gupea_2077_40446_6

gupea_2077_40446_6
Delineating cellular
heterogeneity and
organization of breast
cancer stem cells
Nina Akrap
Department of Pathology
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Sahlgrenska Academy at University of Gothenburg
Gothenburg 2015
Cover illustration: Micrograph of PKH-stained MCF7 mammospheres by
Nina Akrap
Delineating cellular heterogeneity and organization of breast cancer stem
cells
© Nina Akrap 2015
[email protected]
ISBN 978-91-628-9601-0
Printed in Kalmar, Sweden 2015
Lenanders Grafiska AB
Für Baka
Delineating cellular heterogeneity
and organization of breast cancer
stem cells
Nina Akrap
Department of Pathology, Institute of Biomedicine
Sahlgrenska Academy at University of Gothenburg
Göteborg, Sweden
ABSTRACT
Breast cancer is characterized by a high degree of heterogeneity in terms of
histological, molecular and clinical features, affecting disease progression and
treatment response. The cancer stem cell (CSC) model suggests, that cancers are
organized in a hierarchical fashion and driven by small subsets of CSCs, endowed
with the capacity for self-renewal, differentiation, tumorigenicity, invasiveness
and therapeutic resistance. The overall aim of this thesis was to characterize CSC
phenotypes and the cellular organization in estrogen receptor ! + (ER!+) and
ER!- subtypes of breast cancer at the individual cell level. Furthermore, we
aimed to identify novel functional CSC markers in a subtype-independent
manner, allowing for better identification and targeting of breast-specific CSCs.
At present, single-cell quantitative reverse transcription polymerase chain
reaction represents the most commonly applied method to study transcript levels
in individual cells. Inherent to most single-cell techniques is the difficulty to
analyze minute amounts of starting material, which most often requires a
preamplification step to multiply transcript copy numbers in a quantitative
manner. In Paper I we have evaluated effects of variations of relevant parameters
on targeted cDNA preamplification for single-cell applications, improving
reaction sensitivity and specificity, pivotal prerequisites for accurate and
reproducible transcript quantification.
In Paper II we have applied single-cell gene expression profiling in
combination with three functional strategies for CSC enrichment and identified
distinct CSC/progenitor clusters in ER!+ breast cancer. ER!+ tumors display a
hierarchical organization as well as different modes of cell transitions. In contrast,
ER!- breast cancer show less prominent clustering but share a quiescent CSC
pool with ER!+ cancer. This study underlines the importance of taking CSC
heterogeneity into account for successful treatment design.
In Paper III we have used a non-biased genome-wide screening approach to
identify transcriptional networks specific to CSCs in ER!+ and ER!- subtypes.
CSC-enriched models revealed a hyperactivation of the mevalonate metabolic
pathway. When detailing the mevalonate pathway, we identified the mevalonate
precursor enzyme 3-hydroxy-3-methylglutaryl-CoA synthase 1 (HMGCS1) as a
specific marker of CSC-enrichment in ER!+ and ER!- subtypes, highlighting
HMGCS1 as a potential gatekeeper for dysregulated mevalonate metabolism
important for CSC-features. Pharmacological inhibition of HMGCS1 could
therefore be a novel treatment approach for breast cancer patients targeting CSCs.
Keywords: Breast cancer, cancer stem cells, cellular heterogeneity
SAMMANFATTNING PÅ SVENSKA
Bröstcancer är den vanligaste cancerformen hos kvinnor och utgör 30%
(2011) av alla cancerfall hos kvinnor i Sverige. Sjukdomen kännetecknas
av stor variation och bröstcancer kan beskrivas som ett samlingsbegrepp
för olika typer av cancer. Olika varianter av bröstcancer har olika
sjukdomsförlopp och det finns undergrupper med bra respektive dålig
prognos som behandlas på olika sätt.
En tumör består av många olika typer av celler. Flera modeller har försökt
förklara anledningen till denna cellvariation varav en är
cancerstamcellsmodellen. Här tror man att en liten del av cellerna i
tumören, kallade cancerstamceller, är aggressiva, kan bilda metastaser och
är motståndskraftiga mot behandling. Därför tror man att det är viktigt att
hitta behandling riktad mot dessa celler. Syftet med detta arbete är att
studera cancerstamceller i olika typer av bröstcancer och vidare titta på
organisationen av dessa celler och andra celltyper i tumörerna. Ett annat
mål med avhandlingen är att identifiera markörer som är specifika för
cancerstamceller jämfört med andra cellpopulationer i olika typer av
bröstcancer för att kunna använda dessa till att utveckla metoder för
diagnos och behandling.
Cancerstamceller utgör en väldigt liten del av tumörcellerna och för att
studera dessa krävs specifika metoder där man sorterar ut och analyserar
enskilda celler. Enskilda celler innehåller väldigt lite material och därför
måste materialet först amplifieras för att kunna analyseras med
tillgängliga metoder. I artikel I har vi utvecklat en metod för att amplifiera
denna typ av material på ett sätt som ger tillförlitliga resultat.
I artikel II har vi studerat organisationen av olika cellpopulationer i två
typer av bröstcancer. Olika metoder användes för att anrika
cancerstamceller som sedan jämfördes med vanliga cancerceller. Vi tittade
på två olika typer av bröstcancer och i båda fallen identifierades en grupp
av liknande cancerstamceller. I en av cancertyperna identifierades
ytterligare populationer av cancerstamceller och man kunde se en tydlig
organisation av olika celltyper. Denna studie påvisar betydelsen av att
behandla all typer av relevanta cellpopulationer för att eliminera cancer.
I artikel III försökte vi identifiera markörer specifika för cancerstamceller
i olika typer av bröstcancer. Vi använde en speciell metod för att hitta
signalvägar specifika för cancerstamcellerna. Genom att titta närmare på
i
en av signalvägarna som upptäcktes med denna metod identifierade vi en
markör kallad HMGCS1 som är viktig för funktionen av cancerstamceller.
Farmakologisk hämning av HMGCS1 skulle därför kunna vara ett nytt
behandlingssätt för bröstcancerpatienter.
ii
iii
LIST OF PAPERS
This thesis is based on the following studies, referred to in the text by their
Roman numerals.
This thesis is based on the following studies, referred to in the text by their
Roman numerals.
I.
II.
III.
Andersson, D*., Akrap, N*., Svec, D., Godfrey, T.E., Kubista, M.,
Göran Landberg, G. and Ståhlberg, A. Properties of targeted
preamplification in DNA and cDNA quantification Expert Rev
Mol Diagn. 2015 Aug;15(8):1085-100. *Authors contributed
equally.
Akrap, N., Andersson, D., Gregersson, P., Bom, E., Anders
Ståhlberg, A. and Landberg, G. Identification of distinct breast
cancer stem cell subtypes based on single cell PCR analyses of
functionally enriched stem and progenitor pools. Manuscript.
Walsh, C.A., Akrap, N., Magnusson, Y., Harrison, H., Andersson,
D., Rafnsdottir, S., Choudhry, H., Buffa, F.M., Ragoussis, J.,
Ståhlberg, A., Harris, A. and Landberg G. The mevalonate
precursor enzyme HMGCS1 is a novel marker and key mediator of
cancer stem cell enrichment in luminal and basal models of breast
cancer. Manuscript.
iv
CONTENT
SAMMANFATTNING PÅ SVENSKA ...................................................................... I!
LIST OF PAPERS .......................................................................................... IV!
CONTENT .......................................................................................................... V!
ABBREVIATIONS ............................................................................................ VII!
1! INTRODUCTION ............................................................................................1!
1.1! The normal breast and breast cancer ......................................................1!
1.1.1! The normal breast ...........................................................................1!
1.1.2! Breast cancer ..................................................................................3!
1.1.3! Breast cancer subtypes ...................................................................4!
1.1.4! Breast cancer therapy .....................................................................8!
1.2! Tumor heterogeneity ............................................................................10!
1.2.1! Inter-tumor heterogeneity .............................................................10!
1.2.2! Intra-tumor heterogeneity .............................................................12!
1.3! The clonal evolution theory and the cancer stem cell hypothesis ........14!
1.3.1! The clonal evolution theory ..........................................................14!
1.3.2! The cancer stem cell hypothesis ...................................................15!
1.3.3! Attributes of cancer stem cells .....................................................17!
1.3.4! Concluding remarks .....................................................................18!
1.4! Mevalonate pathway in cancer .............................................................18!
1.4.1! Dysregulated metabolism in cancer .............................................18!
1.4.2! The mevalonate pathway for steroid biosynthesis and protein
prenylation ..............................................................................................20!
1.4.3! Mevalonate metabolism is regulated by mutant p53 ....................22!
2! AIMS ..........................................................................................................23!
3! METHODOLOGICAL ASPECTS ...........................................................24!
3.2.1! Growth in anchorage-independent culture ...................................26!
3.2.2! Hypoxic culture ............................................................................26!
3.2.3! Label-retention .............................................................................27!
v
4! RESULTS AND DISCUSSION ........................................................................ 29!
4.1! Results and discussion paper I ............................................................. 29!
4.2! Results and discussion paper II ............................................................ 36!
4.3! Results and discussion paper III .......................................................... 44!
5! CONCLUSIONS ....................................................................................... 51!
ACKNOWLEDGEMENT ..................................................................................... 52!
REFERENCES ................................................................................................... 54!
vi
ABBREVIATIONS
AI
ABCG2
Acetyl-CoA
ALDH
ALDH1A3
ATP
BCSC
BRCA1
BRCA2
!
C
CCNA2
CD
CD49f
CDH1
CDKN2A
cDNA
CFSE
Cq
CSC
DFS
DHCR24
DLL1
DMAPP
DNA
DNER
e.g.
EMT
EPCAM
ER
ERBB2
FACS
FDA
FDG-PET
FFP
FGFR1
FGFR2
FOXA1
Aromatase inhibitors
ATP-binding cassette, sub-family G
(WHITE), member 2
Acetyl-Coenzyme A
Aldehyde dehydrogenase
Aldehyde dehydrogenase 1 family, member A3
Adenosine 5´-triphosphate
Breast cancer stem cell
Breast cancer 1, early onset
Breast cancer 2, early onset
Degree Celsius
Cyclin A2
Cluster of differentiation
Also known as integrin alpha-6
Cadherin 1, type 1
Cyclin-dependent kinase inhibitor 2A
complementary DNA
Carboxyfluorescein succinimidyl ester
Quantification cycle
Cancer stem cell
Disease-free survival
24-dehydrocholesterol reductase
Delta-like 1 (Drosophila)
Dimethylallyl pyrophosphate
Deoxyribonucleic acid
Delta/notch-like EGF repeat containing
Exempli gratia
Epithelial-to-mesenchymal transition
Epithelial cell adhesion molecule
Estrogen receptor
Erb-b2 receptor tyrosine kinase 2, encodes for
HER2
Fluorescence-activated cell sorting
Food and Drug Administration
Fluorodeoxyglucose positron emission tomography
Farnesyl pyrophosphate
Fibroblast growth factor receptor 1
Fibroblast growth factor receptor 2
forkhead box A1
vii
G0/G1
GADD45
GATA3
GGPP
GI/GII/GIII
GRB7
GTPases
HER2
HIF
HMG-CoA
HMGCR
HMGCS1
HRE
i.e.
ID1
IDC-NOC
IHC
IPP
Ki67
MAP3K1
MaSC
min
MMTV
MVA
MVK
MYC
n
N-BP
NANOG
nM
NOD/SCID mouse
NPI
PCA
PCR
PGR
PI3KCA
PR
PTEN
RAS
Gap0/Gap1 cell cycle phase
Growth arrest and DNA-damage-inducible, alpha
GATA binding protein 3
Geranylgeranyl pyrophosphate
Histological grade I-III
Growth factor receptor-bound protein 7
Ras and Rho small guanosine triphosphatases
Human epidermal growth factor receptor 2
Hypoxia-inducible transcription factor
3-hydroxy-3-methylglutaryl-CoA
3-hydroxy-3-methylglutaryl-CoA reductase
3-hydroxy-3-methylglutaryl-CoA synthase 1
Hypoxic-response element
id est
Inhibitor of DNA binding 1
Invasive ductal carcinoma not otherwise specified
Immunohistochemistry
Isopentylpyrophosphate
Marker of proliferation
Mitogen-activated protein kinase kinase kinase 1
Mammary stem cell
Minute
Mouse mammary virus tumor
Mevalonate
Mevalonate kinase
v-myc avian myelocytomatosis viral oncogene
homolog
Sample size
Nitrogen-containing bisphosphate
Nanog homeobox
Nanomolar
Nonobese diabetic/severe combined
immunodeficiency mouse
Nottingham Prognostic Index
Principal component analysis
Polymerase chain reaction
Progesterone receptor
Phosphatidylinositol-4,5-bisphosphate 3-kinase,
catalytic subunit alpha
Progesterone receptor
Phosphatase and tensin homolog
Rat sarcoma viral oncogene homolog
viii
RB1
RNA
RT-qPCR
SD
SEM
SERM
siRNA
SNAI1
SOM
SOX2
TDLU
TCA
TNBC
TP53
Wnt1
ZIC3
Retinoblastoma 1
Ribonucleic acid
Reverse transcription– quantitative polymerase
chain reaction
Standard deviation
Standard error of the mean
Selective estrogen receptor modulator
Small interfering RNA
Snail family zinc finger 1
Self organizing map
SRY (sex determining region Y)-box 2
Terminal ductal lobular unit
Tricarboxylic acid
Triple negative breast cancer
Tumor protein p53
Wingless-type MMTV integration site family,
member 1
Zic family member 3
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Delineating cellular heterogeneity and organization of breast cancer stem cells
1 INTRODUCTION
1.1 The normal breast and breast cancer
1.1.1 The normal breast
Breast development
Mammary gland morphogenesis is initiated in the embryo at around four
weeks. Most of the breast growth takes place at puberty under the
influence of growth hormones and estrogen, leading to an enlargement of
the rudimentary mammary epithelium. During pregnancy alveolar
morphogenesis is induced by several hormones and the mammary
epithelium undergoes rapid proliferation, resulting in increased ductal
branching and the development of the alveolar epithelium, capable of milk
secretion [1].
Breast structure
The mammary epithelium is characterized by a high degree of plasticity
throughout life. The mature epithelium is organized into a series of
branching ducts, which are lined by a bi-layered epithelium, consisting of
luminal and myoepithelial/basal cells adjacent to a basement membrane.
Mammary ducts are surrounded by stromal cells, such as adipocytes and
fibroblasts and infiltrated with blood and lymph vessels. Each duct ends
into the terminal ductal lobular unit (TDLU), which consists of ductules
and alveolar buds. The majority of breast cancers arise in the TDLUs [1,
2] (Fig.1A and 1B).
Cellular hierarchy
Today, it is widely accepted that the mammary epithelium is organized in
a differentiation hierarchy. Bipotent mammary stem cells (MaSCs) form
the apex of the hierarchy, giving rise to unipotent luminal and basal stem
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Nina Akrap
or progenitor cells, which maintain the terminally differentiated cell types.
However, the exact definition of MaSCs and derived progenitor
populations still remains a matter of debate. To interrogate the hierarchical
organization of the mammary epithelium the field has broadly relied on in
vivo and in vitro assays to test self-renewal and differentiation capacity in
subsets of epithelial cells. MaSC of the adult gland are notoriously
difficult to study due to their low frequency and the lack of appropriate
markers. Data derived from these studies have been conflicting, which is
likely the result of different applied tumor dissociation protocols and
assays to assess ‘stemness’ [3]. Several studies indicated that the MaSC
(i.e.
cells
with
highest
repopulating
capacity)
have
an
EPCAMlow/CD49fhigh phenotype and are part of the basal cell
compartment [4, 5], whereas other studies showed that the luminal and
basal compartment contains MaSC and bi-potent progenitors [6].
Additionally, suprabasal luminal cells of the ducts were suggested to
contain MaSC [7, 8]. Besides, there is also evidence for the existence of
unipotent stem/progenitor cells that maintain the luminal and basal
compartment. Luminal progenitor cells can be identified by their
EPCAMhigh/CD49fhigh immunophenotype [9, 10]. No specific marker
profile has yet been identified for basal progenitor cells, but they can be
identified from serial passaging of MaSCs, indicating that these cells lie
downstream in the hierarchy [10]. One feature of adult stem cells is their
slow division cycle, which enables enrichment of these cells by labelretention methods, such as synthetic DNA nucleosides or membrane dyes.
Pece and colleagues have used the lipophilic PKH26 dye in combination
with the mammosphere assay to enrich for MaSC based on their quiescent
nature [8]. The authors identified cells expressing the cell surface marker
profile CD49fhigh/DLL1high/DNERhigh to have the highest mammosphereinitiating potential. Interestingly, the gene signature derived from
PKH26high cells was able to predict biological and molecular features of
breast cancers.
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Delineating cellular heterogeneity and organization of breast cancer stem cells
Figure 1. Schematic illustration of the normal breast. A: Representation of the
human mammary gland. B: Cross section of a mammary duct. Adapted from [2].
1.1.2 Breast cancer
Breast cancer is the most common type of cancer diagnosed in women
worldwide, with an incidence of about 25% [11] corresponding to 1.7
million women being diagnosed with breast cancer in 2012. There was a
sharp rise (20%) in breast cancer incidence since 2008, which can be
partly explained by changes in lifestyle common to industrialized nations
[12]. Despite of the high incidence, breast-cancer related mortality is
decreasing, with 5-year and 10-year survival rates of 87.8% and 78.8% in
Sweden [13].
The risk of developing breast cancer has been linked to numerous factors.
A few well-established risk factors comprise age, lifestyle and
environmental factors, such as body mass index, alcohol consumption and
hormone replacement therapy. Early menarche, late menopause and late
age of first childbirth comprise additional risk factors. Most of the breast
cancers are sporadic and non-familial. Hereditary forms of cancer only
constitute 5-10% of all cancers. However, female carriers of germline
mutations in high penetrance genes, such as BRCA1 and BRCA2 present a
60-80% lifetime risk of developing breast cancer [14]. Additional high
penetrance mutations in for example the PTEN gene (Cowden syndrome)
or TP53 (Li-Fraumeni syndrome) are associated with a significant
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Nina Akrap
increased risk of breast cancer. Mutations in these susceptibility alleles are
rare in the general population and only account for a small fraction of
susceptibility for breast cancer [14].
1.1.3 Breast cancer subtypes
Breast cancer has long been perceived as a complex disease, reflected in
diverse morphological, clinical and molecular characteristics.
Traditionally breast cancer is classified according to histopathological
features, involving tumor size, nodal status and metastasis, also referred to
as the TNM staging system. In addition, immunohistochemical
parameters, such as estrogen receptor (ER), progesterone receptor (PR)
and human epidermal growth factor receptor 2 (HER2) status as well as
proliferation-associated markers (e.g. Ki67) are routinely assessed to
classify breast cancers and to guide appropriate treatment decisions. More
recently, with the invention of microarray-assisted gene expression
profiling, breast cancers have been grouped into distinct molecular
subgroups.
!
Histological classification
Histological grade and histological type are two clinical parameters used
to classify breast cancers into subgroups. Histological grade assesses the
degree of differentiation, whereas the histological type signifies the
growth pattern of the tumor. The most common type of breast carcinoma
are invasive ductal carcinoma not otherwise specified (IDC-NOC), which
accounts for 50-80% of all carcinomas, followed by invasive lobular
carcinomas accounting for about 5-15% of all cases. The remaining cases
of invasive carcinomas comprise at least 17 histological types [15].
TNM staging and the Nottingham Prognostic Index
In breast cancer a useful prognostic factor ideally separates groups of
patients who require no further adjuvant therapy after local surgery from
those patients with poor prognosis for whom additional therapy may
potentially be beneficial. No single prognostic factor meeting these
criteria has been identified [16]. To predict patient outcome and assist
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Delineating cellular heterogeneity and organization of breast cancer stem cells
clinical decision making several methods have been developed, such as
the St. Gallen consensus criteria, the National Comprehensive Cancer
Network guidelines, Adjuvant! Online and the Nottingham Prognostic
Index (NPI). The latter is widely used in clinical practice to stratify the
prognosis of patients. The NPI comprises three prognostic factors, the
presence of lymph node metastasis, tumor size and histological grade,
assembled in a prognostic index formula [17]. Numerical NPI values can
be used to stratify patients into good, moderate and poor prognostic
groups. However, it has been noted that the NPI does not expose the
complete clinical heterogeneity and thus would benefit from taking
additional parameters into account to improve personalized management
of breast cancer patients [18].
Immunohistochemical classification
In addition to the above-described histopathological parameters, ER, PR
and HER2 are used as prognostic, but mainly as predictive markers,
guiding treatment strategies. ER and PR status have been used for many
years to assess if patients are suitable for endocrine therapy. ER is a
transcription factor and required for estrogen-stimulated growth. About
two thirds of all breast cancers express ER. PR expression is regulated by
estrogens and therefore its expression is thought to indicate a functioning
ER pathway, which may assist in predicting response to endocrine
therapy. Immunohistochemistry (IHC) is the standard method for
determination of hormone receptor status. Levels of ER and PR
immunreactivity can be assessed using the Allred scoring system,
combining scores for intensity and the proportion of cells stained. Patients
may be suitable for endocrine therapy with only 1-10% of positively
stained nuclei. The oncogene ERBB2 encodes for HER2, a member of the
epidermal growth factor family of tyrosine kinases. ERBB2 is located on
chromosome 17q21 and its gene product is involved in cell differentiation,
adhesion and motility. The predominant mechanism of overexpression in
breast cancer is gene amplification, occurring in about 20% of all breast
cancers. HER2 expression is used as a predictive marker for specific
systematic therapy with the humanized monoclonal antibody trastuzumab.
HER2 assessment is conducted by IHC and in situ hybridization. The IHC
score takes staining intensity and the percentage of positive cells into
account. Patients presenting more than 10% of highly stained cells are
qualified for targeted treatment. Borderline samples undergo further
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Nina Akrap
assessment by in situ hybridization, applying a dual probe set that targets
the centromere of chromosome 17 as well as the ERBB2 gene locus.
Individuals exhibiting an ERBB2 to chromosome 17 ratio larger than two
are suitable for HER2-specific therapy [19, 20].
Molecular classification
Microarray-based gene expression profiling studies have allowed detailed
insights into the significant degree of heterogeneity of breast cancer [2123]. These studies led to the concept that breast cancer comprises multiple
diseases, affecting the same organ side and originating from the same
anatomical structure (i.e. the TDLU), but display differences in risk
factors, clinical behavior, histopathological features and response to
therapy [24]. By using hierarchical cluster analysis the seminal studies by
Perou et al., (2000) and Sorlie et al., (2001) have revealed the presence of
at least four molecular groups. In addition, these studies demonstrated that
ER+ and ER- breast cancers are distinct diseases at the molecular level
and that the observed clusters were mainly contributed to differential
expression of ER and ER-related genes, proliferation-associated genes and
to a lesser extend to HER2 and genes mapping the region of the HER2
amplicon. Today, at least six different molecular subtypes are recognized;
luminal A and B, basal-like, HER2-enriched, normal breast-like as well as
the more recently discovered claudin-low subgroup [25] (Fig.2A).
Importantly, identified subtypes are associated with differences in clinical
outcome (Fig.2B). Specific features of individual subtypes are
summarized in Table 1.
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Delineating cellular heterogeneity and organization of breast cancer stem cells
Figure 2. Human breast tumors cluster into six molecular groups and exhibit
differences in survival. A: Hierarchical clustering of 547 breast tumors into six
intrinsic subtypes. B: Kaplan-Meyer survival analysis of the six distinct breast
cancer subtypes. DFS, disease-free survival. Adapted from [26].
Table 1. Features of microarray-based defined molecular subtypes of
breast cancers. Adapted from [24].
7
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Nina Akrap
*At the RNA level, breast cancers of this subtype show noticeable similarities with normal
breast tissue and fibroadenomas. It has recently been suggested that this subtype represents
an artifact due to sample contamination with stromal, inflammatory and normal breast cells
[24].
Although the different molecular subtypes are now well recognized, there
are still limitations in regards to the definition and number of subtypes,
and their prognostic and predictive significance. Furthermore, the
information received from gene expression profiling beyond ER, PR,
HER2 and proliferation markers remains to be fully established [24].
1.1.4 Breast cancer therapy
The majority of breast cancers in the developed parts of the world are
diagnosed at early stage of the disease, owing to population-wide
mammogram screenings. Early stage breast cancers can be completely
resected by surgery followed by adjuvant therapy to prevent recurrence,
which has been the gold standard in breast cancer for a long time. More
recently, neoadjuvant treatment has been introduced and is clinically
indicated for patients with large tumor size and high nodal involvement or
patients presenting an inflammatory component [27].
Therapy for hormone receptor positive breast cancers
Hormone receptor (estrogen and progesterone) positive breast cancers
constitute up to 65-75% of all breast cancers [28]. For growth and
survival, hormone receptor positive breast cancers largely depend on
hormone supply, which is essential for endocrine treatment design.
Although, hormone receptor positive breast cancers are associated with
the best prognosis amongst all subtypes, 20% of the patients experience
recurrence within 10 years after surgery. The two main adjuvant
modalities currently provided are cytotoxic chemotherapy and endocrine
therapy, both leading to an improvement of disease-free and overall
survival. There are two main classes of endocrine therapy agents; selective
estrogen receptor modulators (SERMs) and aromatase inhibitors (AIs).
SERMs bind to estrogen receptors in a competitive fashion to inhibit DNA
synthesis by recruitment of co-repressors and inhibition of G0/G1 cell
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Delineating cellular heterogeneity and organization of breast cancer stem cells
cycle progression. The most commonly applied drugs of this class are
tamoxifen, raloxifen and toremifene. AIs inhibit the enzyme aromatase,
which converts circulating androgens into estrogens by an aromatization
reaction, resulting in reductions of serum, tissue and tumor cell estrogen
levels. AIs can exert their function only if the primary source of estrogen
is eliminated, such as in postmenopausal women, after oophorectomy or
in combination with estrogen deprivation therapy [27].
Therapy for HER2 positive breast cancer
HER2 overexpression is one of the most important carcinogenic features
and HER2 amplified breast tumors have the second-poorest prognosis
amongst breast cancer subtypes paralleled by lower disease-free and
overall survival rates. About 20-25% of all breast cancer cases are
characterized by overexpression of the HER2 protein, which is a
prognostic and predictive marker for HER2 targeted therapy. HER2 is a
transmembrane protein with an extracellular ligand-binding domain and
an intracellular tyrosine kinase domain. The receptor is activated upon
ligand binding, leading to homo- or heterodimerization with other HER
protein family members. HER2 signaling is crucial, since it triggers the
downstream activation of multiple pathways involved in cell proliferation
and inhibition of apoptosis. Trastuzumab is a recombinant humanized
monoclonal antibody and was the first FDA-approved targeted treatment
for breast cancer, targeting the extracellular domain of HER2. Clinical
studies have highlighted that combined treatment of trastuzumab with
standard chemotherapy produces improved response rates compared to
chemotherapy alone [29].
Therapy for triple negative breast cancer
Triple negative breast cancer (TNBC) is characterized by the lack of ER,
PR and HER2 expression and accounts for about 10-15% of all breast
cancers, frequently occurring in younger and African women as well as in
BRCA-mutated individuals. TNBC represent a highly heterogeneous
group of tumors and survival of patients with metastatic or recurrent
disease remains poor. Given the lack of effective drug targets,
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Nina Akrap
chemotherapy is used as the standard therapy, which is however more
beneficial than in hormone-receptor positive breast cancers [27].
Personalized breast cancer treatment
Personalized medicine aims to classify individuals into subgroups that
differ in their response to a specific treatment. With the advance of geneexpression profiling, several multi-gene expression tests for determination
of risk relapse in early stage breast cancer have become clinically
available. Molecular diagnostic tests include for example MammaPrint®
(Agendia), Oncotype DX® (Genomic Health) and PAM50® (Prosigna),
using RT-PCR or microarray technology. MammaPrint is a microarraybased gene expression profiling test, analyzing 70 genes involved in cell
cycle regulation, angiogenesis, invasion, metastasis and signal
transduction. The test stratifies patients into low- or high-risk groups of
distant recurrence and proved to be a robust predictor for distant
metastatic-free survival, independent of adjuvant treatment, tumor size,
histological grade, and age. Oncotype DX uses a 21-gene expression
signature to generate a prognostic parameter, termed recurrence score,
predicting the risk of distant recurrence in node-negative ER+ breast
cancer patients treated with tamoxifen. Based on the obtained gene
signatures, patients are classified into low, intermediate and high-risk
groups. Similarly, the PAM50 test uses a 58-gene signature to stratify
patients into low, intermediate and high-risk groups [30, 31].
1.2 Tumor heterogeneity
Breast cancer comprises a diverse group of neoplasms originating in the
epithelial cells of the mammary ducts. Heterogeneity exists between
different tumors (inter-tumor heterogeneity) as well as at the individual
tumor level (intra-tumor heterogeneity) [32].
1.2.1 Inter-tumor heterogeneity
Clinical traits that differ amongst breast cancers include proliferation rate,
invasiveness, metastatic potential and response to treatment [33]. Several
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Delineating cellular heterogeneity and organization of breast cancer stem cells
hypotheses have been developed to explain the underlying reasons for
intertumoral heterogeneity, such as different cells of origin as well as
different oncogenic events. Each breast cancer results from an
accumulation of oncogenic hits in a genetically normal cell. During the
early stage of tumor progression clonal expansion critically determines the
behavior and progression of the resulting tumor. It is thought that
characteristics of the cell of origin are epigenetically conveyed to the
tumor cells and their progeny [33].
DNA and exome sequencing technologies have enabled large-scale
studies of breast cancer cohorts. Comprehensive molecular analyses
revealed associations between tumor subtypes and sets of mutated genes
[34, 35]. An extensive and integrated study by the Cancer Genome Atlas
Network [35] included 852 primary breast cancer patients, which were
analyzed by genomic DNA copy number arrays, DNA methylation,
exome sequencing, mRNA arrays, microRNA sequencing and reversephase protein arrays. The authors found breast cancers to congregate into
four phenotypically different classes (luminal A, luminal B, basal and
HER2 amplified) due to distinct genetic and epigenetic changes. The
lowest mutational rates were identified in luminal A tumors, whereas
basal and HER2 amplified tumors exhibited the highest mutational rate.
Mutated genes were shown to differ between subgroups, luminal A tumors
most frequently displayed mutations in PI3KCA (45%), MAP3K1 (13%),
GATA3 (14%), HER2 amplification was detected in 80% of the HER2
class along with a high frequency of TP53 (72%) and PIK3CA (39%)
mutations, while basal tumors were characterized by high TP53 (80%)
mutations. Interestingly, intrinsic tumor subtypes were not only denoted
by different mutation frequencies, but also by different mutational types.
For example alterations in TP53 were mainly nonsense and frame-shift
mutations in basal tumors, but missense mutations in luminal A tumors.
This and other studies have underlined significant differences in the
mutational profile of breast cancer subtypes and potential subtype-specific
oncogenic drivers.
A second and equally important factor in the creation of breast cancer
heterogeneity is the cell of origin of a tumor and how this cell relates to
the mammary epithelial hierarchy and subtypes. To address this question
two primary approaches have been widely applied; firstly transgenic or
conditional mouse models and secondly genetic alterations of cells and
subsequent in vivo evaluation of their tumorigenic potential in mice [36].
MaSCs have been theorized to play an important role in breast cancer
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initiation due to their long life span, enabling the stepwise accumulation
of genetic mutations over time and additionally because of their inherent
properties of self-renewal and lineage differentiation. Another theory is
that the target cell of the oncogenic transformation is recapitulated in the
phenotype of the breast cancer subtype, i.e. basal-like tumors would be
derived from transformed basal progenitor cells and luminal-like tumors
would arise from transformed luminal progenitor cells [37]. More recently
however, luminal progenitor cells have been put into the spotlight as
putative breast cancer initiating cells. To explore cells of origin in human
cancers Keller et al. [6] isolated luminal cells from breast reduction
tissues and introduced several combinations of oncogenes using lentiviral
transduction. The derived tumors displayed luminal-like and basal-like
phenotypes in immunodeficent mice, comprising much of the
heterogeneity observed in sporadic breast cancers. On the other hand
isolated basal cells generated metaplastic tumors that did not resemble
common forms of breast cancer.
1.2.2 Intra-tumor heterogeneity
Intratumor heterogeneity refers to the coexistence of cancer cell
subpopulations, displaying differences in their genetic, phenotypic or
behavioral traits within a given primary tumor as well as between a
primary tumor and its metastasis. Two models have been suggested to
account for intratumor heterogeneity, clonal evolution and the cancer stem
cell theory. Both concepts are described in more detail below.
Cell oncogenic phenotypes are determined by two components, cellintrinsic and cell-extrinsic factors. Cell-intrinsic factors refer to inherent
properties of a cell and comprise genetic as well as epigenetic aspects. In
normal cells phenotypic identities are mostly always defined by nongenetic mechanisms and genetic heterogeneity is usually very low. In
cancer genetic mutations underlying tumor formation can have profound
impact on cell phenotype and provide options for therapeutic intervention,
such as in the case of HER2 overamplifcation. The differentiation state of
a cell is regulated by epigenetic mechanisms. During tumor progression
the epigenome is modified by two major sources; driver mutations
acquired during tumorigenesis and stochastic alterations during tumor
progression. The extent of epigenetic changes may be side-specific or
global. Cell-intrinsic factors however, should be regarded in a contextdependent manner. Tumor cell behavior is influenced by
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Delineating cellular heterogeneity and organization of breast cancer stem cells
microenvironmental cues, inhibiting or promoting tumor progression.
Multiple factors of the tumor microenvironment contribute to cell
diversity, including blood and lymph vessels, the extracellular matrix and
diverse stromal cells, such as fibroblasts and immune cells as well as
secreted growth factors [38, 39] (Fig.3).
Phenotypic heterogeneity can be classified into two groups, deterministic
and stochastic. Deterministic heterogeneity denotes the existence of
multiple phenotypic states. In normal tissues deterministic heterogeneity
corresponds to distinct stages in the tissue-specific differentiation
hierarchy. In cancer substantial genetic and epigenetic alterations as well
as an atypical microenvironment may cause an increase in deterministic
heterogeneity, including phenotypic states that do normally not occur in
normal tissues. Stochastic heterogeneity on the other hand, defines
transient alterations in phenotypes of cells that share the same
deterministic phenotypic state. These differences stem from the stochastic
nature of biochemical processes and from burst-like gene expression,
leading to considerable cell-to-cell variation. Besides, stochastic processes
can mediate transitions between distinct deterministic phenotypic states.
According to the cancer stem cell (CSC) concept the
phenotypiheterogeneity in cancers reflects the differentiation hierarchies
present in normal tissues [40]. Phenotypic heterogeneity appears to be
dominant over the effects of oncogenic transformations as shown by gene
expression profiles of more differentiated and stem-like subpopulations in
breast cancer cluster more closely to their respective counterparts in
normal tissues then they do to each other. Furthermore, phenotypic
heterogeneity has been associated to important clinical parameters, such
as prognosis, treatment resistance as well as metastatic potential [41-43].
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Figure 3. Determinants of tumor cell heterogeneity. Cell-intrinsic and cellextrinsic factors affect cellular diversity in solid tumors. Intrinsic factors
comprise the biology of the cell of origin as well as genetic and epigenetic
elements. Extrinsic factors arise from the microenvironment, encompassing the
composition of the extracellular matrix, blood and lymph vessel supply and the
recruitment of stromal cells supporting tumor growth.
1.3 The clonal evolution theory and the
cancer stem cell hypothesis
1.3.1 The clonal evolution theory
The clonal evolution theory provides a mechanism to account for intratumor heterogeneity and is focused on random mutations and clonal
selection. According to this paradigm cancer cells in a tumor acquire
several combinations of mutations over time. Eventually due to stepwise
natural selection for the fittest clone, most aggressive cells drive tumor
progression. The clonal evolution model suggests that tumor initiation
occurs in a single cell following the acquisition of multiple mutations,
providing it with a selective growth advantage. During tumor progression,
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Delineating cellular heterogeneity and organization of breast cancer stem cells
genetic instability and uncontrolled proliferation permit the accumulation
of further mutations and hence new characteristics, which may provide a
growth advantage over other tumor cells, e.g. by withstanding apoptosis.
In that way new cellular variant subpopulations are generated as the tumor
progresses and other subpopulations may contract, thereby producing
heterogeneity (Fig.4). Importantly, any cancer cell in a tumor can
potentially become invasive and cause metastasis or develop treatment
resistance and thus lead to recurrence [38]. Mutational analysis has shown
the existence of multiple subclones in diverse cancers including breast
cancer [44]. Moreover, breast cancers have been demonstrated to present
two classes of genetic variation, monogenomic and polygenomic tumors.
Monogenomic tumors contain a single major clonal subpopulation,
whereas polygenomic tumors contain multiple clonal subpopulations,
accounting for tumor heterogeneity [45].
1.3.2 The cancer stem cell hypothesis
An alternative and most likely supplementary concept aiming to account
for the cell diversity in tumors is the CSC hypothesis, according to which
phenotypic heterogeneity in cancers is a reflection of differentiation
hierarchies, existing in normal tissues. The model implies a hierarchical
organization of tumor cells such that a small subpopulation of CSCs form
the apex of the hierarchy and give rise to more differentiated cell types
and thereby establishing the cellular diversity of the primary tumor [40,
46]. Initial evidence for the existence of CSC was shown in acute myeloid
leukemia, in which a minor subset of cells could induce leukemia
following transplantation into immunodeficient mice [47]. In breast
cancer tumor-initiating cells were first isolated by Al-Hajj and co-workers
[48] based on the expression of cell surface marker
CD44high/CD24low/Lineagenegative profile. As few as 100 cells exhibiting
this immunophenotype were able to generate tumors in immunodeficient
mice and could be serially passaged and recapitulate the heterogeneity of
the primary tumor. In contrast, 10,000 cells expressing the reciprocal
marker profile were unable to induce tumors in mice. In follow-up studies
CSCs of breast cancers have been enriched using different combinations
of markers [7, 8] (Fig.4).
Despite of the potential applicability of the CSC model, unequivocal
characterization of cancer cell phenotypes based on their differentiation
states may be impeded by the distorted identity of differentiation states.
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Cancer cells acquire numerous epigenetic and genetic aberrations,
possibly leading to unique mutational phenotypes, which may not exactly
parallel similar states in normal cells [49]. Additionally, several studies
have demonstrated that CSCs can be generated from non-CSCs by
induction of the epithelial-to-mesenchymal transition (EMT) [50, 51] or
convert to a CSC state spontaneously [52, 53], leading to an extension of
the classical CSC model to include the phenomenon of cellular plasticity
(Fig.4). Moreover, stemness of cancer cells can be profoundly affected by
the applied functional assay.
Figure 4. Clonal evolution and the CSC model create tumor heterogeneity. The
clonal evolution model suggests that diverse cancer cell populations evolve
during tumor progression due to the accumulation of random mutations and
clonal selections, thereby contributing to tumor heterogeneity. The cancer stem
cell model proposes that tumor heterogeneity arises when cancer cells reside in
distinct states of stemness or differentiation within an individual tumor. In the
classical CSC model conversions between cell states occur in a unidirectional
manner. The plastic CSC model describes an evolving concept; according to this
paradigm cell-state conversions between CSCs and non-CSCs can occur in a
bidirectional fashion, implying that non-CSCs can generate CSCs throughout
tumorigenesis. CSC, cancer stem cell. Modified from [39].
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Delineating cellular heterogeneity and organization of breast cancer stem cells
1.3.3 Attributes of cancer stem cells
CSC share critical features with normal tissue stem cells, including selfrenewal by symmetric and asymmetric cell division and the capacity to
differentiate, although in an aberrant manner. Multi-lineage differentiation
however is not an obligatory feature of CSCs [46]. In addition, CSCs
often use the same signaling pathways utilized by their normal
counterparts, such as Notch, Wnt and Hedgehog [54]. The cancer stem
cell frequency appears highly variable between different tumor types and
even tumors of the same subtype. CSC numbers may change during the
course of the disease and moreover CSC enumerations strongly depend on
the applied assay to assess stemness, highlighting the need for more
specific markers [2, 46, 55]. For the definitive identification of CSCs
enriched cell fractions should re-establish the phenotypic heterogeneity of
the primary tumor and exhibit self-renewing capacity on serial passaging
in mouse model systems.
Besides, CSCs have been implicated in mediating metastasis [56] and
increased resistance against radiation and chemotherapy, contributing to
relapse following therapy [57-59]. CSC characteristics can vary across
different breast cancer subtypes, for example Harrison et al. [60] have
demonstrated that hypoxia influences CSC numbers in contrasting
directions in ER!+ and ER!- breast cancer, where CSC numbers
increased in the ER!+ disease following hypoxia. CSC heterogeneity has
also been detected within a given tumor. Max Wicha’s lab has shown that
normal and malignant breast cancer stem cells express CD44high/CD24low
phenotype [48] and in addition the enzyme aldehyde dehydrogenase
(ALDH) enriches for cells with CSC characteristics. In primary breast
xenografts, the CD44high/CD24low phenotype and ALDHhigh fractions
identified overlapping, but non-identical cellular populations, both able to
initiate tumors in NOD/SCID mice [7]. More recently the group has
demonstrated that CD44high/CD24low populations exhibit a more
mesenchymal-like phenotype, whereas ALDH populations were
characterized by an epithelial, proliferative phenotype [61]. Moreover,
transitions between these two CSC states were found to be mediated by
epigenetic mechanisms induced by the tumor microenvironment as well as
transcriptional regulation. Based on their studies the authors suggested
that epithelial and mesenchymal-like states of CSCs might enable these
cells to invade and form distant metastasis.
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1.3.4 Concluding remarks
Both, the cancer stem cell model and the clonal evolution theory are likely
to exist in human cancers and are not mutually exclusive. The two
concepts share certain similarities, such as the cellular origin of cancer. In
both views cancer originates from an individual cell that has acquired
multiple mutations and gained the potential to proliferate unlimitedly.
Furthermore, consistent with both paradigms the cell of origin, genetic
aberrations as well as microenvironmental factors will define the
constitution of a tumor, its physical and clinical characteristics.
Differences concern the mechanisms with which tumor heterogeneity is
described. The CSC model proposes a program of aberrant differentiation,
while the clonal evolution model suggests competition between clonal
subpopulations to explain tumor heterogeneity. Furthermore, in the CSC
model only a small subset of cells contribute to tumor progression,
whereas any cell in a tumor has the potential to be involved in tumor
progression according to clonal evolution. According to the CSC concept
only CSCs may acquire further mutations which may lead to more
aggressive phenotypes. Another difference concerns drug-resistance, CSC
are thought to be inherently drug-resistant, while the clonal evolution
models proposes a selection of drug-resistant clones [38]. These two
models implicate differences in the design for new anti-cancer treatments.
In the case of the CSC model, CSCs must be eradicated in order to
achieve curative treatment, requiring knowledge about predominating
pathways and proteins in these cell types. On the other hand, the clonal
evolution model implies that effective treatment regimens should target
multiple cancer cell populations.
1.4 Mevalonate pathway in cancer
1.4.1 Dysregulated metabolism in cancer
The six core hallmarks of cancer as originally postulated by Hanahan and
Weinberg [62] comprise sustaining proliferative signaling, evading
growth suppressors, resisting cell death, enabling replicative immortality,
inducing angiogenesis, and activating invasion and metastasis. Conceptual
progress over the last decade has led to a revised version of the paper [63],
which includes the emerging cancer hallmarks, evading immune
surveillance as well as reprogramming of cellular metabolism. Cancer
cells often proliferate in an uncontrolled manner with corresponding
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Delineating cellular heterogeneity and organization of breast cancer stem cells
alterations of their energy metabolism to ensure sufficient metabolite
supply for cell growth and division. Normal cells under aerobic conditions
metabolize glucose to pyruvate in the cytoplasm, which is then imported
into mitochondria to generate adenosine 5´-triphosphate (ATP) by
oxidative phosphorylation. Under anaerobic conditions pyruvate
production is favored, generating ATP with a considerable lower
efficiency [63].
In the 1920s Otto Warburg discovered that cancer cells, even in the
presence of ample oxygen, prefer to generate ATP through glycolysis, a
seeming paradox as glycolysis is less efficient in terms of ATP production
compared to oxidative phosphorylation [64]. This phenomenon is called
the Warburg effect, also known as aerobic glycolysis. Since then the
Warburg effect has been appreciated in different types of cancers [65] and
its concomitant increase of glucose uptake has been employed clinically
for solid tumor detection by fluorodeoxyglucose positron emission
tomography (FDG-PET). Given the low energy efficiency of the Warburg
metabolism, the functional rationale so far remains unclear.
One idea to explain the Warburg effect is that glycolytic metabolism of
cancer cells presents a selective advantage in the unique tumor
environment. Insufficient and disorganized vessel formation in the
growing tumor leads to limited blood supply, hypoxia and stabilization of
hypoxia-inducible transcription factors (HIFs) [65]. HIF initiates a
pleiotropic transcriptional program that counteracts hypoxic stress,
including a shift towards glycolytic metabolism by upregulation of
glycolytic enzymes, glucose transporters, and inhibitors of mitochondrial
metabolism. With the possible exception of tumors that have lost the von
Hippel-Lindau protein, HIF expression is still linked to oxygen levels, as
evident from its heterogeneous expression in tumors [66, 67]. Thus, the
Warburg effect cannot only be explained by HIF stabilization. Oncogene
activation (e.g. RAS, MYC) and tumor suppressor loss (e.g. TP53, see
below) have been associated with the induction of metabolic changes
independently of HIFs [68]. Rapidly dividing cells require not only ATP,
but also nucleotides, proteins, fatty acids and membrane lipids for biomass
production. More recently, Vander Heiden et al., [69] have proposed that
elevated glycolysis permits the allocation of glycolytic intermediates into
numerous biosynthetic pathways, enabling cells to synthesize
macromolecules and organelles needed to produce a new cell. AcetylCoenzyme A (acetyl-CoA) for example is made available for the synthesis
of several lipid building blocks, including mevalonate (MVA).
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Nina Akrap
1.4.2 The mevalonate pathway for steroid
biosynthesis and protein prenylation
The mevalonate pathway was discovered in the 1950s by Goldstein and
Brown [70] and provides isoprenoid building blocks for the biosynthesis
of diverse classes of vital cellular products, including cholesterol and
prenyl pyrophosphates. The latter function as substrates for
posttranslational prenylation of proteins. Imbalances of mevalonate
metabolism are a well-known cause for cardiovascular diseases [71].
More recently, dysregulation of the mevalonate pathway has been
implicated in various aspects of tumor development and progression [72,
73] and has been linked to CSC survival in breast cancer [74, 75].
Rapidly dividing tumor cells have high energetic requirements, in order to
meet these glucose is converted into pyruvate by aerobic glycolysis as
described above. Pyruvate enters the mitochondria, where it is further
metabolized in the tricarboxylic acid (TCA, citrate or Krebs) cycle.
However, mitochondrial oxidation is incomplete, leading to an increased
export of acetyl-CoA into the cytosol, which is thereby made available for
mevalonate metabolism [76] (Fig.5). In the mevalonate pathway thiolase
condenses two acetyl-CoA molecules to produce acetoacetly-CoA. 3hydroxy-3-methylglutaryl-CoA synthase 1 (HMGCS1) condenses
acetoacetyl-CoA with another acetyl-CoA to form 3-hydroxy-3methylglutaryl-CoA (HMG-CoA). In the first committed step of the
mevalonate
pathway
3-hydroxy-3-methylglutaryl-CoA
reductase
(HMGCR) converts HMG-CoA to mevalonic acid (mevalonate). HMGCR
is regulated by several feedback mechanisms and the target of the
cholesterol-lowering class of drugs, collectively referred to as statins.
Mevalonate is then metabolized to isopentylpyrophosphate (IPP) and its
isomer dimethylallyl pyrophosphate (DMAPP), which both represent
precursors for diverse classes of cellular products [71].
Products formed in the cholesterol branch of the pathway include steroids
like estrogen, bile acids and vitamin D. In normal cells cholesterol is
essential to maintain membrane integrity, modulating membrane fluidity
and is involved in intracellular transport as well as in cell signaling [70].
In the prenylation branch of the pathway farnesyl pyrophosphate (FFP)
and geranylgeranyl pyrophosphate (GGPP) are formed through sequential
condensation reactions of DMAPP. Both, FFP and GGPP are used as
adjuncts for C-terminal posttranslational modifications of various
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Delineating cellular heterogeneity and organization of breast cancer stem cells
proteins, which are referred to as proteinprenylation. Prenylation plays a
role in membrane attachment and protein-protein interaction, which are
essential requirements for biological functioning of proteins and is carried
out by three enzymes, FTase, GGTase I and GGTase II. Prenylation
occurs on many members of the Ras and Rho small guanosine
triphosphatases (GTPases). The role of Ras proteins in cancer
development and progression is well established [77].
Figure 5. Metabolic reprogramming and dysregulation of the mevalonate
pathway in cancer. Metabolic reprogramming of cancer cells causes
upregulation of aerobic glycolysis to the expense of oxidative phosphorylation.
Pyruvate is produced during aerobic glycolysis, which is either converted to
lactate or further metabolized in the TCA cycle. Mitochondrial oxidation is
incomplete and generates excess acetyl-CoA, which is exported into the cytosol.
Cytosolic acetyl-coA can be used to generate HMG-CoA in the mevalonate
metabolism, which is enhanced by certain p53 mutant variants. The mevalonate
pathway can be blocked at several steps; statins inhibit HMG-CoA reductase, the
first committed step of the pathway, while nitrogen-containing bisphosphates (NBPs) inhibit FFP synthase. Modified from [76].
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Nina Akrap
1.4.3 Mevalonate metabolism is regulated by
mutant p53
As the “guardian of the genome” the tumor suppressor protein p53 plays
an important role in the maintenance of genomic integrity and the
prevention of tumor formation. p53 activation occurs through various
extra- and intracellular stressors such as, DNA damage, nutrient
depravation, hypoxia, oncogene deregulation, radiation or chemical agents
[78]. Upon activation p53 is stabilized primarily through posttranslational
modifications, which leads to its activation and accumulation in cells [79,
80]. Wild-type p53 functions as a sequence-specific, homotetrameric
transcription factor, binding to degenerative DNA sequences, termed p53responsive elements to initiate transcription of target genes. Cellular
responses triggered by p53 are stimulus-dependent, i.e. in cells that are
exposed to transient or mild stress p53 promotes cell cycle arrest (e.g. via
p21, GADD45, 14-3-3r) or DNA repair response to facilitate cell survival.
On the other hand, sustained or severe cellular stress triggers p53mediated apoptosis (e.g. via Puma, Bax, Fas) and senescence (e.g. via
p21) [81], to prevent tumorigenesis. Somatic p53 mutations are
appreciated in almost all types of cancer and can be detected in more than
50% of tumors [79, 80]. In contrast to most tumor suppressors, which are
inactivated following mutation, cancer-associated p53 mutations are
frequently missense mutations. Single-base pair substitutions cause the
translation of full-length proteins with an amino acid exchange at the
respective position. These missense mutations cause p53 protein alteration
and prolong the half-life of the protein. In normal, unstressed cells p53 is
maintained at low levels through ubiquitination and proteasomal
degradation by its negative regulators. In addition to loss of tumorsuppressive functions, certain p53 mutants can acquire novel tumorpromoting activities, referred to as gain-of-function mutations. More
recent work has demonstrated that mutant p53 is also involved in many
aspects of metabolic regulation in tumor cells [82, 83] Freed-Pastor et al.
[74] have shown that mutant p53 transcriptionally enhances the expression
of mevalonate pathway members by associating with sterol regulatory
element-binding proteins and binding to sterol gene promoters, resulting
in increased protein prenylation and maintenance of a malignant
phenotype.
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Delineating cellular heterogeneity and organization of breast cancer stem cells
2 AIMS
The overall aim of this thesis is to characterize CSC phenotypes and the
cellular organization in ER!+ and ER!- subtypes of breast cancer at the
individual cell level. Furthermore, we have aimed to identify novel
functional CSC markers in a subtype-independent manner, allowing for
better identification and targeting of CSCs.
Specific aims
Paper I: Quantification of small molecule numbers frequently involves a
preamplification step to generate sufficient copies for accurate
downstream analyses. In paper I we aimed to evaluate the effects of
variations of relevant parameters on targeted cDNA preamplification for
single-cell reverse transcription – quantitative polymerase chain reaction
(RT-qPCR) applications, to improve reaction sensitivity and specificity,
pivotal prerequisites for accurate and reproducible transcript
quantification.
Paper II: The large number of assays currently employed to detect CSC
in breast cancer types indicates either a lack of universal markers or is
reflective of the heterogenetic and dynamic nature of CSCs. In paper II
we aimed to study the diversity of the CSC pool at the individual cell level
in regards to ER!+ and ER!- subtypes, using several functional cancer
stem cell enrichment techniques.
Paper III: Reliable CSC markers common to various breast cancer
subtypes remain to be clearly defined and represent an essential
requirement for clinical identification, monitoring and effective
therapeutic targeting. In paper III we aimed to identify specific molecular
pathways common to CSCs of ER!+ and ER!- subtypes.
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Nina Akrap
3 METHODOLOGICAL ASPECTS
3.1 Single-cell qPCR
Breast cancers are complex entities, composed of heterogeneous cell
types, exhibiting remarkable diversity for many tumorigenesis-related and
therapy-relevant traits, such as their tumorigenic, angiogenic, invasive and
metastatic potential. In addition, responses to specific treatments have
been reported to differ greatly between individual tumor cells [32]. Thus,
there is a vital requirement for reliable tools to scrutinize cellular
behaviors at the single-cell level. One of the major limitations of
conventional gene-expression profiling is that measurements are
performed on composite samples, containing diverse cells in undefined
proportions. Single-cell gene-expression profiling permits the
identification and characterization of different cell types and furthermore,
enables the correlation of gene expression patterns with phenotypical
qualities, providing a comprehensive approach to assess individual cells
under various conditions [84]. Inherent to most single-cell techniques is
the difficulty to analyze minute amounts of starting material, which is
technically more demanding. To date RT-qPCR is the most commonly
applied strategy for single-cell gene-expression profiling [85]. The
technique includes several sequential steps, each of which must be
carefully optimized and validated. Specific steps encompass; cell
collection and lysis, reverse transcription of mRNA and cDNA
preamplification followed by qPCR and multivariate data analysis (Fig.6).
Cells can be collected using various techniques, e.g. fluorescenceactivated cell sorting or microaspiration. During the cDNA
preamplification step transcript copy numbers are multiplied in a
quantitative fashion, theoretically facilitating the analysis of an unlimited
number of transcripts per single cell. Several preamplification approaches
have been described in the literature. For single-cell applications the
preferred method is targeted multiplex PCR, applying gene-specific
primers. Multiplex PCR is a highly complex reaction, due to the presence
of multiple primer pairs and the simultaneous amplification of large
numbers of target molecules. It is critical to not introduce substantial
variation or bias during the preamplification step in order to preserve the
original gene expression pattern.
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Delineating cellular heterogeneity and organization of breast cancer stem cells
Figure 6. Workflow of single-cell qPCR. Individual cells are collected by either
fluorescence-activated cell sorting or microaspiration and lysed directly. Singlecell RNA is reverse transcribed, followed by targeted cDNA preamplification and
quantitative real-time PCR. Single-cell data are typically analyzed using various
uni- and multivariate statistical tools.
3.2 Cancer stem cell enrichment methods
Investigating the role of CSCs during tumorigenesis has become a major
focus in stem cell biology over the last decade. Considerable efforts have
been made to develop clinical applications of the CSC model. Given the
specific CSC attributes of self-renewal and differentiation, each applied
marker and assay needs to be evaluated carefully [86]. The gold standard
to demonstrate CSC identity is serial transplantation of cellular
populations into immunocompromised mouse models. The CSCcontaining population should give rise to the phenotypic heterogeneity
evident in the primary tumor and demonstrate self-renewing competence
upon serial passaging. The isolation of CSCs from epithelial or solid
tumors is accompanied by significant technical issues, in part due to the
difficulty of dissociating these tumors [2]. Furthermore, in the case of
xenotransplantation incomplete immunosuppression and species-specific
variations in cytokine or growth factor signaling represent confounding
factors. In addition to serial passaging in mice, a number of cell surface
markers have been proven useful for CSC enrichment, including CD133
(also known as prominin 1), CD44, CD24, epithelial cell adhesion
molecule (EPCAM) or CD49f (also known as !6-integrin). Other CSC
assays involve the Hoechst33342 side population sorting, which is conferred
by the ABC transporter ABCG2 and the ALDEFLOUR assay, based on
the activity of the detoxifying enzyme aldehyde dehydrogenase,
catalyzing the oxidation of retinol to retinoic acid. CSCs have frequently
been enriched using markers specific for stem cells of the same organ.
However, the utility of CSC markers is limited by variations in
expression, regulation by environmental factors and moreover isolated
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Nina Akrap
CSC fractions may contain considerable numbers of non-CSCs [87].
Therefore, definitive enrichment of CSCs necessitates functional assays.
To circumvent obstacles associated with immunophenotypic CSCisolation, in this work we have applied three different assays to
functionally enrich for CSCs; growth in anchorage-independent culture,
hypoxic culture and label-retention. Each method is explained in more
detail below.
3.2.1 Growth in anchorage-independent culture
Cell culture in non-adherent conditions was originally adapted to normal
breast tissue derived from reduction mammoplasties [88]. Mammary stem
and progenitor cells are equipped with the unique feature of withstanding
anoikis in serum-free suspension culture and generate spherical colonies,
termed mammospheres. These mammospheres were found to be enriched
in stem and progenitor cells. Moreover, mammosphere-derived cells
differentiated along the three mammary epithelial lineages, clonally
produced functional structures in 3D culture systems and reconstituted
mammary glands in mouse model systems. The mammosphere assay has
subsequently been adapted for quantification of stem cell activity and selfrenewal capacity in cancer research and has been applied to enrich for
CSC-like cells in ductal carcinoma in situ [89], invasive ductal carcinoma
[90] and breast cancer cell lines [91]. As an example, Ponti et al. [90]
have demonstrated that breast cancer cell-derived spheres displayed an
increase in the Hoechst33324 side population fraction, CD44+/CD24- cells,
expressed the pluripotency-associated transcription factor OCT4 and
showed high tumorigenic potential in mice. Hence, the mammosphere
assay provides a functional in vitro tool to discover and scrutinize
pathways implicated stem/progenitor cell survival.
3.2.2 Hypoxic culture
Hypoxia is commonly present in solid breast cancers and linked to
malignant progression, invasion, angiogenesis, changes in metabolism and
increased risk of metastasis and consequently to impaired patient
prognosis. Several factors are known to cause intratumoral hypoxia, such
as inadequate vascularization, an increase in diffusion distances that is
associated with tumor expansion as well as tumor or therapy-related
anemia. Cancer cells are able to adapt to a low-oxygen environment,
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Delineating cellular heterogeneity and organization of breast cancer stem cells
which contributes to a more malignant cellular phenotype [92]. The
adaption to hypoxia is controlled by many factors, e.g. transcriptional and
post-transcriptional changes in gene expression. In this regard, 1.5% of the
human genome has been estimated to be responsive to hypoxia [93]. HIF1! is the master regulator of the hypoxic response at the cellular level.
Under hypoxia HIF 1! is stabilized and translocates to the nucleus, where
it binds to the HIF 1" subunit and the co-activator p300 to activate the
transcription of target genes, by binding to the hypoxic-response elements
(HRE). HIF-responsive genes are involved in numerous cellular
processes, including proliferation, survival, metabolism, angiogenesis,
invasion and metastasis, pH regulation and the maintenance of stem cells.
Moreover, cross-talks between the estrogen and hypoxic signaling
pathways have been reported in breast cancer [94-96]. Under hypoxic
conditions HIF-1! facilitates ER! down-regulation by proteasomal
degradation as well as transcriptional repression of ER! expression [97,
98]. Several lines of evidence have reported a change of gene expression
towards a more immature phenotype or an increase of cells with CSC
features in response to hypoxia in different cancer types [99-101].
Furthermore, it has recently been demonstrated that hypoxia leads to
increased CSC numbers in ER!+ breast cancers [60].
3.2.3 Label-retention
A less well-studied feature of CSC is cellular quiescence or dormancy,
which is characterized by a low metabolic activity and entrance into a
reversible G0-G1 arrest [102]. Various studies have used lipophilic
fluorescent dyes, such as carboxyfluorescein succinimidyl ester (CFSE) or
the PKH dye as well as BrdU-label retention to isolate slow-cycling breast
cancer cells [8, 103, 104]. Interestingly, the work of Fillmore and
Kuperwasser [103] has shown, that slow-cycling cells are present in the
CD44+/CD24-/EPCAM+ population of breast cancer cells, suggesting that
these cells represent a specific CSC subset. It has furthermore been
demonstrated that slow-cycling cells exhibit increased xenobiotic efflux
mediated by ABCG2 transporters and increased DNA repair mechanisms
[105, 106]. Taken together, these findings indicate that quiescent CSC
putatively represent a small cellular subpopulation, which could be
associated with resistance to chemo- and radiotherapy, disease recurrence
and the formation of distant metastasis. In the paper II we have combined
PKH26 labeling with the mammosphere assay to functionally enrich for
27
Nina Akrap
quiescent CSC-like cells [8]. The approach is based on the principle that
during mammosphere growth, quiescent or slow dividing cells will retain
the PKH26 dye, whereas the bulk population of transiently proliferating
progenitor cells loses the dye due to successive cell divisions.
28
Delineating cellular heterogeneity and organization of breast cancer stem cells
4 RESULTS AND DISCUSSION
4.1 Results and discussion paper I
The purpose of the preamplification is to multiply transcript copy numbers
in a quantitative manner. Although several preamplification strategies
have been described [107-109], for single-cell gene expression profiling,
the preferred method is targeted multiplex PCR, using gene-specific
primers [85]. In paper I we aimed to evaluate several experimental
parameters in targeted preamplification and their effects on the
reproducibility, specificity and efficiency of RT-qPCR. Specifically,
variations in numbers of primers present in the multiplex reaction, primer
concentrations, annealing temperature and time, cDNA template
concentrations as well as the effect of PCR additives were studied
(Tab.2). To assess its overall performance, we monitored the
preamplification reaction in real-time using the DNA-binding dye SYBR
Green I followed by melting curve analysis, referred to as analysis of
preamplification. By using a non-specific reporter dye this method
allowed us to quantitatively assess overall product formation as well as the
ratios of specific and non-specific PCR products, evaluating the shape of
the melting curves. Furthermore, the formation of specific amplicons was
analyzed with standard RT-qPCR (Fig.7).
For the evaluation of targeted preamplification we optimized 96 individual
PCR assays and purified and quantified the corresponding PCR products
for standardization of template molecule numbers.
!
!
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Nina Akrap
!
Figure 7. Experimental strategy to evaluate parameters on targeted
preamplification. Left: Analysis of preamplification. To evaluate the overall
performance of targeted preamplification the reaction was monitored in realtime using SYBR Green I detection chemistry over 35 PCR cycles followed by
melting curve analysis. Total product formation was quantified via amplification
curves, whereas ratios of specific versus non-specific PCR product formation
were derived from melting curve analyses. Right: Analysis of individual assays.
Individual assays were assessed by downstream RT-qPCR following 20 cycles of
preamplification, applying conventional or high-throughput RT-qPCR
Table 2. Summary of analyzed parameters for targeted preamplification.
30
Delineating cellular heterogeneity and organization of breast cancer stem cells
Theoretical molecule and preamplification cycle numbers and the
dynamic range of targeted preamplification
The required number of preamplification cycles depends on the
downstream qPCR platform and is primarily determined by the reaction
volume, the initial cDNA concentration present in the sample as well as
the dilution factor after preamplification and the preamplification
efficiency [85]. In qPCR, the Poisson distribution can be applied to model
the probability that a reaction chamber contains a particular number of
target cDNA molecules. The variation across reaction chambers
attributable to the Poisson noise leads to considerable uncertainty in the
measured Cq values. Theoretically, an average of 5 molecules per reaction
chamber will yield a 99.3% probability that a reaction chamber contains at
least one molecule. To reduce the variation in Cq due to the Poisson effect
below the variation observed for typical qPCR an average of 35 molecules
is needed [85, 109]. Considering the dilution factor and the effect of
Poisson noise, for 5 initial molecules, we calculate 19 cycles of
preamplification to produce an average of 5 molecules per reaction
chamber on the applied BioMark high-throughput qPCR, assuming a
preamplification efficiency of 80%. In this study, our optimized assays
displayed a preamplification efficiency of approximately 100%, which
results in an average of 36 molecules per reaction chamber.
To assess the dynamic range of the preamplification we conducted two
experiments, to determine the effect of total template concentrations as
well as the effect of only one highly concentrated template. In the first
experiment templates of 6 assays were kept at a constant concentration of
100 molecules each, whereas the remaining 90 templates were varied,
ranging from 0 to 107 molecules per reaction. In the second experiment the
initial target concentration of 95 assays was kept constant at 100
molecules per reaction and only one target was varied between 100 to 109
molecules (Fig.8).
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Nina Akrap
Figure 8. Dynamic range of preamplification – Effect of varied template
concentrations. A. Average Cq ±SD (n=3) of the six assays kept at a constant
initial template concentration of 100 molecules each per reaction. B. Average Cq
±SD (n=3) of six randomly selected assays from the preamplification with an
initial template concentration of 0 to 107 molecules each. C. Average Cq ±SD
(n=3) of six randomly selected assays from the preamplification used at a
constant initial concentration of 100 molecules each per reaction. D. Average Cq
±SD (n=3) of the single assay included in the preamplification with an initial
template concentration of 102 to 109 molecules. The linear fit is to guide the eye
only.
For our specific reaction conditions, the preamplification was within
dynamic range when the 90 templates were initially present at
concentrations <104 molecules, while the remaining six templates were
kept at 100 molecules per reaction (Fig.8A and B). Inhibition occurred at
template concentrations >104 molecules. However, when only the
concentration of one target molecule was increased, the remaining assays
were unaffected. In summary, the preamplification dynamic range of an
32
Delineating cellular heterogeneity and organization of breast cancer stem cells
assay was dependent on the amount of its target molecules and on the total
number of target molecules for all the preamplification assays.
Dependence on assay numbers
To test the effect of different numbers of assays present in the
preamplification, we conducted experiments containing 6, 12, 24, 48 and
96 primer pairs at a constant primer concentration of 40 nM. Analysis of
preamplification showed an increase of the total PCR product yield with
increased assay numbers (Fig.9A). Similar results were obtained using
shorter (0.5 min) or longer (8 min) annealing times. However, Cq values
of template-containing samples did not decrease significantly between 3
and 8 min annealing time, implying that 3 min of annealing is sufficient
for effective target binding under these conditions. Interestingly, not only
the total PCR product formation increased with increasing assay numbers,
but also the PCR product yield of individual assays in downstream RTqPCR (Fig.9B and C), along with improved reproducibility. Due to the
large total number of different primer pairs present in the highly
multiplexed preamplification reaction, non-specific PCR products are
formed at large quantities. One explanation for this observation may be
that increased numbers of primer pairs during preamplification will
increase the formation of possible primer-to-primer interactions as well as
the formation of non-specific PCR products. However, nonspecific PCR
products formed during preamplification will only interfere with the
downstream singleplex PCR if the particular nonspecific PCR product is
complementary to the applied primer pairs.
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Nina Akrap
Figure 9. Assay number dependence. A. Cq-values (average ±SD) for positive
(n=3) and negative samples (n=3) using different number of assays in
preamplification. B. High-throughput qPCR data of individual assays. Average
Cq ±SD (n=3) is shown. Data from all preamplified genes were used. C. Average
Cq ±SD (n=3) of 10 assays included in the preamplification with 12, 24, 48 and
96 pooled assays.
Dependence on primer concentration, annealing time and temperature
Primer concentration, annealing time and duration of the annealing step
are reciprocal factors in preamplification. To reduce the formation of
nonspecific PCR product formation in the multiplex reaction primer
concentrations are 10-20 times lower compared to regular PCR [85, 109].
To maintain high preamplification efficiency at low primer concentration
the annealing time is usually extended up to several minutes. The effect of
variable primer concentrations (10, 40, 160, 240 nM) was tested in
relation to different annealing times (0.5, 3, 8 min). Analysis of
preamplification revealed elevated yields of specific and non-specific
PCR products as primer concentrations and annealing times were
increased. We observed a shift from specific towards non-specific product
formation when primer concentrations were increased from 40 to 160 nM.
The performance of individual assays was dependent on the primer
concentration and annealing time as well. We found individual assays
performed best at a concentration of larger than 40 nM using long
annealing times (3 min and 8 min).
All primers applied for this study were designed to anneal to their specific
target sequence at 60 !C. Using analysis of preamplification of an
annealing temperature gradient ranging between 55.0 !C to 65.3 !C we
34
Delineating cellular heterogeneity and organization of breast cancer stem cells
made two main observations: First, an increase in annealing temperature
lead to a reduction of PCR product yields. Second, we detected a gradual
shift from non-specific to specific product formation as the annealing
temperature increased. For downstream qPCR highest yield, specificity
and reproducibility was observed at annealing temperatures between
58.5 !C and 61.3 !C, using assays optimized to anneal at 60 !C.
Effect of various PCR additives and single-cell gene expression profiling
Analysis of preamplification revealed large amounts of non-specific PCR
products formed for most tested conditions. Therefore, we have tested the
effects of 18 different PCR additives in 35 different reactions, which may
improve enzymatic reactions involving nucleic acids. The formation of
nonspecific PCR products was reduced by 10 cycles (~1000-fold)
compared with preamplification without additives when using and 2
mg/mL bovine serum albumin supplied with 2.5 and 5.0% glycerol,
respectively, 5%, glycerol, 0.5 M formamide and 0.5 M L-carnitine. The
effect of nine selected additives was further evaluated at the individual
assay level, using downstream qPCR of 96 assays. Here, the
preamplification performed equally regardless whether additives were
present or not. Most likely this is because our assays are extensively
optimized for high efficiency, specificity and sensitivity. However, PCR
additives may prove beneficial for less optimized assays or in the context
of next-generation sequencing where formation of non-specific products
may impede sequencing capacity and reduce the amount of informative
reads.
Today, many clinical applications strive towards the use of non-invasive
sampling strategies and small biopsies, including fine needle aspirates and
circulating tumor cells, to detect and quantify biomarkers. Due to the low
abundance of starting material, adequate molecule quantification requires
highly sensitive, robust and specific technologies [110-112]. The preferred
strategy to quantify multiple DNA or cDNA targets in biological samples
of limited size is to first preamplify the material, which theoretically
allows for the analysis of any target sequence by downstream qPCR or
next generation sequencing. Optimized preamplification protocols
typically show high sensitivity, specificity, efficiency, reproducibility and
dynamic range. Targeted preamplification is usually conducted as a
35
Nina Akrap
multiplex PCR, restricting the amplification to the sequences of interest
only [109, 113].
In conclusion our data suggests, that the number of preamplification
cycles should be sufficient to produce at least five (accurate sensitivity),
but preferentially 35 (accurate precision) molecules per downstream
qPCR reaction. A small number of highly abundant targets will likely not
affect the performance of other assays. Furthermore, we found that the
usage of large assay pools, low primer concentration and long annealing
times is beneficial for accurate targeted preamplification.
4.2 Results and discussion paper II
Breast cancer is a distinctly heterogeneous disease with respect to
histological, molecular and clinical features, affecting disease progression
and treatment response [114]. The cancer stem cell model may provide
one explanation for the observed intratumoral heterogeneity, suggesting
that cancers are driven by a cellular subpopulation with stem cell
properties, which give rise to hierarchically structured tumors. Currently,
there is a lack of universal and definite CSC markers, indicating that the
CSC phenotype may not necessarily be uniform between cancer subtypes
or even tumors of the same subtype [55]. Categorization of CSCs is
further complicated by their cellular plasticity [50-53] and a dynamic
microenvironment [39].
In paper II we aimed to characterize putative CSC pools in ER!+ and
ER!- models of breast cancer. To this end, we established single-cell RTqPCR-based gene expression profiling of well-known markers of
differentiation, stemness, the EMT and cell cycle regulators. To
circumvent current obstacles associated with immunophenotype-based
CSC-enrichment methods, in this study we applied three functional in
vitro CSC assays; growth in anchorage-independent culture, hypoxia and
isolation of low proliferative, label-retaining cells derived from
mammospheres (Fig.10A-C). All methods have previously been
demonstrated to enrich for cells with increased cancer initiating potential
in mouse model systems [60, 91, 115].
36
Delineating cellular heterogeneity and organization of breast cancer stem cells
Figure 10. Applied functional CSC enrichment methods. Breast cancer cell
lines were cultured as regular monolayers and cancer stem like cells were
enriched using three established techniques: A. Growth in anchorageindependent culture (ER!+ and ER!- cell lines). B. Hypoxia (1% O2 for 48 h)
(MCF7 cells). C. Non-dividing, PKH26Bright cells cultured as mammospheres
(MCF7 cells).
ER!+ cell lines display distinct subpopulations with CSC-like and
differentiated phenotypes, while proliferative phenotypes define ER!breast cancer cell lines
To detail CSC in the first CSC enrichment approach, we made use of the
ability of CSCs to withstand anoikis in anchorage-independent culture
systems [88, 91]. CSC were enriched following 16-hours growth in
anchorage-independent conditions and analyzed in parallel with matched
monolayer cultures. To investigate cellular organization as well as the
CSC pool in ER!+ and ER!- breast cancer models, individual ER!+ cells
(MCF7, n=157; T47D, n=158) and ER!- cells (CAL120, n=140;
MDA231, n=159) were subjected to single-cell gene expression profiling.
Using principal component analysis (PCA), monolayer and anoikisresistant MCF7 cells displayed three distinct clusters, termed ER!+ I-III.
ER!+ I was characterized by high expression of the pluripotencyassociated genes, lack of proliferation markers and low overall expression
37
Nina Akrap
levels, characteristic for quiescent stem cells [116, 117]. ER!+ II
exhibited high expression of breast cancer stem cell associated genes as
well as high expression of the proliferation markers. ER!+ III was
denoted by high expression of differentiation-associated genes. Anoikisresistant cells were enriched in clusters ER!+ I and II, whereas the
majority of monolayer cells was present in cluster ER!+ III. Similar
clusters were observed for T47D cells; we identified two clusters ER!+ I
and III. Interestingly, differential expressed genes between anoikisresistant cells and monolayer cells were essentially identical for the two
analyzed ER!+ cell lines, suggesting similar CSC enrichment mechanisms
within this breast cancer subtype.
In line with previously published data, single-cell analysis has
demonstrated that the majority of regular grown ER!+ cells displayed a
RNA expression profile reminiscent of a more differentiated luminal
phenotype [118, 119]. In contrast, ER!+ anoikis-resistant cells formed
well-separated clusters with distinct CSC-like gene expression signatures,
indicative of a hierarchical cell organization. Intriguingly, for MCF7 cells
we have identified two clusters with distinct CSC-like gene expression
profiles, which were enriched for anoikis resistant cells. This data points
towards the presence of multiple CSC-like pools. Based on the observed
gene expression profiles the two clusters could represent alternative CSClike or differentiation states. Alternatively, differences in the
transcriptomic phenotype may also result from cellular subpopulations
featuring a distinct genetic/epigenetic background. As has been suggested,
stochastic clonal evolution and the stem cell hypothesis are not mutually
exclusive [120]. Using single-cell transplantation assays, two recent
publications have described genetic diversity and clonal evolution of
leukemic CSCs [121, 122]. Yet, the definite description of various CSC
pools and their therapeutic relevance requires further functional
characterization. In addition, to correlate genotypes with transcriptional or
protein phenotypes, protocols for the detection of DNA, RNA and protein
derived from the same cell have been described [123].
We next scrutinized two ER!- cell lines using the same experimental
setup as for ER!+ cells. For CAL120 cells PCA identified two clusters,
termed ER!- I and III, in accordance with the nomenclature used for
ER!+ cells. ER!- I cells were characterized by low total RNA levels,
while ER!- III were characterized by high expression of 14 genes,
belonging to all defined gene groups. The majority of cells was present in
cluster ER!- III, anoikis-resistant cells were slightly enriched in cluster
ER!- I. MDA231 cells formed two clusters, termed ER!- II and III.
38
Delineating cellular heterogeneity and organization of breast cancer stem cells
Comparison of differential gene expression between anoikis-resistant and
monolayer cells revealed that most genes were down-regulated after 16hours of anchorage-independent culture. As opposed to ER!+ cells, ID1
and CCNA2 were the only commonly down-regulated genes across the
two cell lines, perhaps reflective of the heterogenetic nature of this breast
cancer subgroup.
Compared to the ER!+ cell lines, the segregation of ER!- monolayer and
anoikis-resistant cells was less pronounced. Separation into distinct
clusters was mainly due to differences in their proliferative capacity (data
not shown). The reasons for that could either be that our applied gene
panel did not ideally separate CSC-enriched fractions from regular grown
cells or that ER!- cell lines do not feature a strict hierarchical
organization, in line with observations for melanomas [124]. ER!monolayer and anoikis-resistant cells displayed a characteristic
basal/mesenchymal phenotype [119], which may in part mask
differentiation [103]. Our results nevertheless suggest that ER!- breast
CSC cluster based on proliferative capacity. To further investigate the
applicability of current CSC markers and to identify novel pathways
specific to CSC in both luminal (ER!+) and basal (ER!-) breast cancer
subtypes we have applied a RNA sequencing approach of CSC-enriched
fractions in conjunction to matched monolayer cultures (see paper III).
A common quiescent CSC-like subpopulation can be identified in ER!+
and ER!- cell lines
To scrutinize the relationship between different breast cancer subtypes and
the presence of CSC markers, we conducted combined multivariate
analyses of all cells and grouped them by similarities in their gene
expression profiles. Multiple clustering algorithms defined three discrete
clusters for ER!+ cell lines (ER!+ I-III), whereas ER!- cell lines
congregated into three partly separate clusters (ER!- I-III). ER!+/ER!- I
cluster included cells of all cell lines. Cluster ER!+ II mainly contained
MCF7 AR cells, whereas cluster ER!+ III encompassed the majority of all
differentiated ER!+ ML cells. Clusters ER!- II-III harbored essentially all
MDA231 cells as well as most of the CAL120 cells. The clusters defined
low (ER!- II) or high (ER!- III) proliferative groups. The cellular
organization of both ER!+ and ER!- cells is schematically illustrated in
Figure 11A.
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Nina Akrap
Comprehensive analysis of all cells revealed a clustering characteristic of
hierarchical organization for the analyzed ER!+ cells. Furthermore, the
data may suggest that MCF7 and T47D cells exhibit two separate modes
of differentiation. MCF7 cells seemed to differentiate from a quiescent
CSC-like cell state (ER!+/ER!- I) via a progenitor-like state (ER!+ II) to
acquire a more differentiated phenotype (ER!+ III), while T47D cells did
not seem to pass through this progenitor-like state. ER!- cell lines on the
other hand were mainly separated by their increasing proliferative
capacity from a common quiescent CSC-like pool, shared with ER!+
cells.
Our data indicates the presence of a quiescent CSC-like pool in both
breast cancer subtypes, based on the expression of pluripotency-associated
genes and low overall transcript levels, which has been described for cells
in a dormant state [116, 117, 125]. Upon differentiation, ER!+ and ER!cell lines activate partly different pathways by regulating specific genes
which give rise to the more mature cell types that characterize these breast
cancer subtypes.
To validate our findings in a clinical context we analyzed single-cells
derived from two freshly dissociated primary ductal breast cancer
samples, one ER!+ (n=81) and one ER!- (n=90). Combined PCA of the
two tumors cells revealed a clustering pattern based on their origin (ER!+
or ER!-), but with an overlap of some cells sharing a similar gene
expression profile. This common cell pool was characterized by the
expression of pluripotency markers, while the other cells expressed
markers related to more differentiated cell states. The number of cells with
a common undifferentiated gene expression profile was rather high,
potentially including both common progenitor cells as well as CSCs.
Figure 11B illustrates the differentiation route in primary tumor cells,
which was in line with the cell hierarchy delineated for cell lines. Further
analysis, e.g. by using RNA sequencing of larger cell line and patient
cohorts at the individual cell level will most likely reveal the presence of
additional cellular subpopulations. From a therapeutic point of view the
identification of different CSC is highly relevant. In order to design
curative treatment approaches all tumor-propagating populations need to
be eradicated.
40
Delineating cellular heterogeneity and organization of breast cancer stem cells
Figure 11. ER!+ and ER!- cells define a common quiescent CSC pool. A:
Hypothesized cellular organization of ER!+ and ER!- cell lines. B:
Hypothesized cellular organization of ER!+ and ER!- primary tumors.
ER!+ MCF7 cells comprise distinct cellular states and are organized in a
hierarchical manner
Since the applied gene panel proved more suitable to detect cellular
subpopulations in ER!+ cell lines, for succeeding experiments we
continued with the ER!+ MCF7 cells. For a detailed investigation of
CSC-like/progenitor pools we used two additional functional CSC
enrichment approaches, namely 1% hypoxia (Fig.10B) and PKH26-label
retention in anchorage-independent culture (Fig.10C) and conducted
single-cell analysis. Combined PCA and Kohonen self organizing map
(SOM) analyses of all enriched MCF7 CSC-fractions and matched
monolayer cultures, allowed us to relate and organize phenotypic states.
Using SOM individual cells established four stable clusters (MCF7 I-IV)
based on differential transcriptomic profiles, schematically shown in
Figure 12. Clusters MCF7 I-IV each contained cells from all applied
enrichment methods, although in varying proportions. Cluster MCF7 I
harbored mainly anoikis-resistant cells and displayed high expression of
EMT-, pluripotency-, and certain breast cancer stem cell-related genes.
Cluster MCF7 II primarily contained PKH26Bright cells and was
characterized by high expression of CD44. Cluster MCF7 III was enriched
for hypoxic cells and to a lesser extent for PKH26Bright cells with high
expression of most differentiation markers as well as ABCG2 and ERBB2.
Most monolayer cells were present in cluster MCF7 IV characterized by
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Nina Akrap
high expression of proliferation-associated genes, PGR, ALDH1A3 and
ID1.
The observed gradual gene regulation between the identified clusters
suggests a hierarchical organization of MCF7 cells. The MCF7 I group
features the phenotype of quiescent CSCs and represents the apex of the
hierarchy and differentiation takes place over different cellular states
(MCF7 II and MCF7 III) to the most differentiated cells in group MCF7
IV. First, differentiation-associated genes were activated in immature
CSCs at the same time as EMT and breast cancer associated stem cell
markers were downregulated. Secondly, we observed increased expression
of proliferation markers and downregulation of genes related to stemness.
This progression sequence is further in line with normal stem cell
differentiation and development [126, 127].
Figure 12. ER!+ MCF7 cells feature distinct differentiation states organized in
a hierarchical manner. Proposed model displaying distinct identified cell states
and hierarchical organization of MCF7 cells. The trend of gene expression of
epithelial/differentiation, breast cancer stem cell (BCSC), pluripotency,
EMT/metastasis and proliferation associated genes are indicated outside the box.
In conclusion, our data suggest that ER!+ and ER!- cell lines share a
quiescent cell pool with CSC-like features. This phenotype was partly
recapitulated in two primary tumor samples. Currently, it is not known,
whether progenitor and CSC-like cells are similar across different
molecular subtypes. The CSC concept comprises two separate
42
Delineating cellular heterogeneity and organization of breast cancer stem cells
components; the first concerns the cell of origin of breast cancer and the
second concerns the cell types responsible for tumor maintenance and
progression [120]. Today it is widely believed that the different molecular
subtypes arise from distinct cell types within the mammary hierarchy, but
also particular oncogenic drivers seem to be involved in producing the
various breast cancer phenotypes [39]. Basal (ER!-) cancers for example
are thought to arise from a luminal progenitor cells [128]. The cellular
origin of luminal cancers has yet to be established, however it has been
speculated that a more differentiated luminal progenitor could give rise to
this highly differentiated breast cancer type. In light of this it is possible
that distinct subtypes harbor individual CSC-like/progenitor populations.
Besides, CSCs in particular cells displaying the CD44+ phenotype have
been linked to the formation of metastasis [56]. Clinically, ER!+ and
ER!- breast cancers show distinct organ-specific metastasis. ER!+
preferentially metastasize to the bone, while ER!- breast cancers tend to
metastasis to visceral organs or to the brain [129]. This observation further
underlines the possibility of distinct subtype-specific CSC/progenitor
cells. On the other hand, although different subtypes exhibit a different
mutational spectrum and the predominance of different cell types, it is
possible, that CSCs depend on specific pathways, which may be shared
across the molecular subtypes or even different cancer types. For example
hedgehog signaling and the polycomb protein Bmi-1 have been
demonstrated to regulate self-renewal in both, malignant and nonmalignant stem cells of the breast [130]. Furthermore, a recent study has
analyzed transcriptomic profiles of CSCs with the CD44+/CD24- and
ALDH+ phenotypes across different subtypes and found a remarkable
similarity in CSC-derived gene expression patterns [61]. The
identification of common gene-expression profiles in CSCs across
molecular subtypes indicates that CSC-targeting agents could be effective
in different types of breast cancer in combination with subtype-specific
treatment [120]. One such an agent is the antibiotic salinomycin, which
has been identified in a high-throughput screening [131] and proved
effective in eradicating CSCs in multiple breast cancer types [132, 133].
The multiple CSC enrichment methods used to analyze MCF7 cells
allowed for a detailed description of cell pools present at the individual
cell level. We have identified four different populations, seemingly
organized in a hierarchical manner as displayed by gradual up- and down
regulation of differentiation-, stemness-, EMT-, and proliferation
associated genes. Whether cells transition through multiple cellular
differentiation states in a uni- or bidirectional manner has not explicitly
43
Nina Akrap
been addressed in this study, however several lines of evidence have
recently reported a high degree of cellular plasticity and the capability of
cells to switch between multiple cellular phenotypes [52, 53, 61, 134,
135]. Moreover, mathematical modeling has demonstrated that higher
levels of dedifferentiation reduce the effect of CSC-targeted therapy and
lead to higher rates of resistance [136], laying emphasis on the importance
to take cellular plasticity into account when designing new treatment
approaches. Our data permit the identification of key events in CSC
plasticity. For example, in an attempt to target de-differentiation of
progenitor cells into less-differentiated cells with pluripotent features, in
ER!+ breast cancers, genes associated with differentiation/EMT/breast
cancer
stemness
need
to
be
modulated
rather
than
pluripotency/proliferation, since these processes follow a sequential order.
However, in ER!- cells, proliferation seems to be one of the key
differentiation associated events. Targeting proliferation in both ER!+ and
especially ER!- breast cancer may actually have an effect on
differentiation processes potentially increasing CSC subpopulations and
tumor aggressiveness. Our data highlight the need for proper tumor
characterization and in depth understanding of relevant common as well
as separate differentiation and de-differentiation processes present in
subtypes of breast cancer.
4.3 Results and discussion paper III
In paper III we sought to identify molecular pathways specific to luminal
and basal breast cancer subtypes, using cell line models. To this end, we
conducted RNA sequencing of functionally enriched CSC fractions and
matched monolayer cultures, to identify circuits commonly
overrepresented in CSC fractions of both subtypes.
The mevalonate pathway is a key feature of CSC-enrichment in luminal
and basal breast cancer subtypes
To identify commonly upregulated transcriptional networks in CSCs of
luminal and basal subtypes of breast cancers, we applied a genome-wide
RNA-sequencing approach. For this purpose CSC of luminal and basal
breast cancer cell lines were enriched using a 16-hour anchorageindependent culture system. CSC-derived gene expression signatures were
44
Delineating cellular heterogeneity and organization of breast cancer stem cells
analyzed in conjunction with corresponding adherent populations. A total
of 344 (MCF7), 243 (T47D) and 477 (MDA231) genes were significantly
upregulated in CSC-enriched subpopulations (Fig.13A). Ingenuity®
Pathway Analysis (IPA®) identified mevalonate-associated networks in
six out of the twelve most over-represented pathways (Fig.13B).
Therefore, we have analyzed the expression of 11 mevalonate genes using
single-cell RT-qPCR. Across the three cell lines, we identified similar
expression patterns for HMGCS1, MVK and DHCR24. HMGCS1
expression showed no significant difference between cell lines and was
specific to a tumor cell sub-fraction. Following CSC enrichment, the
percentage of HMGCS1-expressing cells was significantly expanded as
analyzed at the individual cell level. HMGCS1 did not exhibit any gene
associations to other pathway genes, which may indicate mevalonatepathway independent transcriptional regulation. Furthermore, HMGCS1
was significantly over-expressed in all three CSC-enriched cell lines in the
original RNA-sequencing dataset. This observation was confirmed with
RT-qPCR, using RNA extracted from cell lines (Fig.13C). A recent study
recognized the mevalonate pathway overrepresented in CSCs of basal
cancers [75]. In this study gene expression signatures were derived from
mammosphere cultures, containing mammosphere-initiating and
differentiated progenitor cells [137], possibly masking pathways active in
CSCs. By using 16-hours suspension culture we have previously shown to
enrich for cells with increased in vitro mammosphere formation capacity,
in vivo tumor formation as well as for cells with elevated expression of the
CSC-like EPCAM+/CD44+CD24low immunophenotype [91]. Based on our
findings we further explored the putative role of HMGCS1 as a marker of
functionally enriched CSCs.
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Nina Akrap
Figure 13. The mevalonate pathway is a key feature of CSC-enrichment in
luminal and basal breast cancer subtypes. A. RNA-sequencing was conducted on
MCF-7, T47D and MDA231 adherent monolayers and 16-hour CSC-enriched
cultures. Overall analysis of the RNA-sequencing data identified 344 (MCF-7),
243 (T47D) and 477 (MDA231) genes significantly overexpressed in the CSCenriched cultures compared to adherent monolayer cultures. B. Ingenuity®
Pathway Analysis was applied to the 79 genes which were significantly
overexpressed in two or more of the CSC-enriched cell line subpopulations.
Several mevalonate pathway-associated networks were significantly increased
above the statistical threshold (p<0.001) in CSC-enriched subpopulations. C.
MCF-7, T47D and MDA-231 cell lines grown as adherent monolayer or in 16hour CSC-enriched (CSC-en) cultures; HMGCS1 gene expression was assessed
by qPCR. HMGCS1 transcript levels are reported as fold changes relative to
individual monolayer controls, graphs represent average ±SEM, n=3.
HMGCS1 is required for CSC survival in luminal and basal cell lines with
mutated p53
HMGCS1 protein expression was studied in 16-hour anchorageindependent culture and in 5-day mammosphere cultures. Protein
expression was increased in both model systems compared to adherent
monolayer cultures, confirming the RNA data. To assess the effect of
HMGCS in CSC function, gene expression was transiently silenced, using
siRNA (Fig.14A) and survival in 16-hours and 5-days anchorageindependent cultures was analyzed. Following HMGCS1 knockdown
T47D and MDA-231 cells exhibited a significant decrease in survival
46
Delineating cellular heterogeneity and organization of breast cancer stem cells
(Fig.14B) and relative mammosphere forming capacity (Fig.14C).
Dysregulated mevalonate pathway activation has previously been linked
to a gain-of-unction mutation of p53 [74]. The authors identified
functional interaction with sterol regulatory element-binding proteins to
be critical for mutant p53-mediated upregulation of mevalonate pathway
genes.
To block the mevalonate pathway cells were treated with standard doses
of simvastatin, targeting 3-hydroxy-3-methylglutaryl-CoA reductase
(HMGCR), the major rate-limiting enzyme within the mevalonate
pathway and downstream substrate of HMGCS1 [138]. Simvastatin
treatment induced elevated HMGCS1 protein levels, possibly through a
restorative feedback response [139]. This feedback mechanism has been
associated with statin-insensitivity and the lack to induce apoptosis upon
statin exposure in leukemic cells [72]. Simvastatin effects on viability in
16-hour CSC-enrichment and mammosphere formation were negligible.
Given the pronounced effect of HMGCS1 gene-silencing on CSC survival
and seemingly mevalonate-pathway-independent regulation of HMGCS1,
pharmacological inhibition of HMGCS1 may be a superior approach to
specifically target CSC in luminal and basal breast cancer subtypes,
however this requires further investigation.
47
Nina Akrap
Figure 14. HMGCS1 is required for CSC survival in luminal and basal cell
lines with mutated p53. A. HMGCS1 gene expression was silenced using
HMGCS1 siRNA or scrambled negative control. Immunoblot and associated
densitometry confirmed HMGCS1 knockdown relative to scrambled control. "actin was used as loading control. B. Cell lines were transfected with HMGCS1
siRNA or scrambled negative control for 72 hours. Cells were grown in 16-hour
suspension culture. Manual viability counts were conducted with Trypan Blue
exclusion dye. Percentage viable cells of total cell number seeded are shown,
graphs represent average counts ±SEM, n=3. C. Cell lines were transfected with
HMGCS1 siRNA or scrambled negative control for 72 hours; cells were grown in
5-day mammosphere cultures. Mammosphere forming capacity (MFC) was
assessed and is represented as percentage of total cells seeded. Graphs show
average fold-change relative to scrambled control ±SEM, n=3.
HMGCS1 expression transcriptionally regulates CSC-associated genes in
luminal and basal models of breast cancer with mutated p53
To investigate the gene regulatory role of HMGCS1, transcript levels of
proliferation, pluripotency, EMT and breast cancer stem cell genes were
assessed in HMGCS1-knockdown cells and scrambled controls. Following
HMGCS1 knockdown, T47D cells displayed a reduction of ABCG2
expression, while MDA231 cells showed a decrease of SOX2, NANOG
and SNAI1 expression. No significant differences were detected in MCF7
cells, further suggesting a link between p53 mutational status and
deregulated mevalonate metabolism. The adherent monolayer single-cell
data was stratified by HMGCS1 expression. HMGCS1-expressing cells
had remarkably similar frequency in each cell line, however due to the
low number of HMGSC1-expressing cells it was difficult to detect
significant differences.
HMGCS1 expression correlates with disease aggression and associates
with basal tumors in a breast cancer patient cohort
HMGCS1 expression was assessed in a cohort of 149 lymph node positive
breast cancers (Fig.15A). Correlation analysis identified significant
positive associations with p53 mutational status, tumor grade and HIF 1-!.
HMGCS1 exhibited significant inverse correlations with ER! and PR,
there was an obvious increase in the frequency of ER!- tumors in the
HMGCS1-intermediate and -high tumors (Fig.15B) as well as an increase
in p53 mutations in ER!+ tumors (Fig.15C), however due to the small
48
Delineating cellular heterogeneity and organization of breast cancer stem cells
number of patients included in each HMGCS1 subgroup, these results are
only indicative.
Figure 15. HMGCS1 expression correlates with disease aggression and
associates with basal tumors in a breast cancer patient cohort. A. A tissue
microarray of 149 tumor biopsies from patients with lymph-node positive breast
cancers was stained for HMGCS1. Expression was assessed using the Allred
scoring system. B. Comparative pie charts were generated for each HMGCS1
subgroup (negative, low, intermediate and high) to demonstrate the frequency of
ER!+ and ER!- tumor subtypes present in each. C. Percentage of p53 mutations
in ER!+ and ER!- breast cancers for HMGCS1 subgroups.
Hyperactivation of the mevalonate pathway and overexpression of
mevalonate pathway genes has previously been linked to p53 gain-offunction mutations [74]. Here, we establish a specific link to HMGCS1
protein expression and mutated p53. Furthermore, HMGCS1 was
associated with tumor grade and lack of ER! expression. About 30% of
all breast cancers exhibit mutations in the TP53 gene, but the frequency
differs greatly across distinct molecular subtypes [23, 35]. About 80% of
the basal tumors display mutations in the TP53 gene, whereas only about
15% of luminal A tumors carry a p53 mutation [23, 35]. Furthermore,
49
Nina Akrap
basal tumors are characterized by the lack of ER!, PR and HER2
expression as well as high tumor grade [140]. Our data suggests that that
therapeutic modulation of the mevalonate pathway may be beneficial for
patients presenting p53 gain-of-function mutations and consequential
mevalonate pathway hyperactivation, independent of breast cancer
subtype.
In conclusion, by applying genome-wide RNA sequencing of functionally
enriched CSCs in conjunction with matched adherent cultures, we
identified the mevalonate pathway for cholesterol biogenesis and protein
prenylation in CSCs of luminal and basal breast cancers. In breast cancer
the mevalonate pathway has been implicated in tumor cell transformation,
malignancy and the specific regulation of basal-derived CSCs [72, 74, 75].
Dissecting the mevalonate pathway, we identified HMGCS1 as a
functional marker for CSCs. Transient HMGCS1 gene-silencing lead to
reduced survival of CSCs in cell line model displaying mutated p53.
Given the pronounced effect of HMGCS1 gene silencing on CSC survival
pharmacological inhibition of HMGCS1 may be a promising approach to
specifically target CSC in luminal and basal breast cancer subtypes and
likely additional breast cancer types.
50
Delineating cellular heterogeneity and organization of breast cancer stem cells
5 CONCLUSIONS
In paper I we have evaluated experimental parameters of targeted
preamplification for samples with minute starting amount, such as singlecell measurements. Quantification of small molecule numbers often
requires a preamplification step to produce sufficient material for accurate
downstream applications. Based on our findings we were able to provide
general recommendations for conducting robust and accurate
preamplification in conjunction with RT-qPCR or next-generation
sequencing.
In paper II we have evaluated the cellular organization of ER!+ and
ER!- breast cancer cells by combining single-cell gene expression
profiling with three functional enrichment techniques for cancer stem cell
enrichment. ER!+ cells displayed a hierarchical organization with
different modes of cellular transitions. ER!- cells clustered mainly based
on their proliferative capacity. Despite of the seemingly different cellular
organization of both subtypes, we have identified a quiescent cell pool
with CSC-like features common to ER!+ and ER!- cells. In addition, for
ER!+ MCF7 cells we identified several cellular populations, which
displayed distinct CSC-like phenotypes, underlining the importance for
detailed tumor characterization in order to target all therapeutic relevant
cancer cells.
In paper III we have evaluated transcriptional networks in CSC-enriched
populations of ER!+ and ER!- breast cancer cells and thereby identified
the mevalonate pathway commonly overrepresented in both breast cancer
subtypes. Detailing the mevalonate pathway we identified the mevalonate
precursor enzyme HMGCS1 as a specific marker for CSC enrichment,
essential for CSC activities, in particular in cells with mutated p53.
Pharmacological inhibition of HMGCS1 could therefore be a promising
new treatment approach to target CSCs in an appropriate p53 mutational
background.
51
Nina Akrap
ACKNOWLEDGEMENT
First and foremost I would like to express my deep gratitude to my main
supervisor Prof. Göran Landberg for guiding me through my PhD
experience and providing such an excellent and exciting research
environment!
I would also like to express my gratitude to my co-supervisor Prof.
Anders Ståhlberg, whose dedication to science is truly inspiring! Thanks
for sharing your expertise, excitement end enthusiasm about all qPCRrelated matters.
I am deeply grateful for have been given the opportunity to work together
with all the brilliant and talented post-docs in the lab as well as for
constant support, help and scientific inspiration. I have learned so much
from you during the last four years. In no particular order I would like to
thank Paul Fitzpatrick (special thanks for everything, starting in San
Fran, ending in the same office), Eva Bom (special thanks for FACSing
my cells so skillfully and your valuable project input), Susann Busch
(special thanks for proof-reading my thesis and your smart brain), Hanna
Jacobsson (special thanks for proof-reading my thesis, Swedish
translations and an always open ear), Claire Walsh (special thanks for
being able to work on your project and very special birthday songs!),
Éamon Hughes (special thanks for always being so helpful. Be assured
the present thesis is a 100% cabbage-free.) as well as Daniel Andersson
(special thanks for late night single-cell picking sessions and extreme
qPCRing). It was such a pleasure working together with you guys!
I would also like to thank all the wonderful people in my group I have
been working alongside with during the last couple of years. The epicenter
of the lab Pernilla Gregersson (special thanks for advanced single-cell
picking), Ylva Magnusson, Pauline Isakson, and Svanheidur
Rafnsdottir.
In addition, I would like to offer my thanks to a bunch of people loosely
associated to the lab or my project. Isabell Lebküchner (special thanks
for endless single-cell data discussions and horse-riding adventures),
David Svec (special thanks for qPCR expertise and Czech honey),
Soheila Dolatabadi (special thanks for just the right amount of irony) and
last but not least Emma Jonasson (special thanks for last-minute-Fridayafternoon-Swedish-abstract-writing!).
52
Delineating cellular heterogeneity and organization of breast cancer stem cells
Meine Familie und Freunde vielen Dank für all Eure Unterstützung und
Liebe.
Karl my love, the man who was brave enough to put up with crazy
woman for the last few months!
53
Nina Akrap
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