Cancer stem cells as a target population for drug discovery.

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Cancer stem cells as a target population for drug discovery

Cancer stem cells (CSCs) have been identified in a growing list of malignancies and are believed to be responsible for cancer initiation, metastasis and relapse following certain therapies, even though they may only represent a small fraction of the cells in a given cancer. Like somatic stem cells and embryonic stem cells, CSCs are capable of self-renewal and differentiation into more mature, less tumorigenic cells that make up the bulk populations of cancer cells. Elimination of CSCs promises intriguing therapeutic potential and this concept has been adopted in preclinical drug discovery programs. Herein we will discuss the progress of these efforts, general considerations in practice, major challenges and possible solutions.

Intratumor cellular heterogeneity, at both genetic and phenotypic levels, has been documented in almost all cancers [1,2] . The attempt to explain this phenomenon leads to the development of two widely accepted yet highly debated models: the clonal evolution model that explains genetic heterogeneity [3–5] and the cancer stem cell (CSC) model that explains the heterogeneity at phenotypic and functional levels [6,7] . Accumulating evidence supports both models [8] , sparking further debates, although the two models are not mutually exclusive. CSCs, which are also referred to as cancer-initiating cells, tumor-initiating cells and tumor-propagating cells, are speculated to possess selfrenewal capability like other somatic stem cells and may serve as the units of evolution due to their extended lifespan, and thus the accumulation of more genetic mutations [9,10] . CSCs are believed to be responsible for cancer development, resistance to treatment and metastasis [11–14] . The existence of CSCs and their ability to produce heterogeneous cell populations were first demonstrated in serial transplantation experiments using leukemia cells in immunocompromised mice [15] . Since then, a growing list of CSCs have been identified in a variety of cancer types including brain tumors [16] , breast cancer [17] ,

10.4155/FMC.14.106 © 2014 Future Science Ltd

Claire Bouvard1, Colleen Barefield1 & Shoutian Zhu*,1 California Research Institute for Biomedical Research, 11119 North Torrey Pines Road, Suite 100, La Jolla, CA 92037, USA *Author for correspondence: Tel.: +1 858 242 1006 Fax: +1 858 242 1001 [email protected]

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prostate cancer [18] , pancreatic cancer [19] , head and neck squamous carcinoma [20] , melanoma [21] and colon cancer [22] . The identification and study of CSCs have not only drastically improved our understanding of complex cancer biology, but also lead to the development of novel therapeutic strategies that bring us closer to effectively treating these devastating diseases [23,24] . Herein we will review the development of drug-like small molecules targeting CSCs, and discuss promising opportunities and potential issues that warrant careful consideration. Biology of CSCs What defines CSCs?

Stem cells, including embryonic stem cells (ESCs) and somatic stem cells residing in adult tissues, are capable of self-renewal (generating identical copies of themselves) and differentiation (giving rise to progenies with distinct phenotypes). It is the functionality that defines stem cells and puts them on the top of the hierarchical organization of phenotypically and functionally diverse cell populations in the embryos or adult organs [25,26] . Most, if not all, cancers exhibit intra-cancer heterogeneity with hierarchical features that variously represent an aberrant and pathological cellular organization of the tissues

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Review Bouvard, Barefield & Zhu from which they arise. Unsurprisingly, the technologies used to study adult tissue-specific stem cells have been adopted to identify and study CSCs [27,28,29] . For example, the first isolation and identification of leukemia stem cells (LSCs) employed a combination of cell surface makers CD34 + CD38-, which were originally used to identify hematopoietic stem/progenitor cells [15] . Recently, the expression of specific surface markers has been extended to the CSCs of other cancer types including CD44 + CD24low for breast cancer [17] , CD133 for brain tumor [16] and colon cancers [22,30] , and Lgr5 for colorectal cancer [29] . The expression of aldehyde dehydrogenase (ALDH), a metabolic enzyme that promotes detoxification, has been shown to be upregulated in a variety of cancers [31] . ALDH+ populations from these cancers exhibit the characteristics of CSCs, which enables the use of a fluorescent substrate of ALDH to facilitate their identification and purification [31–35] . Other markers used to identify various CSCs include C-X-C chemokine receptor type 4 (CXCR-4) [36] and ATP-binding cassette family P-glycoproteins (ABCB5 and ABCG2) [21,37,38] . Despite their broad usage, it is important to note that none of these markers or their combination can definitively classify CSCs. In fact, isolation and purification of cancer cells using these markers can only achieve CSC enrichment to limited levels of purity, while also missing other tumorigenic cell populations in the marker-excluded fractions [39,40] . In addition to the expression of marker proteins, CSCs have been identified based on their functionalities. The colony-forming cell assays on cytokine-supplemented methylcellulose were originally developed to study the differentiation and formation of different blood cell lineages derived from hematopoietic stem/progenitor cells. This assay is now widely used to assess the presence and frequency of LSCs from patient peripheral blood or bone marrow aspirate [41,42] . Sphere formaKey terms Cancer stem cells: Subpopulations of cells in a given cancer capable of self-renewal and differentiation, thus responsible for fueling cancer growth, relapse and metastasis. Cancer heterogeneity: The presence of genetically and phenotypically diverse populations of cancer cells from different patients (intercancer heterogeneity) and within the same patient (intracancer heterogeneity). Cellular plasticity: The capability of a lineage restricted cell to transit into a developmentally more primitive stage through a process of reprogramming or de-differentiation. Microenvironment: The composition of physiological factors surrounding a given cell, including oxygen and nutrient supply, growth factors, cytokines and chemokines, the components of extracellular matrix as well as stromal cells.

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tion assays, in which the self-renewal capability of a single/few stem cells is monitored by the formation of sphere-like, 3D multicellular structures in serum-free suspension culture conditions, were initially developed to study mammary stem cells (mammosphere formation assay [43,44]) and neural stem cells (neurosphere formation assay [45]); these have been adopted to study breast CSCs and glioblastoma stem cells in vitro. To confirm the identity of the CSCs, their tumorigenic potential must be demonstrated in vivo, which is usually achieved using tumor xenograft assays and serial tumor propagation experiments using limiting dilution analysis [46] . In these assays, CSC-containing and CSC-depleted cell populations are transplanted into immunocompromised mice; CSC frequency is represented by the minimum number of cells required for tumor formation. Secondary and tertiary tumor xenograft models are commonly used to demonstrate CSC maintenance in vivo. The serial transplantation and tumor formation assay is the gold standard for assessing the existence and frequency of CSCs for almost all cancers. Enthused by the in-depth understanding and potential therapeutic promises that the CSC hypothesis is offering, researchers must bear in mind that CSC model systems examine the cells in an ex vivo context and/or mouse models, and thus the CSCs used in these studies may not be fully representative of the biology in patients. Drug discovery programs based on CSC models need to address these challenges, which will be further discussed in this review. Where are CSCs from?

Cancers are a group of diseases exhibiting dysregulated cell growth caused by genetic mutations. CSCs harbor the same genetic driver mutations as the bulk cancer cell population and possess developmental traits that are distinct from nonstem cancer cells including epigenetic modifications and gene expression profiles [9] . In contrast to their widely accepted existence, the origin of CSCs is a topic of ongoing debate (Figure 1) [47] . A number of hypotheses modeling CSC origin have been proposed. Genetic mutations occur in the somatic stem cell compartment, thus CSCs arise with intrinsic stem cell features [29] . Somatic stem cells are long-lived cells, and thus have a higher chance of accumulating genetic mutations compared to transiently propagating and terminally differentiated cells. Certain mutations impart stem cells with accelerated proliferation and compromised differentiation ability, thus giving rise to CSCs [48] . Recently, researchers have identified a preleukemic stem cell population that behaves similar to hematopoietic stem cells (slow cycling and capable of producing functionally normal, terminally differentiated cell populations), even though it harbors the same genetic

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Cancer relapse

CSC resistance to treatment Genetic mutation in stem cell

Cancer cell dedifferentiation/ transdifferentiation

CSC self-renewal and differentiation CSC migration

Fusion between cancer cells and leukocytes or MSCs Cancer metastasis Figure 1. Cancer stem cell biology. CSC: Cancer stem cell; MSC: Mesenchymal stem cell.

mutations as in the bulk leukemic cells from the same patient. The existence of this cell population strongly supports the hypothesis [49] that CSCs arise from somatic stem cells. Cancer cells de-differentiate/transdifferentiate into a stem cell-like stage upon epigenetic deregulation or environmental stimulation. Stem cell differentiation and maturation is not a unidirectional phenomenon; de-differentiation of terminally differentiated cells into a developmentally more primitive stage has been observed in many adult tissues under amenable conditions, such as injury [50,51] . Cellular plasticity is exemplified by the process of somatic cell reprogramming to the pluripotent stage by transient expression of a combination of transcription factors, including OCT4, MYC, SOX2 and KLF4. Under particular conditions, such as hypoxia and exposure to growth factors (e.g., TGF-β and HGF), cancer cells may go through a reprogramming process (to adapt to the microenvironment) for survival. For example, epithelial-mesenchymal transition (EMT) is a commonly observed process in cancers through which cancer cells gain resistance to treatments, as well as mobility to break away from the primary sites and establish metastases. It has been speculated that through EMT, among other transformation processes, cancer cells gain stem cell-like features and


de-differentiate/transdifferentiate into CSCs [52,53] . Epigenetic status and its regulatory machineries play a determining role in controlling cell fate. Elevated genomic and epigenomic instability in cancer cells may also be responsible for the de-differentiation/transdifferentiation processes that give rise to CSCs [2,54] . Cancer cells fuse with other cells such as cancerassociated macrophages and mesenchymal stem cells (MSCs) to acquire stem cell features [55] . Cell–cell fusion happens both in vitro and in vivo and may lead to the acquisition of new functionalities in daughter cells [56,57] . Fusion events between cancer cells and macrophages have been documented in various tumors and have been shown to produce daughter cells exhibiting elevated metastatic capability [58] , one of the key features of CSCs. MSCs have also been observed homing to the malignant sites [59] and in some cases fusing with cancer cells [60–62] , although the purpose and effects of these fusion events remain elusive. Understanding the origin and developmental path of CSCs will not only shed light on our understanding of their biology, but advance the development of preventive medicine for cancer [63] . What roles do CSCs play in cancer?

CSCs are believed to play critical roles in cancers including fueling cancer growth, resisting conventional

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Review Bouvard, Barefield & Zhu therapies (chemo- and radiation therapy) and thereby being responsible for cancer relapse following treatment, and migrating to secondary sites and establishing metastases (Figure 1) . The cellular heterogeneity of many cancers resembles the hierarchical cellular organization of their tissue origin [15] . Like their nonmalignant counterparts [64] , CSCs can self-renew and give rise to the less primitive, more differentiated cancer cell populations, whereas the nonstem cells possess limited proliferation capability and rely on CSCs for their maintenance. Evidence supporting this notion came from several xenograft models, in which specific depletion of CSCs leads to tumor shrinkage [65] . Similar to many adult stem cells, CSCs can be quiescent (slow cycling) and therefore less vulnerable to DNAdamaging therapies that target rapidly dividing cells. While the majority of cancer cells may be depleted following conventional therapies, the CSCs persist and repopulate the bulk cancer population upon treatment withdrawal, resulting in cancer relapses [66–69] . The quiescence of CSCs may also explain the ‘latent period’ frequently observed before cancer recurrence. The resistance to therapies displayed by CSCs may also be explained by the elevated expression levels of certain genes, including ABCB5 and ABCG2 that are responsible for pumping drugs out of the cells [37] and ALDH, an enzyme that metabolizes cytotoxic chemicals [31,32] . Interestingly, differentiated cancer cells have also been shown to protect CSCs from drug effects by increasing the efflux of the drug through ABCB1 upregulation [70] , suggesting additional mechanisms of CSC resistance. In the process of EMT, cancer cells, most of which are from epithelial origin, can undergo a transition to acquire a fibroblast-like morphology and exhibit stem cell-specific characteristics, including increased mobility and drug resistance [53] . There is accumulating evidence implying that stem-like cells arise at the front edges of cancer intrusion through EMT, delaminate and break away from the bulk tumor and establish satellite metastases at remote tissues [71,72] . Furthermore, CSCs may also interact with and modify the microenvironment to favor tumor growth. CSCs are believed to reside in niches such as the hypoxic niche (i.e., perivascular), secreting cytokines that recruit endothelial cells, fibroblasts and leukocytes, which take part in the microenvironment remodeling including neovasculation, remodeling of the extracellular matrix (ECM) and secretion of cytokines and growth factors [73] . In certain situations, CSCs also participate in building their niche through transdifferentiation into endothelial-like cells (vasculogenic mimicry), a phenomenon observed for glioblastoma stem cells [74] . The complex role played by CSCs in various cancers may explain the ineffectiveness of conventional cancer therapies.

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The CSC paradigm challenges the therapeutic strategies that target bulk cancer cells alone and warrants the development of novel strategies for cancer treatment. CSC-targeted drug discovery In parallel to biological studies of CSCs in vitro and in vivo, efforts have been taken to identify potential therapeutic targets and drug candidates specifically tailored for CSCs. Drug discovery based on the mechanisms involved in CSC biology

Since the ground-breaking study on LSCs by Bonnet and Dick, research on CSCs has undergone exponential growth, which has led to a better understanding of the mechanisms regulating CSC functionality. Many of these mechanisms, including cell signaling cascades, transcriptional programs and metabolic processes, have been studied in other cellular systems. Drugs identified from previous studies have been applied to target CSCs and show promising effects. Cell signaling cascades, such as Wnt, hedgehog and Notch, that play critical roles in stem cell self-renewal also regulate CSC maintenance. Accordingly, these signaling pathways have been subject to extensive drug development efforts including high-throughput small molecule screens [75–79] . Other signaling pathways involved in CSC self-renewal and differentiation, such as growth factors (e.g., TGF-βs, BMPs, FGFs and EGF), integrins [80,81] and their downstream kinases (e.g., FAK [82] , SYK [83] , JAK2 [84] and HCK [85]), have been examined and drug candidates targeting these pathways are currently under preclinical and clinical investigation. Transcription factors and their co-regulators, including HIF-1α, aryl hydrocarbon receptor (AhR), RUNX1 and CBFβ, RXR, RAR, STAT3 and MYC, play dominant roles in CSC cell fate determination. Even though transcription factors are conventionally deemed ‘undruggable’, an increasing number of druglike small molecules have been identified that directly or indirectly target these proteins and their effects on stem cells have promising applications. For example, in a high-throughput screen (HTS), we identified a semi-synthetic natural product, stauprimide, which promotes ESC differentiation through MYC transcription suppression (Figure 2A & B) [86] . When supplemented in the culture media of patient-derived glioblastoma stem cells, stauprimide also downregulates MYC transcription, which leads to an obvious differentiation phenotype (Figure 2C & D) . Stauprimide also induces the differentiation of CD34 + LSCs derived from acute myeloid leukemia (AML) patients, which is evidenced by changes in cell morphology (Figure 2E)



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Endoderm Sox17 (red)/ N-Cad (green) N

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Tuj1(red)/ GFAP(green)

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Figure 2. Myc transcription inhibitor induces cancer stem cell differentiation. (A) The structure of stauprimide; (B) stauprimide promotes embryonic stem cell differentiation into multiple germ layer lineages; (C) stauprimidesuppressed Myc transcription in glioblastoma cancer stem cells; (D) stauprimide induces GBM cancer stem cell differentiation in vitro, FBS is used as a reference; (E) Giemsa staining of CD34 + peripheral blood cells derived from acute myeloid leukemia patient upon stauprimide treatment; (F) stauprimide inhibits colony formation of CD34 + peripheral blood cells derived from acute myeloid leukemia patient. DMSO: Dimethyl sulfoxide; FBS: Fetal bovine serum.

and reduced colony formation potential (Figure 2F) . A growing list of small molecules have been found to target various transcription programs, and exhibit promising effects on CSCs. Stemregenin 1 (SR1) and its analogs have been identified in a HTS designed to identify molecules capable of promoting hematopoietic stem cell (HSC) expansion ex vivo. Mechanistic studies revealed that SR1 binds to and antagonizes AhR, a nuclear receptor that plays a critical role in stem cell maintenance in addition to its well-known function in chemical toxin sensing and detoxification. Interestingly, AhR antagonists are also able to expand LSCs in vitro [87] , while AhR agonists induce CSC differentiation in leukemia and breast cancers [88–90] . In addition, Jak2/Stat3 inhibitors have been shown


to suppress breast cancer and glioblastoma CSC selfrenewal in vitro and prolong the survival of the animals receiving CSC transplants [84,91] . The accessibility of gene promoters for transcription factor recognition and transcriptional activation is regulated by epigenetic modification. Many epigenetic modifying enzymes and their regulatory partners have been found playing intriguing roles in CSC maintenance and differentiation [54] . Manipulation of these proteins by drug-like small molecules or functional genomics (e.g., RNAi-mediated gene silencing) exhibits profound effects on CSCs, indicating that epigenetic machineries may be promising therapeutic targets. For example, HDAC inhibitors, in combination with retinoic acid, are able to induce neuroblastoma cell


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Review Bouvard, Barefield & Zhu differentiation in vitro and promote the survival of the animals that received tumor cell transplants [92] . Polycomb protein BMI1 has been shown to be upregulated in leukemia stem cells [41] and colorectal CSCs [93] . Inhibition of BMI1 results in impaired CSC selfrenewal and cancer growth in xenograft animal models. Through an RNAi screen, we identified the protein TRRAP as a potential drug target whose knockdown induces the differentiation of patient-derived glioblastoma stem cells in vitro and the loss of their tumorigenicity upon transplantation into the brains of immunocompromised mice. As a ‘scaffolding’ protein, TRRAP does not possess epigenetic modifying activity itself, however, it is an integral part of many histone acetyltransferase complexes (e.g., NuA4 complex). Knockdown of TRRAP results in the histone deacetylation of cyclin A2 promoter and subsequent downregulation of cyclin A2, which leads to the inhibition of cell cycle progression and elevated susceptibility to differentiation of glioblastoma CSCs [94] . Epigenetic mark readers, such as bromodomain-containing 4 (Brd4), have also been identified as regulators of CSC self-renewal and differentiation. Through a genetic screen, Zuber and colleagues found that Brd4-targeting shRNAs exhibit robust anti-leukemic activity both in vitro and in vivo [95] . A small-molecule inhibitor of Brd4, JQ1, has been administered in multiple cancer models and has shown promising anticancer activities [96–98] . An aberrant metabolism is commonly associated with cancer cells due to their high nutrient demand for enhanced proliferation. Molecular changes (elevated expression level or genetic mutations) in the proteins that regulate metabolism (e.g., hypoxia-HIF) and metabolic enzymes (e.g., ALDH, IDH1 and IDH2) are also frequently observed in CSC compartments [73,99–104] . Inhibiting these metabolic enzymes and their regulatory networks is an effective approach to targeting CSCs both in vitro and in vivo. For example, siRNA and drug-mediated HIF-1α inhibition was shown to eliminate CSCs in AML and glioma in vitro, and suppress cancer growth in vivo [104,105] . In addition, inhibition of heat shock protein 90 (HSP90), a chaperone protein regulating client protein folding and stability, is able to induce HIF-1α degradation in breast cancer cells, which results in the suppression of tumor growth, vascularization, local invasion and metastasis [106] . In another case of CSC-targeted therapy, treatment of stem-like breast cancer cells with an ALDH inhibitor, diethylaminobenzaldhyde (DEAB), sensitizes these cells to chemotherapy and irradiation [107] . Disulfiram, another potent ALDH inhibitor, is also able to decrease CSC populations in breast cancer and glioblastoma and leave cancer cells more vulnerable to chemotherapeutic agents [108] . Inhibition of IDH1

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activity in cancer cells harboring gain-of-function IDH1 mutations affects AML cell survival and growth both in vitro and in vivo [109,110] . Even though they have distinct modes of action, the mechanisms discussed above are not wholly independent of each other. Overlapping effects and interplay between signaling cascades have been documented in CSCs from many cancer types. Targeting these mechanisms in a combined fashion may exert additive effects (for parallel, independent mechanisms) or reduce the amount of each individual regimen to reach maximal effects (for drugs targeting different components along the same or related cascades). For example, the activation of AhR and the inhibition of HDAC are both able to enhance retinoic acid-induced CSC differentiation [90,92] . IDH1 and IDH2 mutations result in epigenetic changes characteristic of CpG island hypermethylation [100,101] and elevated ALDH activity activates HIF-2α in breast cancer cells [111] . Moreover, bromodomain inhibitor JQ1 is speculated to exert its anticancer effect partially through the downregulation of c-Myc [95] . This complex, interwoven network provides multiple options for the manipulation of CSC phenotypes; however, it will be challenging to dissect each pathway and elucidate the roles of associated genes in the regulation of CSCs. Drug discovery through phenotypic screens Strategies targeting CSCs

While significant advances have been made in the field of CSCs, given the intrinsic complexity of cellular machineries and environmental factors involved in CSC biology, our knowledge of the mechanisms regulating CSCs is far from definitive. Cell-based phenotypic studies focus on cellular functions and behaviors. Recognized for their power in unveiling novel mechanisms, phenotypic assays have been widely accepted by researchers to study poorly understood, complex biology, usually in a high-throughput format [26,112] . Both genetic and chemical screens that have been adopted to study CSCs have shed light onto molecular and cellular mechanisms of CSC biology, and afforded potential therapeutic targets and molecular candidates for drug discovery [113] . Strategies that are proposed to target CSCs for drug discovery are discussed below and summarized in Table 1. Selective CSC growth inhibition & apoptosis induction

Due to the elevated resistance of CSCs to conventional treatments, drugs that selectively inhibit the proliferation or induce the cell death of CSCs over nonstem bulk cancer cells have been sought. The identification of salinomycin exemplifies this strategy [114] , in which



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Table 1. Strategies targeting cancer stem cells. Strategy

Drug

Mechanism

Ref.

Selective CSC growth Transformed breast cancer inhibition and apoptosis cells with E-Cadherin knocked induction down

Cancer cell type

Salinomycin

Selectively killing stem-like cancer cells over nonstem cells

[114]

Ependymona cells

5-fluorouracil

Selectively killing ependymoma cells over noncancer neural stem cells

[115]

Glioblastoma-derived neural stem cells

Plk1 inhibitor

Selectively inhibiting GBM stem cells over genetically normal neural stem cells

[116,117]

AML stem cells

Kinetin riboside

Targeting AML stem cells but sparing normal HSCs

[118]

Forced differentiation of CSCs

APL

All-trans retinoic acid

Inducing APL cell differentiation

[120]

Neoplastic human pluripotent stem cells

Thioridazine

Inducing pluripotent stem cell and leukemia stem cell differentiation

[121]

Neuroblastoma stem cells

HDAC inhibitor combined with retinoic acid

Inducing neuroblastoma stem cell differentiation

[92]

Colorectal cancer stem cell

BMI-1 inhibitor

Inducing colorectal CSC differentiation

[93]

Modulating CSC microenvironment

Hypoxia

N/A

Disruption of hypoxia to inhibit CSC expansion

[104]

Growth factors, cytokines and N/A chemokines

Disrupting ligand/receptor interaction or blocking downstream signaling cascades

[75,77,125]

Cancer stromal cells

Blockade of angiogenesis, lymphangiogenesis and metastasis

N/A

[73]

AML: Acute myeloid leukemia; APL: Acute promyelocytic leukemia; CSC: Cancer stem cell; GBM: Glioblastoma; HDAC: Histone deacetylase; HSC: Hematopoietic stem cell; N/A: Not available.

Gupta and colleagues generated CSC-like cells from a breast cancer cell line by knocking down E-cadherin and inducing EMT. They identified compounds that exhibit higher potency in killing EMT-derived stemlike cells compared with ‘non-CSCs.’ Distinguishing between CSCs and bulk cancer cell populations is of great research interest; however, drugs that kill both CSCs and non-stem cancer cells are highly desirable in cancer treatment. This could be achieved by using a combination of CSC-targeting drugs and conventional anticancer drugs targeting bulk cancer cells. In contrast, drugs selectively targeting CSCs, but not noncancerous somatic stem cells, are of great importance. Potential side effects of CSC-targeting drugs arise from the depletion of somatic stem cells that are responsible for the maintenance of tissue homeostasis and repair upon injury, particularly in the tissues with high cellular turnover rates: the hematopoietic system, the intestines and the skin. Great effort has been made to identify compounds with selectivity toward CSCs over noncancerous somatic stem cells. For example,


5-fluorouracil exhibits selective toxicity toward ependymoma cells over normal neural stem cells (NSCs) [115] ; Plk1 inhibitors selectively induce mitotic arrest in glioblastoma-derived neural stem cells and neuroblastoma tumor-initiating cells but not in genetically normal NSCs [116,117] ; kinetin riboside targets AML stem cells but spares normal hematopoietic stem cells [118] . However, the effects these compounds impart on endogenous stem cells in other tissues and non-stem cells are yet elusive. Furthermore, the known toxic effects of these compounds in patients warrant more thorough investigation before they can be used as CSC-targeting agents. Forced differentiation of CSCs

The tumorigenic potential of CSCs is tightly linked to their self-renewal capability. Nonstem-like cells that make up the bulk of most cancers are believed to have limited propagation potential, and thus, are unable to initiate tumor formation or maintain tumor growth [65,119] . Upon differentiation, CSCs become


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Review Bouvard, Barefield & Zhu less primitive cells (progenitors and terminally differentiated cells) and may gradually lose their self-renewal capability. When transplanted into immunocompromised mice, these cells exhibit drastically reduced or no tumorigenic potential. Indeed, differentiation therapy has proven successful in the clinic, as evidenced by the cure of acute promyelocytic leukemia patients harboring PML-RARa fusion protein by all-trans retinoic acid treatment [120] . Forced differentiation has been effective in depleting CSCs and thus inhibiting tumor formation both in vitro and in vivo. Researchers have identified compounds that induce CSC differentiation with demonstrable efficacy in animal models. One example is the antipsychotic drug thioridazine that is capable of inducing neoplastic human pluripotent stem cell differentiation in vitro and impairing leukogenesis by LSCs in vivo [121] . HDAC inhibitors, in combination with retinoic acid, have been shown to induce neuroblastoma differentiation and prolong the survival of animals receiving cancer cell transplantation [92] . Small-molecule inhibitors of BMI-1 have been shown to induce colorectal CSC differentiation, which leads to long-term and irreversible impairment of tumor growth [93] . Modulating CSC microenvironment

The self-renewal and differentiation of stem cells are tightly controlled by the orchestration between their intrinsic regulatory mechanisms (genetic and epigenetic status) and their microenvironment (nutrient and oxygen supply, cytokines and growth factors, ECM components and interaction with stromal cells). For example, the perivascular niche in the bone marrow supports the self-renewal of hematopoietic stem cells. CSCs from patients with hematopoietic malignancies and metastatic tumors have been found homing to this niche [122] . Other niches that have been predicted to influence CSC maintenance include the hypoxic tumor core, the perivascular compartment of the tumor neovasculature and the leading edge of tumor invasion into the adjacent tissues [123] . The dependence of CSCs on their niches has led to the speculation that the CSC microenvironment could be a target for drug development [124] . These factors include hypoxia [73,104] , the presence of growth factors, cytokines and chemokines (e.g., Wnt, Hedgehog, Notch, TGF-β and CXCL12-CXCR4 axis) [75,77,125] , the composition of ECM proteins including fibronectin, collagen and laminin, the ECM-modifying enzymes such as matrix metalloproteinases and cancer-associated stromal cells including fibroblasts, macrophages, endothelial cells and MSCs [73] . Since CSCs share similar niches with somatic stem cells, such as the bone marrow, selectively targeting the CSC microenvironment may be

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challenging. Furthermore, the CSC niches are under constant turnover and remodeling, therefore modifying microenvironment is still a moving target that will push drug discovery to its limits [126] . Due to the similar characteristics shared between CSCs and somatic stem cells, the depletion of the latter by CSC-targeted drugs represents a major safety concern. Thus, selectivity of the drugs toward CSCs over endogenous stem cells, such as HSCs, MSCs, intestinal stem cells and NSCs, is highly desirable. Despite the promising results exhibited thus far, their selectivity has yet been fully assessed. Further studies validating the CSC specificity of these strategies are warranted, and CSC selectivity should be of great importance to scientists searching for drugs targeting these cell populations. Practical considerations for cell-based HTS & follow-up studies CSC isolation & in vitro culture/expansion

The development of tissue culture technologies that maintain CSCs in vitro has allowed for propagation of CSCs (yielding large quantities required for HTS) [127] . For example, growth factors that enhance CSC maintenance and propagation, including Wnt, EGF and FGF, have been used in CSC culture media [127,128] . Small molecules that promote stem cell self-renewal, including AhR antagonists [87] and ROCK inhibitors [129] , have been shown to effectively maintain CSCs and are used in ex vivo CSC cultures. 3D sphere cultures, such as mammospheres and neurospheres that can maintain CSC self-renewal and grow CSCs in compact structures, have become routine culture systems for CSCs. Biodegradable scaffolds that contain ECM components and mechanical properties mimicking the in vivo microenvironment have been developed and used for the enrichment and propagation of CSCs while retaining their stemness and increasing their sphere-forming ability in vitro [130] . Surrogate systems have also been widely used to bypass the limited availability of primary CSCs. ESCs and embryonic carcinoma cells can be cultured in vitro indefinitely without losing their self-renewal and differentiation potential, and thus, have been used as a surrogate system to study CSCs [121] . Genetic manipulation of cancer cell lines, such as EMT induction by the expression of Snail and Twist and the knockdown of E-cadherin, has been shown to impart these cells with stem-like properties and is used in screens mimicking CSCs [114,131] . In addition to in vitro CSC propagation, various cell culture assays have been developed and adapted to HTS formats. For example, researchers have modified adherent culture conditions to facilitate image-based, high content screens using CSCs from glioblastoma



and other cancers [132] . 3D sphere-like cell suspension culture was developed to assess CSC clonogenic potential and study cancer cell function by mimicking tissue architecture in vivo, and has been successfully adapted to 96- and 384-well formats commonly used in HTS [133] . Primary HTS

Despite the emergence of novel and sophisticated technologies in CSC culture, in vitro systems can only recapitulate the in vivo CSC physiology to a limited extent. This is due to both the complexity and the dynamics of the intrinsic regulatory networks and the environmental niche factors that are involved in CSC self-renewal and differentiation. Researchers will need to strike a balance between the sophistication of the assays to faithfully represent CSC physiology and the screening throughput and cost efficiency. Adopted from traditional anticancer drug development programs, cell viability has been one of the early and widely used readouts to monitor CSC survival and proliferation [134] . Through assays monitoring cell numbers, compounds with cytostatic or cytotoxic activities have been identified in stem-like cancer cells in breast cancer [114,131,135] , neuroblastoma [117] and leukemia [118] . Because of their advantageous sensitivity, robustness, throughput and cost efficiency, reporter genes (e.g., luciferases and fluorescent proteins) are well accepted as an assay of choice in HTS. For instance, CK5 promoter-driven luciferase and GFP expression were used to identify small-molecule modulators of luminal breast cancer CSCs [136] . However, these reporter-based systems require the incorporation of exogenous genes into the CSCs and the expansion of the reporter cell lines, which could be challenging due to the low efficiency of delivering genetic materials into primary CSCs and limited in vitro self-renewal and proliferation potential of the CSCs. Technological advances in microscopy, flowcytometry, nucleic acid sequencing and quantification, automation and data analysis have made informationrich assays applicable in HTS. These assays, distinct from reporter-based assays, could be performed on wild-type cells without the introduction of exogenous materials. For example, microscope-based automatic imaging systems have been widely used in cellular phenotypic screens. Proteins whose expression is restricted to a certain lineage or developmental stage are used as markers to monitor stem cell differentiation, such examples include SSEA3 and OCT4, which are specifically expressed in embryonic stem cells and embryonic carcinoma cells [137] . The presence and expression levels of the marker genes are monitored by immunostaining, and images are acquired and analyzed using software coupled with the imaging systems. Image-based assays


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can monitor the signals from individual cells, which offers advantages when compared to reporter assays or cell viability assays that read signals from the entire cell population in each assay well. Furthermore, some imaging systems are capable of acquiring images at multiple focal layers, allowing for the incorporation of physiologically more relevant CSC models into HTS. Examples of other assay formats that are used to assess CSCs include: the expression of a list of ten differentiation signature genes was scored using a ligationmediated amplification assay to identify compounds that, in combination with HDAC inhibitor, promote neuroblastoma CSC differentiation [92] ; and a module of genes that negatively correlate with patient outcome were assessed by quantitative RT-PCR in a screen to identify compounds that target GBM CSCs [138] . Activity confirmation of the hit compounds

When the primary screen is completed, a number of hits are picked for further validation. The hit list usually contains false positives in addition to compounds with desired biological activities, therefore confirmation steps are necessary to valid lead compounds for any screen. The confirmation assays may use the same readout as the primary screen, but test compounds at multiple concentrations to establish a dose dependence for lead compounds and to dismiss random, irreproducible false positives. Biological readouts and assays that are complementary to those used in primary screens are widely used to compensate for the intrinsic limitations of a given technology by using orthogonal detection methods (e.g., RT-PCR, immunostaining and Western blotting to confirm hits from a promoter reporter based assay), filter out nonspecific cytotoxic compounds using cell viability assays, and confirm the biological effects through phenotypes different from the screening assay. For example, a screen assessing the expression of additional cell fate markers, 3D sphere formation or colony formation for CSC differentiation could be used to compare with a reporter assay monitoring the promoter activity of a single CSC-associated gene. Our effort to identify small molecules that promote chondrocyte differentiation exemplifies these strategies [139] . In the primary screen, the presence of chondrocytes was monitored by Rhodamin B staining of the cartilage matrix components in an image-based assay. To confirm the chondrocyte identity, immunostaining for type II collagen, SOX9, aggrecan, CD44 and osteocalcin was performed at the validation step. In parallel, qRT-PCR was applied to confirm the upregulation of these genes at the mRNA level. Furthermore, a 3D pellet culture of MSCs, which mimics the condensed cartilage architecture in vivo, was used to confirm the physiological relevance of the identified


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Review Bouvard, Barefield & Zhu lead compounds. Through these steps we successfully identified the compound kartogenin that specifically induces chondrocyte differentiation. The efficacy of kartogenin in rodent osteoarthritis models justified the physiological relevance of the study and led to a large-scale drug discovery program. In most situations, several rounds of confirmation steps, using these assays one at a time or in combination, are carried out to identify the lead candidates. As the screening progresses, the number of hits to be validated decreases, which then allows for the use of more costly assays for the detailed characterization of a few lead compounds. Thus, 1D cell viability and cytotoxicity assays are usually applied in early steps, whereas assays that analyze the expression of multiple cell fate marker genes/proteins by quantitative RT-PCR, Western blotting or ELISA are used at later validation stages. Besides their activities in CSCs, the hit compounds need to be validated for their selectivity. In the counterscreens, various somatic stem cells including HSCs, MSCs and NSCs, could be used to assess potential deleterious activities of the hit compounds. Ideally, lead compounds are active against CSCs but spare other endogenous stem cells; however, it is rare to find drugs with such perfect selectivity. In practice, a selective window between CSCs and somatic stem cells may qualify the lead compounds for further development. In addition, the selectivity of the lead compounds should be verified in nonstem-cell populations, such as hepatocytes, fibroblasts, endothelial and epithelial cells, to assess potential safety issues. Target identification & mechanism elucidation for the lead compounds

In phenotype-based approaches, the underlying mechanisms of the phenotype of interest are usually unknown at the beginning of the studies and remain to be investigated. The identification of the direct targets of biologically active small molecules is extremely challenging [140–142] . In conventional affinity chromatography-based methodologies [143,144] , the bioactive compound is immobilized onto a solid surface (a resin or a plate) using an inert, flexible linker. The cellular proteins are extracted by cell lysis and incubated with the immobilized compound. The proteins that specifically bind to the compound are retained on the surface and subsequently eluted and identified using proteomics approaches. It has been observed that proteins that nonspecifically interact with the compound, the flexible linker and the solid surface make up the majority of the analyte, which complicates the identification of specific binding proteins, in particular the ones of low abundance. Through this multistep process (cell lysis, binding, washing and elution), the loss of bind-

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ing proteins may be significant so that the visualization of the target proteins becomes increasingly difficult. Furthermore, the proteins inside a living cell reside in highly compartmentalized environments (e.g., mitochondrion, nucleus, ER, Golgi apparatus and other organelles) instead of a homogenous solution, and their subcellular localization and interaction with other cellular components (other proteins, DNA, RNA and metabolites) are crucial for the proteins’ function. The loss of this complexity upon the destruction of a cell and its subcellular compartments may negatively impact the interaction between the compound and its targets. Over the years, we have developed a technology (Figure 3) to successfully identify the direct targets of several small molecules with interesting biological activities [139,145,146] . Our approach relies on an affinity probe that is specifically designed for a given lead compound using knowledge gained from structure–activity relationship studies. Two necessary moieties are incorporated into the probe: a photoactive crosslinking moiety (aryl azide, benzophenone or diazirine) to covalently link the probe to its target proteins, and an affinity tag (e.g., biotin) to facilitate highly specific and sensitive detection of the probe-labeled proteins. Based on the structure-activity relationship of the parent compound, the two moieties can be incorporated at distinct positions, or alternatively a bivalent tag containing both moieties can be linked to the parent compound at a single permeable position. First, the activity of the probe is verified in the biological assays and compared to that of the parent compound. Next, the affinity probe is incubated with cells to allow the interaction with its targets to take place in the appropriate cellular environment. To distinguish between specific and nonspecific-binding proteins, a competition experiment is performed in parallel, in which excessive nonlabeled parent compound (at concentrations usually 20- to 50-times higher than the probe concentration) is included in the probe-cell incubation. Since the interaction between the affinity probe and its targets is specific, and thus, saturable, the nonlabeled parent compound competes with the probe and decreases its labeling of the targets. Alternatively, an inactive analog of the parent compound can be used as a ‘negative’ probe for the indication of nonspecific binding proteins. Following the probe-cell incubation step, the cells are irradiated with UV light to crosslink the affinity probe to its binding proteins. Then the cells are lysed, and the protein targets labeled with the probe are visualized by Western blotting using antibodies specific for the affinity tag. To reduce the number of nonbinding proteins in the cell lysate, fractionation steps including ammonium sulfate precipitation, ion exchange chromatography and hydrophobic



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Figure 3. Target identification strategy. (1) Affinity probe incubated with live cells, probe design using kartogenin as an example, structure in red indicates the aryl azide moiety for photo crosslinking, structure in green indicates the affinity handle (biotin), structure in blue indicates the core structure of the parent compound; (2) UV irradiation for crosslinking; (3) cell lysis; (4) protein fractionation and detection of biotin-labeled proteins; (5) 2D electrophoresis for protein separation; and (6) LC–MS/MS proteomics analysis identifying target proteins.

chromatography may be applied. We typically use 2D electrophoresis followed by Western blotting to visualize the probe-labeled proteins, and Coomassie/silver staining and LC–MS/MS-based proteomics methods for protein identification. The advantages of this technology include: allowing the interaction between the probe and its targets to take place in the native, cellular environment; distinguishing between the specific targets and nonspecific-binding proteins of the probe; preserving the interaction/labeling by photoactivated covalent crosslinking; detecting the target proteins using highly sensitive and specific method (Western blotting); and reducing the complexity of the samples and improving protein separation by fractionation and 2D electrophoresis. Following the identification of the target proteins, their relevance in the biological systems needs to be validated. Genetic methods including RNAi-mediated gene silencing, cDNA overexpression and mutagenesis are widely used for this purpose. The interaction between the compounds and their targets


can be assessed using biochemical methods including in vitro binding assays, enzymatic activity assays and cocrystallization of the compound–protein complex. The underlying mechanisms of the target proteins involved in CSC biology can be studied in further details using molecular and cell biology approaches. The unveiled and validated mechanisms may then serve as novel targets for mechanism-based drug discovery. Efficacy studies for the lead compounds in animal models

Ultimately, the efficacy of the lead compounds need to be assessed using animal models. To be tested in vivo, the lead compounds need to meet certain pharmacological prerequisites including bioavailability, solubility, stability, half-life, tissue distribution and safety. In many cases, a structure–activity relationship study combining medicinal chemistry and cell biology efforts is needed to generate new analogs of the original hit compounds that are suitable for in vivo studies.


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Review Bouvard, Barefield & Zhu Xenograft models are widely used to assess the efficacy of CSC-targeting strategies. CSC-containing cell populations may be treated with the drug candidates in vitro, and then transplanted into immunocompromised mice. The tumorigenic potential of these cells is represented by the minimum number of cells needed to grow tumors in a given in vivo context (e.g., adaptive and/or innate immunodeficiency of the mice). In addition, the frequency of the tumor formation in recipient animals and the dynamics of tumor growth (the length of latent period, the slope of tumor growth curve, etc.) are of interest. Compounds may also be systemically administered using animals harboring (pre-established) CSC-derived xenografts. The effects of the test compounds on CSCs may be assessed by marker-based isolation of CSCs from the xenograft tumor and further analyzed using in vitro assays such as gene expression, surface marker phenotype and sphere and colony formation capabilities. Moreover, the isolated CSCs may be subject to serial transplantation/xenograft assays. Both mechanism- and phenotype-based approaches have been widely used to identify CSC-targeting drug candidates. Clearly, the two strategies can be used synergistically and can cross-validate each other. Confirmed targets identified from genomic screens could serve as the targets for chemical screens for drug discovery; the mode of action studies of lead compounds from chemical phenotype screens could elucidate interesting mechanisms and serve as the target mechanisms for future development. Current challenges in CSC-targeted drug discovery programs CSC heterogeneity

Inter- and intratumor heterogeneity is increasingly recognized as a challenge in cancer research and anticancer drug development. Cancer cells can show genetic, morphological and functional diversity in a single tumor and among different patient tumors. CSCs are conceivably heterogeneous cell populations. For example, glioblastoma stem cells from different patients display distinct morphology and are categorized into four groups – proneural, neural, classical and mesenchymal – and functional and behavioral differences among these phenotypes has been documented [147–149] . Different CSC subclones have been identified from the same patient with distinct genetic mutations and gene expression profiles but all are capable of generating cancers upon transplantation into recipient mice [150] . CSCs isolated from different patients and by different researchers using different protocols may represent different cell populations. Furthermore, CSCs and their progenies may exhibit elevated levels

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of plasticity due to epigenetic deregulation and microenvironmental influence. Thus, even within a genetically homogenous clone of cancer cells, the CSCs may represent a subpopulation subject to dynamic transition. These heterogeneities make it currently impossible to generalize the protocols for CSC identification and purification, and complicate the studies that are developed for CSCs. Therapeutic approaches inspired by personalized medicine, which are tailored to achieve a specialized treatment for individual patient based on their unique genetic background and oncogenic mutations show promising outcomes [151] . Advanced technologies including next-generation sequencing, digital PCR and single cell profiling make this approach not only plausible, but highly effective in assessing the inter- and intra-tumor heterogeneity and predicting efficacious therapies for individual patients. Lineage tracing technologies that are widely used to study adult stem cells and their progenies in diverse tissues may be adapted to study CSCs, especially for their dynamic behaviors in vitro and in vivo [152] . We envision that a greater understanding of the nature of CSC heterogeneity will lead to the development of therapeutic strategies that can effectively target different genotypes and phenotypes of cancer. Despite the heterogeneity within CSCs, certain key mechanisms are shared among CSCs including epigenetic-regulated transcription activation, key extracellular signals (e.g., WNT and CXCL12) and the presence of MYC and other transcription factors that contribute to stem cell maintenance. Thus, it is plausible to predict the emergence of drugs that may be used to treat a variety of CSCs, even though their efficacy needs to be validated in each individual CSC populations, and the outcome may vary significantly. Physiological relevance of CSC models

For any disease, physiological relevance is the basis of success for therapeutic development programs. All in vitro and in vivo assays should recapitulate the physiological conditions at molecular, cellular, tissue and organismal levels. CSC function and behavior are highly regulated by the orchestrated balance between their intrinsic machineries and the microenvironment. Many drug discovery studies rely on in vitro propagated CSCs to meet the need for a large quantity of cells and to simplify the data interpretation. This approach inevitably raises concerns about the physiological relevance of the outcome. To overcome this challenge, protocols have been modified to account for the microenvironmental factors into CSC culture systems. For example, growth factors, cytokines and extracellular matrices that are found in CSC niches in vivo, are widely used



to support CSC maintenance. Spheroid and co-culture systems that mimic the 3D architecture and the presence of stromal cells are also widely used to study CSCs [153–155] . Recently, researchers successfully grew organoids that represent various developing tissues from ESCs and tissue-specific stem cells [156,157] . This development may offer a means of culturing CSCs under more physiologically relevant conditions [158] . Tissue explants such as brain and cartilage slices that preserve the integrity of the tissues may also be adopted for CSC-targeting drug discovery research [159] . Attaining physiological relevance also applies to the proper selection of animal models. Despite the fact that CSC xenograft assays are the gold standard for the assessment of CSC activity, it remains unclear as to what extent these assays recapitulate CSC biology in patients [46,67] . Multiple factors contribute to this debatable divergence. For example, interspecies difference between human and mouse has been documented and casts doubts on murine disease models [160,161] . This difference may be significant in cancer xenograft models since human cells are exposed to murine environment. Furthermore, the lack of immune response of the recipient animals, which are immunocompromised to enhance the survival and engraftment of the xenografts, results in the absence of critical components that immune cells may provide for CSCs in their native environment. The absence of immune surveillance may also lead to cancer cell subclonal evolution in a manner quite different to what occurs in patients. To reconcile this divergence, humanized mice have been developed, in which human hematopoietic stem cells are transplanted into immunocompromised mice to reconstitute the immune system, and are widely used to study human infectious diseases [162] . The adoption of this model into CSC studies may provide valuable insight on the interaction between transplanted CSCs and the immune system. Another concern regarding CSC xenograft model is that the transplantation procedure requires ex vivo handling of patient samples to isolate and enrich CSCs, and subsequent implantation into recipient animals. This process conceivably puts stress onto the primary cancer cells and may lead to a biased selection of subpopulations of more resistant cells, which may not fully represent the biology of the primary patient malignancy. Transgenic models are an alternative to the xenograft models and are widely used to study cancers with known genetic mutations. They have been shown to closely mimic the pathophysiology of human cancers that harbor the same genetic mutations (e.g., KRAS mutations in lung [163] and pancreatic [164] cancers and BRAF mutations in melanoma [165]). However, many different models and a large variety of genetically introduced mutations would be required to


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represent the molecular heterogeneity among patient tumors. Another caveat is the short lifespan of the experimental animals that only allows for limited analysis of the evolution/progression of human cancers. Despite these limitations, mouse models are the most powerful tools available to study CSC biology. A combination of multiple in vitro and in vivo models of CSCs that complement one another may lead to more comprehensive understanding and better predicting the efficacy of the drugs in development. CSC-related data interpretation

Conventional cellular assays for anti-cancer drug discovery have been focused on cell proliferation and survival. These assays have been adopted to CSC-oriented programs, and have proven powerful in identifying cytotoxic and cytostatic drug candidates [114,134] . However, CSCs may only account for a fraction of the cells due to the impossibility of enriching primary CSCs into homogeneous populations and the dynamic transition between CSCs and their differentiated progenies. Thus, the overall cell number-related readouts may over- or underestimate the effects of the compounds on CSCs, especially in the cases where CSCs may be quiescent and the large fraction of the proliferation signal attributes to the activated, transiently proliferating progenitor population differentiated from the stem cells [166] . The subsequent validation steps should include assays that monitor CSC functions such as sphere formation. Furthermore, assays that monitor the CSC ‘stemness’ including cell fate associated marker gene expression, sphere and colony formation have been applied in HTS and proven fruitful [92,94,121] . Care should be taken in interpreting data from animal models; macroscopic tumor growth represents the overall cellularity of the tumor without distinguishing between subpopulations, which could be misleading in cases where CSCs only make up a small fraction of the bulk tumor. To assess the drug effects on CSCs, readouts that represent CSC population should be applied. For example, tumor relapse and metastasis may be monitored in addition to overall tumor growth. Tumorigenecity of CSCs in serial transplantation experiments (as has been done for leukemia stem cells) could be adapted into the assessment of the drug efficacy in other CSC populations. Future perspective Since the identification and characterization of first CSC population in leukemia patient cells, the field of CSC research has seen drastic growth with putative CSCs identified in various cancer types and novel mechanisms unveiled regulating CSC biology. Concurrently, the promising therapeutic potential that CSC-targeting


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Review Bouvard, Barefield & Zhu strategies offer has stimulated drug discovery programs looking for new-generation anticancer drugs. The advance of research on CSCs, in combination with the development of material and detection technologies, allows for the reconstruction of physiologically relevant cellular models of cancer (e.g., 3D sphere cultures, cocultures of CSCs, bulk cancer cells and cancer stromal cells, the application of multicomponent ECM scaffolds) and data acquisition and processing (e.g., imagebased and next-generation sequencing based cellular analysis at single cell level, image acquisition and analysis for 3D cellular structures) in vitro, which may not only lead to the improvement of our knowledge of cancer, but also make possible the identification of effective drug candidates targeting CSCs. Drugs that possess selective activity toward CSCs compared to endogenous noncancerous stem cells and somatic cells will be identified through these CSC-oriented approaches. The development and improvement of animal models (both xenograft and transgenic models) that assess CSC biology will accelerate the drug discovery/development processes and provide critical information to facilitate decision-making. Although there are no CSC-targeting drugs on the market to date, the emergence of this class of therapies

is foreseeable. Due to the roles that CSCs play in various cancers, CSC-targeting drugs may serve as a stand-alone treatment in cancers whose maintenance is dependent on the CSCs, whereas in other cancers CSC-targeting drugs may be co-administered with other standard of care regimens, such as chemotherapy and radiation therapy, to achieve an optimal outcome. CSC-targeting drugs may also prove to be successful in preventing cancer relapse and metastasis following initial surgical removal and other conventional therapies. Acknowledgements The authors are grateful to H Wurdak and LL Lairson for the constructive discussion and painstaking proofreading.

Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties. No writing assistance was utilized in the production of this manuscript.

Executive summary Cancer stem cell biology • Cancer stem cells (CSCs) represent a promising target population for the development of anticancer drugs due to their distinct biology and crucial roles in cancer development and maintenance. • CSCs are capable of self-renewal and differentiation, are believed to be responsible for cancer initiation, growth, drug resistance, relapse and metastasis. • CSC may rise from somatic stem cells harboring oncogenic genetic mutations, cancer cells de-differentiating into a stem-like stage or cancer cells fused with leukocytes or mesenchymal stem cells.

Cancer stem cell-targeted drug discovery • Mechanism- and phenotype-based methods have been applied in CSC-targeted drug discovery programs, afforded promising drug candidates and therapeutic targets for the development of novel anticancer strategies. • Various strategies may effectively target CSCs, including selective inhibition of CSC proliferation, induction of CSC cell death, promotion of CSC differentiation and manipulation of CSC microenvironment. • Drugs selectively targeting CSCs over endogenous stem cells and somatic cells are highly desirable, may be not only effective but also safe. • Affinity-based identification of direct targets of the lead compounds will unveil underlying mechanisms involved in CSC maintenance and provide potential therapeutic targets for future drug development.

Challenges & future direction of CSC-targeted drug discovery • CSC heterogeneity, at both inter- and intratumor levels, complicates the drug discovery efforts, thus the effectiveness of each drug needs to be assessed on a case-by-case basis; whereas key regulatory mechanisms shared among diverse CSCs may allow the identification of drugs targeting different CSC populations and effective for various cancers. • The physiological relevance of the in vitro and in vivo CSC models is critical for the success of CSC-targeted drug discovery programs. • CSCs may only represent a fraction of cells of analysis in both in vitro and in vivo models, the interpretation of the data acquired from the bulk cell population needs to be adjusted, CSC population needs to be identified and signals from this population need to be distinguished from other cells and are used to assess CSC biology and drug effects.

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References 1

Fisher R, Pusztai L, Swanton C. Cancer heterogeneity: implications for targeted therapeutics. Br. J. Cancer 108(3), 479–485 (2013).

2

Meacham CE, Morrison SJ. Tumour heterogeneity and cancer cell plasticity. Nature 501(7467), 328–337 (2013).

3

Greaves M, Maley CC. Clonal evolution in cancer. Nature 481(7381), 306–313 (2012).

4

Nowell PC. The clonal evolution of tumor cell populations. Science 194(4260), 23–28 (1976).

5

Burrell RA, Mcgranahan N, Bartek J, Swanton C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501(7467), 338–345 (2013).

6

Valent P, Bonnet D, De Maria R et al. Cancer stem cell definitions and terminology: the devil is in the details. Nat. Rev. Cancer 12(11), 767–775 (2012).

21

Schatton T, Murphy GF, Frank NY et al. Identification of cells initiating human melanomas. Nature 451(7176), 345–349 (2008).

22

Ricci-Vitiani L, Lombardi DG, Pilozzi E et al. Identification and expansion of human colon-cancer-initiating cells. Nature 445(7123), 111–115 (2007).

23

Frank NY, Schatton T, Frank MH. The therapeutic promise of the cancer stem cell concept. J. Clin. Invest. 120(1), 41–50 (2010).

24

Kitambi SS, Chandrasekar G. Stem cells: a model for screening, discovery and development of drugs. Stem Cells Cloning 4, 51–59 (2011).

25

Zhu S, Wurdak H, Schultz PG. Directed embryonic stem cell differentiation with small molecules. Future Med. Chem. 2(6), 965–973 (2010).

26

Lyssiotis CA, Lairson LL, Boitano AE, Wurdak H, Zhu S, Schultz PG. Chemical control of stem cell fate and developmental potential. Angew. Chem. Int. Ed. Engl. 50(1), 200–242 (2011).

27

Verga Falzacappa MV, Ronchini C, Reavie LB, Pelicci PG. Regulation of self-renewal in normal and cancer stem cells. FEBS J. 279(19), 3559–3572 (2012).

28

Keysar SB, Jimeno A. More than markers: biological significance of cancer stem cell-defining molecules. Mol. Cancer Ther. 9(9), 2450–2457 (2010).

29

Merlos-Suarez A, Barriga FM, Jung P et al. The intestinal stem cell signature identifies colorectal cancer stem cells and predicts disease relapse. Cell Stem Cell 8(5), 511–524 (2011).

7

Visvader JE, Lindeman GJ. Cancer stem cells: current status and evolving complexities. Cell Stem Cell 10(6), 717–728 (2012).

8

Vermeulen L, De Sousa E Melo F, Richel DJ, Medema JP. The developing cancer stem-cell model: clinical challenges and opportunities. Lancet Oncol. 13(2), e83–e89 (2012).

9

Kreso A, Dick JE. Evolution of the cancer stem cell model. Cell Stem Cell 14(3), 275–291 (2014).

10

Greaves M. Cancer stem cells as ‘units of selection’. Evol. Appl. 6(1), 102–108 (2013).

11

Clevers H. The cancer stem cell: premises, promises and challenges. Nat. Med. 17(3), 313–319 (2011).

30

12

Tirino V, Desiderio V, Paino F et al. Cancer stem cells in solid tumors: an overview and new approaches for their isolation and characterization. FASEB J. 27(1), 13–24 (2013).

O’brien CA, Pollett A, Gallinger S, Dick JE. A human colon cancer cell capable of initiating tumour growth in immunodeficient mice. Nature 445(7123), 106–110 (2007).

31

13

Bomken S, Fiser K, Heidenreich O, Vormoor J. Understanding the cancer stem cell. Br. J. Cancer 103(4), 439–445 (2010).

Ma I, Allan AL. The role of human aldehyde dehydrogenase in normal and cancer stem cells. Stem Cell Rev. 7(2), 292–306 (2011).

32

14

Baccelli I, Trumpp A. The evolving concept of cancer and metastasis stem cells. J. Cell Biol. 198(3), 281–293 (2012).

Luo Y, Dallaglio K, Chen Y et al. ALDH1A isozymes are markers of human melanoma stem cells and potential therapeutic targets. Stem Cells 30(10), 2100–2113 (2012).

15

Bonnet D, Dick JE. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat. Med. 3(7), 730–737 (1997).

33

16

Singh SK, Hawkins C, Clarke ID et al. Identification of human brain tumour initiating cells. Nature 432(7015), 396–401 (2004).

Mao P, Joshi K, Li J et al. Mesenchymal glioma stem cells are maintained by activated glycolytic metabolism involving aldehyde dehydrogenase 1A3. Proc. Natl Acad. Sci. USA 110(21), 8644–8649 (2013).

34

Kryczek I, Liu S, Roh M et al. Expression of aldehyde dehydrogenase and CD133 defines ovarian cancer stem cells. Int. J. Cancer 130(1), 29–39 (2012).

35

Fleischman AG. ALDH marks leukemia stem cell. Blood 119(15), 3376–3377 (2012).

36

Cojoc M, Peitzsch C, Trautmann F, Polishchuk L, Telegeev GD, Dubrovska A. Emerging targets in cancer management: role of the CXCL12/CXCR4 axis. Onco. Targets Ther. 6, 1347–1361 (2013).

37

Grimm M, Krimmel M, Polligkeit J et al. ABCB5 expression and cancer stem cell hypothesis in oral squamous cell carcinoma. Eur. J. Cancer 48(17), 3186–3197 (2012).

38

Ma J, Frank MH. Tumor initiation in human malignant melanoma and potential cancer therapies. Anticancer Agents Med. Chem. 10(2), 131–136 (2010).

17

Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ, Clarke MF. Prospective identification of tumorigenic breast cancer cells. Proc. Natl Acad. Sci. USA 100(7), 3983–3988 (2003).

18

Collins AT, Berry PA, Hyde C, Stower MJ, Maitland NJ. Prospective identification of tumorigenic prostate cancer stem cells. Cancer Res. 65(23), 10946–10951 (2005).

19

Li C, Heidt DG, Dalerba P et al. Identification of pancreatic cancer stem cells. Cancer Res. 67(3), 1030–1037 (2007).

20

Prince ME, Sivanandan R, Kaczorowski A et al. Identification of a subpopulation of cells with cancer stem cell properties in head and neck squamous cell carcinoma. Proc. Natl Acad. Sci. USA 104(3), 973–978 (2007).



Review

1581

Review Bouvard, Barefield & Zhu 39

40

Grosse-Gehling P, Fargeas CA, Dittfeld C et al. CD133 as a biomarker for putative cancer stem cells in solid tumours: limitations, problems and challenges. J. Pathol. 229(3), 355–378 (2013).

41

Sengupta A, Ficker AM, Dunn SK, Madhu M, Cancelas JA. Bmi1 reprograms CML B-lymphoid progenitors to become B-ALL-initiating cells. Blood 119(2), 494–502 (2012).

42

Williams BA, Wang XH, Keating A. Clonogenic assays measure leukemia stem cell killing not detectable by chromium release and flow cytometric cytotoxicity assays. Cytotherapy 12(7), 951–960 (2010).

43

Dey D, Saxena M, Paranjape AN et al. Phenotypic and functional characterization of human mammary stem/progenitor cells in long term culture. PLoS ONE 4(4), e5329 (2009).

44

45

1582

Shmelkov SV, Butler JM, Hooper AT et al. CD133 expression is not restricted to stem cells, and both CD133+ and CD133- metastatic colon cancer cells initiate tumors. J. Clin. Invest. 118(6), 2111–2120 (2008).

Dong Q, Wang D, Bandyopadhyay A et al. Mammospheres from murine mammary stem cell-enriched basal cells: clonal characteristics and repopulating potential. Stem Cell Res. 10(3), 396–404 (2013). Deleyrolle LP, Reynolds BA. Isolation, expansion, and differentiation of adult mammalian neural stem and progenitor cells using the neurosphere assay. Methods Mol. Biol. 549, 91–101 (2009).

46

Baiocchi M, Biffoni M, Ricci-Vitiani L, Pilozzi E, De Maria R. New models for cancer research: human cancer stem cell xenografts. Curr. Opin. Pharmacol. 10(4), 380–384 (2010).

47

Bjerkvig R, Tysnes BB, Aboody KS, Najbauer J, Terzis AJ. Opinion: the origin of the cancer stem cell: current controversies and new insights. Nat. Rev. Cancer 5(11), 899–904 (2005).

48

Sell S. On the stem cell origin of cancer. Am. J. Pathol. 176(6), 2584–2494 (2010).

49

Shlush LI, Zandi S, Mitchell A et al. Identification of preleukaemic haematopoietic stem cells in acute leukaemia. Nature 506(7488), 328–333 (2014).

50

Tang DG. Understanding cancer stem cell heterogeneity and plasticity. Cell Res. 22(3), 457–472 (2012).

51

Wagers AJ, Weissman IL. Plasticity of adult stem cells. Cell 116(5), 639–648 (2004).

52

Scheel C, Weinberg RA. Cancer stem cells and epithelialmesenchymal transition: concepts and molecular links. Semin. Cancer Biol. 22(5–6), 396–403 (2012).

53

Marie-Egyptienne DT, Lohse I, Hill RP. Cancer stem cells, the epithelial to mesenchymal transition (EMT) and radioresistance: potential role of hypoxia. Cancer Lett. 341(1), 63–72 (2013).

54

Munoz P, Iliou MS, Esteller M. Epigenetic alterations involved in cancer stem cell reprogramming. Mol. Oncol. 6(6), 620–636 (2012).

55

Lu X, Kang Y. Cell fusion hypothesis of the cancer stem cell. Adv. Exp. Med. Biol. 714, 129–140 (2011).

56

Sanges D, Lluis F, Cosma MP. Cell-fusion-mediated reprogramming: pluripotency or transdifferentiation?


Implications for regenerative medicine. Adv. Exp. Med. Biol. 713, 137–159 (2011). 57

Lluis F, Cosma MP. Cell-fusion-mediated somatic-cell reprogramming: a mechanism for tissue regeneration. J. Cell. Physiol. 223(1), 6–13 (2010).

58

Pawelek JM, Chakraborty AK. The cancer cell--leukocyte fusion theory of metastasis. Adv. Cancer Res. 101, 397–444 (2008).

59

Houghton J, Stoicov C, Nomura S et al. Gastric cancer originating from bone marrow-derived cells. Science 306(5701), 1568–1571 (2004).

60

Pawelek JM, Chakraborty AK. Fusion of tumour cells with bone marrow-derived cells: a unifying explanation for metastasis. Nat. Rev. Cancer 8(5), 377–386 (2008).

61

Aractingi S, Kanitakis J, Euvrard S et al. Skin carcinoma arising from donor cells in a kidney transplant recipient. Cancer Res. 65(5), 1755–1760 (2005).

62

Lazova R, Laberge GS, Duvall E et al. A melanoma brain metastasis with a donor-patient hybrid genome following bone marrow transplantation: first evidence for fusion in human cancer. PLoS ONE 8(6), e66731 (2013).

63

Maund SL, Cramer SD. The tissue-specific stem cell as a target for chemoprevention. Stem Cell Rev. 7(2), 307–314 (2011).

64

Lairson LL, Lyssiotis CA, Zhu S, Schultz PG. Small molecule-based approaches to adult stem cell therapies. Annu. Rev. Pharmacol. Toxicol. 53, 107–125 (2013).

65

Chen J, Li Y, Yu TS et al. A restricted cell population propagates glioblastoma growth after chemotherapy. Nature 488(7412), 522–526 (2012).

66

Abubaker K, Latifi A, Luwor R et al. Short-term single treatment of chemotherapy results in the enrichment of ovarian cancer stem cell-like cells leading to an increased tumor burden. Mol. Cancer 12, 24 (2013).

67

Ghiaur G, Gerber J, Jones RJ. Concise review: Cancer stem cells and minimal residual disease. Stem Cells 30(1), 89–93 (2012).

68

Lin WC, Rajbhandari N, Liu C et al. Dormant cancer cells contribute to residual disease in a model of reversible pancreatic cancer. Cancer Res. 73(6), 1821–1830 (2013).

69

Brunner TB, Kunz-Schughart LA, Grosse-Gehling P, Baumann M. Cancer stem cells as a predictive factor in radiotherapy. Semin. Radiat. Oncol. 22(2), 151–174 (2012).

70

Emmink BL, Van Houdt WJ, Vries RG et al. Differentiated human colorectal cancer cells protect tumor-initiating cells from irinotecan. Gastroenterology 141(1), 269–278 (2011).

71

Oskarsson T, Batlle E, Massague J. Metastatic stem cells: sources, niches, and vital pathways. Cell Stem Cell 14(3), 306–321 (2014).

72

Liu H, Patel MR, Prescher JA et al. Cancer stem cells from human breast tumors are involved in spontaneous metastases in orthotopic mouse models. Proc. Natl Acad. Sci. USA 107(42), 18115–18120 (2010).

73

Semenza GL. Cancer-stromal cell interactions mediated by hypoxia-inducible factors promote angiogenesis, lymphangiogenesis, and metastasis. Oncogene 32(35), 4057–4063 (2013).



74

Cheng L, Huang Z, Zhou W et al. Glioblastoma stem cells generate vascular pericytes to support vessel function and tumor growth. Cell 153(1), 139–152 (2013).

75

Coni S, Infante P, Gulino A. Control of stem cells and cancer stem cells by Hedgehog signaling: pharmacologic clues from pathway dissection. Biochem. Pharmacol. 85(5), 623–628 (2013).

76

non-toxic aryl hydrocarbon receptor agonist. PLoS ONE 5(11), e13831 (2010). 90

Bunaciu RP, Yen A. Activation of the aryl hydrocarbon receptor AhR Promotes retinoic acid-induced differentiation of myeloblastic leukemia cells by restricting expression of the stem cell transcription factor Oct4. Cancer Res. 71(6), 2371–2380 (2011).

Dodge ME, Lum L. Drugging the cancer stem cell compartment: lessons learned from the hedgehog and Wnt signal transduction pathways. Annu. Rev. Pharmacol. Toxicol. 51, 289–310 (2011).

91

Dave B, Landis MD, Tweardy DJ et al. Selective small molecule Stat3 inhibitor reduces breast cancer tumorinitiating cells and improves recurrence free survival in a human-xenograft model. PLoS ONE 7(8), e30207 (2012).

77

Holland JD, Klaus A, Garratt AN, Birchmeier W. Wnt signaling in stem and cancer stem cells. Curr. Opin. Cell Biol. 25(2), 254–264 (2013).

92

78

Santini R, Vinci MC, Pandolfi S et al. Hedgehog-GLI signaling drives self-renewal and tumorigenicity of human melanoma-initiating cells. Stem Cells 30(9), 1808–1818 (2012).

Hahn CK, Ross KN, Warrington IM et al. Expression-based screening identifies the combination of histone deacetylase inhibitors and retinoids for neuroblastoma differentiation. Proc. Natl Acad. Sci. USA 105(28), 9751–9756 (2008).

93

Kreso A, Van Galen P, Pedley NM et al. Self-renewal as a therapeutic target in human colorectal cancer. Nat. Med. 20(1), 29–36 (2014).

79

Takebe N, Harris PJ, Warren RQ, Ivy SP. Targeting cancer stem cells by inhibiting Wnt, Notch, and Hedgehog pathways. Nat. Rev. Clin. Oncol. 8(2), 97–106 (2011).

94

Wurdak H, Zhu S, Romero A et al. An RNAi screen identifies TRRAP as a regulator of brain tumor-initiating cell differentiation. Cell Stem Cell 6(1), 37–47 (2010).

80

Van Den Hoogen C, Van Der Horst G, Cheung H, Buijs JT, Pelger RC, Van Der Pluijm G. Integrin alphav expression is required for the acquisition of a metastatic stem/progenitor cell phenotype in human prostate cancer. Am. J. Pathol. 179(5), 2559–2568 (2011).

95

Zuber J, Shi J, Wang E et al. RNAi screen identifies Brd4 as a therapeutic target in acute myeloid leukaemia. Nature 478(7370), 524–528 (2011).

96

Herrmann H, Blatt K, Shi J et al. Small-molecule inhibition of BRD4 as a new potent approach to eliminate leukemic stem- and progenitor cells in acute myeloid leukemia AML. Oncotarget 3(12), 1588–1599 (2012).

97

Schober M, Fuchs E. Tumor-initiating stem cells of squamous cell carcinomas and their control by TGF-beta and integrin/focal adhesion kinase (FAK) signaling. Proc. Natl Acad. Sci. USA 108(26), 10544–10549 (2011).

Filippakopoulos P, Qi J, Picaud S et al. Selective inhibition of BET bromodomains. Nature 468(7327), 1067–1073 (2010).

98

Hahn CK, Berchuck JE, Ross KN et al. Proteomic and genetic approaches identify Syk as an AML target. Cancer Cell 16(4), 281–294 (2009).

Lamoureux F, Baud’huin M, Rodriguez Calleja L et al. Selective inhibition of BET bromodomain epigenetic signalling interferes with the bone-associated tumour vicious cycle. Nat. Commun. 5, 3511 (2014).

99

Stechishin OD, Luchman HA, Ruan Y et al. On-target JAK2/STAT3 inhibition slows disease progression in orthotopic xenografts of human glioblastoma brain tumor stem cells. Neuro. Oncol. 15(2), 198–207 (2013).

Yang H, Ye D, Guan KL, Xiong Y. IDH1 and IDH2 mutations in tumorigenesis: mechanistic insights and clinical perspectives. Clin. Cancer Res. 18(20), 5562–5571 (2012).

100 Lu C, Ward PS, Kapoor GS et al. IDH mutation impairs

81

82

83

84

85

Lu X, Mu E, Wei Y et al. VCAM-1 promotes osteolytic expansion of indolent bone micrometastasis of breast cancer by engaging alpha4beta1-positive osteoclast progenitors. Cancer Cell 20(6), 701–714 (2011).

Saito Y, Yuki H, Kuratani M et al. A pyrrolo-pyrimidine derivative targets human primary AML stem cells in vivo. Sci. Transl. Med. 5(181), 181ra152 (2013).

86

Zhu S, Wurdak H, Wang J et al. A small molecule primes embryonic stem cells for differentiation. Cell Stem Cell 4(5), 416–426 (2009).

87

Pabst C, Krosl J, Fares I et al. Identification of small molecules that support human leukemia stem cell activity ex vivo. Nat. Methods doi:10.1038/nmeth.2847 (2014) (Epub ahead of print).

88

89

Zhao S, Kanno Y, Nakayama M, Makimura M, Ohara S, Inouye Y. Activation of the aryl hydrocarbon receptor represses mammosphere formation in MCF-7 cells. Cancer Lett. 317(2), 192–198 (2012). Prud’homme GJ, Glinka Y, Toulina A, Ace O, Subramaniam V, Jothy S. Breast cancer stem-like cells are inhibited by a


Review

histone demethylation and results in a block to cell differentiation. Nature 483(7390), 474–478 (2012). 101 Figueroa ME, Abdel-Wahab O, Lu C et al. Leukemic

IDH1 and IDH2 mutations result in a hypermethylation phenotype, disrupt TET2 function, and impair hematopoietic differentiation. Cancer Cell 18(6), 553–567 (2010). 102 Lu C, Venneti S, Akalin A et al. Induction of sarcomas by

mutant IDH2. Genes Dev. 27(18), 1986–1998 (2013). 103 Li Z, Bao S, Wu Q et al. Hypoxia-inducible factors regulate

tumorigenic capacity of glioma stem cells. Cancer Cell 15(6), 501–513 (2009). 104 Soeda A, Park M, Lee D et al. Hypoxia promotes expansion

of the CD133-positive glioma stem cells through activation of HIF-1alpha. Oncogene 28(45), 3949–3959 (2009). 105 Wang Y, Liu Y, Malek SN, Zheng P, Liu Y. Targeting

HIF1alpha eliminates cancer stem cells in hematological malignancies. Cell Stem Cell 8(4), 399–411 (2011).


1583

Review Bouvard, Barefield & Zhu 106 Xiang L, Gilkes DM, Chaturvedi P et al. Ganetespib blocks

HIF-1 activity and inhibits tumor growth, vascularization, stem cell maintenance, invasion, and metastasis in orthotopic mouse models of triple-negative breast cancer. J. Mol. Med. (Berlin) 92(2), 151–164 (2014). 107 Croker AK, Allan AL. Inhibition of aldehyde dehydrogenase

(ALDH) activity reduces chemotherapy and radiation resistance of stem-like ALDHhiCD44(+) human breast cancer cells. Breast Cancer Res. Treat. 133(1), 75–87 (2012). 108 Liu P, Kumar IS, Brown S et al. Disulfiram targets cancer

stem-like cells and reverses resistance and cross-resistance in acquired paclitaxel-resistant triple-negative breast cancer cells. Br. J. Cancer 109(7), 1876–1885 (2013). 109 Chaturvedi A, Araujo Cruz MM, Jyotsana N et al.

Mutant IDH1 promotes leukemogenesis in vivo and can be specifically targeted in human AML. Blood 122(16), 2877–2887 (2013). 110 Kats LM, Reschke M, Taulli R et al. Proto-oncogenic role of

mutant IDH2 in leukemia initiation and maintenance. Cell Stem Cell 14(3), 329–341 (2014). 111 Kim RJ, Park JR, Roh KJ et al. High aldehyde dehydrogenase

activity enhances stem cell features in breast cancer cells by activating hypoxia-inducible factor-2alpha. Cancer Lett. 333(1), 18–31 (2013). 112 Wu X, Schultz PG. Synthesis at the interface of chemistry

and biology. J. Am. Chem. Soc. 131(35), 12497–12515 (2009). 113 Wurdak H. Exploring the cancer stem cell phenotype with

high-throughput screening applications. Future Med. Chem. 4(10), 1229–1241 (2012). 114 Gupta PB, Onder TT, Jiang G et al. Identification of selective

inhibitors of cancer stem cells by high-throughput screening. Cell 138(4), 645–659 (2009). 115 Atkinson JM, Shelat AA, Carcaboso AM et al. An integrated

in vitro and in vivo high-throughput screen identifies treatment leads for ependymoma. Cancer Cell 20(3), 384–399 (2011). 116 Danovi D, Folarin A, Gogolok S et al. A high-content small

molecule screen identifies sensitivity of glioblastoma stem cells to inhibition of polo-like kinase 1. PLoS ONE 8(10), e77053 (2013). 117 Grinshtein N, Datti A, Fujitani M et al. Small molecule

kinase inhibitor screen identifies polo-like kinase 1 as a target for neuroblastoma tumor-initiating cells. Cancer Res. 71(4), 1385–1395 (2011). 118 Mcdermott SP, Eppert K, Notta F et al. A small molecule

screening strategy with validation on human leukemia stem cells uncovers the therapeutic efficacy of kinetin riboside. Blood 119(5), 1200–1207 (2012). 119 O’Brien CA, Kreso A, Jamieson CH. Cancer stem cells and

self-renewal. Clin. Cancer Res. 16(12), 3113–3120 (2010). 120 Ablain J, Rice K, Soilihi H, De Reynies A, Minucci S, De

The H. Activation of a promyelocytic leukemia-tumor protein 53 axis underlies acute promyelocytic leukemia cure. Nat. Med. 20(2), 167–174 (2014). 121 Sachlos E, Risueno RM, Laronde S et al. Identification

of drugs including a dopamine receptor antagonist that

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selectively target cancer stem cells. Cell 149(6), 1284–1297 (2012). 122 Krause DS, Fulzele K, Catic A et al. Differential regulation of

myeloid leukemias by the bone marrow microenvironment. Nat. Med. 19(11), 1513–1517 (2013). 123 Hjelmeland AB, Lathia JD, Sathornsumetee S, Rich JN.

Twisted tango: brain tumor neurovascular interactions. Nat. Neurosci. 14(11), 1375–1381 (2011). 124 Yang ZJ, Wechsler-Reya RJ. Hit ‘em where they live: targeting

the cancer stem cell niche. Cancer Cell 11(1), 3–5 (2007). 125 Koch U, Lehal R, Radtke F. Stem cells living with a Notch.

Development 140(4), 689–704 (2013). 126 Labarge MA. The difficulty of targeting cancer stem cell

niches. Clin. Cancer Res. 16(12), 3121–3129 (2010). 127 Fael Al-Mayhani TM, Ball SL, Zhao JW et al. An efficient

method for derivation and propagation of glioblastoma cell lines that conserves the molecular profile of their original tumours. J. Neurosci. Methods 176(2), 192–199 (2009). 128 Kawaguchi-Ihara N, Murohashi I, Nara N, Tohda S.

Promotion of the self-renewal capacity of human acute leukemia cells by Wnt3A. Anticancer Res. 28(5A), 2701–2704 (2008). 129 Ohata H, Ishiguro T, Aihara Y et al. Induction of the stem-

like cell regulator CD44 by Rho kinase inhibition contributes to the maintenance of colon cancer-initiating cells. Cancer Res. 72(19), 5101–5110 (2012). 130 Sims-Mourtada J, Niamat RA, Samuel S, Eskridge C, Kmiec

EB. Enrichment of breast cancer stem-like cells by growth on electrospun polycaprolactone-chitosan nanofiber scaffolds. Int. J. Nanomedicine 9, 995–1003 (2014). 131 Carmody LC, Germain AR, Verplank L et al. Phenotypic

high-throughput screening elucidates target pathway in breast cancer stem cell-like cells. J. Biomol. Screen 17(9), 1204–1210 (2012). 132 Pollard SM, Yoshikawa K, Clarke ID et al. Glioma stem

cell lines expanded in adherent culture have tumor-specific phenotypes and are suitable for chemical and genetic screens. Cell Stem Cell 4(6), 568–580 (2009). 133 Cavnar SP, Salomonsson E, Luker KE, Luker GD, Takayama

S. Transfer, imaging, and analysis plate for facile handling of 384 hanging drop 3D tissue spheroids. J. Lab. Autom. 19(2), 208–214 (2014). 134 Mathews LA, Keller JM, Goodwin BL et al. A 1536-well

quantitative high-throughput screen to identify compounds targeting cancer stem cells. J. Biomol. Screen 17(9), 1231–1242 (2012). 135 Germain AR, Carmody LC, Morgan B et al. Identification of

a selective small molecule inhibitor of breast cancer stem cells. Bioorg. Med. Chem. Lett. 22(10), 3571–3574 (2012). 136 Yoo BH, Axlund SD, Kabos P et al. A high-content assay

to identify small-molecule modulators of a cancer stem cell population in luminal breast cancer. J. Biomol. Screen. 17(9), 1211–1220 (2012). 137 Barbaric I, Jones M, Harley DJ, Gokhale PJ, Andrews

PW. High-content screening for chemical modulators of embryonal carcinoma cell differentiation and survival. J. Biomol. Screen. 16(6), 603–617 (2011).



138 Visnyei K, Onodera H, Damoiseaux R et al. A molecular

screening approach to identify and characterize inhibitors of glioblastoma stem cells. Mol. Cancer Ther. 10(10), 1818–1828 (2011). 139 Johnson K, Zhu S, Tremblay MS et al. A stem cell-based

approach to cartilage repair. Science 336(6082), 717–721 (2012). 140 Leslie BJ, Hergenrother PJ. Identification of the cellular

targets of bioactive small organic molecules using affinity reagents. Chem. Soc. Rev. 37(7), 1347–1360 (2008). 141 Schenone M, Dancik V, Wagner BK, Clemons PA. Target

identification and mechanism of action in chemical biology and drug discovery. Nat. Chem. Biol. 9(4), 232–240 (2013). 142 Burdine L, Kodadek T. Target identification in chemical

genetics: the (often) missing link. Chem. Biol. 11(5), 593–597 (2004). 143 Ziegler S, Pries V, Hedberg C, Waldmann H. Target

identification for small bioactive molecules: finding the needle in the haystack. Angew. Chem. Int. Ed. Engl. 52(10), 2744–2792 (2013). 144 Chan JN, Nislow C, Emili A. Recent advances and method

development for drug target identification. Trends Pharmacol. Sci. 31(2), 82–88 (2010). 145 Kahsai AW, Zhu S, Wardrop DJ, Lane WS, Fenteany G.

Quinocarmycin analog DX-52–51 inhibits cell migration and targets radixin, disrupting interactions of radixin with actin and CD44. Chem. Biol. 13(9), 973–983 (2006). 146 Wurdak H, Zhu S, Min KH et al. A small molecule

accelerates neuronal differentiation in the adult rat. Proc. Natl Acad. Sci. USA 107(38), 16542–16547 (2010). 147 Denysenko T, Gennero L, Juenemann C et al. Heterogeneous

phenotype of human glioblastoma: in vitro study. Cell Biochem. Funct. 32(2), 164–176 (2014). 148 Verhaak RG, Hoadley KA, Purdom E et al. Integrated

genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17(1), 98–110 (2010). 149 Brennan CW, Verhaak RG, McKenna A et al. The somatic

genomic landscape of glioblastoma. Cell 155(2), 462–477 (2013). 150 Klco JM, Spencer DH, Miller CA et al. Functional

heterogeneity of genetically defined subclones in acute myeloid leukemia. Cancer Cell 25(3), 379–392 (2014). 151 Topol EJ. Individualized medicine from prewomb to tomb.

Cell 157(1), 241–253 (2014). 152 Kretzschmar K, Watt FM. Lineage tracing. Cell 148(1–2),

33–45 (2012).


Review

153 Castellone MD, Laatikainen LE, Laurila JP et al. Brief

report: mesenchymal stromal cell atrophy in coculture increases aggressiveness of transformed cells. Stem Cells 31(6), 1218–1223 (2013). 154 Chaturvedi P, Gilkes DM, Wong CC et al. Hypoxia-

inducible factor-dependent breast cancer-mesenchymal stem cell bidirectional signaling promotes metastasis. J. Clin. Invest. 123(1), 189–205 (2013). 155 Bengsch F, Buck A, Gunther SC et al. Cell type-dependent

pathogenic functions of overexpressed human cathepsin B in murine breast cancer progression. Oncogene 33(36), 4474–4484 (2014). 156 Ranga A, Gjorevski N, Lutolf MP. Drug discovery through

stem cell-based organoid models. Adv. Drug Deliv. Rev. doi:10.1016j.addr.02.006 (2014) (Epub ahead of print). 157 Sato T, Clevers H. Growing self-organizing mini-guts from

a single intestinal stem cell: mechanism and applications. Science 340(6137), 1190–1194 (2013). 158 Sachs N, Clevers H. Organoid cultures for the analysis of

cancer phenotypes. Curr. Opin. Genet. Dev. 24C, 68–73 (2014). 159 Chuang HN, Lohaus R, Hanisch UK, Binder C, Dehghani

F, Pukrop T. Coculture system with an organotypic brain slice and 3D spheroid of carcinoma cells. J. Vis. Exp. doi:10.379150881 (2013) (Epub ahead of print). 160 Mestas J, Hughes CC. Of mice and not men: differences

between mouse and human immunology. J. Immunol. 172(5), 2731–2738 (2004). 161 Seok J, Warren HS, Cuenca AG et al. Genomic responses in

mouse models poorly mimic human inflammatory diseases. Proc. Natl Acad. Sci. USA 110(9), 3507–3512 (2013). 162 Brehm MA, Wiles MV, Greiner DL, Shultz LD. Generation

of improved humanized mouse models for human infectious diseases. J. Immunol. Methods doi:10.1016j.jim.02.011 (2014) (Epub ahead of print). 163 Soucek L, Whitfield JR, Sodir NM et al. Inhibition of Myc

family proteins eradicates KRas-driven lung cancer in mice. Genes Dev. 27(5), 504–513 (2013). 164 Herreros-Villanueva M, Hijona E, Cosme A, Bujanda L.

Mouse models of pancreatic cancer. World J. Gastroenterol. 18(12), 1286–1294 (2012). 165 Dhomen N, Reis-Filho JS, Da Rocha Dias S et al. Oncogenic

Braf induces melanocyte senescence and melanoma in mice. Cancer Cell 15(4), 294–303 (2009). 166 Sloma I, Jiang X, Eaves AC, Eaves CJ. Insights into the

stem cells of chronic myeloid leukemia. Leukemia 24(11), 1823–1833 (2010).


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Drug Normalization for Cancer Therapeutic and Druggable Genome Target Discovery.

Adenosine A2A receptor as a drug discovery target.

Cancer stem cells: a potential target for cancer therapy.

Future target-based drug discovery for tuberculosis?

Human pluripotent stem cells as tools for neurodegenerative and neurodevelopmental disease modeling and drug discovery.

Glioblastoma Stem Cells as a New Therapeutic Target for Glioblastoma.

Induced pluripotent stem cells as cardiac arrhythmic in vitro models and the impact for drug discovery.

Novel drug discovery system for cancer stem cells in human squamous cell carcinoma of the esophagus.

The dengue virus NS5 protein as a target for drug discovery.

Characterization of DNA topoisomerase I from Candida albicans as a target for drug discovery.

Histone lysine methyltransferases as anti-cancer targets for drug discovery.

Cancer stem cells as a potential therapeutic target in breast cancer.

Pluripotent stem cells in disease modelling and drug discovery.

Patient-specific stem cells and cardiovascular drug discovery.

Bioinformatics for cancer immunotherapy target discovery.

The Zebrafish as a Tool to Cancer Drug Discovery.

Stem cells loaded with nanoparticles as a drug carrier for in vivo breast cancer therapy.

Therapeutic approaches to target cancer stem cells.

Myogenic differentiation of VCP disease-induced pluripotent stem cells: A novel platform for drug discovery.

Fruitful research: drug target discovery for neurodegenerative diseases in Drosophila.

Existing drugs and their application in drug discovery targeting cancer stem cells.

Discoidin domain receptor 1 (DDR1) kinase as target for structure-based drug discovery.

Heat-shock protein 90 (Hsp90) as anticancer target for drug discovery: an ample computational perspective.

The histone methyltransferase EZH2 as a druggable target in SHH medulloblastoma cancer stem cells.