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Anti-Tumor Signaling Of Thyroid Hormone Receptor Beta In Breast Anti-Tumor Signaling Of Thyroid Hormone Receptor Beta In Breast

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Eric Bolf University of Vermont

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ANTI-TUMOR SIGNALING OF THYROID HORMONE RECEPTOR BETA IN

BREAST AND THYROID CANCER CELLS

A Dissertation Presented

by

Eric L. Bolf

to

The Faculty of the Graduate College

of

The University of Vermont

In Partial Fulfillment of the Requirements

for the Degree of Doctor of Philosophy

Specializing in Cellular, Molecular, and Biomedical Sciences

May, 2020

Defense Date: March 24, 2020

Dissertation Examination Committee:

Frances E. Carr, Ph.D., Advisor

Karen C. Glass, Ph.D., Chairperson

Jane B. Lian, Ph.D.

Karen M. Lounsbury, Ph.D.

Cynthia J. Forehand, Ph.D., Dean of the Graduate College

ABSTRACT

Dysregulation of the tumor suppressor thyroid hormone receptor beta (TRβ) is a

frequent event in a number of solid tumors. As a nuclear hormone receptor, the primary

function of TRβ is to regulate transcriptional activity in a ligand-dependent manner.

However, TRβ activity is not well-defined and the pathways it regulates are not yet fully

delineated. The two cancer types where TRβ is best studied are thyroid cancer and breast

cancer. Interestingly, thyroid cancer is a risk factor for breast cancer and breast cancer is

a risk factor for thyroid cancer, suggestive of an etiological link. Determining the

molecular mechanisms of a tumor suppressor shared by both diseases may reveal critical

tumor suppressive pathways.

First, in order to discover novel pathways that are regulated by TRβ, TRβ was

stably overexpressed in an anaplastic thyroid cancer (ATC) cell line through lentiviral

transduction. TRβ has not previously been overexpressed in ATC and, through

triiodothyronine (T3) stimulation, TRβ repressed pro-malignant phenotypes including

cellular growth, migration, and stemness. Additionally, TRβ and T3 promoted apoptosis.

RNA-sequencing and subsequent analysis revealed novel TRβ-mediated STAT1

stimulation. Additionally, unmodified anaplastic thyroid cancer cells were sensitive to

treatment with a STAT1 agonist.

Next, to determine if there is common transcriptional signaling between different

types of solid tumors, the TRβ-RUNX2 pathway was examined in breast cell lines. In

thyroid cancer, TRβ represses expression of the pro-metastatic transcription factor

RUNX2. In addition to promoting thyroid cancer, aberrant overexpression of RUNX2 is a

driver of breast carcinogenesis. This informed the hypothesis that TRβ-mediated

repression of RUNX2 and resultant signaling is common to thyroid and breast cancers. T3

treatment reduced RUNX2 expression in breast cell lines. Additionally, TRβ bound to

thyroid hormone response elements in the RUNX2 promoter, the same mechanism of

action for T3-mediated repression as in thyroid cells.

Lastly, to more broadly address mechanisms mediated by TRβ, TRβ was stably

overexpressed in a basal-like aggressive breast cancer cell line. TRβ is already known to

be able to repress tumor growth and metastasis in this type of cancer. However, the

specific molecular mechanisms driving the anti-tumor phenotype are not well delineated.

In order to determine which molecular signaling pathways are regulated by TRβ, RNA-

sequencing was performed. TRβ induced a more epithelial profile of these cells, in

contrast to the typically mesenchymal nature of basal-like breast cancer. Novel TRβ-

mediated repression of pro-invasive cytokeratins was found. The genes regulated by TRβ

in these cells were also compared to the findings in ATC. The set of common genes

included chromatin remodelers and enzymes involved in stearate biosynthesis; the latter

is known to have anti-cancer properties and may be an important driver of TRβ-mediated

tumor suppression in both breast and thyroid cancers.

ii

CITATIONS

Material from this dissertation has been published in the following form:

Bolf E.L., Sprague B.L., Carr F.E.. (2019). A Linkage Between Thyroid and Breast

Cancer: A Common Etiology? Cancer Epidemiology, Biomarkers & Prevention. 4,

643-649.

Bolf E.L., Gillis N.E., Barnum M.S., Beaduet C.M., Yu G.Y., Tomczak J.A., Stein J.L.,

Lian J.B., Stein G.S., Carr F.E.. (2020). The Thyroid Hormone Receptor-RUNX2 Axis:

A Novel Tumor Suppressive Pathway in Breast Cancer. Hormones and Cancer. 11(1),

34-41.

AND

Material from this dissertation has been submitted for publication to Molecular Cancer

Research (March 26, 2020) in the following form:

Bolf, E.L., Gillis N.E., Davidson C.D., Rodriguez P.D., Cozzens L., Tomczak J.A.,

Frietze S., Carr F.E., Thyroid Hormone Receptor Beta Induces a Tumor Suppressive

Program in Anaplastic Thyroid Cancer. Molecular Cancer Research.

iii

ACKNOWLEDGEMENTS

I’d like to start this section with those who have helped me reach graduate school.

I’d be amiss to not start with my parents, Linda and Lewis Bolf, who each instilled a set

of values in me that drove me to pursue higher education and not quit when life is hard.

As a teacher by profession, my mother always encouraged me to learn, ensuring that I

was able to take advantage of the opportunities available to me. My father taught me the

value of hard work, and I like to think that some of his stubbornness rubbed off onto me,

stubbornness that has ensured I stuck with my scientific pursuits, especially when it was

difficult to see a way forward.

I also want to thank my wife, Sanja Raejour, for supporting my desire for further

education, even though it required us to move halfway across the country. She has been a

source of stability during my time in Vermont, a sympathetic ear, and has been more than

willing to help in whatever way she could. I’m grateful for her assisting in the more

artistic ways to my pursuits, including taking a sketch and turning it into a proper figure

for a pilot grant that the lab was ultimately awarded, and which has helped to fund my

research.

Of course, I wouldn’t have the success here at UVM without the continuous help

of my advisor. I have been lucky to have the privilege of working for Dr. Frances Carr,

who encouraged my ideas and allowed my interests to take the lab in a different direction.

She repeatedly places her students first and has been a strong advocate for me.

The members of my committee have also been stellar. My chair, Dr. Karen Glass,

has done a great job of being the impartial voice during meetings. She’s also given

iv

incredibly helpful advice on the necessity of focus when pursuing scientific research. Dr.

Jane Lian has been a great collaborator and helped shape the direction my dissertation

took. She is the one who encouraged me to write my first review and also advocated for

me to pursuing sequencing experiments. Dr. Lian has also been a gracious pre-

submission reviewer, taking time out of her busy days to not only edit manuscripts and

grants, but also to sit down and discuss how to improve the work. I also want to

emphasize how helpful Dr. Karen Lounsbury has been. She always strove to make my

work better. Her kindly-delivered critiques have helped to strengthen the weakest parts of

my writing and research, and ultimately the science I’ve performed.

Lastly, I need to thank the people who I’ve worked with at the bench for many

years. This includes our long-time technician, Jennifer (Jenny) Tomczak, Dr. Carr’s

current graduate students, Cole Davidson and Noelle Gillis, and her former graduate

student, Thomas (Thommy) Taber. Jenny has been a ray of sunshine in even the hardest

moments of graduate school, she is a voice of encouragement and was more than willing

to share her expertise with me when I was first starting out. Cole, Noelle, and Thommy

have been great colleagues and peers to work with. Graduate school can be a lonely

experience, but not with these three. We shared our frustrations with each other, and

celebrated successes. I’m lucky to have been a part of such a great team.

v

TABLE OF CONTENTS

Page

CITATIONS ................................................................................................................ ii

ACKNOWLEDGEMENTS ........................................................................................ iii

LIST OF TABLES ..................................................................................................... ix

LIST OF FIGURES ......................................................................................................x

CHAPTER 1: COMPREHENSIVE LITERATURE REVIEW ......................................1

1.1. Thyroid Hormone Receptor Signaling .................................................................1

1.1.1. Thyroid Hormone Receptor Regulation of Gene Expression ...........................2

1.1.2. Thyroid Hormone Signaling in Cancer ...........................................................4

1.1.3. Structural Biology of Thyroid Hormone Receptor Beta ..................................9

1.2. Thyroid Cancer ................................................................................................. 11

1.2.1. Differentiated Thyroid Cancer ...................................................................... 12

1.2.2. Dedifferentiated Thyroid Cancer .................................................................. 13

1.3. Breast Cancer.................................................................................................... 15

1.3.1. Luminal Breast Cancer ................................................................................. 17

1.3.2. HER2 Positive Breast Cancer ....................................................................... 19

1.3.3. Basal-like Breast Cancer .............................................................................. 21

1.4. Scope of Dissertation ........................................................................................ 23

References ............................................................................................................... 25

vi

CHAPTER 2: THYROID HORMONE RECEPTOR BETA INDUCES A TUMOR

SUPPRESSIVE PROGRAM IN ANAPLASTIC THYROID CANCER ..................... 50

2.1. Abstract ............................................................................................................ 50

2.2. Introduction ...................................................................................................... 51

2.3. Methods ............................................................................................................ 52

2.4. Results .............................................................................................................. 57

2.4.1. Expression of TRβ inhibits pro-tumorigenic characteristics of human

anaplastic thyroid cells ........................................................................................... 57

2.4.2. TRβ reprograms anaplastic thyroid cancer cell signaling .............................. 59

2.4.3. Liganded TRβ reduces ATC stem cell characteristics ................................... 63

2.4.4. TRβ stimulates apoptotic signaling ............................................................... 66

2.4.5. TRβ promotes anti-proliferative signaling .................................................... 68

2.5. Discussion ........................................................................................................ 70

References ............................................................................................................... 73

CHAPTER 3: THE THYROID HORMONE RECEPTOR-RUNX2 AXIS: A NOVEL

TUMOR SUPPRESSIVE PATHWAY IN BREAST CANCER. ................................. 81

3.1. Abstract ............................................................................................................ 81

3.2. Introduction ...................................................................................................... 82

3.3.Materials and Methods ....................................................................................... 84

3.4. Results .............................................................................................................. 88

3.4.1. TR expression in reduced in aggressive breast cancer ................................... 88

3.4.2. Thyroid hormone repression of RUNX2 is driven by TRβ ............................ 89

3.4.3. TRβ binds to the RUNX2 promoter ............................................................... 92

vii

3.5. Discussion ........................................................................................................ 94

References ............................................................................................................... 96

CHAPTER 4: COMMON TUMOR SUPPRESSIVE SIGNALING OF THYROID

HORMONE RECEPTOR BETA IN BREAST AND THYROID CANCER

CELLS. .................................................................................................................... 103

4.1. Abstract .......................................................................................................... 103

4.2. Introduction .................................................................................................... 104

4.3. Methods .......................................................................................................... 105

4.4. Results ............................................................................................................ 107

4.5. Discussion ...................................................................................................... 115

References ............................................................................................................. 116

CHAPTER 5: A LINKAGE BETWEEN THYROID AND BREAST CANCER: A

COMMON ETIOLOGY? ......................................................................................... 123

5.1. Abstract .......................................................................................................... 123

5.2. Introduction .................................................................................................... 123

5.3. What is the Epidemiological Evidence of the Relationship Between Breast and

Thyroid Cancer? .................................................................................................... 125

5.4. What Genetic Factors Link the Diseases? ........................................................ 128

5.5. What is the Impact of Cancer Treatment on Future Risk? ................................ 130

5.6. Are Thyroid Disease and Disruptions of Thyroid Hormone Signaling Risk

Factors? ................................................................................................................. 132

viii

5.7. Does Hormonal Disruption alter Metachronous Cancer Susceptibility? ........... 133

5.8. Are Lifestyle Related Environmental Factors Common Between Breast and

Thyroid Cancer? .................................................................................................... 136

5.9. Concluding Remarks ....................................................................................... 137

References ............................................................................................................. 139

CHAPTER 6: DISCUSSION AND CONCLUDING REMARKS. ............................ 154

6.1. Summary ........................................................................................................ 154

6.2. Implications .................................................................................................... 155

6.3. Future Directions ............................................................................................ 157

6.4. Concluding Remarks ....................................................................................... 158

References ............................................................................................................. 158

COMPREHENSIVE BIBLIOGRAPHY ................................................................... 161

APPENDIX A: Supplemental Materials for Chapter 2 .............................................. 188

APPENDIX B: Supplemental Materials for Chapter 3 .............................................. 210

APPENDIX C: Supplemental Materials for Chapter 4 .............................................. 214

APPENDIX D: List of Abbreviations ....................................................................... 226

ix

LIST OF TABLES

Table Page

Table 1: Summary of Studies linking Breast and Thyroid Cancer Incidence. ............. 126

x

LIST OF FIGURES

Figure Page

Figure 1: The Thyroid Hormone Modulated Pathways MAPK and PI3K are Drivers

of Tumorigenesis .........................................................................................................6

Figure 2: Functional Domains of TRβ ..........................................................................9

Figure 3: Breast Cancer Subtypes Vary in Prevalence and Prognosis. ......................... 17

Figure 4: HER2 stimulates PKC, MAPK, and PI3K pathways .................................... 20

Figure 5: TRβ Repressed Growth of SW1736 ............................................................ 58

Figure 6: TRβ-T3 Altered the Transcriptome of ATC Cells ......................................... 60

Figure 7: TRβ Reduced Stem Cell Characteristics in ATC Cells. ................................ 65

Figure 8: Re-expression of TRβ Increased Apoptotic Signaling. ................................. 68

Figure 9: The Interferon-JAK1-STAT1 Pathway is Activated by TRβ. ....................... 69

Figure 10: TRβ Activates Alternate Apoptotic Pathway. ............................................. 70

Figure 11: Baseline expression of thyroid hormone receptors and RUNX2 in breast

cells. ........................................................................................................................... 89

Figure 12: RUNX2 expression is reduced by Thyroid Hormone Treatment. ................ 90

Figure 13: TRβ, and not TRα, Represses RUNX2 ....................................................... 91

xi

Figure 14: Knockdown of THRB in MCF10A enhances RUNX2 and RUNX2

activity. ...................................................................................................................... 92

Figure 15: TRβ Binds to the RUNX2 Promoter........................................................... 93

Figure 16: Thyroid Hormone Receptor Beta Alters Genes that Determine Breast

Intrinsic Subtype....................................................................................................... 108

Figure 17: Expression of Thyroid Hormone Receptor Beta Induced Alterations to

the Transcriptome and Signaling Networks ............................................................... 110

Figure 18: Chromatin Immunoprecipitation Enrichment Analysis Reveals Putative

Secondary Regulators ............................................................................................... 111

Figure 19: Thyroid Hormone Receptor Beta Represses Invasive Pathways. .............. 113

Figure 20: A Subset of Genes are Regulated by TRβ in Breast and Thyroid Cancer

Cells ......................................................................................................................... 114

Figure 21: Thyroid Cancer and Breast Cancer Incidence are Positively Correlated

among Vermont Counties ......................................................................................... 135

Figure 22: Summary of Possible Etiological Factors in the Thyroid and Breast Cancer

Link .......................................................................................................................... 138

1

CHAPTER 1: Comprehensive Literature Review

1.1. Thyroid Hormone Receptor Signaling

The human genome encodes for two nuclear thyroid hormone receptors (TR),

which are member of the nuclear hormone receptor (NR) superfamily of genes. TRs are

encoded by the genes THRA, which produces the protein TRα, and THRB, which codes

for TRβ. TRs serve as receptors to the hormones produced by the thyroid gland,

thyroxine (T4) and triiodothyronine (T3). Both proteins exhibit greater binding affinity to

T3 over T4. As a result, T4 functions as a pro-hormone for TRs. Although the thyroid does

produce some T3, most T3 is the result of deiodinases cleaving the 5’ iodine atom from T4

in peripheral tissues (1). Nuclear TRs are ubiquitously expressed throughout the body,

however different tissues exhibit different concentrations of each TR. Expression of TRα

is greater in muscle and fat tissue, whereas TRβ is the predominate TR expressed in the

brain and liver (1). TRs, unlike steroid NRs, are predominately nuclear even in the

absence of endogenous ligand. As a consequence, TRs are able to exert transcriptional

effects in the presence and the absence of ligand. Additionally, the integrin αvβ3 is

preferentially responsive to T4, rendering the pro-hormone status of T4 dependent upon

the context of the signaling that occurs within a given cellular system (2). As a cell

membrane bound receptor, αvβ3 triggers a kinase cascade, primarily through stimulation

of the mitogen activated protein kinase (MAPK) pathway (3).

The different modes of signaling from thyroid hormones have recently been

codified based upon broad mechanisms of action (4). Type 1 signaling reflects the

canonical model of thyroid hormone action, transcriptional regulation. In this model,

2

nuclear TRs bind to chromatin directly, either as a monomer, a homodimer, or a

heterodimer, and recruit protein complexes to alter transcription of a gene. Type 2

signaling is where TRs bind to other DNA-bound proteins to modulate their activity. This

mechanism has been demonstrated to occur with AP1 where TRs blocks AP1 signaling

(5-7). Type 3 signaling is the last to occur with nuclear TRs, however the mechanism is

entirely non-genomic. TR binds to a cytoplasmic protein in order to regulation signaling.

An example of this is that both TRα and TRβ are able to bind to the phosphoinositide 3-

kinase (PI3K) complex in the cytoplasm in order to alter downstream AKT

phosphorylation (8). Lastly, type 4 thyroid hormone signaling is through receptors other

than nuclear TRs. This is primarily through the αvβ3 integrin.

1.1.1. Thyroid Hormone Receptor Regulation of Gene Expression

The primary method by which TRs act in a cell is through type 1 signaling. The

canonical model by which TRs exert regulatory effects is through cofactor switching (1).

In the absence of hormone, TR is bound to DNA at a thyroid hormone response element

(TRE) and recruits proteins that repress gene expression. The census element upon which

TRs bind is AGGTCA, which forms a half-site. A full TRE contains two direct binding

sites, typically separated by 0, 4, or 6 nucleotides (9). These binding sets may be direct

repeats, palindromic, or inverted palindromes. Depending upon the nature of the TRE,

TR may bind to a single half site, as a dimer to two consensus elements, or as a

heterodimer with another transcription factor, most commonly retinoid x receptor (RXR).

Notable co-repressors of gene expression are nuclear receptor co-repressor (NCOR) and

silencing mediator for retinoid or thyroid-hormone receptors (SMRT) (10,11). These co-

repressors aid in the further recruitment of histone methyltransferases (12) and histone

3

deacetylases (13), which leads to the modification of chromatin in order to compact DNA

and inhibit the ability of polymerases to synthesize RNA. Upon binding of cognate

ligand, a conformation change in TR occurs and the association between TR and co-

repressor complexes dissociates. Co-activator proteins including nuclear receptor

coactivator 1 (NCOA1), p300, and the vitamin D receptor interacting protein/thyroid

hormone receptor-associated protein (DRIP/TRAP) complex (14,15). Importantly,

NCOA1 and p300 have each been shown to exert histone acetyltransferase (HAT)

activity, thereby modifying chromatin to induce an open structure and promote gene

transcription (15). Although, the DRIP/TRAP complex does not demonstrate HAT

activity and does not require histones to be present to promote transcriptional activity,

demonstrating that varied mechanisms of gene induction are utilized on TREs (16).

In addition to positively inducing the expression of genes, a minority of TREs

are responsible for repressing gene expression in the presence of T3. These negative

TREs are not as well characterized as positive TREs, although many of the same co-

regulatory proteins are involved. In the absence of hormone, TRs can stimulate

expression of a gene regulated by a negative TRE while the addition of T3 drives gene

repression (17). This hormone-null stimulation is blocked in a mutant TR which cannot

interact with co-repressor proteins NCOR and SMRT (18). Additionally, T3 treatment

initiated the dissociation of NCOR and SMRT from a negative TRE, however there was

no accumulation of coactivator proteins (19). These results suggest that corepressor

proteins act in a stimulatory manner in the context of a negative TRE, although the

molecular mechanism is not yet understood.

4

Cistrome wide data of TRβ binding demonstrates the inadequacy of the

canonical type 1 model. A key concept of this model is that, on absence of cognate

ligand, TRs bind to TREs and that addition of T3 is not required to initiate a protein-DNA

interaction. However, chromatin immunoprecipitation (ChIP)-sequencing of TRβ in

mouse liver shows that there is minimal overlap between peaks in hypothyroid and

hyperthyroid animals (20). This is contrary to the canonical model discussed above where

TRs bind to TREs independent of hormonal control. Additionally, evidence from

glucocorticoid receptor (GR), another member of the NR superfamily, show that

interaction between a NR and chromatin are transient (21). Residence times on the

chromatin for GR varied significantly, depending upon the response element, ranging

from 0.2 to 47 seconds. If TRs act in a similar fashion it would mean that the current

model is insufficient to explain how unliganded TRs repress the expression of positive

TREs.

1.1.2. Thyroid Hormone Signaling in Cancer

Thyroid hormones have been investigated in tumor biology since the 19th

century when thyroid extract was used in conjunction with an ovariectomy for

management of surgically unresectable breast cancer (22). Due to there being three

receptors that respond to thyroid hormones in the body, it has been difficult to determine

whether thyroid hormone activity is of a net positive or negative with regard to cancer

risk. There is epidemiological evidence of a pro-oncogenic role of high levels of thyroid

hormones (23) as well as evidence of hypothyroidism protecting against breast cancer

(24). However, the type of tissue matters as hypothyroidism is deleterious in

hepatocellular carcinoma (HCC), where low levels of thyroid hormones are associated

5

with increased risk of HCC (25,26). Importantly, hypothyroidism is associated with

reduced survival following liver transplantation for HCC patients (27). There is also

evidence that hypothyroidism impairs the efficacy of tyrosine kinase inhibitors in cancer

patients (28). Additionally, patients treated with T3 after being rendered hypothyroid

showed improved outcomes in a small, multicancer clinical study (29). These data are

consistent with the different thyroid hormone receptors playing different roles in tumor

biology and further demonstrate the anti-tumor potential of T3 signaling while

highlighting how T4-mediated pathways can promote oncogenesis.

As mentioned previously, the T4 responsive αvβ3 integrin exerts activity through

stimulation of the MAPK pathway. The MAPK pathway is well-characterized to be pro-

tumorigenic and drives the survival of a multitude of types of cancers (Figure 1). Briefly,

following stimulation of a receptor tyrosine kinase (RTK) and subsequent

autophosphorylation the phosphotyrosine residues serve as a docking site for a scaffold

complex. Through this complex a Ras protein is activated by replacement of bound

guanidine diphosphate (GDP) with guanidine triphosphate (GTP). This allows for

binding, and subsequent activation, of a rapidly accelerated fibrosarcoma (RAF) family

protein. In turn, RAF phosphorylates MEK, which activates ERK (also known as MAPK)

by phosphorylation (30). ERK acts upon an array of proteins to promote pro-tumorigenic

signaling to induce unchecked cell proliferation and evade apoptosis. As a result of

MAPK stimulation, The T4-αvβ3 axis promotes tumor cell proliferation and anti-

apoptotic activity (31). Additionally, αvβ3 is a driver of migration and invasiveness in

cancer cell lines (32,33). This activity can result in pro-tumorigenic signaling from T4

when expression of αvβ3 is high.

6

Figure 1: The Thyroid Hormone Modulated Pathways MAPK and PI3K are Drivers of

Tumorigenesis

The nuclear TRs also have a role in tumor biology. The viral oncogene v-erbA is

homologous to TRα (34,35). In chicken embryonic fibroblasts, v-erbA induced

anchorage-independent growth and tumor formation (36). This viral gene lacks functional

hormone receptor binding, and so can act antagonistically to endogenous thyroid

hormone signaling ((37). However, it has also been demonstrated that the majority of

genes regulated by v-erbA are not regulated by TRα, suggesting that the competition

model is incomplete (38). TRα itself has a putative oncogenic role. High levels of TRα

expressed in intestinal tissue increase tumor formation (39). This is likely due to the

known proliferative role of TRα in the colon, primarily through stimulation of the WNT

pathway effector β-catenin (40). TRα is also a putative oncogene in breast tissue; high

levels of the ligand-responsive splice variant TRα1 are associated with reduced survival

odds in breast tumors (41). Additionally, high levels of TRα is linked with worse

outcomes in patients diagnosed with BRCA-associated breast cancer (42). This is in

7

contrast to TRβ, which was reported to have a protective role in the same study (42).

Heightened TRβ expression has also been demonstrated to increase survival odds in

triple-negative breast cancer (43). Some of the protective role of TRβ expression may be

a result of sensitizing tumor cells to chemotherapeutic agents (43).

There are a multitude of studies which highlight the functional significance of

TRβ in tumor biology. Some of the earliest evidence demonstrating that TRβ could have

a tumor suppressing function was the observation that TRβ was frequently mutated in

hepatocellular carcinoma (44) and PTC (45). The tumor suppressive functionality of TRβ

was validated with the discovery that a N-terminal mutation which causes resistance to

thyroid hormone (designated TRβ-PV) can spur the development of FTC in mice (46,47).

When homozygous TRβ-PV mice were crosses with Pten+/- mice, the resulting

THRBPV/PV Pten+/- animals developed more aggressive thyroid cancer, including tumors

with increased anaplasia and metastasis (48). WT TRβ abrogates the tumorigenicity of

FTC cells, as demonstrated by xenograft experiments (49). Further investigation of TRβ

in cancer models showed that overexpression can inhibit the progression of

hepatocellular carcinoma and both luminal and basal breast cancers (50,51). Additionally,

in a breast cancer xenograft, TRβ-PV fails to inhibit tumorigenesis (52).

The cancer studies on TRβ have revealed important anti-tumorigenic signaling.

TRβ-PV exhibits strong binding to the PI3K complex, specifically to the regulatory p85α

subunit in a classic type 3 mechanism (53,54). In the class model of the PI3K pathway

(Figure 1), PI3K is stimulated by an RTK to promote cell growth. PI3K catalyzes the

production of phosphatidylinositol (3,4,5)-trisphosphate (PIP3) from phosphatidylinositol

4,5-bisphosphate (PIP2). PIP3 serves as a docking site for the enzymes 3-

8

phosphoinositide dependent protein kinase-1 (PDK1) and mammalian target of

rapamycin complex 2 (mTORC2). Both PDK1 and mTORC2 phosphorylate the kinase

AKT, also known in the literature as protein kinase B, on threonine 308 and serine 473,

respectively, in order to induce the activity of AKT (55-57). Once activated, AKT

phosphorylates a variety of proteins to promote cell growth, migration, and suppress

apoptosis. TRβ and T3 act to increase PI3K activity in non-cancerous models (58,59),

however in tumor studies WT TRβ consistently reduces AKT phosphorylation,

demonstrating a role in abrogating this pro-tumorigenic pathway (49,52,60).

TRβ anti-tumor activity has been demonstrated through regulation of other

pathways implicated in tumorigenesis. TRβ reduces expression of the oncogenes β-

Catenin (61,62) and RUNX2 (63) in thyroid cancer. Additionally in thyroid cancer, TRβ

represses NF-κβ signaling (64) and modulates inflammatory processes (64). Studies in

breast cancer models demonstrate that TRβ inhibits pro-tumorigenic signaling from

STAT3 (51), the Ras/MAPK pathway (65), and lowers expression of epithelial to

mesenchymal transition (EMT) markers (50).

In addition to the evidence from epidemiological association studies, the TRβ-

PV mutant, and delineation of anti-oncogenic signaling, it has also been demonstrated

that TRβ prevents tumorigenesis from initiating. Hyperthyroid mice are less likely to

develop liver tumors than euthyroid animals, whereas hypothyroid mice are the most

likely to develop hepatocellular carcinoma (66). As TRβ is the predominant thyroid

hormone receptor in liver cells, this action is likely through TRβ signaling. Additionally,

knockout of TRs in mice drive the development of thyroid tumors (67). Collectively,

these data show that TRβ is a potent tumor suppressor of multiple types of solid tumors

9

and further investigation into the molecular activity of TRβ is merited.

1.1.3. Structural Biology of Thyroid Hormone Receptor Beta

The structural features of TRβ drive the molecular role of the protein. TRβ is

comprised of four domains. These are the N-terminal A/B transactivation domain, the

DNA-binding domain (DBD), a hinge domain, and a C-terminal ligand-binding domain

(LBD) (Figure 2) (68). Although not required for binding to DNA ,the A/B domain is

essential for induction of transcriptional activity (69). The LBD is comprised of 12 alpha

helices that, in addition to stabilizing the interaction between receptor and T3, act as

interaction sites for dimerization, co-activators, and co-repressors (70). Additionally, TRβ

contains a nuclear localization signal (NLS) within the hinge domain starting at amino

acid 184 and nuclear export signals (NES) are present in the ligand binding domain of

TRβ (71) (Figure 1). Most NRs are predominately cytoplasmic until binding to ligand,

however, TRs are unique in that they are constrictively nuclear, which does allow for

ligand-independent transcriptional regulation (72).

Figure 2: Functional Domains of TRβ.

Like many proteins within the body, TRβ is a phosphoprotein. Phosphorylation of

TRβ has been shown to enhance transcriptional activity (73). Additionally, kinase

inhibitors can block T3 induced transcription of lipogenic enzymes (74). Inhibition of

serine/threonine kinases have also been shown to reduce the stability of TRβ as well as

reduce the level of THRB mRNA expressed in cells and dimerization with retinoid x

10

receptor (75,76). Additionally, T3 appears to be able a key signal to induce

serine/threonine phosphorylation of TRβ (77). Identified sites of phosphorylation are

T134 and S142 in the DBD and Y406 in the LBD (78-80). T134 is phosphorylated by

protein kinase C and has a role in protein localization, keeping the receptor in the

cytoplasm. Phosphorylation of S142 is trigged by the MAPK pathway and disrupts

interactions between TRβ and SMRT. Src phosphorylates TRβ on the residue Y406 and

phospho-ablative mutants of TRβ are unable to suppress xenograft tumor growth (80,81).

Additional putative phosphorylation of DBD residues Y147 and Y171 was observed in

the hippocampus, and although mutation of the residues blocked TRβ driven induction of

the PI3K pathway direct phosphorylation of Y147 and Y171 was not empirically

confirmed when TRβ was subjected to mass spectroscopy (59).

Other post-translational modifications to TRβ has also been demonstrated to be

important in regulating TRβ activity. Ubiquitin in a small protein that is linked to lysine

residues by a ligase; polyubiquitination serves as a signal for proteasomal degradation.

This process serves to rapidly degrade TRs upon T3 binding, thereby modulating the

transcriptional activity of TR (82). Interestingly, inhibition of ubiquitin-mediated

degradation reduced the transcriptional activity of TRβ in vitro (82). This phenomenon is

similar to what has been observed in estrogen receptor (ER) α and progesterone receptor

(PR) and may be a common feature in the regulation of NRs (83). TRβ is also regulated

by SUMOylation, that is, the addition of small ubiquitin-like modifier (SUMO) to a

lysine residue. SUMOylation enhances the transcriptional activity of TRβ on both

positive and negative TREs and alters affinity for co-regulator interactions (84). These

11

modifications add an additional layer of nuance to regulation of gene expression by TRβ

beyond activation by ligand binding.

1.2. Thyroid Cancer

Thyroid cancer is the most commonly diagnosed cancer in the United States.

Incidence of the disease is increasing rapidly and has been for multiple decades (85).

Thyroid cancer is projected to be the 4th most commonly diagnosed cancer in the

United States by 2030 (86), although in the last few years the increase in thyroid cancer

incidence has started to stabilize (87). This stabilization may be due, in part, to

reclassification of the thyroid cancer subtype “noninvasive encapsulated follicular

variant of papillary thyroid cancer” to “noninvasive follicular thyroid neoplasm with

papillary-like nuclear features” (88). This variant has been estimated to make up

approximately 9% of papillary thyroid cancer diagnoses (89). Regardless, the increase

in incidence is unlikely to be simply the result of improvements in diagnostics due to

the increase in incidence of both relatively benign and more aggressive cancers (90).

The majority of thyroid tumors develop from the epithelial follicular cells,

which are responsible for production of thyroid hormones T4 and T3. This includes the

major subtypes of papillary (PTC), follicular (FTC), poorly differentiated (PDTC), and

anaplastic thyroid cancer (ATC) (91). PTC and FTC are differentiated tumors with

overall positive prognosis, whereas PDTC and ATC are dedifferentiated tumors with

poor prognosis. These subtypes of thyroid cancer are distinguished by differences in

their morphology and genetic drivers. Additionally, different subtypes of thyroid cancer

vary in their prognoses and treatment options.

12

1.2.1. Differentiated Thyroid Cancer

The primary subtypes of differentiated thyroid cancer are PTC and FTC. Over

80% of thyroid cancers are diagnosed as PTC and approximately 10% are FTC (92). Both

subtypes typically exhibit an overall good prognosis, although FTC is the more

aggressive of the two. The primary cytopathological difference is FTC nodules are

typically encapsulated by fibrous connective tissues and PTC is unencapsulated (93).

Additionally, FTC typically presents with larger tumors at time of diagnosis.

The genetic alterations and signaling pathways that induce differentiated thyroid

cancers are well-established. Mutations to the BRAF gene are common, 45% of PTC

cases exhibit the constitutively active BRAFV600E variant (94). The other primary

molecular subtype of PTC are Ras-like carcinomas (95). BRAF and Ras proteins are

effectors within the mitogen activated MAPK pathway (Figure 1), which promotes cell

growth. Increased MAPK signaling is the primary molecular alteration driving PTC

development and malignancy (91). Mutations to RAF proteins and effectors of the

MAPK pathway are common in an array of cancers and inhibitors of the pathway have

demonstrable utility in treating tumors, including thyroid tumors (96). Mutation of the

RAF gene BRAF is predictive of a worse prognosis in thyroid cancer patients (97).

Additionally, ~5% of PTCs have been reported to harbor mutations to EGFR, an

upstream RTK and itself a recognized oncogene (98). The second major driver of thyroid

cancer is the phosphoinositide 3-kinase (PI3K) pathway (Figure 1). Mutations to the

PI3K pathway are common in FTC, which includes mutations to the catalytic subunit of

PI3K, PIK3CA, and mutations or deletions to the phosphatase PTEN (91), which reverses

PI3K driven phosphorylation of PIP2. The PI3K pathway is recognized to be the primary

13

molecular pathway driving FTC malignancy (91).

Differentiated thyroid cancer is treated primarily through surgery and

radioactive iodide (RAI) treatment (99). Depending upon overall risk of the lesion,

surgical options include partial or a complete thyroidectomy. RAI is the first example

of precision medicine applied to cancer, with reports dating back to the 1940s (100).

RAI is effective and selective because thyroid glands express a sodium-iodide

symporter (NIS), unlike most other cells in the body, due to the necessity of iodine for

the synthesis of thyroid hormones. This allows for systemic RAI to preferentially target

the cancerous thyroid tissue if the tumor robustly expresses NIS and if the cells retain

iodide. Without NIS activity, the iodide anion cannot cross through the cell membrane

and, as a result, these cells are not subjected to radiotoxicity. However, during the

process of tumorigenesis resistance to RAI can develop through loss of NIS expression

and activity. The MAPK pathway has been implicated in this process as inhibition of

the MAPK effector MEK1 increases expression of NIS and restores the ability of a

thyroid tumor cell to retain iodide (101). In clinical trials MEK1 inhibition restored NIS

functionality sufficiently to allow formerly RAI retractive patients to be successfully

treated with RAI (102,103).

1.2.2. Dedifferentiated Thyroid Cancer

Dedifferentiated thyroid tumors include both PDTC and ATC. These subtypes

of thyroid cancer are rare; PDTC makes up approximately 3% of thyroid tumors (104)

and ATC comprises 1-2% of thyroid tumors (105). Both tumor types are highly

invasive and exhibit low expression of thyroid markers including NIS. ATC is

distinguished from PDTC pathologically by exhibiting greater necrosis, increased

14

heterogeneity of cellular morphology, and atypical nuclei (105). Additionally, ATC is

definitionally a stage IV neoplasm (106). Both of these subtypes of cancer have a worse

prognosis than differentiated thyroid cancers. Five year survival in PDTC is

approximately 60% (107) and less than 15% of ATC patients survive two years after

diagnosis (105).

In recent years, the genetic markers defining these two thyroid cancer subtypes

have been delineated. These alterations provide insights into the molecular drivers of

the most aggressive variants of thyroid cancer. Both dedifferentiated subtypes exhibit

mutations in the MAPK signaling pathway; mutations to BRAF are particularly

prominent being found in a third of PDTC and nearly half of ATC cases (108).

Mutations to TERT are also common in both subtypes, with 40% of PDTC and over

70% of ATC tumors exhibiting a TERT mutation. Almost three quarters of ATC tumors

exhibit mutations to the transcriptional regulator TP53, which is rarely mutated in other

thyroid cancer subtypes (108,109). Additionally, mutations to SWI/SNF components,

PI3K effectors, and histone methyl transferases are more common in ATC than in

PDTC (108). There was also a difference in the thyroid differentiation score (TDS), a

value derived from the expression of 16 genes (95) (now 13 genes (110)) that defines

thyroid cells. These are primarily genes that are necessary for the synthesis of thyroid

hormones. PDTC tumors had a higher TDS than ATC tumors, additionally, Ras-like

PDTC tumors exhibit an even greater TDS (108).

Due to low expression and activity of NIS in dedifferentiated thyroid cancers,

RAI is not an effective treatment option. Conventional treatment of ATC is largely

palliative, with the goal being to improve quality of life at the expense of life extension

15

(105). Conventional treatments include the use of surgery, beam radiotherapy, and

broad-spectrum chemotherapy. Inhibitors of both MAPK and PI3K signaling are

actively being investigated for use in advanced thyroid cancers, including PDTC and

ATC (111). In 2018, the FDA approved a combinational therapy of a BRAF and MEK

inhibitor for ATC, with a response rate of over 60% in trials (112). In one trial, 80% of

patients with BRAF mutant ATC were alive after 12 months (113). Immunotherapies

have also shown promise, with antibodies against PD-1 exhibiting a 20% response rate

(112).

As in treatment resistant differentiated thyroid cancer, there is an interest in

inducing expression of NIS in dedifferentiated thyroid tumors. Transfection of wild

type p53 into cultured ATC cells restored NIS expression and rendered the cells

sensitive to RAI (114). PI3K inhibition also increased NIS expression in ATC stem

cells (115). An inverse agonist to estrogen-related receptor gamma, GSK5182,

increased expression of NIS and other thyroid markers (116). In ATC xenografts

GSK5182 also restored sensitive to RAI, where co-treatment with the GSK5182 and

131I reduced tumor burden (116).

1.3. Breast Cancer

Breast cancer is the most common cancer diagnosed in women in the United

States with over 250,000 cases per year (87). Additionally, breast cancer is the second

leading cause of cancer mortality in American women (87). Globally, breast cancer is

the most common cancer in women and also responsible for the greatest number of

cancer deaths (117). Breast cancer has also been revealed to be a risk factor for thyroid

16

cancer, similarly a past history of thyroid cancer increases the risk for the development

of a breast malignancy (118).

Clinical classification of breast cancer is primarily based upon the expression of

ERα, PR, and the epidermal growth factor receptor HER2, also known as ERBB2

(119). Expression of ERα and PR are linked to a more differentiated disease whereas

highly aggressive breast cancers exhibit loss of these three receptors. These receptors

are also druggable and are commonly targeted in clinical practice to reduce tumor

burden. Molecular analysis has increased understanding of the differences between

breast tumors resulting in the PAM50 algorithm. A set of 50 genes are now used to

define the intrinsic subtypes of breast cancer and classify breast tumors (120,121).

These subtypes include hormone receptor positive luminal A and luminal B, HER2

positive, normal-breast like, and basal-like breast cancers. In the United States,

Surveillance, Epidemiology, and Endpoints Results classifications includes both

luminal subtypes, HER2 positive, and basal-like subtypes, but not Normal-like.

Luminal A breast cancer is the most commonly diagnosed and HER2 positive is the

rarest (Figure 3) (122). Luminal A exhibits the greatest survival odds, whereas basal-

like breast cancer patients have the worst outcomes (Figure 3) (122).

17

Figure 3: Breast Cancer Subtypes Vary in Prevalence and Prognosis. A) Prevalence of Breast

Cancer Subtypes in the United States of America (2012-2016). B) Relative 5-year Survival of

Breast Cancer Subtypes in the United States of America (2010-2015).

1.3.1. Luminal Breast Cancer

Luminal breast tumors are defined as ERα positive tumors. Luminal subtypes

are distinguished from each other by expression of the proliferation marker Ki-67 and

PR; luminal B breast cancer exhibits either increased Ki-67 or reduced PR whereas

luminal A tumors express comparatively high PR and low Ki-67 (123). Luminal B

tumors may also have increased expression of HER2 (124). Luminal subtypes are less

likely to present with distal metastases, with the least likely being luminal A, consistent

with the observed heightened survival of this subtype (125).

The most prominent genetic feature in luminal breast cancers is the heightened

expression and activity of ERα, however it is not the sole genetic driver of the disease.

Mutations to PIK3CA are relatively common; present in approximately 45% of luminal

A cancers and in approximately 30% of luminal B tumors (126,127). GATA3 is mutated

in approximately 15% of luminal breast cancers and mutually exclusive to PIK3CA

18

alterations. Mutations to MAP3K1 are most prevalent in luminal A, however TP53

mutations are more common in luminal B (126,127).

The primary treatment for luminal breast cancer is surgical intervention with

adjuvant chemotherapy (128). Due to the high expression and activity of ERα in

luminal breast cancers, it is relatively common to treat them with a selective estrogen

receptor modulator (SERM) (128,129). SERMS act on ERα in a tissue selective manner

to reduce estrogenic signaling in the breast while maintaining a degree of estrogenic

function in other body tissues (130). This specificity arises, in part, due to the tissue

specific expression of ERα and ERβ, the dimerization partner for ERα on a response

element, and subtle differences in protein conformation that results from a SERM or

endogenous estrogen binding and resultant co-factor binding (130). This results in an

antagonistic effect in some tissues and an agonistic effect in others. For example, the

prototypical SERM tamoxifen is anti-estrogenic in breast, but pro-estrogenic in uterine

tissue (130). Aromatase inhibitors (AI) are also commonly used as they inhibit the

ability of the enzyme aromatase to synthesize bioactive estrogen (131). AI therapy may

be prescribed separately, or in conjunction with a SERM. Both AIs and SERMs are

used to reduce the odds of recurrence and to control the spread of metastatic nodules.

However, treatment resistance is a relatively common occurrence and limits the utility

of AIs and SERMs. Alterations to multiple pathways can contribute to resistance.

Tamoxifen resistance can arise from upregulation of HER2 and/or PI3K activity (132).

SERM resistance can also arise from a truncated ERα, dubbed ERα-36, which acts

along with HER2 to drive tumor growth (133,134). Mutations to the ligand binding

domain of ERα are associated with AI resistance (135) and have been shown in cell

19

models to be transcriptionally active in absence of cognate ligand (136). These

mechanisms of resistance ultimately limit the utility of anti-estrogen therapies, but also

provide alternate approaches for the treatment of luminal breast tumors.

1.3.2. HER2 Positive Breast Cancer

The HER2 positive subtype of breast cancer is defined by high expression of

HER2 and low expression of the hormone receptors ERα and PR. Increased HER2

expression is associated with an increase in tumor grade (137). A primary or secondary

metastasis occurs within HER2 positive breast cancer approximately 30% of the time,

more often than in any other subtype of breast cancer (125).

HER2 itself is a member of the epidermal growth factor receptor family of

RTKs, however it has no known ligand (138). HER2 can homodimerize and also

heterodimerize with related RTKs, most prominently EGFR and HER3, in order to

initiate a phosphorylation cascade (139). Following dimerization, HER2 stimulates the

previously described PI3K and MAPK pathways. Additionally, HER2 will stimulate

protein kinase c (PKC) though phospholipase c (PLC) (140) (Figure 4). PLC cleaves

PIP2 to release the products diacylglycerol (DAG) and inositol 1,4,5-trisphosphate

(IP3) (141). IP3 is important for calcium release, however DAG directly binds to PKC

in order to stimulate kinase activity. These three pathways are all tumorigenic and

promote proliferation via progression through the cell cycle, increase invasiveness, and

stimulate pro-survival/anti-apoptotic activity (140).

20

Figure 4: HER2 stimulates PKC, MAPK, and PI3K pathways.

In addition to heightened expression of HER2, the HER2 positive subtype of

breast cancer also exhibits significant mutational burdens. Alterations to PI3K effectors

are common, PIK3CA is mutated approximately 40% of the time and PTEN is mutated or

lost in approximately 20% of tumors (126,127). Additionally, TP53 is mutated in 75% of

cases (126,127). HER2 positive breast cancers exhibit a greater overall mutational burden

than other breast cancer subtypes (127).

Similar to luminal breast cancers, the standard of care for HER2 positive breast

cancer is surgery with adjuvant chemotherapy (128). Additionally, the use of a

monoclonal antibody that inhibits HER2 is recommended. The first approved anti-HER2

agent is trastuzumab, which is still the front line therapy for HER2 positive breast cancer

and substantially improved survival for patients (142). Other anti-HER2 agents have

since been developed, including the monoclonal antibody pertuzumab and irreversible

kinase inhibitors afatinib and neratinib (138). Unfortunately, cardiac side effects are

21

relatively common with trastuzumab, as high as 27% in initial studies however that has

been reduced to less than 10% in more recent trials, likely a result of different patients

populations exhibiting varying degrees of sensitivity to these side effects (143). The

HER2 kinase inhibitors exhibit a more favorable cardiac profile than antibody therapy

(143). As with other targeted therapies, resistance to trastuzumab is a problem. A variant

known as p95HER2, which lacks the trastuzumab-binding extracellular domain, is one of

the known mechanisms of resistance (144). Strikingly, this variant does exhibit kinase

activity and stimulates canonical HER2 downstream effectors (145). Loss of PTEN

expression (146) and PIK3CA mutations (147) also inhibit the anti-tumor activity of

trastuzumab.

1.3.3. Basal-like Breast Cancer

Basal-like breast tumors exhibit a triple-negative phenotype, that is, they do not

express ER, PR, or HER2. As a result of this, treatment options are more limited than the

other breast cancer subtypes. Accordingly, outcomes are worse with basal-like breast

cancer exhibiting the lowest 5-year survival rate of the breast cancer subtypes (Figure 3).

In addition, basal-like breast tumors have a high propensity to metastasize, only HER2

positive breast cancer exhibits a greater capacity to develop metastatic lesions (125).

In addition to the defined lack of ER, PR, and HER2, basal-like breast cancer

exhibits a unique and complex molecular signature. TP53 is more likely to be mutated in

basal-like breast cancers than any other breast cancer subtype (126). Additionally, genetic

alterations to the PI3K pathways are common, with mutations to PIK3CA and deletion of

PTEN being relatively common (126). BRCA-associated breast cancers are

disproportionally basal-like (148). The basal-like subtype also has a unique

22

transcriptomic profile, exhibiting increased expression of EGFR and basal cytokeratins

(149).

A further division of basal-like breast cancer can be made based upon the

expression levels of claudins. Claudins are a class of proteins which are responsible for

maintaining the tight junctions between epithelial cells (150). Basal breast tumors that

express low levels of claudins are more mesenchymal than claudin-high breast cancer,

also, claudin-low breast cancer exhibits increased levels of stem-cell markers (151). Both

of these features promote the ability of the tumor cells to survival disaggregation and

colonize a new tumor elsewhere in the body.

Similar to other breast cancers, the standard of care for non-metastatic basal-like

breast cancer is surgical, with supplemental radiation (152). Broad spectrum

chemotherapy is also appropriate for invasive cases and for patients with metastatic

disease. Additionally, combinational approaches are being attempted in order to induce

synthetic lethality. Poly (ADP-Ribose) polymerase (PARP) inhibitors are used to impair

the ability of the cells to repair damaged DNA. PARP inhibitors have been shown in

multiple trials to slow tumor progression, with enhanced efficacy when paired with

another anti-tumor agent (153), and are now approved for use clinically. The RTKs

VEGFR and EGFR are also being actively investigated as potential targets for inhibition

with promising results in clinical trials (154).

BRCA mutations lead to basal-like breast cancer more often than any other

subtype and represent 25% of basal-like tumors (155). Due to BRCA proteins acting to

repair DNA, platinum-based compounds that work by damaging DNA have been

investigated to determine if these cells are more sensitive to this treatment modality. The

23

data so far trends towards BRCA mutant breast cancer being more sensitive to platinum-

based drugs, although recent meta-analysis suggests that the advantage of this therapeutic

approach is relatively minor (156-158).

The androgen receptor (AR) is emerging as another important molecular target

in basal-like breast cancer. Although AR is expressed in luminal breast cancer, there is a

lack of druggable targets in basal-like breast cancer, making it more significant in this

subtype (159). AR functions in a similar function to other nuclear NRs, including ERs. In

the cytoplasm, AR is bound to heat shock proteins (HSP) until a ligand binds, at which

point it translocates into the nucleus. In the nucleus, AR binds to response elements to

recruit a protein complex which regulates transcriptional activity of the gene (159). A

recent phase II trial demonstrated that a subset of breast cancer patients responded

positively to the AR antagonist enzalutamide (160). As the molecular signaling driving

basal-like breast tumors becomes better understood, other new therapeutic strategies are

emerging.

1.4. Scope of Dissertation

TRβ exhibits profound anti-tumorigenic activity in multiple tissue types, most

prominently thyroid and breast. There are effective treatment options for the more

differentiated subtypes of these diseases, however the more aggressive variants present a

challenge. The PI3K-AKT pathway is commonly mutated in both thyroid and breast

tumors, and this is a pathway upon which TRβ has a profound suppressive role. However,

TRβ primarily acts as a transcription factor and the transcriptional activity of TRβ in

cancer cells is not well-delineated, a gap that this dissertation attempts to fill.

24

In chapter 2, the ATC cell line SW1736 to characterize how TRβ alters cellular

pathways and phenotypic characteristics that underly the tumor suppressive response.

This work expands upon the prior study from the Carr lab that detailed the discovery of

the TRβ-RUNX2 pathway (63). In addition to describing how TRβ acts to repress pro-

malignant cellular characteristics, the discovery of novel TRβ-induced re-differentiation

and induction of STAT1 activity are delineated.

Chapter 3 was also the result of expanding on the discovery of the TRβ-RUNX2

pathway in thyroid cancer. In this chapter, the evidence showing this pathway is also

active in breast cells is presented. Additionally, this chapter is a demonstration that the

T3-mediated repression of RUNX2 is a TRβ-specific pathway by highlighting that TRα is

unable to regulate RUNX2 expression.

In chapter 4, TRβ signaling in breast cancer was further investigated by

performing a similar analysis to what is detailed in chapter 2. To determine the effect

TRβ and T3 have on the breast cancer transcriptome, TRβ was overexpressed and RNA-

sequencing was conducted. Novel regulation of genes involved in EMT are described.

Additionally, the differentially expressed genes are compared to the results in thyroid

cancer to reveal common TRβ signaling in thyroid and breast cancer.

Chapter 5 of this dissertation is a further review of the scientific literature, and

the result of serendipitously learning of an epidemiological link between thyroid and

breast cancers. The evidence for the link is discussed as well as putative etiological

factors, including thyroid hormone receptor signaling. This work reframes the results of

the experimental approaches used in prior chapters.

25

This dissertation focuses on the molecular events that the tumor suppressor TRβ

regulates in thyroid and breast cancer. This has revealed novel signaling mechanisms,

potential druggable targets, and demonstrates the importance of cellular identity on the

pathways TRβ regulates.

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CHAPTER 2: Thyroid Hormone Receptor Beta Induces a Tumor Suppressive

Program in Anaplastic Thyroid Cancer.

2.1. Abstract

The thyroid hormone receptor beta (TRβ), a key regulator of cellular growth and

differentiation, is frequently dysregulated in cancers. Diminished expression of TRβ is

noted in thyroid, breast, and other solid tumors and is correlated with more aggressive

disease. Restoration of TRβ levels decreased tumor growth supporting the concept that

TRβ could function as a tumor suppressor. Yet, the TRβ tumor suppression transcriptome

is not well delineated and the impact of TRβ is unknown in aggressive anaplastic thyroid

cancer (ATC). Here, we establish that restoration of TRβ expression in the human ATC

cell line SW1736 (SW-TRβ) reduces the aggressive phenotype, decreases cancer stem-

cell populations and induces cell death in a T3-dependent manner. Transcriptomic

analysis of SW-TRβ cells via RNA-sequencing revealed distinctive expression patterns

induced by ligand-bound TRβ and revealed novel molecular signaling pathways. Of note,

liganded TRβ repressed multiple nodes in the PI3K/AKT pathway, induced expression of

thyroid differentiation markers, and promoted pro-apoptotic pathways. Our results further

revealed the JAK1-STAT1 pathway as a novel, T3-mediated, anti-tumorigenic pathway

that can be activated in additional ATC lines. These findings elucidate a TRβ-driven

tumor suppression transcriptomic signature, highlight unexplored therapeutic options for

ATC, and support TRβ activation as a promising therapeutic option in cancers.

Implications: TRβ-T3 induced a less aggressive phenotype and tumor suppression

program in anaplastic thyroid cancer cells revealing new potential therapeutic targets.

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2.2. Introduction

The non-steroidal nuclear hormone receptor thyroid hormone receptor beta

(TRβ) has been characterized as a tumor suppressor in multiple tumor types, primarily

thyroid, breast, and hepatocellular carcinomas (1,2). Expression of TRβ is typically

reduced in aggressive tumors. Epigenetic silencing of THRB is frequent, while mutations

in the gene are relatively rare. As a nuclear hormone receptor, the canonical function of

TRβ is to regulate gene expression in response to its ligand triiodothyronine (T3). It is

also known that TRβ exerts ligand-independent genomic signaling and has non-genomic

functions that are ligand regulated (3).

Multiple lines of evidence demonstrate the tumor suppressive activity of TRβ in

thyroid cancer. Re-expression of silenced TRβ using demethylating agents delays thyroid

tumor progression in vivo (4), mice expressing a c-terminal frameshift of TRβ (ThrbPV)

spontaneously develop follicular thyroid cancer (5), and ThrbPV/PV KrasG12D mice develop

thyroid tumors with features of dedifferentiated thyroid cancer (6).

TRβ has previously been shown to modulate anti-tumorigenic signaling in

thyroid cancer in part through repression of driver pathways such as PI3K/AKT (7) and

NF-κβ (8); modulation of inflammatory processes (8); and repression of the oncogenes β-

Catenin (9,10) and RUNX2 (11,12). These signaling nodes are important contributors to

the tumor suppressive activity of TRβ, but they likely do not constitute the entire tumor

suppression program.

The current understanding of TRβ tumor suppressive activity has been described

primarily in differentiated thyroid cancers, but there is a need to determine TRβ-mediated

tumor suppressive actions in dedifferentiated anaplastic thyroid cancer (ATC). Although

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this tumor subtype is rare, ATC is responsible for nearly 40% of thyroid cancer deaths

with most patients exhibiting a median survival time of less than six months (13). This is

mainly due to a lack of effective targeted therapeutic options. Characterization of the

activity of tumor suppressors can provide important insights into the development of

rationally designed treatment interventions.

These studies aimed to understand the systemic impact of TRβ and its ligand

thyroid hormone (T3) on ATC cancer cell characteristics. We performed transcriptomic

analysis of TRβ in ATC cells to provide a comprehensive network of cellular signaling

events altered by TRβ and T3. We have paired this transcriptomic data with functional

studies to characterize the TRβ-mediated changes to ATC cell characteristics. Lastly, this

analysis has revealed that activation of a STAT1-dependent anti-proliferative pathway is

a vulnerability in ATC that can be exploited pharmacologically.

2.3. Materials and Methods

Culture of thyroid cell lines.

Anaplastic thyroid cancer cell lines (SW1736, 8505C, OCUT-2, and KTC-2)

were cultured in RPMI 1640 growth media with L-glutamine (300 mg/L), sodium

pyruvate and nonessential amino acids (1%) (Corning Inc, Corning, NY, USA),

supplemented with 10% fetal bovine serum (Thermo Fisher Scientific, Waltham, MA,

USA) and penicillin-streptomycin (200 IU/L) (Corning) at 37°C, 5% CO2, and 100%

humidity. Lentivirally modified SW1736 cells were generated as recently described (12)

with either an empty vector (SW-EV) or to overexpress TRβ (SW-TRβ). SW-EV and

SW-TRβ were grown in the above conditions with the addition of 1 µg/ml puromycin

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(Gold Bio, St Louis, MO, USA). SW1736 and KTC-2 were authenticated by the Vermont

Integrative Genomics Resource at the University of Vermont using short tandem repeat

profiles and Promega GenePrint10 System (SW1736, May 2019; KTC-2, October 2019).

8505C and OCUT-2 were authenticated by University of Colorado by short tandem

repeat profiles (8505C, June 2013; OCUT-2, June 2018).

RNA-seq Library Construction and Quality Control.

80% confluent monolayers of SW-EV and SW-TRβ cells were hormone starved

for 24 hours in phenol red free RPMI with charcoal-stripped fetal bovine serum. 10-8 M

T3 was added and incubated for 24 hours prior to sample collection. Total RNA was

extracted and purified using RNeasy Plus Kit (Qiagen, Venlo, Netherlands) according to

manufacturer’s protocol. Purity of the total RNA samples was assessed via BioAnalyzer

(Agilent Technologies, Santa Clara, CA, USA) and samples with a RIN score >7 were

used for library construction. rRNA was depleted using 1 μg of total RNA with the

RiboErase kit (Roche, Basel, Switzerland). Strand-specific Illumina cDNA libraries were

prepared using the KAPA Stranded RNA-Seq library preparation kit with 10 cycles of

PCR (Roche). Library quality was assessed by BioAnalyzer (Agilent Technologies) to

ensure an average library size of 300 bp and the absence of excess adaptors in each

sample. RNA-Seq libraries were pooled and sequenced on the Illumina HiSeq 1500 with

50 bp single-end reads. Quality scores across sequenced reads were assessed using

FASTQC. All samples were high quality. For alignment and transcript assembly, the

sequencing reads were mapped to hg38 using STAR. Sorted reads were counted using

HTSeq and differential expression analysis was performed using DESeq2. Genes with a

p-value of <0.05 and a fold change of >2 were considered differentially expressed and

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were used for further analysis through Ingenuity Pathway Analysis (IPA) (Qiagen) and

Gene Set Enrichment Analysis (GSEA).

Immunoblot Analysis.

Proteins were isolated from whole cells in lysis buffer (20mM Tris-HCl [pH 8],

137mM NaCl, 10% glycerol, 1% Triton X-100, and 2mM EDTA) containing Protease

Inhibitor Cocktail 78410 (Thermo Fisher Scientific). Proteins were resolved by

polyacrylamide gel electrophoresis on 10% sodium dodecyl sulfate gels EC60752

(Thermo Fisher Scientific) and immobilized onto nitrocellulose membranes (GE

Healthcare, Chicago, IL, USA) by electroblot (Bio-Rad Laboratories, Hercules, CA,

USA). Specific proteins were detected by immunoblotting with the indicated antibodies

(Appendix A-1); immunoreactive proteins were detected by enhanced

chemiluminescence (Thermo Scientific) on a ChemiDoc XRS+ (Bio-Rad Laboratories).

Growth Assays.

Cell growth was measured either through live cell imaging or by cell counting at

discrete points. Live cell imaging was used to assess the growth kinetics of SW-EV and

SW-TRβ. To perform live cell imaging, 5000 cells were plated per well in 24 well plates.

After 24 hours in phenol red free, charcoal stripped media, media was supplemented with

either 10-8 M T3 or equivolume vehicle (NaOH). Every two hours cells were imaged with

a Lionheart automated microscope (Agilent Technologies) and quantified with Gen5

software (Agilent Technologies). The cells entered the log phase of growth at

approximately 48 hours, and we used the portion of the curve following this point to

calculate the doubling time of the cells.

Sulforhodamine B Cell Cytotoxicity Assay Kit (Abcam, Cambridge, UK) was

55

used to stain cell protein as a readout for cell viability. ATC cells were plated in 96-well

clear flat-bottom plates at a density of 5,000 cells/well. Cells were treated with serial

dilutions of the STAT1 activator 2-(1,8-Naphthyridin-2-ly)phenol (2-NP) (Abcam) or

vehicle (DMSO). Plates were then incubated for 72 hours. Cells were fixed, stained, and

then imaged using a plate reader according to manufacturer’s instructions.

Soft Agar Colony Formation Assay.

Soft agar colony forming assays were used to assess anchorage-independent

growth. A layer 0.50% agar in thyroid media (as described) was solidified in 6-well cell

culture plates. SW-EV or SW-TRβ cells were plated in a second layer of 0.25% agar in

thyroid media. 200uL of thyroid media was maintained at the top of each well to prevent

the agar from drying. Where indicated, 10-8 M T3 or vehicle (NaOH) was added to the

media in all layers to evaluate the effects of liganded TRβ on anchorage-independent

growth. Colonies were allowed to grow for 14 days. Live SW-EV and SW-TRβ colonies

were detected via GFP expression using a ChemiDoc XRS+ (Bio-Rad Laboratories).

Colonies were then counted with ImageJ using the Colony Counter plugin.

Tumorsphere Assay.

Tumorsphere-forming assays were used to assess self-renewal and sphere-

forming efficiency. For generating thyrospheres, adherent SW-EV and SW-TRβ

monolayer cells were dissociated with Trypsin-EDTA, and single cells were moved to

round-bottom ultra-low attachment 96-well plates at a density of 1000 cells/well

(Corning). Thyrospheres were cultured in RPMI 1640 growth media supplemented with

20ng/mL each of epidermal growth factor (EGF) and fibroblastic growth factor (FGF)

(Gold Bio). Where indicated, 10-8 M T3 or vehicle (NaOH) was added to the media to

56

evaluate the effects of liganded TRβ on thyrosphere growth. Thyrospheres grew for seven

days and were then counted with an inverted microscope.

Migration Assay.

Cell migration was determined by wound healing assay. Cells were plated and

allowed to grow to 100% confluency. Two hours prior to scratching, cells were treated

with 10 mg/ml Mitomycin C. A scratch was performed with a P1000 pipette tip and

debris was washed away with PBS. Migration media was supplemented with 10-8 M T3.

Images were obtained at 0, 24, 48, and 72 hours. Wound closure was measured using

ImageJ macro “Wound Healing Tool” (http://dev.mri.cnrs.fr/projects/imagej-

macros/wiki/Wound_Healing_Tool). Values were normalized so that the initial scratch

was 0% closure.

Apoptosis Assay.

Cells were plated at a density of 50,000 cells/well and treated with vehicle (1 N

NaOH) or 10-8 M T3. After 5 days the cells were lysed for analysis, poly (ADP-ribose)

polymerase 1 (PARP1) and Caspase 3 cleavage were assessed by immunoblot to measure

apoptotic signaling.

RNA Extraction and Quantitative Real-Time PCR (qRT-PCR).

Total RNA was extracted using RNeasy Plus Kit (Qiagen) according to

manufacturer’s protocol. cDNA was then generated using the 5X RT Mastermix (ABM,

Vancouver, Canada). Gene expression to validate RNASeq analysis was quantified by

qRT-PCR using 2X SuperGreen Mastermix (Thermo Fisher Scientific) on a QuantStudio

3 real-time PCR system (Thermo Fisher Scientific). Fold change in gene expression

compared to endogenous controls was calculated using the ddCT method. Primer

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sequences are indicated in Appendix A-2.

Statistics.

All statistical analyses were performed using GraphPad Prism software. Paired

comparisons were by T-test and group comparisons were made by a 2-way ANOVA

followed by a Tukey multiple comparisons test (p<0.05). Data are represented as mean

standard deviation, or when stated otherwise mean standard error of the mean. Area

under the curve (AUC) at the 95th confidence interval was used to evaluate statistical

differences in growth and migration assays.

2.4. Results

2.4.1. Expression of TRβ inhibits pro-tumorigenic characteristics of human anaplastic

thyroid cells.

Anti-tumorigenic signaling from the tumor suppressor TRβ has not been

comprehensively defined in ATC. We utilized the human ATC cell line SW1736,

modified to overexpress TRβ (previously used in (12)), to examine the effect of both T3

and TRβ on the cellular growth. T3 modestly decreased the growth rate of SW-EV (Figure

5A). Vehicle treated SW-EV also grew faster than vehicle treated SW-TRβ. Importantly,

the combination of TRβ overexpression and T3 treatment profoundly reduced cellular

growth. T3 treated SW-TRβ also exhibited the greatest doubling time (Figure 5B). TRβ

expression and treatment with cognate ligand profoundly reduced the growth of these

cells.

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Figure 5: Liganded TRβ Repressed Growth and Migration of ATC Cells. A) The combination of TRβ

expression and T3 treatment repressed the growth of the ATC cells (n=8). Variability is represented by the

standard error of the mean; significance was determined by calculating the 95th confident interval of AUC

measurements. B) TRβ and T3 acted to increase doubling time of the cells (n=8, * p<0.01). C) SW-TRβ

treated with T3 exhibited significantly reduced scratch closure at the 95th confidence interval by AUC (n=9)

and D) representative images.

In addition to inducing anti-proliferative activity, TRβ is known to block

metastasis in vivo (14). As metastasis and invasiveness require heightened cellular

motility, we also assessed the impact of T3 and TRβ on ATC cell migration by wound

healing assay. Ligand treated SW-TRβ showed reduced migratory potential compared to

ligand treated SW-EV (Figure 5C-D). These data indicate that TRβ in the presence of T3

59

ligand exerts tumor suppressor activity in ATC cells.

2.4.2. TRβ reprograms the anaplastic thyroid cancer cell transcriptome.

Given that T3 significantly reduced ATC cell growth and migration, we then

interrogated the gene expression patterns elicited by TRβ with T3 treatment by RNA-seq.

The expression patterns of differentially expressed genes (DEGs) across each condition

was visualized using a clustered heatmap (Figure 6A). This analysis resulted in 5

distinctive clusters of DEGs. Clusters 1 and 5 are DEGs regulated by T3 in the absence of

TRβ overexpression. Clusters 2 and 3 are DEGs that are T3-regulated only when TRβ is

overexpressed. Cluster 4 includes the DEGs that are repressed by T3 in the absence of

TRβ and induced by T3 in cells that overexpress TRβ.

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Figure 6: TRβ-T3 Altered the Transcriptome of ATC Cells. A) Thresholds for differentially regulated

genes (DEGs) were set at p<0.05 and an absolute log2foldchange of at least 1, upregulated transcripts in

red and repressed transcripts in blue. Genes were clustered according to patterns of expression. Clusters 1

and 5 are genes that are T3 regulated independent of TRβ overexpression. Clusters 2-4 require TRβ for T3 to

exert a regulatory effect. B) Ingenuity Pathway Analysis (IPA) software was utilized to determine pathways

altered within each cluster. Notable cancer-related pathways are highlighted. C) IPA software was used to

ascertain upstream regulators within each cluster. Exogenous chemicals were excluded, however

endogenous chemicals were retained.

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Enriched pathways within these five clusters of DEGs were determined using

IPA software (Figure 6B). Highly enriched processes include Integrin-linked kinase

(ILK) signaling (cluster 1), interferon signaling (cluster 2), endoplasmic reticulum (ER)

stress (cluster 2), nucleotide excision repair (NER) (cluster 3), DNA methylation and

transcriptional repression (cluster 3), and protein ubiquitination (cluster 4). Of these

pathways highlighted in the cluster analysis, TRβ has previously been shown to regulate

ILK (hereafter PI3K/ILK) and interferon signaling (hereafter interferon/JAK1/STAT1)

(7,15).

Following PI3K phosphorylation of PIP2, ILK catalyzes phosphorylation AKT

and activates proliferative and invasive cellular processes (16). A pairwise comparison

between T3-treated SW-EV and SW-TRβ cells demonstrates that expression of TRβ

further represses genes within the PI3K/ILK pathway (Appendix A-3 A-I). Therefore,

changes to the PI3K/ILK pathway are likely a reflection of the known repressive effect

TRβ has on PI3K in cancer cells (7). TRβ repression of PI3K signaling was confirmed by

assessing pAKT/AKT by immunoblot (Appendix A-3 J).

TRβ has recently been shown to alter a set of genes in the

interferon/JAK1/STAT1 pathway in breast cancer cells (15). This pathway was also

significantly upregulated by TRβ in our ATC cell line model as predicted by IPA (Figure

6B, Appendix A-4) and GSEA (Appendix A-5). Since the transcriptomic profiling of

SW1736 was performed in vitro, the observed interferon response is likely intrinsic

signaling (17). In the intrinsic interferon pathway, stimulation of the interferon receptor

in cells drives phosphorylation of JAK proteins, which in turn phosphorylate STAT1 to

initiate a transcriptional response. STAT1 signaling has been reported to promote

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apoptosis and differentiation of tumor cells, as well as inhibit growth (17).

The upstream regulators of each cluster were determined using IPA (Figure 6C).

This analysis further confirmed alteration of the PI3K/ILK and interferon/JAK1/STAT1

pathways. Suppression of PI3K/ILK signaling was consistent with repression of the PI3K

family and the receptor tyrosine kinases FGF2 and EGFR (cluster 1). There was

predicted activation of multiple effectors within the interferon/JAK1/STAT1 signaling

network, including IFNG, IRF1, IFNA2, and STAT1 in cluster 2 as well as IRF7 in

clusters 2 and 5.

Analysis of the upstream regulators highlighted other genes known to be

important in tumorigenesis. Notable upstream regulators were NF-κβ (cluster 1), MAPK1

(cluster 2), CCND1 (cluster 4), ATF4 (clusters 1 and 3), and SMARCB1 (cluster 5). NF-

κβ and MAPK1 are well recognized to be oncogenic (18,19) and were predicted to be

repressed by TRβ and T3. CCND1 encodes the protein cyclin D1, a cell cycle regulator

which is regulated by T3 and TRβ in other cell types (20), and was predicted to be

repressed in this analysis. The ER stress regulator ATF4 was repressed, itself a gene that

is typically upregulated in malignancy and a potential drug target for ATC (21). Genes

positively regulated by SMARCB1, a component of the SWI/SNF chromatin remodeling

complex, were induced by T3. SWI/SNF components are more commonly mutated in

ATC than in other thyroid cancer subtypes (22).

These gene clusters were also used for chromatin immunoprecipitation

enrichment analysis (ChEA) (23) in order to determine which transcription factors have

overrepresented binding sites (Appendix A-6). Of these transcription factors, GATA2,

IRF1, NELFE, VDR, and ZMIZ1 exhibited altered expression and are therefore possible

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drivers of TRβ-mediated signaling.

The majority of the DEGs in this analysis belong to clusters that were only

significantly altered in the TRβ-T3 condition. Therefore, a pairwise comparison between

SW-EV-T3 and SW-TRβ-T3 was performed (Appendix A-7). The long noncoding RNAs

(lncRNA) were pulled from the set of genes due to their emerging role in tumor biology

(24). lncRNAs upregulated included C1QTNF1-AS1, MIR210HG, TBILA, GAS6-AS1,

LUCAT1, DRAIC, KIAA0125, MIR22HG, and UCA1. Conversely, LINC01133 was

repressed. These lncRNAs have all been found to have a role in tumorigenesis (25-33).

In addition to the pathways and key regulators uncovered by the use of

bioinformatics software, we decided to directly examine cell cycle regulators that may

aid in explaining the anti-proliferative phenotype we observed in the SW-TRβ-T3 group.

CDKN1A is a known TRβ-T3 regulated gene in hepatocellular carcinoma, where

CDKN1A mediates the anti-proliferative activity of TRβ (34). Expression of the oncogene

MYC is induced by TRβ-PV in the development of thyroid tumors (6). In SW1736 cells,

TRβ increased expression of CDKN1A and repressed MYC expression (Appendix A-8).

Analysis of the transcriptomic profile induced by overexpression of TRβ and T3

shows that many pathways and key regulators in cancer biology are altered. These

signaling nodes relate to survival signaling, invasiveness, cellular maintenance,

differentiation, and chromatin organization. Thus, T3 treatment of SW-TRβ induces

transcriptomic changes associated with reduced cell growth, migration, cell cycle, and

cell survival.

2.4.3. Liganded TRβ reduces ATC stem-cell characteristics.

Recently, it was demonstrated that TRβ reduces cancer stem cell renewal in

64

luminal breast cancer cell lines (15). We therefore examined whether TRβ expression and

T3 treatment could attenuate the stem-like properties of SW1736 cells. Soft agar colony

formation assays were used to measure changes in anchorage independent growth in the

presence and absence of TRβ and T3. Treatment with T3 reduced anchorage-independent

colony growth in SW-EV cells, and nearly ablated colony growth in SW-TRβ cells

(Figure 7A-B). Tumorsphere assays were used to assess self-renewal and estimate the

size of the cancer stem cell population within our heterogenous cultures. T3 had no effect

on the number of tumorspheres formed from SW-EV cells. Expression of TRβ alone

reduced tumorsphere formation, however the addition of T3 blocked almost all

tumorsphere growth (Figure 7C).

65

Figure 7: TRβ Reduced Stem Cell Characteristics in ATC Cells. A) Treatment of SW1736 cells with T3

was sufficient to decrease anchorage-independent growth; the most pronounced effect was observed in the

SW-TRβ cells (n=4, * p<0.05). B) T3 significantly repressed tumorsphere formation in SW-TRβ, but not

SW-EV (n=4, * p<0.05). D) Thyroid cancer-specific stem cell marker mRNA transcript levels were

significantly repressed by TRβ and T3 by RNA-seq (n=3, * p<0.05). E) Reintroduction of TRβ and

treatment with T3 resulted in a significantly increased thyroid differentiation score (TDS) (n=3, * p<0.05).

Expression of key stem cell markers was also altered. Although the markers for

thyroid cancer stem cells have not been studied with the same rigor as other aggressive

cancer types, a recent review compiled a list of proposed cancer stem cell markers that

66

have been characterized in the thyroid (35). The combination of TRβ overexpression and

T3 significantly reduced expression of the thyroid-specific cancer stem cell genes ALDH,

POU5F1 (encodes OCT3/4), CD44, FUT4 (encodes SSEA-1), and PROM1 (encodes

CD133) (Figure 7D). We also examined the EMT markers CHD1 (E-cadherin), VIM

(Vimentin) (Appendix A-9). The epithelial marker CDH1 exhibited increased expression

in SW1736-TRβ T3 treated cells, whereas expression of the mesenchymal marker VIM

was decreased. Together with the phenotypic assays, these results demonstrate that TRβ

in association with ligand decreases the stem cell population of ATC cells.

To further explore the impact of T3 treatment in this ATC model, the thyroid

differentiation score (TDS) was calculated for the SW1736 cells under each condition to

evaluate whether the transcriptomic data indicates a change in thyroid-specific

differentiation. The TDS has been demonstrated to have utility in predicting the

aggressiveness of a thyroid tumor (36,37). Ten out of the thirteen genes which constitute

the TDS are expressed in at least one condition in this dataset. DIO2, DUOX1, TPO, and

TG, were highly responsive to TRβ expression and the addition of T3 (Appendix A-10).

SW-TRβ cells treated with T3 have a significantly higher TDS, indicating that they have

the most thyroid-like gene expression (Figure 7E). These results suggest that the tumor

suppressive activity of TRβ observed in SW1736 cells may involve cellular

differentiation and a consequential reduction in the cancer stem cell properties.

2.4.4. TRβ stimulates apoptotic signaling.

As a tumor suppressor, TRβ promotes apoptosis in other cancer cell types,

however this has never been described in ATC (6,38). A targeted pathway enrichment

analysis between SW-TRβ treated with vehicle or T3 revealed alterations in pathways that

67

suggested apoptotic programming was occurring (Figure 8A). Additionally, GSEA

analysis using Hallmark gene sets highlighted enrichment of apoptotic signaling (Figure

8B). We further investigated the ability of T3 to stimulate apoptosis in our model. SW-EV

and SW-TRβ cells were cultured in media supplemented with T3 continuously for five

days. Caspase 3 and PARP1 cleavage were detected by immunoblot to assess apoptotic

activity. T3 induced cleavage of these apoptotic effectors only in SW-TRβ (Figure 8C).

Neither T3 nor TRβ alone was sufficient to elicit this response. These data demonstrate

heightened activity of TRβ promotes an apoptotic response in aggressive cancer cells,

further confirming tumor suppressive activity.

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Figure 8: Re-expression of TRβ Increased Apoptotic Signaling. A) In a comparison of DEGs between

SW-EV and SW-TRβ, both T3 treated, IPA predicted changes in pathways important to apoptotic signaling,

and B) GSEA predicted activation of apoptosis. C) Representative immunoblot demonstrates that 5 days of

T3 treatment induces apoptosis in SW-TR cells assessed by cleavage of PARP1 and caspase 3 (n=6).

2.4.5. TRβ stimulates anti-proliferative interferon/JAK1/STAT1 signaling.

Of the pathways altered by TRβ, the interferon/JAK1/STAT1 pathway was

among the most prominent within our transcriptomic analysis (Figure 6, Appendix A-4).

We therefore decided to investigate the activity of this pathway in ATC and whether it

could be modulated to slow tumor growth. Interferon/JAK1/STAT1 signaling is known to

have potent anti-tumor and anti-proliferative activity (17,39). The primary effector of the

interferon response is the transcription factor STAT1.

Expression of the kinase effectors JAK1 and STAT1 were significantly

increased in SW-TRβ cells following treatment with T3 (Figure 9A and Appendix A-11

A-C). Additionally, transcriptional targets of STAT1 were significantly increased in T3

treated SW-TRβ cells T3 also increased the expression of STAT1 target genes IRF1 and

TAP1, and IRF1 target genes APOL6 and TNFSF10 (Figure 9B). TRβ-T3 mediated

induction of these genes was confirmed by qRT-PCR (Appendix A-11 D).

69

Figure 9: The Interferon-JAK1-STAT1 Pathway is Activated by TRβ. A) Interferon pathway effector

proteins JAK1 and STAT1 are expressed at higher levels in SW-TRβ cells following T3 treatment

(*p<0.05, n=3). B) STAT1 and IRF1 upregulated genes were repressed upon overexpression of TRβ and T3

treatment. (n=3, * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001). C) Stimulation of STAT1

transcriptional activity with the agonist 2-NP repressed growth of ATC cell lines (n=8, * p<0.05, **

p<0.01, *** p<0.001).

STAT1 has anti-proliferative activity, raising the question whether it can be

exploited pharmacologically to reduce ATC growth. To identify the effects of STAT1

stimulation the ATC lines SW1736, 8505C, OCUT-2, and KTC-2 were treated for 72

hours with various concentrations of 2-NP, a small molecule activator of STAT1 (40)

(Figure 9C). SW1736 and 8505C were most sensitive to 2-NP, and growth of both KTC-

2 and OCUT-2 were repressed by the compound at 50 µM. Together, these results

70

demonstrate that liganded TRβ stimulates STAT1 signaling, which in turn can repress

growth in ATC cell lines.

2.5. Discussion

While TRβ is a recognized tumor suppressor in thyroid cancer and is known to

be silenced in ATC (11), TRβ-mediated signaling and phenotypic effects are not well

characterized in ATC. Our data in ATC cells demonstrates that TRβ, in conjunction with

T3, acts to regulate pathways important for the process of tumorigenesis and reduces the

aggressive phenotypic characteristics. This change was demonstrated via multiple

measures including reduced growth, migration, and stemness. In addition, the

combination of TRβ and T3 induced the expression of thyroid differentiation markers in

ATC cells, and after 5 days resulted in apoptosis. Indeed, T3 was essential for the

profound anti-tumorigenic molecular signaling and phenotypic remodeling observed in

this study. The major findings of our analysis are represented in Figure 10.

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Figure 10: TRβ Activates Alternate Apoptotic Pathway. TRβ stimulates activity of STAT1, a transducer

of anti-proliferative signaling. Stemness of the cells is reduced, while thyroid differentiation markers are

increased. Additionally, apoptosis was increased in the cells.

Our transcriptomic results reveal new details about TRβ-mediated regulation of

critical cancer-related pathways in ATC. The prominent pathways from our analysis were

repression of PI3K and activation of STAT1. TRβ-mediated repression of PI3K has been

demonstrated in breast cancer and differentiated thyroid tumors (7,38), suggestive that

this signaling is active in a variety of tumor types. Additionally, we highlight the

interferon/JAK1/STAT1 pathway, previously unknown to be modulated by T3 in thyroid

cells, although STAT1 was recently shown to be activated upon overexpression of TRβ

in estrogen receptor positive breast cancer (15). Evidence of MAPK repression was also

observed, another pathway previously shown to be regulated by TRβ in breast cancer

cells (41). Along with alterations to PI3K and interferon activity, there were changes to

cellular repair processes driven by TRβ overexpression. TRβ and T3 altered expression of

genes involved ER stress. This is consistent with alteration of protein ubiquitination,

which may indicate a greater turnover rate of misfolded proteins, an important

component of ER stress. Additionally, alteration of transcriptional repression and NER

are consistent with observed reduction of cell growth. These pathways can initiate an

apoptotic response, such as through an accumulation of misfolded proteins in the ER

(42), or a failure of DNA repair pathways to repair damaged DNA (43). Thus, these

results indicate that these cells are primed for apoptosis to occur after multiple days of

hormone treatment.

The pathways in this analysis overlap substantially with known drivers of ATC.

72

ATC commonly exhibits mutations to the MAPK and PI3K pathways, the SWI/SNF

complex, and DNA repair processes (22,44). These are also shown to be altered by TRβ

in our analysis. The TRβ driven transcriptomic reprogramming is indicative of a reversal

of the process of malignancy. Indeed, these changes in gene expression suggest that TRβ

is promoting a differentiating effect in the cells, which was demonstrated by the TDS and

multiple stemness assays, indicating that TRβ expression may be predictive in

determining the aggressiveness of a tumor. Our results indicate that TRβ reduces the

aggressive malignant phenotype of ATC cells.

A significant finding here is that TRβ stimulates the activity of STAT1 in ATC.

STAT1 is stimulated by interferons, which have been reported to be tumor suppressive in

multiple cancer types (17). Interestingly, cytokine-cytokine receptor pathway was

predicted to be changed in the thyroids of THRBPV/PV PTEN+/- mice (8), a class of

interactions that includes interferon-interferon receptor. STAT1 itself may be a clinically

important drug target as stimulation of STAT1 activity was sufficient to reduce growth in

multiple ATC cell lines. Treatment strategies that focus on activating STAT1-directed

signaling may have value for prolonging the lifespan of ATC patients. Several studies

have examined the utility of treating thyroid cancer with interferon. Interferon γ reduces

proliferation and migration in papillary thyroid cancer (45), and interferon α suppresses

growth in thyroid cancer cells, including in ATC lines (46). Additionally, a phase II trial

combined interferon α with doxorubicin and observed a modest response in advanced

thyroid cancer patients, however toxicity was high (47). Modulation of STAT1 directly to

suppress tumor growth may be better tolerated. In general, targeting multiple effectors in

the same pathway reduces undesirable side effects in cancer patients (48).

73

We have used our investigation into TRβ signaling to elucidate ATC

vulnerabilities and uncover a novel regulatory effect of TRβ in ATC by stimulating

STAT1. Only recently have targeted therapies in ATC shown success in managing

disease burden and progression (49). These treatment modalities focus primarily upon the

mutational landscape of ATC, however epigenetic alterations, including silencing of the

tumor suppressor TRβ, have an unrealized potential to inform drug development.

Additionally, reversing the epigenetic silencing of TRβ may itself be beneficial. Our

work on TRβ signaling reveals novel functions of TRβ in ATC and the need to

investigate STAT signaling in aggressive thyroid cancers.

Finally, our data demonstrates that there is value to maintaining euthyroid status

in ATC patients. Hypothyroidism is a known consequence of certain treatment regimens

(50) and treating chemotherapy-induced hypothyroidism may improve clinical outcomes

due to stimulation of the tumor suppressive activity of TRβ, illustrating the potential for

TRβ as a diagnostic marker.

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CHAPTER 3: The Thyroid Hormone Receptor-RUNX2 Axis: A Novel Tumor

Suppressive Pathway in Breast Cancer.

3.1. Abstract

Metastatic breast cancer is refractory to conventional therapies and an end stage

disease. RUNX2 is a transcription factor that becomes oncogenic when aberrantly

expressed in multiple tumor types, including breast cancer, supporting tumor progression

and metastases. Our previous work demonstrated that the thyroid hormone receptor beta

(TRβ) inhibits RUNX2 expression and tumorigenic characteristics in thyroid cells. As

TRβ is a tumor suppressor, we investigated the compelling question whether TRβ also

regulates RUNX2 in breast cancer. The Cancer Genome Atlas indicates that TRβ

expression is decreased in the most aggressive basal-like subtype of breast cancer. We

established that modulated levels of TRβ results in corresponding changes in the high

levels of RUNX2 expression in metastatic, basal-like breast cells. The MDA-MB-231

triple negative breast cancer cell line exhibit low expression of TRβ and high levels of

RUNX2. Increased expression of TRβ decreased RUNX2 levels. The thyroid hormone

mediated suppression of RUNX2 is TRβ specific as TRα overexpression failed to alter

RUNX2 expression. Consistent with these findings, knock-down of TRβ in non-tumor

MCF10A mammary epithelial-like cells results in an increase in RUNX2 and RUNX2

target genes. Mechanistically, TRβ directly interacts with the proximal promoter of

RUNX2 through a thyroid hormone response element to reduce promoter activity. The

TRβ suppression of the oncogene RUNX2 is a signaling pathway shared by thyroid and

breast cancers.

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Implications: Our findings provide a novel mechanism for TRβ-mediated tumor

suppression in breast cancers. This pathway may be common to many solid tumors and

impact treatment for metastatic cancers.

3.2. Introduction

Breast cancer is the most commonly diagnosed malignancy in women worldwide

[1]. In the United States, one in eight women are expected to develop the disease over the

course of their lives [2]. Endocrine therapies that target the estrogen receptor (ERα) are a

cornerstone of breast cancer treatment for most patients. Yet, these therapies often fail, as

a consequence of distinct resistance pathways. Non-steroid nuclear hormone receptors

have emerged as diagnostic and therapeutic targets in breast cancer, yet the functional

mechanistic and clinically relevant roles of most of this family are minimally understood

[3]. Notably, several lines of evidence from gain of function experiments indicate that

thyroid hormone receptor beta (TRβ) exhibits tumor suppressor activity in breast cancer.

In luminal A MCF7 cells, re-expression of TRβ reduced xenograft tumor mass and pro-

oncogenic STAT3 signaling [4]. Similar results were observed in triple negative MDA-

MB-468 cells. TRβ expression reduced metastases and angiogenesis [5]; further,

expression of TRβ repressed of PI3K signaling and induced apoptosis [6]. Additionally,

TRβ is correlated with improved disease-free survival in both BRCA associated tumors

and in triple negative breast cancer [7, 8]. This may be in part due to increased

sensitization to chemotherapeutics [8], necessitating a thorough understanding of the

mechanisms by which TRβ suppresses tumor growth.

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TRβ also exhibits tumor suppressor activity in thyroid cancer [9], a disease with a

link to breast cancer. Women who develop thyroid cancer are more likely to develop

breast cancer as a second primary tumor and patients with breast cancer are at increased

risk of developing thyroid cancer [10-13]. Further understanding of either of these tumor

types may reveal important insights into the predisposition for the other disease as well as

insights into how both tumor types develop. Reciprocal synergies between these tumors

may be highly informative. Intriguingly, bone is one of the most common distal sites of

metastases in breast cancer [14] and is the second most common site in thyroid

metastases [15]. We recently established a novel tumor suppression pathway by which

the master regulator of osteoblast development, RUNX2, which functions oncogenically

in cancer and promotes invasion and metastasis in thyroid cancer [16, 17], is repressed by

TRβ in thyroid cells [18, 19]. Addition of the ligand, triiodothyronine (T3), enhances the

TRβ mediated transcriptional events, reinforcing this novel pathway. In breast cancer,

RUNX2 drives tumor progression, promoting proliferation and distal metastasis [20, 21]

in part through the upregulation of genes critical to the process of invasion, including

VEGF, OPN, and matrix metalloproteases (MMPs) [17, 22-26]. These findings indicate

that the T3-TRβ-RUNX2 pathway could be active in breast cancer as well as in thyroid

cancer.

Unlike in thyroid cells where the dominant thyroid hormone receptor is TRβ, in

breast cells TRα expression is robust. Although not yet well-defined, TR, in contrast to

TRβ, may be associated with breast tumorigenesis. High TRα expression in BRCA1-

associated breast tumors correlates with a poor prognosis [7]. It has been reported that

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elevated levels of the splice variant TRα1 of TRα are associated with a poor prognosis

and decreased patient survival [27].

A compelling question is whether the TRβ-RUNX2 signaling is active in breast as

well as thyroid cancer. Given that T3 activates both TRβ and TRα, and that TRα

expression is robust in breast cancer cells our experimental strategy was to determine

whether one or both TRs are responsible for repression of RUNX2 in breast cancer cells.

Our results establish that TRβ suppresses the oncogenic RUNX2 activity in breast cancer

cells. These findings demonstrate that the suppressive action of T3 is mediated through

TRβ but not TRα and support mechanistic linkage of thyroid hormone control with breast

cancer activation and repression as well as provide potential therapeutic targets that are

based on selective modifications in thyroid hormone receptor relationships.

3.3. Materials and Methods

Cell Culture and Treatments.

MCF10A cells were grown in DMEM/F-12 (1:1) (Hyclone), supplemented with

5% horse serum (Gibco), human insulin (10 µg/ml), human EGF (20 ng/ml), cholera

toxin (100 ng/ml), hydrocortisone (0.5 µg/ml), and L-glutamine (2 mM) (Millipore

Sigma). MDA-MB-231 were maintained in αMEM supplemented with 10% fetal bovine

serum (Life Technologies), and L-glutamine (2 mM). Both cell lines were grown in the

presence of penicillin-streptomycin (200 IU/L) (Cellgro/Mediatech). Cells were

maintained at 37°C, 5% CO2, and 100% humidity. For hormone and drug treatments,

cells were maintained in charcoal stripped serum (Sigma). MCF10A and MDA-MB-231

85

cells were purchased from American Type Culture Collection and authenticated via Short

Tandem Repeat analysis in January 2018.

Transient Transfections and Cloning Transfections were performed using

Lipofectamine 3000 (Thermo Scientific) following manufacturer’s directions. The

pDEST515-FLAG-THRB plasmid is the same used in prior work, as was the negative

control vector [18]. We cloned pDEST515-FLAG-THRA using MCF10A cDNA

(Appendix A-1). The amplicon was purified from an agarose gel with the Monarch® Gel

Extraction Kit (New England Biolabs) and both the pDEST515-FLAG-THRB backbone

vector and the THRA amplicon were digested with HindIII and BamHI (New England

Biolabs). The THRB cds dropped out from the backbone and the THRA cds

corresponding to the TRα1 variant was ligated into the vector. The identity of

pDEST515-FLAG-THRA was confirmed by Sanger sequencing.

Immunoblot.

Proteins were isolated from whole cells in lysis buffer (20mM Tris-HCl, pH 8,

137mM NaCl, 10% glycerol, 1% Triton X-100, and 2mM EDTA) containing Halt

Protease Inhibitor Cocktail (Thermo Scientific 78410) as previously described [19].

Nuclear proteins were prepared with NE-PER Nuclear and Cytoplasmic extraction

reagents (Thermo Scientific 78833) per manufacturer’s protocol. Proteins were resolved

by polyacrylamide gel electrophoresis on Novex 10% Tris-glycine gels (Invitrogen) and

immobilized onto Protran nitrocellulose membranes (GE Healthcare) by electroblot (Bio-

Rad Laboratories). Specific proteins were detected by immunoblotting with the indicated

antibodies (Appendix A-2). Immunoreactive proteins were detected by enhanced

chemiluminescence (GE Healthcare), visualized by VersaDoc MP3000 (Bio-Rad

86

Laboratories), and intensities were quantified by Image Lab (Bio-Rad Laboratories).

RNA Extraction and Quantitative Real-Time PCR (qRT-PCR).

Total RNA was extracted using RNeasy Plus Kit (Qiagen) according to

manufacturer’s protocol. cDNA was then generated using 5X RT Mastermix (ABM).

Gene expression was quantified by qRT-PCR using 2X BrightGreen Mastermix (ABM)

on a QuantStudio 3 real-time PCR system (Applied Biosystems). Primers are indicated in

Appendix B-1. Fold change in gene expression compared to endogenous control GAPDH

was calculated using the ddCT method.

siRNA Transfection.

Loss of function was assayed by siRNA using On Target Plus siRNAs targeting

human TRβ (L-003447-00) with non-targeting siRNA (D-001810-10-20; GE Healthcare

Dharmacon) as control. Cells were plated at a density of 2.5 x 105 per well and

transfected with 50nM–200nM siRNA with Oligofectamine per manufacturer’s protocol

(Invitrogen/Life Technologies). The effect of knockdown of TRβ on endogenous RUNX2

and metastatic markers was determined after 24 hours

Chromatin Immunoprecipitation (ChIP).

Binding to RUNX2 chromatin was determined by ChIP-PCR. Cultured human

breast cells were cross-linked with 1% formaldehyde for 10 minutes, neutralized with

125mM glycine, rinsed twice with PBS, pelleted, and frozen. Cells were lysed in the

presence of protease inhibitors (Roche) and chromatin was extracted and sonicated to

200–500 bp in size using a Covaris S220 Focused-ultrasonicator. Sonicated cell lysate

was incubated with 2 g of indicated antibody (Appendix B-2) and rotated overnight at

4°C. Twenty microliters of Protein G Dynabeads (Invitrogen) were blocked in 0.5% BSA

87

and resuspended in lysis buffer, added to the lysate/antibody mix, and rotated at 4°C for 3

hours. Complexes were washed extensively, eluted, and incubated at 65°C overnight to

reverse cross-links. Samples were treated with ribonuclease A, incubated with Proteinase

K, phenol/chloroform extracted, and ethanol precipitated. Pellets were resuspended in 70-

L 10mM Tris-HCl, pH 8. Fold binding over IgG was determined by qRT-PCR.

Reporter Assay.

Constructs were described in a prior publication [19]. We co-transfected the -763

to -16 pGL3 luciferase reporter plasmid with pDEST515-FLAG-THRB in MCF10A and

MDA-MB-231 cells and recorded luminescence after 48 hours as previously described.

SV40 renilla construct was omitted due to reports of functional thyroid hormone response

elements (TRE) [28].

DNA Pulldown Assay.

The assay performed was as described in a published protocol with the following

optimizations [29]. MyOne T1 Streptavidin beads (Invitrogen) were rinsed twice with

binding buffer (20 mM HEPES, 30 mM KCl, 1 mM EDTA, 10 mM (NH4)2SO4, 1 mM

DTT, 0.2% (v/v) Tween-20, pH 7.6), incubated with 5 ng of biotinylated probe at 4°C for

1 hour, and then washed twice with binding buffer. 350 µg of nuclear protein extract was

precleared with beads and 8 µg of sheared herring sperm DNA (Promega) for 15 minutes

at room temperature. The lysate was combined with the probe-bound beads. Volume was

brought to 0.75 ml with wash buffer (20 mM Tris-HCl, 150 mM NaCl, 1 mM EDTA,

0.5% (v/v) NP-40, pH 7.5) and KCl was added to a final concentration of 300 mM for

incubation at 4°C for 1.5 hours. The beads were then washed three times with wash

buffer, once with water, and proteins were eluted for immunoblot.

88

Statistics.

All statistical analyses were performed using GraphPad Prism software. Unless

otherwise indicated an unpaired T-test (p < 0.05) was used for comparisons between two

sets, a 2-way ANOVA followed by a multiple comparisons test (p < 0.05) was used to

compare groups, and data are represented as means standard deviation.

3.4. Results

3.4.1 TR expression is reduced in aggressive breast cancer

Basal-like breast tumors exhibit elevated expression of RUNX2 [30]. We have

previously reported an inverse relationship between TRβ and RUNX2 in thyroid cancer,

providing a basis for the compelling question if a similar dynamic relationship is present

in breast cancer. Both TRα and TRβ are both expressed in normal breast tissue [31],

necessitating examination of the expression levels for the THRA and THRB genes. We

interrogated RNA expression data from The Cancer Genome Atlas PanCancer Atlas

using the cBioPortal interface [32, 33] (Fig 11). THRB was significantly repressed in the

most aggressive basal-like tumors compared to other molecular subtypes. THRA

expression was strikingly reduced in the basal-like breast tumors, although there was also

increased expression of THRA in HER2 positive tumors. Similar to THRB, THRA

expression does not vary significantly in the differentiated normal-like, luminal A, and

luminal B cancers. In conclusion, the most aggressive basal-like breast cancers exhibit

the lowest expression of both TRs.

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Figure 11: Baseline expression of thyroid hormone receptors and RUNX2 in breast cells. A)

THRB RNA expression is significantly reduced in basal-like breast tumors in comparison to all other

subtypes. B) THRA RNA expression is significantly repressed in Basal-like breast cancers and greatest in

HER2 expressing tumors. Other significant differences are indicated in the figure. C) MCF10A expresses

higher levels of THRB mRNA than MDA-MB-231 (n=4, p<0.05). Both MCF10A and MDA-MB-231

express comparable THRA mRNA (n=4, p>0.05). D) MCF10A express greater levels of TRβ protein than

MDA-MB-231, while MDA-MB-231 express greater levels of TRα protein than MCF10A cells as

measured by immunoblot (n=6, p<0.05). * p<0.05, *** p<0.001, **** p<0.0001, n.s. not significant

3.4.2 Thyroid hormone repression of RUNX2 is driven by TRβ.

MCF10A mammary epithelial-like cells and MDA-MB-231 triple negative breast

cancer cells were examined to determine whether treatment with T3 can modify RUNX2

expression. Following 24 hours of treatment with 10-8 M of T3 to model euthyroid serum

conditions, RNA was isolated from the cells for analysis by qPCR and protein for

immunoblot analysis. T3 treatment significantly decreased RUNX2 mRNA and protein

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levels in both cell lines after 24 hours (Fig 12 and Appendix B-3).

Figure 12: RUNX2 expression is reduced by Thyroid Hormone Treatment. MCF10A and

MDA-MB-231 were treated with T3 at 10-8 M for 24 hours. A) Expression of RUNX2 was measured by RT-

qPCR (MCF10A: n=6, p<0.001. MDA-MB-231: n=6, p<0.01). B) RUNX2 protein levels were determined

by immunoblot (MCF10A: n=9, p<0.01. MDA-MB-231: n=9, p<0.05). * p <0.05, ** p<0.01, *** p<0.001

Unlike in the thyroid cells where we previously characterized the action of T3 on

expression of the RUNX2 gene, breast cells express both TRα and TRβ. The DNA

binding domain of TRs is highly conserved with significant homology between both TRs

resulting in competency for both receptors to bind the same canonical response element

on chromatin [34], however there are genes that are selectively regulated by a single TR

[35-37]. To evaluate whether one or both TRs can repress RUNX2 expression MDA-MB-

231 cells were transiently transfected with vectors that overexpress either TRα or TRβ.

When RUNX2 protein was quantitated by immunoblot analysis, TRβ expression was

observed to reduce RUNX2 levels. However, when cells were transfected with a TRα

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vector, RUNX2 expression did not change (Fig 13). To determine whether TRα could act

as a negative regulator of TRβ, TRα was also overexpressed in MCF10A cells, which

robustly express TRβ. No change to RUNX2 was observed, showing that increased levels

of TRα did not inhibit the suppressive activity of TRβ. These results provide a functional

indication of the specificity for TRβ regulation of RUNX2 in breast cancer.

Figure 13: TRβ, and not TRα, Represses RUNX2. A) Transient transfection into MDA-MB-231

cells of a plasmid encoding TRβ significantly reduced RUNX2 levels (n=6, p<0.01), however transfection

with a TRα vector did not significantly alter RUNX2 protein expression (n=6, p>0.05) with B)

representative immunoblots. Transient transfection of TRα did not alter RUNX2 mRNA levels in C) and

MCF10A D) MDA-MB-231 cells. ** p <0.01, n.s. not significant

To confirm that TRβ can repress RUNX2, TRβ expression was knocked down by

siRNA in the normal breast cell line MCF10A (Fig 14). Decreased levels of TRβ

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increased RUNX2 expression, and we also observed a significant increase in expression

of the RUNX2 upregulated genes CCND1 [38], MMP9 [22], and VEGFA [24, 25] were

also observed after siRNA knockdown of TRβ. Importantly, each of these have a

demonstrated role in the process of tumorigenesis. These results reinforce the TRβ

repression of RUNX2 expression with the functional consequence of repressing RUNX2

driven pro-tumorigenic activity.

Figure 14: Knockdown of THRB in MCF10A enhances RUNX2 and RUNX2 activity. A)

Treatment of MCF10A with siRNA against TRβ repressed its expression in the cells as confirmed by

immunoblot. B) Loss of TRβ mRNA correlated with significantly increased RUNX2 expression as

measured by RT-qPCR (n=6, P<0.05). There was also an observed increase in the RUNX2 upregulated

genes CCND1 (n=4), MMP9 (n=4) and VEGFA (n=4). * p<0.05

3.4.3 TRβ binds to the RUNX2 promoter

We previously mapped TREs in the P1 promoter of the RUNX2 gene and

demonstrated that TRβ directly interacts with these regions in thyroid cells [19].

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Following ChIP from MCF10A cells there was significant enrichment of the RUNX2

promoter utilizing a TRβ antibody as compared to IgG control (Fig 15A). This was

further validated by a DNA pulldown assay utilizing biotinylated oligonucleotides

containing a previously characterized TRE. Incubation of this oligonucleotide with

MCF10A lysate revealed TRβ binding, which was ablated when the TRE was mutated

(Fig 15B). Additionally, a luciferase reporter vector was co-transfected with a TRβ

expression vector. Nominal, but insignificant repression was observed in MCF10A, in

contrast to the MDA-MB-231 cells that express very low levels of endogenous TRβ

where there was significant repression (Fig 15C). Taken together, these data provide

evidence for direct interaction between TRβ and the RUNX2 gene to alter gene

expression.

Figure 15: TRβ Binds to the RUNX2 Promoter. A) Chromatin immunoprecipitation-PCR of

RUNX2 in MCF10A cells (n=3, p<0.05). B) Biotinylated RUNX2 proximal promoter successfully pulls

down TRβ from MCF10A lysate as shown by immunoblot. Mutation of the TRE ablated this interaction. C)

Cotransfection of an expression vector and luciferase reporter vector in breast cell lines. Nominal

repression of the promoter due to TRβ overexpression was observed in MCF10A (n=2, P>0.05) and MDA-

MB-231 showed significant repression (n=2, p<0.01). * p<0.05, ** p<0.01

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3.5. Discussion

These results demonstrate that T3 mediated RUNX2 repression in breast cancer is

facilitated by TRβ and not via TRα. Importantly, we have identified a gene that is

preferentially regulated by TRβ and not TRα. This specificity provides a mechanistic

insight into the observed TRβ tumor suppressor role and the putative oncogenic role of

TRα [7]. We have also demonstrated a common pathway in breast and thyroid cancers,

which demonstrates the value of this pathway for further understanding the observed

epidemiological link between both diseases [10-13].

To understand the etiology of the link between breast and thyroid tumors, it is

vital to understand the actions of shared tumor suppressors and oncogenes. The

mechanisms by which TRβ represses tumorigenesis is an important question in tumor

biology and pathology but minimally understood. Previously characterized mechanisms

of TRβ-mediated tumor suppression in breast cancer focused on regulation of the

PI3K/Akt [39], Ras/MAPK [40], and JAK2-STAT3 [4] pathways, and induction of

mesenchymal-to-epithelial transitions [41]. In thyroid cancer, TRβ suppresses

tumorigenesis through PI3K/Akt [42] and RUNX2 [19]. Our work here demonstrates that

in addition to shared tumor suppression by PI3K/Akt, TRβ acts to repress RUNX2

expression in both cell types. The Ras/MAPK and JAK2-STAT3 pathways and epithelial

characteristics are all features regulated by TRβ in breast cancer that merit further

investigation in thyroid cancer. Revealing common features can aid in understanding the

metachronous nature of the two diseases.

The T3-TRβ-RUNX2 axis could be a common signaling pathway in a variety of

cancers. In addition to the role of RUNX2 in breast and thyroid tumorigenesis, RUNX2

95

has been proposed to contribute to the development of prostate cancer [43], liver

carcinoma [44], colon cancer [45], and melanoma [46]. TRβ and/or T3 has been shown to

have anti-tumor roles in these cancers as well. Overexpression of TRβ reduces cellular

proliferation in vivo in hepatocellular carcinoma [5]. Thyroid hormone also induces

differentiation of colon cancer stem cells [47]. In melanoma, a loss of heterozygosity of

the THRB gene has been observed [48], consistent with functioning as a tumor suppressor

gene, and T3 stimulation of dendritic cells represses melanoma tumor size in a xenograft

[49]. These findings suggest that the T3-TRβ-RUNX2 signaling program may be

operational in a spectrum of tissues.

This work expands the understanding for involvement of the TRs in

tumorigenesis and are consistent with the potential for TRs to improve diagnostic or

prognostic capabilities for cancer diagnosis and treatment responsiveness. Aside from the

treatment of existing thyroid disorders or to alleviate side effects from therapeutics, direct

modulation of thyroid hormone levels is undesirably as potential confounding effects

must be considered. TRα1 is associated with more advanced breast tumors [7, 27] and

potential side effects for the cardiovascular system, that includes tachycardia and

hypertension which result from TRα signaling in the heart [50]. Additionally, the αVβ3

integrin is responsive to thyroid hormones, primarily thyroxine, promoting pro-oncogenic

activity through the MAPK pathway [51, 52] and, through studies utilizing prostate

cancer cells and osteoblasts, it was shown that αVβ3 induces an increase in expression

and transcriptional activity of RUNX2 [53, 54]. However, it is not clear whether αVβ3

can regulate RUNX2 in response to thyroid hormones. Encouragingly, thyromimetic

compounds have been developed as selective agonists to TRβ [55, 56] and may stimulate

96

TRβ tumor suppressor activity in cancer. There is encouraging evidence that

thyromimetics can enhance the sensitivity of aggressive breast cancer cells to certain

chemotherapies [8]. Our work demonstrates another mechanism by which TRβ

pharmacological stimulation may be beneficial.

In closing, our findings demonstrate a tumor suppressive function of TRβ shared

by multiple tissue types. The T3-TRβ-RUNX2 pathway is active in a variety of cancers.

Understanding the role of this signaling in tumorigenesis may be leveraged for

development of novel clinical interventions with high specificity and minimal off-target

consequences.

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CHAPTER 4: Common Tumor Suppressive Signaling of Thyroid Hormone

Receptor Beta in Breast and Thyroid Cancer Cells.

4.1. Abstract

An epidemiological link between breast cancer and thyroid cancer has been

established. Both of these diseases primarily impact women and prior history of one of

these tumors increases the chance of an individual developing the other cancer in the

future. Although the link has been established, the cause of the association is still

unknown. The thyroid hormone receptor beta (TRβ) is a tumor suppressor in both breast

and thyroid cancer and further understanding of the molecular mechanisms by which TRβ

abrogates tumorigenesis could aid in understanding the link between both malignancies.

Here, we introduce TRβ into the MDA-MB-468 basal-like breast cancer cell line and

perform RNA-sequencing to determine changes in transcriptomic signaling. The TRβ

expressing MDA-MB-468 cells exhibit a more epithelial character as determined by

PCA-PAM50 score and through repression of mesenchymal cytokeratins. The epithelial

to mesenchymal transition pathway is also significantly reduced. The MDA-MB-468

dataset was also compared to RNA sequencing results from the thyroid cancer line

SW1736 to determine which genes are TRβ regulated across both tissue types. Stearate

biosynthesis was revealed as upregulated from the shared gene set, a lipid which is

known to repress breast tumorigenesis. These data provide novel insights into the

molecular mechanism by which TRβ suppresses breast tumorigenesis and suggests a

common metabolic role in breast and thyroid cancer as well a role for TRβ in the

maintenance of epithelial cellular identity.

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4.2. Introduction

A link has been established between breast cancer and thyroid cancer.

International studies show that patients with breast cancer are at an increased risk of

developing a future thyroid tumor and thyroid cancer increases the risk of breast cancer

(1-7). Both diseases are among the most diagnosed cancers in women, making

understanding this association an important public health concern. Breast cancer is the

most commonly diagnosed cancer in women and thyroid cancer is projected to be the 4th

most common cancer in the United States by 2030 and the 2nd most commonly

diagnosed cancer in women, after breast (8). Strikingly, there are no good explanations

available for the etiology of this association.

A common factor in both diseases is loss of expression of the thyroid hormone

receptor beta (TRβ) (9,10). TRβ is a nuclear hormone that is responsive to levels of

triiodothyronine (T3). It is capable of binding to thyroid hormone response elements

(TRE) both in the presence and absence of ligand in order to regulate transcription. The

tumor suppression activity of TRβ has been validated through in vitro and in vivo studies.

In xenograft experiments, restoration of TRβ expression limits tumor growth in follicular

thyroid cancer cell lines (11), in a luminal A breast cancer cell line (12), and in a basal-

like breast cancer cell line (13). Loss or mutation of TRβ also leads to the development of

tumors in mice (14,15). Notably, TRβ regulates pathways that are drivers of both breast

and thyroid cancer. This includes repression of PI3K signaling and repression of RUNX2

expression (9-11,16,17).

Our laboratory has recently performed an analysis of the TRβ transcriptome in an

anaplastic thyroid cancer (ATC) cell line. In this study, we combine that analysis with a

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similar experiment performed in a basal-like breast cancer cell line, MDA-MB-468. TRβ

has previously been demonstrated to exert a strong anti-tumor effect in these cells by

independent laboratories (13,16,18). Briefly, the MDA-MB-468 cell line expresses very

low endogenous TRβ and through viral transduction multiple laboratories have been able

to restore TRβ expression exogenously. Increased expression of TRβ reduced the tumor

burden in vivo. Importantly, this anti-tumor activity included repression of growth

markers and increased anti-metastatic potential; mice with tumors expressing TRβ

exhibited fewer metastatic lesions than control animals (13,18). Additionally, when the

animals were rendered hypothyroid, the number of metastatic lesions in the animals

increased (18). These data are suggestive of a vital role for T3 in TRβ-mediated tumor

suppression. Transcriptomic analysis of the MDA-MB-468 cells demonstrated TRβ

regulates breast differentiation, invasive genes, and metabolic signaling in these cells.

When this analysis was compared to our prior analysis of ATC line SW1736 we were

able to further refine the TRβ regulome to define a set of genes regulated in both these

cell lines.

4.3. Methods

Cell Culture

MDA-MB-468 cells were maintained at 37°C, 5% CO2, and 100% humidity. The

cells were grown in αMEM supplemented with 10% fetal bovine serum (Life

Technologies), and L-glutamine (2 mM) as well as the addition of penicillin-streptomycin

(200 IU/L) (Cellgro/Mediatech). Identify of the cells was confirmed by short tandem

repeats via the Promega GenePrint10 System in the Advanced Genome Technologies

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Core at the University of Vermont (May 2019). The cells were tested for mycoplasma by

PCR using 10 µM of each primer (May 2019) (Appendix C-1).

The MDA-MB-468 cells were transduced as previously described (19). Briefly,

viral particles containing pCDH-MSCV-EF1-GFP-Puro and pCDH-MSCV-EF1-GFP-

Puro-TRβ were generated in human embryo kidney 293 cells and the particles were used

to transduce the cells to express either the empty vector (468-EV) or overexpress TRβ

(468-TRβ). Following lentiviral transduction, the media was further supplemented with 1

µg/ml puromycin (Gold Bio).

Immunoblot

Protein was extracted from the cells by treatment with lysis buffer (20mM Tris-

HCl [pH 8], 137mM NaCl, 10% glycerol, 1% Triton X-100, and 2mM EDTA) containing

Protease Inhibitor Cocktail 78410 (Thermo Scientific). Following quantification by

Bradford assay, the resulting lysate was resolved by polyacrylamide gel electrophoresis

with 10% sodium dodecyl sulfate gels EC60752 (Life Technologies) and immobilized

onto nitrocellulose membranes (GE Healthcare) by electroblot (Bio-Rad Laboratories).

Membranes were probed for TRβ with the antibody MA1-216 (Thermo Scientific) and

for β-actin with MA5-15739 (Thermo Scientific). Blots were imaged using enhanced

chemiluminescence (Thermo Scientific) on a ChemiDoc XRS+ (Bio-Rad Laboratories).

RNA Extraction and Library Generation

468-EV and 468-TRβ cells were plated at 80% confluency and hormone starved

for 24 hours in phenol red free αMEM with charcoal-stripped fetal bovine serum. 10-8 M

T3 was added and incubated for 24 hours prior to sample collection. Total RNA was

extracted and purified using RNeasy Plus Kit (Qiagen) according to manufacturer’s

107

protocol. Purity of the total RNA samples was assessed via BioAnalyzer (Agilent) and

samples with a RIN score >7 were used for library construction. rRNA was depleted

using 1 μg of total RNA and strand-specific Illumina cDNA libraries were prepared using

the NEBNext Ultra II RNA library kit with 7 cycles of PCR (New England Biolabs).

Library quality was assessed by BioAnalyzer (Agilent) to ensure an average library size

of 300 bp and the absence of excess adaptors in each sample. RNA-Seq libraries were

pooled and sequenced with 50 bp single-end reads on the Illumina HiSeq 1500. Quality

scores across sequenced reads were assessed using FASTQC. All samples were high

quality. For alignment and transcript assembly, the sequencing reads were mapped to

hg38 using STAR. Sorted reads were counted using HTSeq and differential expression

analysis was performed using DESeq2. Genes with a p-value of <0.05 and a fold change

of >2 were considered differentially expressed and were used for further analysis through

Ingenuity Pathway Analysis (IPA) (Qiagen), Gene Set Enrichment Analysis (GSEA), and

Chromatin Immunoprecipitation Enrichment Analysis (ChEA)

(https://amp.pharm.mssm.edu/Enrichr/) (20).

4.4 Results

One of the major effects observed in the SW1736 cells was a TRβ-mediated

increase in differentiation markers (as discussed in chapter 3). TRβ has also been

demonstrated to reduce stemness in luminal breast cancer cell lines, further suggestive of

TRβ having a role in tumor differentiation (21). Following confirmation of TRβ

overexpression in the MDA-MB-468 cells we performed RNA sequencing in order to

determine the transcriptomic profile (Appendix C-2). Of interest to the question of

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differentiation is expression of the PAM50 genes, which are used to determine breast

cancer intrinsic subtypes (22,23). We used the Principle Component Analysis-based

iterative PAM50 subtyping (PCA-PAM50) algorithm on our sequencing data to

quantitatively assess subtype scores for the different experimental conditions (Fig 16)

(24). The scores for the luminal A and luminal B subtype were greatest in the 468-TRβ

cells, particularly following T3 treatment. Additionally, there was a significant reduction

in the basal-like breast cancer score in the 468-TRβ cells treated with T3. Notably, there

was a 60% increase in expression of ESR1, a gene which expression effectively defines

the luminal subtypes of breast cancer. These results are suggestive of a decrease in the

aggressive, basal-like phenotype.

Figure 16: Thyroid Hormone Receptor Beta Alters Genes that Determine Breast Intrinsic Subtype.

Each sample of cells was evaluated to determine scoring for the different intrinsic subtypes of breast

cancer. A and B) TRβ increased the luminal subtype scores. C) The combination of TRβ and cognate ligand

reduced the basal-like score of these cells. D) Expression of ESR1 increased following T3 treatment in the

468-TRβ cells.

109

The changes in gene expression were further delineated by cluster analysis

(Figure 17). Using k-means clustering we separated the MDA-MB-468 differentially

expressed genes (DEGs) into 7 clusters representing different patterns of gene expression.

Analysis using IPA revealed alterations in cancer relevant pathways including interferon

signaling, glioblastoma multiforme signaling, and stearate biosynthesis. Interferon

induces anti-tumor activity through the stimulation of JAK1-STAT1 activity, which

upregulates genes that reduce proliferation and induce apoptosis (25). The IPA

glioblastoma multiforme signaling network incorporates multiple pro-tumor pathways

including MAPK, PI3K, and WNT signaling. Stearate biosynthesis results in increased

levels of the fatty acid stearic acid. Stearic acid has been shown to slow the growth of

breast tumors and induce apoptosis (26-29). Notable upstream regulators derived from

the IPA clusters include effectors within the interferon-JAK1-STAT1 pathway, primarily

from clusters 1, 4, and 6. Also predicted are the estrogen receptors and their cognate

ligand, β-estradiol.

110

Figure 17: Expression of Thyroid Hormone Receptor Beta Induced Alterations to the Transcriptome

and Signaling Networks. A) Cluster analysis of DEGs (p-value < 0.05 and an absolute log2FoldChange ≥

1) reveals distinct patterns of gene regulation. Clusters 1 and 3 are genes that can be repressed or induced by TRβ in absence of ligand. Clusters 2, 5, and 6 showcase genes that are T3 regulated in absence of

exogenous TRβ, however genes in clusters 5 and 6 are more strongly regulated when TRβ expression is

increased. The genes in clusters 4 and 7 are dependent upon both TRβ and T3 for differential expression. B)

Ingenuity Pathway Analysis (IPA) was utilized to determine which signaling networks were altered. Up to

two pathways form each cluster are represented, at p-value < 0.05 and absolute z-score >0. C) IPA was also

used to predict upstream regulators, up to 10 were selected for each cluster as determined by p-value with a

threshold of p-value < 0.05.

The effects that TRβ overexpression had on the transcriptome included both direct

111

regulation of genes (primary regulation) and, due to allowing 24 hours of signaling

following hormone treatment, for the directly regulated genes to exert additional changes

in gene signaling through downstream transcription factors (secondary regulation). In

order to determine putative secondary regulators the clusters were subjected to ChEA

(Figure 18) (20). None of the transcription factors overrepresented in the analysis were

induced or repressed at a level necessary to make the threshold to be considered a DEG,

however ESR1 expression was significantly altered as shown in Figure 16. Additionally,

SUZ12, SMAD4, GATA2, NELFE, and TRIM28 were uncovered from the equivalent

analysis performed in SW1736 cells (Appendix B-6). Of these, NELFE was found to be

repressed by TRβ in the thyroid cells (Appendix A-6), and is a transcription factor that

aids in the induction of epithelial-to-mesenchymal transition (EMT) in pancreatic cancer

(30). Additionally, GATA2 is known to interact with TRβ2 to repress thyroid stimulating

hormone β (31). TRIM28 is known to interact with the SWI/SNF complex, a chromatin

remodeling complex TRβ interacts with (19), and promotes mammosphere formation

(32), which TRβ inhibits (21).

112

Figure 18: Chromatin Immunoprecipitation Enrichment Analysis Reveals Putative Secondary

Regulators. The DEGs from each cluster were input into the ChEA webapplet at

https://amp.pharm.mssm.edu/Enrichr/. Transcription factors from the ENCODE and ChEA Consensus TFs

dataset was used to assign statistical values to each factor.

In order to further investigate the pathways that TRβ regulates in breast cancer the

DEGs from 468-EV and 468-TRβ cells, both T3 treated, were directly compared to each

other via GSEA using the Gene Ontology and Hallmarks datasets (Appendix C-3). The

top three pathways, by significance and effect size, were all processes that were driven by

a change in cytokeratin expression (Figure 19). The set of cytokeratins that TRβ

repressed includes markers of breast cancer aggressiveness that included in diagnostic

assays, including the PAM50 gene set (33,34). Additionally, there are studies which

highlight the importance of some of these genes for invasiveness. Notably, expression of

KRT14 correlates with tumor stage and a basal-like phenotype (35). KRT5, KRT14, and

KRT80 are also drivers of an invasiveness in breast tumors (35,36). Lastly, KRT16 is a

prognostic marker as expression correlates with reduced survival (37). Consistent with

the pro-invasive role of these cytokeratins, there was also a significant reduction in the

expression of EMT drivers.

113

Figure 19: Thyroid Hormone Receptor Beta Represses Invasive Pathways. A) A pairwise comparison

between EV-T3 and TRβ-T3 revealed multiple Gene Ontology pathways that are driven by expression of

cytokeratins were repressed. B) KRT genes were significantly repressed in MDA-MB-468 cells that express

exogenous TRβ following T3 treatment. C) Pairwise comparison between EV-T3 and TRβ-T3 showed

repression of epithelial to mesenchymal transition.

We have recently revealed the TRβ transcriptome within the anaplastic thyroid

cancer cell line SW1736 and so we performed pairwise comparisons between SW1736-

EV-T3 and SW1736-TRβ-T3 and between 468-EV-T3 and 468-TRβ-T3 in order to

determine which genes are differentially expressed independent of cell type. The majority

of DEGs were regulated by TRβ-T3 in only one of the two cell types, however a subset of

genes were regulated by TRβ-T3 in both. A total of 255 genes were induced by T3 in both

cell lines and 70 genes were repressed (Figure 20). These genes were used for pathway

analysis using the IPA algorithm and the top predicted pathways were examined to

114

determine cancer relevance. An important shared pathway uncovered was stearate

biosynthesis. Four genes which encode biosynthetic enzymes were T3-induced in both

SW1736 and MDA-MB-468 cells. Additionally, the shared gene set was used for GSEA

using the Gene Ontology dataset (Appendix C-3 and C-4), which revealed repression of

chromatin organization processes and induction of stress responses, including proteolytic

signaling (Appendix C-5).

Figure 20: A Subset of Genes are Regulated by TRβ in Breast and Thyroid Cancer Cells. A) Pairwise

comparisons of 468-EV-T3 and 468-TRβ-T3 along with SW1736-EV-T3 and SW1736-TRβ-T3 were used to

determine which genes were regulated in a tissue agnostic manner. 255 genes were upregulated and 70

genes were repressed independent of cell type. B) IPA was used to predict pathways of these 325 genes and C-D) stearate biosynthesis was examined in closer detail, demonstrating induction of enzymes necessary

for the conversion of palmitate to stearate.

115

4.5. Discussion

Restored expression of the tumor suppressor TRβ in the breast cancer cell line

MDA-MB-468 profoundly altered the transcriptome of the cells. Existing data from the

cell line shows that TRβ acts to repress proliferation and migration in vitro as well as

tumor growth and metastasis in vivo (13,16,18). Our work here adds to that understanding

by providing evidence of the signaling events that result from TRβ activity. Importantly,

there was a change in the set of genes that are used to define breast cancer subtypes and a

change in EMT markers, revealing that the mesenchymal-like MDA-MB-468 cells were

shifting to be more epithelial, and thus differentiated, in character. This is further

supported by the observed reduction of pro-invasion and pro-metastatic cytokeratins. An

induction of cellular differentiation could also explain why loss of TRβ has been found to

decrease the efficacy of chemotherapeutics in basal-like breast cancer cells (38).

Through comparison of a tumor suppressor in both a thyroid and breast cancer

cell line we have revealed common signaling between both cell types. Notably, enzymes

involved in the biosynthesis of stearic acid were revealed, a lipid that has a profound anti-

tumor role in breast cancer apoptosis (26-29). Not only does the data in this study suggest

that TRβ induces expression of stearate synthetic enzymes, but also that stearate could

have unrealized anti-tumor activity in thyroid cancer. Importantly, the anti-tumor

properties of stearate are similar to that which has been observed to be performed by

TRβ. This includes anti-proliferative, anti-metastatic, and pro-apoptotic effects.

Stearic acid has also been found to have a relationship with obesity, a risk factor

for the development of both breast cancer and thyroid cancer (39-42). Levels of stearic

acid in women with larger adipocytes are reduced (43), and stored stearic acid is

116

inversely correlated with BMI (44). Additionally, stearate is a poor substrate for the

synthesis of triglycerides, which further suggest an anti-adiposity effect (45). Stearic acid

levels may be a connection between obesity and risk of malignancy in both breast and

thyroid cancers and are likely regulated in tumors by TRβ.

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CHAPTER 5: A Linkage Between Thyroid and Breast Cancer: A Common

Etiology?

5.1. Abstract

Breast and thyroid cancers are two malignancies with highest incidence in

women. These cancers often occur metachronously. Women with thyroid cancer are at

increased risk for subsequent breast cancer; women with breast cancer have an increased

incidence of later development of thyroid cancer suggesting a common etiology. This

bidirectional relationship is reported worldwide, however the underlying reasons for this

co-occurrence is unknown. In this review, we summarize the current epidemiological

evidence and putative mechanisms of these metachronous or synchronous cancers. Key

potential causative factors are chemotherapy and radiotherapy of the primary tumor,

genetic variants linking the two diseases, hormonal signaling both from the thyroid gland

and from estrogens, lifestyle and environmental factors. There is a critical need for

additional epidemiological studies focused on gender and regional incidence together

with molecular investigations on common tumorigenic pathways in these endocrine

cancers. Understanding the putative mechanisms will aid in the diagnosis and clinical

management of both diseases.

5.2. Introduction

Breast cancer is the most common cancer diagnosed in women in the United

States (U.S.) with over 250,000 cases per year (1). Survivors of breast cancer are at

increased risk of a second cancer, frequently thyroid cancer (2-4). Thyroid cancer, which

also predominantly affects women, is of great concern as the incidence in the U.S. has

124

more than tripled over the past decades, including the aggressive variants (5,6). The

higher incidence is not due to enhanced surveillance and advances in diagnostics;

increases in tumors of all sizes as well as indolent and aggressive tumors are reported (7).

Male thyroid cancer rates have doubled since 1975 whereas female rates have tripled (6).

As 5-year survival rates after thyroid cancer are in excess of 90% (1) and the incidence of

the disease is increasing, there are now more survivors of thyroid cancer than in the past.

The risk of a subsequent second primary cancer, most often breast cancer, is increased for

thyroid cancer survivors (3,4,8). Understanding health risks following treatment is critical

for improving public health.

Due to the rising incidence of thyroid cancer in the U.S., and globally (9), it is

critical to understand the risk factors for thyroid cancer and long-term risks associated

with the disease. Intriguingly, the increase in incidence can vary dramatically depending

upon location (9). An increase in thyroid cancer incidence is most pronounced in Asia,

particularly in South Korea, as compared to other regions. Contributing factors likely

include improved health care networks and surveillance, environmental influences,

hereditary factors, radiation exposure, and lifestyle factors. Iodine deficiency is an

important public health problem in several Asian countries (10), and has been linked to

an increased chance of developing thyroid cancer, in addition to increased risk of

thyroidal disorders (reviewed in (11)).

In contrast to the global increase in thyroid cancer incidence, changes in breast

cancer incidence vary globally. U.S. incidence has been stable for the past few decades.

Strikingly, in the 1980s, there was a 30% increase in female total breast cancer diagnoses,

which is attributed to heightened surveillance (12). In China, breast cancer incidence has

125

also increased, however it is difficult to determine a precise figure due to the varying

quality of studies (13). This may be a pattern similar to what was observed in the U.S.

during the 1980s; the rise of breast cancer rates may be a result of China’s rapid growth

in recent decades and improvements to health care technology and access. Low- and

middle-income nations, such as South American nations, have exhibited the greatest

increase in incidence of breast cancer (12). This may also be due to changes in

surveillance and a result of improvements in health care access and technology.

Regardless, a myriad of factors may underlie the observed increase in breast cancer rates

in some regions of the world and the cause is not yet clear.

Over the last 40 years, studies reveal compelling evidence of a bidirectional, and

potentially causative, relationship between breast cancer and thyroid cancer. Women with

breast cancer are at increased risk for developing thyroid cancer; survivors of thyroid

cancer are more likely to develop breast cancer suggesting common etiological features

in the development of both tumor types (14). A metachronous relationship between

thyroid and breast cancers has significant implications for clinical surveillance and

management of both diseases. Additionally, understanding the etiology of the second

primary tumor is vital for the development of new preventive strategies, diagnostics, and

therapeutics.

5.3. What is the Epidemiological Evidence of the Relationship Between Breast and

Thyroid Cancer?

In the past few years alone, population studies in Asia, Europe, and the U.S.

reveal increased incidence of breast cancer among women previously diagnosed with

126

thyroid cancer (15-21). Of note, aggressive follicular thyroid cancer is detected in

patients with a history of breast cancer more often than the more common papillary

thyroid cancer (18). Further, and importantly for determining etiology, non-malignant

thyroid nodules are more common in women with breast cancer than those without breast

tumors (19). There is an increased risk of developing thyroid cancer following breast

cancer (16,20). Women with prior benign breast disease also are reported to be at a

greater risk of thyroid cancer (21). The increased risk of thyroid cancer following breast

cancer and breast cancer following thyroid cancer is reported in both women and men

(22). Women with breast cancer are two-fold more likely to develop future thyroid cancer

and women with thyroid cancer have a 67% greater chance of developing breast cancer

than the general population. These recent studies are summarized in Table 1. Importantly,

the metachronous relationship was evaluated in nations with widespread cancer screening

and nations where screening is becoming more common. In particular, Zhang and

colleagues evaluated cancer patients between 2001 and 2010 in China, which

corresponded with investments into cancer registries (17,23).

Table 1: Summary of Studies linking Breast and Thyroid Cancer Incidence. Only the studies

published in the last 5 years (since 2013) are presented, organized by which cancer was the initial disease.

Some studies explored the bidirectional relationship and are listed twice. Luo et al (marked with a *)

examined benign breast disease rather than a breast malignancy, and Shi et al (marked with a **) studied

thyroid nodules, rather than thyroid cancer. Fold refers to the fold change of risk for the second tumor as

compared to people who do not have a history of the initial cancer. The sample size classifies patients

based upon the site of the earlier cancer. Abbreviations used are OR (Odds Ratio), SIR (Standard Incidence

Ratio), IRR (Incidence Rate Ratio), AD (Age Dependent), and HR (Hazard Ratio).

127

The clinical characteristics of the initial and metachronous tumors exhibit

distinctive features compared to non-metachronous cancer patients. The metachronous

breast tumors following thyroid cancer are more likely to be hormone receptor positive

than breast cancer patients without this history (16). The metachronous thyroid tumors

following breast cancer are smaller but more aggressive than the control population of

females with thyroid cancer without a history of breast cancer. Metachronous thyroid

cancer is also more common when the initial breast tumor was HER2 positive (24).

Analysis of Surveillance, Epidemiology, and End Results data revealed patients who

Sex Breast →

Thyroid

Increased Risk

Breast→Thyroid

Thyroid →

Breast

Increased Risk

Thyroid→Breast

Sample Size

Female Prinzi et

al 2013

15.24 OR 3921

Female

and Male

Van

Fossen et

al 2013

Female 2-Fold

Male 19-Fold

Van

Fossen

et al

2013

Female 1.67-

Fold

Male 29-Fold

74,650

(Breast)

15,940

(Thyroid)

Female An et al

2015

2.18 SIR An et al

2015

2.45 SIR 6833

(Breast)

4243

(Thyroid)

Female Kuo et al

2016

2.07-2.60

IRR (AD)

53,853

Female Zhang L

et al 2016

4.75 SIR Zhang L

et al

2016

2.40 SIR 18,732

(Breast)

12,877

(Thyroid)

Female Jung et al

2017

2.29 SIR 3444

Female Luo et al

2017*

1.38 HR 133,875

Female Shi et al

2017**

2.5-Fold 4189

128

developed metachronous breast cancer following a thyroid tumor typically were

diagnosed with breast cancer at a younger age than average and exhibited histological

features associated with hormone replacement therapy, implicating a potential role for

estrogen (18).

These recent studies highlight the trends observed in prior research and meta-

analyses (25,26). Studies to date have primarily focused on metachronous cancer

incidence in women as both cancers occur primarily in women (100:1 for breast and 4:1

for thyroid, female:male). Remarkably, the co-occurrence of these diseases is even

greater in men. Men with a prior history of breast cancer are 19 times more likely to

develop thyroid cancer. This link is bidirectional; men with prior thyroid cancer are 29

times more likely to develop breast cancer (22). Independent confirmation of the

metachronous risk in male cases is needed. The epidemiological linkage between thyroid

cancer and breast cancer is bidirectional and data from patients collected globally has

consistently demonstrated the metachronous relationship. However, larger studies are

needed to increase confidence as to how much greater a risk there is of a second primary

tumor as the various studies presented in this review do not report increased risk using

the same parameters. Further research is needed to determine if certain demographics are

at greater risk, the role of socioeconomic factors, or if there are geographic hotspots at

greater risk of metachronous disease.

5.4. What Genetic Factors Link the Diseases?

There is a strong familial component in the development of cancer. A recent,

large study examining twins in the Nordic nations estimated hereditability to be

129

approximately 33% of cancer risk (27). There does appear to be some familial link

between thyroid and breast cancer; a study on Swedish patients found that first-degree

relatives of women diagnosed with breast cancer are at an increased risk of developing

thyroid cancer (28). Similar results were observed in a U.S. population (29). Among

other factors that could drive a familial association, genetics has potentially the greatest

explanatory power in the link between breast tumors and thyroid tumors as many driver

mutations are similar in both cancers. Cowden Syndrome is an already recognized

genetic disorder, arising from mutations to the tumor suppressor PTEN, which increases

the risk of both breast and thyroid cancer, among other cancers including endometrial and

gastrointestinal (30,31). PTEN encodes for phosphatase and tensin homolog (PTEN),

which inhibits the catalytic activity of the enzyme phosphatidylinositol-3,4,5-

trisphosphate 3-phosphatase (PI3K) (32). PI3K upregulates the downstream effector

AKT, which then phosphorylates an array of proteins, ultimately changing gene

expression. The PI3K-AKT pathway promotes survival, proliferation, and migration to

drive tumorigenesis. Cowden Syndrome can also be worsened by co-mutations to KLLN

and genes that make up the succinate dehydrogenase complex (SDHx) (25). KLLN is a

transcription factor that shares the PTEN promoter and upregulates TP53 to promote

apoptosis (33). SDHx mutations also dysregulate TP53 through enhanced proteasomal

degradation of p53 (34). Other genes that feed into the PI3K pathway and apoptosis may

be involved in the link between thyroid and breast cancers.

Additionally, germline mutations in PARP4 were identified in women treated for

both cancers (35). Heightened expression of PARP4 also correlated with longer disease-

free survival and overall survival in breast cancer patients. PARP4 belongs to a family of

130

genes, poly-ADP-ribose polymerases (PARPs) that encode enzymes to catalyze synthesis

of poly-ADP-ribose (36). Levels of poly-ADP-ribose increase in response to genetic

insults and is a critical component of DNA repair. PARP inhibitors are used clinically to

treat ovarian tumors and are in clinical trials for breast cancer (37). However, existing

drugs do not target PARP4. PARP4 is unique in that it can also be found in the

cytoplasm, unlike the other members of the PARP family (36). Unfortunately, PARP4 is

not well characterized and more research is needed to determine the biochemical and

physiological processes it regulates.

To date, no other mutations have been demonstrated to be causal agents linking

metachronous breast and thyroid tumors. More studies to examine patients with histories

of both diseases are needed to uncover new gene variants. Further, epigenetic changes

including altered expression of microRNAs and long-noncoding RNAs (lncRNA),

covalent modification of DNA, and mutations to regulatory elements in the genome could

be critical. Recently, the lncRNA MANCR, has been demonstrated to be upregulated and

a driver of aggressive breast cancer (38). MANCR is also upregulated in thyroid cancers

(39). Such information from these two studies exemplifies common transcriptional

regulation in both diseases.

5.5. What is the Impact of Cancer Treatment on Future Risk?

Radiation, despite being a treatment option for breast cancer, is a well-

documented risk factor in the development of cancer and is a major risk factor for thyroid

cancer (40-42). Multiple recent clinical studies demonstrate alteration of the ability of the

thyroid to produce hormones following radiotherapy (43-45). Despite this evidence that

131

radiotherapy harms the thyroid, a very large study from Taiwan of over 55,000 patients

did not report any association between treating breast cancer with radiation and

subsequent thyroid cancer, nonetheless the results did corroborate the previously

discussed relationship between thyroid and breast cancers (46). Another study revealed

that although the use of radiation therapy to treat breast cancer was associated with an

increased risk of certain tumors, including leukemia and lung cancer, there was not an

observed increase in the risk of thyroid cancer (47).

Childhood radiation therapy is documented to increase the chance of a women

developing breast cancer later in life (48,49) Thyroid tumors are not typically treated with

beam radiotherapy, but one of the most commonly prescribed treatments for thyroid

cancer is to treat the patient with radioactive iodide (131I). 131I selectively targets the

tumor via the sodium-iodide symporter (NIS) to kill the diseased thyroid tissue. However,

breast tissue expresses NIS to transport iodide into milk and so could be impacted by

radioactive 131I (50). There is also evidence that dietary iodide intake is protective against

the development of breast cancer (51). Since the 1960s, numerous studies denote a

concern for secondary tissue effects with the use 131I treatment for thyroid diseases (52).

Although increased risk for development of leukemia was determined, no association

with breast cancer was noted (53-55). Additionally, meta-analysis of studies specifically

examining 131I treatment for thyroid cancer reveal no association between the use of 131I

and subsequent development of breast cancer (56). A recent study found no difference in

breast cancer risk between thyroid cancer patients treated with and without 131I,

confirming prior observations (57).

132

While the impact of radiotherapy on thyroid function has been extensively

studied, data on chemotherapy is sparse. Studies on breast cancer patients receiving

different chemotherapeutic treatments reveal a reduction in serum T4 levels, but

subsequent recovery of thyroid function and thyroid cancer risk was not assessed (58-60).

Critically, the impact of chemotherapeutic agents on thyroid function and cancer risk

needs further study.

5.6. Are Thyroid Disease and Disruptions of Thyroid Hormone Signaling Risk

Factors?

Thyroidal status may be an important factor in second tumor development,

although studies are limited. Clinical implications of thyroid hormone mediated effects

altering initial tumor development are unclear, data on the impact of thyroid hormone

levels on the likelihood of developing a tumor are inconsistent (61-63). Regardless, in a

small clinical study utilizing patients with advanced cancers, including breast tumors,

ablation of de novo thyroid hormone synthesis and supplementation with T3 prolonged

patient survival (64). Strikingly, in a mouse xenograft study, more metastases were

observed in hypothyroid than euthyroid animals, and hypothyroid mice developed

smaller primary tumors, suggestive of an important role for thyroid hormones on

tumorigenesis (65). Additionally, the impact of tyrosine kinase inhibitors (TKI) on

thyroid function has predictive value in determining patient outcomes; patients with

cancers other than thyroid cancer rendered hypothyroid after TKI treatment have a more

favorable prognosis, as defined by overall survival (66). The subpopulation of patients

who experienced drug-induced hypothyroidism and received supplemental T4 exhibited

133

greater odds of overall survival than those with untreated hypothyroidism. The authors

note that thyroidal dysfunction may be indicative of the effectiveness of the cancer

treatment but the mechanisms are unknown (66). Overall, studies of drug-induced

hypothyroidism indicate that thyroid hormone signaling may protect against advanced

breast cancer.

The canonical pathway for thyroid hormone action is through the nuclear

hormone receptors thyroid hormone receptor alpha (TRα) and thyroid hormone receptor

beta (TRβ). Of these receptors, TRβ is expressed in both breast and thyroid tissue (67).

TRβ has also been implicated as a tumor suppressor (68). The pathways which TRβ

regulates have not yet been thoroughly explored, however there is evidence that it

represses PI3K in both breast and thyroid cancer cells (69,70). TRβ also represses

transcription of the oncogene RUNX2 in thyroid cancer through an interaction with a

chromatin-remodeling complex (71,72). RUNX2 is a driver of both breast and thyroid

cancers and common thyroid hormone mediated mechanisms may be active in both

tissues (73,74). There is also a non-classical thyroid hormone receptor, the integrin αVβ3,

which responds to T4 rather than T3, and activates the pro-oncogenic MAPK pathway

(75). It is probable that the response a tumor has to thyroidal status depends upon the

genes expressed in the cells due to the multiple receptors that feed into many different

pathways.

5.7. Does Hormonal Disruption alter Metachronous Cancer Susceptibility?

Breast cancer is often hormonally driven through upregulation of estrogen and

progesterone receptors and mimetics of these compounds can be carcinogenic (reviewed

134

in (76)). This relationship has been extensively studied, and although there are still many

unanswered questions regarding the mechanistic details, there is little doubt concerning

whether estrogenic signaling can promote breast tumorigenesis. Estrogen is also

implicated in the development of thyroid cancer, and may explain why women develop

the disease at roughly 4 times the rate as men (reviewed in (77)). Numerous studies have

linked endocrine disrupting chemicals (EDCs) to obesity, developmental perturbations

and hormone-dependent cancers (78). Exposure to EDCs at prenatal and early

developmental stages are associated with later development of cancers (79), which could

predisposition the same individual to developing both tumor types; potentially explaining

why the same person develops both diseases in a short time frame and at a younger age.

For example, EDCs may stimulate thyroid and breast cancer development through

estrogenic signaling. Bisphenol A (80) and flame-retardants (81), mimetics of

endogenous estrogen or xenoestrogens, have been implicated in the development of

thyroid cancer as well as breast cancers (82). Bisphenol A binds to TRs and antagonizes

thyroid hormone action (83,84). As TRβ is a tumor suppressor in both thyroid and breast

cancers, this inhibition may be a common mechanism.

Epidemiological analysis of hotspots for thyroid cancer development is consistent

with the hypothesis that an increase in thyroid cancer may be linked to environmental as

well as lifestyle factors (85). We performed an exploratory analysis in Vermont,

comparing the breast and thyroid cancer incidence rates in different counties, and

observed a weak correlation between the two cancer types (Fig 21). A linear trend line fit

to the data indicated that an increase of breast cancer incidence by 10 per 100,000 women

corresponded to an increase in thyroid cancer of 1.6 per 100,000 people. An investigation

135

into hotspots where breast and thyroid cancers co-occur could reveal environmental

factors, including EDCs, that drive both tumor types.

Figure 21: Thyroid Cancer and Breast Cancer Incidence are Positively Correlated among

Vermont Counties. County-specific age-adjusted thyroid cancer and invasive breast cancer incidence for

2010-2014 among Vermont females was obtained from the state of Vermont Environmental Public Health

Tracking Data Explorer. A linear regression was performed to yield a slope of 0.1565 with an R-square

value of 0.2102. The counties of Essex and Grand Isle were excluded due to insufficient numbers of thyroid

cancer cases in these small population counties.

Approximately 80% of breast tumors are hormone receptor positive. Endocrine

therapies via selective estrogen receptor modulators (SERMs) that target estrogen

receptor alpha are a cornerstone of breast cancer treatment for most patients (86,87). The

prototypical SERM, Tamoxifen, has been reported to stimulate thyroid stimulating

hormone (TSH) production (88) and, in a rat model, to induce thyroid tissue proliferation

(89). In patients on both endocrine therapy and T4 replacement therapy for

hypothyroidism there is an increase to levels of thyroxine-binding globin, total T4, and

136

TSH (90). What effect SERMs have on thyroid tumorigenesis has not been adequately

investigated.

5.8. Are Lifestyle Related Environmental Factors Common Between Breast and

Thyroid Cancer?

Behaviorally driven environmental factors, include obesity, sedentary lifestyle,

alcohol consumption, and tobacco usage have been demonstrated to increase cancer risk

(91). Breast cancer risk increases with weight gain, alcohol consumption, and from a lack

of regular exercise (92,93). Shared risk factors with thyroid cancer are candidates to the

etiology of metachronous tumors. Obesity is a risk factor to the development of

epithelial-derived thyroid cancers, but not to tumors that arise from the thyroid’s c-cells

(94,95). Despite the association with obesity, there does not appear to be strong evidence

of physical activity altering thyroid cancer risk (96,97). Alcohol related mechanisms of

tumorigenesis are also not shared between the two diseases. A meta-analysis pooled over

7,000 thyroid cancer patients and demonstrated alcohol consumption does not contribute

to a heightened risk of developing a thyroid tumor (98). For the patients that develop

metachronous breast and thyroid tumors, specific studies are needed to determine

whether lifestyle factors alter the risk of a second cancer.

A factor addressed earlier in this review, thyroid hormones, do have an impact on

obesity and metabolism. Changes in signaling resulting from activity of the tumor

suppressor TRβ may be related to obesity as a risk factor. A high fat diet enhanced

thyroid tumor growth by the leptin-mediated JAK2-STAT3 pathway in a mouse model

exhibiting mutant variants of TRβ and PTEN (99). Increased STAT3 activity has been

137

demonstrated to promote breast cancer progression in animal xenograft models and

detected at the invasive tumor front in breast biopsy specimens (100,101). STAT3

activity may be important to the breast-thyroid cancer link.

5.9. Concluding Remarks

There is strong evidence of a relationship between breast and thyroid cancer as

survivors of either cancer are at increased risk for development of thyroid or breast

tumors compared with the general population. The etiology of these cancers and possible

causative factors are at an infancy stage and just beginning to be studied. Further

investigation into the genomics and epigenetics underlying both breast and thyroid cancer

can yield clues into the specifics of tumorigenesis and identifying who are at greatest risk

of a metachronous tumor. A greater understanding of the etiology of male cases of both

breast and thyroid cancer also has the potential to uncover important etiological factors.

Different subtypes of breast cancer are more common in men as compared to women

(102) and the same in true for thyroid (103). These differences could account for the

greater rate of metachronous occurrence and investigations into molecular differences

could reveal novel insights. Additionally, although radiotherapy has been investigated

and is unlikely to be a significant factor, there is a lack of convincing evidence in either

metachronous direction as to a potential role for chemotherapy. Further studies are

needed into the effects of chemotherapy and endocrine therapy on thyroid disease and

cancer susceptibility. EDCs promote development of hormone-dependent cancers,

however there is a lack of information on EDC exposure and the development of thyroid

cancer, an area that merits further investigation. Further research into the role of obesity

138

and JAK-STAT signaling on breast and thyroid tumorigenesis may also provide insights

into the metachronous relationship. The potential for the different possible etiologies in

the breast-thyroid cancer relationship is summarized (Fig 22).

Figure 22: Summary of Possible Etiological Factors in the Thyroid and Breast Cancer Link.

Factors that have the potential to drive the relationship between thyroid and breast cancer are displayed

here on an arbitrary scale to represent potential explanatory power. Factors at the top of the figure, darker,

are more likely than agents at the bottom of the figure, lighter. Likely etiological factors include genes,

obesity, and hormonal influences such as estrogen, thyroid hormone, and environmental endocrine

disruptors. Chemotherapy is a possibility, however there are a lack of studies so the role it has is unclear.

Radioactive iodide, or radiotherapy, is very unlikely to be etiological, and both alcohol use and a sedentary

lifestyle are unlikely factors for the development of metachronous breast and thyroid tumors.

Importantly, studies are needed to perform parallel observations in both tumor

types to enhance understanding of which tumorigenic pathways are common to both

breast and thyroid and narrow down risk factors for these metachronous neoplasms. The

existing public databases for thyroid cancer characteristics need expansion to allow for

extensive cross comparison between thyroid and breast cancers. There also needs to be a

greater focus towards generating thyroid cancer public datasets for comparison to the

many existing breast cancer datasets. With the increase in thyroid cancer incidence and

139

improved survivorship of breast and thyroid cancers, it is an important public health

concern to identify the linkages between these cancers in both sexes.

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CHAPTER 6: Discussion and Concluding Remarks

6.1. Summary

This dissertation provides insights into the mechanisms by which a tumor

suppressor, TRβ, acts to repress the malignant character of both thyroid and breast

cancer. Phenotypic and transcriptomic characterization of TRβ activity using an

anaplastic thyroid cancer cell line, SW1736, was performed and discussed in chapter 2,

demonstrating that TRβ broadly represses the aggressive malignant phenotype of the

cells. Chapter 3 details the evidence that the TRβ-RUNX2 pathway is active in breast

cancer. RUNX2, a transcription factor that is aberrantly overexpressed in dedifferentiated

tumors and drives cancer progression, was already known to be regulated by TRβ in

thyroid cancer. This provides an example of shared tumor suppressive signaling in breast

and thyroid cells, suggestive that TRβ-mediated tumor suppressive mechanisms are

active across different cell types. Additionally, transcriptomic profiling was performed on

the breast cancer cell line MDA-MB-468 and discussed in chapter 4, where it was

compared with the transcriptomic profiling of TRβ performed in chapter 2. These studies

yielded insights both into how TRβ alters the gene expression program of both of these

cancer cells lines, and also revealed a set of genes regulated by TRβ in both cell types.

Intriguingly, there is a link between breast and thyroid cancer, discussed in chapter 5, and

as a shared tumor suppressor, loss TRβ and TRβ-mediated tumor suppressive signaling

may have a role in the etiology.

155

6.2. Implications

The work presented in this dissertation reveals important mechanistic insights into

TRβ-mediated tumor suppression. This work has uncovered novel signaling networks in

both thyroid and breast cancer types. This includes the discovery that STAT1 signaling is

upregulated by TRβ in thyroid cancer cells and that STAT1 is a potential therapeutic

target. Importantly, TRβ expression and activity induced differentiation markers, and a

decrease in the cancer stem cell subpopulation, in the thyroid cancers. Additionally, the

degree to which TRβ activity in cancer is dependent upon cellular identity was examined.

This includes demonstrating that the T3-TRβ-RUNX2 pathway is active in breast cancer

in addition to thyroid cancer (1), as well as identifying the genes TRβ regulates in basal-

like breast cancer. In the MDA-MB-468 breast cancer cell line, TRβ also induced a more

epithelial-like gene expression profile, evidenced by changes in the PAM50 score,

expression of pro-invasive cytokeratins, and in repression in epithelial-to-mesenchymal

transition (EMT) drivers. Importantly, TRβ induced a more differentiated gene

expression profile in both breast and thyroid cells. Although different genes may be

involved in determining cellular character, this result does suggest that robust TRβ

expression and activity could aid in maintenance of cellular identify to prevent

tumorigenesis.

Additionally, this research allowed for pairing of the transcriptomic and the

phenotypic effects of heightened TRβ expression in MDA-MB-468. Prior research has

revealed that TRβ reduces in vivo growth of tumors arising from this cell line and reduces

the metastatic capacity of the cells (2,3). Critically, T3 levels enhanced the anti-metastatic

capacity of TRβ (3). This dissertation adds to that research by highlighting changes in

156

transcriptomic networks that drive metastasis including the aforementioned cytokeratins

and EMT markers as well as novel induction of genes encoding stearate biosynthetic

enzymes. TRβ-mediated induction of the stearate biosynthesis pathway was observed in

both breast and thyroid cell lines. Although stearate is known to have an anti-tumor role

in breast cancer, demonstrated in vitro (4) and in vivo (5), there are a lack of studies on

the role of this lipid in thyroid cancer. The shared analysis also showed there were

significant changes to gene sets involved in chromatin organization and protein

degradation, consistent with the concept that TRβ acts to restore a homeostatic gene

regulation to a cancer cell and blocks the aberrant transcriptional activity intrinsic to

cancer cells (6).

Interestingly, although the PI3K pathway is a driver of both breast and thyroid

tumors and TRβ is known to directly modulate the activity of the PI3K enzyme complex,

repression of the pathway was only predicted from the SW1736 transcriptomic results.

Regardless, TRβ represses phosphorylation of effectors of the PI3K pathway in a breast

cancer model (7) and TRβ induced a similar repression of AKT phosphorylation in

SW1736 cells. The lack of a predicted PI3K pathway repression in the MDA-MB-468

cells is likely a limitation of using a transcriptomic profile to model pathways that are

largely mediated by post-translational modifications to effector kinases. Additionally, as

a master regulatory pathway, the downstream response from altered PI3K activity is most

likely dependent upon the cellular context, including the mutational profile of a cell,

chromatin organization, and pathway cross-talk.

157

6.3. Future Directions

These studies can be built upon by a deeper investigation into the consequences of

the pathways revealed to be regulated by TRβ. There is not yet a study showing that

overexpression of TRβ in ATC reduces tumor growth in vivo and the SW1736 ATC cell

line I modified to overexpression TRβ would serve this purpose. The transcriptomic

results can inform what pathways to examine in the resultant tumors, including STAT1-

regulated genes, PI3K effectors, thyroid differentiation markers, and epithelial markers.

Another intriguing avenue of research is the aforementioned stearate biosynthetic

pathway. Stearic acid levels of the cells overexpressing TRβ should be measured to

confirm that the pathway is functionally stimulated. Stearate has been investigated as an

anti-tumor agent in breast cancer, but the role of this lipid in thyroid cancer is unknown.

An investigation into the pharmacological properties of stearate on thyroid cancer cells

may provide a novel treatment strategy for advanced cases of thyroid cancer. This

includes measuring proliferation, cellular migration, and apoptosis following stearate

treatment of ATC cells. It would also be of value to test whether blocking stearate

biosynthesis impairs TRβ-T3 mediated tumor suppression. Should there be an effect in

vitro, a mouse model of advanced thyroid cancer should be given a stearate high diet to

determine if the lipid represses tumorigenesis.

TRβ itself may be a target of therapeutic intervention. Although the studies in this

dissertation relied upon stimulation with T3, many TRβ-selective thyromimetics have

been developed (8). Importantly, one of these agents, GC-1, elicited the same

transcriptional response as T3 in a liver cell line that expressed TRβ, but lacked TRα (9).

These agents may be able to stimulate endogenous TRβ and elicit an anti-tumor response.

158

As this analysis has revealed a multitude of pathways relevant to cancer progression, a

rational approach can be taken where TRβ agonists can be combined with modulators of

other pathways to enhance efficacy. Candidate pathways from the research presented in

this dissertation include PI3K inhibitors, interferons, and STAT1 agonists. Additionally,

as Gene Set Enrichment Analysis of the differentially expressed genes shared between

both SW-TRβ and 468-TRβ revealed many chromatin organization pathways were

altered, the efficacy of epigenetic modulators may also be increased with a TRβ agonist.

For example, histone deacetylase and bromodomain inhibitors show promise in both

anaplastic thyroid cancer (10) and breast cancer (11,12).

6.4. Concluding Remarks

The work presented in this dissertation adds to the fields of breast and thyroid

cancer research by revealing novel insights into the molecular activity of a tumor

suppressor. TRβ acts upon multiple pathways and regulates numerous cancer-relevant

phenotypes including proliferation, migration, stemness, and apoptosis. Through the

modulation of many cellular networks, including RUNX2, JAK/STAT, and PI3K, TRβ

drives an anti-tumor gene expression program that blunts the aggressive nature of a

tumor. These results support the potential of TRβ as not only a therapeutic target, but also

a biomarker.

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Appendix A

Supplemental Materials for Chapter 2

189

Appendix A-1: Antibody Table

Antibody Manufacturer Protein Species

SAB4300042 Millipore-Sigma pAKT (S142) Rabbit

CST9167

Cell Signaling

Technology STAT1 Mouse

CST9167

Cell Signaling

Technology JAK1 Mouse

MA5-15739

ThermoFisher

Scientific β-Actin Mouse

CST14220

Cell Signaling

Technology Caspase-3 Rabbit

CST964

Cell Signaling

Technology

Cleaved

Caspase-3 Rabbit

CST9542

Cell Signaling

Technology PARP Rabbit

CST5625

Cell Signaling

Technology Cleaved PARP Rabbit

190

Appendix A-2: Primer Table

Gene Forward Oligo Reverse Oligo

MYC AGCGACTCTGAGGAGGGAACAA GTGGGCTGTGAGGAGGTTTG

CDKN1A GCAGACCAGCATGACAGATTT CTGGACTTCGAGCAAGAGATG

TNFSF10 CCGTCAGCTCGTTAGAAAGATGAT TGTGTTGCTTCTTCCTCTGGT

APOL6 CCTGTTCAAAGGCCACCCTA TCAAATGATTTTCTTCTCTCCACGG

TAP1 GCCTTGTTCCGAGAGCTGAT AATGGCCATCTCCCCAAGAG

IRF1 CTTCCAGGTGTCACCCATGC CCATCCACGTTTGTTGGCTG

GAPDH ATGTTCGTCATGGGTGTGAA TGTGGTCATGAGTCCTTCCA

191

Appendix A-3: TRβ Repressed PI3K Signaling.

Appendix A-3. A-I) Expression of cluster 1 differentially expressed genes in the ILK signaling IPA

pathway. All genes were repressed by T3 and the level of most of these genes was repressed in the SW-

TRβ-T3 condition compared to SW-EV-T3. (n=3, * p<0.05, n.s. p>0.05) B) AKT phosphorylation was

repressed upon overexpression of TRβ in SW1736 cells. (representative blot of 3 experiments)

192

Appendix A-4: Significantly Altered Pathways between SW-EV-T3 and SW-TRβ-T3 as

determined by Ingenuity Pathway Analysis.

Ingenuity Canonical Pathways -log(p-value) Ratio z-score

LPS/IL-1 Mediated Inhibition of RXR Function 7.75E+00 1.25E-01 -1.265

VDR/RXR Activation 3.88E+00 1.41E-01 0.378

Prostanoid Biosynthesis 3.70E+00 4.44E-01 2

Interferon Signaling 3.53E+00 1.94E-01 2.646

Dopamine Degradation 3.16E+00 2.00E-01 0

Aryl Hydrocarbon Receptor Signaling 3.08E+00 9.79E-02 -0.816

Cardiac Hypertrophy Signaling (Enhanced) 2.91E+00 6.57E-02 0.365

STAT3 Pathway 2.83E+00 9.63E-02 -0.333

SPINK1 Pancreatic Cancer Pathway 2.81E+00 1.33E-01 -2.828

Tryptophan Degradation X (Mammalian, via Tryptamine) 2.71E+00 2.00E-01 -1.342

Inhibition of Matrix Metalloproteases 2.55E+00 1.54E-01 -1.342

Sirtuin Signaling Pathway 2.54E+00 7.19E-02 -0.229

Intrinsic Prothrombin Activation Pathway 2.38E+00 1.43E-01 1.633

Oxidative Ethanol Degradation III 2.34E+00 2.11E-01 0

Osteoarthritis Pathway 2.24E+00 7.51E-02 0.775

Colorectal Cancer Metastasis Signaling 2.19E+00 7.09E-02 2.5

Putrescine Degradation III 2.18E+00 1.90E-01 -1

Dermatan Sulfate Biosynthesis (Late Stages) 2.18E+00 1.30E-01 0

Ethanol Degradation IV 2.03E+00 1.74E-01 0

p53 Signaling 1.99E+00 9.18E-02 0.378

Neuroprotective Role of THOP1 in Alzheimer's Disease 1.97E+00 8.62E-02 3

Wnt/β-catenin Signaling 1.92E+00 7.51E-02 0

Sumoylation Pathway 1.86E+00 8.74E-02 0.333

LXR/RXR Activation 1.85E+00 8.26E-02 0.707

Leukocyte Extravasation Signaling 1.80E+00 7.04E-02 2.111

Death Receptor Signaling 1.72E+00 8.79E-02 1.414

Endocannabinoid Neuronal Synapse Pathway 1.69E+00 7.81E-02 1.667

HMGB1 Signaling 1.69E+00 7.23E-02 0.632

Dermatan Sulfate Biosynthesis 1.67E+00 1.02E-01 0

Production of Nitric Oxide and Reactive Oxygen Species in Macrophages 1.63E+00 6.88E-02 0.832

PCP pathway 1.61E+00 9.84E-02 0.816

ILK Signaling 1.59E+00 6.77E-02 -0.277

Ethanol Degradation II 1.54E+00 1.25E-01 0

Apelin Adipocyte Signaling Pathway 1.53E+00 8.64E-02 1.134

Chondroitin Sulfate Biosynthesis (Late Stages) 1.51E+00 1.04E-01 0.447

Superpathway of Melatonin Degradation 1.49E+00 9.23E-02 0

IL-8 Signaling 1.46E+00 6.50E-02 1.941

193

FGF Signaling 1.43E+00 8.24E-02 -1.134

Serotonin Degradation 1.43E+00 8.96E-02 0

Noradrenaline and Adrenaline Degradation 1.41E+00 1.14E-01 -1

Type II Diabetes Mellitus Signaling 1.41E+00 6.99E-02 -0.447

B Cell Receptor Signaling 1.36E+00 6.45E-02 0.905

IL-6 Signaling 1.36E+00 7.14E-02 0.707

Glioblastoma Multiforme Signaling 1.36E+00 6.63E-02 0.302

Opioid Signaling Pathway 1.35E+00 6.00E-02 0.258

194

Appendix A-5: Significantly Altered Hallmark and Gene Ontology Gene Set Enrichment

Analysis Pathways between SW-EV-T3 and SW-TRβ-T3.

PATHWAY NAME SIZE ES NES Nominal p-value

HALLMARK IL2 STAT5 SIGNALING 34 0.549212 1.993116 0

HALLMARK INTERFERON ALPHA RESPONSE 36 0.507281 1.866105 0

HALLMARK INTERFERON GAMMA RESPONSE 62 0.457168 1.82303 0

HALLMARK ANDROGEN RESPONSE 28 0.447349 1.576111 0.018626

HALLMARK KRAS SIGNALING DN 26 0.445573 1.555294 0.030588

HALLMARK APOPTOSIS 33 0.42514 1.528914 0.032768

HALLMARK MYOGENESIS 33 0.424435 1.525912 0.037246

HALLMARK KRAS SIGNALING UP 46 0.422374 1.647219 0.011892

HALLMARK HYPOXIA 50 0.420136 1.633861 0.012876

GO REGULATION OF ANION TRANSPORT 16 0.647537 1.961926 0

GO ENSHEATHMENT OF NEURONS 15 0.63384 1.888172 0

GO NEGATIVE REGULATION OF IMMUNE RESPONSE 18 0.623441 1.982653 0

GO MATERNAL PROCESS INVOLVED IN FEMALE PREGNANCY 15 0.605408 1.842508 0.002528

GO NEGATIVE REGULATION OF CELL MORPHOGENESIS INVOLVED IN DIFFERENTIATION 15 0.599959 1.807874 0.003802

GO REGULATION OF G PROTEIN COUPLED RECEPTOR SIGNALING PATHWAY 17 0.589212 1.8504 0.001261

GO NEGATIVE REGULATION OF NEURON PROJECTION DEVELOPMENT 22 0.585012 1.913372 0

GO REGULATION OF AXONOGENESIS 23 0.58306 1.934126 0

GO AXON EXTENSION 16 0.580806 1.76497 0.008706

GO ANTIGEN PROCESSING AND PRESENTATION OF PEPTIDE ANTIGEN VIA MHC CLASS I 19 0.580567 1.84078 0.002421

GO REGULATION OF NEUROLOGICAL SYSTEM PROCESS 15 0.578919 1.743862 0.007673

GO POSITIVE REGULATION OF MULTI ORGANISM PROCESS 16 0.569209 1.734695 0.011598

GO NEGATIVE REGULATION OF IMMUNE EFFECTOR PROCESS 16 0.568819 1.760847 0.002541

GO POSITIVE REGULATION OF PEPTIDE SECRETION 29 0.565288 1.986042 0

GO CHLORIDE TRANSPORT 18 0.563822 1.753195 0.005096

GO REGULATION OF EXTENT OF CELL GROWTH 16 0.558058 1.694238 0.009792

GO POSITIVE REGULATION OF PROTEIN KINASE B SIGNALING 19 0.553993 1.769344 0.008495

GO NEGATIVE REGULATION OF VIRAL PROCESS 19 0.551946 1.739828 0.007194

GO INORGANIC ANION TRANSPORT 23 0.547105 1.845404 0.002364

195

GO NEURON PROJECTION EXTENSION 17 0.545537 1.688651 0.007326

GO RESPONSE TO TYPE I INTERFERON 23 0.541304 1.824775 0

GO LONG CHAIN FATTY ACID METABOLIC PROCESS 19 0.540457 1.722342 0.008696

GO POSITIVE REGULATION OF CHEMOTAXIS 15 0.536159 1.616078 0.019704

GO REGULATION OF DNA TEMPLATED TRANSCRIPTION IN RESPONSE TO STRESS 16 0.53267 1.62916 0.018657

GO TUMOR NECROSIS FACTOR SUPERFAMILY CYTOKINE PRODUCTION 18 0.532292 1.696899 0.01233

GO PRODUCTION OF MOLECULAR MEDIATOR OF IMMUNE RESPONSE 19 0.52714 1.676789 0.008557

GO NEGATIVE REGULATION OF CELL PROJECTION ORGANIZATION 26 0.526901 1.794707 0.001174

GO NEGATIVE REGULATION OF VIRAL GENOME REPLICATION 15 0.526274 1.602835 0.022444

GO RESPONSE TO HEAT 15 0.525898 1.560976 0.040973

GO PRIMARY ACTIVE TRANSMEMBRANE TRANSPORTER ACTIVITY 18 0.525452 1.671375 0.012579

GO ANCHORED COMPONENT OF MEMBRANE 23 0.52515 1.750417 0.004684

GO POSITIVE REGULATION OF AUTOPHAGY 17 0.523293 1.648211 0.012469

GO ATPASE ACTIVITY COUPLED TO MOVEMENT OF SUBSTANCES 20 0.522145 1.708004 0.004831

GO ANTIGEN PROCESSING AND PRESENTATION OF PEPTIDE ANTIGEN 25 0.519054 1.806069 0.004684

GO ENDOTHELIAL CELL PROLIFERATION 18 0.51824 1.630316 0.009721

GO FATTY ACID BIOSYNTHETIC PROCESS 25 0.517154 1.790263 0.002326

GO MEMBRANE FUSION 16 0.515827 1.560734 0.026895

GO UNSATURATED FATTY ACID METABOLIC PROCESS 18 0.515356 1.620658 0.024297

GO RESPONSE TO TEMPERATURE STIMULUS 23 0.514636 1.738781 0.010817

GO POSITIVE REGULATION OF ERK1 AND ERK2 CASCADE 18 0.509508 1.608124 0.021066

GO RESPONSE TO FIBROBLAST GROWTH FACTOR 18 0.507422 1.586456 0.026087

GO POST TRANSLATIONAL PROTEIN MODIFICATION 33 0.50568 1.819322 0.001143

GO ICOSANOID METABOLIC PROCESS 17 0.503684 1.550899 0.036993

GO NEGATIVE REGULATION OF NEURON DIFFERENTIATION 32 0.502984 1.815329 0.003417

GO INORGANIC ANION TRANSMEMBRANE TRANSPORTER ACTIVITY 20 0.500421 1.617689 0.01444

GO CYTOKINE METABOLIC PROCESS 19 0.499549 1.60804 0.027981

GO POSITIVE REGULATION OF TRANSPORTER ACTIVITY 17 0.499204 1.551982 0.0425

GO T CELL RECEPTOR SIGNALING PATHWAY 20 0.498825 1.617687 0.02457

GO RESPONSE TO VITAMIN 16 0.497992 1.517769 0.042875

196

GO SERINE HYDROLASE ACTIVITY 25 0.496751 1.690443 0.008323

GO NEGATIVE REGULATION OF DEFENSE RESPONSE 26 0.494931 1.70787 0.009456

GO VESICLE LOCALIZATION 19 0.493067 1.580967 0.021546

GO DEFENSE RESPONSE TO BACTERIUM 21 0.492649 1.62172 0.014493

GO MACROAUTOPHAGY 25 0.491718 1.66188 0.015495

GO REGULATION OF PRODUCTION OF MOLECULAR MEDIATOR OF IMMUNE RESPONSE 17 0.489345 1.544081 0.03198

GO POSITIVE REGULATION OF LEUKOCYTE MIGRATION 19 0.488579 1.539014 0.043684

GO METALLOPEPTIDASE ACTIVITY 20 0.488505 1.604386 0.021713

GO POSITIVE REGULATION OF SECRETION 49 0.48764 1.910219 0

GO RAB GTPASE BINDING 18 0.486209 1.535886 0.041872

GO REGULATION OF LEUKOCYTE MIGRATION 28 0.485348 1.709499 0.006849

GO REGULATION OF VIRAL LIFE CYCLE 26 0.484676 1.651607 0.012731

GO IRON ION BINDING 20 0.48405 1.547183 0.034105

GO ENDOPEPTIDASE REGULATOR ACTIVITY 28 0.483351 1.703125 0.009368

GO REGULATION OF RESPONSE TO BIOTIC STIMULUS 19 0.482561 1.559634 0.027947

GO TUMOR NECROSIS FACTOR MEDIATED SIGNALING PATHWAY 19 0.4806 1.538006 0.041463

GO ANTIGEN PROCESSING AND PRESENTATION 28 0.479264 1.688396 0.00576

GO NIK NF KAPPAB SIGNALING 22 0.478409 1.597187 0.02974

GO NEGATIVE REGULATION OF CYTOKINE PRODUCTION 41 0.477304 1.81826 0.001098

GO SITE OF POLARIZED GROWTH 19 0.476715 1.516823 0.039663

GO PEPTIDASE REGULATOR ACTIVITY 32 0.475629 1.709681 0.007042

GO REGULATION OF SYMBIOSIS ENCOMPASSING MUTUALISM THROUGH PARASITISM 27 0.474584 1.647228 0.008037

GO TYPE I INTERFERON PRODUCTION 21 0.474526 1.553489 0.020581

GO NEGATIVE REGULATION OF DEVELOPMENTAL GROWTH 21 0.474124 1.528637 0.031863

GO DEVELOPMENTAL CELL GROWTH 23 0.473638 1.573811 0.026474

GO DEVELOPMENTAL GROWTH INVOLVED IN MORPHOGENESIS 27 0.470821 1.643117 0.016

GO ANTIGEN RECEPTOR MEDIATED SIGNALING PATHWAY 26 0.469483 1.61903 0.019883

GO T CELL PROLIFERATION 23 0.468407 1.57022 0.02497

GO SPROUTING ANGIOGENESIS 24 0.467069 1.546203 0.031616

GO REGULATION OF BLOOD PRESSURE 33 0.466682 1.67405 0.005701

GO SPECIFIC GRANULE 19 0.466117 1.510681 0.045056

GO HEART PROCESS 25 0.463807 1.57181 0.029621

GO AXON DEVELOPMENT 60 0.463489 1.876637 0

GO VOLTAGE GATED ION CHANNEL ACTIVITY 22 0.463471 1.501311 0.045949

197

GO INTERFERON GAMMA PRODUCTION 23 0.463215 1.554114 0.019139

GO NEGATIVE REGULATION OF NERVOUS SYSTEM DEVELOPMENT 39 0.463096 1.773813 0.005556

GO ADHERENS JUNCTION ORGANIZATION 19 0.462314 1.505994 0.04896

GO ESTABLISHMENT OF ORGANELLE LOCALIZATION 31 0.462252 1.644556 0.011338

GO NEGATIVE REGULATION OF DNA BINDING TRANSCRIPTION FACTOR ACTIVITY 21 0.460942 1.493413 0.047847

GO REGULATION OF MULTI ORGANISM PROCESS 49 0.458686 1.799577 0

GO PEPTIDASE ACTIVITY 70 0.454856 1.874579 0

GO T CELL DIFFERENTIATION 37 0.454637 1.690113 0.005537

GO REGENERATION 38 0.453457 1.704756 0.005574

GO CELL CHEMOTAXIS 33 0.453449 1.629858 0.01373

GO CATION CHANNEL ACTIVITY 28 0.451402 1.560146 0.028802

GO OXIDOREDUCTASE ACTIVITY ACTING ON PAIRED DONORS WITH INCORPORATION OR REDUCTION OF MOLECULAR OXYGEN 24 0.449344 1.516543 0.043728

GO CYTOKINE RECEPTOR BINDING 27 0.448833 1.575743 0.021004

GO REGULATION OF EXTRINSIC APOPTOTIC SIGNALING PATHWAY 23 0.447673 1.506763 0.040237

GO NEGATIVE REGULATION OF APOPTOTIC SIGNALING PATHWAY 24 0.446991 1.531891 0.039673

GO GOLGI VESICLE TRANSPORT 28 0.444335 1.548446 0.024166

GO NEGATIVE REGULATION OF MULTI ORGANISM PROCESS 27 0.444207 1.5553 0.03169

GO ORGANELLE LOCALIZATION 38 0.4434 1.631574 0.010297

GO REGULATION OF LEUKOCYTE PROLIFERATION 27 0.440103 1.52778 0.036824

GO REGULATION OF CHEMOTAXIS 28 0.439815 1.541717 0.030588

GO REGULATION OF AUTOPHAGY 31 0.439697 1.583541 0.015784

GO POSITIVE REGULATION OF I KAPPAB KINASE NF KAPPAB SIGNALING 28 0.439203 1.556501 0.020548

GO LIPID MODIFICATION 31 0.438909 1.577764 0.017222

GO FATTY ACID METABOLIC PROCESS 33 0.438108 1.592505 0.022497

GO NEGATIVE REGULATION OF CELL DEVELOPMENT 40 0.438 1.662895 0.003371

GO NEURON PROJECTION GUIDANCE 39 0.437973 1.648987 0.005701

GO ATPASE ACTIVITY COUPLED 29 0.436629 1.551395 0.021378

GO PROCESS UTILIZING AUTOPHAGIC MECHANISM 47 0.435348 1.682766 0.002155

GO INTERFERON GAMMA MEDIATED SIGNALING PATHWAY 24 0.433353 1.47067 0.042755

GO REGULATION OF RESPONSE TO CYTOKINE STIMULUS 28 0.433071 1.530637 0.027491

GO CELLULAR RESPONSE TO OXYGEN LEVELS 33 0.432858 1.553396 0.031285

198

GO INTERACTION WITH HOST 26 0.432622 1.477209 0.0454

GO NEGATIVE REGULATION OF PEPTIDASE ACTIVITY 37 0.431201 1.57793 0.016704

GO DEFENSE RESPONSE TO VIRUS 45 0.430754 1.672151 0.006623

GO REGULATION OF CELL MORPHOGENESIS INVOLVED IN DIFFERENTIATION 36 0.430047 1.589398 0.015801

GO NEGATIVE REGULATION OF PROTEOLYSIS 42 0.429535 1.626882 0.007761

GO REGULATION OF EPITHELIAL CELL MIGRATION 38 0.428413 1.601131 0.01

GO CELL MORPHOGENESIS INVOLVED IN NEURON DIFFERENTIATION 62 0.42811 1.745418 0

GO NEGATIVE REGULATION OF CELL GROWTH 29 0.427938 1.51795 0.030928

GO SECRETORY GRANULE MEMBRANE 31 0.426612 1.521126 0.034091

GO ENDOPEPTIDASE ACTIVITY 52 0.424232 1.670582 0.005405

GO CIRCULATORY SYSTEM PROCESS 66 0.421578 1.711263 0.003188

GO EXTRINSIC APOPTOTIC SIGNALING PATHWAY 32 0.421395 1.507483 0.036405

GO SYNAPTIC MEMBRANE 34 0.420783 1.54822 0.029579

GO I KAPPAB KINASE NF KAPPAB SIGNALING 36 0.420671 1.563163 0.025785

GO VIRAL LIFE CYCLE 43 0.419689 1.603734 0.012876

GO MONOCARBOXYLIC ACID BIOSYNTHETIC PROCESS 34 0.419144 1.544815 0.030405

GO INFLAMMATORY RESPONSE 86 0.417094 1.754796 0

GO HORMONE METABOLIC PROCESS 42 0.416015 1.573177 0.013115

GO MULTI MULTICELLULAR ORGANISM PROCESS 43 0.415174 1.596705 0.01547

GO ZINC ION BINDING 78 0.415007 1.733993 0

GO DEVELOPMENTAL GROWTH 78 0.414935 1.74156 0.003128

GO PROTEIN MATURATION 32 0.413693 1.491014 0.039773

GO LEUKOCYTE MIGRATION 63 0.412795 1.662624 0.00106

GO CELLULAR COMPONENT DISASSEMBLY 46 0.412364 1.603382 0.016234

GO TRANSITION METAL ION BINDING 111 0.411783 1.795982 0

GO LYMPHOCYTE DIFFERENTIATION 47 0.411123 1.590951 0.012088

GO CELL PART MORPHOGENESIS 70 0.410375 1.690601 0.005247

GO POSITIVE REGULATION OF ESTABLISHMENT OF PROTEIN LOCALIZATION 42 0.40872 1.566964 0.019956

GO REGULATION OF PEPTIDASE ACTIVITY 56 0.404615 1.618099 0.007487

GO GATED CHANNEL ACTIVITY 35 0.403899 1.477755 0.046083

GO STEM CELL DIFFERENTIATION 39 0.403838 1.515103 0.030939

GO ORGANIC CYCLIC COMPOUND CATABOLIC PROCESS 49 0.403782 1.566806 0.01652

GO RESPONSE TO VIRUS 56 0.396453 1.587233 0.008538

GO REGULATION OF INNATE IMMUNE RESPONSE 55 0.396233 1.572975 0.005342

GO DEFENSE RESPONSE TO OTHER ORGANISM 64 0.395039 1.609834 0.007361

GO REGULATION OF VASCULATURE 55 0.394666 1.563828 0.018182

199

DEVELOPMENT

GO MEMBRANE ORGANIZATION 76 0.394447 1.660607 0.001041

GO NEGATIVE REGULATION OF HYDROLASE ACTIVITY 54 0.393359 1.575005 0.012862

GO REGULATION OF RESPONSE TO EXTERNAL STIMULUS 93 0.392555 1.662111 0.001037

GO REGULATION OF CELL ACTIVATION 67 0.391265 1.605231 0.005236

GO RESPONSE TO OXYGEN LEVELS 53 0.391191 1.54535 0.02063

GO REGULATION OF PEPTIDE SECRETION 52 0.390138 1.522617 0.022508

GO ACTIVE TRANSMEMBRANE TRANSPORTER ACTIVITY 53 0.389852 1.545007 0.021436

GO RESPONSE TO INTERFERON GAMMA 39 0.388208 1.461169 0.048943

GO PASSIVE TRANSMEMBRANE TRANSPORTER ACTIVITY 49 0.38816 1.52367 0.025527

GO ENDOPLASMIC RETICULUM LUMEN 45 0.387492 1.482298 0.035676

GO REGULATION OF LYMPHOCYTE ACTIVATION 52 0.385666 1.510442 0.019334

GO REGULATION OF NEURON PROJECTION DEVELOPMENT 57 0.385644 1.552919 0.016895

GO SYNAPTIC SIGNALING 57 0.385247 1.543757 0.015038

GO NEGATIVE REGULATION OF MULTICELLULAR ORGANISMAL PROCESS 162 0.384751 1.715059 0.001008

GO MONOCARBOXYLIC ACID METABOLIC PROCESS 67 0.383666 1.557665 0.005269

GO RECEPTOR REGULATOR ACTIVITY 57 0.383656 1.540213 0.022532

GO ORGANIC ACID BIOSYNTHETIC PROCESS 47 0.382345 1.500053 0.03337

GO NEGATIVE REGULATION OF IMMUNE SYSTEM PROCESS 59 0.380784 1.518625 0.024758

GO REGULATION OF SECRETION 89 0.380389 1.604804 0.005118

GO RESPONSE TO BACTERIUM 75 0.379729 1.569967 0.011494

GO NEGATIVE REGULATION OF CELL ADHESION 47 0.379591 1.46074 0.027927

GO REGULATION OF NEURON DIFFERENTIATION 79 0.378575 1.585397 0.005171

GO TISSUE MIGRATION 48 0.376939 1.460763 0.039258

GO REGULATION OF CELL PROJECTION ORGANIZATION 75 0.376254 1.548828 0.011628

GO REGULATION OF APOPTOTIC SIGNALING PATHWAY 47 0.375635 1.459788 0.033696

GO CATION TRANSMEMBRANE TRANSPORTER ACTIVITY 63 0.373926 1.521393 0.020299

GO NEURON DIFFERENTIATION 147 0.373014 1.688197 0

GO TAXIS 77 0.370249 1.548929 0.015756

GO T CELL ACTIVATION 63 0.370225 1.530367 0.023305

GO NEGATIVE REGULATION OF CELL DIFFERENTIATION 91 0.369482 1.574338 0.004119

GO SECRETORY GRANULE 95 0.369293 1.593146 0.003099

GO INTERSPECIES INTERACTION BETWEEN 76 0.369035 1.549359 0.013528

200

ORGANISMS

GO PROTEOLYSIS 174 0.368969 1.670815 0

GO NEURON DEVELOPMENT 115 0.368273 1.622731 0.002041

GO IMMUNE RESPONSE REGULATING SIGNALING PATHWAY 59 0.36504 1.45923 0.027572

GO REGULATION OF CELL CELL ADHESION 61 0.361323 1.472242 0.02766

GO REGULATION OF CELL MORPHOGENESIS 58 0.360867 1.441276 0.041314

GO RESPONSE TO BIOTIC STIMULUS 123 0.359989 1.582911 0.002039

GO ANION TRANSPORT 82 0.359777 1.533712 0.012422

GO CALCIUM ION BINDING 76 0.35963 1.51665 0.017801

GO DEFENSE RESPONSE 208 0.35824 1.642828 0.001001

GO REGULATION OF SYSTEM PROCESS 67 0.357123 1.45438 0.02935

GO PEPTIDE SECRETION 70 0.357055 1.472403 0.019895

GO ENZYME INHIBITOR ACTIVITY 53 0.355563 1.418072 0.047468

GO CYTOKINE MEDIATED SIGNALING PATHWAY 119 0.355429 1.565876 0.001017

GO NEUROGENESIS 181 0.353864 1.606674 0

GO CELL SURFACE 107 0.352718 1.535428 0.007172

GO KINASE BINDING 71 0.352442 1.468891 0.025343

GO REGULATION OF CELL DEVELOPMENT 106 0.352033 1.537332 0.012195

GO AMEBOIDAL TYPE CELL MIGRATION 65 0.351985 1.448039 0.035789

GO CELL BODY 65 0.3507 1.439895 0.043524

GO LYMPHOCYTE ACTIVATION 83 0.349816 1.470141 0.028361

GO REGULATION OF SIGNALING RECEPTOR ACTIVITY 70 0.349332 1.444291 0.034006

GO RESPONSE TO WOUNDING 95 0.345693 1.503966 0.018462

GO REGULATION OF IMMUNE RESPONSE 112 0.34521 1.489822 0.01433

GO TRANSMEMBRANE TRANSPORTER ACTIVITY 132 0.34427 1.530914 0.003043

GO IMMUNE EFFECTOR PROCESS 146 0.344127 1.542292 0.00304

GO CYTOKINE PRODUCTION 102 0.343976 1.481774 0.013265

GO INNATE IMMUNE RESPONSE 119 0.343949 1.502394 0.007202

GO VACUOLE 86 0.343637 1.465123 0.014568

GO REGULATION OF DEFENSE RESPONSE 91 0.343565 1.481777 0.015576

GO CELL MORPHOGENESIS INVOLVED IN DIFFERENTIATION 86 0.343448 1.461047 0.028866

GO NERVOUS SYSTEM PROCESS 110 0.341946 1.505712 0.008138

GO GOLGI MEMBRANE 87 0.341716 1.469958 0.023958

GO SECRETORY VESICLE 114 0.340686 1.474996 0.011202

GO POSITIVE REGULATION OF CELL ADHESION 61 0.340118 1.403317 0.036765

GO EXOCYTOSIS 103 0.339306 1.46334 0.019368

GO POSITIVE REGULATION OF IMMUNE RESPONSE 88 0.33867 1.453083 0.022751

GO NEGATIVE REGULATION OF RESPONSE TO STIMULUS 205 0.338208 1.549161 0.001002

GO NEGATIVE REGULATION OF SIGNALING 173 0.337418 1.502875 0.002022

GO NEGATIVE REGULATION OF 133 0.337235 1.486214 0.005071

201

DEVELOPMENTAL PROCESS

GO RESPONSE TO CYTOKINE 169 0.337159 1.537006 0

GO ION TRANSMEMBRANE TRANSPORTER ACTIVITY 99 0.336881 1.447841 0.023736

GO TRANSPORTER ACTIVITY 154 0.335972 1.513632 0.003021

GO GROWTH 113 0.335854 1.454196 0.020471

GO REGULATION OF NERVOUS SYSTEM DEVELOPMENT 104 0.33569 1.446955 0.026694

GO WOUND HEALING 73 0.335517 1.398873 0.049215

GO ENDOPLASMIC RETICULUM PART 170 0.334757 1.527149 0.002016

GO NEGATIVE REGULATION OF CATALYTIC ACTIVITY 91 0.332704 1.434678 0.028896

GO HYDROLASE ACTIVITY ACTING ON ACID ANHYDRIDES 89 0.331295 1.408093 0.038342

GO REGULATION OF CELLULAR COMPONENT MOVEMENT 135 0.330072 1.459029 0.014242

GO REGULATION OF PROTEOLYSIS 82 0.32977 1.39046 0.046973

GO NUCLEAR OUTER MEMBRANE ENDOPLASMIC RETICULUM MEMBRANE NETWORK 136 0.329528 1.452296 0.016113

GO NEURON PART 156 0.328785 1.470799 0.008065

GO RESPONSE TO ABIOTIC STIMULUS 133 0.3277 1.455192 0.007092

GO POSITIVE REGULATION OF INTRACELLULAR SIGNAL TRANSDUCTION 118 0.325479 1.414209 0.021191

GO POSITIVE REGULATION OF TRANSPORT 112 0.321815 1.408163 0.019527

GO REGULATION OF CELL ADHESION 109 0.319826 1.389924 0.038815

GO CELL PROJECTION ORGANIZATION 151 0.318468 1.451611 0.010091

GO NEGATIVE REGULATION OF MOLECULAR FUNCTION 126 0.317113 1.39259 0.027356

GO ORGANIC ACID METABOLIC PROCESS 121 0.316831 1.379301 0.029323

GO CELL CELL ADHESION 99 0.315904 1.363538 0.045596

GO NEURON PROJECTION 121 0.315453 1.374605 0.031568

GO CARDIOVASCULAR SYSTEM DEVELOPMENT 113 0.314171 1.367531 0.033673

GO CYTOPLASMIC VESICLE PART 162 0.313193 1.412517 0.014141

GO GOLGI APPARATUS PART 115 0.313143 1.36674 0.0409

GO TRANSMEMBRANE TRANSPORT 187 0.313142 1.411586 0.011078

GO GOLGI APPARATUS 183 0.312356 1.418048 0.013078

GO SIGNAL TRANSDUCTION BY PROTEIN PHOSPHORYLATION 111 0.311314 1.370082 0.037717

GO REGULATION OF TRANSPORT 199 0.311207 1.423575 0.007021

GO ORGANONITROGEN COMPOUND CATABOLIC PROCESS 123 0.309866 1.359883 0.039594

GO SIGNALING RECEPTOR BINDING 184 0.308256 1.400347 0.019095

GO SMALL MOLECULE METABOLIC PROCESS 222 0.308192 1.424631 0.007

GO INTRINSIC COMPONENT OF PLASMA 182 0.307087 1.385017 0.01712

202

MEMBRANE

GO SECRETION 200 0.306801 1.401896 0.009027

GO REGULATION OF ANATOMICAL STRUCTURE MORPHOGENESIS 156 0.306351 1.367079 0.019173

GO IDENTICAL PROTEIN BINDING 186 0.303694 1.394577 0.015091

GO POSITIVE REGULATION OF SIGNALING 227 0.303549 1.409608 0.005015

GO REGULATION OF RESPONSE TO STRESS 164 0.303474 1.37639 0.01506

GO REGULATION OF CELL DIFFERENTIATION 229 0.298762 1.381317 0.009018

GO ION TRANSPORT 191 0.298041 1.367587 0.022066

GO ENDOPLASMIC RETICULUM 230 0.297184 1.379726 0.014042

GO WHOLE MEMBRANE 186 0.297139 1.359234 0.03009

GO BIOLOGICAL ADHESION 187 0.296793 1.349621 0.023023

GO MOLECULAR FUNCTION REGULATOR 179 0.296147 1.356645 0.022133

GO REGULATION OF INTRACELLULAR SIGNAL TRANSDUCTION 212 0.290747 1.322498 0.029146

GO REGULATION OF IMMUNE SYSTEM PROCESS 187 0.289403 1.32808 0.037149

GO APOPTOTIC PROCESS 221 0.286938 1.316483 0.031093

GO POSITIVE REGULATION OF MULTICELLULAR ORGANISMAL PROCESS 230 0.285401 1.322074 0.02505

GO PROTEIN PHOSPHORYLATION 228 0.281398 1.306697 0.023046

GO CHROMOSOME 65 -0.24549 -1.48646 0

GO PROTEIN HETERODIMERIZATION ACTIVITY 60 -0.25648 -1.59191 0

GO CHROMOSOME ORGANIZATION 69 -0.26622 -1.70404 0.020833

GO CHROMATIN 46 -0.28284 -1.61245 0.037975

GO EPIDERMAL CELL DIFFERENTIATION 32 -0.3013 -1.51752 0.024793

GO NUCLEAR CHROMOSOME 45 -0.31441 -1.86593 0

GO DIGESTIVE SYSTEM DEVELOPMENT 20 -0.31951 -1.44865 0.045714

GO ORGANIC ACID TRANSMEMBRANE TRANSPORT 23 -0.32554 -1.56623 0.029586

GO REGULATION OF CHROMOSOME ORGANIZATION 20 -0.32851 -1.48071 0.031847

GO RNA METABOLIC PROCESS 86 -0.34762 -2.40766 0

GO REGULATION OF EPITHELIAL CELL DIFFERENTIATION 21 -0.34878 -1.54034 0.039773

GO MALE GAMETE GENERATION 29 -0.35003 -1.70536 0.006993

GO GENE SILENCING BY RNA 21 -0.36279 -1.58384 0.03352

GO MESODERM DEVELOPMENT 19 -0.37763 -1.57562 0.041451

GO REGULATION OF DNA METABOLIC PROCESS 16 -0.3806 -1.54878 0.032787

GO POSITIVE REGULATION OF MYELOID CELL DIFFERENTIATION 16 -0.38795 -1.60227 0.033333

GO OXIDOREDUCTASE ACTIVITY ACTING ON CH OH GROUP OF DONORS 19 -0.38807 -1.66139 0.012121

GO CHROMATIN ASSEMBLY OR DISASSEMBLY 21 -0.39308 -1.72442 0.030303

GO REGULATION OF NUCLEOBASE CONTAINING COMPOUND METABOLIC PROCESS 30 -0.41285 -2.09342 0

203

GO UBIQUITIN LIKE PROTEIN LIGASE ACTIVITY 15 -0.41406 -1.6046 0.027027

GO CELLULAR RESPONSE TO REACTIVE OXYGEN SPECIES 16 -0.41808 -1.69832 0.009615

GO AMINO ACID TRANSMEMBRANE TRANSPORT 18 -0.41848 -1.73785 0.015

GO METHYLATION 25 -0.45065 -2.18152 0

GO RIBONUCLEOPROTEIN COMPLEX BIOGENESIS 21 -0.46656 -2.02903 0

GO MACROMOLECULE METHYLATION 17 -0.47267 -2.0032 0

GO CHROMATIN ASSEMBLY 19 -0.48615 -2.09289 0

GO DNA PACKAGING 19 -0.48615 -2.06391 0

GO MEGAKARYOCYTE DIFFERENTIATION 18 -0.49739 -2.02943 0

GO DNA CONFORMATION CHANGE 21 -0.50933 -2.28077 0

GO CATALYTIC ACTIVITY ACTING ON RNA 27 -0.51663 -2.55498 0

GO PROTEIN DNA COMPLEX SUBUNIT ORGANIZATION 23 -0.53245 -2.3819 0

GO NUCLEOSOME ORGANIZATION 18 -0.55132 -2.40781 0

GO PROTEIN DNA COMPLEX 28 -0.5582 -2.77188 0

GO REGULATION OF GENE SILENCING 16 -0.56221 -2.29416 0

GO NCRNA PROCESSING 18 -0.58002 -2.39072 0

GO REGULATION OF MEGAKARYOCYTE DIFFERENTIATION 15 -0.59517 -2.32157 0

GO NCRNA METABOLIC PROCESS 27 -0.62592 -3.11261 0

GO DNA PACKAGING COMPLEX 24 -0.66429 -3.02316 0

GO TELOMERE ORGANIZATION 16 -0.69624 -2.78713 0

GO TRNA METABOLIC PROCESS 15 -0.70427 -2.7583 0

204

Appendix A-6: RNA-sequencing of the TRβ-Regulated Transcriptome Reveals a

Transcription Factor Network.

Appendix A-6. A) Chromatin Enrichment Analysis (ChEA) of differential expression clusters was

performed to evaluate which transcription factor networks were overrepresented in each cluster. B)

Multiple transcription factors elucidated by ChEA were also differentially expressed.

205

Appendix A-7: TRβ Regulates Expression of lncRNAs.

Appendix A-7. A) A pairwise comparison between SW-EV-T3 and SW-TRβ-T3 revealed differential

expression of both mRNAs and lncRNAs (log2(foldchange)>2 and p<0.05). A total of 350 mRNAs and 28

lncRNAs were differentially expressed. B) Expression levels of the differentially expressed lncRNAs in

transcripts per million reads.

206

Appendix A-8: TRβ Regulates Key Cell Cycle Regulators.

Appendix A-8. RNA-sequencing data revealed statistically significant increase of the anti-proliferative

gene CDKN1A and repression the pro-proliferative gene MYC. n=3, ** p<0.01, *** p<0.001

207

Appendix A-9: TRβ Induced a more Differentiated Phenotype.

Appendix A-9. RNA-sequencing data revealed statistically significant increase of the epithelial marker

CDH1 and repression of the mesenchymal marker VIM. Although SNAI1 was expressed, no significant

changes in expression were observed. n=3, * p<0.05, ***** p<0.0001

208

Appendix A-10: Reintroduction of TRβ altered Thyroid Differentiation Markers.

Appendix A-10. Thyroid differentiation markers were significantly induced in SW-TRβ cells treated with

T3 (n=3, * p<0.05).

209

Appendix A-11: Reintroduction of TRβ Increased Expression of interferon/JAK1/STAT1

Pathway Effectors.

Appendix A-11. A-C) Following 24 hours of treatment with T3 or vehicle, protein levels in SW-EV and

SW-TRβ were assessed by western blot. TRβ induced expression of JAK1 and STAT1. Representative

western blot depicted. (*p<0.05, n=6) D) STAT1 and IRF1 regulated genes were induced upon

overexpression of TRβ and T3 treatment. (n=6, * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001).

210

Appendix B


211

Appendix B-1: Primers and Oligonucleotides

Oligonucleotide Sequence Purpose

VEGFA Forward CCTTGCTGCTCTACCTCCAC qPCR

VEGFA Reverse CCATGAACTTCACCACTTTCG qPCR

THRA Forward AGGTCACCAGATGGAAGCG qPCR

THRA Reverse AGTGATAACCAGTTGCCTTGTC qPCR

GAPDH Forward ATGTTCGTCATGGGTGTGAA qPCR

GAPDH Reverse TGTGGTCATGAGTCCTTCCA qPCR

RUNX2 Forward CAGCCCCAACTTCCTGTG qPCR

RUNX2 Reverse CCGGAGCTCAGCAGAATAAT qPCR

MMP9 Forward CCTGGAGACCTGAGAACCATC qPCR

MMP9 Reverse CCACCCGAGTGTAACCATAGC qPCR

RUNX2 P1 Forward GGACAGCAAGAAGTCTCTGGTT qPCR

RUNX2 P1 Reverse GATGCCATAGTCCCTCCTTTTT qPCR

Cyclin D1 Forward GCTGCGAAGTGGAAACCATC qPCR

Cyclin D1 Reverse CCTCCTTCTGCACACATTTGAA qPCR

THRB Forward GGCGCAGCACGTTGAAAAAT qPCR

THRB Reverse CACATCATCVATGGTCCAGATGG qPCR

RUNX2 P1 Forward GATCTCACTTTACTTAAGAGTACTGTGAGGTCACAAACCACATGATTCTGCCTCTCCAGTAAA

DNA Pulldown

RUNX2 P1 Reverse GATCTTTACTGGAGAGGCAGAATCATGTGGTTTGTGACCTCACAGTACTCTTAAGTAAAGTGA

DNA Pulldown

RUNX2 P1 Mutant Forward GATCTCACTTTACTTAAGAGTACTGTGATTTCGCAAACCACATGATTCTGCCTCTCCAGTAAA

DNA Pulldown

RUNX2 P1 Mutant Reverse GATCTTTACTGGAGAGGCAGAATCATGTGGTTTGCGAAATCACAGTACTCTTAAGTAAAGTGA

DNA Pulldown

THRA Cloning Forward ACAAAAGCTTATGGAACAGAAGCCAAGCAAGG

THRA Cloning

THRA Cloning Reverse CACAGGATCCTTAGACTTCCTGATCCTCAAAG

THRA Cloning

212

Appendix B-2: Antibodies

Antibody Manufacturer Target Purpose

MA5-15739 Thermo Fisher Scientific β-Actin Immunoblot

SC-390715 Santa Cruz Biotechnology

RUNX2 Immunoblot

PA1-211A Thermo Fisher Scientific TRα Immunoblot

MA1-216 Thermo Fisher Scientific TRβ Immunoblot

AB53170 Abcam TRβ Immunoblot

PA5-29684 Thermo Fisher Scientific TRβ ChIP

31887 Pierce Scientific N/A (IgG) ChIP

213

Appendix B-3: Repression of RUNX2 by Thyroid Hormone

Appendix A-3. Breast cell lines MCF10A and MDA-MB-231 were treated for 24 hours with 10-8 M T3

and RUNX2 expression was measured by immunoblot. T3 significantly repressed RUNX2 expression in

both cell lines.

214

Appendix C


215

Appendix C-1: Mycoplasma PCR Primers.

Forward Primers Reverse Primers

CGCCTGAGTAGTACGTTCGC GCGGTGTGTACAAGACCCGA

CGCCTGAGTAGTACGTACGC GCGGTGTGTACAAAACCCGA

TGCCTGAGTAGTACATTCGC GCGGTGTGTACAAACCCCGA

TGCCTGGGTAGTACATTCGC

CGCCTGGGTAGTACATTCGC

CGCCTGAGTAGTATGCTCGC

216

Appendix C-2: Reintroduction of TRβ to MDA-MB-468 cells.

Appendix C-2. MDA-MB-468 cells were transfected with either an empty vector (EV) or a vector that

expresses wild type TRβ. Overexpression of TRβ was confirmed by western blot.

217

Appendix C-3: Significantly Altered Hallmark and Gene Ontology Gene Set Enrichment

Analysis Pathways between 468-EV-T3 and 468-TRβ-T3.


HALLMARK INFLAMMATORY RESPONSE 22 -0.37732 -1.94494 0.003185

HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 28 -0.32683 -1.87006 0.003663

HALLMARK TNFA SIGNALING VIA NFKB 21 -0.37503 -1.87435 0.00641

HALLMARK UV RESPONSE DN 18 -0.38381 -1.82522 0.008427

GO POSITIVE REGULATION OF ORGANELLE ORGANIZATION 26 0.41754 1.846908 0.006739

GO CELLULAR LIPID METABOLIC PROCESS 47 0.334884 1.760693 0.009235


GO MEMBRANE ORGANIZATION 32 0.371835 1.770413 0.010825

GO REGULATION OF VESICLE MEDIATED TRANSPORT 33 0.352438 1.723024 0.012195

GO PASSIVE TRANSMEMBRANE TRANSPORTER ACTIVITY 21 0.407297 1.720896 0.014793

GO POSITIVE REGULATION OF RNA BIOSYNTHETIC PROCESS 47 0.312377 1.65367 0.016667

GO REGULATION OF CELL MORPHOGENESIS 31 0.365325 1.745347 0.017931

GO IMMUNE RESPONSE REGULATING SIGNALING PATHWAY 24 0.394224 1.697102 0.019971

GO POSITIVE REGULATION OF DEFENSE RESPONSE 27 0.372784 1.67321 0.022472

GO ENDOCYTOSIS 26 0.37337 1.651849 0.024793

GO LIPID METABOLIC PROCESS 64 0.282495 1.667814 0.025063

GO GATED CHANNEL ACTIVITY 15 0.456657 1.680443 0.025185

GO ACTIVATION OF INNATE IMMUNE RESPONSE 19 0.425724 1.735362 0.0279

GO REGULATION OF IMMUNE RESPONSE 48 0.304758 1.640261 0.030223

GO ACTIVATION OF IMMUNE RESPONSE 25 0.367587 1.62691 0.036111

GO REGULATION OF ANATOMICAL STRUCTURE MORPHOGENESIS 67 0.266734 1.595238 0.037783

GO REGULATION OF INNATE IMMUNE RESPONSE 24 0.389231 1.66464 0.038407

GO POSITIVE REGULATION OF CELLULAR COMPONENT ORGANIZATION 61 0.272602 1.56971 0.038557

GO FATTY ACID METABOLIC PROCESS 16 0.41946 1.611667 0.047267

GO MEMBRANE REGION 22 0.357972 1.539507 0.047955

GO VASCULAR PROCESS IN CIRCULATORY SYSTEM 22 0.370112 1.566991 0.048023

GO REGULATION OF CELL ADHESION 46 0.283399 1.518403 0.048969

218

GO IMPORT INTO CELL 34 0.314766 1.541387 0.049007

GO SKIN DEVELOPMENT 34 -0.58441 -3.50755 0

GO CORNIFICATION 19 -0.7207 -3.49877 0

GO KERATINIZATION 20 -0.69499 -3.46765 0

GO EPIDERMAL CELL DIFFERENTIATION 24 -0.66195 -3.43124 0

GO INTERMEDIATE FILAMENT CYTOSKELETON 19 -0.69527 -3.3382 0

GO EPIDERMIS DEVELOPMENT 33 -0.54663 -3.31345 0

GO EPITHELIAL CELL DIFFERENTIATION 54 -0.44889 -3.14855 0

GO CYTOSKELETAL PART 50 -0.41402 -2.89313 0

GO SUPRAMOLECULAR COMPLEX 44 -0.40219 -2.85011 0

GO EPITHELIUM DEVELOPMENT 79 -0.3363 -2.63464 0

GO POLYMERIC CYTOSKELETAL FIBER 29 -0.46637 -2.58778 0

GO STRUCTURAL MOLECULE ACTIVITY 56 -0.33447 -2.51667 0

GO NEGATIVE REGULATION OF PEPTIDASE ACTIVITY 22 -0.44831 -2.29491 0

GO NEGATIVE REGULATION OF HYDROLASE ACTIVITY 30 -0.37157 -2.14747 0

GO ENDOPEPTIDASE REGULATOR ACTIVITY 16 -0.46235 -2.10286 0

GO AGING 19 -0.42386 -2.10265 0.003205

GO VIRAL LIFE CYCLE 18 -0.44284 -2.08896 0

GO INTERSPECIES INTERACTION BETWEEN ORGANISMS 30 -0.37515 -2.07692 0

GO REGULATION OF ENDOTHELIAL CELL MIGRATION 20 -0.40504 -2.0139 0.006536

GO NEGATIVE REGULATION OF PROTEOLYSIS 26 -0.36542 -2.00984 0

GO COAGULATION 20 -0.40744 -2.0017 0

GO NEGATIVE REGULATION OF LOCOMOTION 23 -0.39279 -1.9916 0.010101

GO RESPONSE TO BIOTIC STIMULUS 71 -0.25196 -1.97639 0

GO NEGATIVE REGULATION OF CATALYTIC ACTIVITY 48 -0.28653 -1.97566 0.009091

GO NEGATIVE REGULATION OF CELL MOTILITY 22 -0.37524 -1.91946 0.006711

GO PEPTIDASE REGULATOR ACTIVITY 19 -0.39199 -1.90271 0.013514

GO REGULATION OF NEUROTRANSMITTER LEVELS 17 -0.42162 -1.88871 0

GO POSITIVE REGULATION OF VASCULATURE DEVELOPMENT 25 -0.34693 -1.85027 0.006431

GO BLOOD VESSEL ENDOTHELIAL CELL MIGRATION 15 -0.42133 -1.84962 0.01791

GO RESPONSE TO VIRUS 24 -0.35377 -1.84828 0.010563

GO POSITIVE REGULATION OF ENDOTHELIAL CELL MIGRATION 17 -0.40436 -1.84612 0.031546

GO CELL JUNCTION ASSEMBLY 23 -0.35777 -1.83409 0.006969

219

GO REGULATION OF SYMBIOSIS ENCOMPASSING MUTUALISM THROUGH PARASITISM 16 -0.41376 -1.81229 0.008746

GO ENZYME INHIBITOR ACTIVITY 25 -0.33106 -1.80354 0.003344

GO TISSUE MIGRATION 29 -0.31383 -1.80289 0.013201

GO NEGATIVE REGULATION OF MOLECULAR FUNCTION 57 -0.24111 -1.80152 0.009434

GO REGULATION OF EPITHELIAL CELL MIGRATION 24 -0.34598 -1.80133 0.022727

GO ENDOTHELIAL CELL MIGRATION 23 -0.35127 -1.77876 0.014235

GO DEFENSE RESPONSE TO VIRUS 17 -0.38976 -1.76128 0.018127

GO NEGATIVE REGULATION OF TRANSCRIPTION BY RNA POLYMERASE II 29 -0.3079 -1.75908 0.016

GO NEGATIVE REGULATION OF RESPONSE TO EXTERNAL STIMULUS 25 -0.32238 -1.75072 0.02807

GO DEFENSE RESPONSE 104 -0.20076 -1.74875 0

GO NEGATIVE REGULATION OF TRANSPORT 30 -0.29859 -1.72403 0.01581

GO POSITIVE REGULATION OF EPITHELIAL CELL MIGRATION 18 -0.37175 -1.71627 0.024768

GO REGULATION OF MULTI ORGANISM PROCESS 28 -0.30311 -1.69656 0.022951

GO AXON 33 -0.28497 -1.69618 0.015385

GO NEGATIVE REGULATION OF CELL DIFFERENTIATION 31 -0.28297 -1.68274 0.02214

GO CELL JUNCTION ORGANIZATION 26 -0.31073 -1.68257 0.025926

GO ENZYME REGULATOR ACTIVITY 51 -0.23892 -1.67848 0.018182

GO REGULATION OF PEPTIDASE ACTIVITY 32 -0.27741 -1.67461 0.019531

GO NEGATIVE REGULATION OF PROTEIN METABOLIC PROCESS 61 -0.22471 -1.6743 0.010753

GO HEPARIN BINDING 17 -0.36739 -1.67375 0.027027

GO REGULATION OF HYDROLASE ACTIVITY 66 -0.22101 -1.66495 0.010309

GO REGULATION OF TRANSMEMBRANE RECEPTOR PROTEIN SERINE THREONINE KINASE SIGNALING PATHWAY 18 -0.34998 -1.64962 0.024316

GO TRANSMEMBRANE RECEPTOR PROTEIN SERINE THREONINE KINASE SIGNALING PATHWAY 23 -0.31389 -1.61431 0.030612

GO DEPHOSPHORYLATION 15 -0.37662 -1.61256 0.043478

GO POSITIVE REGULATION OF DEVELOPMENTAL PROCESS 98 -0.1883 -1.60936 0.019481

GO RESPONSE TO WOUNDING 42 -0.23897 -1.595 0.03861

GO NEGATIVE REGULATION OF DEVELOPMENTAL PROCESS 47 -0.233 -1.56332 0.030172

GO REGULATION OF BODY FLUID LEVELS 39 -0.24598 -1.55248 0.038911

GO RESPONSE TO RADIATION 17 -0.33301 -1.54114 0.047945

GO MOLECULAR FUNCTION REGULATOR 102 -0.17351 -1.52187 0.026316

220

GO RESPONSE TO DRUG 57 -0.21043 -1.48429 0.049774

GO NEGATIVE REGULATION OF RESPONSE TO STIMULUS 90 -0.17652 -1.45039 0.041916

221

Appendix C-3: Significantly Altered Gene Ontology Gene Set Enrichment Analysis

Pathways between 468-EV-T3 and 468-TRβ-T3 Using Shared Differentially Expressed

Genes.


GO PEPTIDASE ACTIVITY 18 0.573847 1.72028 0.009153

GO RESPONSE TO GROWTH FACTOR 15 0.566824 1.630316 0.019608

GO BEHAVIOR 18 0.532005 1.582079 0.020478

GO REGULATION OF PROTEOLYSIS 15 0.529535 1.535544 0.034158

GO REGULATION OF NEURON DIFFERENTIATION 18 0.522837 1.574351 0.017202


GO CELL SURFACE 21 0.508689 1.578421 0.02718

GO REGULATION OF SECRETION 22 0.508067 1.601615 0.023179



GO SECRETORY GRANULE 23 0.497511 1.55795 0.030668

GO INFLAMMATORY RESPONSE 22 0.493079 1.534242 0.023077

GO REGULATION OF PROTEIN LOCALIZATION 22 0.48783 1.513206 0.034483

GO ENZYME LINKED RECEPTOR PROTEIN SIGNALING PATHWAY 19 0.487414 1.48511 0.045913

GO PROTEOLYSIS 39 0.484836 1.680512 0.006336

GO PLASMA MEMBRANE REGION 28 0.482214 1.610188 0.011879


GO NERVOUS SYSTEM PROCESS 27 0.477135 1.552331 0.028078

GO RESPONSE TO ENDOGENOUS STIMULUS 35 0.474273 1.598648 0.019792

GO REGULATION OF NERVOUS SYSTEM DEVELOPMENT 24 0.47253 1.488514 0.044469

GO SECRETORY VESICLE 27 0.471191 1.520926 0.025974

GO RESPONSE TO HORMONE 23 0.467318 1.464294 0.043285

GO RESPONSE TO DRUG 28 0.463248 1.521577 0.031049

GO RESPONSE TO LIPID 32 0.44958 1.5278 0.02234

GO REGULATION OF INTRACELLULAR SIGNAL TRANSDUCTION 45 0.429039 1.515355 0.024819

GO REGULATION OF TRANSPORT 37 0.423213 1.465962 0.046561

GO NEGATIVE REGULATION OF RESPONSE TO STIMULUS 42 0.415829 1.465677 0.039501

GO NEURON DIFFERENTIATION 39 0.414326 1.463045 0.048168

GO NEGATIVE REGULATION OF SIGNALING 33 0.411966 1.401268 0.049841

GO SECRETION 47 0.409263 1.468127 0.031217


GO CELLULAR PROTEIN CONTAINING COMPLEX ASSEMBLY 21 -0.3407 -1.61937 0.018349

GO CHROMATIN ORGANIZATION 23 -0.51729 -2.46573 0

222

GO CHROMOSOME ORGANIZATION 24 -0.53515 -2.62044 0



223

Appendix C-4: Significantly Altered Gene Ontology Gene Set Enrichment Analysis

Pathways between SW-EV-T3 and SW-TRβ-T3 Using Shared Differentially Expressed

Genes.


GO PEPTIDASE ACTIVITY 18 0.578812 2.075959 0.002304

GO POSITIVE REGULATION OF CATABOLIC PROCESS 15 0.56707 1.950009 0

GO INFLAMMATORY RESPONSE 22 0.531233 2.058922 0.001129

GO CYTOKINE PRODUCTION 24 0.50329 1.965671 0.003275

GO REGULATION OF IMMUNE RESPONSE 24 0.500027 1.966478 0

GO REGULATION OF CELLULAR CATABOLIC PROCESS 18 0.482722 1.787873 0.01049


GO ORGANOPHOSPHATE METABOLIC PROCESS 15 0.471951 1.619419 0.031991

GO POSITIVE REGULATION OF SECRETION 15 0.471343 1.643262 0.028037

GO REGULATION OF DEFENSE RESPONSE 16 0.46267 1.641106 0.027381

GO INNATE IMMUNE RESPONSE 19 0.460688 1.715514 0.019653

GO REGULATION OF CATABOLIC PROCESS 20 0.447717 1.70786 0.013857


GO RESPONSE TO BIOTIC STIMULUS 22 0.437278 1.688847 0.022198

GO DEFENSE RESPONSE 39 0.435436 1.921073 0.001063

GO NEGATIVE REGULATION OF IMMUNE SYSTEM PROCESS 17 0.431539 1.567853 0.04

GO REGULATION OF RESPONSE TO EXTERNAL STIMULUS 19 0.430565 1.603309 0.039306

GO PROTEOLYSIS 39 0.426857 1.883387 0.002139

GO IMMUNE EFFECTOR PROCESS 29 0.411864 1.688592 0.010834


GO NEGATIVE REGULATION OF MULTICELLULAR ORGANISMAL PROCESS 38 0.402177 1.777883 0.005313

GO POSITIVE REGULATION OF IMMUNE SYSTEM PROCESS 26 0.397518 1.569866 0.042999



GO ORGANONITROGEN COMPOUND CATABOLIC PROCESS 27 0.38228 1.570028 0.039735

GO NEGATIVE REGULATION OF RNA BIOSYNTHETIC PROCESS 31 -0.22538 -1.55937 0.035294

GO CATION TRANSMEMBRANE TRANSPORT 15 -0.34706 -1.65136 0.018405

GO NUCLEAR CHROMATIN 21 -0.36151 -2.11366 0

GO NUCLEAR CHROMOSOME 21 -0.36151 -2.02312 0.007692

GO MYELOID CELL DIFFERENTIATION 17 -0.36896 -1.8677 0.019231

224

GO CHROMATIN 27 -0.37138 -2.3174 0

GO RNA BINDING 16 -0.37834 -1.92114 0

GO CHROMOSOME 30 -0.3803 -2.56913 0

GO CELLULAR PROTEIN CONTAINING COMPLEX ASSEMBLY 21 -0.38587 -2.22366 0


GO CHROMATIN ORGANIZATION 23 -0.53561 -3.03361 0



225

Appendix C-5: Gene Sets Regulating Chromatin Structure and Protein Degradation Are

Regulated by TRβ in Breast and Thyroid Cancer Cells.

Appendix C-5. Gene Ontology pathways altered by TRβ overexpression included chromatin remodeling

and proteolytic genes in A) MDA-MB-468 and B) SW1736 cells.

226

Appendix D

List of Abbreviations

227

2-(1,8-Naphthyridin-2-ly)phenol (2-NP)

3-Phosphoinositide Dependent Protein Kinase-1 (PDK1)

Anaplastic Thyroid Cancer (ATC)

Androgen Receptor (AR)

Area Under the Curve (AUC)

Aromatase Inhibitor (AI)

Chromatin Immunoprecipitation (ChIP)

Chromatin Immunoprecipitation Enrichment Analysis (ChEA)

Diacylglycerol (DAG)

Differentially Expressed Gene (DEG)

DNA-Binding Domain (DBD)

Endocrine Disrupting Chemical (EDC)

Epidermal Growth Factor (EGF)

Epithelial to Mesenchymal Transition (EMT)

Estrogen Receptor (ER)

Fibroblastic Growth Factor (FGF)

Follicular Thyroid Cancer (FTC)

Gene Set Enrichment Analysis (GSEA)

Guanidine Diphosphate (GDP)

Guanidine Triphosphate (GTP)

Heat Shock Proteins (HSP)

Hepatocellular Carcinoma (HCC)

Inositol 1,4,5-Trisphosphate (IP3)

228

Integrin-Linked Kinase (ILK)

Ligand-Binding Domain (LBD)

Long-Noncoding RNA (LncRNA)

Mammalian Target of Rapamycin Complex 2 (MTORC2)

Matrix Metalloprotease (MMP)

Mitogen Activated Protein Kinase (MAPK)

Nuclear Export Signals (NES)

Nuclear Hormone Receptor (NR)

Nuclear Localization Signal (NLS)

Nuclear Receptor Coactivator 1 (NCOA1)

Nuclear Receptor Co-Repressor (NCOR)

Nucleotide Excision Repair (NER)

Papillary Thyroid Cancer (PTC)

Phosphatase and Tensin Homolog (PTEN)

Phosphatidylinositol (3,4,5)-Trisphosphate (PIP3)

Phosphatidylinositol 4,5-Bisphosphate (PIP2)

Phosphoinositide 3-Kinase (PI3K)

Phospholipase C (PLC)

Poly (ADP-Ribose) Polymerase (PARP)

Poly (ADP-Ribose) Polymerase 1 (PARP1)

Poorly Differentiated Thyroid Cancer (PDTC)

Principle Component Analysis-Based Iterative PAM50 Subtyping (PCA-PAM50)

Progesterone Receptor (PR)

229

Protein Kinase C (PKC)

Quantitative Real-Time PCR (qRT-PCR)

Radioactive Iodide (RAI)

Receptor Tyrosine Kinase (RTK)

Retinoid X Receptor (RXR)

Selective Estrogen Receptor Modulator (SERM)

Silencing Mediator for Retinoid or Thyroid-Hormone Receptors (SMRT)

Small Ubiquitin-Like Modifier (SUMO)

Sodium-Iodide Symporter (NIS)

Succinate Dehydrogenase Complex (SDHx)

Thyroid Differentiation Score (TDS)

Thyroid Hormone Receptor (TR)

Thyroid Hormone Receptor Alpha (TRα)

Thyroid Hormone Receptor Beta (TRβ)

Thyroid Hormone Response Element (TRE)

Thyroid Stimulating Hormone (TSH)

Thyroxine (T4)

Triiodothyronine (T3)

Vitamin D Receptor Interacting Protein/Thyroid Hormone Receptor-Associated Protein

(DRIP/TRAP)

anti-tumor signaling of thyroid hormone receptor beta in

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