REVIEW SPECIAL COLLECTION: CANCER METABOLISM

The metabolic axis of macrophage and immune cell polarizationSpencer A. Hobson-Gutierrez and Carlos Carmona-Fontaine*

ABSTRACTThe extracellular space of solid tumors ranges from beingwell-nurtured to being completely ischemic and can serve as asource of intratumoral heterogeneity, determining the behaviorand molecular profiles of malignant and stromal cells. Here, wediscuss how the metabolic tumor microenvironment modulatesthe phenotypes of the immune cells that infiltrate tumors, withan emphasis on tumor-associated macrophages. These cellsconstitute a diverse population that has pro-tumoral and anti-inflammatory properties, and are likened to anti-inflammatory ‘M2’macrophages. Recent findings show how different metabolicmicroenvironments specify an array of phenotypic changes inmacrophages. In tumors, extracellular metabolite levels varypredictably according to proximity to the vasculature, and phenotypicchanges in tumor-associated macrophages and in other immunecells are also predictable. We speculate that this ‘metabolic axis’ ofmacrophage polarization modulates – and is modulated by – theresponse to inflammatory cues, creating a wide variety of possiblephenotypic states. Understanding how extracellular metabolitesinfluence cell phenotypes allows us to predict how tumor-associatedmacrophages and other tumor cells might change, with the aim ofharnessing this predictability for therapy. Overall, we describe anemerging picture in which chemokines, growth factors and themetabolic tumor microenvironment act together to determinethe phenotypes of tumor-infiltrating immune cells.

KEY WORDS: Cancer metabolism, Immunometabolism,Tumor microenvironment

IntroductionInteractions between cancer and immune cells play a crucial rolein tumor initiation, growth and metastasis. Tumor-associatedmacrophages (TAMs; see Glossary, Box 1) are particularlyabundant within tumors and have been shown to enhance tumormalignancy; the ability to inhibit these pro-tumoral effects couldopen up potentially new therapeutic avenues (De Palma and Lewis,2013; Murray, 2017; Noy and Pollard, 2014; Quail and Joyce,2017). Unfortunately, our understanding of how TAMs acquire theability to promote tumor progression remains incomplete and thedefinition of a bona fide TAM population remains controversial(Franklin et al., 2014; Murray et al., 2014; Ostuni et al., 2015; Qianand Pollard, 2010; Ruffell et al., 2012; Sica and Mantovani, 2012;Wei et al., 2013).

The regulation of cell metabolism is now recognized to be acrucial aspect of the immune system (Ganeshan and Chawla, 2014;O’Neill and Hardie, 2013; Olenchock et al., 2017; Pearce et al.,2009). Virtually every aspect of the immune response, includingantigen presentation and response (Angelini et al., 2002; Doedenset al., 2013; Rubic et al., 2008), clonal expansion of activated T cells(Chang et al., 2015; Man et al., 2013; Wang et al., 2011), immunecell differentiation (Dang et al., 2011; Pearce et al., 2009; Shi et al.,2011b; Sinclair et al., 2013) and the role of the immune system inwound healing (Barbul et al., 1990; Cattin et al., 2015), has beenshown to be intimately linked with cell metabolism. It has alsobecome evident that the immune system has a central role inregulating whole-body metabolism (Buck et al., 2017; Exley et al.,2014; Man et al., 2017; Odegaard and Chawla, 2013). Much of theattention, however, has focused on how different chemokines, andother cell signals, modulate and rewire intracellular metabolicnetworks (Ganeshan and Chawla, 2014; O’Neill and Hardie, 2013;O’Neill and Pearce, 2016; Pearce et al., 2013).

This Review focuses on how metabolites, especially extracellularmetabolites, can act as signals that regulate immune cell function,behavior and phenotype. Although most of our discussion centers onmacrophages, we also discuss how the metabolic microenvironmentcan modulate the phenotype of other immune cells. A key role ofimmune cells is to sense changes to the environment and to respondwhen they identify a threat to homeostasis (Buck et al., 2017; Kotasand Medzhitov, 2015; Okabe and Medzhitov, 2016). With that inmind, it is perhaps not surprising that immune cells are particularlysensitive to changes in metabolic conditions during normalphysiology and disease, requiring them to integrate signals, suchas interleukins, cytokines and chemokines, with cues such asthe local oxygen concentrations and other metabolites. Beforeaddressing the sources of macrophage phenotypic variability, wewill briefly discuss how the metabolic microenvironment of solidtumors is inescapably heterogeneous.

Heterogeneity in the metabolic microenvironment of tumorsTumor cells display remarkable alterations in their metabolism, suchas pseudohypoxia (Box 1) (Frezza et al., 2011), reductivecarboxylation (Mullen et al., 2012) and the generation ofoncometabolites (Box 1). In the best studied of these alterations,the Warburg effect, cancer cells display a highly glycolyticmetabolism even in the presence of oxygen (Dang et al., 2009).This metabolic mode is characterized by high rates of lactateproduction and glucose consumption, which partially compensatesfor the lower adenosine triphosphate (ATP) yield (per glucosemolecule) when we compare this mechanism with the ATP yield ofoxidative phosphorylation (Liberti and Locasale, 2016). Warburgsaw this effect as a detrimental aberration, and he hypothesized thattumor cells had dysfunctional mitochondria (Koppenol et al., 2011).However, it is now clear that although some tumors might havemitochondrial alterations, for the most part, they are still functionalin most cases (Reznik et al., 2017). A new look at this metabolicstrategy has revealed that it enables sufficient ATP to be produced,

Center for Genomics and Systems Biology, Department of Biology, New YorkUniversity, New York City, NY 10003, USA.

*Author for correspondence (carlos.carmona-fontaine@nyu.edu)

C.C., 0000-0002-5238-6852

This is an Open Access article distributed under the terms of the Creative Commons AttributionLicense (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,distribution and reproduction in any medium provided that the original work is properly attributed.

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© 2018. Published by The Company of Biologists Ltd | Disease Models & Mechanisms (2018) 11, dmm034462. doi:10.1242/dmm.034462

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while permitting cells to retain carbon-rich macromolecules withwhich to biosynthesize essential intermediates and the largequantities of biomass that are required for tumor proliferation(Lunt and Vander Heiden, 2011; Pavlova and Thompson, 2016).For example, oncogenic alteration can lead to the accumulation ofphosphoenolpyruvate (Box 1), shunting glycolytic intermediatesinto anabolic pathways for pyrimidine, glycerol, serine and glycinesynthesis (Locasale et al., 2011; Mazurek et al., 2001; Possematoet al., 2011). The complete catabolism of glucose in an entire roundof oxidative phosphorylation yields ∼30 molecules of ATP, but thereare no carbon atoms left over for the production of biomass. Incontrast, glycolysis yields two molecules of ATP per molecule ofglucose, but the two resulting three-carbon molecules of lactate canbe reused by normal and tumor cells as sources of energy and carbon(Faubert et al., 2017; Hui et al., 2017). Since the discovery of theWarburg effect, it has become increasingly clear that similarmetabolic changes also occur in normal, rapidly proliferatingcells, such as in stem cell niches (Zhang et al., 2012), during theclonal expansion of T cells (Wang et al., 2011), and in proliferating

cells during wound healing (Lee et al., 2009). These data suggestthat the Warburg effect, or aerobic glycolysis, is a strategy that iswidely used by proliferating cells rather than being a metabolicaberration (Vander Heiden et al., 2009).

These cancer-related metabolic alterations, and their consequencesin the extracellular space, enhance intratumoral heterogeneity.Rapid cell proliferation in most tumors cannot be matched byrevascularization, which results in inefficient blood perfusion andin regions experiencing extensive ischemia (Gatenby and Gillies,2004; Thomlinson and Gray, 1955). These conditions create largemicroenvironmental differences between cells that are proximal ordistal to the vascular network. Thus, the metabolic microenvironmentof the tumor changes along relatively smooth gradients that rangefrom well-perfused perivascular regions to ischemic regions, wherelactic acid accumulation and nutrient deprivation lead to necroticzones (Carmona-Fontaine et al., 2013; Gatenby and Gillies, 2004;Thomlinson and Gray, 1955; Vaupel, 2004). Under conditions ofnonlethal ischemia, acidosis, hypoxia and nutrient deprivation,metabolites can act as signals that modulate cellular functions(Fig. 1) (Buck et al., 2017; Carmona-Fontaine et al., 2017; Gatenbyand Gillies, 2004). This tumor-induced metabolic diversity suggeststhat infiltrating macrophages could experience a variety of differentmicroenvironments, but first we need to understand howmacrophages alter their phenotype under normal homeostasis.

Macrophages are a diverse cell populationBefore discussing the variability of TAMs and the effect ofmetabolites on this diversity, we need to briefly address the intrinsicvariability of macrophages. Macrophages are an extremely diversecell population even in homeostasis (Gordon and Taylor, 2005).They infiltrate and reside in virtually all of our organs, and thelocal microenvironment of these different compartments plays akey role in establishing and maintaining this diversity (Amit et al.,2016; Gosselin et al., 2014; Lavin et al., 2017). Many aspects ofspecific organs alter macrophage phenotypes, including the tissuemicroarchitecture (Gonzalez et al., 2013; Mebius and Kraal,2005), the rate of metabolic activity (Okabe and Medzhitov, 2014;Thorburn et al., 2014), exposure to commensal microorganisms inthe case of gut and skin macrophages (Chang et al., 2014;Tamoutounour et al., 2013), and the local repertoire of growthfactors and hormones (Gosselin et al., 2014; Lavin et al., 2017).Linked to these regional differences, macrophages that reside indifferent organs might also have distinct embryological origins.Most, if not all, macrophages present at birth develop from yolk-sac-derived erythro-myeloid progenitors (Box 1) (Gomez Perdigueroet al., 2015), and emerge independently of the hematopoieticprocess (Hashimoto et al., 2013; Yona et al., 2013). Microglia,Kupffer cells (Box 1) and other tissue-resident macrophages mostlyoriginate from this embryonic pool (Ginhoux et al., 2010; Schulzet al., 2012). In other organs, such as in the gut, the initialembryonic-derived macrophage population is replaced over time bymacrophages that are derived from circulating monocytes and thatare re-supplied by the bone marrow in the hematopoietic processthroughout adult life (Bain et al., 2014; Perdiguero and Geissmann,2016; Schulz et al., 2012). At a finer scale, changes in the levels andrepertoire of receptors (Gordon and Taylor, 2005; Lavin et al.,2017), and in the versatile biosynthetic capacity of macrophages(Artyomov et al., 2016; Ganeshan and Chawla, 2014), furtheradd to the diversity of macrophages. In summary, even underhomeostasis, macrophage phenotypes vary according to theirlocation, developmental origin, local microenvironmental conditions,and genetic and epigenetic noise. This underlying complexitymust be

Box 1. GlossaryAnergy: A considerably low immune response to an antigen.Cytotoxic T lymphocytes (CTLs): Effector T cells that specialize inkilling cells recognized as being foreign, including bacteria, pathogensand cancer cells, which present mutated proteins that act as antigens.Erythro-myeloid progenitors: A family of undifferentiated blood orbone marrow cells.Inflammasome: A protein complex responsible for the activation ofinflammatory responses.Kupffer cells: Tissue-resident macrophages from the liver.Lipopolysaccharides (LPSs): Large molecules found on the outermembrane of gram-negative bacteria that can activatemacrophages andother immune cells.Microglia: A type of glia in the central nervous system that sharesembryological and functional properties with tissue-resident macrophagesfrom other organs.Myeloid-derived suppressor cell: A type of immune cell from themyeloid lineage that has strong immunosuppressive activities, and that isnormally associated with pathological conditions, such as cancer.Natural killer (NK) cell: A white blood cell that releases cytotoxins inresponse to recognizing dysfunctional or cancerous somatic cells.Oncometabolite: A metabolite that drives malignant transformation in aprocess akin to that driven by oncogenes.Phosphoenolpyruvate: A metabolic intermediate involved in glycolysisand gluconeogenesis.Pseudohypoxia: A condition in which cells mount a hypoxic response(for example, by increasing HIF-1α levels) under normal oxygenconditions.Regulatory T cell (Treg): An immune cell that commonly acts as animmunosuppressive cell and is involved in autoimmune prevention andself-antigen tolerance.T helper cell: a type of T cell that is particularly important in adaptiveimmunity. T helper cells express the surface protein CD4 and are alsoknown as CD4+ cells. They help the activity of other immune cells byreleasing cytokines that suppress or regulate T cell activity, B cellantibody class switching, and increase the bactericidal activity ofmacrophages. Subtypes of this cell population include Type 1 helper T(Th1), Type 2 helper T (Th2) and Type 17 helper (Th17) cells.Reductive carboxylation: A carboxylation step (production of acarboxylic acid group by adding CO2 to a substrate) coupled with areductive reaction. Here, it refers to the production of alpha-ketoglutaratefrom exogenous glutamine, which allows cancer cells to use the TCAcycle in reverse to produce citrate and other metabolites.Tumor-associated macrophage (TAM): A type of macrophage thatinfiltrates solid tumors and that often displays pro-tumoral properties.

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considered when trying to understand the variability of macrophagesin pathological conditions. While most (but not all) TAMs have ahematopoietic origin (Murray, 2017; Qian and Pollard, 2010),macrophages are an intrinsically heterogeneous population.Keeping this in mind might help us to understand the diversity oftheir responses to inflammation and other signals.

The inflammatory axis of macrophage polarizationWhen challenged, tissue-resident and circulating monocyte-derivedmacrophages alter their basal states in a process known as activationor ‘polarization’. The phenotypes of activated macrophages are

often divided between two broad categories: M1 and M2.In response to inflammatory signals, such as interferon gamma(IFNγ) and lipopolysaccharides (LPSs; Box 1), macrophagespolarize into the classical, or ‘M1’, state, which is characterizedby specific changes in their transcriptional, morphological andsecretory profiles (Mantovani et al., 2017; Murray, 2017; Murrayet al., 2014). Gene expression changes include the upregulation ofNos2 [which encodes the enzyme inducible nitric oxide synthase(iNOS)], and the secretion of pro-inflammatory signals, such asinterleukin 6 (IL6) and IL12 (Murray et al., 2014). By contrast,alternatively activated macrophages (known as AAMs or as M2macrophages) are polarized by anti-inflammatory signals, such asIL4 and IL13 (Mantovani et al., 2017; Murray et al., 2014), andupregulate genes, such as Arg1, Mrc1 and Cd163. Although thisbinary distinction of polarized macrophages can be useful, it fails toaccount for the vast diversity of macrophage phenotypes (Lawrenceand Natoli, 2011; Xue et al., 2014). As a result, multiplemacrophage subcategories have emerged in recent years through acombination of multiple factors (Mantovani et al., 2017; Murrayet al., 2014).

How do TAMs fit into this complex tapestry of phenotypes?For the most part, the intrinsic variabilities of macrophages fromdifferent locations are unfortunately ignored in the current system ofmacrophage categorization. In TAMs, the expression of genesnormally associated with M2 macrophages, such Arg1, Mrc1 andothers, led to the likening of these two macrophage populations(Murray, 2018). This idea was further supported by the anti-inflammatory role that TAMs can acquire in tumors, where theyhave been shown to secrete pro-tumoral signals (Kitamura et al.,2015; Quail et al., 2016), recruit other anti-inflammatory cells(Curiel et al., 2004), de-differentiate into and from myeloid-derivedsuppressor cells (MDSCs; Box 1) (Corzo et al., 2010), and dampenthe T cell response (Dong et al., 2002; Gallina et al., 2006;Rodriguez et al., 2004). As with TAMs, M2-like macrophages favortumor growth (see, for example, Hughes et al., 2015; Lujambioet al., 2013; Murray, 2018). Consistently, the repolarization ofTAMs into phenotypes that more closely resembleM1macrophageshas successfully produced anti-tumoral responses in pre-clinicalmurine models (Hughes et al., 2015; Mantovani et al., 2017;Pyonteck et al., 2013).

While there are clear similarities between some TAMs andstereotypical M2 macrophages, there are also some importantdifferences. For example, transcriptional profiling of macrophagesthat reside in tumors in a murine model of spontaneous breast cancer(MMTV-PyMT) has shown that these TAMs represent a distinctpopulation of myeloid cells; this subpopulation was almost absentbefore the onset of the disease but increased with tumor progression(Franklin et al., 2014). Using microarrays, the authors showed thatthis macrophage subpopulation had a different transcriptional profileto AAMs (or to M2 macrophages) and emerged in response to Notch(and not to Stat6) signaling, which transduces the response to IL4 andIL13 (Takeda et al., 1996) to induce M2 macrophages. Perhaps moreimportantly, TAMs display a variety of morphologies, uneven spatialdistributions (Carmona-Fontaine et al., 2013; Joyce and Fearon,2015; Wyckoff et al., 2007, 2011), variable expression ofimmunophenotyping proteins and different signal secretionprofiles (Akkari et al., 2016; Franklin et al., 2014; Mantovaniet al., 2017; Qian and Pollard, 2010; Quail et al., 2016). Inaddition, within tumors there is a combination of inflammatoryand anti-inflammatory signals, such as TNF and IL13, thatmakes the phenotypic polarization of TAMs a dynamic process(Kratochvill et al., 2015).

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Fig. 1. Metabolic gradients in a tumor. (A) Immunofluorescence of a breasttumor section obtained from the mouse mammary tumor virus (MMTV-PyMT)model. Blood vessels were stained with antibodies against CD31 (red),hypoxic regions were labeled by detection of pimonidazole (white), and cellnuclei were counterstained with Hoechst (blue). Note how hypoxic regionscorrelate with distance from the vasculature, with strong hypoxia appearing∼100 mm from capillaries. (B) Changes in the levels of other metabolites,including lactate and glucose, are also expected within the tumor regiondefined by the dashed yellow lines. Based on Vaupel (2004). (C) A schematicshowing how levels of extracellular metabolites can change with increasingischemia and with increasing distance from the nearest capillary. Such changescan alter cell phenotypes and provide spatial information to tumor cells.

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Our definition of TAMs is strongly influenced by flow cytometryand by bulk genetic approaches, such as population RNA sequencing.Although flow cytometry provides rich data, it requires the destructionof tissue architecture and disregards spatial organization. Recently,microscopy has emerged as a powerful tool that can complement ourmolecular characterization of immune cells (Broz et al., 2014;Carmona-Fontaine et al., 2013, 2017; Gerner et al., 2012; Halle et al.,2016; Mukherjee et al., 2017). Using this approach, our group hasrecently shown that TAMs express M2 macrophages markers, suchas Arg1 and Mrc1, but they do so in distinct, nonoverlappingsubpopulations (Carmona-Fontaine et al., 2017). The RNAsequencing of bulk TAM populations in this system would havemixed these two subpopulations together, thus giving the misleadingimpression that these twomarkers were co-expressed by TAMs.Whiletaking tissue architecture into account is crucial, recent technologicaladvances, such as single-cell sequencing, are also helping to uncoverthe full phenotypic diversity of TAMs and other cells (Lavin et al.,2014, 2017). A surprising observation from our microscopy-basedanalysis of TAMs in a spontaneous murine breast tumor model,was that Arg1-expressing TAMs were almost exclusively locatedwithin ischemic tumor regions, while Mrc1-expressing TAMswere found in perivascular and other well-nurtured tumorregions (Carmona-Fontaine et al., 2017). A perivascular MRC1-expressing TAM subpopulation in patients with breast cancer hasbeen shown to be crucial for tumor relapse after chemotherapy,suggesting that the spatial patterns we observed in murine modelsmight be conserved in human tumors (Hughes et al., 2015).Reinforcing the differences between TAMs, and macrophagespolarized by anti/pro-inflammatory cues, ischemic TAMs co-expressArg1 and Nos2, stereotypical M2 and M1 markers, respectively.The correlation between the proximity to the vasculature (and thusto oxygen and nutrient levels) and the distribution of TAMsubpopulations, has prompted us and other researchers toinvestigate how local cell density, accessibility to blood vessels,and proximity to the tumor edge affect and diversify TAMphenotypes. The resulting evidence to date suggests that the range

of metabolic microenvironments that emerge in tumors acts as anunrecognized axis of macrophage polarization with effects that aredistinct from those of the inflammatory axis.

The metabolic axis of macrophage polarizationMetabolic rewiring is an integral aspect of how macrophagesrespond to external signals (Ganeshan and Chawla, 2014; O’Neilland Hardie, 2013). For example, strong inflammatory stimuliprovided by a combination of IFNγ and LPS in murine bonemarrow-derived macrophages lead to an accelerated glycolyticmetabolism (Tannahill et al., 2013) and to a ‘broken’ version of thetri-carboxylic acid cycle (TCA), which leads to an accumulation ofmalate and itaconate (Jha et al., 2015; Lampropoulou et al., 2016).Conversely, murine bone marrow-derived macrophages exhibitenhanced oxidative phosphorylation (Vats et al., 2006) andincreased glutamine metabolism (Jha et al., 2015) when treatedwith anti-inflammatory signals, such as IL4. Consistent with thisevidence, IL10 – another anti-inflammatory signal – inhibits glycolysisand promotes oxidative phosphorylation, possibly via the inhibition ofmTOR signaling (Ip et al., 2017). How macrophage metabolismchanges in response to chemokines and to other cell signals hasbeen extensively reviewed elsewhere (Ganeshan and Chawla, 2014;O’Neill and Hardie, 2013). However, we know much less about howextracellular metabolites act as signals and alter intracellular signaling,cell metabolism andmacrophage function. Here, wewill focus on howthe availability of oxygen, nutrients and other extracellular metabolites,interferes with and modulates other types of cell signals to directlychange macrophage phenotypes (Fig. 2).

Monocyte-derived TAMs are known to migrate towards hypoxic,avascular regions of multiple tumor types (Cramer et al., 2003;Murdoch et al., 2004). The stabilization of hypoxia-inducible factor1-alpha (HIF-1α) by low oxygen levels can activate inflammatoryprograms in macrophages via the recruitment of components of theinflammasome (Box 1) (Wen et al., 2012). HIF-1α activation canalso be driven directly by inflammatory signals [such as LPS(Nishi et al., 2008; Tannahill et al., 2013)] and by pseudohypoxia,

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Fig. 2. Integration of metabolic andinflammatory signals. (A) Schematicto show how the local metabolicmicroenvironment of a cell (gray) canmodulate how its signals, such as byproducing pro- and anti-inflammatorychemokines [such as interleukin 4 (IL4)and IFNγ, respectively], to alter cellphenotypes. (B) Cell signaling molecules,such as IL4 (top) are known to re-wireintracellular metabolism and to directphenotypic change. Here, we highlight thatextracellular metabolites (such as lactate,bottom) can act as cues that triggerintracellular signals and direct phenotypicchanges. (C) The combined action of cellsignaling and metabolites expands therepertoire of cellular responses. Here, wedepict these two sources of cell modulationas part of an AND gate.

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when the accumulation of metabolites, such as succinate(Chouchani et al., 2014; Tannahill et al., 2013) or low pH(Intlekofer et al., 2017), stabilize HIF-1α even under normaloxygen tension. Thus, direct (via low oxygen) or indirect hypoxia(via the activation of HIF-1α independently of oxygen levels) cantrigger an inflammatory program in macrophages in the absence ofpro-inflammatory chemokines.Excessive ischemia in avascular tumor regions can lead to the

accumulation of lactic acid, which lowers the extracellular pH tolevels that are sustainable for some tumor cells but lethal for TAMs(Carmona-Fontaine et al., 2013). A combination of recruitment bychemokines, such as CCL2 (Shi et al., 2011a), attraction towardshypoxia (Cramer et al., 2003; Murdoch et al., 2004) and death bylow pH (Carmona-Fontaine et al., 2013), has been proposed toexplain the complex distribution patterns of TAMs observed inmurine models (Carmona-Fontaine et al., 2013; Gocheva et al.,2010; Wyckoff et al., 2007). Sublethal levels of lactic acid can haveinteresting effects on macrophages. Recent evidence shows thatbone marrow-derived macrophages (BMDMs) and TAMs treatedwith lactate express some of the genes associated with anti-inflammatory macrophages (Colegio et al., 2014). This effect wasnot caused by pH changes, but mediated directly by lactate in anHIF-1α-dependent manner (Colegio et al., 2014).Ischemic environments feature a combination of hypoxia, low

nutrient levels and the accumulation of waste products, such aslactate. Many studies have isolated single ischemic variables – forexample, hypoxia – and have examined their effects on cellularbiology. This approach has helped to establish how cells respond tospecific metabolic cues. But because cells do not sense these cues inisolation, we might be missing important information about howthese metabolic cues interact or synergize to change cell fate.To overcome this, our group recently developed an ex vivo systemto study the effect of ischemia on cells, including macrophages(see ‘Perspective: the need for tools to study the metabolicmicroenvironment’ section). Using this system, we have shown thatthe general macrophage response to ischemia is primarily driven bythe combined effect of lactate and hypoxia (Fig. 3). This combinationdirectly activates MAPK/ERK signaling via cRaf (also known asRAF1), which in turn triggers a transcriptional profile that is quitedistinct from typical ‘M1’ and ‘M2’ macrophages (Carmona-Fontaine et al., 2017). An interesting possibility is thatmacrophages and TAMs integrate these two metabolic cues viaNDRG family member 3 (NDRG3), which has been shown to be alactate-dependent hypoxia sensor that signals via cRaf (Fig. 3)

(Lee et al., 2015). While this pathway has so far not been shown tooperate in macrophages or in other immune cells, NDRG3 couldrepresent an interesting target for therapy and the modulation ofthe tumor microenvironment. JAK-STAT signaling is an essentialpart of the pro-inflammatory (via STAT1) and anti-inflammatory(via STAT6) response that generates ‘M1’ and ‘M2’macrophages,respectively (Murray, 2007). However, JAK-STAT signaling isdispensable for the macrophage response to ischemia, reinforcingthe idea that this response to metabolic cues is distinct to theinflammatory axis of polarization (Carmona-Fontaine et al.,2017). It is also noteworthy that the activation of HIF-1α is notsufficient to recapitulate the macrophage response to ischemia[although HIF-1α might still be required for other aspects ofmacrophage adaptation to hypoxic conditions (Colegio et al.,2014)], emphasizing the relevance of studying how differentmetabolic cues act in concert.

The transcriptional response to ischemia also differs from themacrophage response to inflammatory signals. For example, thequintessential ‘M1’ marker is the expression of Nos2 (that encodesiNOS), while murine ‘M2’ macrophages express high Arg1 levels(Murray et al., 2014) (Fig. 4). In contrast, ischemic macrophagesco-express both of these markers and the combination of hypoxiaand high lactate inhibits the expression of the ‘M2’ marker Mrc1(Carmona-Fontaine et al., 2017). These data suggest that themetabolic polarization of macrophages might lie outside of theM1/M2 axis of macrophage activation. These findings also help toexplain our observation that Arg1 and Mrc1 are expressed inmutually exclusive TAMs subpopulations in a murine model ofbreast cancer (Carmona-Fontaine et al., 2017). Interestingly,Toll-like receptors can signal via the ERK pathway and trigger amacrophage response that resembles the response to intracellularpathogens (El Kasmi et al., 2008). Arg1 levels can also beregulated by a Stat6-independent autocrine signaling mechanismthat is mediated by CCAAT/enhancer-binding protein beta(C/EBPβ) (Qualls et al., 2010). Emerging evidence in the literaturehas shown that Arg1 is also upregulated in other pathologicalischemic conditions, such as in granulomas in tuberculosis(Duque-Correa et al., 2014; Mattila et al., 2013) and duringretinal vein occlusion (Martin et al., 2018; Narayanan et al., 2013),arguing that the metabolic axis of macrophage polarization is notexclusive to tumors but a general aspect of macrophage biology.In summary, in response to ischemia, macrophages adopt a polarizedstate, mediated by the MAPK/ERK pathway, that is different to ‘M1’and ‘M2’ polarization.

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Fig. 3. Effect of ischemia on TAMs. Schematic of a cross-section through a tumor and its associated blood vessel, with blue representing well-perfused tumor cells,red representing ischemic cells and gray representing cells at intermediate locations. Lactate levels (yellow) increase with distance from the blood vessel, whereasoxygen levels (light blue) decreasewith distance from the blood vessel. Lactate combinedwith hypoxia act synergistically to activate gene expression in TAMs,which arepresent within the gradient of ischemia. A putative mechanism by which this could occur is shown in the inset box (right) and involves NDRG3, a lactate-dependenthypoxia sensor that signals via the MAPK/ERK pathway to activate the expression of the TAM markers, including Arg1, Mrc1 and Vegfa.

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Macrophages also face ischemia in noncancerous contexts as partof their physiological roles. For example, when a nerve is severed,neighboring capillaries are also usually damaged, which creates ahypoxic region near the wound (Cattin et al., 2015). Macrophagesrespond directly to hypoxia bymigrating to thewound and attractingendothelial cells via the secretion of vascular endothelial growthfactor (VEGF) proteins. These recruited endothelial cells stimulatethe revascularization of the severed region, which is a required stepin the nerve repair process (Cattin et al., 2015). It is possible thattumor ischemia triggers a similar mechanism in TAMs, and thuspart of the behavior of metabolically polarized macrophages canbe co-opted from physiological wound healing roles (Schäfer andWerner, 2008; Weber et al., 2016). Consistent with this idea,TAMs within ischemic tumor regions express VEGF subtypes andattract endothelial cells, likely to re-vascularize these regions(Carmona-Fontaine et al., 2017; Lewis et al., 2000; Murdoch et al.,2004). It has been proposed that deranged tumor vascularizationand leaky blood vessels facilitate tumor metastasis (Mazzoneet al., 2009), and that part of that leakiness might come fromexcessive re-vascularization (Wenes et al., 2016). This group hasrecently shown that when the angiogenic response of macrophagesis diminished, tumors create better-formed and functional vessels,concomitant with a reduction in metastasis. They achieved this byincreasing the glucose uptake and glycolytic rate of TAMs via theinhibition of REDD-1, a negative regulator of the mTOR pathway,in murine tumors (Wenes et al., 2016). It is well known thathypoxia directly triggers the expression of VEGF proteins

(Fukumura et al., 1998; Shweiki et al., 1992), and a similar effecthas been reported for lactate (Colegio et al., 2014). However, inmouse bone marrow-derived macrophages, the combination oflactate and hypoxia produced a synergistic effect on Vegfaexpression, and consequently on VEGFA secretion, which largelyexceeded the effect of lactate or hypoxia alone (Carmona-Fontaineet al., 2017). The synergy between these signals further supports theidea that macrophages integrate the levels of multiple metabolites togain more robust information about their environments.

The levels of many other nutrients and metabolic waste productsare also likely to alter the phenotype of TAMs. For example, aminoacids are heterogeneously distributed within tumors in which lowglutamine levels are found in blood-deprived tumor regions(Pan et al., 2016). This local glutamine deprivation leads tohistone hypermethylation and to the dedifferentiation of cancer cells(Pan et al., 2016). It would therefore be interesting to study whetherglutamine levels produce similar epigenetic changes in TAMs.Macrophages rely on glucose for ATP production and as a carbonsource (Cramer et al., 2003; Kelly and O’Neill, 2015). Glucose isalso vital for a pro-inflammatory response in macrophages, and theinhibition of glucose metabolism causes a diminished inflammatoryresponse in macrophages (Haschemi et al., 2012; Tan et al., 2015).Consistent with this, the stable overexpression of the glucosetransporter GLUT1 (also known as SLC2A1) has been reported toplace macrophages in a hyperinflammatory state (Freemerman et al.,2014). Thus, glucose levels might contribute to how macrophagesrespond to ischemia and blood perfusion. Considering these data, it

‘M1’ macrophages

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Fig. 4. Themetabolic axis of macrophage polarization. (A) A proposed metabolic axis of macrophage polarization that is orthogonal to (but still able to interactwith) the traditional inflammatory axis that leads to M1/M2 macrophage polarization. Typical input signals are shown (such as oxygen, glucose, lactate andcytokine levels), as well as intracellular mediators, molecular markers used to distinguish them and secreted chemokines for these differently polarized examples.These signals andmarkers are provided as examples rather than reflecting a complete view of these different phenotypes. (B) A conceptual representation of howthe underlying phenotypic heterogeneity of a cell population (such as TAMs) is modulated by inflammatory or metabolic perturbations. In this representation,the basal heterogeneity of the cell population is shown at the center as a two-dimensional landscape that is commonly used to represent heterogeneousmultidimensional data (e.g. as in PCA and tSNE plots). In these types of plots, subpopulations with common features cluster together. After perturbations, allsubpopulations change, and we depict this with similar plots, but now the subpopulations have shifted to a different state (represented with different colors).For example, under nutrient deprivation, all subpopulations change (represented as a shift from gray to magenta hues) but they somehow maintain their originaldiversity as they remain in separate clusters. When analyzing real data, however, this might change as some perturbations can either unify, diversify further,or reconfigure the original phenotypic diversity, affecting the shape and size of the clusters in the plot.

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is not surprising that the activity and levels of nutrient sensors, suchas mTORC1 and AMPK, also affect macrophage function, as hasbeen extensively reviewed elsewhere (Biswas, 2015; Covarrubiaset al., 2015; Hardie, 2015). mTORC1 is implicated in autophagyand the synthesis of lipids, proteins and nucleotides, along withmetabolic reprogramming and differentiation in macrophages(Covarrubias et al., 2015; Weichhart et al., 2015). In macrophagesand other mammalian cells, AMPK (also known as PRKAA2) isassociated with cellular metabolic reprogramming in response tonutrient deprivation (Hardie, 2015) and influences macrophagepolarization (Sag et al., 2008). Overall, there is considerableevidence that macrophages sense their metabolic microenvironmentand modulate their functional immunophenotypes according tothis information. While efforts have been made to classify themacrophage response to extracellular organisms along the M1 andM2 axis, we believe that their metabolic responses are orthogonaland distinct from the responses to the inflammatory axis ofmacrophage polarization. Thus, we propose that the degree ofblood perfusion and ischemia in the microenvironment establishes a‘metabolic axis’ of macrophage polarization. As we discuss in thenext section, similar metabolic cues can affect other immune cellsand thus this metabolic axis could affect the functional polarizationof other immune cells.

Metabolic polarization of other immune cellsMore than 60 years ago, the pioneer in organ transplantation, PeterMedawar, asked how embryos avoid rejection (Billingham et al.,1953). The answer came ∼40 years later, along with the firstevidence of metabolites that affect the immune system directly.Indoleamine-2,3-dioxygenase (IDO) functions as a rate-limiting-stepenzyme in tryptophan catabolism. Cells that express high levels ofIDO rapidly deplete extracellular tryptophan, which diminishes T-cellproliferation and activity (Munn et al., 1999). Ido (also known asIdo1) is expressed in early mammalian embryonic structures, andtryptophan levels drop during pregnancy (Schrocksnadel et al., 1996).This led Munn and colleagues to hypothesize that tryptophanmetabolism creates an immunosuppressive environment that protectsthe embryo from the maternal immune system (Munn et al., 1998).The immunomodulatory effect of tryptophan metabolism could intheory also be relevant for immune evasion in tumors (Sheridan,2015). Cancer-associated fibroblasts (CAFs), macrophages andother antigen-presenting cells (APCs), and sometimes tumor cellsthemselves, upregulate IDO, exacerbating tryptophan catabolism,to create an immunosuppressive tumor microenvironment thatprevents T-cell activation by inducing anergy (Box 1) andapoptosis (Moffett and Namboodiri, 2003; Platten et al., 2012).This immunosuppressive effect occurs via at least two mechanisms:direct depletion of tryptophan, which fuels biomass production duringthe rapid expansion of activated T cells; and the accumulation ofimmunosuppressive metabolites derived from tryptophan catabolism,such as kynurenine, 3-hydroxyanthranilate and quinolinate (Moffettand Namboodiri, 2003; Platten et al., 2012). These tryptophan-derived catabolites appear to have additional immunomodulatoryeffects; for example, they can act as ligands of the aryl hydrocarbonreceptor (AHR). Endogenously produced metabolites, includingkynurenine, directly activate the AHR (Opitz et al., 2011).Kynurenine-dependent activation of AHR has been shown to skewthe differentiation of naive T cells towards FoxP3+ regulatory T cells(Tregs; Box 1) cells rather than to Th17 cells (Box 1) (Mezrich et al.,2010; Nguyen et al., 2010), thus promoting immunosuppressivefeatures in T cells. A careful look at Ido1/2 double-knockoutmice could also illuminate the role of these enzymes in regulating the

immune response. The immunomodulatory effects of IDO andtryptophan metabolism are still under active investigation, and bothare promising targets for new immunotherapy drugs (Sheridan, 2015).

Low oxygen tensions and high levels of lactate appear to bothindependently and synergistically suppress adaptive immunity andto augment innate immune system function through a variety ofmechanisms that target inflammation. In the innate immune system,hypoxia increases the survival of neutrophils and delays apoptosis(Carmeliet and Jain, 2011). HIF-1α has also been shown toconsiderably increase the release of pro-inflammatory cytokinesand pro-stimulatory molecules by dendritic cells (Kumar andGabrilovich, 2014; Nizet and Johnson, 2009). In the adaptiveimmune system, HIF-1α has a variety of functions (Eltzschig andCarmeliet, 2011), including driving the expression of inhibitoryreceptors by activated T cells (Freemerman et al., 2014), increasingthe number of Tregs by modulating FoxP3 levels (Ben-Shoshanet al., 2008), and inducing a shift from a type 1 helper T cell to a type2 helper T cell (Box 1), which in turn increases the production ofIL10 and decreases IFNγ levels (Ben-Shoshan et al., 2009). Thecytotoxic abilities of cytotoxic T lymphocytes (CTL; Box 1) andnatural killer (NK; Box 1) cells become inhibited with increasedlevels of lactate. With high levels of lactate, there is also a decreasein CTL proliferation (Fischer et al., 2007), a reduction in the abilityof NK and T cells to surveil for the presence of tumors (Brand et al.,2016; Rizwan et al., 2013; Xie et al., 2014), and a reduction in T cellmobility (Haas et al., 2015).

Ischemia is also associated with an increase in the activity of lactatedehydrogenase (LDH) and malate dehydrogenase (MDH), whichuse alpha-ketoglutarate as a substrate to make (L)-2-hydroxyglutarate(L-2HG) (Intlekofer et al., 2015, 2017). This metabolite is thestereoisomer of (R)-2-hydroxyglutarate (R-2HG), which is producedby tumorigenic mutations in isocitrate dehydrogenase (IDH) (Danget al., 2009;Ward et al., 2010; Xu et al., 2011). Both isoforms of 2HGcan have profound effects on cell phenotypes and differentiation, asthey can directly affect histone modifications; they can thus play arole in the balance between undifferentiated and differentiated cellstates (Lu et al., 2012). In T cells, L-2HG has dramatic effects onT cell activation, almost wholly abolishing T-cell activation andpromoting instead a regulatory T-cell differentiation program(Tyrakis et al., 2016).

Metabolites are primarily the energy and biomass resources thatare required for cell function. The modulation of glucose (Jha et al.,2015), phosphoenolpyruvate (Ho et al., 2015) and potassium(Eil et al., 2016) levels have been shown to affect immune response orto shift immune cell phenotypes towards a more immunosuppressivestate (Ghaderian et al., 2010; Ghesquiere et al., 2014). Succinate canact as a chemokine that enhances chemotaxis in dendritic cells(Rubic et al., 2008). Glucose availability also modulates immunecell phenotypes. Limiting glucose levels during T-cell activationcan prevent effector T-cell expansion (Buck et al., 2017; Changet al., 2015; Frauwirth et al., 2002; Wang et al., 2011) and IFNγproduction (Cham and Gajewski, 2005). It has also been shown thattumor cells compete with T cells for glucose, and often T cells aresuppressed as a result of this competition (Chang et al., 2015). Theactivation of dendritic cells also depends on glucose levels (Evertset al., 2014; Lawless et al., 2017; Thwe et al., 2017). Intriguingly,recent evidence suggests that the effect of glucose in dendritic cellsdepends on the type – and activation state – of neighboring cells. Forexample, glucose can facilitate inflammatory functions in dendriticcells (Everts et al., 2014), but this effect is inhibited in the presenceof inflammatory T cells (Lawless et al., 2017). The reason for this isthat the high glycolytic rate of inflammatory T cells causes glucose

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to become locally deprived, which hampers the inflammatoryresponse of dendritic cells (Lawless et al., 2017). This is anotherexample of how the spatial structure of the tissue, and the relativeposition of different cells, impact cell phenotypes and behavior.Nutrients and metabolites are essential for biological

organization. Given their crucial role, all life forms have refinedmechanisms for sensing and adapting to extracellular metabolitelevels (Chantranupong et al., 2015; Palm and Thompson, 2017).Without trying to create a complete and exhaustive list of how everymetabolite affects all immune cells, we have shown a few examplesof howmetabolite sensing is at the core of immune cell behavior andfunctions. Although this feature is not exclusive to tumor-infiltratedimmune cells, nutrient sensing and adaptation might be particularlyimportant within the deranged metabolic microenvironment foundin most tumors.

Perspective: the need for tools to study the metabolicmicroenvironmentOur understanding of how cellular phenotypes change in responseto the local metabolic microenvironment is limited by two mainchallenges. The first challenge is the need tomeasure cell phenotypes,within their spatial context, ideally at single-cell resolution. As wementioned before, single-cell sequencing is redefining how we lookat cell populations in vivo (Lavin et al., 2014, 2017). Although this isa fantastic development, it still requires the destruction of the tissuearchitecture and the loss of important spatial information. In addition,sample preparation is relatively slow, especially relative to the timescales at which cellular metabolism changes. A complementaryapproach is cell cytometry, where fixed or live cells can be examinedusing fluorescent dyes, antibodies and genetically-encoded probes(Broz et al., 2014; Carmona-Fontaine et al., 2013, 2017; Gerner et al.,2012; Halle et al., 2016; Mukherjee et al., 2017). We have workedsignificantly on this approach, but the limited number of fluorophoresavailable to detect and discriminate between cells limits the range ofparameters one can analyze simultaneously. The matrix-assisted laserdesorption ionization (MALDI) imaging of antibodies labeled withmetals, which can then be analyzed with mass spectrometry, mighthelp to resolve this issue (Angelo et al., 2014), but this approachrequires sample destruction and it is not widely available.The second major challenge is to measure, without perturbing,

the temporal and spatial distribution of metabolites in tissues.MALDI-based metabolomics is one alternative but retains some ofthe same issues we described above (Chughtai and Heeren, 2010).Genetically encoded fluorescent probes have also recently beendeveloped that report levels of specific metabolites. For example,the probe Laconic can be used to determine intracellular lactatelevels with high accuracy (San Martín et al., 2013). Measuringextracellular metabolite levels is more challenging, partly becauseof the fine spatial resolution required to study them in vivo.In vivo studies of metabolites are complex, and it is often difficult

to determine whether metabolites are directly responsible for theobserved phenotypic changes. A potentially transformative toolwould be a method that could be used to alter the local levels ofspecific metabolites, at will. At the same time, conventional in vitroculture systems lack the spatial structure that provides heterogeneityin metabolic microenvironments. To overcome this, we havedeveloped the metabolic microenvironment chamber (MEMIC),in which cultured cells spontaneously create gradients of ischemia(Carmona-Fontaine et al., 2013, 2017). This tool supports thedetailed analysis of how gradients of metabolites affect tumor andimmune cells ex vivo. However, the MEMIC is not without itslimitations. For example, in order to generate the gradients, cells are

enclosed in a small volume in glass, which hampers access to thecells to perform biochemical analyses. The small dimensions of theMEMIC also complicate certain analyses, such as RNA-sequencingor metabolomics, because of small sample yields. A system similarto the MEMIC, but one that supports better cellular yield andsample accessibility, is urgently required. Our own, and many othergroups, areworking to develop these and other techniques that couldbe used to understand and modulate how metabolites alter cellfunction. By combining these techniques with in vivo observations,manipulations and measurement of local metabolite levels, we hopeto be able to better understand, and potentially to control, thecellular adaptation to different tumor metabolic microenvironments.

This article is part of a special subject collection ‘Cancer Metabolism: models,mechanisms and targets’, which was launched in a dedicated issue guest edited byAlmut Schulze and Mariia Yuneva. See related articles in this collection at http://dmm.biologists.org/collection/cancermetabolism.

AcknowledgementsWe thank all members of the Carmofon Laboratory for feedback and comments onthe manuscript.

Competing interestsThe authors declare no competing or financial interests.

FundingThe authors’work is supported by the National Cancer Institute of NIH (R00CA191021)and the Center for Genomics and Systems Biology at New York University.

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