GLIOMA STEM CELLS ADAPT TO RESTRICTED NUTRITION THROUGH PREFERENTIAL GLUCOSE UPTAKE. by WILLIAM ALEXANDER FLAVAHAN

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1 GLIOMA STEM CELLS ADAPT TO RESTRICTED NUTRITION THROUGH PREFERENTIAL GLUCOSE UPTAKE by WILLIAM ALEXANDER FLAVAHAN Submitted in partial fulfillment of the requirements For the degree of Doctor of Philosophy Dissertation Advisor: Dr. Jeremy Rich Department of Molecular Medicine CASE WESTERN RESERVE UNIVERSITY January 2014

2 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of William Alexander Flavahan candidate for the Doctor of Philosophy degree *. Xiongwei Zhu, PhD (chair of the committee) Jeremy Rich, MD Robert Weil, MD Robert Silverman, PhD Jan Jensen, PhD Anita Hjelmeland, PhD (date) *We also certify that written approval has been obtained for any proprietary material contained therein.

3 TABLE OF CONTENTS Table of Contents 1 List of Figures 2 Acknowledgements 3 Abstract 6 Introduction 8 Glioblastoma 8 Cancer stem cells and the tumor hierarchy 11 Metabolism and Cancer 19 Chapter 1: Glucose restriction increases stem phenotype in GBM 24 Introduction and rationale 24 Results and discussion 25 Materials and methods 42 Chapter 2: Cancer stem cells adapt to glucose restriction via increase uptake 49 Introduction and rationale 49 Results and discussion 50 Materials and methods 64 Chapter 3: GLUT3 expression is a clinical marker of poor survival in cancer 69 Introduction and rationale 69 Results and discussion 70 Materials and methods 94 Conclusions and Future Directions 96 Conclusions 96 Future Directions 100 References 106 1

4 LIST OF FIGURES Figure A. Stochastic and hierarchical models of tumor heterogeneity. 12 Figure B: Definition of cancer stem cells 15 Figure C: Treatment paradigm incorporating the cancer stem cell model. 18 Figure D: Metabolic reprogramming and the Warburg effect. 21 Figure 1. Glucose restriction increases stem markers in glioblastoma. 27 Figure 2. Glucose restriction increases behaviors associated with CSCs in GBM. 30 Figure 3. Potential mechanisms behind the increase in stemness due to glucose. 32 Figure 4. Glucose restriction selects for glioblastoma stem cells. 35 Figure 5. Glucose restriction induces plasticity in non stem glioblastoma cells. 40 Figure 6. Glioblastoma stem cells have higher levels of glucose uptake. 52 Figure 7. Ex vivo glucose uptake imaging confirms higher glucose uptake in GSCs. 54 Figure 8. Glucose transporter expression in glioblastoma. 57 Figure 9. GLUT3 knockdown decreases GSC glucose uptake and growth. 60 Figure 10. GLUT3 is required for the propagation of glioblastoma in vivo. 62 Figure 11. GLUT3 expression is significantly increased in more severe pathologies 71 Figure 12. GLUT3 Expression correlates with radiological criteria in GBM 74 Figure 13. GLUT3 expression correlates with poor survival in brain tumor datasets 75 Figure 14. GLUT1 does not correlate with clinically relevant patient markers 76 Figure 15. GLUT2 does not correlate with clinically relevant patient markers 77 Figure 16. GLUT4 does not correlate with clinically relevant patient markers 78 Figure 17. GLUT3 is reduced in GCIMP tumors and predicts survival in non GCIMP 81 Figure 18. CA9 independently predicts patient survival and correlates with GLUTs 83 Figure 19. GLUT3 predicts survival independently of tumor hypoxia 84 Figure 20. GLUT1 does not predict survival independently or with tumor hypoxia. 85 Figure 21. GLUT3 expression predicts survival in multiple cancers beyond the brain 88 Figure 22. GLUT3 expression predicts metastatic free survival in cancer. 89 Figure 23. GLUT3 is expressed in embryonic and induced stem cells. 92 2

5 ACKNOWLEDGEMENTS The work presented here would not have been possible without the help of many other people. Briefly: I would first like to thank my parents, for instilling in me a strong sense of curiosity about the world and a keen desire to learn as much as possible, and also for putting up with my attempts at amateur astronomy as a child, at the cost of cleanly painted walls. I would also like to thank the Jeremy Rich lab, both past and present members, for being such a supportive and collaborative environment. Discovery is definitely an iterative process, and having such a great environment to bounce ideas around was an immeasurable help. There were a couple of you who deserve special mention, so in no particular order: Qiulian Wu, for superhuman dedication in keeping the lab running smoothly, as well as invaluable assistance with experiments and always having words of encouragement handy. Justin Lathia, for both general support and specifically for introducing me to Oncomine. Little did you realize the monster you would be unleashing upon an unprepared world, but this introduction provided immeasurable clinical relevance and data for both my work and others, and was absolutely instrumental in convincing Jeremy that my patient database mining was not simply screwing around on the computer when I should have been working. 3

6 Monica Venere for always being ready to answer any questions I had, either meaningful science and data questions, or stupid questions about which type of introduction figure looked better. Without your help, many of my coolest experiments would never have been started, and all of my introduction figures would have been far uglier. Anita Hjelmeland, for incredible support and help getting all of this data planned, performed, and put together, even the stuff that Jeremy didn t think was ever going to work. Actually, especially the stuff Jeremy didn t think was ever going to work. Without your help and guidance, I don t think I would have gotten much of anything done. And Jeremy himself, for both paying for everything and putting up with all of my stuff. I remember shortly after I joined the lab, when at a lab meeting you were sort of upset at the lab because the only signature in the liquid nitrogen log on Christmas day was yours, because nobody else was here on Christmas. I thought to myself, wow, if I can actually survive this lab, I m going to get some amazing stuff done here. My doubts about surviving the lab were not alleviated that one time you were upset that I was reading a book at my desk instead of doing more work, while my qpcr was running, at 8pm, on a Sunday, of a three day weekend. But somehow (through an incredible amount of support from both you and Anita), I did not descend into sleep deprivation-induced madness, and we were actually able to get some pretty amazing stuff done. I d also like to thank all of Jeremy s previous graduate students, for being robotic cyborgs sent here from a distant dystopian future, with the sole directive of 4

7 preparing humanity s knowledge base for the ultimate creation of our supreme robotic overlord, through absolutely tireless and unceasing scientific discovery. Thank you for convincing Jeremy that that level of commitment and productivity was obtainable by mere humans. Hopefully my feeble attempts to live up to that standard you all set did not do too much damage to your inevitable robot apocalypse (although future Rich lab grad students: you re all welcome). I also owe a great deal of thanks to an incredibly supportive and helpful committee. You all may not have known exactly what you were signing on to all those years ago, but thanks to all of your help and guidance, my project gradually morphed into something that was pretty interesting, and I couldn t have done it without your help. I also owe a great deal to Kourtney, for always being there with a supportive silly face and for putting up with my tired-induced grumpiness. It s been a unique trip since our late night sailboat and baseball statistic lessons and midnight milkshakes, but I wouldn t change any of it and there s no one I d rather have shared it with. And finally, I would like to thank my mother for giving birth to me. I did already mention my parents, but I feel like this is an important point worth repeating. Much of the work presented in this dissertation required the accumulation and application of advanced molecular biology techniques, and it is likely that I would have been unable to gain and apply this knowledge without first having been born. So, thanks again Mom. 5

8 Glioma Stem Cells Adapt to Restricted Nutrition Through Preferential Glucose Uptake Abstract by WILLIAM ALEXANDER FLAVAHAN Glioblastoma (GBM) remains one of the most lethal human cancers, with conventional treatment offering only palliation. Like all cancers, GBMs display the Warburg effect, a preferential utilization of aerobic glycolysis for energy supplies. This metabolic shift reduces the cells oxygen dependence and provides a steady supply of anabolic material yet requires a steady supply of glucose. In this dissertation, I show that nutrient restriction contributes to tumor progression by enriching for glioblastoma stem cells (GSCs), the most treatment-resistant and regenerative of GBM cells. This enrichment is due to both preferential GSC survival and adaptation of non-gscs through acquisition of stem-like features. GSCs outcompete for glucose uptake by co-opting the high-affinity neuronal glucose transporter isoform, type 3 (GLUT3). In the normal brain, glucose is an essential fuel for neuronal metabolism; yet vascular glucose delivery is physiologically stymied by the blood brain barrier. In response to this, neurons express GLUT3, allowing steady glucose uptake from a glucose-poor 6

9 microenvironment. GSCs preferentially express GLUT3 and targeting GLUT3 inhibits GSC growth and tumorigenic potential, suggesting GLUT3 is a marker of CSCs. GLUT3, but not GLUT1, correlates with poor survival in brain tumors and other solid cancers in clinical patient datasets; thus, CSCs in many cancer types may extract nutrients with high affinity. Given the restricted expression profile of GLUT3 in non-malignant tissue and the critical role of GLUT3 in CSC biology, therapeutic targeting of GLUT3 may prove to be a viable treatment across multiple cancer types. 7

10 INTRODUCTION Glioblastoma Glioblastoma (GBM, WHO grade IV gliomas) are the most lethal and most prevalent primary brain malignancy. Median survival of these patients has been reported to be around 14.6 months, however these reports only consider patients who meet stringent study requirements regarding therapeutic eligibility, and the overall average survival of all patients remains under one year 1. In addition to the poor survival statistics, this disease also has numerous debilitating effects on patients. Common symptoms include headaches, seizures, and hemiparesis, but will often also include tumor-induced cognitive deficits primarily changes in personality, memory loss and loss of executive function. Disease pathology - Diagnostic criteria of GBM include a high degree of angiogenesis and the presence of a type of necrosis referred to as pseudopalisading necrosis. Pseudopalisading refers to the pattern of a central area of necrosis ringed by an aligned pattern of cells, and this type of necrosis is both unique to glioma and has long been recognized as a predictive factor of poor survival 2. These areas, adjacent to the necrosis, are highly hypoxic, and the cells in this ring display very high levels of the hypoxia-induced factors (HIFs), as well as pro-angiogenic factors such as VEGF. It seems likely that the cells that survive in these hypoxic, necrotic regions secrete angiogenesis to drive the formation of blood vessels towards the most poorly vascularized areas of the tumor. These created blood vessels can then support the delivery of nutrients to 8

11 the tumor and allow for further growth until the tumor once again outgrows its vascular supply and creates new necrotic regions, starting the cycle once again. Genomics and GBM Subtypes Analysis of patient data collected through the Cancer Genome Atlas (TCGA) project has revealed the presence of four subgroups of Glioblastoma 3,4,5. The Classical subtype is associated with the characteristic co-occurrence of EGFR amplification or mutation and deletion of the tumor suppressor CDKN2A. TP53, a commonly mutated tumor suppresser with an overall mutation rate in glioblastoma of close to 25%, is almost never mutated in Classical tumors. The Proneural subgroup is associated with amplification of the gene PDGFRA. The Proneural subgroup is also closely tied to the G-CIMP tumors, tumors with IDH1 mutation that causes a hypermethylator phenotype and increased survival, which are universally proneural. The Mesenchymal subgroup is associated with mutation in the Ras GAP NF1, and has fewer EGFR alterations and less EGFR expression than other subtypes. The fourth subgroup, Neural, does not have a hallmark genomic alteration. Overall, the most common copy number alterations in glioblastoma are gain of chromosome 7 (EGFR), loss of chromosome 9 (CDKN2A), and loss of chromosome 10 (PTEN) 3,4. Commonly mutated genes include TP53, PTEN, EGFR, NF1, and RB1 3,4. Standard Treatment Standard treatment for GBM is aggressive, maximal resection of as much tumor as can be safely taken, followed by radiation and concurrent temozolomide 1. Temozolomide, an alkylating agent, will induce the 9

12 formation of N-7 or O-6 alkylation of guanine residues on DNA. This DNA damage can be repaired by the product of the gene MGMT, which is often epigenetically silence in tumor development. As such, MGMT silencing is a predictor of response to temozolomide therapy 6. Randomized clinical trials demonstrated that addition of temozolomide to therapy consisting of surgery followed by radiation increased survival of the patients from 12.1 months to 14.6 months 1. Treatment failure - One of the most consistent features of GBM is the highly regenerative nature of these tumors following treatment while standard therapy is effective at largely debulking the tumor, recurrence and outgrowth of the remaining fraction is an almost universal feature. This appears to be due to two primary factors. The first factor is the inability of current drug and radiation therapies to achieve one hundred percent tumor cell killing. This compounds with the second factor, which is the sensitive nature of the brain as a surgical site. In many non-cns malignancies, it is possible to take very wide surgical margins to remove any tumor cells infiltrating into the surrounding tissue. In some solid tumor settings it is even possible to completely remove the affected tissue, in the case of mastectomies, colectomies, or prostatectomies. This is unlikely to ever be possible, regardless of technological advancement, for the brain. Additionally, hemispherectomies, removal of an entire half of the brain where the tumor is localized, have proven ineffective at stopping tumor recurrence, while also having severe cognitive side effects This suggests that GBM, known as a highly infiltrative tumor, will result in disseminated tumor 10

13 cells throughout the entire brain which require targeted therapy to achieve a true cure. Cancer stem cells and the tumor hierarchy The regenerative property of GBM tumors does not appear to be a universal property of each individual tumor cell. This can be seen following the orthotopic implantation of isolated GBM cells into the brains of immunocompromised mice. Following introduction of tumor cells, the mice will sometimes develop tumors that phenotypically mimic the original patient tumor, they will rarely develop tumors that mimic lower grade tumors (less necrosis, low degree of angiogenesis), and they will frequently fail to develop any disease 11. Performing this assay in a limiting dilution format will reveal that tumor formation potential is in fact limited to a fraction of the tumor. There are two models that attempt to account for this, but importantly, these models are not necessarily mutually exclusive, rather they represent two ends of a spectrum (Figure A) 12. The first model, the stochastic model, suggests that all cancer cells are intrinsically the same, and the difference is due to random, or stochastic, events. This model suggests that whether a given tumor cell will be able to form or propagate a tumor is largely based on random extrinsic events like microenvironmental cues or growth factor signals that the cell receives, and in a truly stochastic system, any cell that receives these signals would be capable of forming or propagating a tumor. At the other end of the spectrum lies 11

14 Figure A. Stochastic and hierarchical models of tumor heterogeneity. (Top) The stochasticc model posits that the cells that are capable of propagation of tumors are essentially randomly selected from the tumor bulk. Conditions that are conducive to tumor formation tend to be extrinsic, such as the presence of growth factors or conducive microenvironmental conditions. (Bottom) The hierarchical model posits that within the tumor exists a hierarchy, similar to differentiation hierarchies in non malignant organs. At the top of this hierarchy sit the cancer stem cells, with the intrinsic regenerativee and proliferative capacity to propagate and maintain tumors. Thesee two models are not mutually exclusivee so much as ends of a single spectrum, and it is likely that both models play a role in tumor development and progression. 12

15 the hierarchical model, which states that within the tumor bulk, there exists a hierarchy of cells. At the apex of this hierarchy sits a population of cells, whose intrinsic properties cause them to form a relationship with the tumor analogous to that of normal stem cells to their counterpart organs. In a purely hierarchical tumor, these cancer stem cells are the only cells in the tumor capable of the sustained proliferation and self-renewal necessary for tumor maintenance and propagation. While experiments have repeatedly demonstrated the presence of a hierarchy in glioblastoma and other solid tumors 11-15, it is likely that most tumors have elements of both models at work: where a defined stem cell hierarchy caused by intrinsic differences in epigenetic modification and gene expression reacts to extrinsic factors in the tumor microenvironment, such as hypoxia, to define and shape tumor growth and maintenance. Implications of the cancer stem cell model on cell of origin The cancer stem cell hypothesis remains controversial in the field, perhaps due to lingering misunderstandings regarding the nature of the stem cell model. The cancer stem cell model does not require that the cell of origin for a CSC-driven tumor to be a non-malignant stem cell as has long been known in the normal stem cell field, terminally differentiated cells can be reprogrammed into stem-like states, first through the usage of nuclear transfer or cell fusion, but also demonstrated within the last decade to be possible through the introduction of a few select stem cell transcription factors With the notoriously unstable genome (and epigenome) of cancer cells, it is entirely possible that a more differentiated cell of origin could activate reprogramming pathways and become a 13

16 cancer stem cell without ever being a normal stem cell. While several studies have proven that it may be easier for a stem cell to become malignant 20 that is, less carcinogenic insults are required to transform a stem cell than a differentiated cell the problem becomes one of Bayesian probability. Using arbitrary numbers, if a stem cell is under any given circumstance ten times more likely to become carcinogenic than a non-stem cell, but a thousand times rarer, then one would expect the majority of tumors to still be derived from the more common non-stem cells. Definition of a cancer stem cell Because of the lingering confusion regarding the conclusions of the cancer stem cell model, it is necessary to construct a definition of what exactly a cancer stem cell is. There are three functional criteria included in this definition (Figure B). First, a putative cancer stem cell must be capable of self-renewal. Second, a cancer stem cell must be capable of sustained proliferation. And finally, a cancer stem cell must be able to propagate the tumor; to drive creation of a tumor phenotypically identical to the original lesion upon introduction into a suitable environment (e.g. orthotopic implantation into an immunocompromised mouse). Cancer stem cells also often possess several other properties which, although common, are not necessary or part of the functional definition of a cancer stem cell. The first of these is the expression of common markers of non-malignant stem cells, such as transcription factors (OCT4, NANOG, SOX2, etc.) and surface markers (CD133, CD44, CD15/SSEA-1, etc.). Additionally, while cancer 14

17 Figure B: Definition of cancer stem cells. There are three criteria required for a cell to be considered a cancer stem cell. First, the cell must be capable of self renewal: upon cell division, at least one of the daughter cells must be identical too the original cell. Second, the cell must be capable of sustained proliferation. Finally, upon introduction to a suitable environment (e.g. orthotopic implantation into an immunocompromised mouse model), the cell must be able to recapitulate the original tumor. Other properties, such as expression of stem cell markers, rarity within a tumor, and ability to give rise to cells with markers of several lineages, may occur in cancer stem cells, but are not definitionally required.. 15

18 stem cells may represent a rare population within the tumor, this is not necessarily the case. It is likely that the proportion of the tumor with stem properties is highly depend on many factors such as the progression of the tumor, response to treatment, and other potential factors. When cancer stem cells lose their stemness, in a process similar to differentiation of non-malignant stem cells, they may also give rise to cells that mimic several different lineages of normal differentiated cells. However, this is not required for a cell to be a cancer stem cell. Indeed, due to the highly aberrant expression profiles of cancer cells in general, this may not even reflect a stem-like process. Since non-malignant stem cells differentiate into lineages with defined functions, while cancer stem cells merely create non-stem progeny, differentiation is likely the wrong term to use for a cancer stem cell that loses stemness. While the analogy to normal stem cells is helpful to define and understand these cells, it is not a perfect one to one relationship between stem cell behavior and cancer stem cell properties. There may be other criteria commonly fulfilled by cancer stem cells, however the above three definitions are the only functionally required for demonstration of the cancer stem cell phenotype. Cancer stem cell niches Much recent work on cancer stem cells has focused on their interaction with the tumor microenvironment. Like normal stem cells, cancer stem cells seem to reside in defined areas, termed niches, which support their self-renewal 21. One of the first characterized regions was the perivascular region 22. Signals such as the extracellular matrix interacting with cancer stem cells induce self-renewal 16

19 signals. More recent works have characterized areas of hypoxia 23,24 and acidic stress 25 as additional microenvironmental niches supporting the cancer stem cell phenotype, suggesting that areas of necrosis, pathological characteristics of GBM, provide residence for the cancer stem cells. There is also a volume of work demonstrating the necessity of oxygen for the therapeutic effects of treatments such as oxygen. The effect of the stem cell paradigm on treatment design The implications of this model cannot be ignored for the design of clinical therapeutics (Figure C). Cancer stem cells possess a number of properties, such as increase DNA repair 11, increased drug efflux 31, and residence in poorly vascularized and hypoxic areas 23,24, which all combine to make cancer stem cells resistant to traditional therapies Following treatment failure, cancer stem cells can drive regeneration of the tumor, with almost the entirety of regrown tumor derived from CSCs 32. Because of this, therapies need to be designed with these factors in mind. Standard treatment fails in GBM because the stem cells are left intact, capable of regrowing the tumor. However, a treatment which only targets cancer stem cells may not be completely effective, due to a potential plasticity of the non-stem cells. If non-stem cells can, due to microenvironmental cues, assume cancer stem cell properties, then these newly formed cancer stem cells could regenerate the tumor like the original cancer stem cells. In order to be truly effective, a treatment must not only target the cancer stem cells, but also block the assumption of stem cell properties in other tumors. 17

20 Figure C: Treatment paradigm incorporating the cancer stem cell model. Conventional treatments fail due to only targeting the non stem cell compartment (top). The cancer stem cells escape therapy and are able to regrow the tumor. Specifically targeting the cancer stem cells, however, may not be sufficient for successful treatment (middle). Some non CSCs may not be targeted by the CSC specific treatment but, due too inherent plasticity or plasticity in response to microenvironmental conditions, thesee non CSCs may gain CSC phenotype, and regrow the tumor. Successful treatment (bottom) requires addressing both the CSC population and the potential for non CSCs to assume CSC properties. 18

21 Metabolism in cancer the Warburg Effect and metabolic reprogramming Metabolism in the healthy brain In the normal brain, vascular glucose delivery is stymied by a physiological feature known as the blood brain barrier, which exists to prevent blood-borne toxins and parasitic organisms or infections from reaching the brain 33. The vessels and microvessels in the brain are completely encircled by astrocytic foot processes, which not only form one level of the barrier but also induce the formation of tight junctions between vascular endothelial cells, further enhancing the barrier function 34. One result of this barrier, however, is a reduction in the glucose permeability of the blood vasculature 35. The glucose level in the extracellular environment that the neurons see is approximately one fifth of what cells in other organs would see. To combat this, the neurons express the high affinity glucose transporter, type 3 (GLUT3). GLUT3 has a fivefold higher glucose affinity and transport capacity, which is necessary for neuron function due to the highly energy intensive processes neurons require to function, including ion transport and constant swings in membrane polarization, axonal transport, and neurotransmitter synthesis, among others 36. Glucose is the most commonly utilized fuel for the neurons, however there alternate energy sources neurons can use. Astrocytes are obligate anaerobes, only utilizing glucose for glycolysis. The resulting lactate can be provided to the neurons through a pathway termed the astrocyte-neuron lactate shuttle

22 Additionally, the neurons can metabolize ketone bodies found in the blood as an alternate energy source 38. Warburg Effect It has long been recognized that cancer cells in general have altered metabolic profiles (Figure D). In 1924, the future Nobel laureate Otto Warburg first discovered that cancer cells obtained their cellular energy almost exclusively through anaerobic glycolysis 39. This metabolic reprogramming reduces the tumor cell s dependence on oxygen and provides a steady anabolic carbon source for processes such as cell division and growth factor production, however the relative inefficiency of glycolysis increases the cell s glucose demand and requires a constant flux of glucose into the cell to maintain cellular energy supplies. This effect is known as the Warburg effect, and it can be exploited clinically, as seen through the usage of radioactive fluoroglucose and positron emission topography, used in GBM to determine whether the core of tumors is necrotic or metabolically active. Additionally, higher levels of glucose in brain tumor patients has been shown to associate with shorter survival 40. The Warburg effect is not limited to cancer, and appears to be common to highly proliferative cells including stem cells 41. Metabolism in Brain Cancer Metabolism is an important area of study in cancer for several reasons. One of the most direct, however, is that rapidly dividing, growth factor producing, and treatment-evading tumor cells need a steady supply of energy. Many of the 20

23 Figure D: Metabolic reprogramming and the Warburg effect. Normal differentiated tissue (left) first breaks glucose down into pyruvate. In the presence of oxygen, this pyruvate is transported to the mitochondria, which first send the pyruvate through the Tricarboxylic Acid (TCA) cycle to generate NADPH and FADH. These components are then used to fuel oxidative phosphorylation, consuming oxygen and creating carbon dioxide. Thiss process is very efficient at extracting energy from glucose, yielding about 36 molecules of ATP per molecule of glucose consumed. Cancer cells and highly proliferative tissue, however, sends only a small fraction of pyruvate to the mitochondria, instead converting it to lactate. This is energy inefficient and requires a greater amount of glucose, but reduces the cell s reliance on oxygen and provides a carbon source in the form of pyruvate/lactate and other glycolysis intermediates. 21

24 defining characteristics of tumors are highly energy demanding, and while tumors are capable of providing their own growth factors or anti-apoptotic factors, there really isn t any way for tumors to cheat the metabolic requirements of these processes. Metabolism is therefore closely linked to tumor progression. Pathways such as the energy-sensing AMPK pathway, which while capable of halting growth and protein synthesis and thus classifiable as tumor suppressor pathways, are very infrequently inactivated in cancer, since even tumor cells need to be able to not outpace their energy supplies 42. Unfortunately for disease management, tumor cells are very good at getting high levels of energy supplies. There are many metabolic pathways known to play a role in tumor progression. One of the most enigmatic of these are mutations in the gene encoding the protein isocitrate dehydrogenase 1, IDH1, a component in the tricarboxylic acid cycle (TCA cycle, also known as the Krebs cycle or citric acid cycle) IDH1 mutations occur frequently in lower grade astrocytomas and occasionally in GBM, especially secondary GBM 44. IDH1 mutations are associated with a hypermethylator phenotype, known as the glioma CpG Island Methylator Phenotype (G-CIMP) 45,46. These tumors tend to be less severe than non-cimp tumors of the same disease. This is curious, since in a highly competitive and proliferative environment like cancer, deleterious mutations will be selected against and replaced by clonal expansion of better adapted cells. How IDH1 mutations remained in the tumors, and more so how a TCA cycle enzyme could induce changes in DNA methylation, remained a mystery for some time. Recent work, however, has shown that the mutations in IDH1, which are almost all 22

25 substitutions of an arginine residue in the active site with a histidine (R132H), are a gain of function mutation, which changes the product of the reaction catalyzed by IDH1 from α-ketogluatarate to 2-hydroxyglutarate. This 2-hydroxyglutarate can interfere with histone lysine demethylates, such as Jumonji C family members, which require α-ketogluatarate as a substrate, and induce the G-CIMP phenotype. 2-hydroxyglutarate is thus a toxic oncometabolite capable of driving tumorigenesis and disease formation 48. An additional example of a metabolic enzyme closely involved in tumor progression is pyruvate kinase M2 (PKM2). PKM2 is the final enzyme in the glycolytic pathway, catalyzing the conversion of phosphoenolpyruvate to pyruvate. In EGFR-driven glioblastomas, EGFR activation can result in translocation of PKM2 to the nucleus. Nuclear PKM2 can phosphorylate histone 3 at the threonine 11 residue, causing the removal of histone deacetylase 3 (HDAC3) from promoters such as CCND1 (Cyclin D1, a critical cell cycle progression component) or c-myc, a well categorized oncogene. The resulting acetylation of H3K9 on these promoters will activate transcription and help drive tumor growth

26 CHAPTER 1: GLUCOSE RESTRICTION INCREASES STEM PHENOTYPE IN GBM Introduction and rationale Prior work has sought to establish the levels of stem behavior in populations of cells. Microenvironmental conditions such as hypoxia 23,24 and acidity 25 have been shown through a number of methods to increase the levels of stem properties in GBM cells. In order to determine the effects of glucose restriction on isolated glioma cells, I adapted some of the approaches of previous studies 23-25, as well as created some novel approaches to specify the nature of the increased stem behaviors. The first step is quantifying the overall levels of stemness. This can, and has been, achieved in a number of ways, notably marker quantification through qpcr of factors such as stem-related transcription factors (NANOG, OCT-4, SOX2), or immunophenotypes of stem markers, such as CD In vitro quantification of the stem behavior of a population of cells can be achieved through assays such as limiting dilution clonogenic sphere formation, which when followed by statistical calculation can reveal the fraction of the population with stem-like phenotype 50,51. Finally, demonstration of in vivo tumor formation potential can be performed through implantation of cells orthotopically (i.e. intracranially) into immunocompromised mice. 24

27 However, the quantification of stemness following a treatment is not a sufficient exploration of the effects on the tumor hierarchy. There are multiple mechanistic possibilities to explain an increase in observed levels of stemness (as seen in Figure 3). For instance, a population of cells can undergo selection effects, causing changes at the population level. That is, glucose restriction could preferentially cause growth or death in a single population (e.g. growth of stem cells, or death of non-stem cells). Differential responses to glucose restriction would cause an observed shift at the population level without necessarily affecting the stem properties of any individual cell. Alternatively, glucose restriction could have effects on individual cells, inducing through glucose responsive pathways a form of stem plasticity, increasing individual cells levels of stem associated factors and behaviors. This is a critical distinction, as the differences between these two mechanisms would have biological and therapeutic implications. If an ability of single-cell plasticity exists, then any therapy that specifically targets only the stem cell fraction is unlikely to result in a disease cure, as this plasticity can result in the creation of new stem cells to propagate the tumor. Additionally, the demonstration of a plasticity is a technically challenging feat, as any experimental paradigm must take into account the possibility of selection for a marker-negative stem cell population. Results and Discussion Media - Standard media formulations are made to a glucose concentration of 4.5 g glucose per liter of media (equal to 450 mg/dl). This concentration is 25

28 extremely supra-physiological, however it does allow for the rapid and sustained growth of cell lines in culture, explaining its continued utilization in research. In order to develop a condition more closely mimicking brain physiology, I decided to investigate the effects of a media formulation of 0.45 g glucose per liter (45 mg/dl), which will more closely mimic the in vivo glucose concentrations that tumor cells might encounter in the brain, especially following depletion over the experimental culture time period. Glucose restriction induces stem marker expression - As a first step, several freshly isolated patient-derived xenograft cells were cultured in either standard, full glucose, media, or in the restricted glucose media condition, for seven days and then harvested. RNA transcript levels of several critical stem-associated transcription factors, including NANOG, OCT-4, and SOX2 were measured, and found to be increased by glucose restriction (Figure 1a-c). NANOG and OCT-4 were consistently increased between seven and ten-fold across multiple patient specimens, while SOX2 was more modestly increased, with levels between 1.5 and two-fold higher across the patient specimens. To confirm that this was not simply due to a global change in transcription, we also measured levels of glial fibrillary acidic protein (GFAP), an intermediate filament protein expressed in astrocytes and previously used as a measure of loss of stemness in glioblastoma. Following culture in restricted glucose, levels of GFAP were decreased compared to cells cultured for the same seven days in full glucose media (Figure 1d). Additionally, the cycle threshold of the housekeeping genes used for normalization (both GAPDH and BACT) were consistent with total RNA 26

29 Figure 1. Glucose restriction increases stem markers in glioblastoma. Unenriched glioma cells isolated from the indicated xenografts were culturedd for 7 days in standard (4. 5 g/l) or restricted glucose (0.45 g/l). Quantitative real time PCR indicates elevation of (a) Oct4 [*, p< with unpaired t test], (b) Nanog [*, p< with unpaired t test], and (c) Sox2 [*, p<0.05 with unpaired t test] mrna under conditions of restrictedd glucose in comparison to standard culture conditions while (d) GFAP is reduced [*, p<0.05 with unpaired t test]. (e) Flow cytometry demonstrates increased percentages of CD133+ cellss after culture of unenriched tumor cells in low glucose [*, p<0.008 with unpaired t test]. An overlay of representative flow plots is shown at right. 27

30 processed, suggesting that these results do in fact reveal a change towards the stem phenotype at the RNA level rather than a global alteration in transcription activity. Cancer stem cells are characterized by expression of specific surface markers. One of the most thoroughly validated of these markers is the glycoprotein CD133 (a glycosylated form of the product of the gene Prominin 1, PROM1). Using flow cytometry, I examined the levels of CD133 expression following standard or restricted glucose culture. Glucose restriction reliably induced a two-fold or greater increase in the fraction of cells staining positive for CD133 across multiple patient specimens (Figure 1e). Combined with the RNA transcript examination, these data show that common markers of the stem phenotype are increased by decreased availability of extracellular glucose. Glucose restriction increases functional behavior associated with cancer stem cells - As cancer stem cells are defined functionally rather than by markers, I next had to demonstrate that glucose restriction induced an increase in cancer stem cell-associated behavior. The defining criteria of cancer stem cells include the ability to self-renew, exhibit sustained proliferation, and the capability to propagate tumors (see Figure B). When introduced into culture, stem cells will exhibit sustained clonal proliferation, forming sphere like growths referred to as neurospheres. The growths of these spheres demonstrates the capacity for both self-renewal and sustained proliferation, capacities required by the definition of a cancer stem cell. By plating cells in a limiting dilution series and counting the fractional sphere formation calculations, it is possible to determine the frequency 28

31 of stem cells in the original population. Following a week of either standard or restricted glucose, cells were plated via flow cytometry into full glucose media. Counting the spheres that formed after two weeks revealed that restricted glucose increased the stem fraction of several patient specimen cell populations by at least two fold (Figure 2a-d). The third property required to demonstrate cancer stem cell functionality is that of tumor propagation. This ability was confirmed via injection of tumor cells, cultured in either standard or restricted glucose for seven days, intracranially into the brains of immunocompromised mice. Of the three human specimens injected into mice, all three displayed a decreased latency until the development of neurological signs (as a surrogate for tumor formation), and one of the specimens had a decreased incidence in the normal glucose injected group when compared to the restricted glucose group (Figure 2e-g). An increase in tumor incidence is a definitive demonstration of an increased cancer stem cell fraction. A decrease in tumor latency is also suggestive of an increased level of cancer stem cells; as the tumor grows, the cancer stem cells perform vital tumor promoting actions such as microenvironmental remodeling and angiogenesis. Injecting more cells will result in a decreased tumor latency as these cells cooperate to initiate tumor formation, and as such, it is likely that the increased tumor latency demonstrated in this experiment resulted from the injection of a population with a larger fraction of cancer stem cells. Discussion, initial restricted glucose results - While these results demonstrate that culture of an unsorted, mixed population of glioblastoma patient specimen 29

32 Figure 2. Glucose restriction increases functional behaviors associated with cancer stem cells in glioblastoma. (a) Neurosphere formation assays indicate the percentage of wells with neurospheres increases after culture of unenriched GBM cells in restricted glucose conditions when 10 cells are plated per well [*, p<0.007 with unpaired t test]. (b d) In vitro limiting dilution assays plating decreasing numbers of bulk tumor cells from (b) M12 [p=0.03 with ELDA analysis], (c) 3832 [p= with ELDA analysis] or (d) 4121 [p= with ELDA analysis] GBM cells indicate the frequency of GSCs increasess after culturee in low glucose. Kaplan Meier survival curves of immunocompromised mice intracranially injected with (e) 4302 [n=5 per arm; *p< <0.05 with log rank analysis] or (f) M43 [n=10 per arm; *p< <0.05 with log rank analysis] cells cultured in restricted glucose or standard glucose conditions. (g) Table summarizing results of tumor formation experiments. 30

33 cells will increase the cancer stem cell levels of that population, the exact nature of this increase was not demonstrated by these experiments. As all of the cells used in this experiment were unsorted, the cell populations used were a mixture of unknown fractions of cancer stem cells and non-stem cancer cells. While these experiments demonstrate that a week of culture in restricted glucose will result in a larger fraction of cancer cells that qualify as cancer stem cells, both by marker expression and functional behavior, these experiments do not elucidate whether this increase is due to a shift in the fractions at a population level, or an assumption of stem characteristics by a non-stem cell through a glucose restriction-induced plasticity (Figure 3). While this distinction may seem to be trivial, there are important clinical and disease implications associated with either possibility. If the glucose restriction induces a selection towards cancer stem cells, for instance by preferentially killing non-stem cells, then this observation could potentially explain much of the observed pathology of GBM. One of the diagnostic criteria of GBM is areas of necrosis, which also seem to function as a cancer stem cell niche. Thus, it is entirely possible that these necrotic areas exist because as the tumor grows and outpaces its vasculature, the restricted glucose induces death of the non-stem cells, leaving only the cancer stem cells to inhabit these areas. These stem cells, demonstrated to have a high degree of VEGF secretion 52 and thus angiogenic potential could drive angiogenesis to the areas of the tumor that need it the most, allowing for more rapid tumor 31

34 Figure 3. Potential mechanisms behind the increase in stemness due to glucose restriction. Glucose restriction could increase the levels of stem phenotype in a population of glioma cells in two ways. First, GSCs could survive more effectively under glucose restriction, causing a selection at the population level favoring GSCs. Alternatively, glucose restriction could cause a shift at the single cell level, inducing a plasticity where non GSCs to acquire stem like properties. 32

35 growth and poorer patient prognosis. Finding a way to block this potential for selection could help slow tumor growth and angiogenesis. If these results also indicate the presence of a single-cell level plasticity, there are also profound therapeutic implications. Much energy is focused on finding targets which would allow therapeutic targeting of cancer stem cells specifically. If glucose restriction is capable of essentially promoting a non-stem cell into a cancer stem cell, then any therapy which only targets cancer stem cells would be ineffective, as any non-stem cell could potentially be able to reprogram into a cancer stem cell through glucose-restriction induced signal and regrow the tumor (see Figure C). Glucose restriction selects for cancer stem cells In order to determine whether glucose restriction induces a selection for the cancer stem cell population, I retrovirally incorporated three patient specimens with either red fluorescent protein (RFP) or green fluorescent protein (GFP). These fluorescent cells were then stained with an anti-cd133 antibody and collected via flow cytometry. This collection allowed for the mixing of defined proportions of CD133 positive cancer stem cells of one color and CD133 negative non-stem cancer cells of the opposite color (e.g. a culture of 10% red cancer stem cells and 90% green nonstem cancer cells). The proportion of stem cells to non-stem cells was chosen based on the specimen and the level of CD133 staining observed. Patient specimens 4121 and IN326 had about 10% positive staining for CD133, so the mixed cross-color cultures were 10% stem cells and 90% non-stem cells. IN528 had a CD133 staining closer to 50%, so stem and non-stem cells were mixed to 33

36 be 50%/50%. Additionally, in order to ensure that any selection was not simply a result of non-glucose factors in the media, such as serum or growth factors, these mixtures were cultured in stem cell media (either full or restricted glucose), supplemented with 1% serum, and on plates coated with Geltrex to induce attachment (Figure 4a). Following a week of standard culture, all specimens displayed only a minor increase, about 10%, in the stem-cell derived population. This indicates that the media and other experimental design conditions were not inducing a strong selection effect towards the stem cells. Under restricted glucose, however, there was a dramatic increase in the fraction of the culture derived from the cancer stem cells after seven days, between 30% and 70% increase by flow cytometry analysis (Figure 4b-e). Additionally, imaging of the cultures demonstrated that not only was there a dramatic difference between the standard and restricted glucose conditions, but this difference seemed to be largely due to a decrease in the levels of the non-stem derived population. Levels of stem cell derived population appeared, by imaging, to be consistent between the two conditions. Additionally, there were less overall cells in the restricted glucose condition. These data combined suggested a shift in populations due to an induction of non-stem cell death by restricted glucose. To test this theory, isolated (nonfluorescent) stem and non-stem cells were cultured separately for seven days in either standard or restricted glucose. At the end of the culture, cells were collected through manual cell lifting and disaggregation (to avoid trypsin digestion of dead cells) and stained via ethidium homodimer to detect necrotic and late 34

37 Figure 4. Glucose restriction selects for glioblastoma stem cells. (a) Strategy for competition experiments with fluorescently labeled GSCs and non GSCs. (b) Fluorescently labeled GSCs and non GSCs were plated at defined numbers via flow cytometry and the percentage of cells derived from them monitored over time in standard (4.5 g/l) or restricted glucose (0.45 g/l). Scale bars represent 75 µm. Representative images of cells after 7 days are shown. Flow cytometry was used to quantify the percentage of GSC and non GSC cells from (c) 4121 [*, p= with unpaired t test], (d) IN528 [*, p<0.02 with unpaired t test], or (e) IN326 [*, p< with unpaired t test] specimens in standard and low glucose and the relative change in the percentage of GSC derived cells is shown. Ethidium homodimer III staining demonstrated elevated cell death in non BTICs derived from (f) M12 [*, p= with unpaired t test] or (g) M43 [*, p<0.001 with unpaired t test] GBMs after nutrient restriction. Example flow trace shown in (h). 35

38 apoptotic cells. Glucose restriction did not induce any cell death in the stem cells, above the baseline levels observed under standard glucose conditions. In the non-stem cell culture, however, restricted glucose conditions induced cell death in 50-70% of cells, while standard glucose levels of cell death were similar to that of stem cells in either condition (Figure 4f-h). These data confirm that, under glucose restriction, cancer stem cells have a survival advantage and will be selected for at the population level. Glucose restriction induces adaptation and plasticity in non-stem cancer cells The existence of a glucose restriction-induced selection for cancer stem cells does not mean that there is not also a single cell-level plasticity occurring, causing individual non-stem cells to acquire stem cell characteristics which may also afford the previously non-stem cells whatever survival benefits the stem cells have. It does, however, greatly complicate any experiments attempting to demonstrate this plasticity. Since any mixed population will become more stem-like simply due to this selection, any experiment attempting to demonstrate plasticity will have to either start with a pure population of non-stem cells, or to track a single cell throughout its transformation. Starting with a pure population of non-stem cells is a practical impossibility. No surface marker has yet been demonstrated to be one hundred percent effective at segregating populations, and even if one could be found, the unstable nature of cancer cells in general means that any conclusions drawn simply from a marker selected population would be suspect. Additionally, the definition of cancer stem cells as self-renewing and capable of sustained 36

39 proliferation means that even extended culture will select for cancer stem cells, so a population that was one stem cell in one or even ten thousand could have a much higher stem proportion following culture in even standard glucose. In order to truly demonstrate plasticity, cells must be tracked at the single cell level. As a first step towards addressing this problem, I created a modified limiting dilution sphere formation assay. A standard sphere-forming assay has three steps. First, cells are treated. Then, cells are plated in a limiting dilution format, using flow cytometry to get precise dilutions. Finally, the plated cells are incubated, usually for between ten and fourteen days, to allow spherogenic outgrowth. The fraction of wells that then form at least one sphere are counted as positive results. Since the only endpoint is formation of at least one sphere, rather than calculation of the number of spheres formed per amount of cells plated, the assay relies on the limiting dilution to use a modified Poisson distribution calculation to determine the original fraction of sphere forming cells. This also means that any stem cell selection effects that occur post-plating would not affect the results of the assay. If a stem cell is present in the well and going to form a sphere, then it doesn t matter what happens to the rest of the cells in that well, since the well will count as a positive result regardless. To take advantage of this statistical design, I modified the assay so that instead of the treatment occurring pre-plating, the treatment would occur post-plating but pregrowth. Basically, one population of (CD133 negative non-stem) cells was plated directly into plates containing either standard or restricted glucose. One week later, glucose was added to all wells to allow for spherogenic growth in identical 37

40 conditions. Two weeks post glucose addition, spheres were counted (Figure 5a). This unique experimental set up allowed for three detectable possibilities for each cell. First, a cell could be a (marker-negative) stem cell at the time of plating, and would form a sphere. Second, a cell could be a non-stem cell at plating and a non-stem cell during sphere formation, and would not form a sphere. Finally, and most interestingly, a cell could be a non-stem cell during plating, and acquire stem cell characteristics sometime before sphere counting and generate a sphere. (There is also the possibility of a cell initially having spherogenic capacity and then losing it or dying due to the restricted glucose, however, the previous data, especially the cell death measurements, indicated that this was unlikely to have a significant effect on the results.) The first and third possibilities mean that there will be a baseline rate of sphere formation in cells simply plated into standard glucose. For cells plated into restricted glucose, however, since the starting population of cells was the same as the standard glucose and the media and sorting conditions were identical with the sole exception of the glucose concentration, any observed increase in spherogenic capacity could only be due to the third possibility: cells that were not stem cells during plating and would not have formed spheres if introduced into normal glucose, but somehow changed due to exposure to restricted glucose and generated spheres. Additionally, since the assay allows for calculation of the stem cell frequency, the frequency of these adaptable non-stem cells could be calculated by subtracting the baseline stem cell frequency of the standard 38

41 glucose condition from the stem cell frequency of the restricted glucose condition. When this experiment was performed, the standard glucose condition, as expected, displayed a baseline stem cell frequency of about one in twenty to one in forty cells, depending on the specimen (Figure 5b,c). Again, this is likely due to either marker negative stem cells, or non-stem cells that gained spherogenic potential due to something besides glucose restriction, for instance growth factors or serum deprivation. In the glucose restriction condition, however, the stem cell frequency was between one in seven and one in nine, a dramatic and significant increase. As the only difference between these conditions was glucose concentration, this increase could only be due to glucose-restriction induced gain of spherogenic potential. Subtracting the restricted glucose frequency from the baseline frequency yielded an adaptable non-stem cell frequency of between one in eleven to one in fourteen. While this assay conclusively demonstrated the plasticity of glioma cells that could be induced by glucose restriction, I wanted to confirm this result via an assay that would be more accessible, persuasive, and understandable; perhaps requiring less than several pages of explanation. To that end, I obtained a construct where green fluorescent protein (GFP) was driven by the promoter of the stem-related transcription factor NANOG. Cells with this construct incorporated would thus be green if they were stem cells (expressing NANOG), and not green if they were not stem cells. Using time lapse microscopy, timelapse imaging of these cells and the capture of a non-green cell becoming green 39

42 Figure 5. Glucose restriction induces plasticity in non stem glioblastoma cells. (a) Schematic for adaptive non GSC in vitro neurosphere formation assay. (b,c) In vitro limiting dilution assays in which non GSCs from (b) IN528 [p=0.014 with ELDA analysis] or (c) M12 [p= with ELDA analysis] were plated directly into normal or low glucose demonstrated adaptation to nutrient restriction through promotion of a GSC spherogenic phenotype. (d f) Time lapse imaging using reporter cells in which the Nanog promoter is driving green fluorescent protein (GFP) demonstrated that Nanog/GFP cells can begin expressing Nanog/GFP after approximately 60 hours of nutrient restriction. Three different panels of time lapse images for individual cells which became GFP+ are shown. Scale bars represent 25 µm. 40

43 due to glucose restriction would be a convincing and simple demonstration of the plasticity demonstrated by the modified L.D.A. Cells were infected with the NANOG-GFP construct as well as a constitutive (CMV-driven) red fluorescent protein (RFP) construct, for visualization purposes. The NANOG-GFP construct did include a Zeomycin selection cassette, however stem cells are also often defined as side population cells that have active multidrug resistance related efflux pumps capable of removing dyes or drugs from the cell, which could artificially select for uninfected stem cells, so following infection I collected via flow cytometry cells that were both GFP and RFP positive. This population of presumably pure stem cells was cultured in a differentiation media containing FBS for two weeks. Following differentiation, this population was then stained with CD133 antibody and flow sorted. RFP positive, CD133 negative, GFP negative cells were collected. A fraction of the collected cells were plated into wells of a six well plate under either standard or restricted glucose conditions on geltrex-coated plates to induce attachment. 24 hours post-plating, this plate was then set up in an incubated time lapse microscope for 72 hours, set to take fluorescence and bright field images of 30 pre-selected fields per condition every 20 minutes. An additional plate of each condition was also plated from the remaining fraction of collected cells, which were kept in a standard tissue culture incubator and observed. Analysis of the time lapse images revealed that, as expected, all cells were initially lacking green fluorescence. In the standard glucose condition, no tracked cell ever acquired green fluorescence. Analysis of the parallel culture also revealed a complete absence of any green fluorescent 41

44 cells. In the restricted glucose condition, however, several of the tracked cells acquired green signal starting at about 60 hours post plating, and remained green and alive throughout the entire 72 hour duration of the experiment (96 hours post-glucose restriction) (Figure 5 d,e,f). Analysis of the parallel plated culture revealed that between ten and twenty percent of the culture expressed GFP at the end of the incubation, consistent with previous results. Discussion, selection and adaptation These experiments confirm that glucose restriction increases the stem fraction of glioma cells both at the population level and at the single cell level. Materials and methods Tissue procurement and Xenograft Passage Glioblastoma patient specimens were obtained directly from patients in accordance with a Duke University or Cleveland Clinic Institutional Review Board-approved protocol, in which informed consent was obtained by the tumor bank. De-identified excess tissue was provided to our laboratory following tumor resection. Immediately upon tissue receipt by our lab, the tumor specimen was dissociated through the use of a papain-based dissociation kit (Worthington Biochemical, cat #LK003150), according to manufacturer s protocol. Briefly, tumor section was minced with sterile razor blades and surgical scissors. Once tumor had been sufficiently dissociated, papain resuspended in Earle s Balanced Salt Solution (EBSS) with DNAse was applied to the tumor and incubated in a 42

45 tissue culture incubator (37 C, 5% CO 2, atmospheric O 2 ) until the digested tumor mixture was easily passed through a 10ml pipet. This took between 15 minutes and one hour, depending on the individual tumor. Following enzymatic digestion, tumor mixture was passed through a 70µm filter twice and resuspended in EBSS. Cell suspension was layered on ovomucoid protease inhibitor solution and gravity centrifuged. Red blood cells in the pellet were lysed through introduction to a 25% PBS:75% water mixture and immediate centrifugation (exposure to hypotonic solution was about 3 minutes total). Cells were allowed to recover overnight in stem cell media (see below) before flank injection of several million cells into immunocompromised mice (either athymic nude (Foxn1 nu/nu ) or NOD scid gamma (NSG) mice). Mice were monitored daily for tumor formation, and harvested when tumors were appropriately size (~1-2 cm in diameter) or at the first signs of animal distress or skin necrosis. Removed tumors were harvested similar to patient specimens. Cell culture and restricted glucose media Cells were cultured in tissue culture incubators, maintained at 37 C, 5% CO 2, and atmospheric O 2. Standard formulations of media are as follows: GBM stem cell media: Neurobasal-A (Invitrogen), supplemented with B27 w/o Vitamin A (Gibco), 20 μg bfgf per 500 ml media, 20 μg EGF per 500 ml media, L-Glutamine to 4mM, Sodium Pyruvate to 1mM, and Pencillin/Streptomycin 50 units / ml media. 43

46 GBM non-stem cell media: DMEM media supplemented with 10% heatinactivated FBS and Pencillin/Streptomycin 50 units / ml media. For restricted glucose conditions, identically supplemented glucose-free media formulations were mixed in a 9 to 1 ratio with standard media formulations. As both of the above media are contain 4.5 g/l glucose, this mixture resulted in a final concentration of 0.45 g/l glucose. For culture of mixed stem and non-stem cells (or unsorted cells), media was GBM stem cell media formulation, supplemented with 1% heat-inactivated FBS. This media formulation was found to keep populations relatively consistent. Culture of stem cells in pure non-stem cell media induced differentiation, and culture of non-stem cells in stem cell media induced death, but stem cell media plus 1% FBS resulted in minimal cell death and kept the stem cell fraction relatively stable. During cell culture under restricted glucose, cells were dissociated mechanically though cell lifters, and triturated to dissociate cell clumps. The usage of standard Trypsin/EDTA was found to induce significant cell death of cells cultured under restricted glucose and was therefore discontinued from experiments involving restricted glucose. For consistency, cells in full glucose that were being compared to restricted glucose conditions were also mechanically disaggregated, but for routine passage of non-experimental cultures, trypsin/edta was used. Quantitative Real Time Polymerase Chain Reaction (qpcr) 44

47 RNA was isolated via RNeasy mini-prep kit (Qiagen), and reverse transcribed into cdna using the Superscript III Reverse Transcription Kit (Invitrogen). : β- actin forward 5 -AGA AAA TCT GGC ACC ACA CC-3 and reverse 5 -AGA GGC GTA CAG GGA TAG CA-3 ; Oct4 5 -TCTCCCATGCATTCAAACTGAG-3 and reverse 5 - CCTTTGTGTTCCCAATTCCTTC-3 ; NANOG forward 5 - GAAATACCTCAGCCTCCAGC-3 and reverse 5 - GCGTCACACCATTGCTATTC-3; Sox2 forward 5 - CACACTGCCCCTCTCAC-3 and reverse 5 -TCCATGCTGTTTCTTACTCTCC-3 Flow cytometry characterization of immunophenotype For FACS staining of surface CD133 expression, live cells were incubated with CD133 antibody (AC133-APC, Miltenyi) for 45 min at dilutions specified in the manufacturer s protocols, and run on a BD flow cytometer. Limiting dilution sphere formation assay For in vitro limiting dilution assays, viable (propidium iodide negative) cells were sorted by FACS with decreasing numbers of cells per well (50, 20, 10, 5, and 1) plated in 96 well plates containing stem cell media. Plate setup was an external border of PBS to prevent well evaporation, two rows of ten wells each of 1 cell per well, two rows of 5 cells per well, one row of ten cells per well, and one row of half (5 wells) 20 cells per well and the other half 50 cells per well. Extreme limiting dilution analysis was performed using software available at Neurosphere formation assays were also performed in a non-limiting dilution format similar to our prior report 23 with 10 45

48 cells per well plated in 96 well plates and the percent of wells with neurospheres measured after 10 days. In vivo tumor formation assay All animal procedures were performed in accordance with Cleveland Clinic IACUC approved protocols. Animals were housed in a temperature-controlled vivarium with a 14 hour light, 10 hour dark cycle at no more than 5 animals per cage. For nutrient deprivation studies, unenriched GBM cells were cultured in standard or low glucose conditions for seven days and 5,000 viable cells were intracranially injected into athymic/nude mice as previously described 23. Animals were maintained until development of neurological signs (e.g. lethargy, ataxia, seizures and/or paralysis), at which point brains were harvested. Harvested brains were fixed in 4% formaldehyde, sunk in 30% sucrose, cryopreserved in OCT, and cryosectioned. Fluorescent cell competitive growth assay Unsorted glioma cells were infected with either RFP or GFP lentivirus. Following infection, cells were sorted for color, viability using LIVE/DEAD Fixable Blue Dead Cell Stain kit (Invitrogen) and CD133-APC status by flow cytometry and directly plated in Neurobasal stem cell media with 1% FBS on Geltrex-coated plates. Cells were imaged on days 4 and 7, and collected for flow cytometry analysis on day 7. Flow cytometry cell death assay 46

49 Apoptosis and Necrosis Quantitation kit was obtained from Biotium and performed according to the manufacturer s protocol, with cell fixation in paraformaldehyde following staining. Adaptive non-gsc limiting dilution neurosphere formation assay For the demonstration of non-btic adaptation to low glucose, a slightly modified version of the above neurosphere formation assay was employed. CD133 negative, propidium iodide negative cells were sorted in decreasing numbers of cells into 96 well plates containing either stem cell media or restricted glucose stem cell media. The cell dilution scheme was as follows: an external border of PBS to prevent evaporation, one row (of ten wells) each of the following dilutions per well: 1, 5, 10, 20, 50, and 100 cells per well. Cells were left in this media for seven days, at which point glucose containing media was added to all wells to eliminate any possible effect of glucose restriction on proliferation, which would affect the sphere formation results. Fourteen days after glucose addition (twenty one days post plating), spheres were counted as above. The frequency of adaptive non-btics was calculated as the difference in sphere formation between the normal glucose condition and the 7 days restricted glucose condition. Time lapse recording of NANOG-GFP acquisition in non-gscs pgreenzeo-nanog plasmid were obtained from System Biosciences, prepared into lentiviral particles, and used to infect cells. Infected cells were sorted via flow cytometry to obtain only GFP expressing cells. Fourteen days post-sort, 47

50 cells were plated on Geltrex in Neurobasal stem cell media with 1% FBS and either full or restricted glucose. Twenty four hours post-plating, images were taken on a Leica DMIRB Inverted Microscope equipped for time-lapse microscopy with a Roper Scientific CoolSNAP HQ Cooled CCD camera (Roper Scientific, Tucson AZ, USA), temperature controller (37 C) and CO2 (5%) incubation chamber (Leica Microsystems GmbH), PeCon incubator (PeCon GmbH, Erbach, Germany), Prior motorized stage with linearly encoded controller with x/y/z drive for time-lapse imaging of multiple fields and heating insert for 6- well plates (Prior Scientific Inc., Rockland, MA, USA), Uniblitz shutter (Vincent Associates, Rochester, NY, USA), and MetaMorph Software (Molecular Devices, Downingtown, PA, USA). Bright field images were taken every ten minutes and green fluorescence images were taken every twenty minutes for 72 hours. 48

51 CHAPTER 2: CANCER STEM CELLS ADAPT TO GLUCOSE RESTRICTION VIA INCREASED UPTAKE Introduction and rationale The last chapter demonstrated that glucose restriction will cause death in a large fraction of the non-stem cancer cells, and of those that survive, a significant proportion will have adapted to the low glucose by gaining cancer stem cell properties. This raises the question: what advantage do cancer stem cells have under restricted glucose that makes either being a stem cell or becoming one so advantageous to survival? One possibility is that cancer stem cells are less inhibited by intracellular energy stress. However, this seems unlikely, as cancer in general maintains highly active energy sensing pathways. While these pathways can inhibit growth, deactivation of energy sensing may result in metabolic collapse as a cell expends all of its energy on growth and loses the ability to maintain membrane integrity and polarity. An alternative possibility is an inherent difference in glucose uptake potential. Glucose is taken into cells through facilitated diffusion through a family of hexose transporters the GLUT family. GLUT1 is the ubiquitous transporter and best characterized member of the family, and almost all previous work in cancer (and all work in GBM) has been focused on the role and expression levels of GLUT1. Changes in glucose transport potential and capacity could theoretically allow a 49

52 cancer stem cell a competitive advantage under glucose restriction; as extracellular glucose availability dwindles, a cancer stem cell could continue to survive with a steady stream of glucose afforded by increased uptake ability. Results and discussion Cancer stem cells display increased levels of glucose uptake In order to determine the levels of glucose uptake displayed by cancer stem cells and nonstem cancer cells, I obtained a fluorescently modified glucose molecule, 2-[N-(7- nitrobenz-2-oxa-1,3-diazol-4-yl) amino]-2-deoxy-d-glucose 53, referred to from here on as 2-NBDG for brevity s sake. The fluorescent modification of this molecule is relatively small and has excitation similar to FITC, and as such this molecule has been utilized in many previous studies to characterize glucose uptake. Additionally, as a 2-deoxy-glucose, the molecule is not a suitable substrate for glycolysis and will not be rapidly degraded in the cell. It does, however, compete with standard glucose and as such can induce metabolic toxicity at high concentrations, meaning any usage of high concentrations of 2- NBDG must be analyzed acutely. The first experiment I performed was flow cytometry assessment of 2-NBDG uptake in unsorted cultures, also stained with CD133. CD133 positive cells had consistently higher levels of 2-NBDG uptake across multiple patient specimens (Figure 6a). To confirm that this was not simply due to the fluorescent modification, I obtained a glucose oxidase kit that would directly measure levels 50

53 of glucose present in lysate. Sorted stem and non-stem cells were washed in glucose-free media three times, and then incubated in 0.45 g/l media for thirty minutes. Cells were then washed twice in PBS, collected, and lysed. Protein lysate was boiled to eliminate any enzyme activity that could cause interference and subjected to the glucose oxidase kit. These results mimicked the flow cytometry data cancer stem cells display a higher level of glucose uptake across multiple patient specimens (Figure 6b). Having demonstrated that cancer stem cells displayed increase glucose uptake, I next attempted to demonstrate the opposite direction that higher glucose uptake cells were stem cells. Collecting cells following glucose uptake and performing PCR for stem markers revealed that the cells that had higher levels of glucose uptake had higher levels of OCT4, NANOG, and SOX2 (Figure 6c). This concentration of 2-NBDG, however, proved toxic to the cells and cells plated for sphere formation were incapable of generating spheres even at 100 cells per well. Using a lower concentration of 2-NBDG resulted in less clear separation between high and low 2-NBDG uptake, but did result in a significant increase in stem cell frequency in the 2-NBDG high population (Figure 6d). These results were convincing but not compelling. In order to truly demonstrate the glucose uptake potential in a relevant microenvironment, I created an ex vivo glucose uptake system (Figure 7a). This was a modification of a system previously published by our lab 32, where a mouse brain slice culture was imaged via a multiphoton microscope. The unique properties of this microscope allows for the usage of wavelengths that allow for greater tissue imaging depth. Isolated 51

54 Figure 6. Glioblastoma stem cells have higher levels of glucose uptake. (a) Flow cytometry demonstrates uptake of the fluorescent glucose 2 NBDG is higher in CD133 expressing glioma cells [*, p<0.001 with unpaired t test]. (b) Glucose oxidase assays demonstrate elevated levels of glucose in the lysate of GSCs in comparison to matched non GSCs [*, p<0.05 with unpaired t test]. (c) qpcr of isolated high and low 2 NBDG uptake cells reveals that cells with higher levels of glucose uptake have higher levels of the stem related transcription factors, OCT4, SOX2, and NANOG [p<0.05 with unpaired t test]. Trace at right demonstrates populations. (d) Using a lower concentration of 2 NBDG to reduce toxicity, high and low 2 NBDG uptake cells were sorted for sphere forming L.D.A. The fraction of spherogenic cells was higher in the NBDG high uptake population. 52

55 stem and non-stem cancer cells were labeled with different colors of fluorescent cell tracking dyes. These cells were then allowed to engraft to a freshly prepared slice culture overnight. The next day, the culture system was incubated for thirty minutes in 2-NBDG, and then imaged with the multiphoton microscope. The resulting images dramatically and compellingly demonstrated that the cancer stem cells had a significantly greater level of glucose uptake than the non-stem cells (Figure 7b-e). Imaging of brains with engrafted labeled cells and no glucose, or unengrafted brains incubated in glucose confirmed that there was no fluorescence channel bleed affecting the images. Discussion, Glucose uptake - Glucose uptake occurs through the GLUT family of hexose transporters, of which GLUT1 through 4 are the most prevalent glucose transporters, composing the class I GLUTs. The other GLUT family members have varying roles. GLUT5, for instance, is a fructose transporter. GLUT13 is a proton/myoinsitol transporter, and GLUT14 appears to be a GLUT3 pseudogene. Within the class I GLUTs, GLUT1 is the ubiquitous transporter responsible for the baseline glucose uptake in most cells. The general rule seems to be, if a cell doesn t have a special specific need for one of the more specialized GLUTs, GLUT1 will be responsible for its glucose uptake. Almost all of the work on functional glucose uptake in cancer focuses on GLUT1. GLUT2 is a lower affinity but bidirectional glucose transporter that is expressed in pancreatic β cells, where its bidirectional transport allows for equilibrium between the bloodstream and the cell interior, which may allow the glucose sensing abilities of β cells to function. GLUT2 is also expressed in the liver, where it allows for the release of 53

56 Figure 7. Ex vivo glucose uptake imaging confirms higher glucose uptake in GSCs. (a) Diagram of the experimental procedure for ex vivo imaging of glucose uptake in brain slices. CD133 sorted BTICs or non BTICs were labeled for 45 minutes with CellTracker Red CMPTX or CellTrace FarRed DDAO SE, respectively. Cells were then engrafted onto live mouse brain slice cultures and allowed to engraft overnight. The next day, the slice culture with engrafted cells was incubated for 30 minutes in 50ug/ml 2 NBDG and imaged via multiphoton microscopy. (b e) Reconstructions of representative fields, showing increased glucose (green) uptake in GSCs (red). Non GSCs (cyan) consistently displayed less glucose uptake than GSCs. Scale bars represent 10 µm for b and 15 µm for c e for the window of the 3D reconstruction. Left to right: cell reconstructions only, GSC reconstructions plus glucose, non GSC reconstructions plus glucose, all cell reconstructions plus glucose. 54

57 glucose into the bloodstream. GLUT4 is the insulin responsive GLUT, it is maintained on intracellular vesicles in the cells that express it (mostly skeletal muscle), and when the cell is stimulated with insulin, GLUT4 will translocate to the plasma membrane and allow for glucose transport. Perhaps the most interesting of the GLUTs, however, is GLUT3. GLUT3 has been referred to as the neuronal glucose transporter, as it is mostly expressed in neurons 54, though there is also expression in sperm cells and scattered reports of expression in some immune cells. In the normal brain, the neurons have a high energy demand their function requires for very active ion transporters continually activating. However, vascular delivery of glucose to the neurons is stymied by the blood brain barrier a physiological feature designed to prevent blood-borne toxins or parasites in the blood from entering the brain. The blood vessels in the brain are completely encircled by astrocytic foot processes. These foot processes not only form a barrier, but they also induce the formation of tight junctions between vascular endothelial cells. This barrier is highly effective at protecting the brain, however one consequence of this is the extracellular glucose concentrations that the neurons see is approximately one fifth of what levels in other organs would be. As an adaption to this, neurons express GLUT3. GLUT3 is the high affinity glucose transporter; its affinity for glucose and glucose transport capacity is five-fold higher than GLUT1. Through GLUT3, neurons can obtain a high level of glucose uptake even in the face of extracellular glucose scarcity. While no studies have looked at the function, there have been scattered reports of GLUT3 expression in some cancers and cancer cell lines 55,56. 55

58 Cancer stem cells uptake more glucose due to GLUT3 expression Starting with the hypothesis that a difference in glucose transporters could explain the difference in glucose uptake, I performed qpcr to determine the levels of the various class I GLUTs in cancer stem cells and non-stem cells (Figure 8a-c). GLUT1 was expressed and slightly yet significantly increased at the RNA level in two of the three patient specimens tested. However, GLUT3 was expressed at similar levels and dramatically increased in all three specimens. GLUT2 was also significantly more expressed in stem cells in all three specimens, however overall levels of GLUT2 were drastically lower than either GLUT1 or GLUT3. To confirm that the RNA differences would also translate into functional protein, I obtained antibodies that could recognize the cell surface epitope of GLUT1 and GLUT3. While there are no reports that GLUT1 or GLUT3 are maintained intracellularly like GLUT4, expression of surface protein by flow cytometry would indicate the amount of transporter that could transport glucose much more specifically than a technique like western blotting, which would not be able to discriminate intracellular protein. While GLUT2 was also significantly different, the lower affinity of GLUT2 combined with the much lower expression levels meant that it was unlikely that GLUT2 was contributing to the increased glucose transport observed. While increase GLUT2 expression may merely be an experimental artifact or a symptom of cancer cells simply having altered expression patterns, it is possible that distinct expression and membrane protein association complexes could form a glucose sensing apparatus in these cancer stem cells a fascinating possibility beyond the scope of this work. 56

59 Figure 8. Glucose transporter expression in glioblastoma. mrna Expression of glucose transporters GLUT1, GLUT2, GLUT3 and GLUT4 in GSCs and non GSCs isolated from (a) 3832 [*, p<0.02 with unpaired t test], (b) 4121 [*, p<0.001 with unpaired t test], or (c) 4597 [*, p< with unpaired t test] GBM cells demonstrates elevated GLUT3 in GSCs. Flow cytometry using fluorescently labeled antibodies against (d) GLUT3 [*, p< with unpaired t test] or (e) GLUT1 [*, p< with unpaired t test] demonstrates elevated GLUT3 protein in GSCs. (f) Immunohistochemistry confirms GSC specific expression of GLUT3, as determined by colocalization with the stem cell marker, nuclear SOX2. 57

60 Flow cytometry staining revealed that GLUT3 was dramatically and significantly increased in CD133 positive cells in multiple patient derived xenografts, on average around 300% increased compared to CD133 negative non-stem cells (Figure 8d). GLUT1, on the other hand, was increased by about 20% in the stem cells in half of the patient specimens tested, with no significant difference in the other half (Figure 8e). Immunofluorescence staining confirmed the stem cellspecific expression of GLUT3, as only cells that expressed nuclear SOX2, a stem cell associated transcription factor, had GLUT3 staining (Figure 8f). Considering the greater differences in GLUT3 transport, as well as the unique transport characteristics of GLUT3, I decided to further investigate GLUT3 as the main driver of cancer stem cell glucose uptake. In order to test the dependence of cancer stem cells on GLUT3, I obtained shrna constructs directed towards GLUT3, introduced to the cells via a lentiviral delivery system. These sequences had no overlap with GLUT1 (or any other gene) by BLAST search. Still, following infection of cancer stem cells with this construct, I performed qpcr to eliminate the possibility that this GLUT3 knockdown would also affect GLUT1. The data, however, showed that the GLUT3 knockdown was specific, with significant knockdown of GLUT3 RNA levels with no effects on GLUT1 levels (Figure 9j). Knockdown of GLUT3 was also confirmed at the protein level by flow cytometry (Figure 9a). To determine the function of GLUT3 in the glioma cells, I infected isolated populations of cancer stem cells and non-stem cells on geltrex. These cells were incubated in 2-NBDG for 30 minutes, washed, and then the fluorescence was measured via 58

61 plate reader. This experiment revealed that GLUT3 inhibition essentially obliterated the glucose uptake potential of the cancer stem cells compared to cells infected with a non-targeting control shrna, yet GLUT3 knockdown had absolutely no effect on the non-stem cancer cells (Figure 9b,c). As might be expected by this level of glucose uptake inhibition, the knockdown was not very well tolerated by the stem cells, resulting in significant cell death after several days of culture, even at the high glucose concentration of standard media. Because of this, I choose to skip selection of these cells, since incubation of a population of cells that had been exposed to virus with puromycin simply resulted in the vast majority of cells dying, either to GLUT3 knockdown or to puromycin selection. To confirm that GLUT3-dependant glucose uptake was necessary for cell growth, I plated cells infected with either GLUT3 knockdown or control non-targeting shrna for a growth assay. Cell number was measured over the course of seven days. In two specimens tested, GLUT3 knockdown resulted in dramatic decrease in cell growth in the cancer stem cells (Figure 9e,f). The non-stem cancer cells were not affected by the GLUT3 knockdown, however, they did also proliferate slower even in the control condition (Figure 9g,h). As sustained proliferation is a definitional characteristic of cancer stem cells, this is not surprising, and has been reported before. I next decided to test the effect of GLUT3 knockdown on stem cell functional behavior. As the effects of GLUT3 knockdown were specific to the stem cell fraction, I utilized only CD133+ isolated cancer stem cells for the functional 59

62 Figure 9. GLUT3 knockdown decreases GSC glucose uptake, growth, and spherogenic potential. (a) Flow cytometry confirms shrnas directed against Glut3 (shrna1 and shrna2) can reduce the expression of Glut3 protein relative to a non targeting control shrna [*, p<0.001 with unpaired t test]. In the representative flow histogram, the grey line is representative of the isotype control. (b) Knockdown of Glut3 shrna in BTICs isolated from 3832, 4121, or M43 reduces the uptake of a fluorescent glucose analogue demonstrating a requirement for Glut3 in BTIC glucose transport [*, p<0.005 with ANOVA comparison]. (c) There is no requirement for Glut3 for glucose uptake in matched non BTICs. To best determine the impact of Glut3 directed shrnas in g h, values were normalized to non targeting shrnas in each group. These GLUT3 shrna constructs also decrease the growth of (a) 3832 [*, p<0.05 with ANOVA] and (b) IN528 [*, p<0.001 with ANOVA] GSCs in comparison to a non targeting control shrna as measured using the cell titer assay. In vitro limiting dilution assays demonstrate that knockdown of GLUT3 in GSC enriched cultures decreases the frequency of (c) 4121 [p=5x10 14 with ELDA analysis] or (d) 3832 [p=5.8x10 11 with ELDA analysis] GSCs. (g) qpcr confirms the GLUT3 knockdown is specific to GLUT3 and does not decrease levels of GLUT1 in GSCs. 60

63 characterization. Cancer stem cells were infected with either non-targeting control shrna or one of the two GLUT3 knockdown shrna constructs. Two days later, viable cells (measured by propidium iodide exclusion) were sorted for a limiting dilution neurosphere formation assay. Both of the specimens tested by this experiment had a dramatic decrease in spherogenic frequency, from a rate of about one in three or one in five to a rate of one in twenty five or one in fifty at the more severe knockdown (Figure 9 h,i). This demonstrates that GLUT3 function is a necessity for self-renewal and sustained proliferation of cancer stem cells. The final test was to examine whether GLUT3 was necessary for the formation of tumors in vivo. Initially, 4121 cells were treated with control shrna virus or one of the two GLUT3 shrna viruses. Two days later, one thousand viable cells were injected intracranially into the brains of immunocompromised mice. The mice were then followed for the development of neurological signs as a surrogate for tumor formation. Mice that had been injected with control shrna all developed tumors between 32 and 70 days post injection (Figure 10a,d). Sixty percent of each knockdown arm developed tumors between 70 and 110 days post injection. Notably, however, when these knockdown tumors were isolated and stained for GLUT3 expression, all of the tumors displayed very strong GLUT3 staining. This suggests that the tumors that formed were due to escape of the knockdown rather than a GLUT3-independent tumor formation capability. To address this, two more patient specimens were prepared for injection. This time, however, cells were introduced to a higher viral titer overnight, and then 61

64 Figure 10. GLUT3 is required for the propagation of glioblastoma in vivo. (a) In vivo tumor propagation assays with classical subtype 4121 derived GSCs demonstrate that targeting of GLUT3 using shrna increases the survival of mice bearing human glioma xenografts relative to the non targeting shrna control. [n=5 for all groups; * p<0.03 with log rank analysis]. (b) In vivo tumor propagation assays with proneural subtype IN528 derived GSCs demonstrate that targeting of GLUT3 using shrna increases the survival of mice bearing human glioma xenografts relative to the non targeting shrna control [n=8 for non targeting control, n=10 for GLUT3 shrna1 and GLUT3 shrna2 groups; * p< with log rank analysis]. (c) In vivo tumor propagation assays with classical subtype M43 derived GSCs demonstrate that targeting of GLUT3 using shrna increases the survival of mice bearing human glioma xenografts relative to the non targeting shrna control [n=10 for all groups; * p= with log rank analysis]. (d,e) Representative images of hematoxylin and eosin stained sections of brains from mice in (a) and (b) demonstrating the presence of tumors (circled) in animals showing neurologic signs. 62

65 injected into the brains of mice the next day. This reduced any potential selection effects of the GLUT3 knockdown, as the time period from viral introduction to injection was too short for viral integration and expression of the shrna construct. With this adapted strategy, the differences in tumor formation between control and GLUT3 knockdown was dramatically increased. In the IN528 specimen, seven of eight control mice developed tumors between 60 and 125 days post-injection. In the weaker shrna arm, two of ten mice developed tumors at around 110 days. In the stronger knockdown arm, no tumors were observed (Figure 10b,e). In the M43 specimen, all of the control mice developed tumors between 38 and 50 days post injection. At 49 days, when the last control mouse developed neurological signs, two randomly selected shrna mice were sacrificed and their brains were examined for the presence of tumors that were simply too small to affect the mice, however, no trace of tumor was found in the brains. In the weaker shrna group, four of the mice would eventually develop neurological signs, with a median neurological deficit-free survival of 115 days, compared to 47 days in the control group. In the stronger knockdown arm, there was no neurological symptom development in over 150 days (Figure 10c). These data demonstrate conclusively, compellingly, and dramatically that GLUT3 is required for the formation of tumors in an in vivo environment. Discussion, cancer stem cell GLUT3 expression The cancer stem cell specific expression of GLUT3 is a fascinating and important finding. This cells, which express essentially no other markers of neuronal lineage, have somehow coopted the high affinity glucose transporter utilized by the neurons. While the 63

66 cancer stem cells are not neuronal in origin, they do find themselves in a similar position to neurons; both cell types have a high energy demand with limited extracellular concentrations of nutrients. The fact that both of these cell types express the same high affinity glucose transporter to address this problem is a testament to the adaptability of cancer, able to utilize a specialized grab bag of non-neoplastic tools to adapt to any situation that may be encountered in the tumor microenvironment. Materials and Methods Fluorescent glucose flow cytometry Following 30 minutes of glucose starvation in glucose-free stem cell media, unsorted GBM cells were incubated for 30 minutes in the presence of the fluorescent glucose analog, 2-[N-(7-nitrobenz-2-oxa-1,3-diaxol-4-yl)amino]-2- deoxyglucose (2-NBDG). For studies comparing uptake in CD133+ and CD133- cells, unenriched cells were co-incubated with AC133-APC (Miltenyi) antibody and analyzed via FACS. For studies with shrna expressing BTICs, cells were washed and fluorescence analyzed using a VICTOR plate reader. Glucose oxidase assay Following 45 minutes of glucose starvation, GBM stem cells or non-stem cells were incubated in 0.45 g/l glucose for 30 minutes, then collected for lysis via mechanical lifting. Cells were lysed in RIPA buffer (Sigma), lysate was boiled, and the protein was quantified as a loading control via Bradford assay. A glucose colorimetric detection kit was obtained (Arbor Bioassays) and performed 64

67 according to protocol, with the exception of an increased incubation time to account for the use of whole cell lysate. Ex vivo glucose uptake assay Prior to transplantation onto brain slices for imaging, GBM stem cells were labeled with Cell Tracker Red CMPTX (Invitrogen) and non-stem cells were labeled with Cell trace far red DDAO-SE (Invitrogen). For ex vivo glucose analysis, slice cultures were prepared from mice according to prior publications cells total were transplanted (at a ratio of 1:1, GSC:non-GSC). Transplanted cells were incubated overnight to ensure integration and survival in the brain slices and prior to imaging, slices were incubated in 50ug/ml NDBG for 30 minutes prior to image acquisition. Imaging was done using a Leica multiphoton microscope as previously described with a 20x liquid immersion objective, NA=1.0. Images were acquired at 820nm and processed using Imaris software (Bitplane). GLUT qpcr RNA was isolated via RNeasy mini-prep kit (Qiagen), and reverse transcribed into cdna using the Superscript III Reverse Transcription Kit (Invitrogen). : β- actin forward 5 -AGA AAA TCT GGC ACC ACA CC-3 and reverse 5 -AGA GGC GTA CAG GGA TAG CA-3 ; Glut1 forward 5 - ATCGTGGCCATCTTTGGCTTTGTG-3 and reverse 5 - CTGGAAGCACATGCCCACAATGAA-3 ; Glut2 forward 5 - AGCTGCATTCAGCAATTGGACCTG-3 and reverse 5-65

68 ATGTGAACAGGGTAAAGGCCAGGA-3 ; Glut3 forward 5 - AGCTCTCTGGGATCAATGCTGTGT-3 and reverse 5 - ATGGTGGCATAGATGGGCTCTTGA-3 ; Glut4 forward 5 - TCGTGGCCATATTTGGCTTTGTGG-3 and reverse 5 - TAAGGACCCATAGCATCCGCAACA-3 GLUT flow cytometry staining For FACS staining of surface Glut expression, live cells were incubated with either Glut3 (R&D, MAB1415) or Glut1 (Abcam AB40084), and CD133 (AC133- APC, Miltenyi) for 45 min at dilutions specified in the manufacturer s protocols (2.5μg GLUT3 antibody per million cells, 1μg GLUT1 antibody per million cells). GLUT3 viral shrna knockdown Lentiviral clones expressing GLUT3 (SLC2A3) shrnas (TRC and TRC ) and control shrna (SHC002) were purchased from Sigma- Aldrich. Viral particles were produced in 293T cells with the ppack set of helper plasmids (System Biosciences) in stem cell medium. Both sequences were designed against the GLUT3 coding sequence. GLUT3 shrna1: 5 - CCGGCTTGGTCTTTGTAGCCTTCTTCTCGAGAAGAAGGCTACAAAGACCAA GTTTTTG-3 ; 66

69 GLUT3 shrna2: 5 - CCGGAGTAGCTAAGTCGGTTGAAATCTCGAGATTTCAACCGACTTAGCTACT TTTTTG-3 GLUT3 knockdown in vitro limiting dilution sphere formation assay For in vitro limiting dilution assays, viable (propidium iodide negative) cells were sorted by FACS with decreasing numbers of cells per well (50, 20, 10, 5, and 1) plated in 96 well plates containing stem cell media. Plate setup was an external border of PBS to prevent well evaporation, two rows of ten wells each of 1 cell per well, two rows of 5 cells per well, one row of ten cells per well, and one row of half (5 wells) 20 cells per well and the other half 50 cells per well. Extreme limiting dilution analysis was performed using software available at GLUT3 knockdown in vivo tumor formation assay All animal procedures were performed in accordance with Cleveland Clinic IACUC approved protocols. Animals were housed in a temperature-controlled vivarium with a 14 hour light, 10 hour dark cycle at no more than 5 animals per cage. For nutrient deprivation studies, unenriched GBM cells were cultured in standard or low glucose conditions for seven days and 5,000 viable cells were intracranially injected into athymic/nude mice as previously described (REF PAPER 12). Animals were maintained until development of neurological signs (e.g. lethargy, ataxia, seizures and/or paralysis), at which point brains were harvested. Harvested brains were fixed in 4% formaldehyde, sunk in 30% 67

70 sucrose, cryopreserved in OCT, and cryosectioned. Tumors were visualized via hematoxylin and eosin staining and imaged via slide scanner. 68

71 CHAPTER 3: GLUT3 EXPRESSION IS A CLINICAL MARKER OF POOR SURVIVAL IN CANCER Introduction and rationale While the previous two chapters demonstrate convincingly that glucose availability is intrinsically linked to the tumor hierarchy, all of the work was done on model cell systems. While our model system, of xenograft passage of patient specimens, is a much closer approximation than cell lines, all of the work was still done in vitro. To truly demonstrate that these effects play a role in the clinical progression of GBM, work has to be done using actual patient data. Thankfully, there is a wealth of patient data that has been collected for GBM. With the abundance of relatively cheap microarray systems, the development of exome and genome sequencing, and other tools such as complete genome hybridization and SNP chips, it is possible to gather a wealth of information for any given tumor or disease. Additionally, the disease characteristics of GBM, including its near universal indication for surgery and its poor prognosis, mean that much effort has been focused on the collection of data from GBM tumors. In fact, GBM was one of the two cancer types first investigated by the Cancer Genome Atlas (TCGA), due to its poor prognosis and overall public health impact, as well as the availability of samples. TCGA joins REMBRANDT, the NCI s Repository for Molecular BRAin Neoplastic DaTa, another large scale effort looking at glioma. In each dataset, there are around 500 patients with survival 69

72 data and microarray expression data, allowing for the in depth examination of how gene expression may interact with patient survival. Given the clinical prognosis of this disease, as well as the wealth of information available, I chose to focus a great deal of effort in examining how GLUT3 affects the clinical management of patients. Demonstration that the effects of the previous two chapters also applies to the clinical patient setting would dramatically underscore the importance of this work in the management of human disease. Results and discussion GLUT3, but not GLUTs 1, 2 or 4, correlate with glioma progression First, I examined the levels of the class I GLUTs, GLUTs 1 through 4, in the Cancer Genome Atlas (TCGA) data. Consistent with prior reports, GLUT1 and GLUT3 were significantly expressed in patients, while GLUT2 and GLUT4 had minimal expression levels (Figure 11a). I next turned to the data aggregation site Oncomine (Compendia Bioscience) to analyze the role of the GLUTs in disease. Analysis of GLUT1-4 in two glioma datasets, the Freije brain database 57 and the Sun brain database 58, showed that GLUT3 was consistently and dramatically upregulated in GBM compared to lower grade glioma (Figure 11b,c). GLUT1 was slightly upregulated in the Sun dataset, however the fold change and significance level were lower than that of GLUT3 (GLUT1 1.1 fold higher in GBM, GLUT fold). GLUT1 was not 70

73 Figure 11. Glut3 Expression is Significantly Increased in Glioblastomas, Recurrent Brain Tumors, and Brain Tumors from Patients with Poor Survival. (a) Expression of Glut isoforms in the TCGA database demonstrates expression of both Glut1 and Glut3 with minimal expression of Glut2 and Glut4. (b) Oncomine analysis of the Freije database indicates elevated Glut3 mrna expression (but not Glut1, 2, or 4) correlates with increased glioma tumor grade [p=4.2x10^ 4 with ANOVA]. (c) Oncomine analysis of the Sun database indicates elevated Glut3 mrna expression correlates with increased glioma tumor grade [p=8.02x10^ 8 with ANOVA]. There is also a significant correlation with Glut1 mrna expression [p=0.014] but not Glut2 or Glut4. (d) Oncomine analysis of the Phillips databases indicates elevated Glut3 mrna expression (but not Glut1, 2, or 4) correlates with glioma recurrence [p=0.008 with unpaired t test]. (e) Oncomine analysis of the Nutt database indicates elevated Glut3 mrna expression (but not Glut1, 2, or 4) correlates with reduced patient survival at three years [p=9.5x10^ 5 with unpaired t test]. 71

74 significantly increased in GBM in the Freije database. GLUT2 and GLUT4 were not significantly associated with any disease type in either dataset. I next queried the Phillips database 59 to see if the levels of any of the GLUTs were changed following disease recurrence. Recurrent tumors tend to be more aggressive and treatment resistant, owing to selection effects. GLUT3 was significantly increased following disease recurrence, while none of the other class I GLUTs showed a difference between primary and recurrent specimens (Figure 11d). Several of the datasets also had survival data for the patients. The default analysis in Oncomine was to compare the expression levels of patients who were alive or dead at a given time point (one, three, or five years post-diagnosis). Comparison of the GLUTs via this method in the Nutt dataset 60 revealed a very strong difference in GLUT3 levels, patients who were alive three years postdiagnosis had a dramatically lower level of GLUT3 (Figure 11e). In fact, of the over 8500 genes in this data set, GLUT3 was the 31 st best at predicting survival, placing it in the top third of a percent. None of the other class I GLUTs showed a significant difference in expression in this analysis. GLUT3 correlates with radiological criteria associated with poor survival To further examine the role of GLUT3 in tumor pathology, I obtained data from the VASARI initiative of REMBRANDT 61. Here, the imaging data of 18 GBM patients whose tumors had been subjected to microarray expression characterization was sent to three radiologists. These scans were scored on a number of criteria, several of which are associated with poor survival, such as radiological presence 72

75 of necrosis, deep white matter invasion, and overall tumor size. Analysis of this data showed that GLUT3 was significantly correlated with several radiological criteria associated with poor survival. First, larger tumors had significantly higher levels of GLUT3 expression (Figure 12a). Tumors that had detectable necrosis also had significantly higher levels of GLUT3 (Figure 12b). Finally, tumors that could be detected invading the deep white matter had a very significant and dramatically higher level of GLUT3 expression than those that were not invasive (Figure 12c). These correlations were not present for GLUT1 (Figure 12d-f). GLUT3 informs glioma patient survival in multiple data sets - These results were very encouraging, so I decided to examine the survival of patients in other datasets. Patient survival is traditionally measured via Kaplan-Meier curves analyzed by log-rank test. Patient survival and expression data was downloaded from Oncomine for several datasets, including the Freije, Phillips, and Nutt datasets (the Sun dataset did not have survival data available), as well as the REMBRANDT and TCGA datasets. Kaplan-Meier analysis was performed on this data. GLUT3 was consistently and dramatically associated with poorer patient survival in all datasets tested (Figure 13). This correlation with survival was unique to GLUT3 among the class I GLUTs; GLUT1, GLUT2 and GLUT4 did not correlate with poorer survival in any tested dataset (Figures 14, 15, and 16). Previously published in-depth analysis of TCGA data reported the presence of several subtypes of glioblastoma, characterized by hallmark mutations and activation of specific pathways. To determine whether GLUT3 played a role in any particular subtype, I examined these TCGA subpopulations individually. 73

76 Figure 12. Glut3 Expression significantly correlates with radiological criteria of poor survival in GBM. (a c) Analysis of VASARI REMBRANDT data reveals that high GLUT3 expression is significantly correlated with radiological criteria of poor tumor prognosis, including increased size (a), presence of necrosis (b), and invasion of the tumor into the white matter (c). GLUT1, however, does not correlate with these criteria (d f). 74

77 Figure 13. Glut3 Expression Correlates with Poor Survival in Multiple Brain Tumor Datasets. Analysis of Brain Datasets available through Oncomine indicates a significant correlation between high Glut3 expression and poor survival in the (a) Freije [n=39 Glut3 low; n=45 Glut3 high; p=0.002 with log rank analysis], (b) Phillips [n=39 Glut3 low; n=38 Glut3 high; p=0.04 with log rank analysis], and (c) Nutt [n=18 Glut3 low; n=10 Glut3 high; p=0.01 with log rank analysis] datasets. (d) Analysis of REMBRANDT data in the National Cancer Institute s repository indicates that greater than two fold elevation of Glut3 mrna expression correlates with poor glioma patient survival [n=193 Glut3 low; n=144 Glut3 medium; n=6 Glut3 high; p=0.002 vs. all other samples and p=0.018 vs. intermediate expression with log rank analysis]. Greater than two fold reduction in Glut3 mrna expression correlates with improved patient survival [**, p=2.2x10 6 vs. all other samples and p=7.7x10 6 vs. intermediate expression with log rank analysis]. Reporter is Affymetrix _s_at (Highest Geometric Mean Intensity). (e) Analysis of TCGA data indicates a significant correlation between reduced Glut3 expression and survival in all patients [n=103 Glut3 low; n=320 Glut3 medium; n=91 Glut3 high; *, p=0.02 with log rank analysis]. Classification of TCGA according to Verhaak subtypes indicates Glut3 does not significantly correlate with survival in (f) Mesenchymal tumors [n=15 Glut3 low; n=39 Glut3 high; p=0.26 with log rank analysis], but reduced Glut3 correlates with improved patient survival in both (g) Classical [n=7 Glut3 low; n=31 Glut3 high; p=0.03 with log rank analysis] and (h) Proneural [n=28 Glut3 low; n=25 Glut3 high; p=7x10 5 with log rank analysis] GBM subtypes. 75

78 Figure 14. Glut1 Does Not Correlate with Clinically Relevant Patient Markers. (a) Analysis of the Freije dataset indicates no significant correlation between increasing grade and expression of Glut1 [n=11 for Oligodendroglioma, n=7 for Mixed Glioma, n=8 for Anaplastic Astrocytoma, n=59 for GBM, p=0.87 with ANOVA comparison]. (b) Analysis of Philips Dataset indicates no significant correlation between Glut1 expression and glioma recurrence [n=77 primary tumors, n=23 recurrent tumors, p=0.71 with unpaired t test comparison]. (c) Analysis of Nutt Dataset indicates no significant difference between Glut1 expression in patients with differential survival at 3 years in GBM [n=4 patients alive at three years, n=21 patients dead at three years, p=0.17 with unpaired t test comparison]. Analysis of (d) Freije [n=37 GLUT1 high patients, n= 47 GLUT1 low patients, p=0.86 with log rank analysis], (e) Phillips [n=49 GLUT1 high patients, n=28 GLUT1 Low patients, p=0.73 with log rank analysis], or (f) Nutt [n=11 GLUT1 high patients, n=17 GLUT1 low patients, p=0.69 with log rank analysis] data indicates no significant correlation between GLUT1 expression and patient survival. (g) Analysis of REMBRANDT data in the National Cancer Institute s repository indicates no significant correlation between GLUT1 levels and glioma patient survival [n=46 GLUT1 high patients, n=250 GLUT1 med patients, n=47 GLUT1 low patients, p=0.30 with log rank analysis]. Analysis of TCGA data for (h) all patients [n=85 GLUT1 high patients, n=362 GLUT1 med patients, n=68 GLUT1 low patients, p=0.91 with log rank analysis] or subgroup classified tumors including (i) Mesenchymal [n=32 GLUT1 high patients, n=22 GLUT1 low patients, p=0.81 with log rank analysis], (j) Classical [n=22 GLUT1 high patients, n=16 GLUT1 low patients, p=0.71 with log rank analysis], and (k) proneural [n=31 GLUT1 high patients, n=22 GLUT1 low patients, p=0.56 with log rank analysis] indicates no significant association between GLUT1 levels and GBM patient survival. 76

79 Figure 15. GLUT2 Does Not Correlate with Clinically Relevant Patient Markers. (a) Analysis of the Freije dataset indicates no significant correlation between increasing grade and expression of GLUT2 [n=11 for Oligodendroglioma, n=7 for Mixed Glioma, n=8 for Anaplastic Astrocytoma, n=59 for GBM, p=0.33 with ANOVA statistical analysis]. (b) Analysis of Philips Dataset indicates no significant correlation between GLUT2 expression and glioma recurrence [n=77 primary tumors, n=23 recurrent tumors, p=0.66 with unpaired t test analysis]. (c) Analysis of Nutt Dataset indicates no significant difference between GLUT2 expression in patients with differential survival at 3 years in GBM [n=4 patients alive at three years, n=21 patients dead at three years, p=0.85 with unpaired t test statistical comparison]. Analysis of (d) Freije [n=31 GLUT2 high patients, n=53 GLUT2 low patients, p=0.28 with log rank analysis], (e) Phillips [n=37 GLUT2 high patients, n=40 GLUT2 low patients, p=0.62 with log rank analysis], or (f) Nutt [n=14 GLUT2 high patients, n=14 GLUT2 low patients, p=0.71 with log rank analysis] data indicates no significant correlation between GLUT2 expression and patient survival. Analysis of (g) REMBRANDT data in the National Cancer Institute s repository indicates no significant correlation between GLUT2 levels and glioma patient survival [n= 13 GLUT2 high patients, n=246 GLUT2 med patients, n=84 GLUT2 low patients, p=0.12 with log rank analysis]. Analysis of TCGA data for (h) all patients [n=100 GLUT2 high patients, n=314 GLUT2 med patients, n=100 GLUT2 low patients, p=0.13 with log rank analysis] or subgroupclassified tumors including (i) Mesenchymal [n=30 GLUT2 high patients, n=24 GLUT2 low patients, p=0.68 with log rank analysis], (j) Classical [n=21 GLUT2 high patients, n=17 GLUT2 low patients, p=0.87 with logrank analysis], and (k) Proneural [n=27 GLUT2 high patients, n=26 GLUT2 low patients, p=0.40 with logrank analysis] indicates no significant association between GLUT2 levels and GBM patient survival. 77

80 Figure 16. Glut4 Does Not Consistently Correlate with Clinically Relevant Patient Markers. (a) Analysis of the Freije dataset indicates no significant correlation between increasing grade and expression of Glut4 [n=11 for Oligodendroglioma, n=7 for Mixed Glioma, n=8 for Anaplastic Astrocytoma, n=59 for GBM, p=0.1 with ANOVA statistical analysis]. (b) Analysis of Philips Dataset indicates no significant correlation between Glut4 expression and glioma recurrence [n=77 primary tumors, n=23 recurrent tumors, p=0.65 with unpaired t test statistical analysis]. (c) Analysis of Nutt Dataset indicates a significant decrease in Glut4 expression in patients who survive less than 3 years [n=4 patients alive at three years, n=21 patients dead at three years, p=0.009 with unpaired t test statistical analysis]. Analysis of (d) Freije [n=46 Glut4 high patients, n=38 Glut4 low patients, p=0.97 with log rank analysis], (e) Phillips [n=39 Glut4 high patients, n=38 Glut4 low patients, p=0.71 with log rank analysis], or (f) Nutt [n=16 Glut4 high patients, n=12 Glut4 low patients, p=0.31 with log rank analysis] data indicates no significant correlation between Glut4 expression and patient survival. Analysis of (g) REMBRANDT data in the National Cancer Institute s repository indicates no significant correlation between Glut4 levels and glioma patient survival [n=36 for Glut4 high patients, n=237 for Glut4 med patients, n=70 for Glut4 low patients, p=0.40 with log rank analysis]. Analysis of TCGA data for (h) all patients [n=106 for Glut4 high patients, n=308 for Glut4 med patients, n=100 for Glut4 low patients, p=0.24 with log rank analysis] or subgroup classified tumors including (i) Mesenchymal [n=29 for Glut4 high patients, n=25 for Glut4 low patients, p=0.95 with logrank analysis], (j) Classical [n=21 for Glut4 high patients, n=17 for Glut4 low patients, p=0.21 with log rank analysis], and (k) Proneural [n=27 for Glut4 high patients, n=26 for Glut4 low patients, p=0.56 with logrank analysis] indicates no significant association between Glut4 levels and GBM patient survival. 78

81 Interestingly, the GLUT3 correlation was the strongest in the Proneural subgroup, characterized by amplification of the PDGFRA gene. GLUT3 was also significantly associated with survival in the Classical subgroup, characterized by EGFR amplification or mutation and CDKN2A deletion. GLUT3 was not significantly associated with survival in the Mesenchymal subgroup, characterized by NF1 deletion or mutation, however it did appear that there might be a trend towards survival. Analysis of IDH1 mutant tumors - The Proneural subgroup is also associated with mutation of the metabolic gene isocitrate dehydrogenase 1, IDH1. Mutation in the active site of IDH1 is associated with better prognosis and a unique disease phenotype, referred to as the Glioma CpG Island Methylator Phenotype (G- CIMP), involving increased histone methylation and decreased 5- hydroxylmethylcytosine. This was a rather enigmatic mutation in the field, as mutations which hinder tumor growth are usually simply selected out. IDH1 mutation, however, appears to be a gain of function mutation, where mutant IDH1 protein, normally a Tricarboxylic Acid (TCA) Cycle enzyme which converts isocitrate to α-ketoglutarate, instead will convert isocitrate to the toxic oncometabolite 2-hydroxyglutarate. 2-hydroxygluatarate will interfere with the enzymes such as histone demethylases, resulting in altered DNA methylation patterns. These altered patterns resulted in tumorigenesis, hence, mutations in IDH1 could drive tumorigenesis. Since GLUT3 seemed to correlate with survival most dramatically in the Proneural subgroup, I investigated whether GLUT3 may be associated with IDH1 79

82 mutation and G-CIMP status of tumors. Interestingly, GLUT3 did appear to correlate with both IDH1 mutation status and G-CIMP status in TCGA data. Every IDH1 mutant or G-CIMP positive patient was in the bottom 50% of GLUT3 expression (Figure 17a,c). This correlation, however, was not sufficient to explain the predictive effect of GLUT3 on patient survival. Increased GLUT3 expression significantly correlated with poorer patient survival in both Proneural non-g-cimp tumors and in IDH1 WT patients (Figure 17b,d). While IDH1 mutation and G-CIMP status does not explain the GLUT3 survival difference, the correlation between G-CIMP and lower GLUT3 expression is fascinating. Since IDH1 is involved in the TCA cycle, it seems possible that the increased glucose afforded to a tumor by GLUT3 could create a higher intracellular level of isocitrate due to a more active TCA cycle. This could create to buildup of toxic levels of the oncometabolite due to catastrophic levels of genomic instability and histone alteration. Thus, these tumors would have a strong selection pressure against acquiring increased GLUT3 expression, which may even explain why these tumors tend to be less deadly, given the strong correlation of GLUT3 with survival. Analysis of possible hypoxia confounding Several microenvironmental factors, most notably hypoxia, have also been reported as negative prognostic indicators of therapeutic response and patient survival, as can be seen by examination of the hypoxia marker carbonic anhydrase IX (CA9) in TCGA data (Figure 18a). As both GLUT1 and GLUT3 are reported to be hypoxia response genes, and contain putative hypoxia response elements in their promoters, this raises the potential 80

83 Figure 17. Glut3 expression is reduced in GCIMP tumors but retains ability to inform survival in GCIMP negative tumors. (a) Glut3 expression is significantly lower in GCIMP positive tumors in the TCGA dataset [n=199 GCIMP negative patients, n=20 GCIMP positive patients, p< with unpaired t test statistical analysis]. (b) Glut3 expression correlates with survival in the proneural subtype restricted to only GCIMP negative tumors [n=25 Glut3 high patients, n=10 Glut3 low patients,p= with log rank statistical analysis]. (c) Glut3 expression is significantly lower in IDH1 mutant tumors in the TCGA dataset [n=123 IDH1 wild type patients, n=10 IDH1 mutant patients, p=0.002 with unpaired t test statistical analysis]. (d) Glut3 expression correlates with survival in IDH1 wild type tumors in the TCGA dataset [n=55 Glut3 high patients, n=65 Glut3 low patients, p=0.001 with log rank statistical analysis]. 81

84 argument that the predictive effect of GLUT3 expression on survival is merely a surrogate for hypoxia; that is, that GLUT3 high tumors are simply hypoxic tumors, already known to fare worse, and GLUT3 itself is doing nothing. Both GLUT3 and GLUT1 significantly correlated with CA9 expression (Figure 18b,c), confirming the hypoxia responsive status of both genes. However, to test which of the two factors, hypoxia or GLUT expression, was more strongly affecting survival, I divided patients into five groups. Boundaries were set one half of a standard deviation away from the mean, and only patients with high or low expression of both CA9 and GLUT3 were analyzed (Figure 19a). This sorted patients into five groups, with the four interesting groups being: patients with low CA9 and low GLUT3, patients with high CA9 and high GLUT3, and then patients with high GLUT3 but low CA9 or high CA9 but low GLUT3. Patients with intermediate levels of either gene comprised the fifth group: excluded patients. Patients with low expression of both genes would be expected to have the longest survival, and patients with high expression of both genes would be expected to have the shortest survival, as both of these genes independently correlate with poor survival (Figure 19b). The interesting part of the analysis is in comparing patients with high GLUT3 but not high CA9 and high CA9 but not high GLUT3. By comparing each of these groups to the high/high and low/low, whether CA9 or GLUT3 is truly linked to survival will be uncovered. This analysis revealed that GLUT3 was the important factor. Analyzing patients with the same level of GLUT3 but different levels of CA9 revealed no survival difference due to CA9 that was not also attributable to GLUT3 (Figure 19e,f). 82

85 Figure 18. CA9, a marker of hypoxia, independently predicts patient survival and correlates with Glut1 and Glut3 expression. (a) Analysis of TCGA patient survival for expression of the standard hypoxia marker, Carbonic Anhydrase IX (CA9) reveals that patients with high levels of CA9 have significantly poorer survival [p=0.037 with log rank statistical analysis]. Analysis of patients for expression of CA9 and (b) Glut3 [p< with regression analysis] or (c) Glut1 [p< with regression analysis] reveals a significant correlation between CA9 levels and Glut3 or Glut1 expression. 83

86 Figure 19. Glut3 predicts survival independently of tumor hypoxia. (a) In order to determine whether the effects of Glut3 informing patient survival could be attributed to hypoxia driven expression of Glut3, we segregated patients based on Glut3 and CA9 expression. Boundaries were set one half of a standard deviation away from the mean, and only patients with high or low expression of both CA9 and Glut3 were analyzed. n=104 for Glut3 low CA9 low patients; n=17 for Glut3 low CA9 high patients; n=29 for Glut3 high CA9 low patients; n=71 for Glut3 high CA9 high patients. (b) Kaplan Meier survival curves for the groups in a are shown. Patients with Glut3 high expression had the worst prognosis regardless of CA9 expression. (c d) In order to determine the effects of Glut3 independently of tumor hypoxia, patients with consistent CA9 expression but different Glut3 expression were compared. Glut3 remained predictive of survival in both patients with (c) high [p=0.004 with log rank statistical analysis] or (d) low [p=0.011 with logrank statistical analysis] CA9 expression, indicating that Glut3 is independently predictive of survival regardless of hypoxia status. (e, f) Performing the same analysis for CA9 expression levels at a constant Glut3 (e) high [p=0.58 with log rank statistical analysis] or (f) low [p=0.32 with log rank statistical analysis] expression level eliminates the correlation between CA9 expression and survival. The predictive effect of CA9/hypoxia was dependent on Glut3 as CA9 was not associated with survival in the subset of either Glut3 high (e) or Glut3 low (f) patients. 84

87 Figure 20. Glut1 does not predict survival independently or in correlation with tumor hypoxia. (a) In order to determine whether the ability of Glut3 to inform patient response to tumor hypoxia was a general phenomenon of the Glut proteins, patients were segregated based on Glut1 and CA9 expression. As in Supplemental Figure 8, boundaries were set one half of a standard deviation away from the mean, and only patients with high or low expression of both CA9 and Glut1 were analyzed. n=110 for Glut1 low CA9 low patients; n=16 for n=104 for Glut1 low CA9 high patients; n=23 for Glut1 high CA9 low patients; n=79 for Glut1 high CA9 high patients. (b) Kaplan Meier survival curves for the groups in a are shown. (c, d) In order to determine the effects of Glut1 independently of tumor hypoxia, patients with consistent CA9 expression but different Glut1 expression were compared. Glut1 remained not predictive of survival in both patients with (c) high [p=0.45 with log rank statistical analysis] or (d) low [p=0.66 with log rank statistical analysis] CA9 expression. (e,f) Survival was determined for CA9 expression levels at a constant Glutlut1 expression. (e) The correlation between CA9 expression and survival remained in in Glutlut1 high patients [p=0.035 with log rank statistical analysis], indicating that the ability to inform patient response to hypoxia is specific to Glut3. (f) There was no statistically significant difference in the survival of Glut1 low CA9 low and Glut1 low CA9 high patients [p=0.11 with log rank statistical analysis]. Unlike Glut3, Glut1 was not independently predictive of survival either alone or at constant CA9 levels and segregation for Glut1 expression did not remove the predictive ability of CA9/hypoxia. 85

88 Analyzing patients with the same level of CA9 but different levels of GLUT3 revealed the same strong survival effect seen by analyzing GLUT3 alone (Figure 19c,d). Interestingly, performing the same analysis with GLUT1, which did not independently correlate with survival, did not have the same result. Patients segregated based on GLUT1 expression retained the predictive effect of CA9 expression (Figure 20). These data demonstrate that not only does GLUT3 correlate with survival independently, rather than as a surrogate for hypoxia, but they also suggest that the reason that hypoxic tumors are associated with poor survival may be the acquisition of GLUT3 in hypoxic tumors. GLUT3 correlates with survival in other cancers beyond the brain While these results were a compelling demonstration of the critical role of GLUT3 in glioma patient disease progression, they did raise an interesting question: does this also apply to non-glioma tumors? Because GLUT3 is referred to as the brain glucose transporter, it is tempting to think that perhaps it is only expressed in brain cancer. However, referring to GLUT3 as the brain glucose transporter is imprecise and misses a key point: in the brain, GLUT3 is only expressed on neurons, not on glia such as astrocytes and oligodendrocytes. To verify this, I queried data from the Cahoy dataset on isolated mouse brain cell types. Neurons however are post-mitotic and are not thought to give rise to cancer. Gliomas, as the name suggests, are thought to arise from glial-like cells, and display morphological hallmarks suggestive of glial cells. How then would these 86

89 tumors that don t widely express neuronal markers express neuronal GLUT3? And if glial-derived tumors can co-opt this pathway, can other solid tumors do the same? To answer this question, I queried datasets from other solid tumors in Oncomine. Perhaps surprisingly, GLUT3 was expressed and displayed an extremely strong correlation with survival in almost every dataset queried, displaying strong predictive effects in multiple lung, breast, and colon datasets, and in an ovarian cancer dataset (Figure 21). In fact some of these predictive effects dwarfed the effects seen in glioma datasets; in the Bild lung dataset, the two year survival of the high GLUT3 patient group was zero, while the five year survival of the low GLUT3 group was over eighty percent (Figure 21h). Additionally, increased GLUT3 expression was strongly correlated with poorer metastatic-free survival in several datasets (Figure 22). In the Loi breast cancer dataset, almost half of the GLUT3 high patient group would go on to develop a metastasis in seven years post-diagnosis (Figure 22a). In the GLUT3 low group, the metastatic event rate over eleven years was zero. This is fascinating but not unexpected, as one of the critical steps of metastasis is extravasation and establishment of a tumor in a new site. Such a process, involving microenvironmental remodeling and sustained proliferation, is highly energy intensive, and a metastatic cell is likely to require a steady supply of glucose during this process, which almost necessitates GLUT3 expression. As metastasis is responsible for almost 90% of solid tumor deaths, this is a hugely important and disease-relevant finding. 87

90 Figure 21. GLUT3 expression predicts survival in multiple cancer types beyond the brain. (a i) Analysis of Carcinoma Datasets available through Oncomine indicates a significant correlation between high Glut3 expression and poor survival for breast (a c), colorectal (d f), ovarian (g) and lung (h,i) cancers. (a) van de Vijver Breast Carcinoma Dataset [n=39 for Glut3 low; n=138 for Glut3 medium; n=112 for Glut3 high; p= with log rank analysis]. (b) Kao Breast Carcinoma dataset [n=145 Glut3 low; n=181 Glut3 high; p= with log rank analysis]. (c) Sorlie breast carcinoma dataset [n=54 Glut3 low; n=18 Glut3 high; p=0.06 with log rank analysis]. (d) Smith 2 Colorectal Adenocarcinoma Dataset [n=9 Glut3 low; n=34 Glut3 medium; n=11 Glut3 high; p=0.004 with log rank analysis]. (e) Staub Colon Carcinoma dataset [n=4 Glut3 low; n=42 Glut3 medium; n=16 Glut3 high; p= with log rank analysis]. (f) TCGA colon carcinoma dataset [n=85 Glut3 low; n=68 Glut3 high; p=0.013 with log rank analysis]. (g) Bonome Ovarian Carcinoma Dataset [n=24 Glut3 low; n=117 Glut3 medium; n=18 Glut3 high; p= with log rank analysis]. (h) Bild Lung Adenocarcinoma Dataset [n=10 Glut3 low; n=33 Glut3 medium; n=7 Glut3 high; p< with log rank analysis]. (i) Raponi Squamous Cell Lung Carcinoma Dataset [n=18 Glut3 low; n=90 Glut3 medium; n=21 Glut3 high; p<0.04 with log rank analysis]. 88

91 Figure 22. GLUT3 expression predicts metastatic free survival in multiple cancer types. (a b) Analysis of cancer datasets available through Oncomine indicates a significant correlation between high Glut3 expression and increased rates of metastasis in breast (a) and head and neck squamous cell carcinoma (b). 89

92 GLUT3 is an embryonic stem cell glucose transporter upregulated during reprogramming - This led me to a fascinating hypothesis what if cancers, including glioblastoma, were not acquiring GLUT3 through neuronal pathways? What if the fact that I found GLUT3, the brain glucose transporter, by looking at brain cancer was merely a coincidence? Neurons express GLUT3 because they have a high energy demand but a low extracellular availability, but there is another important population of cells that have this identical situation: that of a pre-implantation embryo. Before the development of the placenta, the embryo has no vascular support and yet hosts a population of rapidly dividing cells, engaging in very active cytoplasmic segregation and growth factor production. Additionally, GLUT3 is also known to be expressed on the placenta, where it competes with maternal GLUT1 to bring glucose to the developing fetus. To determine whether cancer may be activating embryonic stem cell pathways to acquire GLUT3, I queried several microarray datasets deposited in the NCBI s gene expression omnibus (GEO). These included data from Shinya Yamanaka s original report of induced pluripotent stem (ips) cells in mice 19, as well as data from James Thomson s later work looking at human ips cells 63. These data sets contained data from parental differentiated cells, embryonic stem cell lines, and induced pluripotent stem cells. Importantly, if ips cells express GLUT3 at levels above the parental cells, then that suggests that the type of reprogramming that I demonstrated in chapter 1 could result in GLUT3 upregulation in cancer. In both data sets, differentiated cells had low levels of GLUT3, which were dramatically increased following ips reprogramming (Figure 23a,b). This 90

93 increase mimicked the expression levels of the embryonic stem cell (ESC) lines, which all had very high GLUT3 expression. Some of the ESC lines had slightly higher levels of GLUT1 than parental fibroblasts, and Glut1 was slightly increased during the reprogramming of mouse cells, however the reprogramming of human cells in the Thomson dataset showed no increase in GLUT1 (Figure 23b). A potential mechanism for this can be found by examining the chromosomal structure surrounding the GLUT3 locus (Figure 23c). GLUT3 is located on chromosome 12, adjacent (~120 kb distant from) the stem transcription factor NANOG. Indeed, this region of chromosome 12 is home to multiple stem cell genes, including GDF3 and DPPA3/Stella, as well as an OCT4 pseudogene, POU5F1P3. This region has been reported to be under the control of an OCT4 superenhancer site. This provides a potential mechanism, as embryonic stem cells (and ips cells) have very active OCT4 and NANOG, this could result in relaxed chromatin around the area, allowing for GLUT3 transcription. As cancer stem cells also have active OCT4 and NANOG signaling, this may explain how cancer stem cells acquire GLUT3 expression. Discussion - These data demonstrate that GLUT3 is a critical factor affecting patient survivability in both glioma and other solid tumors. This is a profound finding that may have dramatic therapeutic implications. There is however one potential pitfall how can we target cancer stem cell glucose uptake without targeting neuron glucose uptake, both dependent on GLUT3? 91

94 Figure 23. GLUT3 is an embryonic stem cell glucose transporter upregulated during reprogramming. (a) Analysis of the Yamanaka dataset for induced pluripotent stem cell (ipsc) generation from mouse embryonic fibroblasts (mef) demonstrates a significant increase in Glut3 expression with reprogramming. Mouse embryonic stem cells (mesc) also express high levels of Glut3 relative to fibroblasts [*, p=0.01; **p<0.01 with ANOVA comparison to mouse embryonic fibroblasts]. (b) Analysis of the Thomson dataset for ipsc generation from human foreskin fibroblasts (Parent) demonstrates increases in Glut3 levels with reprogramming. Human embryonic stem cell lines (H1L and H7) also express high levels of Glut3 relative to fibroblasts [*, p=0.01; ** p<0.01 with ANOVA comparison to parental cells]. (c) UCSC Genome browser output of the extended GLUT3 locus. GLUT3 (SLC2A3) is located at the center. About 100kb more telomeric (left) are the stem genes NANOG, GDF3, and DPPA3. 150kb more centromeric (right) lies the OCT4 (POU5F1) pseudogene, POU5F1P3. 92