Identifying and characterizing a unique enhancer for Y537S mutant estrogen receptor (ER) in breast cancer cells

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Content IDENTIFYING AND CHARACTERIZING A UNIQUE ENHANCER FOR Y537S MUTANT ESTROGEN
RECEPTOR (ER) IN BREAST CANCER CELLS
By
Jonathan (Yonatan) Amzaleg
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(CRANIO-FACIAL BIOLOGY)
May 2023
Copyright 2023 Jonathan (Yonatan) Amzaleg
ii

DEDICATION

Dedicated to my grandfathers, Z”L Asher Ben Roza and Chaim Ben Rachel, and my wife’s
grandfathers Z”L Massoud ben Rachel and Reuven Shmuel ben Reitzah

Your dedication to life, Torah, and family helped shaped me into the person that I am today.
You constantly set an example for later generations to aspire to.

Dedicated in the loving memory of my mentor, Dr. Baruch Frenkel. Everyone who came into
your orbit came out a better person.

iii
ACKNOWLEDGEMENTS
I first want to acknowledge Hashem. I can’t do anything without You.
To my parents. Aba and Ima, I love you so much. Thank you for always believing in me
and inspiring me to be the best version of myself. Thank you to my siblings Eric and Rachel; Limor
and Oran; and all their beautiful children. Thank you to my mother-in-law and father-in-law, I
would not be doing this PhD right had it not been your insistence and belief in me. My
grandmothers, Buya and Safta Chana, your empathy for others (even those you’ve never met) is
something that I strive to acquire. Finally, my rock, the love of my life – Tamari. I couldn’t have
survived the past 6 years without your love and support. To my wonderful children Aaron, Sarah,
Shmuel, and Naomi. Please forgive me for not being there every day to tuck you in. I love you all
more than you’ll ever know.
To Dr. Michael Paine, you took a chance on me when you didn’t have to. For this I’ll be
forever grateful. Thank you so much for your guidance over the years, it has been invaluable. To
Dr. Jian Xu, I now understand why Dr. Frenkel used to call you a superstar. Your patience,
diligence and helpfulness both during the quals and as I prepare to defend has been incredibly
helpful. To Dr. Michael Stallcup, you’ve been one of the best mentors I’ve ever had. Thank you
so much for giving as much attention to an unqualified volunteerto your post docs and PhD. It
speaks to the person you are. Thank you to Janice Bea from CCMB who has been so kind and
supportive of me during my PhD. Thank you to Drs. Jeff Chen and Yang Chai for all you do for
CCMB.
Finally, I would like to acknowledge my mentor, Dr. Min Yu. Min, over the past 5 years,
you’ve brought out the best in me. You’ve always put your students first, pushing us because you
know we could always be better. Your scientific curiosity and enthusiasm are infectious. I would
not have been able to deal with the slew of negative data of this project without it. Thank you!
To the best lab mates for whom one can ever ask. Veronica, Diane, Sathish, Aidan, Tal, Des, Remi,
Mohamad, Ebony, Oihana, Yilin, Irving, Amber, Negeen. It’s been a pleasure to get to know each
one of you during this journey. You’ve all helped me so much and for that I am incredibly grateful.

iv
TABLE OF CONTENTS

DEDICATION ................................................................................................................................... ii

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

LIST OF TABLES .............................................................................................................................. vi

LIST OF FIGURES ........................................................................................................................ …vii

LIST OF ABBREVIATIONS ……………………………………………………………………………………………………………ix

ABSTRACT ...................................................................................................................................... xi

Chapter 1: Introduction .................................................................................................................. 1
1.1 Types of breast cancer and their respective therapies ................................................ 1
1.2 Circulating tumor cells (CTCs) and metastasis ............................................................. 2
1.3 Estrogen receptor signaling in breast cancer .............................................................. 3
1.4 Estrogen receptor regulated by transcription factors and post
translational modifications ................................................................................................. 6
1.5 Endocrine therapy and resistance………………………………………………...............................9
1.6 Enhancers and gene regulation ……………………………………………………………………………..12
1.7 IGFBP3 and CD44 …………………………………………………………………………………………………...14

Chapter 2: Identifying and Characterizing a Unique Enhancer in Mutant Y537S Estrogen
Receptor (ER) Breast Cancer Cells. ............................................................................................... 18
2.1 Introduction ................................................................................................................ 18
2.2 ER binding profile of a CTC line harboring the heterozygous Y537S mutant ER ......... 19
2.3 Identifying the intronic region of DPY19L1P1 as a unique Y537S mutant ER binding
site .................................................................................................................................... 25
2.4 DPY19L1P1 Knockout (KO) in MCF7 Y537S cells decreases cellular proliferation
and adhesion ................................................................................................................... 35
2.5 DPY19L1P1 KO in Y537S cells increases its sensitivity to fulvestrant………………………..38
2.6 RNA-seq of DPY19L1P1 KO in WT and Y537S cells reveals potential genes that are
regulated by the region………………………………………………………………………………………………..42
2.7 qPCR data reveals IGFBP3 and CD44 are regulated by DPY19L1P1 region in Y537S
And not WT cells …………………………………………………………………………………………………………46
2.8 Knocking out IGFBP3 in Y537S_control cells recapitulates some of the DPY19L1P1
KO phenotype……………………………………………………………………………………………………………..52
2.9 Materials and methods………………………………………………………………………………………….56
2.10 List of Primers………………………………………………………………………………………………………68

Chapter3: Discussion……………………………………………………………………………………………………………….70
v
3.1 Discussion and future direction .................................................................................. 70
3.2 Conclusion………………………………………………………………………………………………………………73
Bibliography…………………………………………………………………………………………………………………………….75

vi
LIST OF TABLES

Chapter 1: Introduction
Table 1.2 Targeted exome sequencing of ex-vivo patient derived CTC lines………………………………3

Chapter 2: Identifying and Characterizing A Unique Enhancer for Y537S Mutant Estrogen
Receptor (ER) in Breast Cancer Cells
Table 2.3 Identifying Y537S mutant ER-specific binding across multiple studies…………………………29

vii

LIST OF FIGURES

Chapter 1: Introduction
Figure 1.3.1 Structure of Estrogen Receptor-a (ERa) and Estrogen Receptor-b (ERb)……………....4

Figure 1.3.2 Estrogen Receptor signaling and endocrine therapy………………………………………………6

Figure 1.5.1 Point mutations found in ESR1 gene..………………………………………………………………….…12

Figure 1.7.1 Protein structure of IGFBP3 …………………………………………………………………………………..17

Figure 1.7.2 Protein and gene structure of CD44 – Figure source: Chen et al 2018…………………..17

Chapter 2: Identifying and Characterizing A Unique Enhancer for Y537S Mutant Estrogen
Receptor (ER) in Breast Cancer Cells
Figure 2.2.1: PCA plot and Heatmap of ER ChIP-seq data in BRx68 CTC line ................................ 22

Figure 2.2.2 Identifying Unique Binding Profile of the Y537S Mutant ER ..................................... 24

Figure 2.3.1 Importance of statistical power in ChIP-seq data .................................................... 29

Figure 2.3.2 Enhancer properties of Y537S mutant ER-specific binding sites .............................. 30

Figure 2.3.3: Motifs present in DPY19L1P1 and CLASP2 regions .................................................. 31

Figure 2.3.4: Luciferase assay validating enhancer activity of DPY19L1P1 and CLASP2 regions…..31

Figure 2.3.5: Determining open chromatin state and presence of H3K4me1 in DPY19L1P1
and CLASP2 regions………………………………………………………………………………………………………………….31

Figure 2.3.6: ChIP qPCR of GREB1 promotor site of DPY19L1P1 region in WT and Y537S cells……33

Figure 2.3.7: Generating DPY19L1P1 Knockouts (KO) in WT and Y537S MCF7 cells…………………..34

Figure 2.4.1: DPY19L1P1 KO causes decrease in cell proliferation and adhesion in Y537S but
not WT cells……………………………………………………………………………………………………………………………..37

Figure 2.5.1: DPY19L1P1 KO increases sensitivity to fulvestrant in Y537S cells, not in WT cells…40

Figure 2.5.2: DPY19L1P1 KO increases sensitivity to short-term and long-term treatment
of fulvetrant in Y537S cells, not WT cells…………………………………………………………………………………. 41
viii

Figure 2.6.1: RNA-seq analysis of DPY19L1P1 KO in WT and Y537S cells…………………………………..44

Figure 2.6.2: RNA-seq analysis of DPY19L1P1 KO identifies genes upregulate in Y537S cells
but downregulated in DPY19L1P1 KO……………………………………………………………………………………….45

Figure 2.6.3: Rapid Immunoprecipitation Mass Spectrometry of Endogenous Proteins
(RIME) reveals IGFBP3 complexes with Y537S mutant ER………………………………………………………… 46

Figure 2.7.1: qPCR confirmation of downregulation of IGFBP3 and CD44 in Y537S cells in full
and hormone-depleted media………………………………………………………………………………………………… 49

Figure 2.7.2: Relative RNA expression level of IGFBP3 and CD44 in WT and Y537S DPY19L1P1
KO cells in the presence of E2 and fulvestrant…………………………………………………………………………. 50

Figure 2.7.3: Relative RNA expression of IGFBP3 and CD44 in T47D WT and Y537S cells
in the presence of E2 and fulvestrant……………………………………………………………………………………… 51

Figure 2.8.1: Generating IGFBP3 Knockouts in MCF7 Y537S cells……………………………………………….54

Figure 2.8.2: IGFBP3 Knockout in Y537S cells recapitulates cell growth phenotype of
Y537S DPY19LP1 KO………………………………………………………………………………………………………………… 55

ix
LIST OF ABBREVIATIONS
Abbreviation or Term Definition/Explanation
DCIS
CYP19A1
CDK7
CTCs
Dex
ERβ
ERα (ER)
ERE
E1
E2
E3
E4
FSH
GPER
GR
GRE
HAT
HDAC
HSP
HER2
HOX
IDC
IL-1β
ILC
IGF-1
IGF-1R
IGFBP
LBP
LH
MAPK
NLS
PBX-1
Ductal Carcinoma in Situ
Aromatase enzyme
Cyclin-dependent kinase 7
Circulating Tumor Cells
Dexamethasone
Estrogen receptor β
Estrogen receptor-α
Estrogen-response element
Estrone
17β-estradiol
Estriol
Estetrol
Follicle-stimulating hormone
G protein-coupled estrogen receptor
Glucocorticoid receptor
GR response element
Histone acetyltransferases
histone deacetylase
Heat shock protein
Human epidermal growth factor receptor 2
Homeobox
Invasive Ductal Carcinoma
Interleukin-1 beta
Lobular carcinoma
Insulin-like growth factor
IGF type 1 Receptor
IGF binding protein
Ligand binding pocket
Luteinizing hormone
Mitogen-activated protein kinase
Nuclear Localization Signal
PBX homeobox 1
x
PAK1
P13K
PIP-2
PIP-3
PLC
PKA
PKC
PR
PTMs
RIME
SERDs
SERMs
SphK1
S1P
TNF-α

P21 (RAC1) activated kinase 1
Phosphatidylinositol 3 kinase
Phosphatidylinositol 4,5 bisphosphate
Phosphatidylinositol -3,4,5 triphosphate
Phospholipase C
Protein kinase A
Protein kinase C
Progesterone receptor
Post-translational modifications
Rapid Immunoprecipitation Mass Spectrometry of Endogenous proteins
Selective estrogen receptor degraders
Selective estrogen receptor modulators
Sphingosine kinase 1
Sphingosine-1-phosphate
Tumor necrosis factor alpha

xi
ABSTRACT

Point mutations in the ligand binding regions of the ESR1 gene, encoding estrogen
receptor α (ER), have recently been detected in metastatic lesions of breast cancer patients who
become resistant to anti-estrogen endocrine therapy. These mutations lead to conformation
changes in the ER protein that mimic a constitutively active ER even in the absence of estrogens.
Further research is needed to develop effective therapies for breast cancer patients with these
mutations. Recent reports, including our own studies, suggest that in addition to the traditional
ER targets, there is a unique transcriptome and binding profile for the different mutant ERs
compared to the activated WT. For example, preliminary studies have revealed unique chromatin
modifying proteins complexing to the mutant ER and not the WT. Analyzing the ChIP-seq
experiments from publicly available datasets as well as our own circulating tumor cell (CTC) line
which harbors one of the more common ESR1 mutations (Y537S), we identified a genomic region
in the first intron of the pseudogene DPY19L1P1 which showed enriched Y537S ER binding
compared to the WT. Further examination revealed that the DPY19L1P1 region also shows
enhancer properties based on the H3K4me1 and H3K27Ac data from both ENCODE and from data
generated from our lab. Following successful knockout of this region using CRISPR-Cas9
technology in both MCF7 WT and MCF7 harboring the Y537S ER mutation cells, we determined
that the DPY19L1P1 region knockout in the Y537S mutant cells showed a statistically significant
attenuated growth, decrease in adhesive properties, and an increase in sensitivity to commonly
used endocrine therapy (fulvestrant) compared to Y537S control cells. Interestingly, these
functional changes were not observed with the DPY19L1P1 knockout in MCF7 WT cells which
showed no statistical difference in cell growth, adhesive properties, and sensitivity to hormonal
therapy. RNA-seq analysis of the DYP19L1P1 knock out in MCF7 WT or Y537S mutant cell lines
compared to respective controls demonstrate that more genes were affection by DPY19L1P1
deletion in Y537S mutant compared to WT KO cells. Two candidate genes, IGFBP3 and CD44,
were significantly downregulated in Y537S DPY19L1P1 KO cells. Both of these genes were also
observed as Y537S upregulated genes compared to WT when in our RNA-seq analysis of publicly
available datasets. After confirming by qPCR that the IGFBP3 and CD44 genes were
downregulated in DPY19L1P1 region knockout cells in Y537S but not WT cells, we used CRISPR-
xii
Cas9 technology to knockout IGFBP3 in Y537S MCF7 cells to determine the role of this gene
played in the observed phenotype of the DPY19L1P1 region knockout of the same genotype.
While IGFBP3 KO resulted a statistically significant decrease in proliferation, it did not affect cell
adhesion nor fulvestrant sensitivity. Taken together, these data suggest a unique mechanism of
action for the mutant ER and the role of a unique enhancer in promoting mutant ER-mediated
cellular growth, adhesion, and resistance to fulvestrant, which can help determine appropriate
therapies for metastatic patients harboring the Y537S ESR1 mutation.

1
Chapter 1: Introduction

1.1 Types of Breast Cancer and their Respective Therapies
Breast cancer is the most common form of cancer and the second leading cause of cancer-
related deaths among women (Siegel et al 2021). Stage 0 breast cancer (also known as Ductal
Carcinoma in Situ (DCIS)) is found in about 1 in 5 new cases of breast cancer and is a type of non-
invasive breast cancer found on the lining of the breast ducts. Breast cancers which have spread
into surrounding breast tissue are called Invasive Breast Cancer, of which the most common ones
are Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC). The molecular
classification of breast cancer is subdivided into four molecular subtypes: Luminal A, Luminal B,
Basal-like, and human epidermal growth factor receptor 2 (HER2) enriched. While both Luminal
A and B subtypes tend to express the nuclear receptor protein Estrogen Receptor-α (ER), encoded
by the gene ESR1, they differ in tumor grade. Luminal B type breast cancer typically has a larger
tumor size compared to Luminal A. Luminal A breast cancer usually does not express HER2, while
Luminal B may or may not express HER2. Ki-67, a nuclear protein associated with cellular
proliferation, is typically expressed higher in Luminal B than Luminal A breast cancers (Inic et al
2014). The usual treatment for Luminal A and B subtypes is hormonal therapy, or endocrine
therapy which directly or indirectly targets the ER (See Section 1.5). The HER2-enriched molecular
subtype comprise about 5-15% of breast cancer and are characterized by the expression of HER2
and the lack of ER or PR expression. Therapies for this subtype include direct targeting of HER2
with therapies such as the monoclonal antibody Herceptin (Exman et al 2021) and tyrosine kinase
inhibitors (TKIs) such as tucatinib (Schlam et al 2021).
Around 15-20% of breast cancer is basal-like (Alluri et al 2014) and does not usually
express ER, progesterone receptor (PR), nor HER2. Triple-negative breast cancer, sometimes used
interchangeably with basal-like breast cancer, is very similar to basal-like breast cancer in that
neither of the three receptors are expressed, but it differs in that the basal-like subtype is defined
by a specific gene expression signature profile characterized by high expression of basal markers
such as cytokeratins (CK) 5, 6 and 17 (Perou et al 2000). The prognosis for basal-like and triple
negative breast cancer patients is typically more severe as there is no established molecular
2
target. For patients with basal-like subtype, chemotherapy is typically prescribed neo-adjuvantly
and/or adjuvantly.

1.2 Circulating tumor cells (CTCs) and metastasis
More than 90% of breast cancer deaths are due to distal organ metastases, most
commonly in the bone, lungs, liver, and brain (Dillekås et al 2016). Circulating tumor cells (CTCs)
are tumor cells shed from primary or metastatic tumors into blood circulation. The detection of
higher numbers of CTCs has been associated with worse prognoses in patients with breast
cancers (Cristofanilli et al 2004, Cristofanilli et al 2008, Yu et al 2011). Mounting evidence has
demonstrated that CTCs are indicative of disease progression and treatment responses (Yu et al
2013). In metastatic breast cancer patients, CTCs may represent a reservoir of metastatic tumor
cells deposited from tumors at various organ sites. One important aspect of CTCs is the potential
to utilize them in non-invasive liquid biopsies to monitor tumor progression and drug responses,
compared to highly invasive solid-tumor biopsies which are substantially less practical for
monitoring patient responses and guiding personalized medicine (Yu et al 2011). However,
isolating CTCs from blood has presented challenges. Among these challenges is the rarity of CTCs
compared to white (WBC) and red blood cells (RBC). For example, in 1ml of blood there may be
less than 10 CTCs circulating compared to millions of WBCs and billions of RBCs (Kamal, et al
2019). There have been many technologies used to detect and characterize CTCs (Joosse et al
2015, Miller et al 2010, Sollier et al 2014), many of which involve mRNA and protein expression
profiling in order to characterize them based on their biological properties. While these are
excellent ways to detect and characterize CTCs, this would involve fixing or lysing cells precluding
them from expansion for further downstream analysis.
Dr. Min Yu has recently developed a method for ex vivo culture of CTCs isolated from the
peripheral blood of metastatic breast cancer patients (Yu et al 2014). Importantly, among the
CTC lines established from six luminal breast cancer patients, Dr. Yu identified 3 different point
mutations in the ligand binding domain (LBD) of the ESR1 (Yu et al 2014) which are the same hot
spot mutations identified in several other reports published during the same time period as her
3
study (Table 1.3) (See sections 1.3 and 1.5 for further detail). These CTC lines are a unique
resource for the determination of unique mutant ER-binding sites on chromatin.

Table 1.2 Targeted Exome Sequencing of ex-vivo patient-derived CTC lines. Exome sequencing of
the top 1000 genes associated with cancer was performed on CTC lines. ESR1 mutation (Red box) was
found in 3 of the 6 cell lines. Note BRx68 has an allele frequency close to 50% indicating heterozygous
mutation.

1.3 Estrogen Receptor Signaling in Breast Cancer
In women, the Estrogen Receptor is activated by four major estrogens: estrone (E1), 17β-
estradiol (E2), estriol (E3), and estretrol (E4). Chemically, estrogens contain one benzene ring
with a hydroxyl group and either a ketone group (E1) or one, two or three additional hydroxyl
groups (E2, E3, and E4, respectively) (Fuentes, et al 2019). Estrogens are produced primarily by
the ovaries up until menopause, and their synthesis is regulated by pituitary gonadotropins,
follicle-stimulating hormone (FSH), and luteinizing hormone (LH) (Hall et al 2001). Following
menopause, in which the ovaries are no longer producing estrogens, the primary source of
estrogens becomes peripheral tissue, in which the aromatase enzyme (CYP19A1) catalyzes the
4
conversion of androgens to estrogens (I.E. Smith et al 2003). Estrogens are important regulators
of cellular growth and differentiation, with functions in a number of different tissues. They bind
to the ligand binding domain of two estrogen receptors, ERα and ERβ, each encoded by distinct
genes, ESR1 and ESR2 respectively. ERα and ERβ are expressed in a tissue-specific manner, with
ERα being the predominant receptor in breast tissue (Hall et al 2001) and estimated to be
expressed in about 80% of breast cancers (Anderson et al 2002.).
ERα was first discovered in the late 1950s in research led by Dr. Elwood Jansen, who also
showed in follow-up publications that ERα could translocate to the nucleus and promote gene
expression (Jensen et al 1967, Jensen et al 1968). More than 30 years later, another nuclear
receptor was discovered in epithelial cells of the prostate gland and the granulosa cells of the
ovaries that shared a high level of homology to the ERα and was aptly called Estrogen Receptor
β (ERβ) (Kuiper et al 1996). Over the past decade, a new class of estrogen receptor was discovered
called G Protein-Coupled Estrogen Receptor (GPER1), and unlike the two previously discovered
estrogen receptors which were nuclear receptors, this is a membrane receptor involved in
mediating non-genomic estrogen-related signaling (Boonyaratanakornkit et al 2007).
The full-length ERα is comprised of 595 amino acids (about 67 kDa). Despite the high
homology between the ERα and the ERβ, the ERβ is smaller due to a shorter NTD domain,
comprised of a total of 530 amino acid residues (about 59 kDa). The structural domains of both
ERα and ERβ are elucidated in Figure 1.3.1. Briefly, they are both subdivided into six functionally
distinct domains – The N terminal domain, the AF1 domain (where co-activators are recruited,
independent of ligand binding), the DNA-binding domain (DBD), the hinge domain (facilitating
the synergy between AF-1 an AF-2-based complexing proteins), the Ligand Binding Domain, and
the AF-2 domain (where complexing proteins are recruited after binding of an agonist).

5
Figure 1.3.1 Structure of Estrogen Receptor-α (ERα) and Estrogen Receptor-β (ERβ). Protein
structure of Estrogen receptors α and β. Each contain an Activating Function-1 (AF-1) region, DNA binding
domain (DBD), Hinge Domain, and Activating Function-2 (AF-2) domain, and Ligand binding domain (LBD).
Image created with BioRender.com

As ERα is the major receptor expressed in breast tissue and this particular receptor is the
focus of my thesis, moving forward, I will refer to ERα as ER. ER signaling is activated upon binding
of the steroid hormone E2 (Hall et al 2001). The LBD is the gate keeper for agonist-induced ER
activation and within the LBD is a ligand binding pocket (LBP) which is the region in the LBD where
antagonists or agonists bind. The ER LBD consists of 12 α-helices (h1-h12), and one of those loops
(h11-h12) has been shown via X-ray crystallography that it is important for keeping the ER in an
"off", or not active, state (Fanning et al 2016). In order for the ER to be in an activated state, the
h12 helix would need to bend over the LBP. However, due to the string of hydrophobic amino
acid residues in the linker region (V533, V534, P535, and L536), the h11-h12 loop experiences
strain and in the absence of an agonist the h12 would be unable to bend over the LBP and adopt
the activated state (Katzenellenbogen et al 2018). Once, however, the E2 does bind, The E2-ER
interaction leads to a conformational change of the ER which displaces the ER from the heat
shock protein tethered to it (HSP90) in the unliganded state, and then it dimerizes. The
conformational change of ligand-binding leads to an assembly of various co-activators which can
assemble in the AF-2 of the ER to form transcriptional complexes that bind to DNA sequences
known as estrogen-response elements (EREs), and initiates the transcription of target genes (Fig.
1.3.2) (S. Ali et al 2002). Activated ER can also act as a co-activator or complexing partner to other
transcription factors leading to ER binding independent of ERE (see following sections) (Lin et al
2007). Additionally, ER has non-canonical signaling that includes estrogen-independent genomic
activities mediated by the AF-1 domain of ER (Schiff et al 2004) (Figure 1.3.2).
6

Figure 1.3.2 Estrogen Receptor Signaling and Endocrine Therapy. A schematic depicting
estrogen receptor (ER) signaling and the actions of different types of endocrine therapy.
Aromatase inhibitors prevents conversion of peripheral androgens to estrogens. Selective
Estrogen Receptor Modulators (SERMs) and Selective Estrogen Receptor Degraders (SERDs) work
by directly targeting the ER. ER signaling involves estrogen binding to ligand binding domain (LBD)
inducing a confirmational change in which it detaches from heat shock protein, dimerizes and
translocates to the nucleus where it binds to estrogen receptor elements (ERE) to target gene
expression.

1.4 Estrogen Receptor regulated by Transcription Factors and Post-translational Modifications
Genomic landscape and epigenome alterations are a hallmark of transcriptional
programming, facilitating the process of transcription factor binding to elements on the genome
which had been previously inaccessible. PBX Homeobox 1 (PBX1) is well-studied as a co-activator
for homeobox (IHOX) transcription factor genes. PBX1 was shown to behave as a pioneer factor
for ER which contributes to its genomic activity in breast cancer. Indeed, studies revealed that
85% of the ER cistrome (determined via the Cistrome web application from Dr. Shirley Liu's lab
(Liu et al 2011)) contains DNA which recognizes PBX1 and noted to be co-expressed with ER in 41
independent breast cancer expression studies (Magnani et al 2011).
Crosstalk between ER and other transcription factors has been indicated in processes such
as cellular growth and development in breast cancer cells. Crosstalk between ER and
7
glucocorticoid receptor (GR)specifically has previously been shown to contribute to the
progression of breast cancer, but more recently it has been determined that co-activation of ER
and GR leads to an increase in binding sites for ER. Performing ChIP-seq analysis in murine cells
ectopically expressing ER and GR revealed that in the presence of both ligands activating ER and
GR (E2 and dexamethasone (Dex) respectively) there is an increase in binding sites for ER
compared to E2-only treatment, suggesting a mechanism of "assisted-loading" by which the GR
first binds to regions on the genome, which would provide new accessibility to ER (Miranda et al
2013). In fact, new ER binding events that appear in the presence of E2 and Dex corresponded
with areas which showed increased chromatin accessibility in the presence of the dual drug
treatment compared to E2 alone (as determined via DNAseI-seq). This suggests that ER acts as a
binding partner to GR and promotes differential gene expression. Another study from 2017
showed that activated GR can reduce ER-mediated transcriptional activity. The study identified
465 genes upregulated in MCF7 in the presence of E2, that was abrogated in the presence of Dex
and E2 (Yang et al 2017). ChIP-qPCR assay of ER and GR in the presence of E2, Dex, and both of
the 423 enhancer regions which corresponded to the 465 genes upregulated by E2, revealed that
binding of ER to these enhancers did not change when comparing E2 to E2 + Dex, but the binding
of GR dramatically increased. Puzzling, however, given that when performing motif analyses of
the GR gains regions, the GR Response Element (GRE) was not among the significantly enriched
motifs, suggesting GR is binding to the chromatin by way of tethering to the ER. The study
performed a ChIP-seq of co-activators associated with ER activation (called MegaTrans complex
and consists of GATA3, AP-gamma, and RAR-alpha) in the presence of E2, Dex, and both. They
determined that while there was substantial enrichment of these co-activators in 465 ER-
associated enhancers in the presence of E2, the intensity of the peaks decreased dramatically in
the presence of both Dex and E2, suggesting that GR binds to the ER in these regions and
prevented binding of an important subset of ER co-activators to regulate gene expression,
thereby causing a decrease in expression of the E2-mediated ER genes.
Other than activation via ligand binding to ER, there is a plethora of evidence for ligand
independent activation of the ER leading to ER binding along the chromatin – some to classical
EREs and some to non-canonical sites. Various post-translational modifications (PTMs) such as
8
ubiquitination, acetylation, methylation, and SUMOylation can affect ER activity through various
mechanisms. Amongst the most common PTM in the context of ER is phosphorylation (Anbalagan
et al 2012).
Phosphorylation of the Serine at the 118
th
residue (S118) is one of the more commonly
described PTM of ER and is usually mediated via cell cycle regulator Cyclin-Dependent Kinase 7
(CDK7) (Harrod et al 2017). Growth factors have also been noted to play a role in facilitating the
phosphorylation of S118 in a ligand-independent function. Growth factors such as epidermal
growth factor (EGF) activate cellular receptor protein EGFR to then activate the intracellular
mitogen-activated protein kinase (MAPK), inducing S118 phosphorylation and ER ligand-
independent activity (Kato et al 1995). Further studies have shown that this ligand-independent
ER activation via MAPK promotes MCF7 cell line proliferation through interaction with other
protein complexing partners (Mendoza et al 2010). The serine at the 305
th
residue (S305) can be
phosphorylated as well. A non-canonical form of S305 phosphorylation results from cytokines
like tumor necrosis factor alpha (TNF-α) or interleukin-1 beta (IL-1β), binding to their respective
cell membrane receptors, which then causes the activation of IKKβ and phosphorylation of the
serine residue to induce signaling. Cytokine-induced S305 phosphorylation of the ER, results in
ER binding to similar regions in the genome to that of a normal activated ER in the presence of
E2 (Stender et al 2017). Phosphorylation of the S305 via Protein Kinase A (PKA)and P21 (RAC1)
Activated Kinase 1 (PAK1) was shown to activate ER in the absence of E2 (Wang et al 2002). In
addition to S118 and S305, phosphorylation of the S167 residue was identified to be vital for
some ER-mediated transcriptional activity (Joel et al 1998). This phosphorylated residue results
from phosphatidylinositol-3 Kinase (PI3K) signaling which is activated by hormone (such as
insulin) or growth factor (such as EGF).
Acetylation of histone marks (notably H3K27Ac) via histone acetyltransferases (HAT) p300
and CBP is important for "opening" previously "closed" regions of the genome, thus allowing ER
to bind and promote expression of its target genes. ChIP-seq studies performed in MCF7 cells of
ER, p300, and CBP binding revealed more than 50% shared binding regions between ER and the
HAT co-activators (Mohammed et al 2013). Additionally, acetylation of the ER itself has been
observed across many studies. Among the acetylation marks facilitated via p300 are K266, K268,
9
K299, K302, and K303. The acetylation at these different marks has been noted to have varying
effects. For instance, acetylation of K266 and K268 were shown to have a stimulatory effect of
E2-mediated ER transcriptional regulation (Chen et al 1999), while K299, K302, and K303 were
associated with decreased transcriptional activity (M. Kim et al 2001).
In addition, methylation of ER at residue K302 has been shown to be associated with both
ER stability on the genome and transcriptional activation (Subramanian et al 2008). This
particular PTM is mediated through the lysine methyltransferase SET7. Additionally, arginine
methylation at R260, which is found in the DNA binding domain via Protein arginine N-
methyltransferase 1 (PRMT1), helps facilitate ER's interaction with PI3K and SRC leading to
downstream signaling via AKT pathway (Le Romancer et al 2008). All in all, both histone and ER
PTMs are paramount for regulating ER activity on the genome, both in terms of binding to its
cognate recognition sites and regulating gene expression activity.
1.5 Endocrine therapy and resistance.
Due to the importance of ER activation in breast cancer, treatment to adversely affect ER
signaling, known as endocrine therapy, has become the first line treatment in neoadjuvant,
adjuvant, and metastatic settings and dramatically benefited women with ER positive breast
cancers (Patani et al 2014). In early-stage breast cancers, endocrine therapy lowers the
recurrence risk by 50% and mortality by more than 25%. In more advanced breast cancer, it does
extend patient survival, however, resistance usually occurs.
Endocrine therapies are classified into two main categories based on the biology of ER
signaling and estrogen production in pre- and post-menopausal women. In pre-menopausal
women, endocrine therapy mainly targets binding by utilizing inhibitors that competitively bind
to ER. Those agents include selective estrogen receptor modulators (SERMs), such as
lasofoxifene, bazedoxifene, and tamoxifen, and selective estrogen receptor degraders (SERDs),
such as fulvestrant (also called ICI-182780) and AZD9496. They competitively bind to ER and
induce conformational changes that differ from the normal, active ER structure (Lancet et al
2005).
SERMs and SERDs differentially antagonize the ER. While both SERMs and SERDs provide
the required energy to cause the ER to shed from the HSP, SERMs block co-activators' ability to
10
bind to the AF-2 region of the ER. This, however, does not prevent the ER from dimerizing and
other co-activators from complexing with the ligand-independent AF-1 region which would still
promote gene expression (ligand-independent gene expression). SERDs, on the other hand,
inhibit ER-mediated functions by inhibiting receptor dimerization (Parker et al 1993), nuclear
uptake (Davous et al 1992), ERE binding (Gibson et al 1991), and the promote ER level
downregulation (Davous et l 1993). In the context of post-menopausal breast cancer patients,
when the main source of estrogens is aromatase-mediated conversion from androgen in
peripheral tissues, the endocrine therapy is aromatase inhibitors (AIs) (Figure 1.2) [I.E. Smith et
al 2003 (Miller et al 2000). AIs, unlike SERMs and SERDs do not directly interact with the ER.
There are two main types of AIs. One type is the irreversible steroidal inhibitors (such as
exemestane) which bind covalently to the binding site of aromatase preventing the agonist
from binding there. The second type are non-steroidal inhibitors such as letrozole and
anastrozole which work via competitive inhibition and is reversible. Both types of AIs have been
shown in double-blind studies to be effective for ER positive post-menopausal breast cancer
patients in the metastatic setting. Letrozole and Exemestane were respectively compared with
the SERM tamoxifen in a randomized phase II study of patients who had previously received
adjuvant endocrine therapy and each aromatase inhibitor had a better response rate, complete
response rate, and increased time to progression (Mouridsen et al 2001, Paridaens et al 2000).
However, among ER-positive breast cancers, about 30% of patients exhibit innate
resistance, and 40% of the patients who initially respond exhibit acquired resistance to endocrine
therapies (Osborne et al 2001). Until recently, the genetic mechanism of resistance remained
elusive. Many studies have recently performed sequencing on tumor samples from endocrine
therapy-resistant metastatic breast cancer patients and identified point mutations in the N-
terminus of ER, with the most common mutations found in the LBD. These mutations lead to
conformation changes that mimic a constitutively activated form of ER even in the absence of a
ligand, contributing to endocrine therapy resistance (Fanning et al 2016). These mutations are
detected in patient-derived xenograft mouse models (S. Li et al 2013), metastatic tumor biopsies
(Merenbakh-Lamin et al 2013, Robinson et al 2013, Toy et al 2013), and our own data on CTCs
isolated from the peripheral blood of metastatic breast cancer patients (Yu et al 2014), who have
11
developed resistance to endocrine therapies (Figure 1.5.1). Such mutations exist in 18–53% of
patients with breast cancer that has progressed while on endocrine therapies, pointing to one
possible major mechanism of resistance. Structural biology data has shown that, in the absence
of hormones, these mutant ERs recruit co-activators with reduced affinities for estrogen agonist
and antagonist (Fanning et al 2016). In vitro cell line data, including those generated in my lab,
showed that the SERD agent fulvestrant has a reduced effect on constitutively active mutant ERs
(S. Li et al 2013, Robinson et al 2013, Toy et al 2013, Yu et al 2014).
The mechanism by which ER mutant proteins are constitutively active and acquire
resistance to traditional endocrine therapy is determined largely due to the structure of the LBD.
As mentioned in section 1.3, the h12 α-helix of the LBD plays an important role in allowing
complexing proteins to bind to the AF-2 region. In the WT ER, the aspartic acid (D) found at the
538 residue displays a natural repulsion to the aspartic acid at residue 351 of the h3 α-helix,
resulting in a helical characteristic at the beginning of h12. Additionally, as predicated by
molecular dynamics modeling (Fanning, et al 2016), the tyrosine amino acid (Y) at the 537 residue
of WT ER interacts with the asparagine (N) 545 residue closer to the C-terminus of the protein,
which keeps the WT ER at an "off" confirmation in the absence of ligand. These critical
interactions are disrupted through the two most prevalently studied examples of mutant ERs
with the LBD point mutationsY537S and D538G. The Y537 residue is the most prevalently
mutated variant, while the D538 residue is found in about 20% of patients following AI therapy
(Clusan et al 2021) and was shown to have adopted different conformations in the presence and
absence of agonists and antagonists stabilizing the h12 α-helix (Fanning et al, 2016). For the case
of the Y537S mutation, crystal structure studies suggest that a serine (S) in the 537 residue forms
a hydrogen-bond with the aspartic acid (D) on the 351 residue of the h3 α-helix. This tight
interaction allows for the h11-h12 loop to remain in the agonist conformation and thus the Y537S
mutant ER to remain constantly active. With respect to the D538G mutation, which has less
constitutive activity to the Y537S mutation (Katzenellenbogen et al 2018), the h11-h12 loop is
stabilized by the unwinding of the h12 α-helix. The glycine (G) mutation at the 538 residue causes
a deviation in the loop conformation that pushes the beginning of h12 from 538 to 539, thus
"lengthening" the h11-h12 loop which results in greater stability of the D538G (Fanning, 2016).
12
Taken together these data suggest that the different LBD mutations lead to a constitutively active
ER through different processes. This is important to note as many studies have been done on
different ER+ breast cancers harboring different LBD point mutations and have shown unique
transcriptomic and ER binding profile in addition to the traditionally known WT ER binding sites
(Jeselsohn et al 2018, Harrod et al 2017).
These recent discoveries have highlighted a significant mechanism of resistance to AI-
mediated endocrine therapy. With limited existing treatment options for tumors with these
mutations and the decreased efficacy of the current options, it is vital to develop new ways to
target these constitutively active ERs with better inhibitors or combinatorial approaches.

Figure 1.5.1 Point Mutations found in ESR1 gene. Illustration of mutations found in the LBD of ESR1
from recent reports in biopsies or CTCs from endocrine therapy-resistant metastatic patients. The
common Y537 mutated residue is highlighted in red.

1.6 Enhancers and gene regulation
The genome is a vast array of genetic material within the nucleus of each cell of an
organism that is involved with regulating and encoding various genes vital for various biological
13
processes. The precise pattern of activating and repressing gene expression needs to be
coordinated and regulated so as to avoid aberrant gene expression, which can lead to various
diseases including cancers. This regulation of gene expression is usually achieved through a series
of molecular processes which result in "opening" or "closing" regions of the genome allowing for
trans-acting proteins (or transcription factors) to bind to DNA along with their corresponding co-
regulators and transcriptional machinery to promote the expression or repression of genes.
Transcription factors do not solely bind to the respective promoters of the gene it is regulating,
but also to enhancer sites which could be distances away from a target gene or genes. Enhancers
are cis-regulator elements (regions of non-coding DNA) which function in an orientation-
independent fashion and could be located as far as 1Mb (1,000,000 bp) away from the gene it is
targeting. Understanding enhancers and even identifying them has been proven difficult as their
distances from the target genes which they regulate are variable. In fact, a putative enhancer
may be circumventing genes in closer proximity and targeting those further away). In addition to
variations in distance, enhancers can be found both upstream and downstream its potential
targets and they can also be found in the introns of other genes (Pennacchio et al 2013). All this
taken together has made identifying putative enhancers in silico incredibly challenging and has
required high-throughput methods and genome-wide studies to enable identification of
enhancers on the basis of their activity.
Enhancers contain DNA sequences called motifs which can be recognized by the DNA
binding domain of specific transcription factors. Given that enhancers are often located a
substantial distance away from the target genes they regulate, active enhancers bound by the
transcription factors are brought into closer proximity to the promoter of the target gene through
a process called looping. Enhancer-promoter looping works to deliver the transcription factors,
co-factors, and RNA polymerase II bound to the enhancer to the promoter of the target gene it
is regulating. This looping process is mediated through protein complexes like cohesin (Shlyueva
et al 2014). The loop increases concentration of the transcription machinery necessary for gene
expression near the target gene thus allowing for the setup of a pre-initiation complex (PIC) (Koch
et al 2011) and the beginning of the transcriptional process.
14
Ultimately, in order for transcriptional activity to be turned on or off, the enhancer must
be in a state by which the transcription factor can be bound to it. There are three known states
of enhancers: active, poised, or inactive. Active enhancers typically lack nucleosomes allowing
the DNA to be accessible to the transcription factor, structurally determined by particular histone
PTMs (post-translational modifications). Hallmarks of active enhancers are H3K4me1 and
H3K27Ac which are PTMs of the nucleosomes which flank the enhancer regions (Spicuglia et al
2012). Regions may be considered "poised" when H3K27me3 and H3K4me1 are simultaneously
modified in a particular enhancer site or considered “closed” when the site is marked with an
H3K27me3 (Hansen et al 2008). The poised enhancers are primed for activation whether by a
later developmental point or as a result of stimuli (Creyghton et al 2010). These PTMs are very
helpful in identifying an enhancer, as well as the activity status of the enhancer in different cell
types or conditions. Finally, inactive enhancers are those that are unavailable to transcription
factor binding due to the structure of the DNA/nucleosome architecture. Ultimately, the PTMs
and subsequent looping of an enhancer bound by a transcription factor, along with RNA
polymerase II and other transcriptional machinery, set the stage for long-range targeting of the
enhancer to the promoter of the gene or genes of interest.
1.7 IGFBP3 and CD44
Insulin-like growth factors (IGFs) are protein ligands which play an important role in
regulating cellular growth, differentiation, survival, proliferation, development and migration.
IGF-I and IGF-II are the two types of IGFs which circulate through the bloodstream by way of the
liver. IGFs have an increased half-life when traveling in a binary complex with IGF binding proteins
(IGFBP) (Baxter et al, 1993) allowing it to travel to its intended site and release through the
actions of a protease (Nakamura et al, 2005) which then allows the IGFs to bind with
transmembranal IGF type 1 Receptor (IGF-1R).
There are six known types of IGFBPs (IGF Binding Proteins), IGFBP1 – IGFBP6. IGFBP-3 is
the most abundant in the blood circulation – accounting for more than 70% of all IGFBP (Brahim
et al 2009). Some proteins like IGFBP7 and other proteins which are members of connective
tissue growth factor cysteine rich protein (CCN) family share structural homology with IGFBPs,
15
but lack affinity for the IGF ligand. IGFBP3 has been well studied in its role of delivering IGF to
target cells.
The first 27 amino acids of the IGFBP3 precursor protein, before its post-translational
modification, is excised from IGFBP3 when it is shuttled to the endoplasmic reticulum. The
mature, unmodified IGFBP3 consists of 264 amino acids, equating to 29 kDa, and contains three
structural domains: The N-terminal domain, the linker region, and the C-terminal domain (Figure
1.7.1). The N-terminal domain shares about 55% homology with other IGFBPs. It is required for
binding, inducing apoptosis (Shahjee et al, 2008), and contains a transactivation domain for
complexing with transcription factors (Zhao et al 2006). The linker region is the only region of the
three that doesn't contain IGF binding domains, but rather is a site for post-translational
modifications such as glycosylation, proteolysis and phosphorylation. The C-terminus of IGFBP3
contains a Nuclear Localization Signal (NLS), something that only IGFBP5 also has and allows for
translocation of IGFBP3 into the nucleus. Preliminary data we generated using Rapid
Immunoprecipitation Mass Spectrometry of Endogenous proteins (RIME) showed IGFBP3 as a
possible binding partner with the Y537S ER in MCF7 cells harboring the mutation, suggesting
novel interaction between IGFBP3 and the Y537S ER.
Recent studies have begun to elucidate the mechanisms underlying the dichotomous
effects of IGFB3, where in some cases it is pro-apoptotic and attenuates growth and in other
circumstances it can promote growth and survival. The dual role of IGFBP3 is likely dependent on
cellular context, physiological conditions, and the various interacting proteins in which it comes
in contact. Studies have shown that IGFBP3 interacting with GRP78 in the endoplasmic reticulum
induces autophagy (C. Li et al. 2012), while it could also promote proliferation via growth factor
signaling through association with sphingosine rheostat (Maceyka et al, 2012). Given that IGFBP3
can be found in different locations in the cell, it is certainly possible that differing biological
activities of IGFBP3 can be in action simultaneously. Therefore, the interplay and balance
between these multiple effects of IGFBP3 will decide if the cells experience either growth
inhibition or proliferation.
Studies in in vivo animal models suggest that IGFBP3 suppresses the growth of prostate,
lung, colon and head and neck tumors (Alami et al 2008, Liu et al. 2007, Oh et al. 2012,
16
respectively) but is important in promoting proliferation in breast cancer cells (Butt et al. 2004).
Nuclear IGFBP3 specifically has been shown to promote tumor progression in breast cancer
(Julovi et al. 2018), and IGFBP3 was previously linked to a growth advantage phenotype through
sphingosine kinase 1 (SphK1) (Martin et al. 2014, Pyne et al. 2016). One of the main functions of
SphK1 is the generation of sphingosine-1-phosphate (S1P), a lipid mediator of oncogenesis and
bone disease (Zhang et al 2020).
CD44 is a transmembrane glycoprotein protein expressed in various cell types including
embryonic and cancer stem cells, and cells in connective tissue and bone marrow. Due to the fact
that it is upregulated in subpopulation of cancers, it is recognized as a biomarker for cancer stem
cells. When bound to its ligand, hyaluronan (an integral part of the extracellular matrix), it is
involved in promoting cell proliferation and survival, enhancing cellular movement, and
maintaining tissue structure via cell-cell or cell-matrix adhesion. CD44 also binds to other ligands
like osteopontin, serglycin, collagens, fibronectin, and laminin (Chen et al 2018).
CD44 can exist in many different isoforms via alternative splicing and PTMs. CD44 can have up to
19 exons (Chen et al 2018) with each variant possessing a shared 10 exons with multiple
permutations of the other 9 (Figure 1.7.2). CD44 consists of four domains: ligand binding domain,
variable domain, transmembrane domain, and the cytoplasmic tail (in order of outside the
phospholipid bilayer to within the cytoplasm).
The multifaceted roles of the different variants of CD44 in the pathogenesis of cancer are
being thoroughly investigated. . One study showed that CD44 can switch isoforms in breast tumor
cells during EMT, from one variant of CD44 to the classic variant that bore the 10 shared exons
across all variants (RL Brown et al, 2011). Many studies have also showed that different CD44
variants were expressed in metastases of solid tumors in colorectal (Ozawa et al 2014), pancreatic
(Z. Li et al 2014), and prostate (J. Ni et al 2014) cancers and is associated with poor prognosis (M.
Kaufmann et al, 1995).
17

Figure 1.7.1 Protein Structure of IGFBP3. IGFBP3 protein structure contains three basic elements –
the N-terminal Domain containing the trans-activation domain for complexing with transcription factors,
the Linker region, and the C-terminal domain which contains the nuclear localization signal (NLS). Image
created with BioRender.com

Figure 1.7.2 Protein and gene structure of CD44 – Figure source: Chen et al 2018. (A) Protein
structure of CD44 and its four domains: The Ligand binding domain, the variable domain, the
transmembrane domain and the cytoplasmic tail. (B) CD44 gene structure. Exons 1-5; 16-20 appear in all
CD44 variants. Exons 7-15 are variable. Listed are a few examples of the different variants. Source: Chen
et al 2018

A
B
18
Chapter 2: Identifying and Characterizing A Unique Enhancer for
Y537S Mutant Estrogen Receptor (ER) in Breast Cancer Cells

2.1 Introduction
Breast cancer is the most common form of cancer amongst women and the second
leading cause of cancer-related deaths (Siegal et al 2021). Due to the fact that roughly 80% of
breast cancer is Estrogen Receptor (ER)-positive (Anderson et al 2002), targeting ER signaling by
neo-adjuvant endocrine therapy remains effective at treating early-stage ER positive breast
cancer patients (Patani et al 2014). The type of endocrine therapies given to patients depends on
the major source of estrogen for said patient. For post-menopausal women, aromatase inhibitors
that prevent the aromatase enzyme (CYP19A1) from converting androgens to estrogens in the
peripheral tissue are used (I.E. Smith et al 2003). For pre-menopausal women, the endocrine
therapy includes agents that target the ER directly via Selective Estrogen Receptor Modulator
(SERMs) which compete with the receptor agonist to bind to the ER and thus prevents ER
activation, or Selective Estrogen Receptor Degraders (SERDS) which inhibit ER-mediated
functions via, among other things, promoting ER degradation (Gibson et al 1991).
Indeed, in early-stage breast cancer endocrine therapy lowers the cancer recurrence rate
by half and reduces the mortality rate by more than 25%. However, among ER-positive breast
cancer patients, 30% of patients are innately resistance and 40% of patients who initially
responded to the therapy acquire resistance. Studies performing exome sequencing on
endocrine therapy-resistant metastatic tumor samples identified several frequent point
mutations on the ESR1 gene that encodes ER (mostly found in the ligand binding domain)
(Robinson et al 2013). In vitro studies have shown that these single point mutations lead to
conformational changes which mimics the ligand-activated form of ER, in the absence of an
agonist (Fanning et al 2016, Toy et al 2013, Merenbakh-Lamin et al 2013). These mutations occur
in 18-53% of patients with cancer that has advanced while on endocrine therapy, pointing to their
potential roles in developing resistance. Data from Molecular Dynamics (MD) modeling (Fanning
et al 2016, Fanning et al 2018) show an attenuated affinity for ER antagonists in cells harboring
the two most common ER mutations, Y537S and D538G. In vitro data from our own circulating
tumor cell (CTC) line harboring the Y537S mutation (Yu et al 2014) as well as ER-positive cell lines
19
which were edited to harbor the ESR1 mutation exhibit a decreased effect of the SERD fulvestrant
(ICI) on cell proliferation (S. Li et al 2014, Robinson et al 2013).
RNA-seq and ChIP-seq data from different labs using ER-positive cell lines genetically
modified to harbor ESR1 mutations have shown that in addition to expressing traditional ER
target genes and binding to traditional ER binding sites, there is also a unique transcriptome and
ER binding landscape of the mutant ER compared to the activated wild type (WT) counterpart
(Jeselsohn et al 2018, Bahreini et al 2017, Harrod et al 2017, Li et al 2022). Analysis of this data
along with ER ChIP-seq results from our Y537S mutant ER CTC line, BRx68, helped identify an
intronic region of the pseudogene DPY19L1P1 (henceforth called DPY19L1P1 region) on
Chromosome 7 in which Y537S_ER was binding with higher intensity compared to the activated
WT_ER and it also displayed enhancer qualities. Using CRISPR-Cas9 technology to knockout the
region in both MCF7 WT and Y537S cells, we helped characterize the phenotype of the knockout
in both cell genotypes. We also performed RNA-seq to determine the genes whose expression is
potentially influenced by this putative enhancer. Amongst the genes we identified the Insulin
Growth Factor Binding Protein (IGFBP3) as having been downregulated when the DPY19L1P1 is
knocked out in the Y537S cells but not the WT. We then proceeded to knockout IGFBP3 in Y537S
control cells using CRISPR-Cas9 technology to determine if some of the phenotype of the Y537S
DPY19L1P1 knockout cells can be recapitulated by knocking out IGFBP3 in the control Y537S cells.

2.2 ER binding profile of a CTC line harboring the heterozygous Y537S ER mutant
Most mutant ER ChIP-seq data available publicly comes from genetically engineered cell
lines like CRISPR knock-in (Bahreini et al 2017, Harrod et al 2017) and over-expression vectors
(Jeselsohn et al 2018). While these studies are important for studying the mutant receptor, there
may be off-target effects from some of the methods which produced the mutant ER. The BRx68
CTC line, derived from a luminal breast cancer patient was discovered to have a heterozygous
Y537S ESR1 mutation (Yu et al, 2014). We performed ER ChIP-seq on the BRx68 cells in the
presence and absence of ER ligand 17β-estradiol (henceforth called E2). We reasoned as this was
a heterozygous mutation—BRx68 also expresses a copy of the WT ER—it would be important to
study the ER binding landscape in this CTC line both in the absence and presence of activating
20
WT ER along with a constitutively active Y537S mutant ER. In Figure 2.2.1A, we produced a PCA
plot and heat map of biological replicates of BRx68 in the presence and absence of E2 (BRx68_veh
and BRx68_E2, respectively) using the top 1000 variable binding regions of all the samples. While
the PCA plot suggests that replicate one of BRx68_veh separates from its other replicate, the
complimentary heatmap and the corresponding dendrogram suggests that the replicates of both
BRx68_veh and BRx68_E2 cluster together. When using the statistically significance of FDR value
of less than 0.05 compared to its respective input, we noted that BRx68_veh replicates one and
two had 2041 and 3131 called peaks respectively, of which there are 1361 peaks shared between
the two. In the case of the BRx68_E2 samples, the replicates had 1950 and 3231 called peaks
respectively with 1864 shared peaks between the two replicates. Interesting to note is when
visualizing a heatmap from a perspective of the top 1000 binding peaks, the samples seem to
display similar heatmap patterns (Figure 2.2.1B).
We next wanted to elucidate the feature distribution of mutant ER binding in BRx68 cells.
Using the consensus peaks shared between the two replicates, we annotated each set of peaks
using ChIPSeeker (G. Yu et al, 2015) determining the promotor region to be 1kb upstream and
downstream of the transcription start site (TSS). In the same comparison we also analyzed the
ER ChIP-seq data in MCF7 cells with Doxycycline(dox)-inducible expression of Y537S ER mutant
from the lab of Dr. Myles Brown (Jeselsohn et al 2018). The feature distribution of the peaks
shows that in the BRx68_veh, where only the mutant ER is active, a larger percentage of the
peaks are found in the promoter regions, whereas when those cells are treated with E2 to
activate the WT ER copy the distribution changes with more peaks in distal intergenic regions and
a fewer percentage of the peaks in promoter regions. This is intriguing because many ChIP-seq
analyses of the WT ER in ER positive breast cancer cell lines have shown that their binding sites
are typically found at distal enhancer sites (Carrol et al 2006). Indeed, the WT_E2 from the ChIP-
seq performed by the Brown lab showed most of the peaks were found at distal intergenic
regions. In comparison, the Y537S_veh ER binding sites showed enriched promoter binding.
However, the Y537S_E2 ER binding peaks do not show an increase in distal intergenic sites
compared to the Y537S_veh, potentially due to the differences in dox-inducible expression versus
the de novo mutation level of Y537S (Figure 2.2.2 A).
21
We next wanted to compare the ER binding profile of BRx68 against WT and Y537S ER
peaks found in a different data set. Using the same data from the Brown lab we used deeptools
to graph the BRx68 samples (both veh and E2 treated) against peaks common between WT and
Y537S ER (Figure 2.2.2 B left) and those that are specific to Y537S ER in the Brown study (Figure
2.2.2B right). While the heatmap indicates BRx68 ER had many shared regions with peaks
common to WT and Y537S ER (753 of the 1361 peaks were shared), compared to the Y537S ER
unique peaks there were fewer shared regions (124 of the 1361 peaks were shared). This
indicated that between these two cell lines, the commonly shared Y537S ER binding regions are
more likely to be the traditional ER targets bound also by WT ER, whereas the unique Y537S ER
targets are more likely to be cell type specific. This is not unusual as cell line-specific gene
expression (S.Yu et al 2017) and enhancers (Mumbach et al 2016) have been widely reported in
literature suggesting that there could be genes upregulated in the two different cell lines of the
same cell type that are controlled by two distinct enhancers (Mumbach et al 2016). Although
rare, we sought to determine the possibility of a unique mutant ER binding region shared across
different studies.

22

A
B
-2
0
2
-15 -10 -5 0 5
PC1: 94% variance
PC2: 6% variance
Replicate
Rep1
Rep2
Condition
E2
Veh
Veh_rep2
Veh_rep1
E2_rep1
E2_rep2
0
5
10
15
20
25
30
35
BRX68_ERC_Rep1
BRX68_ERC_Rep2
BRX68plusE2_ERC_Rep1
BRX68plusE2_ERC_Rep2
Condition
Replicate
Replicate
Rep1
Rep2
Condition
E2
Veh
4
6
8
10
12
C
23

Figure 2.2.1 PCA plot and Heatmap of ER ChIP-seq data in the BRx68 CTC line. (A)
Principle Component Analysis (PCA) plot of the top 1000 most variable regions by ER ChIP-seq in
BRx68 cultured with or without of E2. Replicates are indicated by shapes and treatment by color.
(B) Heatmap plot of BRx68 ChIP-seq along with dendrogram indicating similarities between the
samples. The darker the blue between the cross of two samples indicates stronger similarity. (C)
Heatmap of the count matrix of the top 1000 “strongly bound” genomic regions. The regions
graphed are represented on the y-axis in descending order of ER binding intensity. Sample names
are listed on the bottom.

24

B
Brown_Y537S_E
Brown_WTE
Brown_Y537S_veh
BRx68_E2
BRx68_veh
0 25 50 75 100
Percentage(%)
Feature
Promoter
5' UTR
3' UTR
1st Exon
Other Exon
1st Intron
Other Intron
Distal Intergenic
Downstream (<=300)
Feature Distribution
BRx68_veh BRx68_E2
BRx68_veh BRx68_E2
WT_E
Y537S only
A
25

2.3 Identifying the intronic region of DPY19L1P1 as a unique Y537S ER binding site
Using ER ChIP-seq data from the labs of Myles Brown (Jeselsohn et al 2018), Steffi
Oesterrich (Li et al 2022), and Simak Ali (Harrod et al 2017) along with our ER ChIP-seq from
BRx68, we sought to identify unique mutant ER binding regions found in multiple studies. One of
the reasons we chose to use many studies to identify a unique region is as these studies were
done in different labs using breast cancer cell lines which were genetically modified to introduce
the Y537S mutation in different ways (the Oesterrich and Ali labs using CRISPR-Cas9 to knock in
the point mutation in one allele, whereas the Brown lab used a dox-inducible overexpression
vector) there will invariably be Y537S binding regions in each study which may be due to
confounding factors and thus not really indicative of a distinct mutant ER binding site.
Additionally, we limited our analysis to genetically modified mutant ER in the ER-positive breast
cancer cell line MCF7 so as to limit cell line-specific mutant ER binding sites. In this way, we could
develop a more concise list in which the mutant ER binding site is shared at least between two
cell lines. The data processing is explained in great detail in the materials and methods section.
Briefly, we processed all the different samples from all the studies using the same parameters for
trimming, aligning, and sorting. The final list of Y537S unique regions in each study was then
crossed with our BRx68 ER ChIP-seq in the vehicle setting to create a compiled list of mutant ER-
unique peaks that are shared between one of the studies and our BRx68 study (Table 2.3A). The
ChIP-seq experiments using the knock-in edited cell lines were performed lacking biological or
technical replicates, therefore we were unable to perform a statistical power-based analysis for
these datasets. Instead, to determine the mutant ER-specific binding regions in those studies, we
first called statistically significant peaks for each sample over its respective input, then we used
bedtools to exclude regions which were found to be statistically significant in the WT ER in that
same study. While this method certainly works, the drawback is that it can overlook certain
Figure 2.2.2 Identifying Unique Binding Profile of the Mutant ER. (A) Feature distribution analysis
of mutant ER compared to activated WT. Statistically significantly called peaks (FDR less than 0.05
compared to input) of mutant ER from BRx68 in the presence and absence of E2 and genetically
engineered MCF7 cells with dox-inducible overexpression of mutant ER in the absence and presence of E2
(Data analyzed from Jeselsohn et al 2018) were annotated using ChIPseeker. Promoter is defined as being
1kb upstream and downstream of transcription start site (TSS). (B) Heatmaps of binding intensity of ER in
BRx68 graphed against statistically significantly called peaks from activated WT (left) or unique Y537S
peaks (right). Unique Y537S peaks were identified by calling statistically significant peaks in MCF7 Y537S
cells from Jeselsohn data and removing from the list peaks also called in MCF7 WT in the presence of E2.

26
regions that both mutant ER and WT ER bind, but the mutant ER binds at a much higher intensity
(an example shown in Figure 2.3.1A). For this reason, we mainly focused on the comparison
between mutant ER enriched regions shared between data from the Brown lab and our BRx68,
and then cross-referencing with the other studies to determine if the same trend was observed.
We initially focused on two regions of interest- one of those regions was a 446 bp region
found in the first intron of a pseudogene called DPY19L1P1 (henceforth called DPY19L1P1 region)
on Chromosome 7 and the second was 254 bp region located in the first intron of a gene called
CLASP2 on Chromosome 3. Both of these regions showed enriched binding of the mutant ER in
BRx68 and statistically significant increased binding of mutant compared to WT (log2 fold change
of more than 2) in the Brown lab data. Using the data from ENCODE, we looked to characterize
the regions based on cumulative ChIP-seq data from seven different human cell lines of histone
marks (H3K4me1, H3K4me3, and H3K27Ac). Both the DPY19L1P1 and CLASP2 region showed a
strong H3K4me1 and H3K27Ac profile (marks associated with enhancer and active element in
general, respectively) while having a very minimal H3K4me3 (a histone mark associated with
promoters – usually found within 1 kb upstream and downstream of the transcription start site)
enrichment suggesting the likelihood that they are enhancer elements (Figure 2.3.2A-B). When
we analyzed both regions using the online bioinformatics toolbox geneXplain which features the
TRANSFAC library to analyze DNA sequence and predict motif enrichments, the DPY1L1P1 region
showed to have a classic palindromic ERE site (the predicted site of WT ER binding as
homodimers following activation with E2 – sense strand aggtcacaaAGACCc, anti-sense strand
aGGTCAcaaagaccc) in both the sense and anti-sense strand, as well as a viable half-site in the anti-
sense strand (caGGTCA) with a matrix similarity score (MSS) and core similarity score (CSS) both
at 1.0 that indicates an exact match (Figure 2.3.3A). On the contrary, no ERE nor half-ERE sites
were identified in the CLASP2 region (Figure 2.3.3B).
We next wanted to determine if these regions do exhibit enhancer properties in cells
which harbor either the WT or two different mutant ERs (Y537S and D538G). In collaboration
with Dr. Steffi Oesterreich whose lab produced knock in Y537S and D538G mutations in MCF7
cells (along with T47D), we first cloned both the forward and reverse orientation of either the
DPY19L1P1 or CLASP2 regions (plus 200 bp upstream and downstream of the region) into
27
Promega’s pGL4.26 luciferase plasmid that contains a minimal promoter followed by the luciferin
gene (luc2) downstream of the minimal promoter. We then transiently transfected these
plasmids into the WT, Y537S or D538G along with a renilla-based reporter (phRL-TK from
Addgene) which we used as a control for transfection efficiency. Each cell line was also
transfected with a negative “control” pGL4.26 without a region cloned into the multiple cloning
region. We then performed a luciferase assay using the Dual Luciferase Assay (Promega) to
determine levels of luminescence generated by the region in both the forward and reverse
direction and corrected the values for renilla expression (Figure 2.3.4A-B). In both cases of the
DPY19L1P1 and CLASP2 regions, the normal orientation produced luciferase activity greater than
seen in control in both WT and Y537S cells. The DPY19L1P1 region, in general, showed more
luciferase activity in all three genotypes compared to the respective control. This was especially
true in the D538G cells which showed very little CLASP2 luciferase activity compared to control
but much more DPY19L1P1 luciferase activity. However, in all three cell types the opposite
orientation of both the CLASP2 and DPY19L1P1 regions didn’t produce meaningful differences
compared to the control. Because an enhancer is an element that operates at a distance from a
promoter regardless of position or orientation, one of the ways to test for enhancer properties
is to determine if both orientations would produce similar luciferase activity. However, this assay
is detecting enhancer activity from an ectopically expressed plasmid and not in the native context
of the actual genome, which could possibly explain the discrepancy. Additionally, one publication
noted that certain gene-enhancer interactions require the enhancer to be in the correct
orientation (Hozumi et al 2012).
We next performed an ATAC-seq assay using cells with the three genotypes to determine
whether there was open chromatin in the two candidate regions (Figure 2.3.5A top). While all
three genotypes showed an open profile in the DPY19L1P1 region indicated by the peaks along
the region, there was a stronger intensity noted for the Y537S mutation. When looking at the
CLASP2 region, only the WT and Y537S genotypes showed an open chromatin profile (though not
as intense as that of the DPY19L1P1) and no open chromatin near the CLASP2 region in the D538G
genotype (Figure 2.3.5 B top). Following this we performed a CUT&Tag assay in WT and Y537S
cells using an antibody against H3K4me1 mark — a histone mark that is typically associated with
28
enhancer activity. The Washington Epigenome Browser showed H3K4me1 enrichment in both
regions of interest in both genotypes (Figure 2.3.5 A-B both bottom). Taking together, based on
the stronger mutant ER enrichment in the DPY19L1P1 region compared to that of the CLASP2
region as well as the predicted motifs showing full- and half- EREs, we decided to proceed forward
with characterizing the DPY19L1P1 region.
As the region was identified via ChIP-seq studies, we next want to validate the binding of
Y537S ER compared to WT ER using ChIP-qPCR, with primers against the DPY19L1P1 region and
a well-known classic ER binding region in the promoter of the gene encoding GREB1 as a positive
control. WT and Y537S cells were grown three days in hormone-depleted serum and then treated
for 24 hours with either 10 nM of E2 or its vehicle (ethanol). As a quality control we first
performed ChIP-qPCR with primers against the promoter of GREB1 where both WT and mutant
ER bind (Lin et al., 2004). As expected, in the presence of E2, Y537S ER showed statistically
significant enriched binding compared to the WT (Figure 2.3.5 C). Additionally, there was a trend
towards statistical significance of increased ER binding of the activated WT (WT_E2) and Y537S
in the absence of E2 compared to the WT in the absence of E2 (P value at .09 and 0.1,
respectively). When we performed the ChIP-qPCR against the DPY19L1P1 region, we detected
statistically significant enrichment of Y537S ER binding compared to activated WT. Interestingly
we also noticed that when the Y537S cells were treated with E2 and thus activating the WT
receptor in those cells, the mutant ER enrichment of the DPY19L1P1 decreased significantly
(Figure 2.3.5 D).
We then used CRISPR-Cas9 technology with sgRNA targeting both sides of the DPY19L1P1
region (Figure 2.3.6 A) to effectively knockout the DPY19L1P1 region in both the MCF7 WT and
Y537S cells (Figure 2.3.6 B). We were able to successfully grow out one knockout for each
genotype (WT_D2 and Y537S_E5) and two clones of “control” knockouts in which plasmids not
carrying the sgRNA sequence were transfected and single cell sorted (WT_C10, WT_C11,
Y537S_D4, Y537S_Z99). The next part of my thesis is the functional characterization of this region
to determine if the knockout resulted any phenotype in either the WT or Y537S cells.
29

Chr Start End Closest Gene Region K27Ac K4me1/me3
10 124133825 124134396 Plekha1 Promoter Strong Weak - both
12 30907815 30908345 LOC645485 Promoter Strong Weak – both
7 8301637 8302054 ICA1 Promoter? Very Strong Weak – both
7 32720197 32720643 Intronic region of DPY19L1P1 Enhancer Strong Good (stronger me1 than me3)
3 33700421 33701054 Clasp2 Enhancer? Strong Encode strong H3K4me1
Chr Start End Closest Gene Region K27Ac K4me1/me3
12 68633740 68634245 Near IL22 Enhancer Moderate Strong H3K4me1
17 73851345 73851880 WBP2 Promoter Strong Strong H3K4me3
19 45988511 45989041 RTN2 Promoter Strong Moderate H3K4me1
19 47551686 47552294 TMEM160 Promoter Strong Weak – both
19 48833247 48833681 EMP3 Intronic Strong Weak – both
3 33700421 33701054 Clasp2 Enhancer? Strong Encode strong H3K4me1
Chr Start End Closest Gene Region K27Ac K4me1/me3
11 82996917 82997678 CCDC90B Promoter Very Strong Weak – Encode shows strong H3K4me3
12 56709523 56710399 PAN2 Promoter Strong Weak
14 77923831 77924462 Between VIPAS39/AHSA1 Promoter Strong Weak
16 30933634 30934905 FBXL19-AS1 Promoter Strong Weak
19 49223770 49224623 RASIP1 Enhancer? Strong Weak – Encode shows strong H3K4me3
Harrod et al 2017
Li et al 2022
Jeselsohn et al 2018
Table 2.3 Identifying Mutant-ER specific binding regions across multiple studies. A list of
shared ER-binding peaks between genetically modified MCF7 with Y537S ER mutation (Jeselsohn et
al 2018, Harrod et al 2017, Z. Li et al 2022) and BRx68. The status of the enriched H3K27Ac and
H3K4me1 histone marks from ENCODE for each region was listed. Red box highlights the DPY19L1P1
and CLASP2 regions. Determination of the strength of the peaks is based on the score from
ENCODE which is based on the average of peak intensity of the histone mark at the
respective region in the cell lines

Figure 2.3.1 Importance of Statistical Power in ChIP-seq Analysis. Example of a region
that may be missed as not Mutant ER enriched when performing analysis in datasets of one
sample per condition. In such studies to find mutant ER specific regions, bedtools –v is used to
eliminate regions in which there is binding from both samples (regardless of the intensity of the
binding). For studies which use biological replicates, differential binding would be used to
determine mutant ER enriched binding and this region wouldn’t be obviated in the study.
Values on the right are intensity counts of binding.

30

CTC
ER mutant
MCF7
ER mutant
MCF7
WT ER
DPY19L1P1
A
CTC
ER mutant
MCF7
ER mutant
MCF7
WT ER
B
Figure 2.3.2 Enhancer properties of Mutant ER-specific binding sites. Bigwig files from
Jeselsohn et al 2018 and BRx68 ER ChIP-seq experiments displayed against (A) first intron of
DPY19L1P1 gene (B) first intron of CLASP2. The bigwig files were graphed on the Washington
Epigenome Browser. Cumulative ENCODE data from H3K4me1, H3K27Ac, and H3K4me3 ChIP-seq
experiments performed on 7 different cell lines. The binding intensity levels of those 3 histone
epigenetic marks, respectively are depicted for the intronic of DPY19L1P1 (A) and intronic region
for CLASP2 (B).

31

A
B
Luciferase/Renilla
Luciferase/Renilla
MCF7_Y537S
Luciferase/Renilla
MCF7_D538G
A
B
C
Figure 2.3.3 Motifs present in DPY19L1P1 and CLASP2 regions. Graph showing the
predicted motif enrichments, for DPY19L1P1 region (A) and the CLASP2 region (B), generated using
online tool geneXplain, which uses the TRANSFAC library to predict motifs. Black box highlights the
full and half-ERE sites found at the DPYL1P1 region.

Figure 2.3.4 Luciferase assay validating enhancer activity of DPY19L1P1 and CLASP2 regions.
Graphs showing relative luciferase/renilla activity 24 hours post transfection in MCF7 WT (A), Y537S (B)
and D538G (C) cell lines with Promega’s pGL4.26 luciferase plasmid (containing either the DPY19L1P1 or
CLASP2 region in forward or reverse direction) and a renilla control plasmid. DPY= DPY19L1P1 region,
Opp= reverse orientation. N=3

32

A
B
MCF7_Y537S
MCF7_D538G
MCF7_WT
MCF7_Y537S
MCF7_WT
B
MCF7_Y537S
MCF7_D538G
MCF7_WT
MCF7_Y537S
MCF7_WT
Figure 2.3.5 Determining open chromatin state and presence of H3K4me1
in DPY19L1P1 and CLASP2 regions. Washington Epigenome Browser was used to graph the
bigwig files of ATAC and H3K4me1 CUT&Tag data of WT, Y537S and D538G MCF7 cells. (A) Top
shows ATAC data of DPY19L1P1 region on all three genotypes, bottom shows H3K4me1
enrichment of DPY19L1P1 region for Y537S and WT. (B) Top shows ATAC data of CLASP2 region
on all three genotypes, bottom shows H3K4me1 enrichment of CLASP2 region for Y537S and
WT

33

WT_veh WT_E2 YS_veh YS_E2
0
200
400
600
800
1000
GREB1
Fold change over IgG
IgG
ER
✱✱✱✱
✱✱✱✱
✱✱✱✱
A
B
WT_veh WT_E2 YS_veh YS_E2
0
10
20
30
40
DPY1L1P1
Fold change over IgG
IgG
ER
✱✱✱✱
✱✱✱ ✱✱✱✱
WT_veh WT_E2 YS_veh YS_E2
0
2
4
6
DPY19L1P1
Fold change over IgG
IgG
ER
✱✱
✱✱
✱✱✱
✱
✱✱✱✱
Figure 2.3.6 ChIP qPCR of GREB1 promoter site and DPY19L1P1 region in WT And
Y537S cells. MCF7 WT and Y537S cells were grown in CSS for three days and then seeded at
a density of 10 million cells per condition. Genotypes were either treater with vehicle (ethanol)
or 10nM of E2 for 24 hours. Following 24 hours, ER ChIP qPCR was performed on the samples
with primers corresponding to the promoter of GREB1 (A) and the DPY19L1P1 region (B). Data
was analyzed using 2-way ANOVA with Tukey’s multiple comparison correction. * P< 0.05 **
P <0.01, *** P < 0.001, **** P<0.0001. (B) Shows data from two different ChIP qPCR
experiments showing increased Y537S ER binding in the region compared to WT.

34

A
B
WT Y537S
WT Y537S
C10 D2 Z99 E5 C11 D4
Figure 2.3.7 Generating DPY19L1P1 Knockouts in WT and Y537S MCF7 cells. (A)
Schematic depicting process for the CRIPSR-Cas9 based knockout. Two different plasmids were
used with an sgRNA targeting the leftmost region cloned into PX458-GFP and an sgRNA targeting
the rightmost region cloned into plKO5.sgRNA-RFP. Following double transfection of both
plasmids in WT and Y537S MCF7 cells for 24 hours, GFP+/RFP+ cells were sorted and plated at
single cell per well. Image created with BioRender.com. (B) Gel images of PCR product using
primers targeting 200 bp upstream and downstream of the expected knockout region on gDNAs
extracted from the established single cell clones. 1 Kb ladder (left) and 100 bp ladder (right) were
run alongside the samples.

35
2.4 DPY19L1P1 Knockout (KO) in MCF7 Y537S Decreases Cellular Proliferation and Adhesion
During the single cell expansion of these cells transfected with sgRNAs and CRISPR-Cas9
plasmids, a curious phenomenon was noted in the E5 well that came from a single Y537S cell
clone. While all the other Y537S and WT single cell clones, which equally could have had the
knockout, were growing at a seemingly normal rate, the E5 well proliferated particularly slower.
We ultimately recovered 4 clones for the Y537S cells and 5 clones for the WT cells transfected
with sgRNAs targeting the DPY19L1P1 region. We next performed gDNA extraction and PCR using
primers that were 200 bp upstream and downstream of where the sgRNA targeted the left and
rightmost side of the region. The result showed that WT_D2 and Y537S_E5 (the slow growing
clone) both had a clear DPY19L1P1 knockout (Figure 2.3.6 B), which was about 700 bp shorter
than the non-edited product. With respect to the controls, we used the clones that were
transfected with the respective control plasmids without sgRNA sequences. For the purposes of
future assays, we pooled the control cells in each genotype (from now on called WT_control and
Y537S_control).
As we noted the slow growth in the Y537S_E5 cells which had the DPY19L1P1 KO, we
decided to validate this result. WT_control, Y537S_control, WT_D2, and Y537S_E5 were seeded
on a 96-well plate with same numbers and analyzed using CellTiter-Glo assay at three different
time points – 0, 3, and 7 days. Indeed, confirming our preliminary observations, the Y537S_E5
cells showed statistically significant attenuated growth compared to the Y537S_control cells in
both the day 3 and 7 time points. As these cells were grown in complete serum which contains
hormones including E2, the lack of statistical difference in growth between the WT_control and
Y537S_contol cells in day 7 is not surprising as publications have shown that the activated WT ER
and mutant ER grow at similar paces (Bahreini et al 2017). Interestingly, the proliferation data at
day 3 shows that the Y537S_control cells as well as the WT_D2 cells grew faster than the
WT_control cells (Figure 2.4.1A). This is fascinating as the DPY19L1P1 region seems to be
exhibiting the opposite effect in the context of cell proliferation in the WT than it does in the
Y537S cells. However, by day 7 there is not a statistical difference in growth between the WT_D2
cells and the WT_control cells.
36
Another observation noted while passaging these cells, is that the Y537S_E5 cells, upon
being trypsinized, displayed reduced adhesion to the cell culture plate. To determine if cell
adhesion was affected by knocking out the DPY19L1P1 region, we performed a cell adhesion
assay in collagen coated plates. 48-well plates were coated with collagen and 30,000 cells with
the DPY19L1P1 KO and their controls were incubated on these wells, fixed, and then stained with
Crystal violet to determine cell adhesion. We performed the experiment in both complete serum
(termed full media) as well hormone-depleted serum (called Charcoal stripped serum (CSS)). In
the full-media condition, the Y537S_E5 cells exhibited decreased adhesion to collagen plate
compared to both the WT_control and the Y537S_controls cells (Figure 2.4.1B). In the CSS media
condition, The Y537S_E5 showed statistically significantly reduced adhesion compared to
Y537S_control cells. When comparing between the Y537S and WT cells in the CSS media
condition, the WT_D2 cells showed decreased adhesive properties compared to the
Y537S_control (Figure 2.4.1B)
These data indicate that deletion of the DPY19L1P1 region significantly reduced cellular
proliferation and adhesion in the Y537S cells, but not in the WT cells.

37

2.5 DPY19L1P1 KO in Y537S cells increases its sensitivity to fulvestrant
Day_0 Day_3 Day_7
0
10
20
30
Day
Relative Luminescence
MCF7_WT_control
MCF7_WT_D2
MCF7_Y537S_control
MCF7_Y537S_E5
✱
✱✱
✱ ✱✱✱✱
A
B
WT_control
WT_D2
Y537S_Control
Y537S_E5
0.0
0.4
0.8
1.2
Adhesion Full media
absorbance/day 0 control
WT_control
WT_D2
Y537S_Control
Y537S_E5
✱✱
✱
WT_control
WT_D2
Y537S_Control
Y537S_E5
0.0
0.4
0.8
1.2
Adhesion CSS Media
absorbance/day 0 control
WT_control
WT_D2
Y537S_Control
Y537S_E5
✱ ✱✱
WT
Y537S
WT
Y537S
Control Knockout
Control Knockout
WT_control
WT_DPY_KO
Y537S_control
Y537S_DPY_KO
WT_control
WT_DPY_KO
Y537S_control
Y537S_DPY_KO
WT_control
WT_DPY_KO
Y537S_control
Y537S_DPY_KO
Figure 2.4.1. DPY19L1P1 KO causes decrease in cell proliferation and adhesion in Y537S but
not WT cells. (A) Graph showing CellTiter-Glo2 reading of cells at day 0, 3, and 7 after culturing in
complete serum (full media). Data was analyzed using two-way ANOVA and Tukey’s multiple
comparison test. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001. (B) Cell adhesion assay results using
crystal violet showing quantifications (left) and representative images (right), on cells grown in either
full media (top) or hormone depleted serum (CSS)(bottom). Left side shows the quantification of three
experiments. Data was analyzed using one-way ANOVA and Tukey’s multiple comparison test. P values
are as indicated for (A).
38
2.5 DPY19L1P1 KO in Y537S cells increases its sensitivity to fulvestrant
As endocrine therapy resistance is a hallmark of mutant ER bearing cells (Toy et al 2016,
Bahreini et al 2017), we were interested in testing whether deletion of the DPY19L1P1 region will
enhance the sensitivity of these cells to a common SERD used to treat breast cancer patients,
Fulvestrant (ICI). Cells with the DPY19L1P1 KO and their controls which were grown for 3 days in
CSS media were seeded in 96 well plates and treated with either the vehicle or increasing
concentrations of ICI (from 1nM to 1000nM). As ICI targets the ER signaling through direct
interactions with ER, we performed this experiment both in the presence and absence of E2.
Following 7 days of growth with and without treatment, CellTiter Glo2 was used to lyse the cells
and then the luminescence was read. In the WT cells (WT_control and WT_D2) in the absence of
E2, there was a strong response to ICI even at 1nM of treatment (Figure 2.5.1A). However, no
statistical difference was noted between the WT_control and the WT_D2 cells at any
concentration. In the WT cells in the presence of E2, as expected, a higher concentration of ICI
was required to attenuate growth substantially. Similar to the WT cells in the absence of E2, there
were no differences in the effect of ICI treatment between WT_control and WT_D2 (Figure
2.5.1A). With respect to the Y537S cells in the absence of E2 (ie, just the mutant ER activated)
there was a statistical difference between Y537S_control and Y537S_E5 at 10 and 100nM of ICI
in which Y537S_E5 was more sensitive to those concentrations of ICI. Interestingly, in the Y537S
cells in the presence of 1nM of E2 (its WT copy activated) there was no observed statistical
difference between the Y537S_control and Y537S_E5 cells, but there is a trend towards statistical
difference at 100nM of ICI (Figure 2.5.1B).
As the CellTiter-Glo2 assay is a luminescence assay based on mitochondrial activity, we
wanted to use crystal violet staining as a different assay to visualize the effect of ICI with or
without the DPY19L1P1 KO. We chose the concentration of 100nM of ICI as the DPY19L1P1 KO
in Y537S cells showed stronger sensitivity compared to the respective control and is also the
concentration with the strongest effect on both WT_control and WT_D2. The cells were seeded
on a 24 well plate and were treated with either a vehicle (DMSO and Ethanol), 1nM of E2, 100nM
of ICI, or E2 + ICI and kept in culture for a 7-day period (changing the media every 3 days). On the
7
th
day, the cells were fixed and treated with crystal violet, the plates were imaged and the crystal
39
violet was then eluted with methanol and the absorbance was read at 590nm. The WT_control
and WT_D2 cells were very comparable upon visualization as well as measuring the absorbance.
In both types of cells, E2 caused an increase in proliferation and that was attenuated and brought
back to control levels when ICI was added to the E2 (Figure 2.5.2A). However, in the case of the
Y537S cells, the Y537S_E5 cells showed statistically significantly reduced reading compared to
the Y537S_control in both ICI treatment and E2+ICI treatment (Figure 2.5.2B).
As both CellTiter-Glo2 and Crystal Violet assays were done with cells treated with ICI for
7 days at the longest, we wanted to determine if the sensitivity to ICI treatment in the Y537S_E5
remained for a longer period of time. To that end we performed a long-term ICI assay in which
WT_control, WT_D2, Y537S_control, Y537S_E5 were sustained in culture for a 3-week period
with either vehicle (or DMSO) or ICI treatment (at final concentration of 10nM). We then
calculated the proliferation from the starting seeding density of 75,000 cells to after 3 weeks of
treatment. Both WT cells showed an average of less than 10% growth compared to vehicle
treated cells (Figure 2.5.2 C). This is consistent with literature suggesting that ICI is very potent
at its normal concentrations in attenuating the growth of WT ER-containing breast cancer cells.
The Y537S_control cells showed an average of 50% growth in ICI treatment compared to vehicle
treatment. The Y537S_E5 cells showed a statistically significant decrease in ratio of proliferation
compared to the Y537S_control cells, indicating that the enhanced sensitivity to ICI treatment
acquired by the knockout of DPY19L1P1 region in Y537S cells is maintained for a long-term.
40

0 1nM 10nM 100nM 1000nM
0.0
0.5
1.0
1.5
2.0
Y537S in presence of E2
concentrations of drug nM
relative Luminenscence
YS_Control
YS_DPY_KO
ns ns ns ns ns
0 1nM 10nM 100nM 1000nM
0.0
0.5
1.0
1.5
Y537S in absence of E2
concentrations of drug nM
relative Luminenscence
YS_DPY_control
YS_DPY_KO
ns ns ✱ ✱✱ ns
0 1nM 10nM 100nM 1000nM
0.0
0.5
1.0
1.5
WT in presence of E2
concentrations of drug nM
relative Luminenscence
WT_DPY_control
WT_DPY_KO
ns ns ns ns ns
0 1nM 10nM 100nM 1000nM
0.0
0.5
1.0
1.5
WT in absence of E2
relative Luminenscence
WT_DPY_control
WT_DPY_KO
ns ns ns ns ns
A
B
C D
Figure 2.5.1 DPY19L1P1 KO increases sensitivity to Fulvestrant in Y537S cells, but not in
WT cells. Graphs showing CellTiter-Glo2 results for MCF7 WT (A and B) and Y537S cells (C and
D) grown in CSS and treated with either vehicle (DMSO/Ethanol) or Fulvestrant at a concentration
ranging from 1nM to 1000nM. (B) and (D) were also treated with 1nM of E2. Treatment was done
over a 7-day period and media changes were performed every 3 days. Values were normalized to
the 0 nM value for each graph. Experiment was performed 6 times. Data was analyzed using 2-way
ANOVA with Sidak’s multiple comparison correction. * P< 0.05 ** P <0.01, *** P < 0.001, ****
P<0.0001; DPY_KO = DPY19L1P1 Knockout; YS = Y537S.

41

Veh E2 ICI E2+ICI
0.0
0.5
1.0
1.5
2.0
2.5
Conditions
relative Absorbance
Y537S_control
Y537S_DPY_KO
ns ✱✱✱✱ ✱✱ ✱✱
Veh E2 ICI E2+ICI
0
1
2
3
4
5
relative Absorbance
WT_Control
WT_DPY_KO
ns ns ns ns
A
B
WT_Control
WT_DPY_KO
Y537S_Control
Y537S_DPY_KO
vehicle
E2
ICI E2+ICI
C
1
0.0
0.5
1.0
1.5
ratio ICI-treated/veh-treated
WT_control
WT_DPY_KO
Y537S_control
Y537S_DPY_KO
✱
Figure 2.5.2 DPY19L1P1 KO increases sensitivity to short-term and long-term treatment of
Fulvestrant in Y537S cells, not in WT cells. Graphs and images showing quantifications and visualization
of crystal violet staining results for MCF7 WT (A) and Y537S cells (B) grown in CSS and treated with either
vehicle (DMSO/Ethanol), 1nM of E2, 100nM of fulvestrant (ICI), or E2+ICI, for 7 days. Values were normalized
to the vehicle value for each graph. Experiment was performed 3 times. Data was analyzed using 2-way ANOVA
with Sidak’s multiple comparison correction. * P< 0.05 ** P <0.01, *** P < 0.001, **** P<0.0001; DPY_KO =
DPY19L1P1 Knockout. For each picture the layout is top left 3 wells are vehicle-treated cells, top right are E2
treated cells, bottom left ICI treated cells, bottom right are E2+ICI treated cells. (C) Graph showing CellTiter-
Glo2 results for ICI treatment for 3 weeks. Fold changes were quantified from the fold change between the
initial seeding and three days later, cells were then reseeded at 75,000 cells/well and the process was
continued for 3-weeks. The 3-week multiplier of the fold change of ICI-treated cells were divided by 3-week
multiplier of the vehicle-treated cells for each point. This experiment was performed 4 times. Data analysis
was a using Mann-Whitney unpaired t-test comparing Y537S_control to Y537S_DPY_KO. P value breakdown
same as for (A) and (B)

42
2.6 RNA-seq of DPY19L1P1 KO in WT and Y537S cells reveals potential genes regulated by the
region
Enhancers are cis-regulatory elements that while usually are located within 1Mb of the
promoter, can also form long distance interactions with the target genes as well (Snetkova, et al
2017). Although the enhancer-promoter interactions are mostly intra-chromosomal, some
studies have recently showed inter-chromosomal interactions (Kyrchanova et al 2021). Given
the strong phenotype displayed by the DPY19L1P1 KO in Y537S cells, we explored the possible
gene targets of the DPY19L1P1 region in both the WT and Y537S cells. We performed an RNA-
seq experiment using 2 replicates for WT_D2 and Y537S_E5 clones and each of 2 control clones
for WT and Y537S cells. The PCA plot (Figure 2.6.1A) showed good clustering within the replicates.
We next performed a differential expression analysis to determine the genes downregulated and
upregulated as a result of DPY19L1P1 KO in Y537S or WT cells. With parameters of FDR less than
or equal to 0.05 and a log2fold change greater than or equal to 0, we identified 504 genes
downregulated and 378 genes upregulated by DPY19L1P1 KO in Y537S cells, and 184 genes
downregulated and 336 genes upregulated in WT cells. We created a venn diagram (Figure
2.6.1B) to determine 451 and 321 uniquely downregulated and upregulated genes in Y537S cells,
and 154 and 257 uniquely downregulated and upregulated genes in WT cells, when the
DPY19L1P1 was knocked out (Figure 2.6.1B).
We then used Integrative Pathway Analysis software to analyze downstream pathways of
the different comparisons, specifically highlighting the uniquely downregulated genes in
DPY19L1P1 KO cells of each genotype. The DPY19L1P1 KO cells in Y537S showed downregulation
of genes involved in the PTEN pathway (11 out of 151 genes) and, interestingly, ER signaling (18
of 404 total genes associated with that pathway) (Figure 2.6.1 C). Amongst the ER signaling genes
were CACNA2D1 (encodes for a subunit in the calcium voltage gate complex), EGFR (a tyrosine
kinase receptor associated with cellular proliferation), GPER1 (G-coupled Estrogen receptor) all
of which were found on chromosome 7 (at about approximately 50Mb, 22 Mb, and 30 Mb away
from the DPY19L1P1 region respectively). By contrast, the downregulated genes in WT
DPY19L1P1 KO cells made up a smaller composition of most of the pathways in the IPA analysis.
Interestingly not only are more genes differentially regulated when knocking out the region in
43
Y537S cells, but more of those genes appeared on the chromosome 7 in Y537S cells (7 genes)
than in WT cells (1 gene).
We also were interested in the genes which were upregulated in the Y537S cells
compared to the WT cells to determine if any of the Y537S-upregulated genes were
downregulated as a result of the DPY19L1P1 knockout. Because we are interested in determining
Y537S-specific upregulated genes, we needed to perform a different analysis which compared
the Y537S ER in the vehicle setting (in which only the mutant ER is active) to the WT ER in the
presence of E2 (activated WT). Using published data of the CRISPR knock in cell lines with which
we have been working (Bahreini et al 2017), we first determined upregulated genes by comparing
Y537S_veh to WT_E2 at an FDR of less than or equal to 0.05 and a log2fold change greater than
1, and then filtered out the genes that are commonly upregulated when comparing WT_veh to
WT_E2 to control for the potential vehicle effect. This analysis revealed 425 Y537S-specfic
upregulated genes, among which 10 genes were also downregulated by deletion of the
DPY19L1P1 region specifically in Y537S cells. Amongst them were Insulin-like Growth Factor (IGF)
Binding Protein 3 (IGFBP3) (found on Chromosome 7 approximately 14 Mb away from the
DPY19L1P1 region) and CD44 (found on Chromosome 11) (Figure 2.6.2 A). IGFBP3 and CD44 were
downregulated when DPY19L1P1 was deleted in Y537S cells at a log2 fold change of 2 and 1.5,
respectively. IGFBP3 and CD44 were upregulated in Y537S cells compared to WT at a log2 fold
change of 4 and 1.3, respectively (Figure 2.6.2 B).
Another reason for us to be interested in exploring IGFBP3 is that it is one of the proteins
identified to be complexing with mutant ER, from a Rapid Immunoprecipitation Mass
Spectrometry of Endogenous Protein (RIME) assay that we performed to determine unique
binding proteins with ER in Y537S cells and WT cells. IGFBP3 is involved mostly in transporting
IGFs in the bloodstream to the trans-membranal IGF type-1 receptor. While IGFBP3 has been
shown to display pre-apoptotic phenotype (Brahim et al 2009, C. Li et al 2012, Agostini-Dreyer et
al 2015), it has been shown in recent studies to promote proliferation in different cancers.
Nuclear IGFBP3 has recently been shown to be associated with tumor progression in breast
cancer (Julovi et al 2018). Given this data, we decided to explore the role of IGFBP3 in the
DPY19L1P1 KO in both Y537S and WT cells.
44

-8
-4
0
4
-10 -5 0 5 10
PC1: 38% variance
PC2: 26% variance
KO_Status
Control
DPY_KO
Genotype
Mutant
WT
A
B
451
16
154
0
0
0
0
0
321
14
0 0
37
257
42
DPY_KO_up_in_YS DPY_KO_up_in_WT
DPY_KO_down_in_YS DPY_KO_down_in_WT
Figure 2.6.1 RNA-seq Analysis of DPY19L1P1 KO in WT and Y537S cells. (A) PCA plot showing all the
samples with replicates clustering nicely together. (B) Venn diagram showing the intersect of differentially
expressed genes derived from pairwise comparison within specific genotypes upon DPY19L1P1 KO, based
on threshold at log2fold change greater than 0 and FDR less than 0.05. Unique genes downregulated by
DPY19L1P1 KO in Y537S or WT cells were analyzed for pathway enrichment using Integrative Pathway
Analysis. Red circle highlights Estrogen Receptor signaling in DPY19L1P1 KO in Y537S cells.

45

A
CD44
B
MCF7_WT_E2
MCF7_WT_Veh
MCF7_Y537S_E2
MCF7_Y537S_Veh
0
100
200
300
400
IGFBP3
normalized RNA seq counts
MCF7_WT_E2
MCF7_WT_Veh
MCF7_Y537S_E2
MCF7_Y537S_Veh
MCF7_WT_E2
MCF7_WT_Veh
MCF7_Y537S_E2
MCF7_Y537S_Veh
0
200
400
600
800
CD44
normalized RNA seq counts
MCF7_WT_E2
MCF7_WT_Veh
MCF7_Y537S_E2
MCF7_Y537S_Veh
WT_control
WT_DPY_KO
YS_control
YS_DPY_KO
0
50
100
150
200
IGFBP3
normalized RNA seq counts
WT_control
WT_DPY_KO
YS_control
YS_DPY_KO
WT_control
WT_DPY_KO
YS_control
YS_DPY_KO
0
500
1000
1500
CD44
normalized RNA seq counts
WT_control
WT_DPY_KO
YS_control
YS_DPY_KO
Bahreini et al
2016
DPY19L1P1
KO RNA-seq
Figure 2.6.2 RNA-seq Analysis of DPY19L1P1 KO in WT and Y537S cells identifies genes
upregulated in Y537S cells but downregulated in DPY19L1P1 Knockout. (A) Venn diagram
showing comparison made between differentially expressed genes from Y537S_veh compared to
WT_E2 (left side) with genes downregulated in Y537S DPY19L1P1 KO compared to Y537S control
(right side). This produced 10 genes and amongst them were IGFBP3 and CD44. (B) Bar graphs
showing normalized RNA-seq counts of data from Bahreini et al 2017 (top) and our DPY19L1P1
RNA-seq (bottom).

46

2.7 qPCR Data Reveals IGFBP3 and CD44 are being regulated by the DPY19L1P1 Region in
Y537S but not WT cells.
The first step following the RNA-seq analysis was to verify the results via qPCR. Indeed,
when the cells were grown in full media, qPCR using primers against IGFBP3 and CD44 showed
that both genes showed a decreased expression in Y537S_E5 comparing to Y537S_control cells.
Interestingly, the qPCR also showed that there is no statistical significance between the
WT_control and Y537S_control cells (Figure 2.7.1A). This can be explained by the fact that the
discovery of IGFBP3 and CD44 as Y537S upregulated genes compared to WT was in the context
of cells grown in hormone-depleted media. We then grew out the cells in hormone-depleted
serum and noted that in both genes there was a statistically significant decrease in expression in
Y537S_E5 compared to Y537S_control, and that Y537S_control cells showed higher expression in
both genes compared to both WT cells (Figure 2.7.1B).
We next decided to perform a qPCR to determine if there is any effect to these target
genes when treated with ICI. DPY19L1P1 KO cells and their respective controls were grown in
CSS-media for three days and then seeded in a 24 well plate at a density of 30,000 cells/well. The
cells were then treated either with a vehicle (DMSO/ethanol), E2, ICI, or E2 + ICI for 24 hours.
RNA was extracted and then qPCR performed for both genes as well as the classical ER target
GREB1 as a control. WT_control and D2 cells followed the classic expectation of GREB1 gene

Figure 2.6.3 Rapid Immunoprecipitation Mass Spectrometry of Endogenous Proteins
(RIME) reveals IGFBP3 complexes with mutant ER. Summary of RIME analysis comparing
MCF7 WT and MCF7 Y537S, of the 60 Y537S ER complexing proteins pulled down compared to 160
WT ER complexing proteins pulled down, IGFBP3 was noted as unique to Y537S ER. Mut = Y537S
ER

IGFBP3
47
expression. WT cells expressed low levels of GREB1 in the absence of E2, with an increase in
expression by about 20-fold in the presence of E2. In the presence of ICI without E2 the
expression of GREB1 in WT cells is similar to in the vehicle condition, and when treated
simultaneously with E2 and ICI the GREB1 expression decreases to below half of the respective
level in E2 condition (Figure 2.7.2A). Similarly, the Y537S cells exhibited the same profile already
showed in previous publications (Jeselsohn et al 2018, Bahreini et al 2017). As Y537S ER is a
constitutively active ER, the vehicle setting already produced a strong expression of GREB1. E2
caused a slight but significant increase in expression (this could be attributed to activating the
WT). ICI caused a decrease in expression in both Y537S cells and the combination treatment of
ICI and E2 generates expression similar to the vehicle treated cells (Figure 2.7.2A). Analyzing the
expression of IGFBP3 in both WT_control and WT_D2 cells in all condition did not produce any
statistically significant changes between the treatments of the respective types of cells (Figure
2.7.2B). The same was the case for CD44 expression as there were no observed statistical
significance between treatments within the respective types of cells, however there is a noted
trend in the cases of both WT_control and WT_D2 cells of a slight increase in expression in the
presence of E2 (Figure 2.7.2C). This was not the case for Y537S cells. Curiously, we observed an
upregulation of IGFBP3 expression when Y537S_control cells were treated with ICI (p value =
0.58). These Y537S_control cells in the presence of ICI showed statistically significantly higher
expression compared to any treatment conditions of the Y537S_E5 cells. There was also a trend
of downregulated expression between any other condition in the Y537S_control cells compared
to the Y537S_E5 cells (Figure 2.7.2B). Expression of CD44 in Y537S_control cells showed a similar
trend in increased expression between the vehicle and E2 treated group. The E2-treated group
showed a strong statistically significant increase in expression compared to each treatment group
of the Y537S_E5 cells. While GREB1 expression is unaffected between Y537S_control and
Y537S_E5 cells, there was a clear decrease in expression across the board in all Y537S_E5
compared to the control cells.
T47D cells are another ER-positive cell line in which the Steffi Oesterrich lab developed a
knock-in Y537S mutation (Bahreini et al 2017). As mentioned previously, regulatory genomic
regions can be cell-type or cell-line specific and hence we focused our entire study on MCF7.
48
Indeed, when we initially examined the bigwig files of T47D ER Chip-seq of both WT in the
presence of E2 and Y537S in absence of E2 we noted a comparable binding to the DPY19L1P1
region between the two conditions. However, it is possible that other enhancers are affecting
expression of CD44 and IGFBP3. To determine if CD44 and IGFBP3 levels are higher in T47D Y537S
cells compared to the WT, we grew out T47D WT and Y537S cells (from the Oesterrich lab) in CSS-
media for three days and then seeded them and treated them with either Vehicle, E2, ICI, or
E2+ICI for 24 hours. We first performed qPCR with primers against GREB1 and noted that there
was an expected significant increase in expression in both genotypes when cells were treated
with E2 and that this expression level was then decreased in the presence of ICI. In the presence
of both E2 and ICI, the GREB1 levels were less than half that of the vehicle treated cells. T47D
Y537S in the vehicle setting shows about a 4.5-fold difference in relative expression compared to
the T47D WT.
IGFBP3 is generally expressed at a higher level in the Y537S cells than that in the WT in all
conditions except the vehicle condition (Figure 2.7.3 B). T47D WT in the presence of E2 has a
statistically significant decreased expression compared to the Y537S treated with E2, the same is
true when comparing the T47D WT treated with ICI to the Y537S cells treated with ICI.
Interestingly, we observe a slight increase in expression of IGFBP3 in the Y537S cells in the
presence of ICI, similar to what we observed in the MCF7 cells. CD44 also show higher levels of
expression in certain conditions when comparing Y537S to WT cells. In both the vehicle and ICI-
treated cells, there was a statistically significant increase in expression in Y537S cells than the
WT. The generally higher levels of IGFBP3 and CD44 in the Y537S compared to WT in T47D cells
suggest that the upregulation of these genes may be common between these two cell lines.

49

MCF7_WT_Control
MCF7_WT_D2
MCF7_Y537S_Control
MCF7_Y537S_E5
0
1
2
3
4
5
relative expression
✱✱
IGFBP3
A
MCF7_WT_Control
MCF7_WT_D2
MCF7_Y537S_Control
MCF7_Y537S_E5
0.0
0.5
1.0
1.5
CD44
relative expression
✱✱✱✱
B
MCF7_WT_Control
MCF7_WT_D2
MCF7_Y537S_Control
MCF7_Y537S_E5
0
5
10
15
IGFBP3
relative expression
✱✱✱✱
✱✱✱✱
✱✱✱✱
MCF7_WT_Control
MCF7_WT_D2
MCF7_Y537S_Control
MCF7_Y537S_E5
0
1
2
3
4
5
CD44
relative expression
✱✱✱
✱✱✱✱
✱✱✱✱
Figure 2.7.1 qPCR confirmation of downregulation of IGFBP3 and CD44 in Y537S cells in full
and hormone-depleted media. Bar graphs showing qPCR analysis of relative RNA expression level
of IGFBP3 and CD44 in WT and Y537S cells with DPY19L1P1 KOs and their respective control in full
media (A) and hormone-depleted (CSS) media (B). Data analysis was performed using one-way ANOVA
analysis with Dunnett’s multiple comparison test. * P< 0.05 ** P <0.01, *** P < 0.001, **** P<0.0001
N=3

50

A
Veh
E2
ICI
E2+ICI
Veh
E2
ICI
E2+ICI
0
50
100
150
200
GREB1
relative expression
YS_Control
YS_DPY_KO
✱✱✱✱
✱✱✱✱
✱✱✱✱
✱✱✱✱
✱✱✱✱ ✱✱✱✱
✱✱✱✱
✱✱✱✱
✱✱✱✱
✱✱✱✱
B
Veh
E2
ICI
E2+ICI
Veh
E2
ICI
E2+ICI
0
2
4
6
8
CD44
relative expression
YS_Control
YS_DPY_KO
✱✱
✱
✱✱✱
✱
Veh
E2
ICI
E2+ICI
Veh
E2
ICI
E2+ICI
0
1
2
3
4
5
CD44
relative Luminenscence
WT_Control
WT_DPY_KO
Veh
E2
ICI
E2+ICI
Veh
E2
ICI
E2+ICI
0
2
4
6
IGFBP3
relative expression
WT_Control
WT_DPY_KO
Veh
E2
ICI
E2+ICI
Veh
E2
ICI
E2+ICI
0
2
4
6
8
10
IGFBP3
relative expression
YS_Control
YS_DPY_KO
✱
✱
✱
p = .058
C
Veh
E2
ICI
E2+ICI
Veh
E2
ICI
E2+ICI
0
20
40
60
GREB1
relative expression
WT_Control
WT_DPY_KO
✱
✱✱
✱ ✱✱ ✱✱
✱✱✱
✱✱
✱✱
✱✱✱
Figure 2.7.2 Relative RNA expression level of IGFBP3 and CD44 in WT and Y537S DPY19L1P1
KO cells in the presence of E2 and Fulvestrant. Bar graphs showing qPCR results of relative
RNA expression level of GREB1 (A), IGFBP3 (B), and CD44 (C) in WT (left side) and Y537S (right side)
DPY19L1P1 KO cells along with their controls grown in CSS media and treated with either vehicle
(DMSO/Ethanol), 1nM of E2, 10nM of fulvestrant (ICI), or E2 + ICI for 24 hours. Data was analyzed using
two-way ANOVA analysis with Tukey’s multiple comparison test * P< 0.05 ** P <0.01, *** P < 0.001,
**** P<0.0001 N=3 or 4

51

Veh
E2
ICI
E2+ICI
Veh
E2
ICI
E2+ICI
0
5
10
15
20
25
GREB1
relative expression
T47D_WT
T47D_Y537S
✱✱✱✱
✱✱✱✱
✱✱✱✱
✱
✱✱✱
✱✱✱✱
✱✱✱✱
✱✱✱✱
✱✱✱✱
✱✱✱✱ ✱✱✱✱
Veh
E2
ICI
E2+ICI
Veh
E2
ICI
E2+ICI
0.0
0.5
1.0
1.5
2.0
IGFBP3
relative expression
T47D_WT
T47D_Y537S
✱✱✱✱
✱✱✱✱
✱✱✱✱
Veh
E2
ICI
E2+ICI
Veh
E2
ICI
E2+ICI
0
1
2
3
CD44
relative expression
T47D_WT
T47D_Y537S
✱✱✱
✱
A
B
C
52

2.8 Knocking out IGFBP3 in Y537S_control Cells recapitulates some of the DPY19L1P1
KO phenotype
One of the major questions we would need to address is whether IGFBP3 or CD44 are
regulated by the DPY19L1P1 region thereby playing a role in the phenotypes observed with the
KO. While it is necessary to determine physical interactions between the enhancer and the
promoter of the candidate genes, another approach we used is to delete the gene of interest in
Y537S_control cells to determine if they recapitulate the phenotype observed in that genotype’s
DPY19L1P1 KO cells (Figure 2.8.1A). Using CRISPR-Cas9 technology we were able to successfully
knockout IGFBP3 in Y537S_control cells in two clones (B2 and E9) (Figure 2.8.1B). We also
transfected Y537S_control cells with a control plasmid and combined the clones to be used in
experiments as the control to the IGFBP3 KOs.
We first tested whether IGFBP3 KO clones recapitulate the attenuated growth phenotype
we observed in the DPY19L1P1 KO Y537S cells. Indeed, observing growth over a 7-day period,
Y537S_B2 and Y537S_E9 showed a statistically significant decrease in growth compared to the
Y537S control cells (Figure 2.8.2A). Interestingly, the difference in growth between the IGFBP3
KO clones and its respective control, while statistically significant, was not as stark as the
difference between the Y537S_control to the Y537S_E5 after seven days (Figure 2.4.1A),
indicating possible additional targets also contributing to the growth phenotype.
We next tested whether the IGFBP3 KO clones have an increased sensitivity to fulvestrant
treatment. As we observed the statistically significant differences at 10nM and 100nM, we
treated the IGFBP3 KO clones and their respective control with either the vehicle or 10nM or
100nM of fulvestrant. Similar to the experiment done with the DPY19L1P1 KO cells, we
performed this experiment in the presence and absence of E2. Our results indicated that one of
the clones (E9) in the absence of E2 showed a statistically significant decrease in growth in the
Figure 2.7.3 Relative RNA expression of IGFBP3 and CD44 in T47D WT and Y537S cells in
the presence of E2 and Fulvestrant. Bar graphs showing qPCR results of relative RNA
expression level of GREB1 (A), IGFBP3 (B), and CD44 (C) in T47D WT (blue) and Y537S (red) cells
grown in CSS media and treated with either vehicle (DMSO/Ethanol), 1nM of E2, 10nM of fulvestrant
(ICI), or E2 + ICI for 24 hours. Data was analyzed using two-way ANOVA analysis with Tukey’s multiple
comparison test * P< 0.05 ** P <0.01, *** P < 0.001, **** P<0.0001; N=3

53
presence of either 10nM or 100nM compared to the control. Curiously, we did not observe this
in the B2 clone (Figure 2.8.2B). There were no statistically significant differences between the
IGFBP3 KO clones and the control when this experiment was done in the presence of E2
(Figure2.8.1B)
To determine if IGFBP3 KO has an affect on cellular adhesion, we performed an adhesion
assay using collagen-coated plate. We observed that not only is there no decrease in adhesion as
observed with Y537S_E5 cells, but there is a statistically significant increase in adhesion to
collagen plate in the B2 clone compared to the control, though this was not observed in the E9
clone (Figure 2.8.1 C), indicating the possibility that IGFBP3 is not responsible for the observed
reduction in cell adhesion.
In summary, both B2 and E9 clones showed a decrease in growth compared to the control,
but when it came to fulvestrant sensitivity, the E9 clone displayed an increased fulvestrant
sensitivity while the B2 clone did not. With respect to the cell adhesion properties B2 showed a
statistically significant increase of adhesion compared to the control, and the E9 clone did not
show any difference compared to the control.

54

A
B
YS_Control
sg1.1_B2
sg1.1_E9
0.0
0.5
1.0
1.5
IGFBP3
Normalized to Beta-Actin
YS_Control
sg1.1_B2
sg1.1_E9
B-actin
IGFBP3
Figure 2.8.1 Generating IGFBP3 Knockouts in MCF7 Y537S cells. (A)Schematic of
CRISPR-Cas9 based targeting of IGFBP3 in Y537S cells. Sg1.1 is sgRNA targeting the first exon
of IGFBP3. Image was created using BioRender.com. (B) Verification of Knockout. Lysates
from Y537S_control, B2, and E9 cells were immunoblotted for B-actin (top) and IGFBP3
(bottom) (B) was quantified (below) using ImageJ software with intensity values of IGFBP3
normalized to B-actin YS= Y537S
55

A
Day0 Day7
0
50
100
150
relative Luminenscence
YS_Control
sg_1.1_B2
sg_1.1_E9
✱✱
✱✱✱✱
B
0 10nM 100nM
0.0
0.5
1.0
1.5
IGFBP3_KO in absence of E2
drug concentration
relative Luminenscence
Y537S_control
sg1.1_B2
sg1.1_E9
✱
C
0 10nM 100nM
0.0
0.5
1.0
1.5
IGFBP3_KO in presence of E2
drug concentration
relative Luminenscence
Y537S_control
Y537S_B2
Y537S_E9
✱
Y537S_control
sg.1.1_B2
sg1.1_E9
0.8
1.0
1.2
1.4
✱✱
Figure 2.8.2 IGFBP3 Knockout in Y537S cells recapitulates cell growth phenotype of Y537S
DPY19LP1 KO. (A) Graph showing CellTiter-Glo2 results of IGFBP3 KO cells (B2 and E9) and the respective
control grown in full media at day 0 or 7. Data was analyzed using using two-way ANOVA Bennet’s with
multiple comparison test * P< 0.05 ** P <0.01, *** P < 0.001, **** P<0.0001; N=3 or 4. (B) Graph showing
CellTiter-Glo2 results of Y537S control, B2 and E9 cells grown in CSS media and treated with either vehicle
(DMSO/ethanol) or Fulvestrant (ICI, either at 10nM or 100nM), with or without 1nM of E2 for 7 days.
Values were normalized to the 0nM value for each graph. The experiment was performed 3 different
times. Data was analyzed using two-way ANOVA and Tukey’s multiple comparison correction. (C) Bar
graph showing crystal violet staining quantification of cell adhesion assay for Y537S control, B2 and E9
cells. Experiment was performed 3 times. Data was analyzed using one-way ANOVA and Dunnett’s
multiple comparisons test.
56
2.9 Materials and Methods
Chromatin Immunoprecipitation Sequencing (ChIP-Seq) and subsequent analysis
Sample Preparation: Ex-vivo CTC BRx68 cells were grown in suspension at a seeding density of 10
million cells in a 500 cm
2
Thermo Scientific NUNC Square Culture dish. The experiment was
performed in biological replicates and consisted of two conditions: BRx68 cells treated with a
final concentration of 10nM of Estradiol (E2) and the respective vehicle, ethanol. Following 24
hours of growth with the drug or vehicle, cells were collected and processed according to a
protocol developed by the Peggy Farnham lab and optimized by the Yu lab. Cells were collected,
centrifuged, and resuspended with 1% formaldehyde for 8 minutes while on a shaker to cross-
link. Cross-linking was stopped by adding glycine at a final concentration of 0.125 M. The cross-
linked cell solution was then centrifuged at 430 rcf for 5 minutes at 4C and washed two times
with ice-cold PBS. To lyse cells and extract the nuclei, the pellets were resuspended in cell lysis
buffer (see Recipe page for recipes for relevant buffers) and incubated on ice for 15 minutes. The
cells were then centrifuged at 430 rcf for 5 minutes, decanted and the pellet was resuspended in
nuclear extraction buffer. Following a 30-minute incubation on ice the samples were sonicated
using a BioRuptor (high setting with cycles of 30 seconds on and off for 15 minutes). Sonication
was verified through running the samples on an agarose gel. 10ug of the sonicated chromatin
was then incubated with either ER antibody (Santa Cruz, HC-20) or IgG (Cell Signaling #2729)
corresponding to the species of the primary antibody (rabbit) overnight. The following day, the
chromatin-antibody incubated samples were resuspended with ChIP-grade Protein A-G beads for
3 hours, washed 3 times with IP buffers, and then de-crosslinked overnight. The samples were
then column purified with ZYMO DNA concentration kit and made into libraries via the KAPA
HyperPrep Kit for Next Generation sequencing at 15 million reads per sample.

Data Processing for BRx68:
Single-end Fastq files of each samples underwent a quality check using the FastQC tool
(Andrews et al 2010). The files were then trimmed of their adaptors using cutadapt tool (Martin
et al 2011) at setting optimized for ChIP-seq. The samples were then aligned to the hg19
genome build using bowtie1 (Langmead et al 2009) (code: bowtie -q -p 8 -m 1 -v 3 --sam –best),
57
converted to bam files and sorted via the samtools tool (H. Li et al 2009), and deduplicated
using the MarkDuplicates.pl tool from picard. For purposes of generating heatmaps, the
bamCoverage tool from the Deeptools package (Ramírez et al 2014) was used to generate
bigwig files normalized using Reads per Genomic Content (RPGC), which takes into
consideration genome size and total number of reads. Once the bigwig files were generated,
the computeMatrix tool from the Deeptools package (Ramírez et al 2014) was used to generate
an intermediary file of all the scores of each sample’s bigwig file against certain genomic
regions. For purposes of determining the putative Wild Type (WT) Estrogen Receptor (ER) and
mutant Y537S ER binding sites, publicly available ChIP-seq data sets from the Myles Brown lab
(Jeselsohn et al 2018) were used, calling statistically significant peaks the case of the BRx68
samples graphed against putative Wild Type (WT) Estrogen Receptor (ER) binding sites. The
MACS2 tool (Zhang et al 2008) was used to call peaks showing statistical significance over input
DNA in MCF7 WT cells in the presence of E2. For the Y537S mutant ER specific peaks, the peaks
for MCF7 Y537S cells were called and the bedtools -v function (Quinlin et al 2010) was used to
remove peaks also called in the WT ER samples. The plotHeatmap tool from the Deeptools
(Ramírez et al 2014) package was then used to graphically display the scores of each sample
against those regions.
For purposes of the PCA plot, a count table was generated using the featureCount tool
(Liao et al 2014). In short, a SAF file was first generated following calling significant peaks in all
BRx68 samples and the bedtools intersect -wa -wb function was used to combine the list of
significant genomic coordinates. This was followed by using the -uniq command to remove
peaks that were duplicated. The featureCount tool was used with the code featureCounts -a
${saf_file} -F SAF --readExtension3 150 -o count_table.txt -O -T 8 to determine a numeric count
for each sample against all possible genomic regions. The PCA plot was then plotted against the
top 1000 variable regions across all samples and visualized using ggplot2.
The feature distribution chart was generated using the ChIPseeker package (G. Yu et al
2015). Statistically significant called peaks were piped into the R program and the transcription
start site (TSS) was identified as being 1 kb upstream and downstream of the promotor site.

58
Data Analysis of Publicly Available Datasets for Enhancer Discovery
Our BRx68 ER ChIP-seq dataset was analyzed along with ER ChIP-seq data sets from the
labs of Miles Brown (Jeselsohn et al 2018), Simak Ali (Harrod et al 2017), and Steffi Oesterrich (Z.
Li et al 2022). The processing of the files was done with the same pipeline used to process the
BRx68 fastq files. For purposes of determining significant peaks, the processed varied based the
datasets. To determine mutant-specific peaks in the datasets that only included one sample per
conditions (i.e., no replicates MACS2 callpeak function (Zhang et al 2008) was called on all
processed bam files with the same parameters called against the respective sample’s genomic
DNA which had been sonicated but not incubated with an antibody (Input). Sample code: macs2
callpeak -t $(sample.bam) -c $(control_Input.bam) -f BAM -g hs -n sample_peaks -q 0.01.
Significant peaks were called for mutant and WT ER samples and then bedtools intersect -v was
used to remove from the called mutant ER peaks those which were also found in the WT ER. The
mutant ER-unique peaks were then crossed against the BRx68 called peaks to look for shared
peaks across the study. As the ER ChIP-seq performed by the Miles Brown lab used replicates
(Jeselsohn et al 2018), we decided to determine mutant-specific ER peaks in their study by using
the featureCount (Liao et al 2014) tool like what was done with the BRx68 samples. In this case
the SAF file featured all possible ER binding regions across all samples. The count table was then
piped into DESeq2 for differential binding analysis with parameters set at Mutant ER log2 fold
change greater 1 compared to WT ER with an FDR value of less than 0.05. This list was then
crossed with the BRx68 list to produce a list of common Mutant-ER specific binding peaks across
the samples. The shared regions were then visualized on the UCSC genome browser to determine
the levels of three different epigenetic marks associated with regulatory elements – H3K4me1
(associated with putative enhancer), H3K4me3 (found at promoter sites), and H3K27Ac (found
near regulatory elements, usually if both H3K27Ac and H3K4me1 are present in a site it is
associated with an enhancer region) across seven different cell lines.

CRISPR-Cas9 to Knockout Genomic Region
In collaboration with the lab of Dr. Steffi Oesterrich, CRISPR-edited MCF7 cells (WT and
those bearing the Y537S mutation) were maintained in DMEM supplemented with 10% FBS.
59
To determine the role of mutant ER in regulating the enhancer activity for the DPY19L1P1 region,
CRISPR-Cas9 was used to delete this genomic region. To perform the deletion, we determined
gRNAs corresponding to 200-300 bp upstream of the leftmost part of the region (called L-gRNA)
and 200-300 bp corresponding to the rightmost part of the region of interest (called R-gRNA).
The L-gRNA was cloned into the backbone vector called pSpCas9(BB)-2A-GFP (henceforth called
px458). Px458 also codes for a Cas9 protein and GFP. The R-gRNA was cloned into another
backbone vector called pLKO5.sgRNA.EFS.tRFP which has a scaffold to clone in an sgRNA (similar
to the px458) but will express an RFP and doesn’t code for a Cas9 protein. Both cloning of the L-
gRNA and R-gRNA were done according to the protocol developed by the Zhang lab in MIT (Ran
et al 2014). MCF7 WT and Y537S cells were seeded at a density of 300,000 cells per well in a 6-
well plate. Using the Lipofectamine 3000 (Thermofischer) Reagent according to the
manufacturers’ optimized protocol, we transfected Px458 containing L-gRNA and
pLKO5.sgRNA.EFS.tRFP containing R-gRNA in both cell genotypes. Following 2 days of
transfection, the cells were washed, trypsinized, and resuspended in 1% BSA media. They were
then FACS sorted to isolate single cells which were RFP positive and GFP positive, as well as DAPI
negative, into a 96 well plate. The individual wells were observed for 2.5 weeks until viable
colonies were identified. To then identify clones harboring a knockout of the DPY19L1P1 region,
we extracted genomic DNA from the different clones of both cell genotypes and ran a Taq
polymerase-based PCR using primers that were 300 bp upstream and downstream of the
approximately 500 bp DPY region. We then ran the PCR product on a 1% Agarose gel that was
supplemented with GreenGlo Safe DNA dye (Denville Scientific, CA3600) and visualized using the
Bio-Rad Gel EQ and the Quantity One analyzing software.
IGFBP3 KO cells were produced by cloning in sgRNAs (Sg1.1, Sg2.1) targeting different
regions within the first exon of IGFBP3 into the px458 vector. Following 2 days of transfection,
the cells were washed, trypsinized, and resuspended in 1% BSA media and then single-cell sorted
for GFP positive and DAPI negative-cells. The single cells were observed under a microscope
regularly for growth with media change taking place following the first week of sorting. When
single cell clones developed colonies, they were grown out, protein was extracted using laemli
buffer to perform Western blot assay to determine whether the knockout was successful.
60

Cell Culture
The BRx68 cell lines were cultured in ultra-low attachment plates with RPMI 1640 media
supplemented with EGF (final concentration at 20ng/ml), bFGF (20ng/ml), 1X B27 and 1X
antibiotic/antimycotic, in 4% O 2 and 5% CO 2. The seeding for the BRx68 ChIP-seq experiment is
described above in the section pertaining to ChIP seq.
MCF7 (and their respective ER genotypes WT and Y537S) along with other CRISPR edited
iterations of the different genotypes of this cell line (such as those harboring the DPY19L1P1
region knockouts and the ones introduced with a lenti-dCas9 plasmid (see later section on
generation of Lenti-dcas9 (LDK) cells) was maintained in high-glucose DMEM media
supplemented with 10% FBS and 1% Penicillin-Streptomycin (P/S). For purposes of assays which
involved treatment of Estradiol (E2) (Sigma-Aldrich, E2758) and Fulvestrant (ICI) (Sigma-Aldrich,
I4409), the cells were washed two times with PBS and then grown in phenol red-free high-glucose
DMEM media supplemented with 10% Charcoal:Dextran Stripped Fetal Bovine Serum (CSS)
(Gemini-Bio, A76G02J), 1% L-Glutamine (L-Q) (Sigma-Aldrich, G3126), and 1% P/S for a period of
at least three days. The cells were then detached using TrypLE Express enzyme (Thermo Fisher,
12604013) at 37C for 5 minutes and then plated for their respective assays. T47D (and their
respective genotypes WT and Y537S) were maintained in RPMI media supplemented with 10%
FBS and 1% P/S. For purposes of assays in which cells needed to be grown in a hormone-deprived
state, the cells were washed twice with PBS and then maintained in RPMI media supplemented
with 10% CSS, 1% L-Q, and 1% P/S for three days before cells were detached using TrypLE Express
enzyme and then plated for their respective assays

Cell Proliferation
MCF7 WT and Y537S cells harboring the DPY191L1 deletion (WT_D2 and Y537S_E5, respectively)
and their respective controls were seeded at a density of 750 cells per well in technical triplicates
in a 96 well plate. The cells were grown for either 0 days, 3 days, or 7 days. To determine the
proliferation change of the cells at the different time points, cells lysed with CellTiter-Glo 2.0
reagent (CTG) (Promega, G924) at a final concentration of 1:2 of reagent to culture media. The
61
entire mix was then transferred over to a flat bottom, white plate (COSTAR, REF 3912) and the
luminescence was read using the CLARIOstar Multiplate reader. The values were normalized to
the seeding control (Day 0). This assay was repeated two more times for a total of three biological
replicates. For the IGFBP3 KO proliferation assay, the same set up was used as above with the
starting seeding density of 750 cells per well and with two time points recorded (Day 0 and Day
7).
For purposes of measuring cell growth in the presence of different concentrations of ICI
with and without E2, WT_D2 and Y537S_E5 cells (along with their respective controls) were
seeded at 750 cells per well in a 96-well plate in CSS media. The following day, the CSS media was
removed and replaced with CSS-media supplemented with differing concentrations of ICI (final
concentrations ranging from 1 – 1000nM) and/or E2 (final concentrations of 1nM). The cells were
also treated with the respective vehicles of ICI and E2 as the controls. The media changes were
done on the fourth day after seeding (3
rd
day following the first treatment) and the cells were
lysed with CTG and the luminescence was read using the CLARIOstar Multiplate reader. To control
for variation in seeding density, the cells were also seeded in another 96-well plate at the same
density. 3 hours after seeding those cells were also treated with CTG and their luminescence
measured. This experiment was performed 5 additional times for 6 total biological replicates.
The experiment above was also performed for the MCF7 Y537S IGFBP3 KO cells, but
treated with a final concentration of either 10 or 100 nM ICI. This experiment was done 3 total
times for 3 biological replicates.

Long-term ICI treatment assay
To determine the effect of long-term ICI treatment on MCF7 DPY191L1 deletion cells and
their respective controls, 75,000 cells were seeded in a 12-well plate in compete serum DMEM
media supplemented with either 10nM of ICI or its vehicle (DMSO). After 3 days, cells were
washed, trypsinized, and counted to determine proliferation. 75,000 cells of this first
proliferation were seeded again, along with its respective treatment. This process was repeated
over the course of 3 weeks with cells trysinized and counted every 3 days. We then multiplied all
the fold changes from each of the 3-day checkpoints, and the final multiplier of the vehicle-
62
treated cells was divided by the multiplier of the respective ICI-treated cells. This experiment was
performed 4 different times for a total of 4 biological replicates.

Crystal Violet Proliferation assay
WT_D2 and Y537S_E5 cells were grown in CSS media for three days and dissociated by
incubating with TrypLE express enzyme (Thermo Fisher, Cat# 12604013) at 37
o
C for 5 minutes.
Cells were plated at a density of 4000 cells/well in 1 mL volume per well in 24-well tissue culture-
treated plates. The following day the media was replaced with CSS media supplemented with
vehicle (0.1% EtOH and 0.1% DMSO), 1nM of E2, 100nM of ICI, and 1nM of E2 + 100nM of ICI.
The media changes were done on the fourth day after seeding (third day following the first
treatment). At the end of treatment, cells were washed twice with cold PBS containing 1 mM
CaCl
2 and 1 mM MgCl
2
. Cells were then fixed with cold 100% methanol for 10 minutes at RT,
followed by washing with PBS 3 times. Crystal violet (0.5% w/v crystal violet in 20% ethanol) was
added to the cells at 150ul per well and incubated on a shaker for 10 minutes at RT. The plates
were then submerged in large 1L beakers of distilled water for 1 minute each with gentle twisting.
Plates were then dried overnight in a fume hood and imaged the following day using Keyence BZ-
X810 microscope at a 4X magnification. Crystal violet was then recovered by adding 200 ul of
100% methanol to the wells and then placed to shake for 15 minutes at RT. 100 ul of the
recovered crystal violet solution was transferred to flat-bottom 96-well plates (Corning, REF
9017) and absorbance measured at 590 nm using a BioTek plate reader.

Cell Adhesion Assay
Rat tail collagen I (Corning, 354236) was diluted in 0.2N acetic acid to a final concentration
of 100 ug/mL and was used to coat the wells of a 24 well plate overnight at 37
o
C. The following
day, the collagen was removed and washed twice with PBS. The collogen-coated wells were then
blocked in DMEM with 10% FBS and incubated for 1 hour at 37
o
C. During this time, cells were
trypsinized, counted, and adjusted to an optimized final seeding density (30,000 cells per well)
and adhered to the collagen coated wells for 30 minutes at 37
o
C. The plate was then inverted
and washed two times with ice-cold PBS supplemented with CaCl 2 and MgCl 2 to a final
63
concentration of 1mM to remove the unadhered cells. Cells were then fixed with cold 100%
methanol for 10 minutes at RT, followed by washing with PBS 3 times. Crystal violet (0.5% wv
crystal violet in 20% ethanol) was added to the cells at 150ul per well and incubated on a shaker
for 10 minutes at RT. Following this the plates were submerged in large 1L beakers of distilled
water for 1 minute each with gentle twisting. Plates were then dried overnight in a fume hood
and imaged the following day using Keyence BZ-X810 microscope at a 4X magnification. Crystal
violet was then recovered by adding 200 ul of 100% methanol to the wells and placed to shake
for 15 minutes at RT. 100 ul of the recovered crystal violet solution was transferred to flat-bottom
96-well plates (Corning, REF 9017) and absorbance measured at 590 nm using a BioTek plate
reader. This experiment was performed the same for the DPY191L1 region deleted cells and their
controls as well as the IGFBP3 KO cells and their controls.

RNA Isolation and Gene Expression profiling (RNA-seq) and Data Processing
MCF7 WT and Y537S cells harboring the DPY191L1 region deletion (WT_D2 and Y537S_E5,
respectively) along with their respective controls (WT_C10, WT_C11, Y537S_D4, Y537S_Z99)
were seeded in a 24 well plate at a seeding density of 25,000 cells per well for two days prior to
RNA extraction. Given that only one clone for either genotype of MCF7 cells that contained the
DPY191L1 region deletion was able to be isolated, we grew out the cells which had the deletion
in technical replicates for RNA-seq. After two days in culture, RNA was extracted using the Zymo
Quick RNA kit according to the manufacturers’ instruction. The RNA quantity and quality were
measured using. Extracted, high-quality RNA was sent to Novogene Corporation for paired-end
sequencing at 15 million reads per sample. Paired-end FASTQ files were quality checked using
FASTQC (Andrews et 2010) and trimmed of their respective adaptors using the Trim Galore tool.
Trimmed reads were then mapped to the human genome build GRCh37 from Ensembl
(ftp://ftp.ensembl.org/pub/grch37/current/fasta/homo_sapiens/dna/Homo_sapiens.GRCh37.d
na_sm.primary_assembly.fa.gz) using STAR (Dobin et al 2012) under optimized parameters for
single-end sequenced data optimized by the Andrew Smith lab. The aligned reads were then
counted via featureCounts (Liao et al 2014) and piped into the R program DESeq2 (Love et al
2014) for normalization to sequencing depth for downstream analysis. For purposes of producing
64
the PCA plot, the count table was transformed using the vst function under the standard
parameters recommended by the developers of DESeq2 (Love et al 2014). The transformation
was done to eliminate the experiment-wide trend of variance over mean. The PCA plot was then
generated using ggplot2 (Wickham et 2009). Deeptools was used to generate bigwig files to
visualize using the Washington Epigenome Browser (D. Li et al 2022) and the UCSC Genome
Browser (Navarro-Gonzales et al 2021). For purposes of differential expression analyses, the
contrast function was used and with each comparison we looked at differentially expressed genes
between two samples to have an FDR value less than 0.05 and a log2fold change value greater
than 0. The Venn.diagram tool on R was used to visually depict shared upregulated genes across
the different comparisons. We used the Intersect function on R in order to identify shared
upregulated genes between 2 or more conditions and the Setdiff function to determined what
genes are upregulated in certain comparisons and not others.
RNA-seq analysis of publicly available datasets (Jeselsohn et al 2018, Harrod et al 2017,
Bahreini et al 2017) involved the same steps as mentioned above to ensure consistency when
analyzing the data. To determine transcriptomic difference between the constitutively active
Y537S mutant ER and the E2-activated WT ER, the differential expression analysis in all of the
studies had to take into consideration that there are genes which may be upregulated because
of the vehicle treatment in the Y537S cells (Ethanol) over the E2-treatment in WT cells. To correct
for this, for each study we performed the following set of comparisons: for all comparisons we
used the parameters of log2 foldchange greater than 1 and an FDR value of less than 0.05. A
standard comparison was first performed determining the upregulated genes in Y537S cells in
the vehicle setting (Y537S_veh) compared with WT cells in the presence of E2 (WT_E2). We then
identified upregulated genes in WT_veh cells compared to WT_E2 cells. As each study also
included Y537S treated with E2, we reasoned this could be used as an extra point of comparison
to compare to WT_E2 to determine Y537S-upregulated genes. After generating the lists, genes
which were statistically significantly upregulated in WT_veh over WT_E2 were removed from the
Y537S_veh vs WT_E2 list. This was then intersected with the Y537S_E2 vs WT_veh list to develop
a comprehensive list of Y537-upregulated genes compared to the activated WT.

65
Chromatin Accessibility Assay (ATAC-seq) and Data Processing
Assay for transposase-accessibility chromatin with high-throughput sequencing (ATAC-
seq) was performed as developed by Dr. Jason Buenrostro in 2013 (Buenrostro et al 2013). In
short, nuclei preparation was performed by resuspending 50,000 MCF7 WT, Y537S, and D538G
cells in ATAC-seq lysis buffer (10 mM Tris pH7.4, 10 mM NaCl, 3 mM MgCl 2, 0.1% Igepal).
Transposition reaction was performed by using the Tn5 transposase (Nextera) at 37°C for 30
minutes with mild shaking. Transposed DNA was amplified by PCR using 100X SYBR I Green
(similar to traditional SYBR used for q-PCR, but lacking any dNTPs and Polymerase enzyme) for
20 cycles labeling. The amplified PCR product was then run on a CFX iCycler real-time PCR
machine (Bio-Rad). The R value, which corresponds to the cycle value in which the RFU of a
certain sample reaches its saturation point, was calculated for each individual sample and divided
by 3 to determine the number of additional cycles necessary for each sample (this was optimized
by the Kaestner lab to avoid over amplification of one product over another which can lead to
spurious open regions). The generated libraries were then purified using Agencourt AMPure XP
(Beckman Coulter). Library quality was determined using TapeStation and the prepared libraries
were sent to Novogene for paired-end sequencing at 10 million reads per sample. The resulting
FASTQ files were processed in the same way as detailed for the ChIP-seq FASTQs. The “differential
open” regions were determined using featureCounts (Liao et al 2014) to produce a count table
similar to the way we performed the differential binding analysis with the BRx68 ChIP seq
samples. The count table was then piped into DESeq2 (Love et al 2014) to perform the different
comparisons using the contrast function, using a log2 foldchange value of greater than 1 and an
FDR value of less than 0.05. The lists of regions were then annotated using the R program biomaRt
(Smedley et al 2009) and the resultant gene lists were then piped into Integrative Pathway
Analysis (IPA) technology (Krämer et al 2014) to determine enriched pathways.

Gene Expression Profiling by RT-qPCR
RNA was extracted using Quick RNA Microprep kit from Zymo. 500 ng of RNA was
reverse transcribed using 5X iScript supermix (Bio-Rad). The cDNA was diluted to 5ng/ul, and 2
ul of cDNA was used per real-time quantitative PCR reaction using iQ SYBR Green Supermix
66
(Bio-Rad). All reactions were run on a CFX iCycler real-time PCR machine (Bio-Rad). The primer
list can be found in a comprehensive list of all primers and sgRNA sequences used.

Western Blot
Cells were washed in PBS twice and lysed in 1.5 X Laemmli Buffer (50 mM Tris pH=6.8,
1.25% SDS, 15% glycerol) and heated at 95
o
C for 15 minutes with light shaking. After protein
quantification by Lowry protein assay (Bio-Rad). 30ug of protein for each sample was reduced
with 5% (v/v) beta-mercaptoethanol (B-mercap) and mixed with bromophenol blue at a final
concentration of 0.01% (v/v). The lysates were then run on 4-15% Mini-PROTEAN TGX Precast
gels (Bio-Rad) for 1-2 hours. Gels were then transferred by semi-dry method to low
fluorescence PVDF membranes (which were methanol-activated) in a Trans-Blot Turbo Transfer
System (Bio-Rad) using the standard Bio-Rad listed protocol (30 minutes, 25 V, 1 A).
Membranes were then blocked in 5% non-fat milk in TBS for at least 30 minutes and was
followed by primary antibody incubation in blocking buffer with 0.1% Tween 20 and shaken at
overnight in a 4
o
C cold room. The following day the membranes were washed three times with
TBS-T with at least 10 minutes per wash and incubated for an hour at RT with a secondary LI-
COR antibody in blocking buffer with 0.1% Tween-20. Membranes were then washed three
times in TBS-T for at least 10 minutes each and then imaged on LI-COR imaging instrument.
Quantification for Western Blot was done using Image J by comparing intensity of IGFBP3 band
to that of the loading control (B-actin). The primary antibody used for IGFBP3 was from
Proteintech (Rabbit, 10189-2-AP) and B-actin was from Cell singaling (Mouse, #3700). The
secondary antibody used were IRDye® 800CW goat anti-rabbit IgG secondary antibody LI-COR
925-32211 and IRDye® 680RD goat anti-mouse IgG secondary antibody LI-COR 926-68070.

Rapid Immunoprecipitation Mass Spectrometry of Endogenous Proteins (RIME)
RIME was performed according to its detailed protocol (Mohammed et al 2019). Briefly,
cells were gown on 15cm dishes and 20 million cells were cross-linked using 1% formaldehyde
in DMEM lacking FBS and P/S for 8 minutes and quenched by 0.125 M glycine for 5 minutes.
Cell lysates were then resuspended in buffers to swell the cells and extract nuclei. The
67
extracted cell nuclei were then sonicated by using a Diagenode sonicator (30 seconds on and
30 seconds off, 13 minutes total). The sonication efficiency and total chromatin were verified
using the same method discussed above in ChIP-seq and all samples were found to have a DNA
size range of 200-600 bp. For each sample, 10ug of mouse monoclonal ER antibody (Santa Cruz,
sc-8002) was incubated with ChIP-grade magnetic protein G beads (Cell Signaling, 9006) for 3
hours to conjugate the beads to the antibody. This protein G-antibody mixture was then
incubated with each sample overnight in a cold room under shaking conditions. The following
day the cells were washed with RIPA buffer for a total of 10 times and the beads were sent to
Taplin Mass Spectrometry Facility to digest the antibodies/protein from the beads and perform
the Mass Spectrometry.

Cleavage Under Targets and Tagmentation (CUT&Tag)
CUT&Tag was performed using an optimized protocol from EpiCypher® CUTANA™ (Kaya-
Okur et al 2019). Briefly, MCF7 WT and Y537S with the DPY191L1 deletion and their respective
controls were seeded at a density of 25,000 cell per well in a 24 well plate and extracted the
following day via trypsinization. Cells were resuspended in 100 ul of wash buffer and 10ul of
pre-washed magnetic Concavalin A (conc-A) beads, which were incubated with the cells for 15
minutes. The conc-A beads are important for tethering the cells thereby increasing efficiency of
the assay. The supernatant was then removed and resuspended with antibody buffer
containing 1ug of Rabbit polyclonal H3K4me1 from Abcam (ab8895) recommended by
Epicypher for the assay and let to incubate in a cold room overnight under shaking conditions.
The following day the supernatants were removed and the beads were resuspended with
Digitonin-based wash buffer supplemented with 1ug of Rabbit secondary from Cell Signaling
(Cell Signaling, 2729). This was incubated for 1 hour in RT. The cells were then washed 3 times
with digitonin buffer and on the 3
rd
wash, digitonin buffer was supplemented with a Tn5
conjugated to ProteinA/G (supplied by EpiCypher) and incubated for 1 hour at RT. This was then
washed 3 times afterwards with digitonin buffer and were resuspened in Tagmentation buffer
(Digitonin-based wash buffer with the addition of MgCl 2 at a final concentration of 10mM to
activate the tagmentation) and incubated at RT for 10 minutes. The tagmentation was then
68
quenched by using an SDS-based quenching buffer. As CUT&Tag uses the similar principles of
ATAC-seq, the library prep method for CUT&Tag is the same. The libraries were then sent to
Novogene for paired-ended read sequencing at 10 million reads per sample.
Data processing followed the same methods used to analyze the ChIP-seq datasets. The
regions unique to the Y537S cells lacking the DPY19L1P1 region was done by graphing the bigwig
files of all samples (against all possible statistically significantly bound peaks across all the
samples) and using a k -means value of 4. The group that showed significantly enriched binding
of Y537S cells lacking the DPY19L1P1 region was isolated and annotated using biomaRt and then
run on IPA to determine enriched pathways.

2.10 List of Primers

Primer Sequences for RT-qPCR,
GREB1 -F 5’ ATG GGA AAT TCT TC GCT GGA C 3’
-R 5’ CAC TCG GCT ACC ACC TTC T 3’

IGFBP3 #1 -F 5’ ATC TGT GCT CTG CTG AGA CTC GT 3’
-R 5’ CCA CCC CCT CCA TTC AAA GAT 3’

IGFBP3 #2 -F 5’ GAG CCT GAC TTT GCC AGA CC 3’
-R 5’ CTG GG AAA GGA GCT CAC GC 3’

CD44 -F 5’ TGG TCG CTA CAG CAT CCTC TC 3’
-R 5’ TGT GGG CAA GGT GCT ATT GA 3’

36B4 -F 5’ GTG TTC GAC AAT GGC AGC AT 3’
-R 5’ AGA CAC TGG CAA CAT TGC GGA 3’

Primer Sequences for ChIP qPCR
GREB1 -F 5’ ACC GCA ACG TGG TTT TCT CAC CCT ATG G 3’
-R 5’AAT CTT GAA TCC CAT AGC TGC TTG AAT C 3’

DPY19L1P1 region -F 5’ GAA GAA CCA AAC TGC ACG ACG AAA 3’
-R 5’ GAG GTA GGA CCA GCA ATC CAT 3’

Primer sequences for Cloning
L-gRNA -F 5’ caccg TGG GAC GTT AAG GTG TTG AT 3’
-R 5’ aaac ATC AAC ACC TTA ACG TCC CA c 3’
69

R-gRNA. -F 5’ caccg CGT GCA GTT TGG TTC TTC AC 3’
-R 5’ aaac GTG AAG AAC CAA ACT GCA CG c 3’

Sg 1.1. -F 5’ caccg CTG ACG TGC GCA CTG AGC GA 3’
-R 5’ aaac TCG CTC AGT GCG CAC GTC AG c 3’

Sg 2.1. -F 5’ caccg CAC CAG CTC CGC GCA CAC GG 3’
-R 5’ aaac CCG TGT GCG CGG AGC TGG TGc 3’

Primer sequence for PCR
DPY19L1P1 -F 5’ ACC AGT AGC TGC CAA AGT TTA 3’
-R 5’ TGA ACA TCA ACA TCC AGT CAT T 3’

70
Chapter 3: Discussion

3.1 Discussion and future directions
Estrogen Receptor (ER) signaling plays a major role in breast cancer development and
ultimate progression. Endocrine therapy, either by reducing levels of estrogen circulating in the
blood or directly targeting the ER, provide an important method for treating luminal breast
cancer patients. However, the innate and acquired resistance to these therapies create a major
obstacle to the successful treatment of patients and is the contributing factor to the ultimate
metastasizing breast cancers (Anurag et al 2018). One mechanism explaining the resistance to
endocrine therapy is single missense mutations found on the ligand binding domain (LBD) of the
ER alpha that is encoded by gene ESR1. These mutations lead to a confirmational change of the
structure of the ER allowing it to be constitutively active in the absence of its agonist, 17β-
estradiol (E2) (Toy et al 2017, Bahreini et al 2017). ESR1 mutations has been studied extensively
over the past decade as next generation sequencing has been performed on metastatic breast
cancer (Jeselsohn et al 2014, S. Li et al 2013, Robinson et al 2013, Toy et al 2013) including in
circulating tumor cells (Yu et al 2014) suggesting a high prevalence of ESR1 mutation in metastatic
tissue in patients that progressed from endocrine therapies. There is a debate as to whether the
ESR1 mutation is directly responsible for the metastasis or is it due to the fact that mutant ERs
are resistant to endocrine therapy and thus are the cells which are selected for during multiple
rounds of therapy (Jeselsohn et al 2015). Even so understanding the mechanism of action by the
mutant ER could provide better insight into how to better target it.
Recently, many genomic and transcriptomic studies on edited breast cancer cell lines
containing the most common hotspot mutations have been performed to determine the
mechanism by which the mutant ERs operate (Bahreini et al 2017, Harrod et al 2017, Jeselsohn
et al 2018). The studies have revealed a unique transcriptome of the mutant ER in which while
still upregulating genes associated with traditional wild type (WT) ER targets (GREB1, PR, TFF1)
(Bahreini et al 2017), additionally regulates genes not traditionally associated with ER targets (Z.
Li et al 2022, Jeselsohn et al 2018). ChIP-seq analysis has also revealed a unique binding profile
for mutant ER. In 2018 Dr. Rinath Jeselsohn and colleagues observed an increase in mutant ER
71
binding in the promoter regions of genes compared to the WT ER, something in which I also
observed with data analysis from ChIP-seq on an ex vivo expanded CTC line with a de novo Y537S
ER mutation. This is remarkable as WT ER binding is typically found along Estrogen Response
Elements (ERE) typically found in distal intergenic regions. While these labs and many others have
utilized next generation sequencing (NGS) techniques to better understand the mechanism by
which mutant ER acts, the large bulk of the data is derived from established ER positive breast
cancer cell lines which have been genetically engineered to have ESR1 mutation. As there are
expected off-target effects of CRISPR-Cas9 technology and there is potential “leakiness” of
doxycycline (dox)-induced overexpression vectors, deriving data from patient samples harboring
the ESR1 mutation and then comparing it to publicly available datasets would help us determine
the mutant ER-specific transcriptome and binding profile that are shared across many studies
and found in patients, leading to less spurious results. This will eventually help us identify
appropriate therapies that could better target mutant ER-bearing cells.
ChIP-seq data on the BRx68 CTC line harboring a heterozygous Y537S mutation, was
analyzed and compared to publicly available datasets of ChIP-seqs performed on an ER-positive
breast cancer cell line, MCF7, which were edited to include the ESR1 mutation. The analysis
yielded a region in which mutant ER in BRx68 has a strong binding intensity as well as showed a
statistically significant increase in binding intensity comparing the mutant ER with WT ER from
the study in Myles Brown’s lab. The region is located at the first intron of a pseudogene called
DPY19L1P1. Using data from ENCODE, we also found that it contained enhancer like properties
as an analysis with 7 cell lines showed that this region contains the common histone marks
associated with enhancers, H3K4me1 and H3K27Ac. ATAC-seq data on MCF7 with Y537S
mutation, D538G mutation as well as the WT showed the region to be open with the intensity
stronger in the Y537S cells. Furthermore, a CUT&Tag assay performed on MCF7 WT and Y537S
cells confirmed the enrichment of H3K4me1 at this particular region in both cell lines. Finally, a
luciferase assay containing this region upstream of a minimal promoter showed enhancer activity
in cells containing mutant ER, more so than that of WT ER.
Our next step was to characterize this candidate mutant-ER specific enhancer. We deleted
the DPY19L1P1 region in MCF7 cells, both the WT and the Y537S mutant, using CRISPR-Cas9
72
technology. We observed that the DPY19L1P1 KO in Y537S cells grew at a slower pace than the
Y537S control cells, this was not the case in the WT genotype. Further, we observed a decreased
cell adhesion as well as an increased sensitivity to fulvestrant treatment in Y537S cells harboring
the DPY19L1P1 deletion compared to its respective control, but not in the WT. Based on the
importance of the DPY19L1P1 region in Y537S cells, we next aimed to determine the potential
genes regulated by this enhancer. RNA-sequencing of the WT and Y537S DPY19L1P1 KO and their
respective controls revealed more genes regulated by the DPY19L1P1 region in Y537S cells than
WT. Furthermore, we identified two genes of interest, IGFBP3 and CD44, that are downregulated
in the Y537S when the DPY19L1P1 region is deleted and are also identified as mutant ER-uniquely
upregulated genes. We confirmed via qPCR that IGFBP3 and CD44 are statistically significantly
downregulated in the mutant cells harboring the DPY19L1P1 deletion, but not the WT.
Intriguingly, we also observed an increase in expression of IGFBP3 in Y537S control cells when
treated with fulvestrant. This is fascinating as it may point to a role for IGFBP3 in endocrine
therapy resistance. By knocking out IGFBP3 in Y537S_control cells, we determined that we could
recapitulate some of the properties of deleting the DPY19L1P1 region in the same cells. IGFBP3
KO curbed growth of Y537S cells. However, when it came to increasing sensitivity to fulvestrant
treatment only one of the two clones we generated exhibited fulvestrant resistance (E9).
Furthermore, decrease in cell adhesion was not observed and, in fact, one of the clones showed
increased cell adhesion (B2). These data suggest that IGFBP3 contribute partially to the
phenotypes observed in DPY19L1P1 enhancer deletion but additional genes regulated by this
enhancer also are important to the phenotypes.
IGFBP3’s role in cancer is controversial as it is identified as a pro-apoptotic, anti-tumoric
gene in some studies (C. Li et al 2012, Cai et al 2020) while others have shown its association with
poor cancer prognosis (Sheen-Chen et al 2009) and promoting tumorigenesis (Zielinska et al
2020). In a publication by Chao Li and colleagues in 2012, they identified GRP78 (a chaperone
protein involved with the unfolded protein response) as an interacting partner of IGFBP3 and
when they overexpressed IGFBP3 in endocrine therapy resistant breast cancers cells it led to a
disruption of the GRP78-caspase-7 complex, causing caspase-7 to be released from the complex
inducing apoptosis. A more recent article published in 2020 suggests that GRP78 expression is
73
important for the pro-apoptotic role of IGFBP3, and they noted that patients that expressed
IGFBP3 but had low expression of GRP78 experienced poor prognosis (Zielinska et al 2020)
suggesting that IGFBP3’s role in apoptosis or tumorigenesis is largely based on its interaction with
GRP78. As IGFBP3 was shown in MCF7 cells with Y537S ER mutation to be important for cell
proliferation, what is important to address is the mechanism by which this happens. A 2018
publication from the lab of Dr. Robert Baxter showed that Nuclear IGFBP3 is associated with
tumor progression in breast cancer (Julovi et al 2018) and which was attributed to the activation
of the receptor tyrosine kinase, EGFR, and the lipid kinase, sphingosine kinase (SphK). SphK1 has
also been linked with ER signaling (Antoon et al 2011) and tumorigenesis (Pyne et al 2016).
Many questions remain to be addressed for future studies. For example, given the clear
phenotype of DPY19L1P1 KO in Y537S cells, understanding what are the genes which contribute
to this phenotype is paramount. My focus was on IGFBP3 due to its relative proximity to the
enhancer site, its association with poor prognosis in breast cancer patients (H. Yu et al 1996,
Sheen-Chen et al 2009), and its potential interaction with mutant ER based on our RIME data.
While IGFBP3 knockout showed reduced proliferation in Y537S cells, it is very likely more genes
are contributing to other phenotypes observed with the deletion of DPY19L1P1 region. Indeed,
as a follow up I am in the process of knocking out CD44 in Y537S cells.
The main goal of my thesis was to study this mutant-specific enhancer and determine the
genes it regulates. While our RNA-seq approach yielded a list of genes may be regulated by the
DPY19L1P1 enhancer and two of which were verified via qPCR, it is important to validate the
physical interaction of DPY19L1P1 region with the promoter of its target genes. This would
require using chromatin capture (3C) technology. If the interest is in determining all potential
interactions of the DPY19L1P1 region, we could use Circular Chromatin capture, which utilizes
the same principles of 3C but reveals all potential genomic interactions associated with the
DPY19L1P1 region rather than between two known loci.

3.2 Conclusion
Resistance to endocrine therapy in metastatic breast cancer is a main challenge for the
field. Activating missense mutations in the LBD of ER have been linked as one of the mechanisms
74
behind this resistance to endocrine therapy. Identifying a mutant-ER specific enhancer, in which
its deletion creates a clear phenotype could be leveraged to better target cells with these
mutations. In my thesis, I was able to show that the DPY19L1P1 region displayed characteristics
of an enhancer and the deletion of this region attenuated growth, reduced cell adhesion, and
increased sensitivity to a common endocrine therapy – fulvestrant.

75
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Asset Metadata

Creator Amzaleg, Jonathan Osher (author)

Core Title Identifying and characterizing a unique enhancer for Y537S mutant estrogen receptor (ER) in breast cancer cells

Contributor Electronically uploaded by the author (provenance)

School School of Dentistry

Degree Doctor of Philosophy

Degree Program Craniofacial Biology

Degree Conferral Date 2023-05

Publication Date 01/31/2025

Defense Date 01/12/2023

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag breast cancer,enhancer,estrogen receptor,next-generation sequencing,OAI-PMH Harvest,Y537S mutation

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Language English

Advisor Yu, Min (committee chair), Paine, Michael (committee member), Xu, Jian (committee member)

Creator Email amzaleg@usc.edu,jamzaleg84@gmail.com

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Abstract (if available)

Abstract Point mutations in the ligand binding regions of the ESR1 gene, encoding estrogen receptor α (ER), have recently been detected in metastatic lesions of breast cancer patients who become resistant to anti-estrogen endocrine therapy. These mutations lead to conformation changes in the ER protein that mimic a constitutively active ER even in the absence of estrogens. Further research is needed to develop effective therapies for breast cancer patients with these mutations. Recent reports, including our own studies, suggest that in addition to the traditional ER targets, there is a unique transcriptome and binding profile for the different mutant ERs compared to the activated WT. For example, preliminary studies have revealed unique chromatin modifying proteins complexing to the mutant ER and not the WT. Analyzing the ChIP-seq experiments from publicly available datasets as well as our own circulating tumor cell (CTC) line which harbors one of the more common ESR1 mutations (Y537S), we identified a genomic region in the first intron of the pseudogene DPY19L1P1 which showed enriched Y537S ER binding compared to the WT. Further examination revealed that the DPY19L1P1 region also shows enhancer properties based on the H3K4me1 and H3K27Ac data from both ENCODE and from data generated from our lab. Following successful knockout of this region using CRISPR-Cas9 technology in both MCF7 WT and MCF7 harboring the Y537S ER mutation cells, we determined that the DPY19L1P1 region knockout in the Y537S mutant cells showed a statistically significant attenuated growth, decrease in adhesive properties, and an increase in sensitivity to commonly used endocrine therapy (fulvestrant) compared to Y537S control cells. Interestingly, these functional changes were not observed with the DPY19L1P1 knockout in MCF7 WT cells which showed no statistical difference in cell growth, adhesive properties, and sensitivity to hormonal therapy. RNA-seq analysis of the DYP19L1P1 knock out in MCF7 WT or Y537S mutant cell lines compared to respective controls demonstrate that more genes were affection by DPY19L1P1 deletion in Y537S mutant compared to WT KO cells. Two candidate genes, IGFBP3 and CD44, were significantly downregulated in Y537S DPY19L1P1 KO cells. Both of these genes were also observed as Y537S upregulated genes compared to WT when in our RNA-seq analysis of publicly available datasets. After confirming by qPCR that the IGFBP3 and CD44 genes were downregulated in DPY19L1P1 region knockout cells in Y537S but not WT cells, we used CRISPR-Cas9 technology to knockout IGFBP3 in Y537S MCF7 cells to determine the role of this gene played in the observed phenotype of the DPY19L1P1 region knockout of the same genotype. While IGFBP3 KO resulted a statistically significant decrease in proliferation, it did not affect cell adhesion nor fulvestrant sensitivity. Taken together, these data suggest a unique mechanism of action for the mutant ER and the role of a unique enhancer in promoting mutant ER-mediated cellular growth, adhesion, and resistance to fulvestrant, which can help determine appropriate therapies for metastatic patients harboring the Y537S ESR1 mutation.