Investigating the Effects of Antipsychotics and the Roles of Dopamine Receptor 2 in Breast Cancer

INVESTIGATING THE EFFECTS OF ANTIPSYCHOTICS AND THE ROLES OF DOPAMINE RECEPTOR 2 IN BREAST CANCER

Matthew Tegowski

A thesis submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Curriculum of Genetics and Molecular Biology.

Chapel Hill 2019

Approved by:

Albert Baldwin

Ian Davis

Charles Perou

Brian Strahl

Bernard Weissman

ABSTRACT

Matthew Tegowski: Investigating the Effects of Antipsychotics and the roles of Dopamine Receptor 2 in Breast Cancer (Under the direction of Albert S. Baldwin)

Breast cancer is the most common malignancy in women worldwide, and over 40,000 people die from breast cancer each year in the US alone. Further, metastasis, the spread of tumor cells and the colonization of distant sites, remains challenging to treat. The cancer stem cell (CSC) hypothesis proposes that not all tumor cells are capable of initiating new tumors or indeterminate growth. Instead, only a subset, the CSCs, are the drivers of metastasis.

Recently, thioridazine, an antipsychotic drug that primarily target dopamine receptor 2

(DRD2) were shown to inhibit certain properties of CSCs, such as self-renewal and tumor initiation. However, little is still known about how thioridazine works, and even whether it would affect breast cancer cells at all. Work presented in this thesis shows that high doses of thioridazine strongly affect the proliferation and cell viability of all breast cancer cell lines tested. However, these effects are independent of DRD2 inhibition. We show that, at lower doses, thioridazine can inhibit self-renewal in a DRD2-depedent manner. Interestingly, we show that DRD2 promotes self-renewal by activating the STAT3 transcription factor, which drives the production of the proinflammatory cytokine IL-6. We further show that the self- renewal of only the basal-like breast cancer cells is inhibited by thioridazine. Despite these interesting findings, there is still much to learn about the effects of thioridazine and the roles of DRD2 in breast cancer, and future studies are needed to fully elucidate how thioridazine will affect breast tumors, and which patients could potentially benefit the most if used therapeutically.

iii

ACKNOWLEDGEMENTS

There are many people to thank, without whom completion of this work would have been impossible or of a lesser quality. First, I have to thank my advisor, Al Baldwin, for the opportunity to work in this lab and to pursue a less than conventional project. This project was fascinating to me from the start, and I appreciate the ability to have worked on this project. I also have to thank my committee, Ian Davis, Charles Perou, Brian Strahl, and Buddy

Weissman. There were a lot of discussions in committee meetings that made me rethink parts of this projects, and even direct advice that led to the development of the project in Chapter

3, as well as help wrapping up our paper, so thank you.

Thank you to all members of the Baldwin lab that I have worked with, past and present.

Without all the discussions in lab, in lab meeting, and even outside lab, I would not have been able to complete this project. Especially Joe Durand and the former 210, Ricki Antonia,

Cortney Lawrence, and Amanda Rinkenbaugh. I know that we have always been down, so if

I didn’t ever thank you, then just let me do it now. I also had many friends during my time in grad school, both scientists and non-scientists, that contributed significantly to my ability to complete this project, you are my Bread and Butter.

I would also like to thank my parents, for the many years of support in raising me, making sure I took education seriously, and making sure that I always knew going to college didn’t have to be a question. And lastly, and definitely mostly, I would like to thank Emily, for being there at every moment during my trip through grad school, from the highest points to the lowest. Thank you for being there and helping put everything into perspective. Without you, this project would have been abandoned years ago and I would never have written this document, thank you.

TABLE OF CONTENTS

LIST OF TABLES………………………………………………………………………………………………………………………. vii

LIST OF FIGURES……………………………………………………………………………………………………………………… ix

LIST OF ABBREVIATIONS…………………………………………………………………………………………………………. xi

CHAPTER 1: INTRODUCTION……………………………………………………………………………………………………. 1

1.1 – Introduction to Breast Cancer and Cancer Stem Cells………………………………………. 1

1.1.1 – Overview of breast cancer……………………………………………………………………. 1

1.1.2 – Breast cancer subtypes………………………………………………………………………… 1

1.1.3 – Cancer stem cells: origins and relevance in breast cancer…………………. 3

1.1.4 – Signaling in cancer stem cells………………………………………………………………. 5

1.2 – Introduction to Dopamine Receptors………………………………………………………………….. 7

1.2.1 – Dopamine Receptors are G-protein-coupled receptors………………………. 7

1.2.2 – Dopamine Receptor genes and structure…………………………………………….. 9

1.2.3 – Dopamine Receptor signaling…………………………………………………………….. 11

1.2.4 – Peripheral DRD2……………………………………………….………………………………… 14

1.2.5 – Antipsychotics and dopamine receptors in cancer……………………………. 17

1.3 – Introduction to the JAK/STAT Pathway…………………………………………………………….. 18

1.3.1 – Overview of the JAK/STAT pathway…………………………………………………… 18

1.3.2 – Activation of STAT3 in cancer…………………………………………………………….. 19

1.4 – Conclusions and views………………………………………………………………………………………. 20

1.5 – REFERENCES………………………………………………………………………………………………………. 28

CHAPTER 2: THIORIDAZINE INHIBITS SELF-RENEWAL IN BREAST CANCER CELLS VIA DRD2-DEPENDENT STAT3 INHIBITION, BUT INDUCES A G1 ARREST INDEPENDENT OF DRD2………………………………………………………………………………….. 40

2.1 – Introduction……………………………………………………………………………………………………….. 40

2.2 – Results……………………………………………………………………………………………………………….. 42

2.2.1 – Thioridazine reduces cell viability of all tested TNBCLs, but inhibits self-renewal of some TNBCLs………………………………………………………. 42

2.2.2 – Thioridazine induces cell-cycle arrest………………………………………………… 44

2.2.3 – Thioridazine inhibits STAT3 activity…………………………………………………… 45

2.2.4 – Thioridazine requires STAT3 to inhibit self-renewal, but not proliferation or survival………………………………………………………………………. 47

2.2.5 – DRD2 promotes sphere formation in SUM149 cells………………………….. 48

2.2.6 – DRD2 promotes STAT3 activation……………………………………………………….49

2.2.7 – Thioridazine inhibits self-renewal, but not cell viability via DRD2 inhibition……………………………………………………………………………. 50

2.3 – Discussion………………………………………………………………………………………………………….. 51

2.4 – Experimental Procedures…………………………………………………………………………………… 56

2.5 – REFERENCES………………………………………………………………………………………………………. 78

CHAPTER 3: SPECULATIVE STUDIES SUGGEST THAT ACTIVE DOPAMINE SIGNALING IN BASAL-LIKE BREAST CANCER VIA ACTIVATION OF PLASMINOGEN PATHWAY MAY PROMOTE SELF-RENEWAL……………………………………………………. 83

3.1 – Introduction…………………………………………………………………………………………..…………… 83

3.2 – Results………………………………………………………………………………………………………………… 86

3.2.1 – Thioridazine preferentially targets the tumorsphere initiating cells of basal-like breast cancer cell lines (BLBCLs)……………………….. 86

3.2.2 – DRD2 expression does not predict thioridazine effects on self-renewal……………………………………………………………………………………. 88

3.2.3 – DRD2 activation promotes sphere formation in non-basal like cells………………………………………………………………………………………. 89

3.2.4 – Expression of plasminogen pathway genes………………………………………. 89

3.2.5 – Activation of the plasminogen pathway promotes sphere formation and sensitivity to thioridazine……………………………………………. 90

3.3 – Discussion………………………………………………………………………………………………………….. 91

3.4 – Experimental Procedures…………………………………………………………………………………… 94

3.5 – REFERENCES..……………………………………………………………………………………………….. 112

CHAPTER 4: CONCLUSIONS AND FUTURE STUDIES…………………………………………….…..……….. 115

4.1 – Conclusions and Discussion…………………………………………………………………………….. 115

4.2 – Future Directions……………………………………………………………………………………...…….. 120

4.3 – REFERENCES…………………………………………………………………………………...……..………. 127

vii

LIST OF TABLES

Table 2.1 – IC50 of thioridazine in 6 TNBCLs…………………………………………………………………………. 64

Table 3.1 – Dopamine detection in a C3-Tag tumor……………………………………………………………. 111

viii

LIST OF FIGURES

Figure 1.1 – The clinical subtypes of breast cancer………………………………………………………………. 21

Figure 1.2 – Experiments that measure tumor initiation activity within a population of cells……………………………………………………………………………………………………… 22

Figure 1.3 – D1-like vs. D2-like dopamine receptors……………………………………………………………. 23

Figure 1.4 – The main downstream mediators of DRD2 signaling………………………………………… 24

Figure 1.5 – DRD2 regulation of prolactin release………………………………………………….…………….. 25

Figure 1.6 – Dopamine receptors in the adrenal glands decrease blood pressure by inhibiting aldosterone release…………………………………………………………………… 26

Figure 1.7 – STAT3 as a mastor regulator of cancer phenotypes………………………………………….27

Figure 2.1 – DRD2 expression across breast cancer subtypes…………….………..…………..……….. 61

Figure 2.2 – Thioridazine inhibits self-renewal in some TNBCLs, but not others, while it inhibits proliferation and cell viability in all TNBCLs…………….……………….. 62

Figure 2.3 – Increased DRD2 expression is sphere-forming cells…………………….………..………… 63

Figure 2.4 – Low doses of thioridazine do not induce apoptosis…………………………………………… 65

Figure 2.5 – Thioridazine treatment does not induce TRAIL production via AKT inhibition and Foxo3a activation……………………………………………………………………………….. 66

Figure 2.6 – Thioridazine induces G1 arrest………………………………………………………………………….. 67

Figure 2.7 – Thioridazine inhibits STAT3 activity and IL-6 production…………………………………. 69

Figure 2.8 – Thioridazine inhibits self-renewal, but not cell viability via STAT3/IL-6……………….………..…………………………………………………………………….………… 70

Figure 2.9 – DRD2 promotes self-renewal……………………….……………………..…..………..……..…….. 71

Figure 2.10 – DRD2 mRNA in knockdown cells……………………………………………….………..………….. 72

Figure 2.11 – gDNA mutation in DRD2 CRISPR cells…………………………………………………………….. 73

Figure 2.12 – DRD2 promotes STAT3 activity……………………………………………………………………….. 74

Figure 2.13 – DRD2 knockdown with RNAi does not consistently decrease pY705-STAT3………………………………………………………………………………….……………..……….. 75

Figure 2.14 – IL-6 is downstream of DRD2 in SUM149 cells……………………………………….……….. 76

Figure 2.15 – Thioridazine inhibits self-renewal, but not cell viability via DRD2 inhibition………………………………………………………………………………………….……………..………. 77

Figure 3.1 – Thioridazine preferentially targets the tumorsphere initiating cells of basal-like breast cancer cell lines (BLBCLs)…………………………………………...... 98

Figure 3.2 – Thioridazine preferentially targets the tumorsphere initiating cells of the basal-like population within the same cell line…………………………………..100

Figure 3.3. – DRD2 expression does not predict thioridazine effects on self-renewal………………………………………………………………………………………………………….. 102

Figure 3.4 – Confirming the specificity of the DRD2 antibody………………………………………...... 104

Figure 3.5 – Quinpirole increases tumorsphere formation in MCF7 and SUM159 cells………………………………………………………………………………………………………………….. 105

Figure 3.6 – Expression of plasminogen pathway genes in breast cancer cell lines and tumors……………………………………………………………………………………..…………. 106

Figure 3.7 – Activation of the plasminogen pathway supports DRD- mediated tumorsphere formation…………………………………………………………………………………………. 107

Figure 3.8 – Model figure depicting the hypothesis that the plasminogen pathway promotes tumorsphere formation in basal-like breast cancer cell lines by regulating dopamine release……………………………………………………………………………. 109

Figure 4.1 – A model figure of effects of thioridazine on breast cancer cells……………………. 124

Figure 4.2 – Thioridazine inhibits JAK2 activation………………………………………………………………. 125

Figure 4.3 – Potential effects on tumor-associated immune cells if dopamine signaling is disrupted…………………………………………………………………………………………… 126

LIST OF ABBREVIATIONS AND SYMBOLS

AA - Arachidonic Acid

AC - Adenylate Cyclase

ATP - Adenosine Triphosphate

BirA - Bifunctional Ligase/Repressor

BLBCL - Basal-Like Breast Cancer Cell Line cAMP - Cyclic Adenosine Monophosphate

CBP - Cyclic Adenosine Monophosphate-Response Element-Binding Protein -Binding Protein

CCND1 - Cyclin D1

CCR5 - C-C Chemokine Receptor 5

CDK4 - Cyclin Dependent Kinase 4

CHO - Chinese Hamster Ovary

CKN1A/p21 - Cyclin Dependent Kinase Inhibitor 1A/Protein 21

CREB - Cyclic Adenosine Monophosphate-Response Element-Binding Protein

CRIPSR - Clustered Regularly Interspaced Short Palindromic Repeats

CSC - Cancer Stem Cell

DARPP-32 - Dopamine and cAMP-Regulated Phosphoprotein-32

DMSO - Dimethyl Sulfoxide

DNA - Deoxyribonucleic Acid

DRD1 - Dopamine Receptor 1

DRD2 - Dopamine Receptor 2

DRD3 - Dopamine Receptor 3

DRD4 - Dopamine Receptor 4

DRD5 - Dopamine Receptor 5

EGFR - Epidermal Growth Factor Receptor

ELISA - Enzyme-Linked Immunosorbent Assay

EMT - Epithelial-to-Mesenchymal Transition

EpCAM - Epithelial Cell Adhesion Molecule

ER - Estrogen Receptor

ERK - Extracellular Signal-Regulated Kinase

Foxo3a - Forkhead Box O3

GDP - Guanosine Diphosphate

GP130 - Glycoprotein 130

GPCR - G-protein-Coupled Receptor

GRK - G-Protein-Coupled Receptor Kinase

GSK3β - Glycogen Synthase Kinase 3β

GTP - Guanosine Triphosphate

HER2 - Human Epidermal Growth Factor Receptor 2

HIV - Human Immunodeficiency Virus

HSC - Hematopoietic Stem Cell

IC50 - Half-Maximal Inhibitory Concentration

IGF-1R - Insulin-Like Growth Factor-Receptor 1

IL-10 - Interleukin-10

IL-4 - Interleukin-4

IL-6 - Interleukin-6

IP3 - Inositol Triphosphate

JAK - Janus Kinase

JAK2 - Janus Kinase 2

MAPK - Mitogen-Activated Protein Kinase

MEK - MAPK/ERK mRNA - Messenger Ribonucleic Acid

NF-κB - Nuclear Factor-Kappa-Light Chain Enhancer of Activated B Cells

PARP-1 - Polyadenosine Diphosphate-Ribose Polymerase 1

PI3K - Phosphoinositide-3 Kinase

xii

PKA - Protein Kinase A

PKC - Protein Kinase C

PLAT/tPA - Tissue Plasminogen Activator

PLAU/uPA - Urokinase Plasminogen Activator

PLAUR/uPAR - Urokinase Plasminogen Activator Receptor

PLC - Phospholipase C

PP1 - Protein Phosphatase 1

PP2 - Protein Phosphatase 2

PR - Progesterone Receptor qPCR - Quantitative Polyermase Chain Reaction

RGS - Regulator of G Protein Signaling

RTK - Receptor Tyrosine Kinase

S1P1R - Sphingosine-1 Phosphate Receptor 1

SERPINE1/PAI-1 - Serine Protease Inhibitor E1/Plasminogen Activator Inhibitor 1 shRNA - Short Hairpin Ribonucleic Acid siRNA - Small Interfering Ribonucleic Acid

Src - Proto-Oncogene Tyrosine Kinase Src

STAT - Signal Transducer and Activator of Transcription

STAT1 - Signal Transducer and Activator of Transcription 1

STAT2 - Signal Transducer and Activator of Transcription 2

STAT3 - Signal Transducer and Activator of Transcription 3

STAT4 - Signal Transducer and Activator of Transcription 4

STAT5a - Signal Transducer and Activator of Transcription 5a

STAT5b - Signal Transducer and Activator of Transcription 5b

TCGA - The Cancer Genome Atlas

TIC - Tumorsphere Initiating Cell

TNBCL - Triple-Negative Breast Cancer Cell Line

xiii

TNFα - Tumor Necrosis Factor α

TRAIL - Tumor Necrosis Factor-Related Apoptosis-Inducing Ligand

Wnt - Wingless-Integration

Wnt1 - Wnt Family Member 1

Wnt5a - Wnt Family Member 5a

xiv

CHAPTER 1: BACKGROUND AND INTRODUCTION

1.1 Introduction to Breast Cancer and Cancer Stem Cells

1.1.1 Overview of breast cancer

Cancer is the second-leading cause of death in the United States (US), and breast cancer is the most common primary tumor site in American women. Breast cancer is also the second leading cause of cancer related deaths in women, and it causes about 40,000 deaths every year in the US alone (Siegel et al., 2018). For several decades, recommendations for routine mammography screenings have been suggesting starting screening at earlier ages, and with increased frequencies. These recommendations were implemented to detect more early stage breast tumors, before they progress to more dangerous invasive and metastatic tumors. Although mammography is highly effective at detecting breast tumors, this increase in the detection of early stage tumors has not led to a similar decrease in metastatic disease

(Kalager et al., 2010). Additionally, because the test is designed to be as sensitive as possible, the rate of false positives is alarmingly high, with over 50% of women who have 10 mammograms having at least one false-positive result (Hubbard et al., 2011). The impact of routine mammography has been questioned (Welch et al., 2016), and improvements in the technology are being investigated. However, despite increased awareness, screening, and targeted therapies metastatic breast cancer remains and important and unsolved problem.

1.1.2 Breast cancer subtypes

The term breast cancer refers to carcinomas derived from the tissues of the mammary gland. However, breast cancer is a highly heterogenous disease and perhaps may be more precisely considered as a collection of different cancer types that happen to occur in the same tissue. These different categorizations of breast cancers are termed subtypes (Perou et al.,

2000). There are several ways in which the heterogenous subtypes of breast cancers have

1 been classified: clinical expression of targetable genes and by gene expression signatures that suggest more specific cell types and origins.

Clinically, breast cancers are classified based on their expression of the estrogen receptor (ER), progesterone receptor (PR), or overexpression of the ERBB-family HER2 receptor. There are effective therapies for hormone receptor positive and HER positive tumors, and so the clinical expression of these proteins can dictate the treatment of the patient (Figure 1.1) (Brenton et al., 2005; Foulkes et al., 2010). However, about 15% of breast cancer patients do not express ER, PR, or the HER2 receptors, and will not benefit from the targeted therapies. These tumors are termed triple-negative (Brenton et al., 2005;

Foulkes et al., 2010). While many triple-negative tumors respond to chemotherapy and are treated with surgery, recurrence and distant disease are more common than in non-triple- negative tumors, especially in the first 3-5 years after treatment (Foulkes et al., 2010; Liedtke et al., 2008). Developing effective treatments for patients with triple-negative tumors remains an important problem that needs to be addressed.

Breast cancers are also classified by molecular subtype, based on gene expression signatures. Most breast cancers can be classified as luminal A, luminal B, HER2-enriched, basal-like, or claudin-low (Foulkes et al., 2010; Herschkowitz et al., 2007; Perou et al., 2000).

Many luminal A and B tumors express ER or PR, while a majority of the HER2-enriched tumors express high levels of the HER2 receptor (Perou et al., 2000; Weigelt et al., 2009). Strikingly, a large majority of basal-like and claudin-low tumors can be clinically classified as triple negative and, as would be expected, basal-like and claudin-low tumors have a worse prognosis than luminal A tumors (Perou et al., 2000; Sabatier et al., 2014; Sørlie et al.,

2001). It is interesting that the molecular subtypes, developed independent of the

ER/PR/HER2 status, nonetheless still hold prognostic value (Brenton et al., 2005; Sørlie et al., 2001). Since the prognosis for basal-like tumors is poor compared to other subtypes, and a majority of basal-like breast tumors are triple-negative and so do not benefit from current

2 targeted therapies, new therapies must be implemented to treat these tumors. Work presented in this thesis suggests that basal-like breast cancer cells may be more sensitive to inhibition of dopamine receptor than the other subtypes.

1.1.3 Cancer stem cells: origins and relevance in breast cancer

Stem cells serve as a reservoir of cellular potential, and they help replenish tissue after a wound, or during the normal turnover of cells. Stem cells serve this function with their ability to self-renew, or to produce daughter cells that will differentiate, but also daughter cells that will remain in the undifferentiated state. Although many different types of stem cells are known, the most well-described hierarchy of differentiation is in the hematopoietic system, where markers for each stage of differentiation have been established (Zhang et al.,

2018). Importantly, this field established functional assays to characterize hematopoietic stem cell activity. The ability of cells to form colonies in colony formation assays or to reconstitute all the cell types of the hematopoietic system following transplantation defines a hematopoietic stem cell (Boulais and Frenette, 2015; Clevers, 2011; Zhang et al., 2018). It was these assays that led to the identification and study of stem-like cancerous cells, termed cancer stem cells (CSCs) or tumor initiating cells (TICs) (Figure 1.2).

Although many tumors have long been known to be less differentiated than the bulk of the tissue of origin, and it had been known since at least 1937 that a single tumor cell could transplant and recapitulate a new tumor in a new host (Furth et al., 1937), it was not until the 1990s that the cancer stem cell field began to proliferate. Work from the Dick laboratory showed that leukemia blasts expressing CD34, but not CD38 (CD34+/CD38-), were the only cells that had the ability to form leukemia when engrafted into a new host mouse. This discovery led to the assertion that a rare population of CSCs may be responsible for the regenerative and metastatic capacity of a tumor (Bonnet and Dick, 1997; Clevers, 2011;

Lapidot et al., 1994). Since this work was done, the gold standard measure of CSC activity has been the ability of a defined population of cells to initiate a new tumor in a transplantation

3 assay, although other in vitro methods (colony formation assays, tumorsphere assays, organoid formation) have been developed to estimate this activity more quickly and cheaply without requiring a large number of mice and months of waiting for tumors to develop.

Therefore, CSCs are largely defined not just by whether they express certain marker genes associated with multipotency, but by their ability to self-renew and initiate a new tumor.

Further, CSCs are generally more mesenchymal and resistant to radiation and cytotoxic therapies (Batlle and Clevers, 2017; Dean et al., 2005; Reya et al., 2001). Therefore, the presence of CSCs, which may be difficult to target using conventional therapies, may explain why metastases are so difficult to treat, and why certain tumors can recur after what is considered a complete clinical response. If most tumors contain CSCs, then those CSCs need to be identified, so that therapies can be developed to target them and prevent metastasis and recurrence.

The discovery of CSCs in human breast cancers initiated a surge of research in the

CSC field. EpCAM+/CD44+/CD24- human breast cancer cells were shown to be highly tumorigenic, with as little as 100 cells initiating a tumor upon transplantation into a mouse

(Al-Hajj et al., 2003). Despite this intriguing start, there does not seem to be a single population of breast CSC markers that can easily distinguish the most tumorigenic cells in every breast cancer. Since the landmark discovery by Al-Hajj et al, other non-overlapping markers of CSC populations have been found in breast cancers, including ALDH1hi cells

(Ginestier et al., 2007) and CD133+ cells (Wright et al., 2008). This suggests that there may not be a single population of CSCs in breast cancer, but instead heterogenous cell types are capable of exhibiting high tumorigenicity and self-renewal.

As mentioned above, cancer cells that acquire mesenchymal characteristics are associated with increased stem-like gene expression patterns as well as increased tumor initiation capacity and ability to migrate (Batlle and Clevers, 2017; Guen et al., 2017; Mani et al., 2008; Prat et al., 2010). This suggests that the primary CSC population is likely be

4 mesenchymal in nature. However, the first breast CSCs discovered by Al-Hajj et al expressed high levels of EpCAM, a cell adhesion molecule highly expressed by epithelial cells. More recently, it has been shown that cells that are highly mesenchymal are actually less metastatic than cells that show more plasticity and can switch between epithelial and mesenchymal states (Celià-Terrassa and Kang, 2018; Celià-Terrassa et al., 2012; Tran et al., 2014). This again suggests that most of the current markers used to identify breast CSCs are not encompassing the entire heterogeneity of breast CSCs. True breast CSCs may be more phenotypically malleable than bulk tumor cells, with ever changing cell surface markers and gene expression patterns. Currently, the only concrete method to identifying breast CSCs, is to functionally test its ability to form a new tumor.

1.1.4 Signaling in cancer stem cells

Since the discovery of CSCs, many signaling pathways have been shown to regulate

CSC-like properties like tumor initiation and self-renewal. In breast cancer the most well- established CSC-promoting pathways are Wnt, Notch, NF-κB, and STAT3. Activation of the

Wnt pathway has long been associated with stemness and differentiation, and indeed Wnt activity has been shown to regulate breast CSCs. Wnt ligands are released from cells, after which they can bind to Frizzled receptors at the cell surface, which leads to the stabilization of β-catenin. β-catenin can enter the nucleus, where is acts as a potent transcriptional activator, and can induce the transcription of many genes that promote survival, proliferation and stemness like CyclinD1 and Myc (Clevers, 2006). Overexpression of Wnt-1, one of the

Wnt ligands, in mice leads to spontaneous tumor formation capable of metastasis (Li et al.,

2000). This suggests that Wnt pathway activation supports the development of tumor and metastasis initiating CSCs. Further, Wnt ligands have been shown to promote tumorsphere formation in vitro, while Wnt pathway antagonists have been shown to inhibit tumorsphere formation (Lamb et al., 2013; Scheel et al., 2011). Further, Wnt pathway activation supports

EMT and even tumor initiation in vivo (Jordan et al., 2013; Scheel et al., 2011).

Notch signaling is activated when membrane-associated ligands bind to membrane- associated Notch receptors on a neighboring cell. This leads to proteolytic cleavage of the intracellular domain (ICD) of Notch receptors, after which the ICD can enter the nucleus and act as a transcriptional activator (Artavanis-Tsakonas et al., 1999). Activation of Notch signaling supports tumorsphere formation and self-renewal in vitro (Dontu et al., 2004).

Importantly, Notch signaling also supports resistance to anti-estrogen therapies and tumor initiation in vivo (Simões et al., 2015).

The tumor microenvironment is critical in determining the ultimate fate of a developing tumor. Infiltrating immune cells, cancer-associated fibroblasts, and even infiltrating neurons have been shown to support tumor growth and metastasis, and prolonged inflammation has long been associated with tumor development and metastasis (Grivennikov et al., 2010a;

Hanahan and Weinberg, 2011; Mantovani et al., 2008). The NF-κB pathway leads to activation of a family of transcription factors that strongly regulate inflammatory cytokines, as well as genes that promote cell survival and proliferation, and chronic activation of the NF-κB pathway is associated with tumorigenesis (Zhang et al., 2017). Inhibition of NF-κB supports tumorsphere formation in breast cancer cell lines through the production of inflammatory cytokines IL-6 and IL-8 (Kendellen et al., 2014; Lawrence and Baldwin, 2016). Additionally, even a transient activation of the NF-κB pathway has been shown to support breast CSCs through the production of IL-6 (Iliopoulos et al., 2009). That study also showed that another rapidly-inducible transcription factor, STAT3, is activated by IL-6 and drives the CSC phenotype (Iliopoulos et al., 2009). 1.3 described in more detail how activated STAT3 is associated with poor outcomes and metastasis in breast cancer, and how a STAT3/IL-6 feed- forward loop supports breast CSCs.

Many different pathways have been shown to regulate breast tumorigenesis and the

CSC phenotype. Importantly, inhibition of a single pathway rarely eliminates tumorsphere formation in vitro or tumor initiation in vivo completely. This suggests that these pathways

6 can exist simultaneously to support different populations of CSCs. There needs to be a stronger understanding of the how these pathways support CSCs, how they interact with each other, and whether they can be targeted for cancer treatment.

1.2 Introduction to Dopamine Receptors

1.2.1 Dopamine Receptors are G-protein-coupled receptors

The dopamine receptor family is comprised of 5 G-protein-coupled receptors (GPCRs) that are most well characterized for their facilitation of neurotransmission, the communication that occurs between firing neurons. Dopamine receptors are activated by the neurotransmitter and catecholamine dopamine, which induces intracellular signaling events. Although the critical functions of dopamine receptors within the central nervous system have been the focus of most of the research on dopamine receptors, there have been numerous dopamine receptor-mediated functions discovered in the periphery. Several recent studies have even indicated a possible role for dopamine receptors, particularly dopamine receptor 2 (DRD2) in promoting cancer cell growth, survival, and the cancer stem cell phenotype. The work presented in this thesis focuses on the roles of DRD2 and antipsychotic drugs that target dopamine receptors on breast cancer cell growth and self-renewal.

Dopamine receptors belong to the GPCR superfamily, which are integral membrane proteins characterized by their 7 transmembrane domains and their association with an intracellular heterotrimeric complex of G proteins. The three classes of G proteins are

GDP/GTP-binding Gα subunits (subclassified into Gαi, Gαs, Gαo, Gαq, and Gα12/13), the Gβ, and

Gγ subunits (Marinissen and Gutkind, 2001). While a receptor is inactive, the αβγ subunits are bound together and associated with the receptor, and the Gα subunit is bound to GDP.

Upon receptor stimulation, the Gα subunit dissociates from the complex, exchanges GDP for

GTP, and becomes active (Lambright et al., 1996; Marinissen and Gutkind, 2001; Wall et al.,

1995). There are a variety of downstream signaling events mediated by GTP-bound Gα proteins, with Gα proteins from different subclasses having different functions (Rosenbaum et

7 al., 2009). The activity of Gα proteins is limited by the rate of GTP hydrolysis. As a consequence of GTP hydrolysis to GDP, the protein becomes inactive and reassociates with the Gβ/γ subunits and the inactive GPCR. The Gα-mediated GTP hydrolysis can be rapidly induced by RGS family kinases (Dohlman and Thorner, 1997). Although signaling downstream of GTP-bound Gα proteins are the most well-studied, the Gβ/γ subunits can also mediate signaling upon receptor activation. Gβ/γ subunits have been shown to regulate a diverse array of effectors including, but not limited to, the activation of calcium and potassium ion channels

(Ikeda, 1996; Logothetis et al., 1987; Wickman et al., 1994), protein lipase C (PLC) (Boyer et al., 1992; Camps et al., 1992), phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)

(Stephens et al., 1994), and the RAS/MEK/ERK pathway (Crespo et al., 1994; Pumiglia et al.,

1995; Smrcka, 2008).

There is even greater complexity to GPCR signaling. Upon receptor activation, GRK family kinases are activated and phosphorylate intracellular residues on the receptor (Kuehn et al., 1973; Moore et al., 2007). These phosphorylation events provide binding sites for β- arrestins, which bind and inhibit GPCR activation. Further, β-arrestin binding induces clathrin- mediated internalization, and the receptor can be either recycled back to the plasma membrane or trafficked to lysosomes for degradation (Attramadal et al., 1992; Lohse et al.,

1990; Pitcher et al., 1998). Despite the important roles for β-arrestin-mediated GPCR inhibition, β-arrestins have been shown to act as scaffold proteins when bound to GPCRs and promote the activation of downstream effectors such as Src family kinases (Luttrell et al.,

1999; Shenoy and Lefkowitz, 2011), MAP kinases (DeFea et al., 2000; Luttrell et al., 2001), and GSK3β (Povsic et al., 2003), independent of G proteins. Due to the high complexity of signaling and many downstream effector pathways, GPCRs are involved in regulating an amazingly diverse number of processes including neurotransmission, scent detection, cell proliferation, migration, immune cell activation, and more (Rosenbaum et al., 2009). As a superfamily, GPCRs are also quite druggable, with many small molecule agonists and

8 antagonists available for study and disease treatment (Rosenbaum et al., 2009). Dopamine receptors are quite typical GPCRs in this sense with a myriad of neuronal and non-neuronal functions, and many small molecules that target each receptor.

1.2.2 Dopamine receptor genes and structure

Although dopamine was discovered in 1957 (Carlsson et al., 1957), and the existence of two subclasses of receptors was proposed in 1978 (Spano et al., 1978), the first receptor,

DRD2, was not cloned until 1988 (Bunzow et al., 1988; Grandy et al., 1989). Since the cloning of the dopamine receptors, much has been learned about their gene regulation and structure.

As mentioned above, the family of 5 dopamine receptors is separated into two subfamilies, the D1-like and D2-like receptors. The D1-like receptors are dopamine receptor 1 (DRD1) and

DRD5, while the D2-like subfamily is comprised of DRD2, DRD3, and DRD4. These two subfamilies were first proposed based on whether the receptors were positively coupled to adenylate cyclase (AC) and promote the formation of cAMP (the D1-like receptors), or whether the receptors were negatively coupled to AC and inhibit cAMP formation (Figure 1.3) (Caron et al., 1978; Spano et al., 1978). Although the original distinction between the D1-like and

D2-like subfamilies was made based on the mode of regulation of AC, many aspects of dopamine receptor function and structure are defined by the subfamily distinction.

The structure of gene loci differs between the D1-like and D2-like receptors.

Interestingly, neither of the D1-like receptors contain any introns, while all of the D2-like receptors have introns, as well as observed splice variants (Missale et al., 1998). The most well-studied dopamine receptor splice variants are the DRD2-long and DRD2-short transcripts. The DRD2-short variant does not contain 29 amino acids within the functionally important third intracellular loop compared to the DRD2-long isoform. The short and long isoforms have been shown to have different expression and effects on neuronal transmission

(Beaulieu and Gainetdinov, 2011). The DRD2-short isoform is generally expressed presynaptically and act as an autoreceptor. That is, it inhibits dopamine release and

9 neurotransmission upon receptor stimulation. However, the DRD2-long isoform is generally expressed postsynaptically, and promotes neuronal excitation (Usiello et al., 2000). The D1- like and D2-like subfamily receptors differ not only in the structure of the gene loci, but also in protein structure.

Like all GPCRs, dopamine receptors contain 7 transmembrane domains, and the proteins start with an extracellular N-terminal domain, then consist of alternating extracellular and intracellular loops, and end with a C-terminal intracellular tail. Interestingly, D2-like receptors have an extensive third intracellular loop and a short C-terminal tail, which is typical of GPCRs that couple to the Gαi family of G proteins. Conversely, D1-like receptors contain a short third intracellular loop, but rather a long C-terminal tail, which is indicative of GPCRs that couple to the Gαs family of G proteins (Beaulieu and Gainetdinov, 2011; Missale et al.,

1998). These differences explain the original functional distinction between the two subfamilies, the opposing regulation of AC and cAMP. Gαi family G proteins generally inhibit

AC activity, while members of the Gαs family of G proteins activate AC activity.

Although the D1-like and D2-like subfamilies have different structures and functions, there is evidence that they can signal cooperatively. Activating both DRD1 and DRD2 with specific agonists can have different effects than activating either receptor alone. This has been shown with dopamine receptor-stimulated arachidonic acid (AA) release. While stimulation with a DRD1-specific agonist alone does not affect AA release, stimulation with a DRD2- specific agonist induced AA release (Piomelli et al., 1991). However, stimulation of both DRD1 and DRD2 induced an increase on AA over DRD2 stimulation alone (Piomelli et al., 1991).

There have also been more direct observations of physical associations between DRD1 and

DRD2. These heteromers have been shown to regulate intracellular calcium and downstream gene expression (Hasbi et al., 2009; Lee et al., 2004; Perreault et al., 2014). Interestingly,

DRD1/DRD2 heteromers were shown to couple to Gαq proteins (Lee et al., 2004), and heteromer activation requires stimulation of both DRD1 and DRD2, while inhibiting either

10 receptor can block heteromer signaling (Hasbi et al., 2009; Verma et al., 2010). Although dopamine receptors can easily be classified into two groups based on gene structure and AC regulation, dopamine receptor biology is complex, and there are numerous downstream signaling pathways regulated by dopamine receptors.

1.2.3 Dopamine Receptor Signaling

Over the past 40 years, there has been extensive research studying the molecular mechanisms of dopamine receptor-mediated signaling. As the scope of the work presented in this thesis focuses on the roles of DRD2 and antipsychotics that target D2-like receptors, this section will also focus mainly on the cellular signaling mediated by DRD2 and the other D2- like receptors (Figure 1.4).

The original distinction of the D2-like receptors is inhibition of AC and a reduction in cAMP levels, which results in reduced protein kinase A (PKA) activity (Beaulieu and

Gainetdinov, 2011; Kebabian and Calne, 1979). PKA is a multi-subunit serine/threonine kinase that is the primary downstream effector of cAMP (Skalhegg and Tasken, 2000).

Classically, active PKA has been shown to phosphorylate cAMP response element-binding protein (CREB), which promotes an association with CREB-binding protein (CBP), which then binds to cAMP response elements (CREs) located in gene promoters (Shaywitz and Greenberg,

1999). Although PKA has been shown to regulate hundreds of targets other than CREB via phosphorylation, the most well characterized target downstream of dopamine receptors is dopamine and cAMP-regulated phosphoprotein (DARPP-32) (Beaulieu and Gainetdinov,

2011). DARPP-32 is a regulatory subunit of protein phosphatase 1 (PP1) that, when associated with PP1, decreases its phosphatase activity, and activated PKA downstream of D1-like like receptors phosphorylates DARPP-32, which increases its association with PP1, decreasing PP1 activity (Hemmings et al., 1984; Svenningsson et al., 2004). This can amplify PKA-mediated dopamine receptor signaling by inhibiting a phosphatase that removes phosphorylation sites deposited by PKA. D2-like receptors, on the other hand, decrease PKA activity, which leads

11 to a decrease in DARPP-32/PP1 interaction, leading to the dephosphorylation of PKA targets

(Bateup et al., 2008).

Although the most well characterized activity of D2-like receptors is the inhibition of

AC activity by the activation of Gαi proteins, activation of D2-like receptors also induces signaling events mediated by the Gβ/γ subunits. There have been many observed roles for

Gβ/γ proteins downstream of D2-like receptors, including the regulation of calcium and potassium ion channels (Hernández-López et al., 2000; Lavine et al., 2002). The most well- studied Gβ/γ-mediated signaling event downstream of D2-like dopamine receptors is the activation of phospholipase C (PLC) family enzymes (Hernández-López et al., 2000). Active

PLC cleaves the phosphatidylinositol head groups from lipids in the plasma membrane, leaving diacyl glycerol, which can activate protein kinase C (PKC) signaling, and releasing inositol

1,4,5-triphosphate (IP3) (Kadamur and Ross, 2013). IP3 induces the release of calcium ions from the endoplasmic reticulum, activating further downstream effectors, such as the members of the CAM-kinase family (Kadamur and Ross, 2013). The effects of PKC activation and cytoplasmic calcium release downstream of PLC activation differ, depending on cell type, but downstream of dopamine receptors in neurons, they can have dramatic effects that are important in diseases like Huntington’s disease and Alzheimer’s disease (Yang et al., 2016).

The G-protein-mediated signaling events discussed above represent only a subset of the known mechanisms in which dopamine receptors are known to signal via G-proteins.

However, dopamine receptors are also known to modulate intracellular signaling via β- arrestins. As mentioned above β-arrestins were discovered and named for their roles in negatively regulating GPCR signaling, but have since been identified as scaffolds that recruit other signaling molecules (Luttrell and Lefkowitz, 2002). β-arrestin-mediated signaling has much slower kinetics than G-protein-mediated signaling, since β-arrestin binding to the GCPR occurs as a feedback mechanism downstream of G-protein activation and occurs from endosomes after GCPR internalization (Ahn et al., 2004; Beaulieu and Gainetdinov, 2011). β-

12 arrestin binding to DRD2 can recruit protein kinase B (AKT) and protein phosphatase 2 (PP2).

While in complex, PP2 dephosphorylates and inactivates AKT (Beaulieu et al., 2005). AKT phosphorylates another kinase GSK3β, which inactivates it. Therefore, stimulation of DRD2 can lead to the activation of GSK3β, which has been shown to be especially important in mediating the effects of dopamine receptor stimulation on locomotion and social interaction in mice (Beaulieu et al., 2015; Urs et al., 2012).

Although the most well-studied signaling mechanisms of dopamine receptors are the regulation of cAMP, intracellular Ca2+, ion channels, and serine/threonine phosphorylation, dopamine receptors have also been shown to regulate tyrosine kinase cascades. Over 25 years ago Lajiness et al showed that dopamine can stimulate the tyrosine phosphorylation of a number of substrates in DRD2-transfected Chinese hamster ovary cells. Further, they showed that DRD2 activity could promote proliferation that was blocked with the broad tyrosine kinase inhibitor genistein (Lajiness et al., 1993). Receptor tyrosine kinases (RTKs) regulate a large and diverse set of cellular processes, including some that can co-opted by cancer cells, such as stemness/self-renewal, cell survival, proliferation, cytokine response and production, and cell migration (Schlessinger, 2000). The epidermal growth factor receptor

(EGFR) is an RTK that has been associated with many pro-tumor functions, and is especially active in many triple-negative breast cancers (Nielsen et al., 2004; Reis-Filho and Tutt, 2007).

Interestingly, DRD2 has been shown to directly promote the transactivation of EGFR, leading to AKT and ERK phosphorylation (Swift et al., 2011). DRD2-mediated transactivation of EGFR can protect cells against apoptosis in cell line model of rat pheochromocytoma via SRC activity

(Nair and Sealfon, 2003). Despite this, DRD2 has also been shown to indirectly activate EGFR by inducing the release of heparin-bound EGF, through the activation of extracellular proteases (Yoon and Baik, 2013), indicating a complex relationship between DRD2 and EGFR activation. In addition to EGFR, DRD2 has been shown to transactivate the insulin-like growth factor receptor (IGF-1R), supporting AKT activation in transfect CHO cells (Mannoury la Cour

13 et al., 2011). Dopamine receptors, including DRD2, have been shown to promote the activity of a number of pathways known to promote cancer growth, invasion, or metastasis, yet only a small fraction of the research on these receptors, and their agonists and antagonists, has been applied to cancer. Most of the research has focused on effects within neurons and neuronal function. However, expression of dopamine receptors in non-neuronal cell types has been known for many decades, and there is a wealth of research uncovering important roles for dopamine receptors in the periphery.

1.2.4 Peripheral DRD2

While the expression of all five dopamine receptors has been observed in peripheral tissues, this introduction will focus primarily on the function of D2-like receptors, especially with a focus on DRD2. The following reviews extensively cover the functions of all five dopamine receptors in the periphery (Beaulieu and Gainetdinov, 2011; Levite, 2016; Missale et al., 1998).

One of the most well-studied effects of dopamine receptor function outside of neurons is the inhibition of prolactin secretion from the pituitary (Figure 1.5). Prolactin is a hormone produced and released from the pituitary gland that, among other functions, stimulates lactation in mammals (Freeman et al., 2000). Dopamine released from hypothalamic neurons stimulate D2-like receptors in the pituitary, which leads to decreased release of prolactin, as well as decreased gene transcription (Elsholtz et al., 1991). In fact, hyperprolactinemia, the overproduction and release of prolactin, is a common side effect of typical antipsychotics, all of which inhibit DRD2 (see below) (Kapur and Seeman, 2001; Missale et al., 1998). Further, in patients with prolactin-secreting pituitary tumors, treatment with DRD2 agonists can manage prolactin levels (Cunnah and Besser, 1991).

Dopamine receptors have other important roles in the endocrine system outside the pituitary gland. Dopamine receptors are also expressed in the adrenal glands, where they affect blood pressure by inhibiting the secretion of the hormone aldosterone (Figure 1.6)

(Missale et al., 1998). Aldosterone is a hormone synthesized in the adrenal glands, but it acts in the kidneys to promote water and sodium retention, which increases blood pressure (Lifton et al., 2001). Inhibition of D2-like receptors in vivo leads to an increase in aldosterone levels

(Carey et al., 1979, 1980). Under normal salt and blood pressure conditions, activation of D2- like receptors does not inhibit aldosterone levels (Carey et al., 1979). This suggests that under normal sodium conditions, D2-like receptors maximally inhibit aldosterone secretion.

Alternatively, when sodium levels are low, renal dopamine secretion is inhibited, which leads to decreased dopamine receptor activity, which in turn allow for an increase in aldosterone secretion (Chugh et al., 2013). After secretion, aldosterone can make its way to the kidney where it promotes water and sodium retention. Interestingly, mice with decreased DRD2 expression, as well as humans with certain polymorphisms at the DRD2 locus, exhibit increased blood pressure (Konkalmatt et al., 2016). This may partly be explained by aldosterone regulation, but D2-like receptors also influence blood pressure more directly by affecting sodium retention in the kidneys. Dopamine receptors inhibit aldosterone secretion from the adrenal gland, which promotes the expression and activity of Na+/K+ pumps in the kidney that lead to sodium retention (Salyer et al., 2013). The activity of dopamine receptors in the proximal tubule inhibits the activity of the Na+/K+ pumps that lead to sodium retention

(Bertorello and Aperia, 1990; Carey, 2001). Through these mechanisms, dopamine receptors in the periphery are critical to blood pressure homeostasis.

Despite the intriguing functions of dopamine and its receptors in several tissues outside the CNS, some of the most well-studied functions of peripheral dopamine are in immune cells.

Not only has the expression of dopamine receptors, to varying degrees, been observed in every major immune cell type, but the expression of dopamine synthesis genes and/or dopamine transporters have also been observed in many immune cells (Amenta et al., 2001;

Arreola et al., 2016; Levite, 2016; Marino et al., 1999; McKenna et al., 2002).

Dopamine has been shown to support hemapoetic cells by inducing the migration and colony-formation activity of hematapoetic stem cells (HSCs) (Spiegel et al., 2007). In macrophages, which are myeloid cells that normally phagocytose pathogens and can act as antigen presenting cells, DRD2 activity can inhibit TNFα and nitric oxide secretion (Haskó et al., 1996). Further, dopamine has been shown to decrease NF-κB DNA binding activity

(Bergquist et al., 2000). These intriguing observations suggest that DRD2 may subvert macrophages away from the inflammatory cytotoxic M1 state, towards a more immunosuppressive phenotype. If true, that could indicate that DRD2 activation in tumor infiltrating macrophages may support tumor growth and immune avoidance. Interestingly,

DRD1 and DRD2 have also been shown to facilitate HIV viral entry into macrophage, which is mediated by CCR5 (Gaskill et al., 2014). Additionally, DRD2, but not DRD1, can stimulate HIV replication within macrophages in an ERK-dependent manner (Gaskill et al., 2009).

The activity of D2-like receptors has also been shown to influence dendritic cell function. Dendritic cells are myeloid cells that primarily act as efficient antigen-presenting cells. Antigen presenting cells activate T and B cells by directly binding and exposing the foreign antigen on their cell surfaces (Banchereau and Steinman, 1998). Dendritic cells can release dopamine while bound to T cells, which supports a Th2 polarization, and if dendritic cells are treated with D2-like receptor antagonists, a stronger release of dopamine was observed, leading to a higher preference for Th2 polarization (Nakano et al., 2009). By activating cytotoxic T cells, Th1 polarized T helper produce a highly inflammatory response toward viruses, bacteria, and other single celled pathogens (Abbas et al., 1996). Th2 responses, on the other hand, are less inflammatory because they release anti-inflammatory cytokines like IL-4 and IL-10. Further, by activating eosinophils, Th2 polarized T helper cells are important for the response to multicellular pathogens, such as helminths (Abbas et al.,

1996). Interestingly, since cytotoxic T cells are important for immune-mediated tumor cell killing, Th1 polarized cells are associated with anti-tumor responses, while Th2 polarized cells

16 are associated with a tumor permissive microenvironment and metastasis in breast cancer

(DeNardo et al., 2009; Grivennikov et al., 2010b).

In addition to T helper cell polarization, dopamine has generally been shown to inhibit the T effector cells. Dopamine, via DRD2 and DRD3 receptors has been shown to inhibit proliferation of activated T cells as well as inhibit the production of cytokines in activated T cells (Bergquist et al., 1994; Ghosh et al., 2003). Together with the previously discussed effects of dopamine on T helper cell and macrophage polarization suggest a possibility that dopamine may permit a microenvironment that promotes tumor growth and immune evadence. Dopamine, via DRD2 and the other D2-like receptors has been shown to have many effects on immune cells. These effects include the activation and polarization of macrophages and T cells, which are known to modulate the tumor microenvironment in ways that can promote tumor growth or tumor clearance.

1.2.5 Antipsychotics and Dopamine Receptors in Cancer

It has been suggested that all antipsychotics inhibit DRD2, which supports the dopamine hypothesis of schizophrenia (Kapur and Seeman, 2000, 2001; Seeman and Kapur,

2000). This posits that the primary driver of the positive symptoms of schizophrenia are caused by an overactive dopaminergic system (Howes and Kapur, 2009). However, many antipsychotics also bind to, and act as agonists or antagonists to, many other receptors

(Besnard et al., 2012; Chibon, 2013; Roth et al., 2004; Vidal and Mestres, 2010). This complicates the use of antipsychotics in molecular studies, as it cannot necessarily be assumed that inhibition of DRD2 is the mechanism of action.

The discovery of antipsychotics as anticancer agents is nearly 30 years old, since pimozide and thioridazine where shown to reduce the proliferation of breast cancer cells in vitro (Strobl and Peterson, 1992; Strobl et al., 1990). Despite this early discovery, the effects of DRD2-targeting antipsychotics went largely unexplored for several decades. A small molecule screen seeking to identify compounds that would reduce the stemness of

17 transformed stem cells, but not normal stem cells, identified the DRD2-targeting antipsychotic thioridazine as a lead candidate (Sachlos et al., 2012). Since then, a rush of papers has been published demonstrating thioridazine, or other antipsychotics such as haloperidol, could target cancer stem cells, induce apoptosis, and block proliferation in many cancer cell types including breast, lung, ovarian, brain, and colon (Caragher et al., 2019; Cheng et al., 2015;

Johannessen et al., 2018; Li et al., 2014; Liu et al., 2018; Luo et al., 2016; Meng et al., 2016;

Wei et al., 2006; Yin et al., 2015; Yong et al., 2017; Yue et al., 2016; Zhang et al., 2016;

Zong et al., 2014). Most of these studies use high concentrations of thioridazine (10-20µM) and observe strong effects on apoptosis induction and reduced viability. Since antipsychotics, especially thioridazine bind numerous receptors tightly, some with a Ki of less than 10nM

(Besnard et al., 2012), it remains unclear how these agents are having such strong effects, and even whether inhibition of DRD2 is required. Not only are the mechanisms of the effects of antipsychotics on cancer cells unknown, but so are the cell types that are most sensitive and most resistant. To this point, no studies have indicated whether there are certain cell lines that are more or less sensitive to thioridazine. If thioridazine, or any other antipsychotic, will be used in the treatment of cancers, it must be known which patients will benefit the most from such a treatment.

The work presented in this thesis attempts to address these issues. First, we searched for the mechanisms of the effects of thioridazine on breast cancer cells, including the roles of

DRD2. Then, we approached the important question of which tumor cells are the most sensitive to thioridazine.

1.3 Introduction to JAK/STAT Pathway

1.3.1 Overview of the JAK/STAT Pathway

The JAK/STAT pathway turns signals from cytokines binding to plasma membrane receptors into changes in gene expression. There are 4 non-receptor tyrosine kinases (TYK2,

JAK1, JAK2, and JAK3), that associate with active cytokine receptors (Schindler et al., 2007).

Activated JAK-family kinases can phosphorylate tyrosine residue on STAT-family transcription factors (STAT1, STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6). The STAT-family transcription factors are normally kept in the cytosol in an inactive state, but upon phosphorylation by JAKs, dimerize and translocate to the nucleus, where they activate transcription (Schindler et al., 2007; Stark and Darnell, 2012). The activation of the STAT- family transcription factors is critical for the innate immune response, the adaptive immune response, autoimmunity, and even embryonic stem cell pluripotency (Forbes et al., 2016; Raz et al., 1999; Schindler et al., 2007; Stark and Darnell, 2012). Although all STAT-family transcription factors are vital for the proper mammalian development and immune responses, activation of STAT3 has been particularly well documented to be pro-tumorigenic (Banerjee and Resat, 2016; Yu et al., 2014).

1.3.2 Activation of STAT3 in cancer

Strong, but transient activation of STAT3 can be vital to appropriate cellular responses to pathogens. However, persistent or chronic activation of STAT3 promotes tumorigenesis by regulating gene expression to promote cell survival, proliferation, migration (EMT), drug resistance, and CSC-like character (Figure 1.7) (Gao et al., 2007; Iliopoulos et al., 2009;

Korkaya et al., 2012; Lee et al., 2010; Levy and Darnell, 2002; Oh et al., 2013; Sonnenblick et al., 2015; Yu et al., 2014). One of the most well-established mechanisms of persistent

STAT3 activation is via the IL-6/STAT3 feed forward loop. Interleukin-6 (IL-6) is a cytokine that, when bound to its receptor in complex with the tyrosine kinase GP130, can activate JAK- family kinases, leading to STAT3 activation. Interestingly, IL-6 is also a STAT3 target gene.

Activation of STAT3 by IL-6 can induce more IL-6 production, thereby leading to further induction of STAT3 activity and establishing persistent STAT3 activation (Yu et al., 2014). This chronic activation of STAT3 can promote CSC-like activities in breast cancer like drug resistance and metastasis (Dethlefsen et al., 2013; Iliopoulos et al., 2009; Korkaya et al.,

2012; Oh et al., 2013). Despite the critical importance of IL-6/STAT3 in the progression of

19 breast cancer, and despite anti-IL6 therapies that already treat immune disorders, the translation to cancer therapeutics has been slow (Hunter and Jones, 2015). Developing therapies that inhibit the chronic activation of the IL-6/STAT3 feed-forward loop can be critical to reducing dangerous breast cancer complications such as drug resistance, recurrence, or metastasis.

1.4 – Conclusions and views

The most dangerous events in the progression of breast cancer are drug resistance, recurrence, and metastasis. CSCs are hypothesized to drive these events in many tumor types, including breast cancers. Additionally, the IL-6/STAT3 feed-forward loop can support

CSCs and their pro-tumorigenic behaviors. The work presented in chapter 2 shows that DRD2 can, in some triple-negative breast cancer cells, promote self-renewal via STAT3/IL-6.

Importantly, we show that thioridazine, a cheap FDA-approved DRD2 antagonist, can inhibit

STAT3 activation, IL-6 production, and tumorsphere initiation in some triple-negative breast cancer cells. In chapter 3, we show that the self-renewal of basal-like breast cancer cell lines is the most sensitive to thioridazine and DRD2-inhibition. Additionally, we provide preliminary evidence that the plasminogen pathway can enhance DRD2-promoted self-renewal. This needs to be investigated further, as it could lead to the development of new therapeutic targets.

Figure 1.1 – The clinical subtypes of breast cancer. The expression or lack of expression of estrogen receptor or the HER2 receptor can inform treatment decisions for breast tumors.

Figure 1.2 – Experiments that measure tumor initiation activity within a population of cells. In all tumor initiation experiments, the rate of tumor or colony initiation is tested in a population of dissociated single cells. The in vitro tumorsphere assay and colony formation assay can quickly estimate the tumor initiation activity of a population of cells. The gold standard assay is the in vivo transplantation assay, in which the tumor initiation rate is measured using decreasing numbers of cells.

Figure 1.3 – D1-like vs. D2-like dopamine receptors. There are two subfamilies of dopamine receptors. The D1-like family receptors couple to Gαs, contain no introns, and have a long C-terminal tail. The D2-like receptors couple to Gαi, contain introns and have splice variants, and have a long third intracellular loop.

Figure 1.4 – The main downstream mediators of DRD2 signaling. DRD2, like many GPCRs, has been shown to signaling through numerous downstream pathways. The most well-characterized are through Gαi, Gβ/γ, β-arrestin, and RTK signaling.

Figure 1.5 – DRD2 regulation of prolactin release. Dopamine released from hypothalamic neurons activates DRD2 in pituitary cells, which inhibits prolactin secretion. Many DRD2-targeting antipsychotics can induce the release of prolactin, leading to hyperprolactinemia.

Figure 1.6 – Dopamine receptors in the adrenal glands decrease blood pressure by inhibiting aldosterone release. When sodium levels are low, dopamine release from renal cells is inhibited. This decreases dopamine receptor activation in nearby adrenal cells. Adrenal dopamine receptors inhibit aldosterone release, and when low sodium limits dopamine release, aldosterone is release to the renal cells, where it promotes sodium resorption and increased blood pressure.

Figure 1.7 – STAT3 is a master regulator of cancer phenotypes.

REFERENCES

Abbas, A.K., Murphy, K.M., and Sher, A. (1996). Functional diversity of helper T lymphocytes. Nature 383, 787–793.

Ahn, S., Shenoy, S.K., Wei, H., and Lefkowitz, R.J. (2004). Differential Kinetic and Spatial Patterns of β-Arrestin and G Protein-mediated ERK Activation by the Angiotensin II Receptor. J. Biol. Chem. 279, 35518–35525.

Al-Hajj, M., Wicha, M.S., Benito-Hernandez, A., Morrison, S.J., and Clarke, M.F. (2003). Prospective identification of tumorigenic breast cancer cells. Proc. Natl. Acad. Sci. 100, 3983–3988.

Amenta, F., Bronzetti, E., Cantalamessa, F., El-Assouad, D., Felici, L., Ricci, A., and Tayebati, S.K. (2001). Identification of dopamine plasma membrane and vesicular transporters in human peripheral blood lymphocytes. J. Neuroimmunol. 117, 133–142.

Arreola, R., Alvarez-Herrera, S., Pérez-Sánchez, G., Becerril-Villanueva, E., Cruz-Fuentes, C., Flores-Gutierrez, E.O., Garcés-Alvarez, M.E., de la Cruz-Aguilera, D.L., Medina-Rivero, E., Hurtado-Alvarado, G., et al. (2016). Immunomodulatory Effects Mediated by Dopamine. J. Immunol. Res. 2016, 3160486.

Artavanis-Tsakonas, S., Rand, M.D., and Lake, R.J. (1999). Notch signaling: cell fate control and signal integration in development. Science 284, 770–776.

Attramadal, H., Arriza, J.L., Aoki, C., Dawson, T.M., Codina, J., Kwatra, M.M., Snyder, S.H., Caron, M.G., and Lefkowitz, R.J. (1992). Beta-arrestin2, a novel member of the arrestin/beta-arrestin gene family. J. Biol. Chem. 267, 17882–17890.

Banchereau, J., and Steinman, R.M. (1998). Dendritic cells and the control of immunity. Nature 392, 245–252.

Banerjee, K., and Resat, H. (2016). Constitutive activation of STAT3 in breast cancer cells: A review. Int. J. Cancer 138, 2570–2578.

Bateup, H.S., Svenningsson, P., Kuroiwa, M., Gong, S., Nishi, A., Heintz, N., and Greengard, P. (2008). Cell type–specific regulation of DARPP-32 phosphorylation by psychostimulant and antipsychotic drugs. Nat. Neurosci. 11, 932–939.

Batlle, E., and Clevers, H. (2017). Cancer stem cells revisited. Nat. Med. 23, 1124–1134.

Beaulieu, J.-M., and Gainetdinov, R.R. (2011). The Physiology, Signaling, and Pharmacology of Dopamine Receptors. Pharmacol. Rev. 63, 182–217.

Beaulieu, J.-M., Sotnikova, T.D., Marion, S., Lefkowitz, R.J., Gainetdinov, R.R., and Caron, M.G. (2005). An Akt/β-Arrestin 2/PP2A Signaling Complex Mediates Dopaminergic Neurotransmission and Behavior. Cell 122, 261–273.

Beaulieu, J.-M., Espinoza, S., and Gainetdinov, R.R. (2015). Dopamine receptors - IUPHAR Review 13. Br. J. Pharmacol. 172, 1–23.

Bergquist, J., Tarkowski, A., Ekman, R., and Ewing, A. (1994). Discovery of endogenous catecholamines in lymphocytes and evidence for catecholamine regulation of lymphocyte function via an autocrine loop. Proc. Natl. Acad. Sci. U. S. A. 91, 12912–12916.

Bergquist, J., Ohlsson, B., and Tarkowski, A. (2000). Nuclear factor-kappa B is involved in the catecholaminergic suppression of immunocompetent cells. Ann. N. Y. Acad. Sci. 917, 281–289.

Bertorello, A., and Aperia, A. (1990). Inhibition of proximal tubule Na(+)-K(+)-ATPase activity requires simultaneous activation of DA1 and DA2 receptors. Am. J. Physiol. Physiol. 259, F924–F928.

Besnard, J., Ruda, G.F., Setola, V., Abecassis, K., Rodriguiz, R.M., Huang, X.-P., Norval, S., Sassano, M.F., Shin, A.I., Webster, L.A., et al. (2012). Automated design of ligands to polypharmacological profiles. Nature 492, 215–220.

Bonnet, D., and Dick, J.E. (1997). Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat. Med. 3, 730–737.

Boulais, P.E., and Frenette, P.S. (2015). Making sense of hematopoietic stem cell niches. Blood 125, 2621–2629.

Boyer, J.L., Waldo, G.L., and Harden, T.K. (1992). Beta gamma-subunit activation of G- protein-regulated phospholipase C. J. Biol. Chem. 267, 25451–25456.

Brenton, J.D., Carey, L.A., Ahmed, A.A., and Caldas, C. (2005). Molecular classification and molecular forecasting of breast cancer: ready for clinical application? J. Clin. Oncol. 23, 7350–7360.

Bunzow, J.R., Tol, H.H.M. Van, Grandy, D.K., Albert, P., Salon, J., Christie, M., Machida, C.A., Neve, K.A., and Civelli, O. (1988). Cloning and expression of a rat D2 dopamine receptor cDNA. Nature 336, 783–787.

Camps, M., Carozzi, A., Schnabel, P., Scheer, A., Parker, P.J., and Gierschik, P. (1992). Isozyme-selective stimulation of phospholipase C-β2 by G protein βγ-subunits. Nature 360, 684–686.

Caragher, S.P., Shireman, J.M., Huang, M., Miska, J., Atashi, F., Baisiwala, S., Hong Park, C., Saathoff, M.R., Warnke, L., Xiao, T., et al. (2019). Activation of dopamine receptor 2 (DRD2) prompts transcriptomic and metabolic plasticity in glioblastoma. J. Neurosci. 1589– 18.

Carey, R.M. (2001). Renal Dopamine System. Hypertension 38, 297–302.

Carey, R.M., Thorner, M.O., and Ortt, E.M. (1979). Effects of Metoclopramide and Bromocriptine on the Renin-Angiotensin-Aldosterone System in Man. J. Clin. Invest. 63, 727–735.

Carey, R.M., Thorner, M.O., and Ortt, E.M. (1980). Dopaminergic inhibition of metoclopramide-induced aldosterone secretion in man. Dissociation of responses to dopamine and bromocriptine. J. Clin. Invest. 66, 10–18.

Carlsson, A., Lindqvist, M., and Magnusson, T. (1957). 3,4-Dihydroxyphenylalanine and 5- hydroxytryptophan as reserpine antagonists. Nature 180, 1200.

Caron, M.G., Beaulieu, M., Raymond, V., Gagné, B., Drouin, J., Lefkowitz, R.J., and Labrie, F. (1978). Dopaminergic receptors in the anterior pituitary gland. Correlation of [3H]dihydroergocryptine binding with the dopaminergic control of prolactin release. J. Biol. Chem. 253, 2244–2253.

Celià-Terrassa, T., and Kang, Y. (2018). Metastatic niche functions and therapeutic opportunities. Nat. Cell Biol. 20, 868–877.

Celià-Terrassa, T., Meca-Cortés, O., Mateo, F., Martínez de Paz, A., Rubio, N., Arnal-Estapé, A., Ell, B.J., Bermudo, R., Díaz, A., Guerra-Rebollo, M., et al. (2012). Epithelial- mesenchymal transition can suppress major attributes of human epithelial

29 tumor-initiating cells. J. Clin. Invest. 122, 1849–1868.

Cheng, H.-W., Liang, Y.-H., Kuo, Y.-L., Chuu, C.-P., Lin, C.-Y., Lee, M.-H., Wu, A.T.H., Yeh, C.-T., Chen, E.I.-T., Whang-Peng, J., et al. (2015). Identification of thioridazine, an antipsychotic drug, as an antiglioblastoma and anticancer stem cell agent using public gene expression data. Cell Death Dis. 6, e1753–e1753.

Chibon, F. (2013). Cancer gene expression signatures – The rise and fall? Eur. J. Cancer 49, 2000–2009.

Chugh, G., Pokkunuri, I., and Asghar, M. (2013). Renal dopamine and angiotensin II receptor signaling in age-related hypertension. Am. J. Physiol. Renal Physiol. 304, F1-7.

Clevers, H. (2006). Wnt/β-Catenin Signaling in Development and Disease. Cell 127, 469– 480.

Clevers, H. (2011). The cancer stem cell: premises, promises and challenges. Nat. Med. 17, 313–319.

Crespo, P., Xu, N., Simonds, W.F., and Gutkind, J.S. (1994). Ras-dependent activation of MAP kinase pathway mediated by G-protein βγ subunits. Nature 369, 418–420.

Cunnah, D., and Besser, M. (1991). Management of prolactinomas. Clin. Endocrinol. (Oxf). 34, 231–235.

Dean, M., Fojo, T., and Bates, S. (2005). Tumour stem cells and drug resistance. Nat. Rev. Cancer 5, 275–284.

DeFea, K.A., Zalevsky, J., Thoma, M.S., Déry, O., Mullins, R.D., and Bunnett, N.W. (2000). beta-arrestin-dependent endocytosis of proteinase-activated receptor 2 is required for intracellular targeting of activated ERK1/2. J. Cell Biol. 148, 1267–1281.

DeNardo, D.G., Barreto, J.B., Andreu, P., Vasquez, L., Tawfik, D., Kolhatkar, N., and Coussens, L.M. (2009). CD4+ T Cells Regulate Pulmonary Metastasis of Mammary Carcinomas by Enhancing Protumor Properties of Macrophages. Cancer Cell 16, 91–102.

Dethlefsen, C., Højfeldt, G., and Hojman, P. (2013). The role of intratumoral and systemic IL-6 in breast cancer. Breast Cancer Res. Treat. 138, 657–664.

Dohlman, H.G., and Thorner, J. (1997). RGS proteins and signaling by heterotrimeric G proteins. J. Biol. Chem. 272, 3871–3874.

Dontu, G., Jackson, K.W., McNicholas, E., Kawamura, M.J., Abdallah, W.M., and Wicha, M.S. (2004). Role of Notch signaling in cell-fate determination of human mammary stem/progenitor cells. Breast Cancer Res. 6, R605.

Elsholtz, H.P., Lew, A.M., Albert, P.R., and Sundmark, V.C. (1991). Inhibitory control of prolactin and Pit-1 gene promoters by dopamine. Dual signaling pathways required for D2 receptor-regulated expression of the prolactin gene. J. Biol. Chem. 266, 22919–22925.

Forbes, L.R., Milner, J., and Haddad, E. (2016). Signal transducer and activator of transcription 3: a year in review. Curr. Opin. Hematol. 23, 23–27.

Foulkes, W.D., Smith, I.E., and Reis-Filho, J.S. (2010). Triple-Negative Breast Cancer. N. Engl. J. Med. 363, 1938–1948.

Freeman, M.E., Kanyicska, B., Lerant, A., and Nagy, G. (2000). Prolactin: Structure, Function, and Regulation of Secretion. Physiol. Rev. 80, 1523–1631.

Furth, J., Kahn, M.C., and Breedis, C. (1937). The Transmission of Leukemia of Mice with a Single Cell. Am. J. Cancer 31, 276–282.

Gao, S.P., Mark, K.G., Leslie, K., Pao, W., Motoi, N., Gerald, W.L., Travis, W.D., Bornmann, W., Veach, D., Clarkson, B., et al. (2007). Mutations in the EGFR kinase domain mediate STAT3 activation via IL-6 production in human lung adenocarcinomas. J. Clin. Invest. 117, 3846–3856.

Gaskill, P.J., Calderon, T.M., Luers, A.J., Eugenin, E.A., Javitch, J.A., and Berman, J.W. (2009). Human Immunodeficiency Virus (HIV) Infection of Human Macrophages Is Increased by Dopamine: A Bridge between HIV-Associated Neurologic Disorders and Drug Abuse. Am. J. Pathol. 175, 1148–1159.

Gaskill, P.J., Yano, H.H., Kalpana, G. V., Javitch, J.A., and Berman, J.W. (2014). Dopamine Receptor Activation Increases HIV Entry into Primary Human Macrophages. PLoS One 9, e108232.

Ghosh, M.C., Mondal, A.C., Basu, S., Banerjee, S., Majumder, J., Bhattacharya, D., and Dasgupta, P.S. (2003). Dopamine inhibits cytokine release and expression of tyrosine kinases, Lck and Fyn in activated T cells. Int. Immunopharmacol. 3, 1019–1026.

Ginestier, C., Hur, M.H., Charafe-Jauffret, E., Monville, F., Dutcher, J., Brown, M., Jacquemier, J., Viens, P., Kleer, C.G., Liu, S., et al. (2007). ALDH1 Is a Marker of Normal and Malignant Human Mammary Stem Cells and a Predictor of Poor Clinical Outcome. Cell Stem Cell 1, 555–567.

Grandy, D.K., Marchionnit, M.A., Makam, H., Stofko, R.E., Alfanot, M., Frothinghamt, L., Fischert, J.B., Burke-Howiet, K.J., Bunzow, J.R., Servert, A.C., et al. (1989). Cloning of the cDNA and gene for a human D2 dopamine receptor (schizophrenia/alternative splicing/DRD2/introns/receptor evolution). Biochemistry 86, 9762–9766.

Grivennikov, S.I., Greten, F.R., and Karin, M. (2010a). Immunity, Inflammation, and Cancer. Cell 140, 883–899.

Grivennikov, S.I., Greten, F.R., and Karin, M. (2010b). Immunity, Inflammation, and Cancer. Cell 140, 883–899.

Guen, V.J., Chavarria, T.E., Kröger, C., Ye, X., Weinberg, R.A., and Lees, J.A. (2017). EMT programs promote basal mammary stem cell and tumor-initiating cell stemness by inducing primary ciliogenesis and Hedgehog signaling. Proc. Natl. Acad. Sci. U. S. A. 114, E10532– E10539.

Hanahan, D., and Weinberg, R.A. (2011). Hallmarks of Cancer: The Next Generation. Cell 144, 646–674.

Hasbi, A., Fan, T., Alijaniaram, M., Nguyen, T., Perreault, M.L., O’Dowd, B.F., and George, S.R. (2009). Calcium signaling cascade links dopamine D1–D2 receptor heteromer to striatal BDNF production and neuronal growth. Proc. Natl. Acad. Sci. 106, 21377–21382.

Haskó, G., Szabó, C., Merkel, K., Bencsics, A., Zingareli, B., Kvetan, V., and Vizi, E.S. (1996). Modulation of lipopolysaccharide-induced tumor necrosis factor-α and nitric oxide production by dopamine receptor agonists and antagonists in mice. Immunol. Lett. 49, 143– 147.

Hemmings, H.C., Greengard, P., Tung, H.Y.L., and Cohen, P. (1984). DARPP-32, a dopamine-regulated neuronal phosphoprotein, is a potent inhibitor of protein phosphatase- 1. Nature 310, 503–505.

Hernández-López, S., Tkatch, T., Perez-Garci, E., Galarraga, E., Bargas, J., Hamm, H., and Surmeier, D.J. (2000). D2 Dopamine Receptors in Striatal Medium Spiny Neurons Reduce L- Type Ca2+ Currents and Excitability via a Novel PLCβ1–IP3–Calcineurin-Signaling Cascade. J. Neurosci. 20, 8987–8995.

Herschkowitz, J.I., Simin, K., Weigman, V.J., Mikaelian, I., Usary, J., Hu, Z., Rasmussen, K.E., Jones, L.P., Assefnia, S., Chandrasekharan, S., et al. (2007). Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors. Genome Biol. 8, R76.

Howes, O.D., and Kapur, S. (2009). The Dopamine Hypothesis of Schizophrenia: Version III--The Final Common Pathway. Schizophr. Bull. 35, 549–562.

Hubbard, R.A., Kerlikowske, K., Flowers, C.I., Yankaskas, B.C., Zhu, W., and Miglioretti, D.L. (2011). Cumulative Probability of False-Positive Recall or Biopsy Recommendation After 10 Years of Screening Mammography. Ann. Intern. Med. 155, 481.

Hunter, C.A., and Jones, S.A. (2015). IL-6 as a keystone cytokine in health and disease. Nat. Immunol. 16, 448–457.

Ikeda, S.R. (1996). Voltage-dependent modulation of N-type calcium channels by G-protein β γsubunits. Nature 380, 255–258.

Iliopoulos, D., Hirsch, H.A., and Struhl, K. (2009). An Epigenetic Switch Involving NF-κB, Lin28, Let-7 MicroRNA, and IL6 Links Inflammation to Cell Transformation. Cell 139, 693– 706.

Johannessen, T.-C.A., Hasan-Olive, M.A.M., Zhu, H., Denisova, O., Grudic, A., Latif, M., Saed, H., Varughese, J.K., Røsland, G.V., Yang, N., et al. (2018). Thioridazine inhibits autophagy and sensitizes glioblastoma cells to temozolomide. Int. J. Cancer.

Jordan, N.V., Prat, A., Abell, A.N., Zawistowski, J.S., Sciaky, N., Karginova, O.A., Zhou, B., Golitz, B.T., Perou, C.M., and Johnson, G.L. (2013). SWI/SNF chromatin-remodeling factor Smarcd3/Baf60c controls epithelial-mesenchymal transition by inducing Wnt5a signaling. Mol. Cell. Biol. 33, 3011–3025.

Kadamur, G., and Ross, E.M. (2013). Mammalian Phospholipase C. Annu. Rev. Physiol. 75, 127–154.

Kalager, M., Zelen, M., Langmark, F., and Adami, H.-O. (2010). Effect of Screening Mammography on Breast-Cancer Mortality in Norway. N. Engl. J. Med. 363, 1203–1210.

Kapur, S., and Seeman, P. (2000). Antipsychotic agents differ in how fast they come off the dopamine D2 receptors. Implications for atypical antipsychotic action. J. Psychiatry Neurosci. 25, 161–166.

Kapur, S., and Seeman, P. (2001). Does Fast Dissociation From the Dopamine D 2 Receptor Explain the Action of Atypical Antipsychotics?: A New Hypothesis. Am. J. Psychiatry 158, 360–369.

Kebabian, J.W., and Calne, D.B. (1979). Multiple receptors for dopamine. Nature 277, 93– 96.

Kendellen, M.F., Bradford, J.W., Lawrence, C.L., Clark, K.S., and Baldwin, A.S. (2014). Canonical and non-canonical NF-κB signaling promotes breast cancer tumor-initiating cells. Oncogene 33, 1297–1305.

Konkalmatt, P.R., Asico, L.D., Zhang, Y., Yang, Y., Drachenberg, C., Zheng, X., Han, F.,

Jose, P.A., and Armando, I. (2016). Renal rescue of dopamine D2 receptor function reverses renal injury and high blood pressure. JCI Insight 1, e85888.

Korkaya, H., Kim, G.-I., Davis, A., Malik, F., Henry, N.L., Ithimakin, S., Quraishi, A.A., Tawakkol, N., D’Angelo, R., Paulson, A.K., et al. (2012). Activation of an IL6 inflammatory loop mediates trastuzumab resistance in HER2+ breast cancer by expanding the cancer stem cell population. Mol. Cell 47, 570–584.

Kuehn, H., Cook, J.H., and Dreyer, W.J. (1973). Phosphorylation of rhodopsin in bovine photoreceptor membranes. Dark reaction after illumination. Biochemistry 12, 2495–2502.

Lajiness, M.E., Chio, C.L., and Huff, R.M. (1993). D2 dopamine receptor stimulation of mitogenesis in transfected Chinese hamster ovary cells: relationship to dopamine stimulation of tyrosine phosphorylations. J. Pharmacol. Exp. Ther. 267, 1573–1581.

Lamb, R., Ablett, M.P., Spence, K., Landberg, G., Sims, A.H., and Clarke, R.B. (2013). Wnt pathway activity in breast cancer sub-types and stem-like cells. PLoS One 8, e67811.

Lambright, D.G., Sondek, J., Bohm, A., Skiba, N.P., Hamm, H.E., and Sigler, P.B. (1996). The 2.0 Å crystal structure of a heterotrimeric G protein. Nature 379, 311–319.

Lapidot, T., Sirard, C., Vormoor, J., Murdoch, B., Hoang, T., Caceres-Cortes, J., Minden, M., Paterson, B., Caligiuri, M.A., and Dick, J.E. (1994). A cell initiating human acute myeloid leukaemia after transplantation into SCID mice. Nature 367, 645–648.

Lavine, N., Ethier, N., Oak, J.N., Pei, L., Liu, F., Trieu, P., Rebois, R.V., Bouvier, M., Hébert, T.E., and Van Tol, H.H.M. (2002). G Protein-coupled Receptors Form Stable Complexes with Inwardly Rectifying Potassium Channels and Adenylyl Cyclase. J. Biol. Chem. 277, 46010– 46019.

Lawrence, C.L., and Baldwin, A.S. (2016). Non-Canonical EZH2 Transcriptionally Activates RelB in Triple Negative Breast Cancer. PLoS One 11, e0165005.

Lee, H., Deng, J., Kujawski, M., Yang, C., Liu, Y., Herrmann, A., Kortylewski, M., Horne, D., Somlo, G., Forman, S., et al. (2010). STAT3-induced S1PR1 expression is crucial for persistent STAT3 activation in tumors. Nat. Med. 16, 1421–1428.

Lee, S.P., So, C.H., Rashid, A.J., Varghese, G., Cheng, R., Lança, A.J., O’Dowd, B.F., and George, S.R. (2004). Dopamine D1 and D2 Receptor Co-activation Generates a Novel Phospholipase C-mediated Calcium Signal. J. Biol. Chem. 279, 35671–35678.

Levite, M. (2016). Dopamine and T cells: dopamine receptors and potent effects on T cells, dopamine production in T cells, and abnormalities in the dopaminergic system in T cells in autoimmune, neurological and psychiatric diseases. Acta Physiol. (Oxf). 216, 42–89.

Levy, D.E., and Darnell, J.E. (2002). STATs: transcriptional control and biological impact. Nat. Rev. Mol. Cell Biol. 3, 651–662.

Li, J., Zhu, S., Kozono, D., Ng, K., Futalan, D., Shen, Y., Akers, J.C., Steed, T., Kushwaha, D., Schlabach, M., et al. (2014). Genome-wide shRNA screen revealed integrated mitogenic signaling between dopamine receptor D2 (DRD2) and epidermal growth factor receptor (EGFR) in glioblastoma. Oncotarget 5, 882–893.

Li, Y., Hively, W.P., and Varmus, H.E. (2000). Use of MMTV-Wnt-1 transgenic mice for studying the genetic basis of breast cancer. Oncogene 19, 1002–1009.

Liedtke, C., Mazouni, C., Hess, K.R., André, F., Tordai, A., Mejia, J.A., Symmans, W.F., Gonzalez-Angulo, A.M., Hennessy, B., Green, M., et al. (2008). Response to neoadjuvant

33 therapy and long-term survival in patients with triple-negative breast cancer. J. Clin. Oncol. 26, 1275–1281.

Lifton, R.P., Gharavi, A.G., and Geller, D.S. (2001). Molecular Mechanisms of Human Hypertension. Cell 104, 545–556.

Liu, S., Fan, Y., Chen, A., Jalali, A., Minami, K., Ogawa, K., Nakshatri, H., Li, B.-Y., and Yokota, H. (2018). Osteocyte-Driven Downregulation of Snail Restrains Effects of Drd2 Inhibitors on Mammary Tumor Cells. Cancer Res. canres.0056.2018.

Logothetis, D.E., Kurachi, Y., Galper, J., Neer, E.J., and Clapham, D.E. (1987). The βγ subunits of GTP-binding proteins activate the muscarinic K+ channel in heart. Nature 325, 321–326.

Lohse, M.J., Benovic, J.L., Codina, J., Caron, M.G., and Lefkowitz, R.J. (1990). β-Arrestin: a protein that regulates beta-adrenergic receptor function. Science 248, 1547–1550.

Luo, L., Jin, X., Zou, B., Zhong, C., Zhang, P., Cheng, H., Guo, Y., and Gou, M. (2016). Codelivery of thioridazine and doxorubicin using nanoparticles for effective breast cancer therapy. Int. J. Nanomedicine Volume 11, 4545–4552.

Luttrell, L.M., and Lefkowitz, R.J. (2002). The role of beta-arrestins in the termination and transduction of G-protein-coupled receptor signals. J. Cell Sci. 115, 455–465.

Luttrell, L.M., Ferguson, S.S., Daaka, Y., Miller, W.E., Maudsley, S., Della Rocca, G.J., Lin, F., Kawakatsu, H., Owada, K., Luttrell, D.K., et al. (1999). Beta-arrestin-dependent formation of beta2 adrenergic receptor-Src protein kinase complexes. Science 283, 655– 661.

Luttrell, L.M., Roudabush, F.L., Choy, E.W., Miller, W.E., Field, M.E., Pierce, K.L., and Lefkowitz, R.J. (2001). Activation and targeting of extracellular signal-regulated kinases by beta-arrestin scaffolds. Proc. Natl. Acad. Sci. U. S. A. 98, 2449–2454.

Mani, S.A., Guo, W., Liao, M.-J., Eaton, E.N., Ayyanan, A., Zhou, A.Y., Brooks, M., Reinhard, F., Zhang, C.C., Shipitsin, M., et al. (2008). The Epithelial-Mesenchymal Transition Generates Cells with Properties of Stem Cells. Cell 133, 704–715.

Mannoury la Cour, C., Salles, M.-J., Pasteau, V., and Millan, M.J. (2011). Signaling Pathways Leading to Phosphorylation of Akt and GSK-3 by Activation of Cloned Human and Rat Cerebral D2 and D3 Receptors. Mol. Pharmacol. 79, 91–105.

Mantovani, A., Allavena, P., Sica, A., and Balkwill, F. (2008). Cancer-related inflammation. Nature 454, 436–444.

Marinissen, M.J., and Gutkind, J.S. (2001). G-protein-coupled receptors and signaling networks: emerging paradigms. Trends Pharmacol. Sci. 22, 368–376.

Marino, F., Cosentino, M., Bombelli, R., Ferrari, M., Lecchini, S., and Frigo, G. (1999). Endogenous catecholamine synthesis, metabolism, storage, and uptake in human peripheral blood mononuclear cells. Exp. Hematol. 27, 489–495.

McKenna, F., McLaughlin, P.J., Lewis, B.J., Sibbring, G.C., Cummerson, J.A., Bowen-Jones, D., and Moots, R.J. (2002). Dopamine receptor expression on human T- and B-lymphocytes, monocytes, neutrophils, eosinophils and NK cells: a flow cytometric study. J. Neuroimmunol. 132, 34–40.

Meng, Q., Sun, X., Wang, J., Wang, Y., and Wang, L. (2016). The important application of thioridazine in the endometrial cancer. Am. J. Transl. Res. 8, 2767–2775.

Missale, C., Nash, S.R., Robinson, S.W., Jaber, M., and Caron, M.G. (1998). Dopamine Receptors: From Structure to Function. Physiol. Rev. 78, 189–225.

Moore, C.A.C., Milano, S.K., and Benovic, J.L. (2007). Regulation of Receptor Trafficking by GRKs and Arrestins. Annu. Rev. Physiol. 69, 451–482.

Nair, V.D., and Sealfon, S.C. (2003). Agonist-specific Transactivation of Phosphoinositide 3- Kinase Signaling Pathway Mediated by the Dopamine D 2 Receptor. J. Biol. Chem. 278, 47053–47061.

Nakano, K., Higashi, T., Takagi, R., Hashimoto, K., Tanaka, Y., and Matsushita, S. (2009). Dopamine released by dendritic cells polarizes Th2 differentiation. Int. Immunol. 21, 645– 654.

Nielsen, T.O., Hsu, F.D., Jensen, K., Cheang, M., Karaca, G., Hu, Z., Hernandez-Boussard, T., Livasy, C., Cowan, D., Dressler, L., et al. (2004). Immunohistochemical and Clinical Characterization of the Basal-Like Subtype of Invasive Breast Carcinoma. Clin. Cancer Res. 10, 5367–5374.

Oh, K., Lee, O.-Y., Shon, S.Y., Nam, O., Ryu, P.M., Seo, M.W., and Lee, D.-S. (2013). A mutual activation loop between breast cancer cells and myeloid-derived suppressor cells facilitates spontaneous metastasis through IL-6 trans-signaling in a murine model. Breast Cancer Res. 15, R79.

Perou, C.M., Sørlie, T., Eisen, M.B., van de Rijn, M., Jeffrey, S.S., Rees, C.A., Pollack, J.R., Ross, D.T., Johnsen, H., Akslen, L.A., et al. (2000). Molecular portraits of human breast tumours. Nature 406, 747–752.

Perreault, M.L., Hasbi, A., O’Dowd, B.F., and George, S.R. (2014). Heteromeric Dopamine Receptor Signaling Complexes: Emerging Neurobiology and Disease Relevance. Neuropsychopharmacology 39, 156–168.

Piomelli, D., Pilon, C., Giros, B., Sokolofff, P., Martres, M.-P., and Schwartz, J.-C. (1991). Dopamine activation of the arachidonic acid cascade as a basis for D1-D2 receptor synergism. Nature 353, 164–167.

Pitcher, J.A., Freedman, N.J., and Lefkowitz, R.J. (1998). G PROTEIN–COUPLED RECEPTOR KINASES. Annu. Rev. Biochem. 67, 653–692.

Povsic, T.J., Kohout, T.A., and Lefkowitz, R.J. (2003). Beta-arrestin1 mediates insulin-like growth factor 1 (IGF-1) activation of phosphatidylinositol 3-kinase (PI3K) and anti- apoptosis. J. Biol. Chem. 278, 51334–51339.

Prat, A., Parker, J.S., Karginova, O., Fan, C., Livasy, C., Herschkowitz, J.I., He, X., and Perou, C.M. (2010). Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res. 12, R68.

Pumiglia, K.M., LeVine, H., Haske, T., Habib, T., Jove, R., and Decker, S.J. (1995). A direct interaction between G-protein beta gamma subunits and the Raf-1 protein kinase. J. Biol. Chem. 270, 14251–14254.

Raz, R., Lee, C.K., Cannizzaro, L.A., d’Eustachio, P., and Levy, D.E. (1999). Essential role of STAT3 for embryonic stem cell pluripotency. Proc. Natl. Acad. Sci. U. S. A. 96, 2846–2851.

Reis-Filho, J.S., and Tutt, A.N.J. (2007). Triple negative tumours: a critical review. Histopathology 52, 108–118.

Reya, T., Morrison, S.J., Clarke, M.F., and Weissman, I.L. (2001). Stem cells, cancer and

35 cancer stem cells. Nature 414, 105–111.

Rosenbaum, D.M., Rasmussen, S.G.F., and Kobilka, B.K. (2009). The structure and function of G-protein-coupled receptors. Nature 459, 356–363.

Roth, B.L., Sheffler, D.J., and Kroeze, W.K. (2004). Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nat. Rev. Drug Discov. 3, 353–359.

Sabatier, R., Finetti, P., Guille, A., Adelaide, J., Chaffanet, M., Viens, P., Birnbaum, D., Bertucci, F., Viale, G., Perou, C., et al. (2014). Claudin-low breast cancers: clinical, pathological, molecular and prognostic characterization. Mol. Cancer 13, 228.

Sachlos, E., Risueño, R.M., Laronde, S., Shapovalova, Z., Lee, J.-H., Russell, J., Malig, M., McNicol, J.D., Fiebig-Comyn, A., Graham, M., et al. (2012). Identification of Drugs Including a Dopamine Receptor Antagonist that Selectively Target Cancer Stem Cells. Cell 149, 1284–1297.

Salyer, S.A., Parks, J., Barati, M.T., Lederer, E.D., Clark, B.J., Klein, J.D., and Khundmiri, S.J. (2013). Aldosterone regulates Na+, K+ ATPase activity in human renal proximal tubule cells through mineralocorticoid receptor. Biochim. Biophys. Acta - Mol. Cell Res. 1833, 2143–2152.

Scheel, C., Eaton, E.N., Li, S.H.-J., Chaffer, C.L., Reinhardt, F., Kah, K.-J., Bell, G., Guo, W., Rubin, J., Richardson, A.L., et al. (2011). Paracrine and autocrine signals induce and maintain mesenchymal and stem cell states in the breast. Cell 145, 926–940.

Schindler, C., Levy, D.E., and Decker, T. (2007). JAK-STAT signaling: from interferons to cytokines. J. Biol. Chem. 282, 20059–20063.

Schlessinger, J. (2000). Cell Signaling by Receptor Tyrosine Kinases. Cell 103, 211–225.

Seeman, P., and Kapur, S. (2000). Schizophrenia: more dopamine, more D2 receptors. Proc. Natl. Acad. Sci. U. S. A. 97, 7673–7675.

Shaywitz, A.J., and Greenberg, M.E. (1999). CREB: A Stimulus-Induced Transcription Factor Activated by A Diverse Array of Extracellular Signals. Annu. Rev. Biochem. 68, 821–861.

Shenoy, S.K., and Lefkowitz, R.J. (2011). β-arrestin-mediated receptor trafficking and signal transduction. Trends Pharmacol. Sci. 32, 521–533.

Siegel, R.L., Miller, K.D., and Jemal, A. (2018). Cancer statistics, 2018. CA. Cancer J. Clin. 68, 7–30.

Simões, B.M., O’Brien, C.S., Eyre, R., Silva, A., Yu, L., Sarmiento-Castro, A., Alférez, D.G., Spence, K., Santiago-Gómez, A., Chemi, F., et al. (2015). Anti-estrogen Resistance in Human Breast Tumors Is Driven by JAG1-NOTCH4-Dependent Cancer Stem Cell Activity. Cell Rep. 12, 1968–1977.

Skalhegg, B.S., and Tasken, K. (2000). Specificity in the cAMP/PKA signaling pathway. Differential expression,regulation, and subcellular localization of subunits of PKA. Front. Biosci. 5, D678-93.

Smrcka, A. V. (2008). G protein βγ subunits: Central mediators of G protein-coupled receptor signaling. Cell. Mol. Life Sci. 65, 2191–2214.

Sonnenblick, A., Brohée, S., Fumagalli, D., Vincent, D., Venet, D., Ignatiadis, M., Salgado, R., Van den Eynden, G., Rothé, F., Desmedt, C., et al. (2015). Constitutive phosphorylated

STAT3-associated gene signature is predictive for trastuzumab resistance in primary HER2- positive breast cancer. BMC Med. 13, 177.

Sørlie, T., Perou, C.M., Tibshirani, R., Aas, T., Geisler, S., Johnsen, H., Hastie, T., Eisen, M.B., van de Rijn, M., Jeffrey, S.S., et al. (2001). Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. U. S. A. 98, 10869–10874.

Spano, P.F., Govoni, S., and Trabucchi, M. (1978). Studies on the pharmacological properties of dopamine receptors in various areas of the central nervous system. Adv. Biochem. Psychopharmacol. 19, 155–165.

Spiegel, A., Shivtiel, S., Kalinkovich, A., Ludin, A., Netzer, N., Goichberg, P., Azaria, Y., Resnick, I., Hardan, I., Ben-Hur, H., et al. (2007). Catecholaminergic neurotransmitters regulate migration and repopulation of immature human CD34+ cells through Wnt signaling. Nat. Immunol. 8, 1123–1131.

Stark, G.R., and Darnell, J.E. (2012). The JAK-STAT Pathway at Twenty. Immunity 36, 503– 514.

Stephens, L., Smrcka, A., Cooke, F.T., Jackson, T.R., Sternweis, P.C., and Hawkins, P.T. (1994). A novel phosphoinositide 3 kinase activity in myeloid-derived cells is activated by G protein beta gamma subunits. Cell 77, 83–93.

Strobl, J.S., and Peterson, V.A. (1992). Tamoxifen-resistant human breast cancer cell growth: inhibition by thioridazine, pimozide and the calmodulin antagonist, W-13. J. Pharmacol. Exp. Ther. 263, 186–193.

Strobl, J.S., Kirkwood, K.L., Lantz, T.K., Lewine, M.A., Peterson, V.A., and Worley, J.F. (1990). Inhibition of human breast cancer cell proliferation in tissue culture by the neuroleptic agents pimozide and thioridazine. Cancer Res. 50, 5399–5405.

Svenningsson, P., Nishi, A., Fisone, G., Girault, J.-A., Nairn, A.C., and Greengard, P. (2004). DARPP-32: An Integrator of Neurotransmission. Annu. Rev. Pharmacol. Toxicol. 44, 269– 296.

Swift, J.L., Godin, A.G., Doré, K., Freland, L., Bouchard, N., Nimmo, C., Sergeev, M., De Koninck, Y., Wiseman, P.W., and Beaulieu, J.-M. (2011). Quantification of receptor tyrosine kinase transactivation through direct dimerization and surface density measurements in single cells. Proc. Natl. Acad. Sci. U. S. A. 108, 7016–7021.

Tran, H.D., Luitel, K., Kim, M., Zhang, K., Longmore, G.D., and Tran, D.D. (2014). Transient SNAIL1 Expression Is Necessary for Metastatic Competence in Breast Cancer. Cancer Res. 74, 6330–6340.

Urs, N.M., Snyder, J.C., Jacobsen, J.P.R., Peterson, S.M., and Caron, M.G. (2012). Deletion of GSK3 in D2R-expressing neurons reveals distinct roles for -arrestin signaling in antipsychotic and lithium action. Proc. Natl. Acad. Sci. 109, 20732–20737.

Usiello, A., Baik, J.-H., Rougé-Pont, F., Picetti, R., Dierich, A., LeMeur, M., Piazza, P.V., and Borrelli, E. (2000). Distinct functions of the two isoforms of dopamine D2 receptors. Nature 408, 199–203.

Verma, V., Hasbi, A., O’Dowd, B.F., and George, S.R. (2010). Dopamine D1-D2 receptor Heteromer-mediated calcium release is desensitized by D1 receptor occupancy with or without signal activation: dual functional regulation by G protein-coupled receptor kinase 2. J. Biol. Chem. 285, 35092–35103.

Vidal, D., and Mestres, J. (2010). In Silico Receptorome Screening of Antipsychotic Drugs. Mol. Inform. 29, 543–551.

Wall, M.A., Coleman, D.E., Lee, E., Iñiguez-Lluhi, J.A., Posner, B.A., Gilman, A.G., and Sprang, S.R. (1995). The structure of the G protein heterotrimer Gi alpha 1 beta 1 gamma 2. Cell 83, 1047–1058.

Wei, Z., Mousseau, D.D., Dai, Y., Cao, X., and Li, X.-M. (2006). Haloperidol induces apoptosis via the σ2 receptor system and Bcl-XS. Pharmacogenomics J. 6, 279–288.

Weigelt, B., Baehner, F.L., and Reis-Filho, J.S. (2009). The contribution of gene expression profiling to breast cancer classification, prognostication and prediction: a retrospective of the last decade. J. Pathol. 220, n/a-n/a.

Welch, H.G., Prorok, P.C., O’Malley, A.J., and Kramer, B.S. (2016). Breast-Cancer Tumor Size, Overdiagnosis, and Mammography Screening Effectiveness. N. Engl. J. Med. 375, 1438–1447.

Wickman, K.D., Iñiguez-Lluhi, J.A., Davenport, P.A., Taussig, R., Krapivinsky, G.B., Linder, M.E., Gilman, A.G., and Clapham, D.E. (1994). Recombinant G-protein βγ-subunits activate the muscarinic-gated atrial potassium channel. Nature 368, 255–257.

Wright, M.H., Calcagno, A.M., Salcido, C.D., Carlson, M.D., Ambudkar, S. V, and Varticovski, L. (2008). Brca1 breast tumors contain distinct CD44+/CD24- and CD133+cells with cancer stem cell characteristics. Breast Cancer Res. 10, R10.

Yang, Y.R., Kang, D.-S., Lee, C., Seok, H., Follo, M.Y., and Cocco, L. (2016). Primary phospholipase C and brain disorders. Adv. Biol. Regul. 61, 80–85.

Yin, T., He, S., Shen, G., Ye, T., Guo, F., and Wang, Y. (2015). Dopamine receptor antagonist thioridazine inhibits tumor growth in a murine breast cancer model. Mol. Med. Rep. 12, 4103–4108.

Yong, M., Yu, T., Tian, S., Liu, S., Xu, J., Hu, J., and Hu, L. (2017). DR2 blocker thioridazine: A promising drug for ovarian cancer therapy. Oncol. Lett. 14, 8171–8177.

Yoon, S., and Baik, J.-H. (2013). Dopamine D2 receptor-mediated epidermal growth factor receptor transactivation through a disintegrin and metalloprotease regulates dopaminergic neuron development via extracellular signal-related kinase activation. J. Biol. Chem. 288, 28435–28446.

Yu, H., Lee, H., Herrmann, A., Buettner, R., and Jove, R. (2014). Revisiting STAT3 signalling in cancer: new and unexpected biological functions. Nat. Rev. Cancer 14, 736–746.

Yue, H., Huang, D., Qin, L., Zheng, Z., Hua, L., Wang, G., Huang, J., and Huang, H. (2016). Targeting Lung Cancer Stem Cells with Antipsychological Drug Thioridazine. Biomed Res. Int. 2016, 6709828.

Zhang, C., Gong, P., Liu, P., Zhou, N., Zhou, Y., and Wang, Y. (2016). Thioridazine elicits potent antitumor effects in colorectal cancer stem cells. Oncol. Rep. 37, 1168–1174.

Zhang, Q., Lenardo, M.J., and Baltimore, D. (2017). 30 Years of NF-κB: A Blossoming of Relevance to Human Pathobiology. Cell 168, 37–57.

Zhang, Y., Gao, S., Xia, J., and Liu, F. (2018). Hematopoietic Hierarchy - An Updated Roadmap. Trends Cell Biol. 28, 976–986.

Zong, D., Zielinska-Chomej, K., Juntti, T., Mörk, B., Lewensohn, R., Hååg, P., and

Viktorsson, K. (2014). Harnessing the lysosome-dependent antitumor activity of phenothiazines in human small cell lung cancer. Cell Death Dis. 5, e1111.

CHAPTER 2: THIORIDAZINE INHIBITS SELF-RENEWAL IN BREAST CANCER CELLS

VIA DRD2-DEPENDENT STAT3 INHIBITION, BUT INDUCES A G1 ARREST

INDEPENDENT OF DRD2

2.1 - Introduction

The family of dopamine receptors consists of 5 G-protein-coupled receptors (GPCRs).

There are two subfamilies of dopamine receptors, which couple to different G-proteins and can have different effects on signaling. The Gαs-coupled D1-like receptors (DRD1 and DRD5) induce cAMP production, whereas the Gαi/o-coupled D2-like receptors (DRD2, DRD3, and

DRD4) inhibit cAMP production (Beaulieu and Gainetdinov, 2011; Missale et al., 1998). DRD2 in particular has been extensively studied due to its disease relevance. Excess or reduced

DRD2 activity is thought to be responsible for diseases such as schizophrenia and Parkinson’s disease, and compounds that preferentially inhibit or activate DRD2, respectively, can be used to manage these diseases (Iversen and Iversen, 2007; Missale et al., 1998). Although much of the research on dopamine receptors has focused on neurons, functions for dopamine and its receptors outside of the CNS have been reported. Expression of dopamine receptors has been observed in renal cells, where they regulate inflammation and blood pressure

(Konkalmatt et al., 2016; Missale et al., 1998; Zhang et al., 2012). Dopamine receptors, including DRD2, have also been identified in immune cells. The effects of dopamine and its receptors have been especially well studied in T-cells, where they regulate T-cell activation and proliferation (Basu et al., 2010; Levite, 2016; Mei et al., 2012).

Recently, evidence has emerged that DRD2-targeting antipsychotics can block the growth of several cancer types, including leukemia, glioblastoma, colorectal, lung, and breast cancer cell lines (Cheng et al., 2015; Li et al., 2014; Sachlos et al., 2012; Yin et al., 2015;

Yue et al., 2016; Zhang et al., 2016). Interestingly, the DRD2-targeting antipsychotic thioridazine had been shown as early as 1992 to inhibit breast cancer cell growth (Strobl and

Peterson, 1992). Further, high expression of DRD2 mRNA has been observed in glioblastoma and pancreatic cancer (Jandaghi et al., 2016; Li et al., 2014). DRD2-targeting antipsychotics such as thioridazine and haloperidol have been reported to induce apoptosis and reduce self- renewal in cancer cells (Cheng et al., 2015; Li et al., 2014; Sachlos et al., 2012; Yue et al.,

2016; Zhang et al., 2016). Despite these discoveries, the molecular mechanisms by which antipsychotics such as thioridazine lead to reduced cancer cell growth and survival are still unclear. Several studies of the effects of thioridazine have been performed using high concentrations (10-20µM) of drug (Cheng et al., 2015; Sachlos et al., 2012; Yue et al., 2016;

Zhang et al., 2016). It remains unclear whether certain cell types may be more or less sensitive to treatment with antipsychotics, whether thioridazine can inhibit self-renewal effects independent of cell toxicity, and whether these effects are mediated by inhibition of

DRD2 as is generally assumed. However, it is clear that DRD2-targeting antipsychotic compounds have very strong effects on many cell types.

STAT3 is an inducible transcription factor that has been shown to promote proliferation, prevent apoptosis, and affect cellular differentiation (Yu et al., 2014). STAT3 is one of 6 STAT family transcription factors that are primarily known for roles in pathogen response and inflammation (Stark and Darnell, 2012). Not only has STAT3 been shown to promote self-renewal in cancer stem cells and normal embryonic stem cells, but its activation also increases breast cancer proliferation and invasion (Banerjee and Resat, 2016; Marotta et al., 2011; Matsuda et al., 1999; Staniszewska et al., 2012; Wei et al., 2014). A number of stimuli are capable of activating STAT3, including the widely-studied cytokine interleukin-6

(IL-6). IL-6 forms a complex with IL-6 receptor and GP130 and activates the tyrosine kinase activity of GP130. Active GP130 can phosphorylate and activate members of the JAK tyrosine kinase family, which then activate STAT3 by phosphorylating Y705 (Hunter and Jones, 2015).

Importantly, IL-6 is also a target gene of STAT3, and a feed-forward loop can be established that leads to chronically activated STAT3. Studies indicate that chronically activated

STAT3/IL-6 promotes more aggressive and drug resistant breast cancers (Korkaya et al.,

2012; Real et al., 2002; Sonnenblick et al., 2015). Although receptor tyrosine kinase signaling is a well-studied mode of STAT3 activation, GPCRs such as S1P1R have been shown to promote STAT3 activation by directly binding to JAKs (Lee et al., 2010). Intriguingly, D2-like receptors have been shown to promote proliferation by activating tyrosine kinase activity

(Lajiness et al., 1993; Luo et al., 1999). This suggests a possible connection between

JAK/STAT/IL-6 and the effects of DRD2-targeting antipsychotics on cancer cells.

While high STAT3 activity is associated with more aggressive cancers, triple-negative breast cancers (TNBCs) in particular are associated with high STAT3 activity (Banerjee and

Resat, 2016; Wei et al., 2014). Previous work from our lab has also shown that IL-6 is a critical factor for the maintenance of tumor initiating cells in triple-negative breast cancer cell lines (TNBCLs) (Kendellen et al., 2014). Triple-negative breast cancer is a subtype of breast cancer that lacks the expression of the HER2, estrogen, and progesterone receptors.

Consequently, there are no targeted therapies for TNBC, which has a poor prognosis compared to other breast cancer subtypes (Bianchini et al., 2016).

In this study, we show that the DRD2-targeting drug thioridazine inhibits the self- renewal of some TNBCLs, but not others. At higher concentrations, thioridazine can induce a

G1 arrest and block cell viability in all tested TNBCLs. We demonstrate that the inhibition of self-renewal is dependent on DRD2 and activated STAT3. However, the strong reduction in cell viability is not only independent of STAT3 activity, but also independent of DRD2.

2.2 - Results

2.2.1 Thioridazine reduces cell viability of all tested TNBCLs, but inhibits self-renewal of some TNBCLs.

Thioridazine, a DRD2 inhibitor, has been reported to decrease cancer cell proliferation and stemness in leukemia, glioblastoma, colorectal, lung, and breast cancer cell lines (Cheng et al., 2015; Sachlos et al., 2012; Strobl and Peterson, 1992; Yin et al., 2015; Yue et al.,

2016; Zhang et al., 2016). DRD2 mRNA expression across different breast cancer subtypes was assessed using TCGA data. Interestingly, basal-like, claudin-low, and lobular tumors had highest expression of DRD2 compared to other breast cancer subtypes (Figure 2.1). Since basal-like and claudin-low tumors are predominantly triple-negative, the effects of thioridazine were assessed on a panel of triple-negative cell lines (Perou, 2010). Although high expression was also observed in lobular breast cancer, our group has previously studied the self-renewal of triple-negative breast cancers (Kendellen et al., 2014), so we studied the effects of DRD2-targeted antipsychotics in triple-negative breast cancer cell lines (TNBCLs).

To test whether thioridazine has effects on the self-renewal of TNBCLs, the in vitro tumorsphere assay was used. Six triple-negative cell lines were treated with DMSO, 1µM,

2µM, or 5µM thioridazine once and were cultured for seven days before the number of spheres were counted. Interestingly, some TNBCLs (SUM149, HCC1143, HCC1937) were found to be sensitive where thioridazine caused a dose-dependent decrease in tumorsphere number; whereas others (SUM159, MDA-MB-231, HCC38) were resistant, showing no significant decrease in tumorsphere numbers at these concentrations of thioridazine (Figure 2.2A). To more strigently test whether thioridazine inhibits self-renewal, secondary tumorsphere formation of SUM149 and SUM159 cells was tested in the presence of thioridazine. Indeed, thioridazine treatment inhibits secondary tumorsphere formation of SUM149 cells, but not of

SUM159 cells (Figure 2.2B). Since thioridazine is an inhibitor of DRD2, the observed differences in sensitivity were predicted to arise from expression of DRD2. Although HCC1143 cells have abundant expression of DRD2 compared to the other TNBCLs, DRD2 mRNA expression did not correlate with whether thioridazine will inhibited sphere formation (Figure

2.2C). Perhaps thioridazine inhibits sphere formation in SUM149 cells but not SUM159 cells,

43 because DRD2 is more highly expressed in the sphere-forming cells in the SUM149 cell line.

To test this, DRD2 mRNA expression was measured in tumorspheres relative to adherently grown SUM149 and SUM159 cells. Indeed, DRD2 is expressed more highly in SUM149 tumorspheres than in adherently grown cells (Figure 2.3). However, it is also expressed more highly in SUM159 tumorspheres than in adherently grown cells (Figure 2.3). This suggests that the lack of response in SUM159 tumorspheres is not due to low or absent expression of the receptor. Unfortunately, we were unable to specifically detect DRD2 protein by western blot. Antibodies against dopamine receptors, as is the case with many other GPCRs, are notoriously nonspecific (Bodei et al., 2009; Stojanovic et al., 2017).

A decrease in tumorsphere formation may be caused by inhibited self-renewal, or indirectly via reduced cell proliferation or increased death. In this regard, thioridazine has been reported to decrease cell viability in a number of cancer cells (Cheng et al., 2015;

Sachlos et al., 2012; Yue et al., 2016; Zhang et al., 2016). To test the effects of thioridazine on adherently grown TNBCLs, cell viability was measured by detecting ATP abundance after

72 hours of thioridazine treatment. In agreement with studies on other cancer cell lines, thioridazine dramatically reduced cell viability in TNBCLs at higher doses (Figure 2.2D).

Interestingly, there is only a 3-fold range in the IC50 of thioridazine across all 6 TNBCLs tested

(Table 2.1). We confirmed these results on SUM149 and SUM159 cells by counting cell number after 72 hours of thioridazine exposure. Both the SUM149 cells and the SUM159 cells show reduced cell numbers when treated with 2µM or 5µM, but not 1µM thioridazine (Figure 2.2E and F). Fewer SUM149 cells are counted after 2-4 day of 5µM thioridazine (Figure 2.2E). On the other hand, there are SUM159 cells still growing when treated with 5µM thioridazine

(Figure 2.2F). It is interesting to note that 1µM thioridazine inhibits tumorsphere formation in

SUM149 cells, even though cell viability is unaffected. This suggests that at 1µM thioridazine has a specific effect on self-renewal in this cell line. On the other hand, doses as high as 5µM thioridazine did not affect tumorsphere number in SUM159 cells, but it does strongly, but not

44 completely, inhibit proliferation. This suggests that thioridazine preferentially targets non- sphere-forming cells in this cell line.

To determine if thioridazine leads to induction of apoptosis, the proportion of sub-G1 cells was analyzed in response to thioridazine treatment. 5µM thioridazine treatment modestly increased the proportion of sub-G1 SUM149 cells (Figure 2.4A). Accordingly, a slight induction of caspase 3/7 activity and PARP-1 cleavage was observed (Figure 2.4B and C). These data indicate that while apoptosis increases modestly upon 5µM thioridazine treatment, the loss in cell viability is primarily non-apoptotic at this concentration. ONC201 is a novel compound known to strongly induce apoptosis in many different cancer cell types including colorectal,

AML, and breast cancer cells (Edwards and Ge, 2018; Greer et al., 2018; Prabhu et al., 2015).

It is also a DRD2 antagonist, like thioridazine (Madhukar et al., 2017). It was originally discovered for its ability to induce apoptosis by inducing TNF-related apoptosis inducing ligand

(TRAIL). ONC201 treatment inhibits AKT, which releases Foxo3a to the nucleus, and Foxo3a induces the transcription of TRAIL (Prabhu et al., 2015). We tested whether thioridazine may work via this mechanism. While thioridazine does dose-dependently inhibit AKT (Figure 2.5A), an increase in nuclear Foxo3a is not observed, nor is there a significant increase in TRAIL production (Figure 2.5B and C). Therefore, while thioridazine does inhibit AKT, like ONC201, it does not induce Foxo3a/TRAIL-mediated apoptosis.

2.2.2 Thioridazine induces cell-cycle arrest

To address whether thioridazine causes a cell-cycle defect, the cell-cycle distribution of SUM149 cells was assessed by flow cytometry after propidium iodide staining in cells that were treated with increasing doses of thioridazine for 48 hours. An increase in the proportion of G0/G1 cells was observed when SUM149 cells were treated with 5µM thioridazine (Figure

2.6A and B). Changes in proteins involved in S-phase entry were assessed by western blot in response to thioridazine treatment. Interestingly, an increase in the CDK inhibitor

CDKN1A/p21 was observed after 4 hours. Further, a decrease in CCND1/Cyclin D1 and CDK4

45 was observed in as little as one hour (Figure 2.6C). These data indicate that 5µM thioridazine treatment blocks the G1-S phase transition, leading to a cell cycle-arrest.

2.2.3 Thioridazine inhibits STAT3 activity

IL-6 is a pro-inflammatory cytokine known to promote tumor growth (Dethlefsen et al., 2013; Grivennikov et al., 2009; Kumari et al., 2016; Oh et al., 2013). Previous work from our group showed that IL-6 promotes tumor imitating cells in TNBCLs (Kendellen et al., 2014), and it has been shown to be a part of an IL-6/STAT3 feed-forward loop that promotes resistance to trastuzumab in Her2+ breast cancer cells (Korkaya et al., 2012). We first measured IL-6 mRNA abundance in all 6 TNBCLs used in the tumorsphere assay.

Interestingly, cell lines that were sensitive to thioridazine in the tumorsphere assay expressed more IL-6 mRNA (Figure 2.7A). To test whether thioridazine treatment affects IL-6 expression

SUM149 cells were treated with thioridazine for 1 – 8 hours, total RNA was isolated and IL-6 mRNA abundance was measured by qPCR. Thioridazine induced a rapid decrease in IL-6 transcript abundance, and that suppression was sustained for at least 8 hours (Figure 2.7B).

Secreted IL-6 protein was measured by ELISA 4 hours after treatment with thioridazine. A dose-dependent decrease of soluble IL-6 in the medium was observed (Figure 2.7C). IL-6 is a target gene of the STAT3 pathway and can also form a feed-forward loop by activating

STAT3. For these reasons we investigated whether thioridazine treatment can inhibit STAT3 activation. To do this, we measured phosphorylation of STAT3 at Y705, which is important for

STAT3 dimerization and transcriptional activity (Hunter and Jones, 2015). Thioridazine treatment caused a dose-dependent decrease in the phosphorylation of STAT3 at Y705 in

SUM149 cells, but not SUM159 cells (Figure 2.7D). Inactive STAT3 is normally excluded from the nucleus, but translocates to the nucleus upon pathway activation. We tested whether

SUM149 cells have nuclear STAT3, and whether thioridazine causes a reduction. Indeed, total nuclear STAT3, as well as pY705-STAT3 decreased upon thioridazine treatment (Figure 2.7E

46 and F). Together, these data show that thioridazine inhibits STAT3 activation and downstream

IL-6 transcription in SUM149 cells.

2.2.4 Thioridazine requires STAT3 to inhibit self-renewal, but not proliferation or survival

Having shown that thioridazine inhibits STAT3, we tested whether STAT3 is required for the ability of thioridazine to inhibit self-renewal and proliferation of SUM149 cells. First, we tested whether STAT3 is required for thioridazine-mediated inhibition of self-renewal. To do this, SUM149 cells were transfected with siControl or siSTAT3. Then they were cultured in a tumorsphere assay and treated with DMSO or 1µM thioridazine and the number of spheres formed were counted after one week. As expected from previous results, thioridazine caused a reduction in sphere formation (Figure 2.8A). Knockdown of STAT3 also reduced tumorsphere formation, consistent with its reported role in maintaining cancer cell self-renewal (Figure

2.8A). Interestingly, 1µM thioridazine did not cause an additional decrease in sphere formation in the siSTAT3-treated SUM149 cells (Figure 2.8 This suggests that thioridazine inhibits self-renewal in these cells by inhibiting STAT3 activity. Since thioridazine reduces IL-

6 production, which itself also promotes STAT3 activation, we tested whether addition of recombinant IL-6 (rIL-6) could rescue tumorsphere formation. Indeed, rIL-6 treatment increased tumorsphere number in DMSO-treated SUM149 cells as well as it does in thioridazine-treated SUM149 cells (Figure 2.8B). To confirm that IL-6 inhibition is downstream of thioridazine, we tested if rIL-6 treatment can induce pY705-STAT3 in the presence of thioridazine. Indeed, rIL-6 induced pY705-STAT3 in control treated SUM149 cells and in thioridazine treated SUM149 cells (Figure 2.8C). Thioridazine also caused a reduction in cell viability at high concentrations. To test whether STAT3 inhibition is also required for thioridazine-induced inhibition of proliferation and cell viability, siControl and siSTAT3 treated

SUM149 cells were treated with 1µM, 2µM, or 5µM thioridazine, and cultured adherently for

72 hours before the number of remaining cells were counted. Interestingly, siSTAT3 treatment had no impact on the proliferation of SUM149 cells, and thioridazine inhibited proliferation

47 similarly in siControl- and siSTAT3-treated cells (Figure 2.8D). These data demonstrate that while thioridazine inhibits self-renewal in SUM149 cells via STAT3 inhibition, the proliferation and cell viability defects are not mediated by STAT3 inhibition.

2.2.5 DRD2 promotes sphere formation in SUM149 cells

To test unique functions for DRD2 in TNBCLs, we first measured tumorsphere formation in SUM149 and SUM159 cells treated with siControl or siDRD2. Interestingly, siDRD2 treatment reduced tumorsphere formation in SUM149 cells, but not in SUM159 cells

(Figure 2.9A and B), consistent with the effects of thioridazine on tumorsphere number in these cells. The magnitude of DRD2 knockdown was confirmed by qPCR (Figure 2.10). To confirm that this decrease in sphere formation was not due to an offtarget effect of the siRNA,

SUM149 cell lines stably expressing shRNA targeting DRD2 were generated, and DRD2 knockdown was confirmed by qPCR (Figure 2.10). Again, knockdown of DRD2 decreased tumorsphere formation in SUM149 cells (Figure 2.9C). To further confirm the specificity of knockdown results, DRD2-CRISPR cells were generated and tumorsphere formation was determined. A decrease in DRD2 mRNA was observed in DRD2-CRISPR cells (Figure 2.10), and presence of mutations at the cut site were also confirmed (Figure 2.11). Indeed, the

CRISPR-DRD2 cells also formed fewer tumorspheres than control cells (Figure 2.9D). In order to more stringently test whether DRD2 promotes self-renewal in SUM149 cells, DRD2-CRISPR spheres were dissociated and cultured again as secondary tumorspheres. Again, a decrease in tumorsphere formation was observed, although more modest than primary tumorsphere formation (Figure 2.9E). We tested whether chlorpromazine, an antipsychotic similar in structure to thioridazine and a known DRD2 antagonist, could also suppress sphere formation.

Indeed, chlorpromazine treatment inhibited sphere formation in a dose-dependent manner

(Figure 2.9F). To further test if DRD2 promotes self-renewal in SUM149 cells, a highly specific

DRD2 inhibitor amisulpride was used (Besnard et al., 2012). Treatment with as little as 500nM amisulpride caused a modest, yet significant decrease in tumorsphere formation in SUM149

48 cells (Figure 2.9G). Interestingly, the reduction in tumorsphere formation induced by 1µM amisulpride is similar to that induced by 1µM thioridazine or chlorpromazine. This suggests that specifically targeting DRD2 affects self-renewal, but not proliferation (see below, see discussion). Tumorsphere formation in response to a well-characterized DRD2/3 specific agonist quinpirole was also tested, which caused an increase in tumorsphere formation (Figure

2.9H). These data confirm that DRD2 promotes self-renewal in the SUM149 cell line.

2.2.6 DRD2 promotes STAT3 activation

Since DRD2 promotes tumorsphere formation in SUM149 cells, and thioridazine inhibits tumorsphere formation via STAT3 inhibition, we tested whether DRD2 itself regulates

STAT3 activation. To do this we first used the DRD2 antagonist chlorpromazine and observed its effect on STAT3 phosphorylation in SUM149 cells. Indeed, chlorpromazine decreased pY705-STAT3 (Figure 2.12A). Additionally, amisulpride, the highly specific DRD2/3 inhibitor also decreased pY705-STAT3 (Figure 2.12B). We were unable to observe decreased pY705-

STAT3 when DRD2 was knocked down with siRNA or shRNA (Figure 2.13). This may be due to compensatory mechanisms of STAT3 activation that arise within the hours or days after knockdown. To directly test if DRD2 can promote STAT3 activation, we overexpressed DRD2 in SUM159 cells. As shown earlier, thioridazine does not decrease pY705-STAT3 in these cells

(Figure 2.7D). Interestingly, DRD2 overexpression increased pY705-STAT3 in SUM159 cells

(Figure 2.12C), and most importantly, the increase in pY705-STAT3 was sensitive to thioridazine treatment (Figure 2.12C). Since 1µM thioridazine reduces tumorsphere formation via STAT3 in SUM149 cells, but very little pY705-STAT3 decrease is observed when SUM149 cells are treated with 1µM thioridazine (Figure 2.7D), we tested whether 1µM thioridazine could reduce pY705-STAT3 in SUM149 cells cultured as spheres. Indeed, a decrease in pY705-

STAT3 was observed in SUM149 spheres treated with 1µM thioridazine (Figure 2.12D).

Further, we show that similar to thioridazine treatment, rIL-6 treatment rescued tumorsphere number in shDRD2 SUM149 cells (Figure 2.14). Although pY705-STAT3 is not decreased in

49 shDRD2 cells, rIL-6 increased pY705-STAT3 (Figure 2.14). This result shows that DRD2 is not required for the effects of IL-6, and that IL-6 is downstream of DRD2/STAT3 in these cells.

These data confirm that DRD2 can promote STAT3 activity in SUM149 cells, and that thioridazine targets DRD2 to inhibit STAT3 activity.

2.2.7 Thioridazine inhibits self-renewal, but not cell viability via DRD2 inhibition

It is still unknown whether thioridazine reduces self-renewal and cell proliferation by inhibiting DRD2, or if those functions result from its activities on other receptors. To test whether the self-renewal inhibition by thioridazine results from DRD2 inhibition, the tumorsphere assay was used. SUM149 cells treated with siControl or siDRD2 were also treated with DMSO or 1µM thioridazine. As expected, both siDRD2 and thioridazine treatment alone caused a reduction in tumorsphere formation. However, thioridazine treatment had no effect on sphere formation in siDRD2 cells (Figure 2.15A). This suggests that DRD2 is required for thioridazine to inhibit tumorsphere formation. To confirm this, the same experiment was repeated, but with DRD2-CRISPR SUM149 cells. And indeed, while 1µM thioridazine decreased tumorsphere formation in control cells, it had no effect on DRD2-CRISPR cells (Figure 2.15B).

Since the ability of thioridazine to inhibit self-renewal is dependent on DRD2, we tested whether the inhibition of proliferation by thioridazine is also a dependent on DRD2. To do this, we first observed the growth of SUM149 DRD2-CRISPR cells by counting the number of cells

72 hours after seeding. Interestingly, a decrease in cell number in DRD2-CRISPR cells was not observed (Figure 2.15C). We also counted cell numbers after treatment with the DRD2 specific inhibitor amisulpride, and no proliferation inhibition was detected at any dose tested

(Figure 2.15D). This indicates that DRD2 does not support cell proliferation in SUM149 cells.

Additionally, we measured whether DRD2 is required for thioridazine to inhibit proliferation in

SUM149 cells. To do this, control or DRD2-CRISPR cells were treated with DMSO or thioridazine for 72 hours and then cells were counted. Thioridazine induced the same dose- dependent decrease in proliferation in DRD2-CRISPR cells as in control cells (Figure 2.15E).

These data show that while thioridazine inhibits sphere formation and self-renewal via DRD2 inhibition, it inhibits proliferation and cell viability by a different mechanism.

2.3 - Discussion

Antipsychotics that target DRD2 have been shown to inhibit proliferation and self- renewal as well as induce apoptosis in leukemia, lung, colon, and brain cancer cells (Cheng et al., 2015; Li et al., 2014; Sachlos et al., 2012; Yue et al., 2016; Zhang et al., 2016). The effects of these compounds are generally assumed to be mediated by DRD2 inhibition.

Notably, these studies only report strong effects when thioridazine is used at 10-20µM (Cheng et al., 2015; Sachlos et al., 2012; Yue et al., 2016; Zhang et al., 2016). At these concentrations, thioridazine may have effects not mediated by DRD2 inhibition, and determining which effects of antipsychotic treatment are DRD2-dependent or independent is an important distinction to make in order to further identify drug targets in cancer. In addition, the mechanisms that mediate the antitumor effects of these compounds remain largely unknown.

In this study, we demonstrate that thioridazine blocks the self-renewal of several

TNBCLs, while the self-renewal of other TNBCLs is unaffected (Figure 2.2B). Importantly, we show that thioridazine inhibits self-renewal via DRD2 (Figure 2.15). Additionally, we demonstrate that additional DRD2-targeting antipsychotics also inhibit self-renewal (Figure

2.9). We also show that thioridazine induces a G1 arrest and dramatically decreases proliferation (Figure 2.2 and 2.6). These proliferation effects of thioridazine are not dependent on DRD2 (Figure 2.15). Further, we demonstrate that DRD2 promotes self-renewal by regulating STAT3/IL-6 activity in SUM149 cells. STAT3, similar to DRD2, does not support proliferation and cell viability, but instead primarily supports self-renewal (Figure 2.8). Based on these data, we developed a model where DRD2 supports STAT3 activation to maintain a

STAT3/IL-6 feed-forward loop and persistent STAT3 activation (Figure 2.15F).

Regarding the effects of thioridazine on self-renewal, we show that concentrations as low as 1µM thioridazine are capable of decreasing tumorsphere formation in SUM149 cells

(Figure 2.2B), and that proliferation and cell viability are unaffected at those doses (Figure

2.2E and F). This suggests that effects on self-renewal are not confounded by effects on cell viability at that dose. Furthermore, the DRD2/3 specific antagonist amisulpride blocks sphere formation to a similar degree as 1µM thioridazine (Figure 2.9G and 2.2B), even though it does not inhibit proliferation (Figure 2.15D). This further supports that DRD2 promotes self- renewal in SUM149 cells, but the proliferation inhibition is DRD2-independent. In contrast, in

SUM159 cells doses of thioridazine as high as 5µM do not affect tumorsphere formation

(Figure 2.2B), although cell viability is dramatically decreased (Figure 2.2E and G). This indicates that while thioridazine can reduce the proliferation and viability of the bulk of

SUM159 cells, it is unable to inhibit the activity of sphere-forming cells at these concentrations. Together, our data show that in certain cells (SUM149) DRD2 supports sphere-forming cells, and that this activity can be targeted with thioridazine, when used at

1µM. As increasing doses of thioridazine are used (5µM), a G1 arrest and loss of cell viability independent of DRD2 activity occurs. In other cells (SUM159), thioridazine cannot inhibit self- renewal via DRD2/STAT3 in sphere-forming cells. However, thioridazine can still inhibit the proliferation of the bulk of cells at 5µM, but the sphere-forming cells are more resistant than the bulk of the cells. Uncovering the cell types in which this DRD2/STAT3/IL-6 pathway can be targeted would be an important step in determining which tumors can be most effectively treated with thioridazine or other DRD2-targeting antipsychotics.

The STAT3/IL-6 activation loop is an established cancer promoting pathway. High systemic IL-6 correlates with poorer prognosis and advanced tumor stage in breast cancer patients (Dethlefsen et al., 2013), and STAT3 is known to promote breast cancer survival, invasion, drug resistance and stemness (Banerjee and Resat, 2016; Real et al., 2002;

Sonnenblick et al., 2015; Yu et al., 2014). For these reasons, there has been significant

52 research into targeting the STAT3/IL-6 pathway. However, STAT3, like many transcription factors, has proven difficult to specifically target with small compounds. Although antibodies that target both IL-6 and the IL-6 receptor have been developed and have made major impacts in inflammation-related diseases, translation into cancer therapies has been slow

(Heo et al., 2016). Mechanisms whereby inhibiting DRD2 has anti-cancer effects remain largely unknown. Here we demonstrate that DRD2 promotes STAT3 activation and that thioridazine and other DRD2-targeting compounds blocks that activity in SUM149 cells. Using tumorsphere assays, we also show that STAT3 is required for DRD2 to promote self-renewal, and thioridazine does not block self-renewal in the absence of STAT3 in SUM149 cells. We observed the strongest decrease of pY705-STAT3 at 5-10µM thioridazine. At these doses loss of cell viability is observed, not just self-renewal defects. However, these western blots were performed after 1 hour of treatment from cells grown adherently. We also show that addition of recombinant IL-6 rescues tumorsphere number of thioridazine-treated and shDRD2

SUM149 cells. IL-6 treatment is also still able to increase pY705-STAT3, even with thioridazine-treatment or in DRD2 knockdown cells. This indicates that IL-6 is downstream of

DRD2/STAT3, and that DRD2 likely supports STAT3 activity, and IL-6 is subsequently promoted via increased STAT3 activity.

Notably, we demonstrated that thioridazine induces a G1 arrest and a decrease in cell proliferation that is independent of DRD2/STAT3 in SUM149 cells. We also determined that

5µM thioridazine causes a reduction in proliferation without a dramatic increase in apoptosis.

(Figure 2.2 and 2.4). We also show using DRD2-CRISPR cells and the highly specific DRD2/3 inhibitor amisulpride that DRD2 does not support proliferation and viability in the bulk of tumor cells (Figure 2.15C and D). Importantly, we demonstrate that thioridazine is not dependent on the presence of DRD2 to inhibit proliferation in SUM149 cells (Figure 2.15E).

This is supported by work from Kline et al, which showed that a novel DRD2 antagonist,

ONC201, can lead to reduced cell proliferation in both control and DRD2 knockout colorectal

53 cancer cells (Kline et al., 2018). The mechanisms by which these DRD2-independent effects occur are unknown, but they may offer insight into interesting new drug targets for TNBC.

The origin of an activating ligand in cancer, if any, for DRD2 is still unknown. The catecholamine dopamine is generally considered the primary activator for all dopamine receptors, including DRD2 (Missale et al., 1998). It is unknown what the potential source of dopamine could be in the breast cancer milieu. Interestingly, T-cells, B-cells, dendritic cells, and macrophages have all been shown to express dopamine receptors (Levite, 2016;

McKenna et al., 2002). These cells have been shown to be capable of dopamine synthesis

(Bergquist et al., 1994; Josefsson et al., 1996; Kokkinou et al., 2009; Levite, 2016; Nakano et al., 2009), and dopamine has been demonstrated to either promote or inhibit their activity

(Cosentino et al., 2007; Josefsson et al., 1996; Levite, 2016; McKenna et al., 2002; Nakano et al., 2009). It is possible that tumor infiltrating immune cells could secrete dopamine and other catecholamines and support tumor growth and/or suppress antitumor immunity.

However, our study is based on cell lines grown in vitro. So even if infiltrating immune cells are a source of dopamine in vivo, dopamine receptors have cancer cell line autonomous effects. Whether this is a result of intrinsic, ligand-independent, activity of dopamine receptors or whether cancer cells themselves maybe secreting dopamine is unclear. Additionally, there is evidence that proteins, such as the non-canonical Wnt5a, can also activate DRD2 (Yoon et al., 2011). The implications of this are intriguing, but mostly unexplored.

It is also unknown which cell types are most sensitive to thioridazine and other DRD2- targeting compounds. We show that the self-renewal of several TNBCLs (SUM149, HCC1143,

HCC1937) is strongly inhibited by thioridazine, whereas the self-renewal of three other

TNBCLs (SUM159, MDA-MB-231, HCC38) is unaffected by thioridazine (Figure 2.2B). The inhibition of self-renewal caused by 1µM thioridazine is dependent on DRD2 (Figure 2.15A and B). Interestingly, we also show that while the TNBCLs in which thioridazine inhibits self- renewal do not express more DRD2 mRNA (Figure 2.2D), they do express more IL-6 mRNA

(Figure 2.7A). Further, thioridazine inhibits STAT3 activation in SUM149 cells, but not SUM159 cells (Figure 2.7D), and STAT3 is required for thioridazine to inhibit self-renewal in SUM149 cells (Figure 2.8). These findings suggest that some cell types, as in SUM149 cells, the DRD2 promotes STAT3/IL-6 activity to support self-renewal, whereas in other cell types, as in

SUM159 cells, STAT3 activity is not supported by DRD2 and therefore DRD2 inhibition does not block self-renewal. The effects of thioridazine on STAT3 activation and tumor growth of the murine 4t1 triple-negative breast cancer model have already been tested. Although 4t1 tumor growth is suppressed by thioridazine treatment, STAT3 phosphorylation is unaffected

(Yin et al., 2015). This is similar to the effects of thioridazine on the TNBCL SUM159. The connection between DRD2 activity and STAT3/IL-6 needs to be explored further to determine the cell types in which targeting DRD2 is likely to lead to a specific inhibition of self-renewal.

We show that the effects of thioridazine on proliferation and cell viability are independent of DRD2. Finding the receptors responsible for these effects may be important, but complicated as thioridazine is known to bind other GPCRs (Besnard et al., 2012).

Interestingly, antagonism of serotonergic receptors has been shown to reduce breast cancer growth (Gwynne et al., 2017). Important roles of adrenergic receptors in promoting progression and angiogenesis of prostate cancer have also been recently reported (Magnon et al., 2013; Zahalka et al., 2017). Additionally, a recent study showed that treatment of glioblastoma cells with DRD4 antagonists inhibited autophagy, led to a G1 arrest, and ultimately induced apoptosis (Dolma et al., 2016). This implicates DRD4 as potentially regulating cancer cell survival in glioblastoma. However, mechanisms by which antipsychotics like thioridazine can achieve such dramatic effects across so many cell types remain elusive.

This study has shown that DRD2 can promote STAT3 activity and self-renewal, and antipsychotics that preferentially target DRD2 can inhibit this pathway in SUM149 cells.

Interestingly, these antipsychotics also have strong effects on cell proliferation and cell viability that are not a result of DRD2 inhibition broadly, in all TNBCLs tested. Thioridazine is

55 generally safe and has shown to be potent at reducing cancer cell viability, and there may be potential therapeutic benefit to using thioridazine or other DRD2-targeting compounds in the cancer clinic.

2.4 – Experimental Procedures

Cell culture and reagents

SUM149 and SUM159 cells were maintained in HuMEC medium (Gibco, Waltham, MA,

USA) with 5% fetal bovine serum (FBS). MDA-MB-231 cells were maintained in DMEM (Gibco) with 10% FBS. HCC38, HCC1143, and HCC1937 cells were maintained in RPMI-1640 with

10% FBS. Thioridazine, chlorpromazine, forskolin, amisulpride, and quinpirole were obtained from (Sigma-Aldrich, St. Louis, MO, USA). All antibodies were obtained from Cell Signaling

Technologies (Danvers, MA, USA). qPCR primer-probe sets targeting IL-6 and GUSB were obtained from Applied Biosystems (Grand Island, NY, USA). DRD2 primer-probe set was obtained from Bio-Rad (Hercules, CA, USA) Recombinant IL-6 was obtained from Peprotech

(Rocky Hill, NJ, USA). Anti-IL-6 receptor antibody was obtained from R & D Systems

(Minneapolis, MN, USA). All siRNAs were SMARTpool siGENOME from Dharmacon (Lafayette,

CO, USA). DRD2 overexpression plasmid in pLENTI6-V5 vector was obtained from the CCSB

- Broad lentiviral ORF collection (Yang et al., 2011).

Stable shDRD2 and DRD2-CRISPR cell lines

DRD2-targeting shRNA constructs (TRCN0000011342 and TRCN0000011343) in pLKO.1 vector backbone was obtained from the Open Biosystems TRC1 shRNA library. The plasmids were transfected into HEK293T cells using FuGeneHD (Promega, Madison, WI, USA).

The supernatant was collected then filtered using 0.4μm filters. The resulting liquid was then concentrated to approximately 150μL using Amicon Ultra-15 Ultracel-100 (Millipore, Billerica,

MA, USA). The virus concentrate was then added to SUM149 cell growing in 10 mL of HuMEC medium before puromycin selection. lentiCRISPR v2 was a gift from Feng Zhang (Addgene

56 plasmid #52961). Genomic sequences to target were identified using predictive software from the Zhang lab. Sequences of the primers to clone the DRD2 CRIPSR constructs are as follows:

5’-CTGCGTTATTGAGTCCGAAG-3’ and 5’-GTAGCGCGTATTGTACAGCA-3’. Lentivirus was made as previously stated. Pooled CRISPR mutants that survived puromycin selection were used for experiments.

Immunoblots, cell fractionation, and immunoprecipitation

Immunoblots and immunoprecipitations were performed as previously described (Hsia et al., 2017), except Mini-PROTEAN TGX SDS-PAGE gels (Bio-Rad) and Clarity ECL (Bio-Rad) were used. To obtain nuclear and cytoplasmic extracts cells were lysed with CE buffer (10mM

HEPES, 60mM KCl, 1mM EDTA, 0.075% NP-40, 1mM DTT and protease and phosphatase inhibitor cocktails). Cytoplasmic extract was removed and the resulting nuclei were washed in CE buffer then lysed in NE buffer (62.5mM Tris-HCl, 5% glycerol, 2% SDS, 5% β- mercatoethanol). The lysates were then immunoblots as described.

RNA isolation and quantitative PCR

Total RNA was isolated using Trizol (Invirogen) according to manufacturer’s protocol. iScript (Bio-Rad) was used to make cDNA. Quantitative PCR was performed with iTaq Probes

Supermix (Bio-Rad) using an QuantStudio 6 (Applied Biosystems, Foster, CA, USA) and analyzed using QuantStudio software to calculate RQ values relative to GUSB housekeeping gene.

Tumorsphere Assay

Cells were trypsinized and resuspended into a single cell suspension and counted using a hemocytometer. 20000 cells were resuspended in 2 mL of complete mammocult medium

(Stem Cell Technologies, Vancouver, BC, CA) and plated into ultra-low adherence 6 well plates

(Corning, Corning, NY, USA). After one week of culture, the spheres were collected and all spheres greater than 60μm in diameter were counted according to manufacturer’s protocol

(Stem Cell Technologies). Secondary tumorsphere formation was assessed by culturing primary spheres as described; the resulting spheres were dissociated with trypsin and 20000 cells were again plated in 2mL of complete mammocult into ultra-low adherence 6 well plates.

Thioridazine treatments were applied once at the time of plating. Quinpirole, chlorpromazine, amisulpride, rIL-6, and anti-IL-6R antibody were applied daily. Experiments with siDRD2 were performed as described except only 200 cells were plated in 2mL of mammocult.

Transfections

Overexpression analysis was achieved by transfecting 10μg of plasmid with 20μL of

FugeneHD (Promega). siRNA transfections were achieved by using 40nM siRNA and

DharmafectI (Dharmacon) according to manufacturer’s protocol.

Proliferation, cell viability, and caspase assays

To assess proliferation, cells were plated in 24 well plates at 4000 cells per well in triplicate (SUM159), or 6500 cells per well in triplicate (SUM149). Cells were counted at indicated times using a hemocytometer. Cell viability assays were performed by plating 1000 cells in a 96 well plate in medium with the appropriate concentration of thioridazine. After 72 hours, cells were treated with 100μL of CellTiterGlo (Promega) for 10 minutes. Luciferase activity was measured using a Synergy 2 Luminometer (BioTek, Winooski, VT, USA). IC50 was determined using GraphPad Prism 5 (Prism, La Jolla, CA, USA). To asses caspase 3/7 activity,

2500 SUM149 cells were plated per well in a 96 well plate and treated with indicated concentrations of thioridazine for 4, 24, or 48hr. Assay was performed according to manufacuterer’s protocol. Luciferase activity was measured using a Synergy 2 Luminometer

(BioTek).

Cell cycle analysis

SUM149 cells were treated with DMSO or thioridazine for 48 hours and fixed in ice- cold 70% ethanol for 2 hours at 4oC. The cells were resuspended in flow buffer (1x PBS, 10%

FBS) and treated with 10μg/mL RNase A for 30 minutes at 37oC. 5μL of 1mg/mL propidium iodide (Invitrogen) was added for 30 mins at room temperature. Cells were then washed in flow buffer and filtered using a 40μm filter (BD). Data were aquired using a Cyan ADP Flow

Cytometer (Becton Dickinson, Franklin Lakes, NJ, USA) and analyzed using FloJo software

(Ashland, Oregon, USA).

ELISA

To measure soluble IL-6, fresh HuMEC medium with DMSO or thioridazine was added to SUM149 cells in 6 well plates. 4 hours later, the medium was collected and centrifuged to remove insoluble material. Then the human IL-6 BD OptEIA kit was used to detect the IL-6 according to manufacturer’s protocol (Becton Dickinson). To measure cAMP, cells in a 6 well plate were treated with 1μM forskolin and either DMSO or quinpirole. Cells were additionally treated with 2μM thioridazine where indicated. After a 15-minute incubation, the cells were harvested and ELISA was performed according to manufacturer’s protocol using the Cyclic

AMP Select ELISA Kit (Cayman Chemical, Ann Arbor, MI, USA).

TCGA analysis

Expression of DRD2 across breast cancer subtypes was analyzed in the breast invasive carcinoma samples in the TCGA dataset. A one-way ANOVA was performed to compare the gene expression values in multiple groups, which are displayed by boxplot.

Statistics

Statistical significance tests were done as described in each figure. GraphPad Prism 7 software (GraphPad) was used for all graphs and significance tests unless otherwise noted.

Figure 2.1 - DRD2 Expression across breast cancer subtypes. Log2 gene expression of DRD2 (Entrez gene ID 1819) is displayed for each breast cancer subtype by with boxplots. (Basal = basal-like breast cancer; CLOW = claudin-low; Her2 = Her2 amplified; ILC = lobular carcinoma; LumA = Luminal A; LumB = Luminal B). Significance was measured using a one- way ANOVA.

Figure 2.2 - Thioridazine inhibits self-renewal is some TNBCLs, but not others, while it inhibits proliferation and cell viability in all TNBCLs. (A) SUM149, HCC1143, HCC1937, SUM159, MDA-MB231, and HCC38 cells were cultured in a tumorsphere assay with DMSO, 1μM, 2μM, or 5μM thioridazine. (B) SUM149 and SUM159 cells were cultured as spheres in DMSO, 0.5μM, 1μM, or 2μM thioridazine. After 7 days, spheres were dissociated, and cultured again as spheres in the presence of the same concentrations of thioridazine before the number of spheres was counted 7 days later. (C) Total RNA was harvested from SUM149, HCC1143, HCC1937, SUM159, MDA-MB231, and HCC38 cells, cDNA was made, and DRD2 mRNA was measured by qPCR. The relative expression of each cell line is normalized to SUM149 expression. (D) SUM149, HCC1143, HCC1937, SUM159, MDA-MB231, and HCC38 cells were treated with DMSO, 1μM, 2μM, 4μM, 8μM, 16μM, or 32μM thioridazine for 72 hours. CellTiter-Glo was used to measure cell viability. (E) Top SUM149 cells were treated with DMSO, 1μM, 2μM, or 5μM thioridazine and cells were counted every 24 hours. Bottom As in top, but 5μM thioridazine curve only is shown. (F) Top SUM159 cells were treated with DMSO, 1μM, 2μM, or 5μM thioridazine and cells were counted every 24 hours. Bottom As in top, but 5μM thioridazine curve only is shown. All experiments where performed with three biological replicates. Significance for (C) was measured using a two-sample t-test, significance for (E) and (F) was measured by repeated measures ANOVA, and significance for all other experiments was measured using a one-sample t-test. Error bars represent standard deviation. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001.

Figure 2.3 - Increased DRD2 expression in sphere-forming cells. (A) SUM149 cells were cultured as spheres or adherently. Total RNA was collected and DRD2 expression was determined using quantitative PCR. (B) SUM159 cells were cultured as spheres or adherently. Total RNA was collected and DRD2 expression was determined using quantitative PCR. All experiments were performed with three biological replicates. Significance is measured using a one-sample t-test. *P<0.05.

Table 2.1 - IC50 of thioridazine in 6 TNBCLs. The IC50 of thioridazine in each cell line from the cell viability assay in (Figure 2.2C) is shown. GraphPad Prism software was used to calculate the IC50.

Figure 2.4 - Low doses of thioridazine do not induce apoptosis. (A) SUM149 cells were treated with thioridazine at the indicated concentrations for 48 hours. Cells were stained with propidium iodide and analyzed by flow cytometry to determine cell cycle stage. (B) SUM149 cells were treated with the indicated concentrations of thioridazine for 24hr, and apoptosis was measured using Caspase-Glo 3/7 Assay. (C) SUM149 cells were treated with DMSO, 1μM, 2μM, or 5μM thioridazine and cultured for 24 hours. Cells were then harvested and full length and cleaved PARP-1 abundance was measured by western blot. All experiments where performed with three biological replicates. Significance for (A) was measured using a two- sample t-test, and significance for (B) was measured using a one-sample t-test. Error bars represent standard deviation. *P<0.05, **P<0.01.

Figure 2.5 - Thioridazine treatment does not induce TRAIL production via AKT inhibition and Foxo3a activation. (A) SUM149 cells were treated with the indicated concentrations of thioridazine for 1 hour. Protein lysates were harvested and abundance of pT308-AKT, total AKT, and actin was measured by western blot. (B) SUM149 cells were treated with 5μM thioridazine for the indicated timepoints. Nuclear extracts were made and Foxo3a abundance was measured by western blot. Histone H3 was used to measure nuclear isolation quality and loading, and tubulin was used to measure cytoplasmic purity. The quantifications are the fold-change of relative Foxo3a normalized to H3. (C) SUM149 cells were treated with 5μM thioridazine for the indicated timepoints, total RNA was isolated, and TRAIL mRNA abundance was measure by quantitative PCR. All experiments were performed with three biological replicates. Significance was measured using a one-sample t-test. *P<0.05, **P<0.01.

Figure 2.6 - Thioridazine induces G1 arrest. (A) SUM149 cells were treated with DMSO, 1μM, 2μM, or 5μM thioridazine for 48 hours. Cells were stained with propidium iodide and analyzed by flow cytometry to determine cell cycle stage. (B) Quantification of cell cycle stage. FloJo software was used to classify cell cycle stage. (C) SUM149 cells were treated with 5µM thioridazine for indicated lengths of time. Cells were harvested and abundance of p21, CyclinD1, CDK4, and Actin proteins were measured by western blot. All experiments were performed with three biological replicates and representative images are shown. Significance for (C) was measured using a one-sample t-test. Error bars represent standard deviation. *P<0.05, *P<0.01.

Figure 2.7 - Thioridazine inhibits STAT3 activity and IL-6 production. (A) Quantitative PCR analysis of relative IL-6 mRNA abundance in 6 TNBCLs. The relative expression of each cell line is normalized to SUM149 expression. (B) SUM149 cells were treated with 5μM thioridazine for the indicated timepoints, total RNA was isolated, and IL-6 mRNA abundance was measure by quantitative PCR. (C) The medium of SUM149 cells was replaced with fresh medium containing either DMSO or the indicated concentration of thioridazine. After 4 hours, the media were harvested and secreted IL-6 protein was measured by ELISA. (D) SUM149 and SUM159 cells were treated with DMSO, 1µM, 2µM, 5µM, or 10µM thioridazine for one hour. Protein lysates were harvested and abundance of pY705-STAT3 was measured by western blot. (E) SUM149 cells were treated with 5μM thioridazine for the indicated timepoints. Nuclear extracts were made and STAT3 abundance was measured by western blot. Histone H3 was used to measure nuclear isolation quality and loading, and tubulin was used to measure cytoplasmic purity. (F) Quantification of nuclear pY705-STAT3 and total STAT3. The quantifications are the fold-change of relative pY705-STAT3 and STAT3 normalized to H3. All experiments were performed with three biological replicates. Significance for (A) is measured using a two-sample t-test, significance for other experiments was measured using a one-sample t-test. Error bars represent standard deviation. *P<0.05, **P<0.01.

Figure 2.8 - Thioridazine inhibits self-renewal, but not cell viability via STAT3/IL-6. (A) Left SUM149 cells were transfected with either siControl or siSTAT3 and then cultured in a tumorsphere assay. Each group was also treated with DMSO or 1μM thioridazine and tumorspheres were counted after 7 days. Right STAT3 knockdown is shown. (B) Left SUM149 cells were cultured in a tumorsphere assay, treated with DMSO or 1μM thioridazine. Each group was additionally treated with mock solution or 60ng/mL rIL-6 for 7 days and spheres were counted. Right Quantification of the fold change in sphere formation induced by rIL-6 treatment. (C) Left SUM149 cells were treated with 5µM thioridazine for 5 min., 60ng/mL recombinant IL-6 was added for 10 min., and protein abundance was measured by western blot. Right Quantification of the fold change in relative pY705-STAT3 signal induced by rIL-6 treatment. (D) SUM149 cells were transfected with either siControl or siSTAT3 and then cultured adherently. Each group was also treated with DMSO 1μM, 2µM, or 5µM thioridazine and cells were counted after 72 hours. All experiments were performed with three biological replicates. Significance for (A) and (B) was measured using a one-sample t-test. Significance for others is measured using two-sample t-test. Error bars represent standard deviation. *P<0.05.

Figure 2.9 - DRD2 promotes self-renewal. (A) SUM149 cells were treated with siControl or siDRD2 and cultured in a tumorsphere assay for one week, and the number of spheres formed was counted. (B) SUM159 cells were treated with siControl or siDRD2 and cultured in a tumorsphere assay for one week, and the number of spheres formed was counted. (C) SUM149 cells stably expressing a control or DRD2-targeting shRNA were cultured in a tumorsphere assay for one week, then the number of spheres formed was counted. (D) SUM149 cells stably expressing a control or DRD2-targeting CRISPR/Cas9 construct were cultured in a tumorsphere assay for one week, then the number of spheres formed was counted. (E) Secondary tumorsphere formation of SUM149 cells stably expressing a control or DRD2-targeting CRISPR/Cas9 contruct. (F) SUM149 cells were treated with DMSO, 1µM, 2µM, or 5µM chlorpromazine once. 7 days later the number of spheres formed where counted. (G) SUM149 cells were treated with DMSO, 250nM, 500nM, or 1µM amisulpride daily in a tumorsphere assay for week before the number of spheres formed was assessed. (H) SUM149 cells were treated with DMSO, 5µM, 15µM, or 50µM quinpirole daily in a tumorsphere assay for week before the number of spheres formed was assessed. All experiments were performed with three biological replicates and significance was measured using one-sample t-test. Error bars represent standard deviation. *P<0.05, **P<0.01, ****P<0.0001.

Figure 2.10 - DRD2 mRNA in knockdown cells. (A) Relative abundance of DRD2 mRNA was measured by qPCR. (B) Total RNA from SUM149 control and DRD2-CRISPR cells, and relative abundance of DRD2 mRNA was measured by qPCR. (C) Total RNA from SUM149 control and siDRD2 cells, and relative abundance of DRD2 mRNA was measured by qPCR. (D) Total RNA from SUM159 control and siDRD2 cells, and relative abundance of DRD2 mRNA was measured by qPCR. All RNA was measured from three independent RNA collections and qPCR runs. Significance was measured using a one-sample t-test. *P<0.05, **P<0.01.

Figure 2.11 - gDNA mutation in DRD2 CRIPSR cells. Genomic DNA was extracted from SUM149 CRISPR A and SUM149 CRISPR B cells. The region surrounding DRD2 exon 3 was amplified by PCR and column purified before sanger sequencing was performed to determine that mutations at the cut site for both CRISPR constructs occurred.

Figure 2.12 - DRD2 promotes STAT3 activity. (A) SUM149 cells were treated with DMSO, 1µM, 5µM, 10µM, or 20µM chlorpromazine for one hour. Protein lysates were harvested and abundance of pY705-STAT3 was measured by western blot. (B) SUM149 cells were treated with DMSO, 500nM, 1µM, or 2µM amisulpride for 18 hours. Protein lysates were harvested and abundance of pY705-STAT3 was measured by western blot. (C) DRD2 was overexpressed in SUM159 cells, which were then treated with 5μM thioridazine for 1 hour. pY705-STAT3 was measured by western blot. (D) SUM149 cells were cultured as spheres for 7 days. The sphere cultured was treated with 1µM thioridazine for 0, 0.5, 1, or 2 hours. pY705-STAT3 was measured by western blot. All experiments were performed with three biological replicates and representative images are shown. Error bars represent standard deviation.

Figure 2.13 - DRD2 knockdown with RNAi does not consistently decrease pY705- STAT3. (A) SUM149 cells were treated with siControl or siDRD2 (n =4) and pY705-STAT3 abundance compared to total STAT3 was measured by western blot and relative quantifications were performed. (B) SUM149 shDRD2 cells harvested (n = 3) and pY705- STAT3 abundance compared to total STAT3 was measured by western blot and relative quantifications were performed. Significance was measured by one-sample t-test. *P<0.05.

Figure 2.14 - IL-6 is downstream of DRD2 in SUM149 cells. (A) SUM149 pLKO and shDRD2 cells were cultured in a tumorsphere assay for 7 days and treated with a mock solution or 60ng/mL rIL-6 daily. (B) Top SUM149 pLKO and shDRD2.1 cells were treated with 60ng/mL rIL-6 for 30min. Cells were then harvested and abundance of pY705-STAT3 was measured by western blot. Bottom SUM149 pLKO and shDRD2.2 cells were treated with 60ng/mL rIL-6 for 30min. Cells were then harvested and abundance of pY705-STAT3 was measured by western blot. Experiments were performed in triplicate. Significance was measured using a one-sample t-test.

Figure 2.15 - Thioridazine inhibits self-renewal, but not cell viability via DRD2 inhibition. (A) SUM149 cells were transfected with either siControl or siDRD2 and then cultured in a tumorsphere assay. Each group was also treated with DMSO or 1μM thioridazine and tumorspheres were counted after one week. (B) Control and DRD2-CRISPR SUM149 cells were cultured in a tumorsphere assay, treated with DMSO or 1μM thioridazine and the number of spheres formed was counted after one week. (C) SUM149 DRD2-CRIPSR cells were seeded and the number of cells were counted after 72 hours. (D) SUM149 cells were seeded and treated with the indicated concentration of amisulpride. Cells were then counted after 72 hours. (E) SUM149 control or DRD2-CRISPR cells were treated with DMSO 1μM, 2µM, or 5µM thioridazine and cells were counted after 72 hours. (F) Model figure describing mechanism of thioridazine effects on TNBCLs. All experiments were performed with three biological replicates. Significance for (E) is measured using a two-sample t-test, significance for other experiments was measured using a one-sample t-test. Error bars represent standard deviation. *P<0.05.

REFERENCES

Banerjee, K., and Resat, H. (2016). Constitutive activation of STAT3 in breast cancer cells: A review. Int. J. Cancer 138, 2570–2578.

Basu, B., Sarkar, C., Chakroborty, D., Ganguly, S., Shome, S., Dasgupta, P.S., and Basu, S. (2010). D1 and D2 dopamine receptor-mediated inhibition of activated normal T cell proliferation is lost in jurkat T leukemic cells. J. Biol. Chem. 285, 27026–27032.

Beaulieu, J.-M., and Gainetdinov, R.R. (2011). The Physiology, Signaling, and Pharmacology of Dopamine Receptors. Pharmacol. Rev. 63, 182–217.

Bianchini, G., Balko, J.M., Mayer, I.A., Sanders, M.E., and Gianni, L. (2016). Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat. Rev. Clin. Oncol. 13, 674–690.

Bodei, S., Arrighi, N., Spano, P., and Sigala, S. (2009). Should we be cautious on the use of commercially available antibodies to dopamine receptors? Naunyn. Schmiedebergs. Arch. Pharmacol. 379, 413–415.

Cosentino, M., Fietta, A.M., Ferrari, M., Rasini, E., Bombelli, R., Carcano, E., Saporiti, F., Meloni, F., Marino, F., and Lecchini, S. (2007). Human CD4+CD25+ regulatory T cells selectively express tyrosine hydroxylase and contain endogenous catecholamines subserving an autocrine/paracrine inhibitory functional loop. Blood 109, 632–642.

Dethlefsen, C., Højfeldt, G., and Hojman, P. (2013). The role of intratumoral and systemic IL-6 in breast cancer. Breast Cancer Res. Treat. 138, 657–664.

Dolma, S., Selvadurai, H.J., Lan, X., Lee, L., Kushida, M., Voisin, V., Whetstone, H., So, M., Aviv, T., Park, N., et al. (2016). Inhibition of Dopamine Receptor D4 Impedes Autophagic Flux, Proliferation, and Survival of Glioblastoma Stem Cells. Cancer Cell 29, 859–873.

Edwards, H., and Ge, Y. (2018). ONC201 shows promise in AML treatment. Cell Cycle 1–1.

Greer, Y.E., Porat-Shliom, N., Nagashima, K., Stuelten, C., Crooks, D., Koparde, V.N., Gilbert, S.F., Islam, C., Ubaldini, A., Ji, Y., et al. (2018). ONC201 kills breast cancer cells in vitro by targeting mitochondria. Oncotarget 9, 18454–18479.

Grivennikov, S., Karin, E., Terzic, J., Mucida, D., Yu, G.-Y., Vallabhapurapu, S., Scheller, J., Rose-John, S., Cheroutre, H., Eckmann, L., et al. (2009). IL-6 and Stat3 are required for survival of intestinal epithelial cells and development of colitis-associated cancer. Cancer Cell 15, 103–113.

Gwynne, W.D., Hallett, R.M., Girgis-Gabardo, A., Bojovic, B., Dvorkin-Gheva, A., Aarts, C., Dias, K., Bane, A., Hassell, J.A., Gwynne, W.D., et al. (2017). Serotonergic system

78 antagonists target breast tumor initiating cells and synergize with chemotherapy to shrink human breast tumor xenografts. Oncotarget 8, 32101–32116.

Heo, T.-H., Wahler, J., and Suh, N. (2016). Potential therapeutic implications of IL-6/IL- 6R/gp130-targeting agents in breast cancer. Oncotarget 7, 15460–15473.

Hsia, H.-C., Hutti, J.E., and Baldwin, A.S. (2017). Cytosolic DNA Promotes Signal Transducer and Activator of Transcription 3 (STAT3) Phosphorylation by TANK-binding Kinase 1 (TBK1) to Restrain STAT3 Activity. J. Biol. Chem. 292, 5405–5417.

Hunter, C.A., and Jones, S.A. (2015). IL-6 as a keystone cytokine in health and disease. Nat. Immunol. 16, 448–457.

Iversen, S.D., and Iversen, L.L. (2007). Dopamine: 50 years in perspective. Trends Neurosci. 30, 188–193.

Jandaghi, P., Najafabadi, H.S., Bauer, A.S., Papadakis, A.I., Fassan, M., Hall, A., Monast, A., von Knebel Doeberitz, M., Neoptolemos, J.P., Costello, E., et al. (2016). Expression of DRD2 is Increased in Human Pancreatic Ductal Adenocarcinoma and Inhibitors Slow Tumor Growth in Mice. Gastroenterology 151, 1218–1231.

Josefsson, E., Bergquist, J., Ekman, R., and Tarkowski, A. (1996). Catecholamines are synthesized by mouse lymphocytes and regulate function of these cells by induction of apoptosis. Immunology 88, 140–146.

Kline, C.L.B., Ralff, M.D., Lulla, A.R., Wagner, J.M., Abbosh, P.H., Dicker, D.T., Allen, J.E., and El-Deiry, W.S. (2018). Role of Dopamine Receptors in the Anticancer Activity of ONC201. Neoplasia 20, 80–91.

Kokkinou, I., Fragoulis, E.G., and Vassilacopoulou, D. (2009). The U937 macrophage cell line expresses enzymatically active l-Dopa decarboxylase. J. Neuroimmunol. 216, 51–58.

Konkalmatt, P.R., Asico, L.D., Zhang, Y., Yang, Y., Drachenberg, C., Zheng, X., Han, F., Jose, P.A., and Armando, I. (2016). Renal rescue of dopamine D2 receptor function reverses renal injury and high blood pressure. JCI Insight 1, e85888.

Kumari, N., Dwarakanath, B.S., Das, A., and Bhatt, A.N. (2016). Role of interleukin-6 in cancer progression and therapeutic resistance. Tumor Biol. 37, 11553–11572.

Levite, M. (2016). Dopamine and T cells: dopamine receptors and potent effects on T cells, dopamine production in T cells, and abnormalities in the dopaminergic system in T cells in

79 autoimmune, neurological and psychiatric diseases. Acta Physiol. (Oxf). 216, 42–89.

Luo, Y., Kokkonen, G.C., Hattori, A., Chrest, F.J., and Roth, G.S. (1999). Dopamine stimulates redox–tyrosine kinase signaling and p38 MAPK in activation of astrocytic C6-D2L cells. Brain Res. 850, 21–38.

Madhukar, N.S., Khade, P., Huang, L., Gayvert, K., Galletti, G., Stogniew, M., Allen, J.E., Giannakakou, P., and Elemento, O. (2017). A New Big-Data Paradigm For Target Identification And Drug Discovery. Doi.Org 134973.

Magnon, C., Hall, S.J., Lin, J., Xue, X., Gerber, L., Freedland, S.J., and Frenette, P.S. (2013). Autonomic Nerve Development Contributes to Prostate Cancer Progression. Science (80-. ). 341, 1236361–1236361.

Marotta, L.L.C., Almendro, V., Marusyk, A., Shipitsin, M., Schemme, J., Walker, S.R., Bloushtain-Qimron, N., Kim, J.J., Choudhury, S.A., Maruyama, R., et al. (2011). The JAK2/STAT3 signaling pathway is required for growth of CD44+CD24− stem cell-like breast cancer cells in human tumors. J. Clin. Invest. 121, 2723–2735.

Matsuda, T., Nakamura, T., Nakao, K., Arai, T., Katsuki, M., Heike, T., and Yokota, T. (1999). STAT3 activation is sufficient to maintain an undifferentiated state of mouse embryonic stem cells. EMBO J. 18, 4261–4269.

Mei, F., Guo, S., He, Y., Wang, L., Wang, H., Niu, J., Kong, J., Li, X., Wu, Y., and Xiao, L. (2012). Quetiapine, an Atypical Antipsychotic, Is Protective against Autoimmune-Mediated Demyelination by Inhibiting Effector T Cell Proliferation. PLoS One 7, e42746.

Missale, C., Nash, S.R., Robinson, S.W., Jaber, M., and Caron, M.G. (1998). Dopamine Receptors: From Structure to Function. Physiol. Rev. 78, 189–225.

Nakano, K., Higashi, T., Takagi, R., Hashimoto, K., Tanaka, Y., and Matsushita, S. (2009). Dopamine released by dendritic cells polarizes Th2 differentiation. Int. Immunol. 21, 645– 654.

Perou, C.M. (2010). Molecular stratification of triple-negative breast cancers. Oncologist 15 Suppl 5, 39–48.

Prabhu, V. V, Allen, J.E., Dicker, D.T., and El-Deiry, W.S. (2015). Small-Molecule ONC201/TIC10 Targets Chemotherapy-Resistant Colorectal Cancer Stem-like Cells in an Akt/Foxo3a/TRAIL-Dependent Manner. Cancer Res. 75, 1423–1432.

Real, P.J., Sierra, A., de Juan, A., Segovia, J.C., Lopez-Vega, J.M., and Fernandez-Luna, J.L.

(2002). Resistance to chemotherapy via Stat3-dependent overexpression of Bcl-2 in metastatic breast cancer cells. Oncogene 21, 7611–7618.

Sonnenblick, A., Brohée, S., Fumagalli, D., Vincent, D., Venet, D., Ignatiadis, M., Salgado, R., Van den Eynden, G., Rothé, F., Desmedt, C., et al. (2015). Constitutive phosphorylated STAT3-associated gene signature is predictive for trastuzumab resistance in primary HER2- positive breast cancer. BMC Med. 13, 177.

Staniszewska, A.D., Pensa, S., Caffarel, M.M., Anderson, L.H., Poli, V., and Watson, C.J. (2012). Stat3 is required to maintain the full differentiation potential of mammary stem cells and the proliferative potential of mammary luminal progenitors. PLoS One 7, e52608.

Stark, G.R., and Darnell, J.E. (2012). The JAK-STAT Pathway at Twenty. Immunity 36, 503– 514.

Stojanovic, T., Orlova, M., Sialana, F.J., Höger, H., Stuchlik, S., Milenkovic, I., Aradska, J., and Lubec, G. (2017). Validation of dopamine receptor DRD1 and DRD2 antibodies using receptor deficient mice. Amino Acids 49, 1101–1109.

Wei, W., Tweardy, D.J., Zhang, M., Zhang, X., Landua, J., Petrovic, I., Bu, W., Roarty, K., Hilsenbeck, S.G., Rosen, J.M., et al. (2014). STAT3 signaling is activated preferentially in tumor-initiating cells in claudin-low models of human breast cancer. Stem Cells 32, 2571– 2582.

Yang, X., Boehm, J.S., Yang, X., Salehi-Ashtiani, K., Hao, T., Shen, Y., Lubonja, R., Thomas, S.R., Alkan, O., Bhimdi, T., et al. (2011). A public genome-scale lentiviral expression library of human ORFs. Nat. Methods 8, 659–661.

Yin, T., He, S., Shen, G., Ye, T., Guo, F., and Wang, Y. (2015). Dopamine receptor antagonist thioridazine inhibits tumor growth in a murine breast cancer model. Mol. Med. Rep. 12, 4103–4108.

Yoon, S., Choi, M., Chang, M.S., and Baik, J.-H. (2011). Wnt5a-dopamine D2 receptor interactions regulate dopamine neuron development via extracellular signal-regulated kinase (ERK) activation. J. Biol. Chem. 286, 15641–15651.

Yu, H., Lee, H., Herrmann, A., Buettner, R., and Jove, R. (2014). Revisiting STAT3 signalling in cancer: new and unexpected biological functions. Nat. Rev. Cancer 14, 736–746.

Yue, H., Huang, D., Qin, L., Zheng, Z., Hua, L., Wang, G., Huang, J., and Huang, H. (2016). Targeting Lung Cancer Stem Cells with Antipsychological Drug Thioridazine. Biomed Res. Int. 2016, 6709828.

Zahalka, A.H., Arnal-Estapé, A., Maryanovich, M., Nakahara, F., Cruz, C.D., Finley, L.W.S., and Frenette, P.S. (2017). Adrenergic nerves activate an angio-metabolic switch in prostate cancer. Science (80-. ). 358, 321–326.

Zhang, C., Gong, P., Liu, P., Zhou, N., Zhou, Y., and Wang, Y. (2016). Thioridazine elicits

81 potent antitumor effects in colorectal cancer stem cells. Oncol. Rep. 37, 1168–1174.

Zhang, Y., Cuevas, S., Asico, L.D., Escano, C., Yang, Y., Pascua, A.M., Wang, X., Jones, J.E., Grandy, D., Eisner, G., et al. (2012). Deficient Dopamine D2 Receptor Function Causes Renal Inflammation Independently of High Blood Pressure. PLoS One 7, e38745.

CHAPTER 3: SPECULATIVE STUDIES SUGGEST THAT ACTIVE DOPAMINE

SIGNALING IN BASAL-LIKE BREAST CANCER CELLS VIA ACTIVATION OF

PLASMINOGEN PATHWAY MAY PROMOTE SELF-RENEWAL

3.1 – Introduction

Despite advances in screening and targeted therapies, breast cancer is the most common malignancy in women, and the second most common cause of cancer-related deaths in women (Siegel et al., 2018). Addressing the treatment of breast cancers is complicated as it is not a single, homogenous disease. Rather, there are at least 5 types of breast cancer, termed subtypes, based on molecular gene expression (Luminal A, Luminal B, HER2+, Basal- like, and Claudin-low) (Herschkowitz et al., 2007; Perou et al., 2000). These subtypes not only describe intrinsic molecular characteristics, but they also hold prognostic value and correlate with known responses to different therapies (Brenton et al., 2005; Sørlie et al.,

2001). While luminal tumors usually express estrogen receptor and are responsive to hormone therapies, and HER2+ tumors overexpress the HER2 receptor and usually respond to HER2-targeted therapies, many basal-like and claudin-low tumors do not express these receptors, and as such there are no targeted therapies to treat these tumors (Brenton et al.,

2005; Foulkes et al., 2010; Sørlie et al., 2001). While basal-like tumors do respond well to chemotherapy, recurrence is still elevated in this group compared to other subtypes, and metastatic disease for all subtypes remains an unsolved problem decades after diagnosis

(Haque et al., 2012; Prat and Perou, 2011).

Despite increased awareness and targeted therapies, over 40,000 people die of breast cancer each year in the US alone (Siegel et al., 2018), and metastatic disease is the primary driver of mortality from breast cancer. The cancer stem cell hypothesis postulates that a small

83 population of tumor cells, cancer stem cells (CSCs), are capable of indefinite self-renewal and colonization of distant sites (Clevers, 2011; Reya et al., 2001). This colonization activity can be measured in vitro using the tumorsphere assay, in which a population of cells are cultured in suspension at low density and without serum, thus limiting survival and proliferation signals from surface and cell-to-cell attachment, as well as from factors in serum.

The plasminogen pathway is known for its roles in thrombosis and clearing blood clots and fibrous tissue (Bezerra et al., 1999; Mekkawy et al., 2014), however, high expression of important plasminogen pathway genes is also associated with poor outcomes and metastasis in breast cancer (Andreasen et al., 1997). The plasminogen pathway ultimately leads to the proteolytic processing of plasminogen to plasmin, an active protease in the extracellular matrix through the activity of one of two activators (PLAT/tPA or PLAU/uPA) (Andreasen et al., 1997). Plasmin and its activation can be blocked by SERPINE1/PAI-1, a serine protease inhibitor (Andreasen et al., 1997). Although this pathway is associated with poor outcomes and metastasis in breast cancer, the mechanisms by which this occurs are not precisely known, but are presumed to be dependent on the degradation of the extracellular matrix

(Andreasen et al., 1997; Li et al., 2018; Mekkawy et al., 2014; Schmitt et al., 2011). However, activated plasmin has also been shown to support the release of dopamine in cultured neurons and in vivo (Bahi et al., 2008; Ito et al., 2006; Nagai et al., 2004, 2005). In fact, depolarization-induced dopamine release was completely abrogated in neurons from tPA-/- mice (Ito et al., 2006). The plasmin pathway is not only associated with poor survival and metastasis in breast cancer, but it also supports the release of dopamine in neurons.

Dopamine is a catecholamine neurotransmitter that, when released from cells, can bind and activate dopamine receptors. Dopamine signaling via dopamine receptors has been shown to regulate numerous neurological processes and diseases from locomotion, learning, and memory to addiction, Parkinson’s disease, and schizophrenia (Beaulieu et al., 2015;

Iversen and Iversen, 2007). There are five dopamine receptors (DRD1-5) that mediate the

84 functions of dopamine. Many drugs targeting DRD2 in particular have been developed, as all drugs with antipsychotic activity inhibit DRD2 (Kapur and Seeman, 2001; Seeman, 2006).

However, dopamine signaling is not restricted to neurons, and many peripheral functions have been elucidated. DRD2 has been shown to modulate blood pressure by regulating sodium secretion in the kidneys (Chugh et al., 2013; Hussain and Lokhandwala,

2003), inhibit prolactin secretion in the pituitary (Kebabian and Calne, 1979), and regulate T- cell activation and proliferation, among other functions (Basu et al., 2010; Levite, 2016;

Missale et al., 1998).

Although thioridazine and pimozide, which are both DRD2-targeting antipsychotic drugs, were shown to reduce the proliferation of cultured cancer cells nearly 30 years ago

(Strobl and Peterson, 1992; Strobl et al., 1990), the effects of these drugs on cancer cells went largely unexplored until a small molecule screen identified thioridazine as an inhibitor of cancer stem cells (Sachlos et al., 2012). Since that publication, several studies have shown that DRD2-targeting antipsychotics can reduce growth, induce apoptosis, or even inhibit self- renewal in many cancer cell types including leukemia, glioblastoma, colorectal, lung, and breast cancer cell lines (Cheng et al., 2015; Li et al., 2014; Sachlos et al., 2012; Yin et al.,

2015; Yue et al., 2016; Zhang et al., 2016). Recently, work from our own lab has shown that the inhibition of proliferation at higher doses of thioridazine (5µM) in triple-negative breast cancer cells occurs independent of DRD2-inhibition, while the inhibition of self-renewal at lower doses (1µM) is mediated through the inhibition of DRD2 and STAT3 activity (Tegowski et al., 2018). That study also demonstrated that the self-renewal of some breast cancer cell lines could be inhibited by thioridazine, while others were unaffected.

In this chapter, we present evidence that the self-renewal of basal-like breast cancer cell lines is most sensitive to thioridazine in vitro, while the self-renewal of cell lines from other subtypes is more resistant. We also demonstrate that DRD2 expression can be detected in all breast cancer cell lines tested, regardless of whether thioridazine inhibits self-renewal.

We postulate that DRD2 regulation may depend on dopamine release regulated by the plasminogen pathway, which may be more highly active in basal-like cells. Activation of this pathway in luminal or claudin-low cell lines could potentially support self-renewal in a manner that is sensitive to thioridazine.

3.2 – Results

3.2.1 – Thioridazine preferentially targets the tumorsphere initiating cells of basal-like breast cancer cell lines (BLBCLs)

Previous work from our lab has shown that 1µM thioridazine decreases self-renewal in two BLBCLs, HCC1143 and SUM149 cells, but not in claudin-low breast cancer cell lines

(CLBCLs) SUM159, MDA-MB-231, and HCC38 cells (Chapter 2). To determine if thioridazine preferentially inhibits self-renewal in the basal-like subtype, we also tested the effects of thioridazine on the tumorsphere formation in two luminal cell lines (MCF7 and ZR751), two

HER2+ cell lines (MDA-MB-361 and BT474), as well as another basal-like cell line (SUM229), and compared the results to the effects previously reported in Chapter 2. Strikingly, 1µM or

2µM thioridazine did not decrease tumorsphere formation in any luminal, HER2+, or claudin- low cell lines (Figure 3.1A). However, doses as low as 1µM thioridazine did reduce tumorsphere formation in 3 out of 4 BLBCLs (HCC1143, SUM149, and SUM229) (Figure 3.1A).

Since the formation of visible spheres can be inhibited by reduced proliferation as well as by the inhibition of self-renewal, we also tested the effects of thioridazine on the proliferation of adherently growing cells. Except for ZR751 cells, 1µM thioridazine did not significantly decrease adherent proliferation in any cell lines (Figure 3.1B). This suggests that the observed differences in tumorsphere formation at 1µM thioridazine are from effects on self-renewal, not from confounding effects on proliferation. Remarkably, the proliferation of all cell lines was strongly decreased at 5µM thioridazine, regardless of whether tumorsphere formation was affected at the same concentration (Figure 3.1B). This suggests that thioridazine can inhibit proliferation and self-renewal through independent mechanisms. These data demonstrate

86 that thioridazine preferentially decreases tumorsphere formation in BLBCLs, but not in other subtypes.

Although cell line models are far more homogenous than a tumor microenvironment, some cell lines stably exist with multiple populations of cells that have different characteristics. In particular, SUM229 and SUM149 BLBCLs consist of a major population of epithelial, basal-like cells, and a subpopulation of mesenchymal, claudin-low cells (Prat et al.,

2013). The epithelial basal-like population is EpCAM+, while the mesenchymal claudin-low population is EpCAM- (Prat et al., 2013). We obtained SUM229 cells sorted into EpCAM+ and

EpCAM- populations and confirmed the EpCAM expression in each population (Figure 3.2C).

We tested whether the tumorsphere formation of the EpCAM+ basal-like population of cells is more sensitive to thioridazine treatment than the EpCAM- claudin-low population within the same cell line. Indeed, while thioridazine dose-dependently decreases tumorsphere formation in EpCAM+ SUM229 cells, the number of tumorspheres formed is unaffected, even at doses as high as 5µM thioridazine in the EpCAM- cells (Figure 3.2A). Despite this dramatic difference, thioridazine inhibits adherent proliferation similarly in both EpCAM+ and EpCAM- cells (Figure

3.2B). To further test whether the self-renewal of basal-like cells is more sensitive than claudin-low cells, we measured tumorsphere formation in SUM149 cells sorted into EpCAM+ and EpCAM- populations (Figure 3.2F). Similar to SUM229 cells, thioridazine inhibits tumorsphere formation in SUM149 EpCAM+ at doses as low as 1µM, but not in SUM149 EpCAM- cells (Figure 3.2D). Unlike SUM229 cells however, 5µM thioridazine almost completely blocks tumorsphere formation, as well as proliferation, in both EpCAM+ and EpCAM- populations

(Figure 3.2D and E). This suggests that thioridazine affects proliferation and cell viability in all SUM149 cells, regardless of EpCAM status, more strongly than in SUM229 cells. The inhibition of proliferation and cell viability is independent of the effects of DRD2 on self- renewal (Tegowski et al., 2018).

Thioridazine decreased tumorsphere formation most potently in cell line classified as basal-like breast cancer cell lines, and within triple-negative breast cancer cell lines, thioridazine inhibited tumorsphere formation most potently in the EpCAM+ cells. Together, these data show that thioridazine targets self-renewal most potently in basal-like breast cancer cells.

3.2.2 DRD2 expression does not predict thioridazine effects on self-renewal

Since self-renewal of 3 out of 4 BLBCLs is most sensitive to thioridazine, and this effect is thought to be DRD2-dependent, we reasoned that DRD2 expression would be highest in the BLBCLs. Further, previously reported analysis of TCGA data indicated that DRD2 mRNA expression is elevated in basal-like breast cancers (Chapter 2). So, we measured DRD2 mRNA expression by qPCR, and DRD2 expression was indeed observed to be higher, on average, in

BLBCLs, than in non-BLBCLs (Figure 3.3A). It is noteworthy that DRD2 expression could be detected in all cell lines tested, regardless of subtype (Figure 3.3B). Further, SUM149 cells, which are highly sensitive to thioridazine in the tumorsphere assay, do not highly express

DRD2 mRNA, relative to the other BLBCLs (Figure 3.3B). In fact, SUM149 cells express lower levels of DRD2 mRNA than several other cell lines whose self-renewal is not sensitive to thioridazine, like HCC38 cells or SUM159 cells (Figure 3.3B). Additionally, although SUM229

EpCAM+ cells express more DRD2 mRNA than SUM229- cells, SUM149 EpCAM+ cells do not express more DRD2 mRNA (Figure 3.3C and D). This suggests that while DRD2 mRNA expression is generally higher in most basal-like cells, DRD2 mRNA expression alone does not predict whether thioridazine can inhibit tumorsphere formation in a specific cell line.

We also analyzed DRD2 protein expression by immunoblot. We first confirmed that the

DRD2 antibody showed specificity for DRD2 by performing immunoblots on siControl and siDRD2-treated cells (Figure 3.4). When DRD2 protein abundance in all 11 cell lines was compared the highest expression of DRD2 protein was observed in luminal and claudin-low cells, with BLBCLs expressing relatively little DRD2 protein (Figure 3.3D and E). This further

88 confirms that simply measuring DRD2 expression alone, does not predict sensitivity to thioridazine.

3.2.3 DRD2 activation promotes sphere formation in non-basal like cells

Since DRD2 mRNA and protein expression was observed in all breast cancer cell lines tested, we speculated that perhaps DRD2 may be expressed but not active in cell lines whose self-renewal is not affected by thioridazine. To test this, we measured tumorsphere formation in MCF7 and SUM159 cells treated with the DRD2-specific agonist quinpirole. Both of these cell lines express DRD2 (Figure 3.3), however the tumorsphere formation of both lines is unaffected by thioridazine (Figure 3.1). Strikingly, the tumorsphere formation of both MCF7 and SUM159 cells is dose-dependently increased by quinpirole treatment (Figure 3.5A and B).

This suggests that functional DRD2 protein is indeed expressed, and can promote self-renewal when activated.

3.2.4 Expression of Plasminogen Pathway Genes

Since expression of DRD2 cannot itself determine whether the self-renewal of a cell line is inhibited by thioridazine, we searched for gene expression differences between the breast cancer cell lines using microarray data (Prat et al., 2013). We especially searched for genes in pathways known to regulate dopamine receptors. Activation of the protease plasmin by tissue plasminogen activator (PLAT) or urokinase plasminogen activator (PLAU) promotes the release of dopamine from cultured neurons, and it is associated with breast cancer metastasis (Andreasen et al., 1997; Ito et al., 2006; Nagai et al., 2004, 2005).

We searched the microarray data for the expression of PLAT, since its expression supports dopamine release in neurons, and is associated with poor breast cancer outcomes.

Further, we searched for the expression of the receptor for PLAU (PLAUR), which is required for PLAU to activate plasmin (Andreasen et al., 1997). We also probed the microarray data for expression of the plasminogen pathway inhibitor, PAI-1. Interestingly, the luminal and

HER2+ cell lines (MCF7, ZR751, BT474, MDA-MB-361) expressed relatively low levels of the

89 plasminogen activators and inhibitors (Figure 3.6A to C). If plasmin activation is not occurring in these cell lines, potentially little dopamine release may occur. While claudin-low cell lines

(SUM159, MDA-MB-231, and HCC38) express more PLAT and PLAUR, SUM159 and MDA-MB-

231 cells also express high levels of the PAI-1 inhibitor of plasmin (Figure 3.6A to C). On the other hand, most of the basal-like cell lines express higher levels of either PLAT and/or PLAU, and generally lower levels of PAI-1 (Figure 3.6A to C). In this regard, we considered whether

PAI-1 expression is generally increased in claudin-low cells and tumors compared with basal- like cells and tumors. We probed the microarray data across many cell lines and human tumors, and we compared the PLAT, PLAUR, and PAI-1 expression between all basal-like samples and all claudin-low samples (Prat et al., 2013). Interestingly, while the expression of

PLAT and PLAUR is similar, if not increased in claudin-low cells and tumors, the claudin-low cells and tumors express significantly elevated levels of PAI-1 (Figure 3.6D). These data suggest that the plasminogen pathway may be most active in the most thioridazine-sensitive cell lines, and could lead to DRD2 activity through the regulation of dopamine release.

3.2.5 Activation of the plasminogen pathway promotes sphere formation and sensitivity to thioridazine

We addressed whether the plasminogen pathway has effects on DRD2-mediated tumorsphere formation in the MCF7 and SUM159 cell lines. We have shown that the tumorsphere formation activity of both of these cell lines is unaffected by thioridazine (Figure

3.1A), yet they both express DRD2, and a DRD2 agonist can stimulate tumorsphere formation

(Figure 3.3 and 3.5). Additionally, MCF7 cells express relatively low levels of the genes of the plasminogen activation system, while SUM159 cells express high levels of PLAT, an activator of plasminogen, but also high levels of PAI-1, the plasminogen inhibitor. Therefore, both cell lines likely have low plasminogen activation, but for different reasons.

To test this, we overexpressed PLAT in MCF7 cells to activate the plasminogen system, and cultured them as tumorspheres. Both the control and PLAT overexpression cells were

90 treated with DMSO or 1µM thioridazine. After one week, the number of tumorspheres were counted. We observed that PLAT overexpression increased tumorsphere formation by 36%

(Figure 3.7A). Interestingly, thioridazine treatment abrogated the induction of tumorsphere formation by PLAT overexpression (Figure 3.7A). We calculated the percent change in tumorsphere formation caused by thioridazine in control MCF7 cells, compared with PLAT overexpression cells. While 1µM thioridazine had no significant effect on tumorsphere formation in control cells, it decreased tumorsphere formation by approximately 30% in

MCF7-PLAT cells (Figure 3.7B). This suggests that PLAT supports DRD2-mediated tumorsphere formation.

To test whether the plasminogen pathway inhibitor PAI-1 inhibits DRD2-mediated tumorsphere formation in claudin-low cells, we cultured SUM159 cells transfected with siControl or siPAI-1 as spheres and treated them with DMSO or 1µM thioridazine. After 1 week, the number of tumorspheres formed was quantified. PAI-1 knockdown alone, led to an increase in tumorsphere formation, while 1µM thioridazine alone had no effect on tumorsphere formation (Figure 3.7C). As observed in MCF7 cells, the increase in tumorsphere formation due to PAI-1 knockdown was abrogated by 1µM thioridazine treatment (Figure 3.7C). While

1µM thioridazine had no effect on siControl SUM159 cells, it decreased tumorsphere formation by approximately 40% in siPAI-1 SUM159 cells (Figure 3.7D). Together, these data suggest that an active plasminogen pathway promotes tumorsphere formation in a DRD2-dependent mechanism.

3.3 – Discussion

The work presented in this chapter shows that the self-renewal of basal-like breast cancer cell lines is more sensitive to thioridazine than that of other subtypes. This was shown by analyzing the effects of thioridazine on a panel of 11 breast cancer cell lines (Figure 3.1).

Further, we used cell lines that were sorted into EpCAM+ and EpCAM- populations to show that the self-renewal of basal-like (EpCAM+) cells were more sensitive than the claudin-low

(EpCAM-) cells, despite the fact that the cells are derived from the same cell line (Figure 3.2).

This suggests basal-like cell lines have a population of tumorsphere-initiating cells (TICs) that are maintained by the activity of DRD2, while cell lines with characteristics of other molecular subtypes do not. It important to note that basal-like breast cancers are the most sensitive to thioridazine, as there are no current targeted therapies for most basal-like breast tumors. It is also noteworthy that thioridazine targets basal-like TICs. Thioridazine is safe, cheap, and already FDA-approved. It could easily be added in addition to current breast cancer therapies to test whether it could reduce the recurrence or metastasis of basal-like breast cancers.

We also demonstrated that DRD2 mRNA and protein is expressed in all cell lines, even if thioridazine does not affect tumorsphere formation (Figure 3.3). Further, treatment with the specific DRD2 agonist quinpirole increases tumorsphere formation in MCF7 and SUM159 cells (Figure 3.5), despite the fact that thioridazine does not affect tumorsphere formation in these cell lines. This suggests that DRD2 is expressed in these cells, and that it is capable of supporting self-renewal, but that it is generally not active. We utilized previously published gene expression data to find pathways that may predict whether a cell line may be more sensitive to DRD2-inhibition (Prat et al., 2013). We found that basal-like cell lines, especially those most sensitive (SUM149, SUM229, and HCC1143) express high levels of plasminogen activating genes, while luminal cells expressed low levels, and claudin-low cells expressed high levels of the plasminogen inhibitor PAI-1 (Figure 3.6). This suggests that DRD2 may support self-renewal in cell lines that have higher activation of the plasminogen pathway. We tested this by overexpressing PLAT, the plasminogen activator, in MCF7 cells and treating with thioridazine. Thioridazine blocked the induction of tumorsphere formation induced by

PLAT overexpression. Additionally, knockdown of PAI-1 in SUM159 cells also increased tumorsphere formation, in a thioridazine-sensitive manner (Figure 3.7). These data suggest that activation of the plasminogen system may support self-renewal in a DRD2-dependent manner. We have developed a model depicting this hypothesis. It proposes that plasmin

92 activation in basal-like cell lines may support dopamine release and DRD2-supported self- renewal (Figure 3.8A). On the other hand, lack of expression of plasmin activators in luminal cells prevents dopamine release and DRD2 activity (Figure 3.8B). Finally, the high expression of PAI-1 in claudin-low cells inhibits plasmin, which also results in dopamine not being released, and DRD2 inactivity (Figure 3.8C).

Since plasminogen activation has been shown to promote dopamine release in neurons

(Ito et al., 2006; Nagai et al., 2004, 2005), it is possible that the thioridazine-sensitive induction in tumorsphere formation caused by activation of this pathway may arise from dopamine release activating DRD2. However, while we have attempted to detect dopamine release in breast cancer cell lines, it has not yet been conclusively shown whether these cell lines can, in fact, release dopamine. It has also not yet been shown whether any human breast tumors release dopamine. This must be investigated further, as we have successfully detected dopamine from tumor tissue obtained from a triple-negative C3-Tag tumor (Figure

3.9). Determining whether plasminogen activation supports the self-renewal of breast cancer cells may lead to new therapeutics. Additionally, it possible that in vivo, that DRD2 inhibition may inhibit self-renewal in other types of cancer than just basal-like tumors. If DRD2 is expressed in many tumors, then any tumor that releases dopamine may benefit from DRD2 inhibition. Dopamine release may come from nerve growth into the tumor, or from infiltrating immune cells, which have also been shown to release dopamine (Arreola et al., 2016; Levite,

2016; Magnon et al., 2013; Zahalka et al., 2017).

The importance of the plasminogen pathway in determining breast cancer self-renewal should be tested further, especially whether the pathway activates DRD2 to support self- renewal. The use of gene expression databases like the cancer cell line encyclopedia can be used to predict whether cell lines have high expression of genes of the plasminogen activation pathway. Then tumor-initiation assays can then be performed with cell lines predicted to have high plasminogen activation in the presence of thioridazine and the results compared to the

93 results from cell lines predicted to have low plasminogen pathway activation. This can be done using cells of many different cancer types, and would test whether plasminogen activation predicts whether the self-renewal of a cell line will be affected by thioridazine.

3.4 – Experimental Procedures

Cell culture and reagents

SUM149, SUM159, SUM229, SUM149-EpCAM+/=, and SUM229 EpCAM+/- cells were maintained in HuMEC medium (Gibco, Waltham, MA, USA) with 5% fetal bovine serum (FBS).

MDA-MB-231 cells were maintained in DMEM (Gibco) with 10% FBS. MCF7, ZR751, BT474,

MDA-MB-361, HCC38, HCC1143, and HCC1937 cells were maintained in RPMI-1640 with 10%

FBS. SUM229-EpCAM-sorted cells were generously provided by the lab of Dr. Gary Johnson.

Thioridazine and quinpirole were obtained from (Sigma-Aldrich, St. Louis, MO, USA). All antibodies were obtained from Cell Signaling Technologies (Danvers, MA, USA). qPCR primer- probe sets targeting GUSB were obtained from Applied Biosystems (Grand Island, NY, USA).

DRD2 primer-probe set was obtained from Bio-Rad (Hercules, CA, USA). DRD2 antibody was obtained from Sigma-Millipore. β-actin antibody was obtained from Cell Signaling

Technologies (Danvers, MA, USA). EpCAM-FITC antibody for flow cytometry and cell sorting was obtained from Stem Cell Technologies (Vancouver, BC, CA). All siRNAs were SMARTpool siGENOME from Dharmacon (Lafayette, CO, USA). PLAT overexpression plasmid in pLENTI6-

V5 vector was obtained from the CCSB - Broad lentiviral ORF collection (Yang et al., 2011).

Transfections

Overexpression of PLAT in MCF7 was achieved by transfecting 4µg of plasmid with 12µL of Fugene6 (Promega, Madison, WI, USA). siRNA transfections were achieved by using 40nM siRNA and DharmafectI (Dharmacon) according to manufacturer’s protocol.

Tumorsphere assays

Tumorsphere assays were performed as previously described (Tegowski et al., 2018).

Cells were trypsinized and resuspended into a single cell suspension and counted using a hemocytometer. 20000 cells were resuspended in 2 mL of complete mammocult medium

(Stem Cell Technologies) and plated into ultra-low adherence 6 well plates (Corning, Corning,

NY, USA). After one week of culture, the spheres were collected and all spheres greater than

60μm in diameter were counted according to manufacturer’s protocol (Stem Cell

Technologies). Transfection for overexpression and siRNA were completed 24 hours prior to plating in the tumorsphere assay. Thioridazine treatments were applied once at the time of plating. Quinpirole, was applied every 48 hours. Tumorsphere formation data in Figure 3.1A for SUM159, MDA-MB-231, HCC38, HCC1937, SUM149, and HCC1143 are taken from

(Tegowski et al., 2018).

Proliferation assays

Proliferation assays for SUM149 and SUM159 were previously reported in (Tegowski et al., 2018). For all other cell lines, 10,000 cells per well were plated in 24 well plates in triplicate and treated with DMSO or the indicated concentration of thioridazine. After 72 hours, the number of cells per well was counted using a hemocytometer.

Flow cytometry and sorting

To perform flow cytometry analysis for EpCAM, cells were detached using TrpLE

Express (Gibco) at 37°C. Cells were resuspended in full growth medium and counted using a hemocytometer. 500,000 cells were resuspended in flow buffer (PBS containing 1% FBS and

1mM EDTA) and 2.5µL EpCAM antibody (Stem Cell Technologies) on ice for 45 minutes. Cells were washed once with flow buffer and fixed for 15 minutes at room temperature in PBS +

10% Formalin. Cells were washed with flow buffer, passed through a 40µm filter, and then data were aquired using a Cyan ADP Flow Cytometer (Becton Dickinson, Franklin Lakes, NJ,

USA) and analyzed using FloJo software (Ashland, Oregon, USA). To sort SUM149 cells by

EpCAM expression, cells were detached using TrpLE Express (Gibco) at 37°C. Cells were resuspended in full growth medium and counted using a hemocytometer. 107 cells were counted and resuspended in 900µL HBSS (Gibco) + 100µL 10x Buffer + 50µL DNAse I

(Promega) at room temperature for 15 minutes. Cells were washed with flow buffer and resuspended in 1mL flow buffer and treated incubated on ice with 50µL EpCAM antibody for

45 mintues. Cells were washed with flow buffer, passed through a 40µm filter, and then sorted using a FACS Aria II (Becton Dickinson). Cells were then cultured at high density for 24 hours.

Cells were cultured for at least 96 hours prior to be used for experiments.

Quantitative PCR

RNA isolation and quantitative PCR

Total RNA was isolated using Trizol (Invirogen) according to manufacturer’s protocol. iScript (Bio-Rad) was used to make cDNA. Quantitative PCR was performed with iTaq Probes

Supermix (Bio-Rad) using a Viia 7 qPCR machine (Applied Biosystems, Foster, CA, USA) and analyzed using QuantStudio software to calculate RQ values relative to GUSB housekeeping gene.

Immunoblots

Immunoblots were performed as previously described (Tegowski et al., 2018).

Dopamine ELISA

C3-Tag tumor tissue was obtained as a gift from the UNC Mouse Phase I Facility. Tumor samples were frozen at -80°C until just before the ELISA was performed. The tumor section used was weighed, washed in PBS (Gibco) before being resuspended in PBS at 1mL

PBS/100mg tumor tissue. The ELISA was performed according to manufacturer’s instruction

(Biovision, Milpitas, CA, USA).

Statistics

Statistics were performed as described for each figure using GraphPad Prism 8.

Figure 3.1 - Thioridazine preferentially targets the tumorsphere initiating cells of basal-like breast cancer cell lines (BLBCLs) (A) Cells were cultured in a tumorsphere assay with DMSO, 1μM, 2μM, or 5μM thioridazine. The number of tumorspheres formed after one week were counted. Tumorsphere formation is shown as fold-change relative to DMSO. Data for SUM159, MDA-MB-231, HCC38, HCC1937, SUM149, and HCC1143 was previously

98 reported (Tegowski et al., 2018). (B) Cells were plated and treated with DMSO, 1μM, 2μM, or 5μM thioridazine. The number of cells were counted after 72 hours. The fold-change in cell number relative to DMSO is shown. Data for SUM159 and SUM149 was previously reported (Tegowski et al., 2018). All experiments where performed with three biological replicates. Significance was measured using a one-sample t-test. Error bars represent standard deviation. *P<0.05, **P<0.01.

Figure 3.2 - Thioridazine preferentially targets the tumorsphere initiating cells of the basal-like population within the same cell line. (A) SUM229-EpCAM+ and SUM229- EpCAM- cells were cultured in a tumorsphere assay with DMSO, 1μM, 2μM, or 5μM thioridazine. The number of tumorspheres formed after one week were counted. Tumorsphere formation is shown as fold-change relative to DMSO. (B) SUM229-EpCAM+ and SUM229-EpCAM- cells were plated and treated with DMSO, 1μM, 2μM, or 5μM thioridazine. The number of cells were counted after 72 hours. The fold-change in cell number relative to DMSO is shown. (C) The EpCAM expression of SUM229-EpCAM+ and SUM229-EpCAM- cells was determined using flow cytometry to check for the purity of the populations. (D) SUM149-EpCAM+ and SUM149-EpCAM- cells were cultured in a tumorsphere assay with DMSO, 1μM, 2μM, or 5μM thioridazine. The number of tumorspheres formed after one week

100 were counted. Tumorsphere formation is shown as fold-change relative to DMSO. (E) SUM149-EpCAM+ and SUM149-EpCAM- cells were plated and treated with DMSO, 1μM, 2μM, or 5μM thioridazine. The number of cells were counted after 72 hours. The fold-change in cell number relative to DMSO is shown. (F) The post-sort EpCAM expression of SUM149- EpCAM+ and SUM149-EpCAM- cells was determined using flow cytometry to check for the purity of the populations. (A to B) and (C to D) where performed with three biological replicates. Significance was measured using a one-sample t-test. Error bars represent standard deviation. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001.

101

Figure 3.3 - DRD2 expression does not predict thioridazine effects on self-renewal. (A) Total RNA was harvested from all cell lines in the 11-cell line panel. DRD2 expression normalized to GUSB was determined by qPCR, and displayed relative to the expression of SUM149, which is set to 1. The average expression of DRD2 mRNA in the basal-like cell lines

102

(HCC1937, HCC1143, SUM149, and SUM229) was compared to the average DRD2 expression in the other 7 cell lines. (B) Total RNA was harvested from the 11-cell line panel. DRD2 expression normalized to GUSB was determined, and displayed relative to the expression of SUM149. (C) Total RNA was harvested from SUM229-EpCAM+ and SUM229-EpCAM- cells. DRD2 expression normalized to GUSB was determine, and displayed relative to the expression in SUM229-EpCAM+ cells. (D) Total RNA was harvested from SUM149-EpCAM+ and SUM149- EpCAM- cells. DRD2 expression normalized to GUSB was determine, and displayed relative to the expression in SUM149-EpCAM+ cells. (E) Cells from the indicated cell lines were harvested and total DRD2 protein was determined by immunoblot. (F) Quantification of DRD2 protein normalized to actin. All experiments where performed with three biological replicates except for (A to C), which were performed with biological duplicates.

103

Figure 3.4 – Confirming the specificity of the DRD2 antibody – SUM229-EpCAM+ cells were treated with siControl or siDRD2 for 72 hours. Then cells were harvested, lysed, and DRD2 protein abundance was measured by immunoblot.

104

Figure 3.5 – Quinpirole increases tumorsphere formation in MCF7 and SUM159 cells. (A) MCF7 cells were cultured in a tumorsphere assay and treated with the indicated concentrations of quinpirole every 48 hours. After 7 days, the number of tumorspheres formed was counted. Tumorsphere formation is shown as fold-change relative to DMSO. (B) SUM159 cells were cultured in a tumorsphere assay and treated with the indicated concentrations of quinpirole every 48 hours. After 7 days, the number of tumorspheres formed was counted. Tumorsphere formation is shown as fold-change relative to DMSO. All experiments where performed with three biological replicates. was measured using a one-sample t-test. Error bars represent standard deviation. *P<0.05, **P<0.01.

105

Figure 3.6 – Expression of plasminogen pathway genes in breast cancer cell lines and tumors. (A) The normalized expression of PLAT from microarray data reported in (Prat et al., 2013) in the indicated cell lines. (B) The normalized expression of PLAUR from microarray data reported in (Prat et al., 2013) in the indicated cell lines. (C) The normalized expression of PAI-1 from microarray data reported in (Prat et al., 2013) in the indicated cell lines. (D) The normalized expression of PLAT, PLAUR, and PAI-1 in all basal-like and claudin- low cell lines and tumors from microarray data reported in (Prat et al., 2013).

106

Figure 3.7 – Activation of the plasminogen pathway supports DRD2-mediated tumorsphere formation. (A) Control and PLAT-overexpressing MCF7 cells were plated in a tumorsphere assay and treated with DMSO or 1µM thioridazine. After 7 days, the number of tumorspheres were counted. Tumorsphere formation is shown as fold-change relative to DMSO control. (B) The fold-change in tumorsphere formation caused by 1µM thioridazine treatment is shown for control and PLAT-overexpressing MCF7 cells. (C) siControl and siPAI-

107

1 treated SUM159 cells were plated in a tumorsphere assay and treated with DMSO or 1µM thioridazine. After 7 days, the number of tumorspheres were counted. Tumorsphere formation is shown as fold-change relative to DMSO control. (D) The fold-change in tumorsphere formation caused by 1µM thioridazine treatment is shown for siControl and siPAI-1 treated SUM159 cells. (E) The expression of PLAT was determined by qPCR in control and PLAT- overexpressing MCF7 cells. (F) The expression of PAI-1 was determined by qPCR in siControl and siPAI-1 treated SUM159 cells. All experiments where performed with three biological replicates. Significance for (A) and (C) was measured using a one-sample t-test. Significance for (B) and (D) was measured using a two-sample t-test. Error bars represent standard deviation. *P<0.05, **P<0.01.

108

Figure 3.8 – Model figure depicting the hypothesis that the plasminogen pathway promotes tumorsphere formation in basal-like breast cancer cells by regulating dopamine release. (A) Basal-like cells may have highly activated plasmin, which supports

109 the release of dopamine and DRD2 activation, leading to tumorsphere formation via STAT3. (B) Luminal cell lines express low levels of plasminogen activators, and therefore may not release much dopamine, and therefore have inactive DRD2. (C) Claudin-low cell lines may express high levels of PAI-1, the plasmin inhibitor, and therefore not release much dopamine, and therefore have inactive DRD2.

110

Dopamine/g of tumor tissue (ng) C3-Tag 5.12512271

Table 3.1 – Dopamine detection in a C3-Tag tumor. C3-Tag tumor tissue was homogenized and an ELISA for dopamine was performed with the extract. The result shown was from one tumor, performed in technical triplicate.

111

REFERENCES

Andreasen, P.A., Kjøller, L., Christensen, L., and Duffy, M.J. (1997). The urokinase-type plasminogen activator system in cancer metastasis: A review. Int. J. Cancer 72, 1–22.

Bahi, A., Kusnecov, A.W., and Dreyer, J.-L. (2008). Effects of urokinase-type plasminogen activator in the acquisition, expression and reinstatement of cocaine-induced conditioned- place preference. Behav. Brain Res. 191, 17–25.

Beaulieu, J.-M., Espinoza, S., and Gainetdinov, R.R. (2015). Dopamine receptors - IUPHAR Review 13. Br. J. Pharmacol. 172, 1–23.

Bezerra, J.A., Bugge, T.H., Melin-Aldana, H., Sabla, G., Kombrinck, K.W., Witte, D.P., and Degen, J.L. (1999). Plasminogen deficiency leads to impaired remodeling after a toxic injury to the liver. Proc. Natl. Acad. Sci. U. S. A. 96, 15143–15148.

Brenton, J.D., Carey, L.A., Ahmed, A.A., and Caldas, C. (2005). Molecular classification and molecular forecasting of breast cancer: ready for clinical application? J. Clin. Oncol. 23, 7350–7360.

Chugh, G., Pokkunuri, I., and Asghar, M. (2013). Renal dopamine and angiotensin II receptor signaling in age-related hypertension. Am. J. Physiol. Renal Physiol. 304, F1-7.

Clevers, H. (2011). The cancer stem cell: premises, promises and challenges. Nat. Med. 17, 313–319.

Foulkes, W.D., Smith, I.E., and Reis-Filho, J.S. (2010). Triple-Negative Breast Cancer. N. Engl. J. Med. 363, 1938–1948.

Haque, R., Ahmed, S.A., Inzhakova, G., Shi, J., Avila, C., Polikoff, J., Bernstein, L., Enger, S.M., and Press, M.F. (2012). Impact of Breast Cancer Subtypes and Treatment on Survival: An Analysis Spanning Two Decades.

Hussain, T., and Lokhandwala, M.F. (2003). Renal Dopamine Receptors and Hypertension. Exp. Biol. Med. 228, 134–142.

Ito, M., Nagai, T., Kamei, H., Nakamichi, N., Nabeshima, T., Takuma, K., and Yamada, K. (2006). Involvement of tissue plasminogen activator-plasmin system in depolarization-

112 evoked dopamine release in the nucleus accumbens of mice. Mol. Pharmacol. 70, 1720– 1725.

Iversen, S.D., and Iversen, L.L. (2007). Dopamine: 50 years in perspective. Trends Neurosci. 30, 188–193.

Kapur, S., and Seeman, P. (2001). Does Fast Dissociation From the Dopamine D 2 Receptor Explain the Action of Atypical Antipsychotics?: A New Hypothesis. Am. J. Psychiatry 158, 360–369.

Kebabian, J.W., and Calne, D.B. (1979). Multiple receptors for dopamine. Nature 277, 93– 96.

Li, S., Wei, X., He, J., Tian, X., Yuan, S., and Sun, L. (2018). Plasminogen activator inhibitor-1 in cancer research. Biomed. Pharmacother. 105, 83–94.

Mekkawy, A.H., Pourgholami, M.H., and Morris, D.L. (2014). Involvement of Urokinase-Type Plasminogen Activator System in Cancer: An Overview. Med. Res. Rev. 34, 918–956.

Missale, C., Nash, S.R., Robinson, S.W., Jaber, M., and Caron, M.G. (1998). Dopamine Receptors: From Structure to Function. Physiol. Rev. 78, 189–225.

Nagai, T., Yamada, K., Yoshimura, M., Ishikawa, K., Miyamoto, Y., Hashimoto, K., Noda, Y., Nitta, A., and Nabeshima, T. (2004). The tissue plasminogen activator-plasmin system participates in the rewarding effect of morphine by regulating dopamine release. Proc. Natl. Acad. Sci. 101, 3650–3655.

Nagai, T., Kamei, H., Ito, M., Hashimoto, K., Takuma, K., Nabeshima, T., and Yamada, K. (2005). Modification by the tissue plasminogen activator-plasmin system of morphine- induced dopamine release and hyperlocomotion, but not anti-nociceptive effect in mice. J. Neurochem. 93, 1272–1279.

Prat, A., and Perou, C.M. (2011). Deconstructing the molecular portraits of breast cancer. Mol. Oncol. 5, 5–23.

Prat, A., Karginova, O., Parker, J.S., Fan, C., He, X., Bixby, L., Harrell, J.C., Roman, E., Adamo, B., Troester, M., et al. (2013). Characterization of cell lines derived from breast cancers and normal mammary tissues for the study of the intrinsic molecular subtypes. Breast Cancer Res. Treat. 142, 237–255.

Reya, T., Morrison, S.J., Clarke, M.F., and Weissman, I.L. (2001). Stem cells, cancer and

113 cancer stem cells. Nature 414, 105–111.

Schmitt, M., Harbeck, N., Brünner, N., Jänicke, F., Meisner, C., Mühlenweg, B., Jansen, H., Dorn, J., Nitz, U., Kantelhardt, E.J., et al. (2011). Cancer therapy trials employing level-of- evidence-1 disease forecast cancer biomarkers uPA and its inhibitor PAI-1. Expert Rev. Mol. Diagn. 11, 617–634.

Seeman, P. (2006). Targeting the dopamine D 2 receptor in schizophrenia. Expert Opin. Ther. Targets 10, 515–531.

Siegel, R.L., Miller, K.D., and Jemal, A. (2018). Cancer statistics, 2018. CA. Cancer J. Clin. 68, 7–30.

Strobl, J.S., and Peterson, V.A. (1992). Tamoxifen-resistant human breast cancer cell growth: inhibition by thioridazine, pimozide and the calmodulin antagonist, W-13. J. Pharmacol. Exp. Ther. 263.

Tegowski, M., Fan, C., and Baldwin, A.S. (2018). Thioridazine inhibits self-renewal in breast cancer cells via DRD2-dependent STAT3 inhibition, but induces a G1 arrest independent of DRD2. J. Biol. Chem. 293, 15977–15990.

Yin, T., He, S., Shen, G., Ye, T., Guo, F., and Wang, Y. (2015). Dopamine receptor antagonist thioridazine inhibits tumor growth in a murine breast cancer model. Mol. Med. Rep. 12, 4103–4108.

Yue, H., Huang, D., Qin, L., Zheng, Z., Hua, L., Wang, G., Huang, J., and Huang, H. (2016). Targeting Lung Cancer Stem Cells with Antipsychological Drug Thioridazine. Biomed Res. Int. 2016, 6709828.

Zhang, C., Gong, P., Liu, P., Zhou, N., Zhou, Y., and Wang, Y. (2016). Thioridazine elicits potent antitumor effects in colorectal cancer stem cells. Oncol. Rep. 37, 1168–1174.

114

CHAPTER 4: CONCLUSIONS AND FUTURE STUDIES

4.1 – Conclusions and Discussion

Although thioridazine, as well as another DRD2-targeting antipsychotic pimozide, were shown to decrease the proliferation of cultured cancer cells nearly 30 years ago (Strobl and

Peterson, 1992; Strobl et al., 1990), the effects of these drugs had been largely ignored until

Sachlos et al discovered thioridazine in a small molecule screen for differentiation-inducing compounds (Sachlos et al., 2012). That study showed that thioridazine could induce cellular differentiation and cause a greater reduction in colony formation of AML blasts than of normal hematopoietic stem cells. They showed that this inexpensive compound, which is already

FDA-approved for treating schizophrenia, could reduce CSC-like activity, without inhibiting normal stem cells. Several studies following the Sachlos et al publication confirmed that higher doses of thioridazine (5-20µM) induces cell cycle arrest and apoptosis (Cheng et al., 2015;

Meng et al., 2016; Yin et al., 2015; Zhang et al., 2016). However, the roles of DRD2, the primary target of thioridazine, have largely gone unexplored. It is important to determine whether thioridazine (or other antipsychotics that inhibit cancer cell growth or self-renewal) are actually working through dopamine receptor inhibition, because even though thioridazine binds and inhibits DRD2 with high affinity, it is also known to bind other GPCRs with high affinity, including some serotonin receptors and adrenergic receptors (Besnard et al., 2012).

Therefore, it must be experimentally determined whether any observed effects of thioridazine are via DRD2 inhibition, as opposed to the inhibition of other receptors. This is especially important since compounds targeting serotonin receptors, which are also known to bind

DRD2, have also been shown to induce cell cycle arrest and apoptosis of cancer cells, as well as possibly targeting CSCs (Gwynne et al., 2017; Hallett et al., 2014).

115

Work presented in this thesis shows that, although thioridazine indeed induces a cell cycle arrest and potentially apoptosis at higher doses (5-10µM) in all breast cancer cell lines tested, this activity is independent of DRD2 (Chapter 2, Figure 4.1). Conversely, the reduction in tumorsphere formation observed when cells are treated with lower doses of thioridazine

(1-2µM), does work through DRD2 inhibition. This suggests that in breast cancer cells, DRD2 is not required to maintain cell survival or cell cycle progression, but that a subset of tumorsphere-initiating cells (TICs) are supported by the activity of DRD2. These DRD2- supported TICs are not present in all breast cancer cell lines, however. We tested the effects of thioridazine on tumorsphere formation in luminal, HER2+, claudin-low, and basal-like breast cancer cell lines, and DRD2 only supported TICs in the basal-like cell lines (Chapter 2 and 3). This discrepancy in sensitivity suggests that, if translated clinically, some patients would likely benefit from DRD2-targeting therapies more than others.

We also showed that DRD2 promotes tumorsphere formation by supporting a

STAT3/IL-6 feedforward loop in SUM149 cells (Chapter 2, Figure 4.1). Our work suggests that

DRD2 activity does not activate STAT3 by supporting IL-6 receptor signaling, but instead, IL-

6 is downstream of DRD2 and IL-6 can rescue tumorsphere formation defects of DRD2 knockdown cells (Chapter 2). Although we have shown that thioridazine treatment reduces active phosphorylated JAK2 (Figure 4.2), it remains uncertain what mechanisms may underlie this regulation. Work from Hua Yu’s laboratory showed that another GPCR, S1P1R, can promote STAT3 activation in bladder cancer cells through direct binding of JAK and Src (Lee et al., 2010). DRD2 has also been shown to activate membrane-associated proteases, which release heparin-bound epidermal growth factor, thereby inducing EGFR activity (Yoon and

Baik, 2013), which has been shown to promote STAT3 activation via Src (Silva, 2004). If

DRD2 support of STAT3 activity is found to be a general mechanism observed in a variety of cells, then the mechanisms by which DRD2 promotes STAT3 activity will need to explored further.

116

Since we noticed that thioridazine could reduce the formation of tumorspheres in some triple-negative breast cancer cell lines, but not others (Figure 2.2), we investigated which breast cancer cells’ self-renewal was most sensitive to thioridazine. Interestingly, the self-renewal of basal-like cells are uniquely sensitive to thioridazine, compared to cells of other subtypes (Figure 3.1). Further, the basal-like cells express more DRD2 mRNA, on average, than cells of other subtypes, suggesting that high DRD2 levels may explain why basal-like cells are more sensitive to DRD2 inhibition (Figure 3.3). However, SUM149 cells, which are highly sensitive to thioridazine, express relatively low levels of DRD2, while

HCC1937 cells, which are only modestly affected by thioridazine, express the most DRD2 mRNA of all the cell lines in the panel (Figure 3.3). This suggests that DRD2 expression alone does not predict whether TIC-activity within a cell line is supported by DRD2. Importantly, we also demonstrated that treatment with quinpirole, a selective DRD2 agonist, promotes tumorsphere formation in two cell lines (SUM159 and MCF7) in which thioridazine does not reduce tumorsphere formation (Figure 3.4). These data suggest that these thioridazine- insensitive cell lines express DRD2, that DRD2 is capable of supporting TIC-activity, but that

DRD2 is not being activated. Presumably this is due to a lack of dopamine production or release, but that is not yet conclusively known. ONC201 is a small molecule in currently in clinical trials that, like thioridazine, is thought to be a DRD2 inhibitor (Madhukar et al., 2017), yet has strong anticancer effects that are not dependent on DRD2 activity (Kline et al., 2018).

High expression of DRD5 has been shown to correlate with resistance to ONC201 (Prabhu et al., 2019). The possibility of antagonism or compensation from other dopamine receptors should also be investigated with thioridazine and other DRD2-targeting anticancer compounds.

It is presently unclear whether any of these breast cancer cell lines release dopamine, let alone whether the more highly sensitive basal-like cell lines actively release more dopamine than the cell lines of other subtypes. However, that is a question we are interested in addressing. For now, the observed effects of the components of the plasminogen pathway

117 components do imply that some cell lines may release more dopamine than others. The plasminogen pathway is well studied for its roles in thrombosis, and treatment with activators of plasminogen, particularly tPA, can be used to degrade blood clots during ischemia (Adams et al., 2007). However, the activators (PLAT/tPA, PLAU/uPA, and PLAUR/uPAR) and the inhibitor (SERPINE1/PAI-1) of plasminogen are correlated with breast cancer metastasis and decreased survival in breast cancer patients (Li et al., 2018; Mekkawy et al., 2014; Schmitt et al., 2011; Smith and Marshall, 2010). Along those lines, we have shown that expression of

PLAT, PLAUR, and/or PAI-1 are highest in claudin-low and basal-like cell lines (Figure 3.5).

Not only does plasminogen pathway biology support breast cancer metastasis, but it also been shown to support the release of dopamine in cultured neurons (Ito et al., 2006; Nagai et al., 2004, 2005; Samson and Medcalf, 2006). In fact, ablation of the PLAT gene can eliminate depolarization-induced dopamine release, but treatment with recombinant plasmin can rescue that effect (Ito et al., 2006). This suggests that perhaps DRD2 would be most active in supporting self-renewal of cells in which the plasmin pathway is most active, supporting the release of dopamine.

We tested this hypothesis by expressing PLAT, the gene that produces tPA, in MCF7 cells. Tumorsphere formation of MCF7 cells is not affected by thioridazine (Figure 3.1), and

MCF7 cells express relatively low levels of both activators and inhibitors of the plasminogen pathway (Figure 3.5). We reasoned that PLAT overexpression may be able to support plasminogen activation and subsequent dopamine release, increasing tumorsphere formation in a manner that should then be thioridazine sensitive. Remarkably, that is what we observed

(Figure 3.6). To further test whether the plasminogen pathway supports DRD2-dependent self-renewal, we used siRNA to knockdown PAI-1 expression in SUM159 cells. Tumorsphere formation in SUM159 cells is also not affected by thioridazine, and although they express relatively high levels of PLAT, they also express high levels of PAI-1, the inhibitor. So we reasoned that if expression of the inhibitor was released, tPA could be more active and support increased tumorsphere formation, again in a thioridazine-sensitive manner. Indeed, that is

118 what we observed (Figure 3.6). Together, these data tentatively suggest that the plasminogen activation system can support DRD2-dependent self-renewal. However, it remains critical to measure whether this activity is mediated through the regulation of dopamine release, or possibly, an unrelated mechanism.

Although we have found that thioridazine inhibits self-renewal in a DRD2-dependent manner most strongly in basal-like breast cancers, it is possible that DRD2 inhibition may have an effect on other tumor types in vivo. First, many groups have shown that doses of thioridazine greater than 5-10µM are broadly cytotoxic (Johannessen et al., 2018; Sachlos et al., 2012; Strobl and Peterson, 1992; Tegowski et al., 2018; Yin et al., 2015; Yin et al., 2015;

Yue et al., 2016). Therefore, treatment with high, yet tolerable, doses of thioridazine could complement chemotherapy and reduce tumor size (Luo et al., 2016; Yin et al., 2015), even if self-renewal is not specifically affected. The mechanisms by which thioridazine leads to cell cycle arrest and apoptosis are important consequences of thioridazine treatment that need further elucidation. Also, our data suggest that basal-like breast cancer cell lines both express and activate DRD2 signaling. However, if dopamine release is the cause of DRD2 activation, then any tumor that expresses DRD2 could potentially have a population of TICs maintained in a DRD2-dependent manner. This is because the complex tumor microenvironment can harbor cell types that are capable of synthesizing and releasing dopamine.

Dopamine can be synthesized by immune cells, including B cells, T cells, and macrophages (Arreola et al., 2016; Levite, 2016). This indicates that in the in vivo tumor microenvironment, infiltrating immune cells, some of which have already been shown to support tumor growth and metastasis (Grivennikov et al., 2010), may synthesize and release dopamine, even if the tumor cells are not. Additionally, dopamine may promote the polarization of macrophages towards the M2 type (Haskó et al., 1996) and promote the polarization of activated T cells to the Th2 state, rather than the Th1 (Figure 4.3) (Levite,

2016; Nakano et al., 2009). Both M2 and Th2 macrophages and T cells are associated with cancer progression, metastasis, and immune evasion (Grivennikov et al., 2010). Therefore,

119 the use of dopamine receptor antagonist may be able to modulate the tumor microenvironment, making it less amenable to tumor growth and metastasis, even if the tumor cells themselves do not express dopamine receptors or produce dopamine. However, this has not yet been conclusively shown, and instead is a potentially important area of future investigation. Even further, work in prostate cancer has shown that neurons can extend into the tumor boundaries and support growth via adrenergic receptors (Magnon et al., 2013;

Zahalka et al., 2017). This suggests that innervation of dopaminergic neurons may be possible, which then could release dopamine into the tumor milieu.

The work presented in this thesis contributes to a growing field, and there is evidence that thioridazine, or other DRD2-targeting agents, may have therapeutic effects in cancer.

However, many important questions remain unaddressed. Further studies addressing the impacts of antipsychotics in cancer, the roles of dopamine receptors, and even roles of other neurotransmitter receptors in cancer may lead to new, safe, and cheap therapies for patients.

4.2 – Future Directions

While there are many potential avenues to explore the effects of antipsychotics and the roles for dopamine receptors in cancer, there are several important areas that have received little attention.

First, the source of dopamine receptor activation remains elusive. Dopamine released into the tumor microenvironment is presumed to be the most likely source of dopamine receptor activation. However, the measurement of dopamine in tumor tissue and tumor cells has received little attention, with one exception. Pheochromocytoma tumors, which arise from dopamine-producing cells in the adrenal gland (Lenders et al., 2005). Released dopamine, epinephrine, and norepinephrine (catecholamines) can cause dangerous side effects, but the release of catecholamines is not thought to be the dominant driver of tumor growth. Instead many patients have mutations in the hypoxia-inducible factor (HIF) pathway, or in the succinate dehydrogenase complex, mutations in which leads to HIF activation (Kantorovich et al., 2010). In order to test whether dopamine release is occurring in other cancer types, HPLC

120 can be used on samples obtained from tumor tissue, conditioned cultured cell medium, and conditioned organoid medium. Additionally, orthotopic mouse tumor models such as 4T1 can be used to genetically probe the effects of dopamine receptors in both tumor tissue and non- tumor tissue. 4T1 is an especially promising model as it is a triple-negative breast cancer model, and its growth is reduced by thioridazine treatment (Yin et al., 2015). To test whether

DRD2 has functions in the tumor microenvironment, wild-type 4T1 tumors can be grown orthotopically in DRD2-/- mice. Tumor growth, as well as metastatic frequency or tropism can then be measured. If differential growth or metastasis is observed in DRD2 knockout mice, the effects be further investigated using inducible knockouts induced by tissue-specific expression of cre-recombinase. Similarly, the origins and effects of dopamine can be studied by using tyrosine hydroxylase (TH) knockout mice. Tyrosine hydroxylase is the enzyme that catalyzes the rate-limiting step in dopamine synthesis. Since homozygous TH knockout mice do not reach adulthood (Kobayashi et al., 1995), tissue-specific deletions will be required.

However, much can be learned from the growth of TH-deleted tumor cells, or from tumors grown in mice that have TH deleted from leukocyte or lymphocyte populations.

Detection of dopamine in tumor tissue is critical to understanding the effects of dopamine receptors on tumor growth. However, a report that Wnt5a, a non-canconical Wnt- family member, can bind DRD2 and affect neurite outgrowth in a DRD2-dependent manner

(Yoon et al., 2011) suggests the possibility that factors other than dopamine may activate

DRD2 signaling in tumors. Further, Wnt5a is known to promote EMT in breast cancer cells

(Jordan et al., 2013), as well as the increase the production of dopaminergic neurons from neural stem cells (Parish et al., 2008). These data suggest that Wnt5a may be to able functionally activate DRD2 and affect the tumorigenicity of breast cancer cells.

Another major area of need is to determine the molecular mechanisms of the effects of thioridazine and other antipsychotics on cancer cells. It is especially important to investigate the molecular effects of dopamine receptor inhibition on self-renewal pathways.

To accomplish this, cells should ideally be cultured as spheres to enrich for TICs, a highly

121 sensitive cell line should be compared with a similar but insensitive cell line (SUM229 EpCAM+ compared to SUM229 EpCAM-), and low doses of thioridazine should be used (1-2µM). If

SUM229 EpCAM+ and SUM229 EpCAM- cells are grown in sphere culture, treated with 1µM thioridazine, and total RNA harvested after 1-4 hours for RNAseq, then changes in gene expression patterns can be analyzed. This experiment should provide evidence of gene expression changes regulated by DRD2 in order to promote self-renewal, especially by directly comparing the thioridazine-induced changes in the EpCAM+ cells to the EpCAM-. We predict that STAT3-target genes would be affected (discussed in Chapter 2). However, other potential pathways may be elucidated.

Proteomics approaches can also be used to elucidate mechanisms of DRD2 activity, especially within basal-like breast cancer cells. Expression of DRD2 fused to a mutated biotin ligase (BirA) will label any protein sequences that come into close proximity with biotin (Roux et al., 2012). Then the biotinylated proteins can be isolated and analyzed by mass spectrometry. If this experiment is done in SUM229 EpCAM+ tumorspheres treated with 1µM thioridazine, and compared with SUM229 EpCAM- tumorspheres treated with 1µM thioridazine, then downstream signaling components interacting with DRD2 can be identified. Ideally, this experiment can be compared with data from the RNAseq experiment described above to identify important signaling pathways regulated by DRD2 that support the self-renewal of

TICs through changes in gene expression patterns.

Although thioridazine, as well as other DRD2-targeting antipsychotic drugs, have been shown to have strong anti-cancer effects, there are still many important questions unanswered. Addressing these questions and learning more about how these inhibitors work, and what the roles of dopamine receptors are in cancer, might lead to new and effective treatments.

122

Figure 4.1 – A model figure of effects of thioridazine on breast cancer cells. In cells of all subtypes, 5µM thioridazine reduces proliferation through a mechanism that does not depend on DRD2. In basal-like breast cancer cells, thioridazine can inhibit self-renewal in a DRD2-dependent manner. This disrupts a STAT3/IL-6 feed-forward loop that supports self- renewal.

123

Figure 4.2 – Thioridazine inhibits JAK2 activation. (A) SUM149 cells were cultured as tumorspheres for 1 week before treated with 1µM thioridazine for 15 minutes to 1 hour. The spheres were harvested, and immunoblots for pJAK2 (Y1007/1008), total JAK2, and actin were performed. (B) Quantification of the relative ratio of pJAK2/JAK. Experiment was performed in biological triplicate. Error bars represent standard deviation. Significance was tested using a one-sided t-test.

124

Figure 4.3 – Potential effects on tumor-associated immune cells if dopamine signaling is disrupted. Dopamine release during T cell activation can shift T cells toward a Th2 polarization, which is less inflammatory and associated with promoting metastasis in a tumor setting. DRD2 activity can inhibit the production of M1-macrophage-associated genes, potentially promoting M2 polarization, which is associated with tumor progression.

125

REFERENCES

Adams, H.P., del Zoppo, G., Alberts, M.J., Bhatt, D.L., Brass, L., Furlan, A., Grubb, R.L., Higashida, R.T., Jauch, E.C., Kidwell, C., et al. (2007). Guidelines for the Early Management of Adults With Ischemic Stroke. Stroke 38, 1655–1711.

Grivennikov, S.I., Greten, F.R., and Karin, M. (2010). Immunity, Inflammation, and Cancer. Cell 140, 883–899.

Gwynne, W.D., Hallett, R.M., Girgis-Gabardo, A., Bojovic, B., Dvorkin-Gheva, A., Aarts, C., Dias, K., Bane, A., Hassell, J.A., Gwynne, W.D., et al. (2017). Serotonergic system antagonists target breast tumor initiating cells and synergize with chemotherapy to shrink human breast tumor xenografts. Oncotarget 8, 32101–32116.

Hallett, R.M., Girgis-Gabardo, A., Gwynne, W.D., Giacomelli, A.O., Bisson, J.N.P., Jensen, J.E., Dvorkin-Gheva, A., and Hassell, J.A. (2014). Serotonin transporter antagonists target tumor-initiating cells in a transgenic mouse model of breast cancer. Oncotarget.

Ito, M., Nagai, T., Kamei, H., Nakamichi, N., Nabeshima, T., Takuma, K., and Yamada, K. (2006). Involvement of tissue plasminogen activator-plasmin system in depolarization- evoked dopamine release in the nucleus accumbens of mice. Mol. Pharmacol. 70, 1720– 1725.

Kantorovich, V., King, K.S., and Pacak, K. (2010). SDH-related pheochromocytoma and paraganglioma. Best Pract. Res. Clin. Endocrinol. Metab. 24, 415–424.

126

Kobayashi, K., Morita, S., Sawada, H., Mizuguchi, T., Yamada, K., Nagatsu, I., Hata, T., Watanabe, Y., Fujita, K., and Nagatsu, T. (1995). Targeted disruption of the tyrosine hydroxylase locus results in severe catecholamine depletion and perinatal lethality in mice. J. Biol. Chem. 270, 27235–27243.

Lenders, J.W., Eisenhofer, G., Mannelli, M., and Pacak, K. (2005). Phaeochromocytoma. Lancet 366, 665–675.

Li, S., Wei, X., He, J., Tian, X., Yuan, S., and Sun, L. (2018). Plasminogen activator inhibitor-1 in cancer research. Biomed. Pharmacother. 105, 83–94.

Mekkawy, A.H., Pourgholami, M.H., and Morris, D.L. (2014). Involvement of Urokinase-Type Plasminogen Activator System in Cancer: An Overview. Med. Res. Rev. 34, 918–956.

Meng, Q., Sun, X., Wang, J., Wang, Y., and Wang, L. (2016). The important application of thioridazine in the endometrial cancer. Am. J. Transl. Res. 8, 2767–2775.

Nakano, K., Higashi, T., Takagi, R., Hashimoto, K., Tanaka, Y., and Matsushita, S. (2009). Dopamine released by dendritic cells polarizes Th2 differentiation. Int. Immunol. 21, 645– 654.

Parish, C.L., Castelo-Branco, G., Rawal, N., Tonnesen, J., Sorensen, A.T., Salto, C., Kokaia, M., Lindvall, O., and Arenas, E. (2008). Wnt5a-treated midbrain neural stem cells improve dopamine cell replacement therapy in parkinsonian mice. J. Clin. Invest. 118, 149–160.

Prabhu, V. V., Madhukar, N.S., Gilvary, C., Kline, C.L.B., Oster, S., El-Deiry, W.S., Elemento, O., Doherty, F., VanEngelenburg, A., Durrant, J., et al. (2019). Dopamine Receptor D5 is a Modulator of Tumor Response to Dopamine Receptor D2 Antagonism. Clin.

127

Cancer Res. 25, 2305–2313.

Roux, K.J., Kim, D.I., Raida, M., and Burke, B. (2012). A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells. J. Cell Biol. 196, 801–810.

Samson, A.L., and Medcalf, R.L. (2006). Tissue-Type Plasminogen Activator: A Multifaceted Modulator of Neurotransmission and Synaptic Plasticity. Neuron 50, 673–678.

Silva, C.M. (2004). Role of STATs as downstream signal transducers in Src family kinase- mediated tumorigenesis. Oncogene 23, 8017–8023.

Smith, H.W., and Marshall, C.J. (2010). Regulation of cell signalling by uPAR. Nat. Rev. Mol. Cell Biol. 11, 23–36.

Strobl, J.S., and Peterson, V.A. (1992). Tamoxifen-resistant human breast cancer cell growth: inhibition by thioridazine, pimozide and the calmodulin antagonist, W-13. J. Pharmacol. Exp. Ther. 263.

Yin, T., He, S., Shen, G., Ye, T., Guo, F., and Wang, Y. (2015). Dopamine receptor antagonist thioridazine inhibits tumor growth in a murine breast cancer model. Mol. Med. Rep. 12, 4103–4108.

YIN, T., HE, S., SHEN, G., YE, T., GUO, F., and WANG, Y. (2015). Dopamine receptor antagonist thioridazine inhibits tumor growth in a murine breast cancer model. Mol. Med. Rep. 12, 4103–4108.

Yue, H., Huang, D., Qin, L., Zheng, Z., Hua, L., Wang, G., Huang, J., and Huang, H. (2016). Targeting Lung Cancer Stem Cells with Antipsychological Drug Thioridazine. Biomed Res. Int. 2016, 6709828.

128

Zhang, C., Gong, P., Liu, P., Zhou, N., Zhou, Y., and Wang, Y. (2016). Thioridazine elicits potent antitumor effects in colorectal cancer stem cells. Oncol. Rep. 37, 1168–1174.

129