Proteomics and metabolomics in cancer drug development

Proteomics and metabolomicsin cancer drug development

5Expert Rev. Proteomics 10(5), 000–000 (2013)

Angelo D’Alessandroand Lello Zolla*Department of Ecological and Biological

Sciences, University of Tuscia, Largo

dell’Universita, snc, 01100 Viterbo, Italy

*Author for correspondence:

Tel.: +39 0761 357 100

Fax: +39 0761 357 630

[email protected]

In this review article, the main recent advancements in the field of proteomics andmetabolomics and their application in cancer research are described. In the second part of

10the review the main metabolic alterations observed in cancer cells are thoroughly dissected,especially those involving anabolic pathways and NADPH-generating pathways, whichindirectly affect anabolic reactions, other than the maintenance of the redox poise.Alterations to mitochondrial pathways and thereby deriving oncometabolites are also detailed.The third section of the review is a discussion of how and to what extent (mutations to)

15tumor suppressors and oncogenes end up influencing cancer cell metabolism and cell fate,either promoting survival and proliferation or autophagy and apoptosis. In the last section ofthe review, an overview is provided of therapeutic strategies that make use of metabolicreprogramming approaches.

KEYWORDS: cancer • drug development • mass spectrometry • metabolism • proteomics

Metabolomics, the new/old hallmark of25cancer

Human tumor pathogenesis is characterized bythe progressive accumulation of changes to nor-mal cells, changes that make cells evolve to aneoplastic state through the gradual acquisition

30of a series of hallmark capabilities. This multi-step process utterly enables normal cells tobecome tumorigenic and, ultimately malig-nant [1]. While metabolic reprogramming hasonly recently been included in the list of the so-

35called ‘hallmarks of cancer’ [1], echoes from thelast (at least) 60 years of research already sug-gested a crucial correlation between chronic anduncontrolled cell proliferation and deregulatedmetabolism [2]. The ‘Warburg effect’, named

40after Otto Warburg, the first researcher to docu-ment an exception to the Pasteur effect (inhibi-tion of glycolysis in presence of oxygen) inhighly proliferating cancer cells, is based uponthe appreciation of an increased glycolytic rate,

45at the expenses of mitochondrial metabolism(preferentially exploited by normal differentiatedcells for energy production purposes), even inthe presence of oxygen [2]. This phenomenon,often referred to as ‘aerobic glycolysis’, was at

50first deemed to be counterintuitive, since rapidlyproliferating cells are supposed to have higherenergy requirements, while a strictly glycolyticmetabolism is less efficient than one relying

upon mitochondrial oxidative phosphorylationin terms of ATP production (~18-fold lowerefficiency) [3]. However, since generalization of aWarburg-like metabolism seems to be also appli-cable to many rapidly dividing embryonic tis-sues, a tentative explanatory and evidence-basedtheory posits that aerobic glycolysis might haveevolved to meet the elevated anabolic demand(for biosynthetic purposes) and favor the uptakeand incorporation of nutrients into biomass byrapidly dividing cells [3].

Conversely, over generalizations should beavoided as well, since tumor cells do notalways display a Warburg-like metabolism.Indeed, some tumors are characterized by twosubpopulations of cancer cells, one consistingof glucose-dependent cells that secrete lactate(Warburg-wise), while a second subpopulationalmost symbiotically relies upon the secretedlactate to sustain their energy production viathe tricarboxylic acid cycle (TCA cycle, alsoknown as Krebs cycle) [1].

During the last decade, molecular evidenceshave underpinned a role for genetic reprog-ramming in the metabolic regulation observedin cancer cells, a phenomenon that is oftenaccompanied by the preferential expression ofcancer-specific isoforms of certain metabolicenzymes, or rather by peculiar and recurrentcancer-associated mutations, especially in genescoding for TCA cycle enzymes [4]. In the light

Review

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mailto:[email protected]

of these observations, Warburg’s hypothesis was recently revived55 and expanded, as it became to be considered one key target for

therapeutic treatments [5]. The implementation of novel massspectrometry (MS)-based metabolomics and proteomicsapproaches has boosted this area of research, delivering promis-ing results and suggesting new avenues for further research

60 development in the field of cancer biology, as we will attemptto review in this paper.

Mass spectrometry-based proteomics & metabolomicsCancer proteomics still represents a mainstay in cancer researchsince the dawn of the post-genomic era [6]. The underlying

65 assumption is that proteins can be regarded as the ‘effectors’ ofcellular functions and thus, in biological terms, protein profil-ing might be more informative than mRNA profiling [7].Nevertheless, despite almost two decades of efforts, the ambi-tious agenda pursuing the complete annotation of the physio-

70 logical role of all known genes still remains unfulfilled [8].From a technical standpoint, recent technical advancements

have opened new scenarios in the field of proteomics. While adecade ago separative and quantitative proteomics approachesmainly relied upon 2DE-based analyses and the implementa-

75 tion of HPLC-MS-based workflows was only auspicated, cur-rent proteomics analyses actually take advantage of quantitativeanalyses via chromatography-MS approaches. Novel instru-ments have been indeed implemented (both at the HPLC andMS level–the interested reader is referred to reference [7] for

80 further details), as well as bioinformatics suites and tools, whichsimplified quantitative analysis by allowing peak alignment,detection, protein identification and attribution of post-translational modifications (PTMs).

Quantitative proteomics

85 Among quantitative proteomics approaches that have beenextensively applied in the field of cancer research, three mainstrategies have gained momentum: i) in vivo labeling with stableisotopes; ii) in vitro labeling and iii) label-free approaches [7].

The basic concept behind labeling strategies is that a stable-90 isotope labeled peptide shares identical chemical features with

its native (unlabeled) counterpart, which results in identicalbehavior during chromatographic separation and mass spectro-metric analysis, though it still allows them to be differentiatedowing to their mass difference. Relative abundances can be

95 then grasped by measuring the ratio of signal intensities for thelabeled and unlabeled peptide pairs under different biologicalconditions [7].

Stable isotope labeling of amino acid in culture (SILAC), isprobably the most extensively adopted in vivo labeling

100 approach in cancer research [9]. The SILAC protocol envisagescell culturing in media containing either normal amino acidsor amino acids labeled with heavy isotopes. The labeled aminoacids are often lysine and arginine (with different combinationsof 13C, 15N, and 3H). The choice to label these basic amino

105 acids stems from the broadly diffused adoption of trypsin asthe protease of choice upstream of HPLC-MS proteomics

analyses. In SILAC, trypsin cleavage thus exposes C-terminallylabeled arginine or lysine, which allows relative quantitation ofeach digestion-generated peptide, except for the C-terminus

110peptide of the protein [7,9]. While SILAC was at first optimizedfor unicellular model organisms [7,9], its experimental designmakes it suitable for cell culture experiments and thus amena-ble for in vivo/ex vivo cancer research investigations. A directevolution of SILAC is super-SILAC [10], a method that com-

115bines a mixture of multiple SILAC-labeled cell lines. Indeed,SILAC is a particularly accurate quantitative method, althoughuntil recently it was limited to cell lines or animals that couldbe metabolically labeled with heavy amino acids. This limita-tion of SILAC in studying patient tumor samples has been

120overcome through the use of a mix of multiple SILAC-labeledcell lines as an internal standard, a technique called super-SILAC [10]. This mix achieved superior quantification accuracycompared with a single SILAC-labeled cell line, owing to thegeneration of hundreds of thousands of isotopically labeled

125peptides in appropriate amounts to serve as internal standardsfor MS [11].

Isotope-coded affinity tags (ICAT) are one of the most rap-idly expanding in vitro labeling techniques for protein quantita-tion. ICAT is based upon specific tagging of cysteine residues

130with a reagent containing either eight or no deuterium atoms,along with a biotin group that enables affinity purification strat-egies to recover and enrich labeled peptides prior to MS analy-sis [7]. One major limitation of this technique is that it can beonly applied to those proteins that contain cysteine residues.

135Isobaric tags for relative and absolute quantification(iTRAQ) is another important in vitro labeling strategy. IniTRAQ, tagging requires a reporter group, a balance group anda peptide reactive group. The reactive group binds the N-terminus and side-chain amines of peptides, while the reporter

140(up to eight different labeling patterns are possible) and thebalance group are designed as to achieve isobaric balancing inMS mode, and discriminating fragments in collision induceddissociation (CID) mode for relative quantitation on the basisof the relative abundance of different reporter groups [7].

145Another labeling strategy for quantitative proteomics impliesthe use of isotopomer labels, referred to as ‘tandem mass tags’(TMTs) [12]. TMTs are designed to ensure that identical pepti-des labeled with different isotopomers exactly comigrate in allchromatographic separations. On the other hand, peptides

150from different samples can be identified and relatively quanti-fied using CID-based analysis method. Relative abundancemeasurements made in the MS/MS mode using the new tagsare accurate and sensitive [12].

Another strategy aims at quantifying protein abundances by155targeting a so-called proteotypic peptide (defined as ‘an experi-

mentally observable peptide that uniquely identifies a specificprotein or protein isoform’ [13]) through selected reaction moni-toring (SRM)-MS, which enables isolation and quantitativeassaying of the expected mass to charge ratio (against standards

160or in silico predicted values) [14]. Proteotypic standard peptidescould be used as external references to determine calibration

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2 Expert Rev. Proteomics 10(5), (2013)

curves, as resulting from the correlation of MS readings (eitherpeak intensities or peak areas) in response to a variation in pep-tide concentrations. However, SRM is not necessarily a synonym

165 for label free quantitation. Early evolutions of the SRMapproach imply the use of isotopically labeled synthetic peptides(SPIKETIDES [15,16] or AQUA peptides) to enable absolutequantification of specific proteins in a targeted fashion. The clearadvantage over non-labeled peptides is that isotopically labeled

170 proteotypic peptides can be directly spiked in the sample to beused as an internal reference, which also helps coping with anyuntoward technical bias at the nano HPLC or MS level.

While labeling-based quantitative strategies are rather expen-sive and often time consuming (especially in vivo, which might

175 require four to six replication cycles of cultured cells to achievefull labeling), label free approaches are increasingly attracting agreat deal of interest, owing to the reduced costs and ease ofimplementation to classic HPLC-MS proteomics workflows.Recently introduced post hoc algorithms allow calculation of the

180 so-called ‘exponentially-modified protein abundance index’(emPAI), where the number of identified peptides, normalizedagainst the number of all the possibly identifiable peptides fora given protein, is used as an indicator of the absolute proteinabundance on a logarithmic basis. Other indicators of absolute

185 protein abundances include spectral counting and peakintensities [7].

Redox proteomics

Deregulation of metabolism in cancer cells is also intercon-nected with increased susceptibility to, and exacerbation of, oxi-

190 dative stress (as it will be extensively described in the followingparagraphs). The key approaches to redox proteomics havebeen recently reviewed [14,17]. Redox proteomics is a recentbranch of proteomics that is devoted to the determination and,possibly, quantification of oxidative modifications to proteins

195 (including protein carbonylation, oxidation/nitrosylation ofthiol groups and nitrosylation of tyrosines).

Among all redox modifications, oxidation of thiol groupsmight affect the functional activity of several key enzymes (suchas metabolic and redox-homeostasis-related enzymes, including

200 glyceraldehyde 3-phosphate dehydrogenase and peroxiredoxin2) [14,17]. Owing to the labile nature of thiol groups-targetingoxidative modifications, an experimental strategy envisages thetemporary quenching of free thiols (by means of trichloroaceticacid-based acidification or through the use of cell permeable

205 reagents for thiol-groups, such as the alkylating agents iodoace-tamide or N-ethylmaleimide) and subsequent specific reduction(also with dithiothreitol, sodium arsenite or dimedone) [14,17].On the other hand, commercially available antibodies can benow exploited to determine the extent of S-nitrosylations, via

210 enabling antibody-based enrichment strategies (immunoprecipi-tation) or direct immunoassay detection (ELISA, western blot).Other analytical approaches also include biotin labeling for theenrichment of S-glutathionylated peptides or isotope labeling(especially ICAT, see above) to enrich, determine and quantify

215 S-oxidative modifications [14,17].

Further developments in the field of cancer redox proteomicsare awaited in the next few years.

Phosphoproteomics

Despite the significant body of accumulating knowledge about220genes involved in the development of human cancer (at least

300 have been discovered so far), only a limited number of can-cer genes encode for proteins that are suitable targets for effectivedrugs. In this view, protein kinases (such as Abl tyrosine kinase)are among the best eligible targets for small molecule inhibi-

225tors [18,19]. Indeed, sustaining proliferative signaling is a key hall-mark of cancer, which is often achieved through kinase-triggeredphosphorylation cascades [1]. It is thus pivotal to further ourunderstanding of the biological role of protein phosphorylationsthrough the introduction of novel analytical strategies to enhance

230their detection. Within this context, big strides have beenrecently made in the field of phosphoproteomics. Phosphoryla-tion (mostly of S/T, and to a lesser extent to Y amino acid resi-dues) is a reversible PTM that plays important regulatoryfunctions in cellular signaling pathways, which can influence cell

235growth, differentiation, invasion, metastasis and apoptosis [18,19].Since protein phosphorylations are often sub-stoichiometric,

enrichment strategies are often necessary to enable determina-tion of differential phosphorylation events. Enrichmentstrategies are either based upon affinity chromatography

240(immobilized metal ion affinity chromatography), titaniumdioxide, zirconium dioxide, calcium phosphonate precipitationor strong cation exchange or immunoprecipitation strategies.Detection via non-MS methods mainly involves antibody-basedapproaches, while MS-based approaches rather rely on isotopic

245labeling (ICAT, iTRAQ or 32P/33P) and/or alternative (to colli-sion induced dissociation–CID) fragmentation strategies, suchas electron transfer dissociation (ETD), which favors generationof c and z ions upon peptide fragmentation (instead of b and yions, which are predominant in CID) [19].

250Glycoproteomics

Evading growth suppression and activating invasion and meta-stasis (two key hallmarks of cancer [1]) is mainly achieved bycancer cells through the modulation of membrane proteins,which allow cancer cells to bypass contact-triggered growth-

255inhibitory signaling. Glycosylation is one of the most commonPTMs, estimated to be found in over 50% of human pro-teins [20] and more than 80% of membrane proteins [21]. Mostmembrane biomarkers of cancer cells, which are amenable toantibody-based therapies, are glycosylated proteins. Other than

260extracellular membrane proteins, secreted proteins (which canbe thus searched for in the patient’s body fluids) are oftenN-glycosylated in the endoplasmic reticulum or Golgi appara-tus. It is thus small a wonder that protein glycosylation isincreasingly attracting a great deal of interest in the field of

265cancer research. Glycosylations can be further distinguishedinto: i) N-linked glycosylation, ii) O-linked glycosylationand iii) C-glycosylations. In like fashion to phosphoproteomics,glycoproteomics approaches often rely upon preliminary

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enrichment strategies (lectin affinity or boronic acid chromatog-270 raphy or immunoprecipitation) to selectively enrich those pro-

teins bearing N-, O- or C-glycosylations. Glycosylated peptidescan then be screened via the use of MS, either MALDI or ESI-MS, the latter relying both on CID and ETD fragmentationmodes [22].

275 Enriched glycosylated peptides can be thus released via enzy-matic digestion (with PNGase F for N-glycosylations) and bychemical methods (for O-glycan release) [20]. Alternative enzy-matic digestion combinations can help further define thearrangement of side-chain branches, the most challenging task

280 in glycoproteomics analyses to date [23].

Imaging mass spectrometry

One of the greatest advances over the last decade in the field ofMS-based cancer research has been the introduction of MALDIimaging approaches. Imaging MS is a molecular analytical tech-

285 nology that enables the simultaneous measurement of multipleanalytes directly from intact tissue sections [24]. Histological fea-tures of the sample, as gleaned through classic immunohisto-chemistry staining, can indeed be correlated with molecularspecies (proteins, peptides, lipids and metabolites) without the

290 need for target-speci?c reagents such as antibodies.Imaging MS is based upon matrix spraying on suitably-

treated cryostat sections mounted on conductive indium titai-num oxide-coated slides. The preliminary treatment dependson the molecular species under investigation (e.g., organic sol-

295 vents might interfere with on tissue lipid analyses). MALDIimaging can indeed be applied to determine molecular signa-tures that are specific of a tumor tissue, while theoretically eas-ing the individuation of tumor biomarkers and theirdiscrimination from tumor border biomarkers [25]. The possi-

300 bility to combine it with to routine immunohistochemicalapproaches offers the opportunity to validate and complementthe information attainable from a biopsy while obtaining addi-tional complimentary proteomics/lipidomics/metabolomics-rele-vant results.

305 Indeed, imaging MS can be applied to obtain tissue profilesof proteins [26], peptides [27], lipids and metabolites [28] (includ-ing drug metabolites, thus helping monitoring the efficiency ofa therapeutical treatment). Additionally, protocols have beendeveloped also to allow imaging analyses of formalin-fixed par-

310 affin-embedded tissue slices [29].One main limitation of imaging techniques is related to repro-

ducibility (mainly affected by matrix depositing issues), althoughrecently introduced automatic sprayers dramatically abated tech-nical variability. Other limitations include the difficulty of moni-

315 toring high molecular weight compounds (especially proteinsabove the 50–60 kDa threshold) and the constraints related tothe relative abundances of molecular species (e.g., most abundantcompounds are often easily visible, while low abundance ones arehardly detectable through this approach).

320 However, the flexibility of the method makes it suitable fortargeted quantitative approaches (such as SRM to low molecu-lar weight compounds, such as drugs [30]) directly on tissue.

From proteomics to metabolomics: MALDI imaging &

MALDI-based metabolomics

325Metabolomics is the global quantitative assessment of metabo-lites (low molecular weight compounds below the 1.5 kDathreshold, including sugars, phosphate compounds, organicacids, nucleosides, lipids and fatty acids or exogenous com-pounds) in a biological system [31].

330While proteomics investigates the effectors influencing thephenotype, metabolites are the phenotype itself. Indeed, thehistorical precursor to metabolomics can be traced back to earlyclinical biochemistry approaches [32], while technical advance-ments in the field NMR and, subsequently, of MS [32,33] have

335boosted the refinement of this old/new omics discipline. NMRwas at first favored by machine accessibility, established datahandling and the conservative nature of the analysis, whichallows further testing downstream of NMR analyses on thesame samples. Conversely, MS has gradually complemented

340and often replaced NMR owing to its higher sensitivity, whichresults in MS being less demanding in terms of minimumdetectable concentrations of the analyte [33]. Also, MS-basedmetabolomics holds the potential to better discriminate metab-olites, thus improving coverage of the metabolome space, espe-

345cially when performing upstream compound-class-specificchromatographic separations [32,33].

In likewise fashion to quantitative proteomics, quantitativeMS-based metabolomics can also rely upon SRM or multiplereaction monitoring (MRM) approaches [34–36], which allow

350detection and absolute quantification of a compound and itsfragments against a pure standard.

However, most advanced metabolomics studies today relyupon post hoc alignment, peak detection and metabolite dis-crimination without any a priori restriction: this approach also

355goes by the name of untargeted metabolomics [37] and isalready providing decisive insights into cancer biology. In par-ticular, a rather recent application of MS-based untargetedmetabolomics is based upon carbon flux during catabolism andanabolism, via supplying 13C-labeled metabolic substrates

360(mainly glucose and glutamine) [38]. This approach allows thekinetics of energy fluxes to be monitored during molecularbiology experiments on cell lines (e.g., upon induction orsilencing of an oncogene or tumor suppressor protein), thushelping further refine the understanding of metabolic networks

365in normal and cancer cells.Biomedical application of MALDI MS is technically suited

to monitoring metabolic variations directly on tissue sectionsfrom biopsies, but it also allows the screening of whole bodytissue sections from model organisms (e.g., mice) while looking

370for the metabolites derived from catabolism of a specific drugunder testing. This is relevant in the context of cancer researchsince one of the key steps in drug discovery is the determina-tion of the areas targeted by a therapeutic (bodyaccumulation) [39].

375MALDI-imaging has recently been applied in cancer metab-olomics research, especially in lipidomics analyses, since lipidsare highly preponderant and easily detectable through imaging



approaches. One clear advantage withMALDI imaging approaches is that a

380 molecule can be directly detected on tis-sue, with a lateral resolution down to10–15 mm with commonly availableinstruments (while in secondary ionimaging MS it can be improved up to

385 >50 nm [40]). These advancements havepaved the way for a broader applicationof MALDI-based proteomics andmetabolomics strategies [41].

MALDI-based metabolomics, on cell390 lysates or tissue homogenates instead of

tissue sections, would have severaladvantages over routinely used MS-based metabolomics platforms (such asLC-MS), in that it would be amenable

395 to automatization and multiplexing,especially in combination with roboticauto samplers.

Anabolism & the Warburg effectThe recent introduction of specific

400 metabolomics analytical platforms helpedelucidating metabolic fluxes in highlyproliferating cells [42,43]. In line withKilburn’s observations [44], dating back tofour decades ago, highly proliferating cells do not have extremely

405 higher energy demands (in terms of glucose metabolization) incomparison to resting cells. This at least in part justifies the met-abolic choice of oxidizing glucose via glycolysis while depressingthe TCA cycle in cancer cells. However, an elevated replicationrate is based upon the accumulation of building blocks to build

410 up mass and cell constituents before replication. In this scenario,metabolomics analyses contributed significant insights by demon-strating how alternative metabolic pathways are indeed activatedalong with glycolysis, which promote anabolism by constantlyproviding key reducing coenzymes such as NADPH (that is piv-

415 otal, e.g., in lipid synthesis) (FIGURE 1). In parallel to aerobic glycol-ysis, glucose utilization fuels the main NADPH-generatingpathway, the (oxidative phase of the) pentose phosphate pathway(PPP). Over-activation of the PPP at the non-oxidative phasefuels the generation of ribose phosphate substrates for nucleoside

420 biosynthesis, another central step toward DNA replication inproliferating cells. It is perhaps worthwhile to recall thatNADPH also plays a fundamental role in the recycling of oxi-dized glutathione (GSSG) back to the reduced form (GSH),thus contributing substantially to the redox poise (FIGURE 1).

425 In this complex metabolic scenario, mitochondria are notjust innocent and inactive bystanders in cancer cells [45].Mitochondrial metabolism is indeed fueled by glutamine,which is one major nitrogen source for biosynthesis reactionsand carbon source (via glutamate-a-ketoglutarate intermedi-

430 ate conversion) for the TCA cycle (FIGURE 1). At the same time,mitochondrial activation fuels production of reactive oxygen

species (ROS) in the form of superoxide anions and hydroxylradicals, which ends up ‘fertilizing’ the tumor microenviron-ment [46] and promotes the accumulation of further muta-

435tions to oncogenes and tumor suppressors [47]. Of note, theimpairment in ROS modulation/production in mitochondriais often accompanied by mutations to electron transportchain components [48].

In parallel, most cancers share distinct features such as440defects in certain mitochondrial enzymes, including isoci-

trate dehydrogenase (IDH), fumarate dehydrogenase (FD)and succinate dehydrogenase (SD) [49,50]. Alterations to IDHor isoform switching (IDH1 vs IDH2) promote the utiliza-tion of a-ketoglutarate from glutamine metabolism via

445reductive carboxylation to isocitrate, to fuel acetyl-CoA pro-duction for fatty acid biosynthetic purposes [51–53] or aminoacid synthesis via oxaloacetate intermediates (FIGURE 2). Ofnote, isocitrate to a-ketoglutarate conversion by cytosolicIDH is associated with the production of NADPH, analo-

450gous to the conversion of malate (another TCA cycle inter-mediate) to pyruvate by malic enzyme. Anomalies toIDH1 enzyme (R132H) result in 2-hydroxyglutarate pro-duction from a-ketoglutarate, which negatively affects a-ketoglutarate-dependent dioxygenase enzyme activity and

455promotes malignant progression of brain tumors and, inparticular, gliomas [54].

Anomalies to FD and SD results in the accumulation offumarate and succinate, respectively. These metabolites (alongwith the aforementioned 2-hydroxyglutarate) have been recently

Glucose

G6P

Pyruvate

Lactate

F6P

FBP

DHAP G3P

BPG

3PG

2PG

PEP

HXK

GAPDH

GPI

PFK FBPase

PGK

PGAM

ENO

PKM

LDH

TPI

ALDOA

GlcN-6P GlcNAc-6P

GlcNAc-1P

UDP-GlcNAc

GFPT1 GNPNAT1

PGM

UAP1

3PHPYR PSER SER 3PGDH PSAT PSP

GLY

SHMT

Glut1

GL6P 6PG

X5P R5P

F6P

E4P

S7P

G3P

SBP

E4P DHAP

G6PDH PGLS PGD RPE RPIA

TKT

TKT

NADPH NADPH

TALDO1

ALDOA

Acetyl-CoA

MAL FUM

SUCC

SUCC-CoA

α-KET

OAA

CITR

ISOCIT

PDH

FD

SD

IDH ACO

OGDH

SUCCLG1

MDH

ACLY

GSH GSSG GLY

γGLCY

CYST GLUT

MCT

GLTM

GSS

GCLM

GSR SLC38A

GLS

GLUD1

NADPH

Ru5P

Figure 1. An overview of the main catabolic pathways in normal and proliferat-ing cells. Glycolysis, Krebs cycle, PPP, serine synthesis, hexosamine synthesis and gluta-thione homeostasis are included. Enzymes are indicated in light grey, according to theirrelative UniProt names.PPP: Pentose phosphate pathway.



460 found to play a key role as direct inhibitor of dioxygenase andprolyl hydrolaseAQ2 , enzymes that indirectly catalyze the degrada-tion of the hypoxia-inducible factor (HIF) [51–53]. This inhibi-tory phenomenon is relevant since accumulation of HIF, whichnormally occurs under hypoxia, mediates the activation of pyr-

465 uvate dehydrogenase kinase (PDK1), which in turn inhibitspyruvate dehydrogenase and thus hinders conversion of pyru-vate to acetyl-CoA and shunts pyruvate to lactate [55]. In this

view, fumarate and succinate have been recently referred to asoncometabolites. Also, HIF accumulation diverts IDH-

470dependent reductive carboxylation fluxes toward fatty acidanabolism [56], which further stresses the intertwinement ofmetabolic deregulation with proliferative capacity (FIGURE 3) [57].

Cell proliferation, tumor suppressors, oncogenes &metabolism

475Metabolic deregulation in cancer cells is partly the cause andmostly the effect of genetic deregulation, at the oncogene andtumor suppressor level [58]. As described in the previous para-graph, these metabolic adjustments serve to build up anabolicproducts to pursue cell proliferation [59]. At the same time, they

480promote deregulation of oxidative phosphorylation and mito-chondrial events, thus favoring ROS accumulation and altera-tions to the tumor microenvironment. Exacerbation of oxidativestress promotes senescence-like phenomena in cancer cells, whiledecreasing glucose uptake, deregulating matrix attachment [59].

485At the same time, cancer cells cope with the excess of ROS byactivating pro-survival pathways, through the deregulation ofspecific oncogenes [60], often complementary to inactivating orgain of function mutations to tumor suppressor proteins, suchas proteins from the p53 family (p53, p63 and p73), which

490normally act as the guardians of the genome stability. One para-digmatic example is indeed represented by mutations to p53 (e.g., R175H and R273H [61]), resulting in cell survival, increasedproliferation and promotion of invasiveness.

The double role of p53 family members495While early cancer investigation studies indicated that p53 and

retinoblastoma protein (RB) mutations (comprehensivelydetected in the great majority of tumors) mainly resulted in theloss of function of their tumor-suppressor activity [62], recentstudy indicate how these proteins (especially p53) might play a

500more complex role in modulating the balance of pro-survivaland pro-apoptotic signaling, a function that escapes regulationupon the acquisition of specific mutations to these proteins [63].Tp53, for example, is now known to take part in metabolicmodulation at several levels [64]. Analogous roles are increas-

505ingly emerging for all the members of the p53 family [65]. Forexample, p53 can inhibit the expression of the glucose trans-porters GLUT1 and GLUT4 [64], and can increase the expres-sion of Tp53-inducible glycolysis and apoptosis regulator(TIGAR), a fructose-bisphosphatase that inhibits glycolysis by

510reducing cellular levels of fructose-2,6-bisphosphate and thuspromoting a shift backward to the PPP, a process that pro-motes NADPH accumulation and plays a role in anti-apoptoticsignaling via ROS-damage protection and promotes anabolicpathways, as summarized in the previous paragraphs [66–68]

515(FIGURE 4). Besides, p53-responsive elements are present in thepromoters of PGM and hexokinase II (HK2), which is sugges-tive of the fact that p53 can promote at least some steps in gly-colysis. Of note, under hypoxic conditions, mitochondriallocalization of TIGAR stimulates HK2 (often bound to mito-

520chondrial membrane in tumors) [66].

Acetyl-CoA

MAL FUM

SUCC

SUCC-CoA

α-KET

OAA

CITR

ISOCIT IDH

Amino acidSynthesis

GLUT GLTM

Reductive carboxylation

Fatty acidSynthesis

Figure 2. Cancer is often associated with mutations to TCAcycle enzymes, or expression of specific isoforms. In thecase of IDH, this results in the promotion of reductive carboxyla-tion from glutamine-derived a-ketoglutarate, instead of regularTCA cycle fluxing towards succinyl Co-A. This utterly results inacetyl Co-A accumulation, which promotes fatty acid synthesisand, thus, cell growth and proliferation.IDH: Isocitrate dehydrogenase; TCA: Tricarboxylic acid cycle.

Pyruvate

Lactate

Glucose

Acetyl-CoA

MAL FUM

SUCC

SUCC-CoA

α-KET

OAA

CITR

ISOCIT

PDH

FD

SDMDHPDK

HIF PDH

Figure 3. Cancer is often associated to mutations to Krebscycle enzymes, including SD, FD and MDH. These mutationsend up blocking TCA cycle catabolic fluxes, and promote theaccumulation of the respective substrates of these enzymes, suc-cinate, fumarate ad malate. These oncometabolites inhibit prolylAQ3hydrolase activity, thereby indirectly resulting in HIF stabilization.In turn, this promotes PDK1 activity, an inhibitory enzyme ofpyruvate dehydrogenase. This results in increased lactic acidfermentation and decreased fluxes from glycolysis to theTCA cycle.FD: Fumarate dehydrogenase; HIF: Hypoxia-inducible factor;MDH: Malate dehydrogenase; PDK1: Pyruvate dehydrogenase kin-ase; SD: Succinate dehydrogenase; TCA: Tricarboxylic acid cycle.



Other than p53, a transactivation-proficient isoform ofp73 has been recently shown to play a role in the promotionof a metabolic shift toward the PPP, via the upregulation ofenzymes involved in the oxidative phase of the pentose phos-

525 phate shunt such as 6-phosphogluconolactonase [69].Tp53 also regulates the expression of sestrin proteins, which

activate AMPK to regulate growth and autophagy but alsofunction as antioxidants, protecting cells from hydrogenperoxide-induced damage [65]. Furthermore, also in terms of

530 antioxidant defenses, p53 triggers the activation of glutathioneperoxidase, aldehyde dehydrogenase and tumor proteinp53-inducible nuclear protein 1 (TP53INP1), all of them dis-playing antioxidant functions.

However, it should not be forgotten that many p53-induci-535 ble proteins participate in apoptotic responses via the promo-

tion of ROS production, including p53-induced gene3 (PIG3), proline oxidase, BAX, PUMA and p66SHC [65], aswell as of cytochrome C oxidase 2 [70].

Tp53 further influences mitochondrial metabolism by pro-540 moting the expression of glutaminase 2, glutaminases being a

family of enzymes responsible for glutamine to glutamate con-version [71]. Analogous effects on glutaminase expression havebeen recently reported also for p63 [72]. In turn, glutamate is apivotal constituent of the tripeptide GSH, or rather it can be

545 further metabolized to a-ketoglutarate, that can either undergoreductive carboxylation to isocitrate or further oxidation tosuccinyl-CoA via the TCA cycle.

Energy or nutrient deprivation, extreme environmental con-ditions or Ca2+ release from the lumen of the endoplasmic

550 reticulum (ER) results in the disruption of proper protein-folding activity in this organelle, a condition that promotes theso-called ER-stress. ER-stress has been observed to influencep53 stability by modulating its differential phosphorylation toserine 315 and serine 376, which promotes p53 localization in

555 the cytoplasm and its degradation [73,74]. Conversely, otherp53 family members, p63 and p73, have been shown to pro-mote ER stress and scotin (protein shisa-5) [75,76], suggesting anintricate cross-talk among p53-family members, other thandirect competition for p53 responsive elements or oligomeriza-

560 tion through direct binding. In this view, it is interesting tonote that almost pleiotropic metabolic effects are also expectedfor other p53 family members, since, for example,TAp73 deletion reduces cellular ATP levels, oxygen consump-tion and mitochondrial complex IV activity, with increased

565 ROS production and oxidative stress sensitivity [77]. This phe-nomenon involves the mitochondrial complex IV subunit cyto-chrome C oxidase subunit 4 (Cox4i1), which is a directTAp73 [77].

Metabolic starvation experiments570 On the basis of the evidences for modest energy demands,

albeit extreme substrate uptake, especially of glucose and gluta-mine (we hereby purposely neglect uptake of lipids from themedium, which would deserve an entirely dedicated review) foranabolic and redox homeostasis purposes, new trends in the

575field of cancer drug research are aimed at evaluating the effectsof starvation on cancer cells. One simple approach to promotecancer cell starvation in vivo would be to prevent angiogenesis,which would reduce the blood flux and thus oxygen andnutrient delivery [78]. Anti-angiogenic therapy mainly relies

580upon the administration of anti-VEGF-targeting monoclonalantibodies (e.g., bevacizumab).

Within this framework, in vitro metabolomics experimentshave recently provided a clearer understanding of the mecha-nisms underlying the effects of starvation on cancer cells. Since

585the main metabolic substrates for carbon and nitrogen build upin cancer cells are both glucose and glutamine, starvationexperiments have been so far performed through the supple-mentation of cell media that are depleted of these two com-pounds [79–83]. A complex scenario emerged whereby glucose

590might represent the main energy source for certain cell lines(such as head and neck squamous carcinoma cells [79]), whileonly glutamine depletion actually triggered apoptosis in othercell lines [83].

Other than glucose and glutamine, cancer cells necessitate595serine to support anabolism by providing precursors for biosyn-

thesis of proteins, nucleotides, creatine, porphyrins, phospholi-pids and glutathione. Also, up-regulation of the serine synthesispathway occurs in some breast cancers [84,85]. It has beenrecently demonstrated that p53-mediated cell responses to ser-

600ine starvation involve over-activation of the serine synthesispathway. Besides, it promotes inhibition of glycolysis, since arate-limiting enzyme, in particular, the specific and less efficientcancer isoform, pyruvate kinase M2 (PKM2) [86], is allosteri-cally activated by serine and thus, serine starvation, ends up

605inhibiting glycolysis (FIGURE 4) [87]. This prompts two maineffects: increase in TCA cycle fluxes to cope with the decreasedATP production, and an increase in PPP fluxes, to generateNADPH and thus cope with oxidative stress arising from theelevation in TCA cycle-dependent energy production [85]. Of

610note, PKM2 expression in cancer cells results in relativelydecreased glycolytic rates, which promotes accumulation ofearly glycolysis intermediates, including 3-phosphoglycerate, aprecursor to serine de novo synthesis.

Serine metabolism is also at the crossroads between p53 and615starvation-induced autophagic responses [88]. Autophagy is a

catabolic mechanism that promotes cell degradation of unneces-sary or dysfunctional cellular components through the lysoso-mal machinery to cope with the decrease in energy andanabolic resources, and it is often regarded as: i) an alternative

620option cells might choose to commit suicide, other than apop-tosis, ii) a cell’s defensive strategy upon cell damage or iii) acell’s major adaptive (survival) strategy to cope with metabolicstress, such as nutrient deprivation, or starvation in general.Starvation, the most extensively investigated inducer of auto-

625phagic responses, triggers the activation of AMPK [89,90], a kin-ase that is activated by increased AMP/ATP ratios, and isregulated to some extent by mammalian target of rapamycin(mTOR), a key molecular sensor for nutrient availability and aregulator of cell growth and proliferation [91–95]. In particular,



630 autophagy initiation is associated with downregulation ofmTOR complex 1 (mTORC1) activity. mTOR is a well-conserved serine/threonine kinase that belongs to the phosphoi-nositide 3-kinase (PI3K)-related kinase family, and it plays animportant role in the signaling network that controls growth

635 and metabolism in response to environmental cues. The activa-tion of mTORC1 (one of the two distinct multi-protein com-plexes involving mTOR) requires glutamine and essentialamino acids such as leucine, while it has been recently demon-strated to also depend on availability of serine [87]. At the same

640 time, Akt and AMPK communicate directly with mTORC1,by phosphorylating raptor (an mTORC1 component), leadingto 14-3-3 binding and the allosteric inhibition of mTORC1 [94].Activation of mTORC1 promotes protein synthesis by phos-phorylating the kinase S6K and the translation regulator 4E-

645 BP1 and lipid biogenesis, via activation of SREBP and PPARgtranscription factors [94,95]. The phosphatase and tensin homo-log (PTEN) interferes with the whole phosphoinositide 3-kin-ase (PI3K)/Akt/mTORC1 axis, by dephosphorylatingphosphatidylinositol (3,4,5)-trisphosphate (PIP3) at the 3‘ posi-

650 tion of the phosphate of the inositol ring, thus producing

phosphatidylinositol (4,5)-biphosphate(PIP2). In this way, PTEN modulatesPIP3/PIP2 ratios and, indirectly, cellsurvival [96].

655The list of oncogenes involved in meta-bolic regulation is not only limited to HIFand mTOR, but includes many other play-ers. One of those is MYC, which encodesa transcription factor c-Myc that links

660altered cellular metabolism to tumorigene-sis [97]. Indeed, c-Myc regulates genesinvolved in the biogenesis of ribosomesand mitochondria, affects glucose and glu-tamine metabolism, nucleotide metabolism

665and, along with E2F1, DNA replicationand miRNA expression [97]. Ectopic c-Myccooperates with HIF to promote theinduction of a transcriptional program forhypoxic adaptation, involving up-

670regulation of glycolytic genes including lac-tate dehydrogenase A, or the repression ofmicroRNAs (miRNAs) miR-23a/b toincrease glutaminase protein expressionand glutamine metabolism.

675Recent flux-balance analyses have indi-cated a key role in glycolytic modulation(toward increase) and glutamine metabo-lism (towards reductive carboxylation) foranother oncogene, KRAS [98,99]. In partic-

680ular, KRAS appears to be involved in theincrease in glycolytic metabolism andchanneling of glycolytic intermediates tonon-oxidative phase PPP reactions andhexosamine biosynthesis (in turn promot-

685ing protein glycosylation) [98]. Also, KRAS seems to promote analternative pathway for glutamine metabolism to glutamate andalternative downstream pathway. Such alternative route involvingglutamate metabolism appears to be dependent on transami-nases, especially aspartate transaminases (GOT), which promote

690glutamate and oxalacetate production from aspartate and a-ketoglutarate (FIGURE 5). This results in oxaloacetate cytosolic accu-mulation (certain GOTs operate only in the cytosol), whichprompts its conversion to malate (via malate dehydrogenase)and, through malic enzyme, to pyruvate, a reaction that concom-

695itantly fuels NADPH production [99].Analogously, the tumor suppressor promyelocytic leukemia

(PML) gene acted as both a negative regulator of PPARg coac-tivator 1A (PGC1A) acetylation and a potent activator ofPPAR signaling and fatty acid oxidation in breast cancer

700cells [100]. Finally, it is at least worth mentioning the long timeestablished role in metabolic regulation of the insulin-likegrowth factors (IGF-I and IGF-II) system [101].

MicroRNAs (miRNAs) are small RNA molecules that regu-late gene expression post-transcriptionally [97]. As anticipated in

705the previous paragraphs, miRNA expression can be more or

Glucose

G6P

Pyruvate Lactate

GLUT1, GLUT4

F6P

FBP

3PG PEP

HXK

PKM2

Oxidative phase PPP

NADPH NADPH

SER

p53 TIGAR

Mitochondria

Anabolism and Antioxidant responses

Non-oxidative phase PPP Nucleosidebiosynthesis

GLUT

GLTM GLS2

ROS

Sestrins Glutathione peroxidase Aldehyde dehydrogenase TP53INP1

Bax PUMA p66SHC Cytochrome C oxidase 2

Figure 4. Metabolic regulation by p53 at a glance. As a tumor suppressor, p53 islong known to promote apoptosis via enhancing pro-apoptotic factors and ROS-generating mitochondrial metabolism. However, it recently emerged as a role for p53, asa pro-survival mediator under mild stress conditions. This phenomenon appears to involvea TIGAR, a fructose-2,6-bisphosphatase that promotes accumulation of early glycolyticprecursors thus boosting a diversion towards the PPP. The production of NADPH at thenon-oxidative phase of the PPP sustains anabolic and antioxidant processes. Evidencesalso indicate a role for p53 in the induction of anti-oxidant defenses (sestrins, glutathioneperoxidase, aldehyde dehydrogenase, TP53INP1). At the same time, non-oxidative phaseproducts fuel de novo nucleoside synthesis. Pro-survival effects mediated by p53 undermild stress conditions appear to involve the homeostasis of serine metabolism (SER).PPP: Pentose phosphate pathway; ROS: Reactive oxygen species; TIGAR: Tp53-inducedglycolysis and apoptosis regulator; TP53INP1: Tumor protein p53-inducible nuclearprotein 1.



less directly involved in metabolic mod-ulation [102–105]. Small RNAs may havean intrinsic function in tumor suppres-sion, since their levels are globally

710 decreased in human cancers cells [88]. Inline with this, the transcription of somemiRNA genes (such as miR-34) is regu-lated by p53 [102]. Of note, miR-34aappears to be a key regulator of hepatic

715 lipid homeostasis. Together withmiR-34, other miRNAs play a key rolein metabolic modulation, includingmiR-33a and miR-33b, which have acrucial role in controlling cholesterol

720 and lipid metabolism in concert withtheir host genes, the sterol-regulatoryelement-binding protein (SREBP) tran-scription factors [104]. Other metabolicmiRNAs, such as miR-103 and

725 miR-107, regulate insulin and glucosehomeostasis [104], whilemiR-143 regulates hexokinase-2 expres-sion in cancer cells [105].

Induction of apoptosis via gene730 therapy & metabolic

reprogrammingIn the previous paragraphs we have sum-marized how cancer cells often sufferfrom mutations that provide a competi-

735 tive edge over normal cells in terms of biomass accumulation andproliferative capacity. Starting from Warburg’s initial hypothesis,latest research has revealed that metabolic reprogramming occursas a consequence of mutations in cancer genes and alterations incellular signaling. Thus, we described how these mutations end

740 up promoting certain pathways (glycolysis, PPP, nucleoside andfatty acid biosynthesis, NADPH-generating reactions) at theexpenses of others (above all, oxidative phosphorylation). Appreci-ation of these phenomena was a step forward from the canonicconception of the Warburg effect and opened new avenues for

745 future developments in the field of cancer treatment [106]. There-fore, ‘untuning the metabolic machine’ [99] might represent thenew trend in cancer therapies, which might surprisingly beboosted by decades of research in the field of metabolism-relateddiseases, including diabetes and drugs for diabetes treatment (such

750 as metformin) [107].Other metabolic reprogramming strategies envisage the pro-

motion of oxidative stress via mitochondrial uncoupling [108].Another approach is related to caloric restriction, ketogenic dietsand modulation of circulating nutrient levels through enzymes

755 such as asparaginase, these strategies imply that tumor cells aremore demanding in terms of nutrients in comparison to normalcells [106–110].

In this section, we will briefly describe the main metabolicreprogramming strategies [105–109] that are currently either under

760laboratory testing or already under clinical evaluation (sincethey are based on already commercialized drugs).

Targeting glycolysis to modulate the Warburg effect

Substantiation of the Warburg effect derives from the increasedglucose uptake by cancer cells and increased lactate production

765via glycolysis, even in presence of oxygen. On this ground,these characteristics have fostered the concept of new classes ofdrugs that can be used either to monitor cancer cell metabo-lism via state of the art diagnostic tools or to make it amenableto therapeutic interventions [111–114].

770For example, since tumors consume higher levels of glu-cose, clinicians have long been able to monitor tumor uptakeof a fluorine radioisotope of glucose, 18F-deoxyglucose, byFDG-PET. This technique has proven its usefulness in deter-mining the cancer stage, to identify metastatic sites and mon-

775itor treatment effectiveness. In parallel, a correlation has beenobserved between the initial degree of FDG-PET positivityand the overall patient outcome across cancer types andsubtypes [111].

Therapeutic intervention based upon drugs that target glyco-780lytic enzyme activities are currently under evaluation. Among

the possible targets, hexokinase (HXK), phosphofructokinase(PFK), glyceraldehyde 3-phosphate dehydrogenase (GADPH)and lactate dehydrogenase (LDH) represent ideal therapeutic

Acetyl-CoA TCA cycle

α-KET

OAA

GSH

Reductive

carboxylation

GLY

CYST GLUT

GLTM GLS

ASP OAA MAL PYR GOT1 MDH1 ME1

CO2NADPH NAD+

Lactate

Cytosol

Mitochondrion

TC

A

Figure 5. Glutamine metabolism can follow different fates in normal and cancercells. Glutamine can be converted to glutamate (via glutaminase enzymes–GLS), and thusbe metabolized to ketoglutarateAQ4 , whereby it enters TCA cycle or rather promotes reduc-tive carboxylation. In parallel, glutamate can represent a building block of the tripeptideglutathione (GSH). An alternative route involves the malate-aspartate shuttle, a pathwaythat involves glutamine-derived aspartate towards the accumulation of oxaloacetate andmalate intermediates in the cytosol. This pathway is relevant in that NADPH is producedto sustain anabolic and anti-oxidant pathways.GSH: Glutathione; TCA: Tricarboxylic acid cycle.



targets, since chemical inhibitors are already known (though785 they are often not extremely specific).

Known inhibitors of hexokinase include 2-deoxyglucose,3-bromopyruvate, 5-thioglucose and mannoheptulose. In par-ticular, 3-bromopyruvate is a strong alkylating agent towardthe free SH groups of cysteine residues in proteins, which

790 might thus also affect those enzymes with thiol groups in theiractive sites (such as GAPDH).

Lonidamine is known to inhibit only the mitochondria-bound hexokinase, which is a distinctive feature of cancer cells(please, refer to the previous paragraphs) [111–114].

795 Use of a PFK inhibition strategy implies the suppression ofthe PFKB3 isozyme, which controls the cellular level of fruc-tose-2,6-bisphosphate and thus affects the glycolytic flow byallosterically-modulating PFK activity.

Known inhibitors of GAPDH include a-chlorohydrin,800 ornidazole and iodoacetate, as well as the pentovalent

arsenate [111].LDH-A can be knocked down in tumor cells by shRNAs,

thereby stimulating energy fluxes from pyruvate to mitochon-dria, which in turn promotes mitochondrial uncoupling

805 (ROS production, accumulation of pro-oxidant intermedi-ates) in those tumors bearing mutation of mitochondrialenzymes. Targeting LDH might represent a cancer-cell sup-pressing preferential strategy, while it could be less toxic tonormal cells [111].

810 Other therapeutical interventions might involve the use ofoxythiamine (a thiamine antagonist that inhibits transketolaseand pyruvate dehydrogenase) or glufosfamide. Glufosfamide isa conjugate of glucose (highly consumed by cancer cells) andifosfamide, an alkylating agent with cytotoxic effects. Of note,

815 glufosfamide is uptaken via the SAAT1 glucose transporter,which is overexpressed in cancer cells [111–114].

Owing to its pro-glycolytic potential, HIF-1-targeting drugsshould be included, to a certain extent, in the same category ofglycolysis inhibitors [115]. Several classes of drugs have been

820 designed and tested over the years, targeting HIF transcription,synthesis, stability, heterodimerization, DNA-binding activityor targets downstream to HIF-controlled signaling [115].

Targeting NAD-metabolism has recently emerged as apotential therapeutic approach to tackle cancer cell prolifera-

825 tion, since NAD undergoes crucial changes in cancer cells,whereby its use in transcription, DNA repair, cell cycle progres-sion, apoptosis and metabolism processes is deregulated incomparison to normal cells [116].

Targeting autophagy

830 Although autophagy can result in the suppression of tumordevelopment, it may also mirror an extreme and desperateattempt of the cancer cells to survive. Owing to the complexityof autophagic responses in mediating cancer survival/suppres-sion, several strategies have been proposed to tackle autophagy

835 over the last few years [117–119].Treatments with rapamycin (targeting mTOR), for example,

induce glucose starvation-like effects in cancer cell [119]. Another

recent strategy to promote autophagic responses relies upon theadministration of cannabinoid receptor agonists [120].

840Starvation (via nutrient deprivation) of cancer cells has beenproposed as a viable strategy to tackle cancer cell prolifera-tion [121,122], especially when used in combination withchemotherapy [123,124].

Old drugs, new benefits

845Drug discovery is an extremely complicated, expensive and,most of the time a challenging (if not discouraging) area ofresearch. Patenting, testing and commercializing new effectivedrugs is a prohibitive task even for multi-national companies,which need to invest resources (in terms of funds and trained

850personnel) for more than two decades, before (when lucky)receiving the final approval by US FDA. A record of currentlyavailable drugs, the latest version of DrugBank (release 2.0),includes a list of approximately 4900 drug entries, in which areenlisted both FDA-approved small molecule and biotech

855drugs [125]. Since metabolic diseases have long been investi-gated, especially those involving deregulation of glucose homeo-stasis, such as diabetes, a long list of currently commercializeddrugs already exists that might be amenable for cancer treat-ment, in the light of the revisited role of the Warburg effect

860and metabolic reprogramming in cancer progression. Bigua-nides (such as metformin) belong to this category of old drugswith potential new benefits. Biguanides were first isolated in1920 from the French lilac Galega officinalis, which was knownto contain an agent that reduced the frequent urination associ-

865ated with diabetes. Biguanides have step up to the spotlight fortheir ability to suppress liver gluconeogenesis, which is believedto occur through activation of hepatic AMP-activated proteinkinase (AMPK) signaling [107]. It was but in recent years thatthe antitumor effect of metformin could be observed, an effect

870that is mediated by the activation of AMPK and thereby mod-ulating the AMPK/mTOR pathways. At higher doses (thanphysiologically achievable in vivo), metformin appears todirectly affect mitochondrial oxidative phosphorylation incancer cells.

875Targeting fatty acid synthesis & metabolism

Owing to their highly proliferating nature, tumor cells need tobuild up new membrane to favor replication into daughtercells. In this view, fatty acid synthesis and uptake from themedium (tumor microenvironment) are two key pathways that

880have attracted a great deal of interest at least during the last10 years [126]. Targeting fatty acid synthesis involves promotinglipid lowering PPAR-pathways (via fenofibrate, also decreasinglocal angiogenesis) [127] or rather by addressing fatty acid oxida-tion (FAO) [126,128]. While most cancer researchers focused on

885glycolysis, glutaminolysis and fatty acid synthesis, the role offatty acid oxidation in cancer cell metabolic transformation hasnot been hitherto carefully examined [129].

FAO is inhibited by oxidative stress and, though indirectly,it might contribute to counteracting ROS accumulation in can-

890cer cells [129]. Indeed, FAO generates one molecule of acetyl



CoA in each oxidation cycle and two in the last cycle. AcetylCoA enters the Krebs cycle, and combines with oxaloacetate togive rise to citrate. As described in the previous paragraphs,IDH-mediated cytosolic conversion of isocitrate to a-

895 ketoglutarate also produces cytosolic NADPH (for anabolicand antioxidant purposes). This is the same pathway (thoughin the opposite direction), according to which fatty acid synthe-sis under hypoxic conditions (or when mitochondrial respira-tion is limited) might rely upon glutamine-glutamate-a-

900 ketoglutarate generating reactions, thereby driving reductivecarboxylation toward the accumulation of acetyl CoA, as abuilding block for fatty acid synthesis and elongation [130].

It is also worthwhile recalling that glutamine-derived gluta-mate generates NADPH when converted to a-ketoglutarate by

905 glutamate dehydrogenase (GLUD1). Also, since glutamine-derived a-ketoglutarate might fuel acetyl CoA accumulationand thus fatty acid synthesis, glutamine might represent a keytherapeutic target (e.g., at the glutaminase level, through DONand azaserine) also when attempting to tackle lipid

910 synthesis [131].Potential pharmacological targets to inhibit FAO are repre-

sented by carnitine palmitoyl transferase (CPT1), the rate-limiting enzyme in FAO, and 3-ketoacylthiolase (3-KAT),which catalyzes the final step in FAO [129,132] and ATP-citrate

915 lyase (ACLY), a cytosolic enzyme that catalyzes the generationof acetyl CoA from citrate [133].

Targeting protein markers of cancer

In the present paper we mainly focused on metabolites or pro-tein targets mainly related to cancer metabolism. However,

920 there is a long list of emerging molecular markers of cancerthat is continuously expanding [134].

One of the most promising classes of protein biomarkersthat are currently undergoing clinical testing is represented bymolecular chaperones of the heat shock protein (HSP) family.

925 A wide range of human cancers is accompanied by overex-pression of HSPs, which are implicated in tumor cell prolifera-tion, differentiation, invasion, metastasis, death and recognitionby the immune system [135]. HSPs are useful biomarkers forcarcinogenesis in some tissues and might be used as a valid

930 indicators of the degree of differentiation and the aggressivenessof some cancers. Serum levels of HSP and HSP-specific anti-bodies in cancer patients might help tumor diagnosis and berelated to prognosis of specific cancers. Overexpression ofHSP27, for example, is associated with poor prognosis in gas-

935 tric, liver and prostate carcinoma and osteosarcomas [135]. Onthe other hand, HSP70 [136] is correlated with poor prognosisin breast, endometrial, uterine cervical and bladder carcinomas.HSPs might interfere with therapetuic treatments and/orinduce resistance to chemotherapy in breast cancer, leukemia

940 patients and osteosarcomas. Two main strategies have been pro-posed to target HSPs, depending on whether they are relatedto an improved or worsened prognosis of a specific cancer.These strategies include: i) pharmacological modification ofHSP expression or molecular chaperone activity and ii) use of

945HSPs in anticancer vaccines, exploiting their ability to act asimmunological adjuvants [135].

Expert commentaryBig strides have been made over the last decade in the biologi-cal understanding of the phenomena underpinning cancer

950metabolism deregulation. This accumulating body of laboratoryscience has paved the way for designing new therapeutical strat-egies and re-discovering of older drugs, with metabolism-regulatory aptitude.

Mass spectrometry-based proteomics and metabolomics have955been at the core of these basic science advancements, which

will undoubtedly translate into actual pharmacological applica-tions within the next decades. Without the broader distributionof highly sensitive and accurate MS instruments and new quan-titative (isotope labeling-based) strategies, cancer research would

960have hardly had any chance to set even one single step into thedeep forest of metabolic intricacies that we described in theprevious sections. Many molecular biologists have revised theirpositions, firmly standing on a reductionistic ground, whilestarting to complement classic molecular biology experimental

965approaches (to put it cursorily, knock out one gene, induceanother gene, knock down another) with emergingomics disciplines, theoretically encompassing the whole pro-teome and metabolome (within the current capabilities of theapplied technique).

970Metabolism-targeting compounds already include a broadlist of patented drugs and food derived nutrient/pharmaceuti-cal-like molecules, also known as nutraceuticals [137]. Revisitingthe Warburg effect with proteomics and metabolomics toolshas revealed that we might already have an old answer (com-

975mercialized drugs) for a new, compelling question (tacklingcancer proliferation via metabolic reprogramming).

Five-year viewFurther advancements will soon be achieved through the imple-mentation of in silico prediction tools, based upon machine

980learning algorithms, a wind of change that embraces thebroader concept of systems biology [138,139]. Based upon bioin-formatic models, computer predictions will also help predictinguntoward effects (scarce efficiency, scarce specificity) of in vitrodesigned drugs [140]. Improvements in drug specificity and

985selectivity at the design phase will make it amenable to targetcancer specific enzyme isoforms or mutations (PKM2, mito-chondrial HXK2, monocarboxylate transporters MCT4 for lac-tate secretion [141]), by exploiting the concept of ‘syntheticlethality’. As summarized by Kaelin [142], ‘two genes are syn-

990thetic lethal if mutation of either alone is compatible with via-bility but mutation of both leads to death.’ Therefore, it couldbe possible to target a gene that is synthetic lethal to a cancer-relevant mutation, as to kill only cancer cells while sparing nor-mal cells [142].

995Finally, as we hope it emerged from this paper, cancer cellsdisplay an intricate network of intertwined and mutually-compensating metabolic pathways. Altering one node of the



network might result in the compensation effect promoted bythe so-called plasticity of the metabolic network itself. There-

1000 fore, omics/systems biology-wise efforts should be pursued tounveil as many pathways as possible, and to gain an improvedunderstanding of the role of already known, albeit underinvesti-gated ones, such as the folate and mevalonate pathways (bothproducing NADPH at some steps of the cycles). The final

1005 goal will be to determine, case by case, cancer by cancer, themost suitable targets for multi-targeted pharmaceuticalinterventions [143].

Financial & competing interests disclosure

A D’Alessandro and L Zolla are supported by funds from the Italian

1010National Blood Centre (Centro Nazionale Sangue–CNS–Istituto Superiore

Sanita‘–Rome, Italy). The authors have no relevant affiliations or finan-

cial involvement with any organization or entity with a financial interest

in or financial conflict with the subject matter or materials discussed in

the manuscript. This includes employment, consultancies, honoraria, stock

1015ownership or options, expert testimony, grants or patents received or pend-

ing or royalties.

No writing assistance was utilized in the production of this manuscript.

1020 Key issues

• The introduction of label-based and label-free quantitative proteomics and metabolomics analyses revived the field of

cancer metabolism.

• Eighty years after Warburg’s early observations, cancer metabolism is now deemed to be one key hallmark of cancer.

• Most of the cancer-specific frequent mutations to tumor suppressors and oncogenes have the potential to affect cancer

1025 cells metabolism.

• These changes often promote glycolysis at the expenses of oxidative phosphorylation in terms of energy production, other than anabolic

and NADPH-generating pathways. However, mitochondrial metabolism still plays a key role in producing oncometabolites with regula-

tory effects on key oncogenes, other than providing substrates for anabolic reactions (especially fatty acid de novo synthesis) and contri-

buting to the redox poise (via NADPH generation).

1030 • Starvation promotes autophagy, which could provide a therapeutical intervention opportunity for some cancers. Induction of autophagy

might represent a viable alternative to induction of apoptosis.

• Small molecule inhibitors of most of the key enzymes involved in the pathways described above are already available and deserve

further testing to understand their specificity and effectiveness. Cancer isoform-targeting (‘synthetic lethality’) specific drugs must also

be designed as well.

1035 • Among the thousands of commercially available drugs, those having a validated effect in the treatment of metabolic diseases may also

be suitable for the treatment of some cancers.

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