seven shades of tamoxifen resistance - helda

Susanne Hultsch

Seven shadesof tamoxifen resistance

Molecular mechanisms of drug resistance in breast cancer

Seven shades of tamoxifen resistance

Molecular mechanisms of drug resistance in breast cancer

Susanne Hultsch

Institute of Molecular Medicine Finland (FIMM)and

Doctoral Program in Biomedicine (DPBM) Faculty of Medicine,

University of Helsinki

Academic Dissertation

To be presented with the permission of the Faculty of Medicine, University of Helsinki, for public examination in Porthania, PIII

(119), Yliopistonkatu 3 on the 23rd of October 2018 at 12 noon.

Helsinki 2018

ii

Supervisors: Olli Kallioniemi, MD, PhD, Professor, DirectorInstitute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, FinlandScience for Life Laboratory (SciLifeLab), Karolinska Institutet, Sweden

Vilja Pietiänen, PhDInstitute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Finland

Thesis Committee: Kaisa Lehti, PhDResearch Director and Senior Cancer ResearcherUniversity of Helsinki, Finland,Karolinska Institute, Sweden

Johanna Mattson, MD, Docent, Chief Medical Officer HYKS Cancer Center and University of Helsinki, Finland

Pre-examiners: Tuula Kallunki, PhD, Adjunct Professor Danish Cancer Society Research Center, Copenhagen, Denmark.

Katri Pylkäs, PhD, DocentLaboratory of Cancer Genetics and Tumor BiologyBiocenter Oulu, University of Oulu, Finland

Opponent: Leonie Young, PhD, Associate ProfessorRCSI Surgery, Royal College of Surgeons in Ireland

©Susanne HultschCover image and layout by Susanne Hultsch

ISBN 978-951-51-4491-1 (paperback)ISBN 978-951-51-4492-8 (PDF)

Unigrafia, Helsinki 2018

ABSTRACT iii

ABSTRACTTamoxifen treatment of estrogen receptor-positive breast cancer re-

duces breast cancer mortality. However, resistance to tamoxifen de-

velops frequently. A plethora of resistance mechanisms have been

described but their biological importance, clinical significance, and

possibilities for diagnostic or therapeutic intervention are poorly un-

derstood. Fusion genes, for example, have the potential as thera-

peutic targets or diagnostic tools as they are highly cancer-specific.

In order to determine the mechanisms underlying endocrine therapy

resistance and to identify new opportunities to defy resistance in

breast cancer, we created seven tamoxifen-resistant breast cancer

cell lines. We characterized the resistant cell lines by exome-se-

quencing to identify possible mutations or genomic rearrangements

involved in drug resistance. RNA-sequencing was applied to shed

light on the nature of fusion genes in the parental cell line as well as

their contribution to acquired drug resistance. RNA-sequencing also

exposed gene expression and pathway changes, which were followed

up in detail in one of the resistant cell lines. In addition to drug sen-

sitivity and resistance testing combined with high-content imaging,

network analysis determined the drug response profiles. We further

uncovered potential drug targets of tamoxifen resistance.

This intensive molecular profiling revealed that each tamoxifen-resis-

tant cell line developed its own resistance mechanism and acquired

individual drug vulnerabilities. However, we were able to detect a

common increased sensitivity towards an ERK1/2-inhibitor. On the

other hand, we discovered co-resistance to paclitaxel, which is

mostly driven by the slower growth rate of the resistant cells. Anal-

ysis of differentially expressed genes identified pathways which were

associated with cell cycle, protein modification, and metabolism, es-

iv ABSTRACT

pecially with the cholesterol pathway. After further investigation we

identified that the prevention of lysosomal membrane permeabilization

was associated with drug resistance in the T-47D tamoxifen-resistant

cell lines. Targeting this mechanism remains challenging. We further

revealed the complex nature of fusion genes, which include the high

prevalence of alternative splicing and the lack of recurrence across

different breast cancer cell lines. Additionally, we identified fusion

genes only present in the resistant cell lines. However, these were

mainly cell clone-specific or recurrent read-through fusions. Exome-se-

quencing revealed no known or common mutations or copy number

changes related to resistance development.

With the diversity of fusion genes, mutations, copy number changes,

differentially expressed genes, pathway changes, and drug responses

in tamoxifen-resistant cells, it is safe to say that tamoxifen resistance

cannot simply be explained by one common mechanism. Therefore, it

is likely that countering the resistance will require different thera-

peutic approaches.

TABLE OF CONTENTS v

TABLE OF CONTENTS

ABSTRACT ..........................................................................................iii

TABLE OF CONTENTS ........................................................................v

LIST OF ORIGINAL PUBLICATIONS ................................................vii

ABBREVIATIONS ..............................................................................viii

INTRODUCTION ...............................................................................1

REVIEW OF LITERATURE ................................................................2

1 The Breast ...................................................................................22 Estrogen Receptor – it's complicated .........................................33 Breast Cancer ..............................................................................6

3.1 Breast Cancer Subtypes ......................................................64 Fusion genes ...............................................................................95 Tamoxifen ..................................................................................11

5.1 A summary of tamoxifen's history .....................................115.2 Mode of Action ...................................................................12

6 Drug resistance in cancer .........................................................136.1 Resistance mechanisms .....................................................14

6.1.1 Drug uptake, efflux, and metabolism .........................156.1.2 Drug target alterations ...............................................166.1.3 Cell cycle alteration and DNA damage repair ...........176.1.4 Avoiding apoptosis ......................................................186.1.5 Resistance mediated by autophagy ............................196.1.6 Adaption of signaling ..................................................206.1.7 Epithelial-mesenchymal transition (EMT) .................206.1.8 Tumor microenvironment ...........................................216.1.9 Extracellular vesicles .................................................22

6.2 Resistance to Tamoxifen ....................................................24 AIM OF THE STUDY .......................................................................29

MATERIALS AND METHODS .........................................................30

1 Cell culture and generation of tamoxifen-resistant cell lines (I, II, III) .............................................................................................302 Characterization of tamoxifen-resistant cell lines (II, III) ........31

2.1 Growth analysis (II) ...........................................................312.2 Responsiveness to estrogens and downstream signaling (II) ...................................................................................................322.3 Measurements and drug induced changes in protein levels

vi TABLE OF CONTENTS

(II, III) ........................................................................................322.4 Immunofluorescence staining (III) ....................................332.5 Lysosomal membrane permeabilization (LMP) assay (III) 342.6 Measurement of triglycerides and cholesterol esters (III) 34

3 Paired-end RNA-sequencing (I, III) ...........................................344 RNA-sequencing analysis (I, III) ...............................................35

4.1 Fusion gene detection, characterization, and validation (I, III) .............................................................................................354.2 Integration of transcriptomic data and differential gene expression (III) ..........................................................................36

5 Genomic profiling by exome-sequencing (II) ............................376 Drug sensitivity and resistance testing (DSRT) (II, III) ............38

6.1 Cell viability measurement (II, III) ....................................386.2 High-content phenotypic drug profiling (III) .....................38

7 DSRT data analysis (II, III) ........................................................397.1 Analysis of cell viability (II, III) .........................................397.2 Construction of drug sensitivity and co-resistance networks (II) .............................................................................................397.3 Analysis of high-content phenotypic drug profiling (III) ...40

RESULTS & DISCUSSION ..............................................................41

1 Fusion genes .............................................................................411.1 Fusion gene characteristics in breast cancer ...................411.2 The role of fusion genes in tamoxifen resistance ..............43

2 Characterization of tamoxifen-resistant cell lines ....................442.1 Tamoxifen-resistant cells alter their cell cycle ..................452.2 Changes in ERα protein-levels and estrogen-responsiveness ...................................................................................................462.3 Genetic alterations in the genomes of tamoxifen-resistant cell lines ....................................................................................472.4 Transcriptional changes in tamoxifen-resistant cell lines and their resemblance to patients ...................................................482.5 Drug responses of tamoxifen-resistant cell lines ..............51

3 Lipid changes in T-47D and their consequences ......................53 CONCLUSION .................................................................................57

ACKNOWLEDGMENTS .......................................................................x

REFERENCES ..................................................................................xiii

INDEX OF FIGURES AND TABLES ..............................................xxxvi

ORIGINAL PUBLICATIONS ..........................................................xxxvii

LIST OF ORIGINAL PUBLICATIONS vii

LIST OF ORIGINAL PUBLICATIONS

I. Kangaspeska S, Hultsch S, Edgren H, Nicorici D, Murumägi

A, Kallioniemi O. Reanalysis of RNA-sequencing data reveals

several additional fusion genes with multiple isoforms. PLoS

One. 2012;7(10):e48745.

II. Kangaspeska S*, Hultsch S*, Jaiswal A, Edgren H, Mpindi JP,

Eldfors S, Brück O, Aittokallio T, Kallioniemi O. Comprehen-

sive drug sensitivity testing reveals specific vulnerabilities

and co-resistance patterns in endocrine-resistant breast

cancer. BMC Cancer. 2016 Jul 4;16:378.

III. Hultsch S, Kankainen M, Paavolainen L, Kovanen RM, Ikonen

E, Kangaspeska S, Pietiäinen V, Kallioniemi O. Lipid repro-

gramming in tamoxifen-resistant breast cancer cells and po-

tential therapeutic vulnerabilities (Manuscript accepted, BMC

Cancer

* Equal contribution

viii ABBREVIATIONS

ABBREVIATIONSABC ATP-binding cassette

Abl1 Abelson murine leukemia viral oncogene homolog 1

AEBS antiestrogen binding sites

AF-1 activation function domain 1

AR androgen receptor

BCL-2 B-cell lymphoma 2

BCR breakpoint cluster region protein

bp base pair

BRCA1/2 breast cancer 1/2 early onset

CAR chimeric antigen receptor

CML chronic myelogenous leukemia

CNV copy number variation

CP ceruloplasmin

CPM counts per million

CTG CellTiter-Glo

CTSD cathepsin D

DBD DNA-binding domain

DSRT drug sensitivity and resistance testing

DSS drug sensitivity score

ECM extracellular matrix

EGF epidermal growth factor

EGFR epidermal growth factor receptor

EMT epithelial-to-mesenchymal transition

ER estrogen receptor

ERα/ESR1 estrogen receptor α

ERβ/ESR2 estrogen receptor β

ERE estrogen response element

ETS E26 transformation-specific

EV extracellular vesicle

ABBREVIATIONS ix

FDA food and drug administration

GS-X glutathione S-conjugate export

HER2 human epidermal growth factor receptor 2

HGF hepatocyte growth factor

HIF-1 hypoxia-inducible factor 1

ICI Imperial Clinical Industries

IGF insulin growth-factor

LBD/AF-2 ligand-binding domain/activation function domain 2

LLOMe L-leucyl-L-leucine methyl ester

LMP lysosomal membrane permeabilization

lncRNA long non-coding RNA

MCL-1 Induced myeloid leukemia cell differentiation protein

MET Met proto-oncogene tyrosine kinase

miRNA microRNA

MRP1 multidrug resistance-associated protein 1

NCoR nuclear receptor co-repressor

NF-κBB nuclear factor kappa-light-chain-enhancer of activated B cells

NSCLC non-small-cell lung cancer

PI3K phosphatidylinositol-3-kinase

PKC protein kinase C

PR progesterone receptor

ROS reactive oxygen species

SERM selective estrogen receptor modulator

SOD1 superoxide dismutase 1

SRC steroid receptor co-activator

TGF-β Transforming growth factor beta

TMPRSS2 transmembrane protease, serine 2

VEGF vascular endothelial growth factor

1 INTRODUCTION

INTRODUCTIONImmediately, with the discovery of usefulness of cytotoxic agents in

cancer therapy, the phenomenon of cancer drug resistance was ob-

served in the 1940s (Goodman et al. 1946). To study drug resistance,

researchers applied lessons learned from the selection process of an-

tibiotic-resistant bacteria and cultivated patient-derived cell lines ex-

posed to the drug of interest and developed drug-resistant cancer

cell lines (McDermott et al. 2014). Samples were also taken from

drug-resistant tumors and investigated in animal models, for ex-

ample, as xenografts (Houghton, Houghton, and Green 1982;

Bertotti et al. 2011; S. Li et al. 2013). Results from cell line or animal

studies were not always easily translatable to the clinic. Hence, even

after 70 years of research, drug resistance is still a problem not only

in the case of chemotherapeutics but also of targeted therapy. Even

though cancer is a genetic disease, it became obvious that this might

not always be the case in terms of drug resistance. Drug resistance

can arise from genetic, epigenetic, as well as environmental factors

and those factors can act simultaneously. Another contributor to

drug resistance is the molecular diversity of cancer in which breast

cancer, with its multiple subtypes, is a poster child. In breast cancer,

commercial diagnostic tests, such as Oncotype DX (Carlson 2006),

MammaPrint (Glas et al. 2006), or EndoPredict (Filipits et al. 2011)

may provide prognostic information but also guide therapy. Given

that we can now detect circulating tumor cells as well as circulating

free DNA using so-called liquid biopsies from the blood (Friedlander,

Premasekharan, and Paris 2014), it would be very beneficial to iden-

tify drug resistance as soon as it occurs. Maybe even by imple-

menting such a test. However, we still need to learn more about drug

resistance mechanisms and vulnerabilities associated with these to

be able to treat drug-resistant cancers.

REVIEW OF LITERATURE 2

REVIEW OF LITERATURE

1 The BreastThe breast, or mammary gland, is not fully developed at birth. In its

post-natal development, it undergoes a course of transformations

such as cell proliferation, differentiation, and morphogenesis. Var-

ious hormones and growth factors regulate these transformations.

During puberty estrogens, together with progesterone, are respon-

sible for the expansion of the gland and the development of the

ductal epithelium, which give rise to its glandular tissue structure

(Brisken et al. 1998; Mallepell et al. 2006). The fully developed adult

mammary gland is composed of different cell types. The inner lumen

of the ductal and lobular structures are lined with a continuous layer

of epithelial cells that are surrounded by a layer of contractile my-

oepithelial cells. The myoepithelial cells are further enclosed by a

basement membrane which separates the epithelial from the stroma.

The stroma consists of adipocytes, lymphocytes, neurons, fibroblasts,

and endothelial cells (Anderson, Clarke, and Howell 2000). The hor-

mone-sensing cells such as estrogen- and progesterone receptor pos-

itive cells are located within the luminal layer ranging from 7-20% of

cells. Interestingly, those ER-positive cells do not divide in normal

tissue but are in close proximity to them (Clarke et al. 1997).

Overall, the female mammary gland experiences a lot of structural

and functional changes that are characterized by proliferation, dif-

ferentiation, and apoptosis of cells due to hormonal changes during

the menstrual cycle, pregnancy, lactation, and menopause (Macias

and Hinck 2012).

3 REVIEW OF LITERATURE

2 Estrogen Receptor – it's complicatedThe activities of estrogens are not only restricted to the development

and maintenance of sexual and reproductive functions, they are addi-

tionally involved in biological processes of the musculoskeletal, im-

mune, cardiovascular and central nervous system in men and women

(Gustafsson 2003). There are three types of estrogens: estrone (E1),

estriol (E3) and the predominant 17β-estradiol (E2), which binds to

the nuclear transcription factor receptors ERα and ERβ. The recep-

tors are encoded by ESR1 and ESR2 genes and have three functional

domains. The hormone-independent transcriptional activation func-

tion domain (AF-1) at the N-terminus harbors the biggest functional

differences between the two receptors. The DNA-binding domain

(DBD) shares about 55% homology and the ligand-binding domain

(LBD/AF-2) at the C-terminus is 95% homologous between the two

receptors (Kuiper et al. 1996). Nevertheless, primarily the activation

of ERα target genes is responsible for promoting cancer cell prolifer-

ation and a decrease in cancer cell apoptosis (Iwao et al. 2000;

Roger et al. 2001). There are several paths by which estrogens and

ER convey signaling (Figure 1). The ligand-dependent arm, as the

name implies, involves the binding of estrogen to the LBD/AF-2 of

ER, forming an estrogen-ER complex, which promotes different gene

activation scenarios. The complex can directly bind to estrogen re-

sponse elements (EREs) in the promoter region of a target gene

(Figure 1A). The ERE is defined by two inverted DNA-sequences sep-

arated by three random nucleotides (GGTCAnnnTGACC) (Klinge

2001). However, it is now clear that the complex also binds to en-

hancer regions in the genome (Carroll et al. 2006).


Alternatively, the estrogen-ER complex can interact with other tran-

scription factors such as activator protein 1 (AP1), specificity protein

1 (SP1), NF-κBB, or cAMP response element-binding protein (CREB)

to name just a few amongst many others (Galien and Garcia 1997;

Kushner et al. 2000; Lazennec, Thomas, and Katzenellenbogen 2001;

W. Porter et al. 1997). This co-activation facilitates the transcrip-

tional regulation of genes that do not harbor EREs (Figure 1B). In

order to fully facilitate transcriptional regulation the estrogen-ER

complex needs co-regulators that can modify the chromatin structure

or interact with the transcriptional machinery. Plenty of those co-reg-

Figure 1: Simplistic overview of ER signaling pathways. Ligand-dependent arm includes A the classical (direct) pathway, B the non-classical (tethered) pathway, and C the non-genomic pathway. The ligend-independent pathway is depicted in D. Grey ovals depict second messenger cascades; E2, Estradiol; ER, Estrogen receptor, ERE, Estrogen response element, TF, transcription factor; RE, Response elements to other transcription factors; P, phosphorylation; GF, Growth factor; RTK, tyrosine kinase receptor (modified from Le Romancer et al. 2011)

ERETF

Co-reg .

ER

E2

?

RE RE

P P

PP

GF

ERE

RTK

A B C D


ulators have been identified (Métivier et al. 2003). The most in-

tensely studied co-regulators belong to the p160 family proteins (or

SRCs). They can either directly or indirectly recruit co-activators and

so remodel chromatin and modify histone activity (Koh et al. 2001),

for example, by recruiting histone acetyltransferase (HAT), which is

known to play a role in the initiation of transcription (Brownell and

Allis 1996; Kraus and Kadonaga 1998). Cathepsin D (CTSD) and tre-

foil factor 1 (TFF-1, also known as pS2) have been among the first

identified target genes of the estrogen-ER and the p160 co-regula-

tors complex in vivo (Shang et al. 2000). As if this isn't already com-

plex enough, the estrogen-ER complex has been shown to activate

multiple signaling cascades through direct interactions with various

proteins including tyrosine kinase Src, phosphatidylinositol 3-kinase

(PI3K), and insulin-like growth factor 1 receptor (IGF-1R) (Castoria

et al. 2001; Kahlert et al. 2000; Simoncini et al. 2000). This so-called

non-genomic mode of action leads to an immediate response by the

estrogen-ER complex (Figure 1C).

Finally, there is a ligand-independent activation of ER signaling with

various effects on cellular processes (Cenni and Picard 1999). The

best-studied signals inducing ER activities are growth factors, for ex-

ample, EGF and even IGF-1, which induce signaling cascades that

lead to the phosphorylation and thereby activation of ER in an es-

trogen-independent manner (Kato et al. 1995, Figure 1D).

The different mechanisms of action demonstrate the complex nature

of ER signaling. However, it needs to be stated that most of the func-

tional studies have been conducted in cancer cell lines and little is

known about the interactions of ER in healthy human mammary

tissue.


3 Breast CancerDespite losing its first place in cancer mortality among young Eu-

ropean woman to lung cancer, breast cancer is still with around

460’000 cases, the most often diagnosed cancer among European

woman, and remains a major public health concern (Malvezzi et

al. 2015; Ervik et al. 2016). Fortunately, breast cancer is often

treatable if it represents one of the subtypes that have treatment

options available. If breast cancer is diagnosed late or belongs to

a subtype with limited treatment possibilities it is still deadly. An-

other major drawback is the development of therapy resistance,

which is followed by tumor progression and development of

metastasis.

3.1 Breast Cancer SubtypesBreast cancer is extremely heterogeneous. According to the

World Health Organization, the invasive ductal carcinoma, not

otherwise specified (ductal NOS) represents 40-75% of breast

cancer cases. These cases, as the NOS addition suggests, repre-

sent a heterogeneous group that does not exhibit sufficient histo-

logical properties in comparison to the invasive lobular carcinoma

(ILC, 5-15%) or tubular carcinoma (<2%) (Lakhani et al. 2012).

Breast cancer is routinely clinically characterized by tumor type,

size, grade, lymph node status as well as four immunohistochem-

istry markers ER, PR, Ki67, and HER2. Alternatively, the more

sensitive fluorescent in situ hybridization (FISH) is used to detect

HER2. However, a combination of IHC and FISH is recommended

(Lakhani et al. 2012; Wolff et al. 2018). Intensive molecular pro-

filing has shed light on this cancer's biology, which can also guide

clinical decisions.


More than 15 years ago, Perou, Sørlie, and colleagues divided

breast cancer into five different subtypes based on gene expres-

sion profiles (Perou et al. 2000; Sørlie et al. 2001). The most domi-

nant subtypes are the luminal A and luminal B types and as their

name suggests, they mainly express genes of the lumen of the

mammary duct (Table 1). Both subtypes are ER-positive. Addition-

ally, they can be either positive or negative for the progesterone

receptor (PR). These subgroups are usually responsive to en-

docrine- and chemotherapy (Early Breast Cancer Trialists’ Collab-

orative Group (EBCTCG) 2005). The luminal B type is also HER2-

positive and has high levels of Ki67 compared to luminal A and

has, therefore, a higher proliferation rate. Importantly, the HER2-

positivity makes it responsive to therapeutic approaches against

HER2, such as herceptin. Other subtypes do not express ER and

PR, including the HER2-positive subtype (Table 1), which is non-

responsive to endocrine therapy (Loibl and Gianni 2017). Finally,

the basal-like (or triple negative) subtype has a similar expression

profile to the myoepithelial cells. The treatment options are re-

stricted to surgery, radiation and/or chemotherapy, as this type

does not express ER, PR, and/or HER2 (Fulford et al. 2007; Holl-

iday and Speirs 2011). A controversial subtype is the normal

breast-like type. It has gene expression similar to normal breast

tissue and high expression of genes that reassemble the charac-

teristic for adipose tissue (Sørlie et al. 2001; Fan et al. 2006). How-

ever, the normal breast-like type has been regarded as an artifact

due to high contamination of “true” normal breast tissue in the

sample (Parker et al. 2009).


Table 1: Breast cancer subtypes (based on http://www.pathophys.org/ breast-cancer/ by Eric Wong)

Subtype Characteristics Prevalence Medical therapy

Luminal A ER PR HER2 Low Ki67

+++-

40 % Endocrine therapy

Luminal B ER PR HER2 high Ki67

++/-+/-

20 % Chemotherapy, Endocrine therapy, anti-HER2 targeted therapy

HER2-positive

ER PR HER2 high Ki67

--

++

10-15 % Chemotherapy with anti-HER2 targeted therapy

Triple negative

ER PR HER2 high Ki67

---

15-20 % Chemotherapy

This first classification (Perou et al. 2000; Sørlie et al. 2001) was

followed by several more suggestions. By additionally utilizing

copy number alterations, a study by Curtis et al. divided the ex-

isting classification in even 10 different subgroups (Curtis et al.

2012), whereas others suggested an extra claudin-low group

(Herschkowitz et al. 2007), or identified six TNBC subgroups of

which one is androgen receptor-positive (Lehmann et al. 2011).

For patients with the latter subtype there are currently several

clinical trials with anti-androgen-therapy ongoing (clinicaltrial-

s.gov: NCT01889238; NCT02689427; NCT03383679).

The examples of the different subgroups (Table 1) show that even

within the subtypes tumor-specific dissimilarities occur, which

likely contribute to tumor development and progression. It also

http://www.pathophys.org/breast-cancer/

http://www.pathophys.org/


highlights that a “one-size-fits-all” therapy is not applicable in

breast cancer and a more personalized treatment is needed.

4 Fusion genesAs mentioned above, breast cancer is a heterogeneous disease

with frequent genetic alterations. These alterations can activate

cancer-promoting genes, so-called oncogenes, and inactivate

tumor suppressor genes thereby shifting the balance towards

cancer progression. Germ-line mutations, for example in

BRCA1/2, TP53 or CHEK2, account for about 25% of breast can-

cers (Olopade et al. 2008). More than 30 genes such as somatic

mutations in PIK3CA, GATA3, and TP53, as well as gene amplifi-

cations like the HER2 amplification, have been recurrently identi-

fied and are responsible for driving growth in a subset of breast

cancers (Cancer Genome Atlas Network 2012; Desmedt, Yates,

and Kulka 2016).

However, a potentially relevant but still overlooked class of

cancer mutations are fusion genes. A fusion gene is a chimeric

DNA molecule where either by chromosomal translocation, inver-

sion, deletion, insertion, or tandem duplication a part of a gene

shifts to a new genomic locus (Figure 2).

Figure 2: Main fusion gene types. A chromosomal translocation, B inversion, C deletion and D duplication (modified from Guy Leonard, 2012, Wikimedia Commons)

A B C D


This event might just inactivate a gene but can also result in a fu-

sion protein, which can drive neoplastic cell growth and cancer

progression (Edwards 2010). Mechanisms such as the proximity

between different chromosomes during the interphase or incor-

rect recombination due to shared sequence motifs at the chromo-

somal breakpoints have been suggested to give rise to fusion

genes (Mitelman, Johansson, and Mertens 2007).

The fusion of BCR and ABL1, for example, leads to an abnormal

tyrosine kinase in 90% of chronic myelogenous leukemia (CML)

cases. This fusion is formed by a reciprocal translocation of chro-

mosome 9 and 22 (Rowley 1973). The subsequent fusion protein,

encoded by the smaller so-called Philadelphia chromosome, is

based on its molecular weight associated with different leukemia

types. The 210kDa protein (p210BCR-ABL1) is detectable in CML, the

185/190kDa protein (p190BCR-ABL1) is linked to B-cell acute lym-

phoblastic leukemia (B-ALL) and the heavier 230kDA (p230BCR-

ABL1) has been found in a subtype of CML, which was once called

chronic neutrophilic leukemia (Clark et al. 1987; Hermans et al.

1987; Wada et al. 1995). The Philadelphia chromosome was al-

ready discovered in 1960 by Peter Nowell and David Hungerford

(Nowell and Hungerfood 1960) and several years later imatinib, a

drug that inhibits the fusion protein's enzyme activity, was devel-

oped (Thiesing et al. 2000).

Several decades after the discovery of the Philadelphia chromo-

some, TMPRSS2-ETS was the first recurrent fusion gene discov-

ered in solid tumors, namely prostate cancer (Tomlins et al. 2005).

It has been suggested that TMPRSS2-ETS can function as an ad-

ditional diagnostic marker, which is even detectable from urine

samples (Cornu et al. 2013; Rostad et al. 2009). Furthermore, in a


subgroup of non-small cell lung carcinoma patients, the EML4-

ALK fusion was detected of which the protein domains of the ALK

fusion partner can be targeted with crizotinib (Shaw et al. 2011).

In breast cancer, the ETV6–NTRK3 fusion, for example, has been

identified as the cause for about 90% of secretory breast cancers.

This fusion facilitates a ligand-independent activation of a sig-

naling cascade and thereby transforming mammary epithelial

cells into tumor-forming cells (Tognon et al. 2002). Larotrectinib,

a drug targeting NTRK1/2/3, is available and clinical trials with

patients harboring NTRK-fusions are ongoing and showing

promising results (Drilon et al. 2018).

As these few examples show, fusion genes have the potential to be

used as therapeutic targets or diagnostic tools as they are highly

cancer-specific. Additionally, they can be utilized as an indicators

of drug treatment success or failure as they are detectable in

urine (Cornu et al. 2013; Rostad et al. 2009) and blood (R. J.

Leary et al. 2010; McBride et al. 2010).

5 TamoxifenTamoxifen belongs to the drug class of selective estrogen receptor

modulators (SERMs). Due to its anti-estrogen behavior in breast

tissue, high efficiency and low price it is the preferred treatment of

ER-positive breast cancer in pre- and postmenopausal woman

(Jordan 2008; Senkus et al. 2015).

5.1 A summary of tamoxifen's historyTamoxifen failed to be a contraceptive, but got repurposed as an

anti-cancer drug and became helpful for millions of women with

breast cancer (Jordan 2003). In 1967 the non-steroidal compound


ICI46,474, the drug which became tamoxifen was synthesized by

Dora Richards and reported by Arthur Walpole and Michael Harper

from ICI Pharmaceuticals Division (today AstraZeneca). This team of

reproductive endocrinologists discovered that the drug was an es-

trogen agonist in mouse, but functions in the immature rat as an ag-

onist/antagonist (Harper and Walpole 1967). Further clinical studies

showed that tamoxifen could be used as a treatment for advanced

breast cancer because it caused fewer side effects and was as effec-

tive as contemporary treatments when given for 1 year (Cole, Jones,

and Todd 1971; Ward 1973). As the focus of the company was not

cancer research, it took Walpole a bit of persuasion to market the

drug as an advanced breast cancer treatment in the UK. In addition,

there were patent issues in the US (Jordan 2003).

Further, a lot of interpersonal connections helped to advance the re-

search on tamoxifen, which showed that 4-hydroxytamoxifen, the ac-

tive metabolite of tamoxifen, binds with high affinity to the estrogen

receptor (Jordan et al. 1977). These findings and further research

into the duration of tamoxifen treatment (Baum et al. 1983; Jordan

and Allen 1980) led to the more direct use of tamoxifen in ER-posi-

tive breast cancer, making tamoxifen the first targeted breast cancer

therapy and one of the widest used anticancer drug (Jordan 2003).

5.2 Mode of ActionAs complex as the molecular mechanism of ER is, so is the mecha-

nism of tamoxifen. The active metabolite 4-hydroxytamoxifen binds

to the LBD/AF-2 domain of ER and changes the protein structure of

the receptor, thereby preventing the binding of certain co-regulators

such as p160 (Norris et al. 1998; Shiau et al. 1998). However, this

conformation change enables the binding of nuclear receptor co-re-

pressors (NCoRs), which inhibit the gene transcription (Huang,


Norris, and McDonnell 2002; Lavinsky et al. 1998). The ER-depen-

dent mode of action already takes place at low concentrations of ta-

moxifen (< 0,1µM) (Coezy, Borgna, and Rochefort 1982; Murphy and

Sutherland 1985). However, in patients' plasma tamoxifen concentra-

tions of 1 µM and even higher within the tumor have been measured

(MacCallum et al. 2000). Therefore, other mechanisms by which ta-

moxifen induces cell death have been suggested.

Tamoxifen has been shown to inhibit protein kinase C (PKC), an en-

zyme that modulates cell growth regulating signals, among others

(Horgan et al. 1986; Lavie et al. 1998; O’Brian, Housey, and Wein-

stein 1988). Tamoxifen also induces transforming growth factor-β

(TGF-β), which is a hormonally regulated growth inhibitor (Butta et

al. 1992; Knabbe et al. 1987). Other signaling mediators influenced

by tamoxifen include calmodulin (Gulino et al. 1986; O’Brian,

Housey, and Weinstein 1988), the mitogen insulin-like growth factor I

(IGF-I) and the proto-oncogene c-myc (Kang, Cortina, and Perry

1996). Also, other proteins involved in apoptosis such as Bcl-2, Bax

or different caspases are impacted by tamoxifen (Thiantanawat,

Long, and Brodie 2003; G. J. Zhang et al. 1999). Additionally, tamox-

ifen has been shown to bind to so-called microsomal antiestrogen

binding sites (AEBS), which are associated with cholesterol metabo-

lism (Kedjouar et al. 2004). This mechanism, together with the induc-

tion of oxidative stress by tamoxifen, has been shown to induce apop-

tosis as well (Bekele et al. 2016; de Medina et al. 2011).

It now becomes more and more clear that tamoxifen has an array of

ways to induce cell death in breast cancer patients, and could even

provide benefits for ER-negative tumors.

6 Drug resistance in cancerDespite a patient's overall ability to absorb and metabolize a drug


as well as the drug's accessibility to the tumor, there are factors

within a tumor that cause drug resistance (Pluen et al. 2001). Sci-

entists differentiate between de-novo/primary and acquired resis-

tance (Giaccone and Pinedo 1996). Primary resistance is defined

as follows: a tumor consists of cells with diverse genetic back-

grounds and some of those cells are resistant to the given therapy

to begin with. In contrast, in acquired resistance, all cells within a

tumor, despite their genetic and epigenetic diversity, are initially

responsive to the therapy, but during the treatment, some cells

undergo changes that lead to drug resistance (Figure 3).

6.1 Resistance mechanismsDrug resistance can occur for just handful of drugs, whose functions

are similar to each other or to a plethora of drugs with no functional

relation. In the second case, scientists talk about multidrug resis-

tance (Gottesman et al. 1994).

Figure 3: Comparison of de-novo and acquired resistance. Pills indicate the start of drug treatment and color drug resistant cells (modified from Anna Azvolinsky 2017)

De-novo resistance

Acquired resistance


6.1.1 Drug uptake, efflux, and metabolism

Some drugs need to enter cancer cells via transporters. Resistance

to those drugs can occur if the transporters are downregulated or

mutated resulting in structural changes (Gottesman 2002). For in-

stance, expression changes in the folate transporters lead to resis-

tance of folate analogs, such as methotrexate (Gorlick et al. 1997).

Also, the uptake of cytarabine (AraC), a nucleoside drug, is impaired

due to alterations in the nucleoside transporter (Galmarini et al.

2002; Gati et al. 1998).

Another way to confer drug resistance is to increase the drug efflux,

which is mainly facilitated by the membrane-embedded transporters

of the ABC (ATP-binding cassette) superfamily (C. J. Chen et al.

1990). The efflux mechanism is important to clean out toxins within a

healthy cell. However, an overexpression of ABC-transporters has

been identified in multiple cancers (Goldstein et al. 1989; Nooter et

al. 1995). The activity of the ABC-transporter ABCB1 or P-glycopro-

tein, encoded by the MDR1 gene, was the first one to be identified to

be responsible for multidrug resistance. When a drug binds to the

transporter it activates an ATP-binding domain and further hydrol-

ysis of ATP, which in turn changes the conformation of ABCB1 and

the drug gets secreted. This cycle is repeated after the transporter

restores its original state by hydrolyzing another ATP (Sauna and

Ambudkar 2001).

Some drugs are administered as inactive prodrugs and need to be

activated in order to induce cell death. However, cancer cells have

found a way to decrease this activation. For example, the gene en-

coding for thymidine phosphorylase, an enzyme which converts the

prodrug capecitabine into the active form of 5-fluorouracil (Miwa et

al. 1998), can be downregulated by DNA methylation leading to drug

resistance (Kosuri et al. 2010). Further, drugs can get inactivated if


they get conjugated to glutathione, a mechanism which has been

found to inactivate platinum-based drugs (Meijer et al. 1992). In ad-

dition, the ABC-transporter MRP-1, sometimes referred as GS-X

pump, has been found to clear out glutathione-conjugated drugs

(Ishikawa and Ali-Osman 1993). Recently it has been shown that in

pancreatic cancer, even bacteria play a role in drug resistance. Bac-

teria with a longer version of the cytidine deaminase (CDD) gene

were able to inactivate gemcitabine and the effect was reversed

when antibiotics were added (Geller et al. 2017).

6.1.2 Drug target alterations

Mutations and expression alterations also occur as means of drug re-

sistance in targeted therapy, where the drugs affect oncogenes that

drive tumor growth. These targeted therapies have the general ad-

vantage of causing fewer side effects due to their cancer specificity.

Nevertheless, drug resistance is a major issue with these therapies

as well. One of the best examples of an oncogene treated with tar-

geted therapy is the activated EGFR-receptor, which is inhibited by

gefitinib or erlotinib (Shepherd et al. 2005; Lynch et al. 2004). How-

ever, resistance frequently occurs within a year and in 50% of the

cases, it is due to an EGFR-T790M mutation (Bell et al. 2005).

Fusion genes, more specifically the proteins they encode, are also

highly cancer-specific and therefore offer good targets for cancer

therapy. The activities of the BCR-ABL1 fusion protein are suc-

cessfully blocked by imatinib (Thiesing et al. 2000) until a mis-

sense mutation in the kinase domain at position T315 occurs and

imatinib cannot bind anymore (Gorre et al. 2001).

The efficacy of targeted therapy can be lowered by an increase in

drug target expression, as more molecules must be inhibited by the

drug. A good example is the genomically amplified androgen re-


ceptor in about 30% of prostate cancers leading to resistance to-

wards bicalutamide, an androgen receptor antagonist (Palmberg et

al. 1997).

Alternative splicing is another possibility by which cancer cells can

evade their fate of therapy-induced cell death. This escape has been

shown for the newly FDA approved cancer immunotherapy with

chimeric antigen receptor (CAR) T-cells. In this therapy approach,

the patient's own T-cells get genetically modified so that they target

CD19 expressing leukemia cells (D. L. Porter et al. 2011). Sotillo et

al. discovered that, among other mutations, exon 2 which encodes

the binding domain for the CAR therapy was missing in the ex-

pressed CD19 gene thereby preventing binding of the anti-CD19

CAR T-cells (Sotillo et al. 2015).

Glycosylation is a form of post-translational protein modification

where glycans get attached to lipids and proteins. Glycosylation has

been linked to modulate the response of cancer cells to drug treat-

ments. Deglycosylation of HER2+ breast cancer cells improves the

response to chemotherapeutics and herceptin (Peiris et al. 2017)

and branching N-glycans on VEGF2-receptor are associated with

resistance towards anti-VEGF treatment (Croci et al. 2014). Even

though the link between glycosylation and cancer formation and

progression is well established (reviewed in Pinho and Reis 2015),

research about the interaction of glycosylation and drug re-

sponses is in its early stages.

6.1.3 Cell cycle alteration and DNA damage repair

Chemotherapy leads to cell death due to DNA damage induced

during the S-phase (DNA synthesis) of the cell cycle. If a cell is in

the resting- (G0) or growth-phase (G1), it is, therefore, resistant


to chemotherapy (Stewart et al. 2007). One of the cell cycle regu-

lating proteins is cyclin E, which associates with Cdk2 (cyclin-de-

pendent kinase 2) to regulate the transition into the S-phase,

where it gets degraded (Ohtsubo et al. 1995). If this mechanism is

out of balance, it can lead to reduced cell size, growth factor-in-

dependent proliferation, as well as lower cell growth rates (D. C.

Porter et al. 2001; Ohtsubo et al. 1995; Dulić et al. 1993). Addi-

tionally, a halt in the cell cycle gives cells time to repair therapy-

induced cell damage and therefore cancer cells can evade their

destiny of cell death. The DNA repair mechanisms nucleotide ex-

cision repair and homologous recombination reverse the effects of

DNA crosslinks of platinum-based drugs (Pennington et al. 2014;

Selvakumaran et al. 2003).

6.1.4 Avoiding apoptosis

The avoidance of apoptosis is a cancer hallmark (Hanahan and Wein-

berg 2000). Primary tumors present mechanisms that can lead to

failure of cancer therapy, especially if the drugs fail to induce other

forms of cell death, such as necroptosis (Krysko et al. 2017). For ex-

ample, it has been shown that an overexpression of the anti-apop-

totic protein Bcl-2 is present when resistance towards chemothera-

peutic drugs occurs (Ellis et al. 1998; Sartorius and Krammer 2002;

Miyashita and Reed 1993; Dole et al. 1994) and expression changes

in other anti- and pro-apoptotic family members were observed as

well (Kitada et al. 1998; G.-Q. Wang et al. 2001; Ni Chonghaile et al.

2011). However, whereas overexpression of MCL-1 can be connected

to poor patient survival after chemotherapy, there have been studies

that could not establish the link between overexpression of Bcl-2 and

poor treatment outcome (Kitada et al. 1998; Bosari et al. 1995). Nev-

ertheless, targeting mechanisms that lead to inhibition of apoptosis


are showing promising results. For example, preclinical data demon-

strate the effectiveness of navitoclax, a drug that promotes pro-apop-

totic and hinders anti-apoptotic signaling, when given as a single

agent or in combination with chemotherapy (van Delft et al. 2006;

Oltersdorf et al. 2005). However, overexpression of MCL-1 over-

writes the cytotoxicity of this drug (Konopleva et al. 2006). Therapy

that induces apoptosis by activating caspase-8 in combination with

chemotherapy has also shown promising results (Soria et al. 2010).

Even though not all changes in apoptotic signals are connected to

drug resistance, research shows that some are, meaning that

avoiding apoptosis is not only a cancer hallmark but also a hallmark

of drug resistance.

6.1.5 Resistance mediated by autophagy

Autophagy has an important role in intracellular homeostasis as it is

responsible for the recycling of unnecessary, damaged or aged cel-

lular components by lysosomal degradation. The removal of a cell's

damaged organelles, toxic proteins or oncogenic substrates reflects

the tumor suppressive character of autophagy, whereas intracellular

recycling provides food and energy for a cell and is tumor-promoting

(White 2012). Even though autophagy has been shown to promote

chemosensitization (Eum and Lee 2011; Zou et al. 2011) the latter

effect may facilitate drug resistance as it enables a tumor to respond

to environmental stress by increasing the breakdown of unnecessary

proteins and organelles. Targeting the p210BCR-ABL expressing

CML cells with imatinib can also induce autophagy. By inhibiting the

autophagous process with chloroquine an increase of imatinib-in-

duced cell death has been observed (Bellodi et al. 2009). Similar ef-

fects have been observed with the EGFR inhibitors gefitinib and er-

lotinib in human lung cancer cells (Han et al. 2011), in glioblastoma

multiforme where chloroquine was administered as an additional op-


tional adjuvant therapy (Briceño, Calderon, and Sotelo 2007), and in

colorectal cancer that was treated with the standard chemothera-

peutic 5-fluorouracil and 3-methyladenine, which is another au-

tophagy inhibitor (Jie Li et al. 2010; Sasaki et al. 2012).

6.1.6 Adaption of signaling

Cancer cells can also activate pro-survival signaling. For example,

the EGFR signaling cascade is activated to prevent chemothera-

peutic induced toxicity (Sumitomo et al. 2004; Van Schaeybroeck et

al. 2006, 2005). Even when EGFR is targeted cells can reprogram

their kinome and bypass the oncogene by activating HER3 (Sergina

et al. 2007; Wheeler et al. 2008). In addition, amplification of MET

has been observed to be the reason for drug resistance in 20% of

EGFR-driven lung cancers. MET bypasses the drug blocked EGFR by

inducing PI3K signaling via HER3 (Engelman et al. 2007).

6.1.7 Epithelial-mesenchymal transition (EMT)

During EMT, epithelial cells lose their polarization and cell-cell adhe-

sion and gain the more migratory phenotype of mesenchymal cells.

Several factors such as cytokines, chemokines, integrins, cadherins,

and metalloproteinases are involved in this phenotype change, which

has been undoubtedly linked to drug resistance (Singh and Set-

tleman 2010). To name a few examples, overexpression of Nanog, a

transcription factor linked to EMT, leads to cisplatin resistance in

ovarian cancer cells (Liu et al. 2016). An upregulation of TGF-β sig-

naling induces EMT in colon and triple-negative breast cancer cells,

which leads to doxorubicin resistance (W.-C. Chen et al. 2013; Jin-

peng Li et al. 2015). Also, EMT has been observed in patients with

non-small-cell lung carcinoma (NSCLC) treated with EGFR-inhibitors

(Uramoto et al. 2011; Sequist et al. 2011).


6.1.8 Tumor microenvironment

Not only can the tumor microenvironment contribute to tumor pro-

gression, invasion, and metastasis, it can also promote drug resis-

tance. The tumor microenvironment comprises the extracellular ma-

trix (ECM), blood and/or lymph vessels, cells associated to the im-

mune system as well as inflammation, and cancer-associated fibrob-

lasts (Bissell and Radisky 2001; Tlsty and Coussens 2006).

The heterodimeric integrin receptors are major connectors of cells to

the ECM. These interactions between integrins and ECM can render

different signaling pathways, such as the NF-κBB and PI3K pathways,

active (Danen 2005). High levels of β1-integrins have been associ-

ated with herceptin resistance in HER2-positive breast cancer (Les-

niak et al. 2009). In a cell line model of oral carcinoma, the adhe-

sion of β1-integrin even leads to cancer proliferation induced by

chemotherapeutic treatment with cisplatin (Eberle et al. 2011).

Furthermore, soluble factors released by the tumor itself or its

microenvironment have been shown to influence the outcome of

drug treatment. Interleukin-6 (IL-6), for example, confers mul-

tidrug resistance in breast cancer cells lines (Conze et al. 2001)

or cisplatin resistance in ovarian cancer (Cohen et al. 2013).

Other soluble factors such as fibroblast growth factor (FGF),

neuregulin (NRG1), hepatocyte growth factor (HGF) and epi-

dermal growth factor (EGF) to name just a few, have also been

identified to play a role in drug resistance (Wilson et al. 2012).

Some of these soluble factors are released by another important

component of the tumor microenvironment, the cancer-associated

fibroblasts (CAFs). HGF secretion by CAFs leads to resistance to-

wards RAF inhibition in BRAF-mutant melanoma (Straussman et

al. 2012). In addition, CAFs secrete platelet-derived growth factor C


and by this means induce resistance towards the inhibition of VEGF-

mediated angiogenesis (Crawford et al. 2009). Matrix metallopro-

teinase secretion leads to cetuximab resistance in head and neck

squamous cell carcinoma cell lines when co-cultured with CAFs (Jo-

hansson et al. 2012). Further, CAFs promote gemcitabine resistance

in pancreatic cancer by stimulating proliferation and survival path-

ways (Hwang et al. 2008).

The tumor vasculature is abnormal and defective compared to the

healthy tissue, leading to improper penetration of drugs, but also to

tissue areas that are nutrient-deprived and hypoxic (Tong et al.

2004). Hypoxic conditions induce HIF-1 transcriptions factor (con-

sisting of HIF-1α and HIF-1β, G. L. Wang et al. 1995), whose target

gene is MDR1 among others (see 5.1.1 Drug uptake, efflux, and me-

tabolism). Therefore, it does not come as a surprise that HIF-1α

overexpression has been observed in plenty of drug-resistant cancer

cell lines (Comerford et al. 2002; Nardinocchi et al. 2009; Zhu et al.

2005) as well as human colon and bladder cancer samples (Ding et

al. 2010; Sun et al. 2016). In addition, other resistance mechanisms

such as avoiding apoptosis (Chapter 5.1.4) and induction of au-

tophagy (Chapter 5.1.5) can involve HIF-1 (Rohwer and Cramer

2011).

6.1.9 Extracellular vesicles

Extracellular vesicles (EVs) facilitate the communication between

cells and can thereby influence the recipient cell's function. EVs can

simultaneously deliver all the different biomolecule categories

ranging from proteins to lipids, nucleic acids, and sugars (Yáñez-Mó

et al. 2015). Therefore, it is not surprising that EVs take part in the

communication between the tumor and its surrounding environment

(Milane et al. 2015) and are able to mediate drug resistance between


stromal and tumor cells. Examples such as the 5-fluorouracil resis-

tance in gastric cancer cells (Ji et al. 2015) or bortezomib resistance

in multiple myeloma cells (J. Wang et al. 2014) have been reported.

In addition, EVs are able to mediate the transfer of resistance from a

drug-resistant cell to a drug-sensitive cell. The best example is the

transfer of ABCB1 (P-glycoprotein) and even its protein-coding

mRNA MDR1 (see 5.1.1 Drug uptake, efflux, and metabolism) in

prostate (Corcoran et al. 2012), leukemia (Bebawy et al. 2009),

ovarian (F. Zhang et al. 2014), and breast cancer models (Pasquier et

al. 2012; Dong et al. 2014). Further, in leukemia, the MRP-1 trans-

porter (see 5.1.1 Drug uptake, efflux, and metabolism) has been

transferred from resistant to drug-sensitive cells (Lu et al. 2013).

Not only can EVs transfer drug efflux transporters, they can also se-

questrate drugs. The ABCG2 transporter, for example, localizes to

vesicles and the drug is then taken up into the EVs and released

from the cell (Ifergan, Scheffer, and Assaraf 2005).

Other molecules that are transferred by EVs and have been associ-

ated with promoting drug resistance and sensitivity are non-coding

RNAs such as miRNAs (19–25 nucleotides long) and lncRNAs (not

translated long RNA-transcripts). miRNAs from docetaxel-resistant

breast cancer cells target pathways related to drug therapy failure

and convey docetaxel resistance in a previous drug-sensitive cell line

(W.-X. Chen et al. 2014). The same has been observed with gemc-

itabine-resistant pancreatic cancer (Mikamori et al. 2017) or cis-

platin-resistant ovarian cancer (R. C. Pink et al. 2015) as well as lung

cancer cell lines (Wu et al. 2017). Even though drug resistance medi-

ated by long non-coding RNAs is a newly emerging research field,

several examples of how vesicles transfer lncRNAs exist. LincRNA-

ROR induces sorafenib or doxorubicin resistance and linc-VLDLR in-

duces the ABCG2 drug transporter (see above) in hepatocellular car-


cinoma cells (Takahashi, Yan, Kogure, et al. 2014; Takahashi, Yan,

Wood, et al. 2014).

It is obvious that drug resistance is a big complex research field as

there is not just one drug resistance mechanism. It is clear that

cancer cells can activate multiple mechanisms at the same time to

avoid drug-induced cancer cell death. Furthermore, one drug can be

inactivated by different resistance mechanisms as seen with cis-

platin, gemcitabine, imatinib, and 5-fluorouracil (see above). In addi-

tion, researchers continuously discover new mechanisms.

6.2 Resistance to TamoxifenEven 15 years post-treatment, tamoxifen has been shown to reduce

the death due to breast cancer by 30 %, compared to patients that

did not receive tamoxifen over a five-year period. However, the

cancer recurrence rate of 15 % five years after diagnosis of early-

stage ER-positive breast cancer has risen to 25 % after ten years

(Early Breast Cancer Trialists’ Collaborative Group (EBCTCG) 2005).

Almost all patients with metastatic breast cancer treated additionally

with tamoxifen eventually relapse. Various examples of tamoxifen re-

sistance have been proposed (Table 2), and more are to be discov-

ered.

Table 2: Possible mechanisms of tamoxifen resistance (modified from Tryfonidis et al. 2016)

Factors related to Examples

ER Loss of ER; ESR1 mutations; ER regulator modifications

Growth and survival Cell cycle regulation; deregulation of apoptosis; autophagy; cancer cell dormacy

Modification of signaling pathways

EGFR/HER2 signaling; AR signaling; PI3K/mTOR signaling


Factors related to Examples

Drug metabolism Decreased uptake; increased drug efflux/sequestration; metabolism of tamoxifen

Tumor microenvironment

CAFs; tumor stroma; extra cellular matrix; exosomes; immunomodulation

The most obvious mechanisms are related to the estrogen receptor,

tamoxifen's primary target. Mutations in ESR1 have been reported:

for example, a K303R gain-of-function mutation was detected in a

hinge region before the LB domain in hyperplastic lesions (Fuqua et

al. 2000). Cell line studies showed that if overexpressed and a

crosstalk with growth receptor pathways exists, this mutation can

lead to estrogen hypersensitivity and thus cells are less sensitive to

anti-estrogen treatment (Barone et al. 2010). However, several

studies failed to detect this mutation in primary and metastatic tu-

mors (Davies et al. 2005; Cancer Genome Atlas Network 2012; D. R.

Robinson et al. 2013; S. Li et al. 2013; Toy et al. 2013; Merenbakh-

Lamin et al. 2013; Jeselsohn et al. 2014). Less controversial is the

detection of mutations in the LBD of ESR1, which have been re-

ported in studies investigating metastatic ER-positive breast cancers.

The LBD ESR1 mutations were detected in 14%-54% of metastatic

ER+ tumor patients who received hormonal therapy including ta-

moxifen. The amino acid residues Y537 and D538 were most fre-

quently mutated. Further cell line studies showed that these muta-

tions lead to a ligand-independent activity as well as enhanced

ligand-stimulated activity, resulting in decreased tamoxifen sensi-

tivity (S. Li et al. 2013; D. R. Robinson et al. 2013; Toy et al. 2013;

Merenbakh-Lamin et al. 2013; Jeselsohn et al. 2014). In addition,

ESR1 amplification, as well as fusion genes, have been detected in

patients. However, their role in tamoxifen resistance is still unclear

(Jeselsohn et al. 2015).


Loss of ERα expression has been observed under tamoxifen treat-

ment (Johnston et al. 1995). Events such as CpG island methylation

and histone deacetylation (Parl 2003), hypoxia (Stoner et al. 2002),

EGFR and HER2 overexpression (Stoica et al. 2000; Creighton et al.

2006) as well as the interaction of p53 with ERα (Angeloni et al.

2004) have been proposed to be responsible for ERα downregula-

tion. However, roughly only 20% of patients with acquired tamoxifen

resistance lose ERα expression (Johnston et al. 1995; Kuukasjärvi et

al. 1996) indicating other means of tamoxifen resistance are at play.

Further, different expression levels of ERα co-regulatory proteins

have been linked to tamoxifen resistance. Well-studied examples in-

clude AIB1 (SRC3), SRC-1, NcoR and SMRT (Osborne et al. 2003;

Redmond et al. 2009; Girault et al. 2003; L. Zhang et al. 2013).

As mentioned above, administered tamoxifen is a prodrug that needs

to be metabolized in the liver to more potent metabolites, mainly en-

doxifen and 4-hydroxytamoxifen. One of the enzymes participating in

the conversion is cytochrome P450 2D6 (CYP2D6) (Klein et al. 2013).

Polymorphisms in this enzyme have been linked to a higher breast

cancer recurrence rate during tamoxifen only treatment (Province et

al. 2014). In addition, it has been observed that endoxifen is a sub-

strate of ABC-transporter ABCB1 (Teft, Mansell, and Kim 2011) and

a subsequent upregulation of the MDR1 gene could lead to tamox-

ifen resistance.

Probably the main mechanism by which tumor cells circumvent ta-

moxifen-induced death is the activation of alternative pathways and

factors. Especially HER2, EGFR, and IGFR play an important role,

but also the PI3K/AKT/mTOR signaling pathway, which is essential

for metabolism, cell survival and growth (García-Becerra et al.

2012). Results from the phase II TAMRAD trial or the phase III trial

BOLERO2 indicate that patients could benefit from additional


mTOR1 inhibition with everolimus. Nevertheless, these studies also

report a higher proportion of discontinuations due to adverse side

effects (Piccart et al. 2014; Treilleux et al. 2015). Further, the com-

plexity of the PI3K/AKT/mTOR pathway harbors the potential of de-

veloping drug resistance to the single inhibitors (Bihani et al. 2015;

Choi et al. 2016). As the targeting of this pathway is currently an ac-

tively studied field, time will tell whether this approach will lead to a

blockage of tumor progression.

As growth factors are known to induce EMT (Kalluri and Weinberg

2009), the upregulation of their receptors can be indirectly linked to

drug resistance. An upregulation of HER2 inhibits E-cadherin

leading to a reduced adherence of cells and then to EMT (D’souza

and Taylor-Papadimitriou 1994; Ingthorsson et al. 2016). Further,

other EMT promoting factors such as β-catenin, SLUG and SNAIL,

Notch and Wnt signaling have been associated with tamoxifen resis-

tance (Hiscox et al. 2006; Ye et al. 2010; Dhasarathy, Kajita, and

Wade 2007; Bui et al. 2017; Loh et al. 2013).

Immune cells within the tumor microenvironment have been known

to release growth factors that can lead to remodeling of ECM (see

above). Moreover, predictive gene-profiling of patients who were to

receive tamoxifen treatment linked an inflammation-related gene

cluster to therapy failure (Loi et al. 2008). In addition, cancer-associ-

ated fibroblasts, as well as their release of ILβ1, are associated with

tamoxifen resistance (Martinez-Outschoorn et al. 2011; Pontiggia et

al. 2012; Jiménez-Garduño et al. 2017).

Proteins involved in cell cycle regulation have also been detected to

impact tamoxifen resistance. Overexpression of ERα's direct target

cyclin D1, which promotes the progression through G1 to S phase,

has been linked to tamoxifen resistance in cell lines (Wilcken et al.

1997) and correlates with poor treatment outcome (Stendahl et al.


2004; Rudas et al. 2008). Also, cyclin E has been associated with ta-

moxifen resistance (Dhillon and Mudryj 2002; Hui et al. 2002).

The role of apoptosis avoidance in tamoxifen resistance is highly de-

batable, as changes in pro and antiapoptotic genes can be merely

due to activation of other resistance pathways (Butt, Sutherland, and

Musgrove 2007). However, increased expression of GRP78, a protein

that balances prodeath apoptosis and prosurvival autophagy, has

been reported to promote tamoxifen resistance (Cook et al. 2012).

Further, hydroxychloroquine, an autophagy inhibitor has been shown

to restore tamoxifen sensitivity (Cook et al. 2014).

In addition, it has been shown that miRNAs play a role in tamoxifen

resistance development and can function as a predictive marker (Xin

et al. 2009; Lyng et al. 2012; Huber-Keener et al. 2012). Interest-

ingly, miR-221/222, a well studied miRNA conveying tamoxifen resis-

tance (reviewed in Alamolhodaei et al. 2016), can be transferred via

exosomes to other cells and transform a tamoxifen sensitive cell to a

resistant one (Wei et al. 2014). The same phenomenon has been ob-

served for lncRNA UCA1 (Xu et al. 2016, 1).

As these examples show, it becomes more and more evident that a

cancer cell has a plethora of resistance mechanism towards tamox-

ifen at hand to prevent cell death. There is a need to define

biomarkers for each resistance mechanism and multiple therapeutic

options to combat them.

29 AIM OF THE STUDY

AIM OF THE STUDYSeveral therapies, such as surgery, radiation, chemo-, targeted, and

hormonal therapy, are used to treat breast cancer. Survival rates

have been improving as a result of therapies and earlier diagnoses.

However, cancer can develop drug resistances often leading to

therapy failure. Development of drug resistance and tumor progres-

sion are believed to be caused by a variety of mechanisms. The het-

erogeneity of resistance mechanisms most likely reflects the molec-

ular subtypes and intrinsic molecular properties of individual breast

cancers.

Therefore, the aims of my PhD work was to utilize breast cancer cell

line models to explore molecular pathogeneses and resistance mech-

anisms of breast cancer:

• Identify fusion genes in breast cancer, which could potentially

open new therapeutic approaches.

• Establish the molecular basis of tamoxifen resistance in breast

cancer by integrating exome-sequencing and drug profiling

using newly generated isogenic drug-resistant variants of

ER-positive breast cancers.

• Characterize the transcriptome of the tamoxifen-resistant cell

lines and define pathways as well as biomarkers of drug resis-

tance and identify ways to overcome resistance mechanisms.

MATERIALS AND METHODS 30

MATERIALS AND METHODSIn this chapter, materials and methods used in this thesis are briefly

portrayed. A detailed description can be found in the original publi-

cations, referred to by Roman numbers (I, II and III).

1 Cell culture and generation of tamoxifen-resistant cell lines (I, II, III)

BT-474 (HTB-20), MCF-7 (HTB-22), T-47D (HTB-133) and ZR-75-1

(CRL-1500) cells were obtained from American Type Culture Collec-

tion (Table 3). KPL-4 was a gift from Dr. Junichi Kurebayashi, D

(Tabuchi et al. 2009).

Table 3: Cell lines from human mammary gland

Cell line Description Used in

BT-474 Ductal carcinoma I, II, III

MCF-7 Adenocarcinoma derived by pleural effusion I, II, III

KPL-4 Inflammatory breast carcinoma derived by pleural effusion

I

T-47D Ductal carcinoma derived by pleural effusion II, III

ZR-75-1 Ductal carcinoma derived from ascites II, III

BT-474, MCF-7 and KPL-4 cells were grown in DMEM (PAN Biotech/

Euro Clone) supplemented with 10 % FCS (Gibco), 1 % penicillin/

streptomycin (Gibco) and 0,1 % bovine insulin (Sigma) except KPL-4.

ZR-75-1 and T-47D were grown in RPMI-1640 (PAN Biotech) supple-

mented with 10 % FCS (Gibco, Life Technologies, Carlsbad, CA), 1 %

penicillin/streptomycin (Gibco) and 0,1 % bovine insulin (Sigma) for

T-47D cell line.

Due to continuous exposure to 1 µM 4-OH-tamoxifen (Sigma) over a

time frame of 8-12 months seven tamoxifen-resistant cell lines (MCF-

31 MATERIALS AND METHODS

7 Tam1, T-47D Tam1 & Tam2, ZR-75-1 Tam1 & Tam2, BT-474 Tam1 &

Tam2) were derived from the parental cell lines (Figure 4). The cul-

ture media needed to be replaced every 2–3 days. Cells were pas-

saged when about 80 % confluent and incubated at 37 °C with 5 %

CO2. As the cell lines were grown in the presence of estrogen (FCS

and phenol), our studies mimic tamoxifen resistance in pre-

menopausal woman.

2 Characterization of tamoxifen-resistant cell lines (II, III)

2.1 Growth analysis (II)To measure the viability of the tamoxifen-resistant cells in study II,

CellTiter-Glo Cell Viability Assay (Promega) was performed with in-

creasing tamoxifen concentrations (0–1,8 μM). After 120M). After 120 h of incuba-

tion luminescence was measured with the PHERAstar plate reader

(Agilent Technologies). The active DNA synthesis and cell prolifera-

tion were measured with the Click-iT® EdU Alexa Fluor® 488 Flow

Figure 4: Schematic overview of tamoxifen-resistant cell lines. Tamoxifen resistant cell lines (color) were generated from parental cell line (no color) by exposure to 1µM 4-OH-tamoxifen (pills) for 8-12 months.

MCF-7

MCF-7 Tam1

T-47D

T-47D Tam1

ZR-75-1

ZR-75-1 Tam1

BT-474

BT-474 Tam1

T-47D Tam2

ZR-75-1 Tam2

BT-474 Tam2


Cytometry Assay Kit (Life Technologies). The EdU Alexa Fluor® 488

incubation time was 4 h for T-47D and MCF-7 and 28 h for the BT-

474 and ZR-75-1 cells. The permeabilization of the ZR-75-1 cells had

to be adjusted to 0,1 % TritonX-100-PBS. After RibonucleaseA treat-

ment, the DNA content was detected using the FxCycle™ Far Red

(Life Technologies). The stained cells in suspension were analyzed

using Accuri C6 flow cytometer and its software (BD Biosciences).

2.2 Responsiveness to estrogens and downstream signaling (II)

The response to estrogen was measured by depriving all the cell

lines of estrogen. Therefore, phenol red-free medium (PAN Biotech)

was supplemented with 2,5 % dextran–charcoal-treated FCS (Sigma-

Aldrich). After 72 h, 17β-estradiol (Sigma, 10-8 M in ethanol) was

added for 4, 8 or 24 h. RNA was isolated and converted it into cDNA.

Quantitative-PCR with the DyNAmo ColorFlash SYBRGreen PCR kit

(Thermo Scientific) on the LightCycler 480 system (Roche) was per-

formed, to detect changes in pS2, PGR, and GREB1, the direct target

genes of ERα. Primer sequences can be found in Additional file 1 of

the original publication (Kangaspeska et al. 2016).

2.3 Measurements and drug induced changes in protein levels (II, III)

Changes in ERα in all the cell lines (Study II) and cathepsin D in the

T-47D cells (Study III) were investigated by Western blotting as de-

scribed earlier (Östling et al. 2011). Β-actin/-tubulin were used as

reference proteins. The effects of VX-11E (ERK inhibitor) and

selumetinib (MEK inhibitor) on several proteins were assessed with

increasing concentrations of VX-11E (50 nM, 100 nM, and 250 nM)

and with 100 nM VX-11E combined with 1 μM). After 120M selumetinib. Informa-

tion about all antibodies used for Western blotting is in Table 4.


Table 4: Primary antibodies used for Western blotting (WB) and immunofluorescence (IF)

Antibody Product number and vendor

Application Study

ERα ab16660, Abcam WB II

EGFR CST4267, Cell Signaling Technologies (CST)

WB II

pEGFR CST3777, CST WB II

ERK1/2 CST9207, CST WB II

pERK1/2 CST4370, CST WB II

Cathepsin D 610800, BD Transduction Laboratories

WB III

β-actin A1978, Sigma WB II, III

β-tubulin ab6046, Abcam WB III

Ceruloplasmin ab110449, Abcam IF III

Lamp 1 H4A3, Developmental studies hybridoma bank (DSHB)

IF III

Lamp 2 H4B4, DSHB IF III

Galectin-3 556904, BD Biosciences IF III

Filipin F9765, Sigma IF III

2.4 Immunofluorescence staining (III)The parental and resistant T-47D cells were seeded on coverslips

with and without 1 µM 4-OH tamoxifen. Cells were fixed and

stained either directly with filipin for free cholesterol,

LipidTOXGreen neutral lipid stain, or primary antibodies (Table

4). Nuclei were detected with DRAQ5 (Biostatus) or Hoechst.

Images were acquired with a Nikon 90i microscope (Nikon) using

the 40x objective and processed with ImageJ (Schneider,

Rasband, and Eliceiri 2012) and the Adobe Photoshop software.


2.5 Lysosomal membrane permeabilization (LMP) assay (III)

Galectin-3 translocation in the parental and resistant T-47D cells

was measured to investigate the lysosomal membrane integrity as

described earlier (Aits, Jäättelä, and Nylandsted 2015). The cells

were grown for 72 h +/- 1 µM 4-OH-tamoxifen and then incubated

with 1 mM LLMOe to induce LMP. Cells were fixed and stained

with galectin-3, ceruloplasmin as cell segmentation marker (Table

4) and Hoechst (405nm) to detect the nuclei. Confocal images

were taken with the PE Opera Phenix HCS system (PerkinElmer)

with the 40x water immersion objective (NA 1.1). Images were

analyzed with the Columbus Image Data Storage and Analysis

System (PerkinElmer). LMP was assessed by calculating the av-

erage spots per cell and the percentage of galectin-3 positive

cells within an image.

2.6 Measurement of triglycerides and cholesterol esters (III)

Triglycerides and cholesterol esters were extracted from the

parental and resistant T-47D cells and measured by thin layer

chromatography (TLC) as previously described (Bligh and Dyer

1959). The lipid bands were quantified using ImageJ (Schneider,

Rasband, and Eliceiri 2012).

3 Paired-end RNA-sequencing (I, III)For study I, data from our previously produced paired-end RNA li-

braries of the BT-474, MCF-7, and KPL-4 cell lines were used (Ed-

gren et al. 2011).

For study III, after RNA isolation with miRNeasy kit (Qiagen),


RNA quality control with the Agilent Bioanalyzer using the

RNApico chip (Agilent) and determination of RNA amount with

Qubit RNA-kit (Life Technologies), a strand-specific paired-end

RNA-sequencing library was prepared with ScriptSeq™ Complete

kit for human/mouse/rat (Illumina). The mean fragment size was

ca 300-400 nucleotides, as fragments smaller than 200 bp were

removed by SPRI beads (Agencourt AMPure XP). The paired-end

library was sequenced with the Illumina HiSeq 2000 (Illumina) in-

strument. A detailed description can be found in Kumar et al.

(Kumar et al. 2017).

4 RNA-sequencing analysis (I, III)

4.1 Fusion gene detection, characterization, and validation (I, III)

Fusion genes were detected in study I and III with the

FusionCatcher algorithm using raw, unprocessed read files

(Edgren et al. 2011; Nicorici et al. 2014). Ensembl version 61

(Study I) and Ensembl version 80 (Study III) were used for se-

quence alignment to the human genome. A database containing

exon-exon fusion points and the junction sequences, as well as infor-

mation about both fusion partners, was generated. A fusion partner

was allowed to have multiple partner genes.

Fusion genes were further characterized and validated in study I.

Protein domains were annotated using SMART (Schultz et al. 1998;

Letunic, Doerks, and Bork 2012) and the 1M oligo Agilent aCGH data

(Edgren et al. 2011) was used to determine copy number changes

and gene coverage. Fusion genes were validated by Sanger-se-

quencing. Shortly, isolated RNA was reverse transcribed into cDNA.

With fusion gene-specific primers, the region of interest was ampli-


fied and the gel-purified PCR-product was cloned into pCR2.1-TOPO

vector (Invitrogen). The vector was transferred into bacteria and

colony PCR was used to confirm a successful transfection. After

plasmid purification, the product was Sanger-sequenced and the ob-

tained sequences were compared with the predicted fusion se-

quence, using the multiple sequence alignment tool (Corpet 1988).

The chromosomal breakpoints of the fusion genes were determined

by Sanger-sequencing of the genomic DNA of the BT-474 cell line.

Primers for amplification of the region around the fusion breakpoint

were designed based on already existing exome-sequencing data.

The PCR product was directly Sanger-sequenced and the exact chro-

mosomal breakpoints were located using BLAT (Kent 2002) with the

“Near-exact matches” option. The primers of this study can be found

in Table S4 of the original publication (Kangaspeska et al. 2012).

4.2 Integration of transcriptomic data and differential gene expression (III)

Public patient transcriptome data (McBryan et al. 2015) was

downloaded from GEO database (Barrett et al. 2013). RNA-se-

quencing data analysis, which contained read pre-processing,

read alignment to the human reference genome (Ensembl

GRCh38) guided by the Ensembl reference gene models and fea-

tures (Ensembl v80), and count estimation are described in detail

in Kumar et al. (Kumar et al. 2017). The estimated expression

counts of cell line and patient data were combined, normalized

using the trimmed mean of M-values (TMM) method (M. D.

Robinson and Oshlack 2010), converted to counts per million

(CPM) estimates using edgeR (M. D. Robinson, McCarthy, and

Smyth 2010), corrected for the batch effect associated with the

study origin using limma (Smyth and Speed 2003), and assigned


to Ensembl information using biomaRt (Smedley et al. 2015) with

default settings. Lists of differentially expressed genes, defined as

log2 ratio of >= |1| and CPM difference of >= |10| when com-

paring parental versus resistant cell line was generated and up-

loaded to Enricher (E. Y. Chen et al. 2013; Kuleshov et al. 2016)

to determine pathways involved in tamoxifen resistance develop-

ment. The data was visualized using principle component anal-

ysis, Venn diagrams as well as heat maps.

5 Genomic profiling by exome-sequencing (II)

We utilized exome-sequencing to detect point mutations or copy

number changes that arose during the development of tamoxifen re-

sistance by comparing parental with the isogenic resistant cell line.

The exomes were captured from genomic DNA with NimbleGen Se-

qCap EZ Human Exome v2.0 kit (Roche NimbleGen) and paired-end

sequenced on the Illumina HiSeq platform. The detection of the point

mutations has been previously described (Pemovska et al. 2013). The

parental cell lines were used as controls and Ensembl v66 was used

as annotation database. We excluded known false positive point mu-

tations and considered mutations with p < 0,05 and resistant variant

frequency >30 % with high confidence as present. Copy number

variants (CNVs) were detected with a variant calling pipeline (Su-

lonen et al. 2011). Copy number values were calculated as RPKM

(reads per kilobase length per million mapped reads) for exome

target regions and the log2 copy number ratios determined by using

the parental cell line as a reference. The values were then assigned

to the human genome based on Ensembl database v67 using the ex-

treme method option in GISTIC2 (Mermel et al. 2011). Thresholds to

identify CNVs were set at -0,4 (heterozygous deletion), -1,2 (homozy-


gous deletion), +0,5 (gain) and +1,3 (amplification).

6 Drug sensitivity and resistance testing (DSRT) (II, III)

6.1 Cell viability measurement (II, III)The DSRT platform has been described in detail before (Pemovska et

al. 2013). For study II we used the FIMM FO2Baq library, which con-

tains 279 investigational and approved cancer drugs, on all the

parental and resistant cell lines. For study III we used 33 drugs tar-

geting the lipid and cholesterol metabolism, the lysosomal mem-

brane, and oxidation processes, in addition to drugs that have been

part of study II (Kangaspeska et al. 2016). We tested the 33 drugs

with the T-47D parental and resistant cell lines. In both studies, neg-

ative (0,1 % DMSO only) as well as positive (100 μM). After 120mol/l benzetho-

nium chloride) controls were included. Five different concentrations

of one drug covered a 10000-fold concentration range. The optimal

amount of cells was grown in the cell's normal media, and supple-

mented with 1 μM). After 120M 4-OH-tamoxifen. The incubation time was 72 h at

37 °C. CellTiter-Glo Cell Viability Assay (Promega) was used to mea-

sure the viability with the PHERAstar plate reader.

6.2 High-content phenotypic drug profiling (III)

In study III we additionally fixed plates from the DSRT with the

33 drugs with 4% PFA in PBS for image-based phenotyping. We

stained the plates with LipidTOXGreen neutral lipid stain

(ThermoFisher Scientific) and Hoechst for nuclei detection.

Confocal images were acquired with the PE Opera Phenix HCS

system (PerkinElmer) with the 40x water immersion objective (NA


1.1). Laser power and exposure time were unchanged during the

imaging of the different plates with the same staining.

7 DSRT data analysis (II, III)

7.1 Analysis of cell viability (II, III)The raw ATP-measurements were analyzed with our in-house

bioinformatic pipeline Breeze, which gives dose response curves,

IC50 values as well as a Drug Sensitivity Score (DSS) for each in-

dividual drug treatment (Yadav et al. 2014). DSS scores (Study

III) and DSS differences between parental and resistant cell lines

(Study II) were visualized as a heat map. Rank product analysis

(Breitling et al. 2004) was used to identify drugs that significantly

changed their efficacy while tamoxifen resistance is obtained

(Study II).

7.2 Construction of drug sensitivity and co-resistance networks (II)

In study II, drug sensitivity and resistance networks were con-

structed based on drugs that showed a DSS difference of at least five

when comparing resistant with parental cell line DSS. Drug targets

were extracted from the ChEMBL database (https://www.ebi.ac.uk /

chembl) and assigned a Kinase Inhibitor BioActivity (KIBA)-score

(Tang et al. 2014). Based on the KIBA-score, point mutations, copy

number changes and canonical pathway for Estrogen Receptor Sig-

naling (www.qiagen.com) we used the Ingenuity Pathway Analysis

application (Ingenuity® Systems, Qiagen) (Krämer et al. 2014) to

construct sensitivity and co-resistance networks.

http://www.qiagen.com/

https://www.ebi.ac.uk/chembl

https://www.ebi.ac.uk/chembl

https://www.ebi.ac.uk/


7.3 Analysis of high-content phenotypic drug profiling (III)

Images were utilized to measure LipidTOXGreen signal and calcu-

late a DSS based on cell counts. Images were flatfield corrected

using CIDRE method (Smith et al. 2015), stitched to a single

multi-channel image per well, as well as segmented and analyzed

with CellProfiler 2.2.0 (Kamentsky et al. 2011). The LipidTOX-

Green signal was measured for each individual cell and a mean of

the intensities per well calculated. Dose-response curves were

drawn for the cell counts and used to calculate a DSS for each

drug in Breeze.

41 RESULTS & DISCUSSION

RESULTS & DISCUSSION

1 Fusion genes In study I we reanalyzed our already published RNA-sequencing data

(Edgren et al. 2011) by changing parameters such as the annotation

version of Ensembl to map the reads to the human genome, allowing

a fusion partner to be fused to more than one gene and having as

little as two reads to indicate the presence of a fusion gene. The re-

analysis predicted 16 additional fusion genes, which we validated

and characterized. Additionally, in study III we evaluated the

changes of fusion genes after tamoxifen resistance had developed.

1.1 Fusion gene characteristics in breast cancer

Out of the 16 newly predicted fusion genes, we were able to Sanger-

sequence 14 (Study I, Table 1 and S1). Our previously published and

validated 27 breast cancer fusion genes (Edgren et al. 2011), and the

results of others from solid tumors, show that fusion genes are not

just an event prevalent to hematological cancers. Indeed, in 2015,

9189 fusion genes have been found in solid tumors compared to 759

fusion genes in hematological diseases. However, only around 2 %

were found to be recurrent in solid tumors, compared to about 20 %

in hematological diseases (Mertens et al. 2015). The fusion genes we

identified were not recurrent. Nevertheless, one of the fusion part-

ners, for example, MED1 was found to be fused to different genes

(STXBP4, USP32, and ACSF2) in our study (Study I, Figure 3). Other

studies, for example, identified genes of the MAST- and NOTCH-gene

families to be fused to different partners in 4 or 6 % of breast cancer

patients, respectively (D. R. Robinson et al. 2011). The clinical

phase I trials with MK-0752, a γ-secretase inhibitor, which prevents

RESULTS & DISCUSSION 42

NOTCH activation, have shown promising results in advanced breast

cancer (Schott et al. 2013) and solid tumor patients (Krop et al.

2012). However, those studies were not selective for the NOTCH-fu-

sion genes and an evaluation of the fusion genes as predictive

markers or even therapy targets would be necessary. Even though

we did not detect NOTCH gene fusions, results like these show that

a disease-driving fusion partner could be targeted by therapy. In-

deed, FDA-approved drugs are already in use to treat NSCLC har-

boring ALK fusions and myeloproliferative neoplasms with PDGFR

fusions (Mertens et al. 2015).

Alternative splicing events such as cis- (intrachromosomal) and

trans-splicing (interchromosomal) or genomic rearrangements are

underlying fusion gene formation. The majority of fusion genes are

formed by genomic rearrangements due to genomic instability and

are frequently found at high-level amplifications (Study I, Table 1

and Figure 2, Stephens et al. 2009; Edgren et al. 2011; Kalyana-Sun-

daram et al. 2012). Most of the studied fusion genes are at a ge-

nomic breakpoint. We were able to sequence the exact chromosomal

breakpoints of three fusion genes (Study I, Figure 1b). The THRA-

AC090627.1 and MED1-ACSF2 fusion shared sequence microho-

mology of 1 bp at the breakpoint (Study I, Figure 1b). It is therefore

suggested that microhomology-mediated break-induced replication

(Lawson et al. 2011) is the mechanism by which these two genes

were joined.

We further identified the EEF1DP3-FRY fusion in the KPL-4 cell line

as a read-through event (Study I, Table S1). A read-through fusion,

also known as cis-transcription-induced gene fusion (cis-TIGF), oc-

curs when a large pre-mRNA, which contains two neighboring genes,

is transcribed and, due to splicing events, the two genes are fused

together (Akiva et al. 2006). Around 5 % of genes have been pro-


posed to form read-through fusions (Parra et al. 2006). However, the

majority of read-through fusions have little or no cellular impact and

may even be artifacts (Yu et al. 2014; He et al. 2018).

1.2 The role of fusion genes in tamoxifen resistance

In addition to the reanalysis of fusion genes in study I, we investi-

gated the fusion genes in the tamoxifen-resistant, as well as their iso-

genic parental cell lines (Study III). At first sight it appeared that the

cells acquire fusion genes during the resistance development as

some fusion genes were not found in the parental cell lines

(Study III, Additional file 6). After thorough investigation, all fusion

genes annotated in the Fusion Catcher results file as “known_fusion”

or “cell_lines” have been identified in the same parental cell line be-

fore (D. Kim and Salzberg 2011; Edgren et al. 2011; Kangaspeska et

al. 2012; Kalyana-Sundaram et al. 2012; Klijn et al. 2015). The elimi-

nation of all previously reported fusion genes and read-through fu-

sions, left only two acquired fusion genes (SMARCC1-EMCN in MCF-

7 Tam1 and RAD9A-FUT10 in ZR-75-1 Tam1), which have not been

detected in the parental cell line before. However, none are recur-

rent, suggesting that they do not play a major role in the develop-

ment of tamoxifen resistance. We were able to detect two shared

read-through fusions ABCC11-ABCC12 (BT-474 Tam1, T-47D Tam1

and ZR-75-1 Tam2) and TRIM3-HPX (T-47D Tam1 and ZR-75-1

Tam2, Study III, Additional file 6). The TRIM3-HPX has been de-

tected in prostate adenocarcinoma (Nacu et al. 2011), indicating

that it is neither breast cancer- and nor tamoxifen resistance-spe-

cific. ABCC11 and ABCC12 are highly homologous, which implies

that this read-through event is an artifact due to the mapping of

reads to similar genomes.


We were able to detect the ESR1-CCDC170 fusion in the MCF-7, as

well as in the tamoxifen-resistant MCF-7 Tam1, but not in the ZR-75-

1 as previously reported (Veeraraghavan et al. 2014) or in its iso-

genic tamoxifen-resistant clones (Study III, Additional file 6). The

ESR1-CCDC170 fusion has been identified in about 4 % of ER-posi-

tive breast tumors and tandem duplication has been proposed as the

underlying mechanism for the fusion formation (Veeraraghavan et al.

2014; Holst 2016). ESR1 is downstream of CCDC170 and a fusion of

the two genes should result in a CCDC170-ESR1 read-through fu-

sion, which we detected in MCF-7, but not in MCF-7 Tam1 (Study III,

Additional file 6). Whether the fusion contributes to endocrine resis-

tance is highly questionable. ESR1 contributes only with its pro-

moter region to form a fusion gene with CCDC170's coding regions

and a still fully functional ESR1 gene encodes for ERα. Indeed, dif-

ferential allele-specific expression (DASE) studies indicate that

CCDC170 alone is responsible for the breast cancer susceptibility

(Jiang et al. 2017), which has been earlier associated to the

CCDC170/C6orf97-ESR1 locus (6q25.1) by genome-wide association

studies (GWAS) (Turnbull et al. 2010; Dunning et al. 2016).

Nevertheless, true ESR1 fusion genes have been detected in ER-posi-

tive breast cancers. These include, ESR1-YAP1, which lacks the

ligand-binding domain (LBD/AF-2) and has therefore been shown to

drive endocrine resistance by promoting estrogen-independent cell

growth (S. Li et al. 2013).

2 Characterization of tamoxifen-resistant cell lines

We generated seven tamoxifen-resistant cell lines out of four

parental cell lines, by exposing them to 1 µM 4-OH-tamoxifen over a

period of 8-12 months (Figure 4, Study II, Figure 1A). The selected


cell lines reflect the molecular subtypes which would benefit from

endocrine-therapy. MCF-7, T-47D and ZR-75-1 were classified as lu-

minal A and the HER2-positive BT-474 cells as the luminal B-subtype

(Subik et al. 2010). The BT-474 cells functioned primarily as a con-

trol as HER2 overexpression is a known resistance mechanism to ta-

moxifen (García-Becerra et al. 2012). Before we obtained stable

growing tamoxifen-resistant cells, each of the clones underwent at

least one round of mass mortality, leaving just a few regrowing cells.

Once regrown, we assessed their tolerance towards tamoxifen and

found that the resistant cells were more tolerant, confirming that ta-

moxifen resistance has developed (Study II, Additional file 3A).

We further evaluated their growth, estrogen-responsiveness, ge-

nomic and transcriptional changes as well as their drug profiles.

2.1 Tamoxifen-resistant cells alter their cell cycle

Overall, we noticed during cell culturing that the resistant cells grew

slower than the parental cells and increased their doubling time dra-

matically. Further, FACS analysis revealed that all the resistant cells

accumulate in the G0/G1 phase (Study II, Additional file 3B). Studies

with another anti-estrogen resistant as well as a tamoxifen-resistant

model have also reported an increase in the doubling time and con-

sequently G0/G1 accumulation of the cells (Fog, Christensen, and

Lykkesfeldt 2005; H. S. Kim et al. 2015). Interestingly, the MCF-7

Tam1 cells accumulate less in G0/G1 (59 %), than the parental cells

under tamoxifen treatment (68 %). When ZR-75-1 Tam1 cells are ex-

posed to media without tamoxifen they dramatically halt in G0/G1

compared to tamoxifen treatment (86 % vs. 68 %), implying a tamox-

ifen addiction, which has been observed previously (van den Berg et

al. 1989). Withdrawal response to tamoxifen has also been reported


in breast cancer patients and can even lead to cancer remission

(Howell et al. 1992; Canney et al. 1987). On the other hand, non-ad-

herence to tamoxifen therapy is associated with a higher mortality

(McCowan et al. 2008). These results show that there would be a

need to define markers that could identify tamoxifen-addicted tu-

mors and help to stratify patients for therapy.

Interestingly, RNA-sequencing and subsequent pathway analysis of

differentially expressed genes showed that genes involved in cell

cycle were especially enriched in MCF-7 Tam1 & ZR-75-1 Tam1

(Study III, Table 2, Additional file 8). The results indicate that al-

tering the cell cycle properties is one way to overcome tamoxifen-in-

duced cell death.

2.2 Changes in ERα protein-levels and estrogen-responsiveness

Western blotting revealed that ERα was decreased in the ZR-75-1-

and T-47D-derived resistance models (Study II, Additional file 4C),

which is in line with previous research (van den Berg et al. 1989;

Graham et al. 1992). Acquired tamoxifen resistance has been associ-

ated with the loss of ERα expression (Oh et al. 2010) and epigenetic

mechanisms such as DNA methylation and histone deacetylation

have been proposed to underlay these changes (Sharma et al. 2005;

Y. Zhou et al. 2007). By contrast, in MCF-7 Tam1 resistant cells, the

levels of ERα remained almost unchanged, and in BT-474 resistant

clones, an increase was observed (Study II, Additional file 4C).

Therefore, depending on the molecular background, tamoxifen either

renders ERα stable (Lykkesfeldt, Madsen, and Briand 1994; J. J. Pink

and Jordan 1996; A. F. Leary et al. 2010) or promotes its destabiliza-

tion via proteasome-mediated degradation (Marsaud et al. 2003). At

the same time, we observed a decrease in ERα target gene transcrip-


tion (pS2, PGR, GREB1) as well as impaired estradiol-responsivity in

all resistant cell lines (Study II, Additional file 4A and 4B), sug-

gesting that even if the ERα is stabilized, its function may be im-

paired. Further, the loss of progesterone receptor frequently occurs

upon acquiring anti-estrogen resistance (Lykkesfeldt, Madsen, and

Briand 1994; Drury et al. 2011; Arnedos et al. 2014). Our results

suggest that different resistant clones may exhibit either steroid-de-

pendent or -independent growth and differ in their ER-mediated re-

sponses.

2.3 Genetic alterations in the genomes of tamoxifen-resistant cell lines

It has been reported that tamoxifen resistance is driven by genetic

adaptations such as point mutations or copy number changes (Karnik

et al. 1994; Borg et al. 1994; Dowsett et al. 2001; Moelans et al.

2010; Burandt et al. 2013; Lundgren et al. 2008; S. Li et al. 2013, 1;

D. R. Robinson et al. 2013; Toy et al. 2013; Merenbakh-Lamin et al.

2013; Jeselsohn et al. 2015). We, therefore, performed exome-se-

quencing on all the cell lines and determined resistance-specific

point mutations and copy number variations by comparing each ta-

moxifen-resistant cell line to its parental cell line. From around 250

to 350 acquired non-synonymous mutations per resistant cell, we

compared the high confident ones and found that no acquired point

mutation was shared across all the resistant cell lines (Study II, Addi-

tional file 8 and 9). Nevertheless, between the clones derived from

the same parental cell line, we found some shared mutations, which

involved TNS1, PTH2R, and NHLRC2 in BT-474 Tam1 and Tam2 and

TIMM23 and RP11-368 J21.2.1 in ZR-75-1 Tam1 and Tam2 (Study II,

Additional file 8 and 9). Copy number changes were predominantly

large heterozygous deletions, affecting many of the loci across the

genome, including chromosomes 7 and X. The ZR-75-1 Tam1 resis-


tant cell clone, on the other hand, acquired only copy number gains.

Whereas T-47D Tam1 and Tam2 had some shared copy number alter-

ations (mainly 7q), almost none were shared between the ZR-75-1

and BT-474 derived resistant clones (Study II, Additional file 8 and

10). Contrary to previous reports (Karnik et al. 1994; S. Li et al.

2013; Toy et al. 2013; D. R. Robinson et al. 2013; Merenbakh-Lamin

et al. 2013; Jeselsohn et al. 2015), we observed no acquired loss or

mutations of PTEN, PIK3CA or ESR1 in any of the drug-resistant

variants. However, unlike with ESR1 mutations, the causal relation-

ship between PTEN and PIK3CA mutations and tamoxifen resistance

has been questioned (López-Knowles et al. 2010; Beelen et al. 2014).

Thus, our results, and those from others (Block et al. 2012), suggest

that accumulation of numerous genomic aberrations can trigger re-

sistance development. In fact, antiestrogen resistance can be seen

even in the absence of any evident mutations (Brünner et al. 1997),

also without any in ESR1 (Martin et al. 2017). An absence of resis-

tance driving mutations has been observed in leukemic cells as well

(Pisco et al. 2013). The results imply that the emerged mutations and

copy number changes are rather passengers due to the selection

pressure than true drivers of tamoxifen resistance (Pisco and Huang

2015).

2.4 Transcriptional changes in tamoxifen-resistant cell lines and their resemblance to patients

Due to a lack of apparent genomic changes that could explain tamox-

ifen resistance, we investigated in study III the transcriptome of the

tamoxifen-resistant cell lines, to detect gene expression as well as

pathway changes. Further, we were able to compare our findings to

sequential tumor samples from patients that developed endocrine re-

sistance (McBryan et al. 2015).


In line with the exome-sequencing results (Study II), we did not iden-

tify a single common differentially expressed gene across all the re-

sistant cell lines (Study III, Figure 1B). Even within the clones de-

rived from the same parental cell line, a maximum of 38 % of genes

were commonly deregulated (Study III, Additional file 5 S1A). A

handful of common point mutations, shared copy number alterations

(Study II, Additional file 8 and 10) and diversified gene expression

changes (Study III, Figure 1A), indicate that each of the resistant

clones chooses its own way to acquire tamoxifen resistance.

Interestingly, the only study so far that has investigated global ex-

pression changes of sequential tumors from tamoxifen-treated pa-

tients identified less than 3 % of differentially expressed genes

(McBryan et al. 2015). Our reanalysis of their data set, where we

compared primary with the corresponding metastatic tumor, con-

firmed the results (Study III, Figure 1C). Further, principle compo-

nent analysis with the expressed genes of patients and cell lines clas-

sified as luminal A subtype (Subik et al. 2010) showed that the pa-

tient samples cluster together with the luminal A cell lines (Study III,

Figure 1D). In addition, we were able to identify SERPINA1,

PLXDC2, NAV1, DOCK10, and LRP1 as differentially expressed when

looking within the luminal A subtype. Almost nothing is known about

these genes and their relationship to tamoxifen resistance. Only

SERPINA1, which is upregulated in the patient samples as well, has

been shown to be upregulated by ligand-independent binding of ERα

(Chan et al. 2015). Indeed, when ERα protein expression is down

(Study II, Additional file 4C), we see a decrease of SERPINA1 in T-

47D Tam1, T-47D Tam2, ZR-75-1 Tam1 and ZR-75-1 Tam2 and vice

versa for the MCF-7 Tam1. In the patient samples, we see a slight

increase in ESR1 expression (Study III, Additional file 7). Another

interesting finding was the enormous increase in ceruloplasmin (CP)


expression in the resistant T-47D cell lines (240-290 fold-increase), in

MCF-7 Tam1 (26 fold-increase) and the three metastatic patient

samples (12 to 57 fold-increase; Study III, Additional file 7).

Ceruloplasmin has been suggested to be a marker for metastatic

breast cancer (Schapira and Schapira 1983) and it has been seen to

increase in the blood during tamoxifen treatment (Rössner and Wall-

gren 1984; Kailajärvi et al. 2000). Unfortunately, those studies did

not investigate the survival or recurrence rate of the patients with

increased ceruloplasmin-levels. In addition, it has been reported that

high levels of ceruloplasmin correlate with lower remission rate

(Ozyilkan et al. 1992), questioning the predictive value of

ceruloplasmin as a marker for metastatic disease.

In addition to the analysis of expression changes of single genes,

we used the differentially expressed genes and performed a

pathway analysis (Study III, Table 2, Additional file 8). Also, the

results of the pathway analysis supported the notion that every

cell line finds its own way to cope with the tamoxifen pressure. As

discussed earlier (2.1. Tamoxifen-resistant cells alter their cell

cycle), MCF-7 Tam1 & ZR-75-1 Tam1 mainly changed the

expression of genes involved in cell cycle. In the other cell lines,

altered genes were mainly involved in protein modification (BT-

474 Tam2), or extracellular matrix organization (ZR-75-1 Tam2 &

BT-474 Tam1), whereas T-47D Tam1 & Tam2 displayed changes in

metabolism associated genes, especially in those of the

cholesterol pathway (Study III, Table 2, Figure 2A). The results of

the ZR-75-1 Tam2 and BT-474 Tam1 needs to be viewed with

caution as the adjusted p-value is low due to a low amount of

input genes (Study III, Table 2).

Interestingly, we also observed an increase of cholesterol


pathway related genes in the metastatic patient samples

(Study III, Figure 2A). McBryan et al. did, however, remove all the

liver-related genes from their analysis as the breast cancer

metastasis samples were taken from the liver (McBryan et al.

2015). Nevertheless, our results raise the hen and egg question:

What came first, the changes in cholesterol pathway related

genes in breast cancer cells as a resistance mechanism or the

settlement of the metastatic breast cancer cells in the liver and

subsequent adjustment of cholesterol pathway related genes due

to the liver environment? The reprogramming of lipid metabolism,

however, is currently emerging as a player in metastatic cancer

(reviewed in Luo et al. 2017) and in drug resistance (C.-L. Chen et

al. 2016).

2.5 Drug responses of tamoxifen-resistant cell lines

When drug-resistance develops and signaling pathways in tumor

cells change, new therapy vulnerabilities or even co-resistance to

other drugs may arise. Therefore, we investigated in study II the

drug responses of the seven tamoxifen-resistant and their parental

cell lines towards 279 investigational and approved oncology drugs.

We identified common, cell type-, and cell clone-specific sensitivities

and co-resistances by calculating the DSS for each compound and

comparing parental with resistant cell line (Study II,

Figure 2, 3, 4, 5, 6, 7, Additional file 2, 5, 11, 12). We drew drug re-

sponse networks by integrating drug targets with copy number

changes (Study II, Figure 4, 5, 6, 7, Additional file 11 and 12). How-

ever, as the majority of the copy number changes are heterozygous

deletions, their role in the drug responses are potentially minor. The

networks are highly cell line specific, confirming once more that dif-


ferent resistance mechanisms develop in these cell lines.

We observed only a few common effector drugs (drugs with DSS dif-

ference < |5|) across the cell lines (Study II, Figure 2, 5A and 5B). As

the MCF-7 Tam 1 cell lines developed a rather overwhelming co-re-

sistance profile, there were no drugs to which all the resistant cell

lines became more sensitive (Study II, Figure 5A). However, one of

the drugs to which the other resistant cell lines were more sensitive,

was VX-11e (Study II, Figure 5A and 5B). It has been shown that VX-

11e induces ERK1/2 phosphorylation (Chaikuad et al. 2014, 1). The

resistant cells had reduced ERK1/2 phosphorylation compared with

the parental cell line, which was then increased under VX-11e treat-

ment (Study II, Figure 5C). Interestingly, a study by Zhou et al. ob-

served that tamoxifen induces cell death when ERK1/2 is continu-

ously activated (J.-H. Zhou et al. 2007). As VX-11e also induced cell

death in the parental cell line when supplemented with 1 µM tamox-

ifen (Study II, Additional File 5), the proposed mechanism by Zhou et

al. might explain the increased sensitivity to VX-11e.

On the other spectra, all of the tamoxifen-resistant cell lines devel-

oped a co-resistance towards paclitaxel (Study II, Figure 5A and 5B),

but also other chemotherapeutics were less effective in a more cell-

specific manner (Study II, Figure 3, 4, 6, Additional file 11 and 12).

As stated in study II, ER has been associated with chemoresistance

(Shi et al. 2014; Kurebayashi et al. 2010; Tabuchi et al. 2009), but in

the case of the tamoxifen-resistant cell lines we assume the lower

effectiveness of chemotherapeutics is rather due to the cells' slow

growth. Indeed, in study III we performed another DSRT, including

paclitaxel, with the T-47D cells (Study III, Figure 4A, Additional

file 9). Paclitaxel was more effective in the T-47D Tam2 cells in

this screen than in study II. However, we also observed an in-

creased proliferation in the T-47D Tam2 cells (Study III, unpub-


lished) compared to our first screen (Study II). Importantly, T-47D

Tam1, which grow at the same speed as in the first screen, con-

firmed the results in study II of reduced sensitivity towards pacli-

taxel (Study III, Figure 4A, Additional file 9).

In the HER2 overexpressing BT-474 cell lines, we saw that HER2/

EGFR inhibitors (gefitinib, ibrutinib, neratinib) were even more

effective in the tamoxifen-resistant cells compared to the already

sensitive parental cells (Study II, Figure 5, 6, Additional file 2). In-

terestingly, neratinib has been recently approved by the FDA for

treatment of early-stage HER2-positive breast cancer (Center for

Devices and Radiological Health, 2017). Analyzing the RNA-se-

quencing data, we detected changes in genes associated with pro-

tein modification (BT-474 Tam1 & Tam2), especially glycosylation

(Study III, Table 2). Glycosylation has been connected to oncogen-

esis (Ng et al. 1987; Vavasseur et al. 1994) and shown to modu-

late the response of HER2+ breast cancer cells to chemotherapy

and herceptin treatments (Peiris et al. 2017). Therefore, one

could speculate that changes in glycosylation of the EGFR re-

ceptor family members potentially make them more accessible to

their ligands, or in this case, drugs.

In addition to our results from the exome- and RNA-sequencing,

the drug profiling confirmed that clone-specific responses un-

derlay the tamoxifen-resistance and that different drugs are

needed to overcome each resistance mechanism.

3 Lipid changes in T-47D and their consequences

In study III, we investigated the T-47D cell lines more in depth. RNA-

sequencing indicated changes in cholesterol and lipid metabolism,


which were found in the patient samples as well (Study III, Table 2,

Figure 2A). We were able to observe an enormous increase in choles-

terol and neutral lipids by immunofluorescence as well as with thin

layer chromatography (Study III, Figure 2 and 4B). It has recently

been reported that the use of cholesterol-lowering drugs in en-

docrine-therapy prevents breast cancer recurrence. Even though this

study did not detect any benefits in the tamoxifen treatment only

arm, cholesterol levels were decreased (Borgquist et al. 2017). This

indicates that the increase of cholesterol does not seem to be a

normal drug response and might indeed be a resistance mechanism.

We further observed that the cholesterol accumulates in highly en-

larged lysosomes (Study III, Figure 3A and B). Lysosomes are in-

volved in mediating different types of cell death, such as apoptosis,

necroptosis, necrosis, and ferroptosis, through lysosomal mem-

brane permeabilization (LMP; Galluzzi, Bravo-San Pedro, and

Kroemer 2014). Therefore, one could say that preventing LMP can

lead to drug resistance. Indeed, when we treated the tamoxifen-re-

sistant T-47D cells with LLOMe, an LMP inducing agent, we ob-

served a decrease in the number of galectin-3 positive lysosomes

compared to the parental cells (Study III, Figure 3C and D). Fur-

thermore, it has been shown that accumulation of cholesterol (Ap-

pelqvist et al. 2011), an increase in lysosomal-associated mem-

brane protein 1 (Lamp1) and Lamp2 (Fehrenbacher et al. 2008),

as well as downregulation of cathepsins prevent LMP (Liaudet-

Coopman et al. 2006). All of these LMP inhibiting mechanisms

were seen in T-47D Tam1 and Tam2 (Study III, Figure 3).

We also measured an increase in triglycerides and lipid droplets

in these cells (Study III, Figure 2C, D and 4B). However, when

targeting the lipid phenotype with drugs affecting the lipid me-

tabolism, we did not observe a reversal of the lipid phenotype or a


major reduction in cell viability (Study III, Figure 4, Additional

file 9 and 10). We, therefore, suggest that the lipid droplets are

mainly functioning more as energy storage than a resistance

mechanism.

Compounds targeting pathways involved in regulating the reac-

tive oxygen species (ROS), such as SOD1 inhibitor LCS-1, were

able to induce cell death in all the T-47D cells. ROS are able to in-

duce LMP (Aits and Jäättelä 2013) and we did see an upregulation

of SOD1 expression in both of the tamoxifen-resistant cell clones

(Study III, Additional file 7). SOD1 encodes for a superoxide dis-

mutase, which turns superoxide into either O2 or H2O2, lowering

the ROS that could induce LMP. Also RSL-3, which inhibits the

glutathione peroxidase 4 that prevents lipid peroxidation, was

able to induce cell death in all the cell lines. In addition, we can

see an upregulation of ceruloplasmin, an enzyme that converts

Fe2+ to Fe3+ and thus prevents the Fenton-reaction, which would

produce superoxide that would then induce LMP. When putting all

these results in perspective, it seems that the tamoxifen-resistant

T-47D cells circumvent tamoxifen-induced cell death by pre-

venting LMP due to the reduction in superoxide, downregulation

of cathepsin D, and an increase in cholesterol and LAMPs (Figure

5).


These results also show that resistance mechanisms are very well

ingrained and targeting a particular resistance mechanism might

not always be successful or harbor only a small window of oppor-

tunity.

Figure 5: Proposed resistance mechanism of tamoxifen-resistant T-47D cells. The lysosomal membrane is stabilized by upregultation of Lamp1, Lamp2 and cholesterol. Upregulation of cerulopasmin (CP) and superoxide dismutase (SOD1) prevents the A Fenton as well as B Haber-Weiss reaction, respectively. Thereby ROS gets reduced and lysosomal membrane permeabilization (LMP) is not induced. LMP would, together with cathepsins, trigger various types of cell death. Lamp1/2, lysosomal-associated membrane protein 1/2; CAT, catalase; GPx, glutathion peroxidase

Cholesterol

Cathepsin D

Lamp1

Lamp2LMP

Necroptosis

Apoptosis

Necrosis

Ferroptosis

H2O2Fe2++

H2O2 + Fe2+ Fe3++ OH-

HO +

Fe3+O2-

+

CP

Fe2+ + O2

SOD1

H2O + O2

CAT/GPx

A

B

57 CONCLUSION

CONCLUSIONTamoxifen is one of the most widely used targeted therapies for ER-

positive breast cancer. However, patients that initially respond to the

therapy can acquire drug resistance and the cancer ultimately re-

lapses. A thorough understanding of the complex mechanisms of

drug resistance is needed in order to use tamoxifen more effectively.

This understanding can guide development of combinations with

other therapies to prevent drug resistance. It can also enable the de-

tection resistance when it occurs, and present an opportunity to

switch therapies or target the resistance mechanism.

In my thesis work I, therefore, investigated the genomic and tran-

scriptomic changes of seven tamoxifen-resistant cell lines, tried to

identify compounds that can target those changes, and explored the

possibility of fusion genes as possible prognostic or diagnostic

markers. It became evident relatively quickly that each of the resis-

tant cell lines had developed its own drug resistance profile, or as I

would say I discovered the “Seven shades of tamoxifen resistance”. I

observed the different molecular changes in the genetic-, transcrip-

tomic- and drug-profile with limited overlaps. I identified that the

MCF-7 resistant cell line mainly regulated their growth properties

and displayed multi-drug resistance. The resistant ZR-75-1s still

need a thorough investigation about their mechanisms, but there are

hints of cell cycle and adhesion protein modification. The resistant

BT-474s cells modified their protein glycosylation and adhesion ac-

cording to RNA-Sequencing analysis. And last but by no means least,

the T-47D cells prevented lysosomal membrane permeabilization by

upregulating their cholesterol metabolism. The example of the T-47D

cell lines additionally show how difficult it is to target the resistance

mechanisms, and one might need to look beyond the field of cancer

CONCLUSION 58

research in order to understand and combat them.

On one hand, it is confirming that the models represent already

known resistance mechanisms, as well as sharing similarities with

patient samples, making them appropriate models to study the

molecular mechanisms of resistance development and discover

possible drug vulnerabilities. On the other hand, seeing the diver-

sity of these mechanisms in only seven resistant cell lines enor-

mously highlights the need of personalized diagnosis and therapies

in treatment of tamoxifen-resistant breast cancer.

Whether fusion genes can be used as diagnostic markers or even

drug-targets as in hematological cancers such as CML, can be

doubted because the diversity of breast cancer is so high. Never-

theless, maybe one of the recurrent fusion gene partners could be

utilized as a therapeutic target and on a personalized therapy

level they could function as cancer progression markers and indi-

cate possible therapy success.

I was once asked by a scientist that actually works in this field if

this breast cancer field is highly competitive. I pointed to my re-

sults and said whoever develops another resistance model will

find other mechanisms and only when we have enough of the data

freely available we might be able to stratify patients. However,

more important than models would be the existence and access to

clinical data sets, such as the set published by McBryan et al.,

which not only contains the primary tumor information, but also

the information of diagnosed acquired tamoxifen resistance with

response/survival information. This would enable researchers to

compare the relevance of their models and prove that the resis-

tance mechanism of interest exists in human cancer.

x ACKNOWLEDGMENTS

ACKNOWLEDGMENTSFirst and foremost I want to thank Sara Kangaspeska. I am thankful

for bringing me onto the project. Even though you were not my official

supervisor, I enjoyed your excellent supervision with valuable com-

ments, guidance, encouragement and little pushes even after you left

the research group. I admire your drive to sit down and wrap things

up.

This study was carried out at the Institute for Molecular Medicine Fin-

land (FIMM) over quite some years. I would like to thank everyone at

the Institute for making it a wonderful and encouraging workplace.

Which brings me to one of my supervisors, Olli Kallioniemi who has

played an existential role in creating this excellent work environment.

I am highly grateful for letting me continue with this project even

though all the breast cancer expertise had supposedly left the group. I

am thankful for giving me the freedom and the trust to design the last

study. I also want to express my gratitude to Taija af Hällström, Päivi

Östling, and Vilja Pietiäinen for your interest to work with me. I espe-

cially want to thank Vilja, who became one of my supervisors, for ex-

ploring with me the lysosomes and the iron. I highly appreciate your

motivational enthusiasm and happiness about science. I hope you can

preserve this attitude in the years to come.

I would like to thank my thesis reviewers Tuula Kallunki and Katri

Pylkäs for their kind comments and my thesis committee members

Kaisa Lehti and Johanna Mattson for their encouragement to wrap

things up. In the end, it took longer than expected. Further, I would

like to thank Robert Mills for having a final glance over the thesis.

Of course, such a study could not have been done without the support

of the people in the lab, namely Anna, Mariliina, Katja, Sanna and Piia

who helped with culturing and generating the cells, the experiments,

ACKNOWLEDGMENTS xi

and the general lab stuff. The FIMM Tech Centers such as the se-

quencing unit with Pekka Ellonen and Pirkko Mattila, the HTB unit

with Ida Lindenschmidt, Laura Turunen, Maria Nurmi and most defi-

nitely Swapnil Podar, and finally the HCA unit with Antti Hassinen

and Peter Horvath, without these units such an extensive screening

and sequencing would not have been possible. I also want to thank

all my co-authors: Henrik Edgren, Daniel Nicorici, Astrid Murumägi,

Alok Jaiswal, John-Patrick Mpindi, Samuli Eldfors, Oscar Brück, Tero

Aittokallio, Matti Kankainen, Lassi Paavolainen, Ruusu-Maria Ko-

vanen and Elina Ikonen. And I want to give my sincerest apologies

for all the guilt that the bioinformaticians felt when they “acciden-

tally” met me in the aisle.

Many thanks to old and new lab mates that have not directly been in-

volved in my studies but participated in the group discussions and

were always good for a joke: Sami, Vesa, Teijo, Maija W., Anna A.,

Anna L., Dima, Riku, Tanja, Kalle, Minxia, Akira, Clement and Maija

P.. An extra big thanks go to Susanna Rosas for all the help and

making the impossible possible.

I spent a great deal of my FIMM time in the back office. I would like

to thank everyone, who shared the office with me, for being such

nice roomies. Of course, I want to highlight Disha, Khalid and Poo-

jitha for all the on- and off-topic discussions we had.

Along those lines, I would like to thank the not so secret club (you

know who you are) for having thoughtful and silly discussions over a

good and sometimes not so good whisky.

I want to sincerely thank Gretchen and Mari for encouraging and

supporting me to organize the science and art exhibitions.

Of course, a PhD would not have been possible without the (mental)

support of friends. A big thank you goes out to all my friends that

xii ACKNOWLEDGMENTS

kept me company during travel, hockey matches, lunch, beer and bar

explorations, parties, girls evenings, ravitolapäivät, issue discussions,

concerts, espresso and tea breaks, to the ones that let me be part of

their weddings and introduced me to their families and friends. A big

hug to Jenni M. & Vesa L., Annabrita, Pauliina, Jenni L, Manu, Daria,

Jennifer, Maren, Malu, Piia, Rita, Yuexi, Prson, Alok, Arjan, Kamila,

Mamun, Tini, Sami, Elsa, Stefano, Riikka, Dima, Alex, Poojitha &

Tuomas, Disha & Ashwini, the people from Kuopio, the Canadian and

the German hockey gang and all the others I have met over the past

years.

Finally, I want to thank my family for all their support during my life,

for letting me explore the world and for encouraging me no matter

what. Not to forget, thank you for accepting that I am sometimes not

reachable. DANKE für Alles!

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xxxvi INDEX OF FIGURES AND TABLES

INDEX OF FIGURES AND TABLES

Index of figuresFigure 1: Simplistic overview of ER signaling pathways.(modified from Le Romancer et al. 2011) 3Figure 2: Main fusion gene types. (modified from Guy Leonard, 2012) 8Figure 3: Comparison of de-novo and acquired resistance.(modified from Anna Azvolinsky 2017) 12Figure 4: Schematic overview of tamoxifen-resistant cell lines. 28Figure 5: Proposed resistance mechanism of tamoxifen-resistant T-47D cells. 48

Index of tablesTable 1: Breast cancer subtypes (based on Eric Wong) 6Table 2: Possible mechanisms of tamoxifen resistance (modified from Tryfonidis et al. 2016) 19Table 3: Cell lines from human mammary gland 24Table 4: Primary antibodies used for Western blotting (WB) and immunofluorescence (IF) 26

ORIGINAL PUBLICATIONS xxxvii

ORIGINAL PUBLICATIONS

I. Kangaspeska S, Hultsch S, Edgren H, Nicorici D, Murumägi

A, Kallioniemi O. Reanalysis of RNA-sequencing data reveals

several additional fusion genes with multiple isoforms. PLoS

One. 2012;7(10):e48745.

https://doi.org/10.1371/journal.pone.0048745

II. Kangaspeska S*, Hultsch S*, Jaiswal A, Edgren H, Mpindi JP,

Eldfors S, Brück O, Aittokallio T, Kallioniemi O. Comprehen-

sive drug sensitivity testing reveals specific vulnerabilities

and co-resistance patterns in endocrine-resistant breast

cancer. BMC Cancer. 2016 Jul 4;16:378. * Equal contribution

https://doi.org/10.1186/s12885-016-2452-5

III. Hultsch S, Kankainen M, Paavolainen L, Kovanen RM, Ikonen

E, Kangaspeska S, Pietiäinen V, Kallioniemi O. Lipid repro-

gramming in tamoxifen-resistant breast cancer cells and po-

tential therapeutic vulnerabilities. BMC Cancer. 2018 Aug

24;18(1):850.

https://doi.org/10.1186/s12885-018-4757-z

https://doi.org/10.1186/s12885-018-4757-z

https://doi.org/10.1186/s12885-016-2452-5

https://doi.org/10.1371/journal.pone.0048745

seven shades of tamoxifen resistance - helda

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