Breast Cancer Stem Cells and the Move Toward High-Resolution Stem Cell Systems

C H AP TER 5

Breast Cancer Stem Cells and the Move Toward High-Resolution Stem Cell Systems B.T. Spike University of Utah, Salt Lake City, UT, United States

INTRODUCTION At its core the cancer stem cell (CSC) hypothesis is intended to functionally explain the cellular heterogeneity frequently observed in tumors. It proposes that a subset of tumor cells referred to as CSCs act in a manner analogous to self-renewing stem cells of a normal tissue. These CSCs are thought to propagate the cancer long term by giving rise to more CSCs while also giving rise to bulk tumor cells that lack long-term cancer maintenance properties. From its inception, this straightforward description of cellular functional heterogeneity has been intertwined with questions of its relationship to normal stem cell biology at the tissue, cellular, and molecular levels. Accordingly, there have been three major trajectories of research that all fall under the theme of CSC research: (1) studies which seek to isolate and characterize the cells responsible for tumor propagation using specific biomarkers, (2) studies which seek to determine broader molecular profiles and mechanisms governing cancer cells and relate them to molecular profiles from normal stem cells, and (3) studies that seek to determine the normal cell of origin for tumors bearing a given molecular profile. These three types of studies are not mutually exclusive and often occur in combination in a single report with little distinction between them, though they are in fact separate ideas. Breast cancer has been extensively studied in all three contexts. Yet, it often remains unclear how well the CSC model fits a particular instance of the disease, how closely breast cancer cells resemble normal mammary stem cells at the molecular level and whence the stem cell related feature of a tumor derive. For instance, it is generally unknown whether stem cell phenotypes are inherited by tumor cells from a transformed normal stem cell or whether they are acquired through tumor-associated reprogramming events.

CONTENTS Introduction������� 121 Origins in Development����� 122 The Mammary Gland as a Classic Stem Cell–Driven System�������������� 123 Identification of Breast Cancer Stem Cells������������������ 124 Caveats of the Cancer Stem Cell Assay����������������� 125 Associated Cancer Stem Cell Markers and Regulators��� 127 Genome Wide Molecular Profiling in Breast Cancer��������������� 131 Stem Cell Programs in Breast Cancer��������������� 133 An Inducible Stemness���������� 135 121

Cancer Stem Cells. http://dx.doi.org/10.1016/B978-0-12-803892-5.00005-X Copyright © 2016 Elsevier Inc. All rights reserved.

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Tumor-Associated Reprogramming� 136 Tackling Heterogeneity at Single-Cell Resolution��������� 137 Conclusion�������� 139 List of Acronyms and Abbreviations���� 140 References�������� 141

In addition to highlighting some the most common markers, regulators, and biological processes uncovered for breast cancer stem cells (BCSCs) to date, this chapter will address several of the open questions and limitations inherent in the commonly used stem cell assays and their interpretation. However, the chief aim of the chapter is to emphasize the need for, and propose avenues to obtain, a more comprehensive understanding of molecular and cellular mechanisms fueling stem cell phenotypes in breast and other cancers, including how these parallel or contrast with normal developmental biology. A deeper and broader understanding of both normal and tumorigenic stem cell biology as systems may have profound impact on how we identify good molecular targets and conceptualize curative cancer therapies.

ORIGINS IN DEVELOPMENT The idea that tumors parallel or caricature normal development is not new (see Triolo 1965 [1]). From the mid-19th century tenets of cell theory advanced by Remak and Virchow describing the production of daughter cells from the division of parent cells [2,3], it logically follows that the generation of complex tissues would be the result of these daughter cells differentiating and, further, that the generation of tumor tissue that is also comprised of cells should represent some derangement of the same process. The outward similarities between rapidly dividing and plastic embryonic tissue and tumorigenic tissue (especially teratomas) led a number of 19th century researchers to propose a causative relationship whereby deviations in the embryo or embryonic remnants were responsible for tumorigenesis in the adult [1]; a concept that Cohnheim formalized as the embryonic rest hypothesis of cancer [4]. In the same vein, Durante opined more than 140 years ago that: Elements which have retained their […] embryonal characteristics in the adult organism, or have regained them through some chemico-physiologic deviation, represent […] the generative elements of every tumor variety and specifically those of a malignant nature. Such elements may remain enclosed within matured tissues for many years, giving no indication of their presence, until an irritation – a simple stimulus suffices – rekindles their vital cellular activities… F. Durante 1874 [1].

Consider how timely Durante’s hypothesis would still be were it offered today with these “elements” representing today’s CSCs. Over the next century a number of additional studies corroborated this relationship between early development and cancer at the molecular level, identifying immune antigens shared between cancers and embryonic material [5]. However, it was not until the landmark works from John Dick’s group, in the 1990s in

The Mammary Gland as a Classic Stem Cell–Driven System

the hematopoietic system, that the CSC hypothesis was formalized [6,7]. They reported that leukemias followed a cellular hierarchy related to that of the normal tissue where only a subset of cells were fully tumorigenic upon transplantation. Like their normal counterparts these cells were capable of giving rise to all of the heterogeneous cell types observed in the original tumor tissue and were inferred to be responsible for tumor propagation/perpetuation in situ [6,7]. Of course, this work drew inspiration from earlier work including, the seminal work of Till and McCullough demonstrating the presence of stem cell activity in bone marrow transplantation of irradiated mice [8]; work showing that leukemias could similarly be transferred from donor to host [9]; and a considerable body of work defining surface markers and differentiation hierarchies in the normal hematopoietic system and relating these to various blood cancers [10]. In this regard, it may also have been significant that expansion of recognizable blast populations in many leukemias correlate with blast populations in healthy hematopoiesis except for their number, emphasizing the role of defective differentiation in tumorigenesis over enhanced proliferation from mutated oncogenes per se. Thus, the many points of analogy between normal developmental cellular hierarchies and those observed in cancers undoubtedly played a central role in forming the concept of CSCs and in their experimental discovery.

THE MAMMARY GLAND AS A CLASSIC STEM CELL– DRIVEN SYSTEM In solid tissues, cellular hierarchies and markers (especially surface markers) that define parent/progeny relationships are typically less well defined than they are in the blood system. Nevertheless, over the past decade, the cancer stem cell paradigm has been applied to a wide variety solid tissue cancers including brain [11], colon [12], pancreas [13,14], prostate [15,16], and mammary gland [17]. Like the blood system, the mammary gland is a natural candidate for stem cell–driven biology with repetitive cycles of tissue expansion and involution. This was substantiated experimentally in classic studies by DeOme and subsequent work by others showing reconstitution of mammary tissue upon transplantation in mice [18]. Viral tagging experiments used in combination with DeOmes transplant approach further showed that individual cells could recapitulate the heterogeneity of the mammary gland and could self-renew [19]. Despite this clear evidence for the existence of mammary stem cells, no reproducible prospective markers for identifying and isolating the cells exhibiting stem cell activity in these assays were known until 2006. In that year, back to back papers in Nature prospectively isolated subpopulations of cells from mouse mammary glands based on their expression of the surface markers CD24 and either CD49f or CD29 that could reconstitute the mammary gland upon transplantation much more efficiently than their counterparts and could occasionally even do so as single cells [20,21].

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IDENTIFICATION OF BREAST CANCER STEM CELLS However, 3 years prior to the prospective isolation of mammary stem cells in mice, the groups of Michael Clarke and Max Wicha, borrowing markers from studies on melanoma and neural stem cells and applying them to human breast cancer, isolated the first solid tumor CSCs to be identified [17]. In their study, the majority of human breast cancers examined (8 of 9) contained a subset of cells that were CD44+CD24- and exhibited an elevated capacity to form xenografted tumors in NOD/SCID mice that bore the same cellular heterogeneity as the original tumor. The relationship of these BCSCs to normal stem cells of the mammary gland could not initially be determined, however, owing to a lack normal mammary stem cell markers, and the authors cautiously and perhaps more aptly referred to these cells as the tumorigenic cells or tumor-initiating cells. In 2008 Bob Weinberg’s group showed that the CD44+CD24- phenotype in human breast cells from both normal and cancer contexts was associated with markers of epithelial-to-mesenchymal transition (EMT) and enhanced performance in nonadherent mammosphere growth and differentiation assays developed by Dontu et al. [22,23]. These assays ostensibly measure stem cell activity as it was demonstrated that the 3D structures called mammospheres could be dissociated and reseeded in multiple generations demonstrating self-renewal and that they produced multilineage outgrowths when plated in differentiation promoting conditions [23]. Therefore, a link was postulated between EMT, CD44+/CD24- marker phenotype and both normal and tumorigenic stem cells [22]. In further support of this interpretation, the Weinberg study profiled a set of EMT markers in the recently identified mouse mammary stem cell enriched fraction and showed that these cells also exhibited relative mesenchymal gene expression profiles when compared to the non-stem cell– enriched epithelial compartment. Based on these initial studies, much of the subsequent mechanistic work in the BCSC field has focused on this marker set, this experimental setting and the EMT phenotype as a stem cell hallmark in both the normal and tumorigenic setting, as well as in tumor progression, metastasis, and therapy resistance [24]. However, while the aforementioned studies were under way the Wicha group reported a second strategy for identifying BCSCs based on their aldehyde dehydrogenase (ALDH) activity [25]. They reported that ALDH+ breast cancer cells as well as ALDH+ normal cells exhibited robust stem cell activity in mammosphere culture and in vivo, though it should be noted that the clonality of multilineage outgrowths from normal cells in vivo was not firmly established in these experiments. Surprisingly, they also found that the CD44+CD24- population and ALDH+ population only partially overlapped in breast cancers and that xenografts could be generated from both CD44+CD24- cells and

Caveats of the Cancer Stem Cell Assay

CD44-CD24+ cells provided these cells were ALDH+. In addition, cells jointly exhibiting both types of BCSC markers were reported to be the most competent subpopulation in these experiments. Thus, despite their discrepancies, these studies support well the idea that at any given point in time a tumor is likely comprised of heterogeneous cell types with differing potencies and associated risks, and therefore, that treatments targeting the most dangerous cell types in a tumor and reducing or eliminating them could perform better in the long run than treatments targeting the tumor bulk [25a]. In spite of this, there are a number of critical caveats to consider in these experiments.

CAVEATS OF THE CANCER STEM CELL ASSAY The process of isolating cells, sorting them, and culturing them in vitro and/or transplanting them into a xenogeneic host presents a number of selective pressures on cells that may not be universally germane in the context of tumors in situ. Thus, it is quite possible that some cells will be classified as non-CSCs because they perform poorly during these ex vivo manipulations but they may nevertheless harbor significant potential to drive tumor progression or recurrence in vivo. In this regard, the EMT phenotype is particularly suspect as a stem cell hallmark. In the Weinberg study correlating EMT with stem cells, the authors demonstrated that artificially inducing EMT through molecular genetic means appeared to increase the number of stem cells in assays using nontransformed mammary epithelial cell lines [22]. Though this was interpreted to mean that EMT “generates” stemness, an alternate explanation is that mesenchymal characteristics specifically confer competence in a particular facet of these assays (eg, anoikis resistance, attachment to substrata, etc.), such that cells already harboring stem cell potential are not lost. Indeed, a number of groups have pointed out that while mammospheres can arise from individual cells, they frequently represent aggregates when plated at higher densities [26] so it is also possible that the presence of epithelial-to-mesenchymal transitioned (EMT’d) cells may facilitate the survival of non-EMT’d or reverted cells in these assays through aggregation. The EMT phenotype may play a similar role in transplantation, by promoting critical cell–cell or cell–matrix interactions or facilitating migration toward nutrient supply or specific niches. This would be expected to favor the naturally more mesenchymal compartment, again quite independently of other core stem cell programs. Consistent with this caveat, lineage-tracing studies in the adult mouse mammary gland indicate the existence of two independent stem cell pools responsible for producing luminal or basal cells, respectively [27]. In those studies, as in previous studies only the basal compartment performed

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well upon transplantation as measured by mammary reconstitution and multilineage differentiation potential [20,21,27]. Thus, in both the normal and cancer situation, the requirements of the assay could overly restrict our view of the true stem cell pool. It is also noteworthy that most studies on BCSC have proven to be enrichment studies not purification studies as tumor-initiating activity is also present (albeit much reduced) in the “non-BCSC” compartment. For example, in the original report by Al-Hajj and colleagues, the findings held for eight out of nine cancers examined and but even among the eight examples with demonstrative BCSCs there were sporadic successes in xenografting from the non-CD44+CD24- population [17], and in other studies take rates from the non-CD44+CD24- population were quite appreciable [25]. This has rather obvious implications for the development of truly curative therapies that seek to exclusively target and track a single BCSC-enriched fraction. Namely, if residual cells with a low but measurable potential to regenerate the tumor exist in the so-called non-BCSC fraction then a paradox emerges where these cells are both non-BCSC by marker expression but retain BCSC like properties over the longer clinically relevant term. It may be that more effective or comprehensive CSC markers will emerge and rectify this apparent tautology. Alternatively, it may not be possible to find a marker set with the ability to purify stem cells from breast cancers (or normal tissues) if the pool of stem cells is itself inherently heterogeneous. Even more disruptive to the cancer stem cell model is the potential for bidirectional interconversion between different “stem cell” states (Fig. 5.1). Indeed, some studies examining breast cancer cell lines have demonstrated instances where tumors appear to be seeded by multiple cell compartments though they may exhibit somewhat different rates of interconversion [28–30]. Similarly, ALDH+ and CD44+CD24- BCSC populations were also shown to interconvert, but also to occupy largely distinct anatomical locations in the tumor in vivo suggesting an important role for the microenvironment in determining their molecular phenotype [31]. Again, even in cases where there is a relatively strong directionality to the BCSC/Non-BCSC hierarchy, given enough time retrograde conversion of the non-BCSC compartment to BCSC activity could lead to treatment failure. Such cellular plasticity may be a common mechanism for generating tumor cell heterogeneity. Finally, perhaps the most notable deficiency of CSC models is a very general one—that we just do not yet know enough about their broader molecular and cellular underpinnings. As indicated earlier, initial studies were aimed at identifying markers for prospective CSC isolation or identification in situ and much of the CSC field remains focused on biomarker identification for clinical detection irrespective of molecular function.

Associated Cancer Stem Cell Markers and Regulators

FIGURE 5.1  Breast cancer stem cell (BCSC) differentiation and interconversion. The best characterized BCSCs, CD44+CD24- cells (top left) and ALDH+ cells (top right), have been demonstrated to comprise largely independent pools and to interconvert. Although it has been shown that factors promoting EMT increase the number of CD44+CD24- BCSCs and that hypoxia can promote an increase in ALDH+ cells, it remains to be determined if other molecular mechanism can contribute to their interconversion. Additionally, there may exist alternative stem cell states (yellow cell, bottom right) that interconvert with those identified and characterized to date, but their markers and mechanisms of plasticity naturally remain to be determined. BCSC differentiation into non-BCSCs has been shown to accompany the action of various signaling molecules, epigenetic regulators, transcription factors, and microRNAs that promote changes in cell state (eg, MET, differentiation, metabolic changes, etc.) (see Table 5.1). However, the extent to which these changes are permanent in vivo generally remains to be determined, and there may be specific microenvironmental cues or genetic lesions that promote reversion of apparent non-BCSCs to BCSCs.

ASSOCIATED CANCER STEM CELL MARKERS AND REGULATORS Markers—These caveats aside, a growing body of work has begun to pick apart the molecular biology driving tumor propagation both globally and in select subsets of tumor cells such as the BCSC populations described earlier (see Table 5.1). Additional BCSC markers have also been proposed. PROCR and ESA were in fact proposed to be superior BCSC enrichment markers when compared to CD44+/CD24-cells in a panel of cell lines and primary tumors and the same study also found correlations between a variety of other proposed BCSC markers and progression to metastasis [32]. Among these the chemokine receptor CXCR4, has been implicated in EMT, stem cells and metastasis in breast and other cancers previously, and was specifically shown to increase mammosphere

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Table 5.1  Molecular Regulators in BCSCs BCSC Regulator

Direction of Effect

Proposed Mechanism

BMI1 c-JUN

Promotes Promotes

CD90 CXCR4 EPHA4 HH/Smoothened

Promotes Promotes Promotes Promotes

HIF1-α/2-α

Promotes

Silencing of differentiation genes Promotes EMT and BCSC growth in trans via expression of SCF and CCL5 Promotes niche interaction and Src and NFκB activation Unknown; promotes production of mammospheres Promotes niche interaction and Src and NFκB activation Multiple targets including STAT-3, BCL-2, CYCLIN-D3 and matrix metaloproteinases Multiple targets including IL-8, MDR-1, TAZ, SIAH-1 and potentialy ESC regulators and others.

ID4 IL-8/CXCR1/2 miR9 mTOR

Promotes promotes promotes Promotes

Blocks luminal differentiation genes Unknown; EGFR/Her2 dependent Blocks E-Cadherin expression Promotes drug resistance and colony formation

NOTCH1/4

Promotes

Differentiation blockade

P63 SLUG

Promotes Promotes

Activates CXCR4 Promotes CD44 expression; niche factor regulation

SNAIL SOX10 SOX9

Promotes Promotes Promotes

Transcriptional activation of ZEB1 Cooperates with SLUG Cooperates with SLUG; niche factor regulation

STAT3 TAZ

Promotes Promotes

Maintains ALDH+ cellular subset Promotes EMT, metastasis, and chemoresistance

TGF-β

Promotes

Classic promoter of EMT

TWIST

Promotes

Promotes EMT; suppresses CD24

WNT ZEB1

Promotes Promotes

Promotes colony growth in nonadherent conditions Promotes EMT and radioresistance

BMP2/7 DACH1 Let-7

Inhibits Inhibits Inhibits

Blocks TGF-β Blocks NANOG and SOX2 Inhibits multiple BCSC promoting genes including H-RAS and HMGA2

[73] [50] [72] [33] [72] [48] [105] [106] [107] [108] [109] [110] [111] [69] [77] [45] [46] [112] [62] [61] [113] [59] [62] [62] [113] [53] [114] [51] [115] [49] [63] [57] [43] [116] [117] [118] [119] [120] [121]

Associated Cancer Stem Cell Markers and Regulators

Table 5.1  Molecular Regulators in BCSCs—cont’d BCSC Regulator

Direction of Effect

Proposed Mechanism

MEL18 miR200c miR27b

Inhibits Inhibits Inhibits

miR30C

Inhibits

Blocks WNT and NOTCH via BMI1 Reduces chemoresistance Inhibits ABCG2 mediated drug resistance by suppressing ENPP1 Blocks migration and chemorsesistance through cytoskeletal effects and IL-11 downregulation

miR34a miR99 P53

Inhibits Inhibits Inhibits

Downregulates NOTCH Blocks mTOR Multiple targets including transactivation of miR200c

[76] [77] [75] [78] [124]

UTX

Inhibits

Epigenetic repression of EMT genes

[54]

forming ability of breast cancer cell lines under stimulation with ligand [33]. However, most studies have continued to utilize the CD44+/24- profile or ALDH+ to mark BCSCs. Functionally, ALDH is a detoxifying enzyme with reported roles in retinoic acid synthesis and signaling [34]. The CD44 BCSC marker is a transmembrane protein that binds components in the extracellular matrix including fibronectin and hyaluronan, among others [35]. It has been demonstrated to be functionally relevant to the CD44+/CD24- BCSC as its downregulation promotes their differentiation to the non-BCSC phenotype and inhibits their tumorigenic capacity [36]. More recently, genetically encoded marker systems have been developed that permit isolation and tracking of putative cancer stem cells based on the activity of stem cell–related gene promoters. One such system, utilizing lentiviral delivery of a green fluorescent protein (GFP) and luciferase reporter construct that responds to the Notch intracellular domain (NICD)-transcription complex, determined that breast cancer cells with active Notch signaling exhibit increased self-renewal relative to reporter-negative cells [37]. This is consistent with known roles for Notch in the regulation of normal stem and progenitor phenotypes. Critically, Notch-reporter activity also correlated with increased xenograft initiating ability in diverse breast cancer cell lines [37]. Similarly, two other recent studies used lentiviral molecular genetic systems that report the activity of the core embryonic stem (ES) cell transcription factors, Oct4 and Sox2 to isolate BCSCs [38,39]. On average, the expression of these factors and their stem cell target gene, Nanog, appears to be hundreds to thousands of times lower in breast cancer cells than it is in ES cell cultures possibly explaining their low and sporadic detection in several earlier breast cancer studies focused on bulk samples [40–42]. Nevertheless, despite these low levels, cells showing reporter construct activation exhibited enhanced self-renewal, enhanced xenograft and metastasis initiating ability, and intrinsic insensitivity

[122] [74] [123]

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to chemotherapeutic agents in vitro [38]. Reporter positive cells were highly correlated to the ALDH+ BCSC marker type but showed even greater xenograft initiating capacity than the ALDH+ population suggesting that Nanog is a higher specificity BCSC marker and leading to the identification of the tight junction protein JAM-A, as an improved specificity BCSC surface marker with therapeutic potential [39]. Of course, more studies will be needed to establish the generality of this promising new marker and its utility as a therapeutic target. It will also be interesting to determine if lentiviral delivered Notch, Sox2, and Oct4 responsive vectors show such high fidelity in reporting specific pathway signaling and identifying CSCs in other experimental settings and tumor types, and whether cancers are capable of exploiting alternative factors to achieve similar malignant cellular phenotypes. Signaling Pathways—Beyond these markers, myriad other molecular players ranging from cell surface signaling pathways to transcription factor networks have been functionally implicated in BCSCs. For example, there are reported roles for Wnt [43,44], Notch [45,46], HH [47,48], and Tgf-β [49] signaling and at the transcriptional level critical roles for c-Jun [50], Taz [51,52], Stat3 [53], and others. In addition, chromatin modifiers are thought to add an additional layer of transcriptional control [54–56]. Potential regulators of EMT have garnered the most attention among transcription factors in BCSCs including Zeb1 [54], Twist [57,58], Snail [59,60], and Slug [61]. Weinberg’s group reported cooperativity between Slug and Sox9 (or Sox10) in promoting the stem cell state in mammary cells [62], although recently they have also reported that normal stem cells and BCSCs use different molecular variations on the EMT theme, with BCSC preferring Snail to Slug [59]. Another EMT-promoting transcription factor, Twist, has also been recognized to regulate breast cancer cell EMT and metastasis and to directly repress CD24 expression in BCSC [63,57]. Microenvironment and Stroma—However, CD24 is also under the control of hypoxia inducible factor, Hif-1α [64], and broader roles for hypoxia in regulating BCSC have been reported (see Semenza [65]) and may be particularly relevant in defining the ALDH+ BCSC niche [66]. In addition to hypoxia, a number of additional reports suggest BCSCs may be sensitive to other microenvironmental cues and stresses. These stresses may specifically impinge on unique BCSC metabolism as there are several reports indicating BCSC sensitivity to Metformin [67,68]. mTOR dysregulation has also been implicated in BCSC survival [69]. Recently, Dong et al. described a mechanism coupling EMT with glucose metabolism and a reduction of mitochondrial ROS and this could play a critical role in chemotherapy insensitivity of BCSCs [60]. Autophagy was also recently reported to play a critical role in BCSC maintenance through regulation of CD24 and IL-6 [70]. BCSC may also be sensitive to their cellular microenvironment as several reports indicate immune cell modulation of BCSC function. CD8T cells were reported to actively promote the

Genome Wide Molecular Profiling in Breast Cancer

BCSC phenotype in tumors in FVBneu-tg mice [71] though the precise mechanism remains to be elucidated. Monocytes and macrophages may also contribute to BCSCs maintenance through juxtacrine signaling involving CD90 and EphA4 [72]. MicroRNAs—A sizeable proportion of the mechanistic literature concerning BCSCs has focused on their regulation by microRNAs (miRNAs, miR). In 2009, Clarke’s group identified a set of 37 miRNAs that were differentially expressed between BCSC and non-BCSCs [73]. Several of these miRNAs have been shown to target pathways critical to BCSC function. Among these miR200c was found to regulate Bmi1, a polycomb chromatin modifier critical in normal mammary cells and stemlike embryonal carcinoma cells where it is similarly targeted by miR200c [73]. miR200c expression was also subsequently shown to enhance chemosensitivity and reverse the metastatic potential of Claudin-low breast cancer cells [74]. The MCF7 breast cancer cell line has a high proportion of CD44+/CD24- cells, and another microRNA, miR34a, was reported to function through Notch to sensitize MCF7 cells to Doxorubicin [75]. Similarly, miR30c was shown to effect BCSC chemoresistence, though in this case it acts through regulation of the actin-binding protein TWF and the cytokine, IL-11 [76]. miR30c was also shown to regulate cellular migration and metastasis through its effects on cytoskeletal components [77]. In two other examples, among many, Let-7 repression downstream of Wnt has been shown to promote BCSCs [44], and miR99 [78] has been reported to integrate their EMT and mTor signaling. Together these studies, a brief survey of the total literature really, have provided inroads for understanding BCSC at the molecular level. It will be important to continue expanding this mechanistic knowledge base within and outside of the BCSC compartment and to identify associated opportunities for therapeutic intervention in breast cancer, especially for those tumor types where molecularly targeted therapies are lacking and where existing approaches are frequently ineffective and bear harmful side effects.

GENOME WIDE MOLECULAR PROFILING IN BREAST CANCER Historically, the molecular biology of cancer has been understood only in terms of a handful of markers and pathways that could be feasibly picked apart through gene-by-gene molecular genetic approaches similar to those described earlier. Though enlightening in its own right, the resulting somewhat narrow view of tumor biology is naturally prone to miss many integrated biological processes. The advent of genome wide technologies (eg, genomics, transcriptomics, proteomics, etc.) has promised to permit a more comprehensive and systems-oriented view. Through such comprehensive methods, we might expect identification of novel candidate therapeutic targets even when these are not

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directly or commonly mutated, or even when they are not proximal to a commonly mutated gene in the cellular signaling network. In addition to facilitating the identification of new molecular targets for the bulk of tumor cells, comprehensive knowledge of tumor cell genes and transcripts can facilitate comparisons to the molecular underpinnings of the normal stem cells state and potentially, by broad molecular similarity, point to a cell of origin for a given manifestation of the disease. Genome wide approaches, particularly genomics and transcriptomics, have already been extensively applied in breast cancer. Large-scale sequencing and cataloging projects, most notably The Cancer Genome Atlas (TCGA) have begun compiling genetic lesion databases across many samples in many tumor types. In breast cancer these studies reveal that (1) the most prevalent driver mutations were those already uncovered by traditional molecular genetic approaches, although novel significantly mutated genes were identified and (2) these mutations alone and in combination are insufficient to wholly describe tumor type, since several of the common genetic lesions (eg, PIK3CA mutation, p53 inactivation) are distributed across multiple subtypes [79]. Therefore, these studies further support the idea that understanding tumor cell biology and ultimately BCSC biology will require not only an understanding of the genetic lesions in a tumor, but how these integrate with the tumor cells’ broader molecular circuitries. Ultimately this should extend to analysis of the epigenome, transcriptome, proteome, metabolome, etc. and will need to be considered not only on the level of the bulk tumor but also in terms of its subpopulations and infiltrating nontransformed cells. Among these technologies transcriptomics (gene expression profiling) is by far the most well-developed and widely used approach to date. For more than a decade in fact, multisample data sets comprised of expression profiles from various normal tissues, cancers and cell lines have been produced by multiple laboratories and made publically available for analysis. The resulting data sets have been used to identify candidate genes for study in experimental systems, for tumor classification, for prognostic and predictive clinical tool development, and in comparative studies; for instance, those examining the similarity of a given tumor to a normal stem or progenitor cell population. Studies of this later type in the mammary gland and breast cancer are among the most prolific. Breast cancer has long been recognized as a diverse set of diseases, with some cases exhibiting significant cellular differentiation and others less. Clinically, it is defined by this heterogeneity and other standard histopathological features such as nuclear atypia, mitotic index, the integrity of tissue boundaries, etc. Over the past couple of decades, a handful of

Stem Cell Programs in Breast Cancer

molecular markers have also become routine in the clinical classification of breast cancers. The hormone receptors for estrogen and progesterone (ER and PR, respectively), and HER2/neu, an epithelial growth factor receptor family member (ErbB2), serve not only as clinical designations but as targets for molecular therapy. In contrast, triple-negative breast cancers (TNBCs) lacking these markers are considered the most clinically recalcitrant and are presently treated with relatively nonspecific chemotherapy approaches in the clinical arsenal [80]. However, beginning in 2000, gene expression profiling efforts began to vastly broaden the molecular profile available in breast cancer classification and analysis [81]. Perou and colleagues identified “intrinsic” subtypes of breast cancer based on clusters of similar gene expression within large multitumor gene expression data sets. These intrinsic subtypes were named based on their differential expression of genes known to be expressed in various normal mammary cellular compartments or deregulated in particular types of breast cancer. Ultimately, although alternative clustering strategies have been proposed, six distinct clusters of tumor samples were identified [82]. These included two subtypes with expression of luminal characteristics referred to as Luminal A and Luminal B, a Her2-enriched subtype with many tumors showing elevated Her2 expression, a Basal-like subtype, and a more mesenchymal subtype called Claudin-low due to reduced expression of claudins 3, 4, 7, and other markers, as well as a cluster that was more similar to normal mammary tissue, the so-called Normal-like subtype [83].

STEM CELL PROGRAMS IN BREAST CANCER Though the overlap between clinically defined tumor types and intrinsic subtypes is not an exact match, the Basal-like intrinsic subtype includes the vast majority of TNBCs and these were initially thought to best reflect a dedifferentiated or stem cell–like molecular phenotype [83]. However, based partly on mesenchymal features reported in the CSC studies described earlier and partly based on comparative transcriptomic studies between luminal progenitor–enriched populations and Basal-like breast cancers [84], Perou and colleagues generated a differentiation score for tumors as a prognostic indicator that equated Claudin-low tumors with the stem cell transcriptional state [28]. In this schema, Basal-like breast cancers were further along the differentiation scale toward the phenotype of the normal luminal progenitors. These studies also inferred like many others that the observed relationships were likely to reflect the cell of origin for the tumors or at least the state of differentiation arrest since a tumor could be driven by rare BCSCs with a more primitive molecular phenotype than the tumor bulk. Indeed, one study reported that the

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degree to which a given tumor exhibits a stem cell gene expression profile is directly correlated to the number of stem cells in that tumor [85]. However, in contrast to the differentiation score, comparative analysis from Ben-Porath et al. demonstrated significant enrichment for ES cell signatures in Basal-like breast cancers [41]; a finding that we subsequently showed was tightly correlated to the tumor’s p53 status [42]. Interestingly, attenuated p53 may be a natural feature of the stem cells state [86], and several groups demonstrated that p53 attenuation facilitates cellular reprogramming [87]. In our study, we also showed that induced pluripotent stem (iPS) cell–derived signatures were enriched in Basal-like and p53 mutated cancers, an analysis meant to highlight the potential for tumor associate reprogramming as means to acquiring a stem cell profile during tumorigenesis. The stem cell signatures we used in these analyses were typically comprised of hundreds of genes including Oct4, Sox2, and Nanog, but we did not see a correlation between expression of these three factors and any group of breast cancers although Ben-Porath et al. reported that expression of their target genes correlates with the Basal-like subtype and high tumor grade. In our analysis, neither the iPS cell signature nor the ES cell signatures were well represented in the highly EMT-related Claudin-low breast cancer intrinsic subtype. Both the ES cell and iPS cell signatures were derived from culture contexts where the percentage of stem cells is expected to be very high and where the expression profile of the bulk sample is consequently unlikely to diverge significantly from the average stem cell. This is in contrast to the putative adult stem cell signatures analyzed to date where the actual stem cells are a relatively small minority of cells. The differences in the spectrum of tumors enriched for the ES/iPS cell signatures verses the putative adult stem cell signatures could relate to the different stem cell content of their source populations. It could also be true that the ES cell expression profile is significantly different from tissue specific stem cell profiles at the cellular level, leading to different enrichment patterns in breast cancers. However, we subsequently isolated and profiled a highly enriched population of mammary stem cells from the mouse embryo that did show strong enrichment for Basal-like breast cancers similar to ES and iPS cell signatures [88]. In examining the fetal mammary stem cell profiles, we noted that these fetal mammary stem cells were clearly more epithelial in nature than the associated stroma as they express high levels of CD24 and coexpress both basal and luminal cytokeratins though they did express some mesenchymal markers (eg, vimentin). This is in contrast to previous studies on adult mammary stem cells and BCSCs that would have predicted them to be highly EMT’d. Also in contrast to the common expectation for stem cells, the fetal stem cell fraction did not grow efficiently in nonadherent mammosphere culture though the more mesenchymal stromal, non-stem cells did. It remains to be determined how similar the expression profiles of fetal mammary stem

An Inducible Stemness

cells and highly purified adult mammary stem cells would be pending purification of the later, and it is also unknown how much of a role microenvironment would play in these differences.

AN INDUCIBLE STEMNESS Normal stem cells in other tissue systems are well known to require highly controlled culture conditions in vitro and to depend upon highly specialized niches in vivo to self-renew, differentiate, or possibly dedifferentiate. One notable example of the later is that of tuft cells in the intestine, which do not fit the definition of tissue stem cells “until an irritation—a simple stimulus suffices” to induce them to behave as such; in this case chronic injury and inflammation [89]. Though the mammary stem cell niche and the cues that can evoke latent stem cell capacity remain largely undefined, several groups have demonstrated that mammary stem cells are under similar contextual regulation. Transplantation analysis is significantly affected by the genetics, hormone levels and innate immune system of the host [90]. Furthermore, transplantation efficiencies are dramatically augmented by supplementation with reconstituted extracellular matrix [90]. The performance of normal mammary stem cells in culture is similarly sensitive to matrix and growth factor control, with some factors promoting self-renewal and others promoting differentiation [91]. Furthermore, fetal mammary stem cells can be dramatically and reversibly shifted from an epithelial state to a mesenchymal state by modulating levels of Sox10 potentially in coordination with specific fibroblast growth factor (FGF) signaling [92]. Such flexibility may be essential to normal organogenesis and wound healing. In addition, recent studies show that oncogene activation can reactivate bilineage differentiation potential in adult basal and luminal cells in vivo prior to significant tumor progression [93], similar to the bilineage potential we observed for fetal mammary stem cells (fMaSC) in the rapidly developing and remodeling mammary rudiment [88]. This context dependence for both molecular profile and stem cell fitness (ie, performance in certain assays or tumor propagation in vivo) is likely at play in cancer as well. For instance, the distinct anatomical niche of ALDH+ cells appears to be in the more hypoxic zones of the tumor relative to the CD44+CD24- pool. This raises the hypothesis that hypoxia may specifically evoke the ALDH+ phenotype and/or that the ALDH+ cells are particularly fit for survival and growth in hypoxia [31]. Studies showing that ALDH+ BCSC increase in tumors after antiangiogenic treatment with Avastin advance this idea [66] and fits well with hypotheses we and others have also raised that nutrient deprivation and hypoxic stresses could specifically promote stem cell phenotypes [91]. Thus, it may be a general principal in complex tissues that many cells possess latent stem cell potential needing only the right cue to “rekindle their vital cellular activities….”

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TUMOR-ASSOCIATED REPROGRAMMING The ability of particular stimuli or stresses to restore primitive, stem cell–like behaviors in cancer cells, akin to in vitro cellular reprogramming, would no doubt also be colored by oncogene activation and loss of tumor suppressor activity (eg, p53). Given these added factors, it seems unlikely that such a process would achieve complete and faithful recapitulation of normal stem cell gene expression programs. It may be relevant in this regard that the stem cell signature enrichment we have observed in cancers are virtually always modular [88,94]. That is, when we derive stem cell expression profiles and compare them to human tumor data, we typically find large coordinately expressed groups of genes as well as some genes and groups of genes that are not expressed. Critically, we do not find that this undermines the value of comparisons between developmental programs and cancer, and recently, we reported that modules from both luminal progenitors and fetal mammary stem cells were useful in predicting breast cancer response to neoadjuvant chemotherapy [94]. However, it is not always clear why tumors would selectively reactivate a given portion of a stem cell gene expression program but we can speculate that cancers are accessing combinatorial biological programs not only from the repertoire of what does happen in normal development but also what can happen in all possible functional combinations. Thus, stem cell activity in tumors may be achieved through “unnatural” stem cell states, crafted by co-opting and corrupting whichever developmental programs can respond to the stress at hand and there could be many such states (Fig. 5.2). Such a model could explain

FIGURE 5.2  Model of normal and cancer stem cell (CSC) hierarchies. In this take on a Waddington diagram, (A) represents a normal differentiation hierarchy (cyan) where the stem cell (SC) pool cascades along several paths of lineage commitment to produce three distinct differentiated cell types within the tissue. A different tissue (yellow) is shown in the background with a similar hierarchy. (B) Panel B represents a model cancer hierarchy. In this model, the bulk of the tumor (purple) is derived from a CSC pool resembling the normal stem cell (red, CSC1) but is potentially fed or interconverting with a second CSC pool (CSC2) representing a more primitive stem cell state or alternative niche not usually utilized in the adult. A third CSC pool (CSC3) may be able to yield cells atypical for the tissue that are functional in some other capacity such as providing blood flow (orange) but these are also not normally represented in the adult. There may be many possible CSC states potentially available to the growing cancer represented by the additional valleys in the diagram.

Tackling Heterogeneity at Single-Cell Resolution

Weinberg’s recent observation that different EMT programs are used by BCSCs than are used by normal mammary stem cells [59]. This model could also explain reports of transdifferentiation and non-tissue of origin mimicry and could help to explain a significant amount of intertumor heterogeneity even within an intrinsic subtype as there could be multiple ways to achieve the same overall tumor phenotype. While the model is perhaps not as simple as the original CSC model, it would predict that along with the significant similarities between normal stem cells and CSCs there are fundamental differences that could be exploited to target tumor cells without damaging the host. However, under this model it is unclear how many stem cell states might be achievable by a tumor or how many of them would need to be targeted to cure the disease.

TACKLING HETEROGENEITY AT SINGLE-CELL RESOLUTION Ultimately, it will be clinically essential to know how many functional/dangerous cell states are accessed by a tumor. Here, “dangerous” means those cells capable of operating as CSCs in the broadest sense of propagating or disseminating tumor growth and perpetuating the disease in active or dormant states. Single-cell sequencing has begun to provide inroads into this daunting task. At the genomic level, a study by Navin et al. analyzed 100 single TNBC cells for copy number variations identifying punctuated disequilibrium in cancer evolution and rare subclones with massive KRAS amplification, two novel observations that were unlikely to have emerged from traditional analysis [95]. A subsequent work identified an even broader potential heterogeneity of genetic subclones and speculated that these could represent differing fitness for specific environments in the tumor and in the tumor’s future including the stresses we have highlighted earlier such as nutrient deprivation and hypoxia [96]. At the gene expression level, single-cell studies are just now beginning to emerge in numbers. While there are some quite early studies that amplified single-cell cDNA libraries for analysis on arrays. Many studies have turned to a “many cells × many genes” RT-PCR approach, sometimes with the aid of rapidly advancing microfluidic technologies. These studies are proving informative in terms of identifying and validating the expression of various candidate stem cell regulator genes, but also revealing general principles of cell biology overlooked by bulk gene expression analysis. For instance, single-cell profiling on fMaSC revealed the expression of key candidates stem cell regulators in subsets of cells (eg, lgr5, Sox10), validated the coexpression of luminal and basal characteristics within individual cells and uncovered a striking amount of diversity of gene expression patterns even within a cluster of significantly related cells [88]. A very recent study used a similar approach to identify stem cell programs in metastatic breast cancer [97]. However, a

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significant drawback to these approaches is their limitation on the discovery of novel-involved genes and splice variants since a limited set of genes must be chosen in the assay. Recent, advances in single-cell transcriptional profiling using RNA sequencing (RNA-Seq), which lacks this limitation, have raised the unprecedented possibility of deconstructing complex tissues at single-cell resolution and reintegrating this information for a systems-level understanding of cancer and cancer stem cells. For instance, single-cell RNA-Seq may aid in establishing how many distinct cancer stem cell pools there are in a particular tumor as well as how internally heterogeneous these pools are. One recent study profiled 430 single cells from glioblastoma and determined that cells of the tumor exhibit a gradient of relationships to stem cell profiles rather than having a distinct and clearly identifiable subset of stemlike cells [98]. Recently, Lee et al. profiled individual MDA-MB-231 cells before and after treatment with Paclitaxel to study drug resistance dynamics using RNA-Seq and identified microtubule stabilization effects correlated with drug tolerance [99]. In addition, a recent methodological report suggested that cell types that are extremely rare and even those represented by an individual cell in a given experiment can be identified and characterized [100]. Though this procedure still requires some vetting in practice, it raises the very exciting prospect of characterizing early neoplasias, residual disease following therapy, dormant cells, circulating tumor cells, micrometastases, and many other rare, previously inaccessible or unculturable cell types. The extensive cellular heterogeneity indicated by existing single-cell profiling data in both normal and neoplastic contexts represents a daunting challenge but also a tremendous opportunity to uncover new principals of tissue organization and new therapeutic targets. In this regard it may be worth remembering that several clinical tests for existing molecularly targeted therapies (eg, Her2) are based not only on the identification of receptor expression in the tumor but on the number of cells expressing the target. Despite this heterogeneity, molecularly targeted therapies can sometimes be effective although response is often significantly lower in patients with heterogeneous versus homogenous HER2 positivity [101]. There are several possible reasons for this. For instance, it may be that the Her2 positive fraction of cells represents the sole BCSC for this disease. Alternatively, all tumorigenic cells may need to traffic through a receptor positive status even if the individual cell would have been diagnostically negative at the time of the test, or there may be bystander effects where targeting of the positive cells leads to destruction of nearby negative cells. In some tumors there may be obligate heterogeneity with some cells playing niche roles and others playing stem cell roles, and either stem cell or niche may provide valid therapeutic target, as

Conclusion

would targeting their physical interaction. Some tumors may recruit a normal niche cell from the stroma. Such a recruited niche might have less potential for developing therapy resistance. Still other tumors may differentiate to provide their own niche cells, while still others may be relatively cell autonomous for niche signaling. All of these possibilities may color the hierarchical structure of a given tumor and influence how CSCs are best defined in each case. Understanding these scenarios could easily prove impenetrable without single-cell resolution studies and potentially the integration of this information across genomics, epigenomics, proteomics, metabolomics analyses as new technologies enable these to function at high resolution.

CONCLUSION Since the identification of BCSCs in 2003, the cancer stem cell literature has grown dramatically. Indeed, it would be somewhat impractical to recount here in any appreciable detail the myriad molecules and pathways that have been reported to play critical, stem cell–related roles in breast cancer. Furthermore, as several dozen reviews covering many specialized molecular aspects of BCSC biology have been published even within the last year, this chapter has focused instead on challenges and critical next steps. The hope is that future work along these and related trajectories will lead to clinically relevant improvements in our understanding of the problem that the cancer stem cell hypothesis at its core has sought to address; namely that of cellular heterogeneity and plasticity in breast and other cancers [102–104]. Though some pieces of the puzzle have been assembled, no consensus regulatory circuitry has been defined for the mammary gland stem cells or BCSCs. We can anticipate that BCSC circuitries would borrow extensively from developmental paradigms, that they will involve multigene networks and systems-level processes, and that these could point to new strategies for molecularly targeted therapies. However, we do not yet know the catalog of molecular regulators, microenvironmental/niche cues and selective pressures that diversify upon developmental themes in breast cancer or BCSCs. We do know that multiple stem cell states exist as they have been reported in both the normal mammary gland and in tumors. However, we know little about their underlying mechanisms or mechanisms facilitating their functional plasticity. Indeed, it may be necessary to piece together many individual stem cell profiles to understand this continuum. Recent advances in molecular profiling especially as these progress to higher resolution and even single cells hold great promise for obtaining the systems-level view of stem cell function in normal mammary tissue and in breast cancer. They promise to help answer a number of critical questions: How

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similar are the mammary stem cell and BCSC states at the molecular level? How many BCSC states are there? How do they interconvert? Do molecular profiles identify cells with latent stem cell potential? What cues can “rekindle” them? Answering these questions will in turn allow us to make better sense of the outward similarities between stem cells, development and cancer and to better choose cellular and molecular therapeutic targets at all stages of the disease.

List of Acronyms and Abbreviations ALDH  Aldehyde dehydrogenase Bmi1  B lymphoma Moloney murine leukemia virus insertion region 1 homolog BCSC  Breast cancer stem cell c-Jun  Cellular Jun protooncogene CD24,49f,44,90  Cluster of differentiation CSC  Cancer stem cell CXCR4  c-x-c chemokine receptor type 4 EMT  Epithelial-to-mesenchymal transition EphA4  Ephrin type-4 receptor 4 ER  Estrogen receptor ErbB2  Erythroblastic leukemia B2 protooncogene ES  Embryonic stem (cell) ESA  Epithelial surface antigen FGF  Fibroblast growth factor fMaSC  Fetal mammary stem cell FVBneu-tg  Friend Virus B-Type mouse strain with neu transgene GFP  Green fluorescent protein HER2  Human epidermal growth factor receptor 2 HH  Hedge hog Hif1-a  Hypoxia inducible factor IL-11  Interleukin 11 iPS  Induced pluripotent stem (cell) JAM-A  Junctional adhesion molecule -A Let-7  Lethal 7 gene Lgr5  Leucine-rich repeat-containing G-protein receptor 5 MCF7  Michigan cancer foundation 7 brest cancer cell line MDAMB231  MD Anderson metastatic breast cancer 231 miR  Micro-ribonucleic acid miRNA  Micro-ribonucleic acid mTOR  Mechanistic target of rapamycin neu  Neuronal protooncogene NICD  Notch intracellular domain NOD/SCID  Nonobese diabetic/severe combined immunodeficiency mouse strain Oct4  Octamer binding transcription factor 4 p53  Protein product of transformation-related protein 53 gene PIK3CA  Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit A PR  Progesterone receptor

References

PROCR  Protein c receptor RNA-Seq  Ribonucleic acid sequencing Sox2, 9, 10  (Sex determining region y)-box region 2, 9, 10 Stat3  Signal transducer and activator of transcription 3 TAZ  Tafazzin TCGA  The cancer genome atlas Tgf-β  Transforming growth factor-β TNBC  Triple-negative breast cancer Wnt  Wingless/integration Zeb1  Zinc finger e-box homeobox 1

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