Frontiers reaches 6.4 on Journal Impact Factors

Hypothesis and Theory ARTICLE

Front. Oncol., 29 September 2014 | https://doi.org/10.3389/fonc.2014.00262

Metabostemness: a new cancer hallmark

imageJavier A. Menendez1,2* and imageTomás Alarcón3
  • 1Metabolism and Cancer Group, Translational Research Laboratory, Catalan Institute of Oncology-Girona (ICO-Girona), Girona, Spain
  • 2Girona Biomedical Research Institute (IDIBGI), Girona, Spain
  • 3Computational and Mathematical Biology Research Group, Centre de Recerca Matemàtica (CRM), Barcelona, Spain

The acquisition of and departure from stemness in cancer tissues might not only be hardwired by genetic controllers, but also by the pivotal regulatory role of the cellular metabotype, which may act as a “starter dough” for cancer stemness traits. We have coined the term metabostemness to refer to the metabolic parameters causally controlling or functionally substituting the epitranscriptional orchestration of the genetic reprograming that redirects normal and tumor cells toward less-differentiated cancer stem cell (CSC) cellular states. Certain metabotypic alterations might operate as pivotal molecular events rendering a cell of origin susceptible to epigenetic rewiring required for the acquisition of aberrant stemness and, concurrently, of refractoriness to differentiation. The metabostemness attribute can remove, diminish, or modify the nature of molecular barriers present in Waddington’s epigenetic landscapes, thus allowing differentiated cells to more easily (re)-enter into CSC cellular macrostates. Activation of the metabostemness trait can poise cells with chromatin states competent for rapid dedifferentiation while concomitantly setting the idoneous metabolic stage for later reprograming stimuli to finish the journey from non-cancerous into tumor-initiating cells. Because only a few permitted metabotypes will be compatible with the operational properties owned by CSC cellular states, the metabostemness property provides a new framework through which to pharmacologically resolve the apparently impossible problem of discovering drugs aimed to target the molecular biology of the cancer stemness itself. The metabostemness cancer hallmark generates a shifting oncology theory that should guide a new era of metabolo-epigenetic cancer precision medicine.

Introduction

We are accumulating ever-growing evidence that metabolism and stemness are highly intertwined processes in tumor tissues. Cancer is beginning to be understood as a disease of reprograming that appears to involve the progressive resetting of the metabolic infrastructure and metabolite levels concomitantly with changes in cellular differentiation (19). The modulation of metabolism and associated signaling is being increasingly implicated in the determination of cell identity during nuclear reprograming and oncogenesis. The transformation of cellular metabolism precedes changes in stemness and, therefore, metabolic reprograming appears to reflect the molecular dynamics fundamental for the rearranging and redirection of cell-fate (1030). Moreover, we have recently learned that certain metabolites can be oncogenic themselves and, crucially, the malignant activity of these oncometabolites, i.e., small-molecule components (or enantiomers) of normal metabolism whose accumulation causes signaling dysregulation to establish a milieu that initiates and drives carcinogenesis (3145) likely relies on their ability to epigenetically block the acquisition of differentiation markers while inducing the expression of stem cell maintenance genes. A key question, however, remains unanswered: how can metabolism and metabolites exert influence over the transcriptional factors, the chromatin structure, and the epigenetic circuits that establish and maintain the self-renewal and differentiation capacities owned by cancer stem cells (CSCs), which are suggested to drive tumor-initiation and metastatic progression?

Herein, we propose that the molecular logic behind the conversion of non-CSCs into CSCs can be better understood in terms of cellular metabotypes that operate as pathways or roadblocks by facilitating or impeding, respectively, the epitranscriptional orchestration of the genetic reprograming that drives the intrinsic and microenvironmental paths to CSC cellular states. We therefore postulate that a bona fide metabolo-epigenetic reprograming of stemness exists in pre-malignant and cancer tissues, a new cancer trait to which we have coined the name “metabostemness.”

Metabolism: Emerging as a Cancer Hallmark

When Hanahan and Weinberg revisited the hallmarks of cancer in 2011 (46), they raised the question of whether the deregulation of cellular metabolism in tumor tissues might be viewed as a core hallmark capability of cancer cells that is as fundamental as the six well-established core hallmarks formerly proposed in 2000 (47). Alternatively, the reprograming of cancer metabolism might be merely viewed as an evolutionary conserved target that is upstream programed by oncogenic gain-of-function events and the loss of tumor-suppressors (48). The latter view implies that a stereotyped pattern of cancer-associated metabolic changes including accelerated glucose transport, reduced mitochondrial oxidative phosphorylation (OXPHOS) accompanied by aerobic glycolysis and lactate production (i.e., the Warburg effect), and augmented de novo fatty acid biogenesis (i.e., the lipogenic phenotype), can be all induced by most common genetic alterations in the oncogenic PI3K/AKT/mTOR/HIF axis and in the tumor-suppressor p53 system (4953). Not surprisingly, the metabolic signatures of cancer cells have been frequently perceived by traditional biochemists as indirect, secondary phenomena that are merely required to support oncogene-directed anabolic proliferation and survival. Instead of adopting the challenging notion that tumor cells might essentially exhibit increased autonomy in maintaining an anabolic phenotype because proto-oncogenes and tumor-suppressors originated through evolution as components of metabolic regulation, Hanahan and Weinberg rather considered cluster analyses showing that several cancer-driving mutations converge on metabolic pathways. Subsequently, they designed cancer metabolic reprograming as an “emerging hallmark” to highlight the unresolved issues surrounding its functional independence from the bona fide cancer hallmarks (46, 47).

Stemness: A forgotten Core Cancer Capability

Several researchers have advocated incorporating the two key properties of stem cells, i.e., the ability to proliferate without lineage commitment (i.e., self-renewal), and the capacity to differentiate into one or more specialized cell types (i.e., pluripotency), as a new-dimensional hallmark of cancer (5458). The role of stemness as a cancer attribute was originally identified from the analysis of the outcomes of high-throughput gene expression datasets revealing that biologically aggressive, poorly differentiated tumors display transcriptional profiles characterized by the overrepresentation of gene signatures usually enriched in embryonic stem cells (ESCs) (5963). Some carcinomas appear to hijack the stemness transcriptional factors’ machinery to support tumor-initiation, aberrant proliferation, and metastasis; accordingly, the activation of reprograming-like dedifferentiation mechanisms driven by master regulators of self-renewal and pluripotency (e.g., Sox2, Oct4, and Lin28) has been repeatedly shown to generate cell populations enriched with CSC-like cells that possess tumor-initiation and colonization capacities (6472). However, the pioneer suggestion by Bond et al. (73) almost 20 years ago that the apparent dedifferentiation accompanying malignant progression can play a causal rather than passive role in the critical tumors-behavior-switch from well-differentiated to highly aggressive forms has been commonly forgotten. Most cancer researchers have adopted an alternative view, in which tumors adhere to essentially irreversible top-down hierarchies of CSC-driven cellular differentiation that caricature those occurring in normal tissues. As for metabolic reprograming, the stemness-related loss of differentiation, one fundamental characteristic of most tumor tissues, was not considered a distinct hallmark in the framework provided by Hanahan and Weinberg in 2011.

Stemness in Cancer Tissues: What is the Origin of Cancer Stem Cells?

Carcinogenesis involves the accumulation of numerous mutational events over long periods of time. In tumors that originate from tissues with high cellular turnover, only adult stem cells (ASCs), with their innate self-renewal capacity, can remain in the tissue long enough to accumulate the number of oncogenic alterations that are necessary to support a complete malignant transformation. This has led to the hypothesis that tumor-initiation and progression are driven by CSCs, commonly defined as the fraction of tumor cells specifically endowed with self-renewal and tumor-seeding potential and the ability to spawn non-CSC progeny (7476). Not surprisingly, ASCs have been commonly hypothesized to represent the cells of origin in most tumors because they can be directly targeted with primary transforming events; more committed progenitors can also similarly gain the ability of self-renewal and function as CSCs through oncogenic transformation. In this hierarchical organization with rare, self-renewing CSCs residing at the top, the disorganized tumoral mass should be merely viewed as an aberrant version of the ASC-driven mechanisms that govern the corresponding tissue’s normal development (7779). However, while this is the case for cancers with a stem cell origin such as hematopoietic malignancies, for those with non-stem cell origin including liver, breast, lung, pancreatic, and prostate cancers, although CSCs obviously exhibit stem cell properties, they do not necessarily originate from the direct transformation of normal tissue stem cells or progenitor cells.

We now know that non-cancerous and differentiated cancerous cells possess enough plasticity to aberrantly reprogram and acquire bona fide CSC properties. Indeed, the inherent aggressiveness of carcinomas appears to derive not from the pre-existing content of CSCs, but rather from the intrinsic proclivity of a given tumor tissue to generate new CSC from non-CSC cell populations (8090). Such plasticity potential of non-CSCs to acquire a CSC cellular state depending on their epigenetic/transcriptional signature and in their interpretation of multiple microenvironmental signals (e.g., hypoxia, starvation, inflammation, and therapeutics) is fully absent in the conventional depiction of the one-way stem/progenitor cell hierarchy and is revolutionary changing our current perception of the CSCs’ biology. By understanding cancer as a disease of differentiation, CSCs can be generated at any time during cancer progression so long as appropriate oncogenic lesions that can enable epigenetic reprograming to a stem-like cellular state are present or, alternatively, tumor-suppressors are hindered. Because CSCs are emergent, dynamic cellular states, i.e., CSCs are also made and not just born, multiple independently derived and molecularly distinct CSC cell populations may evolve depending on the likelihood of reprograming phenomena within tumors. The resulting heterogeneity manifests as diverse clones of CSC that vary in terms of their dormancy, proliferative, biomarkers, metastatic, and/or chemo-sensitivity profiles. Indeed, the molecular heterogeneity and stochasticity of gene expression in cancer tissues can drive a continuum of cancer cell states to rapidly shape cancer’s evolution through a greater probability of entering CSC cellular states.

Cancer Stem Cells Reprograming: A New Paradigm for Understanding Tumors’ Social Structure

Cancer stem cell reprograming is a molecular process able to establish the bidirectional control of tumors’ epigenetic hierarchy. On the one hand, cancer genetic alterations can reset the epigenetic and transcriptional status of an initially healthy cell to establish a newly acquired, pathological differentiation program of aberrant stemness (i.e., a CSC-like cellular state) that ultimately leads to cancer development. On the other hand, differentiated tumor cells can dynamically alter their transcriptional and epigenetic circuits to acquire a less-differentiated CSC cellular state. Differentiated (normal and tumor) cells and CSCs are therefore distinct cellular states that could convert each other to achieve a balanced equilibrium within heterogeneous cancer cell populations. Crucially, the reprograming-differentiation cancer model easily explains many of the apparently paradoxical aspects of the CSC-related tumor biology: (1) the apparently contradictory reconciliation of the rarity (of CSC number) with robustness (of CSC properties) in some tumors; (2) the challenging application of hierarchical models to some tumors such as metastatic melanoma [i.e., an extreme example of stem cell reprograming in which certain epigenetic makeups endow almost the entire tumor cell population with the easiest ability to acquire CSC qualities; (91)]; (3) the lack of bona fide CSC markers enabling the general identification of stemness across different cancer types and even during the natural history of a given tumor type (61, 9294); (4) the occurrence of unique stem-like states due to the continuous evolution and adaptation to new constraints [e.g., the conversion of tumor cells into functional vascular endothelial cells that resist antiangiogenic therapy or the transient assumption by individual cells of a reversible drug-tolerant state to protect the cancer cell population from eradication due to potentially lethal exposures; (95, 96)]; and (5) the accumulation of CSCs following treatment with therapeutics [i.e., cancer therapies do not necessarily enrich cancer tissues with pre-existing, treatment-refractory CSCs, as an accelerated production of de novo CSC cellular states from the residual cancer cells may easily repopulate their ranks while the older CSCs die; (97)]. The reprograming-dedifferentiation cancer model illuminates the fact the “hierarchy” within tumors’ social structure is not rigid because self-renewal and differentiation are acquired traits. Importantly, CSC reprograming does not exclude the pivotal role of non-strictly genetic factors (e.g., metabolites and microenvironment), which can significantly impact the conversion probability between non-CSCs’ and CSCs’ cell stages.

Cancer Metabostemness: A Conceptual Description

A key peculiarity of the abovementioned model of stem cell reprograming is that the dysregulation of specific signaling pathways, rather than the type of the cell of origin, dictates the emergence and phenotype of CSCs in a given tissue. If any differentiated cell can be reprogramed to an induced pluripotent state through the right combination of transcription factors, then, following the same line of reasoning and in theory, non-CSC cells could similarly dedifferentiate to a CSC cellular state given that an appropriate stemness transcription factor is strongly activated. Moreover, by balancing counteracting differentiation forces (98), reprograming to a CSC functional state might be also achieved through the establishment of a fine-tuned equilibrium that might not require of the traditionally considered master regulators of stemness. However, by following the numerous parallels between the process of reprogramming differentiated cells-to-induced pluripotent stem cells (iPSCs) and differentiated cells-to-CSCs, when considering the slow kinetics and efficiencies of iPSCs generation (99) it might tempting to suggest that not all of the normal or cancerous cells would possess the equivalent ability ab initio to permit their successful reprograming to CSC cellular states. Whereas certain populations are seemingly refractory to reprograming, we now know that the ability to generate iPSCs is intrinsic to any cell given sufficient time and the appropriate reprograming push. Thus, additional expression of the so-called Yamanaka factors, the use of additional stemness transcription factors, the use of chemicals, or the direct modification of critical epigenetic components can greatly enhance (even to efficiencies nearing 100%) and accelerate reprograming to iPSCs (100104). Ever-growing iPSCs-based findings showing that metabolic reprograming phenomena might be essential for transcription factor-induced stemness have further confirmed that the elaborate regulation of key master metabolic switches seems to contribute to the metabolic changes that take place in the transition between a differentiated cell and a stem cell, to the maintenance of the stemness properties in stem cell cellular states, and to the exit from the pluripotent state to become primed for differentiation (1030, 105). In this scenario, we recently reasoned that the dedifferentiation of somatic cells into iPSCs as well as the de novo generation of CSC cellular states from non-CSCs may represent mechanistically related, metabolically dependent reprograming phenomena in which epigenetic remodeling, the activation of genes related to the establishment and maintenance of stemness, and/or the scavenging of fate determinants related to cell differentiation might be co-opted in the absence of functional tumor-suppressing mechanisms. In other words, the metabolic infrastructure and functioning might operate as the key molecular constraint controlling the kinetics of stemness reprograming for the optimal routing of non-CSC to CSC-like cellular states during cancer genesis and progression.

We propose that the acquisition of and departure from stemness in pre-malignant and cancer tissues might not be governed exclusively by genetic and epigenetic controllers, but also by the pivotal regulatory role of the cellular metabotype, which may act as “starter dough” for cancer stemness traits. When viewing cancer stemness as a flexible quality that might be gained and lost in a metabolic-dependent manner, the cellular metabotype then operates as a supra-genetic dimension guiding the ability of epigenetic and transcriptional circuitries to redirect normal and non-CSC tumor cells toward a CSC-like cellular state. Certain metabotypic shifts might function as very early molecular events that render a (normal or cancerous) differentiated cell more susceptible to transcriptional and epigenetic rewiring required for the acquisition of aberrant stemness and, concurrently, of refractoriness not only to apoptosis, but also to differentiation. Subsequent hits, occurring in variable orders, combinations, and/or intensities can then confer the definitive quality of cancer stemness and dictate the dynamic cellular hierarchy within a tumor tissue. We have therefore coined the term “metabostemness” to refer to the metabolic parameters causally controlling or functionally substituting the epitranscriptional orchestration of the genetic reprograming that redirects normal and non-CSC tumor cells toward less-differentiated CSC cellular states (Figure 1). As such, the metabostemness trait can be understood as the physiological glue that metabolically connects all the omic layers with a self-autonomous CSC cellular quality; operatively, metabostemness is a systeomic function of observable metabolic phenotypes (i.e., the CSC metabolophenome) that predate systems biology and its sub-disciplines (i.e., genomics, transcriptomics, proteomics, and metabolomics) at the level of CSC cellular states. From a holistic perspective, the metabostemness hallmark comprises the intrinsically, microenvironmental, and physiologically determined metabolic parameters that enable the self-renewal and differentiation capacities owned by the CSC cellular states in tumor tissues (Figure 1). Four main features can conceptually define the metabostemness property in cancer tissues:

(1) the cellular metabotype determines the global success of the epitranscriptional reprograming that redirects normal and non-CSC tumor cells toward less-differentiated CSC cellular states;

(2) the cellular metabotype imposes the “permitted” and “protected” cellular modes that allow or prevent, respectively, the completion of the molecular journey from non-CSC to CSC cellular states;

(3) the closer a cellular metabotype is to that of a CSC, the higher its reprograming capacity for acquiring a CSC cellular state; and

(4) metabolic interventions can reprogram cellular metabotypes in a manner that successfully impedes the aberrant acquisition and functioning of stemness in cancer tissues.

FIGURE 1
www.frontiersin.org

Figure 1. Metabostemness: a new-dimensional cancer hallmark. We have recently hypothesized that CSC-driven malignant progression might be envisioned as an evolving spatio-temporal heterogeneous structure that might not be driven solely by irreversible genomic hits but also by metabolic means; thus, the acquisition of, and departure from, stemness in cancer tissues might be governed not only by transcriptional and epigenetic controllers but also by the pivotal regulatory role of metabolic reprograming in cell-fate decisions. In this scenario, we propose the actual existence of a new phenomic cancer hallmark to which we have coined the term “metabostemness.” Metabostemness refers to the metabolic parameters at the cell-intrinsic, tissue-microenvironmental, and systemic levels that enable the unique functional properties owned by the CSC cellular states. A metabotype-based infrastructure and functioning of CSC can therefore operate as a supra-genetic dimension controlling over or functionally substituting the epitranscriptional orchestration of the genetic reprograming that redirects normal and non-CSC tumor cells toward less-differentiated CSC cellular states.

Cancer Metabostemness: An Operational Delineation

Transcription factors are commonly viewed as the key intrinsic regulators of cell-fate, i.e., the cell-fate choice exclusively involves modulating networks of transcription factors. It is also generally accepted that a given cellular type including that of stem cells must not be excessively sensitive to random, or unpredictable, small fluctuations in the levels of specific signals. This indispensable requirement to withstand modulating factors that may perturb the gene regulatory network (GRN) architecture – i.e., the gene–gene relationships, their directionality (“who controls whom”), their interaction modalities (inhibition versus stimulation), and the modes of cooperation – defines a cell type in a timely manner and strongly limits the number of solutions or “cellular states” for a given genetic system including those of cancer tissues (106108). The evolving dynamics of molecular connections between the genes and gene products that can interact with each other within a cell, including the underlying regulatory logic that govern these interactions, must therefore provide robustness with respect to varying extrinsic signals and intrinsic factors, i.e., the noise. Such stability of the cell state is obviously required in stem cells to support the self-renewal and maintenance of an uncommitted state, but must also afford certain flexibility in the choice of cell-fate to permit the diversification and differentiation of cell types in response to intrinsic cues or extrinsic signals.

During normal development, cells make very precise transitions between network states of gene expression patterns, which must be stable and irreversible in terminally differentiated cells, at least under homeostatic or physiological conditions. Conversely, the potential for reverse differentiated-to-stem cell state conversions has been revolutionarily exemplified by the nuclear reprograming of somatic cells to a pluripotent stem cell state driven by a small number of stemness transcription factors. Crucially, a very similar consideration of cell states transitions apply to pathological states such as cancer, where the misexpression of transcriptional regulators can reset the status of an initially healthy cell to establish a newly acquired, pathological differentiation program of aberrant stemness that ultimately leads to cancer development. Considering the above-depicted scenario, it is not surprising that the major challenge that is being faced by regulatory biology in the postgenomic era of understanding cancer diseases is to map the core transcription factors’ networks associated with different cancer cell types, especially the underlying regulatory logic that governs their behavior as differentiated versus CSC cellular states. The accurate delineation of such a map will provide crucial insights into the rules that define cancer cellular states (i.e., cancer heterogeneity) and how transitions between cancer cellular states are achieved, thus providing unforeseen therapeutic solutions to the apparently irresoluble problem of targeting cancer dynamical models such as the model of reversibility, or cancer cell reprograming, in which heterogeneous populations of CSC can arise by reversion of more differentiated cancer cells. Indeed, multiple different CSC populations have been described for a given cancer type, despite presenting different gene and protein expression signatures, thus leading to the currently accepted view that the characterization of CSCs can no longer be based on marker expression, but instead at their functional level, strongly supporting the model of reversibility, or CSC reprograming, in which the populations of CSCs arise by reversion of more differentiated cells (85, 109113).

As for bona fide pluripotency, CSCs should be viewed as functional states rather than discrete cellular entities characterized by well-defined and static gene networks, thus highlighting cancer stemness as a statistical property resembling a macrostate in statistical physics (108, 114, 115). If these macrostate entities of functional CSCs are correct, the gene expression signature for a CSC in a given tumor tissue may be dynamic or non-unique, which can create a challenge when trying to unambiguously establish mathematical frameworks describing CSC reprograming phenomena. We propose that incorporating metabolism and metabolites into the intrinsic variability of the CSCs’ epigenetic and genetic signatures can significantly reduce the high-dimensional problem of understanding reprograming dynamics both at the single-cell level and the population level. But how can we model the incorporation of the metabolism and metabolites into the epigenetic and genetic signatures accounting for CSC variability during the generation, maintenance, and evolution of CSC cellular states in cancer tissues? To definitely consider the process of reprograming to CSC into a rigorous, quantifiable theory, the establishment of a mathematical framework able to accurately mapping the landscape pertaining the transition between non-CSC and CSC cell states needs to incorporate a never before considered metabolic dimension.

Dynamic Perspective by Developing Probabilistic Descriptions of Cell States

Cancer cell states (e.g., non-CSC versus CSC) can be parameterized as vectors of molecular characteristics, Ŝ, which are generally considered a set of gene expression levels (Ŝ = [g1, g2, g3, …, gN]) (Figure 2). A cancer cell at any time t can exist in a point of this state space, and its state can change with time in response to particular cell-autonomous and non-cell-autonomous conditions, i.e., in a CSC reprograming scenario, Ŝ is a function of time Ŝ(t) as a result of noise, reprograming, or differentiation. By plotting one trajectory during a state change from t0 to t1, we can fully describe the transition between cancer cell states during this time S(t) = [g1(t), g2(t), g3(t), …, gN(t)]. For simplicity, we show a cancer cell state space generated by the level of N different genes, g1 to gN, where each arrow represents an axis corresponding to the expression level of that particular gene transcript. Such a cancer cell state space provides an accurate means to quantitatively organize and visualize different states of a cancer cell with a fixed genome. Indeed, often cancer cells will cluster in particular regions of the cancer cell state space, which can be viewed as stable types of cancer cells that express particular markers. Those regions where no cancer cells are found correspond to cell states that are somehow unstable for the given genome of the cancer cell in the microenvironmental conditions considered. Thus, N is generally reduced to two to three more manageable dimensions through statistical techniques [e.g., principal component analysis (PCA)]. Figure 2 shows a two-dimensional representation of the cancer cell state space, where each axis (ga and gb from PCA) is a linear combination of genes g1 to gN and, therefore, stable types of cancer cells exist at particular points of this graph. By quantitatively mapping the gene expression levels of a large sample of single stable cancer cells in the same space, the probability of occupying each point in this space can be plotted in a continuous fashion, and regions with high densities of spots can define observable cancer cell types. The continuous probabilistic description of cancer cell types staying at a particular point in state space can be represented as a landscape, by calculating − ln [P (Ŝ)] at each point. In this landscape, V (Ŝ) represents “energy barriers” between transitions involving any two cellular states and thus may provide a more thorough description of non-CSC-to-CSC transitions. However, we should acknowledge that while the nature of the cancer cell substates can be elucidated in terms of a landscape picture in which stable differentiated, non-CSC, and CSC cellular states are mathematically defined as “attractors” – i.e., observable cancer cell types – the architecture of these apparently stable “network states” is described exclusively in terms of genes, or at the most epigenetic, expression patterns. Indeed, when selecting a particular set of characteristics as being informative for the transition between two cell states of interest, functional cell types are almost exclusively defined based on levels of gene expression changes on the timescale of days to weeks. In this probabilistic framework, the genetic background defines the abovementioned architecture of the cell state space, meaning that particular gene–gene relationships, interaction modalities, and integrating transfer functions (i.e., the GRN) are largely “hardwired” by the genome. A more detailed characterization not only of gene expression, but also of biochemical noise in signaling processes (e.g., binding of epigenetic modifiers to particular genome loci, inherent stochastic nature of molecular binding events, secondary messengers, and variable cell–cell contact) and of the epigenetic state (e.g., levels of epigenetic methylation or acetylation marks on DNA or chromatin) is expected to provide more insight into the missing key parameters and required transcriptional changes controlling the kinetics of CSC reprograming. In this regard, we propose that the regulatory network architecture of a cancer landscape comprises not only the genes and proteins that can interact within a cancer cell, but also the cellular metabotype, which can necessarily and sufficiently characterize different functional cell states within heterogeneous cancer cell populations through different types and/or levels of certain metabolites.

FIGURE 2
www.frontiersin.org

Figure 2. Metabostemness: developing probabilistic descriptions of non-CSC and CSC cell states. (See text for a more detailed explanation).

But how do metabolism and metabolites hierarchically integrate with genetic expression programs to coordinately regulate CSC function and fate? Because DNA transcription is regulated by chromatin organization, the occurrence of metabolic inputs into epigenetic modifications of chromatin and transcription should be viewed as the molecular bridge that links metabolism to epigenetics and gene expression during non-CSC to CSC transitions. We are accumulating ever-growing evidence suggesting that, beyond the classically delineated transcriptional output of growth factor/hormonal signaling pathways, a variety of metabolic signals can play also critical roles in determining chromatin structure, thus directly linking metabolic perturbations to the dysregulation of cellular differentiation (116118) (Figure 3). Indeed, CSC metabolism is linked to epigenetics and gene expression in a multifaceted and bidirectional manner:

– Metabolic fluxes can be controlled by metabolic enzymes that are directly regulated by stemness transcription factors such as c-Myc, which can act both locally and globally on chromatin to exert wide-ranging effects on the biology of stem and tumor cells (119122).

– Epigenetic alterations (e.g., demethylation or methylation of gene promoter regions, protein acetylation) can contribute to the deregulated expression of key enzymes involved in cell metabolism. Thus, the reversible acetylation of histones and non-histone proteins has been shown to affect cell metabolism, and glycolytic versus OXPHOS pathway gene expression and DNA methylation patterns change during reprograming to stemness. The rate-limiting glycolytic enzyme Hexokinase II (HK2) and the key enzyme of gluconeogenesis fructose-1,6-biphosphatase (FBP1), with opposing roles in glycolysis, have recently been identified as epigenetically regulated by promoter demethylation and methylation, respectively (123126). PKM2, which catalyzes the conversion of phosphoenol pyruvate to pyruvate, is targeted for degradation in a glucose-dependent manner by acetylation at lysine K305; the epigenetic silencing of FBP1, which catalyzes the energy-consuming conversion of fructose-1,6-biphosphate to fructose-6-phosphate, is employed by CSC as a mechanism of glucose flux maintenance via glycolysis and other associated biosynthetic pathways (127). The tumor tissues’ extra requirements of glucose, an essential nutrient for CSC that, when present in the culture environment, significantly increases the percentage of CSC-like cells in the overall cancer cell population (128), can be achieved via the epigenetic silencing of DERL3, the gene responsible for degrading the glucose transporter SLC2A1 [glucose transporter 1 (GLUT1)] (129). Moreover, posttranscriptional modifications of p53, the key connector of reprograming to pluripotency and tumorigenesis (130136), may affect cell metabolism through downstream targets such as TIGAR (TP53-induced glycolysis and apoptosis regulator) and interaction with PGC-1α [peroxisomal proliferator-activated receptor (PPAR) gamma coactivator 1] (137142).

– Epigenetic modifications of DNA and histones by methylation and acetylation reactions require cofactors that are derived from various metabolic pathways including glycolysis, fatty acid oxidation, tricarboxylic acid (TCA) cycle, and OXPHOS (Figure 3). Among these cofactors, we can mention the following: (a) S-adenosyl-L-methionine (SAM), which is a cofactor for methylation reactions by DNA- and histone-methyltransferases (DNMTs and HMTs) (143145); (b) Flavin adenine dinucleotide (FAD), which is a cofactor for lysine specific demethylase 1 (LSD1) (144, 146); (c) α-ketoglutarate, which is an electron donor and cofactor for the α-KG/Fe2+-dependent dioxygenases Jumonji-C domain (JmjC) histone demethylases (HDMs) and ten–eleven translocation (TET) proteins, respectively (143, 144); (d) Nicotinamide adenine dinucleotide (NAD), which is a cofactor for the sirtuins family of histone deacetylases (HDACs) and poly(ADP-ribose)polymerase (PARP) (143, 147151); and (e) Acetyl Coenzyme A, which is not only an important precursor for the de novo biogenesis of fatty acids, but also an essential cofactor for histone acetyl transferases (HATs) and the acetylation of non-histone proteins involved in cell metabolism (145, 152155). Because alterations in the supply of these cofactors may affect DNA methylation, alter chromatin structure, and change posttranslational modifications of non-histone proteins that influence the regulation of gene expression reprograming to stemness, and because cofactors and modifying enzymes are always present at some level, the current challenge is to understand how localized fluctuations in levels of metabolites control chromatin modifiers in space and time to operate as mechanisms of specificity that prevent all genes from being regulated synchronously. Enzyme recruitment with DNA-binding factors, local depletion or excess cofactors, or the modification of spatial and temporal sublocalization of enzymes within the nucleus might likely account for a hierarchy of target chromatin regions and associated genes (116, 156), thus translating CSC metabotypes into CSC methylation maps.

– Beyond the more general effects on epigenetics brought about by changes in the availability of substrates or cofactors for enzymes that regulate chromatin structure and gene expression, mutations in metabolic enzymes have been linked to the generation of oncometabolites, which directly drives epigenetic reprograming in cancer cells. Ever-growing in vitro and in vivo studies have provided strong biochemical, cell biological, and genetic evidence that oncometabolites can drive tumorigenesis and tumor maintenance by regulating histone and DNA modifications and engaging a metabolic block in cellular differentiation. This is the case of the currently recognized oncometabolites R(-)-2-hydroxyglutarate (2-HG), fumarate, and succinate, which accumulate due to defects in the TCA cycle enzymes isocitrate dehydrogenases (IDHs), fumarate hydratase (FH), and succinate dehydrogenase (SDH), respectively. Defects in these TCA cycle enzymes caused inherited benign or malignant tumors by altering DNA and histone modifications to cause widespread transcriptional dysregulation (3145). Indeed, the identification of cancer-associated mutations in metabolic enzymes has provided the strongest support ever for the argument that the metabolism rewiring observed in cancer plays a significant role in cancer development. Crucially, by highlighting the similarities in the nuclear reprograming pathways that are involved in the generation of iPSCs and of the tumor-initiating action of CSC-like cells, it has recently been suggested that a stemness-related hallmark of cancers may be mutations or expression changes in metabolic genes that are implicated in the regulation of DNA methylation plasticity such as IDHs (7). Lu and Thompson (116) originally suggested that, during progenitor cell differentiation, the inhibition of the HDM JHDM by the oncometabolite 2-HG, which is aberrantly synthesized by the neomorphic mutations of IDH enzymes, causes defective histone demethylation and blocks the accessibility of differentiation-related genes. The 2-HG-driven inhibition of JHDM as well as that of the TET family of DNA demethylases, which operate as failsafe mechanisms to protect promoters from aberrant DNA methylation, should lead to progressive DNA hypermethylation and permanently “lock” differentiation-related genes in a silent state; the resulting differentiation arrest might facilitate cancer development through the accumulation of undifferentiated cells capable of self-renewal. Goding et al. (7) now propose that the cell of origin dictates the metabolo-epigenetic relationship between reprograming and cancer. As originally proposed by Lu and Thompson (116), gain-of-functions IDH mutations can lead to global hypermethylation, preventing the demethylation of genes that are implicated in differentiation, and consequently promoting an increase in the number of stem cells that may be targetable by oncogenic mutations in cancers with a stem cell of origin, such as hematopoietic malignancies. For cancers with a non-stem cell origin including liver, breast, lung, pancreatic, and prostate cancers, in which mutations on metabolic genes are not widespread, Goding et al. (7) propose that an intact metabolic function of IDH would be necessary to maintain DNA methylation plasticity and a flexible epigenetic landscape, as seen during the generation of iPSCs.

FIGURE 3
www.frontiersin.org

Figure 3. Metabostemness: a metabolo-epitranscriptional switcher. A highly active crosstalk between metabolism and epigenetics allows the causal integration of metabolism and metabolites with genetic programs to coordinately regulate CSC function and fate. Nutrient levels and cell metabolism will significantly affect levels of the metabolites, which are required substrates of chromatin-modifying enzymes that use them to modify both histones and DNA (116). A minor portion of the lucose that enters the glycolytic pathway is branched to hexosamine biosynthetic pathway to produce GlcNAc, a substrate for histone GlcNAcylation by the OGT enzyme. It is well known that the flux through glycolysis determines the NAD+/NADH ratio, a crucial metabolic parameter for the activities of sirtuin histone deacetylases. Citrate and α-ketoglutarate (α-KG) are intermediates of the TCA cycle that can be exported out of mitochondria; cytosolic citrate is converted to acetyl-CoA that is employed as a donor for HAT-mediated histone acetylation. JHDM and TET use α-KG as cofactor for histone and DNA demethylation reactions, respectively. S-adenosylmethionine (SAM), which is synthesized from essential amino acid methionine, is a donor for DNA methylation via HMT and DNMT. A low ATP/AMP ratio indicative of metabolic stress can activate AMPK, which can translocate to chromatin and phosphorylate histones. In this scenario, we propose that certain metabotypes might operate as pivotal molecular events rendering a non-CSC cell susceptible to epigenetic and transcriptional rewiring required for the acquisition of aberrant stemness and, concurrently, of refractoriness not only to apoptosis but also to differentiation. Only certain cell metabotypes would be compatible with the operational properties exclusively owned by CSC cellular states; conversely, certain metabotypes would impose cellular modes protected against CSC reprograming. In other words, the molecular logic behind the conversion of non-CSC into CSC can be better understood in terms of cellular metabotypes that operate as pathways (“permissive”) or roadblocks (“protected”) by facilitating or impeding, respectively, the transcriptional events and signal transduction pathways that lastly coordinate the intrinsic and microenvironmental paths to CSC cellular states.

Nevertheless, as metabolic abnormalities in cancer continue to be uncovered, it can be expected that new metabolites or metabolic perturbations that affect chromatin structure will be identified (53, 157). Moreover, during reprograming to a CSC cellular state, a positive feedback loop might be established between bioenergetics reprograming and the activity of stemness transcriptional factors; accordingly, there is a progressive resetting of metabolite levels that parallels the progressive changes seen in global epigenetic modifications and gene expression over prolonged times of induced pluripotency maintenance. We propose that the probabilistic description of cell states in cancer tissues can be better described by incorporating the connections between metabolism and chromatin dynamics; therefore, in order to determine how metabolism and metabolites exert influence over epigenetic and genetic reprograming circuits that establish and maintain CSCs’ self-renewal and differentiation abilities, the transition between cancer cell states should incorporate not only a set of gene expression levels, but also the metabolo-epigenetic state, i.e., S(t) = [meg1(t), meg2(t), meg3(t), …, megN(t)] (Figure 2).

Dynamic Perspective Using Waddingtonian Landscapes

It is well-recognized that the Waddington landscape (158), a metaphoric representation of cell differentiation in which pluripotent stem cells are positioned at the top of a hill progressively losing differentiation potential while going downhill into different valleys representing irreversible differentiated cellular states, allows for the modeling of a complex network of molecular barriers governing cell-fate transitions. The complexity of the enormous number of possible intracellular factors that can mitigate or impede cellular reprograming is notably reduced into an effective energy landscape in the Waddington model, in which cell-fates are defined by stable states, known as “attractors,” and cell transitions are represented as flows from one energy state to another. In this quasi-potential Waddingtonian landscape that captures the global dynamics of high-dimensional attractor states (i.e., distinct entities and of regulatory networks representing the equilibrium solutions of how the concentrations of interacting transcripts, proteins, and other variables in a GRN evolve over time to exhibit the natural properties of cell types), the cancer cell types within a tumor tissue are therefore self-stabilizing states basically defined by their “energetic” minimum (i.e., the commitment is defined as the progression toward the minimum energy) and the shape of the potential cleft. While they are robust to small perturbations, they still allow “all-or-nothing” transitions to other attractors including those of CSCs given sufficiently high perturbations, as they are separated by “hills” that correspond to “unstable” states. The latter truly represent the “epigenetic barriers” originally proposed in the 1940s to explain cell-fate determination in contraposition to the commonly, but erroneously, usually claimed “epigenetic landscape” in which unique chromatin marks such as DNA methylation and histone acetylation/methylation control the activity of specific genes, thereby acting as the crucial modulators of cell-fate. Thus, according to a bona fide Waddingtonian model based on a rather reversed role of chromatin modification as the prima causa of lineage-specific gene expression patterns by “upstream” controlling the access of transcription factors to DNA target sites (i.e., the stemness transcription factors themselves, endowed with sequence-recognition capability, are in charge of initiating the opening of chromatin at specific sites, followed by a directional cooperation with the chromatin-modifying enzymes that they recruit to their target loci), we argue that the metabolo-epigenetic nature of the potential barriers between non-CSC and CSC cell states actually determines how metabolism and metabolites exert influence over or functionally substitute the genetic and epigenetic circuits that establish and maintain stem cell reprograming in cancer tissues. In this new scenario, the underlying regulatory logic that governs the interactions between genes and proteins should include the cellular metabotype to better define the actual “metabolo-epitranscriptional regulatory network (MGRN)” architecture of cancer tissues.

A glossary of terms might facilitate the understanding of the metabolo-epigenetic landscapes in cancer tissues. The term “metabotype” refers to a cellular phenotype characterized by different metabolites’ levels that can be described by means of four variability criteria: (a) the presence–absence of metabolites; (b) the concentration levels of metabolites; (c) the relative levels or ratios between metabolites; and (d) metabolic profiles. The term “MGRN” comprises the epigenetic state, genes transcripts, proteins, and metabolites that can interact with each other within a cell, including the underlying regulatory logics that govern the interactions between chromatin dynamics, transcripts, proteins, and metabolites. The delineation of “metabolo-epitranscriptional rate equations” refers to mathematical representations of how the concentrations of interacting cellular methylome, gene transcripts, proteins, and metabolites in an MGRN evolve over time in a cell. A “metabolo-epitranscriptional attractor” describes the equilibrium solutions of the metabolo-epitranscriptional rate equations that represent observable cellular states and can be visualized as wells, or depressions, in a metabolo-epigenetic landscape. A “metabolo-epitranscriptional basin of attraction” includes the set of initial conditions of the metabolo-epitranscriptional rate equations that describe how a particular cell moves to particular metabolo-epitranscriptional attractors. It therefore represents a fate-primed subset of a given population, i.e., a particular cellular state.

The concepts of attractors and the associated landscape pictures provide a very useful conceptual framework in considering the metabolo-epigenetic nature of the non-CSC and CSC substates. Although it might be argued that the power of a dynamic perspective of cancer metabostemness using Waddingtonian metabolo-epigenetic landscapes does not extend beyond intuition, we should acknowledge that, in some examples, it has been possible to mathematically describe the relevant attractors and to make predictions about the paths that differentiating cells follow from the undifferentiated, undetermined stem cell state, and vice versa (106, 115, 159). Moreover, the importance of pursuing mathematical models for stemness and differentiation issues should be emphasized when considering that a CSC cellular state should represent a “metabolo-epitranscriptional attractor” in which the maintenance of stemness might not only involve an active process of maintaining this “ground-state,” but also prevent cells from leaving it (160). In this scenario, where CSC cellular states are expected to have an innate program for self-replication that might not require extrinsic instruction, which may certainly account for their latent tumorigenicity, the development of the methods and procedures to mathematically model MGRNs involved in determining self-renewal and pluripotency will not only provide new insights into switch and reprograming properties in cancer tissues, but will also reveal specific, metabolically driven fluctuating behaviors; in particular, the delineation of minimal metabolo-epitranscriptional requirements for cancer self-renewal can pinpoint missing metabolic components and interactions from CSC functionality that can be confirmed in the laboratory, thus generating an iterative procedure in which the occurrence of “metabostemness factors” and “metabolic rules of attraction” is modeled.

In a first attempt to provide a defined platform for the precise description and dissection of the CSC state in minimal metabolo-epitranscriptional terms, we recently developed the methods and procedures to mathematically model the MGRNs that might be involved in determining pluripotency and self-renewal in mammary epithelial cells; similarly to previous studies, our model formulation considered that the establishment of a balance between Oct4 and Sox2 – the core regulators of pluripotency – and/or counteracting lineage specifiers facilitate reprograming to stemness (98). Our model formulation, however, introduced for the first time ever the epigenetic regulation of the lineage-specific genes, which is an essential element that allows the effects of metabolic regulation in the nuclear reprograming process to be taken into account (Menendez et al., unpublished observations). In particular, we added nucleosome modification by HDM as an essential reprograming element regulated by oncometabolites such as 2-HG. When studying the impact of 2-HG-induced reduction of HDM activity on both the epigenetic landscape of pluripotency and the reprograming kinetics (Figure 4), the following was predicted:

(a) a metabolically driven small reduction of HDM activity should be sufficient to notably lower the barriers of the epigenetic landscape, thus allowing differentiated cells to more easily enter into stem cell macrostates;

(b) the reduction in average reprograming time should vary exponentially with the metabolically imposed reduction of HDM activity, suggesting that even modest metabolic decreases of histone demethylation would produce a considerable increase in reprograming efficiency; and

(c) the metabolic-induced reduction of HDM activity should transfer a portion of the basin of attraction of the differentiated to the pluripotent state thus increasing the size of the basin of attraction of the macrostate occupied by stem cells.

FIGURE 4
www.frontiersin.org

Figure 4. Metabostemness: metabolic regulation of the efficient and rapid reprograming-to-cancer stemness in cancer tissues. The cellular metabotype can significantly alter the efficiency and speed of CSC reprograming in cancer tissues by lowering the barriers of the epigenetic landscape and by increasing the size of the basin of attraction of CSC cellular states. In this scenario, even modest changes in the protected versus permissive nature of the cellular metabotype are expected to produce a considerable change on the kinetic efficiency of the reprograming process.

Second, the creation of non-transformed mammary epithelial cells with clinically relevant heterozygous knock-in of the 2-HG-producing IDH1 R132H mutation allowed us to generate proof-of-concept data supporting the actual reprograming activity of oncometabolites predicted in our mathematical model. The experimental validation of functional predictions confirmed the following:

(a) the endogenous accumulation of oncometabolites such as 2-HG is likewise sufficient to significantly enhance and accelerate the efficient generation of induced CSC-like-cells when using the four transcription factors Oct4, Sox2, Klf4, and c-Myc;

(b) even transient exposure to oncometabolites can operate as a microenvironment-driven dedifferentiation event that facilitate “pathological reprograming” because several hours of exposure with a cell-permeable form of 2-HG can fully recapitulate the promoting effects of IDH1 mutations on switching differentiated mammary epithelial cells into induced CSC-like cells; and, more importantly

(c) 2-HG can substitute for Klf4 and c-Myc in reprograming-factor combinations leading to the enhancement and acceleration of reprograming non-CSC into induced CSC-like cells when solely using Oct4 and Sox2.

By impacting the epigenetic remodeling that facilitates the transition between non-CSC and CSC functional states, oncometabolites such as 2-HG likewise appear to operate as bona fide “metabostemness reprograming factors” that determine the cancer tissue proclivity of generating a given number of CSCs in a given time. Because we employed a non-tumorigenic, non-transformed genetic background that, upon introduction of defined reprograming factors, has been found to generate tumorigenic cells with CSC properties (161), our findings demonstrating how small-molecule components of metabolism such as 2-HG efficiently drive significant enhancement and acceleration of CSC reprograming strongly support the notion that metabostemness factors could poise cells with chromatin states competent for rapid dedifferentiation, creating a persistent pre-neoplastic state suitably primed for later transcriptional alterations to finish the reprograming of non-CSC into CSC cellular states.

Given that metabolism represents a junction system receiving cumulative signals from upstream (genome, transcriptome, and proteome) and downstream (microenvironment) systems, pre-malignant, and cancer cells can adapt, resist, or react to multi-omic effects through different types of metabolism and, therefore, based on differential regulation, synthesis, and availability of cellular metabolites. Indeed, all the – omic characteristics that drive cancer plasticity concurrently provide to metabolism a flexibility that can be described by means of, at least, four variability criteria, i.e., the occurrence of certain “elite” metabolites, their concentration levels, their relative levels or ratios between metabolites, and metabolic profiles. The occurrence of particular metabolites should be viewed as a qualitative criterion concerning a strong metabolomic parameter specifically stimulated by particular cancer cell states or that locks cancer cells in a given state by merely being present (e.g., oncometabolites). Although apparently simplistic, this binary aspect of associating unique metabolomic parameters with radically different non-CSC versus CSC cellular “species” is strongly supported by the abovementioned findings with the oncometabolite 2-HG. In this scenario, metabotypes based on the presence–absence of some particular metabolites can have high taxonomic value at specifically differentiating non-CSC versus CSC cellular states in cancer tissues. The increase in the concentration levels of some metabolites could also operate as intrinsic factors governing the proclivity of non-CSC-to-CSC transitions. For instance, the upregulation of the middle glycolysis intermediate fructose-1,6-bisphosphate upon epigenetic silencing of the gluconeogenic enzyme fructose-1,6-biphosphatase is differentially employed by CSCs as a mechanism of glucose flux maintenance and the lowering of reactive oxygen species (ROS) via glycolysis and other associated biosynthetic pathways (127). Forthcoming studies should elucidate whether metabolic ratios between the concentration levels of structurally close metabolites can also provide biochemical functional discrimination between non-CSC and CSC cellular states in heterogeneous cancer populations. In the same regard, should CSC cellular states possess specific changes in the capacities and kinetics of certain metabolic modes, the discovery of unique, CSC-associated metabolic flux imprints will remain a challenge for the field (see below).

Tissue-Dependent Cancer Metabolic Programs: The “Metabolic Memory” of Reprogramed CSC

A dynamic perspective of the metabostemness trait using Waddingtonian metabolo-epitranscriptional landscapes can explain why we are accumulating evidence that tumor cells’ metabolism, often considered a single entity differing from normal cell metabolism, rather exhibits a wide diversity of metabolic phenotypes (162). The heterogeneous expression of metabolic genes is observed across tissue types and, therefore, the metabotypic expression pattern appears to reflect cancer cells’ propensity to adapt the pre-existing metabolic network to successfully support the cancer tissue’s altered needs. Where it has been studied, the metabotype of tumors is a function of both the genetic lesions driving tumorigenesis and the tissue from which the cancer arose (163). Although it has been suggested that this heterogenous metabolic pattern across tissue types can reflect cancer cells’ propensity to adapt the pre-existing metabolic network to support the neoplastic tissue’s altered metabolic needs (162), the metabostemness trait can provide a radically different view of the occurrence of tissue-dependent cancer metabolic phenotypes.

A shift in the balance between mitochondrial OXPHOS and glycolysis that reconfigures the cellular anabolic requirements (i.e., high glycolytic carbon flux and the increased decoupling from ATP production in mitochondria) precedes the appropriate acquisition of stemness traits in iPSCs (1030, 105). Similar to well-recognized genetic and epigenetic factors, bioenergetics reprograming crucially operates as an enabling regulator of cellular reprograming to stemness because the self-renewal and pluripotency attributes cannot be efficiently acquired in the presence of an inadequate bioenergetic metabotype. Thus, the efficiency of reprograming is higher the closer the glycolytic/OXPHOS energy metabolism profiles of the starting somatic cells are to the pattern observed in ESCs. Moreover, bioenergetics resetting has an early, active role during reprograming because manipulations that inhibit glycolysis reduce, whereas augmenting glycolysis enhances reprograming efficiency, respectively. Therefore, only when a crucial, very early step of engagement to the stemness’ bioenergetics metabotype is correctly initiated can transcriptional regulators of self-renewal and pluripotency then induce additional endogenous factors to acquire a bona fide stem cell cellular state while maintaining the reprograming cells’ bioenergetics’ competence. Accordingly, induced pluripotency can be achieved with a combination of only one stemness transcription factor and small molecules able to facilitate the metabolic transition from mitochondrial OXPHOS to glycolysis. Moreover, there is a progressive resetting of metabolite levels that parallels the progressive changes seen in global epigenetic modifications and gene expression with late passage iPSCs significantly closer to the metabolo-epigenetic profiles of ESCs (19, 164).

The abovementioned findings strongly suggest that, similar to the “epigenetic memory,” a “metabolic memory” also exists that can be partially retained through the reprograming process and might significantly influence the functioning and differentiation potential of iPSCs from cells of different tissues. Although little is known about the bioenergetics resetting of CSCs, it appears that, similarly to iPSCs, a direct link might exist between the occurrence of a metabolic switch from OXPHOS to aerobic glycolysis and the occurrence and maintenance of CSC cellular states. Compared to their more differentiated progeny, the acquisition of a stem cell cellular state might necessarily promote changes in the bioenergetics metabotype because CSCs appear to preferentially perform glycolysis over OXPHOS, at least in some cancer types. Accordingly, recent studies have confirmed that breast CSCs shift from mitochondrial OXPHOS toward fermentative glycolysis and are sensitive to treatment with 2-deoxyglucose, a well-known inhibitor of glycolysis (165). A direct link between glucose metabolism and cancer stem/initiating cells has similarly been established in glioblastoma cancer tissues (166). Once again, by following the numerous parallels between the processes of reprograming differentiated cells-to-iPSCs and differentiated cells-to-CSC, it seems reasonable to suggest that certain bioenergetics features such as the Warburg effect can no longer be viewed as the single metabolic entity shared by all the cancer tissues, but rather as the archetypical aspect of the undifferentiated state owned by CSCs. As recently pointed out by Pacini and Borziani (167), the specific metabolic phenotype known as the Warburg effect might not be considered a metabolic signature that is acquired during the oncogenesis process; conversely, the Warburg effect might represent an aberrant expression of a metabolic layout that is distinctive from the undifferentiated state owned by CSCs. Pacini and Borziani (167) further consider that there is a gradual and irreversible establishment of an undifferentiated state, with a gradual or complete loss of OXPHOS, rather than respiration in itself, which is often present in neoplasms, as originally described by Otto Warburg almost a century ago. Thus, these authors view the permanent shift toward a Warburgian energetic and metabolic state as an essential contributory cause of cancer, as it might represent the central link between genetic/epigenetic instability and the CSC theory considering that a characteristic and essential feature of each neoplasm is the lack of differentiation.

Our metabostemness proposal provides an alternative explanation to the apparently contradictory fact that certain bioenergetic metabotypes such as the OXPHOS-to-glycolysis bioenergetics resetting can be commonly adopted by CSC cellular states from different cancer tissues, whereas, the metabolic networks of individual tumors more closely resemble those of the normal tissue from which the tumor arose than other tumors that develop in different organ sites (168). From a Waddingtonian perspective, establishing a CSC cellular state is an “uphill battle” against the global slope in the landscape, which accounts for the arrow of time of cancer development. Certain metabolic events (e.g., the Warburg effect) might lower the barriers of the epigenetic landscape to provide “smoothly ascending slopes” while concomitantly enlarging the CSC attractor basin and thus promoting the ground-state character of the CSC cell state that is self-maintaining (Figure 4). However, the Warburgian metabolic state of CSC will be nevertheless globally situated at a “high altitude,” which affords the state a strong urge to “differentiate away” and populate all other cancer states attractors situated at a lower “altitude”; upon certain perturbations the metabolo-network state will “flow down” the valleys to the much lower attractors of differentiated cancer cells, which, because of their “metabolic memory” will convey their tissue-specific metabolic patterns without needing any “instructive” signal.

Cancer Metabostemness: The Challenges Ahead

Description of the CSC-Metabolic Phenome: The Metabolic Importance of the Host

Could we determine whether the cellular states of CSCs specifically engage structural or functional changes in metabolic enzymes to produce either specific or differentially enriched CSC-metabolites? We should acknowledge that there are currently no studies on the intracellular metabolic fluxes of CSCs. The elucidation of CSCs’ shift between OXPHOS and aerobic glycolysis based solely on transcriptomic data would imply there are no specific changes in CSC metabolism, as most metabolic changes are not accompanied by significant changes in the transcript levels of metabolic genes. The inclusion of proteomics data would shed further light on CSC-associated metabolism; for example, increased protein expression throughout the glycolytic pathway could suggest that this pathway’s activity is increased in CSC cellular states. However, proteomics data will not be conclusive regarding changes in the activity of certain metabolic pathways. Indeed, detailed knowledge of cell physiology, in particular at the level of cellular metabolism, is entirely lacking for CSCs due to the difficulty of measuring the in vivo metabolic fluxes of mammalian cells. A better understanding of these fluxes will be critical in order to exploit the metabostemness trait to eliminate tumor tissues in patients, especially when considering that the tumor cell microenvironment can profoundly affect metabolism and influence how nutrients are metabolized (169, 170). Moreover, tumor tissues are composed of a heterogeneous mixture of cancer cells and normal cells, and symbiotic metabolic relationships have been described in both normal and malignant-tissues contexts (171175). Because whole body metabolic regulation can also affect tumor tissue metabolism (176), incorporating the complex interplay between genetics, microenvironment, tissue heterogeneity, and whole body metabolism at the CSC-metabolic level remains an enormous challenge for the field. In this regard, we recently reasoned that certain extracellular nutrients might be the major determinant of the maintenance and/or expansion of the metabolic states characteristic of CSC cellular states. Using a standardized high-throughput metabolic phenotyping platform [i.e., the Phenotype MicroArrays for Mammalian Cells (PMM) technology] based on a single-assay metabolic profile of several hundred nutrient sources, we performed a comprehensive evaluation of the phenotypic variations among non-CSC and CSC-like isogenic cells as a starting point for uncovering CSC cells’ microecological nutritional niches (5). Our study revealed that the acquisition of stemness traits by breast epithelial cells is sufficient to cell-autonomously enable the vectorial transfer of energy-rich nutrients from the extracellular microenvironment to energy-producing catabolic pathways in the CSC-like cells. We presented the first evidence for the existence of a cell-autonomous “reverse Warburg effect” where CSC reprograming appears to pre-activate energy-producing pathways that can efficiently use bona fide ketone bodies (e.g., β-hydroxy-butyric acid) and other high-energy metabolites such as lactate and pyruvate from the extracellular milieu to feed mitochondrial energy production, especially upon starvation. Because cancer cells’ utilization of the high-energy metabolites pyruvate, lactate, and ketones has been shown to increase the transcriptional expression of gene profiles normally associated with stemness, including genes commonly upregulated in ESCs (177, 178), our results strongly suggest that global nutrient utilization analyses should be viewed as crucial complements of the metabolo-phenotypic characterization of CSC cellular states.

But how should a definitive elucidation of the metabolo-phenomic maps in CSC be approached? Metabolic flux analysis (fluxomics) is known to represent the physiological counterpart of its sibling’s transcriptomics, proteomics, and metabolomics. Fluxomics integrates in vivo measurements of metabolic fluxes with stoichiometric network models to allow the determination of absolute flux through large networks of the central carbon metabolism. Thus, fluxomics, by comprising all metabolic conversion rates in a cell, is being increasingly used in fundamental and applied sciences to unravel metabolic infrastructures and activities of metabolic networks and their regulation (Figure 5). Accordingly, forthcoming studies should afford the systematic determination of “CSC fluxomes” by directly inferring the immeasurable in vivo central metabolic reaction rates through rigorous mathematical modeling. By performing metabolomic flux analyses using isotopic tracers and mass spectrometry (MS), it might be possible to determine whether CSC cellular states possess highly specific structural or functional changes in metabolic enzymes and nodes that allow them to produce specific or differentially enriched CSC-associated metabolites. CSC fluxomic experiments are currently underway in our laboratory, which consist of feeding the culture of EMT-induced CSC-like cells and isogenic parental cells with a defined 13C-labeled substrate, and measuring, through nuclear magnetic resonance (NMR) or MS, the isotopic enrichment in intracellular metabolites. This information is expected to be “stored” in terms of isotopomers (i.e., each of the possible labeling states in which a particular metabolite can be found). The resultant 13C-labeling in the intracellular metabolites is expected to impose important constraints on how the labeled carbon substrate is distributed throughout the metabolic network and, hence, on the identity of the non-CSC- and CSC-associated metabolic fluxes.

FIGURE 5
www.frontiersin.org

Figure 5. Metabostemness: a fluxomic perspective. CSC cellular states should include changes in the capacities (enzyme abundance) and kinetics (enzyme activity) of certain metabolic nodes that might then generate CSC-associated metabolites or metabolomic flux imprints. Metabolic flux analysis (MFA) has become a standard tool to probe cellular metabolism and elucidate in vivo metabolic fluxes, which are estimated from isotopic labeling measurements combined with extracellular uptake and excretion rates. Because 13C-MFA has been shown to provide an inherently more realistic representation of in vivo cellular metabolism, the application of the isotopic 13C-MFA methodology might allow obtaining an integrated picture of the metabolic fluxes specifically or differentially occurring in CSC cellular states. In brief, a 13C-labeled substrate can be incorporated into the carbon backbone of a wide range of CSC-metabolites and the CSC metabolome, either through exchange or synthesis. The dynamic distribution of labeled carbon traversing along CSC-metabolic pathways is expected to generate a characteristic imprint of labeling patterns whose mass signature will be observed by MS. All fluxes can be determined on an absolute scale because the physiological fluxes in and out of non-CSC and CSC cells will be available under experimental conditions. Mass isotopomers can be analyzed at different times to follow the label incorporation immediately after incubating cells with a labeled substrate. To translate the time-series labeling data into metabolic fluxes, MS-related mathematical models could combine the balances of the total metabolite pools and of individual isotopomers to obtain complete information about the transitions of the labeled carbons within metabolites. The model inputs will be the isotopomer 13C time-courses and some metabolite pools determined experimentally, in addition to the consumption or production rates of metabolites measured in cell supernatants. Because the isotopic transient 13C-MFA methodology does not rely on uncertain cofactor balances it allows the estimation of fluxes through both parallel and cyclic pathways as well as through bidirectional reactions.

Geroncogenesis and Metabostemness

The recently proposed “geroncogenesis” hypothesis states that metabolic changes during aging (i.e., the normal decline in oxidative metabolism and the development of a Warburgian glycolytic metabolism in normal tissues) constitute an early and important “hit” that pushes cells toward complete cellular transformation. Wu et al. (179) propose that “is not simply the time taken to accumulate genomic hits that accounts for the increased rate of cancer with age, but the decline in metabolic homeostasis and gene regulation that occurs normally as we age.” The authors place the sirtuins, a family of NAD+-dependent deacetylases (180) that have evolved as coordinators of physiological responses to nutrient intake and energetic demand, as central to this aging-induced dysregulation of mitochondrial metabolism. In the geroncogenic scenario, aging induces a gradual reprograming of metabolism toward a “cancer-like” state. This pre-metabolic transformation certainly correlates well with our current proposal of metabostemness, which calls for the need to broaden our current perception on how metabolism and metabolites exert influence over the transcriptional factors, the chromatin structure, and the epigenetic circuits that establish and maintain the self-renewal and differentiation capacities of CSCs. Certain aging-related metabotypic alterations (e.g., a shift toward a predominantly glycolytic, Warburg-like metabolism) might operate as pivotal molecular events rendering any type of cell of origin susceptible to epigenetic rewiring required for the acquisition of aberrant stemness and, concurrently, of refractoriness not only to apoptosis, but also to differentiation. From a geroncogenic perspective (179), certain “gerometabolites” (43) can operate as bona fide metabostemness reprograming factors that poise cells with chromatin states competent for rapid dedifferentiation while concomitantly setting the idoneous metabolic stage for later genetic alterations to finish the reprograming of non-cancerous into tumor-initiating cells. From a dynamic perspective using Waddingtonian landscapes, metabolic changes during aging could causally control or functionally substitute the epitranscriptional orchestration of the genetic reprograming redirecting normal and non-CSC tumor cells toward less-differentiated CSC cellular states by accelerating the number of “stochastic transitions” (i.e., a case in which the metabolo-epitranscriptional rate equations occurring in a given MGRN cannot specify how a cellular state will move from non-CSC attractors to CSC attractors) and increasing the probability of “deterministic transitions” (i.e., a case in which the metabolo-epitranscriptional rate equations occurring in a given MGNR specify how a cellular state will move from non-CSC attractors to CSC attractors once appropriate initial conditions such as genomic hits are defined). By impacting the epitranscriptional remodeling that facilitates the transition between non-CSC and CSC functional states, the metabostemness trait determines the cancer tissue proclivity of generating a given number of CSCs in a given timeframe, thus providing a functional extension of the geroncogenesis hypothesis at the stem cell level. The gero-metabostemness scenario is consistent with the strong association between cancer prevalence and type 2 diabetes, obesity, and sedentary lifestyle and might be counteracted by exercise, calorie restriction (CR), and CR mimetics that may prevent aging tissues from undergoing the metabolic switch in the first place. Forthcoming studies should mechanistically evaluate how organismal diet, stem cell function, and cancer initiation are interconnected (181) and the implications for cancer prevention using metabolic drugs (e.g., biguanides) or natural dietary products including polyphenolic xenohormetins (8, 182, 183).

Metabostemness and Therapeutics: From Metabo-Stemotoxic Drugs to Anti-CSC Smart Foods

The fact that CSC can be generated de novo from more differentiated cells adds a crucial concern to the therapeutic elimination of CSC cellular states: how can we resolve the apparently impossible problem of suppressing the molecular biology of the stemness itself as the sole credible target against CSC? The proposed metabostemness hallmark critically contributes to the gaining and retaining of the stem cell-like fate in pre- and malignant-tissues because, as mentioned above, it operates as the physiological glue that connects all the omic layers with a self-autonomous CSC-metabolic quality, thus establishing a therapeutically targetable metabolic continuum in the reprograming-differentiation model of cancer genesis and progression. The metabostemness hallmark not only dictates the plasticity of the original differentiated cell that is targeted so as to be reprogrammable by early or late genetic and/or epigenetic hits, but, by removing or remodeling the potential barriers between non-CSC and CSC cellular states, it can also operate as a bona fide accelerator of the reprograming process, allowing cells to rapidly progress toward the acquisition of a CSC-like status. Because gains and losses of the metabostemness trait can shift the balance of the non-CSC-to-CSC interconversion in one direction or another, it then follows that only certain cell metabotypes will be compatible with the operational properties exclusively owned by CSC cellular states; conversely, certain metabotypes will impose cellular modes refractory to CSC reprograming. In other words, the molecular logic behind the conversion of non-CSC into CSC can be understood in terms of cellular metabotypes that operate as pathways or roadblocks by facilitating or impeding, respectively, the transcriptional events and signal transduction pathways that lastly coordinate the reprograming paths to CSC cellular states. Since metabolic (like epigenetic) programs, and unlike most genetic actors involve in stemness (e.g., loss-of-function mutations in tumor-suppressors, the activation of non-catalytic transcription factors), can be erased, manipulated, and reinitiated, the inclusion of the metabostemness hallmark as a new-dimensional cancer driver opens an entirely new framework for cancer prevention and treatment based on CSCs’ unique metabolic dependencies. Because small perturbations in a particular metabolic pathway or metabolite might have drastic consequences on the formation, maintenance, and evolution of CSC, an unambiguous elucidation of a putative metabo-stem connection remains mandatory before we could develop a new generation of therapies directed against CSCs’ metabolic dependencies. Accordingly, anti-metabolic reprograming strategies begin to provide a roadmap for the generation of novel “metabo-stemotoxic” therapies that metabolically target CSCs in biologically aggressive tumor types; in this regard, perhaps it is not surprising that the potential applications for biguanides in oncology (184) closely relate to their differential metabolic effects during the cellular transformation and CSC stages (185). A growing number of studies have demonstrated that the biguanide metformin selectively ablates CSCs, as evidenced by the decreased expression of pluripotency-associated genes, CSC-associated surface markers, and other CSC-specific properties including tumor-initiation (83, 84, 166, 185204).

Moreover, a definitive elucidation of the metabolo-phenomic maps of CSC cells may reveal an unforeseen route to the development of therapies against not only the intrinsic metabolic energy-generating machinery of CSC cells, but also their nutritional niches. The emerging discipline of nutritional genomics or nutrigenomics includes both the study of the effects of diet on an individual’s gene activity and health and the study of how genetic composition affects nutrient metabolism (205208). Through an understanding of the unique roles of specific nutrients and their possible roles in boosting CSC-like cell phenotypes, it might be possible to customize “smart foods” or systemic “metabolic nichotherapies” tailored to the specific nutritional phenomes possessed by cells with CSC cellular states. Nevertheless, because new techniques such as multi-isotope imaging mass spectrometry (MIMS) permit the high-resolution tracking of heavy isotope-labeled molecules upon utilization by specific types of cells (209, 210), the unique metabolic fluxes generated by the catabolic energy-producing machinery of CSC could be implemented in a novel manner to monitor the spatio-temporal distributions and functionality of CSC cellular states in real-time.

Corollary

In view of recent groundbreaking studies showing that aging-related decline of certain metabolites can set the metabolic stage for later mutations to drive tumorigenesis (i.e., the geroncogenesis hypothesis) and that the epigenetic fate determination of differentiated cells might be converted to pluripotency by microenvironmental cues, it is time to paradigmatically rethink our perception of the regulatory role of metabolic reprograming in cancer cell-fate decisions. Frustrated by the gene-centric guidelines that usually govern of conventional approaches against CSC, we recently envisioned that the incorporation of robust metabolic phenomics, i.e., the systematic acquisition and objective documentation of cancer metabolic data at the level of CSC cellular states, might revolutionize the advancement of CSC-related cancer precision medicine. Our current proposal of the metabostemness cancer hallmark should forge a new and different path to treating and monitoring cancer through the metabolic phenome of CSC cellular states. Under the assumption that the molecular biology of the stemness transformation itself would be the sole credible target in CSC, a systematic combination of biology, biochemistry, pharmacology, genetics, fluxomics, and mathematical approaches should unambiguously provide the first qualitative and quantitative phenomic representation of the metabolic state of CSC, thus unlocking an almost unexplored field of discovery in cancer research. By generating a robust product engine to interrogate the differential cellular metabolism of CSCs relative to normal and non-CSC tumor cells, we might discover an unforeseen new phenomic hallmark of cancer that we have called cancer metabostemness.

Previously, scientists believed that metabolic changes were a mere consequence of aberrant cancer cell growth. We propose that metabolic reprograming of CSC has cancer-causing activity. This proposal suggests an alternative explanation and offers the possibility of delineating a metabolic roadmap for the acquisition and maintenance of CSC cellular states that might not even require pre-existing mutations or rearrangements of well-established “cancer genes.” Moreover, identifying the metabotypic infrastructure of CSCs will add a novel dynamic, phenomic dimension to well-recognized cancer hallmarks. Should CSC possess specific changes in the capacities and kinetics of certain metabolic nodes, the unique metabolic flux imprints generated by CSC could be revolutionarily implemented to monitor CSCs’ spatio-temporal distribution and functionality in real-time. Once in the clinic, this approach can be used to pursue unique and potentially more rapid clinical development pathways by theranostically focusing on cancer populations defined by specific CSC-metabolites or CSC-metabolic imprints. Indeed, we will be able to pharmacologically establish cell metabotypes that are protected against the reprograming events leading to CSC cellular states, thus providing the molecular bases to accelerate and diversify our current therapeutic capacity in new clinical trials designed to metabolically prevent and target CSC cellular states. The metabostemness cancer hallmark generates a shifting oncology theory that should guide a new era of metabolo-epigenetic cancer precision medicine. The tremendous health and social impacts of this paradigmatic shift in the origins of CSC-driven cancer heterogeneity are suited to becoming the platform for new biopharmaceutical strategies based on CSC-metabolic phenomics and dedicated to the research and development of the almost unexplored field of CSC-metabolic theranostics (CSC-metabolic diagnosis + CSC-metabolic treatment; Figure 6) in cancer diseases.

FIGURE 6
www.frontiersin.org

Figure 6. Metabostemness: a therasnostic perspective. A qualitative and quantitative representation of the CSC-metabolic state will add a novel dynamic dimension to other well-known cancer hallmarks. Such a description of the CSC-metabolic phenome would make it possible to metabolically create non-permissive (or “hostile”) metabotypes to prevent the occurrence of cellular states with tumor- and metastasis-initiating capacities. In the clinic, the cancer metabostemness attribute will be the basis to rapidly pursue unique therapeutic approaches that target the addiction of CSCs to certain metabolic infrastructures and metabolic fluxes at the cell-intrinsic, microenvironmental, and/or systemic levels. Importantly, the opportunities and challenges for targeting the metabolic infrastructure of CSCs might be rapidly achieved because existing metabolic drugs may be easily repositioned from pre-clinical stages to clinical approaches. For instance, the drugs that would arise from the knowledge-based drug repositioning strategies selected to metabolically suppress the functionality of CSCs could silently operate as “cancer tissue sweepers” of cells capable of initiating and propagating tumors while sparing their normal counterparts. Moreover, differentiating between the specific characteristics of normal tissue SCs and CSCs may be an essential requisite for assessing the best treatment targets for CSCs while minimizing sequelae. The specific, efficient elimination of the malignant teratocarcinoma-initiating cells using the metabolic drug metformin (198) strongly supports the suggestion that the phenomic metabostemness hallmark is a previously unrecognized, indispensable component of the CSC machinery. In addition, the phenomic metabostemness hallmark will allow an “all-in-one” therasnostic approach in cancer diseases, an emerging tool in drug discovery and commercialization that might allow us to take pharmacometabolomics-based precision medicine from the lab to the “point-of-care,” the patient. Should CSC possess specific changes in the capacities and kinetics of certain metabolic nodes, the unique metabolic flux imprints generated by CSC could be revolutionarily implemented to monitor the spatio-temporal distribution and functionality of CSCs in real-time. Coupling metabostemness-based drug testing with the use of non-invasive devices (e.g., MIMS, NMR/MRS, and circulating metabolomics) that can accurately monitor in real-time the spatio-temporal distribution and functionality of CSC could be rapidly implemented to help surgeons, radio-oncologists, and oncologists accelerate and improve metabostemness-based drug discovery and development (note: the illustrations accompanying the figure were created by “Formas Naturales”: http://www.formasnaturales.com/).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This work was financially supported by the Ministerio de Ciencia e Innovación (SAF2012-38914), Plan Nacional de I+D+I, MICINN, Spain.

References

1. Suvà ML, Riggi N, Bernstein BE. Epigenetic reprogramming in cancer. Science (2013) 339:1567–70. doi: 10.1126/science.1230184

CrossRef Full Text

2. Menendez JA, Joven J, Cufí S, Corominas-Faja B, Oliveras-Ferraros C, Cuyàs E, et al. The Warburg effect version 2.0: metabolic reprogramming of cancer stem cells. Cell Cycle (2013) 12:1166–79. doi:10.4161/cc.24479

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

3. Menendez JA, Alarcón T, Corominas-Faja B, Cuyàs E, López-Bonet E, Martin AG, et al. Xenopatients 2.0: reprogramming the epigenetic landscapes of patient-derived cancer genomes. Cell Cycle (2014) 13:358–70. doi:10.4161/cc.27770

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

4. Campos-Sánchez E, Cobaleda C. Tumoral reprogramming: plasticity takes a walk on the wild side. Biochim Biophys Acta (2014). doi:10.1016/j.bbagrm.2014.07.003

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

5. Cuyàs E, Corominas-Faja B, Menendez JA. The nutritional phenome of EMT-induced cancer stem-like cells. Oncotarget (2014) 5:3970–82.

Pubmed Abstract | Pubmed Full Text

6. Friedmann-Morvinski D, Verma IM. Dedifferentiation and reprogramming: origins of cancer stem cells. EMBO Rep (2014) 15:244–53. doi:10.1002/embr.201338254

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

7. Goding CR, Pei D, Lu X. Cancer: pathological nuclear reprogramming? Nat Rev Cancer (2014) 14:568–73. doi:10.1038/nrc3781

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

8. Menendez JA, Joven J. Energy metabolism and metabolic sensors in stem cells: the metabostem crossroads of aging and cancer. Adv Exp Med Biol (2014) 824:117–40. doi:10.1007/978-3-319-07320-0_10

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

9. Vicente-Dueñas C, Hauer J, Ruiz-Roca L, Ingenhag D, Rodríguez-Meira A, Auer F, et al. Tumoral stem cell reprogramming as a driver of cancer: theory, biological models, implications in cancer therapy. Semin Cancer Biol (2014). doi:10.1016/j.semcancer.2014.02.001

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

10. Prigione A, Fauler B, Lurz R, Lehrach H, Adjaye J. The senescence-related mitochondrial/oxidative stress pathway is repressed in human induced pluripotent stem cells. Stem Cells (2010) 28(4):721–33. doi:10.1002/stem.404

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

11. Chen T, Shen L, Yu J, Wan H, Guo A, Chen J, et al. Rapamycin and other longevity-promoting compounds enhance the generation of mouse induced pluripotent stem cells. Aging Cell (2011) 10:908–11. doi:10.1111/j.1474-9726.2011.00722.x

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

12. Folmes CD, Nelson TJ, Martinez-Fernandez A, Arrell DK, Lindor JZ, Dzeja PP, et al. Somatic oxidative bioenergetics transitions into pluripotency-dependent glycolysis to facilitate nuclear reprogramming. Cell Metab (2011) 14:264–71. doi:10.1016/j.cmet.2011.06.011

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

13. Folmes CD, Nelson TJ, Terzic A. Energy metabolism in nuclear reprogramming. Biomark Med (2011) 5:715–29. doi:10.2217/bmm.11.87

CrossRef Full Text

14. Folmes CD, Nelson TJ, Dzeja PP, Terzic A. Energy metabolism plasticity enables stemness programs. Ann N Y Acad Sci (2012) 1254:82–9. doi:10.1111/j.1749-6632.2012.06487.x

CrossRef Full Text

15. Folmes CD, Dzeja PP, Nelson TJ, Terzic A. Metabolic plasticity in stem cell homeostasis and differentiation. Cell Stem Cell (2012) 11:596–606. doi:10.1016/j.stem.2012.10.002

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

16. Folmes CD, Arrell DK, Zlatkovic-Lindor J, Martinez-Fernandez A, Perez-Terzic C, Nelson TJ, et al. Metabolome and metaboproteome remodeling in nuclear reprogramming. Cell Cycle (2013) 12:2355–65. doi:10.4161/cc.25509

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

17. Menendez JA, Vellon L, Oliveras-Ferraros C, Cufí S, Vazquez-Martin A. mTOR-regulated senescence and autophagy during reprogramming of somatic cells to pluripotency: a roadmap from energy metabolism to stem cell renewal and aging. Cell Cycle (2011) 10:3658–77. doi:10.4161/cc.10.21.18128

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

18. Mahmoudi S, Brunet A. Aging and reprogramming: a two-way street. Curr Opin Cell Biol (2012) 24:744–56. doi:10.1016/j.ceb.2012.10.004

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

19. Panopoulos AD, Yanes O, Ruiz S, Kida YS, Diep D, Tautenhahn R, et al. The metabolome of induced pluripotent stem cells reveals metabolic changes occurring in somatic cell reprogramming. Cell Res (2012) 22:168–77. doi:10.1038/cr.2011.177

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

20. Rafalski VA, Mancini E, Brunet A. Energy metabolism and energy-sensing pathways in mammalian embryonic and adult stem cell fate. J Cell Sci (2012) 125:5597–608. doi:10.1242/jcs.114827

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

21. Vazquez-Martin A, Cufi S, Corominas-Faja B, Oliveras-Ferraros C, Vellon L, Menendez JA. Mitochondrial fusion by pharmacological manipulation impedes somatic cell reprogramming to pluripotency: new insight into the role of mitophagy in cell stemness. Aging (Albany NY) (2012) 4:393–401.

Pubmed Abstract | Pubmed Full Text

22. Vazquez-Martin A, Vellon L, Quirós PM, Cufí S, Ruiz de Galarreta E, Oliveras-Ferraros C, et al. Activation of AMP-activated protein kinase (AMPK) provides a metabolic barrier to reprogramming somatic cells into stem cells. Cell Cycle (2012) 11:974–89. doi:10.4161/cc.11.5.19450

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

23. Vazquez-Martin A, Corominas-Faja B, Cufi S, Vellon L, Oliveras-Ferraros C, Menendez OJ, et al. The mitochondrial H(+)-ATP synthase and the lipogenic switch: new core components of metabolic reprogramming in induced pluripotent stem (iPS) cells. Cell Cycle (2013) 12:207–18. doi:10.4161/cc.23352

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

24. Corominas-Faja B, Cufí S, Oliveras-Ferraros C, Cuyàs E, López-Bonet E, Lupu R, et al. Nuclear reprogramming of luminal-like breast cancer cells generates Sox2-overexpressing cancer stem-like cellular states harboring transcriptional activation of the mTOR pathway. Cell Cycle (2013) 12:3109–24. doi:10.4161/cc.26173

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

25. Guan JL, Simon AK, Prescott M, Menendez JA, Liu F, Wang F, et al. Autophagy in stem cells. Autophagy (2013) 9:830–49. doi:10.4161/auto.24132

CrossRef Full Text

26. Wang S, Xia P, Ye B, Huang G, Liu J, Fan Z. Transient activation of autophagy via Sox2-mediated suppression of mTOR is an important early step in reprogramming to pluripotency. Cell Stem Cell (2013) 13:617–25. doi:10.1016/j.stem.2013.10.005

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

27. Xu X, Duan S, Yi F, Ocampo A, Liu GH, Izpisua Belmonte JC. Mitochondrial regulation in pluripotent stem cells. Cell Metab (2013) 18:325–32. doi:10.1016/j.cmet.2013.06.005

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

28. Bukowiecki R, Adjaye J, Prigione A. Mitochondrial function in pluripotent stem cells and cellular reprogramming. Gerontology (2014) 60:174–82. doi:10.1159/000355050

CrossRef Full Text

29. Ito K, Suda T. Metabolic requirements for the maintenance of self-renewing stem cells. Nat Rev Mol Cell Biol (2014) 15:243–56. doi:10.1038/nrm3772

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

30. Perales-Clemente E, Folmes CD, Terzic A. Metabolic regulation of redox status in stem cells. Antioxid Redox Signal (2014). doi:10.1089/ars.2014.6000

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

31. Ward PS, Patel J, Wise DR, Abdel-Wahab O, Bennett BD, Coller HA, et al. The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting alpha-ketoglutarate to 2-hydroxyglutarate. Cancer Cell (2010) 17:225–34. doi:10.1016/j.ccr.2010.01.020

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

32. Xu W, Yang H, Liu Y, Yang Y, Wang P, Kim SH, et al. Oncometabolite 2-hydroxyglutarate is a competitive inhibitor of α-ketoglutarate-dependent dioxygenases. Cancer Cell (2011) 19:17–30. doi:10.1016/j.ccr.2010.12.014

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

33. Chowdhury R, Yeoh KK, Tian YM, Hillringhaus L, Bagg EA, Rose NR, et al. The oncometabolite 2-hydroxyglutarate inhibits histone lysine demethylases. EMBO Rep (2011) 12:463–9. doi:10.1038/embor.2011.43

CrossRef Full Text

34. Frezza C, Pollard PJ, Gottlieb E. Inborn and acquired metabolic defects in cancer. J Mol Med (Berl) (2011) 89:213–20. doi:10.1007/s00109-011-0728-4

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

35. Yang M, Soga T, Pollard PJ, Adam J. The emerging role of fumarate as an oncometabolite. Front Oncol (2012) 2:85. doi:10.3389/fonc.2012.00085

CrossRef Full Text

36. Yang M, Soga T, Pollard PJ. Oncometabolites: linking altered metabolism with cancer. J Clin Invest (2013) 123:3652–8. doi:10.1172/JCI67228

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

37. Cairns RA, Mak TW. Oncogenic isocitrate dehydrogenase mutations: mechanisms, models, and clinical opportunities. Cancer Discov (2013) 3:730–41. doi:10.1158/2159-8290.CD-13-0083

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

38. Krell D, Mulholland P, Frampton AE, Krell J, Stebbing J, Bardella C. IDH mutations in tumorigenesis and their potential role as novel therapeutic targets. Future Oncol (2013) 9:1923–35. doi:10.2217/fon.13.143

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

39. Sullivan LB, Martinez-Garcia E, Nguyen H, Mullen AR, Dufour E, Sudarshan S, et al. The proto-oncometabolite fumarate binds glutathione to amplify ROS-dependent signaling. Mol Cell (2013) 51:236–48. doi:10.1016/j.molcel.2013.05.003

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

40. Adam J, Yang M, Soga T, Pollard PJ. Rare insights into cancer biology. Oncogene (2014) 33:2547–56. doi:10.1038/onc.2013.222

CrossRef Full Text

41. Borger DR, Goyal L, Yau T, Poon RT, Ancukiewicz M, Deshpande V, et al. Circulating oncometabolite 2-hydroxyglutarate is a potential surrogate biomarker in patients with isocitrate dehydrogenase-mutant intrahepatic cholangiocarcinoma. Clin Cancer Res (2014) 20:1884–90. doi:10.1158/1078-0432.CCR-13-2649

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

42. Gaude E, Frezza C. Defects in mitochondrial metabolism and cancer. Cancer Metab (2014) 2:10. doi:10.1186/2049-3002-2-10

CrossRef Full Text

43. Menendez JA, Alarcón T, Joven J. Gerometabolites: the pseudohypoxic aging side of cancer oncometabolites. Cell Cycle (2014) 13:699–709. doi:10.4161/cc.28079

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

44. Saha SK, Parachoniak CA, Ghanta KS, Fitamant J, Ross KN, Najem MS, et al. Mutant IDH inhibits HNF-4α to block hepatocyte differentiation and promote biliary cancer. Nature (2014) 513(7516):110–4. doi:10.1038/nature13441

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

45. Terunuma A, Putluri N, Mishra P, Mathé EA, Dorsey TH, Yi M, et al. MYC-driven accumulation of 2-hydroxyglutarate is associated with breast cancer prognosis. J Clin Invest (2014) 124:398–412. doi:10.1172/JCI71180

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

46. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell (2011) 144:646–74. doi:10.1016/j.cell.2011.02.013

CrossRef Full Text

47. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell (2000) 100:57–70. doi:10.1016/S0092-8674(00)81683-9

CrossRef Full Text

48. Kroemer G, Pouyssegur J. Tumor cell metabolism: cancer’s Achilles’ heel. Cancer Cell (2008) 13:472–82. doi:10.1016/j.ccr.2008.05.005

CrossRef Full Text

49. Menendez JA, Lupu R. Fatty acid synthase and the lipogenic phenotype in cancer pathogenesis. Nat Rev Cancer (2007) 7:763–77. doi:10.1038/nrc2222

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

50. DeBerardinis RJ, Lum JJ, Hatzivassiliou G, Thompson CB. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab (2008) 7:11–20. doi:10.1016/j.cmet.2007.10.002

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

51. Jones RG, Thompson CB. Tumor suppressors and cell metabolism: a recipe for cancer growth. Genes Dev (2009) 23:537–48. doi:10.1101/gad.1756509

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

52. Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science (2009) 324:1029–33. doi:10.1126/science.1160809

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

53. Locasale JW. Serine, glycine and one-carbon units: cancer metabolism in full circle. Nat Rev Cancer (2013) 13:572–83. doi:10.1038/nrc3557

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

54. Hernandez-Vargas H, Sincic N, Ouzounova M, Herceg Z. Epigenetic signatures in stem cells and cancer stem cells. Epigenomics (2009) 1:261–80. doi:10.2217/epi.09.19

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

55. Schoenhals M, Kassambara A, De Vos J, Hose D, Moreaux J, Klein B. Embryonic stem cell markers expression in cancers. Biochem Biophys Res Commun (2009) 383:157–62. doi:10.1016/j.bbrc.2009.02.156

CrossRef Full Text

56. Maenhaut C, Dumont JE, Roger PP, van Staveren WC. Cancer stem cells: a reality, a myth, a fuzzy concept or a misnomer? An analysis. Carcinogenesis (2010) 31:149–58. doi:10.1093/carcin/bgp259

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

57. Floor S, van Staveren WC, Larsimont D, Dumont JE, Maenhaut C. Cancer cells in epithelial-to-mesenchymal transition and tumor-propagating-cancer stem cells: distinct, overlapping or same populations. Oncogene (2011) 30:4609–21. doi:10.1038/onc.2011.184

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

58. Floor SL, Dumont JE, Maenhaut C, Raspe E. Hallmarks of cancer: of all cancer cells, all the time? Trends Mol Med (2012) 18:509–15. doi:10.1016/j.molmed.2012.06.005

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

59. Ben-Porath I, Thomson MW, Carey VJ, Ge R, Bell GW, Regev A, et al. An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors. Nat Genet (2008) 40:499–507. doi:10.1038/ng.127

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

60. Lengerke C, Fehm T, Kurth R, Neubauer H, Scheble V, Müller F, et al. Expression of the embryonic stem cell marker SOX2 in early-stage breast carcinoma. BMC Cancer (2011) 11:42. doi:10.1186/1471-2407-11-42

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

61. Liu CG, Lu Y, Wang BB, Zhang YJ, Zhang RS, Lu Y, et al. Clinical implications of stem cell gene Oct-4 expression in breast cancer. Ann Surg (2011) 253:1165–71. doi:10.1097/SLA.0b013e318214c54e

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

62. Liu C, Cao X, Zhang Y, Xu H, Zhang R, Wu Y, et al. Co-expression of Oct-4 and Nestin in human breast cancers. Mol Biol Rep (2012) 39:5875–81. doi:10.1007/s11033-011-1398-6

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

63. Leis O, Eguiara A, Lopez-Arribillaga E, Alberdi MJ, Hernandez-Garcia S, Elorriaga K, et al. Sox2 expression in breast tumours and activation in breast cancer stem cells. Oncogene (2012) 31:1354–65. doi:10.1038/onc.2011.338

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

64. Peng S, Maihle NJ, Huang Y. Pluripotency factors Lin28 and Oct4 identify a sub-population of stem cell-like cells in ovarian cancer. Oncogene (2010) 29:2153–9. doi:10.1038/onc.2009.500

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

65. Yang X, Lin X, Zhong X, Kaur S, Li N, Liang S, et al. Double-negative feedback loop between reprogramming factor LIN28 and microRNA let-7 regulates aldehyde dehydrogenase 1-positive cancer stem cells. Cancer Res (2010) 70:9463–72. doi:10.1158/0008-5472.CAN-10-2388

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

66. Zhong X, Li N, Liang S, Huang Q, Coukos G, Zhang L. Identification of microRNAs regulating reprogramming factor LIN28 in embryonic stem cells and cancer cells. J Biol Chem (2010) 285:41961–71. doi:10.1074/jbc.M110.169607

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

67. Beltran AS, Rivenbark AG, Richardson BT, Yuan X, Quian H, Hunt JP, et al. Generation of tumor-initiating cells by exogenous delivery of OCT4 transcription factor. Breast Cancer Res (2011) 13:R94. doi:10.1186/bcr3019

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

68. Hassiotou F, Hepworth AR, Beltran AS, Mathews MM, Stuebe AM, Hartmann PE, et al. Expression of the pluripotency transcription factor OCT4 in the normal and aberrant mammary gland. Front Oncol (2013) 3:79. doi:10.3389/fonc.2013.00079

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

69. Madison BB, Liu Q, Zhong X, Hahn CM, Lin N, Emmett MJ, et al. LIN28B promotes growth and tumorigenesis of the intestinal epithelium via Let-7. Genes Dev (2013) 27:2233–45. doi:10.1101/gad.224659.113

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

70. Boumahdi S, Driessens G, Lapouge G, Rorive S, Nassar D, Le Mercier M, et al. SOX2 controls tumour initiation and cancer stem-cell functions in squamous-cell carcinoma. Nature (2014) 511:246–50. doi:10.1038/nature13305

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

71. Tam WL, Ng HH. Sox2: masterminding the root of cancer. Cancer Cell (2014) 26:3–5. doi:10.1016/j.ccr.2014.06.024

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

72. Vanner RJ, Remke M, Gallo M, Selvadurai HJ, Coutinho F, Lee L, et al. Quiescent sox2(+) cells drive hierarchical growth and relapse in sonic hedgehog subgroup medulloblastoma. Cancer Cell (2014) 26:33–47. doi:10.1016/j.ccr.2014.05.005

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

73. Bond JA, Oddweig Ness G, Rowson J, Ivan M, White D, Wynford-Thomas D. Spontaneous de-differentiation correlates with extended lifespan in transformed thyroid epithelial cells: an epigenetic mechanism of tumour progression? Int J Cancer (1996) 67:563–72. doi:10.1002/(SICI)1097-0215(19960807)67:4<563::AID-IJC16>3.0.CO;2-8

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

74. Bonnet D, Dick JE. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat Med (1997) 3:730–7. doi:10.1038/nm0797-730

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

75. Reya T, Morrison SJ, Clarke MF, Weissman IL. Stem cells, cancer, and cancer stem cells. Nature (2001) 414:105–11. doi:10.1038/35102167

CrossRef Full Text

76. Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ, Clarke MF. Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci U S A (2003) 100:3983–8. doi:10.1073/pnas.0530291100

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

77. Kleinsmith LJ, Pierce GB Jr. Multipotentiality of single embryonal carcinoma cells. Cancer Res (1964) 24:1544–51.

78. Pierce GB, Speers WC. Tumors as caricatures of the process of tissue renewal: prospects for therapy by directing differentiation. Cancer Res (1988) 48:1996–2004.

Pubmed Abstract | Pubmed Full Text

79. Passegue E, Jamieson CH, Ailles LE, Weissman IL. Normal and leukemic hematopoiesis: are leukemias a stem cell disorder or a reacquisition of stem cell characteristics? Proc Natl Acad Sci U S A (2003) 100:11842–9. doi:10.1073/pnas.2034201100

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

80. Mani SA, Guo W, Liao MJ, Eaton EN, Ayyanan A, Zhou AY, et al. The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell (2008) 133:704–15. doi:10.1016/j.cell.2008.03.027

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

81. Shackleton M, Quintana E, Fearon ER, Morrison SJ. Heterogeneity in cancer: cancer stem cells versus clonal evolution. Cell (2009) 138:822–9. doi:10.1016/j.cell.2009.08.017

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

82. Iliopoulos D, Hirsch HA, Struhl K. An epigenetic switch involving NF-kappaB, Lin28, Let-7 MicroRNA, and IL6 links inflammation to cell transformation. Cell (2009) 139:693–706. doi:10.1016/j.cell.2009.10.014

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

83. Iliopoulos D, Hirsch HA, Wang G, Struhl K. Inducible formation of breast cancer stem cells and their dynamic equilibrium with non-stem cancer cells via IL6 secretion. Proc Natl Acad Sci U S A (2011) 108:1397–402. doi:10.1073/pnas.1018898108

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

84. Iliopoulos D, Hirsch HA, Struhl K. Metformin decreases the dose of chemotherapy for prolonging tumor remission in mouse xenografts involving multiple cancer cell types. Cancer Res (2011) 71:3196–201. doi:10.1158/0008-5472.CAN-10-3471

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

85. Gupta PB, Fillmore CM, Jiang G, Shapira SD, Tao K, Kuperwasser C, et al. Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell (2011) 146:633–44. doi:10.1016/j.cell.2011.07.026

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

86. Chaffer CL, Brueckmann I, Scheel C, Kaestli AJ, Wiggins PA, Rodrigues LO, et al. Normal and neoplastic nonstem cells can spontaneously convert to a stem-like state. Proc Natl Acad Sci U S A (2011) 108:7950–5. doi:10.1073/pnas.1102454108

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

87. Polytarchou C, Iliopoulos D, Struhl K. An integrated transcriptional regulatory circuit that reinforces the breast cancer stem cell state. Proc Natl Acad Sci U S A (2012) 109:14470–5. doi:10.1073/pnas.1212811109

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

88. Spike BT, Wahl GM. p53, stem cells, and reprogramming: tumor suppression beyond guarding the genome. Genes Cancer (2011) 2:404–19. doi:10.1177/1947601911410224

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

89. Magee JA, Piskounova E, Morrison SJ. Cancer stem cells: impact, heterogeneity, and uncertainty. Cancer Cell (2012) 21:283–96. doi:10.1016/j.ccr.2012.03.003

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

90. Meacham CE, Morrison SJ. Tumour heterogeneity and cancer cell plasticity. Nature (2013) 501:328–37. doi:10.1038/nature12624

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

91. Quintana E, Shackleton M, Foster HR, Fullen DR, Sabel MS, Johnson TM, et al. Phenotypic heterogeneity among tumorigenic melanoma cells from patients that is reversible and not hierarchically organized. Cancer Cell (2010) 18:510–23. doi:10.1016/j.ccr.2010.10.012

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

92. Godar S, Ince TA, Bell GW, Feldser D, Donaher JL, Bergh J, et al. Growth-inhibitory and tumor-suppressive functions of p53 depend on its repression of CD44 expression. Cell (2008) 134:62–73. doi:10.1016/j.cell.2008.06.006

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

93. Meyer MJ, Fleming JM, Lin AF, Hussnain SA, Ginsburg E, Vonderhaar BK. CD44posCD49fhiCD133/2hi defines xenograft-initiating cells in estrogen receptor-negative breast cancer. Cancer Res (2010) 70:4624–33. doi:10.1158/0008-5472.CAN-09-3619

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

94. Liu C, Kelnar K, Liu B, Chen X, Calhoun-Davis T, Li H, et al. The microRNA miR-34a inhibits prostate cancer stem cells and metastasis by directly repressing CD44. Nat Med (2011) 17:211–5. doi:10.1038/nm.2284

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

95. Sharma SV, Lee DY, Li B, Quinlan MP, Takahashi F, Maheswaran S. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell (2010) 141:69–80. doi:10.1016/j.cell.2010.02.027

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

96. Soda Y, Marumoto T, Friedmann-Morvinski D, Soda M, Liu F, Michiue H, et al. Transdifferentiation of glioblastoma cells into vascular endothelial cells. Proc Natl Acad Sci U S A (2011) 108:4274–80. doi:10.1073/pnas.1016030108

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

97. Norton L. Cancer stem cells, EMT, and seeding: a rose is a rose is a rose? Oncology (Williston Park) (2011) 25:30–2.

98. Shu J, Wu C, Wu Y, Li Z, Shao S, Zhao W, et al. Induction of pluripotency in mouse somatic cells with lineage specifiers. Cell (2013) 153:963–75. doi:10.1016/j.cell.2013.05.001

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

99. Yamanaka S. Elite and stochastic models for induced pluripotent stem cell generation. Nature (2009) 460:49–52. doi:10.1038/nature08180

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

100. Hanna J, Saha K, Pando B, van Zon J, Lengner CJ, Creyghton MP, et al. Direct cell reprogramming is a stochastic process amenable to acceleration. Nature (2009) 462:595–601. doi:10.1038/nature08592

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

101. Hanna JH, Saha K, Jaenisch R. Pluripotency and cellular reprogramming: facts, hypotheses, unresolved issues. Cell (2010) 143:508–25. doi:10.1016/j.cell.2010.10.008

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

102. Zhang Y, Li W, Laurent T, Ding S. Small molecules, big roles – the chemical manipulation of stem cell fate and somatic cell reprogramming. J Cell Sci (2012) 125:5609–20. doi:10.1242/jcs.096032

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

103. Li W, Li K, Wei W, Ding S. Chemical approaches to stem cell biology and therapeutics. Cell Stem Cell (2013) 13:270–83. doi:10.1016/j.stem.2013.08.002

CrossRef Full Text

104. Rais Y, Zviran A, Geula S, Gafni O, Chomsky E, Viukov S, et al. Deterministic direct reprogramming of somatic cells to pluripotency. Nature (2013) 502:65–70. doi:10.1038/nature12587

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

105. Prigione A, Adjaye J. Modulation of mitochondrial biogenesis and bioenergetic metabolism upon in vitro and in vivo differentiation of human ES and iPS cells. Int J Dev Biol (2010) 54:1729–41. doi:10.1387/ijdb.103198ap

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

106. Enver T, Pera M, Peterson C, Andrews PW. Stem cell states, fates, and the rules of attraction. Cell Stem Cell (2009) 4:387–97. doi:10.1016/j.stem.2009.04.011

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

107. Huang S. Reprogramming cell fates: reconciling rarity with robustness. Bioessays (2009) 31:546–60. doi:10.1002/bies.200800189

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

108. MacArthur BD, Ma’ayan A, Lemischka IR. Systems biology of stem cell fate and cellular reprogramming. Nat Rev Mol Cell Biol (2009) 10:672–81. doi:10.1038/nrm2766

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

109. Visvader JE. Cells of origin in cancer. Nature (2011) 469:314–22. doi:10.1038/nature09781

CrossRef Full Text

110. Visvader JE, Lindeman GJ. Cancer stem cells: current status and evolving complexities. Cell Stem Cell (2012) 10:717–28. doi:10.1016/j.stem.2012.05.007

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

111. Vermeulen L, de Sousa e Melo F, Richel DJ, Medema JP. The developing cancer stem-cell model: clinical challenges and opportunities. Lancet Oncol (2012) 13:e83–9. doi:10.1016/S1470-2045(11)70257-1

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

112. Fessler E, Dijkgraaf FE, De Sousa E, Melo F, Medema JP. Cancer stem cell dynamics in tumor progression and metastasis: is the microenvironment to blame? Cancer Lett (2013) 341:97–104. doi:10.1016/j.canlet.2012.10.015

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

113. Medema JP. Cancer stem cells: the challenges ahead. Nat Cell Biol (2013) 15:338–44. doi:10.1038/ncb2717

CrossRef Full Text

114. MacArthur BD, Lemischka IR. Statistical mechanics of pluripotency. Cell (2013) 154:484–9. doi:10.1016/j.cell.2013.07.024

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

115. Morris R, Sancho-Martinez I, Sharpee TO, Izpisua Belmonte JC. Mathematical approaches to modeling development and reprogramming. Proc Natl Acad Sci U S A (2014) 111:5076–82. doi:10.1073/pnas.1317150111

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

116. Lu C, Thompson CB. Metabolic regulation of epigenetics. Cell Metab (2012) 16:9–17. doi:10.1016/j.cmet.2012.06.001

CrossRef Full Text

117. Ward PS, Thompson CB. Metabolic reprogramming: a cancer hallmark even Warburg did not anticipate. Cancer Cell (2012) 21:297–308. doi:10.1016/j.ccr.2012.02.014

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

118. Zhang J, Nuebel E, Daley GQ, Koehler CM, Teitell MA. Metabolic regulation in pluripotent stem cells during reprogramming and self-renewal. Cell Stem Cell (2012) 11:589–95. doi:10.1016/j.stem.2012.10.005

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

119. Varlakhanova NV, Knoepfler PS. Acting locally and globally: Myc’s ever-expanding roles on chromatin. Cancer Res (2009) 69:7487–90. doi:10.1158/0008-5472.CAN-08-4832

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

120. Dang CV, Le A, Gao P. MYC-induced cancer cell energy metabolism and therapeutic opportunities. Clin Cancer Res (2009) 15:6479–83. doi:10.1158/1078-0432.CCR-09-0889

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

121. Dang CV. Rethinking the Warburg effect with Myc micromanaging glutamine metabolism. Cancer Res (2010) 70:859–62. doi:10.1158/0008-5472.CAN-09-3556

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

122. Wahlström T, Arsenian Henriksson M. Impact of MYC in regulation of tumor cell metabolism. Biochim Biophys Acta (2014). doi:10.1016/j.bbagrm.2014.07.004

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

123. Goel A, Mathupala SP, Pedersen PL. Glucose metabolism in cancer. Evidence that demethylation events play a role in activating type II hexokinase gene expression. J Biol Chem (2003) 278:15333–40. doi:10.1074/jbc.M300608200

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

124. Liu X, Wang X, Zhang J, Lam EK, Shin VY, Cheng AS, et al. effect revisited: an epigenetic link between glycolysis and gastric carcinogenesis. Oncogene (2010) 29:442–50. doi:10.1038/onc.2009.332

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

125. Wolf A, Agnihotri S, Munoz D, Guha A. Developmental profile and regulation of the glycolytic enzyme hexokinase 2 in normal brain and glioblastoma multiforme. Neurobiol Dis (2011) 44:84–91. doi:10.1016/j.nbd.2011.06.007

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

126. Chen M, Zhang J, Li N, Qian Z, Zhu M, Li Q, et al. Promoter hypermethylation mediated downregulation of FBP1 in human hepatocellular carcinoma and colon cancer. PLoS One (2011) 6:e25564. doi:10.1371/journal.pone.0025564

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

127. Dong C, Yuan T, Wu Y, Wang Y, Fan TW, Miriyala S, et al. Loss of FBP1 by Snail-mediated repression provides metabolic advantages in basal-like breast cancer. Cancer Cell (2013) 23:316–31. doi:10.1016/j.ccr.2013.01.022

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

128. Liu PP, Liao J, Tang ZJ, Wu WJ, Yang J, Zeng ZL, et al. Metabolic regulation of cancer cell side population by glucose through activation of the Akt pathway. Cell Death Differ (2014) 21:124–35. doi:10.1038/cdd.2013.131

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

129. Lopez-Serra P, Marcilla M, Villanueva A, Ramos-Fernandez A, Palau A, Leal L, et al. A DERL3-associated defect in the degradation of SLC2A1 mediates the Warburg effect. Nat Commun (2014) 5:3608. doi:10.1038/ncomms4608

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

130. Hong H, Takahashi K, Ichisaka T, Aoi T, Kanagawa O, Nakagawa M, et al. Suppression of induced pluripotent stem cell generation by the p53-p21 pathway. Nature (2009) 460:1132–5. doi:10.1038/nature08235

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

131. Kawamura T, Suzuki J, Wang YV, Menendez S, Morera LB, Raya A, et al. Linking the p53 tumour suppressor pathway to somatic cell reprogramming. Nature (2009) 460:1140–4. doi:10.1038/nature08311

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

132. Li H, Collado M, Villasante A, Strati K, Ortega S, Cañamero M, et al. The Ink4/Arf locus is a barrier for iPS cell reprogramming. Nature (2009) 460:1136–9. doi:10.1038/nature08290

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

133. Marión RM, Strati K, Li H, Murga M, Blanco R, Ortega S, et al. A p53-mediated DNA damage response limits reprogramming to ensure iPS cell genomic integrity. Nature (2009) 460:1149–53. doi:10.1038/nature08287

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

134. Utikal J, Polo JM, Stadtfeld M, Maherali N, Kulalert W, Walsh RM, et al. Immortalization eliminates a roadblock during cellular reprogramming into iPS cells. Nature (2009) 460:1145–8. doi:10.1038/nature08285

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

135. Sarig R, Rivlin N, Brosh R, Bornstein C, Kamer I, Ezra O, et al. Mutant p53 facilitates somatic cell reprogramming and augments the malignant potential of reprogrammed cells. J Exp Med (2010) 207:2127–40. doi:10.1084/jem.20100797

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

136. Tapia N, Schöler HR. p53 connects tumorigenesis and reprogramming to pluripotency. J Exp Med (2010) 207:2045–8. doi:10.1084/jem.20101866

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

137. Bensaad K, Tsuruta A, Selak MA, Vidal MN, Nakano K, Bartrons R, et al. TIGAR, a p53-inducible regulator of glycolysis and apoptosis. Cell (2006) 126:107–20. doi:10.1016/j.cell.2006.05.036

CrossRef Full Text

138. Kawauchi K, Araki K, Tobiume K, Tanaka N. p53 regulates glucose metabolism through an IKK-NF-kappaB pathway and inhibits cell transformation. Nat Cell Biol (2008) 10:611–8. doi:10.1038/ncb1724

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

139. Maddocks OD, Vousden KH. Metabolic regulation by p53. J Mol Med (Berl) (2011) 89:237–45. doi:10.1007/s00109-011-0735-5

CrossRef Full Text

140. Sen N, Satija YK, Das S. PGC-1α, a key modulator of p53, promotes cell survival upon metabolic stress. Mol Cell (2011) 44:621–34. doi:10.1016/j.molcel.2011.08.044

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

141. Sen N, Satija YK, Das S. p53 and metabolism: old player in a new game. Transcription (2012) 3:119–23. doi:10.4161/trns.20094

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

142. Berkers CR, Maddocks OD, Cheung EC, Mor I, Vousden KH. Metabolic regulation by p53 family members. Cell Metab (2013) 18:617–33. doi:10.1016/j.cmet.2013.06.019

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

143. Hitchler MJ, Domann FE. Metabolic defects provide a spark for the epigenetic switch in cancer. Free Radic Biol Med (2009) 47:115–27. doi:10.1016/j.freeradbiomed.2009.04.010

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

144. Teperino R, Schoonjans K, Auwerx J. Histone methyl transferases and demethylases; can they link metabolism and transcription? Cell Metab (2010) 12:321–7. doi:10.1016/j.cmet.2010.09.004

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

145. Wallace DC, Fan W. Energetics, epigenetics, mitochondrial genetics. Mitochondrion (2010) 10:12–31. doi:10.1016/j.mito.2009.09.006

CrossRef Full Text

146. Huang J, Sengupta R, Espejo AB, Lee MG, Dorsey JA, Richter M, et al. p53 is regulated by the lysine demethylase LSD1. Nature (2007) 449(7158):105–8. doi:10.1038/nature06092

CrossRef Full Text

147. Sauve AA, Wolberger C, Schramm VL, Boeke JD. The biochemistry of sirtuins. Annu Rev Biochem (2006) 75:435–65. doi:10.1146/annurev.biochem.74.082803.133500

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

148. Schreiber V, Dantzer F, Ame JC, de Murcia G. Poly(ADP-ribose): novel functions for an old molecule. Nat Rev Mol Cell Biol (2006) 7:517–28. doi:10.1038/nrm1963

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

149. Belenky P, Bogan KL, Brenner C. NAD+ metabolism in health and disease. Trends Biochem Sci (2007) 32:12–9. doi:10.1016/j.tibs.2006.11.006

CrossRef Full Text

150. Quénet D, El Ramy R, Schreiber V, Dantzer F. The role of poly(ADP-ribosyl)ation in epigenetic events. Int J Biochem Cell Biol (2009) 41:60–5. doi:10.1016/j.biocel.2008.07.023

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

151. Li X, Kazgan N. Mammalian sirtuins and energy metabolism. Int J Biol Sci (2011) 7:575–87. doi:10.7150/ijbs.7.575

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

152. Kim SC, Sprung R, Chen Y, Xu Y, Ball H, Pei J, et al. Substrate and functional diversity of lysine acetylation revealed by a proteomics survey. Mol Cell (2006) 23:607–18. doi:10.1016/j.molcel.2006.06.026

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

153. Wellen KE, Hatzivassiliou G, Sachdeva UM, Bui TV, Cross JR, Thompson CB. ATP-citrate lyase links cellular metabolism to histone acetylation. Science (2009) 324:1076–80. doi:10.1126/science.1164097

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

154. Zhao S, Xu W, Jiang W, Yu W, Lin Y, Zhang T, et al. Regulation of cellular metabolism by protein lysine acetylation. Science (2010) 327:1000–4. doi:10.1126/science.1179689

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

155. Choudhary C, Kumar C, Gnad F, Nielsen ML, Rehman M, Walther TC, et al. Lysine acetylation targets protein complexes and co-regulates major cellular functions. Science (2009) 325:834–40. doi:10.1126/science.1175371

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

156. Katada S, Imhof A, Sassone-Corsi P. Connecting threads: epigenetics and metabolism. Cell (2012) 148:24–8. doi:10.1016/j.cell.2012.01.001

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

157. Johnson C, Warmoes MO, Shen X, Locasale JW. Epigenetics and cancer metabolism. Cancer Lett (2013). doi:10.1016/j.canlet.2013.09.043

CrossRef Full Text

158. Waddington CH. The Strategy of the Genes: A Discussion of Some Aspects of Theoretical Biology. London: Allen & Unwin (1957).

159. Armond JW, Saha K, Rana AA, Oates CJ, Jaenisch R, Nicodemi M, et al. A stochastic model dissects cell states in biological transition processes. Sci Rep (2014) 4:3692. doi:10.1038/srep03692

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

160. Ying QL, Wray J, Nichols J, Batlle-Morera L, Doble B, Woodgett J, et al. The ground state of embryonic stem cell self-renewal. Nature (2008) 453:519–23. doi:10.1038/nature06968

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

161. Nishi M, Sakai Y, Akutsu H, Nagashima Y, Quinn G, Masui S, et al. Induction of cells with cancer stem cell properties from nontumorigenic human mammary epithelial cells by defined reprogramming factors. Oncogene (2014) 33:643–52. doi:10.1038/onc.2012.614

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

162. Vander Heiden MG. Exploiting tumor metabolism: challenges for clinical translation. J Clin Invest (2013) 123:3648–51. doi:10.1172/JCI72391

CrossRef Full Text

163. Yuneva MO, Fan TW, Allen TD, Higashi RM, Ferraris DV, Tsukamoto T, et al. The metabolic profile of tumors depends on both the responsible genetic lesion and tissue type. Cell Metab (2012) 15:157–70. doi:10.1016/j.cmet.2011.12.015

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

164. Chin MH, Mason MJ, Xie W, Volinia S, Singer M, Peterson C, et al. Induced pluripotent stem cells and embryonic stem cells are distinguished by gene expression signatures. Cell Stem Cell (2009) 5:111–23. doi:10.1016/j.stem.2009.06.008

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

165. Ciavardelli D, Rossi C, Barcaroli D, Volpe S, Consalvo A, Zucchelli M, et al. Breast cancer stem cells rely on fermentative glycolysis and are sensitive to 2-deoxyglucose treatment. Cell Death Dis (2014) 5:e1336. doi:10.1038/cddis.2014.285

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

166. Sato A, Sunayama J, Okada M, Watanabe E, Seino S, Shibuya K, et al. Glioma-initiating cell elimination by metformin activation of FOXO3 via AMPK. Stem Cells Transl Med (2012) 1:811–24. doi:10.5966/sctm.2012-0058

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

167. Pacini N, Borziani F. Cancer stem cell theory and the Warburg effect, two sides of the same coin? Int J Mol Sci (2014) 15:8893–930. doi:10.3390/ijms15058893

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

168. Hu J, Locasale JW, Bielas JH, O’Sullivan J, Sheahan K, Cantley LC, et al. Heterogeneity of tumor-induced gene expression changes in the human metabolic network. Nat Biotechnol (2013) 31:522–9. doi:10.1038/nbt.2530

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

169. Yun J, Rago C, Cheong I, Pagliarini R, Angenendt P, Rajagopalan H, et al. Glucose deprivation contributes to the development of KRAS pathway mutations in tumor cells. Science (2009) 325(5947):1555–9. doi:10.1126/science.1174229

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

170. Metallo CM, Gameiro PA, Bell EL, Mattaini KR, Yang J, Hiller K, et al. Reductive glutamine metabolism by IDH1 mediates lipogenesis under hypoxia. Nature (2011) 481(7381):380–4. doi:10.1038/nature10602

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

171. Pellerin L, Magistretti PJ. Glutamate uptake into astrocytes stimulates aerobic glycolysis: a mechanism coupling neuronal activity to glucose utilization. Proc Natl Acad Sci U S A (1994) 91:10625–9. doi:10.1073/pnas.91.22.10625

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

172. Sonveaux P, Végran F, Schroeder T, Wergin MC, Verrax J, Rabbani ZN, et al. Targeting lactate-fueled respiration selectively kills hypoxic tumor cells in mice. J Clin Invest (2008) 118:3930–42. doi:10.1172/JCI36843

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

173. Kung HN, Marks JR, Chi JT. Glutamine synthetase is a genetic determinant of cell type-specific glutamine independence in breast epithelia. PLoS Genet (2011) 7:e1002229. doi:10.1371/journal.pgen.1002229

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

174. Salem AF, Whitaker-Menezes D, Lin Z, Martinez-Outschoorn UE, Tanowitz HB, Al-Zoubi MS, et al. Two-compartment tumor metabolism: autophagy in the tumor microenvironment and oxidative mitochondrial metabolism (OXPHOS) in cancer cells. Cell Cycle (2012) 11:2545–56. doi:10.4161/cc.20920

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

175. Sotgia F, Martinez-Outschoorn UE, Howell A, Pestell RG, Pavlides S, Lisanti MP. Caveolin-1 and cancer metabolism in the tumor microenvironment: markers, models, and mechanisms. Annu Rev Pathol (2012) 7:423–67. doi:10.1146/annurev-pathol-011811-120856

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

176. Pollak M. Insulin and insulin-like growth factor signalling in neoplasia. Nat Rev Cancer (2008) 8:915–28. doi:10.1038/nrc2536

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

177. Bonuccelli G, Tsirigos A, Whitaker-Menezes D, Pavlides S, Pestell RG, Chiavarina B, et al. Ketones and lactate “fuel” tumor growth and metastasis: evidence that epithelial cancer cells use oxidative mitochondrial metabolism. Cell Cycle (2010) 9:3506–14. doi:10.4161/cc.9.17.12731

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

178. Martinez-Outschoorn UE, Prisco M, Ertel A, Tsirigos A, Lin Z, Pavlides S, et al. Ketones and lactate increase cancer cell “stemness,” driving recurrence, metastasis and poor clinical outcome in breast cancer: achieving personalized medicine via Metabolo-Genomics. Cell Cycle (2011) 10:1271–86. doi:10.4161/cc.10.8.15330

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

179. Wu LE, Gomes AP, Sinclair DA. Geroncogenesis: metabolic changes during aging as a driver of tumorigenesis. Cancer Cell (2014) 25:12–9. doi:10.1016/j.ccr.2013.12.005

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

180. Feldman JL, Dittenhafer-Reed KE, Denu JM. Sirtuin catalysis and regulation. J Biol Chem (2012) 287:42419–27. doi:10.1074/jbc.R112.378877

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

181. Mihaylova MM, Sabatini DM, Yilmaz ÖH. Dietary and metabolic control of stem cell function in physiology and cancer. Cell Stem Cell (2014) 14:292–305. doi:10.1016/j.stem.2014.02.008

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

182. Li Y, Wicha MS, Schwartz SJ, Sun D. Implications of cancer stem cell theory for cancer chemoprevention by natural dietary compounds. J Nutr Biochem (2011) 22:799–806. doi:10.1016/j.jnutbio.2010.11.001

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

183. Menendez JA, Joven J, Aragonès G, Barrajón-Catalán E, Beltrán-Debón R, Borrás-Linares I, et al. Xenohormetic and anti-aging activity of secoiridoid polyphenols present in extra virgin olive oil: a new family of gerosuppressant agents. Cell Cycle (2013) 12:555–78. doi:10.4161/cc.23756

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

184. Pollak M. Potential applications for biguanides in oncology. J Clin Invest (2013) 123:3693–700. doi:10.1172/JCI67232

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

185. Janzer A, German NJ, Gonzalez-Herrera KN, Asara JM, Haigis MC, Struhl K. Metformin and phenformin deplete tricarboxylic acid cycle and glycolytic intermediates during cell transformation and NTPs in cancer stem cells. Proc Natl Acad Sci U S A (2014) 111:10574–9. doi:10.1073/pnas.1409844111

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

186. Hirsch HA, Iliopoulos D, Tsichlis PN, Struhl K. Metformin selectively targets cancer stem cells, and acts together with chemotherapy to block tumor growth and prolong remission. Cancer Res (2009) 69:7507–11. doi:10.1158/0008-5472.CAN-09-2994

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

187. Hirsch HA, Iliopoulos D, Struhl K. Metformin inhibits the inflammatory response associated with cellular transformation and cancer stem cell growth. Proc Natl Acad Sci U S A (2013) 110:972–7. doi:10.1073/pnas.1221055110

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

188. Martin-Castillo B, Vazquez-Martin A, Oliveras-Ferraros C, Menendez JA. Metformin and cancer: doses, mechanisms and the dandelion and hormetic phenomena. Cell Cycle (2010) 9:1057–64. doi:10.4161/cc.9.6.10994

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

189. Cufí S, Vazquez-Martin A, Oliveras-Ferraros C, Martin-Castillo B, Joven J, Menendez JA. Metformin against TGFβ-induced epithelial-to-mesenchymal transition (EMT): from cancer stem cells to aging-associated fibrosis. Cell Cycle (2010) 9:4461–8. doi:10.4161/cc.9.22.14048

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

190. Cufi S, Corominas-Faja B, Vazquez-Martin A, Oliveras-Ferraros C, Dorca J, Bosch-Barrera J, et al. Metformin-induced preferential killing of breast cancer initiating CD44+CD24-/low cells is sufficient to overcome primary resistance to trastuzumab in HER2+ human breast cancer xenografts. Oncotarget (2012) 3:395–8.

Pubmed Abstract | Pubmed Full Text

191. Del Barco S, Vazquez-Martin A, Cufí S, Oliveras-Ferraros C, Bosch-Barrera J, Joven J, et al. Metformin: multi-faceted protection against cancer. Oncotarget (2011) 2:896–917.

Pubmed Abstract | Pubmed Full Text

192. Jung JW, Park SB, Lee SJ, Seo MS, Trosko JE, Kang KS. Metformin represses self-renewal of the human breast carcinoma stem cells via inhibition of estrogen receptor-mediated OCT4 expression. PLoS One (2011) 6:e28068. doi:10.1371/journal.pone.0028068

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

193. Oliveras-Ferraros C, Cufí S, Vazquez-Martin A, Torres-Garcia VZ, Del Barco S, Martin-Castillo B, et al. Micro(mi)RNA expression profile of breast cancer epithelial cells treated with the anti-diabetic drug metformin: induction of the tumor suppressor miRNA let-7a and suppression of the TGFβ-induced oncomiR miRNA-181a. Cell Cycle (2011) 10:1144–51. doi:10.4161/cc.10.7.15210

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

194. Vazquez-Martin A, Oliveras-Ferraros C, Cufí S, Martin-Castillo B, Menendez JA. Metformin and energy metabolism in breast cancer: from insulin physiology to tumour-initiating stem cells. Curr Mol Med (2010) 10:674–91. doi:10.2174/156652410792630625

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

195. Vazquez-Martin A, Oliveras-Ferraros C, Cufí S, Del Barco S, Martin-Castillo B, Menendez JA. Metformin regulates breast cancer stem cell ontogeny by transcriptional regulation of the epithelial-mesenchymal transition (EMT) status. Cell Cycle (2010) 9:3807–14. doi:10.4161/cc.9.18.13131

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

196. Vazquez-Martin A, Oliveras-Ferraros C, Del Barco S, Martin-Castillo B, Menendez JA. The anti-diabetic drug metformin suppresses self-renewal and proliferation of trastuzumab-resistant tumor-initiating breast cancer stem cells. Breast Cancer Res Treat (2011) 126:355–64. doi:10.1007/s10549-010-0924-x

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

197. Vazquez-Martin A, López-Bonetc E, Cufí S, Oliveras-Ferraros C, Del Barco S, Martin-Castillo B, et al. Repositioning chloroquine and metformin to eliminate cancer stem cell traits in pre-malignant lesions. Drug Resist Updat (2011) 14:212–23. doi:10.1016/j.drup.2011.04.003

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

198. Vazquez-Martin A, Cufi S, Lopez-Bonet E, Corominas-Faja B, Oliveras-Ferraros C, Martin-Castillo B, et al. Metformin limits the tumourigenicity of iPS cells without affecting their pluripotency. Sci Rep (2012) 2:964. doi:10.1038/srep00964

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

199. Bednar F, Simeone DM. Metformin and cancer stem cells: old drug, new targets. Cancer Prev Res (Phila) (2012) 5:351–4. doi:10.1158/1940-6207.CAPR-12-0026

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

200. Song CW, Lee H, Dings RP, Williams B, Powers J, Santos TD, et al. Metformin kills and radiosensitizes cancer cells and preferentially kills cancer stem cells. Sci Rep (2012) 2:362. doi:10.1038/srep00362

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

201. Lonardo E, Cioffi M, Sancho P, Sanchez-Ripoll Y, Trabulo SM, Dorado J, et al. Metformin targets the metabolic achilles heel of human pancreatic cancer stem cells. PLoS One (2013) 8:e76518. doi:10.1371/journal.pone.0076518

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

202. Mohammed A, Janakiram NB, Brewer M, Ritchie RL, Marya A, Lightfoot S, et al. Antidiabetic drug metformin prevents progression of pancreatic cancer by targeting in part cancer stem cells and mTOR signaling. Transl Oncol (2013) 6:649–59. doi:10.1593/tlo.13556

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

203. Würth R, Pattarozzi A, Gatti M, Bajetto A, Corsaro A, Parodi A, et al. Metformin selectively affects human glioblastoma tumor-initiating cell viability: a role for metformin-induced inhibition of Akt. Cell Cycle (2013) 12:145–56. doi:10.4161/cc.23050

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

204. Zhu P, Davis M, Blackwelder AJ, Bachman N, Liu B, Edgerton S, et al. Metformin selectively targets tumor-initiating cells in ErbB2-overexpressing breast cancer models. Cancer Prev Res (Phila) (2014) 7:199–210. doi:10.1158/1940-6207.CAPR-13-0181

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

205. Riscuta G, Dumitrescu RG. Nutrigenomics: implications for breast and colon cancer prevention. Methods Mol Biol (2012) 863:343–58. doi:10.1007/978-1-61779-612-8_22

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

206. Ferguson LR, Schlothauer RC. The potential role of nutritional genomics tools in validating high health foods for cancer control: broccoli as example. Mol Nutr Food Res (2012) 56:126–46. doi:10.1002/mnfr.201100507

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

207. Levesque JP, Winkler IG, Rasko JE. Nichotherapy for stem cells: there goes the neighborhood. Bioessays (2013) 35:183–90. doi:10.1002/bies.201200111

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

208. Lundstrom K. Past, present and future of nutrigenomics and its influence on drug development. Curr Drug Discov Technol (2013) 10:35–46. doi:10.2174/1570163811310010006

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

209. Steinhauser ML, Bailey AP, Senyo SE, Guillermier C, Perlstein TS, Gould AP, et al. Multi-isotope imaging mass spectrometry quantifies stem cell division and metabolism. Nature (2012) 481:516–9. doi:10.1038/nature10734

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

210. Steinhauser ML, Lechene CP. Quantitative imaging of subcellular metabolism with stable isotopes and multi-isotope imaging mass spectrometry. Semin Cell Dev Biol (2013) 24:661–7. doi:10.1016/j.semcdb.2013.05.001

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

Keywords: stemness, metabolism, reprograming, cancer stem cells, oncometabolites, Waddington, epigenetic landscapes

Citation: Menendez JA and Alarcón T (2014) Metabostemness: a new cancer hallmark. Front. Oncol. 4:262. doi: 10.3389/fonc.2014.00262

Received: 03 August 2014; Accepted: 07 September 2014;
Published online: 29 September 2014.

Edited by:

Pilar Blancafort, The University of Western Australia, Australia

Reviewed by:

Keisuke Ito, Albert Einstein College of Medicine, USA
Valerio Donato, New York University Medical Center, USA

Copyright: © 2014 Menendez and Alarcón. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Javier A. Menendez, Catalan Institute of Oncology, Girona (ICO-Girona), Hospital Dr. Josep Trueta de Girona, Ctra. França s/n, Girona, Catalonia E-17007, Spain e-mail: jmenendez@iconcologia.net; jmenendez@idibgi.org