Research Collection
Doctoral Thesis
Identification of inhibitors and surface markers of breast cancer stem cells
Author(s): Cui, Jihong
Publication Date: 2015
Permanent Link: https://doi.org/10.3929/ethz-a-010502765
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ETH Library DISS. ETH NO. 22665 Identification of inhibitors and surface markers of breast cancer stem cells
A thesis submitted to attain the degree of
DOCTOR OF SCIENCES of ETH ZURICH
(Dr. sc. ETH Zurich)
presented by
Jihong Cui
MSc Physiology, Sun-Yat sen University, China
born on 19. 10. 1984
Citizen of China
accepted on the recommendation of
Prof. Dr. Michael Detmar, examiner
Prof. Dr. Gisbert Schneider, co-examiner
2015
“Don’t even try to understand all complex ways of tumorigenesis – just cure it”
James D. Watson
Hallmarks and Horizons of Cancer
September 2011
Lausanne, Switzerland
Table of Content
TABLE OF CONTENT ...... I
TABLE OF FIGURES ...... V
TABLE OF TABLES ...... VII
1 SUMMARY ...... 1
1.1 SUMMARY ...... 1
1.2 ZUSAMMENFASSUNG ...... 3
2 INTRODUCTION ...... 5
2.1 BREAST CANCER ...... 5
2.1.1 The structure of the mammary gland ...... 5
2.1.2 Molecular classification of breast cancer ...... 6
2.1.3 Differentiation status of breast cancer ...... 8
2.2 CANCER STEM CELLS ...... 9
2.2.1 Evolution of CSC research ...... 10
2.2.2 The characteristics and possible origin of CSCs ...... 11
2.2.3 The dynamic changes and plasticity of CSCs ...... 12
2.2.4 Controversies regarding CSCs ...... 13
2.2.5 CSCs and drug resistance ...... 15
2.2.6 Strategies for the isolation and enrichment of CSCs ...... 17
2.2.6.1 CSC marker-positive cell isolation ...... 18
2.2.6.2 Dye-exclusion side-population cell isolation ...... 19
2.2.6.3 Anchorage-independent cell culture (Sphere formation assay) ...... 20
2.2.6.4 High-activity aldehyde dehydrogenase (ALDH) cell isolation ...... 21
2.2.6.5 EMT-induced CSCs ...... 22
2.2.6.6 Chemotherapeutic and radiotherapeutic selection of CSCs ...... 23
2.2.6.7 Hypoxia-induced CSCs ...... 24
2.2.7 Cancer metastasis and CSCs ...... 25
I 2.2.8 In vivo and in vitro models for CSC studies ...... 27
2.2.8.1 In vitro models ...... 27
2.2.8.2 In vivo models ...... 28
2.2.9 Key CSC signaling pathways ...... 32
2.3 TARGETING CSCS ...... 37
2.3.1 Clinical implication of CSCs ...... 37
2.3.2 Therapeutic strategies for targeting CSCs ...... 37
2.3.2.1 Direct strategies ...... 38
2.3.2.1.1 Ablation of CSCs by prospective markers ...... 38
2.3.2.1.2 Self-renewal related pathway interference ...... 39
2.3.2.1.3 Induction of CSC differentiation ...... 40
2.3.2.1.4 Change of the quiescent status of CSCs ...... 41
2.3.2.1.5 Targeting survival/apoptosis pathways of CSCs ...... 42
2.3.2.1.6 Targeting the metabolism of CSCs ...... 43
2.3.2.2 Indirect strategies ...... 44
2.3.2.2.1 Targeting the tumor microenvironment ...... 44
2.3.2.2.2 Disruption of immune evasion ...... 47
2.3.3 Identified inhibitors of CSCs ...... 48
2.3.3.1 Monoclonal antibodies for inhibiting CSCs ...... 48
2.3.3.2 Small molecular compounds for inhibiting CSCs ...... 50
2.3.3.3 Natural compounds for inhibiting CSCs ...... 51
2.3.3.4 Human vaccines for targeting CSCs ...... 53
2.3.4 Combination chemotherapy and anti-CSC treatment strategies ...... 53
2.4 ANTICANCER DRUG DISCOVERY PLATFORMS TO TARGET CSCS ...... 55
2.4.1 In vitro cell-based screening ...... 55
2.4.2 In vivo tumor models ...... 58
3 AIMS OF THIS THESIS ...... 61
4 RESULTS AND DISCUSSION ...... 63
4.1 CELL-BASED PHENOTYPIC SCREENING IDENTIFIES NOVEL INHIBITORS OF BCSCS ...... 63
4.1.1 Abstract ...... 63
II 4.1.2 Introduction ...... 64
4.1.3 Results ...... 66
4.1.4 Discussion ...... 74
4.2 BENZTROPINE MESYLATE AND DEPTROPINE CITRATE ARE NOVEL INHIBITORS OF
BREAST CANCER STEM CELLS ...... 77
4.2.1 Abstract ...... 77
4.2.2 Introduction ...... 78
4.2.3 Results ...... 79
4.2.4 Discussion ...... 94
4.3 Α9 NICOTINIC ACETYLCHOLINE RECEPTOR (Α9-NACHR) AS A POTENTIAL BIOMARKER
FOR BREAST CANCER STEM CELLS ...... 97
4.3.1 Abstract ...... 97
4.3.2 Introduction ...... 98
4.3.3 Results ...... 101
4.3.4 Discussion ...... 107
5 CONCLUSION AND OUTLOOK ...... 109
6 MATERIAL AND METHODS ...... 113
7 CURRICULUM VITAE ...... 123
8 ACKNOWLEDGEMENTS ...... 125
9 REFERENCES ...... 127
10 LIST OF ABBREVIATIONS ...... 157
III
IV Table of Figures
Figure 1. Structure of the mammary gland...... 6
Figure 2. Model of human mammary epithelial hierarchy linked to intrinsic subtype and
therapeutic subtype ...... 9
Figure 3. Timeline of important discoveries in the field of CSCs...... 11
Figure 4. Timeline of milestone studies that demonstrate how CSCs contribute to the
acquisition of chemotherapy resistance...... 16
Figure 5. Xenograft assay to measure CSCs...... 30
Figure 6. Signaling pathways that regulate self renewal ...... 35
Figure 7. Therapeutic strategies targeting CSCs ...... 38
Figure 8. Modulating effects of bioactive food components on signaling pathways ...... 53
Figure 9. Enrichment of BCSCs by mammosphere culture of EMT-induced breast cancer
cells (HMLER shEcad)...... 67
Figure 10. Schematic overview of the chemical screening strategy for compounds that
selectively inhibit the survival of HMLER-shEcad spheres...... 69
Figure 11. Chemical screening for compounds that selectively inhibit the survival of HMLER
shEcad spheres...... 70
Figure 12. Identification of compounds that exhibit selective inhibitory effects on HMLER
shEcad sphere...... 71
Figure 13. Identification and validation of compounds that exhibit selective inhibitory effects
on HMLER shEcad spheres...... 73
Figure 14. Cell viability assays...... 79
Figure 15. Mammosphere formation assay...... 80
Figure 16. Cell viability of mammospheres with or without treatment...... 81
Figure 17. Self-renewal assay...... 82
Figure 18. FACS analysis of the expression of CSC markers (ALDH+ and CD44+/CD24-) in
MDA-MB-231 spheres with or without benztropine mesylate treatment...... 84
V Figure 19. FACS analysis of ALDH+ cell population in SKBR3 and 4T1-luc2 cells with or
without benztropine mesylate treatment...... 86
Figure 20. Benztropine mesylate inhibits migration and invasion of HMLER shEcad and
MDA-MB-231 cells in vitro...... 86
Figure 21. Benztropine mesylate treatment improves the efficiency of chemotherapy in vitro.
...... 88
Figure 22. Effects of benztropine mesylate treatment on tumor growth, metastasis and tumor
seeding in vivo...... 90
Figure 23. Benztropine mesylate partially impairs mammosphere formation of BCSCs
through acetylcholine receptors, dopamine receptors/transporters and/or histamine
receptors...... 92
Figure 24. Effects of a panel of antagonists of the dopamine receptor, the histaminergic
receptor or the acetylcholine receptor on mammosphere formation...... 93
Figure 25. CHRNA9 gene expression was increased by EMT and sphere formation in breast
cancer cells...... 102
Figure 26. CHRNA9 gene expression in breast cancer cells was increased by chemotherapy
treatment...... 104
Figure 27. Determination of choline levels in CSC medium...... 104
Figure 28. Blockade α9-nAchRs by ACV1 inhibits cell viability, sphere formation and self-
renewal of BCSCs...... 106
VI Table of Tables
Table 1. Overview of mechanisms of CSC resistance to therapy and strategies to overcome
resistance ...... 17
Table 2. CSC markers for distinct solid tumor types ...... 19
Table 3. Limitations of xenotransplant assays of human CSCs ...... 31
Table 4. Combined chemotherapy and anti-CSC treatment strategies...... 54
Table 5. Target prediction of benztropine mesylate by SPiDER 1.0 software ...... 95
Table 6. Primer sequences for qRT-PCR analysis of stemness-associated genes in HMLER
shEcad cells and spheres (Project 1) ...... 116
Table 7. Primer sequences for qRT-PCR analysis in human cancer cells (Project 2) ...... 117
VII
VIII 1 Summary
1.1 Summary
Breast cancer is the most common cancer in women and has the second highest morbidity rate worldwide. Therapeutic resistance and cancer relapse with metastasis after systemic treatment represent the major roadblock to a cure of this disease. One of the established reasons for this is the presence of cancer stem cells (CSCs), i.e., breast CSCs (BCSCs). The CSC hypothesis suggests that CSCs contribute to tumor initiation, development, progress and metastasis due to their self-renewal and differentiation characteristics. From the therapeutic perspective, selective killing of CSCs or impairing their capacities could be an efficient approach to eradicate cancer.
In our study (Chapter 4.1 and 4.2), we first established a screening platform using spheres formed by malignant human breast gland-derived cells (HMLER shEcad cells, representing
BCSCs) and control immortalized non-tumorigenic human mammary cells (HMLE cells, representing normal stem cells, NSCs) for identification of novel compounds with specific toxicity for spheroid-derived BCSCs, but not for breast cells or NSCs. We applied this platform to a chemical screen including 2,546 compounds from two chemical libraries (the
NCI-DTP diversity set II and the Prestwick library), and discovered nineteen compounds that induced a >50% inhibition of HMLER shEcad sphere formation and had a <30% inhibition of
HMLE spheres and of HMLE adherent cells. We then focused on characterizing the anti-CSC effects of two compounds with similar core structure, deptropine citrate and benztropine mesylate. Benztropine mesylate reduced the ALDH+ and CD44+/CD24- CSC population in a dose-dependent manner. Systemic treatment with benztropine mesylate inhibited tumor growth and reduced liver and lymph node metastasis in a 4T1 mouse breast cancer model. A mechanism exploration indicated that benztropine mesylate impaired the sphere formation ability of CSCs through inhibition of acetylcholine receptors, dopamine transporters/receptors
1 and histamine receptors. This study indicates that benztropine mesylate might represent a novel anti-BCSC agent. The screening platform established here could also be applied for further large-scale anti-CSC compound screens.
CSC populations have been defined by the presence or absence of various cell surface markers, but the limited overlap between distinct CSC marker-positive populations represents a great challenge for CSC related research. Thus, more surface markers are needed for identification of different CSC pools. In the second project, we aimed to investigate whether the alpha9-nicotinic acetylcholine receptor (α9-nAchR) might represent a novel surface marker for BCSCs. Our results showed that the gene expression level of α9-nAchR
(CHRNA9) significantly increased along with the enrichment of BCSCs. Blocking α9-nAchR using a pharmacological inhibitor significantly reduced the CSC subpopulation and inhibited the CSC-associated properties such as sphere-formation and self-renewal.
In conclusion, this work has established a simple, reliable and cost-efficient method to screen for novel CSC-targeting drugs. Using this screening platform, we identified and characterized benztropine mesylate as a novel anti-CSC agent in in vitro and in vivo models. Moreover, our results indicated that α9-nAchR represents a potential marker for BCSCs.
2 1.2 Zusammenfassung
Mit der zweithöchsten Morbiditätsrate weltweit gehört Brustkrebs zu den wichtigsten
Tumorerkrankungen der Frau. Eine der grössten Herausforderungen ist das Auftreten von
Therapieresistenzen und das Wiederkehren von Metastasen trotz Chemotherapie.
Krebsstammzellen (KSZ) haben laut der KSZ-Hypothese die Fähigkeit Krebszellpopulationen zu erneuern und sich zu verschiedenen Zelltypen zu differenzieren. Dadurch spielen diese
Zellen eine wichtige Rolle in der Tumorentstehung, -entwicklung und -metastasierung. Die
Hypothese folgert daraus, dass KSZ mitverantwortlich für die Entstehung von
Therapieresistenzen sind.
Im ersten Teil dieser Arbeit (Kapitel 4.1 und 4.2) wurde eine Screening-Plattform für die
Suche nach neuen potentiellen Wirkstoffen etabliert. Dadurch sollen Verbindungen mit einer spezifischen Toxizität gegenüber KSZ und gleichzeitig geringer Toxizität gegenüber immortalisierten Kontrollstammzellen identifiziert werden. Dazu wurde die
Sphärenbildung von Zellen aus humanem malignem Brustdrüsengewebe (HMLER shEcad
Sphären, als KSZ) und von Zellen aus humanem immortalisiertem nicht tumorösem
Brustgewebe (HMLE Sphären, als normale Stammzellen) analysiert. Mit dieser Methode wurden insgesamt 2,546 Präparate aus zwei Molekülbanken (NCI-DTP diversity set II und
Prestwick library) untersucht. Dabei konnten 19 Kandidaten identifiziert werden, welche die
Bildung der HMLER shEcad Sphären um mehr als 50% reduzierten. Des Weiteren zeigten diese 19 Verbindungen eine Hemmung der Sphärenbildung von weniger als 30% für HMLE sowie von adhärenten HMLE Zellen auf. Für die weitere Charakterisierung der Wirkung auf die KSZ wurden drei dieser Verbindungen, welche eine ähnliche chemische Grundstruktur haben, ausgewählt: Deptropincitrat and Benztropinmesylat. Davon zeigte Benztropinmesylat in KSZ die stärkste Inhibition der Tumorsphärenbildung und Verminderung der
Selbsterneuerungsrate. Zudem wurde eine dosisabhängige Reduktion der ALDH+ und
CD44+/CD24- KSZ-Population festgestellt. Im 4T1 Mausmodell für Brustkrebs zeigte die
3 systemische Applikation von Benztropinemesylate eine Verminderung des Tumorwachstums sowie eine reduzierte Metastasierungsrate in Leber und Lymphknoten.
Weiter wurde der Wirkungsmechanismus von Benztropinemesylate auf die Hemmung der
Sphärenbildung untersucht. Diese Analysen konnten Hinweise auf eine Beteiligung vom
Acetylcholin-Rezeptor, Dopamin-Transporter/Rezeptor und Histamin-Rezeptor aufzeigen.
In der vorliegenden Arbeit konnte Benztropinmesylat als neuer potentieller anti-KSZ
Wirkstoff identifiziert werden. Ausserdem kann die hier beschriebene KSZ-Screening-
Methode für die Suche nach weiteren anti-KSZ Wirkstoffkandidaten verwendet werden.
Die KSZ-Populationen werden über das Vorhandensein oder Fehlen von bestimmten
Zelloberflächenmarkern definiert. Da jedoch viele dieser Marker lediglich Subpopulationen markieren, fehlt es oft an Übereinstimmung der verschiedenen Markierungen. Daher werden dringend neue KSZ-spezifische Marker für die gleichzeitige Identifizierung verschiedener
KSZ-Subpopulationen benötigt. Im zweiten Teil dieser Arbeit wird der α9 nikotinische
Acetylcholin Rezeptor (α9-nAChR) als ein solcher neuer Marker für KSZ beschrieben. Es konnte aufgezeigt werden, dass sich die Genexpression von α9-nAChR (CHRNA9) proportional zum Anstieg des KSZ-Anteils in einer Zellpopulation verhält. Durch die
Blockierung von α9-nAChR mittels eines Inhibitors konnte die KSZ-Population sowie deren
Fähigkeit zur Sphärenbildung und Selbsterneuerung signifikant reduziert werden.
Zusammenfassend wurde in dieser Arbeit eine einfache, zuverlässige und kosteneffiziente
Methode zur Identifizierung neuer anti-KSZ-Wirkstoffkandidaten entwickelt. Dabei wurde
Benztropinmesylat als neuer Wirkstoffkandidat identifiziert und durch in vitro und in vivo
Modelle charakterisiert. Zudem wurde α9-nAchR als potentieller neuer Marker für KSZ beschrieben.
4 2 Introduction
2.1 Breast cancer
Breast cancer is the most common cancer in women worldwide, and is also the second leading cause of cancer death in women, exceeded only by lung cancer. The American Cancer
Society's estimates for breast cancer in the United States for 2014 showed that 232,670 new cases of invasive breast cancer were diagnosed in women and about 1 in 8 women in the US develops invasive breast cancer during their lifetime [1]. In 2015, an estimated 62,290 new cases of breast carcinoma in situ will be diagnosed, 83% of which will be ductal carcinoma in situ (DCIS) and 12% lobular carcinoma in situ (LCIS). About 231,840 new case of invasive breast cancer will be detected and nearly 40,290 patients will die from breast cancer. The 5- year relative survival for women diagnosed with localized breast cancer is 99%; if the cancer has spread to nearby tissues or lymph nodes (regional stage) or distant lymph nodes or organs
(distant stage), the survival rate drops to 84% or 24%, respectively [2].
Breast cancer patients are treated with cytotoxic, hormonal, and immunotherapeutic agents in the adjuvant, neoadjuvant, and metastatic settings, depending on the molecular and biological characteristics of breast cancer. These systemic agents are active at the beginning of therapy in 90% of primary breast cancers and 50% of metastatic breast cancer [3]. Despite the significant progress in treatment, relapse of breast cancer or drug resistance occurring after a variable periods of systemic treatment still represents a major challenge in cancer therapy, which prompted us to develop new therapeutic strategies.
2.1.1 The structure of the mammary gland
The mammary gland consists of a branching ductal system that ends in terminal ducts with their associated acinar structures, termed the terminal ductal-lobular units (TDLUs), together with interlobular fat and fibrous tissue (Figure 1). Histological examination showed that different cell subtypes exist in the TDLUs, including luminal, basal, myoepithelial and stem
5 cells [4-6]. Molecular profiling studies indicated the existence of multiple subtypes of breast cancers probably originating from luminal, basal, and possibly stem cell compartments [5].
Figure 1. Structure of the mammary gland (adapted from [5]). The terminal ductal-lobular unit (TDLU), composed of ductal cells, is the unit thought to be the origin of most breast cancers. The stroma is composed of fatty tissue (adipocytes) and fibroblasts. Also shown are the two primary types of cells in normal ducts: outer contractile myoepithelial and inner columnar luminal cells. A putative progenitor/stem cell is also indicated.
2.1.2 Molecular classification of breast cancer
Breast cancer is a heterogeneous disease in terms of pathological features, therapeutic response, clinical course, patient outcomes and dissemination patterns to distant sites. The most common and traditional classification method is based on the receptor status (estrogen receptor (ER); progesterone receptor (PR) and human epidermal growth factor receptor 2
(HER2)) of breast cancer, which is identified by immunohistochemistry. Along with the development of molecular technology, this traditional classification has been refined by molecular classifications based on the gene expression patterns of breast cancers. There are five subgroups of breast cancer defined by their relatively unique molecular profiles: a luminal type (further subdivided in to variants A and B), a “basal-like,” a HER2-positive, a claudin-low and a normal breast-like variant [7-9].
6 The luminal A is the most common subtype, representing 50-60% of all breast cancers. It is characterized by the expression of genes activated by the ER transcription factor that are typically expressed in the luminal epithelium lining the mammary ducts. Furthermore, it shows a low expression of genes related to cell proliferation. According to their molecular profile, all cases of lobular carcinoma in situ are luminal A tumors, as are most of the infiltrating lobular carcinomas [8, 10].
A further 10-20% of all breast cancers are of the luminal B subtype. Compared to the luminal
A, it has a more aggressive phenotype, higher histological grade and proliferative index and poorer prognosis. Luminal B breast cancer typically has an increased expression of proliferation genes, such as Ki67 and cyclin B1 and often expresses epidermal growth factor receptor (EGFR) and HER2 [10].
The HER2 enriched subtype corresponds to 15-20% of all breast cancer cases. They are characterized by a high expression of the HER2 gene and other genes associated with the
HER2 pathway. These cancers exhibit an overexpression of genes related to cellular proliferation. This subtype does not express genes of the basal-like cluster and may show a low expression of characteristic luminal genes [10]. HER2+ tumors have amplification and overexpression of the ERBB2 oncogene and can be effectively controlled with a diverse array of anti-HER2 therapies [11].
The basal-like subtype represents 10-20% of all breast carcinomas. Basal-like tumors are associated with poor clinical outcome and mostly (~75%) lack the expression of ER, PR and
HER2 and are clinically referred to as “triple-negative” breast cancer (TNBC) [10]. Currently, there is no molecular-based targeted therapy for TNBC, and unfortunately only approximately
20% of these tumors respond well to standard chemotherapy [11]. Additionally, expression features of basal-like tumors include a characteristic signature containing keratins 5, 6 and 17 and high expression of genes associated with cell proliferation [12, 13].
7 The normal breast-like subtype accounts for about 5-10% of all breast carcinomas. These tumors are also classified as TNBC, without being considered basal-like, as they are negative for keratins 5 and EGFR. They have an intermediate prognosis between luminal and basal- like and are insensitive to neo-adjuvant chemotherapy [10].
The claudin-low subtype was identified in 2007 [14]. It is a rare subtype present in human and mouse models and is characterized by high expression of mesenchymal and stem cell- associated genes and the lack of expression of luminal differentiation markers like claudin-3, claudin-4, claudin-7, E-cadherin, epithelial cell adhesion molecule (EpCAM) and mucin-17.
Clinically, the majority of claudin-low tumors are triple-negative invasive ductal carcinomas with poor prognosis [15].
2.1.3 Differentiation status of breast cancer
The hierarchy of breast cancer is also indicated by the differentiation degree of distinct breast cancer subtypes. The transcriptional programs that control luminal and basal lineage identity in the normal mammary epithelium as well as progenitor and stem cell function are active in breast cancers, and show distinct associations with different disease subtypes. Breast cancer differentiation is often viewed along an axis between a claudin-low/basal-mesenchymal-stem cell state and a luminal-epithelial-differentiated state. Figure 2 shows the possible overlap of molecular portraits, immunohistochemistry, and degree of differentiation-based classes together with the grade of breast cancer [16, 17]. Accordingly, luminal and HER2+ tumors may originate from luminal lineage-committed progenitors, whereas basal-like cases arise from less differentiated progenitor cells and claudin-low tumors arise from stem cell-like cells. However, in most breast tumors, cancer cells with stem cell-like and more differentiated features can be detected, but the existence of a simple differentiation hierarchy has been questioned by the high degree of genetic diversity within and between these two cell populations.
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Figure 2. Model of human mammary epithelial hierarchy linked to intrinsic subtype and therapeutic subtype (modified from [17]).
2.2 Cancer stem cells
Most tumors are characterized as complex and heterogeneous in both phenotype and behavior. A subpopulation of malignant cells with stem cell properties and contributing to this heterogeneity were termed CSCs or tumor initiating cells (TICs) [18]. The term TICs is often used to denote the cell that causes a tumor (or leukemia) in xenograft models of human cancer. Some researchers have extrapolated that the cell that initiates a tumor xenograft is the same as the cell that received the first oncogenic “hits” in the patient. It is clear that the CSCs capable of forming a tumor at one point in time might change during the progression of the disease. Thus, a tumor may be initiated by a set of mutations leading to transformation of one cell type, but progressive mutations occurring during the evolution of the tumor may result in the acquisition of stem cell properties by a second cell type at a later time [19]. The two most important properties of CSCs are self-renewal capacity and differentiation [20]. CSCs are resistant to many conventional cancer treatments, including chemotherapy and radiation
9 therapy, enabling them to give rise to the almost inevitable local recurrence and relapse. Also,
CSCs contribute to the high potential of metastasis, which is responsible for more than 90% of cancer-associated mortality [21]. Due to their proposed importance in cancer pathogenesis,
CSCs have attracted enormous attention from researchers worldwide.
2.2.1 Evolution of CSC research
The first connection between cancer and stem cells dates back to the 18th century when histological similarities were noted between tumors and embryonic tissue [22]. In 1937, Furth and Kahn established that a single cell from a mouse tumor could initiate a new tumor in a recipient mouse. In 1964, Pierce showed single cells in teratocarcinomas that can differentiate into multiple differentiated, non-tumorigenic cell types. Later, hematological tumors were characterized as having proliferative heterogeneity and a hierarchical organization, leading to the proposition that slow-cycling leukemic stem cells (LSCs) caused tumor relapse [23]. In
1995, functional heterogeneity studies of acute myeloid leukemia (AML) showed that leukemic engraftment could only be initiated from limited CD34+/CD38- subpopulations [24] and the frequency of initiating cells accounts for only one per million tumor cells [25].
In 2003, the CSC concept and the heterogeneous structure of tumors were confirmed in breast cancer by showing the unique tumorigenic capacity of CD44+/CD24-/low subpopulations. In eight out of the nine patients examined CD44+/CD24-/low subpopulations were able to form tumors [26]. As few as 100 BCSCs injected into the breasts of mice formed tumors, although tens of thousands of the other cancer cells were unable to do so [27]. Al-Hajj and colleagues also found that even after secondary and tertiary serial passages, the new tumors were phenocopies of the original cancer, confirming the self-renewal capacity of the tumorigenic population in vivo [26] (Figure 3).
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Figure 3. Timeline of important discoveries in the field of CSCs (modified from [22, 23]).
2.2.2 The characteristics and possible origin of CSCs
Based on the CSC theory, it was shown that CSCs are cancer cells that posses characteristics associated with NSCs. Two essential qualities, self-renewal and differentiation drive NSCs to perform their natural functions. Self-renewal denotes a special cell division that enables a stem cell to produce another stem cell with essentially the same development and replication potential [28]. The ability to self-renew enables expansion of the stem cell compartment in response to systemic or local signals, which trigger massive proliferation and maintenance of a tissue-specific undifferentiated pool of cells in the organ or tissue. The self-renewal of
CSCs allows them to maintain themselves and drives tumorigenesis. Another important functional property of stem cells is differentiation. The differentiation ability corresponds to the production of daughter cells that become tissue-specific specialized cells. The differentiation capability of CSCs contributes to tumor cellular heterogeneity and it can give rise to a hierarchy of proliferative and progressively differentiating cells, which can generate
11 the full repertoire of tumor cells including both tumorigenic cells and non-tumorigenic cells
[20, 28].
The original hypothesis postulated that CSCs arose from cells with stem cell properties or directly from normal tissue-specific stem cells. It was found that LSCs share the expression of some markers with normal stem cells and that leukemia cells are capable of differentiating into multiple mature cell lineages [24]. But the term CSCs has led to some confusion. CSCs are not only derived from the stem cells from corresponding tissue, but could also arise from more differentiated progenitor cells when these cells acquire the capacity of self-renewal by accumulation of genetic or epigenetic abnormalities [19]. Tavil and colleagues found that the phenotype of the LSC population in many patients matches that of progenitor cells, not hematopoietic stem cells (HSCs) [29]. In AML, the functional LSCs were detected in a Thy1- progenitor cell state by an in vitro colony-forming assay [30, 31]. Additionally, there is extensive evidence to support the possibility that differentiated cells could generate CSCs by accumulation of mutations in certain oncogenes and tumor suppressor genes, leading to reprograming of cells and activation of a self-renewal program. Scaffidi and Misteli experimentally reprogramed human skin fibroblasts by stable expression of human telomerase
(hTERT), oncogenic allele of ras (H-RasV12), and simian immunodeficiency virus 40 large T
(SV40 LT) and small T (SV40 ST) antigens in vitro led to the generation of cells with CSC properties, able to form hierarchically organized tumors in mice [32]. Mani and colleagues induced expression of the epithelial-to-mesenchymal transition (EMT)-inducing transcription factors Twist or Snail in immortalized human mammary epithelial cells (HMLE), which resulted in the acquisition of mesenchymal traits and in the gain of epithelial stem cell properties [33].
2.2.3 The dynamic changes and plasticity of CSCs
Tumors are genetically unstable entities. CSCs represent a highly dynamic cell population.
Within the individual tumor, multiple CSC pools exist. For example, two hierarchically
12 organized distinct LSC populations defined by CD34, CD38, and/or IL3Rα expression have been found to coexist in AML patients [34]. In breast cancer, the cell subpopulations with phenotypes like CD44+/CD24-/low, CD49f+/EpCAM-/low or high aldehyde dehydrogenase
(ALDH) activity exhibit CSC-like properties [35].
Additionally, mounting evidence indicates that distinct CSC subpopulations exist after serial xenotransplanatation, during tumor progression and after therapy. In ovarian cancer, substantial differences of the CSC subpopulation were found between primary cancer specimens, which contained CD133+ CSCs, and their corresponding xenografted tumors, which were governed by CD133- TICs subpopulations [36]. During tumor progression, different tumor stages might be governed by distinct CSC clones. In the early stage tumor, a single CSC clone may control the tumor initiation, but the advanced stage tumors might contain several distinct clones, either arising from the initial CSC clone or from the differentiated cells via mutations or via induction by the tumor microenvironment. Among these distinct clones, some acquired more aggressive properties like enhanced self-renewal ability as well as decreased differentiation, which help them become the dominant type in the tumor. Late stage tumors may thus be comprised almost exclusively of aggressive, multi- resistant CSCs [30]. Compared to the CSC clones in primary tumors, the metastases in distant organs contained different CSC clones. Only a subset of disseminating CSCs, which undergo
EMT and a mesenchymal-epithelial transition (MET) program has the capacity of seeding in a new microenvironment to initiate metastasis. After chemotherapy and/or CSCs-targeted therapy, the majority of tumor cells including some types of CSCs are eliminated, but one or a few CSC clones which are resistant to the therapies or undergo certain mutations could survive and expand, leading to a change in the clonal and cellular composition of the relapsing cancer [30].
2.2.4 Controversies regarding CSCs
13 Although the CSC concept has attracted great interest, concerns about its scientific foundation remain because significant gaps in supporting research findings and knowledge still exist.
Some researchers actually question the existence of CSCs and argue that the relative importance of the putative population is negligible [37]. Firstly, a common misperception that continues to pervade discussions is that a cell labeled as a “CSC” must have arisen from a
NSC. In fact, this is not the case and was never the intention of the adopted nomenclature
[38]. A second area of confusion relates to assumptions regarding the nature of CSC properties. This problem has also arisen from the misunderstanding concerning the origin of
CSCs mentioned before. CSCs are proposed to exhibit similar qualitative and quantitative traits as present in normal systems, as NSCs. Most NSC systems behave according to relatively well-conserved and predictable rules, which typically include a hierarchical developmental process. At steady state, the frequency, biological properties and surface immunophenotype of a particular stem cell compartment generally do not vary much within defined cell lineages, whereas CSCs show plasticity during tumor progression, and different stage tumors contain distinct CSC pools. It is speculated that CSCs are of relatively low- frequency in early stages of tumorigenesis, but become an increasingly prevalent (or perhaps even dominant) component of the tumor population during tumor progression [30]. Therefore, the lack of consistent biological features will inevitably lead to controversy and confusion regarding the existence of CSCs.
Additionally, niches or microenvironments are essential for supporting and limiting the characteristics of stem cells. One of the differences between NSCs and CSCs is their degree of dependence on the stem cell niche, a specialized microenvironment in which stem cells reside. The stem cell niche is composed of a group of cells in a special location for maintaining stem cells. It is a physical anchoring site and generates extrinsic factors (e.g.
Hedegehog (Hh), Wnt, Notch, fibroblast growth factor (FGF), bone morphogenetic protein
(BMP)) to control their proliferation, fate determination and the number of stem cells. The niche also controls normal asymmetrical division [39, 40]. For NSCs, the niche controls the
14 activity of stem cells, but the CSCs are governed by their niche while simultaneously instructing it as well, thus leading to CSC division, proliferation, differentiation, invasion, and metastasis [37].
Thirdly, some researchers also argue that the existence of CSCs or their frequency is of little clinical relevance. In order for directed CSC eradication to be efficacious, the CSCs should maintain stable phenotypes. However, CSC subpopulations demonstrate significant plasticity of genetic, epigenetic, and cellular properties. Their status is easily changed by the local microenvironment. Thus, targeting CSCs may bring the same problems encountered for decades in treating the bulk tumor population, such as emergence of drug resistance and selection of increasingly refractory cell types [38].
Nevertheless, the basis for the CSC model continues to evolve. With the development of new technologies, more and more theoretical and experimental challenges to the CSC model are being explored [41].
2.2.5 CSCs and drug resistance
CSCs are considered to be resistant to conventional chemotherapy and radiation therapy. For example, it was shown that BCSCs were about twenty times more resistant to paclitaxel than non-CSCs. Furthermore, paclitaxel treatment uniformly enriched CSCs in vivo in several breast cancer models [42]. In breast cancer patients, the percentage of CD44+/CD24-/low tumor cells increased after chemotherapy [43]. Besides breast cancer, the resistance to conventional therapies is shared by CSCs in various cancers (Figure 4, [44]).
Various mechanisms contribute to the resistance to conventional therapies. On the one hand,
CSCs are naturally resistant to traditional therapy through their quiescent or slow proliferation rate, the high expression level of ATP-binding cassette (ABC) drug pumps, the intrinsic high levels of anti-apoptotic molecules, the relative resistance to oxidative or DNA damage and the efficiency of DNA repair [45]. On the other hand, CSCs can acquire resistance by
15 accumulating a wide variety of mutations, whereby they can establish a population of multidrug-resistant tumor cells to drive the tumor relapse.
Figure 4. Timeline of milestone studies that demonstrate how CSCs contribute to the acquisition of chemotherapy resistance [44]. Schematic representation of a patient with disease who initially responds to chemotherapy and how CSCs survive chemotherapy exposure. The red box indicates when the hypothesis that CSCs survive chemotherapy was first formulated. Black boxes indicate the studies that have demonstrated how CSCs survive chemotherapy in a wide range of tumor types. AML, acute myeloid leukemia; CML, chronic myeloid leukemia; GBM, glioblastoma multiforme.
Table 1 provides a detailed summary of mechanisms and relative therapeutic strategies that contribute to the responses exhibited by CSCs to known drugs. However, many cancers are not effectively eliminated by these strategies. Thus, further studies aimed at selectively eliminating CSCs have attracted widespread interest.
16 Table 1. Overview of mechanisms of CSC resistance to therapy and strategies to overcome resistance (adapted from [46])
Mechanism Therapeutic approach/strategy Intrinsic resistance of CSCs:
CSC quiescence Pretreatment with CSC-activating (tissue/organ- specific) factors Plasticity in CSC subclones Early eradication of all neoplastic stem cells, drug combinations, broadly-acting drugs Overexpression of efflux transporter molecules Transporter inhibition (e.g., verapamil, CSA) (e.g., MDR, ABCG2) Expression of survival molecules and stress Blockers or antagonists of survival molecules, proteins (e.g., HSPs) HSP targeting drugs Interactions between CSCs and their Target both CSCs and niche cells, mobilization of microenvironment (CSC niche) CSCs away from the niche, targeting niche-CSC interactions Acquired Resistance of CSCs:
Additional mutations in oncoproteins Alternative oncoprotein inhibitors, multi-kinase- inhibitors, drug combinations (± Compound mutations)*
Gene amplification, elevated Higher doses of inhibitors, alternative inhibitors, drug combinations load (amount) of oncoproteins Activation of additional prooncogenic signalling Broadly-acting kinase inhibitors, other targeted pathway drugs, drug combinations Epigenetic silencing of critical tumour suppressor Demethylating agents and/or other epigenetic genes drugs, drug combinations MDR, multidrug resistance gene; CSA, cyclosporin A; HSPs, heat shock proteins.
*: Compound mutation: ≥ 2 mutations in the same oncoprotein molecule [47, 48].
2.2.6 Strategies for the isolation and enrichment of CSCs
Due to the fact that the CSC subpopulations in a tumor are very small, collecting larger numbers of CSCs that can be used for well-validated functional assays is a great challenge.
Thus, strategies to efficiently and reliably isolate CSCs from a heterogeneous tumor mass or cancer cell lines play a fundamental role in CSC research, the results of which will have profound implications both for tumor development and for therapeutic outcomes. In this part, we briefly discuss different strategies for isolating and enriching CSCs in different labs.
17 2.2.6.1 CSC marker-positive cell isolation
CSC subpopulations are commonly defined by the presence or absence of various combinations of cell-surface makers. By staining cells with specific antibodies against these markers, populations of interest are easily identified and isolated by fluorescence-activated cell sorting (FACS). Thus far, a number of markers have proven useful for the isolation of cell subsets enriched for CSCs in multiple types of solid tumors (Table 2).
It should, however, be noted that none of these markers are exclusively expressed by CSCs.
For example, CD133 (Prominin) was initially described as a CSC marker for glioblastoma multiforme and was then widely explored as a CSC marker in different types of cancer [34].
However, it has also been implicated to be a NSC marker, while later evidence showed that it is also expressed in proliferative cells in many organs [49]. Additionally, different CSC pools exist in the individual tumor and CSC markers are not universal. It is unfeasible to isolate or enrich the different types of CSCs by limited CSC markers. A combination of markers can refine the CSC phenotype. For example, CD44 expression combined with high levels of the tyrosine kinase receptor c-MET provided robust selection of pancreatic CSCs [50], and absence of CD24 together with presence of CD44 expression resulted in significant enrichment of BCSCs [26]. Unfortunately, in some cases, limited overlaps between distinct
CSC marker-positive populations represent a big challenge for CSC related researches. The
ALDHhi and CD44+/CD24-/low CSC-enriched subsets in breast cancer bear little overlap within the same tumor [51]. The EpCAMhiCD44+ CSC subpopulation shared minor overlap with the
CD133+ CSC subpopulation in colorectal cancer [52].
Another problem is that CSCs are genetically unstable. The subpopulations sorted by markers do not keep the specific marker expression pattern stably, and have been reported to readily regenerate the original marker expression pattern before sorting. Thus, despite the fact that
CSC markers allow the faithful sorting of marker-positive and marker-negative populations, a deep understanding of the underlying stem cell biology is still poorly developed. At present,
18 CSC markers must be clearly defined for each tissue and more investigation is needed to determine whether they can be used as therapeutic target in different tumors. Moreover, clarifying cellular and signaling functions of markers will provide more therapeutic options to destroy CSCs.
Table 2. CSC markers for distinct solid tumor types (adapted from [53]).
Solid Cancer CSC marker
Breast ALDH1, CD44, CD24, CD90, CD133, Hedgehog-Gli activity, α6-intergrin
Colon ABCB5, ALDH1, β-catenin activity, CD24, CD26, CD29 CD44, CD133, CD166, LGR5
Glioma CD15, CD90, CD133, α6-integrin, nestin
Liver CD13, CD24, CD44, CD90, CD133
Lung ABCG2, ALDH1, CD90, CD117, CD133
Melanoma ABCB5, ALDH1, CD20, CD133, CD271
Ovarian CD24, CD44, CD117, CD133
Pancreatic ABCG2, ALDH1, CD24, CD44, CD133, c-Met, CXCR4, Nestin, Nodal-Activin
Prostate ALDH1, CD44, CD133, CD166, Trop2, α2β1-integrin, α6-integrin
2.2.6.2 Dye-exclusion side-population cell isolation
Many studies reported that the “side population” (SP), a subset of stem cells which has been identified in several normal tissues and human cancers using dual wavelength flow cytometry
(FACS) combined with Hoechst 33342 dye efflux. SP cells are capable of self-renewal, can undergo asymmetric division, and express common stem cell markers [54]. For example, purified SP cells from the MCF-7 breast cancer cell line showed the capacity of asymmetric division in vitro and increased expression of the “stemness genes” Notch1 and β-catenin [55].
In vivo studies revealed that SP cells are more tumorigenic than non-side population (NSP) cells when transplanted into immune-deficient mice. Purified SP cells from two cell lines
(U373 glioma cell line and MCF7 breast cancer cell line) and a xenograft prostate tumor
(LAPC-9) are more tumorigenic than the corresponding NSP cells [55]. In a clinical study,
19 Nakanishi and colleagues have identified SP cells in ER+ luminal breast cancer specimens
[56]. SP cells also showed increased invasive capacity, which contributed to the metastatic spread of tumor. SP cells isolated from the pancreatic cancer cell line PANC-1 have been shown to have an increased invasive potential in vitro and increased metastatic potential in vivo when compared to NSP cells using a murine liver metastasis model [57].
SP cells exhibit drug-resistance properties. This is due to high expression of drug efflux transport proteins (ABC transporters) in SP cells, which leads to their high drug efflux capacity [54]. For example, gastrointestinal stromal tumors express high levels of ABCB1
[58], and neuroblastoma cells highly express of ABCG2 and ABCA3 [59]. However, ABC transporter overexpression in SP cells is not the only mechanism for their chemoresistance.
Luo and colleagues provided evidence that purified SP cells from melanoma cells are resistant to temozolomide, which is not a substrate for ABC transporters, through IL8 up- regulation [60].
Actually, SP cannot completely define CSC-like cells. For example, purified SP cells in glioblastoma multiforme were unable to self-renew and had a lower tumourigenic potential than NSP cells [61]. SP does not define CSC-like cells in adrenocortical tumors, which lack the ability of self-renewal and are not more chemoresistant than the NSP population [62].
2.2.6.3 Anchorage-independent cell culture (Sphere formation assay)
Anchorage-independent sphere forming assays are widely used to characterize and enrich stem cells and progenitor cells. In 1992, Reynolds and Weiss first cultured cells with stem cell-like properties as free-floating spheres, termed neurospheres, from the adult brain [63].
To assess the capacity of unlimited self-renewal of stem cells, the neurospheres were mechanically dissociated and cultured again under low-attachment conditions with minimal growth factor supplementation, with a subset for secondary generation of neurospheres.
Additionally, differentiation ability is another intrinsic property of stem cells; when plated
20 under adherent condition, neurospheres differentiated in both neurons and glial cells.
Therefore, the neurosphere assay provided a simple method to identify cells exhibiting both functional properties of stem cells [64]. For the mammary gland, Dontu and colleagues developed an in vitro cultivation system to enrich mammary stem cells and progenitor cells as non-adherent mammospheres and demonstrated that non-adherent mammospheres are able to differentiate along mammary epithelial lineages and to clonally generate complex functional structures in reconstituted 3D culture systems [65].
For tumor studies, the tumorsphere assay, which is analogous to the neurosphere or mammosphere assay, allows to assess whether CSCs harbor the potential to both initiate and maintain tumors in the absence of cellular interaction and adhesion. Cancer cells lacking stem cell properties have limited sphere-forming potential due to telomere loss and cellular senescence. Therefore, the sphere forming efficiency could reflect the percentage of CSCs to some extent. The tumorigenic efficiency of a subset of cancer cells can be determined based on the number of spheres that emerge from single cells [64]. The sphere-forming assay provides a useful tool to enrich CSCs which lack unique cell surface markers and in the absence of a distinct and discernable morphological phenotype.
2.2.6.4 High-activity aldehyde dehydrogenase (ALDH) cell isolation
ALDH activity has been mostly attributed to the function of ALDH1, a detoxifying enzyme responsible for the oxidation of retinal to retinoic acid. ALDH1 plays an important role in the differentiation of normal and malignant human stem cells from multiple sources such as brain, lung, breast, and colon [51, 66-68]. Many studies showed that the ALDH1+ cancer cells isolated from tumor masses using the ALDEFLUOR assay and FACS analysis expressed stem cell markers and exhibited properties of CSCs including self-renewal, multipotency, and drug resistance. For example, Rasper et al. showed that high protein levels of ALDH1 facilitate neurosphere formation and that inhibition of ALDH1 decreased both the number of neurospheres and their size in vitro, while high levels of ALDH1 seem to keep tumor cells in
21 an undifferentiated, stem-cell like state [68]. Ginestier et al. found ALDH1 to be a stem cell marker in breast carcinoma associated with poor clinical outcome. High ALDH activity identified the tumorigenic cell fraction, capable of self-renewal and of generating tumors [51,
69]. Additionally, ALDH+ cells are resistant to sequential paclitaxel and epirubicin-based chemotherapy for breast cancer [70]. Huang et al. found that the isolated ALDH+ populations from malignant colon cells could generate xenograft tumors, while ALDH- cells failed to form xenograft tumors [67].
However, using ALDH as a stem-cell marker does have some limitations for the isolation of
CSCs. In some cancer cell lines and tumor samples, ALDH activity fails to identify cells with stem cell-like properties. For example, in the lung carcinoma cell line H522, the ALDH+ population is not CSC-enriched compared to the ALDH- population. Both populations are able to initiate tumors after inoculation into NOD/SCID mice and tumors generated from
ALDH- cells grow faster and bigger than the tumors from ALDH+ and this remains true after multiple passages [71, 72]. For prostate cancer, ALDH activity does not identify prostate
CSCs, because ALDHlow CD44- cells were able to develop tumors [73].
2.2.6.5 EMT-induced CSCs
EMT is a biological process that allows epithelial cells to undergo multiple biochemical changes that enable it to transdifferentiate and acquire mesenchymal traits including enhanced migratory capacity, invasiveness, and elevated resistance to apoptosis. It is important in various developmental processes as well as tumor progression and metastasis [74-76]. The activation of EMT has also been shown to generate cells with properties of stem cells, and the
EMT program could provide a ready source of CSCs by enabling the dedifferentiation of many epithelial carcinomas. Mani et al. found that induction of an EMT in normal or neoplastic mammary epithelial cells either upon transforming growth factor β (TGF-β) treatment or by forced expression of E-cadherin transcriptional repressors like Twist or Snail, could result in the enrichment of stem cell-like cells with increased mammosphere forming
22 ability, higher proportion of CD44+/CD24- cells, drug resistance and increased tumorigenic ability [33, 42]. Additionally, stem cells isolated from normal breast tissue or breast cancers express a number of canonical EMT markers.
In fact, many studies provide evidence for the molecular connections between the EMT program and CSC generation. For example, proteins or pathways implicated in self-renewal regulate EMT transcription factors (EMT-TFs), while EMT-TFs may regulate proteins involved in self-renewal. For example, Wnt-β-catenin signaling is involved in embryonic development and controls self-renewal of stem cells in a number of normal tissues as well as cancer. Wnt signaling also regulates the EMT program by the transcription factor Snail, which interferes with the expression of E-cadherin, leading to suppression of the epithelial characteristics [77, 78]. However, it remains unclear whether passage through an EMT directly generates CSCs or simply cells that are poised to become CSCs.
2.2.6.6 Chemotherapeutic and radiotherapeutic selection of CSCs
Many studies have shown that CSCs are resistant to chemotherapy and radiotherapy through complex mechanisms. Using this property, many laboratories successfully enriched CSCs from different types of tumors such as breast cancer, non small-cell lung cancer (NSCLC), colon cancer, pancreatic cancer and prostate cancer by chemotherapy and radiotherapy. Using non-BCSCs sorted from patient samples, Lagadec and colleagues found that ionizing radiation reprogrammed differentiated breast cancer cells into BCSCs, which showed increased mammosphere formation, increased tumorigenicity, and expressed a variety of stemness-related genes [79]. Li et al. found that the cells that were isolated from biopsy samples after chemotherapy exhibited a higher percentage of CD44+/CD24- CSCs and stronger mammosphere-forming ability compared to that before therapy [43].
Chemoradiation-resistant pancreatic cancer cells underwent EMT and showed similar characteristic as CSCs, i.e. they expressed more anti-apoptotic Bcl-2, apoptosis-inhibitory protein survivin and the stem cell markers Oct4, ABCG2, CD24 and CD133, and they were
23 more tumorigenic in vitro as well as in vivo [80]. Bao et al. demonstrated that the population of cells enriched for glioma CSCs was dramatically increased by irradiation and that radio- resistant gliomas showed an increased percentage of CD133-positive cells [81].
In summary, there are several methods for isolating and enriching CSCs in distinct cancers.
However, the advantages and limitations of these different strategies remain to be elucidated.
An ideal method for isolating CSCs should have the following characteristics. (i) Specificity:
The isolated cells should not contain any nontumorigenic cells, progenitor cells, or differentiated cells and should be able to form the original tumor in mice using as few as one cell. (ii) Sensitivity: All CSC subpopulations that can initiate tumor formation should be included in the isolated cells. (iii) Versatility: The method should be applicable for the isolation of CSCs from different types of tumors. (iv) Convenience: The isolation procedures should be simple, use limited resources (such as time, effort, and energy), and be user friendly. Thus, all strategies mentioned above have obvious limitations [82] but a combination of several methods for isolating CSCs may provide reliable and efficient results.
In the near future, it is still reasonable to believe that additional reliable, convenient and efficient methods for isolating high-purity CSCs and enriching CSCs will be developed.
2.2.6.7 Hypoxia-induced CSCs
Rapid cell division and aberrant blood vessel formation lead to the formation of hypoxic regions in a tumor. The hypoxia-inducible factors (HIFs) mediate transcriptional responses to localized hypoxia in normal tissues and in cancers and can promote tumor progression by altering cellular metabolism and stimulating angiogenesis [83]. In recent years, mounting evidence showed a strong link between hypoxia and CSCs derived from solid tumors. Soeda et al. found that hypoxia promotes expansion of CD133-positive glioma stem cells through activation of HIF-1α [84]. Conley et al. demonstrated that the anti-angiogenic agents sunitinib and bevacizumab increased the population of BCSCs by generating intratumoral hypoxia in human breast cancer xenografts. Furthermore, in vitro studies revealed that hypoxia-driven
24 stem/progenitor cell enrichment is primarily mediated by HIF-1α [85]. Li et al. showed that
HIF-2α is highly expressed in CSCs in gliomas and neuroblastomas and that loss of HIF-2α leads to a significant decrease in proliferation and self-renewal of CSCs [86].
Interestingly, there are several characteristics of CSCs that help them to survive and enrich in hypoxic conditions [82]. First, several core stemness-related transcription factors, including
Oct4, Nanog, Snail, c-Myc, Twist and Notch, are the direct or indirect targets of HIF [83, 86].
Furthermore, solid tumor CSCs are predominantly localized in hypoxic zones in vivo. One attractive hypothesis is that stem cells, particularly in long-lived animals, might benefit from residing in hypoxic niches where oxidative DNA damage may be reduced [83]. Another advantage could be that CSCs possess greater resistance to hypoxic conditions and can therefore be enriched under these conditions. A direct HIF target family is the ABC glycoprotein transporter, which is highly expressed on CSCs and confers multidrug resistance in different carcinomas [83, 87]. For example, Bcrp/ABCG2, a HIF-regulated ABC transporter, is expressed on BCSCs and is implicated in chemotherapeutic drug resistance in breast cancers [88]. Finally, sustained telomerase activity plays a critical role in stem cell function. Nishi et al. indicated that HIF-1 mediates up-regulation of human telomerase
(hTERT) [89].
2.2.7 Cancer metastasis and CSCs
Cancer metastasis accounts for more than 90% of lethality in cancer patients. Metastasis occurs when genetically unstable cancer cells adapt to a tissue microenvironment and originate in distant organs and tissues to disseminate and colonize a secondary site, which is distinct from the primary tumor [21]. Metastasis is a complex, multi-step process. EMT and
MET processes are considered to be the crucial events in tumor metastasis. Loss of epithelial traits and gain of migration ability by EMT lead the cancer cells to disseminate from the primary tumor to distant organs, while the reverse biological process, MET facilitates colonization and macrometastasis [90]. Several observations showed that the induction of
25 EMT can co-induce stem cell properties, including resistance to apoptosis, transient quiescence and self-renewal capacities [33]. The EMT process can therefore be described as a de-differentiation process, while the MET is a re-differentiation process. The invasive de- differentiated cancer cells are termed “migrating CSCs (mCSCs)”. They are characterized by both EMT properties and a stem cell-like phenotype and are considered as potential sources of metastases [91].
Several characteristics of mCSCs suggest that they are key drivers of metastasis. First, CSCs lead to tumor initiation, so even if non-CSCs migrate through the vascular system, they would not be able to generate a new tumor in distant organs. Second, primary tumors and corresponding metastases both have a heterogeneous structure. Therefore, just CSCs have the ability to differentiate into progenitor cells as well as differentiated cells and propagate into heterogeneously diverse metastatic lesions. Furthermore, the inherent plasticity of stem cells and genetic instability make CSCs more adept to survive in a particular foreign environment
(albeit primed by the pre-metastatic niche) where growth factors and other signaling molecules are different than in the primary tumor site. Moreover, tumor-initiating capacity is required at any metastasis site along with a niche for it [92].
Many studies have provided supporting evidence for the above-mentioned hypotheses. It has been shown that a distinct subpopulation of CD133+ CSCs, which express both CD133 and
CXCR4 are present in the invasive front of pancreatic tumors. This subpopulation of cancer cells is migratory and could determine the metastatic phenotype of the individual tumor [93].
Pang et al. reported that isolated CD26+ cells, but not CD26- cells, led to development of distant metastasis when injected into the mouse cecal wall. CD26+ cells isolated from human colorectal cancer were also associated with enhanced invasiveness and chemoresistance [94].
The essential role of CSCs in metastasis is also supported by clinical data. For example, it has been found that the majority of early-disseminated cancer cells detected in the bone marrow of breast cancer patients have a putative BCSC phenotype [73]. Similarly, it has been reported that a major proportion of circulating tumor cells in blood samples from metastatic breast
26 cancer patients shows EMT and CSC characteristics, while the blood samples from healthy donors are negative for EMT and ALDH1 transcripts [95].
2.2.8 In vivo and in vitro models for CSC studies
2.2.8.1 In vitro models
An ideal in vitro assay would be (a) quantitative; (b) highly specific, measuring only the cells of interest; (c) sufficiently sensitive to measure candidate stem cells when present at low frequency; and (d) rapid. Several in vitro assays have been used to identify stem cells, including sphere-forming assays, serial colony-forming unit assays (replating assays), and label-retention assays. Colony-forming unit assays are based on the ability of stem cells and progenitors to proliferate and differentiate into colonies in semi-solid media in response to cytokine stimulation. The colonies formed can be enumerated and characterized according to their unique morphology [96]. Sphere-formation assays in ultra-low attachment culture have been widely used to characterize normal and malignant stem cells based on their capacity to evaluate self-renewal and differentiation at the single cell level in vitro when removed from their in vivo niche. It provides a relatively simple method to assay stem cell potential in vitro.
Sphere-forming assays are also frequently used to dissect the molecular regulation of self- renewal and differentiation, and to investigate the intrinsic properties of stem cell and progenitors cells in many tissues during development and disease [64]. A label-retaining assay contains two essential parts: a pulse period and a chase period. Historically, bromo- deoxyuridine (BrdU) or radiolabelled DNA analogues were administrated to animals for a certain time (the pulse) to label all of the proliferating cells. Administration of the labeling reagents was then stopped and following a certain period (the chase), the tissues of interest were examined. Fast-cycling cells are constantly dividing and dilute the label through each round of division. Therefore, after the chase, their original label is diluted to a degree at which it can no longer be detected. Conversely, slow-cycling cells divide infrequently during
27 the chase period. For this reason, CSCs, as slow-cycling cells, retain significant amounts of the label and appear as label-retaining cells (LRCs) [97].
However, such assays also have obvious limitations. First, considering technical aspects, many critical experimental parameters need to be accounted for and a substantial amount of experimental variability is introduced into these assays including medium composition, cell density, digestion methods, surface area of the culture dish and culture duration [64, 98].
Second, spheres or colonies are prone to aggregation. It is important to ensure that spheres/colonies are due to proliferation not aggregation. Third, stem cell frequency is often calculated based on the number of spheres/colonies generated from a given tissue sample based on the premise that all spheres are derived from a stem cell. Actually, sphere-forming assays and colony forming assay are not a read-out of in vivo stem cell frequency. Multiple populations in stem cell lineages, including both stem cells and transit amplifying cells, are able to form spheres. And for this reason, serial passaging should be performed to eliminate more committed progenitors and to select for self-renewing stem cells. Fourth, sphere/colony size shows significant heterogeneity. It is has been proposed that sphere size reflects the property of the founder clone and it is believed that stem cells give rise to large spheres and progenitors cells to small spheres. However, the sphere size is not an exact read-out of in vivo stem cells. Independent of aggregation issues, sphere size not only reflects the proliferation/differentiation status but also demonstrates the responsiveness to growth factors of the parental clone-forming cell [64]. Moreover, in vivo factors to stimulate growth and to maintain self-renewal are largely absent in in vitro settings, which leads to anomalous and rapid differentiation; and the three-dimensional in vivo structure and environment cannot be fully replicated in vitro.
2.2.8.2 In vivo models
28 The gold standard for the characterization of CSCs is represented by their capability to initiate in vivo tumors that first recapitulate all the differentiated cellular populations of the primary tumor, and that can subsequently be serially re-transplanted without loss of tumorigenic potential, demonstrating CSC self-renewal. In vivo, the studies of CSCs rely on the model of xenograft transplantation into immunodeficient mice (Figure 5). The candidate human CSC populations have the ability to initiate tumors in immunodeficient animals, and the cellular composition of the xenografted tumor should resemble the primary tumor from which the selected cells were enriched [99, 100]. The first evidence of CSCs in human AML was obtained by transplantation experiments in the severe combined immunodeficient mouse, showing that leukemia is transferred to the host by CD34+/CD38- LSCs. This in vivo model replicates many aspects of human AML [24]. Later, it was demonstrated that transplanted
CD34+/CD38- LSCs give rise to a hierarchy of differentiated leukemic cells in the non-obese diabetic/severe combined immunodeficiency (NOD/SCID) mouse [25]. In the following years, CSCs have been described in xenografting studies in several human solid tumors. For example, the CD44+/CD24- BCSC subpopulation was able to induce tumors upon transplantation in the mammary fat pad, and this tumorigenic subpopulation could be serially passaged and generated new tumors containing additional CD44+/CD24- tumorigenic cells as well as phenotypically diverse mixed populations of nontumorigenic cells [26]. Ricci-Vitian et al. found that subcutaneous injection of CD133+ colon CSCs readily reproduced the original tumor in immunodeficient mice and such tumors were serially transplanted for several generations [101].
29
Figure 5. Xenograft assay to measure CSCs (adapted from [22]) Limiting-dilution transplants or other clonal tracking strategies are typically used to determine the frequency of CSCs in the initial tumor-derived cell suspension. Ideally, the tumors that form in primary hosts are again tested for their content of cells with CSC activity (demonstrable in injected secondary hosts) to formally confirm that initial CSCs had self-replicating ability. These principles apply to both the measurements of the CSC frequency and the total CSC content, either in the bulk population or in isolated subpopulations of dissociated tumor cells. The most sensitive assays are those in which there is no immunological difference between the host and the tumor. When this is not possible (such as for human tumors), xenografts into highly immunodeficient mice are used.
The limited dilution assay (LDA) is used to estimate the active cell frequency, and in recent years it has been commonly used to estimate CSC frequencies [102-105]. Tumor cells are transplanted into recipient animals at increasing doses; the proportion of animals that develop tumors is used to calculate the number of self-renewing cells within the original tumor sample. Ideally, a large number of animals would be used in each LDA to accurately determine the frequency of tumor-propagating cells. However, large-scale experiments involving mice are costly, and most limiting dilution assays use only 10-15 mice per experiment [106]. For other purposes like investigating whether a particular marker enriches for CSC activity, in vivo LDA needs to be done with both the tumor-initiating and non-tumor- initiating fractions; additional attention must be paid to the latter in order to ensure that the injected cells are viable tumor cells.
30 However, the capability of a cell to produce a detectable malignant population in a transplanted mouse might not accurately reflect its behavior in a cancer patient due to the limitations of currently available xenotransplantation (Table 3).
Table 3. Limitations of xenotransplant assays of human CSCs (adapted from [46])
Limitations Possible solutions
Short lifetime of mice Second-generation recipients or other animals
Altered homing of CSCs Orthotopic injection, humanized vasculature, humanized organs, humanized mice and growth-supporting synthetic scaffolds embedded in local tissue sites
Lack of cytokines that can stimulate Injection of cytokines, cytokine-transgenic mice, growth of CSCs humanized organs and humanized mice
Lack of tumor-specific Local injection of human microenvironmental cells, microenvironment humanized mice and supportive scaffolds
Lack of intact immune cells; lack of Prior transplantation of CD34+ hematopoietic cells, natural immunosurveillance humanized lymphoid organs, humanized mice and co- transplanation of immune cells (for example, natural killer cells)
The major issues are the lack of cross-species activity of many factors (e.g. cytokines) between mouse and human and the absence of immunosurveillance in immunocompromised mice. The short lifespan of mice (~ 2 years) compared to humans may compromise the detection of slow-growing CSCs and could possibly impose unknown, age-related differences in host factors on the behavior of tumors [22]. Conversely, tumor cells with transient self- sustaining ability may be prematurely identified as CSCs in immunodeficient mouse assays if the assays are not of sufficient duration [22, 107]. Continued genetic modifications of host mice might provide some possible solutions. For example, transgenic mice could be generated to express human-specific cytokines and humanized mice could be used to create more human-like microenvironments. Injections of specific immune cells may help to establish natural tumor immunosurveillance.
31 2.2.9 Key CSC signaling pathways
Dys-regulation of signaling pathway networks plays an important role for maintaining the stemness of CSCs. Signaling pathways involved in the control of self-renewal and differentiation of CSCs and NSCs include nuclear factor kappa B (NF-κB), PTEN/PI3K/Akt, mitogen-activated protein kinase/extracellular signal-regulated kinases (MAPK/ERK), Janus kinase/signal transducers and activators of transcription (JAK/STAT), Wnt/β-catenin, hedgehog (Hh), Notch, transforming growth factor β (TGF-β) and others.
NF-κB, a transcription factor that regulates the expression of multiple genes for pro- inflammatory cytokines and chemokines, has been shown to be a regulator of CSC function.
Liu et al. reported that NF-κB governed tumor stem cell expansion and mammary tumorigenesis in transgenic mice. Inhibition of NF-κB correlated with reduction in EMT and
BCSCs expansion [108]. NF-κB activation triggers Lin28B expression, which in turn decreases Let-7 microRNA levels [109]. Let-7 is able to down-regulate self-renewal and tumorigenicity of breast cancer cells [110].
PTEN/PI3K/Akt constitutes an important pathway regulating the signaling of multiple biological processes such as apoptosis, metabolism, cell proliferation and cell growth.
Activating mutations of PI3K and Akt in tumors are able to confer tumorigenic properties in several cellular systems [111]. For example, inhibiting the PI3K/Akt pathway strongly decreases the activity of the ABCG2 transporter by modulating its localization at the cell membrane. Furthermore, therapeutic blockade of either PI3K or Akt might reduce ABCG2 activity and SP phenotype in glioma CSCs in vivo and improve chemotherapy efficacy of drugs that are substrates of this transporter. Loss of the tumor suppressor PTEN greatly increases the SP and further enhances the MDR phenotype upon the standard treatment with temozolomide. [112].
MAPK/ERK signaling promotes cell proliferation, cell survival and metastasis. Several lines of evidence indicate that aberrant activation of MAPK/ERK, particular through upstream
32 activation by the EGFR and the Ras small guanosine triphosphatases (GTPases) plays an important part in the progression of cancer [113]. In colon cancer, the activation of Akt and
MAPK pathways proved to be vital for the CSC tumorigenicity. It was found that the activation levels of Akt and Erk1/2 are significantly higher in CD133+ cells than in CD133- cells. In addition, inhibitors of Akt and MAPK reduced the colony formation abilities of the colon CSCs [114]. In basal-like breast cancer, activation of the MAPK pathway due to loss of dual specificity phosphatase-4 promoted the CSC phenotype including increased tumorsphere formation, higher expression of the CSC-promoting cytokines interleukin (IL) 6 as well as
IL8, and an increased CD44+/CD24- population [115].
The JAK/STAT signaling pathway plays an important role in self-renewal and maintenance of the capacity of stem cells [116, 117]. It has been found that the JAK/STAT pathway is frequently dysregulated in a wide variety of cancer types. Recently, growing evidence supports that JAK/STAT activation may represent a characteristic feature of CSCs. In breast cancer, hypomethylation of several gene components of the JAK/STAT pathway has been correlated with an increased expression in mammospheres relative to parental cells. Sorted
CD44+/CD24-/low BCSCs exhibit a constitutive activation of the JAK/STAT pathway [118]. In prostate cancer, stem-like and progenitor cells express the IL6 receptor gp80 with concomitant expression of pSTAT3. Blockade of activated STAT3, by either the anti-IL6 antibody siltuximab or by LLL12, a specific pSTAT3 inhibitor, suppresses the clonogenicity of the stem-like cells in patients with high-grade prostate cancer. An in vivo study showed that JAK/STAT blockade inhibits tumor initiation [119].
Proper functioning of the Wnt, Hedgehog (Hh), and Notch signaling pathways is required for normal development during early life, and these pathways also play a key role in regulation and maintenance of stem cell functions including self-renewal and differentiation (Figure 6).
Increasing evidence implicates dysregulation of these pathways in the development and progression of a number of malignancies, including breast cancer [120-122]. Furthermore, emerging data from many human tumors including glioblastoma [123-125], breast cancer
33 [120, 126-128], pancreatic adenocarcinoma [129, 130], skin cancer [131], colon cancer [132,
133] and CML [134, 135] have suggested that these signaling pathways regulate CSCs. The
Wnt/β-catenin signaling pathway has been identified in mice to be a tumor-promoting integration site of the mouse mammary tumor virus (MMTV) (named INT) and a segment polarity gene in Drosophila (named WINGLESS) [120]. Increased evidence indicated a significant link between aberrant activity of Wnt signaling and CSCs in different tumors.
Constitutive Wnt signaling activation, resulting from mutations in genes encoding its downstream components, underlies tumorigenesis in different tissues [132]. For example, in skin cancer, the CD34+ CSC population, which was identified in mouse epidermal tumors, exhibits a preferential location of β-catenin in the nucleus. Conditional ablation of β-catenin by tamoxifen-induced mutagenesis in the DMBA-TPA or Ras-induced tumors results in complete regression of the tumors and in terminal differentiation of the tissues [131]. In colon cancer, tumor cells with nuclear β-catenin accumulation appear to undergo cell-cycle arrest and EMT to generate cells with stem cell-like properties [133].
Hedgehog signaling pathway activation is initiated by binding of one of the three secreted and lipid-modified ligands found in mammals, Sonic (SHh), Desert (DHh), and Indian (IHh)
Hedgehog, to Patched (Ptch1), a 12-pass transmembrane receptor [136]. Hh signaling in adults is significantly reduced compared to the embryo and neonate, being detected only in a few sites in adults such as central nervous system stem cells [137] and the gut epithelium
[138]. In breast cancer, the Hh signaling pathway is activated in human CD44+/CD24-/Lin-
BCSCs. The components of this pathway, such as PTCH1, Gli1, and Gli2 are highly expressed in normal and malignant human mammary stem/progenitor cells cultured as mammospheres and these genes are down-regulated when cells are induced to differentiate
[126]. In glioblastoma multiforme, Clement et al. found that Hh-Gli signaling regulates the expression of stemness genes in and the self-renewal of CD133+ glioma CSCs. Interference of
Hh-GLI signaling with cyclopamine or through lentiviral-mediated silencing decreased the tumorigenicity of human gliomas in mice [123]. In CML, Hh pathway activation through up-
34 regulation of Smo leads to expansion of the imatinib-resistant Bcr-Abl-positive leukemic stem cells [135].
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Figure 6. Signaling pathways that regulate self renewal (modified from [45]). Activation of the Hedgehog (Hh) signaling pathway is initiated by binding of a Hh ligand to protein patched homologue (PTCH). This lifts suppression of Smoothened homologue (SMO), activating a cascade that leads to the translocation of glioma-associated oncogene homologue (Gli) into the nucleus and the activation of target genes. Serine–threonine protein kinase 36 (STK36, also known as FU) and suppressor of fused homologue (SUFU) act downstream of PTCH and SMO to regulate Gli. Cyclopamine is a potent antagonist of SMO and has been used as a tool compound to study Hh signaling pathways both in vitro and in vivo. The core Notch pathway is activated by interaction between the Notch ligand (Delta-like or Jagged) on one cell with the Notch receptor on another cell, resulting in two proteolytic cleavages of the receptor. This mediates the release of the Notch intracellular domain, which enters the nucleus and interacts with transcription factors including recombination signal binding protein for immunoglobulin region (RBPJ, also known as CBF1). It has been suggested that alternative pathways involving AKT exist downstream of Notch activation. Various γ-secretase inhibitors can inhibit Notch cleavage and activation.
35 The Notch pathway is one of the most intensively studied putative therapeutic targets in
CSCs. Inappropriate Notch activation stimulates proliferation, restricts differentiation and/or prevents apoptosis [121]. Fan et al. showed that Notch inhibition selectively depletes CSCs as determined by CD133-high status or dye exclusion in medulloblastoma and glioblastoma
[124, 139]. Additionally, Notch activation contributed to radio-resistance of glioma stem cells
[125]. In breast cancer, Sansone et al. demonstrated that p66Shc/Notch-3 interplay controls self-renewal and hypoxia survival in human stem/progenitor cells of the mammary gland expanded in vitro as mammospheres [128]. For HER2-positive breast cancer, there is a link between HER2, Notch and CSCs. The HER2 promoter contains Notch-binding sequences, activation and subsequent nuclear localization of the Notch intracellular domain induces the transcription of target genes including HER2. Increased levels of HER2 in turn activate the
PI3K/Akt pathway that drives stem cell self-renewal [140].
TGF-βs and their family members, including bone morphogenetic protein (BMPs), Nodal and activins, play important roles in the maintenance and differentiation of CSCs. TGF-βs are capable to induced EMT, which in turn can induce CSCs [33], by up-regulating the expression of Snail transcription factor family members [141]. Piccirillo et al. reported that
BMPs, amongst which BMP4 elicits the strongest effect, activate their receptors (BMPRs) and trigger the Smad signaling cascade in cells isolated from human glioblastomas. This is followed by a reduction in clonogenic ability, in the size of the CD133+ CSCs population, and increased expression of markers of neural differentiation, with no effect on cell viability
[142].
In summary, these pathways all contribute to stem cell regulation and self-renewal. Therefore, targeting these pathways in cancer may specifically impact CSCs. Combination of CSC- targeting substances with standard therapy is a concept worthy of further investigation.
36 2.3 Targeting CSCs
From a clinical perspective, the CSC concept has significant implications, since these cells need to be eradicated in order to provide long-term disease-free survival.
2.3.1 Clinical implication of CSCs
Clinically, many tumor patients, particularly those with metastasized solid tumors, either do not respond to existing cancer therapies (including chemotherapeutics, radiotherapy and tumor-targeted agents) or relapse quickly after initial remission [45]. The CSC hypothesis provides a powerful explanation for the failure of current therapeutic strategies against cancer. Therefore, targeting CSCs represents a potential method for cancer treatment.
In addition to cancer treatment, the CSC hypothesis has profound implications for cancer prognosis. It is hypothesized that the CSC population, either in quantitative terms such as the relative or absolute number of CSCs, or qualitative aspects related to biologic features of
CSCs is related to cancer behavior [143]. In AML for example, a higher percentage of blasts with the CD34+/CD38- LSC phenotype is correlated with poorer overall survival [144]. The relationship between CSCs and cancer prognosis, however, is still unclear, and more evidence is needed to determine the prognostic significance of CSCs.
Considering that the CSC hypothesis has profound clinical implications, focusing research efforts on the CSCs may help to understand the cancer biology more clearly and to promote the development of anti-CSC targeting-therapeutic methods.
2.3.2 Therapeutic strategies for targeting CSCs
Considering the functional and molecular properties of CSCs, there are several potential therapeutic strategies for targeting CSCs including direct and indirect approaches. The direct strategies include CSC ablation using agents that target their molecular markers, reversal of resistance mechanisms operative in CSCs, and differentiation therapy. The indirect strategies
37 are related to the angiogenic/vasculogenic functions, microenvironment impacts and the immunoevasive properties of CSCs (Figure 7) [145]. Nowadays, a number of therapeutic strategies directed at CSCs are beginning to be experimentally validated. These approaches could potentially enhance responsiveness to current anticancer treatment regimens and might reduce the risk of relapse and dissemination.
Figure 7. Therapeutic strategies targeting CSCs (modified from [145])
2.3.2.1 Direct strategies
2.3.2.1.1 Ablation of CSCs by prospective markers
CSC surface markers help to identify CSCs, but also provide a strategy for targeting CSCs. In human malignant melanoma, killing of CSCs through their prospective identifier, ABCB5, could halt experimental tumor initiation and growth in vivo. These findings have provided an initial proof of principle that using an agent directly targeting a CSC-defining molecular marker is sufficient to inhibit tumorigenesis and tumor growth [146]. Additionally, ABCB5 gene silencing substantially increases the sensitivity of human melanoma cells to the anticancer chemotherapeutics 5-fluorouracil (5-FU) and camptothecin [147]. In AML, Majeti et al. found that CD47 in human LSC is an adverse prognostic factor. Blocking monoclonal antibodies (mAb) directed against CD47 preferentially enabled phagocytosis of AML LSCs and inhibited their engraftment in vivo [148]. In gliomas, the expression levels of L1CAM, a
38 neuronal cell adhesion molecule, were higher in CD133+ glioma CSCs than in normal neural progenitors. Targeting L1CAM using lentiviral-mediated short hairpin RNA (shRNA) interference in CD133+ glioma cells potently disrupted neurosphere formation, induced apoptosis in vitro and inhibited tumor growth in vivo [149]. Using mAb- and shRNA-based strategies directly targeting CSCs may prove to be especially useful in this regard because of their high degree of target specificity.
2.3.2.1.2 Self-renewal related pathway interference
Self-renewal, the essential function of CSCs, promotes tumor recurrence after standard chemotherapy, and targeting this property may constitute a promising strategy to inhibit disease progression. As mentioned before, several developmental pathways such as Wnt,
Hedgehog and Notch regulate CSC self-renewal. Thus, inhibiting the components of these pathways could block the self-renewal of CSCs.
For example, the Wnt/β-catenin pathway regulates CSC self-renewal in different tumors. The activity of the Wnt/β-catenin signaling pathway is dependent on the amount of β-catenin in the cytoplasm [122]. β-catenin deletion by treatment with the cyclooxygenase inhibitor indomethacin resulted in a profound loss of residual CML stem cells in the bone marrow of mice following imatinib therapy [150]. Non-steroidal anti-inflammatory drugs such as sulindac inhibit Wnt-signaling, suppress the activity of 5-lipoxygenase and reverse aberrant self-renewal of stem cells in AML or CML models [151, 152]. In head and neck squamous carcinomas (HNSC), the Wnt pathway receptor FZD8 was essential for interactions of c-Met and Wnt/β-catenin signaling in HNSC-CSCs, and targeting the c-Met/FZD8 signaling axis eliminated patient-derived CSCs in HNSC [153].
Hh signaling is another essential pathway for maintenance of CSCs. Hh signaling is activated in LSCs through up-regulation of Smo. Loss of Hh signaling by genetically disrupting Smo results in the inhibition of LSCs and prolongs the time to relapse after the end
39 of treatment [135]. Thus, Smo represents a drug target in LSCs. Active Hh signaling has also been identified in glioblastoma CSCs, and pathway inhibition with cyclopamine or shRNA directed against pathway components resulted in the loss of tumorigenic potential [154].
Similarly, Notch and its ligands are overexpressed in breast cancer and one method of effectively blocking Notch activity is preventing its cleavage at the cell surface with γ- secretase inhibitors (GSIs) [121]. Notch pathway inhibition by GSIs is capable of reducing self-renewal and improving the efficacy of docetaxel in patient-derived breast cancer xenografts [155]. A Notch-4 monoclonal antibody or gefitinib dramatically decreases the ability to form tumorspheres of BCSCs by inhibiting the Notch pathway [156]. Additionally, antibodies against either Dll4 [157] or Notch1 [158] improved the efficacy of taxane therapy in patient-derived xenografts.
Finally, similar to the role of other developmental signaling pathways, TGF-β also regulates tumorigenesis and tumor progression. TGF-β inhibition enhances chemotherapy action against triple-negative breast cancer (TNBC). The TGF-β type I receptor kinase inhibitor
LY2157299, a neutralizing TGF-β type II receptor antibody, and SMAD4 siRNA all blocked paclitaxel-induced IL8 transcription and CSC expansion in vitro. Treatment of TNBC xenografts with LY2157299 prevented reestablishment of tumors after paclitaxel treatment
[159].
2.3.2.1.3 Induction of CSC differentiation
A number of studies have reported that CSCs lack differentiation markers. For example, breast, colon and prostate CSCs lack cytokeratin epithelial differentiation markers, whereas gliblastoma CSCs lack glial fibrillary acidic protein [26, 44, 101, 160, 161]. Induction of CSC differentiation might be a successful therapeutic strategy, as the bulk of the tumor has limited proliferation potential and is more sensitive to chemotherapy or radiation therapy.
40 Potential strategies that induce quiescent CSCs to differentiate into more mature tumor cells include activation of distinct signaling pathways such as morphogen-driven signaling cascades, alteration of gene expression profiles using microRNAs and epigenetic differentiation therapy [145]. For example, Piccirillo et al. used BMP signaling to induce differentiation of CSCs in human brain cancer models. Specifically, administration of BMP4 either in vitro to glioblastoma cultures or in vivo to human brain cancer bearing mice induced differentiation of glioblastomas and significantly reduced CD133+ cell frequency [142].
Polycomb group proteins, such as enhancer of zeste homologue 2 (EZH2) are exploited by
CSCs to repress genes that are related to differentiation, and thus provide a potential target for inducing CSC differentiation [162]. miRNAs represent an additional class of molecules that could sensitize CSCs to conventional anticancer therapies through differentiation. Let-7 miRNAs are markedly reduced in BCSCs and increase with differentiation. Yu et al. found that increased let-7 parallels reduced H-RAS and HMGA2, known let-7 targets. Silencing H-
RAS in BCSCs reduced self-renewal while silencing HMGA2 enhanced differentiation [110].
2.3.2.1.4 Change of the quiescent status of CSCs
In clinical practice, it has been observed that patients after therapy showed a long-standing period of tumor quiescence and then might experienced recurrence after long periods of disease free-survival. Quiescence is considered to be one of the mechanisms of resistance to some chemotherapeutics. Killing CSCs could also be achieved by sensitizing them to chemotherapeutic drugs. Targeting regulators of the cell cycle represents a method for forcing quiescent CSCs to enter the cell cycle.
For example, primitive quiescent CML cells are biologically resistant to imatinib mesylate, an inhibitor of the p210 (BCR-ABL) kinase. Intermittent exposure to granulocyte-colony stimulating factor (G-CSF), a cell-cycle inducer, can enhance the effect of imatinib mesylate on CML cells by specifically targeting the primitive quiescent leukemic cells [163]. The addition of G-CSF also significantly decreases the percentage of G0/G1-phase cells and
41 significantly increases that of S-phase cells among leukemia cell lines [164]. p21, as cyclin- dependent kinase (CDK) inhibitor 1 or CDK-interacting protein 1, regulates cell cycle progression at the G1 and S phase [165]. In the colon cancer cell line HCT116, p21 null cells were found to produce tenfold smaller tumor in growth assays when compared to normal cells expressing p21 and were unable to form spheres. They ceased to proliferate and eventually died. Small molecule targeting of p21 or downstream p21 targets may therefore prove to be an effective means of forcing quiescent CSCs to enter the cell cycle or to undergo apoptosis
[166]. Cycling CSCs would be susceptible to chemotherapy and hopefully eliminated.
2.3.2.1.5 Targeting survival/apoptosis pathways of CSCs
Aberrancies in apoptosis and survival pathways are a hallmark of carcinogenesis [167]. These deregulations may for example result from alterations in mitochondrial proteins, cytokine signaling and/or developmental pathways. Inhibitors of apoptosis proteins are frequently overexpressed in tumors and have become promising targets for developing anti-cancer drugs.
In addition, a growing body of evidence suggests that CSCs exploit several mechanisms to deregulate the related signaling pathways and to promote chemotherapy resistance.
The mitochondrial Bcl-2 family proteins are among the best-characterized regulators of apoptosis. Lang et al. showed that BIKDD, a constitutively active mutant form of the pro- apoptotic gene BIK, effectively induces apoptosis of BCSCs and synergizes with lapatinib through co-antagonism of its binding partners Bcl-2, Bcl-xL, and Mcl-1, suggesting a potential therapeutic strategy targeting CSCs [168]. Another study showed that the alternative splicing of multiple Bcl-2 family genes promotes malignant transformation of myeloid progenitors into blast crisis LSCs that are quiescent in the marrow niche and contribute to therapeutic resistance. Sabutoclax, a pan-BCL2 inhibitor, renders marrow-niche-resident blast crisis LSCs sensitive to tyrosine kinase inhibitors at doses that spare normal progenitors
[169].
42 Secreted cytokines may also constitute a source of pro-survival signaling in cancer. Todaro et al. reported that the CD133+ colon CSCs produce and utilize IL4 to protect themselves from apoptosis. Consistently, treatment with an IL4Rα antagonist or an anti-IL4 neutralizing antibody strongly enhanced the antitumor efficacy of standard chemotherapeutic drugs through selective sensitization of CD133+ cells [170]. Death receptor 5 (DR5), a new member of the tumor necrosis factor receptor (TNFR) family, is enriched in pancreatic CSCs compared with the bulk of the tumor cells. A combination of tigatuzumab, a fully humanized
DR5 agonist mAb, with gemcitabine proved to be more efficacious by providing a double hit to kill both CSCs and bulk tumor cells [171, 172].
Developmental signaling pathways are also known to promote CSC survival. In human leukemia, inhibition of NF-κB induced apoptosis of CD34+/CD38– CSCs in vitro and inhibited tumor growth in experimental animal models in vivo, while sparing the physiologic hematopoietic cells compartment [173]. In prostate cancer, targeting Notch and Hedgehog signaling to deplete the CSC population through inhibition of the survival molecules AKT and Bcl-2, improved the therapeutic effect by of docetaxel [160].
2.3.2.1.6 Targeting the metabolism of CSCs
Recently, an increasing number of studies suggest that metabolic reprogramming of CSCs may have cancer-causing activity. This might be an essential characteristic that allows dynamic, multidimensional and evolving cancer populations to compete successfully for their expansion in the organism. Thus, CSC bioenergetics might be another cancer hallmark [44,
174-179].
The Warburg effect implies that one of the best-characterized metabolic phenotypes of cancer is the shift from oxidative phosphorylation to aerobic glycolysis [175]. The glycolytic phenotype appears to be closely associated with stemness. Liao et al. reported that CSC- enriched spheroid cells route glucose predominantly to anaerobic glycolysis and the pentose
43 cycle to the detriment of rerouting glucose for anabolic purposes [180]. Metformin is an oral antidiabetic drug of the biguanide class and the first-line drug for the treatment of type 2 diabetes. Many groups reported that metformin shows selective toxicity on CSCs in various cancers including breast cancer, lung adenocarcinomas, prostate cancer, melanoma and ovarian cancer [181-186].
Another study by Ginestier et al. found the enzymes of the mevalonate metabolic pathway to be overexpressed in basal/mesenchymal tumorspheres, compared to those in cognate adherent cells [187]. Inhibition of this pathway with hydroxy-3-methylglutaryl CoA reductase blockers resulted in a reduction of BCSCs independent of inhibition of cholesterol biosynthesis and of protein farnesylation. Further modulation of this metabolic pathway demonstrated that protein geranylgeranylation (GG) is critical to BCSC maintenance. A small molecule inhibitor of the geranylgeranyl transferase I (GGTI) enzyme reduced the BCSC subpopulation both in vitro and in primary breast cancer xenografts [187].
Zhang et al. showed that the metabolic enzyme glycine decarboxylase (GLDC) is critical for
CSCs in NSCLC. GLDC induces dramatic changes in glycolysis and glycine/serine metabolism, leading to changes in pyrimidine metabolism to regulate cancer cell proliferation. Overexpression of GLDC and other glycine/serine enzymes promotes tumorigenesis. In the clinic, aberrant activation of GLDC correlates with poorer survival in lung cancer patients. Thus, this link between glycine metabolism and tumorigenesis may provide new evidence for metabolic reprogramming of CSCs as well as novel targets for anticancer therapy [188].
2.3.2.2 Indirect strategies
2.3.2.2.1 Targeting the tumor microenvironment
44 A specialized microenvironment consisting of cells, matrix proteins and growth factors, known as a ‘niche’, is thought to physically restrain stem cells and to enable them to maintain their stemness by providing the necessary factors. At least two possibilities exist for generating CSC niches: Either they are engineered as nascent domains by tumor cells, or
CSCs usurp existing tissue-specific stem cell niches [189]. The CSC niche provides appropriate signals to regulate self-renewal and the normal homeostatic processes such as inflammation, EMT, hypoxia and angiogenesis. The presence or architecture of such a niche has not been physiologically identified, but its conception has led to a number of approaches for CSC therapy [190].
Targeting extracellular matrix (ECM) molecules could be a potential way to damage the microenvironment of CSCs. For example, CD44, a cell-surface ECM receptor, is probably the most established and common CSC marker. It mediates signaling including proliferation, apoptosis, survival, migration and differentiation through many protein interactions ranging from matrix metalloproteases, growth factor receptors, Src/Rho kinases and transcriptional regulators. Anti-CD44 antibody therapy represents the major anti-CSC approach in different cancers such as leukemia [191], breast cancer[192] and melanoma [193]. Another example, the CXCR4 chemokine (Cys-X-Cys motif) receptor is essential for the homing, retention and maintenance of hematopoietic stem cells in stromal niches in the bone marrow. CXCR4 up- regulation by imatinib induces CML cell migration to bone marrow stroma and promotes survival of quiescent CML cells. Inhibition of CXCR4 in CML cells disrupts their interaction with the bone marrow microenvironment and sensitizes them to nilotinib [194-196]. CXCR4 antagonists, such as Plerixafor (AMD3100) and T14003 analogs, can damage adhesive tumor-stroma interactions and induce leukemia cell mobilization away from the bone marrow stromal microenvironment, causing the cells to become more vulnerable to cytotoxic drugs
[197, 198].
Targeting growth factors secreted by stroma cells can influence the microenvironment of
CSCs. In colon cancer, high activity of the Wnt pathway is observed preferentially in CSCs
45 located close to stromal myofibroblasts. Myofibroblast-secreted factors such as hepatocyte growth factor (HGF) also restore the CSC phenotype in more differentiated tumor cells both in vitro and in vivo. Thus, inhibitors of HGF are expected to inhibit the properties of CSCs
[199].
Furthermore, many signals from the CSC niche are able to induce EMT, which promotes
CSCs. For example, expression of the Snail protein, is an EMT-inducer, was detected at the tumor-stroma interface in diverse human cancers including colon cancer [200]. It has also been shown that β-catenin, a marker of colon CSCs on the one hand and an inducer of EMT on the other hand, is mostly located at the host-tumor interface [133]. TGF-β is also known to stimulate proliferation and expansion of the pre-existing CSC pools, thereby further increasing the chances for metastatic spread [33, 201]. Inhibiting the EMT pathway could decrease the population of CSCs.
CSCs can reciprocally modulate their microenvironment through either the secretion of paracrine factors or direct cell-cell contact. Tumor angiogenesis has been reported to be related to CSC survival and drug resistance [145]. For example, CD133+/Nestin+ CSCs in brain cancers have been found to reside within a perivascular niche. Compared to bulk tumor populations, this CSC population secretes higher levels of vascular endothelial growth factor
(VEGF), which has been recognized to correlate with microvasculature formation and tumor growth. VEGF-specific neutralizing mAb treatment suppressed the growth of the
CD133+ human glioma cell-derived xenografts. Moreover, targeting VEGF with bevacizumab leads to the normalization of tumor vasculature, resulting in a disruption of the CSC niche
[202-204].
Although it is thought that the tumor vasculature is mostly composed of nonmalignant endothelial cell populations originating from host angiogenesis, there is evidence that tumor cells with high degrees of differentiation plasticity might contribute to the tumor vasculature via a process termed vasculogenic mimicry (i.e., de novo vessel formation that occurs
46 independently of angiogenesis through the production of endothelial cells) [205, 206]. In human melanoma, CSCs have been found to be responsible for vasculogenic mimicry, which is related to melanoma aggressiveness [207]. Thus, vasculogenic mimicry has also been proposed to provide a potential target for therapeutic intervention [145].
In summary, targeting the microenvironmental molecules that can promote stem-like functions, or the signaling pathways in cells that mediate those functions, may represent worthwhile therapeutic paradigms.
2.3.2.2.2 Disruption of immune evasion
A hallmark of cancer is its ability to evade the immune system [167]. CSCs have active mechanisms of immune-evasion and they might preferentially initiate and sustain neoplastic growth and disease progression through immune-evasive and immune-modulatory functions.
For example, CD200 is an immunoglobulin superfamily-related receptor that plays multiple roles in autoimmunity, inflammation and the adaptive immune response. It was found that
CSCs with CD200 as a surface marker have the ability to escape the immune system, presumably by inducing a down-regulation of the TH1 immune response (e.g. by IL2 and
IFNγ expression), thereby promoting tumor growth [190]. Together with other immunosuppressive molecules such as programed cell death-1 (PD-1), PD-1 ligand, TGF-β and CTLA-4, CD200 is emerging as a potential candidate for CSC immunotherapy.
In summary, several factors should be taken into account when designing CSC-directed treatment strategies: (i) given the similarities between CSCs and physiologic stem cells, CSC- targeted therapeutic agents could exert adverse effects on the renewal and maintenance of physiologic tissues due to potential toxic effects on a tumor host’s NSC compartment.
Therefore, preferred CSC targets would comprise those molecules or pathways that are preferentially induced or operative in malignant as opposed to physiological stem cells. (ii)
CSCs can represent heterogeneous cell populations that might differ in resistance profiles and
47 might therefore not be efficiently targeted by a single therapeutic agent. (iii) CSCs, like tumor bulk populations, might develop resistance to CSC directed therapies. It will be more effective to combine CSC-targeting with conventional agents to eliminate tumors and reduce the relapse and recurrence.
2.3.3 Identified inhibitors of CSCs
2.3.3.1 Monoclonal antibodies for inhibiting CSCs
The Notch pathway is the core signaling pathway for regulating self-renewal and survival of
CSCs. Targeting the Notch pathway has therapeutic potential for CSCs. Demcizumab (OMP-
21M18) is a humanized IgG2 antibody optimized to target Delta-like ligand 4 (DLL4), an activator of the Notch signaling pathway and to block Notch signaling in CSCs. Blocking
DLL4 has been shown in preclinical studies to result in broad-spectrum anti-tumor activity via multiple mechanisms, including disrupting angiogenesis, inhibiting CSC growth, and promoting cell differentiation and potentially immune activation. OMP-21M18 was observed to have activity against colorectal cancer, breast cancer, lung cancer, pancreatic cancer and melanoma (http://www.oncomed.com/Pipeline.html; Clinicaltrails.gov: NCT01189968:
Phase I). An anti-DLL4/VEGF bispecific antibody (OMP-305B83) targets both DLL4 in the
Notch CSC pathway and VEGF. This antibody is intended to have both anti-CSC and anti- angiogenic activity. OncoMed is currently enrolling patients with advanced refractory solid tumors in a Phase Ia clinical trial (http://www.oncomed.com/Pipeline.html). Tarextumab
(TRXT, OMP-59R5) is a fully human IgG2 antibody that inhibits the signaling of both
Notch2 and Notch3 receptors. Tarextumab has been shown in preclinical models to have broad-spectrum anti-tumor activity via inhibition of CSC growth and promotion of cell differentiation, as well as disruption of tumor angiogenesis by inhibiting vascular pericytes.
OncoMed has completed a single-agent Phase I clinical trial testing tarextumab in advanced solid tumor patients in 2013. Tarextumab was well tolerated as a single agent, and evidence
48 of Notch pathway modulation and potential early single-agent activity manifested by prolonged stable disease has been observed (http://www.oncomed.com/Pipeline.html). Anti-
Notch1 (OMP-52M51) is a novel anti-CSC antibody that binds the Notch1 receptor. Anti-
Notch1 is being studied as a single agent in two ongoing Phase Ia clinical trials among patients with advanced solid tumors or hematologic malignancies
(http://www.oncomed.com/Pipeline.html).
Wnt pathway inhibition via the targeting of Frizzled receptors results in decreased growth and tumorigenicity of human tumors [208]. Vantictumab (anti-Fzd7, OMP-18R5) and ipafricept
(FZD8-Fc, OMP-54F28) demonstrate evidence of Wnt pathway modulation and potential early single-agent activity manifested by prolonged stable disease has been observed
(http://www.oncomed.com/Pipeline.html). Human R-spondin (RSPO) proteins signal through the leucine-rich repeat-containing G-coupled receptors (LGRs) and the RSPO-LGR pathway is believed to be an important CSC pathway. RSPO proteins are secreted proteins defined by two N-terminal furin domains and a thrombospondin domain. They enhance Wnt/β-catenin signals in various contexts [209-211]. RSPO proteins use members of the LGR family as receptors in particular in stem cells and thereby can influence the canonical Wnt pathway
[212, 213]. Antibodies that disrupt binding of RSPO proteins to LGRs or disrupt RSPO activation of LGR signaling are potential anti-cancer agents. Antibodies targeting the RSPO-
LGR pathway demonstrated anti-CSC activity in preclinical studies
(http://www.oncomed.com/Pipeline.html).
CD44 is expressed in various kinds of cancer cells and CSCs. It is an indicator of tumors and metastasis in malignant diseases. RO5429083, an anti-CD44 recombinant human monoclonal antibody, has been used to treat patients with metastatic and/or locally advanced, CD44- expressing, malignant solid tumors (Clinicaltrails.gov: NCT01358903, Phase I).
Chemoresistant CSCs show increased sensitivity to insulin-like growth factor-Ι receptor (IGF-
ΙR) inhibition [214]. AVE1642 is a humanized version of the murine monoclonal IGF-1R
49 antibody EM164; both antibodies have been shown to induce regression of human tumor xenografts, inhibit metastasis, and enhance chemosensitivity. AVE1642 also enhances bortezomib-induced apoptosis in CD45 negative multiple myeloma cells [215-218].
MT110 (EpCAM/CD3-bispecific antibody) is a bispecific T cell engaging antibody construct
(BiTE) that directs CD3 expressing T cells to kill target cells that express epithelial cell adhesion molecule (EpCAM; CD326), which is widely found on solid tumors. Interestingly,
EpCAM is frequently overexpressed and functionally altered in CSCs [219]. MT110 and related EpCAM-specific BiTE antibodies have already shown high antitumor activity in diverse xenograft tumor models including breast cancer, colon cancer as well as pancreatic cancer [220-223]. A phase I study showed that the EpCAM/CD3-bispecific antibody
(MT110) had anti-tumor activity in patients with advanced solid tumors.
2.3.3.2 Small molecular compounds for inhibiting CSCs
As discussed earlier in this thesis, stem cell signaling pathways play important roles in tumorigenesis and CSC renewal and maintenance. A number of existing drugs and natural compounds have been identified as inhibitors and/or modulators of the Wnt/β-catenin, Hh and
Notch signaling pathways. For example, the Hh signaling antagonist GDC-0449
(Vismodegib) has been found to inhibit pancreatic CSC and BCSC characteristics [224-226].
Salinomycin, which selectively acts on CSCs, has been identified by a screening program for drugs that selectively target cancer cells with an EMT-stem cell-like phenotype. Salinomycin reduced the proportion of CSCs by >100 fold relative to paclitaxel, inhibited mammary tumor growth in vivo and induced increased epithelial differentiation of tumor cells [42]. Lu et al. reported that salinomycin inhibits Wnt signaling by blocking the phosphorylation of the Wnt co-receptor LRP6 and by inducing its degradation. In addition, it selectively induces apoptosis in chronic lymphocytic leukemia cells [227]. It has also been reported that MK-
0752, a γ-secretase inhibitor (GSI), blocks the Notch pathway on which BCSCs are dependent for their survival. A combination of MK-0752 and chemotherapy led to the successful
50 conclusion of a phase I clinical trial in 35 women with advanced breast cancer; breast biopsy samples obtained before and after MK-0752 treatment showed a significant reduction in the number of BCSCs [228].
Metformin, a first-line drug used for treating type II diabetes, has been reported to selectively kill a chemo-resistant subpopulation of CSCs in an in vivo model of breast cancer, although the molecular mechanism of its action and selectivity is unknown [183, 229]. Dasatinib, an orally active inhibitor of both SRC and ABL kinases, can preferentially inhibit the growth of breast cancers with an EMT-stem cell-like phenotype, particularly triple-negative cancers of the basal-like subtype [230]. Tranilast, a non-toxic orally active anti-allergic drug, has been found to strongly inhibit mammosphere formation and to have prominent anti-metastatic effects in vivo. It targets the cell cycle, TGF-β activity, MAPK signaling, EMT, cell migration and invasion and is considered as a CSC inhibitor with anti-proliferative and anti-cancer activity [231]. Based on a strategy to identify compounds that selectively target patient- derived TICs while sparing normal pediatric stem cells by high-throughput screening, DECA-
14 and rapamycin were identified as specific inhibitors of neuroblastoma stem cells [231]. In a screening of neoplastic and normal human pluripotent stem cells, thioridazine was identified as a selective inhibitor of neoplastic cells. Thioridazine, an antipsychotic drug, impairs human somatic CSCs capable of in vivo leukemic disease initiation by inducing differentiation to overcome neoplastic self-renewal while having no effect on normal blood stem cells [232].
2.3.3.3 Natural compounds for inhibiting CSCs
Beside chemical compounds, diverse dietary constituents and natural compounds, such as vitamins A, C and D, genistein, (-)-epigallocatechin-3-gallate (EGCG), sulforaphane, curcumin, piperine, theanine and choline, have been proposed to modify self-renewal properties of CSCs and to help to prevent cancer recurrence (Figure 8) [169, 233]. For example, broccoli has been reported to contain sulforaphane, a natural compound that down- regulates the Wnt/β-catenin self-renewal pathway and thus has the ability to kill BCSCs
51 [234]. Genistein (a prominent isoflavone) inhibits cell growth and induces apoptosis by suppressing the Notch signaling pathway [235]. The soy isoflavone genistein and blueberry polyphenolic acids repressed mammosphere formation of BCSCs [236, 237], and 20 (s)- ginsenoside Rg3 inhibited the proliferation of colon CSCs and induced apoptosis through caspase-9 and caspase-3 pathways [238, 239]. Parthenolide (PTL), a naturally occurring small molecule, induced robust apoptosis in primary human AML cells and blast crisis CML cells while sparing normal hematopoietic cells [240]. Other studies have reported that a PTL analog selectively eradicated AML stem and progenitor cells [173, 240]. Broussoflavonol B, a chemical purified from the bark of the Paper Mulberry tree (broussonetia papyrifera), inhibited the growth of ER-negative breast cancer stem-like cells and induced apoptotic cell death [241]. Curcumin inhibited BCSC migration by amplifying the E-cadherin/β-catenin negative feedback loop [242]. Another study reported that Curcumin and piperine, alone or in combination, could suppress BCSC growth [243]. Curcumin also induced CD133+ rectal CSC apoptosis and increased the radiosensitivity of CSCs [244]. In addition, resveratrol, a natural polyphenolic compound, has been reported to suppress the growth of BCSCs by inhibiting fatty acid synthase [245]. The topoisomerase I inhibitors shikonin and topotecan inhibited the growth of glioma cells and glioma stem cells [246]. Moreover, morusin was suggested to induce apoptosis of cervical CSCs by downregulating NF-κB/p65 and Bcl-2 and upregulating
Bax and caspase-3 [247].
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52 Figure 8. Modulating effects of bioactive food components on signaling pathways (modified from [248]). Arrows represent stimulation and lines with blunted ends indicate inhibition. EGCG: (−)- epigallocatechin-3-gallate.
2.3.3.4 Human vaccines for targeting CSCs
Clinical trials to treat cancer patients utilizing adoptively transferred T cells [249-251] or dendritic cells (DCs) have shown therapeutic efficacy in patients with advanced disease [252-
254]. An autologous hCD40 ligand/IL2 tumor vaccine selectively eliminated a chemoresistant
SP of B-Chronic Lymphocytic Leukemia (B-CLL) cells by cytotoxic T lymphocytes [251]
(Clinicaltrails.gov. NCT00458679: Phase I). Pellegatta et al. found that DC vaccination against neurospheres restrained the growth of a highly infiltrating and aggressive model of glioma, which might suggest that dendritic cell vaccines target CSCs in glioblastoma multiforme [254] (Clinicaltrails.gov. NCT00846456, Phase II). The DC-006 vaccine is a cancer vaccine containing autologous DCs that are transfected with mRNAs extracted from amplified ovarian CSCs and with mRNAs of the tumor antigens hTERT and survivin with potential immunostimulatory and antineoplastic activities. Upon administration, ovarian cancer stem cell/hTERT/survivin mRNAs-loaded autologous DC-006 vaccine may elicit a specific cytotoxic T-cell response against ovarian cancer cells expressing hTERT, survivin, and specific ovarian CSC antigens (Clinicaltrails.gov. NCT01334047, Phase II). Ning and colleagues evaluated the immunogenicity induced by purified CSCs used as a source of antigen to prime DCs as a further vaccination strategy. They found that CSC-based vaccines conferred effective protective anti-tumor immunity which has been associated with the induction of humoral and cellular responses that directly target CSCs via complement- dependent cytotoxicity and cytotoxic T lymphocytes, respectively [255].
2.3.4 Combination chemotherapy and anti-CSC treatment strategies
Considering the clinical implications of CSCs, combined cancer therapy approaches targeting the CSCs and the non-stem cells (chemotherapy or radiotherapy) may be developed with
53 increased efficacy. Table 4 summarizes the progress of clinical research on CSC-targeting therapy combined with chemotherapy based on different mechanisms.
Table 4. Combined chemotherapy and anti-CSC treatment strategies (Adapted from [44]).
Mechanism Pathway Tumor type Combination strategy Ref. Clinical development Self-renewal Hedgehog CML Imatinib + [134, 135] Preclinical Cyclopamine Wnt/β-catenin CML Imatinib + [150] Preclinical Indomethacin Wnt Ovarian Carboplatin/Paclitaxel Ipafricept_HCC Phase I + Ipafricept _Ovarian_Xeno graft_AACR_2 014 #
Wnt HCC Sorafenib + Ipafricept Phase I Wnt Pancreas Gemcitabine + nab- WNT_Taxane_ Phase I paclitaxel + Ipafricept AACR_2014 #
Wnt Breast Paclitaxel + NCT01973309* Phase I Vantictumab Wnt NSCLC Docetaxel + NCT01957007* Phase I Vantictumab Wnt Pancreas Gemcitabine + nab- NCT02005315* Phase I paclitaxel + Vantictumab Nodal/Activin Pancreas Gemcitabine [256] Preclinical + Hedgehog +SB431542+ CUR199691 TGF-β Breast Paclitaxel + [159] Phase II Ly2157299 Notch Breast Docetaxel +Anti- [158] Preclinical Notch1 (OMP-52M51) Notch Breast Docetaxel + MK-0752 [155] Phase II Notch Pancreas Gemcitabine + nab- • Tarextumab_Ph Phase Ib paclitaxel + 1b_ALPINE_A Tarextumab (OMP- SCOGI_2014 # 59R5) Notch ED-SCLC EP + Tarextumab Tarextumab_Ph Phase I (OMP-59R5) 1b_PINNACLE _ASCO_2014 #
Notch NSCLC Carboplatin + NCT1189968* Phase I Pemetrexed+ Demcizumab (OMP- 21M18) Notch PPC Taxol + Demcizumab (OMP-21M18) Notch Pancreas Gemcitabine +/- NCT01189929* Phase I Abraxane + Demcizumab (OMP- 21M18) Differentiation BMP2 GBM Temozolomide + [257] Preclinical BMP2 BMP4 Colon Oxaliplatin + 5-FU + [258] Preclinical BMP4 Quiescence G-CSF AML Cytarabine + G-CSF [259] Phase III Apoptosis/ BCL2 CML Dasatinib + [169] Preclinical Survival Sabutoclax IL4 Colon Oxaliplatin + 5-FU [170] Preclinical +IL4 ATB
54 IL6 Breast Trastuzumab + [260] Phase II Tocilizumab IL8/CXCR1 Breast Docetaxel + [261] Phase II Repertaxin TGF-β CML Imatinib + Ly364947 [262] Preclinical DR5 Pancreas Gemcitabine + [172] Phase II Tigatuzumab Notch Colon/ Irinotecan/Paclitaxel + [157] Phase I Breast 21M18 Notch + Prostate Docetaxel + [160] Phase II Hedgehog Cylopamine + DBZ/ Docetaxel + GDC- 0449 + Compound E Metabolism Metabolism Breast Doxorubicin + [183] Phase II Stress Metformin Metabolism Breast Trastuzumab + [263] Phase II Stress Metformin Microenvironment CXCR4 CML Nilotinib + Plerixafor [196] Phase II Others DNA damage Breast Trastuzumab + [264] Preclinical Salinomycin c-Met Pancreas Gemcitabine + XL184 [50] Phase I Epigenetic- CML Imatinib + LBH589 [265] Phase II HDAC Epigenetic- CML Imatinib + TV-6 [266] Preclinical HDAC Anti-CSC compounds are highlighted in red. Anti-CSC compounds in bold are currently in clinical development. PPC: Primary peritoneal carcinoma; GBM: Glioblastoma multiforme NSCLC: non small-cell lung caner; ED-SCLC: untreated extensive-stage small-cell lung cancer; HCC: Hepatocellular carcinoma #: information from http://www.oncomed.com/Pipeline.html; *: information from clinicaltrails.gov
2.4 Anticancer drug discovery platforms to target CSCs
2.4.1 In vitro cell-based screening
Due to the fact that the CSC population in tumors is very small, collecting large numbers of
BCSCs that can be used for cell-based screening is a great challenge.
As described before, there are several ways to enrich for CSCs including but not limited to using cell sorting techniques to select for combinations of cell-surface markers, sorting to select for a subpopulation of cells that efflux dyes or various serum-free stem cell culture conditions. Using the CSCs enriched by different methods, many groups have successfully established cell-based screening platforms and identified potential selective inhibitors of
CSCs.
55 As the serum-free, non-adherent culture method is simple, economical and reliable, it was widely used to enrich CSCs for screening. For example, Smith et al. established a screening platform using neuroblastoma CSCs, which are isolated from the bone marrow of patients and propagated in serum-free, sphere-forming conditions. 51 compounds were identified that were preferentially active against neuroblastoma CSCs compared to neural stem cells [231].
Visnyei et al. used a non-adherent culture method to enrich glioblastoma multiforme CSC subpopulations across virtually all subtypes of glioblastoma, screened 31,624 small molecules from seven chemical libraries and identified four compounds, which inhibited the self- renewal ability of CSCs in vitro and in a xenograft model in vivo [267]. Mathews et al. established a 1536-well quantitative high-throughput screening using PANC1 human pancreatic cancer and LNCaP human prostate cancer spheres to identify compounds targeting
CSCs [268].
Monitoring stem cell markers with immunofluorescence or fluorescent reporter gene expression is amenable to high-throughput analysis, and both readouts have been successfully used to screen for novel agents targeting characteristic properties of CSCs. For example,
CSCs highly express ABCB1, ABCC1 and ABCG2 transporters to support their chemotherapy-resistance. Ivnitski-Steele and colleagues chose J-aggregate-forming lipophilic cation 5, 5’, 6, 6’-tetrachloro-1, 1’, 3, 3’-tetraethylbenzimidazol- carbocyanine iodide and calcein acetoxymethyl ester (CaAM) as well-characterized fluorescent substrates with overlapping specificity for the ABCB1 transporter as well as ABCG2 and ABCC1 transporters and used high-throughput flow cytometry to detect selective inhibitors of
ABCB1, ABCC1 and ABCG2 transporters. The assay was applied to a screen of the
Prestwick Chemical library, a collection of 880 off-patent small organic molecule drugs and alkaloids. Of the 23 compounds that satisfied hit selection criteria in primary high-throughput screening, 17 were confirmed in secondary dose-response assays to have IC50 values of 10
µM or less for one or more transporters [269].
56 Differentiation markers have also been used as reporters to screen for small molecules or genes that drive or inhibit stem cell differentiation [270]. A successful in vitro screening strategy might be to quantify and track stem cells using various markers or built-in quantitative fluorescent or enzymatic reporters. For example, several groups established an adherent culture method in the presence of EGF and fibroblast growth factor 2 (FGF2) to propagate human neural stem cells, which stably express neural precursor markers (such as nestin, Sox2, CD44, Oct4, Vimentin and Tuj1) and show negligible differentiation into neurons or glia in vitro. These cells can be stably transfected to provide reporter lines (green fluorescent protein (GFP)-labeled cell lines) and readily imaged in live monolayer cultures, creating the potential for high content genetic and chemical screens [271, 272]. Pollard and colleagues performed the adherent culture method mentioned above to generate glioma- derived stem cells (GNS), which share the same properties of normal neural stem cells. They then undertook a proof-of-principle chemical screen using a live-cell imaging system
(IncucyteHD) to monitor the effects of 450 compounds of the NIH Clinical Collection [273-
275]. Based on a similar hypothesis, Sachlos and colleagues generated GFP reporter lines by transduction of neoplastic human pluripotent stem cells with the EOS-GFP reporter (v1H9-
Oct4-GFP and v1H9-Sox2-GFP, respectively) to detect the loss of Oct4 or Sox2 directly, while GFP intensity correlated with Oct4 and Sox2 expression in treatments that favored self- renewal stability and conditions that induced differentiation with the addition of BMP4. They screened 590 well-established annotated compounds from the NIH Clinical Collection and
Canadian Compound Collection and 11 compounds were identified to induce differentiation as indicated by a reduction in GFP percentage residual activity [232]. Nevertheless, efficiently and reliably obtaining quantitative data from images presents design challenges in terms of the data collection, data handling and image processing. Another challenge is that few markers and reporter genes have been established for various CSCs and their differentiated progeny [45].
57 Alternatively, using genetic manipulations to achieve a stabilized mesenchymal-like state that embodies many CSC properties can facilitate high-throughput screening in vitro, which has recently generated novel leads against CSCs. For example, using high-throughput screening,
Gupta et al. identified inhibitors selectively inhibiting the EMT-induced BCSCs [33, 42].
2.4.2 In vivo tumor models
Primary tumor xenografts that have been passaged in vivo offer a suitable system for the study of tumor heterogeneity and hierarchy in preclinical models. Fragments of a surgically resected tumor are implanted directly into immunocompromised mice (orthotopically or subcutaneously) [276]. The resulting xenografts are passaged to new animals and are therefore maintained exclusively in vivo. The cellular architecture and heterogeneity of a primary tumor xenograft closely resemble those of the original patient tumor and are more complex than the corresponding features of traditional cell line xenografts. Therefore, primary tumor xenografts coupled with appropriate experimental analysis tools constitute a valuable preclinical model for effectively evaluating lead compounds and developing drug combination and biomarker strategies [277]. However, in vivo drug discovery screening necessitates a reproducible, cost-effective system. CSC models involving xenotransplantation of primary tumor cells have limitations for medium- or high-throughput assays due to the intrinsic variation between tumors and practical challenges of using freshly resected material, but could be a choice for testing candidate agents [45].
Considering the limitation of screening methods in mice, Zhang et al. developed a novel phenotype-based in vivo screening method using LSCs xenotransplanted into zebrafish.
ALDH+ cells were purified from chronic myelogenous leukemia K562 cells tagged with a fluorescent protein (Kusabira-orange) and then implanted in young zebrafish at 48 h post- fertilization. 24 h after transplantation, the animals were treated with one of several therapeutic agents, followed by determining cancer cell proliferation as well as cell migration by high-content imaging [278, 279]. In another approach, taking advantage of the zebrafish
58 having a transparent body, Zhao et al. established a screening platform for glioma growth and invasion using bioluminescence imaging [280].
Hopefully, the improvement and modification of anticancer drug discovery platforms in light of the CSC hypothesis will improve the clinical relevance of preclinical assays and models.
These models will not only help to understand how current chemotherapeutic and tumor- targeted agents affect different levels of the tumor hierarchy, but also reveal novel agents that target CSCs.
59
60 3 Aims of this thesis
CSCs have the ability to self-renew and to give rise to differentiated tumor cells, which are responsible for the heterogeneity of tumor tissue, tumor initiation and metastasis [281]. These
CSCs are often resistant to existing cancer therapies including chemo- and radiation therapy
[43, 44, 282]. In order to develop truly effective treatments that can create a durable clinical response it is important to develop drugs that can target and kill CSCs. Therefore, the specific aims of this thesis were as follows:
In project 1 (Chapter 4.1), we aimed to develop a phenotypic-based screening assay for the identification of CSC-specific inhibitors that have only minor effects on normal stem cells, employing two chemical libraries (the NCI-DTP diversity set II and the Prestwick library).
The screening platform used the enrichment of CSCs by EMT and sphere formation of malignant human breast gland-derived cells (HMLER shEcad cells), and sphere formation of control immortalized non-tumorigenic human mammary cells (HMLE cells). Based on a cell viability assay, we selected nineteen compounds that induced a >50% inhibition of HMLER shEcad cell sphere formation and had a <30% inhibition of HMLE spheres and of HMLE adherent cells.
Chapter 4.2 in this thesis follows up the “hits” identified in the screening described in chapter 4.1. We validated two compounds benztropine mesylate and deptropine citrate, with the same chemical core structure by CSC-associated in vitro functional assay including cell viability assays, tumorsphere formation assays, and self-renewal assays, which reflect the frequency of CSCs. We then found that benztropine mesylate was the most efficient compound for impairing the capacity of CSCs among these three chemicals. We then measured the CSC subpopulations with high ALDH activity or a CD44+/CD24- phenotype in the presence or absence of benztropine mesylate. In vivo, the effects of benztropine mesylate on tumor initiation, growth and metastasis were evaluated in a 4T1 mouse breast cancer model. For the mechanism exploration, based on the published literature and on target
61 prediction by the SPiDER 1.0 software, we investigated whether benztropine mesylate inhibits the functions of CSCs through the acetylcholine receptors, dopamine transporters/receptors and/or histamine receptors.
The aim of project 2 (Chapter 4.3) was to identify novel BCSC surface markers, which might provide a tool for CSC isolation and characterizations. We found that α9-nAchR- positive cells can be enriched by different CSC enrichment methods such as EMT induction, sphere formation and chemotherapeutic treatments. We then tested several functions of CSCs after blocking α9-nAchRs by using a pharmacological inhibitor. Future plans aim to validate the stem cell properties and tumorigenicity of the newly identified α9-nAchR-positive cells in vitro and in vivo.
62 4 Results and Discussion
4.1 Cell-based phenotypic screening identifies novel inhibitors of BCSCs
4.1.1 Abstract
CSCs, a small subpopulation of cancer cells, possess long-term self-renewal capacity and are thought to drive tumor formation and tumor metastasis. Several studies reported that these cells are resistant to chemotherapy and radiation therapy, leading to cancer recurrence.
Therefore, the identification of drugs and/or drug combinations that target CSCs is of critical medical need. In this study, we established a screening platform using spheres formed by malignant human breast gland-derived cells (HMLER shEcad cells, representing BCSCs) and control immortalized non-tumorigenic human mammary cells (HMLE cells, representing normal stem cells, NSCs) for identification of compounds with specific inhibition of spheroid-derived BCSCs, but not of breast cells or NSCs. We applied this platform to a chemical screen including 2,546 compounds from two chemical libraries (the NCI-DTP diversity set II and the Prestwick library). Hits were selected based on cell viability parameters. We discovered nineteen compounds that induced a >50% inhibition of HMLER shEcad sphere formation and had a <30% inhibition of HMLE spheres and of HMLE adherent cells. Among these potential candidates, there are three groups of compounds with similar chemical core structures (Group 1: NSC 42199 (benztropine mesylate from NCI-DTP diversity set II), Pre 1013 (deptropine citrate) and Pre 1236 (benztropine mesylate from
Prestwick library); Group 2: Pre 899 (adrenosterone) and NSC 27592 (tomatidine); Group 3:
Pre 1229 (aripiprazole) and Pre 389 (ketoconazole)). In conclusion, this work has established a simple, reliable and cost-efficient method to screen for novel CSC-targeting drugs.
63 4.1.2 Introduction
Breast cancer is the most common cancer in women worldwide, and is also the second leading cause of cancer death in women, exceeded only by lung cancer. Breast cancer patients are treated with cytotoxic, hormonal and immunotherapeutic agents in the adjuvant, neoadjuvant and metastatic settings, depending on the molecular and biological characteristics of breast cancer [3]. However, drug resistance occurs after a variable period of systemic treatments. Mounting evidence indicates that one possible cause for treatment failure is the existence of CSCs [43, 44, 282-284]. The CSC hypothesis proposes that a small subpopulation of slow-growing tumor cells have self-renewal ability, driving tumorigenesis, progression and metastasis [26, 285-288]. The differentiation ability of CSCs contributes to tumor cellular heterogeneity and it can give rise to a hierarchy of proliferative and progressively differentiating cells, which can generate the full repertoire of tumor cells including both tumorigenic cells and non-tumorigenic cells [20]. From the therapeutic prospective, selective targeting CSCs using novel small molecular compounds could be an efficient approach to eradicate cancer. In our present study, we screened a drug library containing FDA-approved compounds (Prestwick library) and a small chemical library with high structural and chemical diversity (NCI-DTP diversity set II) to identify novel inhibitors of BCSCs.
Due to the fact that the CSC subpopulations in a tumor are very small, collecting large numbers of CSCs that can be used for drug screening is a great challenge. Different strategies have been applied to enrich CSCs, including sorting based on cell-surface markers [26], isolation of dye-exclusion side population cells [54, 59], sphere formation [65], resistance to chemotherapeutic compounds [283], EMT induction [33] and high-activity of intracellular
ALDH [51, 289]. A combination of different methods for CSC enrichment may enrich for cancer cells at a higher level of cancer hierarchy and be more suitable for drug development.
64 In an effort to derive sufficient CSCs for primary screening, we used EMT-induced CSCs
(HMLER shEcad cells) and applied the spheroid culture technique to enrich CSCs further. In the present study, we identified nineteen compounds that inhibited survival of BCSC-enriched spheres selectively, while no major influence on NSC-enriched spheres was detected. This study provides a screening platform for identification of anti-CSC agents.
65 4.1.3 Results
Mammospheres generated from HMLER shEcad BCSCs.
A major challenge in drug discovery by cell-based phenotypic screening is the limited number of CSCs in cancer cell cultures. To increase CSC numbers, we generated mammospheres from EMT-induced CSCs (HMLER shEcad cells) and examined whether this might further enrich CSCs compared to adherent culture conditions. CSCs mostly maintain quiescence or are slow-cycling states [290]. The HMLER shEcad spheres demonstrated a significant decrease in proliferation compared to adherent HMLER shEcad cells (Figure 9A; two-way ANOVA, p < 0.001). Furthermore, HMLER shEcad spheres exhibited more resistance to both paclitaxel and doxorubicin than adherent HMLER shEcad cells (Figure 9B, two-way ANOVA, p < 0.001). ALDH is used as a biomarker to identify and characterize the
BCSC phenotype [291]. FACS data indicated a higher percentage of ALDH+ cells in HMLER shEcad spheres than that in adherent cells (sphere vs adherent cells: 6.36 ± 1.012% vs 1.48 ±
0.155%, p < 0.01) (Figure 9C-D). Gene expression measurements by qRT-PCR showed that expression of BCSC related genes, including CD44, ALDH1, CD133, SLUG, FOXC2 and
OCT4 was increased in HMLER shEcad spheres compared with the adherent cells (Figure
9E).
66
Figure 9. Enrichment of BCSCs by mammosphere culture of EMT-induced breast cancer cells (HMLER shEcad).
(A) Proliferation curves of 1,000 cells isolated from HMLER shEcad adherent cells and spheres grown for 96 h. Cell viability was measured by the CCk-8 kit. Data are expressed as mean ± SD (n = 6). ***p < 0.001 (two-way ANOVA). (B) Dose-response curves of HMLER shEcad adherent cells and spheres treated with doxorubicin or paclitaxel. (C) ALDH enzymatic activity in sphere-forming cells compared to adherent cells measured using the ALDEFLUOR kit. An aliquot of each sample of cells was treated with ALDH inhibitor DEAB as negative control for setting the FACS gate. (D) Summary data showing
67 the percentage of ALDH+ cells in the HMLER shEcad cells and mammospheres. (E) qRT-PCR analysis of CSC-related genes in HMLER shEcad adherent cells and spheres. Data are normalized to ACTB expression and are presented as fold change in gene expression relative to adherent cells. Data are expressed as mean ± SD (n = 3). **p < 0.01, ***p < 0.001 (Student's t-test).
Identification of compounds with specific inhibition of spheroid CSC via cell-based phenotypic screening
The above results confirmed that a subpopulation of cells with CSC properties became enriched during mammosphere formation. Therefore, we speculated that compounds exhibiting selective inhibition of the HMLER shEcad spheres could be found subsequently to exhibit inhibition of CSCs. For performing the primary screening, we first cultured the
HMLE cells and HMLER shEcad cells in suspension with stem cell culture medium (SCM) to generate sufficient spheres for screening. The primary spheres were dissociated and used to generate subsequent sphere generations, which were used in the screening platform (from the third to the fifth generation). Cells from each cell line were seeded in 96-well plates, allowed to proliferate for 24 h, treated with compounds from chemical libraries at 10 µM, and assayed for cell viability by the CCk-8 assay after 3 days of incubation (Figure 10). The screening of
2,546 small molecules was done as two independent experiments with very high inter assay correlation (Figure 11A-B, r > 0.7). Thus, the used protocol enabled consistent generation of high quality sample spots, which was necessary to ensure that sufficient precision in determining deficient samples was achieved and the risk of producing false negative hits was minimized.
Approximately 6.0% (152 of 2,546) of the test compounds reduced the viability of HMLER shEcad spheres by more than 50% (Figure 11C-D). Because we aimed to find compounds specifically inhibiting BCSCs, the cut off criteria for the viability decrease in control HMLE spheres were set to 30% or less. With these criteria, nine compounds from the NCI-DTP diversity Set II (hit ratio: 0.660%) (Figure 12A) and ten compounds from the Prestwick
68 library (hit ratio: 0.800%) were identified (Figure 12B). The structures of these compounds are shown in Figure 12C.
Figure 10. Schematic overview of the chemical screening strategy for compounds that selectively inhibit the survival of HMLER-shEcad spheres. 1,000 HMLE cells or HMLER shEcad cells were inoculated in a mixed medium of MEGM and DMEM at adherent conditions, while 3,000 sphere- forming cells isolated from related spheres were grown in SCM as suspension. After 24 h, cells were treated with compounds from the chemical libraries at 10 µM or with 1% DMSO, respectively. Cell viability was determined by the CCk-8 assay after 72 h treatment.
69
Figure 11. Chemical screening for compounds that selectively inhibit the survival of HMLER shEcad spheres. (A-B) Replicate correlation plots of raw values from two replicates of the same compounds in HMLE and HMLER shEcad adherent cells and spheres, respectively, showing good agreement and suggesting overall good reproducibility. (C-D) Summary of the cell viability of HMLE and HMLER shEcad adherent cells and spheres, with all compounds from both libraries (NCI-DTP diversity set II and Prestwick library).
70
Figure 12. Identification of compounds that exhibit selective inhibitory effects on HMLER shEcad sphere. (A-B) Nineteen potential candidate compounds identified from chemical libraries based on cell viability assays. (C) Chemical structures of identified compounds from NCI-DTP diversity set II and Prestwick library.
Validation of compounds from primary screening
71
Among these nineteen hits, three groups of compounds with the same chemical core structures were identified from the distinct libraries (Figure 13A). NSC 42199 (from NCI-
DTP diversity Set II) and Pre 1236 (from Prestwick library) are benztropine mesylate. Pre
1013 (deptropine citrate) and benztropine mesylate share the same chemical core structure of diphenylmethane, Pre 389 (ketoconazole) and Pre 1229 (aripiprazole) of phenylpiperazine, and Pre 899 (adrenosterone) and NSC 27592 (tomatidin) of dimethyldecahydronaphthalene.
Reduction of cell viability was further assessed over a wide range of doses to calculate the half-maximal inhibitory concentration (IC50) for each compound. The dose-response curves demonstrated that the cell viability of HMLER shEcad spheres was inhibited more potently than that of HMLE spheres using the selected compounds (Figure 13B).
72
Figure 13. Identification and validation of compounds that exhibit selective inhibitory effects on HMLER shEcad spheres. (A) Chemical structures of compounds from three compound groups with related chemical core structures (red color). (B) Dose-response curves of HMLE spheres and HMLER shEcad spheres treated with selected compounds. Data are expressed as mean ± SD.
73 4.1.4 Discussion
CSCs exist across a range of hematological as well as solid malignancies and they display capacities for self-renewal and differentiation, which are critical for tumor initiation, progression, metastasis and recurrence [22]. The CSC model not only provides an explanation for the failure of conventional cancer therapies that target proliferating tumor cells, but also provides an important drug target in cancer [282, 283, 286]. Identification of agents that selectively prohibit the traits of BCSCs has therefore become a key goal in the challenge to achieve complete eradication of cancer.
In the present study, we screened a small molecule collection by a 96-well plate spheroid- derived CSC growth assay with several advantages. First, we used HMLER shEcad spheres as a model for CSCs. It has been previously shown that HMLER shEcad cells are enriched with EMT-induced CSCs. These cells are transformed to have a mesenchymal phenotype (a hallmark of CSCs) by down-regulation of E-cadherin, producing a high percentage of a
CD44+/CD24- population [33]. Additionally, Dontu and colleagues indicated that suspension mammospheres are enriched in early progenitor/stem cells and are able to differentiate and generate complex functional structures in a reconstituted 3D-culture system with SCM [65].
Our study combined the two CSC enrichment methods mentioned above and generated
HMLER shEcad spheres. We found that HMLER shEcad spheres showed a higher population of slow-cycling cells, higher percentage of cells with high ALDH activity and increased chemotherapy-resistance, compared with HMLER shEcad adherent cells. These results indicated that HMLER shEcad spheres contained a higher proportion of BCSCs.
Several groups have tried to identify compounds that specifically inhibit the CSC-related molecular properties or kill CSCs directly. For example, Marx et al. screened the NCI-DTP diversity set II library and identified four cell membrane permeable compounds capable of selective silencing of ErB2 transcription in breast cancer cells [292]. Three inhibitors
(loxapine, pimozide and acacetin) of the ABC transporters (ABCB1, ABCC1, and ABCG2
74 transporters), which are highly expressed in chemoresistant-CSCs, were identified by high- throughput FACS with the Prestwick library [269]. Sun et al. performed a cell-based rapid screening using the Prestwick library to identify potential inhibitors of survivin in prostate cancer cells; survivin is a broadly expressed tumor antigen associated with CSCs [293].
Because CSC populations are very complex and multiple CSC pools exist within individual tumors [34], we designed the screening platform based on the function of CSCs, instead of just based on a single CSCs-related molecular property.
A major challenge for high-throughput screening is to isolate and scale up sufficient amounts of CSCs. Other CSC enrichment methods based on cell surface markers, Hoechst dye exclusion, or cell auto-fluorescence, are time- and money-consuming. It is also unclear whether any of these approaches can be used reliably and routinely to enrich CSCs across all subtypes. The spheroid technique we used here allows the production of large amounts of
CSCs and therefore enables the availability of these cells for screening. Additionally, our assay allows the exclusion of compounds that are not selective only for CSCs, but also for
NSCs, since NSCs share many similar properties. In fact, salinomycin was found to inhibit
BCSC properties, however, it exhibited equal toxicity to NSCs in vivo, and was less likely to enter into the clinic [233]. To validate the selectivity of compounds for CSCs, we used
HMLE adherent cells and spheres as control, which contain distinctive and discrete naturally present subpopulations of stem-like and non-stem-like cells [33, 294]. Both HMLE adherent cells and spheres control conditions were used to eliminate the compounds with general toxicity for normal breast cells and stem cell-like cells as well as to eliminate the compounds with inhibitory effects due to the suspension culture system.
We used a small-molecule collection consisting of 2,546 compounds from two chemical libraries including the NCI-DTP diversity set II and the Prestwick library for screening. The
NCI-DTP diversity set II is an uncharacterized compound library, which provides the possibility of identifying novel lead candidates, with the drawback that the active mechanism of these compounds are largely unknown. In contrast, the compounds from the Prestwick
75 library are already FDA-approved, well-characterized drugs, which may render the translation of discoveries from the basic laboratory to the clinical application more simple. Also, the already known mechanisms of action of the potential hit compounds may provide some ideas for the mechanism exploration of their new function.
Based on this robust cell-based screening method, we identified nineteen compounds, including three groups of compounds with related chemical core structures, preferentially targeting the viability of HMLER shEcad spheres but not HMLE adherent cells and spheres.
The results of the screens provide the basis for more detailed explorations of the activity and mechanisms of action of the identified hits. Our screening platform can also be applied to larger compound collections to discover additional anti-CSC agents.
76 4.2 Benztropine mesylate and deptropine citrate are novel inhibitors of breast cancer
stem cells
4.2.1 Abstract
Selective targeting of CSCs offers promise for a new generation of cancer therapeutics. In the pursuit of identifying new anti-CSC agents, we performed a phenotypic-based screening employing two chemical libraries (the NCI-DTP diversity set II and the Prestwick library) and identified nineteen CSC-specific inhibitors that have only minor effects on normal stem cells. We then validated two compounds, benztropine mesylate and deptropine citrate, with the same chemical core structure, in in vitro functional assays including cell viability assays, tumorsphere formation assays, and self-renewal assays, which reflect the frequency of CSCs.
We found that benztropine mesylate was the more efficient than deptropine citrate for impairing the capacity of CSCs. CSC subpopulations with high ALDH activity or a
CD44+/CD24- phenotype were decreased in the presence of benztropine mesylate. In vivo, systemic treatment with benztropine mesylate inhibited tumor growth and reduced liver and lymph node metastasis in a 4T1 mouse model. A mechanism exploration indicated that benztropine mesylate inhibits the functions of BCSCs through the acetylcholine receptors, dopamine transporters/receptors, and/or histamine receptors.
77 4.2.2 Introduction
Selective targeting of CSCs offers promise for a new generation of cancer therapeutics. Some agents have been identified that can selectively target CSCs. Salinomycin exhibits inhibitory effects on EMT-induced BCSCs and reduces the CD44+/CD24- BCSC subpopulation [42].
Metformin, a first-line drug used for treating type II diabetes, was reported to selectively kill a chemotherapy-resistant subpopulation of CSCs in an in vivo breast cancer model, although the molecular mechanism of its action and selectivity is unknown [183]. Dasatinib can preferentially inhibit the growth of breast cancers with an EMT-stem cell-like phenotype, particularly triple-negative cancers of the basal-like subtype [230]. Additionally, bioactive food components such as vitamin A [295], (-)-epigallocatechin-3-gallate [296], sulforaphane
[234], curcumin [294] and piperine [243] have been shown to influence self-renewal related signaling pathways of stem cells including Wnt, Notch and Hedgehog [297]. Although many research groups have invested efforts into anti-CSC drug discovery, the progress is still slow.
One group of compounds with the same chemical core structure (benztropine mesylate and deptropine citrate) was identified in the cell-based phenotypic screening in Chapter 4.1. Here we further analyzed the compounds with regard to the inhibition of functional properties of
CSCs in vitro and in vivo.
78 4.2.3 Results
Benztropine mesylate and deptropine citrate inhibit the cell viability of BCSCs enriched by sphere formation
The results shown in Figure 13, the dose-response curves demonstrated that the cell viability of HMLER shEcad spheres was inhibited more potently than that of HMLE spheres using all three groups of compounds. Considering the fact that benztropine mesylate (NSC 42199 from
NCI-DTP diversity Set II; Pre 1236 from Prestwick library) and deptropine citrate (Pre 1013) were identified from two different libraries, as well as their strong selective inhibitory effects on HMLER shEcad spheres, we focused our investigations on benztropine mesylate and deptropine citrate. We studied their effects on the cell viability of spheres induced from two other CSC-enriched human and mouse cell lines, namely MDA-MB-231 cells and 4T1-luc2 cells [17, 298]. The IC50 values of all compounds for MDA-MB-231 spheres were ~5 µM. For
4T1-luc 2 spheres, the IC50 value of benztropine mesylate was around 5 µM (Figure 14).
Figure 14. Cell viability assays. Cell viability of MDA-MB-231 and 4T1-luc2 spheres treated with different concentrations of benztropine mesylate and deptropine citrate for 72 h are shown. Data are expressed as mean ± SD.
79 Benztropine mesylate and deptropine citrate suppress mammosphere formation and self- renewal ability of BCSCs in vitro
Figure 15. Mammosphere formation assay. Morphology (A-B) and sphere number (C-D) of mammospheres from MDA-MB-231 cells and 4T1-luc2 cells which were treated with different concentrations of benztropine mesylate, deptropine citrate, salinomycin or paclitaxel for 6 days. Scale
80 bars indicate 200 µm. Data are expressed as mean ± SD (n = 6). ***p < 0.001, compared with DMSO control (one-way ANOVA).
The ability to form mammospheres is correlated with the frequency of CSCs and progenitor cells in tumor cell lines. Thus, we first analyzed the effects of different concentrations of benztropine mesylate and deptropine citrate on mammosphere formation of MDA-MB-231 and 4T1-luc2 cells. Paclitaxel served as a conventional chemotherapy drug control, whereas salinomycin served as a positive control [42]. The mammosphere growth in SCM with or without compounds was observed after 6 days. In MDA-MB-231 cells, benztropine mesylate and deptropine citrate reduced the size as well as the number of mammospheres significantly in a dose-dependent manner (Figure 15A, C, p < 0.001). In 4T1-luc2 cells, treatment with 5 or
10 µM, but not 1 µM, of the compounds had a significant inhibitory effect on the number and size of the mammospheres formed after 6 days (Figure 15B, D, p < 0.001). In contrast, paclitaxel failed to influence the number of spheres in both cell lines (Figure 15A-D). The inhibitory effects of different concentrations of the compounds on sphere formation corresponded to their effects on cell viability (Figure 16A-B).
Figure 16. Cell viability of mammospheres with or without treatment. Cell viability of MDA-MB- 231 spheres (A) and 4T1-luc2 spheres (B) which were treated with different concentrations of benztropine mesylate, deptropine citrate, salinomycin or paclitaxel for 6 days. Data are expressed as mean ± SD (n = 6). **p < 0.01, ***p < 0.001, compared with DMSO control (one-way ANOVA).
The ability of self-renewal is a unique characteristic of stem cells. We tested the ability of
MDA-MB-231 cells and 4T1-luc2 cells to form subsequent sphere generations in suspension
81 (without treatment) after a 4-day pretreatment of the cells with the selected compounds under adherent conditions.
Figure 17. Self-renewal assay. Inhibitory effects of benztropine mesylate and deptropine citrate on the self-renewal ability of MDA-MB-231 (A) and 4T1-luc2 (B) cells. Adherent cells were pretreated with or without benztropine mesylate, deptropine citrate, salinomycin or paclitaxel with indicated concentrations for 4 days and mammosphere formation was evaluated in sequential sphere generations without any treatment. Sphere numbers are represented as relative to the number of cells plated for each passage (1,000 cells/well) from three independent experiments. Data are expressed as mean ± SD (n = 6). **p < 0.01, ***p < 0.001, compared with DMSO control (one-way ANOVA).
The sphere forming efficiency of MDA-MB-231 cells in different generations was markedly suppressed by pretreatment with 5 µM NSC benztropine mesylate and deptropine citrate, compared to DMSO (Figure 17A). Moreover, a significant inhibitory effect on sphere formation by 5 µM benztropine mesylate was maintained even in the quaternary spheres, suggesting that treatment with these two compounds reduced the stem cell-like subpopulation, and thus prevented the recovery of sphere formation (Figure 17A). A similar effect was seen for the sphere formation of 4T1-luc2 cells after pretreatment with the compounds (Figure
17B). Thus, benztropine mesylate had an apparent effect on the self-renewal capability of breast cancer cells which persisted after drug withdrawal. Salinomycin potently inhibited sphere formation of both cell lines in all generations, whereas paclitaxel showed inhibition in
MDA-MB-231 cells but not in 4T1-luc2 cells.
Benztropine mesylate decreases the percentage of breast cancer cells expressing CSC markers.
82
In light of the above data, we focused further on the anti-CSC properties of benztropine mesylate. To confirm that benztropine mesylate targets the CSC subpopulation, we analyzed the expression of prospective BCSC marker combinations (CD44+/CD24-) and of ALDH after benztropine mesylate treatment. Incubation with different concentrations of benztropine mesylate resulted in a dose-dependent reduction of the cell percentage with high ALDH activity in MDA-MB-231 spheres (Figure 18A-B). The proportion of ALDH+ cells was
11.1% in the DMSO-treated group, and it decreased to 7.1, 5.5 and 4.9% after 6 days treatment with 1, 5 and 10 µM benztropine mesylate, respectively (Figure 18A-B). In contrast, paclitaxel (10 nM) increased the percentage of ALDH+ cells (Figure 18B; 16.8 ±
1.28%, p < 0.001). The percentage of the CD44+/CD24- subpopulation was significantly decreased when MDA-MB-231 spheres were exposed to 5 µM (20.0 ± 12.64%) and 10 µM benztropine mesylate (13.3 ± 13.36%) for 6 days, compared to DMSO-treated cells (46.5 ±
3.30%, p < 0.01, n = 3) (Figure 18C-D). Salinomycin, which targets CSCs, significantly reduced the CD44+/CD24- subpopulation to 11.3% (±2.21%, p < 0.001), whereas paclitaxel had no influence on the percentage of the CD44+/CD24- subpopulation in MDA-MB-231 spheres (42.5 ± 3.28%, p > 0.05).
We also examined whether benztropine mesylate decreased the proportion of cells with high
ALDH activity in other breast cancer cell lines such as SKBR3 cells which show highly
ALDH activity [299] and 4T1-luc2 cells which represent a claudin-low TNBC cell line.
Incubation with different concentrations of benztropine mesylate resulted in a dose-dependent reduction of the percentage of ALDH+ cell subpopulation in SKBR3 and 4T1-luc2 cells
(Figure 19).
83
Figure 18. FACS analysis of the expression of CSC markers (ALDH+ and CD44+/CD24-) in MDA-MB-231 spheres with or without benztropine mesylate treatment. MDA-MB-231 spheres were treated with benztropine mesylate (1, 5 or 10 µM), salinomycin (2 µM), paclitaxel (10 nM) or DMSO for 6 days. Single cell suspensions were used for FACS analysis. Representative data for ALDH+ (A) and CD44+/CD24- (C) populations in benztropine mesylate-treated MDA-MB-231 spheres show a reduction compared with DMSO-treated cells. DEAB was used to inhibit the reaction of ALDH
84 with the ALDEFLUOR reagent, providing a negative control. The proportions of ALDH+ (B) and CD44+/CD24- (D) cells are shown as mean ± SD. Experiments (n = 3) were conducted in triplicate. **p < 0.01, ***p < 0.001, compared with DMSO control (one-way ANOVA).
85 Figure 19. FACS analysis of ALDH+ cell population in SKBR3 and 4T1-luc2 cells with or without benztropine mesylate treatment. SKBR3 and 4T1-luc2 cells were treated with benztropine mesylate (1, 5 or 10 µM), paclitaxel (10nM) or DMSO for 4 days. Single cell suspensions were used for FACS analysis. Representative data for the ALDH+ population in benztropine mesylate-treated SKBR3 cells (A) and 4T1-luc2 cells (C) show a reduction compared with DMSO-treated cells. DEAB was used to inhibit the reaction of ALDH with the ALDEFLUOR reagent, providing a negative control. Data of the proportion of ALDH+ cells are shown as mean ± SD (B, D). Experiments (n = 3) were conducted in triplicates. *p < 0.05, **p < 0.01, compared with DMSO control (one-way ANOVA).
Benztropine mesylate inhibits migration and invasion of breast cancer cells.
The CSC hypothesis postulates that a small subpopulation of cancer cells drives not only tumor growth but also tumor metastasis. Chemotactic transwell migration and invasion assays were conducted in HMLER shEcad and MDA-MB-231 cells treated with 5 µM benztropine mesylate and deptropine citrate, respectively. Benztropine mesylate reduced HMLER shEcad cell migration significantly after 12 h. Paclitaxel and salinomycin inhibited the migration of both cell lines (Figure 20A). To evaluate the effects of the compounds on the invasion of
HMLER shEcad cells and MDA-MB-231 cells, one layer matrigel was added into the upper chamber of the 24-well transwell set. The number of invading cells was decreased by benztropine mesylate as well as paclitaxel, in both cell lines. Deptropine citrate only inhibited
MDA-MB-231 invasion significantly (Figure 20B).
Figure 20. Benztropine mesylate inhibits migration and invasion of HMLER shEcad and MDA- MB-231 cells in vitro. Data are expressed as mean ± SD (A: migration assay, 12 h treatment; B:
86 invasion assay, 24 h treatment). Experiments (n = 3) were conducted in triplicates. *p < 0.05, **p < 0.01, ***p < 0.001, compared with DMSO control (one-way ANOVA).
Benztropine mesylate improves the efficiency of chemotherapy in vitro.
Accumulating evidence indicates that CSCs are chemotherapy-resistant cancer cells [283]. It has been proposed that combined chemotherapy and anti-CSC treatment could improve the efficacy of standard chemotherapy [44]. We hypothesized that combining benztropine mesylate with paclitaxel might inhibit tumor initiation and progression more efficiently. To investigate this hypothesis, 1,000 4T1-luc2 cells or MDA-MB-231 cells were grown in suspension with SCM in the presence of 10 nM paclitaxel alone, 5 µM benztropine mesylate alone or both drugs combined. After 6 days, compared to DMSO (sphere number/well: 4T1- luc2 spheres: 50.0 ± 6.45; MDA-MB-231 spheres: 55.3 ± 6.83) or paclitaxel (4T1-luc2 spheres: 47.5 ± 4.76; MDA-MB-231 spheres: 57.5 ± 7.18), the combination treatment inhibited the sphere formation significantly (4T1-luc2 spheres: 11.3 ± 2.34; MDA-MB-231 spheres: 10.7 ± 2.73; p < 0.001). Compared to benztropine mesylate alone (MDA-MB-231 spheres: 22.8 ± 6.27), the combination treatment decreased sphere formation efficiency by
53.1% in MDA-MB-231 spheres (p < 0.05, Figure 21A-B).
We then pretreated adherent 4T1-luc2 cells or MDA-MB-231 cells with 10 nM paclitaxel alone or combined with 5 µM benztropine mesylate for 4 days and performed mammosphere formation assays. Compared to DMSO (sphere number/well: 55.0 ± 6.07) or paclitaxel (51.8
± 6.40) alone, the combination treatment significantly reduced the sphere formation of 4T1- luc2 cells (0.50 ± 0.548), which was more efficient than benztropine mesylate alone (8.33 ±
3.50, p < 0.05). For MDA-MB-231 cells, both benztropine mesylate alone (sphere number/well: 20.7 ± 4.41) and the combination treatment (15.8 ± 2.93) impaired the sphere- formation by > 57%, compared to DMSO (47.8 ± 4.54) and paclitaxel alone (46.5 ± 1.23, p <
0.001). There was no significant difference between benztropine mesylate and combination treatment (Figure 21C-D).
87
Figure 21. Benztropine mesylate treatment improves the efficiency of chemotherapy in vitro. (A) Schematic representation of the experimental approach taken to quantify the mammosphere formation efficiency of 4T1-luc2 and MDA-MB-231 cells by combination treatment with benztropine mesylate (5 µM) and paclitaxel (10 nM) or as single agent treatment. (B) Representative images and quantification
88 of mammosphere number after 6 days. (C) Schematic representation of the experimental approach taken to quantify the mammosphere formation efficiency of pretreated-4T1-luc2 cells and MDA-MB- 231 cells by combination treatment of benztropine mesylate and paclitaxel or as single agent treatment. (D) Representative images and quantification of mammosphere number after 6 days. Data are expressed as mean ± SD (n = 6). ***p < 0.001, compared with DMSO control (one-way ANOVA); # p < 0.05, compared with benztropine mesylate group.
Benztropine mesylate inhibits tumor growth and metastasis but has no influence on the tumor seeding ability in vivo
To test whether benztropine mesylate might have any anti-CSC activity in vivo, we treated
Balb/c mice bearing 4T1 breast tumors with paclitaxel (10 mg/kg), benztropine mesylate (1.5 mg/kg) or both combined. Both tumor size and tumor weight were significantly reduced after benztropine mesylate treatment compared to the saline treated control group (p < 0.01), comparable to the efficacy of the paclitaxel treatment (Figure 22A-B). Unexpectedly, the combination treatment did not inhibit tumor progression. The mice showed no difference in weight loss between the different treatment groups (Figure 22C).
Benztropine mesylate treatment also significantly decreased the metastatic burden in the inguinal lymph nodes (p < 0.05, 11.11% vs 66.67%), and the liver was free of metastasis in all benztropine mesylate treated animals compared to the control group (p < 0.001) where 8/10 had liver metastases. No significant differences were seen with regard to lung and axillary lymph node metastasis between the different treatments (Figure 22D).
We also assessed the functional presence of CSCs by assaying for in vivo tumor-seeding ability after benztropine mesylate treatment in vitro. For these experiments, 4T1-luc2 cancer cells were treated with 5 µM benztropine mesylate or 0.1% DMSO for 4 days in vitro, and then injected in serial limiting dilutions (102, 103, 104, 105 cells from DMSO or benztropine mesylate pretreated groups, respectively) into mice. We found that all mice in both groups developed tumors during the monitoring days and that benztropine mesylate did not inhibit the tumor seeding ability of 4T1-luc2 cells (Figure 22E).
89
Figure 22. Effects of benztropine mesylate treatment on tumor growth, metastasis and tumor seeding in vivo. Tumor volume (A), tumor weight (B), and body weight change (C) after treatment with benztropine mesylate (1.5 mg/kg), paclitaxel (10 mg/kg) or both combined. Data are expressed as mean ± SD (n = 10). *p < 0.05, **p < 0.01, ***p < 0.001, benztropine mesylate treatment group was compared with 0.9% saline group; ## p < 0.01, ### p < 0.001, paclitaxel treatment group was compared to 0.9% saline group (one-way ANOVA or two-way ANOVA). (D) The number of mice
90 with different organ metastases detected by IVIS imaging. *p < 0.05, **p < 0.01, ***p < 0.001, compared with 0.9% saline group (Fisher’s exact test). (E) The percentage of tumor free mice in different groups over time.
Benztropine mesylate partially inhibits the CSC properties through acetylcholine receptors, dopamine transporters/receptors and/or histamine receptors.
Benztropine mesylate is used clinically for the management of Parkinson’s disease and its pharmacological effects are thought to result from its anticholinergic activity [300]. However, benztropine mesylate is also a centrally acting anti-histamine [301] and dopamine re-uptake inhibitor [302]. To determine which, if any, of these activities play a role in the inhibition of
CSC properties, we evaluated the ability of selective agonists of muscarinic acetycholine receptors (mAChRs), nicotinic acetylcholine receptors (carbachol), dopamine receptors
(dopamine) or histaminergic receptors (histamine) to reduce the inhibitory activity of benztropine mesylate on mammosphere formation of BCSCs. Benztropine mesylate-induced inhibition on mammosphere formation in 4T1-luc2 cells was significantly reduced in the presence of carbachol, histamine and dopamine, as well as in a combination treatment (Figure
23A-B, p < 0.001). Carbachol and histamine also significantly reduced the inhibitory effect of benztropine mesylate on MDA-MB-231 sphere formation (Figure 23D-E). The combination treatment of carbachol, histamine and dopamine partially but not completely blocked the inhibitory effects of benztropine mesylate on mammosphere formation of BCSCs (Figure
23A, C, E).
We then evaluated a panel of antagonists for these receptors, including the dopamine receptor antagonist haloperidol, the histaminergic receptor antagonist pyrilamine, and the acetylcholine receptor antagonists atropine, hexamethonium bromide and pancuronium on mammosphere formation of 4T1-luc2 cells (Figure 24A-E) and MDA-MB-231 cells (Figure
24F-J). We found that 100 µM of atropine and 10 µM of haloperidol inhibited mammosphere formation of 4T1-luc2 cells. The mammosphere formation ability of MDA-MB-231 cells was also significantly reduced when cell were supplied with 10 nM of pyrilamine, 100 µM of
91 atropine or different doses of haloperidol (500 nM, 1 and 10 µM). Based on these results, we conclude that benztropine mesylate inhibits CSC properties partially through acetylcholine receptors, dopamine transporters/receptors and histamine receptors.
Figure 23. Benztropine mesylate partially impairs mammosphere formation of BCSCs through acetylcholine receptors, dopamine receptors/transporters and/or histamine receptors. Representative images (A) and quantification of mammosphere formation efficiency of 1,000 4T1-luc2 cells (B-C) or MDA-MB-231 cells (D-E) co-treated with benztropine mesylate (5 µM), and dopamine (10 µM), histamine (5 µM) or carbachol (5 µM) alone or with an agonist combination (dopamine + histamine + carbachol) for 6 days. Data are expressed as mean ± SD (n = 6). ***p < 0.001, compared with DMSO control; # p < 0.05, ## p < 0.01, ### p < 0.001, compared to benztropine mesylate alone (one-way ANOVA).
92
Figure 24. Effects of a panel of antagonists of the dopamine receptor, the histaminergic receptor or the acetylcholine receptor on mammosphere formation. Quantification of mammosphere number from 1,000 breast cancer cells (4T1-luc2 and MDA-MB-231) treated with dopamine receptor antagonist haloperidol (A, F), histamine receptor antagonist pyrilamine (B, G), muscarinic receptor antagonist atropine (C, H), nicotine receptor antagonist hexamethonium bromide (D, I) or the antagonist of both type of the receptors, Pancuronium bromide (E, J) for 6 days. Data are expressed as mean ± SD (n = 6). *** p < 0.01, *** p < 0.001, compared with DMSO control (One-way ANOVA).
93 4.2.4 Discussion
Based on the screening results shown in paragraph 4.1.4, we focused on characterizing the anti-CSC properties of the compounds benztropine mesylate and deptropine citrate which have a related chemical structure. Deptropine citrate, a well-known H1-histamine receptor antagonist and muscarinic receptor antagonist, showed inhibitory effects on cell viability and mammosphere formation of BCSCs, but it failed to inhibit self-renewal capacities of MDA-
MB-231 cells. Benztropine mesylate exhibited significant inhibition of mammosphere formation and self-renewal of BCSCs. And it also decreased the ALDH+ and CD44+/CD24-
CSC subpopulations and inhibited tumor growth and metastases significantly. Thus, benztropine mesylate is a potential anti-CSC drug candidate that can alter tumorigenic properties. Combination treatment of benztropine mesylate and paclitaxel inhibited sphere formation more efficiently than single treatment in vitro, but did not inhibit tumor progression in vivo. So combination strategies with benztropine mesylate and standard chemotherapy should be further evaluated in vivo.
Benztropine mesylate is a centrally acting anticholinergic agent for the treatment of
Parkinson’s disease [300]. Deshmukh et al. identified that benztropine induced the differentiation of oligodendrocytes through M1 and M3 muscarinic receptors and enhances re-myelination in a multiple sclerosis mouse model [303]. Benztropine also acts as an anti- histamine [301] and a dopamine re-uptake inhibitor [302]. David Sibley has identified that benztropine acts as an allosteric antagonist of the human D2 dopamine receptor (Pubchem
BioAssay: AID 485344). In the present study, the pharmacological data indicated that benztropine mesylate partially inhibited the BCSC properties through acetylcholine receptors, dopamine receptors/transporters and/or histamine receptors. A previous study reported that thioridazine, an antagonist of the dopamine receptor, impairs human somatic CSCs capable of in vivo leukemic disease initiation by inducing differentiation to overcome neoplastic self- renewal, while having no effect on normal blood stem cells [232]. Haloperidol, which
94 exhibits high affinity dopamine D2 receptor antagonism and slows receptor dissociation kinetics [304], inhibited mammosphere formation of BCSCs markedly. Thus, our findings further confirm that dopamine receptors plays an important role in mediating BCSC functions, and indicate that dopamine receptors could be potential CSC markers in breast cancer. Further investigation is required to understand the connection of dopamine receptor signaling and CSC biology in human cancer.
Other, yet unidentified pathways might also possibly be involved in the anti-CSC effects of benztropine mesylate. Increasing evidence indicated that CCL5 and CCR5 are overexpressed in the basal breast cancer subtype, and CCR5 antagonists block metastasis of breast cancer
[305, 306]. As predicted by SPiDER 1.0 software (Table 5), benztropine mesylate also possibly acts as a CCR5 antagonist (S = 0.7079, P = 0.1063), suggesting another potential mechanism of benztropine mesylate-mediated inhibition of BCSC properties; this needs to be investigated in further studies.
In summary, our findings provide evidence for anti-CSC effects of benztropine mesylate (Pre
1236). Modification of the chemical structure of this compound, followed by analysis of structure-function relationships, might be suitable to identify more potent anti-CSC compounds in the future.
Table 5. Target prediction of benztropine mesylate by SPiDER 1.0 software
Responsible subtypes Predicted target Score P-value
Protein Kinase AKT (Protein Kinase B) ATP Competitive Inhibitor; "Rho Kinase Serine Threonine Kinase 0.857 0.0701 (Rho-associated Kinase, Rho-associated Coiled-coil Forming Protein Kinase, ROCK, AGC Kinase Family)" Muscarinic Acetylcholine Receptor Muscarinic Acetylcholine Receptor 0.854 0.0718 M1/2/3 Antagonist
Neuropeptide Y 0.834 0.0804 Neuropeptide Y 5 (NPY) Antagonist
Serotonin Receptor 0.823 0.0844 5-Hydroxytryptamin 3 /4/7
95 Cyclin-dependent Kinase 0.817 0.0863 Cyclin-dependent Kinase 2 Inhibitor (Serine-Threonine Kinase) "Opioid Receptor-like-1 (aka kappaOP3, Nociceptin Receptor (Orphanin FQ Opioid Receptor 0.802 0.0903 Receptor) (Kappa-type 3 Opioid Receptor) (KOR-3))" Polymerase 0.800 0.0909 Poly-(ADP-ribose) Polymerase 1 (ADP-Ribosyltransferase)
Sodium:Neurotransmitter Symporter Choline Transporter (Uptake); Dopamin 0.788 0.0937 (SNF) Transporter (Reuptake) Membrane Copper Amine Oxidase Membrane Copper Amine Oxidase (Semicarbazide-sensitive amine oxidase, 0.734 0.103 (Primary-amine Oxidase) SSAO, Vascular adhesion protein 1, VAP-1
Protein Tyrosine Phosphatase 0.723 0.105 Protein Tyrosine Phosphatase 1B
"Endopeptidase (Serine Endopeptidase, 0.713 0.106 Factor Xa Serine Protease)"
Chemokines 0.708 0.106 CCR5 Receptor Antagonist
"alpha IIb Integrins (Glykoprotein IIb, Integrins 0.706 0.107 IIIa, GP IIa, IIIb, Fibrinogen Receptor)"
"Exopeptidase (Serine Exopeptidase, 0.642 0.112 Dipeptidyl Peptidase IV Serine Protease)"
Arachidonate 5-Lipoxygenase 0.639 0.112 5-Lipoxygenase LOX (Leukotriene A4 Synthase)
96 4.3 α9 Nicotinic acetylcholine receptor (α9-nAchR) as a potential biomarker for breast cancer stem cells
4.3.1 Abstract
CSCs have an increased resistance to conventional therapies and are capable of establishing metastasis. However, only a few biomarkers of CSCs have been identified. Here, we report that α9-nicotinic acetylcholine receptor (α9-nAchR)-expressing cells can be enriched by different CSC enrichment methods, including EMT induction, sphere formation and chemotherapeutic treatment. The blockade of α9-nAchRs using the pharmacological inhibitor
ACV1 significantly reduced the CSC population and inhibited CSC-associated properties, such as sphere formation and self-renewal in HMLER shEcad, MDA-MB-231 cells. The α9- nAchRs might represent a new CSC-specific cell-surface marker and might serve as a potential target for therapeutic strategies aiming at CSCs.
97 4.3.2 Introduction
Breast cancer is the most common cancer in women worldwide. It is also the second leading cause of cancer deaths in women, exceeded only by lung cancer. These breast tumors are composed of a phenotypically diverse population of breast cancer cells [285]. Mounting evidence shows that only a minority of cancer cells, called CSCs, has the ability to form new colonies in an in vitro clonogenic assay and to form new tumors in in vivo xenograft models
[24-26, 51, 54, 289, 307-309]. The CSC hypothesis indicates that CSCs have the ability of self-renewal, which drives tumorigenesis, progression and metastasis [34, 285, 286]. The differentiation capability of CSCs contributes to tumor cell heterogeneity and can give rise to a hierarchy of proliferative and progressively differentiating cells that can generate the full repertoire of tumor cells, both tumorigenic cells and non-tumorigenic cells [20]. Additionally, the CSC hypothesis potentially explains the failure of conventional cancer treatment [43-45].
For example, CSCs are naturally resistant to traditional therapy because of their quiescence or slow proliferation rate, high expression levels of ABC drug pumps, intrinsically high levels of anti-apoptotic molecules, relative resistance to oxidative or DNA damage, and efficient DNA repair [45]. Thus, selectively targeting CSCs may be an efficient approach to eradicate cancer.
However, CSCs are a highly dynamic cell population. Within individual tumors, multiple pools of CSCs exist. Accordingly, much effort is being expended to clinically identify relevant biomarkers of different CSC pools.
In breast cancer, Al-Hajj et al. identified and isolated tumorigenic cells as CD44+CD24−/low lineage from patients. They found that as few as 100 cells with this phenotype were able to form tumors in mice, whereas tens of thousands of cells with alternate phenotypes failed to form tumors, and the tumorigenic subpopulation could be serially passaged [26].
Furthermore, the percentage of CD44+/CD24-/low tumor cells increased after chemotherapy in several mouse models of breast cancer and in patients [42, 43]. Ginestier et al. found that
ALDH1 is a stem cell marker in breast carcinoma associated with poor clinical outcome.
High ALDH activity identified the tumorigenic cell fraction that is capable of self-renewal
98 and generating tumors, which recapitulated the heterogeneity of the parental tumor [51, 69].
ALDH+ cells are also resistant to sequential paclitaxel- and epirubicin-based chemotherapy for breast cancer [70]. Battula el al. reported that the glycosphingolipid ganglioside GD2 identified a small fraction of cells in human breast cancer cell lines and patient samples that are capable of forming mammospheres and initiating tumors with as few as 10 GD2+ cells.
The majority of GD2+ cells are also CD44hiCD24lo cells [310]. Unfortunately, in some cases, limited overlap between distinct CSC marker-positive populations represents a great challenge for CSC-related research. ALDHhi and CD44hiCD24lo CSC-enriched subsets in breast cancer have little overlap within the same tumor [51]. Thus, more potential CSC biomarkers need to be identified.
nAchRs are complexes of protein subunits that co-assemble to form an ion channel that is gated through the binding of the neurotransmitter acetylcholine (Ach) to its ligand-binding site [311]. Structurally, the nAchR family is composed of 17 subunits (α1-α10, β1-β4, ε, and
δ). These receptors are composed of either heteropentamers that consist of a combination of α
(α1-α6) and β (β2-β4) subunits or homopentamers that consist of α7-α10 subunits that are symmetrically arranged around a central ion pore [312]. Recently, accumulating studies have reported roles for nAchRs in carcinogenesis, proliferation, angiogenesis, the inhibition of apoptosis, and metastasis in breast, lung and colon cancers [313-327].
The α9 subunit forms a homomeric receptor-channel complex or heteromeric receptor- channel complex with the α10 subunit, which is activated by Ach but not by nicotine. The α9 subunit also has a very distinct pharmacological profile that falls into neither the nicotinic nor the muscarinic subdivision of the pharmacological classification scheme of cholinergic receptors. Moreover, the α9 subunit gene has a unique and restricted expression pattern. It is present in cochlear and vestibular hair cells but not in the central nervous system [328-330].
In this study, we found that the gene expression of α9-nAchRs is increased by the enrichment of BCSCs with different methods. The blockade of α9-nAchRs with a specific inhibitor
99 hampered CSC-associated properties, such as mammosphere formation and self-renewal.
These results indicated that α9-nAchR represents a potential marker for BCSCs.
100 4.3.3 Results
α9-nAchR expression is upregulated during CSC enrichment by EMT and mammosphere formation.
Increasing evidence indicates that CSCs can be enriched by EMT and mammosphere formation. We compared the gene expression levels of different Ach receptors in HMLE,
HMLER shCtrl and HMLER shEcad adherent cell and spheres as shown in Figure 25.
HMLER shCtrl cells were HMLER cells that were transfected with a control vector. HMLER shEcad cells were established by knocking down the human CDH1 gene, which encodes E- cadherin in experimentally transformed HMLER breast cancer cells. CDH1 silencing triggered EMT, and cells with the mesenchymal phenotype gained stem cell properties [33].
α2-nAChR (CHRNA2), α5-nAChR (CHRNA5), α6-nAChR (CHRNA6), α9-nAChR
(CHRNA9), α10-nAChR (CHRNA10) and β1-nAChR (CHRNB1) mRNA expression was higher in HMLER shEcad (CHRNA5: >4-fold; CHRNA9: >12-fold; CHRNB1: >4-fold;
CHRNA2, 6, 10: ~ 2-fold) than in HMLER shCtrl cells. We cannot detect the expression of
CHRNA4, CHRNB3, CHRND and CHRNE in all cell lines. And there was no significant different of the mRNA expression of other Ach receptors (Figure 25 A-B). Our previous study confirmed that a subpopulation of cells with CSC properties became enriched during mammosphere formation in HMLER shEcad cells (Figure 9). We then compared the mRNA expression of Ach receptors in HMLER shEcad spheres with HMLER shEcad adherent cells.
The expression of CHRNA9 mRNA increased further (17.4-fold) by generating spheres from
HMLER shEcad cells (Figure 25A, C). HMLE is an immortalized, non-tumorigenic human mammary epithelial cell line that contains distinctive and discrete naturally present subpopulations that are stem cell-like and non-stem-like [33]. We generated HMLE spheres from HMLE adherent cells to serve as a normal stem cell control. CHRNA9 mRNA was more highly expressed in HMLER shEcad spheres than in HMLE spheres (Figure 25 A, D). These results suggest a correlation between CHRNA9 mRNA expression levels and a subpopulation of CSCs.
101
Figure 25. CHRNA9 gene expression was increased by EMT and sphere formation in breast cancer cells. Comparison of the relative expression of acetylcholine receptors subunits genes in HMLE adherent cells and spheres, HMLER shCtrl cells, and HMLER shEcad adherent cells and spheres. The Ct values of distinct genes in different cell types are shown in A. Data were normalized to ACTB expression. The fold changes in relative gene expression were compared in (B) HMLER shEcad cells vs HMLER shCtrl cells, (C) HMLER shEcad spheres vs HMLER shEcad adherent cells, and (D) HMLER shEcad spheres vs HMLE spheres. Data are expressed as mean ± SD (n = 3).
α9-nAchR expression is upregulated during CSC enrichment by chemotherapeutic treatment and downregulated by anti-CSC treatment
102
The expression of CHRNA9 mRNA was evaluated in breast cancer cells after treatment with paclitaxel and doxorubicin or anti-CSC treatment with salinomycin. Treatment with 10 nM paclitaxel for 24 h led to an approximately 8-fold increase in CHRNA9 mRNA expression in
HMLER shEcad cells (Figure 26A-B), a 6-fold increase in MDA-MB-231 cells (Figure 26C-
D), and a 2-fold increase in T47D cells (Figure 26E-F). A dramatic increase in CHRNA9 mRNA was also found after 24 h treatment of HMLER shEcad cells (~10-fold, Figure 26A-B) and T47D cells (~7-fold, Figure 26E-F) with 200 nM doxorubicin. In contrast, CHRNA9 mRNA was decreased by approximately 50% in salinomycin-treated MDA-MB-231 cells
(Figure 26D), but no significant changes were found in other salinomycin-treated breast cancer cell lines (Figure 26B, E). Treatment with paclitaxel and doxorubicin increased
ALDH1 mRNA expression in HMLER shEcad cells (Figure 26).
103 Figure 26. CHRNA9 gene expression in breast cancer cells was increased by chemotherapy treatment. Comparison of the relative expression of CHRNA9, CHRNA10, and stem cell-associate genes (CD44, CD133 and ALDH1) in breast cancer cells with anti-CSC or chemotherapy treatment. The Ct values of distinct genes in different cell lines with the indicated treatment are shown in HMLER shEcad cells (A), MDA-MB-231 cells (C) and T47D cells (E). The fold changes in relative gene expression were compared in salinomycin-, doxorubicin-, paclitaxel- and DMSO-treated HMLER shEcad cells (B), MDA-MB-231 cells (D) and T47D cells (F). Data are expressed as mean ± SD (n = 3). *p < 0.5, **p < 0.01, ***p < 0.001 (One-way ANOVA).
Determination of choline levels in CSC medium
Acetylcholine receptors responding to the natural ligand acetylcholine and choline are involved in the control of physiological responses, as well as being a major drug target in human disease [331-334]. Here, the levels of total choline (acetylcholine and free choline) were measured in fresh culture medium and conditioned medium after 6 days of culture of
HMLER shEcad and MDA-MB-231 spheres, respectively. As shown in Figure 27, the conditioned medium from the sphere cultures had higher levels of total choline relative to the fresh culture medium: HMLER shEcad spheres (fresh medium: 90.60 ± 7.213 µM; conditioned medium: 111.5 ± 7.516 µM, p < 0.01), MDA-MB-231 spheres (fresh medium:
48.71 ± 3.176 µM; conditioned medium: 59.07 ± 3.960 µM, p < 0.01).
Figure 27. Determination of choline levels in CSC medium. Total choline (acetylcholine and free choline) in fresh culture medium and conditioned medium after 6 days of culture of HMLER shEcad and MDA-MB-231 spheres, respectively. Data are expressed as mean ± SD (n = 4). **p < 0.01 (Student’s t-test).
Blockade of α9-nAchR inhibits the properties of BCSCs
104
ACV1, also namely conotoxin Vc1.1, is a specific inhibitor for α9-nAchRs [335, 336]. We used it to block α9-nAchRs and investigated the subsequent effects on BCSC properties. A 3- day treatment with 50 and 100 nM ACV1 inhibited the cell viability of HMLER shEcad spheres by approximately 25%, compared with the PBS-treated group (Figure 28A). No significant inhibition of the cell viability of MDA-MB-231 spheres was found after treatment with different doses of ACV1 (Figure 28B).
The ability to form mammospheres is correlated with the frequency of CSCs and progenitor cells in tumor cell lines. Thus, we first analyzed the effects of different concentrations of
ACV1 on mammosphere formation in HMLER shEcad and MDA-MB-231 cells. The development of mammosphere growth in SCM with or without compounds was observed after 6 days. Salinomycin served as a positive control for CSC selectivity and significantly inhibited HMLER shEcad and MDA-MB-231 sphere formation. In HMLER shEcad cells,
ACV1 significantly reduced the number of mammospheres at concentration as low as 20 nM
(p < 0.001), but no dose-dependent effect was observed (Figure 28C). For MDA-MB-231 cells, treatment with 100 or 200 nM of ACV1 had a significant inhibitory effect on the number of mammosphere that were formed after 6 days (Figure 28D).
The ability of self-renewal is a unique characteristic of stem cells. We tested the ability of
HMLER shEcad cells to form subsequent sphere generations in suspension (without treatment) after 4-day pretreatment of cells with ACV1 under adherent conditions. The sphere-forming efficiency of HMLER shEcad cells in different generations was markedly suppressed by pretreatment with different concentrations of ACV1 (50, 100, and 200 nM), compared with the control group. Moreover, a significant inhibitory effect of 50 and 100 nM
ACV1 on sphere formation was maintained even in the quaternary spheres, suggesting that treatment with ACV1 reduced the stem cell-like subpopulation and thus prevented the recovery of sphere formation (Figure 28E).
105
Figure 28. Blockade α9-nAchRs by ACV1 inhibits cell viability, sphere formation and self- renewal of BCSCs. Cell viability assay: cell viability of HMLER shEcad spheres (A) and MDA-MB- 231 spheres (B) that were treated with different concentrations of ACV1 for 72h. Mammosphere formation assay: The number of mammospheres from HMLER shEcad cells (C) and MDA-MB-231 cells (D) that were treated with different concentrations of ACV1 and salinomycin after 6 days. Self- renewal assay: Inhibitory effects of ACV1 on the self-renewal of HMLER shEcad cells (E). Adherent cells were pretreated with or without ACV1, salinomycin or paclitaxel for 4 days at the indicated concentrations, and mammosphere formation was evaluated in sequential sphere generations without any treatment. Sphere numbers are expressed relative to the number of cells plated for each passage (1,000 cells/well) from three independent experiments. Data are expressed as mean ± SD (n = 6). *p < 0.05, **p < 0.01, ***p < 0.001, compared with DMSO control (one-way ANOVA).
106 4.3.4 Discussion
The CSC hypothesis has fundamental implications for cancer biology and clinical assessment.
The development of more effective cancer therapies may thus require targeting this important
CSC population. The identification, isolation and characterization of CSCs are needed for the development of successful new therapeutic strategies.
In our study, we hypothesized that the α9-nAchRs are a potential biomarker for BCSCs.
Based on our screening results, we found that benztropine mesylate and deptropine citrate inhibited the capacity of BCSCs in vitro and/or in vivo (Chapter 4.1 and 4.2). Deptropine citrate [337, 338] and benztropine mesylate [303, 339] are well known to have anticholinergic effects. We therefore compared different subunits of nAchRs in breast cancer cells, BCSCs and NSC-like cells. Higher levels of CHRNA9 mRNA were expressed in BCSCs compared with breast cancer cells and NSC-like cells. In breast cancer, Lee et al. found that α9-nAchRs are overexpressed and activated during tumorigenesis in human breast epithelial cells [316,
317]. They reported that α9-nAChR mRNA was expressed at higher levels in breast cancer tissue than in surrounding normal tissue in paired patient samples. The reduction of α9- nAChR subunit expression by RNA interference in human breast cancer cells substantially inhibited tumor growth in vitro and in vivo [316, 317]. Hung et al. reported that α9-nAChRs are key mediators of nicotine-enhanced cancer metastasis in breast cancer cells [340].
Accordingly, CSCs are responsible for tumor initiation and metastasis. CSCs have higher resistance to chemotherapeutic drugs. We found that treatment with paclitaxel or doxorubicin for 24 h elevated CHRNA9 mRNA levels in breast cancer cells further supporting the concept that the expression of α9-nAChR can be correlated with the population of CSCs.
The blockade of α9-nAChRs by a specific pharmacological inhibitor impaired the sphere formation and self-renewal ability of CSCs [335, 336]. Further studies are needed to isolate the α9-nAChR-positive subpopulation in breast cancer cells and patient samples and to evaluate their CSC-related properties in vitro and in vivo. Additionally, there are different
107 CSC pools in tumors, and it is currently unclear whether the α9-nAChR-positive subpopulation is a distinct or overlapping population with other CSC pools, such as CSCs that are CD44+/CD24- and ALDH+. It also remains to be determined whether these phenotypes are associated with stem cells in other cancer types.
108 5 Conclusion and Outlook
Although there are still controversies regarding CSCs, accumulating experimental evidence indicates that CSCs exist across a range of hematological as well as solid malignancies including breast cancer, melanoma, lung cancer, colon cancer, prostate cancer, ovarian cancer, brain cancer and others [26, 142, 341-347]. CSCs are capable of self-renewal and differentiation, properties that are regarded critical for tumor initiation, progression, metastasis and recurrence [22]. The CSC model not only provides an explanation for the failure of conventional cancer therapies that target proliferating tumor cells, but also provides an important drug target in cancers [282, 283, 286]. Therefore, identification of agents that selectively inhibit the properties of CSCs has become a key goal in the challenge to achieve complete eradication of cancer.
In our study, we confirmed that non-adherent tumorspheres from EMT-induced breast cancer cells (HMLER shEcad cells) cultured in SCM show CSCs properties. The proliferation rate of
HMLER shEcad spheres was slower compared to parental cells. HMLER shEcad spheres are more resistance to chemotherapeutic treatment compared to adherent cells. We observed a larger population of cells with high ALDH activity in HMLER shEcad spheres than that in parental cells. HMLER shEcad spheres contained a higher percentage of CSCs than adherent
HMLER shEcad cells. Therefore, targeting the survival of these cells should lead to impaired sphere CSC functions.
Based on this premise, we performed a cell-based phenotypic screening using a small collection of 2,546 chemicals (Prestwick library: 1,200 compounds and NCI-DTP diversity set II: 1,346 compounds) for identifying selective inhibitors of HMLER shEcad spheres. We also used HMLE adherent cells and spheres as control to eliminate the compounds with general toxicity for normal breast cells and stem-like cells as well as to eliminate the compounds with inhibitory effects due to the suspension culture system. Nineteen compounds, including three groups of compounds with related core structures, were identified
109 that preferentially inhibit the viability of HMLER shEcad spheres compared to HMLE adherent cells and spheres. The dose-response curves based on cell viability assays reflected that all three groups of selected compounds inhibited the cell viability of HMLER shEcad spheres more potently than that of HMLE spheres. This work has established a simple, reliable and cost-efficient method to screen for novel CSC-targeting drugs. Future studies may be performed to identify additional compounds that prohibit CSC properties in large- scale analyses (Chapter 4.1).
In Chapter 4.2, we then focused on characterizing the anti-CSC properties of deptropine citrate (Pre 1013 from Prestwick library) and benztropine mesylate (NSC 42199 from NCI-
DTP diversity set II and Pre 1236 from Prestwick library). We validated these compounds by
CSC-associated in vitro functional assays including cell viability assays, tumorsphere formation assays and self-renewal assays, which reflect the frequency of CSCs. We found that deptropine citrate showed inhibitory effects on cell viability and mammosphere formation of CSCs, but it failed to inhibit self-renewal capacities of MDA-MB-231 cells.
Benztropine mesylate exhibited significant inhibition of the cell viability, mammosphere formation and self-renewal of CSCs. Benztropine mesylate decreased CSC subpopulations with a CD44+/CD24- phenotype or with high ALDH activity. In vivo data also indicated that benztropine mesylate (1.5 mg/kg) inhibited tumor growth and liver and lymph node metastases. In conclusion, benztropine mesylate was identified as a novel potential anti-CSC compound.
However, substantial future work is still required. Firstly, increased evidence indicates that
CSCs are resistant to conventional chemotherapy and radiotherapy, thus combination of anti-
CSC targeting agents and conventional therapies provides a potential strategy for curing cancer completely [28, 45, 53, 60, 70, 80, 99, 228, 258, 282-284]. We found that targeting
CSCs with benztropine mesylate suppressed paclitaxel-induced resistance in vitro but that benztropine mesylate was unable to enhance the anti-tumor effects of paclitaxel in vivo. One of the possible reasons is that antagonistic drug interactions exist between paclitaxel and
110 benztropine mesylate that lead to a decrease or a complete loss of their inhibitory effect after combination. Other possible reasons might be that we used unsuitable drug dosages and application schedule, or that this combination treatment is not suitable for the 4T1 mouse breast cancer model. Future studies should focus on the determination of the most effective combination treatment methods. The drug combination should also be tested on other breast cancer models to identify the influence of tumor models on drug discovery.
It would be of interest to complete a comparison of the structure-activity relationship of benztropine analogues with regard to their anti-CSC properties. Our pharmacological data indicated that benztropine mesylate partially inhibited the CSC properties through acetylcholine receptors, dopamine receptors/transporters and/or histamine receptors. Kulkarni and colleagues [348] conducted a comparative analysis of binding affinities of its analogues for dopamine transporters, muscarinic acetylcholine and histamine receptors, and they found different analogues exhibited varied binding affinities for different receptors. Thus, based on the elucidation of the structure-activity relationship, it might be possible to obtain more efficient compounds for inhibiting CSCs. Last but not least, it is also necessary to dedicate efforts to identify the molecular pathways involved in the connection between acetylcholine receptors, dopamine receptors/transporters and histamine receptors and the maintenance of
CSCs in human breast cancers. In this regard, our approach would have provided useful information not only to identify new targets for cancer therapy but also to improve the understanding of molecular pathways involved in the maintenance of CSCs.
For project 2 (Chapter 4.3), we found that α9-nAchR-expressing cells can be enriched by different CSC enrichment methods, such as EMT induction, sphere formation and chemotherapeutic treatments. The blockade of α9-nAchRs using a pharmacological inhibitor significantly reduced the CSC population and inhibited CSC-associated properties, such as sphere formation and self-renewal. We might expect that α9-nAchRs could represent a new
CSC-specific cell-surface marker and a potential therapeutic target for CSCs. However, the data obtained thus far are rather preliminary and further in-detail analyses are needed. It will
111 be of interest to study the existence of a small fraction of cells with an α9-nAchR positive phenotype in different human breast cancer cell lines and patient samples. Using FACS and western blot analyses, it should be studied whether the protein levels of α9-nAchR are also increased during the enrichment of CSCs. This would facilitate the isolation of α9-nAchR positive cells and the investigation of their capacity to form mammospheres and to initiate tumors, compared to α9-nAchR negative cells. Knockdown α9-nAchR in breast cancer cells might then investigate the biological relevance with regard to CSC-related activities in vitro and in vivo.
112 6 Material and Methods
Cell lines, monolayer and mammosphere cultures
HMLE, HMLER shCtrl and HMLER shEcad cell lines were kindly provided by Prof. Robert
Weinberg (MIT, Cambridge, USA) and cultured as previously described [42, 349]. Briefly, cells were cultured in 1:1 (v/v) mixed medium of Mammary Epithelial Cell Growth Medium
(MEGM, Lonza) with 10 µg/ml insulin (Sigma-Aldrich), 4 ng/ml epidermal growth factor
(EGF, Peprotech Inc.), 20 µg/ml hydrocortisone (Sigma-Aldrich) and Dulbecco's Modified
Eagle Medium (DMEM, Gibco) with 10% (v/v) fetal bovine serum (FBS, Invitrogen), 1%
(v/v) antibiotic-antimycotic solution at humidified atmosphere at 37°C, 5% CO2 and 95% room air. The MDA-MB-231 and T47D cell lines were kindly provided by Prof. Nancy E.
Hynes (FMI, Basel, CH). The 4T1-luc2 cell line was purchased from Caliper. All these three cell lines were cultured in DMEM with 10% (v/v) FBS and 1% (v/v) antibiotic-antimycotic solution.
Mammospheres were generated by incubating single cell suspension in serum-free stem cell medium (SCM) containing MEGM (Lonza, for HMLE and HMLER shEcad spheres) or
Dulbecco’s Modified Eagle Medium: Nutrient Mixture F-12 (DMEM/F12) (Gibco, for MDA-
MB-231 and 4T1-luc2 spheres) supplemented with 2% (v/v) B27 (Gibco), 20 ng/ml EGF
(Peprotech Inc.), 20 ng/ml basic fibroblast growth factor (bFGF, Peprotech Inc.), 10 µg/ml insulin, 20 µg/ml hydrocortisone (Sigma-Aldrich) and antibiotic-antimycotic as previously described [42, 349] in flasks coated with poly (2-hydroxyethyl methacrylate) (poly-HEMA,
Polysciences Inc.) solution. The spheres were passaged every 7-9 days. Spheres can be centrifuged (1,000 rpm), dissociated with 0.05% trypsin/EDTA and then passed through a filter with 40 µM diameter pores to obtain single cell suspension. 5×105 cells were re- suspended in SCM and allowed to re-form next passage spheres.
Chemical libraries and reagents
113
Two commercially available chemical libraries the Prestwick chemical library®
(http://www.prestwickchemical.com/) and the NCI-DTP diversity Set II
(http://dtp.cancer.gov/index.html) were used. The Prestwick chemical library® contains 1,200 small molecules, which are 100% FDA approved drugs and have been selected for their high chemical and pharmacological diversity as well as for their known bioavailability and safety in humans. The NCI-DTP diversity Set II includes a collection of 1,346 synthetic small molecules with structural and chemical diversities. Compounds were solubilized at 1 mM in dimethyl sulfoxide (DMSO) and all compounds were diluted in assay media for a final concentration of 10 µM in the screen. The concentration of DMSO in each assay well, including all control wells was 1%.
The compound NSC 42199 (benztropine mesylate) identified from the NCI-DTP diversity Set
II used for follow-up in in vitro experiments was kindly provided by NCI/NIH, while Pre
1013 (deptropine citrate) and Pre 1236 (benztropine mesylate) were purchased from
Prestwick Chemical Inc. All other compounds used in in vitro assays for Project 1 were bought from Sigma and dissolved in DMSO. The concentrations of paclitaxel and salinomycin were used in our study based on their IC50 values for breast cancer cells and
EMT-induced CSCs [42]. For the in vivo assay, benztropine mesylate was purchased from
Santa Cruz Biotechnology and dissolved in 0.9% saline. Paclitaxel was purchased from LC
Laboratories and dissolved in Cremophor EL: ethanol (1:1, v/v, Sigma-Aldrich) at a concentration of 6 mg/ml and used in dilution with 0.9% saline.
ACV1 was bought from R&D Systems Europe Ltd and dissolved in PBS. All other compounds used in Project 2 (salinomycin, doxorubicin and paclitaxel) were bought from
Sigma and dissolved in DMSO.
Chemical screening and data analysis
114 The chemical screening was performed in a 96-well plate format. 1,000 cells isolated from
HMLE and HMLER shEcad cells were inoculated in a mixed medium of MEGM and DMEM at adherent conditions, while 3,000 sphere-forming cells isolated from related spheres were grown in SCM as suspension. After 24 h, cells were treated with compounds from the chemical libraries at 10 µM or DMSO only. Cell viability was determined by the CCk-8 assay after 72 h treatment according to the manufacturer’s instruction. The optical density (OD) at
450 nm was measured by a microplate reader (Tecan Inc). The cell viability fraction (%) was calculated as follows: OD450nm-test compound / OD450nm-DMSO × 100%. The screening was done for two independent replicates and the quality of the experiment was determined by principal component analysis, calculation of the Pearson correlation coefficients (r) of biological replicates using Prism 5.0.
Validation of compounds from primary screening
The activities of compounds were quantified by generation of dose-response curves for
HMLE spheres and HMLER shEcad spheres under the same cell density and culture conditions described for the primary screen.
Cell Proliferation assay
The proliferation of HMLER shEcad adherent cells and spheres was indirectly assayed using the CCk-8 kit. Approximately 1,000 cells were cultured in monolayer or mammosphere culture conditions. At 24, 48, 72 and 96 h, the cell viability assay using CCk-8 was measured as indicated above.
Chemotherapy sensitivity assays
The sensitivity of the HMLER shEcad adherent cells and spheres to chemotherapeutic drugs was measured by CCk-8 cell viability assay. Briefly, 1,000 cells were seeded in 96-well plates, and various concentrations of paclitaxel and doxorubicin (both from Sigma-Aldrich)
115 were added after 24 h, and co-incubated for 72 h. The CCk-8 cell viability assays were performed according to the manufacturer’s instructions.
RNA Extraction and Quantitative Real-Time PCR (qRT-PCR)
Total RNA was isolated from cells or sphere-containing pellets using Nucleo Spin® RNA kit
(Macherey-Nagel AG) and cDNA synthesis was performed by High Capacity cDNA Reverse
Transcription Kit (Applied Biosystems) as per manufacturer’s instructions. qRT-PCR was run in an Applied Biosystems 7900HT fast real-time PCR machine. qRT-PCR reactions were carried out with SYBR Green PCR Master Mix (Applied Biosystems) and ATCB (β-actin) levels were used as controls. The mean cycle threshold value (Ct) for the gene of interest, normalized to the Ct value of the housekeeping gene (ATCB) was used to calculate gene expression values. The primers for human genes were custom-made oligonucleotide primers
(Microsynth, Switzerland). The primer sequences used in Project 1 (Chapter 4.1) are shown in Table 6 and those used in Project 2 (Chapter 4.3) are shown in Table 7
Table 6. Primer sequences for qRT-PCR analysis of stemness-associated genes in HMLER shEcad cells and spheres (Project 1)
Gene Forward (5’ -> 3’) Reverse (5’ -> 3’)
ACTB CATGTACGTTGCTATCCAGGC CTCCTTAATGTCACGCACGAT CD44 ACCCCAGCAACCCTACTGCTGAT TAGCAGGGATTCTGTCTGTG CD24 CTCCTACCCACGCAGATTTATTC AGAGTGAGACCACGAAGAGAC ALDH1 CCGTGGCGTACTATGGATGC GCAGCAGACGATCTCTTTCGAT CD133 AGTCGGAAACTGGCAGATAGC GGTAGTGTTGTACTGGGCCAAT SLUG TGTGACAAGGAATATGTGAGCC TGAGCCCTCAGATTTGACCTG TWIST1 GTCCGCAGTCTTACGAGGAG GCTTGAGGGTCTGAATCTTGCT FOXC2 CCTCCTGGTATCTCAACCACA GAGGGTCGAGTTCTCAATCCC OCT4 CTTGAATCCCGAATGGAAAGGG GTGTATATCCCAGGGTGATCCTC NANOG TGATTTGTGGGCCTGAAGAAAA GAGGCATCTCAGCAGAAGACA
Cell preparation for FACS
Single cell suspensions were required for staining of samples for FACS. Adherent cells were washed twice with phosphate-buffered saline (PBS) and then harvested with trypsin-EDTA.
116 Mammopheres were collected by gentle centrifugation at 1,000 rpm for 5 min at 4 °C, then dissociated with trypsin-EDTA. Single cell suspensions from adherent or sphere cultures were rinsed with FACS buffer (2 mM EDTA, 1% (w/v) bovine serum albumin (BSA) in PBS) twice, and adjusted to a concentration of 106 cells/ml in FACS buffer for FACS analysis.
Table 7. Primer sequences for qRT-PCR analysis in human cancer cells (Project 2)
Gene Forward (5’ -> 3’) Reverse (5’ -> 3’) ACTB CATGTACGTTGCTATCCAGGC CTCCTTAATGTCACGCACGAT CHRM1 CTCTATACCACGTACCTGCTCA CCGAGTCACGGAGAAGTAGC CHRM2 AACTCCTCTAACAATAGCCTGGC GTTCCCGATAATGGTCACCAAA CHRM3 CACAATAACAGTACAACCTCGCC GCCAGGATGCCCGTTAAGAAA CHRM4 GTTTGTGGTGGGTAAGCGGA TGCTTCATTAGTGGGCTCTTG CHRM5 CAATGCAACCACCGTCAATGG ATCTGCACAGGCTAAGCTGAG CHRNA1 TCCTGGGCTCCGAACATGA ACATTGGTTGTCACGATCTGATT CHRNA2 AGGCTCGCATACCGAGACT TCACCACGTCTGAAGTGTTGG CHRNA3 TGAGCACCGTCTATTTGAGCG TGGACACCTCGAAATGGATGAT CHRNA4 GGAGGGCGTCCAGTACATTG GAAGATGCGGTCGATGACCA CHRNA5 AAAGATGGGTTCGTCCTGTGG CAAACAAAACGATGTCTGGTGTC CHRNA6 TGAGACTCTTCGCGTTCCTG ATTTCAGCTTTGTCATACGTCCA CHRNA7 GCTGGTCAAGAACTACAATCCC CTCATCCACGTCCATGATCTG CHRNA9 AAATCTGGCACGATGCCTATC GCAGGACCACATTGGTGTTCA CHRNA10 TCGACATGGATGAACGGAACC ATCGTAGGTAGGCATCTGTCC CHRNB1 CTCTGGACATTAGCGTCGTGG GCTGAACACCATAGTGCAATTCT CHRNB2 GGTGACAGTACAGCTTATGGTG AGGCGATAATCTTCCCACTCC CHRNB3 TGCTGGTTCTCATCGTCCTTG GCATCTTCATTTTCGGCGATTGA CHRNB4 AACCCGTTACAATAACCTGATCC ATTCACGCTGATAAGCTGGGC CHRND ATGGTCAACCTGGTCTTCTACC CAGGAACTTGCCGATAAGGG CHRNG ACGAGACTCGGATGTGGTCAA GACACCGTCCACGTTGTTCT CHRNE GTGGATGCCGTGAACTTCGT GCACCCAGTCGGACACTTC CD44 ACCCCAGCAACCCTACTGCTGAT TAGCAGGGATTCTGTCTGTG CD24 CTCCTACCCACGCAGATTTATTC AGAGTGAGACCACGAAGAGAC ALDH1 CCGTGGCGTACTATGGATGC GCAGCAGACGATCTCTTTCGAT CD133 AGTCGGAAACTGGCAGATAGC GGTAGTGTTGTACTGGGCCAAT
Aldefluor assay
The ALDEFLUOR assay (STEMCELL Technologies) was used to profile stem and progenitor cells based on their high expression of ALDH1. The basis for this assay is that
117 uncharged ALDH substrate [BODIPY-aminoacetaldehyde (BAAA)] is taken up by living cells via passive diffusion. Once inside the cell, BAAA is converted into negatively charged
BODIPY-aminoacetate (BAA−) by intracellular ALDH. BAA− is then retained inside the cell, causing the cell to become highly fluorescent. The Aldefluor assay was conducted essentially according to the manufacturer’s instructions. Briefly, single cell suspensions from treated- or untreated-tumor cells were harvested, washed with Aldefluor assay buffer, the cell density was adjusted to 106 cells/ml in Aldefluor assay buffer supplement with ALDH substrate and cells were incubated for 40 min at 37°C to allow substrate conversion. As a negative control for all experiments, an aliquot of Aldefluor-stained cells was immediately quenched with diethylaminobenzaldehyde (DEAB), a specific ALDH inhibitor. Cells were analyzed using the FITC channel on the FACS Canto (BD Biosciences). Data were analyzed with FlowJo software (FlowJo X 10.0.7). The ALDH+ fraction was calculated based on the disappearance of that fraction in the presence of DEAB using the formula:
+ + + ALDH fraction = ALDH percentage (-DEAB) - ALDH percentage (+DEAB)
FACS analysis for CD44 and CD24 CSC surface markers
All antibodies were obtained from BD Biosciences (San Diego, CA, USA). Combinations of fluorochrome-conjugated monoclonal antibodies against human CD44 (APC; cat. # 559942) and CD24 (PE; cat. # 555428) or their respective isotype controls (APC mouse IgG2b,κ: cat.
#555745; PE mouse IgG2a,κ: cat. #555574) were added to the single cell suspension at concentrations recommended by the manufacturer (1:20) and incubated at 4°C in the dark for
30 min. The labeled cells were washed in the FACS buffer twice, and then acquired with a
FACS Canto (BD Biosciences). Data were analyzed with Flow Jo software (FlowJo X 10.0.7) and illustrated as percentage of cells with a CD44+/CD24- phenotype ± SD.
Mammosphere formation assay
118 Mammosphere formation assays were performed as described previously, but with addition of
0.5% methylcellulose to prevent cell aggregation [65]. 1,000 cells were seeded per well in ultra-low attachment 96-well plates with SCM. After incubation with tested compounds for 6 days, the mammosphere numbers were counted and photographed.
Self-renewal assay
The adherent cells were pre-treated with compounds or 0.1% DMSO for 4 days. 1,000 treated or untreated cells were dissociated and seeded in ultra-low attachment 96-well plates with 100
µl of SCM. Cells were seeded in parallel at the same density in 6-well plates. 6 days later, the primary mammospheres formed in 96-well plates were counted and photographed. The parallel seeding compartments in 6-well plates were dissociated into single cells and seeded as next generation of mammospheres in both 96-well and 6-well plates without treatments.
The mammosphere number was measured in different generations of spheres without treatment.
Chemotaxis transwell migration and invasion assay
Transwell migration and invasion assays were conducted using polycarbonate 8 µm pore size- membrane 24-well plates (Corning Life Sciences). For transwell migration assay, 25,000 cells per well in serum-free medium were seeded in the upper chamber of 24-well plates (Costar).
Medium with 10% of serum, used as a chemoattractant, was placed in the lower compartment. DMSO, salinomycin (2 µM), paclitaxel (10 nM), benztropine mesylate (5 µM), or deptropine citrate (5 µM) were applied to the medium in both upper and lower chamber.
After the incubation time, cells in the top chamber were removed with a cotton swab, and cells in the lower compartment were stained with Hochest 33342. Five image fields were captured per insert by camera (AxioCam MRc) on a Zeiss Axiovert 200 M microscope (Carl
Zeiss, Inc.), using AxioVison (Version 4.7.1) software under a 10× magnification. The cell numbers on each pictures were counted using Image J (Version 1.46) software. The average numbers within the five pictures taken from the insert were taken for statistic calculations.
119
For invasion assays, growth factor reduced matrigel (BD Matrigel™) was diluted by 1:3 with cold serum-free medium. 30 µl diluted matrigel were added to each insert on ice and incubated at 37°C for 0.5-1 h. After pre-coating, the experimental procedure was the same as for the chemotaxis transwell migration assay described above.
Animal studies
All animal studies were carried out according to the ethical guidelines established by our
Institution (ETH Zürich), under an approved animal protocol (12/2011) by the Veterinäramt
Kanton Zürich. All animals were housed in microisolator cages and in pathogen free conditions until imaging. All surgical procedures were performed under anesthesia and all efforts were made to minimize suffering of the animals.
For the tumor seeding study, 4T1-luc2 cells were pretreated for 4 days with benztropine mesylate (5 µM) or DMSO (0.1%) in vitro. Different number of cells were injected in 50 µl
1:1 (v/v) Matrigel:DMEM solution into the fourth mammary gland of 6-8 week old female
Balb/c mice (Janvier, Le Genest Saint Isle, France). Tumor incidence was monitored for 41 days after injection.
For the in vivo treatment study, 4T1-luc2 mammary carcinoma cells (Caliper) were maintained in DMEM medium containing 10% FBS. 1×105 4T1-luc2 cells were mixed 1:1 by volume with Matrigel injected subcutaneously into the fourth mammary fat pad of 8-week old female Balb/c mice (Janvier, Le Genest Saint Isle, France). Compound treatment was initiated
24 h after injection. Animals were administered 0.9% saline (vehicle, daily), benztropine mesylate (1.5 mg/kg, daily), paclitaxel (10 mg/kg, twice a week) or a combination by intraperitoneal (i.p.) injection for 3 weeks or until the largest tumor reached approximately 12 mm in diameter. The smallest and largest tumor diameters were serially measured with a digital caliper, and mouse weight was determined every 3 days. Tumor volumes were
120 calculated using the following formula: Tumor volume = [4/3] × π × (1/2 × smaller diameter)2 × (1/2 × larger diameter).
Bioluminescence imaging
After 3 weeks of drug treatment, tumor-bearing mice were given an i.p. injection of D- luciferin substrate in PBS (15 mg/ml, 100 µl/10 g body weight, Caliper Life Sciences). After
5 min, the mice were anesthetized with a lethal dose (100 µl) of Narketan-Domitor 4:1 solution (ketamine 400 mg/kg and medetomidine 10 mg/kg). The tissues of interest (primary tumor, lung, liver, inguinal lymph node, axillary lymph node and inguinal non-draining lymph node) were resected for ex vivo imagining. The tissues were imaged with an IVIS
Spectrum imaging system (Caliper Life Sciences). The exposure time was adjusted based on different organs from 1 s (primary tumor), 2 s (lung) to 1 min (liver and lymph node). Images were analyzed with the Living Image v 4.3.1 software (Caliper Life Sciences).
Target prediction by SPiDER 1.0
SPiDER 1.0 software (self-organizing map-based prediction of drug equivalence relationships) was used for target prediction of interested compounds based on the method established previously by Prof. Gisbert Schneider’s group at the ETH Zürich. Briefly, a value of S close to 1 indicates that the coclustered reference ligands are close to the query compound in descriptor space. P values for new predictions were computed based on the likelihood of a certain confidence in the prediction of known drug targets.
Acetylcholine assay
The Choline/Acetylcholine Assay Kit (Abcam, UK, ab65345) was used to determine total choline levels in fresh medium and conditioned medium of BCSCs. The assay was carried out in accordance with the manufacturer's instruction. The assay was conducted in the absence of acetylcholinesterase in order to identify values of total choline.
121 Statistics
Data are represented as mean ± SD. Statistical tests were performed with GraphPad Prism
V5.0 (San Diego, CA). A two-tailed Student’s t-test was used for comparisons of continuous variables between two groups. One-way ANOVA with Tukey or Dunnett post tests or two- way ANOVA was used when three of more groups were compared. Fisher’s exact test was used for the comparisons in the tumor metastasis assay.
122 7 Curriculum Vitae
123
124 8 Acknowledgements
I would like to express my sincere gratitude to Prof. Dr. Michael Detmar for giving me the opportunity to perform my PhD Thesis in his laboratory at the exciting interface of anti- cancer stem cell drug discovery. I greatly enjoyed the scientific discussions with Michael, appreciated his innovative thoughts and plenty of inputs, encouragement and support. My thanks also go to Prof. Dr. Gisbert Schneider, Prof. Dr. Karl-Heinz Altmann and Prof. Dr.
Lukas Sommer for being on my thesis committee. I am really grateful for their scientific input and discussions during my thesis committee meetings. Their expertise on drug structures and development helped me a lot.
I thank my co-authors and master and semester students who significantly contributed to my thesis projects: Dr. Maija Hollmén, Dr. Steven Proulx, Dr. Yong Chen, Lina Li and Daniel
Reker. In particular, I am very thankful to Yong and Lina for help with the screening process,
Maija and Steven for their contribution to animal experiments, and Daniel for target predictions of potential hits.
I would like to thank all previous and current members of the groups of Prof. Michael Detmar and Prof. Cornelia Halin for being always helpful and for the great atmosphere. Your dedication, diligence and enthusiasm for research highly influenced and motivated me.
Particularly, Dr. Lothar Dieterich for his help with data analysis and microscope operations, and Dr. Sinem Karaman for her help and IT support.
I thank Jeannette Scholl and Heidi Baumberger for excellent technical support and smooth ordering procedures. I also thank Susanne Holliger for her administrative effort and Sven
Nowok and Carlos Ochoa for animal caretaking. I thank Dr. Tao Sun help me draw the chemical structures of 19 compounds. I thank Simon Schwager for his time and effort in critical proof-reading of the thesis. I also thank Samia Bachmann and Macro D'Addio for their time in English-German translation work.
125
I would also like to express my gratitude to my friends in Switzerland, Huihui Chen, Tao Sun,
Lu Jin, Xiangang Huang, Yijian Gong, Huan Ma, Delong Xie, Yu Zou, Mi Liu, et al. for their help and support. I felt really funny with you guys for hiking, sports, dinner, shopping, travelling and so on.
My deepest gratitude to my beloved family, my parents, Zhong Cui and Xiuying Liang, my grandparents, Yongzhan Cui and Yuxiang Gu, Jinzai Liang and Yumei Xin, you are the source of inspiration for me and whose permanent assistance I can always thankfully rely on.
126 9 References
1. American Cancer Society, Breast Cancer Facts & Figures 2014-2015. American Cancer Society, ed. American Cancer Society, 2014.
2. American Cancer Society, Cancer Treatment & Survivorship Facts & Figures 2015. American Cancer Society, 2015.
3. Gonzalez-Angulo, A.M., F. Morales-Vasquez, and G.N. Hortobagyi, Overview of resistance to systemic therapy in patients with breast cancer. Adv Exp Med Biol, 2007. 608: p. 1-22.
4. Cardiff, R.D. and S.R. Wellings, The comparative pathology of human and mouse mammary glands. J Mammary Gland Biol Neoplasia, 1999. 4(1): p. 105-22.
5. Dimri, G., H. Band, and V. Band, Mammary epithelial cell transformation: insights from cell culture and mouse models. Breast Cancer Res, 2005. 7(4): p. 171-9.
6. Smalley, M. and A. Ashworth, Stem cells and breast cancer: A field in transit. Nat Rev Cancer, 2003. 3(11): p. 832-44.
7. Sorlie, T., C.M. Perou, R. Tibshirani, T. Aas, S. Geisler, H. Johnsen, T. Hastie, M.B. Eisen, M. van de Rijn, S.S. Jeffrey, T. Thorsen, H. Quist, J.C. Matese, P.O. Brown, D. Botstein, P.E. Lonning, and A.L. Borresen-Dale, Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A, 2001. 98(19): p. 10869-74.
8. Perou, C.M., T. Sorlie, M.B. Eisen, M. van de Rijn, S.S. Jeffrey, C.A. Rees, J.R. Pollack, D.T. Ross, H. Johnsen, L.A. Akslen, O. Fluge, A. Pergamenschikov, C. Williams, S.X. Zhu, P.E. Lonning, A.L. Borresen-Dale, P.O. Brown, and D. Botstein, Molecular portraits of human breast tumours. Nature, 2000. 406(6797): p. 747-52.
9. Prat, A. and C.M. Perou, Deconstructing the molecular portraits of breast cancer. Mol Oncol, 2011. 5(1): p. 5-23.
10. Eroles, P., A. Bosch, J.A. Perez-Fidalgo, and A. Lluch, Molecular biology in breast cancer: intrinsic subtypes and signaling pathways. Cancer Treat Rev, 2012. 38(6): p. 698-707.
11. Polyak, K., Heterogeneity in breast cancer. J Clin Invest, 2011. 121(10): p. 3786-8.
12. Cheang, M.C., D. Voduc, C. Bajdik, S. Leung, S. McKinney, S.K. Chia, C.M. Perou, and T.O. Nielsen, Basal-like breast cancer defined by five biomarkers has superior prognostic value than triple-negative phenotype. Clin Cancer Res, 2008. 14(5): p. 1368-76.
13. Comprehensive molecular portraits of human breast tumours. Nature, 2012. 490(7418): p. 61-70.
14. Herschkowitz, J.I., K. Simin, V.J. Weigman, I. Mikaelian, J. Usary, Z. Hu, K.E. Rasmussen, L.P. Jones, S. Assefnia, S. Chandrasekharan, M.G. Backlund, Y. Yin, A.I. Khramtsov, R. Bastein, J. Quackenbush, R.I. Glazer, P.H. Brown, J.E. Green, L.
127 Kopelovich, P.A. Furth, J.P. Palazzo, O.I. Olopade, P.S. Bernard, G.A. Churchill, T. Van Dyke, and C.M. Perou, Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors. Genome Biol, 2007. 8(5): p. R76.
15. Prat, A., J.S. Parker, O. Karginova, C. Fan, C. Livasy, J.I. Herschkowitz, X. He, and C.M. Perou, Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res, 2010. 12(5): p. R68.
16. Granit, R.Z., M. Slyper, and I. Ben-Porath, Axes of differentiation in breast cancer: untangling stemness, lineage identity, and the epithelial to mesenchymal transition. Wiley Interdiscip Rev Syst Biol Med, 2014. 6(1): p. 93-106.
17. Prat, A. and C.M. Perou, Mammary development meets cancer genomics. nature medicine, 2009. 15(8): p. 842-4.
18. Visvader, J.E., Cells of origin in cancer. Nature, 2011. 469(7330): p. 314-22.
19. Marsden, C.G., M.J. Wright, R. Pochampally, and B.G. Rowan, Breast tumor-initiating cells isolated from patient core biopsies for study of hormone action. Methods Mol Biol, 2009. 590: p. 363-75.
20. Meacham, C.E. and S.J. Morrison, Tumour heterogeneity and cancer cell plasticity. Nature, 2013. 501(7467): p. 328-37.
21. Gupta, G.P. and J. Massague, Cancer metastasis: building a framework. Cell, 2006. 127(4): p. 679-95.
22. Nguyen, L.V., R. Vanner, P. Dirks, and C.J. Eaves, Cancer stem cells: an evolving concept. Nat Rev Cancer, 2012. 12(2): p. 133-43.
23. Clevers, H., The cancer stem cell: premises, promises and challenges. nature medicine, 2011. 17(3): p. 313-9.
24. Lapidot, T., C. Sirard, J. Vormoor, B. Murdoch, T. Hoang, J. Caceres-Cortes, M. Minden, B. Paterson, M.A. Caligiuri, and J.E. Dick, A cell initiating human acute myeloid leukaemia after transplantation into SCID mice. Nature, 1994. 367(6464): p. 645-8.
25. Bonnet, D. and J.E. Dick, Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. nature medicine, 1997. 3(7): p. 730-7.
26. Al-Hajj, M., M.S. Wicha, A. Benito-Hernandez, S.J. Morrison, and M.F. Clarke, Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci U S A, 2003. 100(7): p. 3983-8.
27. Al-Hajj, M. and M.F. Clarke, Self-renewal and solid tumor stem cells. Oncogene, 2004. 23(43): p. 7274-82.
28. Lobo, N.A., Y. Shimono, D. Qian, and M.F. Clarke, The biology of cancer stem cells. Annu Rev Cell Dev Biol, 2007. 23: p. 675-99.
128 29. Tavil, B., M. Cetin, and M. Tuncer, CD34/CD117 positivity in assessment of prognosis in children with myelodysplastic syndrome. Leuk Res, 2006. 30(2): p. 222- 4.
30. Baccelli, I. and A. Trumpp, The evolving concept of cancer and metastasis stem cells. J Cell Biol, 2012. 198(3): p. 281-93.
31. Miyamoto, T., I.L. Weissman, and K. Akashi, AML1/ETO-expressing nonleukemic stem cells in acute myelogenous leukemia with 8;21 chromosomal translocation. Proc Natl Acad Sci U S A, 2000. 97(13): p. 7521-6.
32. Scaffidi, P. and T. Misteli, In vitro generation of human cells with cancer stem cell properties. Nat Cell Biol, 2011. 13(9): p. 1051-61.
33. Mani, S.A., W. Guo, M.J. Liao, E.N. Eaton, A. Ayyanan, A.Y. Zhou, M. Brooks, F. Reinhard, C.C. Zhang, M. Shipitsin, L.L. Campbell, K. Polyak, C. Brisken, J. Yang, and R.A. Weinberg, The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell, 2008. 133(4): p. 704-15.
34. Visvader, J.E. and G.J. Lindeman, Cancer stem cells: current status and evolving complexities. Cell Stem Cell, 2012. 10(6): p. 717-28.
35. May, C.D., N. Sphyris, K.W. Evans, S.J. Werden, W. Guo, and S.A. Mani, Epithelial- mesenchymal transition and cancer stem cells: a dangerously dynamic duo in breast cancer progression. Breast Cancer Res, 2011. 13(1): p. 202.
36. Stewart, J.M., P.A. Shaw, C. Gedye, M.Q. Bernardini, B.G. Neel, and L.E. Ailles, Phenotypic heterogeneity and instability of human ovarian tumor-initiating cells. Proc Natl Acad Sci U S A, 2011. 108(16): p. 6468-73.
37. Takebe, N. and S.P. Ivy, Controversies in cancer stem cells: targeting embryonic signaling pathways. Clin Cancer Res, 2010. 16(12): p. 3106-12.
38. Jordan, C.T., Cancer Stem Cells: Controversial or Just Misunderstood? Cell Stem Cell, 2009. 4(3): p. 203-5.
39. Li, L. and T. Xie, Stem cell niche: structure and function. Annu Rev Cell Dev Biol, 2005. 21: p. 605-31.
40. Li, L. and W.B. Neaves, Normal stem cells and cancer stem cells: the niche matters. Cancer Res, 2006. 66(9): p. 4553-7.
41. Clarke, M.F., J.E. Dick, P.B. Dirks, C.J. Eaves, C.H. Jamieson, D.L. Jones, J. Visvader, I.L. Weissman, and G.M. Wahl, Cancer stem cells--perspectives on current status and future directions: AACR Workshop on cancer stem cells. Cancer Res, 2006. 66(19): p. 9339-44.
42. Gupta, P.B., T.T. Onder, G. Jiang, K. Tao, C. Kuperwasser, R.A. Weinberg, and E.S. Lander, Identification of selective inhibitors of cancer stem cells by high-throughput screening. Cell, 2009. 138(4): p. 645-59.
129 43. Li, X., M.T. Lewis, J. Huang, C. Gutierrez, C.K. Osborne, M.F. Wu, S.G. Hilsenbeck, A. Pavlick, X. Zhang, G.C. Chamness, H. Wong, J. Rosen, and J.C. Chang, Intrinsic resistance of tumorigenic breast cancer cells to chemotherapy. J Natl Cancer Inst, 2008. 100(9): p. 672-9.
44. Vidal, S.J., V. Rodriguez-Bravo, M. Galsky, C. Cordon-Cardo, and J. Domingo- Domenech, Targeting cancer stem cells to suppress acquired chemotherapy resistance. Oncogene, 2014. 33(36): p. 4451-63.
45. Zhou, B.B., H. Zhang, M. Damelin, K.G. Geles, J.C. Grindley, and P.B. Dirks, Tumour-initiating cells: challenges and opportunities for anticancer drug discovery. Nat Rev Drug Discov, 2009. 8(10): p. 806-23.
46. Valent, P., D. Bonnet, R. De Maria, T. Lapidot, M. Copland, J.V. Melo, C. Chomienne, F. Ishikawa, J.J. Schuringa, G. Stassi, B. Huntly, H. Herrmann, J. Soulier, A. Roesch, G.J. Schuurhuis, S. Wohrer, M. Arock, J. Zuber, S. Cerny-Reiterer, H.E. Johnsen, M. Andreeff, and C. Eaves, Cancer stem cell definitions and terminology: the devil is in the details. Nat Rev Cancer, 2012. 12(11): p. 767-75.
47. Zabriskie, M.S., C.A. Eide, S.K. Tantravahi, N.A. Vellore, J. Estrada, F.E. Nicolini, H.J. Khoury, R.A. Larson, M. Konopleva, J.E. Cortes, H. Kantarjian, E.J. Jabbour, S.M. Kornblau, J.H. Lipton, D. Rea, L. Stenke, G. Barbany, T. Lange, J.C. Hernandez-Boluda, G.J. Ossenkoppele, R.D. Press, C. Chuah, S.L. Goldberg, M. Wetzler, F.X. Mahon, G. Etienne, M. Baccarani, S. Soverini, G. Rosti, P. Rousselot, R. Friedman, M. Deininger, K.R. Reynolds, W.L. Heaton, A.M. Eiring, A.D. Pomicter, J.S. Khorashad, T.W. Kelley, R. Baron, B.J. Druker, M.W. Deininger, and T. O'Hare, BCR-ABL1 compound mutations combining key kinase domain positions confer clinical resistance to ponatinib in Ph chromosome-positive leukemia. Cancer Cell, 2014. 26(3): p. 428-42.
48. Khorashad, J.S., T.W. Kelley, P. Szankasi, C.C. Mason, S. Soverini, L.T. Adrian, C.A. Eide, M.S. Zabriskie, T. Lange, J.C. Estrada, A.D. Pomicter, A.M. Eiring, I.L. Kraft, D.J. Anderson, Z. Gu, M. Alikian, A.G. Reid, L. Foroni, D. Marin, B.J. Druker, T. O'Hare, and M.W. Deininger, BCR-ABL1 compound mutations in tyrosine kinase inhibitor-resistant CML: frequency and clonal relationships. Blood, 2013. 121(3): p. 489-98.
49. Shmelkov, S.V., J.M. Butler, A.T. Hooper, A. Hormigo, J. Kushner, T. Milde, R. St Clair, M. Baljevic, I. White, D.K. Jin, A. Chadburn, A.J. Murphy, D.M. Valenzuela, N.W. Gale, G. Thurston, G.D. Yancopoulos, M. D'Angelica, N. Kemeny, D. Lyden, and S. Rafii, CD133 expression is not restricted to stem cells, and both CD133+ and CD133- metastatic colon cancer cells initiate tumors. J Clin Invest, 2008. 118(6): p. 2111-20.
50. Li, C., J.J. Wu, M. Hynes, J. Dosch, B. Sarkar, T.H. Welling, M. Pasca di Magliano, and D.M. Simeone, c-Met is a marker of pancreatic cancer stem cells and therapeutic target. Gastroenterology, 2011. 141(6): p. 2218-27 e5.
51. Ginestier, C., M.H. Hur, E. Charafe-Jauffret, F. Monville, J. Dutcher, M. Brown, J. Jacquemier, P. Viens, C.G. Kleer, S. Liu, A. Schott, D. Hayes, D. Birnbaum, M.S. Wicha, and G. Dontu, ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome. Cell Stem Cell, 2007. 1(5): p. 555- 67.
130 52. Dalerba, P., S.J. Dylla, I.K. Park, R. Liu, X. Wang, R.W. Cho, T. Hoey, A. Gurney, E.H. Huang, D.M. Simeone, A.A. Shelton, G. Parmiani, C. Castelli, and M.F. Clarke, Phenotypic characterization of human colorectal cancer stem cells. Proc Natl Acad Sci U S A, 2007. 104(24): p. 10158-63.
53. Medema, J.P., Cancer stem cells: the challenges ahead. Nat Cell Biol, 2013. 15(4): p. 338-44.
54. Britton, K.M., J.A. Kirby, T.W. Lennard, and A.P. Meeson, Cancer stem cells and side population cells in breast cancer and metastasis. Cancers (Basel), 2011. 3(2): p. 2106-30.
55. Patrawala, L., T. Calhoun, R. Schneider-Broussard, J. Zhou, K. Claypool, and D.G. Tang, Side population is enriched in tumorigenic, stem-like cancer cells, whereas ABCG2+ and ABCG2- cancer cells are similarly tumorigenic. Cancer Res, 2005. 65(14): p. 6207-19.
56. Nakanishi, T., S. Chumsri, N. Khakpour, A.H. Brodie, B. Leyland-Jones, A.W. Hamburger, D.D. Ross, and A.M. Burger, Side-population cells in luminal-type breast cancer have tumour-initiating cell properties, and are regulated by HER2 expression and signalling. Br J Cancer, 2010. 102(5): p. 815-26.
57. Kabashima, A., H. Higuchi, H. Takaishi, Y. Matsuzaki, S. Suzuki, M. Izumiya, H. Iizuka, G. Sakai, S. Hozawa, T. Azuma, and T. Hibi, Side population of pancreatic cancer cells predominates in TGF-beta-mediated epithelial to mesenchymal transition and invasion. Int J Cancer, 2009. 124(12): p. 2771-9.
58. Theou, N., S. Gil, A. Devocelle, C. Julie, A. Lavergne-Slove, A. Beauchet, P. Callard, R. Farinotti, A. Le Cesne, A. Lemoine, L. Faivre-Bonhomme, and J.F. Emile, Multidrug resistance proteins in gastrointestinal stromal tumors: site-dependent expression and initial response to imatinib. Clin Cancer Res, 2005. 11(21): p. 7593-8.
59. Hirschmann-Jax, C., A.E. Foster, G.G. Wulf, J.G. Nuchtern, T.W. Jax, U. Gobel, M.A. Goodell, and M.K. Brenner, A distinct "side population" of cells with high drug efflux capacity in human tumor cells. Proc Natl Acad Sci U S A, 2004. 101(39): p. 14228- 33.
60. Luo, Y., L.Z. Ellis, K. Dallaglio, M. Takeda, W.A. Robinson, S.E. Robinson, W. Liu, K.D. Lewis, M.D. McCarter, R. Gonzalez, D.A. Norris, D.R. Roop, R.A. Spritz, N.G. Ahn, and M. Fujita, Side population cells from human melanoma tumors reveal diverse mechanisms for chemoresistance. J Invest Dermatol, 2012. 132(10): p. 2440- 50.
61. Broadley, K.W., M.K. Hunn, K.J. Farrand, K.M. Price, C. Grasso, R.J. Miller, I.F. Hermans, and M.J. McConnell, Side population is not necessary or sufficient for a cancer stem cell phenotype in glioblastoma multiforme. Stem Cells, 2011. 29(3): p. 452-61.
62. Lichtenauer, U.D., I. Shapiro, K. Geiger, M. Quinkler, M. Fassnacht, R. Nitschke, K.D. Ruckauer, and F. Beuschlein, Side population does not define stem cell-like cancer cells in the adrenocortical carcinoma cell line NCI h295R. Endocrinology, 2008. 149(3): p. 1314-22.
131 63. Reynolds, B.A. and S. Weiss, Generation of neurons and astrocytes from isolated cells of the adult mammalian central nervous system. Science, 1992. 255(5052): p. 1707-10.
64. Pastrana, E., V. Silva-Vargas, and F. Doetsch, Eyes wide open: a critical review of sphere-formation as an assay for stem cells. Cell Stem Cell, 2011. 8(5): p. 486-98.
65. Dontu, G., W.M. Abdallah, J.M. Foley, K.W. Jackson, M.F. Clarke, M.J. Kawamura, and M.S. Wicha, In vitro propagation and transcriptional profiling of human mammary stem/progenitor cells. Genes Dev, 2003. 17(10): p. 1253-70.
66. Jiang, T., B.J. Collins, N. Jin, D.N. Watkins, M.V. Brock, W. Matsui, B.D. Nelkin, and D.W. Ball, Achaete-scute complex homologue 1 regulates tumor-initiating capacity in human small cell lung cancer. Cancer Res, 2009. 69(3): p. 845-54.
67. Huang, E.H., M.J. Hynes, T. Zhang, C. Ginestier, G. Dontu, H. Appelman, J.Z. Fields, M.S. Wicha, and B.M. Boman, Aldehyde dehydrogenase 1 is a marker for normal and malignant human colonic stem cells (SC) and tracks SC overpopulation during colon tumorigenesis. Cancer Res, 2009. 69(8): p. 3382-9.
68. Rasper, M., A. Schafer, G. Piontek, J. Teufel, G. Brockhoff, F. Ringel, S. Heindl, C. Zimmer, and J. Schlegel, Aldehyde dehydrogenase 1 positive glioblastoma cells show brain tumor stem cell capacity. Neuro Oncol, 2010. 12(10): p. 1024-33.
69. Balicki, D., Moving forward in human mammary stem cell biology and breast cancer prognostication using ALDH1. Cell Stem Cell, 2007. 1(5): p. 485-7.
70. Tanei, T., K. Morimoto, K. Shimazu, S.J. Kim, Y. Tanji, T. Taguchi, Y. Tamaki, and S. Noguchi, Association of breast cancer stem cells identified by aldehyde dehydrogenase 1 expression with resistance to sequential Paclitaxel and epirubicin- based chemotherapy for breast cancers. Clin Cancer Res, 2009. 15(12): p. 4234-41.
71. Ucar, D., C.R. Cogle, J.R. Zucali, B. Ostmark, E.W. Scott, R. Zori, B.A. Gray, and J.S. Moreb, Aldehyde dehydrogenase activity as a functional marker for lung cancer. Chem Biol Interact, 2009. 178(1-3): p. 48-55.
72. Charafe-Jauffret, E., C. Ginestier, and D. Birnbaum, Breast cancer stem cells: tools and models to rely on. BMC Cancer, 2009. 9: p. 202.
73. Yu, C., Z. Yao, J. Dai, H. Zhang, J. Escara-Wilke, X. Zhang, and E.T. Keller, ALDH activity indicates increased tumorigenic cells, but not cancer stem cells, in prostate cancer cell lines. In Vivo, 2011. 25(1): p. 69-76.
74. Kalluri, R. and E.G. Neilson, Epithelial-mesenchymal transition and its implications for fibrosis. J Clin Invest, 2003. 112(12): p. 1776-84.
75. Kalluri, R. and R.A. Weinberg, The basics of epithelial-mesenchymal transition. J Clin Invest, 2009. 119(6): p. 1420-8.
76. Thiery, J.P., Epithelial-mesenchymal transitions in tumour progression. Nat Rev Cancer, 2002. 2(6): p. 442-54.
132 77. Yook, J.I., X.Y. Li, I. Ota, C. Hu, H.S. Kim, N.H. Kim, S.Y. Cha, J.K. Ryu, Y.J. Choi, J. Kim, E.R. Fearon, and S.J. Weiss, A Wnt-Axin2-GSK3beta cascade regulates Snail1 activity in breast cancer cells. Nat Cell Biol, 2006. 8(12): p. 1398-406.
78. Clevers, H., Wnt/beta-catenin signaling in development and disease. Cell, 2006. 127(3): p. 469-80.
79. Lagadec, C., E. Vlashi, L. Della Donna, C. Dekmezian, and F. Pajonk, Radiation- induced reprogramming of breast cancer cells. Stem Cells, 2012. 30(5): p. 833-44.
80. Du, Z., R. Qin, C. Wei, M. Wang, C. Shi, R. Tian, and C. Peng, Pancreatic cancer cells resistant to chemoradiotherapy rich in "stem-cell-like" tumor cells. Dig Dis Sci, 2011. 56(3): p. 741-50.
81. Bao, S., Q. Wu, R.E. McLendon, Y. Hao, Q. Shi, A.B. Hjelmeland, M.W. Dewhirst, D.D. Bigner, and J.N. Rich, Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature, 2006. 444(7120): p. 756-60.
82. Duan, J.J., W. Qiu, S.L. Xu, B. Wang, X.Z. Ye, Y.F. Ping, X. Zhang, X.W. Bian, and S.C. Yu, Strategies for isolating and enriching cancer stem cells: well begun is half done. Stem Cells Dev, 2013. 22(16): p. 2221-39.
83. Keith, B. and M.C. Simon, Hypoxia-inducible factors, stem cells, and cancer. Cell, 2007. 129(3): p. 465-72.
84. Soeda, A., M. Park, D. Lee, A. Mintz, A. Androutsellis-Theotokis, R.D. McKay, J. Engh, T. Iwama, T. Kunisada, A.B. Kassam, I.F. Pollack, and D.M. Park, Hypoxia promotes expansion of the CD133-positive glioma stem cells through activation of HIF-1alpha. Oncogene, 2009. 28(45): p. 3949-59.
85. Conley, S.J., E. Gheordunescu, P. Kakarala, B. Newman, H. Korkaya, A.N. Heath, S.G. Clouthier, and M.S. Wicha, Antiangiogenic agents increase breast cancer stem cells via the generation of tumor hypoxia. Proc Natl Acad Sci U S A, 2012. 109(8): p. 2784-9.
86. Li, Z. and J.N. Rich, Hypoxia and hypoxia inducible factors in cancer stem cell maintenance. Curr Top Microbiol Immunol, 2010. 345: p. 21-30.
87. Comerford, K.M., T.J. Wallace, J. Karhausen, N.A. Louis, M.C. Montalto, and S.P. Colgan, Hypoxia-inducible factor-1-dependent regulation of the multidrug resistance (MDR1) gene. Cancer Res, 2002. 62(12): p. 3387-94.
88. Krishnamurthy, P., D.D. Ross, T. Nakanishi, K. Bailey-Dell, S. Zhou, K.E. Mercer, B. Sarkadi, B.P. Sorrentino, and J.D. Schuetz, The stem cell marker Bcrp/ABCG2 enhances hypoxic cell survival through interactions with heme. J Biol Chem, 2004. 279(23): p. 24218-25.
89. Nishi, H., T. Nakada, S. Kyo, M. Inoue, J.W. Shay, and K. Isaka, Hypoxia-inducible factor 1 mediates upregulation of telomerase (hTERT). Mol Cell Biol, 2004. 24(13): p. 6076-83.
90. Brabletz, T., EMT and MET in metastasis: where are the cancer stem cells? Cancer Cell, 2012. 22(6): p. 699-701.
133 91. Brabletz, T., A. Jung, S. Spaderna, F. Hlubek, and T. Kirchner, Opinion: migrating cancer stem cells - an integrated concept of malignant tumour progression. Nat Rev Cancer, 2005. 5(9): p. 744-9.
92. Li, F., B. Tiede, J. Massague, and Y. Kang, Beyond tumorigenesis: cancer stem cells in metastasis. Cell Res, 2007. 17(1): p. 3-14.
93. Hermann, P.C., S.L. Huber, T. Herrler, A. Aicher, J.W. Ellwart, M. Guba, C.J. Bruns, and C. Heeschen, Distinct populations of cancer stem cells determine tumor growth and metastatic activity in human pancreatic cancer. Cell Stem Cell, 2007. 1(3): p. 313-23.
94. Pang, R., W.L. Law, A.C. Chu, J.T. Poon, C.S. Lam, A.K. Chow, L. Ng, L.W. Cheung, X.R. Lan, H.Y. Lan, V.P. Tan, T.C. Yau, R.T. Poon, and B.C. Wong, A subpopulation of CD26+ cancer stem cells with metastatic capacity in human colorectal cancer. Cell Stem Cell, 2010. 6(6): p. 603-15.
95. Aktas, B., M. Tewes, T. Fehm, S. Hauch, R. Kimmig, and S. Kasimir-Bauer, Stem cell and epithelial-mesenchymal transition markers are frequently overexpressed in circulating tumor cells of metastatic breast cancer patients. Breast Cancer Res, 2009. 11(4): p. R46.
96. Pluznik, D.H. and L. Sachs, The cloning of normal "mast" cells in tissue culture. J Cell Physiol, 1965. 66(3): p. 319-24.
97. Hsu, Y.C. and E. Fuchs, A family business: stem cell progeny join the niche to regulate homeostasis. Nat Rev Mol Cell Biol, 2012. 13(2): p. 103-14.
98. Chaichana, K., G. Zamora-Berridi, J. Camara-Quintana, and A. Quinones-Hinojosa, Neurosphere assays: growth factors and hormone differences in tumor and nontumor studies. Stem Cells, 2006. 24(12): p. 2851-7.
99. Alison, M.R., S.M. Lim, and L.J. Nicholson, Cancer stem cells: problems for therapy? J Pathol, 2011. 223(2): p. 147-61.
100. Baiocchi, M., M. Biffoni, L. Ricci-Vitiani, E. Pilozzi, and R. De Maria, New models for cancer research: human cancer stem cell xenografts. Curr Opin Pharmacol, 2010. 10(4): p. 380-4.
101. Ricci-Vitiani, L., D.G. Lombardi, E. Pilozzi, M. Biffoni, M. Todaro, C. Peschle, and R. De Maria, Identification and expansion of human colon-cancer-initiating cells. Nature, 2007. 445(7123): p. 111-5.
102. O'Brien, C.A., A. Kreso, and C.H. Jamieson, Cancer stem cells and self-renewal. Clin Cancer Res, 2010. 16(12): p. 3113-20.
103. Hu, Y. and G.K. Smyth, ELDA: extreme limiting dilution analysis for comparing depleted and enriched populations in stem cell and other assays. J Immunol Methods, 2009. 347(1-2): p. 70-8.
104. Fazekas de St, G., The evaluation of limiting dilution assays. J Immunol Methods, 1982. 49(2): p. R11-23.
134 105. Strijbosch, L.W., W.A. Buurman, R.J. Does, P.H. Zinken, and G. Groenewegen, Limiting dilution assays. Experimental design and statistical analysis. J Immunol Methods, 1987. 97(1): p. 133-40.
106. Bonnefoix, T., P. Bonnefoix, P. Verdiel, and J.J. Sotto, Fitting limiting dilution experiments with generalized linear models results in a test of the single-hit Poisson assumption. J Immunol Methods, 1996. 194(2): p. 113-9.
107. Dieter, S.M., C.R. Ball, C.M. Hoffmann, A. Nowrouzi, F. Herbst, O. Zavidij, U. Abel, A. Arens, W. Weichert, K. Brand, M. Koch, J. Weitz, M. Schmidt, C. von Kalle, and H. Glimm, Distinct types of tumor-initiating cells form human colon cancer tumors and metastases. Cell Stem Cell, 2011. 9(4): p. 357-65.
108. Liu, M., T. Sakamaki, M.C. Casimiro, N.E. Willmarth, A.A. Quong, X. Ju, J. Ojeifo, X. Jiao, W.S. Yeow, S. Katiyar, L.A. Shirley, D. Joyce, M.P. Lisanti, C. Albanese, and R.G. Pestell, The canonical NF-kappaB pathway governs mammary tumorigenesis in transgenic mice and tumor stem cell expansion. Cancer Res, 2010. 70(24): p. 10464- 73.
109. Iliopoulos, D., H.A. Hirsch, and K. Struhl, An epigenetic switch involving NF-kappaB, Lin28, Let-7 MicroRNA, and IL6 links inflammation to cell transformation. Cell, 2009. 139(4): p. 693-706.
110. Yu, F., H. Yao, P. Zhu, X. Zhang, Q. Pan, C. Gong, Y. Huang, X. Hu, F. Su, J. Lieberman, and E. Song, let-7 regulates self renewal and tumorigenicity of breast cancer cells. Cell, 2007. 131(6): p. 1109-23.
111. Carnero, A., C. Blanco-Aparicio, O. Renner, W. Link, and J.F. Leal, The PTEN/PI3K/AKT signalling pathway in cancer, therapeutic implications. Curr Cancer Drug Targets, 2008. 8(3): p. 187-98.
112. Bleau, A.M., D. Hambardzumyan, T. Ozawa, E.I. Fomchenko, J.T. Huse, C.W. Brennan, and E.C. Holland, PTEN/PI3K/Akt pathway regulates the side population phenotype and ABCG2 activity in glioma tumor stem-like cells. Cell Stem Cell, 2009. 4(3): p. 226-35.
113. Roberts, P.J. and C.J. Der, Targeting the Raf-MEK-ERK mitogen-activated protein kinase cascade for the treatment of cancer. Oncogene, 2007. 26(22): p. 3291-310.
114. Wang, Y.K., Y.L. Zhu, F.M. Qiu, T. Zhang, Z.G. Chen, S. Zheng, and J. Huang, Activation of Akt and MAPK pathways enhances the tumorigenicity of CD133+ primary colon cancer cells. Carcinogenesis, 2010. 31(8): p. 1376-80.
115. Balko, J.M., L.J. Schwarz, N.E. Bhola, R. Kurupi, P. Owens, T.W. Miller, H. Gomez, R.S. Cook, and C.L. Arteaga, Activation of MAPK pathways due to DUSP4 loss promotes cancer stem cell-like phenotypes in basal-like breast cancer. Cancer Res, 2013. 73(20): p. 6346-58.
116. Kiger, A.A., D.L. Jones, C. Schulz, M.B. Rogers, and M.T. Fuller, Stem cell self- renewal specified by JAK-STAT activation in response to a support cell cue. Science, 2001. 294(5551): p. 2542-5.
135 117. Tulina, N. and E. Matunis, Control of stem cell self-renewal in Drosophila spermatogenesis by JAK-STAT signaling. Science, 2001. 294(5551): p. 2546-9.
118. Hernandez-Vargas, H., M. Ouzounova, F. Le Calvez-Kelm, M.P. Lambert, S. McKay- Chopin, S.V. Tavtigian, A. Puisieux, C. Matar, and Z. Herceg, Methylome analysis reveals Jak-STAT pathway deregulation in putative breast cancer stem cells. Epigenetics, 2011. 6(4): p. 428-39.
119. Kroon, P., P.A. Berry, M.J. Stower, G. Rodrigues, V.M. Mann, M. Simms, D. Bhasin, S. Chettiar, C. Li, P.K. Li, N.J. Maitland, and A.T. Collins, JAK-STAT blockade inhibits tumor initiation and clonogenic recovery of prostate cancer stem-like cells. Cancer Res, 2013. 73(16): p. 5288-98.
120. Zardawi, S.J., S.A. O'Toole, R.L. Sutherland, and E.A. Musgrove, Dysregulation of Hedgehog, Wnt and Notch signalling pathways in breast cancer. Histol Histopathol, 2009. 24(3): p. 385-98.
121. Pannuti, A., K. Foreman, P. Rizzo, C. Osipo, T. Golde, B. Osborne, and L. Miele, Targeting Notch to target cancer stem cells. Clin Cancer Res, 2010. 16(12): p. 3141- 52.
122. Takahashi-Yanaga, F. and M. Kahn, Targeting Wnt signaling: can we safely eradicate cancer stem cells? Clin Cancer Res, 2010. 16(12): p. 3153-62.
123. Clement, V., P. Sanchez, N. de Tribolet, I. Radovanovic, and A. Ruiz i Altaba, HEDGEHOG-GLI1 signaling regulates human glioma growth, cancer stem cell self- renewal, and tumorigenicity. Curr Biol, 2007. 17(2): p. 165-72.
124. Fan, X., L. Khaki, T.S. Zhu, M.E. Soules, C.E. Talsma, N. Gul, C. Koh, J. Zhang, Y.M. Li, J. Maciaczyk, G. Nikkhah, F. Dimeco, S. Piccirillo, A.L. Vescovi, and C.G. Eberhart, NOTCH pathway blockade depletes CD133-positive glioblastoma cells and inhibits growth of tumor neurospheres and xenografts. Stem Cells, 2010. 28(1): p. 5- 16.
125. Wang, J., T.P. Wakeman, J.D. Lathia, A.B. Hjelmeland, X.F. Wang, R.R. White, J.N. Rich, and B.A. Sullenger, Notch promotes radioresistance of glioma stem cells. Stem Cells, 2010. 28(1): p. 17-28.
126. Liu, S., G. Dontu, I.D. Mantle, S. Patel, N.S. Ahn, K.W. Jackson, P. Suri, and M.S. Wicha, Hedgehog signaling and Bmi-1 regulate self-renewal of normal and malignant human mammary stem cells. Cancer Res, 2006. 66(12): p. 6063-71.
127. Farnie, G. and R.B. Clarke, Mammary stem cells and breast cancer--role of Notch signalling. Stem Cell Rev, 2007. 3(2): p. 169-75.
128. Sansone, P., G. Storci, C. Giovannini, S. Pandolfi, S. Pianetti, M. Taffurelli, D. Santini, C. Ceccarelli, P. Chieco, and M. Bonafe, p66Shc/Notch-3 interplay controls self-renewal and hypoxia survival in human stem/progenitor cells of the mammary gland expanded in vitro as mammospheres. Stem Cells, 2007. 25(3): p. 807-15.
129. Huang, F.T., Y.X. Zhuan-Sun, Y.Y. Zhuang, S.L. Wei, J. Tang, W.B. Chen, and S.N. Zhang, Inhibition of hedgehog signaling depresses self-renewal of pancreatic cancer stem cells and reverses chemoresistance. Int J Oncol, 2012. 41(5): p. 1707-14.
136 130. Wang, Z., Y. Li, D. Kong, S. Banerjee, A. Ahmad, A.S. Azmi, S. Ali, J.L. Abbruzzese, G.E. Gallick, and F.H. Sarkar, Acquisition of epithelial-mesenchymal transition phenotype of gemcitabine-resistant pancreatic cancer cells is linked with activation of the notch signaling pathway. Cancer Res, 2009. 69(6): p. 2400-7.
131. Malanchi, I., H. Peinado, D. Kassen, T. Hussenet, D. Metzger, P. Chambon, M. Huber, D. Hohl, A. Cano, W. Birchmeier, and J. Huelsken, Cutaneous cancer stem cell maintenance is dependent on beta-catenin signalling. Nature, 2008. 452(7187): p. 650-3.
132. Fodde, R. and T. Brabletz, Wnt/beta-catenin signaling in cancer stemness and malignant behavior. Curr Opin Cell Biol, 2007. 19(2): p. 150-8.
133. Brabletz, T., A. Jung, S. Reu, M. Porzner, F. Hlubek, L.A. Kunz-Schughart, R. Knuechel, and T. Kirchner, Variable beta-catenin expression in colorectal cancers indicates tumor progression driven by the tumor environment. Proc Natl Acad Sci U S A, 2001. 98(18): p. 10356-61.
134. Zhao, C., A. Chen, C.H. Jamieson, M. Fereshteh, A. Abrahamsson, J. Blum, H.Y. Kwon, J. Kim, J.P. Chute, D. Rizzieri, M. Munchhof, T. VanArsdale, P.A. Beachy, and T. Reya, Hedgehog signalling is essential for maintenance of cancer stem cells in myeloid leukaemia. Nature, 2009. 458(7239): p. 776-9.
135. Dierks, C., R. Beigi, G.R. Guo, K. Zirlik, M.R. Stegert, P. Manley, C. Trussell, A. Schmitt-Graeff, K. Landwerlin, H. Veelken, and M. Warmuth, Expansion of Bcr-Abl- positive leukemic stem cells is dependent on Hedgehog pathway activation. Cancer Cell, 2008. 14(3): p. 238-49.
136. Merchant, A.A. and W. Matsui, Targeting Hedgehog--a cancer stem cell pathway. Clin Cancer Res, 2010. 16(12): p. 3130-40.
137. Machold, R., S. Hayashi, M. Rutlin, M.D. Muzumdar, S. Nery, J.G. Corbin, A. Gritli- Linde, T. Dellovade, J.A. Porter, L.L. Rubin, H. Dudek, A.P. McMahon, and G. Fishell, Sonic hedgehog is required for progenitor cell maintenance in telencephalic stem cell niches. Neuron, 2003. 39(6): p. 937-50.
138. van den Brink, G.R., S.A. Bleuming, J.C. Hardwick, B.L. Schepman, G.J. Offerhaus, J.J. Keller, C. Nielsen, W. Gaffield, S.J. van Deventer, D.J. Roberts, and M.P. Peppelenbosch, Indian Hedgehog is an antagonist of Wnt signaling in colonic epithelial cell differentiation. Nat Genet, 2004. 36(3): p. 277-82.
139. Fan, X., W. Matsui, L. Khaki, D. Stearns, J. Chun, Y.M. Li, and C.G. Eberhart, Notch pathway inhibition depletes stem-like cells and blocks engraftment in embryonal brain tumors. Cancer Res, 2006. 66(15): p. 7445-52.
140. Korkaya, H. and M.S. Wicha, HER-2, notch, and breast cancer stem cells: targeting an axis of evil. Clin Cancer Res, 2009. 15(6): p. 1845-7.
141. Xu, J., S. Lamouille, and R. Derynck, TGF-beta-induced epithelial to mesenchymal transition. Cell Res, 2009. 19(2): p. 156-72.
142. Piccirillo, S.G., B.A. Reynolds, N. Zanetti, G. Lamorte, E. Binda, G. Broggi, H. Brem, A. Olivi, F. Dimeco, and A.L. Vescovi, Bone morphogenetic proteins inhibit the
137 tumorigenic potential of human brain tumour-initiating cells. Nature, 2006. 444(7120): p. 761-5.
143. Tan, B.T., C.Y. Park, L.E. Ailles, and I.L. Weissman, The cancer stem cell hypothesis: a work in progress. Lab Invest, 2006. 86(12): p. 1203-7.
144. van Rhenen, A., N. Feller, A. Kelder, A.H. Westra, E. Rombouts, S. Zweegman, M.A. van der Pol, Q. Waisfisz, G.J. Ossenkoppele, and G.J. Schuurhuis, High stem cell frequency in acute myeloid leukemia at diagnosis predicts high minimal residual disease and poor survival. Clin Cancer Res, 2005. 11(18): p. 6520-7.
145. Frank, N.Y., T. Schatton, and M.H. Frank, The therapeutic promise of the cancer stem cell concept. J Clin Invest, 2010. 120(1): p. 41-50.
146. Schatton, T., G.F. Murphy, N.Y. Frank, K. Yamaura, A.M. Waaga-Gasser, M. Gasser, Q. Zhan, S. Jordan, L.M. Duncan, C. Weishaupt, R.C. Fuhlbrigge, T.S. Kupper, M.H. Sayegh, and M.H. Frank, Identification of cells initiating human melanomas. Nature, 2008. 451(7176): p. 345-9.
147. Huang, Y., P. Anderle, K.J. Bussey, C. Barbacioru, U. Shankavaram, Z. Dai, W.C. Reinhold, A. Papp, J.N. Weinstein, and W. Sadee, Membrane transporters and channels: role of the transportome in cancer chemosensitivity and chemoresistance. Cancer Res, 2004. 64(12): p. 4294-301.
148. Majeti, R., M.P. Chao, A.A. Alizadeh, W.W. Pang, S. Jaiswal, K.D. Gibbs, Jr., N. van Rooijen, and I.L. Weissman, CD47 is an adverse prognostic factor and therapeutic antibody target on human acute myeloid leukemia stem cells. Cell, 2009. 138(2): p. 286-99.
149. Bao, S., Q. Wu, Z. Li, S. Sathornsumetee, H. Wang, R.E. McLendon, A.B. Hjelmeland, and J.N. Rich, Targeting cancer stem cells through L1CAM suppresses glioma growth. Cancer Res, 2008. 68(15): p. 6043-8.
150. Heidel, F.H., L. Bullinger, Z. Feng, Z. Wang, T.A. Neff, L. Stein, D. Kalaitzidis, S.W. Lane, and S.A. Armstrong, Genetic and pharmacologic inhibition of beta-catenin targets imatinib-resistant leukemia stem cells in CML. Cell Stem Cell, 2012. 10(4): p. 412-24.
151. Steinert, G., C. Oancea, J. Roos, H. Hagemeyer, T. Maier, M. Ruthardt, and E. Puccetti, Sulindac sulfide reverses aberrant self-renewal of progenitor cells induced by the AML-associated fusion proteins PML/RARalpha and PLZF/RARalpha. PLoS One, 2011. 6(7): p. e22540.
152. Roos, J., C. Oancea, M. Heinssmann, D. Khan, H. Held, A.S. Kahnt, R. Capelo, E. la Buscato, E. Proschak, E. Puccetti, D. Steinhilber, I. Fleming, T.J. Maier, and M. Ruthardt, 5-Lipoxygenase is a candidate target for therapeutic management of stem cell-like cells in acute myeloid leukemia. Cancer Res, 2014. 74(18): p. 5244-55.
153. Sun, S., S. Liu, S.Z. Duan, L. Zhang, H. Zhou, Y. Hu, X. Zhou, C. Shi, R. Zhou, and Z. Zhang, Targeting the c-Met/FZD8 Signaling Axis Eliminates Patient-Derived Cancer Stem-like Cells in Head and Neck Squamous Carcinomas. Cancer Res, 2014. 74(24): p. 7546-59.
138 154. Bar, E.E., A. Chaudhry, A. Lin, X. Fan, K. Schreck, W. Matsui, S. Piccirillo, A.L. Vescovi, F. DiMeco, A. Olivi, and C.G. Eberhart, Cyclopamine-mediated hedgehog pathway inhibition depletes stem-like cancer cells in glioblastoma. Stem Cells, 2007. 25(10): p. 2524-33.
155. Schott, A.F., M.D. Landis, G. Dontu, K.A. Griffith, R.M. Layman, I. Krop, L.A. Paskett, H. Wong, L.E. Dobrolecki, M.T. Lewis, A.M. Froehlich, J. Paranilam, D.F. Hayes, M.S. Wicha, and J.C. Chang, Preclinical and clinical studies of gamma secretase inhibitors with docetaxel on human breast tumors. Clin Cancer Res, 2013. 19(6): p. 1512-24.
156. Farnie, G., R.B. Clarke, K. Spence, N. Pinnock, K. Brennan, N.G. Anderson, and N.J. Bundred, Novel cell culture technique for primary ductal carcinoma in situ: role of Notch and epidermal growth factor receptor signaling pathways. J Natl Cancer Inst, 2007. 99(8): p. 616-27.
157. Hoey, T., W.C. Yen, F. Axelrod, J. Basi, L. Donigian, S. Dylla, M. Fitch-Bruhns, S. Lazetic, I.K. Park, A. Sato, S. Satyal, X. Wang, M.F. Clarke, J. Lewicki, and A. Gurney, DLL4 blockade inhibits tumor growth and reduces tumor-initiating cell frequency. Cell Stem Cell, 2009. 5(2): p. 168-77.
158. Qiu, M., Q. Peng, I. Jiang, C. Carroll, G. Han, I. Rymer, J. Lippincott, J. Zachwieja, K. Gajiwala, E. Kraynov, S. Thibault, D. Stone, Y. Gao, S. Sofia, J. Gallo, G. Li, J. Yang, K. Li, and P. Wei, Specific inhibition of Notch1 signaling enhances the antitumor efficacy of chemotherapy in triple negative breast cancer through reduction of cancer stem cells. Cancer Lett, 2013. 328(2): p. 261-70.
159. Bhola, N.E., J.M. Balko, T.C. Dugger, M.G. Kuba, V. Sanchez, M. Sanders, J. Stanford, R.S. Cook, and C.L. Arteaga, TGF-beta inhibition enhances chemotherapy action against triple-negative breast cancer. J Clin Invest, 2013. 123(3): p. 1348-58.
160. Domingo-Domenech, J., S.J. Vidal, V. Rodriguez-Bravo, M. Castillo-Martin, S.A. Quinn, R. Rodriguez-Barrueco, D.M. Bonal, E. Charytonowicz, N. Gladoun, J. de la Iglesia-Vicente, D.P. Petrylak, M.C. Benson, J.M. Silva, and C. Cordon-Cardo, Suppression of acquired docetaxel resistance in prostate cancer through depletion of notch- and hedgehog-dependent tumor-initiating cells. Cancer Cell, 2012. 22(3): p. 373-88.
161. Singh, S.K., C. Hawkins, I.D. Clarke, J.A. Squire, J. Bayani, T. Hide, R.M. Henkelman, M.D. Cusimano, and P.B. Dirks, Identification of human brain tumour initiating cells. Nature, 2004. 432(7015): p. 396-401.
162. Sparmann, A. and M. van Lohuizen, Polycomb silencers control cell fate, development and cancer. Nat Rev Cancer, 2006. 6(11): p. 846-56.
163. Jorgensen, H.G., M. Copland, E.K. Allan, X. Jiang, A. Eaves, C. Eaves, and T.L. Holyoake, Intermittent exposure of primitive quiescent chronic myeloid leukemia cells to granulocyte-colony stimulating factor in vitro promotes their elimination by imatinib mesylate. Clin Cancer Res, 2006. 12(2): p. 626-33.
164. Kitagawa, J., T. Hara, H. Tsurumi, N. Kanemura, S. Kasahara, M. Shimizu, and H. Moriwaki, Cell cycle-dependent priming action of granulocyte colony-stimulating factor (G-CSF) enhances in vitro apoptosis induction by cytarabine and etoposide in leukemia cell lines. J Clin Exp Hematop, 2010. 50(2): p. 99-105.
139 165. Gartel, A.L. and S.K. Radhakrishnan, Lost in transcription: p21 repression, mechanisms, and consequences. Cancer Res, 2005. 65(10): p. 3980-5.
166. Nakshatri, H., M.S. Mendonca, P. Bhat-Nakshatri, N.M. Patel, R.J. Goulet, Jr., and K. Cornetta, The orphan receptor COUP-TFII regulates G2/M progression of breast cancer cells by modulating the expression/activity of p21(WAF1/CIP1), cyclin D1, and cdk2. Biochem Biophys Res Commun, 2000. 270(3): p. 1144-53.
167. Hanahan, D. and R.A. Weinberg, Hallmarks of cancer: the next generation. Cell, 2011. 144(5): p. 646-74.
168. Lang, J.Y., J.L. Hsu, F. Meric-Bernstam, C.J. Chang, Q. Wang, Y. Bao, H. Yamaguchi, X. Xie, W.A. Woodward, D. Yu, G.N. Hortobagyi, and M.C. Hung, BikDD eliminates breast cancer initiating cells and synergizes with lapatinib for breast cancer treatment. Cancer Cell, 2011. 20(3): p. 341-56.
169. Goff, D.J., A. Court Recart, A. Sadarangani, H.J. Chun, C.L. Barrett, M. Krajewska, H. Leu, J. Low-Marchelli, W. Ma, A.Y. Shih, J. Wei, D. Zhai, I. Geron, M. Pu, L. Bao, R. Chuang, L. Balaian, J. Gotlib, M. Minden, G. Martinelli, J. Rusert, K.H. Dao, K. Shazand, P. Wentworth, K.M. Smith, C.A. Jamieson, S.R. Morris, K. Messer, L.S. Goldstein, T.J. Hudson, M. Marra, K.A. Frazer, M. Pellecchia, J.C. Reed, and C.H. Jamieson, A Pan-BCL2 inhibitor renders bone-marrow-resident human leukemia stem cells sensitive to tyrosine kinase inhibition. Cell Stem Cell, 2013. 12(3): p. 316- 28.
170. Todaro, M., M.P. Alea, A.B. Di Stefano, P. Cammareri, L. Vermeulen, F. Iovino, C. Tripodo, A. Russo, G. Gulotta, J.P. Medema, and G. Stassi, Colon cancer stem cells dictate tumor growth and resist cell death by production of interleukin-4. Cell Stem Cell, 2007. 1(4): p. 389-402.
171. Chaudhary, P.M., M. Eby, A. Jasmin, A. Bookwalter, J. Murray, and L. Hood, Death receptor 5, a new member of the TNFR family, and DR4 induce FADD-dependent apoptosis and activate the NF-kappaB pathway. Immunity, 1997. 7(6): p. 821-30.
172. Rajeshkumar, N.V., Z.A. Rasheed, E. Garcia-Garcia, F. Lopez-Rios, K. Fujiwara, W.H. Matsui, and M. Hidalgo, A combination of DR5 agonistic monoclonal antibody with gemcitabine targets pancreatic cancer stem cells and results in long-term disease control in human pancreatic cancer model. Mol Cancer Ther, 2010. 9(9): p. 2582-92.
173. Guzman, M.L., R.M. Rossi, S. Neelakantan, X. Li, C.A. Corbett, D.C. Hassane, M.W. Becker, J.M. Bennett, E. Sullivan, J.L. Lachowicz, A. Vaughan, C.J. Sweeney, W. Matthews, M. Carroll, J.L. Liesveld, P.A. Crooks, and C.T. Jordan, An orally bioavailable parthenolide analog selectively eradicates acute myelogenous leukemia stem and progenitor cells. Blood, 2007. 110(13): p. 4427-35.
174. Feng, W., A. Gentles, R.V. Nair, M. Huang, Y. Lin, C.Y. Lee, S. Cai, F.A. Scheeren, A.H. Kuo, and M. Diehn, Targeting unique metabolic properties of breast tumor initiating cells. Stem Cells, 2014. 32(7): p. 1734-45.
175. Menendez, J.A., J. Joven, S. Cufi, B. Corominas-Faja, C. Oliveras-Ferraros, E. Cuyas, B. Martin-Castillo, E. Lopez-Bonet, T. Alarcon, and A. Vazquez-Martin, The Warburg effect version 2.0: metabolic reprogramming of cancer stem cells. Cell Cycle, 2013. 12(8): p. 1166-79.
140 176. Zhang, G., P. Yang, P. Guo, L. Miele, F.H. Sarkar, Z. Wang, and Q. Zhou, Unraveling the mystery of cancer metabolism in the genesis of tumor-initiating cells and development of cancer. Biochim Biophys Acta, 2013. 1836(1): p. 49-59.
177. Vlashi, E., C. Lagadec, L. Vergnes, T. Matsutani, K. Masui, M. Poulou, R. Popescu, L. Della Donna, P. Evers, C. Dekmezian, K. Reue, H. Christofk, P.S. Mischel, and F. Pajonk, Metabolic state of glioma stem cells and nontumorigenic cells. Proc Natl Acad Sci U S A, 2011. 108(38): p. 16062-7.
178. Michelakis, E.D., G. Sutendra, P. Dromparis, L. Webster, A. Haromy, E. Niven, C. Maguire, T.L. Gammer, J.R. Mackey, D. Fulton, B. Abdulkarim, M.S. McMurtry, and K.C. Petruk, Metabolic modulation of glioblastoma with dichloroacetate. Sci Transl Med, 2010. 2(31): p. 31ra34.
179. Pecqueur, C., L. Oliver, K. Oizel, L. Lalier, and F.M. Vallette, Targeting metabolism to induce cell death in cancer cells and cancer stem cells. Int J Cell Biol, 2013. 2013: p. 805975.
180. Liao, J., F. Qian, N. Tchabo, P. Mhawech-Fauceglia, A. Beck, Z. Qian, X. Wang, W.J. Huss, S.B. Lele, C.D. Morrison, and K. Odunsi, Ovarian cancer spheroid cells with stem cell-like properties contribute to tumor generation, metastasis and chemotherapy resistance through hypoxia-resistant metabolism. PLoS One, 2014. 9(1): p. e84941.
181. Rattan, R., R. Ali Fehmi, and A. Munkarah, Metformin: an emerging new therapeutic option for targeting cancer stem cells and metastasis. J Oncol, 2012. 2012: p. 928127.
182. Bednar, F. and D.M. Simeone, Metformin and cancer stem cells: old drug, new targets. Cancer Prev Res (Phila), 2012. 5(3): p. 351-4.
183. Hirsch, H.A., D. Iliopoulos, P.N. Tsichlis, and K. Struhl, Metformin selectively targets cancer stem cells, and acts together with chemotherapy to block tumor growth and prolong remission. Cancer Res, 2009. 69(19): p. 7507-11.
184. Hirsch, H.A., D. Iliopoulos, and K. Struhl, Metformin inhibits the inflammatory response associated with cellular transformation and cancer stem cell growth. Proc Natl Acad Sci U S A, 2013. 110(3): p. 972-7.
185. Shank, J.J., K. Yang, J. Ghannam, L. Cabrera, C.J. Johnston, R.K. Reynolds, and R.J. Buckanovich, Metformin targets ovarian cancer stem cells in vitro and in vivo. Gynecol Oncol, 2012. 127(2): p. 390-7.
186. Kim, T.H., D.H. Suh, M.K. Kim, and Y.S. Song, Metformin against cancer stem cells through the modulation of energy metabolism: special considerations on ovarian cancer. Biomed Res Int, 2014. 2014: p. 132702.
187. Ginestier, C., F. Monville, J. Wicinski, O. Cabaud, N. Cervera, E. Josselin, P. Finetti, A. Guille, G. Larderet, P. Viens, S. Sebti, F. Bertucci, D. Birnbaum, and E. Charafe- Jauffret, Mevalonate metabolism regulates Basal breast cancer stem cells and is a potential therapeutic target. Stem Cells, 2012. 30(7): p. 1327-37.
141 188. Zhang, W.C., N. Shyh-Chang, H. Yang, A. Rai, S. Umashankar, S. Ma, B.S. Soh, L.L. Sun, B.C. Tai, M.E. Nga, K.K. Bhakoo, S.R. Jayapal, M. Nichane, Q. Yu, D.A. Ahmed, C. Tan, W.P. Sing, J. Tam, A. Thirugananam, M.S. Noghabi, Y.H. Pang, H.S. Ang, W. Mitchell, P. Robson, P. Kaldis, R.A. Soo, S. Swarup, E.H. Lim, and B. Lim, Glycine decarboxylase activity drives non-small cell lung cancer tumor-initiating cells and tumorigenesis. Cell, 2012. 148(1-2): p. 259-72.
189. LaBarge, M.A., The difficulty of targeting cancer stem cell niches. Clin Cancer Res, 2010. 16(12): p. 3121-9.
190. Deonarain, M.P., C.A. Kousparou, and A.A. Epenetos, Antibodies targeting cancer stem cells: a new paradigm in immunotherapy? MAbs, 2009. 1(1): p. 12-25.
191. Gadhoum, Z., J. Delaunay, E. Maquarre, L. Durand, V. Lancereaux, J. Qi, J. Robert- Lezenes, C. Chomienne, and F. Smadja-Joffe, The effect of anti-CD44 monoclonal antibodies on differentiation and proliferation of human acute myeloid leukemia cells. Leuk Lymphoma, 2004. 45(8): p. 1501-10.
192. Palyi-Krekk, Z., M. Barok, J. Isola, M. Tammi, J. Szollosi, and P. Nagy, Hyaluronan- induced masking of ErbB2 and CD44-enhanced trastuzumab internalisation in trastuzumab resistant breast cancer. Eur J Cancer, 2007. 43(16): p. 2423-33.
193. Guo, Y., J. Ma, J. Wang, X. Che, J. Narula, M. Bigby, M. Wu, and M.S. Sy, Inhibition of human melanoma growth and metastasis in vivo by anti-CD44 monoclonal antibody. Cancer Res, 1994. 54(6): p. 1561-5.
194. Sugiyama, T., H. Kohara, M. Noda, and T. Nagasawa, Maintenance of the hematopoietic stem cell pool by CXCL12-CXCR4 chemokine signaling in bone marrow stromal cell niches. Immunity, 2006. 25(6): p. 977-88.
195. Jin, L., Y. Tabe, S. Konoplev, Y. Xu, C.E. Leysath, H. Lu, S. Kimura, A. Ohsaka, M.B. Rios, L. Calvert, H. Kantarjian, M. Andreeff, and M. Konopleva, CXCR4 up-regulation by imatinib induces chronic myelogenous leukemia (CML) cell migration to bone marrow stroma and promotes survival of quiescent CML cells. Mol Cancer Ther, 2008. 7(1): p. 48-58.
196. Weisberg, E., A.K. Azab, P.W. Manley, A.L. Kung, A.L. Christie, R. Bronson, I.M. Ghobrial, and J.D. Griffin, Inhibition of CXCR4 in CML cells disrupts their interaction with the bone marrow microenvironment and sensitizes them to nilotinib. Leukemia, 2012. 26(5): p. 985-90.
197. Burger, J.A. and A. Peled, CXCR4 antagonists: targeting the microenvironment in leukemia and other cancers. Leukemia, 2009. 23(1): p. 43-52.
198. Konopleva, M., Y. Tabe, Z. Zeng, and M. Andreeff, Therapeutic targeting of microenvironmental interactions in leukemia: mechanisms and approaches. Drug Resist Updat, 2009. 12(4-5): p. 103-13.
199. Vermeulen, L., E.M.F. De Sousa, M. van der Heijden, K. Cameron, J.H. de Jong, T. Borovski, J.B. Tuynman, M. Todaro, C. Merz, H. Rodermond, M.R. Sprick, K. Kemper, D.J. Richel, G. Stassi, and J.P. Medema, Wnt activity defines colon cancer stem cells and is regulated by the microenvironment. Nat Cell Biol, 2010. 12(5): p. 468-76.
142 200. Franci, C., M. Takkunen, N. Dave, F. Alameda, S. Gomez, R. Rodriguez, M. Escriva, B. Montserrat-Sentis, T. Baro, M. Garrido, F. Bonilla, I. Virtanen, and A. Garcia de Herreros, Expression of Snail protein in tumor-stroma interface. Oncogene, 2006. 25(37): p. 5134-44.
201. Das, B., R. Tsuchida, D. Malkin, G. Koren, S. Baruchel, and H. Yeger, Hypoxia enhances tumor stemness by increasing the invasive and tumorigenic side population fraction. Stem Cells, 2008. 26(7): p. 1818-30.
202. Burkhardt, J.K., C.P. Hofstetter, A. Santillan, B.J. Shin, C.P. Foley, D.J. Ballon, Y. Pierre Gobin, and J.A. Boockvar, Orthotopic glioblastoma stem-like cell xenograft model in mice to evaluate intra-arterial delivery of bevacizumab: from bedside to bench. J Clin Neurosci, 2012. 19(11): p. 1568-72.
203. Bao, S., Q. Wu, S. Sathornsumetee, Y. Hao, Z. Li, A.B. Hjelmeland, Q. Shi, R.E. McLendon, D.D. Bigner, and J.N. Rich, Stem cell-like glioma cells promote tumor angiogenesis through vascular endothelial growth factor. Cancer Res, 2006. 66(16): p. 7843-8.
204. Calabrese, C., H. Poppleton, M. Kocak, T.L. Hogg, C. Fuller, B. Hamner, E.Y. Oh, M.W. Gaber, D. Finklestein, M. Allen, A. Frank, I.T. Bayazitov, S.S. Zakharenko, A. Gajjar, A. Davidoff, and R.J. Gilbertson, A perivascular niche for brain tumor stem cells. Cancer Cell, 2007. 11(1): p. 69-82.
205. Folkman, J., Seminars in Medicine of the Beth Israel Hospital, Boston. Clinical applications of research on angiogenesis. N Engl J Med, 1995. 333(26): p. 1757-63.
206. Risau, W., Mechanisms of angiogenesis. Nature, 1997. 386(6626): p. 671-4.
207. Hendrix, M.J., E.A. Seftor, A.R. Hess, and R.E. Seftor, Vasculogenic mimicry and tumour-cell plasticity: lessons from melanoma. Nat Rev Cancer, 2003. 3(6): p. 411- 21.
208. Gurney, A., F. Axelrod, C.J. Bond, J. Cain, C. Chartier, L. Donigan, M. Fischer, A. Chaudhari, M. Ji, A.M. Kapoun, A. Lam, S. Lazetic, S. Ma, S. Mitra, I.K. Park, K. Pickell, A. Sato, S. Satyal, M. Stroud, H. Tran, W.C. Yen, J. Lewicki, and T. Hoey, Wnt pathway inhibition via the targeting of Frizzled receptors results in decreased growth and tumorigenicity of human tumors. Proc Natl Acad Sci U S A, 2012. 109(29): p. 11717-22.
209. Kazanskaya, O., A. Glinka, I. del Barco Barrantes, P. Stannek, C. Niehrs, and W. Wu, R-Spondin2 is a secreted activator of Wnt/beta-catenin signaling and is required for Xenopus myogenesis. Dev Cell, 2004. 7(4): p. 525-34.
210. MacDonald, B.T. and X. He, Frizzled and LRP5/6 receptors for Wnt/beta-catenin signaling. Cold Spring Harb Perspect Biol, 2012. 4(12).
211. Niehrs, C., The complex world of WNT receptor signalling. Nat Rev Mol Cell Biol, 2012. 13(12): p. 767-79.
212. Glinka, A., C. Dolde, N. Kirsch, Y.L. Huang, O. Kazanskaya, D. Ingelfinger, M. Boutros, C.M. Cruciat, and C. Niehrs, LGR4 and LGR5 are R-spondin receptors
143 mediating Wnt/beta-catenin and Wnt/PCP signalling. EMBO Rep, 2011. 12(10): p. 1055-61.
213. Carmon, K.S., X. Gong, Q. Lin, A. Thomas, and Q. Liu, R-spondins function as ligands of the orphan receptors LGR4 and LGR5 to regulate Wnt/beta-catenin signaling. Proc Natl Acad Sci U S A, 2011. 108(28): p. 11452-7.
214. Dallas, N.A., L. Xia, F. Fan, M.J. Gray, P. Gaur, G. van Buren, S. Samuel, M.P. Kim, S.J. Lim, and L.M. Ellis, Chemoresistant Colorectal Cancer Cells, the Cancer Stem Cell Phenotype, and Increased Sensitivity to Insulin-like Growth Factor-I Receptor Inhibition. Cancer Res, 2009. 69(5): p. 1951-7.
215. Maloney, E.K., J.L. McLaughlin, N.E. Dagdigian, L.M. Garrett, K.M. Connors, X.M. Zhou, W.A. Blattler, T. Chittenden, and R. Singh, An anti-insulin-like growth factor I receptor antibody that is a potent inhibitor of cancer cell proliferation. Cancer Res, 2003. 63(16): p. 5073-83.
216. Sachdev, D., X. Zhang, I. Matise, M. Gaillard-Kelly, and D. Yee, The type I insulin-like growth factor receptor regulates cancer metastasis independently of primary tumor growth by promoting invasion and survival. Oncogene, 2010. 29(2): p. 251-62.
217. Spiliotaki, M., H. Markomanolaki, M. Mela, D. Mavroudis, V. Georgoulias, and S. Agelaki, Targeting the insulin-like growth factor I receptor inhibits proliferation and VEGF production of non-small cell lung cancer cells and enhances paclitaxel- mediated anti-tumor effect. Lung Cancer, 2011. 73(2): p. 158-65.
218. Geoerger, B., J.F. Brasme, E. Daudigeos-Dubus, P. Opolon, C. Venot, L. Debussche, P. Vrignaud, and G. Vassal, Anti-insulin-like growth factor 1 receptor antibody EM164 (murine AVE1642) exhibits anti-tumour activity alone and in combination with temozolomide against neuroblastoma. Eur J Cancer, 2010. 46(18): p. 3251-62.
219. Gires, O., C.A. Klein, and P.A. Baeuerle, On the abundance of EpCAM on cancer stem cells. Nat Rev Cancer, 2009. 9(2): p. 143.
220. Brischwein, K., B. Schlereth, B. Guller, C. Steiger, A. Wolf, R. Lutterbuese, S. Offner, M. Locher, T. Urbig, T. Raum, P. Kleindienst, P. Wimberger, R. Kimmig, I. Fichtner, P. Kufer, R. Hofmeister, A.J. da Silva, and P.A. Baeuerle, MT110: a novel bispecific single-chain antibody construct with high efficacy in eradicating established tumors. Mol Immunol, 2006. 43(8): p. 1129-43.
221. Schlereth, B., I. Fichtner, G. Lorenczewski, P. Kleindienst, K. Brischwein, A. da Silva, P. Kufer, R. Lutterbuese, I. Junghahn, S. Kasimir-Bauer, P. Wimberger, R. Kimmig, and P.A. Baeuerle, Eradication of tumors from a human colon cancer cell line and from ovarian cancer metastases in immunodeficient mice by a single-chain Ep-CAM- /CD3-bispecific antibody construct. Cancer Res, 2005. 65(7): p. 2882-9.
222. Schlereth, B., P. Kleindienst, I. Fichtner, G. Lorenczewski, K. Brischwein, S. Lippold, A. da Silva, M. Locher, R. Kischel, R. Lutterbuse, P. Kufer, and P.A. Baeuerle, Potent inhibition of local and disseminated tumor growth in immunocompetent mouse models by a bispecific antibody construct specific for Murine CD3. Cancer Immunol Immunother, 2006. 55(7): p. 785-96.
144 223. Cioffi, M., J. Dorado, P.A. Baeuerle, and C. Heeschen, EpCAM/CD3-Bispecific T-cell engaging antibody MT110 eliminates primary human pancreatic cancer stem cells. Clin Cancer Res, 2012. 18(2): p. 465-74.
224. Singh, B.N., J. Fu, R.K. Srivastava, and S. Shankar, Hedgehog signaling antagonist GDC-0449 (Vismodegib) inhibits pancreatic cancer stem cell characteristics: molecular mechanisms. PLoS One, 2011. 6(11): p. e27306.
225. Cirrone, F. and C.S. Harris, Vismodegib and the hedgehog pathway: a new treatment for basal cell carcinoma. Clin Ther, 2012. 34(10): p. 2039-50.
226. Dijkgraaf, G.J., B. Alicke, L. Weinmann, T. Januario, K. West, Z. Modrusan, D. Burdick, R. Goldsmith, K. Robarge, D. Sutherlin, S.J. Scales, S.E. Gould, R.L. Yauch, and F.J. de Sauvage, Small molecule inhibition of GDC-0449 refractory smoothened mutants and downstream mechanisms of drug resistance. Cancer Res, 2011. 71(2): p. 435-44.
227. Lu, D., M.Y. Choi, J. Yu, J.E. Castro, T.J. Kipps, and D.A. Carson, Salinomycin inhibits Wnt signaling and selectively induces apoptosis in chronic lymphocytic leukemia cells. Proc Natl Acad Sci U S A, 2011. 108(32): p. 13253-7.
228. Gangopadhyay, S., A. Nandy, P. Hor, and A. Mukhopadhyay, Breast cancer stem cells: a novel therapeutic target. Clin Breast Cancer, 2013. 13(1): p. 7-15.
229. Iliopoulos, D., H.A. Hirsch, and K. Struhl, Metformin decreases the dose of chemotherapy for prolonging tumor remission in mouse xenografts involving multiple cancer cell types. Cancer Res, 2011. 71(9): p. 3196-201.
230. Finn, R.S., J. Dering, C. Ginther, C.A. Wilson, P. Glaspy, N. Tchekmedyian, and D.J. Slamon, Dasatinib, an orally active small molecule inhibitor of both the src and abl kinases, selectively inhibits growth of basal-type/"triple-negative" breast cancer cell lines growing in vitro. Breast Cancer Res Treat, 2007. 105(3): p. 319-26.
231. Smith, K.M., A. Datti, M. Fujitani, N. Grinshtein, L. Zhang, O. Morozova, K.M. Blakely, S.A. Rotenberg, L.M. Hansford, F.D. Miller, H. Yeger, M.S. Irwin, J. Moffat, M.A. Marra, S. Baruchel, J.L. Wrana, and D.R. Kaplan, Selective targeting of neuroblastoma tumour-initiating cells by compounds identified in stem cell-based small molecule screens. EMBO Mol Med, 2010. 2(9): p. 371-84.
232. Sachlos, E., R.M. Risueno, S. Laronde, Z. Shapovalova, J.H. Lee, J. Russell, M. Malig, J.D. McNicol, A. Fiebig-Comyn, M. Graham, M. Levadoux-Martin, J.B. Lee, A.O. Giacomelli, J.A. Hassell, D. Fischer-Russell, M.R. Trus, R. Foley, B. Leber, A. Xenocostas, E.D. Brown, T.J. Collins, and M. Bhatia, Identification of drugs including a dopamine receptor antagonist that selectively target cancer stem cells. Cell, 2012. 149(6): p. 1284-97.
233. Bhat-Nakshatri, P., C.P. Goswami, S. Badve, G.W. Sledge, Jr., and H. Nakshatri, Identification of FDA-approved drugs targeting breast cancer stem cells along with biomarkers of sensitivity. Sci Rep, 2013. 3: p. 2530.
234. Li, Y., T. Zhang, H. Korkaya, S. Liu, H.F. Lee, B. Newman, Y. Yu, S.G. Clouthier, S.J. Schwartz, M.S. Wicha, and D. Sun, Sulforaphane, a dietary component of broccoli/broccoli sprouts, inhibits breast cancer stem cells. Clin Cancer Res, 2010. 16(9): p. 2580-90.
145 235. Wang, Z., Y. Zhang, Y. Li, S. Banerjee, J. Liao, and F.H. Sarkar, Down-regulation of Notch-1 contributes to cell growth inhibition and apoptosis in pancreatic cancer cells. Mol Cancer Ther, 2006. 5(3): p. 483-93.
236. Montales, M.T., O.M. Rahal, J. Kang, T.J. Rogers, R.L. Prior, X. Wu, and R.C. Simmen, Repression of mammosphere formation of human breast cancer cells by soy isoflavone genistein and blueberry polyphenolic acids suggests diet-mediated targeting of cancer stem-like/progenitor cells. Carcinogenesis, 2012. 33(3): p. 652-60.
237. Montales, M.T., O.M. Rahal, H. Nakatani, T. Matsuda, and R.C. Simmen, Repression of mammary adipogenesis by genistein limits mammosphere formation of human MCF-7 cells. J Endocrinol, 2013. 218(1): p. 135-49.
238. Niu, J., Z.F. Pi, H. Yue, H. Yang, Y. Wang, Q. Yu, and S.Y. Liu, Effect of 20(S)- ginsenoside Rg3 on streptozotocin-induced experimental type 2 diabetic rats: a urinary metabonomics study by rapid-resolution liquid chromatography/mass spectrometry. Rapid Commun Mass Spectrom, 2012. 26(23): p. 2683-9.
239. Yang, L.Q., B. Wang, H. Gan, S.T. Fu, X.X. Zhu, Z.N. Wu, D.W. Zhan, R.L. Gu, G.F. Dou, and Z.Y. Meng, Enhanced oral bioavailability and anti-tumour effect of paclitaxel by 20(s)-ginsenoside Rg3 in vivo. Biopharm Drug Dispos, 2012. 33(8): p. 425-36.
240. Guzman, M.L., R.M. Rossi, L. Karnischky, X. Li, D.R. Peterson, D.S. Howard, and C.T. Jordan, The sesquiterpene lactone parthenolide induces apoptosis of human acute myelogenous leukemia stem and progenitor cells. Blood, 2005. 105(11): p. 4163-9.
241. Guo, M., M. Wang, X. Zhang, H. Deng, and Z.Y. Wang, Broussoflavonol B restricts growth of ER-negative breast cancer stem-like cells. Anticancer Res, 2013. 33(5): p. 1873-9.
242. Mukherjee, S., M. Mazumdar, S. Chakraborty, A. Manna, S. Saha, P. Khan, P. Bhattacharjee, D. Guha, A. Adhikary, S. Mukhjerjee, and T. Das, Curcumin inhibits breast cancer stem cell migration by amplifying the E-cadherin/beta-catenin negative feedback loop. Stem Cell Res Ther, 2014. 5(5): p. 116.
243. Kakarala, M., D.E. Brenner, H. Korkaya, C. Cheng, K. Tazi, C. Ginestier, S. Liu, G. Dontu, and M.S. Wicha, Targeting breast stem cells with the cancer preventive compounds curcumin and piperine. Breast Cancer Res Treat, 2010. 122(3): p. 777- 85.
244. Chendil, D., R.S. Ranga, D. Meigooni, S. Sathishkumar, and M.M. Ahmed, Curcumin confers radiosensitizing effect in prostate cancer cell line PC-3. Oncogene, 2004. 23(8): p. 1599-607.
245. Pandey, P.R., H. Okuda, M. Watabe, S.K. Pai, W. Liu, A. Kobayashi, F. Xing, K. Fukuda, S. Hirota, T. Sugai, G. Wakabayashi, K. Koeda, M. Kashiwaba, K. Suzuki, T. Chiba, M. Endo, T. Fujioka, S. Tanji, Y.Y. Mo, D. Cao, A.C. Wilber, and K. Watabe, Resveratrol suppresses growth of cancer stem-like cells by inhibiting fatty acid synthase. Breast Cancer Res Treat, 2011. 130(2): p. 387-98.
246. Zhang, F.L., P. Wang, Y.H. Liu, L.B. Liu, X.B. Liu, Z. Li, and Y.X. Xue, Topoisomerase I inhibitors, shikonin and topotecan, inhibit growth and induce apoptosis of glioma cells and glioma stem cells. PLoS One, 2013. 8(11): p. e81815.
146 247. Wang, L., H. Guo, L. Yang, L. Dong, C. Lin, J. Zhang, P. Lin, and X. Wang, Morusin inhibits human cervical cancer stem cell growth and migration through attenuation of NF-kappaB activity and apoptosis induction. Mol Cell Biochem, 2013. 379(1-2): p. 7- 18.
248. Kim, Y.S., W. Farrar, N.H. Colburn, and J.A. Milner, Cancer stem cells: potential target for bioactive food components. J Nutr Biochem, 2012. 23(7): p. 691-8.
249. Chang, A.E., Q. Li, G.H. Jiang, D.M. Sayre, T.M. Braun, and B.G. Redman, Phase II trial of autologous tumor vaccination, anti-CD3-activated vaccine-primed lymphocytes, and interleukin-2 in stage IV renal cell cancer. Journal of Clinical Oncology, 2003. 21(5): p. 884-90.
250. Prieto, P.A., K.H. Durflinger, J.R. Wunderlich, S.A. Rosenberg, and M.E. Dudley, Enrichment of CD8(+) Cells From Melanoma Tumor-infiltrating Lymphocyte Cultures Reveals Tumor Reactivity for Use in Adoptive Cell Therapy. J Immunother, 2010. 33(5): p. 547-56.
251. Foster, A.E., F.V. Okur, E. Biagi, A. Lu, G. Dotti, E. Yvon, B. Savoldo, G. Carrum, M.A. Goodell, H.E. Heslop, and M.K. Brenner, Selective elimination of a chemoresistant side population of B-CLL cells by cytotoxic T lymphocytes in subjects receiving an autologous hCD40L/IL-2 tumor vaccine. Leukemia, 2010. 24(3): p. 563- 72.
252. Redman, B.G., A.E. Chang, J. Whitfield, P. Esper, G. Jiang, T. Braun, B. Roessler, and J.J. Mule, Phase Ib trial assessing autologous, tumor-pulsed dendritic cells as a vaccine administered with or without IL-2 in patients with metastatic melanoma. J Immunother, 2008. 31(6): p. 591-8.
253. Chang, A.E., B.G. Redman, J.R. Whitfield, B.J. Nickoloff, T.M. Braun, P.P. Lee, J.D. Geiger, and J.J. Mule, A phase I trial of tumor lysate-pulsed dendritic cells in the treatment of advanced cancer. Clinical Cancer Research, 2002. 8(4): p. 1021-32.
254. Pellegatta, S. and G. Finocchiaro, Dendritic cell vaccines for cancer stem cells. Methods Mol Biol, 2009. 568: p. 233-47.
255. Ning, N., Q. Pan, F. Zheng, S. Teitz-Tennenbaum, M. Egenti, C. Ginestier, M. Wicha, J. Moyer, M. Prince, A.R. Chang, and Q. Li, Cancer Stem Cell Vaccination Confers Significant Anti-Tumor Immunity by Selectively Targeting Cancer Stem Cells. J Clin Immunol, 2012. 32(2): p. 358.
256. Lonardo, E., P.C. Hermann, M.T. Mueller, S. Huber, A. Balic, I. Miranda-Lorenzo, S. Zagorac, S. Alcala, I. Rodriguez-Arabaolaza, J.C. Ramirez, R. Torres-Ruiz, E. Garcia, M. Hidalgo, D.A. Cebrian, R. Heuchel, M. Lohr, F. Berger, P. Bartenstein, A. Aicher, and C. Heeschen, Nodal/Activin signaling drives self-renewal and tumorigenicity of pancreatic cancer stem cells and provides a target for combined drug therapy. Cell Stem Cell, 2011. 9(5): p. 433-46.
257. Persano, L., F. Pistollato, E. Rampazzo, A. Della Puppa, S. Abbadi, C. Frasson, F. Volpin, S. Indraccolo, R. Scienza, and G. Basso, BMP2 sensitizes glioblastoma stem- like cells to Temozolomide by affecting HIF-1alpha stability and MGMT expression. Cell Death Dis, 2012. 3: p. e412.
147 258. Lombardo, Y., A. Scopelliti, P. Cammareri, M. Todaro, F. Iovino, L. Ricci-Vitiani, G. Gulotta, F. Dieli, R. de Maria, and G. Stassi, Bone morphogenetic protein 4 induces differentiation of colorectal cancer stem cells and increases their response to chemotherapy in mice. Gastroenterology, 2011. 140(1): p. 297-309.
259. Saito, Y., N. Uchida, S. Tanaka, N. Suzuki, M. Tomizawa-Murasawa, A. Sone, Y. Najima, S. Takagi, Y. Aoki, A. Wake, S. Taniguchi, L.D. Shultz, and F. Ishikawa, Induction of cell cycle entry eliminates human leukemia stem cells in a mouse model of AML. Nat Biotechnol, 2010. 28(3): p. 275-80.
260. Korkaya, H., G.I. Kim, A. Davis, F. Malik, N.L. Henry, S. Ithimakin, A.A. Quraishi, N. Tawakkol, R. D'Angelo, A.K. Paulson, S. Chung, T. Luther, H.J. Paholak, S. Liu, K.A. Hassan, Q. Zen, S.G. Clouthier, and M.S. Wicha, Activation of an IL6 inflammatory loop mediates trastuzumab resistance in HER2+ breast cancer by expanding the cancer stem cell population. Mol Cell, 2012. 47(4): p. 570-84.
261. Ginestier, C., S. Liu, M.E. Diebel, H. Korkaya, M. Luo, M. Brown, J. Wicinski, O. Cabaud, E. Charafe-Jauffret, D. Birnbaum, J.L. Guan, G. Dontu, and M.S. Wicha, CXCR1 blockade selectively targets human breast cancer stem cells in vitro and in xenografts. J Clin Invest, 2010. 120(2): p. 485-97.
262. Naka, K., T. Hoshii, T. Muraguchi, Y. Tadokoro, T. Ooshio, Y. Kondo, S. Nakao, N. Motoyama, and A. Hirao, TGF-beta-FOXO signalling maintains leukaemia-initiating cells in chronic myeloid leukaemia. Nature, 2010. 463(7281): p. 676-80.
263. Vazquez-Martin, A., C. Oliveras-Ferraros, S. Del Barco, B. Martin-Castillo, and J.A. Menendez, The anti-diabetic drug metformin suppresses self-renewal and proliferation of trastuzumab-resistant tumor-initiating breast cancer stem cells. Breast Cancer Res Treat, 2011. 126(2): p. 355-64.
264. Oak, P.S., F. Kopp, C. Thakur, J.W. Ellwart, U.R. Rapp, A. Ullrich, E. Wagner, P. Knyazev, and A. Roidl, Combinatorial treatment of mammospheres with trastuzumab and salinomycin efficiently targets HER2-positive cancer cells and cancer stem cells. Int J Cancer, 2012. 131(12): p. 2808-19.
265. Zhang, B., A.C. Strauss, S. Chu, M. Li, Y. Ho, K.D. Shiang, D.S. Snyder, C.S. Huettner, L. Shultz, T. Holyoake, and R. Bhatia, Effective targeting of quiescent chronic myelogenous leukemia stem cells by histone deacetylase inhibitors in combination with imatinib mesylate. Cancer Cell, 2010. 17(5): p. 427-42.
266. Li, L., L. Wang, Z. Wang, Y. Ho, T. McDonald, T.L. Holyoake, W. Chen, and R. Bhatia, Activation of p53 by SIRT1 inhibition enhances elimination of CML leukemia stem cells in combination with imatinib. Cancer Cell, 2012. 21(2): p. 266-81.
267. Visnyei, K., H. Onodera, R. Damoiseaux, K. Saigusa, S. Petrosyan, D. De Vries, D. Ferrari, J. Saxe, E.H. Panosyan, M. Masterman-Smith, J. Mottahedeh, K.A. Bradley, J. Huang, C. Sabatti, I. Nakano, and H.I. Kornblum, A molecular screening approach to identify and characterize inhibitors of glioblastoma stem cells. Mol Cancer Ther, 2011. 10(10): p. 1818-28.
268. Mathews, L.A., J.M. Keller, B.L. Goodwin, R. Guha, P. Shinn, R. Mull, C.J. Thomas, R.L. de Kluyver, T.J. Sayers, and M. Ferrer, A 1536-well quantitative high-throughput screen to identify compounds targeting cancer stem cells. J Biomol Screen, 2012. 17(9): p. 1231-42.
148 269. Ivnitski-Steele, I., R.S. Larson, D.M. Lovato, H.M. Khawaja, S.S. Winter, T.I. Oprea, L.A. Sklar, and B.S. Edwards, High-throughput flow cytometry to detect selective inhibitors of ABCB1, ABCC1, and ABCG2 transporters. Assay Drug Dev Technol, 2008. 6(2): p. 263-76.
270. Falk, A., T.E. Karlsson, S. Kurdija, J. Frisen, and J. Zupicich, High-throughput identification of genes promoting neuron formation and lineage choice in mouse embryonic stem cells. Stem Cells, 2007. 25(6): p. 1539-45.
271. Sun, Y., S. Pollard, L. Conti, M. Toselli, G. Biella, G. Parkin, L. Willatt, A. Falk, E. Cattaneo, and A. Smith, Long-term tripotent differentiation capacity of human neural stem (NS) cells in adherent culture. Mol Cell Neurosci, 2008. 38(2): p. 245-58.
272. Conti, L., S.M. Pollard, T. Gorba, E. Reitano, M. Toselli, G. Biella, Y. Sun, S. Sanzone, Q.L. Ying, E. Cattaneo, and A. Smith, Niche-independent symmetrical self- renewal of a mammalian tissue stem cell. PLoS Biol, 2005. 3(9): p. e283.
273. Pollard, S.M., K. Yoshikawa, I.D. Clarke, D. Danovi, S. Stricker, R. Russell, J. Bayani, R. Head, M. Lee, M. Bernstein, J.A. Squire, A. Smith, and P. Dirks, Glioma stem cell lines expanded in adherent culture have tumor-specific phenotypes and are suitable for chemical and genetic screens. Cell Stem Cell, 2009. 4(6): p. 568-80.
274. Dirks, P.B., Glioma migration: clues from the biology of neural progenitor cells and embryonic CNS cell migration. J Neurooncol, 2001. 53(2): p. 203-12.
275. Ravin, R., D.J. Hoeppner, D.M. Munno, L. Carmel, J. Sullivan, D.L. Levitt, J.L. Miller, C. Athaide, D.M. Panchision, and R.D. McKay, Potency and fate specification in CNS stem cell populations in vitro. Cell Stem Cell, 2008. 3(6): p. 670-80.
276. Shimosato, Y., T. Kameya, K. Nagai, S. Hirohashi, T. Koide, H. Hayashi, and T. Nomura, Transplantation of human tumors in nude mice. J Natl Cancer Inst, 1976. 56(6): p. 1251-60.
277. Jimeno, A., G. Feldmann, A. Suarez-Gauthier, Z. Rasheed, A. Solomon, G.M. Zou, B. Rubio-Viqueira, E. Garcia-Garcia, F. Lopez-Rios, W. Matsui, A. Maitra, and M. Hidalgo, A direct pancreatic cancer xenograft model as a platform for cancer stem cell therapeutic development. Mol Cancer Ther, 2009. 8(2): p. 310-4.
278. Zhang, B., Y. Shimada, J. Kuroyanagi, Y. Nishimura, N. Umemoto, T. Nomoto, T. Shintou, T. Miyazaki, and T. Tanaka, Zebrafish xenotransplantation model for cancer stem-like cell study and high-throughput screening of inhibitors. Tumour Biol, 2014. 35(12): p. 11861-9.
279. Zhang, B., Y. Shimada, J. Kuroyanagi, N. Umemoto, Y. Nishimura, and T. Tanaka, Quantitative phenotyping-based in vivo chemical screening in a zebrafish model of leukemia stem cell xenotransplantation. PLoS One, 2014. 9(1): p. e85439.
280. Zhao, H., C. Tang, K. Cui, B.T. Ang, and S.T. Wong, A screening platform for glioma growth and invasion using bioluminescence imaging. Laboratory investigation. J Neurosurg, 2009. 111(2): p. 238-46.
281. Sugihara, E. and H. Saya, Complexity of cancer stem cells. Int J Cancer, 2013. 132(6): p. 1249-59.
149 282. Diehn, M. and M.F. Clarke, Cancer stem cells and radiotherapy: new insights into tumor radioresistance. J Natl Cancer Inst, 2006. 98(24): p. 1755-7.
283. Dean, M., T. Fojo, and S. Bates, Tumour stem cells and drug resistance. Nat Rev Cancer, 2005. 5(4): p. 275-84.
284. Colak, S. and J.P. Medema, Cancer stem cells - important players in tumor therapy resistance. Febs J, 2014. 281(21): p. 4779-91.
285. Reya, T., S.J. Morrison, M.F. Clarke, and I.L. Weissman, Stem cells, cancer, and cancer stem cells. Nature, 2001. 414(6859): p. 105-11.
286. Visvader, J.E. and G.J. Lindeman, Cancer stem cells in solid tumours: accumulating evidence and unresolved questions. Nat Rev Cancer, 2008. 8(10): p. 755-68.
287. Malanchi, I., A. Santamaria-Martinez, E. Susanto, H. Peng, H.A. Lehr, J.F. Delaloye, and J. Huelsken, Interactions between cancer stem cells and their niche govern metastatic colonization. Nature, 2012. 481(7379): p. 85-9.
288. Karnoub, A.E., A.B. Dash, A.P. Vo, A. Sullivan, M.W. Brooks, G.W. Bell, A.L. Richardson, K. Polyak, R. Tubo, and R.A. Weinberg, Mesenchymal stem cells within tumour stroma promote breast cancer metastasis. Nature, 2007. 449(7162): p. 557- 63.
289. Dontu, G., ALDH1 is a marker of normal and cancer breast stem cells and a predictor of poor clinical outcome. Ejc Suppl, 2007. 5(4): p. 11.
290. Moore, N. and S. Lyle, Quiescent, slow-cycling stem cell populations in cancer: a review of the evidence and discussion of significance. J Oncol, 2011. 2011.
291. de Beca, F.F., P. Caetano, R. Gerhard, C.A. Alvarenga, M. Gomes, J. Paredes, and F. Schmitt, Cancer stem cells markers CD44, CD24 and ALDH1 in breast cancer special histological types. J Clin Pathol, 2013. 66(3): p. 187-91.
292. Marx, C., C. Berger, F. Xu, C. Amend, G.K. Scott, B. Hann, J.W. Park, and C.C. Benz, Validated high-throughput screening of drug-like small molecules for inhibitors of ErbB2 transcription. Assay Drug Dev Technol, 2006. 4(3): p. 273-84.
293. Sun, M., W. Lou, J.Y. Chun, D.S. Cho, N. Nadiminty, C.P. Evans, J. Chen, J. Yue, Q. Zhou, and A.C. Gao, Sanguinarine suppresses prostate tumor growth and inhibits survivin expression. Genes Cancer, 2010. 1(3): p. 283-92.
294. Charpentier, M.S., R.A. Whipple, M.I. Vitolo, A.E. Boggs, J. Slovic, K.N. Thompson, L. Bhandary, and S.S. Martin, Curcumin targets breast cancer stem-like cells with microtentacles that persist in mammospheres and promote reattachment. Cancer Res, 2014. 74(4): p. 1250-60.
295. Ginestier, C., J. Wicinski, N. Cervera, F. Monville, P. Finetti, F. Bertucci, M.S. Wicha, D. Birnbaum, and E. Charafe-Jauffret, Retinoid signaling regulates breast cancer stem cell differentiation. Cell Cycle, 2009. 8(20): p. 3297-302.
150 296. Mineva, N.D., K.E. Paulson, S.P. Naber, A.S. Yee, and G.E. Sonenshein, Epigallocatechin-3-gallate inhibits stem-like inflammatory breast cancer cells. PLoS One, 2013. 8(9): p. e73464.
297. Li, Y., M.S. Wicha, S.J. Schwartz, and D. Sun, Implications of cancer stem cell theory for cancer chemoprevention by natural dietary compounds. J Nutr Biochem, 2011. 22(9): p. 799-806.
298. Hennessy, B.T., A.M. Gonzalez-Angulo, K. Stemke-Hale, M.Z. Gilcrease, S. Krishnamurthy, J.S. Lee, J. Fridlyand, A. Sahin, R. Agarwal, C. Joy, W. Liu, D. Stivers, K. Baggerly, M. Carey, A. Lluch, C. Monteagudo, X. He, V. Weigman, C. Fan, J. Palazzo, G.N. Hortobagyi, L.K. Nolden, N.J. Wang, V. Valero, J.W. Gray, C.M. Perou, and G.B. Mills, Characterization of a naturally occurring breast cancer subset enriched in epithelial-to-mesenchymal transition and stem cell characteristics. Cancer Res, 2009. 69(10): p. 4116-24.
299. Marcato, P., C.A. Dean, D. Pan, R. Araslanova, M. Gillis, M. Joshi, L. Helyer, L. Pan, A. Leidal, S. Gujar, C.A. Giacomantonio, and P.W. Lee, Aldehyde dehydrogenase activity of breast cancer stem cells is primarily due to isoform ALDH1A3 and its expression is predictive of metastasis. Stem Cells, 2011. 29(1): p. 32-45.
300. Eshleman, A.J., R.A. Henningsen, K.A. Neve, and A. Janowsky, Release of dopamine via the human transporter. Mol Pharmacol, 1994. 45(2): p. 312-6.
301. McKearney, J.W., Stimulantactions of histamineH1 antagonists on operant behavior in the squirrel monkey. Psychopharmacol, 1982. 77: p. 156–8.
302. Gregory E. Agoston, J.H.W., Sari Izenwasser, Clifford George,† Jonathan Katz, Richard H. Kline, Amy Hauck Newman, Novel N-Substituted 3r-[Bis(4′- fluorophenyl)methoxy]tropane Analogues- Selective Ligands for the Dopamine Transporter. J. Med. Chem., 1997. 40: p. 4329-39.
303. Deshmukh, V.A., V. Tardif, C.A. Lyssiotis, C.C. Green, B. Kerman, H.J. Kim, K. Padmanabhan, J.G. Swoboda, I. Ahmad, T. Kondo, F.H. Gage, A.N. Theofilopoulos, B.R. Lawson, P.G. Schultz, and L.L. Lairson, A regenerative approach to the treatment of multiple sclerosis. Nature, 2013. 502(7471): p. 327-32.
304. Seeman, P. and T. Tallerico, Antipsychotic drugs which elicit little or no parkinsonism bind more loosely than dopamine to brain D2 receptors, yet occupy high levels of these receptors. Mol Psychiatry, 1998. 3(2): p. 123-34.
305. Velasco-Velazquez, M. and R.G. Pestell, The CCL5/CCR5 axis promotes metastasis in basal breast cancer. Oncoimmunology, 2013. 2(4): p. e23660.
306. Zhang, Y., F. Yao, X. Yao, C. Yi, C. Tan, L. Wei, and S. Sun, Role of CCL5 in invasion, proliferation and proportion of CD44+/CD24- phenotype of MCF-7 cells and correlation of CCL5 and CCR5 expression with breast cancer progression. Oncol Rep, 2009. 21(4): p. 1113-21.
307. Heppner, G.H., Tumor heterogeneity. Cancer Res, 1984. 44(6): p. 2259-65.
308. Hamburger, A.W. and S.E. Salmon, Primary bioassay of human tumor stem cells. Science, 1977. 197(4302): p. 461-3.
151 309. Campbell, L.L. and K. Polyak, Breast tumor heterogeneity: cancer stem cells or clonal evolution? Cell Cycle, 2007. 6(19): p. 2332-8.
310. Battula, V.L., Y. Shi, K.W. Evans, R.Y. Wang, E.L. Spaeth, R.O. Jacamo, R. Guerra, A.A. Sahin, F.C. Marini, G. Hortobagyi, S.A. Mani, and M. Andreeff, Ganglioside GD2 identifies breast cancer stem cells and promotes tumorigenesis. J Clin Invest, 2012. 122(6): p. 2066-78.
311. Changeux, J.P., J. Giraudat, and M. Dennis, The Nicotinic Acetylcholine-Receptor - Molecular Architecture of a Ligand-Regulated Ion Channel. Trends Pharmacol Sci, 1987. 8(12): p. 459-65.
312. Brejc, K., W.J. van Dijk, R.V. Klaassen, M. Schuurmans, J. van der Oost, A.B. Smit, and T.K. Sixma, Crystal structure of an ACh-binding protein reveals the ligand- binding domain of nicotinic receptors. Nature, 2001. 411(6835): p. 269-76.
313. Catassi, A., D. Servent, L. Paleari, A. Cesario, and P. Russo, Multiple roles of nicotine on cell proliferation and inhibition of apoptosis: Implications on lung carcinogenesis. Mutat Res-Rev Mutat, 2008. 659(3): p. 221-31.
314. Egleton, R.D., K.C. Brown, and P. Dasgupta, Nicotinic acetylcholine receptors in cancer: multiple roles in proliferation and inhibition of apoptosis. Trends Pharmacol Sci, 2008. 29(3): p. 151-8.
315. Guha, P., G. Bandyopadhyaya, S.K. Polumuri, S. Chumsri, P. Gade, D.V. Kalvakolanu, and H. Ahmed, Nicotine promotes apoptosis resistance of breast cancer cells and enrichment of side population cells with cancer stem cell-like properties via a signaling cascade involving galectin-3, alpha9 nicotinic acetylcholine receptor and STAT3. Breast Cancer Res Treat, 2014. 145(1): p. 5-22.
316. Lee, C.H., Y.C. Chang, C.S. Chen, S.H. Tu, Y.J. Wang, L.C. Chen, Y.J. Chang, P.L. Wei, H.W. Chang, C.H. Chang, C.S. Huang, C.H. Wu, and Y.S. Ho, Crosstalk between nicotine and estrogen-induced estrogen receptor activation induces alpha9- nicotinic acetylcholine receptor expression in human breast cancer cells. Breast Cancer Res Treat, 2011. 129(2): p. 331-45.
317. Lee, C.H., C.S. Huang, C.S. Chen, S.H. Tu, Y.J. Wang, Y.J. Chang, K.W. Tam, P.L. Wei, T.C. Cheng, J.S. Chu, L.C. Chen, C.H. Wu, and Y.S. Ho, Overexpression and Activation of the alpha 9-Nicotinic Receptor During Tumorigenesis in Human Breast Epithelial Cells. J Natl Cancer Inst, 2010. 102(17): p. 1322-35.
318. Dasgupta, P., W. Rizwani, S. Pillai, R. Kinkade, M. Kovacs, S. Rastogi, S. Banerjee, M. Carless, E. Kim, D. Coppola, E. Haura, and S. Chellappan, Nicotine induces cell proliferation, invasion and epithelial-mesenchymal transition in a variety of human cancer cell lines. International Journal of Cancer, 2009. 124(1): p. 36-45.
319. Wei, P.L., Y.J. Chang, Y.S. Ho, C.H. Lee, Y.Y. Yang, J. An, and S.Y. Lin, Tobacco- Specific Carcinogen Enhances Colon Cancer Cell Migration Through Alpha7- Nicotinic Acetylcholine Receptor (vol 249, pg 978, 2009). Ann Surg, 2009. 250(6): p. 978-85.
320. Schaal, C. and S.P. Chellappan, Nicotine-Mediated Cell Proliferation and Tumor Progression in Smoking-Related Cancers. Mol Cancer Res, 2014. 12(1): p. 14-23.
152 321. Lien, Y.C., W. Wang, L.J. Kuo, J.J. Liu, P.L. Wei, Y.S. Ho, W.C. Ting, C.H. Wu, and Y.J. Chang, Nicotine Promotes Cell Migration Through Alpha7 Nicotinic Acetylcholine Receptor in Gastric Cancer Cells. Ann Surg Oncol, 2011. 18(9): p. 2671-9.
322. Momi, N., M.P. Ponnusamy, S. Kaur, S. Rachagani, S.S. Kunigal, S. Chellappan, M.M. Ouellette, and S.K. Batra, Nicotine/cigarette smoke promotes metastasis of pancreatic cancer through alpha 7nAChR-mediated MUC4 upregulation. Oncogene, 2013. 32(11): p. 1384-95.
323. Pillai, S. and S. Chellappan, alpha 7 Nicotinic Acetylcholine Receptor Subunit in Angiogenesis and Epithelial to Mesenchymal Transition. Curr Drug Targets, 2012. 13(5): p. 671-9.
324. Pillai, S., W. Rizwani, X.L. Li, B. Rawal, S. Nair, M.J. Schell, G. Bepler, E. Haura, D. Coppola, and S. Chellappan, ID1 Facilitates the Growth and Metastasis of Non-Small Cell Lung Cancer in Response to Nicotinic Acetylcholine Receptor and Epidermal Growth Factor Receptor Signaling. Mol Cell Biol, 2011. 31(14): p. 3052-67.
325. Schuller, H.M., Regulatory Role of the alpha 7nAChR in Cancer. Curr Drug Targets, 2012. 13(5): p. 680-7.
326. Wei, P.L., L.J. Kuo, M.T. Huang, W.C. Ting, Y.S. Ho, W. Wang, J. An, and Y.J. Chang, Nicotine Enhances Colon Cancer Cell Migration by Induction of Fibronectin. Ann Surg Oncol, 2011. 18(6): p. 1782-90.
327. Zhang, Q.Z., X.D. Tang, Z.F. Zhang, R. Velikina, S. Shi, and A.D. Le, Nicotine induces hypoxia-inducible factor-1 alpha expression in human lung cancer cells via nicotinic acetylcholine receptor-mediated signaling pathways. Clinical Cancer Research, 2007. 13(16): p. 4686-94.
328. Rothlin, C.V., E. Katz, M. Verbitsky, and A.B. Elgoyhen, The alpha9 nicotinic acetylcholine receptor shares pharmacological properties with type A gamma- aminobutyric acid, glycine, and type 3 serotonin receptors. Mol Pharmacol, 1999. 55(2): p. 248-54.
329. Elgoyhen, A.B., D.S. Johnson, J. Boulter, D.E. Vetter, and S. Heinemann, Alpha 9: an acetylcholine receptor with novel pharmacological properties expressed in rat cochlear hair cells. Cell, 1994. 79(4): p. 705-15.
330. Morley, B.J., H.S. Li, H. Hiel, D.G. Drescher, and A.B. Elgoyhen, Identification of the subunits of the nicotinic cholinergic receptors in the rat cochlea using RT-PCR and in situ hybridization. Brain Res Mol Brain Res, 1998. 53(1-2): p. 78-87.
331. Matera, C. and A.M. Tata, Pharmacological approaches to targeting muscarinic acetylcholine receptors. Recent Pat CNS Drug Discov, 2014. 9(2): p. 85-100.
332. Shah, N., S. Khurana, K. Cheng, and J.P. Raufman, Muscarinic receptors and ligands in cancer. Am J Physiol Cell Physiol, 2009. 296(2): p. C221-32.
333. Wessler, I., C.J. Kirkpatrick, and K. Racke, Non-neuronal acetylcholine, a locally acting molecule, widely distributed in biological systems: expression and function in humans. Pharmacol Ther, 1998. 77(1): p. 59-79.
153 334. Arias, H.R., Positive and negative modulation of nicotinic receptors. Adv Protein Chem Struct Biol, 2010. 80: p. 153-203.
335. Livett, B.G., D.W. Sandall, D. Keays, J. Down, K.R. Gayler, N. Satkunanathan, and Z. Khalil, Therapeutic applications of conotoxins that target the neuronal nicotinic acetylcholine receptor. Toxicon, 2006. 48(7): p. 810-29.
336. Wu, C.H., C.H. Lee, and Y.S. Ho, Nicotinic acetylcholine receptor-based blockade: applications of molecular targets for cancer therapy. Clin Cancer Res, 2011. 17(11): p. 3533-41.
337. Barber, P.V., S.S. Chatterjee, and R. Scott, Comparison of Ipratropium Bromide, Deptropine Citrate and Placebo in Asthma and Chronic-Bronchitis. Brit J Dis Chest, 1977. 71(2): p. 101-4.
338. Leckie, W.J.H. and N.W. Horne, Preliminary Assessment of Deptropine Dihydrogen Citrate in Chronic Airways Obstruction. Thorax, 1965. 20(4): p. 317-23.
339. Brocks, D.R., Anticholinergic drugs used in Parkinson's disease: An overlooked class of drugs from a pharmacokinetic perspective. J Pharm Pharm Sci, 1999. 2(2): p. 39- 46.
340. Hung, C.-S., Y.-J. Peng, P.-L. Wei, C.-H. Lee, H.-Y. Su, Y.-S. Ho, S.-Y. Lin, C.-H. Wu, and Y.-J. Chang, The alpha9 Nicotinic Acetylcholine Receptor is the Key Mediator in Nicotine-enhanced Cancer Metastasis in Breast Cancer Cells. Journal of Experimental & Clinical Medicine, 2011. 3(6): p. 283-92.
341. Szotek, P.P., R. Pieretti-Vanmarcke, P.T. Masiakos, D.M. Dinulescu, D. Connolly, R. Foster, D. Dombkowski, F. Preffer, D.T. Maclaughlin, and P.K. Donahoe, Ovarian cancer side population defines cells with stem cell-like characteristics and Mullerian Inhibiting Substance responsiveness. Proc Natl Acad Sci U S A, 2006. 103(30): p. 11154-9.
342. O'Brien, C.A., A. Pollett, S. Gallinger, and J.E. Dick, A human colon cancer cell capable of initiating tumour growth in immunodeficient mice. Nature, 2007. 445(7123): p. 106-10.
343. Li, C., C.J. Lee, and D.M. Simeone, Identification of human pancreatic cancer stem cells. Methods Mol Biol, 2009. 568: p. 161-73.
344. Lee, N., S.R. Barthel, and T. Schatton, Melanoma stem cells and metastasis: mimicking hematopoietic cell trafficking? Lab Invest, 2014. 94(1): p. 13-30.
345. Kim, C.F., E.L. Jackson, A.E. Woolfenden, S. Lawrence, I. Babar, S. Vogel, D. Crowley, R.T. Bronson, and T. Jacks, Identification of bronchioalveolar stem cells in normal lung and lung cancer. Cell, 2005. 121(6): p. 823-35.
346. Fang, D., T.K. Nguyen, K. Leishear, R. Finko, A.N. Kulp, S. Hotz, P.A. Van Belle, X. Xu, D.E. Elder, and M. Herlyn, A tumorigenic subpopulation with stem cell properties in melanomas. Cancer Res, 2005. 65(20): p. 9328-37.
154 347. Collins, A.T., P.A. Berry, C. Hyde, M.J. Stower, and N.J. Maitland, Prospective identification of tumorigenic prostate cancer stem cells. Cancer Res, 2005. 65(23): p. 10946-51.
348. Kulkarni, S.S., T.A. Kopajtic, J.L. Katz, and A.H. Newman, Comparative structure- activity relationships of benztropine analogues at the dopamine transporter and histamine H(1) receptors. Bioorg Med Chem, 2006. 14(11): p. 3625-34.
349. Elenbaas, B., L. Spirio, F. Koerner, M.D. Fleming, D.B. Zimonjic, J.L. Donaher, N.C. Popescu, W.C. Hahn, and R.A. Weinberg, Human breast cancer cells generated by oncogenic transformation of primary mammary epithelial cells. Genes Dev, 2001. 15(1): p. 50-65.
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156 10 List of abbreviations
5-FU 5-fluorouracil ABC ATP-binding cassette Ach Acetylcholine ALDH Aldehyde dehydrogenase AML Acute myeloid leukemia B-CLL B-Chronic lymphocytic leukemia BAA- BODIPY-aminoacetate BAAA BODIPY-aminoacetaldehyde BCSCs Breast cancer stem cells bFGF Basic fibroblast growth factor BMP Bone morphogenetic protein BMPR Bone morphogenetic protein receptor BrdU Bromo-deoxyuridine BSA Bovine serum albumin CaAM Calcein acetoxymethyl ester CML Chronic myeloid leukemia CSA Cyclosporin A CSCs Cancer stem cells Ct Cycle threshold DC Dendritic cells DCIS Ductal carcinoma in situ DEAB Diethylaminobenzaldehyde DHh Desert Hedgehog DLL Delta-like ligand DMEM Dulbecco's Modified Eagle Medium DMEM/F12 Dulbecco’s Modified Eagle Medium: Nutrient Mixture F-12 DMSO Dimethyl sulfoxide DR5 Death receptor 5 ECM Extracellular matrix EGCG (-)-epigallocatechin-3-gallate EGF Epidermal growth factor EGFR Epidermal growth factor receptor EMT Epithelial-to-mesenchymal transition EpCAM Epithelial cell adhesion molecule ER Estrogen receptor EZH2 Zeste homologue 2 FACS Fluorescence-activated cell sorting FBS Fetal bovine serum FGF Fibroblast growth factor G-CSF Granulocyte-colony stimulating factor GFP Green fluorescent protein GG Geranylgeranylation GGTI Geranylgeranyl transferase I GLDC Glycine decarboxylase Gli Glioma-associated oncogene homologue GNS Glioma-derived stem cells GSIs γ-secretase inhibitors GTPases Guanosine triphosphatases H-RasV12 Oncogenic allele of ras HER2 Human epidermal growth factor receptor 2 HGF Hepatocyte growth factor
157 Hh Hedgehog HIFs Hypoxia-inducible factors HMLE Immortalized human mammary epithelial cells HNSC Head and neck squamous carcinomas HSCs Hematopoietic stem cells HSPs Heat shock proteins hTERT Human telomerase reverse transcriptase i.p. Intraperitoneal injection IC50 Half-maximal inhibition concentration IHh Indian Hedgehog IL Interleukin JAK/STAT Janus kinase/signal transducers and activators of transcription LCIS Lobular carcinoma in situ LDA Limited dilution assay LGRs Leucine-rich repeat-containing G-coupled receptors LRCs Lable-retaining cells LSC Leukemic stem cells mAb monoclonal Antibodies mAchR Muscarinic acetylcholine receptors MAPK/ERK Mitogen-activated protein kinase/extracellular signal-regulated kinases mCSCs Migrating cancer stem cells MDR Multidrug resistance gene MEGM Mammary Epithelial Cell Growth Medium MET Mesenchymal-epithelial transition MMTV Mouse mammary tumor virus nAchR Nicotinic acetylcholine receptor NF-κB Nuclear factor kappa B NOD/SCID Non-obese diabetic/severe combined immunodeficiency NSCLC Non-small lung cancer NSCs Normal stem cells NSP Non-side population OD Optical density PBS Phosphate-buffered saline PD-1 Programed cell death-1 poly-HEMA poly (2-hydroxyethyl methacrylate) PR Progesterone receptors Ptch1 Patched PTL Parthenolide RSPO R-spondin SCM Serum-free stem cell medium SHh Sonic Hedgehog shRNA Lentiviral-mediated short hairpin RNA SMO Smoothened homologue SP Side population STK36 Serine–threonine protein kinase 36 SUFU Suppressor of fused homologue SV40 LT Simian immunodeficiency virus 40 large T antigen SV40 ST Simian immunodeficiency virus 40 small T antigen TDLU Terminal ductal-lobular unit TFs Transcription factors TGF-β Transforming growth factor β TICs Tumor initiating cells TNBC Triple-negative breast cancer
158 TNFR Tumor necrosis factor receptor VEGF Vascular endothelial growth factor α9-nAchR Alpha9 nicotinic acetylcholine receptor
159