cell growth in diabetes and obesity by limited TCA cycle anaplerosis, an observation that provides a hypothesis for further testing.

In dose escalating studies, 1 mM MET has been reported to potentiate the cell proliferation inhibitory effect of the hexokinase inhibitor 2DG (200). At a higher concentration (5 mM), MET was shown to cause cell death when combined with 2DG

(177). In the present study, we show that a therapeutically relevant dosage of MET (100

μM) (171) is able to impair glucose utilization through inhibition of FAS when new cholesterol synthesis is limited. We raise for the first time that MET may inhibit pyruvate carboxylase flux, indicated by decreased m1 but increased m2 in glutamate, TCA cycle output and likely ATP production (not measured) in the CHS-MIA PaCa-2 cancer

103 cell line. In support of the role of MET in ATP depletion, others have published evidence indicating that MET only and when combined with 2DG decreases total ATP in human gastric cancer parenteral p-SK4 (177) and prostate cancer cells LNCaP (175, 201), compared to their untreated controls. Previous studies have also implicated contextual factors that enable MET’s anti-cancer properties (202).

Palmitate is the sole product of FAS and its dependence on acetyl- and malonyl-CoA availabilities is evident; palmitate’s 13C positional labeling from glucose-derived acetate demonstrates a robust, over twofold increase in response to CHS. As cellular metabolic reprogramming is evident after cholesterol pre-treatment in pancreatic cancer cells, the same may occur in the obese diabetic cancer patient with increased circulating cholesterol. The presence of cholesterol establishes the flux-based context in which efficacies of MET are high because of tissue specificities in which FAS gene expression is already high due to negative feedback (low product concentrations). Such modalities include pancreatic cancer (203).

Interestingly, in primary cultured rat hepatocytes, MET affected neither fatty acid oxidation nor triglyceride synthesis (142), yet in an in vivo model of colon (178) and hepatocellular carcinoma (HCC) (140) with circulating cholesterol, MET readily decreased FAS expression. In our study MET was effective in altering palmitate synthesis only after glucose-derived acetyl-CoA was re-directed towards acetyl-CoA carboxylase from biosynthetic thiolase, HMG-CoA and cholesterol synthesis by CHS administration. This finding suggests that MET may inhibit acetyl-CoA carboxylase, which has been suggested as a cancer promoting enzyme (204), providing malonyl-CoA precursor directly for FAS.

104

Determining the cause of the apparent differences in the effects of MET between

BxPC-3 and MIA PaCa-2 cell lines represents an exciting research endeavor. A recent study has shown that, in vitro, RAS diffusion is slowed after cholesterol loading in COS-

7 cells (205). Given the evidence that mutations in K-RAS show distinct metabolic phenotypes (168), it is possible that difference in K-RAS status between BxPC-3 (WT K-

RAS) and MIA PaCa-2 (mutated K-RAS), besides apparent differences in the culture media, contribute significantly to their diverse response to cholesterol, with MIA PaCa-2 being responsive by increasing acetyl-CoA avail-ability for FAS, comparable to that of

BxPC-3. After this metabolic adaptation of MIA PaCa-2 cells to glucose-derived acetyl-

CoA shuttling towards FAS, MET acts as an inhibitor of new fatty acid synthesis, while in BxPC-3 MET dilutes glucose-derived acetate with no apparent decrease in the rate of new palmitate formation via FAS. Despite the numerous genetic and phenotypic differences between BxPC-3 and MIA PaCa-2 cells (192), it is evident that extracellular cholesterol uniformly decreases 13C labeling for intracellular cholesterol synthesis in both cell lines. Consequently, extracellular cholesterol increases acetyl-CoA shuttling towards

FAS from glucose in MIA PaCa-2 cells. The sterol ring is an unrecyclable carbon sink when newly synthesized from glucose derived acetyl-CoA in cells; therefore CHS as an external supply introduces significant effects in redistributing acetyl-CoA among cholesterol and fatty acid synthesis pathways, as shown in our paper. This necessitates the introduction of 13C tracer-based metabolic flux research tools in the genetic and signaling research agendas of human cancers as well as metabolic diseases in order to better understand the response of whole biological systems to common drugs.

We acknowledge a potential limitation of this study, succinate of CHS being a

105 potential substrate for TCA cycle metabolism. The dose at which CHS was administrated

(1 mM) is 1/25th of that of glucose (4.5 g/glucose/L (25 mM)) in media. We observed no

13 significant decrease in CO2∆ values after CHS treatment, which is an important assurance that this hemisuccinate did not dilute the TCA cycle substrate pool to any measurable extent. No such dilution was expected from cholesterol under any circumstance due to its stable C:27 carbon ring that lacks oxidation by mammalian cells.

Another limitation may be that this study did not test cell membrane synthesis/turnover directly from isolated membranes for their labeled palmitate pool. We use the connection between inhibited FAS and limited cell membrane synthesis because undifferentiated cells contain the majority, over 90%, of phospho-sphingo- and triglyceride-derived fatty acids in nuclear and plasma membranes. This fraction yields most derivatized methyl-palmitate for GC– MS analyses after saponification of tumor cell pellets. Previous work with fractionated fat pools of cultured undifferentiated murine myoblasts (206) confirms the assumption that transformed cell use FAS for new membrane synthesis and proliferation. Palmitate synthesis via FAS for new membrane formation became a target to treat cancer (207). A similar mechanism is suggested herein for MET in the presence of cholesterol.

Whilst the four measured metabolites and their 13C isotopomer ratios from glucose generate a highly informative matrix, they do not describe the full extent of glucose metabolism. Published methods are available for isotopolome-wide labeling studies with

LC-MS (208) and GC-MS (209). Targeted tracer fate association studies (TTFA or

TTFAS) after drug treatment may provide significantly more information in the future than do either a non-targeted tracer fate detection (NTFD) approach or a limited product

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IWAS. It is important to point out that even a relatively low but steady increase in the rate of glucose-derived new acetate can contribute to enlarged palmitate pools, over time.

Even though there are only a few percent increases in glucose- derived acetyl-CoA to new palmitate synthesis above that in control cells, this surrogate marker of newly contributed acetyl-CoA yields a potentially large new palmitate pool for membrane synthesis; although the majority, > 85% of acetyl-CoA are still recycled from existing

(unlabeled) fatty acids, similar to other transformed cell systems (210). Another important point is that glucose is a reliable source for new acetyl-CoA synthesis as plasma concentrations, especially in diabetes, are constantly high. In the metabolic syndrome this is combined with high circulating cholesterol, which together yields a reliable new acetyl-CoA pool (glucose) and an inhibitor of new cholesterol synthesis

(cholesterol) for tumor cells to thrive with more new palmitate. MET limits this new fraction of palmitate synthesis in the context of metabolic changes in a diabetic host, potentially, based on our observations.

Using the same principles as genome-wide association studies (GWAS), this paper demonstrates the effect of MET by a targeted isotopolome-wide association study

(IWAS) approach. This is readily expanded towards system-wide associations (SWAS) when comparing specific metabolic fingerprints, as well as the effect of MET in the presence of nutritional factor cholesterol in obesity, in two genetically diverse tumor cell lines. Although it may seem ambitious, IWAS presented in a heat map (EZtopolome) reveals that MET under high cholesterol contributes to limit new fatty acid and potentially plasma and nuclear membrane synthesis, demonstrating a previously unknown mechanism for inhibiting cancer growth during the metabolic syndrome.

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V. Concluding Remarks

In conclusion, MET possesses FAS inhibitory properties in the context of the combined metabolic effects of available acetyl-CoA and extracellular cholesterol. Such contextual synthetic inhibition of FAS by MET may partly explain the drug’s demonstrated ability to decelerate growth in some cancers of the diabetic patient (7) or patients with metabolic syndrome. One of the observed side effects, lactic acidosis, is also consistent with our report that the product of glucose metabolism is lactic acid when cholesterol and fatty acid new syntheses are inhibited in the presence of MET.

108

CHAPTER III: METABOLIC EFFECTS OF CHRONIC METFORMIN

TREATMENT IN VITRO

Mary Jo Cantoria and Emmanuelle J. Meuillet designed the experiments. Mary Jo

Cantoria performed the experiments, data analysis and wrote the manuscript. Jana J.

Jandova helped with RT-PCR data interpretation.

109

CHAPTER III: METABOLIC EFFECTS OF CHRONIC METFORMIN

TREATMENT IN VITRO

I. Introduction

Numerous retrospective studies report that MET decreases the incidence of cancer and cancer-related mortality among diabetics (123). The consistency in the chemopreventive association between MET and various types of cancer has fueled a renewed interest on whether this old drug possesses chemotherapeutic properties (197).

There are numerous preclinical studies that demonstrate MET’s anti-cancer effects. The identified mechanisms of action include activation of AMPK by acting as a calorie restriction mimetic that directly affects the insulin/insulin-like growth factor

(IGF)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) pathway (119-

121, 124). MET has been shown to decrease endogenous production of reactive oxygen species (ROS) and decrease ROS-induced DNA damage (125). MET is able to activate the tumor suppressor p53 which results in senescence and cell cycle arrest (115-117).

MET also possesses anti-inflammatory effects and is able to inhibit the Sp transcription factors that activate genes involved in cell proliferation, metabolism, angiogenesis and apoptosis (128).

Up-regulated fatty acid synthesis is a notable hallmark of cancer and is one of the biosynthetic downstream events supported by the Warburg phenotype (68). MET has been reported to impair lipid synthesis in a variety of pre-clinical models by inhibiting the expression and activities of the lipogenic enzymes acetyl-CoA carboxylase (ACC), fatty acid synthase (FAS), 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGCR), Acyl-

110

CoA cholesterol acyltransferase (ACAT), steroyl-CoA desaturase (SCD), and the transcription factors Sterol Regulatory Element-Binding Proteins isoforms 1c and 2

(SREBP-1c and (SREBP-2) (140, 143-145, 148, 178, 179).

13 Using 1,2- C2-D-glucose as the sole tracer, GC/MS and the SiDMAP platform

(http://sidmap.com/), our group has previously demonstrated that a therapeutically relevant acute (24 h) dose of MET (100 μM) for type 2 diabetes management inhibits new glucose-derived de novo fatty acid synthesis in the presence of a) a KRAS mutation and b) when glucose-derived acetyl-CoA is shifted towards fatty acid synthesis (by supplementing cholesterol in the media) (211). In this chapter, we sought out to: a) determine whether MET is a chemopreventive or chemotherapeutic drug in the context of therapeutically relevant dose for diabetes management and b) identify what other regulatory mechanisms, beside alteration of metabolic flux, govern chronic MET treatment and inhibition of lipid synthesis. The retrospective studies that show MET’s protective benefits against cancer and cancer-related mortality have been published based on data from diabetics who have been taking the drug for decades (123). We selected to assess the metabolic effects of chronic MET treatment in order to mimic a more clinically relevant model system.

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II. Materials and Methods

A. Cell Culture

BxPC-3 (wild type KRAS) and MIA PaCa-2 (mutant KRASG12C) pancreatic cancer cells were purchased from American Type Culture Collection (Manassas, VA, USA).

Cell culture media, penicillin-streptomycin (P/S) and trypsin–EDTA were purchased from Mediatech (Manassas, VA, USA). BxPC-3 cells were cultured in RPMI media and

MIA PaCa-2 cells were grown in DMEM. Both media contained 4.5 g glucose/L and were supplemented with 10% FBS from PAA Laboratories, Inc., (Pasching, Austria), 1% fatty acid-free BSA (Gemini Bio-products, Sacramento, CA, USA) and 1% P/S. The cells were incubated at 37 ⁰C, 5% CO2 and 95 % humidity and passaged with 0.25% trypsin–

EDTA once the cells reached 75–80% conﬂuence. Cells treated with cholesteryl hemisuccinate (CHS; Sigma-Aldrich, St. Louis, MO), from now on referred to as BxPC3-

CHS and MIA PaCa-2-CHS, were incubated in media supplemented with 1 mM CHS complexed to 1% BSA for 2 weeks prior to preparation for treatments.

For chronic MET treatment, cells were grown in T25 flasks and treated with 100 μM

MET or an equal volume of vehicle control (PBS) every two days for a total of 30 days.

B. Cell Viability (MTT Assay)

BxPC-3 and MIA PaCa-2 pancreatic cancer cells were plated at 2,000 and 500 cells/well, respectively, in 96-well plates. Cells were allowed to attach overnight. The following day, cells were treated either with vehicle control (PBS) or increasing doses of

MET (0.1mM, 1mM and 10mM), with each control and treatment group in

112 quadruplicate. Cells were allowed to incubate for 72 h. 50 μL of 3-(4,5-dimethylthiazol-

2-yl)2,5-diphenyltetrazolium bromide (MTT) was then added to the cells followed by incubation for 4 h. The liquid was gently aspirated and the resulting precipitates were dissolved in 100 μL DMSO. Plates were read at 540 nm using the Synergy 2 Microplate

Reader (Winooski, VT, USA).

MTT assay was conducted in cells grown in media supplemented with 10% delipidated FBS instead of regular FBS to rule out possible confounding by lipids in the media. The protocol above was followed except the volume of DMSO used to dissolve the formazan precipitates were 50 μL for BxPC-3 + CHS cells and 25 μL for MIA PaCa-

2 + CHS to account for potentially slower cell proliferation due to reduced lipid levels in the media.

Data were calculated as percent of vehicle control (VC) and expressed as fold change vs VC.

C. Gene expression as Determined by Reverse Transcription Polymerase Chain

Reaction (RT-PCR)

RNA was extracted using the Qiagen RNEasy Mini Kit (Hilden, Germany) and RNA yield was measured using the Nanodrop 1000. With one sample used as no amplification control (no reverse transcriptase added) cDNA was created using the qScript™ cDNA synthesis kit under the following PCR conditions: one cycle each at 22⁰C for 5 min, 42⁰C for 30 min, 85⁰C for 5 min. cDNA was placed in 4⁰C and frozen in -20⁰C until use. cDNA was diluted to 5ng/μL. fasn (hs01005622_m1) and hmgcr (hs00168352_m1) primer/probes were purchased from Life Technologies (Grand Island, NY, USA) and

113

ABI 7000 was used amplification under the following conditions: one rep of 95.0⁰C for

10 min, 40 reps of 95.0⁰C for 15 s and 60⁰C for 1 min. Data was analyzed using the

∆∆Ct method (212) using β-actin as control.

D. Protein Expression Analysis by Western Blotting

BxPC-3 (+ CHS) and MIA PaCa-2 (+ CHS) cells treated with either PBS or MET

(100 μM) every 2 days for 30 days were lysed in lysis buffer containing protease and phosphatase inhibitors. 35μg of protein were loaded in 3-8% Tris-acetate gels (Life

Technologies), electrophoresed at 140V for about 1.2 h and then transferred to a PVDF membrane. After blocking for 1h, the membrane was probed for FAS (Cell Signaling,

Danvers, MA, USA) and HMGCR (Millipore, Billerica, MA, USA) antibodies at a concentration of 1:1,000 and incubated overnight. Membranes were washed thrice with

TBST (0.1% Tween-20) and incubated with secondary antibodies (Sigma, St. Louis, MS,

USA) at a concentration of 1:5,000. Membranes were rinsed thrice and developed using the Amersham ECL Western Blotting detection reagents (GE Life Sciences,

Bukinghamshire, UK). β-actin was used as loading control. ImageJ

(http://rsbweb.nih.gov/ij/download.html) was used for densitometry analysis and treatment groups were expressed as VC for each cell line.

E. Cholesterol and Triglyceride Measurement

Intracellular cholesterol was measured using the Amplex Red Cholesterol Assay Kit

(Life Technologies) according to manufacturer’s instructions. Briefly, lipids from cell lysates were extracted according the method of Folch (213). Samples were dissolved in equal amounts of 1X reaction buffer. To 50 μL of sample, an equal volume of working

114 solution containing Amplex Red dye, horseradish peroxidase, cholesterol oxidase, cholesterol esterase and reaction buffer was added. Samples were covered protected from light and incubated at 37⁰C for 30 min and fluorescence was read at 560/590 nm.

Readings were normalized to protein concentrations and reported as fold change vs VC for each cell line.

Intracellular triglyceride in cell lysates was measured using the Pointe triglyceride kit

(Canton, MI, USA). To 5 μL of cell lysate, 100 μL of reconstituted solution containing lipase, glycerol kinase, glycerophosphate oxidase and peroxidase was added. The solution is incubated at 37⁰C for 10 min and read at 540 nm using the Synergy 2

Microplate Reader (Winooski, VT, USA). Cells were normalized to protein concentrations and reported as fold change vs VC for each cell line.

F. Data Analysis

Analysis of variance (ANOVA) was used to calculate statistical significance at α =

0.05 to compare control and treatment groups for each cell line.

115

III. Results

A. Chronic MET Treatment Does not Inhibit PDAC Cell Proliferation

MET has been reported to possess anti-cancer properties (82, 123). Current clinical trials of MET on cancer patients use doses prescribed for diabetic patients while preclinical models employ supra-therapeutic MET concentrations. Before the utility of MET can be used against cancer, it needs to be established whether MET is chemopreventive or chemotherapeutic. We have previously reported that acute MET treatment using a therapeutically relevant dose (100 μM) for diabetes management was unable to decrease

PDAC cell viability (211). We now sought out to determine whether chronic MET treatment sensitizes PDAC cells to future MET treatment as manifested by decreased cell survivability.

We performed an MTT assay where BxPC-3 (+CHS) and MIA PaCa-2 (+CHS) cells that were chronically treated with either VC (PBS) or MET (100 μM) were subject to dose escalating acute (72 h) MET treatment. We can answer the following questions from these experiments. First, is MET cytotoxic to PDAC cells? Figure 27 (10 mM in all groups vs VC) shows that a high dose of 10 mM MET is required to induce a significant decrease in PDAC cell viability when compared to vehicle control.

116

A B

C D

FIGURE 27 Chronic MET treatment shows no significant effect on PDAC viability.

Survival of BxPC-3 + CHS (A, B) and MIA PaCa-2 + CHS (C, D) pancreatic cancer cells chronically treated with MET (100 μM, MET) was assessed through MTT assay using escalating doses of MET treatment for 72 h. Drugged groups are expressed as percent of

117 vehicle control (VC), N=5. Different letters indicate statistical significance from one another, P<0.05.

Second, does chronic MET treatment cause the cells to be more sensitive to acute

MET treatment? In Table 8, we show that chronic MET treatment does not make PDAC cells more susceptible to MET’s inhibitory effects on cell viability. In both cell lines, there are no significant differences among groups chronically treated with vehicle compared to cells chronically treated with MET.

Finally, does chronic CHS treatment make the cancer cells more susceptible to the cytotoxic effects of MET? We found that CHS treatment does not sensitize the cells to

MET (Table 9).

The dose for chronic treatment (100 μM) used in our study reflects a dosage that is commensurate to therapeutically relevant concentrations for diabetes management (171).

Using MTT assay we sought to address whether MET has cytotoxic effects on pancreatic cancer cells. In general, 10 mM of MET is required to significantly decrease cancer cell survival (Fig 27). Hence, the observed metabolic effects due to MET treatment cannot be attributed to inhibition of cell proliferation since the concentration used for chronic treatment, 100 μM, is not cytotoxic to cells.

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TABLE 7 Viability of BxPC-3 and MIA PaCa-2 + CHS relative to vehicle control

(VC) with acute (72 h) increasing doses of MET

Group 1 MEAN (%) Group 2 MEAN (%) Significant? Summary 95%

+ SE + SE P<0.05? CI

BxPC-3 BxPC-3 PBS -18.78

PBS VC 100.00 + 0.00 0.1 mM MET 101.40 + 3.46 No ns to 16.00

BxPC-3 BxPC-3 PBS -9.565

PBS VC 100.00 + 0.00 1 mM MET 92.18 + 4.89 No ns to 25.21

BxPC-3 BxPC-3 PBS 32.42 to

PBS VC 100.00 + 0.00 10 mM MET 50.19 + 2.15 Yes *** 67.20

BxPC-3 BxPC-3 MET -17.53

MET VC 100.00 + 0.00 0.1 mM MET 100.1 + 1.89 No ns to 17.25

BxPC-3 BxPC-3 MET -9.150

MET VC 100.00 + 0.00 1 mM MET 91.76 + 3.01 No ns to 25.63

BxPC-3 BxPC-3 MET 28.37 to

MET VC 100.00 + 0.00 10 mM MET 54.24 + 5.48 Yes *** 63.15

BxPC-3

CHS PBS 100.00 + 0.00 BxPC-3 CHS PBS 97.69 + 3.41 -15.08

VC 0.1 mM MET No ns to 19.70

BxPC-3

CHS PBS 100.00 + 0.00 BxPC-3 CHS PBS 84.58 + 3.66 -1.970

VC 1 mM MET No ns to 32.81

BxPC-3

CHS PBS 100.00 + 0.00 BxPC-3 CHS PBS 33.67 + 1.95 48.94 to

VC 10 mM MET Yes *** 83.72

BxPC-3 BxPC-3 CHS -13.61

CHS 100.00 + 0.00 MET 0.1 mM 96.22 + 1.75 No ns to 21.17

119

MET VC MET

BxPC-3 -4.546

CHS 100.00 + 0.00 BxPC-3 CHS 87.16 + 2.47 No ns to 30.23

MET VC MET 1 mM MET

BxPC-3 BxPC-3 CHS 47.37 to

CHS 100.00 + 0.00 MET 10 mM 35.24 + 3.02 Yes *** 82.15

MET VC MET

MIA -13.23

PaCa-2 100.00 + 0.00 MIA PaCa-2 PBS 95.84 + 2.04 No ns to 21.55

PBS VC 0.1 mM MET

MIA 0.7515

PaCa-2 100.00 + 0.00 MIA PaCa-2 PBS 81.86 + 3.76 Yes * to 35.53

PBS VC 1 mM MET

MIA 29.00 to

PaCa-2 100.00 + 0.00 MIA PaCa-2 PBS 53.61 + 5.02 Yes *** 63.78

PBS VC 10 mM MET

MIA

PaCa-2 100.00 + 0.00 MIA PaCa-2 MET 100.70 + 3.49 -18.08

MET VC 0.1 mM MET No ns to 16.70

MIA

PaCa-2 100.00 + 0.00 MIA PaCa-2 MET 86.33 + 3.68 -3.721

MET VC 1 mM MET No ns to 31.06

MIA

PaCa-2 100.00 + 0.00 MIA PaCa-2 MET 56.52 + 4.41 26.09 to

MET VC 10 mM MET Yes *** 60.87

MIA MIA PaCa-2 CHS -17.50

PaCa-2 100.00 + 0.00 PBS 0.1 mM MET 100.1 + 2.54 No ns to 17.28

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CHS PBS

MIA

PaCa-2 100.00 + 0.00 87.87 + 2.34

CHS PBS MIA PaCa-2 CHS -5.258

VC PBS 1 mM MET No ns to 29.52

MIA

PaCa-2 100.00 + 0.00 64.15 + 4.51

CHS PBS MIA PaCa-2 CHS 18.46 to

VC PBS 10 mM MET Yes *** 53.24

MIA

PaCa-2 100.00 + 0.00 MIA PaCa-2 CHS 96.73 + 2.75

CHS MET 0.1 mM -14.12

MET VC MET No ns to 20.66

MIA

PaCa-2 100.00 + 0.00 95.14 + 6.16

CHS MIA PaCa-2 CHS -12.53

MET VC MET 1 mM MET No ns to 22.25

MIA

PaCa-2 100.00 + 0.00 MIA PaCa-2 CHS 58.95 + 5.05

CHS MET 10 mM 23.66 to

MET VC MET Yes *** 58.44

Cells were grown either in PBS (chronic treatment with vehicle (PBS) every 2 days in 30 days) or MET

(chronic treatment with metformin (100 μM) every 2 days in 30 days) with or without CHS in the media.

Cells were then subject to acute dose-escalating treatment with MET for 72 h using MTT assay. ANOVA was used to determine statistical significance among groups. Ns, not significant. ***, P < 0.0001. **,

0.05>P>0.0001. *, P < 0.05, N=5.

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TABLE 8 Viability of of BxPC-3 and MIA PaCa-2 + CHS chronically treated with

MET and then acutely treated with MET

Group 1 MEAN (%) Group 2 MEAN (%) Significant? Summary 95%

+ SE + SE P<0.05? CI

101.40 + 3.46 100.10 + 16.14

BxPC-3 PBS BxPC-3 MET 1.89 to

0.1 mM MET 0.1 mM MET No ns 18.64

92.18 + 4.89 91.76 + 3.01 16.97

BxPC-3 PBS BxPC-3 MET to

1 mM MET 1 mM MET No ns 17.80

50.19 + 2.15 54.24 + 5.48 21.44

BxPC-3 PBS BxPC-3 MET to

10 mM MET 10 mM MET No ns 13.34

BxPC-3 CHS -

BxPC-3 CHS 97.69 + 3.41 MET 96.22 + 1.75 15.92

PBS 0.1 mM 0.1 mM MET to

MET No ns 18.86

BxPC-3 CHS -

84.58 + 3.66 MET 87.16 + 2.47 19.97

BxPC-3 CHS 1 mM MET to

PBS 1 mM MET No ns 14.81

BxPC-3 CHS BxPC-3 CHS -

PBS 10 mM 33.67 + 1.95 MET 35.24 + 3.02 18.95

MET 10 mM MET No ns to

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15.82

MIA PaCa-2 -

95.84 + 2.04 MET 100.70 + 22.24

MIA PaCa-2 PBS 0.1 mM MET 3.49 to

0.1 mM MET No ns 12.54

MIA PaCa-2 -

81.86 + 3.76 MET 86.33 + 3.68 21.86

MIA PaCa-2 PBS 1 mM MET to

1 mM MET No ns 12.92

MIA PaCa-2 -

53.61 + 5.02 MET 56.52 + 4.41 20.31

MIA PaCa-2 PBS 10 mM MET to

10 mM MET No ns 14.47

MIA PaCa-2 -

MIA PaCa-2 100.1 + 2.54 CHS MET 96.73 + 2.75 14.00

CHS PBS 0.1 0.1 mM MET to mM MET No ns 20.78

MIA PaCa-2 -

MIA PaCa-2 87.87 + 2.34 CHS MET 95.14 + 6.16 24.66

CHS PBS 1 mM 1 mM MET to

MET No ns 10.12

MIA PaCa-2 -

MIA PaCa-2 64.15 + 4.51 CHS MET 58.95 + 5.05 12.19

CHS PBS 10 mM 10 mM MET to

MET No ns 22.59

Cells were grown either in PBS (chronic treatment with vehicle (PBS) every 2 days in 30 days) or MET

(chronic treatment with metformin (100 μM) every 2 days in 30 days) with or without CHS in the media.

Cells were then subject to acute dose-escalating treatment with MET for 72 h using MTT assay. For

123 example, BxPC-3 PBS 0.1mM MET is BxPC-3 chronically treated with PBS and then acutely treated with

0.1mM MET. ANOVA was used to determine statistical significance among groups. Ns, not significant,

N=5.

124

TABLE 9 Effect of CHS on the viability of BxPC-3 and MIA PaCa-2

Group 1 MEAN Group 2 MEAN (%) Significant? Summary 95%

(%) + SE + SE P<0.05? CI

BxPC-3 CHS -13.69

BxPC-3 PBS 101.4 + PBS 0.1 mM 97.69 + 3.41 to

0.1 mM MET 3.46 MET No ns 21.09

BxPC-3 CHS -9.795

BxPC-3 PBS 92.18 + PBS 1 mM 84.58 + 3.66 to

1 mM MET 4.89 MET No ns 24.98

50.19 + BxPC-3 CHS 33.67 + 1.95 0.8712

BxPC-3 PBS 2.15 PBS 10 mM to

10 mM MET MET No ns 33.91

BxPC-3 CHS -13.46

BxPC-3 MET 100.10 + MET 0.1mM 96.22 + 1.75 to

0.1mM MET 1.89 MET No ns 21.32

BxPC-3 CHS -12.79

BxPC-3 MET 91.76 + MET 1 mM 87.16 + 2.47 to

1 mM MET 3.01 MET No ns 21.99

BxPC-3 CHS 1.617

BxPC-3 MET 54.24 + MET 10 mM 35.24 + 3.02 to

10 mM MET 5.48 MET Yes * 36.40

MIA PaCa-2 MIA PaCa-2 -21.66

PBS 0.1 mM 95.84 + CHS PBS 0.1 100.1 + 2.54 to

MET 2.04 mM MET No ns 13.12

MIA PaCa-2 MIA PaCa-2 -23.40

PBS 1 mM 81.86 + CHS PBS 1 mM 87.87 + 2.34 No ns to

125

MET 3.76 MET 11.38

MIA PaCa-2 MIA PaCa-2 -27.93

PBS 10 mM 53.61 + CHS PBS 10 64.15 + 4.51 to

MET 5.02 mM MET No ns 6.850

MIA PaCa-2 MIA PaCa-2 -13.42

MET 0.1 mM 100.7 + CHS MET 0.1 96.73 + 2.75 to

MET 3.49 mM MET No ns 21.35

MIA PaCa-2 MIA PaCa-2 -26.20

MET 1 mM 86.33 + CHS MET 1 95.14 + 6.16 to

MET 3.68 mM MET No ns 8.580

MIA PaCa-2 MIA PaCa-2 -19.82

MET 10 mM 56.52 + CHS MET 10 58.95 + 5.05 to

MET 4.41 mM MET No ns 14.96

Cells were grown either in PBS (chronic treatment with vehicle (PBS) every 2 days in 30 days) or MET

(chronic treatment with metformin (100 μM) every 2 days in 30 days) with or without CHS in the media.

Cells were then subject to acute dose-escalating treatment with MET for 72 h using MTT assay. For example, BxPC-3 PBS 0.1mM MET is BxPC-3 chronically treated with PBS and then acutely treated with

0.1mM MET. ANOVA was used to determine statistical significance among groups. Ns, not significant,

N=5.

If MET is an anti-lipogenic drug, and de novo lipid synthesis is very critical for cancer cell survival and proliferation, why then does MET not cause cell death or impaired cell proliferation in PDAC cells? It is possible that the lipids derived from FBS supplementation of the media may have confounded the effects of MET on cell survival.

To address this question we performed an MTT assay on PDAC cells grown in media supplemented with 10% delipidated FBS. This technique ensures that the major source

126 of acetyl-CoA for lipid synthesis is glucose. Under this condition, 100 μM MET is still unable to induce a cytotoxic effect on BxPC-3 + CHS cells. MIA PaCa-2 cells were unable to form detectable levels of reduced formazan precipitates and MIA PaCa-2 +

CHS failed to survive in the reduced lipid environment. We have previously shown that

MIA PaCa-2 cells incorporate less glucose towards acetate enrichment for fatty acid synthesis and are hence, less lipogenic than BxPC-3 cells (211). Providing an environment that significantly reduced the substrate (lipids from FBS) for lipid synthesis resulted in inability to proliferate. As shown in Figure 28, 10 mM MET is required to induce cytotoxicity in BxPC-3 (+CHS) cells grown in delipidated FBS-supplemented media. Regardless of the changes in lipid levels in the media, high concentration of MET are required to cause a significant decrease in cell viability.

FIGURE 28 Acute MET treatment shows no significant effect on BxPC-3 + CHS viability. Survival of BxPC-3 + CHS pancreatic cancer cells in delipidated FBS- supplemented media was assessed by MTT assay. BxPC-3 cells were plated and grown

127 in media supplemented with delipidated FBS and treated with either vehicle (PBS) or escalating doses of MET for 72 h, N=1 done in two quadruplicate plates. Drugged groups are expressed as percent of vehicle control (VC).

B. Intracellular Cholesterol is Regulated in PDAC Cells

We have previously shown that the glucose-derived acetyl-CoA flux towards cholesterol synthesis is decreased in BxPC-3 and MIA PaCa-2 cells upon CHS treatment

(211). We now sought out to measure total intracellular cholesterol levels in PDAC cells grown in media with and without CHS and chronically treated with 100 μM MET. A significant decrease in intracellular cholesterol is observed only in BxPC-3 CHS-MET group (Fig 29, A). The total cholesterol in cell lysates shows no change from chronic

CHS or MET treatment in MIA PaCa-2 cells (Fig 29, B). This provides a clue that a cell- specific regulatory mechanism operates depending on the baseline cholesterol content of

PDAC cells.

128

A B

FIGURE 29 Total intracellular cholesterol is decreased in BxPC-3 cells treated with CHS and MET. BxPC-3 (+CHS) and MIA PaCa-2 (+CHS) were chronically treated with either vehicle (PBS) or 100 μM MET every 2 d in 30 d cells. Intracellular cholesterol was measured using the Amplex Cholesterol Assay Kit. Values are normalized to protein concentration and expressed as fold change compared to vehicle control, N=2. ANOVA was used to calculate statistical significance. Different letters indicate statistical significance from one another, P<0.05.

C. MET Decreases Intracellular Triglyceride in PDAC Cells

We have previously shown that MET inhibits de novo palmitate synthesis in MIA

PaCa-2 cells when acetyl-CoA is dedicated for fatty acid synthesis (211). In order to interrogate the downstream effect of fatty acid synthesis inhibition, we measured total intracellular triglyceride in PDAC cells. In both cell lines, combined treatment of CHS

129 and MET caused a significant decrease in intracellular triglyceride (Fig 30). An increase in triglyceride content is observed in MIA PaCa-2 cells after CHS supplementation in the media (Fig 30 B). Similar to the observation with total intracellular cholesterol, we believe that a cell line-specific regulatory mechanism is involved in the different responses of BxPC-3 and MIA PaCa-2 to CHS and MET.

A B

FIGURE 30 MET decreases intracellular triglyceride in PDAC cells. BxPC-3

(+CHS) and MIA PaCa-2 (+CHS) were chronically treated with either vehicle (PBS) or

100 μM MET every 2 d in 30 d cells. Intracellular triglyceride was measured using the

Pointe Triglyceride Assay Kit. Values are normalized to protein concentration and expressed fold change compared to vehicle control, N=3. ANOVA was used to calculate statistical significance. Different letters indicate statistical significance from one another,

P<0.05.

130

D. MET has No Effect on HMGCR and FASN Gene Expression

In order to understand how MET regulates metabolic flux in PDAC cells, we measured the gene expression of HMGCR and FASN, which code for the HMGCR and

FAS enzymes, respectively. HMGCR is the rate-limiting enzyme in cholesterol synthesis through the mevalonate pathway while FAS is the enzyme that catalyzes the synthesis of palmitate from glucose.

Figures 31 shows the effect of chronic MET and/or CHS treatment on the gene expression of FASN and HMGCR in BxPC-3 (+CHS) and MIA PaCa-2 (+CHS). There is a cell line-dependent difference in response to CHS and MET treatment. Chronic CHS treatment caused a significant increase in the expression of the HMGCR gene in BxPC-3 cells while neither CHS nor MET caused a change in HMGCR expression in MIA PaCa-2 cells.

There is a significant increase in FASN gene expression in both cell lines (Fig 31 A) after chronic CHS treatment. Chronic MET treatment has no significant effect on FASN gene expression. These results point out that MET’s regulation of lipid synthesis is not dependent on gene regulation.

131

FIGURE 31 FASN gene expression is up-regulated with CHS treatment. BxPC-3 and MIA PaCa-2 were chronically treated with either vehicle (PBS) or 100 μM MET every 2 d in 30 d cells. RT-PCR was used to assess changes in gene expression of

HMGCR (A) and FASN (B). Values are expressed as fold change compared

132 to vehicle control, N=3. ANOVA was used to calculate statistical significance. Similar letters indicate statistical significance from one another, P<0.05. Different letters indicate statistical significance from one another.

E. MET Decreases FAS Protein Expression in MIA PaCa-2-CHS Treated

Cells

In order to understand how MET regulates metabolic flux in PDAC cells, we measured the protein expression of HMGCR and FAS proteins. No significant change is observed in HMGCR protein expression after chronic CHS and/or MET treatment (Fig

32). Interestingly, a decrease FAS protein expression is seen only after combined CHS and MET treatment (Fig 33). These results indicate that prolonged treatment with CHS and MET decreases fatty acid synthesis by down-regulating FAS protein expression and decreasing TG storage in MIA PaCa-2 PDAC cells.

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FIGURE 32 Chronic CHS and MET treatment do not affect HMGCR protein expression in PDAC cells. BxPC-3 and MIA PaCa-2 were chronically treated with either vehicle (PBS) or 100 μM MET every 2 d in 30 d cells. Western blotting was used to assess changes in protein expression of HMGCR, N=3 and densitometry was performed using Image J software. ANOVA was used to calculate statistical significance. Values are expressed as fold change compared to vehicle control. Values are not significantly different.

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FIGURE 33 Chronic CHS and MET treatment decrease FAS protein expression in

MIA PaCa-2-CHS cells. BxPC-3 and MIA PaCa-2 were chronically treated with either vehicle (PBS) or 100 μM MET every 2 d in 30 d cells. Western blotting was used to assess changes in protein expression of FAS, N=3 and densitometry was performed using

Image J software. ANOVA was used to calculate statistical significance. Values are expressed as fold change compared to vehicle control. Different letters indicate statistical significance from one another, P<0.05.

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IV. Discussion

The dilemma exists as to whether the widely-prescribed diabetic drug MET is a preventive drug against PDAC (chemopreventive) or is able to decrease PDAC cell proliferation (chemotherapeutic). Our results with chronic MET treatment of PDAC cells indicate that a supra-therapeutic concentration (10 mM) is required to induce cytotoxic effects in vitro. The peak plasma concentration (Cmax) of MET (after oral dosing) in adult diabetics is 1.48 to 1.90 μg/mL (214) which translates to 9 - 12 μM. In our experiments, we show that a dosage that is ten times the Cmax value failed to induce cytotoxic effects in two PDAC cell lines. The dosages (in mM range) that cause cell death in pre-clinical models of PDAC exceed the diabetic therapeutic dose (120, 128, 215-219). While some studies focus on the diabetes management-relevant dose for anti-cancer application (211,

220-222), it remains to be established what is the benefit of MET in patients afflicted with PDAC. We argue that MET possesses both chemopreventive and chemotherapeutic properties depending on the dosage. As shown in our model, the chemotherapeutic properties of MET can be harnessed if the dosage is increased significantly from the dose for diabetes management. This dose-dependent effect of MET must be carefully considered for clinical trials in order to see the drug’s anti-cancer effects with of course, consideration of the drug’s potential side effects (e.g., gastrointestinal distress and lactic acidosis).

These results verify numerous studies showing that doses of MET (in the mM range) beyond the therapeutic concentration used for diabetes management are required in order to induce significant cell death or cell cycle arrest in cancer cells in vitro (117, 120, 128,

140, 175, 177, 215). Preclinical studies often use MET doses that are much higher than

136 the therapeutic dose prescribed for diabetic patients. Indeed, concentrations of MET <

1mM do not affect the proliferation of cancer cells (217, 222-224). In the present study, we show that when MET is used at a dose indicated for diabetes, it is chemopreventive at best.

We have previously reported that CHS supplementation halts the flux of glucose- derived acetyl-CoA towards cholesterol synthesis while up-regulating acetyl-CoA enrichment towards palmitate synthesis (211). The absence of any regulatory effects by

MET on the gene expression of FAS in the MIA PaCa-2 CHS MET group reflects the enormous complexity of metabolic control. It remains a challenge to clearly demonstrate how changes in gene expression modify metabolic flux and vice versa (225).

Modifications in genes, although able to alter protein synthesis, exert little effect on substrate availability or reaction kinetics (226). It is, therefore, important to employ a technique, such as the use of stable isotopes to capture the dynamic property of metabolism and gain insight into a metabolic network (227) influenced by the cells’ genetic make-up (228). Nevertheless, chronic MET treatment was able to decrease acetyl-CoA enrichment towards new palmitate synthesis (211). We now report that this is associated with decreased FAS protein expression in MIA PaCa-2 cells pre-treated with CHS, verifying the inhibitory effect of MET on the fatty acid synthesis pathway.

Surprisingly, we did not see a significant increase in total cholesterol levels in non-

CHS versus CHS-treated groups. In fact, BxPC-3 CHS treated with vehicle control even showed a significant decrease in total intracellular cholesterol concentrations relative to

BxPC-3 vehicle control. We propose the following explanations for these results. First, the existence of a negative feedback loop caused by increased cholesterol levels in the

137 cell (due to CHS supplementation in the media) prevented the intracellular cholesterol concentrations from exceeding the requirements of the cancer cells. Cellular cholesterol is highly regulated (229). Particularly, rigidification of the plasma membrane may contribute to decreased tumor growth. In support of this theory: a) Meuillet et al. have shown that supplementing the media with CHS caused a decrease in plasma membrane fluidity in HepG2 cells (190) and b) there are numerous reports that agents which have the capability to interact with the lipid bilayer of the plasma membrane are able to decrease the membrane fluidity of cancer cells and antagonize cancer characteristics. For example, genistein, a bioactive found in soy, has been shown to decrease membrane fluidity in LnCAP and PC-3 prostate cancer cell lines with an associated inhibition of cell proliferation and invasion through Matrigel-coated transwells (230). The polyphenols cyanidin, quercetin and epigallocatechin gallate have been shown to rigidify the membrane and cause a decrease in proliferation of mouse myeloma cells (231).

Tamoxifen and 17β-estradiol treatment of MDA-MB-436 and MCF7 breast cancer cells reduced membrane fluidity and caused significant inhibition of DNA synthesis and cell proliferation (232). We believe that in order to maintain their cancer phenotype, PDAC cells must regulate their intracellular cholesterol content by preventing plasma membrane rigidification.

Another aspect of cholesterol content maintenance relies on the transcription factors sterol regulatory element-binding protein (SREBPs). Mammalian hepatic SREBPs have three isoforms, SREPB-1a, SREPB-1c and SREPB-2 (147). When expressed at normal physiologic levels, SREBP-1c preferentially activates sterol response element (SRE)- containing genes involved in fatty acid synthesis. SREBP-2 activates cholesterol

138 synthesis genes and SREBP-1a turns on genes involved in both fatty acid and cholesterol synthesis.

SREBPs are synthesized as inactive proteins that reside in the endoplasmic reticulum

(ER) (Fig 34). Low cholesterol levels trigger the binding of SREBP to the SREBP cleavage activating protein (SCAP). The SREBP/SCAP complex moves from the ER to the Golgi apparatus where successive cleavage by sites 1 and 2 proteases (S1P and S2P) releases the SREBP N-terminal basic helix-loop-helix leucine zipper (bHLH-Zip) domain. This enters the nucleus and sits on the SRE at the promoter region of and activates the transcription of target genes.

FIGURE 34 SREBP-mediated regulation of lipid synthesis during low cholesterol concentrations in the cell. At low cellular concentrations, the carboxy terminal of inactive SREBP binds to the carboxy terminal of SCAP. This allows the SREBP/SCAP complex to migrate from the ER to the Golgi where the proteases S1P and S2P activate the SREBP by cleaving the amino terminal, and allowing it to translocate to the nucleus

139 to stimulate the transcription of SRE-containing genes. Image obtained from Horton et al (147).

High cellular cholesterol concentrations allow cholesterol to bind directly to the sterol sensing domain of the SREBP chaperone SCAP (Fig 35). Upon cholesterol binding,

SCAP changes its conformation and instead of binding SREBP, binds to insulin induced protein (Insig), resulting in the loss of SCAP/SREBP complex transport from the endoplasmic reticulum to the Golgi apparatus preventing SREBP’s subsequent activation and its ability to act as a transcription factor for cholesterol synthesis genes (233). Our results may also be influenced by the negative feedback involving the SREBP pathway.

Future studies should address how low dose MET regulates SREBP’s in pancreatic cancer.

FIGURE 35 SREBP-mediated regulation of lipid synthesis during high cholesterol concentrations in the cell. When the cellular cholesterol content is high, SREBP binds to Insig 1/2 instead of SCAP, preventing its translocation and activation in the Golgi.

Image obtained from Sato (233).

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Second, it is also possible that supplemented cholesterol was metabolized into oxysterols. Oxysterols are derivatives of cholesterol produced by 24-, 25- and 27- hydroxylases (234) and reactive oxygen species in the case of 7-ketocholesterol and 7β- hydroxycholesterol (235). They are potent inhibitors of 3-hydroxy-3-methylglutaryl-

CoA reductase (HMGCR) and cholesterol synthesis (236-238). In addition, the formation of oxysterols may induce the expression of the cholesterol efflux gene ATP- binding cassette transporter 1 (abca1) (239), promoting cellular removal of cholesterol, to further safeguard the cell from cholesterol overload.

The increase in glucose-derived cholesterol observed in the SiDMAP study after CHS treatment of BxPC-3 and MIA PaCa-2 cells (Table 1 Cholesterol 238) does not contradict our current results. Rather, it indicates substrate preference for glucose-derived de novo cholesterol synthesis observed in extrahepatic cells (240) while maintaining the cell’s internal check that prevents excessive cholesterol uptake.

The difference in the responses between BxPC-3 and MIA PaCa-2 after cholesterol supplementation potentially reflects variation in terms of baseline lipogenic phenotype.

We have shown that BxPC-3 VC have higher rates of fraction of new palmitate synthesis and cholesterol total ion current than MIA PaCa-2 VC cells (211). These data suggest that BxPC-3 cells are more lipogenic at baseline compared to MIA PaCa-2 cells. As expected, the negative feedback is more pronounced in BxPC-3 cells (i.e., more resistant to cholesterol loading) than in MIA PaCa-2.

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We show that MET significantly decreased triglyceride levels in BxPC-3 and MIA

PaCa-2 cells that have been pre-treated with CHS. Interestingly, MIA PaCa-2 cells pre- treated with CHS exhibited a marked increase in cellular triglyceride compared to vehicle control. This is in accordance with our previous publication showing that K-RAS mutant

MIA PaCa-2 cells redirect glucose-derived acetyl-CoA towards fatty acid synthesis when exogenous cholesterol is supplied in the media (211). Glucose-derived acetyl-CoA is a shared substrate for both cholesterol and fatty acid synthetic pathways (167). Using 1,2-

13 C2-D-glucose tracer, GC/MS and the SiDMAP technology, we have demonstrated that upon CHS supplementation, the cholesterol synthesis pathway is repressed while the fatty acid synthesis pathway is up-regulated (211). The increased acetyl-CoA flux towards fatty acid synthesis combined with K-RAS mutation present the metabolic and genetic contexts by which MET inhibits de novo fatty acid synthesis (211).

There is literature supporting that MET possesses inhibitory effects towards lipid synthesis (140, 143-146, 178, 186, 211, 241-249). Some of the reports implicated

AMPK activation as a precedent to this effect. In rodent muscles, MET led to the activation of AMPK (via phosphorylation at its α1 catalytic subunit) and caused a decrease in insulin-stimulated [1-14C] palmitate uptake (245, 249). Decrease in lipid uptake due to MET treatment is not isolated in muscles but is also observed in the hepatocytes (145, 244, 250), macrophages (246) and adipocytes (241) of preclinical models. MET was shown to decrease lipid accumulation in HepG2 hepatocellular carcinoma cells in high glucose + insulin environment through preventing the nuclear translocation of SREBP-1 (145). Besides SREBP-1, Forkhead Box O1 (Foxo1) is another transcription factor that is implicated in mediating the anti-lipogenic effects of

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MET. Foxo1 is a member of a family of forkhead box O transcription factors with a wide array of functions including regulation of growth, differentiation, metabolism, tumorigenesis and longevity (251, 252). In macrophages, Foxo1 has been reported to decrease palmitic acid-induced lipid accumulation by inhibition of Foxo1-mediated fatty acid binding protein 4 (FABP4) transcription (246). In adipocytes, MET acts like a nutrient restriction mimetic, up-regulating the nuclear expression of Foxo1 to increase the expression of lysosomal acid lipase (Lipa), an enzyme that catalyzes the catabolism of lipid droplets in lysosomes (241). Because high amounts of stored lipids in organs are positively associated with insulin resistance, inhibition of fat storage may present one of the mechanisms by which MET sensitizes tissues to insulin. Recently, the anti- hyperglycemic effect of MET has been unified with its inhibitory effect on fatty acid synthesis. Using mice with knockin alanine mutations in acetyl-CoA carboxylase

(ACC1) (Ser79) and ACC2 (Ser212), it was shown what the glucose-sensitizing effects of MET is dependent on the drug’s ability to phosphorylate ACC1 and ACC2. This leads to the down-regulation of fatty acid synthesis which results in the amelioration of obesity-induced insulin resistance (250). We now show that MET decreases the ability of pancreatic tumor cells to store fatty acids in the form of triglyceride. Overall, MET is able to prevent lipid storage in various non-malignant and malignant cells.

Cancer cells up-regulate glucose-derived de novo fatty acid synthesis by increasing the expression and activities of key lipogenic enzymes namely, FAS, ACC, ATP citrate lyase (ACLY); by up-regulating the malic enzyme-catalyzed conversion of malate to pyruvate that generates the NADPH required for fatty acid synthesis and by up-regulating glutaminolysis (64, 68, 130). Increased fatty acid synthesis is necessary to meet the

143 proliferating cells’ need for membrane production as well as for post-translational modifications of proteins (68, 253). In addition, prostate cancer cells treated with the

ACC inhibitor soraphen exhibited a lower degree of cellular lipid saturation and a higher levels of polyunsaturation compared to vehicle control treated prostate tumor cells (254).

Besides modulation of lipid composition, soraphen treatment also led to increased susceptibility of cancer cells to lipid peroxidation, cell death and uptake of the chemotherapeutic drug doxorubicin (254). In our study, chronic MET treatment, at a clinical dose comparable to that prescribed for diabetic patients, is able to inhibit pancreatic cancer cells’ lipogenic phenotype by decreasing the gene and protein expression of the FAS enzyme and the storage of intracellular triglycerides. It will be of interest to further explore how this dosage and treatment duration of MET may make pancreatic cancer cells more prone to damage caused by oxidative stress. Combination therapy with fatty acid synthesis inhibitors with anti-cancer properties such as soraphen

(254) , orlistat, cerulenin and triclosan (255) may create a synergistic effect towards blocking the lipogenic phenotype of pancreatic tumor cells and cause cytotoxicity to cancer cells and is of interest for future studies.

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V. Concluding Remarks

In summary, we provide more evidence of the metabolic effects of the generic antidiabetic drug MET. When used in a therapeutically relevant dosage (100 μM) for an extended period of time, MET decreases FAS protein expression in MIA PaCa-2 pancreatic cancer cells only when cholesterol is used to derail acetyl-CoA towards new fatty acid synthesis (via CHS supplementation). In BxPC-3 and MIA PaCa-2, chronic

MET treatment decreases triglyceride storage only after cholesterol supplementation.

Our study shows that MET is indeed, an anti-lipogenic drug with chemopreventive properties and that this drug exerts these effects when in used in a pancreatic cancer cell model with up-regulated fatty acid synthesis. These results highlight the metabolic requirements that must be satisfied before the lipid inhibitory effects of MET can be observed. It would be of public health significance to investigate whether this preclinical model holds true in cancer patients. Although the anti-cancer properties of MET may be an unintentional effect of chronic drug use among the diabetic population, the consistency of associations between MET and decreased cancer risk and cancer mortality

(123) reported in epidemiological studies warrant the reinvestigation of this “old” drug.

The potential of MET as a chemotherapeutic drug needs further investigation and optimization of the dosage that will result in cancer cell death needs to be determined.

The identification of drugs that can potentially synergize with the anti-lipogenic effects of

MET provides another avenue for improving the potential of MET as an anti-cancer drug.

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CHAPTER IV: EVIDENCE OF THE ANTI-CANCER EFFECTS OF

METFORMIN IN VIVO

Emmanuelle J. Meuillet and Natalia Ignatenko designed the animal experiments. AZ

Cancer Center EMSS core facility performed the animal experiments, AZ Cancer Center

TACMASS core facility performed immunohistochemistry experiments, Mary Jo

Cantoria performed the metabolomics studies. Mary Jo Cantoria, Emmanuelle J.

Meuillet and Laszlo G. Boros analyzed the data. Mary Jo Cantoria wrote the manuscript.

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CHAPTER IV: EVIDENCE OF THE ANTI-CANCER EFFECTS OF

METFORMIN IN VIVO

I. Introduction

A. The KPC Mouse Model

The LSL-K-rasG12D/+;LSL-Trp53R172H/+;Pdx-1-Cre (KPC) mouse represents the first representative transgenic mouse model that recapitulates the pathological, histological, genomic and clinical signatures of human pancreatic cancer (256). The K-RASG12D/+ and p53R172H/+ mutations are found in >90% and 50% of human PDAC patients, respectively

(16). This mouse model for PDAC was generated in the laboratories of Drs. Sunil R.

Hingorani and David A. Tuveson and reported in 2005 (257). Briefly, a targeting vector containing elements that prevent transcription and translation with LoxP sites on each end was inserted into the mouse K-ras locus. In the Lox-Stop-Lox (LSL) design, the expression of K-ras is under the control of a Stop element and excision of the stop element occurs after Cre-recombinase recognizes the LoxP sites (Fig 36) (258).

The KPC mouse’s genomic K-ras locus is engineered to contain a guanine to adenine transition in codon 12 which results in glycine (G) to aspartic acid (D) substitution in the expressed protein, constitutively activating the Ras downstream pathways (259). These pathways include the canonical rapidly accelerated fibrosarcoma/mitogen activated protein kinase (RAF/MAPK) pathway and the phosphatidylinositol-4,5-bisphosphate 3- kinase/protein kinase B (PI3K/AKT) pathway, key pathways highly involved in cell survival, cell cycle progression and carcinogenesis (260). In PDAC, K-RAS mutation induces oncogene addiction (261) and silencing of this pathway results in failure to form

147 colonies in various PDAC cell lines (262). Also, it has been documented that K-RAS and

PIK3CA mutations are highly correlated to coexist in human cancer patients (263, 264).

The K-ras mutant mice (heterozygous) were mated with mice expressing pancreatic

Cre-recombinase, driven by the pancreatic and duodenal homeobox 1 (Pdx1) promoter or by the P48 promoter, transcription factors expressed in the murine embryo that are required for the formation of the mouse adult pancreas. When the Cre-recombinase recognizes the LoxP sites, the silencing cassette is excised, recombination occurs, and the resulting littermate possesses both a LoxP site and the mutated K-rasG12D. The LSL-

Trp53R172H/+ mouse is generated similarly to the technique used to generate the LSL-K- rasG12D/+ except that a point mutation is found in codon 172 of the transformation related protein 53 (Trp53), an ortholog of human tumor protein p53 (p53) (257, 265).

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FIGURE 36 K-ras and Tp53 gene expression in the KPC mouse. A Lox-stop-lox is inserted before the K-ras (A) and Tp53 (B) genes designed to contain hyper-activating and loss-of-functions, respectively. Mating with Cre-recombinase containing mice excises the Stop element and induces the transcription of the mutant K-ras and Tp53 genes. The expression of the mutant K-ras and Trp53 genes are controlled by the Pdx-1-

Cre promoter in the KPC mice (B). Note that the K-ras and Tp53 mutations are specific to pancreas (P) and not found elsewhere, such as the tail (T). Image obtained from

Hingorani et al (257, 259).

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B. Similarities with the Human Disease

It is noteworthy that there are numerous characteristics shared by murine and human

PDAC, making the KPC model relevant for in-depth study of this malignancy (256, 266).

The KPC mouse presents with disease burden as early as 10 weeks of age with a median survival of about 5 months (257). Hingorani et al. have characterized this model well

(256, 257) and key features will be summarized in this introduction. KPC mice develop pre-cancerous lesions called pancreatic intraepithelial lesions (PanIN), with characteristics of each progressive level sharing morphological characteristics with human PanINs (Fig 37).

FIGURE 37 Similarities in PanIN lesions between humans and the KPC mouse model of PDAC. Human and KPC PanIN lesions present morphological similarities at each level. PanIN lesions are indistinguishable from one species to another. Image obtained from Tuveson (256) © Cold Spring Harbor.

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Murine cancer expresses cytokeratin-19 (CK-19) and produces mucin, similar to human pancreatic cancer. Elevated expression of key signaling proteins SMAD4, p16/Ink4a, p19/ARF, Rb, AKT and MYC are present (256). Centrosome amplification is observed. Mice present cachexia, abdominal distention and ascites as secondary symptoms. Metastasis in the liver, lungs, adrenals, peritoneum and lymph nodes also occur in the KPC mouse model

The predecessor of the KPC mouse model is the KC (LSL-K-rasG12D/+) mouse (Fig

36, A) by Hingorani and others (259). The mouse was generated using the methods described above. Like the KPC mouse, the KC mouse also developed PanIN lesions that express CK-19 and mucin, prior to developing the full-blown cancer. PDAC is characterized by high desmoplastic reaction and metastasis. However, only about 82% of ducts are neoplastic by 7-10 months of age, compared to 2-3 months for the KPC model

(259, 266). The shortened latency period to develop pancreatic cancer in the KPC mouse indicates that the combined activation of oncogenic K-ras and loss of heterozygosity of the tumor suppressor p53 cooperate to accelerate tumorigenesis in the mouse (257).

C. Importance of the Microenvironment

It is well-known that cellular and molecular heterogeneity exists in tumors i.e., considerable differences in cellular and genetic markers of the primary tumor exist from one patient to another and different regions of the tumor from the same patient can present varying molecular profile (267). Also, the tumor microenvironment plays a key role in the creation of permissive conditions for the maintenance and evolution of cancer

151 hallmarks. More importantly, pancreatic cancer is characterized by desmoplasia or the formation of a highly fibrotic and reactive tissue that surrounds the pancreatic tumor.

The desmoplastic tissue is comprised of fibroblasts, cancer stem cells, stellate cells, immune cells, endothelial cells, nerve cells and extracellular matrix (268). There is considerable interaction between the tumor and the stroma and the stromal components with one another. Growth factors such as fibroblast growth factor, transforming growth factor-β, insulin-like growth factor-1 and platelet-derived growth factor are stored in the stroma, fueling important signaling pathways to cancer cells (269). Recently, it was reported that cancer-associated fibroblasts produce lactate and pyruvate, metabolites that are taken up by cancer cells as substrates for the TCA cycle (270). In addition, stromal components make-up a protective barrier that surrounds the tumor which, severely limits chemotherapeutic drug penetrance. Traditional cell culture conditions are optimized to provide the optimum settings for cancer cell growth. Unfortunately, neither heterogeneity nor the complex tumor microenvironment is captured in typical cell culture methods. Therefore, in vivo models that incorporate these tumor characteristics are crucial to understanding the disease. Thus, the KPC mouse model represents a significant conundrum of features of human pancreatic cancer and is a widely used animal model for studying this malignancy.

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D. Rationale and Hypothesis of Our Animal Studies

We have previously shown that acute MET treatment using a therapeutically relevant dosage (100 μM) for type 2 diabetes management in vitro inhibits new glucose-derived palmitate synthesis in a pancreatic cancer cell line with mutated K-RAS (MIA PaCa-2) when acetyl-CoA availability is shifted towards new palmitate synthesis (211). In addition, MET administered intraperitoneally (I.P.) has been shown to dose-dependently

(50-250 mg/kg once daily) inhibit tumor growth rate of PANC-1 PDAC cells in a xenograft model using male nu/nu mice (271). No solid mechanism of action was provided for MET’s anti-cancer effects on this model. In addition, the xenograft tumor model failed to capture the genetic mutations and significant desmoplastic reaction observed in PDAC patients. Hence, our study, through the use of the KPC mouse model addresses the genetic and the microenvironment factors lacking in the other group’s study.

13 Using uniformly labeled glucose ( C6-glucose), we now perform a metabolomic study in KC, KPC and background control C57BL/6 mice to determine how acute high- dose (250 mg/kg body weight consecutively for five days) intraperitoneal injections of

MET changes tumor metabolism in K-ras mutated (KC and KPC) versus K-ras wild type

(WT) mice (C57BL/6) in the context of the tumor environment. We sought out to investigate the effects of MET on tumor glycolysis, the TCA cycle and fatty acid synthesis in order to understand how acute, high-dose MET treatment metabolically affects pancreatic tumor metabolism in the context of K-ras mutation. We now show the metabolic differences in K-ras mutatnt mice (KC and KPC) compared to WT K-ras mice

(C57BL/6). K-ras mutants are more glycolytic and more gluconeogenic compared to WT

153 mice. More specifically, KPC mice exhibit impaired glucose oxidation compared to KC and C57BL/6 mice. Interestingly, all mice exhibited a trend towards decreased fatty acid synthesis after MET treatment.

II. Materials and Methods

A. Animal Treatment

All animal procedures were approved by The University of Arizona Institutional

Animal Care and Use Committee (IACUC) and animal handling was performed by the

Arizona Cancer Center Experimental Mouse Shared Service (EMSS). Intraperitoneal

(IP) glucose tolerance test (IPGTT) C57BL/6 (background control; N=4), KC (LSL-K- rasG12D/+;p48-Cre; N=2) and KPC mice (LSL-K-rasG12D/+;LSL-Trp53R172H/+;Pdx-1-Cre;

N=4) was performed on day 0 (no vehicle or MET treatment) using uniformly labeled

13 glucose ( C6-glucose) at a dose of 1g/kg body weight. Mice were approximately two months of age. At the time of the experiment, only the KPC mice had developed pancreatic tumors. This is expected as in the previous model of K-ras only mutation, KC mice developed PDAC after 6-8 months (266). From day 1 to day 6, half of the mice received either IP injections of vehicle control (PBS) or MET (250 mg/kg body weight) once a day. On day 6, IPGTT was performed one hour after MET injection, mice were sacrificed and livers and pancreases were snap frozen. Blood samples were placed in heparin tubes and centrifuged to collect plasma component. Tissues were stored in -80⁰C until sample extraction and derivatization for GC/MS analysis.

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B. Immunohistochemistry

On day 5, half of the pancreases and livers were stored in neutral-buffered formalin overnight and embedded in paraffin block for immunohistochemistry (IHC) analysis. Sections of 3 microns were cut. IHC was performed by Ventana automated platform using a borate buffered antigen retrieval by the AZCC Tissue Acquisition and

Cellular/Molecular Analysis (TACMASS) using antibodies for FAS (Cell Signaling,

Danvers, MA, USA), Ki67 (from Leica) and HMGA2 (Cell Signaling, Danvers, MA,

USA).Stained with DAB and counterstained with and counterstained with hemoatxylin.

Slides were dehydrated in graded alcohols and Xylenes then coversliped. Long scores for

FAS and HMGA2 were calculated. The long scores represent staining intensity ranging from 1+ - 3+ and a percentage of positively stained cells ranging from 1-100%. Long scores were calculated by multiplying the intensity by the percentage. The long score minimum is 1 and the long score maximum is 300.

C. GC/MS and SiDMAP

Tissues were weighed and homogenized in the same volume of distilled deionized water. Sample preparation and derivatization, GC/MS conditions and data download were performed as described in Chapter 1.

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D. Data Analysis

Student’s independent two-tailed t-test (α=0.05) was used to calculate significance for the SiDMAP analysis. IHC staining intensity was scored by TACMASS.

III. Results

We sought out to determine how an acute (five days), high dose (250 mg/kg body weight) MET treatment would alter systemic (plasma) and pancreatic metabolism in mice with different K-ras status. We performed a SiDMAP study using uniformly labeled

13 glucose ( C6-glucose) to interrogate changes in glycolysis, TCA cycle and fatty acid synthesis pathways after MET treatment (Fig 38). We also performed immunohistochemistry and probed for expression of cell proliferation (Ki67) and the

FAS enzyme.

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FIGURE 38 A simplified diagram showing metabolic pathways investigated in the in vivo study. Glucose undergoes glycolysis producing lactate and pyruvate. Pyruvate dehydrogenase (PDH) catalyzes the decarboxylation of pyruvate to two molecules of acetyl-CoA. Acetyl-CoA is oxidized via the TCA cycle. Pyruvate carboxylase (PC) replenishes oxaloacetate into the TCA cycle as an anaplerotic reaction. Acetyl-CoA is a substrate for cholesterol (catalyzed by thiolase) and fatty acid syntheses (catalyzed by fatty acid synthase).

A. MET Modulates the Warburg Effect in KC and KPC Mice

Increased lactate production in the tumor is characteristic of increased glycolysis, reflecting the high dependence of tumor cells on glucose or what is known as the

Warburg effect (42). Both K-ras mutant mice (compare KC vehicle control (VC) and

157

KPC VC to C57BL/6 VC) exhibit increased pancreatic lactate production compared to

WT K-ras mice (C67BL/6 VC) (Fig 39). MET decreased the lactate production in the pancreas of mutated (KC) and mutated/tumor-bearing (KPC) mice independent of the presence of pancreatic tumor. This indicates that the presence of the K-ras mutation is sufficient to drive increased glycolysis in the pancreas and that the shift towards the

Warburg phenotype may be an early event in PanIN lesion development.

FIGURE 39 MET decreases pancreatic lactate 13C labeled fraction via all pathways production in KPC and KC mice pancreas. Pancreases of mice were dissected after the fifth day of MET treatment. Intraperitoneal glucose tolerance test

13 (IPGTT) using C6-glucose was performed 1h post-MET injection on the fifth and last day of MET treatment (250 mg/kg body weight I.P.). Pancreases were collected and subject to GC/MS as described in Methods under Chapter 2. 13C labeled lactate is a surrogate marker of glycolysis.

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Figure 40 A shows that K-ras mutation (KC VC), and the presence of the tumor (KPC

VC), induce an increase in plasma glucose production via its de novo synthesis using futile cycling of glucose-derived lactate and pyruvate by the liver while Figure 40 B (KC

VC and KPC VC vs C57BL/6 VC) shows a similar trend in glucose generated from the pentose phosphate pathway (PPP). MET treatment decreases fluxes towards glucose production in K-ras-mutated (KC MET) and tumor-bearing animals (KPC MET), but increases this flux in control liver. The PPP is an important source of ribose sugars that are used as nucleotide backbones and this pathway also generates NADPH. Together with the lactate data (Fig 40) these results indicate that the pancreas in MET-treated animals become less Warburg-like and use less glucose for RNA/DNA and fatty acid synthesis.

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FIGURE 40 Hepatic glucose production is decreased in KC and KPC mice treated with MET. Pancreases of mice were dissected after the fifth day of MET treatment.

13 Intraperitoneal glucose tolerance test (IPGTT) using C6-glucose was performed 1h post-

MET injection on the fifth and last day MET treatment (250 mg/kg body weight I.P.).

Pancreases and plasma were collected and subject to GC/MS as described in Methods under Chapter 2. Shown is 13C glucose derived from all pathways (A) and glucose derived from the pentose phosphate pathway (B) as markers of hepatic gluconeogenesis.

The TCA cycle allows for the complete oxidation of glucose into CO2, water and ATP.

Although cancer cells heavily rely on glycolysis even in the presence of oxygen (termed the Warburg effect or aerobic glycolysis) (43), they are capable of carrying out glucose oxidation via the TCA cycle (270). As seen in Figure 41 (C57BL/6 VC and KC VC vs

KPC VC), KPC mice are able to oxidize glucose albeit half of that of KC and C57BL/6 mice. The inhibitory effect of MET on glycolysis in KPC pancreas is further confirmed by the increased glucose oxidation post MET treatment in Figure 41. Figure 41 (KPC

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VC vs KPC MET) shows that the tumor decreases, but MET increases, complete glucose

13 oxidation into CO2 in the pancreas of KPC mice. KC VC appears to be unchanged compared to C57BL/6 VC whereas KPC VC shows a trend towards a decrease in pancreatic complete glucose oxidation. Therefore, for CO2 production, we found that tumor growth has to be established; the mutation is not enough to induce changes in this flux. Although, K-RAS mutation primes the pancreatic cancer for increased glycolysis

(Fig 39), the mutation and the tumor have to be present to create an anabolic, non- oxidative phenotype in the pancreas. Only in the presence of the tumor does MET restore this metabolic profile back into a more oxidative phenotype, underscoring MET’s specificity towards malignant tissues.

FIGURE 41 KPC mice oxidize glucose one half of that of KC and C57BL/6 mice pancreas. Pancreases of mice were dissected after the fifth day of MET treatment.

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13 Intraperitoneal glucose tolerance test (IPGTT) using C6-glucose was performed 1h post-

MET injection on the fifth and last day MET treatment (250 mg/kg body weight I.P.).

Pancreases were collected and subject to GC/MS as described in Methods under Chapter

13 2. Shown is C labeled CO2 as a surrogate marker of glucose oxidation via the TCA cycle.

Pyruvate carboxylase is the enzyme that catalyzes the replenishment of oxaloacetate from acetyl-CoA into the TCA cycle and its activity is a marker of TCA cycle anaplerosis

(Fig 38). The tumorigenic pancreas shows increased pyruvate carboxylase activity after

MET treatment (KPC VC vs KPC MET) (Fig 42), indicating improved glucose oxidation via the TCA cycle. The restoration of the oxidative phenotype in MET-treated KPC mice confirms the observations in Figure 41.

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FIGURE 42 MET restores TCA anaplerosis in the KPC mice pancreas. Pancreases of mice were dissected after the fifth day of MET treatment. Intraperitoneal glucose

13 tolerance test (IPGTT) using C6-glucose was performed 1h post-MET injection on the fifth and last day MET treatment (250 mg/kg body weight I.P.). Pancreases were collected and subject to GC/MS as described in Methods under Chapter 2. Shown is 13C

M4 glutamate as a surrogate marker of pyruvate carboxylase activity.

Our previous study showed that MET is an inhibitor of glucose-derived new fatty acid synthesis in MIA PaCa-2 cells (human KRAS mutant PDAC cells) so we assessed the effects of MET on fatty acid metabolism in K-ras WT and K-ras mutant mice.

Glucose-derived acetyl-CoA is a substrate for fatty acid synthesis (Fig 38). There is a consistent decrease in acetate enrichment towards palmitate synthesis in all mice treated with MET (Fig 43 A, C57BL/6 VC vs MET, KC VC vs MET and KPC VC vs Met), supporting MET’s anti-lipogenic effects in pancreatic cancer (211). In addition, a decrease in M2 palmitate, which is palmitate derived from glycolysis and citrate shuttling, is observed in MET-treated mice regardless of genetic background (Fig 43 B,

C57BL/6 VC vs Met, KC VC vs MET and KPC VC vs Met). These results demonstrate the ability of MET to impair glucose-derived new fatty acid synthesis in vivo regardless of K-ras or tumor status.

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FIGURE 43 MET inhibits palmitate synthesis in K-ras WT and K-ras mutant mice.

13 Intraperitoneal glucose tolerance test (IPGTT) using C6-glucose was performed 1h post-

MET injection on the fifth and last day of MET treatment (250 mg/kg body weight I.P.).

Pancreases were collected and subject to GC/MS as described in Methods under Chapter

2. Shown are: A) Tracer-derived acetate enrichment in palmitate and B) M2 palmitate, palmitate derived from glycolysis and citrate shuttling (Cytosolic acetyl-CoA and oxaloacetate are formed by the action of ATP citrate lyase on mitochondrial citrate.

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Acetyl-CoA can then be utilized as a substrate for fatty acid synthesis). Shown is tracer- derived acetate for palmitate synthesis (A) and (B).

B. MET Decreases the Expression of FAS, Ki67 and HMGA2 in the

Pancreas of KPC Mice

Cancer cells exhibit a lipogenic phenotype in order to supply the high demand for cell membrane formation for cell division (68). In order to associate the observed decrease in glucose-derived palmitate synthesis after MET treatment, we probed parrafinized pancreases of KPC mice with the fatty acid synthase (FAS) antibody. We show that staining intensity for FAS is decreased by 43% in the pancreas of MET-treated animals

(Figure 44).

We then sought to answer whether this lipid-inhibitory effect of MET decreases tumor cell proliferation. Interestingly, we see a marked decrease in the cell proliferation marker Ki67 (by 80%) in MET-treated KPC pancreas. We also show a decrease in the expression of the EMT marker, HMGA2 in the pancreas of MET-treated KPC mice.

Altogether, the effects of MET on tumor metabolism occurs by modulating metabolic fluxes of the glycolytic, TCA and fatty acid synthesis pathways and by decreasing the expression of the fatty acid synthase enzyme. These anti-cancer effects result in decreasing cell proliferation and the expression of the EMT marker HMGA2 alluding to the fact that MET does possess anti-cancer properties when given at high dosages.

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Ki67

FAS

HMGA2

FIGURE 44 MET decreases the protein expression of Ki67, FAS, and HMGA2 in

13 KPC mice. Intraperitoneal glucose tolerance test (IPGTT) using C6-glucose was performed 1h post-MET injection on the fifth and last day MET treatment (250 mg/kg body weight I.P.). Pancreases were collected and probed for markers of cell proliferation, fatty acid synthesis enzyme and epithelial-to-mesenchymal transition

(EMT). The cell proliferation marker Ki67 (brown) shows an 80% decrease in staining intensity by in MET-treated KPC mice. FAS staining (brown) in MET-treated KPC mice

(Long score = 146.67 + 29.30) show a decrease in staining when compared to VC

(vehicle control; Long score = 218.33 + 56.20). HMGA2 (brown) also show a decrease

166 in staining in MET-treated KPC (Long score = 91.67 + 7.64) versus VC (Long score =

140.00 + 20.00). Long scores are expressed as means + s.d.

IV. Discussion

We have previously shown that an acute treatment (24h) of MET exerts its anti- lipogenic effects in cancer in vitro when the following requirements are met: a) presence of K-RASG12D mutation and b) glucose-derived acetyl-CoA redirected towards fatty acid synthesis (211). In this study, we explored how an acute (five days), high dose (250 mg/kg body weight) MET treatment alters the glucose-derived metabolism of the pancreas with respect to the murine tumor microenvironment and K-ras status.

An interesting observation from this study is the trend towards a similar metabolic profile in terms of pancreatic lactate production and hepatic glucose production of K-ras mutant mice (KC VC and KPC VC) which are distinct from the background control

(C57BL/6 VC) (Fig 39 and 40).

These indicate that the increased dependence on glycolysis (indicated by increased lactate formation and futile cycling of pyruvate and lactate during hepatic gluconeogenesis) is an early event in the development of pancreatic cancer. Indeed, the current model of pancreatic cancer progression identifies oncogenic activation of K-RAS to be present in the earliest pre-malignant step in pancreatic carcinogenesis, which is

PanIN-1a formation (16, 268). Vizan et al. showed that NIH3T3 fibroblasts that have been transfected with codon 12 K-ras mutant increased glycolysis and decreased glucose oxidation in the TCA cycle compared to cells transfected with codon 13 K-ras mutation

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(168). In addition, ras-transfected rat-1 cells demonstrate up-regulated glycolysis (272).

Yun et al. constructed colorectal cancer cell lines with K-RASG13D mutation, and observed that these K-RAS mutant cell lines were able to survive in media containing a low glucose concentration of 0.5mM (273). Although the mutation is in codon 13 instead of codon 12

(used in our study), the Yun et al. paper showed the failure of K-RAS WT cells to survive in hypoglycemic conditions (273). These studies along with ours demonstrate that mutated K-RAS status allows pre-malignant and cancer cells to acquire a glycolytic phenotype.

The ability of MET to selectively decrease lactate production (Fig 39), inhibit glucose

(Fig 40) synthesis and improve glucose oxidation (Fig 41) in KC and KPC mice but not in C57BL/6 mice presents a therapeutic window where MET may be able to derail the glycolytic phenotype of pre-malignant and malignant tissues. This is important because of potential selective effects of the drug on transformed cells. The therapeutic benefit also lies in the potential ability of MET to “reverse” or inhibit the glycolytic switch in cancer to counter the biosynthetic reactions that generate DNA, RNA, amino acids, proteins, ATP and lipids in cancer, downstream of the glycolysis.

Another interesting finding of our study is that MET restored the oxidative phenotype selectively in KPC mice only (Fig 41 C57BL/6 MET vs KC MET vs KPC Met). This underscores the context that the tumor itself modulates pancreatic metabolism and that

MET reverses the glycolytic anabolic phenotype back to a more oxidative one, specifically in tumor-bearing pancreas. The observed ability of MET to decrease glycolytic flux and increase glucose oxidation are contrary to what we observed during acute MET treatment of MIA PaCa-2-cholesterol (CHS) in vitro, where 100 μM of MET

168 induced TCA cycle inhibition and increased flux towards lactate production (211). The major difference between the previous in vitro and the current in vivo study is the influence of the microenvironment present in the current study. The phenomenon of

Reverse Warburg effect has recently been reported and during which, the intact oxidative phosphorylation capacity of cancer cells usurps the stromal fibroblasts’ ability to carry out glycolysis (270). By-products of glycolysis, pyruvate and lactate, are taken up by cancer cells and oxidized in the TCA cycle (270). In support of this theory, M4 glutamate appears to be similar in all vehicle-treated mice (Fig 42), which indicates an intact TCA cycle in C57BL/6, KC and KPC mice. While the effect of MET on stromal metabolism may be one way to interpret the seemingly conflicting result between the in vitro and in vivo study, one of the ways to verify this theory would be to perform a microdissection of the actual tumor and surrounding stroma and separately analyze the metabolic profile of these two compartments.

Similar to our in vitro work, we now present that MET truly possesses anti-fatty acid synthetic properties (Fig 43). What is interesting is that the effect appears to be universal for WT K-ras and K-ras mutated mice. In the current study, a high dose of MET was used whereas in our previous in vitro work, we used a therapeutically relevant MET dose for diabetes management. The dosage used in the present in vivo work is about the maximum recommended for human daily dose on a body surface basis (214). This means that the dose used for the present in vivo work is still within the range of therapeutic dose for diabetics. Although there is a significant difference in MET concentrations between the in vitro and in vivo studies, both studies show that MET inhibits glucose-derived new fatty acid synthesis. This MET effect is associated with a

169 decrease in FAS expression and also resulted in reduced cell proliferation and EMT marker expression high mobility AT-hook group 2 (HMGA2). HMGA2 is a nuclear, non-histone chromatin-binding protein that binds to adenine-thymine (A-T) regions of the

DNA via an Arg/Lys-Arg-Gly-Arg-Pro-Arg/Lys repeat called the AT-hook motif (274).

HMGA2 DNA binding enhances DNA transcription, replication, recombination and repair (274). Recently, it was found that HMGA2 is overexpressed in oral (275), stomach (276), lung (277, 278) and pancreatic cancers (279). Knock-down of HMGA2 via siRNA in various PDAC cell lines resulted in lower cell proliferation, increased protein expression of E-Cadherin, and down-regulation of the protein vimentin and maintenance of an epithelial-like morphology (279). As discussed in Chapter 1, epithelial-to-mesenchymal transition (EMT) is one of the initial steps in metastasis which allows cancer cells to intravasate or invade blood vessels and eventually hone in a local or distal site from the primary tumor (40). Thus, future studies that will be pursued in our lab will include how MET affects the EMT profile (loss of polarity, and up-regulated expression of vimentin, fibronectin, N-Cadherin, alphavbeta3-integrins and fibroblast specific protein-1 (FSP-1)) of pancreatic tumors and how MET affects primary PDAC tumor metastasis in various organs such as the liver, kidneys, adrenals, lungs, peritoneum and lymph nodes.

We hypothesize that a greater effect by MET in terms of reverting PDAC metabolic phenotype to a more oxidative profile would be more apparent if the KPC mice were metabolically challenged with a high cholesterol-high sugar diet. This will induce up- regulation in fatty acid synthesis, which will make the tumor more sensitive to Met’s anti-lipogenic effects. A mouse model developed in Dr. G. Eibl’s laboratory at University

170 of California Los Angeles (UCLA, USA) showed that KPC mice fed a high fat-high- energy diet developed greater PanIN lesions than mice under normal chow (280). While associations were made between the diet and diet-induced inflammation in this model, metabolic analyses were limited to assessments of insulin, glucose, IGF-1, leptin, cholesterol and triglyceride levels (280). It will help gain a better understanding of how the diet remodels the pancreas in the context of metabolic fluxes during precursor PanIN development. A time course study where the metabolic prolife of mice that developed

PanIN lesions are compared to those that developed PDAC needs to be studied. This presents a way to identify chemopreventive and chemotherapeutic targets in terms of the metabolic reactions and enzymes that are dysregulated during PanIN and PDAC development.

V. Concluding Remarks

Although the sample sizes were small in this “proof-of-concept” study, the present work has generated useful information which can be used to design future in vivo work.

It verified that pancreatic tumor could completely oxidize glucose via the TCA cycle. It also showed that glucose metabolism is K-ras status dependent. This study also emphasized the importance of context-specific effect of MET in the absence or presence of the tumor stroma in relation to K-ras status i.e., K-ras up-regulates glycolysis while

MET enhances complete glucose oxidation. The most consistent data presented in this chapter is that MET inhibits glucose-derived fatty acid synthesis through impairment of metabolic flux and reduction of FAS expression. Overall, it appears that acute high-dose

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MET treatment (5 consecutive days, 250 mg/kg body weight I.P.) is a mild modulator of tumor metabolism. Future studies should consider increasing the duration of treatment and metabolically challenging the mice with a high cholesterol-high sucrose diet in order to meet the requirement of context-specific shift of glucose-derived acetyl-CoA towards palmitate synthesis before MET can be a more potent anti-lipogenic drug. As a proof-of- principle, Algire et al. showed that MET decreased tumor size only in mice bearing lung or colon xenografts and fed a high-energy diet (178, 186). In order to create a more translational PDAC model, our lab is currently pursuing the MET study in combination with the high cholesterol-high sucrose dietary challenge in collaboration with University of California, Los Angeles (UCLA). This model will address the hyperglycemia observed with diabetes and the KRAS and p53 mutations observed in PDAC tissues.

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CHAPTER V. FUTURE DIRECTIONS AND OVERALL CONCLUSION

I. Exploring Deregulating Cellular Energetics in PDAC

Our metabolomics experiments show that MET is an inhibitor of glucose-derived fatty acid synthesis in PDAC cells only when K-RAS mutation is present and when glucose-derived acetyl-CoA is shifted towards de novo palmitate synthesis through increasing cholesterol concentrations in the media. MET’s anti-lipogenic effect is regulated at the metabolic flux level and at the level of decreased FAS protein expression under these conditions.

We have shown that intracellular triglyceride (TG) is decreased in BxPC-3 and MIA

PaCa-2 chronically treated with CHS and MET. This is a result of MET’s fatty acid synthesis inhibitory effects. Another reason may be an increase in fatty acid β-oxidation and there is less fat to store as TG. In order to probe for fatty acid oxidation, we propose to use 13C palmitate (281) in KPC mice fed normal chow or challenged with high sugar- high cholesterol diet + MET. Analysis of fatty acid oxidation is important because this pathway can provide ATP to cancer cells (282) and bioactive lipids such as lysophosphatidic acid, phosphatidic acid and lysophosphatidyl ethanolamines (283) that signal to promote cancer aggressiveness, migration, invasion and survival (284-286).

The decrease in intracellular TG in BxPC-3 and MIA PaCa-2 chronically treated with

CHS and MET may make pancreatic cancer cells more prone to damage caused by oxidative stress. Recently, it has been reported that lipogenic cancer cells have increased fatty acid saturation and decreased fatty acid unsaturation (254). Treatment with the

ACC inhibitor soraphen resulted in decreased fatty acid saturation, increased fatty acid

173 unsaturation, rendering these treated cells more susceptible to oxidative stress (254). In vitro studies on potential susceptibility of MET-treated to oxidative damage can be measured through formation of lipid peroxidation products malondialdehyde (MDA) and

4-hydroxynonenal (4-HNE).

We also propose the combination of lipid synthesis inhibitors with MET in future studies. The FDA-approved drug orlistat (lipase inhibitor) may synergize or add to the anti-lipogenic effects of MET and may decrease tumor growth as a consequence. Other investigational drugs include C75 (FAS inhibitor), cerulenin (FAS inhibitor) and soraphen (ACC inhibitor).

A pathway that we were unable to explore was the generation of ribose via the pentose phosphate pathway (PPP). This pathway is significant because the ribose that is generated via the non-oxidative arm of the PPP serves as nucleotide backbone. While

13 1,2- C2-D-glucose can be used to interrogate this pathway (196), we experienced technical difficulties in measurement. Ribose has low abundance from cell lysates and from tissues (unpublished observations) and future studies should include extra samples dedicated for the measurement of this metabolite.

II. Addressing the Dose-Dependence of MET Against PDAC

We show that MET, when used at a dose (100 μM) that is within the therapeutic range for diabetic management, fails to decrease cell proliferation and cell survival of

PDAC cells. We conclude that although MET has anti-lipogenic properties against

PDAC, at this dosage, it is chemopreventive at best. As shown by our MTT assays, a

174 dose a hundred times this therapeutic dose is required to induce significant decrease in

PDAC cell viability.

In order to determine the optimum dosage for the use of MET as a chemotherapeutic drug, a clinical trial that tests whether MET at doses beyond 2,500 mg/day can improve progression-free survival and overall median survival without causing adverse effect- related deaths. It is important that patients who have metabolic up-regulation of fatty acid synthesis (hyperlipidemia and hypercholesterolemia) be selected for this study since we have shown in vitro that this condition is where the anti-lipogenic effects of MET are observed. In order to identify what other metabolic pathways are affected by MET, preclinical models should use MET doses that are within reasonable doses that are useable in the clinic. Otherwise, these preclinical studies will be unable to generate data that complement the clinical scenario and results generated from “super-dosing” may yield results that are due to methodological artifacts.

This is why our in vitro and in vivo studies are extremely relevant to cancer prevention and treatment. In the era of personalized medicine, one needs to consider personalized metabolomics studies for the right combination of anti-metabolic drugs with

MET.

III. Studies on KPC Mice

We also demonstrate the importance of using a clinically relevant in vivo PDAC model that includes the tumor microenvironment. In vivo, we show that a high-dose

MET treatment (250mg/kg body weight) administered once daily for five days, reverses

175 the glycolytic phenotype of KPC mice into a more oxidative one. We need to revise the research design of the in vivo study from Chapter IV by making the following changes.

1) First, identify the maximum tolerated dose and duration of MET treatment in KPC mice and background control. This will allow us to establish the dosage by which mechanistic studies will be based on. In drug development, for anti-cancer agents, this is a requirement and thus this should be performed for the case where MET would be used as an adjuvant treatment for cancer thereapy.

2) Second, increase the sample sizes of the background control and KPC mice (N=12 per group per time point for both genotypes). The time points are defined as: a) PanIN lesion formation and b) PDAC development. The increased sample size should address the difficulty of ribose analysis.

3) Third, divide the mice into: a) fed normal chow or b) challenged with high sugar-high cholesterol diet + PBS or MET.

4) Fourth, probe the metabolic changes during the development of PanIN lesions and

PDAC formation using uniformly-labeled glucose and GC/MS in the plasma and pancreases of the control and KPC mice. This approach will give us information about the step-wise progression of metabolic changes prior to the formation of the tumor and when the tumor is established. As mentioned, early detection of PDAC is very challenging due to the anatomical location of the pancreas and the asymptomatic nature of early PDAC. The identification of the early changes in pancreas during lesion formation can enable the identification of potential drug targets and interventions for

176 patients at high risk for this disease (e.g., patients with chronic pancreatitis, diabetes, family history of PDAC).

5) Fifth, microdissection of the tumors and comparison of the tumor metabolism against surrounding tissue will isolate how the diet and MET affect tumor metabolism. In our study, we homogenized the entire pancreas of the mice. This means that the metabolic effects we observed represented the sum of the tumor, normal tissue and stroma. If there is considerable difference between tumor metabolism and its surrounding tissue dual targeting of these compartments must be considered for drug development and drug delivery.

6) Sixth, MET can be investigated whether it will be able to attenuate chronic pancreatitis

(CP) in KPC mice. CP is a known risk factor for PDAC (16). It has been shown that treatment of K-ras mutant mice with the glucagon-like peptide analog exendin-4 can induce CP (287). It would be interesting to study whether MET, via its anti-lipogenic properties, would decelerate CP development in KPC mice. This is supported by the fact that the cyclooxygenase/prostaglandin (COX/PGE) inflammation pathway is closely linked to lipid synthesis via release of arachidonic acid from the plasma membrane as the primary step as well as the formation of signaling lipids (e.g., prostaglandin, thromboxane, leukotrienes etc). Whether MET can modulate inflammation in KPC mice remains to be determined.

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IV. Overall Conclusion

In summary, MET’s anti-cancer properties rest on its ability to impair cancer cell lipogenesis, a critical mechanism by which cancer cells maintain their survival advantage over normal cells. The downstream effects of MET extend far beyond increased production of lipids for membrane synthesis. The implications of MET against PDAC metabolism can extend to decreased ATP production, decreased lipids for signalling protein post-translational modification and decreased protection against oxidative damage.

Combining metabolomics with molecular biology provides a powerful way to understand how a treatment (e.g., CHS and MET) or condition (e.g., K-RAS mutation) affects a model system’s metabolic phenotype. In particular, the use of targeted metabolomics allows for the establishment of a causal relationship between a treatment and/or condition and a metabolic phenotype. The complicated data analysis, involvement of intense labor and high cost to conduct targeted metabolomics can serve as a barrier to the accessibility of this technology. A collaborative approach is recommended to help overcome these obstacles.

We have shown that MET’s chemopreventive role is effective in select metabolic phenotypes and probably a particular cancer genotype. Thus, it is important to understand the genetic and metabolic context by which MET exerts anti-cancer effects so that the correct patient population can be selected for therapeutic purposes. However, the optimum dose range by which MET exerts a potential chemotherapeutic dose remains to be determined.

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