Exploiting Amino Acid Catabolism As a Metabolic Vulnerability in Breast Cancer

Exploiting Amino Acid Catabolism as a Metabolic Vulnerability in Breast Cancer

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Citation Geck, Renee Catherine. 2020. Exploiting Amino Acid Catabolism as a Metabolic Vulnerability in Breast Cancer. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.

Citable link https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37365887

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Exploiting amino acid catabolism as a metabolic vulnerability in breast cancer

A dissertation presented by Renee Catherine Geck to The Division of Medical Sciences

in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Biological and Biomedical Sciences

Harvard University Cambridge, Massachusetts March 2020

Dissertation Advisor: Alex Toker Renee Catherine Geck

Exploiting amino acid catabolism as a metabolic vulnerability in breast cancer

Abstract

A growing tumor is faced with many challenges, including how to obtain and utilize nutrients.

Reprogramming of metabolism has emerged as a hallmark of cancer and numerous studies have identified specific alterations in metabolic pathways in tumors and how they can lead to targetable vulnerabilities.

This thesis investigates the metabolism of triple-negative breast cancer (TNBC), a disease where treatment is limited by a lack of effective molecularly targeted therapies. We have focused on the effects of genotoxic chemotherapy drugs on the metabolism of the amino acid arginine. First, we investigated how chemotherapy drugs alter the levels of metabolites and enzymes involved in arginine metabolism. We found that exposure of TNBC cells in vitro to cytotoxic chemotherapy drugs leads to alterations in polyamines due to a reduction in the levels and activity of the rate-limiting polyamine biosynthetic enzyme ornithine decarboxylase (ODC). We also observed this decrease in ODC in receptor-positive breast cancer cells, suggesting that it is widespread. The reduction in ODC was mediated by its negative regulator, antizyme, targeting ODC to the proteasome for degradation. Secondly, we investigated the efficacy of targeting polyamine synthesis as an anti-cancer strategy in TNBC. We found that the ODC inhibitor DFMO increased killing of TNBC cells in combination with chemotherapy or alone, and ODC levels were elevated in TNBC patient samples. Alterations in polyamine metabolism in response to chemotherapy, as well as preferential sensitization of TNBC cells to chemotherapy by DFMO, suggest that ODC may be a targetable metabolic vulnerability in TNBC. Together, the data in this thesis highlight novel alterations and potential therapeutic targets in arginine and polyamine metabolism in TNBC.

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Table of Contents

Chapter 1 Introduction……………………………………………………………………………1-49

Chapter 2 Chemotherapy alters polyamine metabolism through ornithine decarboxylase.……..50-68

Chapter 3 Inhibition of ornithine decarboxylase sensitizes triple-negative breast cancer to cytotoxic chemotherapy………………………………………………………………69-90

Chapter 4 Discussion.…………………………………………………………………………..91-113

Appendix I Differential sensitivity of breast cancer cells to arginine deprivation……………..114-123

Appendix II Regulation of arginase II in response to cytotoxic chemotherapy…………………124-137

Appendix III Supporting information…………………………………………………………….138-144

Acknowledgements

Research is never an individual endeavor, so all of this work is thanks to my community of fellow scientists, friends, and family. All the successes I have had are thanks to your input and advice, and the setbacks I overcame were achieved with your support and encouragement.

One of the best pieces of advice I received when I entered graduate school was to find a lab where, no matter how frustrated I might be with my science, I would still want to get up each day and go to lab. I don’t think that can be said truthfully about many jobs, but it has been true each day in the Toker lab. You are all incredibly supportive of me and each other, in our science and lives, far beyond what is required.

The fact that this thesis is completed is thanks to you.

Particular thanks go to Alex Toker: your lab is a wonderful place to work because of your attention and efforts to promote a positive environment. Thank you for taking me in, and supporting me in pursuing my interests in metabolism even when they strayed away from your favorite molecule, Akt. Your advocacy for me and others in the lab has raised the bar for my goals of effectively mentoring students in my future. I am also grateful that, even with how much you love science, you agree that it is only one aspect of our lives, and plan and promote lab outings and events. You made the lab a family, and I am so thankful to be a member.

I can’t adequately thank everyone in the lab here, but know that you have all contributed to my development as a person and as a scientist. Thank you for every time you let me interrupt your work with a question, listened while I tried to process or vent about something, or took a coffee break to watch animal videos. There are pieces of each of you that I aspire to incorporate as I grow in my own practices and perspectives on research and life. I owe you each an induvial letter of appreciation because if I wrote it all here this would quickly become the longest chapter in my thesis. A brief special thank-you to

Whitney and Evan, for being both friends and fellow graduate students I could look up to. You set examples of how to progress through studies and research with grace that I aspire to. Thank you not only for your advice when we were students together, but also for being available and continuing to give me

v guidance after you moved on. Your perspectives have encouraged me throughout graduate school.

To all of my friends and peers in BBS, I am so glad to be in this program with you! When I was interviewing for programs, one of my “pros” for BBS was that with a class of 50-plus people, I figured I would at least want to be friends with a few of you. It didn’t take long to realize it would be a lot more than a few. I’m glad every time I get to see you, now that we’re all in different labs, and encouraged by seeing how we all progress towards different goals and in different ways, but still maintain our friendships. Very special thanks to Janice and Chiara for being amazing roommates, and bearing with me as you helped me grow. Thanks to you, I have learned not just as a scientist, but also as a person. Thank you too for tangible efforts in bringing this thesis to completion: Janice, our writing parties helped me put all of my thoughts together and kept me in good spirits while I worked, and Chiara, your creativity and critical thinking enabled me to develop my model figures and remember to always bring things back to the big picture.

I would not have made it to this point without all of the support of my friends, classmates, teachers, and professors – particularly all of my math and science teachers at Everett High School, and professors at George Fox University. The broad scope of my education better prepared me to interact with the world not just as a scientist but also as an enthusiast for many different fields. Everyone who taught me about literature, art, and music, you reminded me that the world is a beautiful and diverse place. Special thanks to John Schmitt, for giving me my first tour of the biology department that made me want to go to George

Fox, and then inviting me to join your lab when you found out I was interested in research. You showed me what to look for in a mentor and a lab environment, and have continued to be integral to my growth even after I graduated.

If I only talked about science all the time, I would lose a lot of perspective. So thank you to all of my friends who let me ramble on about science and then reminded me that there is more to life, and kept me balanced. I have been blessed with amazing, supportive friends near and far throughout graduate school.

Thank you to all of the wonderful people I have met at Mosaic Boston Church for building a community and welcoming me into it, and always listening and sharing your insight. Thank you to all my West Coast

vi friends for staying in close touch despite the weird hours we schedule to have sufficiently lengthy conversations, and for forgiving me every time I replied to a text way too early in your morning. I have missed being in geographical proximity to all of you, but you always make me feel like I am still close.

Your wisdom, humor, and honesty have enriched my life. Everyone needs people they can be genuinely themselves with, and I am so glad you are my people.

And of course, many many thanks to my wonderful family. Not many people could spend a week on a boat with their extended family, and leave not only in one piece, but wishing they could be together even longer. You make me immediately feel at home, whether I am visiting you or just talking on the phone, whether I need to have a deep conversation or share about some new nerdy fascination. As I have grown,

I have begun to see how much you sacrificed and the great efforts you went to in order to raise me with patience, grace, and love. Your support, and teaching me that above all it is important to be genuine and kind and steadfast, has influenced my growth as much as any intellectual knowledge I could gain in a degree. I count it as the most important progress I could make. If the goal of education is to create lifelong learners, you instilled and fostered that desire in me, and that is what has driven me to complete this work and continue to learn and explore throughout my life.

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Chapter 1

Introduction

1.1 Preface: Tumor Metabolism

A growing tumor is faced with many challenges, both from the environment and within its cells. The availability of nutrients, and how tumor cells obtain and utilize them, is known as tumor metabolism.

Recently, reprogramming of metabolism has been recognized as a hallmark of cancer, and many studies have focused on specific metabolic alterations in tumors and how they can render tumors sensitive to metabolic perturbations1,2.

1.1.1 Why rewire metabolism?

When previously non-proliferative cells become tumor cells, they must balance catabolic and anabolic needs instead of primarily performing catabolic reactions. This leads to a metabolic program that is distinct from other cells in the tissue of origin3. Tumor cells have higher rates of metabolite uptake, transfer, and utilization to meet three major needs faced by a rapidly proliferating tumor cell: energy production, generation of biosynthetic precursors, and ability to tolerate stressors4-6.

One of the first discoveries in tumor metabolism was that cancer cells convert glucose to lactate even in the presence of oxygen, which has been termed the “Warburg Effect”7,8. For this reason, it is often said that cancers rely more on glycolysis than oxidative phosphorylation (OXPHOS) for energy production9.

However, this is a general feature of proliferative cells, not specific to tumors, and respiration is still required for tumor growth8. Tumor cells do not often have impaired OXPHOS machinery, and thus still use it to produce energy9. The primary benefit of glycolysis is that the tumor cells not only produce energy, but also the anabolic precursors necessary for synthesis of lipids, amino acids, and nucleotides5,8.

It is also important to note that tumors are heterogenous, and their metabolism is no exception. Often, some cells within a tumor are more glycolytic while others rely primarily on OXPHOS4. These subpopulations can exchange metabolites in the environment, highlighting the importance of both tumor heterogeneity and the tumor microenvironment when studying tumor metabolism.

In order to fuel their increased biosynthetic demands, many tumor cells overexpress various

transporters or scavenge extracellular nutrients using macropinocytosis2,10. They also increase flux through metabolic pathways that produce precursors, such as the pentose phosphate pathway (PPP) to produce nucleotide precursors and NADPH to combat oxidative stress, and glutaminolysis to replenish the tricarboxylic acid (TCA) cycle and produce NADPH9. This leads to increased consumption of many nutrients by tumors cells, including glucose, lactate, pyruvate, hydroxybutyrate, acetate, glutamine, and fatty acids4. Some tumor cells also become dependent on extracellular supplies of nutrients such as fatty acids, lactate, branched-chain amino acids, serine, and glycine8.

1.1.2 How tumor metabolism is rewired

There are many ways in which tumor metabolism is rewired. Some are more frequently used to promote specific metabolic phenotypes, but often many co-occur within a tumor. These mechanisms include mutations in metabolic genes, changes in metabolic gene expression, mutations in pathways that regulate metabolism, and changes in the tumor microenvironment.

An oncometabolite is a metabolite that accumulates in tumors due to a mutation, and that leads to the development or progression of the tumor. To date, there are three molecules considered to be oncometabolites: D-2-hydroxyglutarate (D2HG), succinate, and fumarate5. A mutant form of isocitrate dehydrogenase (IDH) catalyzes an additional reaction that produces D2HG instead of stopping after the production of α-ketoglutarate (αKG) from isocitrate. D2HG then inhibits αKG-dependent oxygenases, including DNA demethylases, leading to hypermethylation of DNA in IDH-mutant cancers9. Mutations in succinate dehydrogenase and fumarase cause accumulation of succinate and fumarate to the point that they interfere with dioxygenase function and inhibit regulators of hypoxia-inducible factors (HIFs), leading to expression of HIF targets under normoxic conditions3,8. These metabolic alterations are not transformative because of the metabolic changes, but because of the non-metabolic effects of inhibiting

αKG-dependent enzymes such as DNA demethylases8,11.

While these are the only three bona-fide oncometabolites that result from mutations, other genetic and regulatory alterations can lead to changes in expression of metabolic genes. Amplification or deletion of

genes, signaling alterations, and changes in epigenetic markers can all result in altered metabolic enzymes and metabolite levels3. In breast cancer, overexpression of ACSS2 and MTHFD2 meet demands for increased lipid synthesis and folate catabolism, respectively, and correlate with poor prognosis12-14.

Mutations in signaling pathways and regulatory factors can also re-wire metabolism in tumors.

Metabolic pathways are closely coordinated with signaling networks, and thus affected by oncogenic signaling (Figure 1.1)6. The transcription factor Myc is activated in many cancers and leads to transcription of transporters and genes in glycolysis, favoring the production of glycolytic intermediates for nucleotide and NADPH production, and also increases energy production by promoting mitochondrial biogenesis8,9. The tumor suppressor p53 normally regulates aspects of energy metabolism, so its loss in many cancers leads to changes in metabolism9. Additionally, changes in KRAS, PI3K, and mTORC1 signaling regulate nutrient acquisition and activate metabolic processes to support tumor cell growth8,15.

Because nutrient availability is often limited for tumors, metabolic flexibility is advantageous. Tumor cells often use multiple fuel sources from their environment, and may also employ autophagy or scavenging of nutrients from the tumor microenvironment. This flexibility also enables acquired resistance to therapies, such as how AMPK inhibitors in breast cancer lead to a more oxidative phenotype as resistance arises8.

Figure 1.1: Intersection of signaling and metabolism. Oncogenic signaling can control cellular metabolism. Especially relevant are mTORC1 and Myc for their roles in promoting anabolic metabolism. From DeBerardinis and Chandel5. Abbreviations: 3-PG, 3-phosphoglycerate; α-KG, α-ketoglutarate; ATP, adenosine 5´-triphosphate; G6P, glucose-6-phosphate; mTORC1, mTOR complex 1; PPP, pentose phosphate pathway; RTK, receptor tyrosine kinase; ROS, reactive oxygen species.

1.1.3 Taking advantage of metabolic vulnerabilities

Observations of metabolic changes in tumor cells has led to the hypothesis that these changes may create dependencies that can be used to more selectively target tumor cells. Many groups have proposed new therapies and combinations, or built upon and improved extant therapies targeting metabolism2.

One of the most widely used applications of altered tumor metabolism is fluorodeoxyglucose (FDG)- positron emission tomography (PET) imaging. The increased uptake of glucose by tumors allows clinicians to view them following administration of labeled glucose9. Some of the most commonly used chemotherapeutic drugs are antimetabolites that resemble nucleotides, and thus inhibit enzymes in nucleotide synthesis or are incorporated into DNA and inhibit polymerases3. 5-fluorouracil (5FU) is an analog of the nucleotide uracil that inhibits thymidylate synthase, and is one of the most commonly used anti-tumor drugs3,4. That chemotherapy targeting nucleotide synthesis is effective indicates that this pathway can be a bottleneck in tumors, and thus presents a metabolic vulnerability that has been successfully targeted8.

Successfully targeting a metallic vulnerability requires a therapeutic window: normal cells must be able to survive the blockade5. Many non-tumor cells, including bone marrow, intestinal crypts, and hair follicles, are also rapidly proliferating, often at a faster rate than tumor cells3. Pathways that are upregulated in tumors can also be activated under certain conditions in normal tissues, so this also contributes to the on-target toxicity of many metabolic therapies4. Thus, while glucose metabolism is one of the clearest alterations in tumors, targeting it has been largely unsuccessful due to systemic toxicity4,12.

When anti-glycolytic drugs are used at low enough doses to avoid toxicity, they no longer exhibit anti- tumor effects3. Therefore, an area of current research is identifying which metabolic pathways cancer cells are more reliant on as compared to normal cells11.

It is also important to consider the metabolism of specific tumor types, since the metabolism of a tumor more closely reflects its tissue of origin than tumors from another tissue8. For instance, differences in the basal activity of a metabolic enzyme between tumor and normal from the same tissue type could indicate a vulnerability in a tumor. However, one of the biggest challenges in targeting tumor metabolism

is identifying patients that are most likely to respond, since in many cases expression of an enzyme has not been found to correlate with its necessity for tumor growth, and expression varies greatly between patients3.

Most metabolic therapies do not exhibit significant anti-tumor activity alone, so an active area of research is identifying combinations of metabolic perturbations that synergize with other therapies.

Rational combinations can also combat resistance that may arise from metabolic rewiring4. There are many extant drugs that target metabolic processes, so repurposing generally well-tolerated drugs in combination with tumor therapies has garnered attention. As one case, statins can decrease proliferation, migration, and growth of triple-negative breast cancer (TNBC) cells, and their safety make them intriguing for further work towards clinical applications16.

This thesis will focus on the concept of tumor metabolism in the specific setting of TNBC. Since one of the main areas of metabolic rewiring in tumors is to promote biomass accumulation necessary for cell division, we focus on amino acids, which contribute to the majority of tumor biomass17. We show that specific alterations in arginine and polyamine metabolism in TNBC present a metabolic vulnerability that can be targeted to improve our ability to kill TNBC cells.

1.2 Breast cancer classification and treatment

Breast cancer is the second most frequently occurring cancer in women after skin cancer, and will affect

12% of women in the United States during their lifetime18,19. Despite the development of new therapies, there has been little decline in the breast cancer mortality rate over the past decade20. This is partly due to the genetic diversity of breast cancers, such that there is no single therapy that is effective against all breast cancer subtypes. Treatment options for breast cancer include surgery, radiation, hormone therapy, chemotherapy, and targeted therapies18.

1.2.1 Breast cancer classification

Breast cancer is a very heterogeneous disease, but is clinically classified by the expression of three markers: the hormone receptors for estrogen (ER) and progesterone (PR), and human epidermal growth factor receptor 2 (HER2). Breast tumors that lack expression of all three markers are classified as triple- negative21. Tumors can also be classified by whether they are derived from luminal or basal mammary cells, which differ by keratin expression and that luminal cells are positive for ER and PR. Using these classification systems and immunohistochemistry, tumors and cell lines can be assigned to five general subtypes (Figure 1.2)22-27.

Tumors can also be classified by molecular signatures of gene expression. When considering molecular classification across cancer types, most cancers cluster by tissue of origin. However, breast cancer is an exception, since basal-like breast cancer forms its own cluster despite a common tissue of origin; thus it has been proposed that it should be treated as a separate disease21. Basal-like breast cancer generally correlates with TNBC, since 70-80% of TNBCs are basal-like, and approximately 70% of basal- like breast tumors are TNBC16,28.

Figure 1.2: Histopathological classification of breast cancers and cell lines. Classification according to Kumar et al.22. Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor.

1.2.2 Triple-negative breast cancer

TNBC is admittedly a negative classification rather than a biological subtype, but has clinical relevance for how the disease is treated since it does not respond to endocrine or anti-HER2 therapy29. TNBC comprises 15-20% of breast cancer cases, but accounts for a disproportionately high percentage of breast- cancer related deaths30. It is the most aggressive subtype of breast cancer, with a younger age of onset and high risk factors in women of African and Hispanic ancestry30,31. It is more proliferative and has a higher likelihood of brain metastases than ER-positive breast cancer. These, combined with a high rate of early relapse, contribute to the increased mortality rate of TNBC, which is twice that of ER-positive breast cancer22,31.

While TNBC is clinically treated as a single disease, it is very heterogenous and thus further classified into subtypes (Table 1.1)30,32,33. It is important to note that one tumor may be composed of multiple molecular subtypes, which could be relevant for determining its response to therapy30.

Table 1.1: Classification of triple-negative breast cancers.

Subtype % of TNBC Gene Expression Profile Representative Cell Lines HCC-1143 Basal-like 1 35 Cell cycle, DNA damage repair MDA-MB-468 GF signaling and receptors, HCC-1806 Basal-like 2 22 glycolysis and gluconeogenesis SUM-149PT Luminal androgen CAL-148 16 Luminal, androgen receptor receptor MDA-MB-453 EMT, cell motility, cancer stem BT-549 Mesenchymal 25 cells, GF signaling SUM-159PT

Classification according to Lehmann et al.32,33. Abbreviations: EMT, epithelial-mesenchymal transition; GF, growth factor.

1.2.3 Treatment of TNBC with chemotherapy

Since TNBCs do not respond to hormone therapy, chemotherapy is considered the standard of care29,31.

Surgery and radiotherapy are applied similarly in TNBC and non-TNBC, but chemotherapeutics are the

only FDA-approved drugs for treating non-metastatic TNBC16,19. TNBC tumors are initially more sensitive to chemotherapeutics than non-TNBC28,30. This leads to what is termed the “triple-negative paradox”: the highly proliferative nature of TNBC leads to overall poor prognosis, but also leads to greater sensitivity to chemotherapeutics and more pathologic complete responses (pCR). Most of the adverse outcomes of TNBC are the result of its rapid onset, or from the subset of patients that do not respond to chemotherapy29.

There is no evidence that any particular chemotherapeutic agent is optimal for neoadjuvant or adjuvant treatment of TNBC, but the standard of care in TNBC is a regimen of anthracycline and a taxane, usually the AC-T regimen of anthracycline adriamycin (doxorubicin), alkylating agent cyclophosphamide, and taxane paclitaxel16,19,22,28,29. 30-40% of early stage TNBC patients that receive anthracycline-taxane neoadjuvant therapy have pCR, and nearly 80% of breast cancer patients receive multiagent chemotherapy18,21.

Anthracyclines such as doxorubicin and epirubicin are the most active drugs for breast cancer treatment, and also the most common agents used for treatment of metastatic breast cancer16,34.

Anthracyclines are antibiotics that are used for treating many different types of tumors. They have multiple mechanisms of action, mostly related to DNA integrity and replication: intercalating, alkylating, and cross-linking DNA, inhibiting topoisomerase II, and producing free radicals that damage DNA35.

Taxanes such as docetaxel and paclitaxel are microtubule inhibitors. They are used in patients that are resistant to anthracyclines as a first-line therapy, but it has also been shown that combining anthracyclines and taxanes leads to a better quality of life than either alone34.

There have also been may studies on the efficacy of platins for TNBC treatment, since they are the most widely used chemotherapeutic across all cancers. Platins induce DNA damage and oxidative stress, interact with nucleophiles in the cytosol, and initiate apoptosis through induction of BAK1 and BAX36.

They have shown efficacy against breast tumors in small studies, particularly in patients with low BRCA1 expression or when combined with taxanes22,30,37.

1.2.4 Emerging therapeutic strategies for TNBC

TNBC tumors have an average of 60 mutations per tumor, but these are varied and the only frequently mutated gene is TP53. This lack of common mutations makes traditional drug target prediction problematic for TNBC as a whole21. Many attempts have been made to target a variety of pathways in

TNBC, but most lack effective biomarkers16,22,30. Two areas that show more promise are poly ADP-ribose polymerase (PARP) inhibitors and immunotherapy.

10-20% of TNBCs have mutations in the DNA damage repair gene BRCA1, and others have mutations in other repair factors that lead to a “BRCA-ness” phenotype30. Targeting another DNA repair factor, PARP, can lead to synthetic lethality38. PARP inhibition led to partial responses in a cohort with

BRCA-mutant advanced breast cancer39. Attempts to improve response to PARP inhibitors include combinations with chemotherapy, but this led to toxicity, so work is ongoing22,30. It may also be possible to sensitize TNBCs that do not have BRCA mutations by using targeted inhibitors of compensatory DNA repair pathways, such as CDK1 inhibitors30.

TNBC is also the optimal subtype of breast cancer for immunotherapy because of the higher mutational burden and increased tumor infiltrating lymphocytes21,29. 80% of TNBCs express PD-L1, making it an attractive target. Current strategies include combining immune checkpoint inhibition with chemotherapy, so that cells that are killed by the chemotherapeutic drugs release neoantigens to stimulate an anti-tumor immune response30.

While many different therapeutic strategies are under investigation for TNBC treatment, chemotherapy remains the only FDA-approved therapy19. Additionally, many combinations that have been studied are too toxic to be applied in the clinic21. Therefore, more research is required to identify targetable vulnerabilities in TNBC.

1.3 Non-essential amino acid metabolism in breast cancer

This section is adapted from the paper Geck, R.C. & Toker, A. Nonessential amino acid metabolism in breast cancer. Advances in Biological Regulation 62, 11-17, doi:10.1016/j.jbior.2016.01.001 (2016). The paper was modified and updated for this section.

1.3.1 Breast cancer metabolism

Many breast cancers do not contain a single, targetable driver mutation, and targeted therapies are unlikely to eliminate all breast cancer cells due to tumor heterogeneity. Recently, new interest in studying the relationship between cancer and metabolism has arisen as a potential avenue to identify novel biomarkers and therapeutic targets effective across multiple breast cancer subtypes.

Acquiring amino acids is essential to support the proliferation of cancer cells17. They are necessary for protein, nucleotide, and lipid synthesis, maintenance of homeostasis and redox balance, and as allosteric and epigenetic regulators40. Non-essential amino acids (NEAAs) are defined by dietary necessity, such that the organism cannot synthesize the amino acid from readily available nutrients. There are eleven NEAAs in humans, though some can become essential under specific conditions, and are dependent on tissue-specific gene expression and local availability40. While NEAAs can be synthesized by cells (Figure 1.3), they can also be taken up from the environment41. Due to the high metabolic and energetic demand of proliferating tumor cells, some of these NEAAs can become essential42.

Figure 1.3: Non-essential amino acid metabolism. Nonessential amino acid pathways are interconnected and often feed the TCA cycle or enter one-carbon metabolism via the folate cycle, contributing to cancer cell growth. Enzyme abbreviations: ABAT, 4-Aminobutyrate Aminotransferase; ALAT, Alanine Aminotransferase; ARG, Arginase; AS, Argininosuccinate Synthase; ASL, Argininosuccinate Lyase; ASNS, Asparagine Synthase; AST, Aspartate Transaminase; BHMT, Betaine- Homocysteine S-Methyltransferase; CBS, Cystathionine Beta-Synthase; CDO, Cysteine Dioxygenase; CHAC, Glutathione-Specific Gamma-Glutamylcyclotransferase; CTH, Cystathionine Lyase; GCL, Glutamate Cysteine Ligase; GDH, Glutamate Dehydrogenase; GLDC, Glycine Decarboxylase; GLS, Glutaminase; GS, Glutamine Synthetase; GSS, Glutathione Synthase; OAT, Ornithine Aminotransferase; ODC, Ornithine Decarboxylase; OTC, Ornithine Carbamoyltransferase; P5CS, Pyrroline-5-Carboxylate Synthase; PHGDH, Phosphoglycerate Dehydrogenase; PRODH, Proline Dehydrogenase; PSAT, Phosphoserine Aminotransferase; PSPH, Phosphoserine Phosphatase; PYCR, Pyrroline-5-Carboxylate Reductase; SHMT, Serine Hydroxymethyltransferase.

1.3.2 Glutamine, glutamate, and alanine

Of all the amino acids, glutamine is the most consumed by cancer cells43, and thus has been extensively studied and reviewed42,44-47. Glutamine is used for biosynthesis of nucleotides, amino sugars, and lipids, and stimulates increased glucose uptake and mTOR activation41. The majority of intracellular glutamine is hydrolyzed by glutaminase (GLS) to synthesize glutamate, which can then be converted to αKG and feed the TCA cycle, contribute to synthesis of glutathione, or be used to make other NEAAs (Figure

1.3)4,41,48,49. Glutaminolysis, the conversion of glutamine to glutamate for the production of energy via lactate, is increased in cancer cells50. Glutamine can also drive uptake of essential amino acids, lead to mTORC1 activation, recycle ammonia, and balance intracellular pH46.

Glutamine is the most abundant circulating amino acid, but tumor glutamine is lower than that of plasma or surrounding tissue, perhaps because of increased consumption by the tumor cells11,47. Plasma glutamine is reduced in breast cancer patients, and in fluid and tissue surrounding tumors, likely because tumors consume it47,51. Low tumor glutamine levels correlate with poor prognosis in breast cancer, while increased glutamate levels in breast tumor versus normal tissue samples correlate with ER status and endocrine resistance, indicating a change in regulation at the first step of glutaminolysis48,52-54. The high consumption rate of glutamine by many cancers led to development of a labeled glutamine for use in PET imaging, which was able to highlight breast tumors. These tumors were found to have genetic mutations that are implicated in promoting increased glutamine metabolism55.

Some breast cancers, especially TNBC that overexpress Myc, require a supply of glutamine because

Myc increases GLS, rendering them sensitive to glutamine deprivation41,56,57. This is primarily due to the use of glutamine to synthesize glutamate and other amino acids through aminotransferases. Inhibiting aminotransferases with aminooxyacetate (AOA) decreases growth of TNBC xenografts, and sensitivity to

AOA correlates with Myc expression58,59. AOA may also synergize well with chemotherapy, since inhibition of aminotransferases interferes with redox homeostasis11.

The synthesis of alanine from glutamate and pyruvate is another important role of aminotransferases such as alanine aminotransferase (ALAT), and proposed as a major reason for the efficacy of

aminotransferase inhibitors such as AOA. In a study that profiled metabolites in breast tumors, alanine was the most significantly altered metabolite, with especially high levels in ER-negative breast cancers and a two-fold increase in tumor over normal. This most closely correlated with downregulation of 4- aminobutyrate aminotransferase (ABAT), which converts alanine to malonate semialdehyde to feed the

TCA cycle. Low ABAT expression is associated with poor prognosis, so high levels of alanine may contribute to aggressive breast cancer48. One reason for this may be the importance of alanine-derived

αKG for modification of collagen during preparation of the metastatic niche40. This illustrates the need for significant future work to determine the merit of targeting alanine metabolism for breast cancer therapy, and also indicates that potential new clinical targets can be identified through investigating metabolic pathways in cancer.

Another major use of glutamine and glutamate is to contribute carbon to the TCA cycle in a process called TCA anapleurosis8. This is primarily from glutamine or glucose, but in some tumors also from branched chain amino acids (BCAAs)5. To feed into the TCA cycle, glutamine is hydrolyzed to glutamate, which is then deaminated by glutamate dehydrogenase (GDH) to form αKG and ammonia. GDH expression is increased in advanced stage breast cancer, where it is necessary for effective redox regulation, but dispensable for ATP production60. GDH knockdown decreases growth of TNBC cells, though some highly proliferative breast cancer cells have low GDH expression and instead rely on aminotransferases to catabolize glutamate and produce NEAAs60,61.

Glutamine can be transported into cells or synthesized from glutamate. Expression of the glutamine transporters SLC1A5 (ASCT2) and SLC6A14 are increased in breast cancer57. ASCT2 expression is highest in TNBC and correlates with poor survival in mouse models62. SLC6A14 is highly expressed in

ER-positive breast tumor tissue and cell lines, allowing for increased uptake of glutamine as well as arginine and leucine. Thus, inhibition of SLC6A14 lead to decreased tumor growth in a mouse xenograft of ER-positive cells63. Together, these illustrate differences in glutamine uptake between ER-positive and

TNBC tumors.

Glutamine is synthesized from glutamate and free ammonium by glutamine synthase (GS) in an ATP-

dependent reaction46,47. GS is elevated in breast cancer cells compared to normal mammary cells.

Overexpression of GS in breast cancer cells with low GS increases their survival under glutamine starvation. Silencing of GS decreases proliferation of breast cancer cells and TNBC xenografts, and in some lines induces cell death, but does not decrease proliferation of non-tumor cells64. In general, luminal breast cancer cells are less sensitive to glutamine deprivation than basal-like cells because of increased

GS65. These indicate that GS levels are one important biomarker when considering patients who may benefit from glutamine deprivation strategies.

Another key target within glutamine metabolism is GLS, which hydrolyzes glutamine into glutamate and ammonia. This reaction is the main source of cellular glutamate, though it can also be made from

BCAAs40. Overexpression of GLS correlated with poor survival in breast cancer, and elevated GLS in some TNBC cases correlated with accumulation of oncometabolite 2HG and poor prognosis42,52. TNBC and HER2+ breast cancer are hypothesized to have elevated glutamine usage due to increased GLS and

GDH in HER2+ tumors, and a higher glutamate/glutamine ratio in TNBC57. Another study found that basal-like breast tumors have higher levels of the kidney isoform GLS but lower levels of the liver isoform GLS2 when compared to luminal tumors65; perhaps as observed in hepatocellular carcinoma

(HCC), GLS2 may be a p53 target and tumor suppressor in TNBC66.

To take advantage of the elevated GLS activity in breast and other tumors, multiple inhibitors have been developed67. BPTES is a specific inhibitor of kidney isoform GLS, and CB-839 targets both isoforms46. These inhibitors decrease proliferation and transformation of breast cancer cells, and have more pronounced effects on glutamate consumption and glutathione production in TNBC than in non-

TNBC cells12,67,68. These observed patterns suggest that ER-negative breast cancer patients may benefit most from GLS inhibition48,62. GLS inhibitors have also been successfully combined with other therapies, with BPTES improving response of TNBC cells to cisplatin or etoposide, and CB-839 displaying anti- tumor activity in a patient-derived TNBC xenograft68,69. In aromatase inhibitor (AI)-resistant breast cancer, crosstalk between HER2 and ER upregulates glutamine metabolism via c-Myc, increasing the sensitivity of AI-resistant cells to glutaminase inhibitors and indicating that this may be a target

population for application of GLS inhibitors54,70. Breast cancer cell lines that are sensitive to CB-839 have higher expression of the glutamate antiporter xCT, so xCT expression has also been suggested as a biomarker for predicting efficacy of glutaminase inhibitors40,71.

While it has long been acknowledged that glutamine metabolism could be a therapeutic target, many early glutamine therapies presented significant toxicity or were ineffective in vivo. Currently, CB-839 is the only glutaminse inhibitor in clinical trials47. A greater understanding of the mechanisms by which glutamine and glutamate sustain cancer cells may lead to the development or repurposing of other successful targeted metabolic therapies and use of the pathway enzymes and metabolites as biomarkers.

1.3.3 Serine and glycine

Serine and glycine are synthesized from the glycolysis intermediate 3-phosphoglycerate by a series of enzymatic reactions (Figure 1.3). The methyl groups of glycine can feed one-carbon metabolism through the folate cycle, which is utilized by cancer cells for the synthesis of proteins, nucleic acids, lipids, and cofactors40,72,73. Serine and glycine are also involved in the synthesis of antioxidants, increase the survival of cancer cells in hypoxic environments, and promote cell proliferation through mTOR41,72. Glutamate is also a product of serine synthesis, and its conversion to αKG can feed energy production by the TCA cycle72.

Serine can also be taken up from the extracellular environment, though serine and glycine plasma levels are elevated in breast cancer patients51,74. This may be because breast cancers primarily synthesize their serine and glycine from TCA cycle-derived carbons12. Serine synthesis is further increased in ER- negative breast cancer74. However, removal of exogenous serine and glycine still leads to reduced proliferation of breast cancer cells, an effect that can be completely rescued by re-addition of serine but only partially by glycine75.

The serine synthesis enzymes phosphoglycerate dehydrogenase (PHGDH) and phosphoserine phosphatase (PSPH) are both elevated in TNBC cells and patient tumors, and the glycine synthesis enzyme serine hydroxymethyltransferase 1 (SHMT1) is increased in TNBC tumors. High PSPH and

PHGDH correlate with poor overall survival in breast cancer76. Many studies have shown that PHGDH promotes the oncogenic potential of breast cancer through its role as the rate-limiting step in serine synthesis4,74. Breast cancer cells, but not non-tumorigenic breast cells, are dependent on PHGDH for proliferation12. Overexpression of PHGDH in non-tumorigenic breast cells also leads to increased proliferation and anchorage-independent growth, indicating a critical role in transformation77. PHGDH is amplified in 6-18% of breast cancers, and its expression is increased in approximately 70% of ER- negative breast cancers. Suppression of PHGDH expression decreases cell proliferation by lowering production of αKG, as serine contributes up to 50% of glutamate flux into the TCA cycle in PHGDH- overexpressing cells78. However, other studies found that PHGDH expression is not required for TNBC xenograft growth because serine is abundant in plasma40,79. However, it is important to note that PHGDH inhibition does not lower intracellular serine in cell culture since serine transport can compensate, but this may not be the case in a nutrient poor environment74. PHGDH contains many binding sites for which specific inhibitors could be designed, and nonspecific inhibitors are already available80. It is important to consider that PHGDH deficiencies in children lead to neurological defects, so agents would need to be developed with limited access to the central nervous system, and with therapeutic windows carefully calculated3,81.

Limiting serine and glycine in the diet has also been proposed. This diet decreases serum serine and glycine by 50%, and decreases growth of intestinal tumors and lymphomas in murine models40,82. This may also be effective in breast cancers, as one report showed that breast cells that have decreased expression of PHGDH due to spliceosome mutations exhibit decreased growth when starved of serine and glycine, which can be partially rescued by overexpression of PHGDH83.

Glycine uptake and catabolism is also important in cancers, and correlates with growth across cell lines from many different tumor types. In breast cancer, consumption of glycine and expression of biosynthetic enzyme transcripts SHMT2, MTHFD2, and MTHFD1L are increased and correlate with poorer prognosis. Inhibiting glycine uptake and synthesis can lead to death of breast cancer cells4,43. The rate of consumption as compared to production of glycine can be predictive of tumor proliferation rate,

though it is not known if it is a cause or result of increased proliferation43,74.

1.3.4 Cysteine

It has long been known that many tumor cells, including some breast cancer cell lines, cannot survive when the essential amino acid methionine is replaced by homocysteine, while non-malignant cells survive84,85. However, prolonged methionine restriction can result in toxic side effects, so treatment options other than dietary restriction and methioninase treatment have gained attention84. One such option is targeting cysteine metabolism, as it is related to methionine metabolism (Figure 1.3).

The reactive thiol group on cysteine enables it to have unique functions in catalytic sites of enzymes, to form disulfide bonds that stabilize proteins, synthesize taurine needed for mitochondrial function, contribute sulfur to production of hydrogen sulfide and iron-sulfur cluster cofactors, and form functional parts of antioxidants such as glutathione10,40. Glutathione is needed to buffer oxidative stress that would otherwise lead to cell death, particularly in cancer cells10. It is one of most increased metabolites in tumors, and oncogenic signaling can promote glutathione synthesis in breast cancer86,87. Since cysteine is limiting for glutathione synthesis, investigating its acquisition and usage is relevant to targeting the capacity of tumors to cope with oxidative stress10.

Cysteine can be synthesized through transsulfuration pathway or taken up from environment40. Most extracellular cysteine in the oxidized form cystine, which is taken up by the cell and then reduced10. Most intracellular cysteine is from cystine taken up through the cystine-glutamate antiporter SLC7A11 (xCT)40.

Under some conditions, cysteine is synthesized through the transsulfuration pathway from methionine and serine. Through a series of reactions, methionine is converted into homocysteine (Hcy), which can either be recycled back to methionine or used to make cysteine. Only some Hcy is available for synthesis of cysteine because methionine and other transsulfuration products are used for protein synthesis and methylation. Hcy is condensed with serine by cystathionine beta-synthase (CBS) to form cystathionine, which is then made into cystine by cystathionine lyase (CTH)10. Cysteine can then be further metabolized into glutathione or taurine (Figure 1.3). The gene encoding cysteine dioxygenase (CDO), which catalyzes

the first step in taurine synthesis, is hypermethylated in 60% of tested breast cancer samples, leading to increased reactive oxygen species (ROS) detoxification and tumor cell survival. Restoring functional levels of CDO decreases tumor cell growth and sensitizes cells to doxorubicin88.

Cysteine uptake by xCT is limiting for glutathione production, and thus has been studied in the context of oxidative damage and as a way to sensitize tumor cells to oxidizing drugs57. xCT expression is increased in breast cancer, and is especially important in ER-positive cells for synthesis of glutathione89-91.

In breast cancer cells with oncogenic PI3K pathway activation, AKT phosphorylation of xCT suppresses xCT activity and leads to dependency on methionine for cysteine synthesis85. This may be unique to breast cancer or cells with PI3K pathway mutations, since many other cells are strictly dependent on exogenous cystine and xCT activity10. Cysteine levels are increased in PIK3CA-mutant breast cancer cells, and coupled with the concomitant increase in glutamine, this could account for the observed increase in glutathione levels and allow cells to survive oxidative stress92.

Some TNBC cells also require cystine import, though high extracellular glutamate in some TNBCs inhibits xCT and decreases intracellular cysteine93,94. The glutamate antiporter function of xCT has also made its expression an important consideration for the efficacy of therapies targeting glutamine and glutamate metabolism. Breast cancer cell lines that are sensitive to the glutaminase inhibitor CB-839 have higher xCT expression, and expression of xCT in breast cancer cells with low xCT levels led to increased glutamine uptake and increased sensitivity to CB-83971. High xCT can also lead to and dependency on glucose for TCA anapleurosis, and to NADPH deficiency because it is consumed reducing cystine to cysteine10.

There have been many attempts to inhibit xCT, including in breast cancer. The xCT inhibitors sulfasalazine (SAS) and elastin lead to cell death through ferroptosis. However, SAS exhibited toxicity in glioma patients, even though it is safely used to treat rheumatoid arthritis, so xCT inhibition must be carefully studied in different tumor contexts10. In breast cancer, cells that have an active transsulfuration pathway are less susceptible to death from xCT inhibition, so transsulfuration pathway enzymes may be important biomarkers for sensitivity85.

Another source of cysteine is import through SLC3A1. SLC3A1 was proposed to be a cysteine transporter, but may instead transport cystine10. Increased expression of SLC3A1 is associated with breast cancer tumorigenesis95. Cysteine can also be made from glutathione by glutathione-specific gamma- glutamylcyclotransferase (CHAC1). High CHAC1 correlates with poor prognosis and increased proliferation and migration in breast cancer96.

From its myriad roles in tumor-related metabolism, removal of exogenous cysteine has also been considered for therapy. It is very challenging to restrict dietary cysteine, but a form of cystathionine lyase called cyst(e)inase has been developed to deplete plasma cysteine and cystine. Cyste(e)inase administration decreased tumor cysteine and was not toxic in mice, and reduced the growth of breast cancer xenografts10,97. Altogether, the ability to affect the antioxidant capabilities of cancer cells by targeting cysteine metabolism is an attractive proposition, since modulation of cysteine is likely to be better tolerated by normal cells than methionine restriction.

1.3.5 Arginine and proline

Arginine and proline are NEAAs in humans, although arginine can become conditionally essential in pathophysiology. Arginine metabolism intersects with many other metabolic pathways, including nitric oxide, creatine, urea, and polyamine metabolism (Figure 1.3), which can promote tumor growth98.

Arginine also promotes growth through activation of mTOR signaling and increased secretion of growth hormone, insulin, and insulin-like growth factor 1 (IGF1)41. Plasma arginine is decreased in cancer patients, including breast cancer patients, due to high consumption99,100. Arginine depletion has been considered for therapeutic application; however, since arginine promotes T-cell survival, depleting tumor arginine can promote immune suppression8. Thus, many new studies are moving to focus on altering the ability of tumors to use arginine.

Arginine can become conditionally essential in tumors where the urea cycle is suppressed in order to free nitrogen to make aspartate for pyrimidine synthesis3,40,101. Classic examples of arginine auxotrophic tumors are malignant melanoma, HCC, prostate cancer, and acute lymphoblastic leukemia, which cannot

synthesize arginine due to a lack of argininosuccinate synthase (AS)102,103. Because of this, arginine depletion via conversion to citrulline by arginine deiminase (ADI) is an effective treatment for these cancers, though citrulline itself is a highly effective precursor of arginine in cells expressing AS and arginosuccinate lyase (ASL) 104,105. Certain breast cancer cell lines are arginine auxotrophs due to low levels of AS, including MDA-MB-231 and ZR- 75-1, and in a study of 55 breast tumor specimens, 5 were reported to not express AS106,107. These arginine auxotrophs do respond to arginine deprivation via ADI, which induces mitochondrial oxidative stress and autophagy106. While breast cancers that express higher levels of AS do not respond to ADI alone, ADI has been reported to sensitize breast cancer to ionizing radiation via downregulation of c-Myc and upregulation of p21103,108. This may therefore be a more general combination therapy strategy for breast cancer patients. A Phase 1 clinical trial of ADI in combination with doxorubicin was carried out in HER2-negative breast cancer patients (NCT01948843), but the results have not yet been reported.

Due to metabolic variations, it is important to consider arginine metabolism and dependency in specific contexts to identify precise patterns. This is best illustrated for AS: while AS deficiency correlates with worse prognosis in sarcomas, AS positivity correlates with poor prognosis in gastric cancer and some breast cancers104,109,110. Effects on cell migration also vary, as AS overexpression and knockdown have both been shown to inhibit migration in different tumor settings109,110. The reasons for these opposing observations are not known, nor is it clear if there are underlying expression patterns that can predict response to AS and arginine deprivation by ADI, so this is an area that requires additional investigation.

It is also important to note that even within breast tissue, distinct cell lines do not display the same response to arginine metabolic therapy. One study observed that breast cells became quiescent upon arginine removal, but upon arginine re-addition tumorigenic MCF-7 cells recovered while normal breast

MCF-10A cells senesced and did not recover111. A separate study noted the same effects in MCF-7 cells, whereas ZR-75-1 cells underwent cell death in the absence of arginine112. While these are intriguing results, the mechanistic basis for these observations were not fully elucidated. One study concluded that

expression levels of AS or rates of polyamine synthesis determine response to arginine deprivation, while another study implicated arginase and nitric oxide synthase (NOS) expression113. A clear pattern across multiple breast cell lines has yet to emerge, indicating a need to identify biomarkers that can predict tumor response to arginine metabolic therapy before such therapies can become clinically relevant.

Of the therapeutic options available to alter arginine metabolism, ADI and recombinant human arginase both metabolize arginine, but require a deficiency in at least one urea cycle enzyme to prevent the re-synthesis of arginine103,104. One study found that the arginase inhibitor N-omega-hydroxy-L- arginine (NOHA) increases apoptosis of breast cancer cells that overexpress arginase, so inhibitors of arginine metabolism as well as arginine depletion strategies have promise for breast cancer therapy113,114.

However, as exhibited by the highly variable responses to ADI based on AS status, it is important to identify biomarkers to accompany each of these proposed therapeutic strategies in order to apply treatment that will elicit the most robust response106.

Proline and arginine metabolism intersect through ornithine in the urea cycle, though most proline is synthesized from glutamate by pyrroline-5-carboxylate synthase (P5CS) and P5C reductase (PYCR).

Proline can also be degraded by PRODH and feed into other metabolic pathways. Due to its unique shape, proline is important for the synthesis of particular proteins such as collagen, and also for regulation of cellular energy, osmotic pressure, stress, and apoptosis40. Proline can be limiting for TNBC tumorigenesis due to issues during translation, and PYCR is elevated in invasive ductal breast carcinoma56. PYCR depletion reduces tumor forming ability, indicating that some breast cancers may be highly dependent on proline synthesis78.

There are reports supproting both tumor-suppressive and tumor-promoting roles for PRODH, but current evidence supports that in breast cancer PRODH is tumor-promoting40. Ras-transformed breast cells require PRODH-driven proline catabolism and PYCR-driven recycling, relying on ATP production from electrons that PRODH feeds into the electron transport chain via FADH2. PRODH is specifically elevated in breast cancer metastases, and treatment of a murine model of breast cancer with PRODH inhibitor L-THFA decreased lung metastases, though primary tumors were unaffected115. PRODH is also

increased by hypoxia in the microenvironment, spurring breast xenograft tumors in mice116. Together, these studies highlight an important role for proline catabolism in breast cancer.

1.3.6 Aspartate and asparagine

Since the circulating plasma levels and plasma membrane permeability of aspartate are very low, it is primarily synthesized in cells from oxaloacetate and glutamate via aspartate aminotransferases (Figure

1.3)40,74. It is used to make nucleotides and NADPH, and to shuttle electrons between the mitochondria and cytosol as part of the malate-aspartate shuttle40. There are many reports that aspartate can be limiting for proliferation8,117,118. Uptake of aspartate by SLC1A3 or release from the mitochondria is particularly important for proliferation in low glutamine or survival under glutaminase inhibition47,119.

In cells that express asparagine synthase (ASNS), aspartate can be used to synthesize asparagine.

ASNS is elevated in breast cancer metastases and aggressive subtypes, and generally correlates with poor survival120. Asparagine is used only for protein synthesis, not as an intermediate in any metabolic reactions74. The bacterial enzyme asparaginase converts asparagine back to aspartate and ammonia, and is applied therapeutically in acute lymphocytic leukemia as one of the most prominent examples of successfully targeting a metabolic vulnerability in amino acid metabolism4,40. Intracellular asparagine is required for breast cancer metastasis, so asparaginase may also have efficacy in breast cancer. Decreasing asparagine by asparaginase or dietary asparagine restriction decreases breast cancer metastases to the lung in a murine model, although it does not affect primary tumor growth120. Asparagine is also required in breast cancer cells under low glutamine conditions: it is not glutamine itself that is limiting, but the production of asparagine, because its synthesis is the most energetically expensive of the nine glycolysis/TCA derived NEAAs47. Therefore, asparaginase may have therapeutic benefit in breast cancer, particularly in combination with GLS inhibition.

1.3.7 Applications of NEAA metabolism

Targeting nonessential amino acid metabolism is an emerging field for cancer therapy, especially in

breast cancer, as it shows promise to selectively target aspects of tumor metabolism with minimal toxic side effects. It is also worth noting that while glutamine and serine metabolism have been well studied in the context of breast cancer, the metabolism of other NEAAs is a more nascent field. The only NEAA with few known effects in breast cancer is tyrosine, though it has been applied to development of a PET sensor (FMT-PET) to identify tumors with increased expression of the amino acid transporter LAT1121.

LAT1 is over expressed in breast cancer and increases as the tumor progresses, thus FMT-PET could be a valuable tool for detecting advanced breast tumors122. A tyrosine mimetic, SM-88, is also taken up like tyrosine through LAT1 but interrupts protein synthesis, particularly of mucin; it removes protective effects of mucin against ROS and renders cells more susceptible to apoptotic death. SM-88 is in trials in numerous tumor types, and has shown promising effects in breast cancer123. Therefore, even though it seems to be solely used for protein synthesis in breast cancer, tyrosine can also be used to develop therapeutic agents.

In conclusion, we posit that NEAA metabolism provides opportunities for therapeutic applications, as well as the ability to enhance the effects of existing regimens. As interest in studying cancer metabolism grows, new therapeutic targets can be identified and further developed to provide new treatment modalities for breast cancer patients.

1.4 Polyamine metabolism

1.4.1 Functions of polyamines

An important product of the catabolism of NEAAs arginine, glutamine, and glutamate are the polyamines.

Polyamines are essential for eukaryotic cell growth and differentiation124. They interact with nucleic acids and chromatin, stabilize cellular membranes, regulate ion channels, and scavenge free radicals125,126. Most polyamines exist in an RNA-bound state, which functions to increase protein synthesis, particularly of eukaryotic translation factors such as eIF5A (Figure 1.4)125. Therefore, polyamine depletion generally results in cytostasis127, and metabolites necessary for their synthesis are important for progressing through

the cell cycle128.

The polyamine spermine was first described by Leuwenhoek in 1678126,129. In 1978, Hershey showed that polyamines can bind to DNA, and the secondary structures of polyamines bound to DNA were shown by X-ray diffraction129.

Polyamines are found primarily in the cytoplasm130. The total intracellular concentration of polyamines is usually in the millimolar range, but the amounts of free polyamines is lower since most are bound to cellular anions126,127. They form hydrogen bonds with various cellular anions, including DNA, proteins, phospholipids, mRNA, tRNA, and rRNA125,129,130. This leads to many different conformations due to the formation of intra- and intermolecular hydrogen bonds124.

Nuclear polyamines can lead to the condensation of DNA and chromatin, either by binding to the major or minor groves of dsDNA or through electrostatic interactions with the phosphate backbone124.

This occurs at physiological pH, with only 50-100µM spermine, which is within the physiological concentration131.

Aside from their effects on DNA, polyamines have effects on the cell via interactions with other anions. Spermidine is required for the activation of eIF5A by hypusination to regulate eIF5A-controlled translation129. Polyamines also stimulate synthesis of nucleic acids and proteins, and may play a role in regulating circadian rhythms129,130.

The products of polyamine metabolism can also have effects on cellular phenotypes. Deregulation of polyamine metabolism can induce apoptosis, at least partially due to production of cytotoxic metabolites from spermine and spermidine124,125. However, polyamines also have protective effects, such as the ability of spermine to scavenge ROS and reduce expression of proinflammatory cytokines125.

Figure 1.4: Functions of polyamines. Polyamines are required for eukaryotic cell growth and differentiation due to their roles in many cellular processes.

1.4.2 Polyamine transport

There are multiple sources of polyamines, from the diet as well as synthesis. The human diet is rich in polyamines, and they are also produced by gut microbiota126. However, it is not clear to what extent dietary polyamines contribute to intracellular polyamines in the absence of synthesis inhibitors127.

Polyamines cannot diffuse through the plasma membrane because they are fully protonated at cellular pH127. Though it is known that they can be transported into cells via a system or systems, the specific components and mechanism remain to be elucidated.

The polyamine transport system is energy-dependent and saturable, with three main models proposed to account for these and other observed features127. It is possible that there are multiple mechanisms of polyamine transport, and that all of these models contribute to the explanation of polyamine transport mechanisms. The first model is that an as-yet-unidentified transporter uses membrane potential to transport polyamines through the plasma membrane, and they are then sequestered into endosomes. This model accounts for the cellular ability to rapidly change polyamine levels, and the low level of free cytoplasmic polyamines despite high total intracellular polyamine levels. A second model from gastrointestinal cells proposes that polyamines bind to an unknown cell-surface receptor, which is internalized by a caveolin-dependent process. Putrescine is released from the vesicles by SLC3A2, and spermine freed from the receptor by a NO-mediated oxidation127,131. A third model suggests that spermine can be specifically transported into cells by binding to heparin sulfate moieties of glypican-1 on the cell surface, is internalized into endosomes, and then freed by NO-mediated oxidation126,131. More research must be done to determine if these systems can act in concert, or if different cell types use only one specific system.

Figure 1.5: Polyamine metabolism and regulation. (A) Structures of polyamines. Black sections derived from ornithine, green sections derived from dcSAM. (B) Polyamine catabolism and anabolism. Enzymes in blue, metabolites in black. Adapted from Casero et al.127. Metabolite abbreviations: dcSAM,

Decarboxylated S-Adenosylmethionine; H2O2, hydrogen peroxide; Met, Methionine; MTA, Methylthioadenosine; MTR-1P, Methylthioribose-1-Phosphate; NAcSpd, N-Acetyl-Spermidine; NAcSpm, N-Acetyl-Spermine; SAM, S-Adenosylmethionine. Enzyme abbreviations: AdoMetDC, Adenosylmethionine Decarboxylase; MAT2, Methionine Adenosyltransferase; MTAP, Methylthioadenosine Phosphorylase; ODC, Ornithine Decarboxylase; PAOX, Polyamine Oxidase; SMOX, Spermine Oxidase; SPMSY, Spermine Synthase; SPDSY, Spermidine Synthase; SSAT, Spermidine/Spermine N1-Acetyltransferase.

1.4.3 Polyamine anabolism

Polyamines can be synthesized de novo from ornithine and dcSAM (Figure 1.5A). The pyridoxal phosphate-dependent enzyme ornithine decarboxylase (ODC protein,

ODC1 gene) converts ornithine and dcSAM to putrescine, releasing carbon dioxide (Figure 1.5B)125,129.

This is one of the rate-limiting steps in de novo polyamine synthesis. ODC is active as a homodimer, creating two active sites at the dimer interface. Its activity is dependent on the dimerization, such that a catalytic-dead mutant has dominant-negative effects in a dimer. However, the interactions are weak, so there is rapid cycling between the monomeric and dimeric forms129.

ODC is regulated at each level, from transcription to post translational stability (Figure 1.6A)126.

ODC1 is transcriptionally regulated by nuclear factor κB (NFκB), peroxisome-proliferator- activated

receptor γ (PPARγ), and was the first known direct target of c-Myc131-133. ODC1 has canonical c-Myc E- box motif binding sites in its promoter, and c-Myc leads to growth-factor independent ODC1 expression129. ODC1 is translationally regulated by eIF4E and the levels of intracellular polyamines, the latter via an unknown mechanism132.

The ODC protein has a short half-life of only 10-30 minutes, one of the shortest of any known mammalian protein134. This is due to the activity of ornithine antizyme (AZ protein, OAZ1 gene), which prevents ODC homodimer formation to promote ODC degradation by the proteasome in a ubiquitin- independent manner (Figure 1.6B)127,129,132. AZ levels increase in response to elevated free intracellular polyamines, which induce the +1 ribosomal frameshift required for translation of OAZ1 ORF2132. The mechanism remains unclear but may involve pseudoknot structure formation131. There are three isoforms of AZ: AZ1 is the most ubiquitous form, AZ2 is present in much lower concentrations, and AZ3 is expressed only in the testes127; thus, here we focus on AZ1. AZ is also closely regulated at the protein level by antizyme inhibitor (AZI protein, AZIN1/2 genes), which binds to AZ and prevents its association with ODC (Figure 1.6B). AZI has a structure similar to ODC, and relieves ODC from AZ-mediated degradation by competing away AZ, since AZ has a higher affinity for AZI than ODC126.

Adenosylmethionine decarboxylase (AdoMetDC protein, AMD1 gene) is a pyruvoyl-containing decarboxylase that converts S-adenosyl-methionine (SAM) to dcSAM, which is the other rate-limiting step in polyamine synthesis (Figure 1.5B)131. Polyamine synthesis is the only known use of dcSAM, and formation of dcSAM depletes the SAM pool necessary for transmethylation reactions126. Therefore,

AdoMetDC activity can be limiting for polyamine synthesis when dcSAM levels are low125. AMD1 translation is negatively regulated by elevated polyamines, since polyamines stabilize a peptidyl-tRNA conjugate at the 5’-UTR of AMD1 transcripts to promote ribosomal stalling132.

Spermidine synthase (SPDSY protein, SRM gene) and spermine synthase (SPMSY protein, SMS gene) are aminopropytransferases that, respectively, produce spermidine from putrescine and dcSAM, and spermine from spermidine and dcSAM (Figure 1.5B). Both are rate-limited by substrate availability, and inhibited by the polyamine product 5’-deoxy-5’-(methylthio) adenosine (MTA)126.

Figure 1.6: Regulation of ornithine decarboxylase. (A) ODC activation and (B) degradation are mediated at multiple steps. Abbreviations: AZ, Ornithine Decarboxylase Antizyme (protein); AZI, Ornithine Decarboxylase Antizyme Inhibitor; eIF4E, Eukaryotic Translation Initiation Factor 4E; ODC, Ornithine Decarboxylase; OAZ1, Ornithine Decarboxylase Antizyme 1 (transcript).

1.4.4 Polyamine catabolism

Many different amine oxidases can catabolize polyamines, such as copper-containing serum amine oxidases and FAD-dependent monoamine oxidases, but most catalysis is carried out by through the two- step reactions of SSAT and PAOX or in a single step by SMOX (Figure 1.5B)125,126.

Spermidine/Spermine N1-Acetyltransferase 1 (SSAT protein, SAT1 gene) is a propylamine acetyltransferase homodimer that transfers the acetyl group from acetyl-CoA to spermidine or spermine.

The resultant N1-acetylspermidine or N1-acetylspermine can be excreted from the cell or further catabolized by PAOX125,132,135. SSAT is rate-limiting for the two-step catabolism by SSAT/PAOX132.

SSAT is high inducible, and responds to increased polyamine levels, hormones, cytokines, stressors, and various drugs. The SAT1 promoter has binding sites for specificity protein 1 (Sp1), activator protein 1

(AP-1), CCAAT/enhancer-binding protein β (C/EBPβ), cAMP-response-element-binding protein

(CREB), NF-κB, and PPARs. It is also known to be regulated by polyamine levels through a polyamine- responsive element (PRE), which is bound by nuclear factor erythroid 2-related factor 2 (NRF-2) and polyamine-modulated factor-1 (PMF-1) in response to increased polyamines135. However, transcription control of SAT1 by NRF2 has little effect on SSAT protein levels; it is suggested that SAT1 is mainly controlled at the level of translation though a poorly understood mechanism125,132. SAT1 can be alternatively spliced into SSAT-X, which is rapidly degraded by nonsense-mediated RNA decay.

Polyamines inhibit this alternative splicing, thus increasing the levels of SSAT. SSAT is located in the cytosol; a mitochondrial homolog has been identified but its function is unknown135. SSAT has a short half-life of 15 minutes before it is degraded by the 26S proteasome, and interactions with HIF-1⍺ promote its degradation through an unknown mechanism135.

Polyamine oxidase (PAOX) is an FAD-dependent peroxisomal enzyme that completes the catabolic reaction begun by SSAT to produce spermidine or putrescine, 3-aceto-aminopropanal (3-AAP), and hydrogen peroxide125. It is located in the peroxisome, such that the hydrogen peroxide produced by its reaction is degraded by peroxisomal catalase126. PAOX is constitutively expressed and its activity is controlled by substrate availability125.

Spermine oxidase (SMOX) was discovered in 2001 during an attempt to clone PAOX, but identified as a novel enzyme since it oxidized spermine rather than N1-acetylspermine126. It is an inducible FAD- dependent enzyme with high homology to PAOX, but it catabolizes the conversion of spermine to spermidine, 3-aminopropanal (3-AP), and hydrogen peroxide125,126. Since it is located in the cytoplasm and nucleus, not in the peroxisome like PAOX, there is a higher likelihood for DNA damage from hydrogen peroxide production. SMOX expression is induced under cellular stress, including bacterial infection and inflammatory cytokines, and it is negatively regulated by the tumor suppressive microRNA miR-124126.

1.5 Polyamines and cancer

Dysregulation of polyamine metabolism is a frequent event in cancers, with elevated levels of polyamines measured in breast, colon, prostate, and skin cancers125,131. Elevated polyamine levels are necessary for transformation and progression, and the loss of growth control renders transformed cells more sensitive to depletion of polyamines than normal cells127.

Polyamines genes can be controlled downstream of oncogenes such as c-Myc and Ras131. Regulation of polyamine synthesis by c-Myc plays a role in leukemias, lung, neural, and breast cancers, and polyamine synthesis can be regulated by AKT signaling in neovascularization and some HCCs and colon cancers127.

Polyamines as a therapeutic target have varied effects, since depending on the setting, they may protect from or enhance apoptosis125. There has been success in specific settings; AS-negative cells rely on increased polyamine synthesis and can be killed with polyamine inhibitors136. Polyamines can also serve as biomarkers for implementation of other therapies, such as high serum spermine and low tryptophan in HER2+ breast cancer indicating good responders to trastuzumab-paclitaxel therapy127.

1.5.1 Polyamine anabolism in cancer

Several studies in various tumor types indicate that ODC1 can act as an oncogene. ODC1 expression is elevated in many tumors, and also increases the likelihood of developing cancers in response to chemical carcinogens and radiation124,127. ODC is sufficient to promote skin tumorigenesis in mice, and is elevated in human non-melanoma skin cancer. Overexpression of negative regulator AZ decreases formation of skin papillomas, highlighting the tumor-promoting role of ODC activity131. In medulloblastoma, hedgehog signaling increases ODC1 transcription by activation of AMPK and phospho-CNBP127. Several correlative studies have also highlighted roles for ODC in tumors: there is correlation between a SNP in

ODC1 and risk for colon cancer, and there is a positive correlation between MYC and ODC1 expression in breast cancer129. ODC1 is increased in MYCN-amplified neuroblastoma patients, and correlates with

poor survival independent of Myc status126.

A few studies indicate that AdoMetDC may function as a tumor suppressor. It is deleted concurrently with eIF5A in lymphomas, and one study observed decreased skin tumor formation with AdoMetDC overexpression, but others did not observe any phenotype of overexpression131.

Little is known about the effects of SPDSY and SPMSY in cancer, although spermidine is increased in breast tumors and further increases with tumor grade86. However, overexpression of SPDSY did not alter tumor formation in mouse models131. Since SPDSY and SPMSY are inhibited by MTA, and the gene encoding the MTA-degrading enzyme MTAP is often lost in cancers due to its location near several tumor suppressor genes, MTAP-deleted tumors may have lower SPDSY and SPMSY activity126.

1.5.2 Polyamine catabolism in cancer

Increased levels of SSAT and SMOX are commonly associated with inflammation, which can contribute to cancer development, and thus led to interest in therapies targeting SSAT127,132. However, current studies on the effects of SSAT on tumorigenesis have been inconsistent, perhaps due to differences in localized versus ubiquitous SSAT expression, and the effects of ubiquitous expression on acetyl-CoA pools131.

Support for SSAT promoting tumorigenesis is provided by studies that show overexpression of SSAT increases the formation of intestinal tumors and skin carcinomas in murine models125, and that mice overexpressing SSAT in their skin have a higher rate of skin tumors. However, SSAT can also act as a tumor suppressor, since SSAT overexpression decreased prostate tumor formation in a murine model131.

SSAT may also play a role in therapy resistance, since cisplatin-resistant ovarian cancer cells lines have lower SSAT induction, and overexpression of SSAT re-sensitizes them to cisplatin. This may be due to decreasing polyamines bound to DNA, enabling cisplatin to more readily interact with and damage

DNA135. It is also worth noting that p53 increases SAT1 transcription, but it is not known if this affects polyamine levels127.

SMOX may also play a role as an oncogene. Elevated levels of SMOX have been measured in prostate cancer, and may link chronic inflammation and tumorigenesis. Studies of SMOX in an infection-

driven colon cancer model indicate that hydrogen peroxide produced by SMOX leads to recruitment of silencing complexes to chromatin, which can silence tumor suppressors. Combining SMOX inhibition with epigenetic therapies to re-express these silenced genes may increase efficacy, and inhibition of

SMOX in colon and gastric cancer decreases ROS, DNA damage, and tumor formation126,127. Polyamine catabolism is also important for renal toxicity of platinum agents, such that SMOX inhibition may decrease this toxicity127.

One study found the breast cancers have higher SSAT and lower PAOX than surrounding normal tissue, contributing to previous observations of increased acetylated polyamines in breast tumors. PAOX inversely correlated with tumor grade and size. They hypothesized that PAOX is down-regulated to decreased hydrogen peroxide production, and thus that inducing PAOX expression in breast cancers could lead to apoptosis through elevation of hydrogen peroxide137.

1.5.3 Polyamine-drug conjugates

Polyamines can be used as tools to improve the efficacy of other drugs by taking advantage of polyamine transport systems and polyamines’ affinity for DNA126,131. Polyamine-linked platins increase platin efficacy via formation of novel DNA adducts124. These combination molecules may also help overcome platin resistance, since platins decrease ODC, and platin-spermine downregulates arginase II138.

Additionally, using polyamines’ charge to target histone deacetylase (HDAC) inhibitors to chromatin led to 60% inhibition of HDAC activity131.

Polaymine-drug conjugates can also provide some cell-type specificity. Naphthalimide-spermine is selectively toxic to liver cancer cells, anthracene-poylamine conjugate is toxic to leukemia cells, and all- trans-retionic acid coupled to spermine showed specific effects in prostate cancer126.

1.5.4 Targeting polyamine metabolism

There are inhibitors available to each step of polyamine metabolism, but none have shown significant effects against tumors in clinical trials125,135. Therefore, there is interest in determining specific tumor

types or combination therapies where these drugs can be implemented.

2-difluoromethylornithine (DFMO) is an irreversible enzyme-activated inhibitor of ODC that was developed in 1978127,129,131. It is the most widely used ODC inhibitors, primarily used to treat trypanosomiasis129. DMFO depletes putrescine and spermidine but has variable effects on spermine levels; adding back spermidine rescues growth without increasing putrescine, suggesting that spermidine is what is required for growth. DFMO has had limited effects in vivo because of its short half-life and rapid excretion, but is safe despite a review that erroneously reported trials stopped due to toxicity127.

DFMO delays the cells cycle and inhibits DNA synthesis, but generally has cytostatic, not cytotoxic, effects125,126. Cytotoxic effects of DFMO in small-cell lung cancer prompted clinical trials; while it was well tolerated it had little effect, likely due to poor transport or compensatory upregulation of polyamine transport and AdoMetDC131.

DFMO is being tested as a single agent in trials for neuroblastoma treatment, due to the importance of

Myc and its targets in neuroblastoma progression126. Otherwise, there are few successes for DFMO as a mono therapy, except in chemoprevention129. It has been tested in combination therapies, mostly in brain cancers; a cytotoxic regimen with DFMO in glioma led to a small increase in survival, and DFMO inhibited proliferation and combined with cytotoxic agents in neuroblastoma139. Trials of DFMO with bortezomib or etoposide, or with apoptosis-inducing ligands and radiation, are ongoing in brain cancer126.

Most of DFMO’s promise as a cancer therapy are in combination with cytotoxic agents, and thus studies are focusing on potential rational combinations. Cisplatin and doxorubicin increase NF-κB and

Myc in Myc-driven gastric cancer cells, in which Myc protects from DNA damage and apoptosis by increasing ODC140. Overexpression of ODC protected leukemia cells from apoptosis, ROS, and mitochondrial damage induced by cisplatin, and combining DFMO and cisplatin in glioma cells injected into rats decreased proliferation141,142. However, a study in ovarian cancer cells observed a decrease in

ODC1 mRNA in response to 8-24 hours of cisplatin or oxaliplatin treatment143.

Changes in ODC and DFMO sensitivity have also been observed in response to anthracyclines such as doxorubicin. The combination of doxorubicin with DFMO had additive effects on pancreatic cancer

cells, and prolonged survival in rat models of prostate cancer144,145. In MCF-7 cells made resistant to

DFMO, ODC activity increased 10-fold, leading to increased polyamine levels and a decrease in the growth-inhibitory effects of doxorubicin146.

Inhibitors of AdoMetDC increase putrescine and decrease spermidine and spermine, inhibiting cell growth for generally cytostatic effects131. Methylglyoxal bis(guanylhydrazone) (MGBG) is an analog of spermidine that competitively inhibits AdoMetDC126. MGBG was used to treat leukemia before its effects on AdoMetDC were known; however, it is not well tolerated due to mitochondrial toxicity131. Since this toxicity occured before polyamine depletion, it is likely that the mitochondrial toxicity accounted for most observed effects of MGBG126. 4-amidinoindan-1-one-2’-amidinhydrazone (SAM486A/CGP48664) is a competitive inhibitor of AdoMetDC with low mitochondrial toxicity, based on the structure of MGBG.

Clinical trials in non-Hodgkin’s lymphoma led to partial responses126,131. The nucleoside-based inhibitor

5’([(Z)-4-amino-2-butenyl]methylamino)-5’-deoxy-adenosine (AbeAdo) is an irreversible inhibitor of

AdoMetDC that decreases spermidine and spermine and increases putrescine, but its effects on tumors are not well characterized131.

A major caveat to inhibition of ODC or AdoMetDC is that it leads to a compensatory increase in polyamine uptake and compensatory upregulation of the other enzymes. This led to the development of

Polyamine Blocking Therapy (PBT): DFMO with inhibitors of polyamine transport to block uptake of dietary polyamines126. While it has been suggested to reduce dietary intake of polyamines, this is not practical since polyamines present in most foods and produced by normal gut flora127. Thus, transport inhibitors are used, which are structural analogs of polyamines that bind or block the transport system126.

Synergy between DFMO and polyamine transport inhibitor AMXT-1501 has been observed in breast, prostate, melanoma, neuroblastoma, and squamous cell carcinoma cell models126,127. PBT can also relieve

T-cell immunosuppression, inhibiting breast tumor proliferation and leading to immune memory formation in immunocompetent mice126. AMXT-1501 in combination with DFMO also promoted anti- tumor immune responses in models of lymphoma, melanoma, and colon cancer127. However, one study of

PBT in ovarian cancer cells showed that the resulting decrease in spermidine led to a decrease in DNA

synthesis and a decrease in sensitivity to topoisomerase II inhibitors, including doxorubicin147.

Less is known about the effects of inhibiting other polyamine metabolism enzymes. There are no clinical trials of SPDSY/SPMSY inhibitors, though SPDSY inhibitor S-adenosyl-3-thio-1,8- diaminooctane (AdoDATO) induced a small decrease in cancer cell growth131. The SPMSY inhibitor S- adenosyl-1,12-diamino-3-thio-9-azadodecane (AdoDATAD) did not have any effect on cancer cells, and it is unlikely that AdoDATO or AdoDATAD will have any clinical benefit since both can serve as substrates for SSAT and amine oxidases and are thus rapidly catabolized126,131.

There are no clinical-grade inhibitors of polyamine catabolism; MDL72527 inhibits SMOX and

PAOX but is extremely toxic and thus used only as a molecular tool, and there are no specific inhibitors of SMOX, partly because its crystal structure has not yet been determined126.

Another area of interest is polyamine analogs, which take advantage of negative feedback regulation of polyamine synthesis. These analogs are transported into cells and inhibit polyamine metabolism enzymes, but do not fulfill the growth-promoting effects of polyamines and are not rapidly catabolized126.

In general, polyamine analogs increase SSAT, SMOX, and AZ, and repress ODC and AdoMetDC, decreasing polyamine levels and proliferation135. Some analogs inhibited growth of lung, colon, and basal breast cancer cells, and are in clinical trials131. The spermine analog PG-11047 was well-tolerated, but clinical results were not published and development discontinued due to business decisions126.

Oligoamine analogs that bind chromatin and condense DNA inhibited growth of prostate and breast cancer models, but have not been tested in the clinic126,131.

1.6 Summary

Non-essential amino acids are important in proliferating cells, not only for protein synthesis, but also for the synthesis of other metabolites that can promote growth, proliferation, and invasive tumor phenotypes.

In this thesis, we focus on the amino acid arginine and its role in breast cancer.

Because TNBC is currently treated with chemotherapy, in Chapter 2 we investigated the effects of

standard-of-care and emerging chemotherapy drugs on arginine-related metabolites in TNBC cells. We show that chemotherapy increases levels of polyamines and the synthesis enzyme, ODC, through its negative regulator AZ. In Chapter 3, we show that treatment with the ODC inhibitor DFMO specifically sensitizes TNBC cells to chemotherapy, and that this correlates with increased ODC expression. In the appendices, we further explore the effects of arginine on breast cancer cell growth and viability, as well as the regulation of arginine catabolism enzyme ARG2. Together, this thesis highlights the importance of arginine and polyamine metabolism in breast cancer, and proposes that polyamine synthesis is a targetable metabolic vulnerability in TNBC.

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49 Chapter 2

Chemotherapy alters polyamine metabolism through ornithine decarboxylase

Renee C. Geck, Jackson R. Foley, Tracy R. Murray Stewart, John M. Asara, Robert A. Casero, Jr. and Alex Toker.

Author Contributions

R.C.G. and A.T. designed the study and interpreted the results. R.C.G performed all experiments unless otherwise noted. J.R.F. conducted HPLC measurement of polyamines and T.R.M.S. conducted ODC activity assays, both under the direction of R.A.C. J.M.A. assisted with the LC-MS/MS metabolomic studies and data analysis. R.C.G. and A.T. wrote the manuscript.

This chapter is adapted from the paper Geck, R.C. et al. Inhibition of the polyamine synthesis enzyme ornithine decarboxylase sensitizes triple-negative breast cancer cells to cytotoxic chemotherapy. J Biol Chem, doi: 10.1101/2020.01.08.899492 (2020).

2.1 Summary

Chemotherapy regimens are currently the only FDA-approved therapy for treatment of triple-negative breast cancer (TNBC). Previous work by our lab and others have shown that chemotherapy can alter the metabolism of tumor cells, revealing targetable metabolic vulnerabilities. We found that exposure of

ODC to the proteasome for degradation. These suggest that changes in ODC and polyamine metabolism occur in response to chemotherapy and could inform identification of metabolic vulnerabilities in TNBC.

2.2 Introduction

In the past two decades, a renewed interest in tumor metabolism has led to the identification of novel therapeutic vulnerabilities in a variety of cancers1,2. We have investigated these changes in TNBC in order to identify vulnerabilities in a disease that is currently treated only with chemotherapy3,4. Previous studies from our lab have identified changes in pyrimidine nucleotide metabolism in response to chemotherapy, and shown that targeting these changes increases sensitivity to chemotherapy5. Here, we endeavored to identify additional changes in TNBC metabolism that could reveal novel therapeutic targets.

The amino acid arginine has been extensively studied in the context of cancer metabolism and has

been suggested to contribute to the development and progression of cancer6. Arginine is involved in numerous cell growth control processes, including protein synthesis, nitric oxide (NO) production and polyamine biosynthesis, as well as cellular energy production via the TCA cycle7. Furthermore, arginine plays an important role in the immune system, as it is required for full activation of natural killer and T- cells8,9. A number of studies aimed at targeting arginine metabolism have focused on arginine auxotrophic tumors, since they are sensitive to arginine depletion10,11. However, less than 10% of breast tumors lack the rate-limiting arginine synthesis enzyme argininosuccinate synthase (AS)12. Therefore, additional branches of arginine metabolism may be altered in breast cancer and represent potential metabolic vulnerabilities.

The polyamines putrescine, spermidine, and spermine are cationic molecules that are synthesized from arginine following its conversion to ornithine and are essential for eukaryotic cell growth and differentiation13. Functions attributed to polyamines include binding to nucleic acids and chromatin, stabilizing cellular membranes, regulating ion channels and scavenging free radicals14. Polyamines can form hydrogen bonds with anions, such as nucleic acids, proteins and phospholipids, and lead to condensation of DNA and chromatin15,16. ODC, a rate-limiting enzyme in polyamine synthesis, is regulated transcriptionally, translationally, and post-translationally17. Moreover, ODC levels and activity are altered in response to extracellular stimuli such as hormones and growth factors, as well as changes in intracellular free polyamine levels14,17,18. Here we present data supporting that polyamine synthesis is suppressed in response to DNA damaging chemotherapy through the proteasomal degradation of ODC.

2.3 Results

2.3.1 Genotoxic chemotherapy alters levels of polyamines and related metabolites

To evaluate the alterations in intracellular metabolites in breast cancer cells exposed to genotoxic chemotherapy drugs, we measured the levels of polar metabolites in MDA-MB-468 and SUM-159PT

TNBC cells, which represent the Basal A and B subtypes according to the classification of Lehmann et

al.19, following treatment with cisplatin or doxorubicin for 8, 24, or 48 hours. These drugs were selected since doxorubicin is a standard-of-care as part of AC-T (adriamycin (doxorubicin), cyclophosphamide, paclitaxel (taxol)) therapy for TNBC patients, and neoadjuvant cisplatin has shown some therapeutic efficacy in a subset of TNBC patients, particularly those with hereditary BRCA1-mutant breast cancer20-23.

We first determined the concentration of both drugs required for the induction of DNA damage as measured by increased histone H2A.X phosphorylation, while remaining below the IC50 for cell death

(Figure 2.1A-B). Based on this analysis, both cell lines were treated with 0.5 µM doxorubicin and 2.5 µM cisplatin, and targeted metabolomics profiling of 186 metabolites was performed using LC-MS/MS

(Figure 2.1C, Table S124). Increases in pyrimidine nucleotides were observed in response to DNA damage, consistent with previous reports5.

We focused on metabolites involved in arginine metabolism, including the urea cycle, NO cycle, polyamine metabolism, proline metabolism, and creatine synthesis (Figure 2.2A). Twelve metabolites involved in arginine metabolism were detected by our LC-MS/MS platform (Figure 2.2A-B). In response to genotoxic drugs, the most upregulated metabolite in arginine metabolism was ornithine, and the most decreased was S-adenosyl methionine (SAM) (Figure 2.2B-C). Ornithine can be synthesized either from arginine by arginase, or from glutamine via ornithine aminotransferase, although previous reports have shown that in transformed cells, ornithine is derived exclusively from arginine25. In MDA-MB-468 and

13 13 SUM-159PT TNBC cells, carbons from C6-arginine, but not C5-glutamine, were incorporated into

13 13 ornithine, even though both exogenous C6-arginine and C5-glutamine efficiently labeled their respective intracellular pools, and this was unaffected in doxorubicin-treated cells (Figure 2.2D). Since both ornithine and SAM are required for the synthesis of polyamines, but polyamines were not detectable on our LC-MS/MS platform, we measured polyamine levels by conventional HPLC analysis (Figure

2.2E). After 48 hours of exposure to genotoxic drugs, putrescine and spermidine were significantly decreased by doxorubicin, and also decreased in response to cisplatin. These data suggest that genotoxic drugs alter arginine metabolites involved in polyamine synthesis in TNBC cells.

Figure 2.1: Genotoxic chemotherapy alters TNBC metabolism. (A) Viability measured by propidium iodide uptake following 24 or 48 hours of exposure to chemotherapy agents. Selected concentrations (2.5µM cisplatin, 0.5µM doxorubicin) denoted by dashed lines. Nonlinear curve fit by four parameter logistic regression. (B) Representative immunoblot of phospho-S139 histone H2A.X (p-H2A.X) following exposure to chemotherapy agents at times and doses used for metabolite measurements. (C) Fold change metabolite abundance over 24h vehicle control for 186 metabolites measured by LC-MS/S in MDA-MB-468 and SUM-159PT cells treated with 2.5µM cisplatin (CDDP) or 0.5µM doxorubicin (DOXO).

Figure 2.2: Genotoxic chemotherapy alters arginine and polyamine metabolism. (A) Arginine synthesis and degradation. Metabolites in black, genes in blue. Bold text indicates metabolites detected by LC-MS/MS. (B) Fold change metabolite abundance over 24h vehicle control for arginine metabolites in MDA-MB-468 and SUM-159PT cells treated with 2.5µM cisplatin (CDDP) or 0.5µM doxorubicin (DOXO) (n=3). (C) Relative abundance of ornithine and S-adenosyl methionine (SAM) by LC-MS/MS; vehicle at 24h (n=3). (D) Fraction metabolite pool labeled as measured by LC-MS/MS following 13 13 incubation with C6-arginine or C5-glutamine in the presence of vehicle or 0.5µM doxorubicin for 48 hours (n=2 biological replicates; technical replicates denoted by shared symbol). (E) Relative abundance of polyamines by HPLC; vehicle control at 24h (n=3-5). All error bars represent SEM. *, P<0.05; **, P<0.01 by 2-way ANOVA. Gene abbreviations: ALDH18A1, Aldehyde Dehydrogenase 18 Family Member A1; AMD1, Adenosylmethionine Decarboxylase 1; ARG2, Arginase 2; ASL, Argininosuccinate Lyase; ASS1, Argininosuccinate Synthase 1; CPS1, Carbamoyl-Phosphate Synthase 1; dcSAM, decarboxylated S-adenosyl methionine; GAMT, Guanidinoacetate N-Methyltransferase; GATM, Glycine Amidinotransferase; NOS, Nitric Oxide Synthase; OAT, Ornithine Aminotransferase; OAZ1, Ornithine Decarboxylase Antizyme 1; ODC1, Ornithine Decarboxylase; OTC, Ornithine Carbamoyltransferase; PAO, Polyamine Oxidase; PRODH, Proline Dehydrogenase 1; PYCR1, Pyrroline-5-Carboxylate Reductase 1; SAM, S-adenosyl methionine; SAT1, Spermidine/Spermine N1-Acetyltransferase 1; SMOX, Spermine Oxidase; SMS, Spermine Synthase; SRM, Spermidine Synthase. Metabolite abbreviations: Arg,

Figure 2.2 (Continued) L-arginine; AS, L-argininosuccinate; Cit, citrulline; Cr, creatine; Crn, creatinine; GAA, guanidoacetate; Hyp, hydroxyproline; Orn; ornithine; Pcr, phosphocreatine; Pro, proline; Put, putrescine; SAM, S- adenosyl-L-methionine; Spm, spermine; Spd, spermidine.

2.3.2 Chemotherapy decreases levels and activity of ornithine decarboxylase

We reasoned that alterations in polyamine metabolites were due to changes in enzyme levels or activity in response to chemotherapy exposure. ODC catalyzes the first rate-limiting step in polyamine synthesis, specifically the conversion of ornithine to putrescine (Figure 2.2A). Decreased ODC levels or activity could account for the observed decreases in polyamines and increases in ornithine (Figure 2.2C,E), though ornithine could also be elevated by increased activity of arginase II (ARG2) (Figure 2.2A).

Cisplatin and doxorubicin increased total ODC protein at early timepoints (8 hours), consistent with a stress response26, but led to decreased ODC and increased ARG2 at later timepoints (Figure 2.3A, Figure

S3.1). This pattern of ODC expression was also observed in multiple TNBC and non-TNBC breast cancer cell lines (Figure 2.3B-D, Table S3.1). By contrast, alterations in ARG2 expression were not significant across all lines. We also confirmed a concomitant decrease in ODC activity after 24 and 48 hours of exposure to cisplatin or doxorubicin (Figure 2.4A). A significant decrease in putrescine at 72 and 96 hours was also observed, in addition to significantly reduced spermidine after 96 hours of doxorubicin treatment and a time-dependent decreasing trend in spermine concentration (Figure 2.4B).

Figure 2.3: Chemotherapy decreases ODC and increases ARG2 proteins. (A) Representative immunoblots and quantification (n=4) of total ornithine decarboxylase (ODC) and arginase II (ARG2) proteins, and phospho-S139 histone H2A.X (p-H2A.X) following exposure to chemotherapy agents in MDA-MB-468 and SUM-159PT cells, (B) other TNBC cells, and (C) non-TNBC cells. ‘0h’ chemotherapy treatment indicates 24h vehicle control. (D) Average quantification of n=4 immunoblots of ODC and ARG2 from nine breast cancer cell lines in A-C following chemotherapy exposure. Band above 55kD in ODC blots is non-specific. All error bars represent SEM. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001 by 2-way ANOVA.

Figure 2.4: Chemotherapy decreases ODC activity and polyamine levels. (A) ODC activity measured by CO2 release following exposure to 2.5µM cisplatin or 0.5µM doxorubicin; vehicle control at 24h (n=3). (B) Relative abundance of polyamines in MDA-MB-468 cells by HPLC; vehicle control at 72h (n=3). All error bars represent SEM. **, P<0.01; ***, P<0.001; ****, P<0.0001 by 2-way ANOVA.

To investigate the mechanism by which ODC protein and activity are decreased following chemotherapy exposure, we first evaluated transcriptional regulation of ODC1, which is a well- characterized target of c-Myc27. Depletion of c-Myc using siRNA did not alter the overall ODC response to chemotherapy (Figure 2.5A), although transcripts of ODC1 and ARG2 were increased following chemotherapy exposure (Figure 2.5B, Figure S3.2A). ODC protein is post-translationally regulated by the activity of antizyme, which binds ODC to promote its ubiquitin-independent degradation by the 26S proteasome28. Pre-treatment with the proteasome inhibitor MG132 rescued the decrease in ODC protein following doxorubicin exposure (Figure 2.5C). siRNA against antizyme decreased the corresponding transcript of OAZ1 by over 80% (Figure 2.5D) and blocked the decrease in ODC in response to doxorubicin in both TNBC and ER-positive cells (Figure 2.5E-F). No tested antibodies specifically detected antizyme protein in our cells, though OAZ1 transcript levels increased in MDA-MB-468 cells in response to chemotherapy (Figure S3.2B). Therefore, it appears that genotoxic drugs decrease polyamines

by reducing ODC protein and activity, possibly through the negative regulator antizyme. We also inhibited multiple DNA-damage signaling proteins, including ATM, ATR, DNA-PK, Chk1, and PARP, but none of the tested inhibitors blocked the decrease in ODC following chemotherapy (Figure S3.2C).

Figure 2.5: Chemotherapy regulates ODC via proteasomal degradation. (A) Representative immunoblots of ODC and c-Myc in cells pre-treated for 24h with 20nM non-targeting siRNA or siRNA MYC, then treated with doxorubicin for indicated times (n=2). (B) qRT-PCR of ODC1 transcript following treatment with 2.5µM cisplatin or 0.5µM doxorubicin, relative to 24h vehicle control. (C) Representative immunoblots of ODC in SUM-159PT cells pre-treated for 2h with 0.5µM proteasome inhibitor MG132 followed by addition of vehicle or doxorubicin. (n=3) (D) qRT-PCR of OAZ1 transcript following treatment with 20nM indicated siRNA (n=3). (E) Representative immunoblots and quantification of ODC in 20µg/lane MDA-MB-468 or (F) 40µg/lane ZR-75-1 cells pre-treated for 24 hours with 20nM siOAZ1 followed by 48 hours addition of vehicle or 0.5µM doxorubicin (n=3). Band above 55kD in ODC blots is non-specific. All error bars represent SEM. *, P<0.05; **, P<0.01; ***, P<0.001 by 2-way ANOVA.

2.4 Discussion

Chemotherapy is the current standard-of-care for TNBC, but addition of other targeted therapies could improve response and outcomes4. We have previously shown that measuring metabolic changes in response to chemotherapy can be used to identify novel therapeutic targets5. Here, we investigated changes in arginine and polyamine-related metabolites in response to genotoxic chemotherapy in TNBC cells and found that genotoxic drugs decrease ODC levels and activity, with a corresponding reduction in the polyamines putrescine and spermidine.

TNBC cell lines exposed to chemotherapy showed a significant decrease in the expression and activity of ODC (Figures 2.3 and 2.4). This decrease was not due to alterations in transcript levels nor is it mediated by c-Myc, a major transcriptional regulator of ODC (Figure 1.6A)29. Instead, reduced ODC expression in response to chemotherapy was mediated through degradation by the 26S proteasome

(Figure 2.5). ODC has a half-life of 10-30 minutes, one of the shortest of any mammalian protein30. ODC turnover is regulated by antizyme, a protein that binds ODC and targets it for destruction by non-ubiquitin mediated degradation (Figure 1.6B). Antizyme itself is translationally regulated through the binding of polyamines to its transcript OAZ1, thereby inducing a +1 ribosomal frameshift28. Thus, antizyme levels are increased when intracellular polyamine concentrations are elevated. The observed initial increases in polyamines after 8 hours of chemotherapy exposure may be sufficient to increase translation of OAZ1 and subsequently increase antizyme expression (Figure 2.3, S3.2B), resulting in ODC degradation. One previous study demonstrated that doxorubicin increases antizyme expression mediated by c-Jun, though the mechanistic basis was not determined31. An alternative mechanism that could account for ODC regulation is increased antizyme binding to ODC in response to chemotherapy. Moreover, since antizyme inhibitor (AZI) binds antizyme with a greater affinity than ODC, thereby protecting ODC from antizyme- mediated degradation (Figure 1.6A)14,27, decreased AZI would free antizyme to bind ODC. Future studies are necessary to address which of these mechanisms is responsible for regulation of ODC in response to chemotherapy, and to delineate the signals connecting DNA damage or other effects of chemotherapy to

antizyme and ODC.

In summary, we have identified changes in arginine and polyamine metabolism in TNBC cells exposed to chemotherapy, due to a decrease of the polyamine synthesis enzyme ODC. Further studies on the mechanisms linking genotoxic damage to ODC degradation, and the effects of targeting ODC in

TNBC, will improve our understanding of the role of polyamine synthesis in TNBC and its applications as a metabolic vulnerability.

2.5 Materials and Methods

Cell culture

SUM-159PT and SUM-149PT cells were obtained from Asterand Bioscience; all other cell lines were obtained from the ATCC. Features of cell lines are listed in Table S3.1. Cell lines were authenticated using short tandem repeat profiling, and no cell lines used in this study were found in the database of commonly misidentified cell lines, which is maintained by the International Cell Line Authentication

Committee and National Center for Biotechnology Information BioSample. All cells were maintained in

RPMI medium (Wisent Bioproducts) containing 10% FBS (Gibco). Cells were passaged for no more than

4 months and routinely assayed for mycoplasma contamination.

Chemotherapy agents and inhibitors

Doxorubicin was purchased from Cell Signaling Technology and dissolved in DMSO at 10mM. MG132 was purchased from Cayman Chemical and dissolved in DMSO at 10mM. Cisplatin was obtained from the Dana Farber Cancer Institute pharmacy at 1mg/mL in PBS. Inhibitors used in Figure S3.2C were a generous gift from Alexander Valvezan in the laboratory of Brendan Manning (Harvard T.H. Chan School of Public Health). DMSO for use as an organic solvent and vehicle control was purchased from Fisher

Scientific.

Antibodies

ODC (MABS36, 1:100) was purchased from Millipore. ARG2 (ab137069, 1:1000) was purchased from

Abcam. pH2A.XS139 (9718, 1:1000), beta-actin (4970, 1:1000), c-Myc (5605, 1:1000), and vinculin

(13901, 1:1000) were purchased from Cell Signaling Technology. Antibodies were used at indicated dilutions in 5% milk (Andwin Scientific) in TBST buffer (Boston Bioproducts), except for p-H2A.X in

5% BSA (Boston Bioproducts) in TBST.

LC/MS-MS metabolomics profiling

Cells were maintained in RPMI + 10% FBS, and fresh medium was added at the time cells were treated.

For metabolite extraction, medium was aspirated and ice-cold 80% (v/v) methanol was added. Cells and the metabolite-containing supernatants were collected. Insoluble material was pelleted by centrifugation at 20,000 g for 5 minutes. The resulting supernatant was evaporated under nitrogen gas. Samples were resuspended using 20 µL HPLC-grade water for mass spectrometry. For polar metabolite profiling, five microliters from each sample were injected and analyzed using a 5500 QTRAP hybrid triple quadrupole mass spectrometer (AB/SCIEX) coupled to a Prominence UFLC HPLC system (Shimadzu) with HILIC chromatography (Waters Amide XBridge) via selected reaction monitoring (SRM) with polarity switching. A total of 293 endogenous water-soluble metabolites were targeted for steady-state analyses.

Electrospray source voltage was +4950 V in positive ion mode and −4500 V in negative ion mode. The dwell time was 3 ms per SRM transition 32. Peak areas from the total ion current for each metabolite were integrated using MultiQuant v2.1.1 software (AB/SCIEX). For 13C-labeled experiments, five microliters from each sample (20 uL) were injected with similar methodology as above using a 6500 QTRAP

(AB/SCIEX) and integrated using MultiQuant v3.0 software33. SRMs were created for expected 13C incorporation in various forms. Metabolite total ion counts were the integrated total ion current from a single SRM transition and normalized by cellular protein content. Nonhierarchical clustering was performed by Metaboanalyst 4.0 using a Euclidean distance measure and Ward’s clustering algorithm34.

Polyamine concentration determinations and ODC activity assays

Cells were washed with PBS and harvested in ODC breaking buffer (25mM Tris-HCl (pH 7.5), 0.1mM

EDTA, 2.5mM DTT). Intracellular polyamine concentrations of cell lysates were determined by HPLC following acid extraction and dansylation of the supernatant, as originally described by Kabra et al.35.

Standards prepared for HPLC included diaminoheptane (internal standard), PUT, SPD, and SPM, all of which were purchased from Sigma Chemical Co. (St. Louis, MO, USA). Enzyme activity assays were performed for ODC using radiolabeled substrates, as previously described36. All enzyme activities and intracellular polyamine concentrations are presented relative to total cellular protein, as determined using

Bio-Rad protein dye (Hercules, CA) with interpolation on a bovine serum albumin standard curve.

Isotope labeling

RPMI powder lacking glutamine, arginine, tryptophan, and glucose was obtained from US Biological Life

Sciences and supplemented with 25 µM L-tryptophan (Sigma-Aldrich), 11.1mM D-glucose (Gibco), and

13 10% dialyzed FBS (Gibco). For arginine labeling, 2mM glutamine (Gibco) and 1.1mM L-arginine- C6

13 hydrochloride (Aldrich) were added; for glutamine labeling, 2mM L-glutamine- C5 (Cambridge Isotope

Laboratories) and 1.1mM L-arginine hydrochloride (Sigma) were added. Labeled medium, with or without chemotherapy agents, was added to cells, and cellular metabolites were extracted as described above after 48 hours.

Immunoblotting

Cells were washed with ice-cold PBS (Boston Bioproducts) and lysed in radioimmunoprecipitation buffer

(1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS, 150 mM NaCl, 50 mM Tris-HCl (pH 7.5), protease inhibitor cocktail, 50 nM calyculin A, 1mM sodium pyrophosphate, and 20 mM sodium fluoride) for 15 min at 4°C. Cell extracts were cleared by centrifugation at 14,000 rpm for 10 min at 4°C, and protein concentration was measured with the Bio-Rad DC protein assay. Lysates were resolved on acrylamide gels with 20µg/lane by SDS-PAGE and transferred electrophoretically to nitrocellulose membrane (Bio-

Rad) at 100 V for 90 min. Blots were blocked in TBS buffer (10 mmol/L Tris-HCl, pH 8, 150 mmol/L

NaCl, Boston Bioproducts) containing 5% (w/v) nonfat dry milk (Andwin Scientific). Membranes were incubated with near-infrared dye-conjugated IRDye 800CW secondary antibodies (LiCor, 1:20,000 in 5% milk-TBST) and imaged on a LiCor Odyssey CLx.

Quantitative real-time polymerase chain reaction

Total RNA was isolated with the NucleoSpin RNA Plus (MACHEREY-NAGEL) according to the manufacturer’s protocol. Reverse transcription was performed using the TaqMan Reverse Transcription

Reagents (Applied Biosciences). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed using a CFX384 Touch Real-Time PCR Detection System (Bio-Rad). Quantification of mRNA expression was calculated by the DCT method with 18S ribosomal RNA as the reference gene. Primer sequences are listed in Table S3.2.

RNA interference siRNA ON-TARGETplus SMARTpools against human ARG2 (L-009454-01), ODC1 (L-006668-00),

OAZ1 (L-019216-00), and Myc (L-003282-02) were purchased from Dharmacon and dissolved in siRNA buffer (Dharmacon). Cells were transfected with Lipofectamine RNAiMAX Reagent (Invitrogen) in Opti-

MEM (Gibco) according to the manufacturer’s protocol (Invitrogen MAN0007825), with a final concentration of 20nM siRNA and 3µL/mL Lipofectamine.

Propidium iodide viability assay

Cell viability was assayed with a propidium iodide-based plate-reader assay, as previously described37.

Briefly, cells in 96-well plates were treated with a final concentration of 30mM propidium iodide

(Cayman Chemical) for 20 min at 37°C. The initial fluorescence intensity was measured in a GENios FL

measured. The fraction of dead cells was calculated by dividing the background-corrected initial fluorescence intensity by the final fluorescence intensity. Viability was calculated by (1 – fraction of dead cells).

Statistics and reproducibility

Sample sizes, reproducibility, and statistical tests for each figure are denoted in the figure legends. All replicates are biological unless otherwise noted. All error bars represent SEM, and significance between conditions is denoted as *, P<0.05; **, P<0.01; ***, P<0.001; and ****, P<0.0001.

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68 Chapter 3

Inhibition of ornithine decarboxylase sensitizes triple-negative breast cancer to cytotoxic chemotherapy

Renee C. Geck, Jackson R. Foley, John M. Asara, Robert A. Casero, Jr. and Alex Toker

Author Contributions

R.C.G. and A.T. designed the study and interpreted the results. R.C.G performed all experiments unless otherwise noted. J.R.F. conducted HPLC measurement of polyamines under the direction of R.A.C. J.M.A. assisted with the LC-MS/MS metabolomic studies and data analysis. R.C.G. and A.T. wrote the manuscript.

3.1 Summary

Treatment of triple-negative breast cancer (TNBC) is limited by a lack of effective molecular targeted therapies. Recent studies have identified metabolic alterations in cancer cells that can be targeted to improve responses to standard-of-care chemotherapy regimens. We previously found that exposure of

TNBC cells to cytotoxic chemotherapy drugs leads to alterations in arginine and polyamine metabolites due to a reduction in the levels and activity of a rate-limiting polyamine biosynthetic enzyme ornithine decarboxylase (ODC). Treatment with the ODC inhibitor DFMO sensitized TNBC cells to chemotherapy, but this was not observed in receptor-positive breast cancer cells. Moreover, TNBC cell lines showed greater sensitivity to single-agent DFMO, and ODC levels were elevated in TNBC patient samples.

Alterations in polyamine metabolism in response to chemotherapy, as well as preferential sensitization of

TNBC cells to chemotherapy by DFMO, suggest that ODC may be a targetable metabolic vulnerability in

TNBC.

3.2 Introduction

The identification of novel metabolic vulnerabilities is especially promising for tumor types that lack effective targeted therapies, such as TNBC. TNBC comprises 15-20% of breast cancer cases, but accounts for a disproportionately high percentage of breast cancer-related deaths1. This is in part due to a lack of targeted therapies for this molecular subtype of breast cancer, and the standard of care treatment for

TNBC remains combination genotoxic chemotherapy regimens such as AC-T (adriamycin (doxorubicin), cyclophosphamide, paclitaxel (taxol))2,3. Previous studies have identified metabolic vulnerabilities in breast cancer cells including nucleotide metabolism, glutathione biosynthesis and glutamine metabolism that improve response to chemotherapy drugs in pre-clinical settings4-6.

Elevated polyamine levels have been detected in tumors, including breast tumors, and increased polyamine synthesis has been shown to promote tumor initiation and growth7,8. Combined inhibition of polyamine uptake and synthesis results in antiproliferative effects in breast cancer cell lines and murine models, suggesting that targeting polyamine metabolism may be therapeutically effective in breast cancer patients9,10.

2-difluoromethylornithine (DFMO) is an inhibitor of ODC that is used clinically to treat trypanosomiasis11. It is well-tolerated but is generally most effective as part of combination therapy, since alone it primarily induces cytostatic effects12. Although DFMO and other drugs that target polyamine metabolism have not yet been approved for therapeutic use in cancer, clinical trials using polyamine metabolism inhibitors are underway for a number of indications, although to date none in breast cancer13.

In this chapter, we describe the role of ODC in TNBC, where DFMO increases sensitivity to standard-of- care genotoxic drugs.

3.3 Results

3.3.1 Targeting polyamine synthesis increases sensitivity to chemotherapy

Polyamines promote cell cycle progression, and depletion of ODC or polyamines induces cell cycle arrest at the G2/M phase, where cells are more sensitive to DNA damage induced by cisplatin and doxorubicin14-20. Since we observed a decrease in polyamines and ODC activity following chemotherapy treatment (see Chapter 2), we reasoned that targeting ODC to further decrease polyamines could increase tumor cell killing. We proceeded with doxorubicin since it is a standard-of-care chemotherapeutic agent used for the majority of TNBC patients21,22. Treatment with the irreversible suicide inhibitor of ODC,

DFMO, sensitized both MDA-MB-468 and SUM-159PT cells to doxorubicin (Figure 3.1A). Addition of exogenous putrescine or spermidine did not rescue this sensitization (Figure 3.1B). A previous study reported that DFMO can decrease colon cancer cell growth by increasing polyamine recycling, leading to a futile cycle that depletes SAM and nucleotides, and that this effect can be rescued by exogenous thymidine23. However, in TNBC cells, thymidine addition, alone or in combination with putrescine or spermidine, did not rescue the sensitization to doxorubicin by DFMO (Figure 3.1C). Addition of other related metabolites, folic acid and methionine, also failed to alter sensitivity (Figure S3.3A-B). Treatment of MDA-MB-468 cells with the arginase inhibitor Nω-hydroxy-nor-arginine (NOHA) also increased sensitivity to doxorubicin (Figure 3.1D), consistent with its ability to decrease polyamine levels24,25.

To confirm the on-target effects of DFMO, we measured polyamine levels after 72 hours of treatment with DFMO. As expected, putrescine was decreased in MDA-MB-468 cells and undetectable in SUM-

159PT cells, and spermidine was also reduced (Figure 3.1E). To further investigate the effects of DFMO, we measured polar metabolites following exposure to DFMO (Table S226). The putrescine metabolite 4- aminobutyrate was significantly decreased, and other metabolites related to nucleotide, one-carbon, and amino acid metabolism were also decreased (Figure 3.1F). This suggests that these metabolites must be replenished to reverse the effects of DFMO and resulting sensitization to doxorubicin.

Across a panel of breast cancer cell lines representing both estrogen receptor (ER) positive luminal A,

HER2+ and TNBC subtypes (Table S3.1), we observed that all TNBC cell lines tested were sensitized to doxorubicin by pretreatment with DFMO, whereas most non-TNBC lines were not (Figure 3.2A-B).

Overall, cell lines that displayed the greatest sensitization to doxorubicin by DFMO are classified as

TNBC (Figure 3.2C). These findings prompted us to determine whether there exists an intrinsic property of TNBC that makes this molecular subtype of breast cancer more dependent on ODC.

Figure 3.1: ODC inhibition increases sensitivity to doxorubicin. (A) Viability measured by propidium iodide uptake following 72h pre-treatment with 1mM DFMO or vehicle and 72h exposure to doxorubicin in the presence of DFMO or vehicle (n=3). (B) MDA=MB-468 viability following 72h pretreatment with 1mM DFMO with or without 10µM putrescine or 10µM spermidine and 1mM aminoguanidine, and addition of doxorubicin for 72h (n=2 and n=3). (C) Viability following 72h pretreatment with 1mM DFMO with or without 0.1mM thymidine (T), 10µM putrescine (P) or 10µM spermidine (S), followed by addition of doxorubicin for 72h (n=2). 1mM aminoguanidine was added with putrescine and spermidine. (D) Viability following 72h pre-treatment with 0.5mM NOHA and addition of doxorubicin for 72h (n=4) (E) HPLC measurements of polyamines following 72h treatment with 1mM DFMO (n=2). ‘ND’ indicates not detected. (F) Top 10% most significantly altered polar metabolites measured by LC-MS/MS following treatment with 1mM DFMO (n=2). All error bars represent SEM. Nonlinear curve fit by four parameter

Figure 3.1 (Continued) logistic regression. P-values by unpaired two-tailed t-test. Metabolite abbreviations: 4AB, 4- aminobutyrate; AcCarn, acetylcarnitine-DL; ADP, adenosine diphosphate; AICAR, aminoimidazole carboxamide ribonucleotide; Ald, aldehyde; Asn, asparagine; Asp, aspartate; dGDP, deoxyguanosine diphosphate; Hyp, hydroxyproline; Ile, isoleucine; Leu, leucine; m7G, 7-methylguanosine; meCys, methylcysteine; meHis, 1-methylhistidine; NAcAsp, N-acetyl-L-aspartate; Pro, proline; Put, putrescine; SAH, S-adenosyl-L-homocysteine; Spm, spermine; Spd, spermidine; Thym, thymine; Tyr, tyrosine; UDP- Gluc, uridine diphosphate D-glucose.

Figure 3.2: ODC inhibition increases sensitivity of TNBC cells to doxorubicin. (A) Viability measured by propidium iodide uptake following 72h pre-treatment with 1mM DFMO or vehicle and 72h exposure to doxorubicin in the presence of DFMO or vehicle in TNBC and (B) non-TNBC cells (n=3); P- value by paired two-tailed t-test. (C) Percent change doxorubicin IC50 for DFMO over vehicle control from 6A and 7A-B; IC50 calculated by four parameter logistic nonlinear curve fit. All error bars represent SEM. *, P<0.05 by unpaired two-tailed t-test.

3.3.2 Ornithine decarboxylase is a metabolic vulnerability in TNBC

To investigate the role of arginine and polyamine metabolism in TNBC, we queried the alterations in the transcripts of 1,270 genes in the KEGG ‘metabolic pathways’ (KEGG:hsa01100) in the METABRIC breast cancer dataset27-29. Of these transcripts, 1,134 were analyzed in 1,904 tumors (Figure S3.3C, Table

S326). We found that ODC1 is one of the top 5 most significantly enriched transcripts in TNBC samples

(Figure 3.3), and that it is often co-amplified with neighboring metabolic gene LPIN1 (Figure S3.3D).

There was a significant increase in overall survival for patients with the lowest 10% ODC1 expression compared to 10% highest (Figure S3.3E). This trend was also observed in the subset of patients with basal-like tumors (Figure S3.3F), but ODC1 expression and survival did not correlate across all TNBC patients (Figure S3.3G). ODC1 was also enriched in TNBC patient samples from the TCGA provisional breast dataset (Figure 3.3C). Although we did not previously observe the same trends for transcript and protein levels of ODC in response to chemotherapy (Figure 2.3A and 2.5B), baseline transcript and protein levels positively correlated in the TCGA breast samples for which protein mass spectrometry data is available (Figure 3.3D). ODC1 transcript levels were also enriched in the basal subtype of breast cancer, which is commonly associated with TNBC (Figure 3.3E)30.

The increased ODC1 levels in TNBC could be due to changes in ODC1 copy number, since the average ODC1 copy number was higher in TNBC compared to non-TNBC (Figure 3.3F). Since ODC1 is a target of c-Myc, we also evaluated the relationship between MYC amplification and ODC1 transcript levels in TNBC, and observed a significant correlation (Figure 3.3G). ODC stability is regulated by antizyme and antizyme inhibitor (AZI), and we also observed decreased copy number and transcript levels for multiple antizyme genes and transcripts (OAZ1/2/3) in TNBC (Figure 3.3H,I), as well as increased copy number of AZI gene AZIN1 (Figure 3.3J)31.

Figure 3.3: Ornithine decarboxylase levels are increased in TNBC tumors. (A) Comparison of mRNA z-scores from 1,134 metabolic gene transcripts between 299 TNBC and 1605 non-TNBC patient samples from METABRIC breast cancer dataset. (B) Comparison of ODC1 mRNA z-scores between METABRIC breast cancer patient samples or (C) TCGA provision breast cancer patient samples, separated by TNBC status. (D) Correlation of ODC1 mRNA z-scores and ODC protein expression CPTAC z-scores in TCGA provision breast cancer patient samples; dashed lines indicate 90% confidence interval. (E) ODC1 mRNA z-scores in METABRIC samples grouped by PAM50 subtype. (F) Average copy number alterations of ODC1 in METABRIC and TCGA provisional breast cancer patient samples scored by type as -1, shallow deletion; 0, diploid; 1, gain; 2, amplification. (G) Comparison of ODC1 mRNA z-scores between METABRIC breast cancer patient samples separated by TNBC status and grouped by MYC copy number alterations. (H) Copy number alterations in METABRIC patient samples for antizyme genes, scored as in F. (I) mRNA z-scores of antizyme transcripts in METABRIC samples grouped by TNBC status. (J) Copy number alterations in METABRIC patient samples for antizyme inhibitor gene AZIN1, scored as in F. All error bars represent SEM. *, P<0.05; **, P<0.01 ***, P<0.001; ****, P<0.0001 by Welch’s t-test (A-C,I), one-way ANOVA (E,G), or two-tailed unpaired t-test (F,H,J); linear curve fit, p-value, and R2 by linear regression (D).

Figure 3.4: Ornithine decarboxylase is a metabolic vulnerability in TNBC. (A) Comparison of ODC1 mRNA z-scores in 25 TNBC and 25 non-TNBC breast cancer cell lines Cancer Cell Line Encyclopedia. (B) Comparison of ODC1 transcript levels measured by qRT-PCR. (C) Representative immunoblots and quantification (n=3) of ODC protein in untreated breast cancer cell lines; band above 55kD in ODC blots is non-specific. (D-E) Growth of breast cancer cell lines measured as population size relative to vehicle after 72h treatment with 2mM DFMO. (F-G) Viability of breast cancer cell lines measured by propidium iodide uptake following 72h treatment with 2mM DFMO. (H) Doubling time of breast cancer cell lines compared to average viability from F; R2 by linear regression. All error bars represent SEM. *, P<0.05; **, P<0.01 ***, P<0.001; ****, P<0.0001 by two-tailed unpaired t-test (A-C,E,G) or two-way ANOVA (D,F).

Enrichment of ODC1 transcript was also observed in TNBC cells lines according to the Cancer Cell

Line Encyclopedia32 and confirmed by RT-PCR analysis of select breast cancer cell lines (Figure 3.4A-B), consistent with a previous study of four breast cancer cell lines33. A trend towards higher baseline protein expression of ODC in TNBC cells was also observed (Figure 3.4C, S3.1A). Moreover, DFMO treatment significantly reduced proliferation of breast cancer cell lines regardless of their ER/PR/HER2 status

(Figure 3.4D-E). By contrast, DFMO treatment of TNBC cell lines resulted in increased cell death when compared to non-TNBC lines, regardless of doubling time (Figure 3.4F-H).

3.4 Discussion

Due to the poor prognosis for patients with TNBC, many new strategies have been proposed and evaluated for improved therapeutic efficacy. In this context, the implementation of poly ADP ribose polymerase (PARP) inhibitors in BRCA1-mutant TNBC and immune checkpoint therapy in TNBC patients with infiltrating lymphocytes and PD-L1 expression has provided considerable success2,34,35.

However, chemotherapy remains the only FDA-approved therapy for the majority of patients with

TNBC3. Advances in understanding the reprogramming of metabolic pathways in human cancers have led to the identification of druggable targets that have served as the basis for clinical trials, with the goal of targeting tumor-specific metabolic vulnerabilities. In breast cancer, alterations in glutathione, nucleotide and glutamine metabolism occur in response to genotoxic chemotherapy4-6. In the present study, we found that ODC expression is increased in TNBC patient samples and cell lines, to the extent that targeting

ODC with the inhibitor DFMO sensitizes TNBC cells to doxorubicin.

Treatment with the ODC inhibitor DFMO sensitizes TNBC cells to chemotherapy drugs, consistent with previous observations that polyamines bind and stabilize DNA and in turn occlude genotoxic agents36. Conversely, depleting polyamines increases DNA damage and induces cell cycle arrest37-39.

Depletion of ODC or polyamines induces arrest in G2, the cell cycle phase in which tumor cells are most sensitive to damage by cisplatin and doxorubicin16,18-20. Depending on the cell type, DFMO arrests cells in

either G1 or G215,40,41. Our observations are more consistent with a G2 arrest in response to ODC and

DFMO treatment, since this is the phase when cells are most sensitive to genotoxic damage.

Targeting ODC and polyamine synthesis is an attractive antitumor target since high levels of polyamines have been measured in multiple tumor types, and are necessary for transformation and progression7,8,13. We observed a correlation between high ODC1 expression and decreased overall patient survival (Figure S3.3E); however, this is confounded by the correlation between high ODC1 expression and TNBC. When considering the tumor samples from the METABRIC dataset with the highest 10% versus lowest 10% ODC1 expression, the observed increase in survival for ODC1-low patients is unsurprising because 115/190 (61%) of the ODC1-high tumors are TNBC, whereas only 1/190 ODC1- low tumors is TNBC, and TNBC is known to have worse prognosis than non-TNBC42. When considering the top and bottom 10% ODC1-expressing tumors in basal cancers, there is a trend towards decreased survival correlating with increased ODC1 (Figure S3.3F), but when considering all TNBC tumors from the dataset, there is no correlation between ODC1 expression and survival (Figure S3.3G). Taken together, this suggests that ODC1 expression does not contribute to the severity of breast cancer at baseline, but may still affect reliance on polyamine synthesis and thus sensitivity to DFMO.

DFMO is approved for treatment of trypanosomiasis and facial hirsutism, and is in clinical trials for treatment or prevention of various tumor types13. DFMO is of particular chemotherapeutic interest in

MYCN-amplified neuroblastoma, where Myc-driven overexpression of ODC1 contributes to tumor hyperproliferation11. At the time of this study, the only studies of DFMO in combination with genotoxic chemotherapies are in brain tumors, and presently there are no clinical trials in breast cancer using DFMO as single agent therapy or in combination with other drugs. Clinical trials in the late 1990’s established that DFMO reduces polyamines in breast cancer patients, but it did not reduce tumor burden as a single agent43,44. One study found that DFMO decreased growth of some TNBC cells as xenografts, and decreased metastases to the lungs45. Application of DFMO in combination with doxorubicin to TNBC in pre-clinical models would lend further support for the efficacy of this combination, with likely promise due to the low toxicity of DFMO. Combining DFMO with genotoxic chemotherapies could also be

effective in other tumor types with elevated ODC activity or expression, including melanoma, esophageal, prostate, and colorectal cancers16,46-48.

The increased expression of ODC in TNBC could be due to changes in ODC1 copy number, but may also be due to other regulatory factors (Figure 3.3). For example, MYC amplification also correlated with increased ODC1 transcript in TNBC, but not in non-TNBC patient samples, and copy number and transcript of antizyme and AZIN1 were altered in TNBC patient samples in a manner that would lead to increased ODC protein stability. Changes in polyamine metabolism downstream of ODC also indicate that this pathway in general is of critical importance in TNBC etiology as well as advanced disease. The levels of spermine synthase transcript and its catabolic product diacetylspermine are elevated in TNBC patients and predictive of poor survival, and spermidine is elevated in breast tumors and increases further with tumor grade49,50. Determining which of these factors are predictive of DFMO sensitivity will be important to identify a biomarker that can identify which patients are most likely to benefit from combination therapy strategies that include DFMO.

It is also interesting to note that the ODC1 gene is on the same amplicon as LPIN1, which encodes the rate-limiting enzyme in phospholipid synthesis. LPIN1 is frequently overexpressed in TNBC as has been proposed as part of a gene signature to identify TNBC, and is essential for TNBC growth and survival as LPIN1 knockdown decreases growth and induces autophagy and apoptosis51-53. In the

METABRIC dataset, LPIN1 was also one of the top 5 metabolic genes enriched in TNBC (Figure S3.3C).

Of tumors in the METABRIC dataset with amplification in ODC1 or LPIN1, 26% had amplification of

ODC1 alone, and 70% both, indicating that they are frequently co-amplified (Figure S3.3D). Though

LPIN1 has been shown to promote mammary cell transformation and tumorigenesis, it may be that co- amplification of LPIN1-ODC1 provides additional benefit to the tumor, such that ODC1 is not merely a passenger on the amplicon54. Regardless of the role of ODC1 in the selection for the initial amplification event in tumors, tumors with LPIN1-ODC1 co-amplification may be more sensitive to DFMO than tumors without amplification of this region.

The pronounced effects of DFMO as a single agent on TNBC cell viability (Figure 3.4F-G) indicate

that this molecular subtype of breast cancer is especially reliant on de novo polyamine biosynthesis, since in many other cell types, targeting polyamine synthesis is not effective unless combined with a polyamine uptake inhibitor12. While the specific polyamine transport mechanisms are not well defined, it will be interesting to investigate the expression and function of polyamine transporters in TNBC, as deficiencies in transport systems may contribute to a more pronounced reliance on intracellular biosynthesis13. Further characterization of polyamine metabolism and the effects of DFMO treatment in TNBC will help identify why this subtype is particularly sensitive.

To our knowledge, this is the first study to profile metabolic changes in response to DFMO in breast cancer. Adding exogenous polyamines or thymidine was insufficient to rescue the effects of DFMO in

TNBC (Figure 3.1). Importantly, it is evident that treatment of TNBC cells with DFMO also affects metabolites outside of polyamine metabolism, related to nucleotide, one-carbon, and other amino acid metabolism. While in colon tumor cells the effects of DFMO were the result of depleting SAM and nucleotide pools and could be reversed with exogenous thymidine, this was not the case in TNBC cells

(Figure 3.1)23. Since TNBC cells are more sensitive to DFMO than non-TNBC cells, this implies that an intrinsic genetic or epigenetic property of TNBC renders these tumors more susceptible to ODC inhibition, at least in vitro. Whether this represents a metabolic vulnerability in TNBC that can be exploited therapeutically remains to be determined, but given the efficacy of DFMO used in other indications, this warrants exploration in pre-clinical models.

In summary, we have shown that by targeting ODC with the inhibitor DFMO, we can sensitize TNBC cells to doxorubicin, and that this may be specific to TNBC cells due to increased ODC expression.

Further studies on the mechanisms of sensitization, reliance of TNBC cells on de novo polyamine synthesis, and evaluation of combinations in pre-clinical models will provide additional insight for eventual therapeutic applications.

3.5 Materials and Methods

Cell culture

SUM-159PT and SUM-149PT cells were obtained from Asterand Bioscience; all other cell lines were obtained from the ATCC. Cell lines were authenticated using short tandem repeat profiling, and no cell lines used in this study were found in the database of commonly misidentified cell lines, which is maintained by the International Cell Line Authentication Committee and National Center for

Biotechnology Information BioSample. All cells were maintained in RPMI medium (Wisent Bioproducts) containing 10% FBS (Gibco). Cells were passaged for no more than 4 months and routinely assayed for mycoplasma contamination.

Chemotherapy agents and inhibitors

Doxorubicin was purchased from Cell Signaling Technology and dissolved in DMSO at 10mM; DL-α-

Difluoromethylornithine (hydrochloride hydrate) was purchased from Cayman Chemical and dissolved in

DMSO at 50mM; spermidine (10mM), thymidine (100mM), methionine (100mM), and aminoguanidine hydrochloride (1M) were purchased from Sigma and dissolved in water at indicated concentrations, except for aminoguanidine which was dissolved in DMSO; putrescine (dihydrochloride) was purchased from Santa Cruz Biotechnology and dissolved in water at 10mM; folic acid was purchased from Gibco and dissolved in DMSO at 10mM; N-ω-Hydroxy-L-norarginine acetate salt was purchased from Bachem and dissolved in water at 50mg/mL. DMSO for use as an organic solvent and vehicle control was purchased from Fisher Scientific.

Antibodies

ODC (MABS36, 1:100) was purchased from Millipore. Vinculin (13901, 1:1000) was purchased from

Cell Signaling Technology. Antibodies were used at indicated dilutions in 5% milk (Andwin Scientific) in

TBST buffer (Boston Bioproducts).

LC/MS-MS metabolomics profiling

Cells were maintained in RPMI + 10% FBS, and fresh medium was added at the time cells were treated.

Electrospray source voltage was +4950 V in positive ion mode and −4500 V in negative ion mode. The dwell time was 3 ms per SRM transition55. Peak areas from the total ion current for each metabolite were integrated using MultiQuant v2.1.1 software (AB/SCIEX). Metabolite total ion counts were the integrated total ion current from a single SRM transition and normalized by cellular protein content. Nonhierarchical clustering was performed by Metaboanalyst 4.0 using a Euclidean distance measure and Ward’s clustering algorithm56.

Polyamine concentration determinations

Cells were washed with PBS and harvested in ODC breaking buffer (25mM Tris-HCl (pH 7.5), 0.1mM

Standards prepared for HPLC included diaminoheptane (internal standard), PUT, SPD, and SPM, all of which were purchased from Sigma Chemical Co. (St. Louis, MO, USA). Intracellular polyamine concentrations are presented relative to total cellular protein, as determined using Bio-Rad protein dye

(Hercules, CA) with interpolation on a bovine serum albumin standard curve.

Immunoblotting

Cells were washed with ice-cold PBS (Boston Bioproducts) and lysed in radioimmunoprecipitation buffer

(1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS, 150 mM NaCl, 50 mM Tris-HCl (pH 7.5), protease inhibitor cocktail, 50 nM calyculin A, 1mM sodium pyrophosphate, and 20 mM sodium fluoride) for 15 min at 4°C. Cell extracts were cleared by centrifugation at 14,000 rpm for 10 min at 4°C, and protein concentration was measured with the Bio-Rad DC protein assay. Lysates were resolved on acrylamide gels by SDS-PAGE with 20µg/lane and transferred electrophoretically to nitrocellulose membrane (Bio-

Rad) at 100 V for 90 min. Blots were blocked in TBS buffer (10 mmol/L Tris-HCl, pH 8, 150 mmol/L

Quantitative real-time polymerase chain reaction

Total RNA was isolated with the NucleoSpin RNA Plus (MACHEREY-NAGEL) according to the manufacturer’s protocol. Reverse transcription was performed using the TaqMan Reverse Transcription

Propidium iodide viability assay

Cell viability was assayed with a propidium iodide-based plate-reader assay, as previously described58.

Briefly, cells in 96-well plates were treated with a final concentration of 30mM propidium iodide

(Cayman Chemical) for 20 min at 37°C. The initial fluorescence intensity was measured in a GENios FL

Sulforhodamine B growth assay

Population size as cell confluency was assayed by sulforhodamine B staining, as previously described59.

Briefly, cells in 96-well plates were fixed in 8.3% final concentration of tricarboxylic acid of 1 hour at

4°C, washed three times with water, and stained for 30 minutes with 0.5% sulforhodamine B in 1% acetic acid. Plates were washed 3 times with 1% acetic acid and dye was solubilized in 10mM Tris pH 10.5 before measuring absorbance at 510nm on an Epoch plate reader (BioTek). Relative growth was determined as (treatment absorbance / control absorbance) following 72 hours of growth. Doubling time was determined by fitting an exponential growth equation to population sizes over four days.

Analysis of public data

Data from METABRIC and CCLE were accessed through cBioPortal, www.cbioportal.org. Data was analyzed using MATLAB R2017b and Prism 7.

Statistics and reproducibility

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90 Chapter 4

Discussion

4.1 Chemotherapy and polyamines in TNBC: Summary and future perspectives on thesis

Advances in our understanding of tumor metabolism have identified druggable targets in multiple tumor types, which we endeavored to do in triple-negative breast cancer (TNBC) for combination with standard- of-care genotoxic chemotherapies. Focusing on arginine metabolism for its role in tumor development and progression, we identified changes in arginine catabolism and polyamine synthesis in response to genotoxic chemotherapy drugs cisplatin and doxorubicin. We found that genotoxic chemotherapies reduced the protein levels and activity of ornithine decarboxylase (ODC), a rate-limiting enzyme in polyamine synthesis (Chapter 2), and that the ODC inhibitor DFMO specifically sensitized TNBC cells to doxorubicin (Chapter 3). However, there is still much to be investigated through future studies, particularly the mechanism connecting DNA damage to ODC regulation, and how altered polyamine metabolism protects TNBC from doxorubicin-induced cell death.

4.1.1 Chemotherapy drugs alter ODC and polyamine metabolism

We identified a decrease in ODC in response to chemotherapy drugs that is dependent on the proteasome and antizyme (AZ) (Figure 4.1). AZ binds ODC, preventing it from forming a functional homodimer, and targets it to the proteasome for non-ubiquitin mediated degradation; AZ itself is not degraded (Figure

1.6B)1. We observed that AZ is required for the reduction of ODC protein following doxorubicin exposure

(Figure 2.5E), but due to a lack of sensitive and specific antibodies, we could not measure changes in AZ protein following chemotherapy. AZ is regulated at the level of translation by polyamines binding to

OAZ1 transcript and inducing a +1 ribosomal frameshift1; thus, AZ levels are increased when the intracellular polyamine concentration is high. It is possible that the initial increase of polyamines after 8h chemotherapy exposure (Figure 2.3) is sufficient to increase translation of OAZ1 and elevate AZ, targeting ODC for degradation. Improved reagents to measure AZ protein, or alternate methods such as mass spectrometry, would be useful to determine if AZ levels change in response to chemotherapy.

Figure 4.1: Chemotherapy decreases ODC and polyamines. Genotoxic chemotherapy leads to a decrease in ODC dependent on AZ and the proteasome. The resulting decrease in polyamines could interfere with cell growth and survival, and decreased protection from DNA damaging agents. Abbreviations: AZ, Ornithine Decarboxylase Antizyme; ODC, Ornithine Decarboxylase.

It is also possible that AZ levels are not altered by chemotherapy exposure in our system, but activity is. AZI is structurally similar to ODC, and though it lacks catalytic activity, it is bound by AZ with a greater affinity than ODC, thus protecting ODC from AZ binding and degradation2,3. Decreased levels of AZI would free AZ to bind ODC, so chemotherapy could also be affecting ODC levels by decreasing AZI. This could be partially addressed by rescue experiments, to see if overexpression of AZI can maintain levels of ODC in the presence of chemotherapy. Measuring AZI protein following chemotherapy would also be necessary to determine if it is affected, and additional work would be

required to determine the mechanism.

Whether the decrease in ODC is regulated through AZ or AZI, it remains to be determined how chemotherapy and DNA damage lead to these changes in polyamine regulatory proteins. One study showed that doxorubicin can increase the protein levels of AZ in H1299 non-small cell lung carcinoma cells and mouse embryonic fibroblasts, dependent on c-Jun, though the complete mechanism connecting c-Jun and AZ elevation was not determined4. We investigated the effects of blocking DNA damage response signaling pathways on ODC response to doxorubicin, but no inhibitors showed pronounced effects (Figure S3.2C). It is possible that inhibition of single pathways is insufficient due to compensatory signaling through other arms, or that we did not yet interrogate the responsible pathway. However, it is unlikely to be solely through BRCA1 or TP53-mediated pathways since we observed decreased ODC in cell lines with mutations in these pathways (Figure 2.3, Table S3.1). Future studies should focus systematically on DNA damage signaling pathways, including targeting multiple arms at once. This could also be informative for determining which patients would be most likely to exhibit altered polyamine metabolism after chemotherapy, based on their mutational and functional status of different DNA damage repair and response pathways.

It is unclear what, if any, adaptive effects result from altering ODC levels in response to chemotherapy. ODC is known to be elevated in response to cellular stresses, specifically changes in osmolarity and redox state, and is induced by oxidative stresses including hypoxia and chemical sources such as hydrogen peroxide and tert-butylhydroquinone, mediated by Nrf25-7. The observed increase in

ODC and polyamine at 8 hours following chemotherapy may be a stress response to damage inflicted by the chemotherapy. Perturbing different stress response proteins, including Nrf2, and observing their effect on ODC response to chemotherapy may be more informative than focusing specifically on DNA damage response factors. The resulting increase in polyamine levels (Figure 2.2E) may be sufficient to induce antizyme translation1,8, leading to the observed antizyme-mediated degradation of ODC (Figure 2.5).

While this would suggest that the decrease in ODC in response to chemotherapy is not adaptive, that does not imply that it cannot be exploited for therapy; indeed, it informed our combination of DFMO and

chemotherapy, which increased chemosensitivity (Figures 3.1 and 3.2).

Alternatively, the decrease in ODC and polyamine levels may be an adaptive response to chemotherapy. Polyamines can bind to and compact DNA, which can protect it from damage but may also hinder access by repair machinery9,10. The synthesis of polyamines also requires methyl donors derived from the folate cycle, decreasing their availability for other methylation reactions including DNA and histone methylation11,12. Increased ODC activity and polyamine synthesis decrease the availability of

SAM, leading to inhibition of DNA methyltransferases11. It is possible that polyamine synthesis is decreased following DNA damage in order to increase availability of methyl groups for methylation of repaired DNA or production of nucleotides, especially since we observed depleted pools of methyl donors such as SAM following chemotherapy (Figure 2.2C), and depletion of methyl donors can increase DNA damage13.

Interestingly, one study examining ODC-independent functions of antizyme observed that antizyme increased expression of both the catalytic and regulatory subunits of the DNA repair factor

DNA-dependent protein kinase (DNA-PK) in oral cancer cells. Antizyme expression also protected cells from DNA damage induced by gamma irradiation by increasing the repair of double-stranded breaks14. It is possible that antizyme expression is increased following chemotherapy exposure in order to adapt by increasing DNA-PK activity, and the decrease in ODC is a result of increased antizyme levels. A more thorough mechanistic understanding of ODC and its regulatory factors in response to chemotherapy, and the potential adaptive effects of decreased polyamines, could reveal novel ways to exploit this metabolic vulnerability in breast cancer.

4.1.2 A specific role for ODC and polyamine synthesis in TNBC

Although a reduction in ODC was observed in all tested breast cancer cell lines (Figure 2.3), we discovered that the ODC inhibitor DFMO sensitized TNBC cells to doxorubicin, while most non-TNBC cells were not sensitized (Figure 3.2C). We hypothesized that there were intrinsic differences in ODC or polyamine metabolism between TNBC and non-TNBC, and found that ODC is expressed at higher levels

in patients with TNBC (Figure 3.3A,C). This was corroborated in cell lines, as TNBC cells were more sensitive to DFMO as a single agent, indicating that targeting DFMO is a metabolic vulnerability in

TNBC (Figure 3.4).

It is not particularly surprising that depleting polyamines sensitizes TNBC cells to genotoxic agents, since polyamines are known to interact with cellular anions, including DNA, and by binding DNA can occlude genotoxic agents. Polyamines in the nucleus can stabilize DNA and promote its condensation, thus protecting it from genotoxic agents10. Polyamines can also enhance repair of double-stranded DNA breaks by increasing strand exchange by RAD51 recombinase by capturing the homologous duplex

DNA15. DFMO treatment and reduction of polyamines delays repair of UV-induced DNA breaks though defects in gap-filling16. Thus, depleting polyamines has been shown to increase DNA damage and induce cell cycle arrest17,18.

The cell cycle arrest would be concerning if polyamine depletion arrested cells in a state where they were less sensitive to genotoxic damage by our agents of interest, though studies from the 1980’s set precedent for the effectiveness of this combination, showing that combining doxorubicin and DFMO have additive effects on pancreatic cancer cells, and prolong survival in a rat model of prostate cancer19,20.

Tumor cells are most sensitive to damage by cisplatin in G2/M, and doxorubicin induces more damage in

G2 cells such that they are more sensitive to doxorubicin than cells in G121-23. Reports differ on the effects of DFMO treatment on the cell cycle, of either G1 or G2 arrest, indicating that it may have different effects in different cells types24-27. However, ODC depletion induces G2 arrest, and ornithine and putrescine are important for progression through S and G228,29, indicating that our observed decrease in

ODC could lead to arrest of cells in G2, where they are more sensitive to genotoxic damage from cisplatin and doxorubicin. Additionally, ARG2 inhibition and the resultant decrease in polyamines leads to G2/M arrest, unlike arginine deprivation which leads to G1/G0 arrest30, and thus could account for the ability of arginase inhibition to sensitize cells to doxorubicin (Figure 3.1C).

Studying the effects of polyamines combined with chemotherapy will be important to determine the functional mechanism linking polyamine metabolism and chemosensitivity in TNBC. Analysis of the

effects of DFMO on cell cycle distribution of TNBC cells could inform if that is a major mechanism for its effects on sensitivity to doxorubicin. Comparison of DFMO to other drugs that lead to similar cell cycle profiles, and how those affect sensitivity to doxorubicin, could indicate the functional importance of altering the cell cycle. Further studies on the effects of polyamine depletion and ODC inhibition on the cell cycle of TNBC cells would also be informative for selecting optimal combinations and dosing schedules.

It is possible that the effects of DFMO on sensitivity to doxorubicin are not primarily through cell cycle regulation, and other consequences bear further investigation. DFMO could instead be increasing the apoptotic priming of the cells, and thus rendering them more susceptible to apoptosis when treated with doxorubicin. Apoptotic profiling, as well as investigation of the method or methods of cell death from the combination of DFMO and doxorubicin, could point to a role for polyamines in priming31.

Alternatively, DFMO may be removing a protective effect of polyamines9,32,33. Measuring DNA damage induction after DFMO pretreatment could indicate if DFMO renders cells more susceptible to DNA damage. DFMO pretreatment may preferentially sensitize to specific types of damage, which could be interrogated by comparing damage inflicted by genotoxic agents with different mechanisms of action.

The broad changes in intracellular metabolites in response to DFMO treatment (Figure 3.1E-F) indicate that DFMO has broad effects reaching beyond polyamine metabolism. Polyamines intersect with many other metabolic pathways, and some of these known interactions are reflected in our results. An area of particular interest for combination with genotoxic chemotherapy is the competition for SAM between polyamine metabolism and folate metabolism. DFMO treatment can lead to a cycle of SAM consumption and regeneration that depletes tetrahydrofolate pools, thus decreasing the cells’ ability to synthesize thymidine34. However, thymidine alone or in combination with polyamines did not rescue the sensitivity to doxorubicin imparted by DFMO (Figure 3.1C). DFMO treatment may have broader effects, particularly on epigenetics, due to alterations in one-carbon metabolism and availability of methyl group donors. In a mouse model of tuberous sclerosis complex, increased ODC activity in the brain correlated with decreases in transmethylation metabolites including SAM, and DFMO treatment decreased

astrogliosis due to epigenetic effects35. Since these epigenetic alterations are less transient than changes in metabolite pools, addition of one or two exogenous metabolites may be insufficient to revert these changes. Given the inability of polyamines and thymidine, folic acid, or methionine to rescue the effects of DFMO in our cells (Figures 3.1C, S3.3A-B), and the broad metabolic effects of DFMO (Figure 3.1F), it is clear that further study is needed on how DFMO and polyamine metabolism alter other cellular metabolic pathways, particularly in TNBC. Measuring polar metabolites in response to DFMO as in

Figure 3.1F, but also comparing those to changes in cells treated with DFMO and exogenous polyamines, could indicate which effects of DFMO are not through decreasing polyamine levels. This could have two different applications: firstly to identify other metabolites that could be combine to rescue the effects of

DFMO on sensitivity to doxorubicin, and secondly to identify metabolic effects of DFMO treatment that could lead to sensitivity to other agents, thus informing rational combinations.

The sensitivity of TNBC cells to DFMO indicates that an effect of their increased basal ODC levels is an increased reliance on ODC for survival. Polyamines are essential for all eukaryotic cells, but they can be acquired from the environment as well as synthesized within the cell36. For this reason, many therapies targeting polyamine synthesis are not effective unless combined with a polyamine uptake inhibitor, which is referred to as “polyamine blocking therapy” (PBT)2. The pronounced effects of DFMO as a single agent on TNBC cell viability (Figure 3.4F-G) indicates that TNBC cells may be more reliant on synthesis than uptake to fulfill their polyamine requirements, while non-TNBC cells are proficient at polyamine uptake and rely less on synthesis (Figure 4.2). While the specific polyamine transport mechanisms are not well defined36, as more details and transporters are identified it would be interesting to investigate their expression and function in TNBC, as deficiencies in transport systems may contribute to a reliance on intracellular synthesis. Even without knowing the components of the transport systems, it would be informative to measure polyamine uptake rates of TNBC and non-TNBC cells lines, which can be done using polyamine analogs37. Additionally, a combination of DFMO with PBT may increase the sensitivity of non-TNBC cells to doxorubicin, and prevent DFMO-doxorubicin combination resistance from developing in TNBC cells from compensatory upregulation of polyamine import.

Figure 4.2: Model for specific sensitivity of TNBC to DFMO. TNBC is more sensitive to ODC inhibitor DFMO, and has increased expression of ODC. This could indicate that TNBC cells are more reliant on de novo synthesis of polyamines, while non-TNBC cells may survive DFMO treatment by obtaining polyamines from other sources, such as uptake of extracellular polyamines. Abbreviations: DFMO, Difluoromethylornithine; ODC, Ornithine Decarboxylase; TNBC, Triple-Negative Breast Cancer.

4.1.3 Identifying additional metabolic vulnerabilities in TNBC

While this thesis focused on ODC and its particular role in TNBC, our data indicate that other metabolic pathways may also be of special importance in TNBC. It is clear that other amino acid metabolism pathways are relevant, particularly serine and glycine, since both PSAT1 and MTHFD1L transcripts are increased in TNBC (Figure S3.3C). PSAT1 is known to enhance proliferation of ER-negative breast cancer38, which supports the potential of our enrichment analysis to identify metabolic transcripts that are important for tumor phenotypes. Less is known about MTHFD1L in breast cancer, though one study observed its correlation with poorer prognosis in breast cancer39. It also promotes growth in hepatocellular carcinoma and esophageal cancer40,41, suggesting that it would be interesting to study these effects in

TNBC. Transcripts in lipid and sugar metabolism are also enriched and have been previously connected to transformation and invasion in breast cancer42-44, indicating the importance of multiple macromolecules in tumor metabolism.

The decreased expression levels of other metabolic transcripts (Figure S3.3C) may also indicate metabolic vulnerabilities in TNBC, particularly if loss of certain transcripts could lead to requirements for exogenous sources of metabolites. NAT1 loss alters the energetic profile of TNBC cells, increasing oxidative and glycolytic reserve capacity, indicating that its loss may allow cells to adapt to oxidative stress45. Other studies have implicated a role for NAT1 in TNBC cell morphology and invasion, indicating that it can affect multiple tumor-associated phenotypes46. It is known that ER-negative breast cancers have high levels of alanine due to decreased ABAT expression, as we measured, and that this correlates with poor prognosis47. Altogether, the known functional roles for these metabolic enzymes in TNBC indicates that our analysis was able to identify relevant transcripts, and that further studies on those without well-characterized roles in TNBC may elucidate more novel metabolic vulnerabilities in TNBC.

4.2 Therapeutic potential of targeting tumor metabolism

In the past twenty years, there has been a renewed focus on targeting tumor metabolism as a therapeutic strategy. This has been fueled by identification of metabolic alterations in tumors, but also by the application and repurposing of tool compounds and drugs already available to perturb metabolic pathways. Moving forward, it is important to not only identify these potential targets, but also to focus on which metabolites or pathways are required in specific tumor types, and what molecular signatures can be used as biomarkers to select patients most likely to benefit from these proposed therapies.

4.2.1 Strategies for targeting tumor metabolism

There are two main modalities for targeting tumor metabolism: restriction or supplementation of dietary metabolites, or application of drugs that target metabolic enzymes48. For many metabolic pathways, both

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have been explored, but their efficacy depends on the prevalence of the metabolite in the diet, the efficacy of current drugs and compounds, and any on-target toxicity of pathway perturbations.

Dietary restriction has been historically successful for inborn errors of metabolism (IEMs) such as phenylketonuria (PKU), in which mutations inactivate the enzyme that catabolizes phenylalanine so it builds up to toxic levels. Since phenylalanine can be reduced in the diet by restricting consumption of proteins and supplementing with amino acids other than phenylalanine, this diet is greatly beneficial to patients with PKU49. However, IEMs result from single specific genetic changes that present clear targets, whereas tumors harbor many mutations or alterations and expression, which vary between patients as well as within a single tumor. Therefore, identifying dietary restrictions that can significantly affect tumor progression while still containing nutrients necessary for the patient’s general health is challenging.

Some dietary strategies have exhibited success in specific tumors, generally where the tumor requires a metabolite that is not required for whole-body nutritional health. Non-essential amino acids

(NEAAs) are one such metabolite. Since many tumors can synthesize these amino acids, targeting

NEAAs is most promising for auxotrophic tumors48. Dietary restriction of serine can lower plasma serine and glycine, and has been effective in mouse models of intestinal cancer and lymphoma50. However, due to increased expression of serine synthesis genes such as PHGDH, TNBCs are unlikely to respond to serine deprivation48. Mice fed a synthetic amino acid diet deficient in arginine had fewer skin papillomas due to the reduction in arginine, ornithine, and polyamines51. It is not known how effective this would be in breast cancer; a study of multiple cancer cell lines found that one breast cancer line survived while another died in the absence of exogenous arginine, so the arginine dependency of breast cancer cells is variable52. Some NEAAs are difficult to remove from the diet, but can be modulated by altering consumption of other metabolites. It is very challenging to limit cysteine intake, but in breast cancer cells that rely on methionine for cysteine synthesis, methionine-restricted conditions decrease proliferation53.

Dietary restriction may not always affect the primary tumor, but it can be used to decrease potential for metastases and thus prolong survival. A study in a murine model of breast cancer found that limiting dietary asparagine decreased metastases, whereas supplementary asparagine promoted metastases54.

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Dietary restriction can also have adverse effects on normal physiology, so should be carefully considered55.

It is extremely important to consider the effects of metabolic changes in tumor-specific settings56.

The ketogenic diet, which replaces most carbohydrates with proteins and fats, has varied effects in different tumors types. It has anti-tumor effects in neuroblastoma, prostate, colon, pancreatic, and lung cancers, but there are reports of pro-tumor activities in melanoma and prostate cancer57. There is little evidence that the ketogenic diet has any effect in breast cancer outside of individual case studies that reported anti-tumor effects58,59. Specific perturbations of lipid metabolism by blocking ACC also have opposing effects in different models even in the same tissue: ACC knockdown increased growth of A549 lung epithelial cells as xenograft tumors, but treatment of murine models of lung cancer with an ACC inhibitor inhibited tumor formation60,61.

Most attempts to alter tumor metabolism are achieved through application of specific inhibitors and drugs. As new metabolic vulnerabilities are identified, as discussed below, novel compounds can be developed. However, a major advantage in the tumor metabolism field is the multitude of drugs already available. Drugs that are used to manage cholesterol, diabetes, and infectious diseases target metabolic enzymes, and can be repurposed for cancer therapy36,48. Metformin is used to treat diabetes but can also reduce breast cancer risk by decreasing mTOR signaling and lipid synthesis, and increase sensitivity to mTOR inhibitors and other anti-tumor drugs48,62,63. A combination of metformin and propranolol, a beta- adrenergic receptor blocker used to treat hypertension and anxiety, decreased proliferation, invasion, and mitochondrial metabolism of TNBC cells in culture and in mouse xenograft tumors64. Cholesterol- lowering statins decrease proliferation and increase apoptosis of breast cancer cells, and a clinical trial of atorvastatin decreased tumor cell proliferation in breast tumor that express cholesterol synthesis enzyme

HMGCR65. Aspirin may also have antitumor effects through increasing AMPK and decreasing mTOR activity and lipid synthesis, and can decrease the growth and colony forming ability of SUM-159PT cells in culture, or as a xenograft in combination with a PI3K inhibitor48,66. Additionally, as discussed in this thesis and other works, the ODC inhibitor DFMO shows promise for anti-tumor effects, and is relatively

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safe and clinically approved for treatment of trypanosomiasis36. A benefit of repurposing extant drugs is not only their availability, but also their relative safety compared to most cancer therapies. This is promising for use in combination therapy, since most of these metabolic drugs have low or manageable toxicity, and thus would not greatly increase whole-body toxicity as part of a cancer therapy regimen.

When determining how to alter tumor metabolism, it is important to consider these different modalities. Dietary restriction or supplementation can be efficacious and relatively safe if the metabolite is uniquely required by the tumor and easy to limit from the diet. However, in many cases it is necessary to target specific metabolic enzymes or pathways. There are many drugs available to do this, so the most important questions moving forward are how to identify druggable metabolic vulnerabilities and what are the tumor settings and combinations in which they should be applied.

4.2.2 Identifying targetable metabolic vulnerabilities

Numerous studies have identified changes in metabolism within tumors or their microenvironments, and proposed strategies to target these changes in order to alter tumor progression or resistance to other therapies. However, it is clear from conflicting reports on tumor-promoting or protective roles of metabolic pathways that vulnerabilities will vary between tumors by tissue of origin, or even amongst patients with the same type of tumor. Two areas that require additional study for the advancement of metabolic perturbations as cancer therapies are treatment efficacy in different tumor types, and identification of biomarkers to stratify patients.

As discussed above, implementation of the ketogenic diet is an example of a metabolic alteration that has opposite effects in different tumor types. Though not all differences will be this drastic, many potential targets have been identified and characterized in one tumor type, but then shown little or no effect in other tumors. This highlights the diversity of tumors and their metabolism, and the importance of identifying vulnerabilities in specific tumor types.

In this thesis, we compared altered expression of metabolic genes between TNBC and non-

TNBC. Molecular profiling across tumor types has shown that basal breast cancer is a distinct disease

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from other breast cancers67; since there is high correlation between TNBC and basal breast cancer, our analysis compares two distinct tumor types to identify changes in metabolism68,69. Most of the top enriched transcripts in TNBC were related to NEAA metabolism, highlighting the importance of studying

NEAA metabolism specifically in TNBC.

In order to identify targets that are most likely to have an effect on tumor progression, it has been suggested that future research first focus on the identification of metabolic processes that are limiting for tumor cell proliferation70. As seen with many targeted tumor therapies, resistance can arise by rewiring and compensatory upregulation of parallel pathways. However, some metabolic reactions are non- redundant; thus, if the reaction is required by a tumor, targeting it would be predicted to have a greater effect than targeting a less essential or redundant reaction. Studying the metabolic states of tumors to identify these non-redundant reactions, and perturbing them to determine their requirement for tumor growth or progression, is likely to reveal targetable metabolic vulnerabilities. Understanding the patterns underlying these metabolic dependencies can also help identify which tumor types are likely to respond similarly based on their metabolic wiring.

An example of taking advantage of these metabolic bottlenecks is arginine deprivation strategies in tumors that are arginine auxotrophs. Decreased expression of arginine synthesis enzymes AS and ASL render cells unable to synthesize arginine from citrulline71. Thus, when arginine is converted to citrulline by administration of modified bacterial enzyme arginine deaminase (ADI), these tumor cells die. This has been most promising in melanoma and hepatocellular carcinoma, where the majority of tumors are AS- negative and thus respond well to ADI72. Clinical trials of ADI led to depletion of plasma arginine below detectable levels and stabilization of disease, though further studies and stratification will be necessary to determine its therapeutic merit48,73-75. Additionally, cisplatin treatment can lead to suppression of AS or

ASL in order to divert nitrogen into pyrimidine synthesis, so combination with ADI could be used to eliminate cisplatin-resistant cells76,77. This highlights the importance of identifying specific tumor types that are most likely to response to a metabolic therapy, as well as different cells within a heterogeneous tumor that are most likely to be affected. These metabolic perturbations have promise to overcome

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resistance to current and emerging therapies.

Once targetable vulnerabilities are identified, it is equally important to identify predictors of response. Many targeted therapies fail in clinical trials because biomarkers of response are not identified, and thus the overall response rate is poor. For clinical application, development of simple assays will be important if they are to be implemented in trials or therapies. Genetic and transcriptomic analysis is not always predictive of enzyme activity or abundance since many metabolic enzymes are regulated post- transcriptionally or by product and substrate availability. Additionally, it has been shown that responsiveness to metabolic therapies often does not correlate with enzyme expression56, and thus further work is necessary to identify what are the factors affecting response to these therapies, and if they have predictive potential. Development of assays to reliably measure enzyme activity, or identification of metabolites that can be measured in serum, will be most useful as clinical biomarkers.

4.2.3 Polyamine metabolism is a targetable vulnerability in tumors

This thesis specifically focused on changes in polyamine metabolism in TNBC, and the ability of the polyamine synthesis inhibitor DFMO to target TNBC and sensitize it to standard-of-care chemotherapy.

This response is promising for clinical application due to the safety of DFMO, and also informative for understanding how polyamine synthesis may be uniquely required in TNBC.

The therapeutic window for DFMO is also promising since transformed cells are more sensitive to polyamine depletion than non-transformed cells, and DFMO is a well-tolerated drug36. A role for

DFMO in anti-tumor therapies has been most notable in colon cancer. In patients with familial adenomatous polyposis, who are highly likely to develop colon cancer, and in mouse models with mutant

APC, ODC activity and polyamine levels are increased. Treatment with DFMO and an NSAID drug reduced risk factors and tumor burden in patients prone to developing colon cancer78. At the time of this study, the only combinations of DFMO with genotoxic chemotherapies were in brain tumors

(clinicaltrials.gov). No trials have been done of DFMO in breast cancer, though a general solid-tumor trial of DFMO combined with a polyamine transport inhibitor (PTI) recently began accrual (NCT03536728).

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However, it is important also to consider expression in tumor subtypes, as the enrichment of ODC and sensitivity to ODC inhibition would have been less pronounced in a study across breast cancer without separately considering TNBC.

The sensitivity of TNBC to DFMO as a single agent suggests that they are reliant on de novo synthesis of polyamines (Figure 4.2). Polyamines are synthesized de novo from ornithine by ODC, but can also be recycled or taken up from exogenous sources (Figure 1.5B). The sensitivity to DFMO indicates that de novo synthesis may operate as a bottleneck in TNBC, if recycling and uptake do not contribute significantly to the polyamine pool. Further work is necessary to determine if this is the case, by measuring polyamine recycling and uptake, as well as investigating potential upregulation of these compensatory pathways as a resistance mechanism under long-term DFMO treatment.

Currently, little is known about specific mechanisms of polyamine transport, and it has been highlighted as one of the most important areas of ongoing research in the polyamine metabolism field2,36.

Though the mechanisms are incompletely understood, polyamine analogs and PTIs take advantage of polyamine transport to alter intracellular polyamine metabolism. Many different PTIs have been developed, including ones with improved effects against breast cancer cells 79. The most promising therapeutic perturbation of polyamine metabolism is implementing a combination of synthesis and transport inhibition, termed “polyamine blocking therapy” (PBT). Cells can increase polyamine import to compensate for the effects of DFMO, so either extracellular polyamines must be depleted, or transport inhibited. Dietary restriction of polyamines is unlikely to be effective due to their prevalence in many foods and contributions from microbiota, so instead PTIs are combined with DFMO in PBT. PBT has been effective in many tumor models, including neuroblastoma and pancreatic cancer36. The greatest promise of PBT comes from its additional effect on the tumor microenvironment and alleviation of immune suppression. In a murine model of breast cancer, mice treated with PBT prior to tumor resection did not form tumors when re-injected with mammary tumor cells; this is likely due to PBT promoting an increase in CD3+ T-cells and decrease in myeloid suppressor cells80. These combinations may also be more effective in TNBC than DFMO alone, by preempting resistance through upregulation of polaymine

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import and by increasing anti-tumor immune activities. Testing PBT in TNBC as well as non-TNBC could further the application of polyamine metabolism inhibition as an anti-tumor therapy in breast cancer and other tumor types.

4.3 Summary

Studying tumor metabolism, by comparing different tumor types as well as responses of tumors to therapeutic perturbations, has led to identification of potential targets that can have anti-tumor effects alone or in combination with current clinical therapies. Targeting these metabolic vulnerabilities can improve standard-of-care therapies by taking advantage of many metabolic drugs that have low toxicity and are already approved for treatment of other diseases. As the study of tumor metabolism continues to grow, it is evident that there are successes in targeting specific metabolic reactions in specific tumor types. As the field progresses towards clinical applications of these metabolic vulnerabilities, it is essential to proceed with the most promising tumor-essential targets and identification of biomarkers to indicate the patients most likely to respond from targeted metabolic therapies.

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53 Lien, E. C., Ghisolfi, L., Geck, R. C., Asara, J. M. & Toker, A. Oncogenic PI3K promotes methionine dependency in breast cancer cells through the cystine-glutamate antiporter xCT. Science Signaling 10, doi:10.1126/scisignal.aao6604 (2017).

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66 Henry, W. S. et al. Aspirin Suppresses Growth in PI3K-Mutant Breast Cancer by Activating AMPK and Inhibiting mTORC1 Signaling. Cancer Res 77, 790-801, doi:10.1158/0008- 5472.CAN-16-2400 (2017).

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113 Appendix I

Differential sensitivity of breast cancer cells to arginine deprivation

Summary

Numerous studies and our own research have indicated that arginine and its metabolites are important for tumor growth and progression. Here, we investigated the effects of altering exogenous arginine levels on breast cancer cell proliferation and viability. In all cell lines tested, decreasing exogenous arginine led to decreased cell growth. In a subset of lines, complete removal of arginine led to induction of cell death.

This correlated with hormone receptor status, indicating that triple-negative breast cancer (TNBC) cells are particularly reliant on exogenous arginine for survival.

Introduction

Arginine is a nonessential amino acid in human adults, though it can become conditionally essential in vascular and infectious diseases and in cancer. Arginine can be taken up from the environment or synthesized from glutamate. Arginine is metabolized in the cytosol and mitochondria, intersecting with the urea cycle, nitric oxide synthesis, polyamine synthesis, and proline metabolism. Arginine can promote cell growth by contributing to the synthesis of biomolecules, and energy production via the TCA cycle1.

Arginine metabolism has been implicated in multiple cancers. Since specific nonessential amino acids may become conditionally essential in tumors, studies have evaluated tumor-specific amino acid requirements1,2. These differences in metabolic requirements can be exploited to selectively inhibit tumor cell growth by metabolite starvation or treatment with specific metabolic inhibitors3-8. Arginine auxotrophic cancers, such as malignant melanoma, hepatocellular carcinoma, and prostate cancer, require arginine because they do not express arginine synthesis enzymes argininosuccinate synthase (ASS1 gene,

AS protein) or argininosuccinate lyase (ASL)9,10. They are sensitive to arginine depletion by the bacterial enzyme arginine deiminase (ADI), which converts arginine to citrulline11. However, ADI is only effective in cells lacking AS or ASL; otherwise, citrulline is an effective precursor of arginine12. Resistance to ADI can develop through up-regulation of PI3K signaling and ASS1 transcription13. Consequently, melanoma cells with elevated AS are more sensitive to PI3K/Akt inhibitors14.

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Due to metabolic variation, it is important to consider arginine therapies in cancer-specific contexts. An exemplary case is AS: it has opposite effects on cell migration and overall prognosis in different tumor types11,15,16. AS expression has been used as a biomarker for response to arginine deprivation in malignant melanoma, but has not yet been applied in breast cancer11,15,16. Identification of biomarkers for arginine dependency and implementation of drugs targeting weaknesses other than AS deficiency are necessary, as less than 10% of breast tumors lack AS17.

Breast cell lines respond differently to arginine starvation. For example, MCF-7 and MCF-10A cells become quiescent upon arginine removal, but only MCF-7 cells recover upon arginine re-addition18.

In contrast, ZR-75-1 cells undergo cell death in the absence of arginine19. The determinants of these different responses to arginine deprivation are poorly understood, in large part due to the small numbers of breast cancer cell lines that have been analyzed in these contexts20. Therefore, a systematic characterization of arginine metabolism and the molecular determinants of cellular responses to arginine availability across many breast cancer cell lines is needed.

Results

To determine the requirements of breast cancer cells for supplemental arginine, a panel of breast cancer cell lines (Table S3.1) were grown in full media arginine (200mg/L) or low arginine (6mg/L), which is below physiological levels of 80-120µM in healthy adults21. Low arginine was selected over complete arginine deprivation since it is established that cells in culture require exogenous arginine22. Low arginine led to decreased growth rate of all cell lines (Figure S1.1A), which correlated with their doubling time in full arginine (Figure S1.1B).

In order to better differentiate the response to arginine deprivation, we also cultured cells in the absence of arginine and measured viability. Some cells remained viable, while others died in the absence of arginine (Figure S1.2A). Hormone receptor status was predictive of response to arginine deprivation

(Figure S1.2B), and response did not correlate with doubling time (Figure S1.2C).

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Figure S1.1: Comparison of proliferation in high and low arginine. (A) Proliferation of breast cancer cell lines over 6 days measured by SRB assay relative to day 0 population size (n=2-3). All error bars represent SEM. (B) Comparison of doubling time (DT) of breast cancer cell lines in high or low arginine from A; R2 by linear regression. Dotted lines indicate 90% confidence interval.

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Figure S1.2: Effects of arginine deprivation on breast cancer cells. (A) Decrease in viability defined as K < -0.02 in (curve fit) (n=). (B) Comparison of viability at day 8 from A between TNBC and non- TNBC lines. *, p<0.05 by t-test. (C) Comparison of viability at day 8 from A to doubling time (DT) of breast cancer cell lines from 1A (200mg/L arginine); R2 by linear regression. Dotted lines indicate 90% confidence interval. (D) Viability of SUM-159PT cells after 8 days of culture in the presence of absence of arginine and indicated doses of chloroquine (CQ) (n=3 technical replicates). **, P<0.01; ***, P<0.001 by 2-way ANOVA. All error bars represent SEM.

We hypothesized that reliance on autophagy could be a mechanism underlying death under arginine deprivation, such that when starved of arginine some cells over-induce autophagic pathways, leading to death. However, while inhibition of autophagy increased viability in full arginine, it further decreased viability in the absence of arginine (Figure S1.2D).

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Discussion

Altering arginine in the tumor and tumor microenvironment has been proposed as a way to manage tumor growth and immune suppression23. Here we observed that breast cancer cells exhibit decreased growth when cultured in low arginine, and in a subset of cell lines removal of arginine leads to cell death. This death was not due to over-induction of autophagy, but correlated with hormone receptor status.

The correlation between doubling time in full and low arginine (Figure 1.1B) suggests that highly proliferative breast cancer cells have increased reliance on exogenous arginine. This is consistent with recent reports that amino acids and their derivatives account for the majority of cell mass in proliferating cells24, and suggests that these cells may rely on arginine primarily for protein synthesis required to proliferate.

Further study is needed to investigate the reliance on exogenous arginine for viability. TNBC cells were most sensitive to arginine deprivation (Figure 1.2B), and we have previously shown that they are also more sensitive to inhibition of polyamine synthesis from arginine (Figure 2.2D and 3.4G). This could indicate that polyamine synthesis is a major use of arginine in TNBC cells, or that they are reliant on arginine for other cellular processes.

In summary, this study highlights the importance of arginine for growth and survival of breast cancer cells in culture. Future studies on the fate of arginine in these cells, as well as the fate of arginine in breast tumors in vivo, will further elucidate the role of this amino acid in breast tumor growth.

Materials and Methods

Cell culture

SUM-159PT and SUM-149PT were obtained from Asterand Bioscience; all other cell lines were obtained from the ATCC. Cell lines were authenticated using short tandem repeat profiling, and no cell lines used in this study were found in the database of commonly misidentified cell lines, which is maintained by the

International Cell Line Authentication Committee and National Center for Biotechnology Information

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BioSample. All cells were maintained in RPMI medium (Wisent Bioproducts) containing 10% FBS

(Gibco). Cells were passaged for no more than 4 months and routinely assayed for mycoplasma contamination.

Arginine deprivation

Cells were washed with PBS and grown in SILAC RPMI (Thermo Fisher) containing 10% dialyzed FBS

(Gibco), 40mg/L L-lysine-HCl (Sigma) , and 0, 6, or 200mg/L L-arginine (Sigma) for indicated times.

Propidium iodide viability assay

Cell viability was assayed with a propidium iodide-based plate-reader assay, as previously described25.

Briefly, cells in 96-well plates were treated with a final concentration of 30mM propidium iodide

(Cayman Chemical) for 20 min at 37°C. The initial fluorescence intensity was measured in a GENios FL

(Tecan) at 560nm excitation/635nm emission. Digitonin (Millipore) was then added to each well at a final concentration of 600mM. After incubating for 20 min at 37°C, the final fluorescence intensity was measured. The fraction of dead cells was calculated by dividing the background-corrected initial fluorescence intensity by the final fluorescence intensity. Viability was calculated by (1 – fraction of dead cells).

Sulforhodamine B growth assay

Population size as cell confluency was assayed by sulforhodamine B staining, as previously described26.

Briefly, cells in 96-well plates were fixed in 8.3% final concentration of tricarboxylic acid of 1 hour at

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Statistics and reproducibility

Sample sizes, reproducibility, and statistical tests for each figure are denoted in the figure legends. All replicates are biological unless otherwise noted. Data was analyzed using Prism 7. All error bars represent

SEM, and significance between conditions is denoted as *, P<0.05; **, P<0.01; ***, P<0.001; and ****,

P<0.0001.

121

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Appendix II

Regulation of arginase II in response to cytotoxic chemotherapy

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Summary

Many recent studied have highlighted the importance of amino acid metabolism in breast cancer. We observed that altering arginine and polyamine metabolism can affect sensitivity of breast cancer cells to genotoxic chemotherapy. Here, we measured the changes in arginine metabolism enzymes and focued on arginase II (ARG2). We proposed and tested two putative regulators of ARG2, the transcription factor

ATF3 and a micro-RNA, miR-4286. However, neither alters ARG2 levels or response to chemotherapy, indicating that additional study is required to determine how ARG2 is regulated in response to chemotherapy.

Introduction

In the past two decades, a renewed interest in tumor metabolism has led to the identification of many novel therapeutic vulnerabilities in a variety of cancers1,2. This is especially promising for tumor types that thus far lack effective targeted therapies, such as triple-negative breast cancer (TNBC). TNBC comprises 15-20% of breast cancer cases, but accounts for a disproportionately high percentage of breast- cancer related deaths3. This is partially due to a lack of targeted therapies, as the standard of care treatment for TNBC remains genotoxic chemotherapy. Previous work by our lab and others have identified metabolic vulnerabilities in breast cancer that can be used to improve response to chemotherapy4-6. Here we aimed to identify additional metabolic vulnerabilities that arise following chemotherapy treatment in TNBC.

The amino acid arginine is involved many proliferation-promoting processes, including protein synthesis, production of nitric oxide (NO) and polyamines, and cellular energy production via the TCA cycle7. Furthermore, arginine plays an important role in the immune system, as it is needed for full activation of NK- and T-cells8,9. For these reasons, arginine metabolism has been studied to determine possible involvement in tumor development and maintenance. Most recent work on targeting arginine metabolism has been in tumors that do not express the enzyme argininosuccinate synthase (AS), which is

125

necessary to synthesize arginine from citrulline10,11. These tumors respond to treatment with enzymes that deplete arginine, commonly arginine deiminase or arginase12,13. However, less than 10% of breast tumors lack AS14; therefore, other branches of arginine metabolism merit further study to identify novel metabolic vulnerabilities.

Studies of breast cancer cell lines have indicated that some preferentially shunt arginine towards catabolism by nitric oxide synthase (NOS) or by arginase, and that NOS and arginase compete for the available pool of arginine15,16. One group found that the NOS intermediate and arginase inhibitor N- omega-hydroxy-L-arginine (NOHA) increases apoptosis of breast cancer cells that overexpress arginase15,16. NOHA also exhibited cytotoxic effects in models of lung cancer and renal cell carcinoma17,18. Additionally, arginase consumption of arginine has immunosuppressive effects19,20, so inhibition of arginase may have anticancer effects on both the tumor and the microenvironment.

Enzymes in the arginine metabolism pathway are primarily regulated by allosteric interactions with their substrates or products. Transcriptional and post-translational regulation also occurs. Some enzymes, including AS and argininosuccinate lyase (ASL), are regulated transcriptionally in response to arginine availability, though the mechanism remains unknown21,22. Ornithine aminotransferase (OAT) and

AS have proposed phosphorylation sites, and endothelial NOS (NOS3) is phosphorylated and activated by Akt23-25.

Arginase catalyzes the hydrolysis of arginine to ornithine and urea20. The mitochondrial isoform arginase II (ARG2) is expressed ubiquitously, while the cytosolic isoform arginase I (ARG1) is limited to the liver20,26. ARG2 is regulated primarily at the transcriptional level and through product inhibition by ornithine and NOHA20,26. Arginase has two main functions: to detoxify ammonia by producing urea, and to produce ornithine for the synthesis of polyamines and proline20. Here we propose that arginine catabolism is upregulated in response to DNA damage, and investigate potential regulatory mechanisms of ARG2.

126

Results

Chemotherapy increases expression of several arginine metabolism enzymes

We reasoned that the previously observed altered levels of arginine metabolites (Figure 2.2B) were due to changes in enzyme activity in response to chemotherapy exposure. We analyzed effects on transcriptional regulation by measuring transcript levels for 21 enzymes in arginine and proline metabolism. Transcripts for 15 enzymes were detectable in at least one of the three TNBC cell lines used (Figure S2.1A). GATM,

NOS1, NOS2, NOS3, PAOX, and PRODH were not expressed at detectable levels in any of the cell lines.

Of the measured transcripts, ARG2 and spermidine oxidase (SMOX) were significantly increased following chemotherapy exposure in all three cell lines, and ASS1 and ornithine decarboxylase (ODC1) and were significantly increased in two lines (Figure S2.1A-B).

We confirmed that the elevated transcript levels of ARG2 translate to increased protein levels.

When DNA damage is inflicted, ARG2 protein levels are increased (Figure S2.1C-D, S3.1B). Treatment with the DNA-damaging agent etoposide also increased ARG2. It has been previously observed that AS protein levels increase following cisplatin treatment, which corroborates our observations (Figure

S2.1C,E), though others have observed a decrease in AS, suggesting that the response to cisplatin varies in different settings27-29.

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Figure S2.1: Chemotherapy exposure increases ARG2 levels. (A) Fold change expression of arginine metabolism genes measured by qRT-PCR following exposure to 2.5µM cisplatin (CDDP) or 0.5µM doxorubicin (DOXO) compared to 24h vehicle control (n=3). ND indicates not detected. (B) Quantification of ARG2 transcript from A (n=3). (C) Expression of ARG2 protein following chemotherapy exposure. 0h chemotherapy indicates 24h vehicle control. Representative of n=4. (D) Quantification of n=4 blots from C. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001 by 2-way ANOVA. All error bars represent SEM. Gene abbreviations: ALDH18A1, Aldehyde Dehydrogenase 18 Family Member A1; AMD1, Adenosylmethionine Decarboxylase 1; ARG2, Arginase 2; ASL, Argininosuccinate Lyase; ASS1, Argininosuccinate Synthase 1; CPS1, Carbamoyl-Phosphate Synthase 1; GAMT, Guanidinoacetate N-Methyltransferase; OAT, Ornithine Aminotransferase; OAZ1, Ornithine Decarboxylase Antizyme 1; ODC1, Ornithine Decarboxylase; PYCR1, Pyrroline-5-Carboxylate Reductase 1; SAT1, Spermidine/Spermine N1-Acetyltransferase 1; SMOX, Spermine Oxidase; SMS, Spermine Synthase; SRM, Spermidine Synthase.

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Investigation of ATF3 as a putative regulator of ARG2 transcription

To better understand the connection between chemotherapy and ARG2, we investigated possible mechanisms of ARG2 regulation. Since we observed the initial response of increased ARG2 transcript

(Figure S2.1A-B), we applied transcription factor prediction algorithms to the ARG2 promoter and identified two ATF3 binding consensus sequences (Figure S2.2A). ATF3 is a general stress-response transcription factor, and its transcript and protein levels have been observed to increase in response to cisplatin and doxorubicin30-32.

ATF3 can either activate or repress the transcription of its target genes 33,34. Since we observed increased ATF3 binding and increased ARG2 transcript levels after chemotherapy, we hypothesized that

ATF3 activates ARG2 transcription. To test this, we knocked down and knocked out ATF3. However, this did not block the increase in ARG2 protein levels after doxorubicin exposure (Figure S2.2B-C).

Figure S2.2: Identification of ATF3 as a potential regulator of ARG2.

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Figure S2.2 (Continued) (A) Predicted ATF3 binding sequences in the promoter of the human ARG2 gene. (B) Expression of ARG2 protein following chemotherapy exposure after 24h pretreatment with indicated siRNA. 0h chemotherapy indicates 24h vehicle control. Representative of n=2. (C) Expression of ARG2 protein following chemotherapy exposure in pooled knockout cells created by infection with CRISPR-Cas9 system and indicated guides. 0h chemotherapy indicates 24h vehicle control.

Figure S2.3: Identification of miR-4286 as a potential regulator of ARG2. (A) Predicted targets of miR-4286 in arginine metabolism. (B) Fold change expression of miR-4286 measured by qRT-PCR following exposure to 24h 1µM cisplatin (CDDP) or 0.5µM doxorubicin (DOXO) compared to vehicle control (n=4-5). (C) Fold change expression of indicated transcripts measured by qRT-PCR following 24h treatment with control or miR-4286 mimic (n=3). ***, P<0.001 by 2-way ANOVA. All error bars represent SEM.

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Characterization of miR-4286 as a putative regulator of arginine-glutamine metabolism

To investigate other possible regulatory mechanisms contributing to increased ARG2 following chemotherapy exposure, we submitted a list of arginine metabolism genes for micro-RNA target prediction. This analysis predicted that miR-4286 could regulate ARG2 and other transcripts in arginine and glutamine metabolism, particularly GLS2 (Figure S2.3A). We confirmed that miR-4286 decreases in response to chemotherapy (Figure S2.3B), and hypothesized that chemotherapy alleviates miR-4286 suppression of ARG2 and GLS2. However, expressing a mimic of miR-4286 did not alter ARG2 or GLS2 transcript levels, though it did decrease transcript levels of a top predicted miR-4286 target, RNF43

(Figure S2.3C).

Discussion

Based on our previous observations of changes in arginine and polyamine metabolism in response to genotoxic chemotherapy, we investigated global changes in arginine metabolism enzymes and identified significant changes in ODC1, ARG2, and ASS1. Our work on ODC is presented elsewhere (see Chapter

2), and others have shown that ASS1 can contribute to cisplatin resistance, so here we focused on regulatory mechanisms for ARG2.

It has been stated that arginase may be one of the most important targets in arginine metabolism due to the therapeutic relevance of its products12. Since the production of ornithine can limiting for polyamine production, with a 3.5-fold increase in arginase shown to induce a two-fold increase in cellular polyamines, these elevated levels of ARG2 could contribute to observed increases in ornithine and polyamines (Figure 2.2C,E)22,26. Increased levels of ODC may also contribute, as ODC is a rate-limiting step in polyamine synthesis35; these data are presented elsewhere (see Chapter 2).

Here, we observed decreases in ARG2 transcript and protein, indicating that it may be regulated at the transcriptional level rather than post-translationally, at least in response to chemotherapy. However, altering the predicted regulators ATF3 and miR-4286 did not change ARG2 levels or response to

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chemotherapy. It is possible that ATF3 may regulate ARG2 in response to other stimuli, which bears further investigation. Determination of ATF3 occupancy at the ARG2 promoter could inform future work on putative regulators of ARG2.

Materials and Methods

Cell culture

SUM-159PT cells were obtained from Asterand Bioscience; all other cell lines were obtained from the

ATCC. Cell lines were authenticated using short tandem repeat profiling, and no cell lines used in this study were found in the database of commonly misidentified cell lines, which is maintained by the

International Cell Line Authentication Committee and National Center for Biotechnology Information

BioSample. All cells were maintained in RPMI medium (Wisent Bioproducts) containing 10% FBS

(Gibco). Cells were passaged for no more than 4 months and routinely assayed for mycoplasma contamination.

Antibodies

ARG2 (ab137069) was purchased from Abcam. pH2A.XS139 (9718), ATF3 (33593), and vinculin (13901) were purchased from Cell Signaling Technology. Antibodies were used at 1:1000 dilutions in 5% milk

(Andwin Scientific) in TBST buffer (Boston Bioproducts), except for p-H2A.X in 5% BSA (Boston

Bioproducts) in TBST.

Quantitative real-time polymerase chain reaction

Total RNA was isolated with the NucleoSpin RNA Plus (MACHEREY-NAGEL) according to the manufacturer’s protocol. Reverse transcription was performed using the TaqMan Reverse Transcription

Reagents (Applied Biosciences). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed using a CFX384 Touch Real-Time PCR Detection System (Bio-Rad). Quantification of mRNA

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expression was calculated by the DCT method with 18S ribosomal RNA as the reference gene. Primer sequences are listed in Table S3.2.

miRNA-containing RNA was isolated with the Trizol (Thermo Fisher) and the Direct-zol RNA

MiniPrep Plus (Zymo Research) according to the manufacturer’s protocol. Reverse transcription and detection were performed using the miScript PCR Starter Kit with included U6 primers for reference

RNA (Qiagen), and miR-4286 specific primer 5’-ACCCCACTCCTGGTACC-3’.

Immunoblotting

Cells were washed with ice-cold PBS (Boston Bioproducts) and lysed in radioimmunoprecipitation buffer

(1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS, 150 mM NaCl, 50 mM Tris-HCl (pH 7.5), protease inhibitor cocktail, 50 nM calyculin A, 1mM sodium pyrophosphate, and 20 mM sodium fluoride) for 15 min at 4°C. Cell extracts were cleared by centrifugation at 14,000 rpm for 10 min at 4°C, and protein concentration was measured with the Bio-Rad DC protein assay. Lysates were resolved on acrylamide gels by SDS-PAGE with 20µg/lane and transferred electrophoretically to nitrocellulose membrane (Bio-

Rad) at 100 V for 90 min. Blots were blocked in TBS buffer (10 mmol/L Tris-HCl, pH 8, 150 mmol/L

Transcription factor prediction

The presence of ATF3 binding sites in the ARG2 promoter was predicted using Max Planck Institute’s

Transcription factor Affinity Prediction (TRAP) web tool Predicting Associated Transcription factors from Annotated Affinities (PASTAA)36,37. Specific ATF3 binding sites in the ARG2 promoter were identified by the Eukaryotic Promoter Database with JASPAR Core 2018 vertebrates motifs38,39.

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RNA interference and mirVana miRNA mimic siRNA ON-TARGETplus SMARTpool against human ATF3 (L-008663) and control (D-001810-10) were purchased from Dharmacon and dissolved in siRNA buffer (Dharmacon). mirVana miR-4286 mimic

(4464066) and control (4464058) were purchased from Invitrogen. Cells were transfected with

Lipofectamine RNAiMAX Reagent (Invitrogen) in Opti-MEM (Gibco) according to the manufacturer’s protocol (Invitrogen MAN0007825), with a final concentration of 20nM siRNA or 10nM mirVana mimic and 3µL/mL Lipofectamine.

CRISPR/Cas9 Editing

The oligonucleotide sense and antisense pair (Table S3.3) was annealed and inserted into the lentiCRISPRv2-puro backbone (Addgene #98290). To produce lentiviral supernatants, HEK-293T cells were cotransfected with sgRNA-containing lentiCRISPRv2-puro vectors, VSVG, and psPAX2 for 48 hours. Indicated cells were infected with lentiviral supernatants for 48 hours, and then selected and maintained in 1µg/mL puromycin.

miRNA prediction miRNA prediction was performed by Brian Adams in the laboratory of Frank Slack (Beth Israel

Deaconess Medical Center) through a collaboration at the Ludwig Center at Harvard. Targets were predicted using miRanda algorithms40 from the following set of arginine and glutamine metabolism genes: ARG2, ASL, ASS1, GLS, GLS2, GLUD1, GLUL, NOS1, NOS1AP, NOS2, NOS3, OTC. Additional targets of miR-4286 were identified using microRNA.org41.

Statistics and reproducibility

Sample sizes, reproducibility, and statistical tests for each figure are denoted in the legends. All replicates are biological unless otherwise noted. Data was analyzed using Prism 7. All error bars represent SEM; significance between conditions is denoted as *, P<0.05; **, P<0.01; ***, P<0.001; and ****, P<0.0001.

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137 Appendix 3

Supporting information

Table S3.1: Breast cancer cell lines.

Cell Line Subtype ER PR HER2 TP53 Other mutations or deletions BT474 Luminal B + + + E285K MAPK, PIK3CA BT549 Basal B - - - R249S PTEN, RB1, CLTC-VMP1

HCC1143 Basal A - - - R248Q HCC1806 Basal A - - - T256fs*90 CDKN2A, RB1 HCC1937 Basal A - - - R306* BRCA1, PTEN, RB1 HS578T Basal B - - - V157F CDK2NA, HRAS, MT-CYB MCF7 Luminal A + + - WT CDKN2A, PIK3CA MDAMB231 Basal B - - - R280K BRAF, CDK2NA, KRAS, TERT MDAMB468 Basal A - - - R273H PTEN, RB1, SMAD4 SKBR3 HER2+ - - + R175H CDH1 SUM149PT Basal B - - - M237I BRCA1, CDKN2A, EP300 SUM159PT Basal B - - - V157R158insL HRAS, PIK3CA T47D Luminal A + + - L194F PIK3CA ZR751 Luminal A + - - WT HRAS, PTEN

Breast cancer cell line receptor status and key mutations1-6. Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; PR, progesterone receptor.

Figure S3.1: Validation of antibodies to detect ODC and ARG2. (A) Treatment of cells with siRNA for 48 hours validates a band just below the 55kD marker as ODC, whereas the band above is nonspecific. C indicates siCtl. (B) Treatment of MDA-MB-468 cells with 10nM siRNA for 70 hours validates that the antibody detects a prominent band at 40kD as ARG2.

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Table S3.2: RT-PCR primers.

Gene Forward Primer Reverse Primer ALDH18A1 GCTCTTGTCTCCCAGTTCCT TTGGCAAAGTCCCTATCGGT AMD1 7 GGTAATCAGTCAGCCAGATCAA TTTGCAGTAACACCATCTTTCATG ARG2 TCCACCCTGATCTTGGAGTC TACAGGGAGTCACCCAGGAG ASL TTCAAGGCAGCAAAGCCTAC TGGATGTCCTCATTGGA ASS1 GGATGAGAACCTCATGCACA CTTCACAGGGACCCCTTTTT CPS1 8 ATTCCTTGGTGTGGCTGAAC ATGGAAGAGAGGCTGGGATT GAMT TGACGGTCACTTTGATGGGA CCTGCGTCTCCTCAAACATG GATM GCTTCTTTGAGTACCGAGCG CCAGCTCGAATGAAGTCAGC GATM GCTTCTTTGAGTACCGAGCG CCAGCTCGAATGAAGTCAGC GLS AGTGAGCAGTTTCCTACCCC TTTAGTCACACAAAGCGGGC GLS2 CTGCAGCTGAAGGACACATC TGTCCATGACCTGTGCTCAT NOS1 9 GAATACCAGCCTGATCCCTGGAA TCCAGGAGGGTGTCCACAGCGTG NOS2 TCCAGTGACACAGGATGACC GGCAAGATTTGGACCTGCAA NOS2 9 TCCGAGGCAAACAGCACATTCA GATATCTTCGTGATAGCGCTTCTGGC NOS3 ACATGCTGCTGGAAATTGGG CGATGGTGACTTTGGCTAGC NOS3 9 GTGATGGCGAAGCGAGTGAAG CCGAGCCCGAACACACAGAAC OAT CTTCTGGGGTAGGACGTTGT GGTAACCTGGATCCGGAACA OAZ1 7 CGAGCCGACCATGTCTTCAT CCGGTCTCACAATCTCAAAG ODC1 CGCTCTGAGATTGTCACTGC ATCGAGGAAGTGGCAGTCAA OTC TACACCCTGCCCTGCAGTAT GGACGATTCTATGCCCTTGA OTC TTGCACTTCTGGGAGGACAT TAGTGTTCCTGGAGCGTGAG PAOX 7 GTAGGCTGGGACCGTCATT AAGGTGGCCCTGGTTACCA PRODH ACTGAGGAGGAGGAGCTACA ATGTGTTGAAGATGAGCGGC PRODH GGCTGAGATTGAGGACTGGT TGTAGCTCCTCCTCCTCAGT PYCR1 GCCACCACCTTCTCTTCTCT CGAGCTCTAGAGGAAGGTGG RNF43 AGCACTAAAAGGAGGGGTCC TGCCCAGATAAAGTCCAGCA SAT1 7 GGTCCGCAAAGGGAAGAA TGCCAATCCACGGGTCATA SMOX 7 GATCCCCAGGACGTGGTT TTCAGCATTGACTGGTTTATCGT SMS CCCCTAATCACATGTGCTGC CGCTGTGCTGTCTAACCATC SRM CGAAAGGTGCTGATCATCGG CAAAACCGTCACCCACATGT 18S GTAACCCGTTGAACCCCATT CCATCCAATCGGTAGTAGCG

All primers written 5' to 3'. Primers without reference paper designed using Primer310. Multiple primer pairs were tested for transcripts that were not detected in any cell line.

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Figure S3.2: Supporting data for Chapter 2. (A) qRT-PCR of ARG2 and (B) ODC1 transcript following treatment with 2.5µM cisplatin or 0.5µM doxorubicin, relative to 24h vehicle control. (C) Western blot of MDA-MB-468 cells treated with 0.5µM doxorubicin for 48h and SUM-159PT cells treated with 0.5µM doxorubicin for 24h, following 30min pretreatment with indicated DNA-damage signaling inhibitors. –, vehicle control; K, 10µM ATM inhibitor KU55933; A, 2µM ATR inhibitor AZD6738; V, 5µM ATR inhibitor VE821; N, 1µM DNA-PK inhibitor NU7441; L, 0.5µM Chk1 inhibitor LY2603618; O, 20µM PARP inhibitor Olaparib.

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Figure S3.3: Supporting data for Chapter 3. (A) Viability following 72h pretreatment with 1mM DFMO with or without 10µM folic acid or (B) indicated concentrations of methionine (Met), followed by addition of doxorubicin for 72h (n=2 technical replicates). (C) Comparison of mRNA z-scores from 1,134 metabolic gene transcripts between 299 TNBC and 1605 non-TNBC patient samples from METABRIC breast cancer dataset. (D) Copy number alterations of ODC1 and LPIN1 in METABRIC breast cancer patient samples. (E) Comparison of overall survival between patients with 10% highest and lowest ODC1 transcript expression in the METABRIC dataset or (F) in basal breast cancer patients from the

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Figure S3.3 (Continnued) METABRIC dataset. (G) Correlation between overall survival and ODC1 transcript expression in patients from the METABRIC dataset lacking expression of ER, PR, and HER2. Nonlinear curve fit by four parameter logistic regression (A-B); p-value by two-tailed unpaired t-test (C) or Mantel-Cox Log-rank test (E-F); R2 by linear regression (G).

Table S3.3: sgRNAs for CRISPR-Cas9 targeting.

Primer set Genomic Reverse Complement sgCtl caccgGTAACGCGAACTACGCGGGT aaacACCCGCGTAGTTCGCGTTAC sgATF3#1 caccgGGTGTCCATCACAAAAGCCG aaacCGGCTTTTGTGATGGACACC sgATF3#2 caccgGTCCATCACAAAAGCCGAGG aaacCCTCGGCTTTTGTGATGGAC sgATF3#3 caccGGCAGAAGCACTCACTTCCG aaacCGGAAGTGAGTGCTTCTGCC sgATF3#4 caccGTATTGTCCGGGCTCAGAAT aaacATTCTGAGCCCGGACAATAC

Guide sequences were designed using eCRISP11.

143

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