From cancer metabolism to new biomarkers and drug targets

Biotechnology Advances 30 (2012) 30–51

Contents lists available at ScienceDirect

Biotechnology Advances j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / b i o t e c h a d v

Research review paper

From cancer metabolism to new biomarkers and drug targets F. Chiaradonna a,⁎, R.M. Moresco b, c, d, C. Airoldi a, D. Gaglio a, R. Palorini a, F. Nicotra a, C. Messa c, d, e, L. Alberghina a,⁎ a

Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan (MI), Italy Tecnomed Foundation University of Milano-Bicocca, Via Pergolesi 33, 20052 Monza (MB), Italy San Raffaele Institute, Via Olgettina 60, 20132 Milan (MI), Italy d IBFM-CNR, Via Fratelli Cervi 93, 20090 Segrate (MI), Italy e Tecnomed Foundation University of Milano-Bicocca and San Gerardo Hospital, Via Pergolesi 33, 20052 Monza (MB), Italy b c

a r t i c l e

i n f o

Available online 23 July 2011 Keywords: Cancer Metabolism NMR Mass spectrometry PET Drug discovery Systems biology

a b s t r a c t Great interest is presently given to the analysis of metabolic changes that take place specifically in cancer cells. In this review we summarize the alterations in glycolysis, glutamine utilization, fatty acid synthesis and mitochondrial function that have been reported to occur in cancer cells and in human tumors. We then propose considering cancer as a system-level disease and argue how two hallmarks of cancer, enhanced cell proliferation and evasion from apoptosis, may be evaluated as system-level properties, and how this perspective is going to modify drug discovery. Given the relevance of the analysis of metabolism both for studies on the molecular basis of cancer cell phenotype and for clinical applications, the more relevant technologies for this purpose, from metabolome and metabolic flux analysis in cells by Nuclear Magnetic Resonance and Mass Spectrometry technologies to positron emission tomography on patients, are analyzed. The perspectives offered by specific changes in metabolism for a new drug discovery strategy for cancer are discussed and a survey of the industrial activity already going on in the field is reported. © 2011 Elsevier Inc. All rights reserved.

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Metabolic pathways altered in cancer cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Glycolysis: changes in regulatory steps and alterations in transcriptional activity . . . . . . . . 2.2. Rerouting of glutamine utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Fatty acid metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mitochondria: central role in the regulation of cellular metabolism and of apoptosis execution . . . . . . . 3.1. Mitochondrial DNA mutations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Change in gene expression for mitochondrial proteins . . . . . . . . . . . . . . . . . . . . . 3.3. Mitochondrial fusion and fission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Post-translational regulation of mitochondrial protein activity . . . . . . . . . . . . . . . . . 3.5. Role of oncogenes and oncosuppressors in the insurgence of mitochondrial metabolic alterations Cancer as system-level disease: mitochondria at the crossroads of two cancer hallmarks . . . . . . . . 4.1. A new systems biology approach to cancer . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Enhanced cell proliferation as a system-level property . . . . . . . . . . . . . . . . . . . . . 4.3. Evasion from apoptosis as a system-level property . . . . . . . . . . . . . . . . . . . . . . Methods to analyze cancer cell metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Nuclear magnetic resonance (NMR) spectroscopy . . . . . . . . . . . . . . . . . . . . . . . 5.2. Gas chromatography (GC)– and liquid chromatography (LC)–mass spectrometry (MS) . . . . . 5.3. Examples of applications: snapshot analysis and metabolic flux analysis . . . . . . . . . . . .

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⁎ Corresponding authors. E-mail addresses: [email protected] (F. Chiaradonna), [email protected] (R.M. Moresco), [email protected] (C. Airoldi), [email protected] (D. Gaglio), [email protected] (R. Palorini), [email protected] (F. Nicotra), [email protected] (C. Messa), [email protected] (L. Alberghina). 0734-9750/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.biotechadv.2011.07.006

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In vivo imaging cell metabolism using positron emission tomography (PET) based molecular imaging techniques . . . . . 6.1. Clinical applications of PET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. Metabolism as a target for anti-cancer drugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1. Drugs interfering with cancer metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2. Targeting signal transduction pathways controlling cellular metabolism . . . . . . . . . . . . . . . . . . . . . 7.3. Recent advances in the identification of metabolic targeting compounds able to combine cytotoxic and cytostatic effects 8. Conclusions and outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction The intriguing complexity of the cancer phenotype has been shown to derive as a collective property from six basic alterations of cell physiology that taken together bring to malignant proliferation (Hanahan and Weinberg, 2000). Four of them are easily detectable in cancer cells grown in vitro, yielding a phenotype of sustained proliferation and evasion from apoptosis. The other two (cell migration/metastasis and angiogenesis) may be only observed in vivo. Genetic mutations in oncogenes as well as in oncosuppressors and environmental conditions (hypoxia or inflammation) have been shown to cooperate to generate malignant growth (Coussens and Werb, 2002; Stass and Mixson, 1997; Vaupel and Mayer, 2007). A seminal observation by O. Warburg (1956) indicated that cancer cells are very often characterized by an intense glycolytic activity that leads to a large production of lactic acid even at normal oxygen availability, the so called “Warburg effect”. A sustained glycolysis is often associated with enhanced cell proliferation and tumor aggressiveness in vivo. The contribution of glycolytic ATP to total energy supply is very variable (from 10% to 50–70%) even in fast growing tumor cells, thereby indicating that the enhancement of glycolysis is not functional only to a higher energy demand from cancer cell proliferation. Mitochondrial dysfunctions have been reported in cancer cells in vitro and in vivo (Chandra and Singh, 2010), even in conditions in which the contribution of mitochondrial ATP to energy metabolism remains substantial (Srivastava et al., 2007). Glutamine, an exclusively mitochondrially utilizable compound, is known to be an actively metabolized substrate for fast-growing cancer cells (DeBerardinis et al., 2008a,b; Medina et al., 1992). Although several steps of glutamine utilization pathway in cancer cells have been elucidated (DeBerardinis et al., 2007), it is not yet clear how glutamine and/or glucose are required to support the sustained cancer cell growth. In addition, in several cancer cells it has been observed that glucose deprivation specifically brings to cell death by apoptosis and necrosis, while glutamine deprivation arrests cancer cell proliferation (Chiaradonna et al., 2006a; Drogat et al., 2007; Gaglio et al., 2009; Simons et al., 2009; Spitz et al., 2000). Taken together, the above-presented findings clearly indicate that several differences are present in the metabolism of cancer cells as compared to that of normal ones. Since metabolism is a prerequisite for cellular growth, one may wonder whether a new strategy for developing novel, specific and more effective anticancer drugs may not be derived from a deeper understanding of cancer cell metabolism. Chemotherapeutics and several biological anticancer drugs have been designed to act on cell proliferation (by targeting signaling or cell cycle events), a property that is not peculiar to cancer but also to fast growing normal cells (Greider et al., 1983; Lee et al., 2010). If a difference in metabolism could be found that is strictly specific for cancer cells, the enzyme(s) that catalyze this step should represent convenient targets and/or biomarkers to be used in neoplastic diseases. In this review we are going to collect and discuss the wealth of information on cancer cell metabolism and to critically describe the analytical techniques (metabolomics, metabolic flux analysis, Positron Emission Tomography-PET-, Magnetic Resonance Imaging-MRI-) that

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allow to achieve a deeper understanding of the activity of the various metabolic pathways both at cell and tissue levels. In addition we will describe the most recent advances in the identification of anticancer drugs targeting cancer cell metabolism. Finally we will present and discuss a working hypothesis on the strategy to be followed to aim at the rational identification based on specificities of cancer metabolism of new effective biomarkers and/or drug-targets. 2. Metabolic pathways altered in cancer cells 2.1. Glycolysis: changes in regulatory steps and alterations in transcriptional activity Cancer cell growth is tightly correlated to an altered cellular metabolism. In fact, cancer cells rely on glucose fermentation rather than on respiratory metabolism even in the presence of oxygen. This robust cancer metabolic hallmark represents today the biochemical basis for 2-[18F]fluoro-2-deoxy-D-glucose-positron emission tomography (FDG-PET) used in clinical diagnosis (Rajendran et al., 2004). Several reports show an increase in the activity of the glycolytic enzymes such as hexokinase (HK1 and HK2), lactate dehydrogenase A (LDHA) and lactate extruding enzyme monocarboxylate transporter 4 (MCT4) in various types of tumors and cancer cell lines (Kallinowski et al., 1988; Sauer et al., 1982). In addition the up-regulation of glucose transporter isoforms 1 and 3 (GLUT1 and GLUT3), as well as of other more recently described isoforms, which represent a widespread mechanism utilized by malignant cells to sustain their proliferation, has been shown in several tumors (gastrointestinal carcinoma, breast carcinoma, squamous cell carcinoma of the head and neck, renal cell carcinoma, ovarian and gastric cancer) (Brown and Wahl, 1993; Mellanen et al., 1994; Nagase et al., 1995; Yamamoto et al., 1990; Younes et al., 1997) (Fig. 1). Interestingly, a recent work has demonstrated the enhanced expression of M2 isoform of pyruvate kinase (PKM2) in tumor cells. In particular, its up-regulation promotes glucose metabolism in cancer cells, increasing lactate production and reducing oxygen consumption as compared to cells expressing M1 isoform of pyruvate kinase (PKM1) (Christofk et al., 2008). Furthermore, another study from the same authors has demonstrated, in PKM2-expressing cells, that phosphoenolpyruvate (PEP), the pyruvate kinase substrate, provides an alternative glycolytic pathway to sustain the high rate of glycolysis in cancer cells, through its phosphate donation to phosphoglycerate mutase (PGAM) (Vander Heiden et al., 2010). An enhanced glycolytic metabolism seems to be essential for Aktmediated cell survival upon growth factor withdrawal (Rathmell et al., 2003) and other studies have shown that Akt promotes a dosedependent stimulation of glycolysis that correlates with tumor aggressiveness in vivo (Elstrom et al., 2004). Activation of Akt alone is sufficient to functionally drive glucose uptake and aerobic glycolysis, besides tumor cells bearing activated Akt uniquely undergo rapid cell death following medium replacement with one containing low glucose (Elstrom et al., 2004). The strong reduction of ATP production, resulting from inhibition of glycolysis at low glucose

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Fig. 1. Metabolic pathways altered in cancer cells. Schematic representation of principal alterations identified in glycolysis, glutamine utilization and fatty acid metabolism of cancer cells.

availability, has been shown to be responsible for cell death. This could be circumvented by activators of fatty acid oxidation, which produce ATP using an alternative pathway in mitochondria (Buzzai et al., 2005). Cells carrying active oncogenes (e.g. Ras, Her2, Akt) or even primary mouse cells lacking single tumor suppressors (e.g. TSC1/2, LKB1, p53) preferentially undergo apoptosis under low glucose availability in a quite striking way (Chiaradonna et al., 2006b; Corradetti et al., 2004; Inoki et al., 2003; Jones et al., 2005; Kroemer and Pouyssegur, 2008; Shaw et al., 2004; Vander Heiden et al., 2009). In addition, in non-invasive radial-growth melanoma, the overexpression of Akt stimulates glycolysis, by inducing the expression of glycolytic markers, yielding the transformation of tumor into an invasive phenotype (Govindarajan et al., 2007) (Fig. 1). Another signaling pathway involved in the modified glucose metabolism characterizing cancer cells is that of phosphoinositide 3-kinase (PI3K) (DeBerardinis et al., 2008a,b). Indeed, PI3K signaling through Akt regulates glucose transporter (particularly that of glutamine) expression, enhances hexokinase affinity for glucose, stimulates phosphofructokinase activity (DeBerardinis et al., 2008a,b) and in general renders cells dependent on high levels of glucose flux (Buzzai et al., 2005). Finally, PTEN, a negative regulator of PI3K pathway, has been reported to be inactivated in some type of cancer (Engelman et al., 2008) and genetic alterations of PI3K or PTEN lead to chronic activation of Akt with a consequent increase of mTOR activity. The activation of mTOR increases amino acid transporters expression and promotes protein translation, thereby stimulating in a coordinated way protein synthesis in proliferating tumor cells (Vander Heiden et al., 2009) (Fig. 1). Several studies have also demonstrated that the enhanced glycolytic activity is a tumor cell metabolic strategy to survive at low oxygen availability (hypoxia). Indeed, it has been observed that over-expression of the hypoxia-inducible factors-1 (HIF-1) positively regulates the expression of glucose transporters and glycolytic enzymes genes (Brahimi-Horn et al., 2007; Semenza, 2007). In particular, it has been shown that under hypoxic conditions, HIF-1α

induces the expression of pyruvate dehydrogenase kinase 1 (PDK1) (Kim et al., 2006; Papandreou et al., 2006). Since PDK1 inhibits pyruvate dehydrogenase (PDH), an enzyme that catalyzes the conversion of pyruvate to acetyl-CoA, its expression reduces pyruvate entry into the TCA cycle, with the consequent deregulation of mitochondrial oxygen consumption (Dang et al., 2008). Through these regulatory mechanisms, HIF-1α is able to mediate aerobic glycolysis and to contribute to carcinogenesis (Wenger et al., 2010) (Fig. 1). Interestingly, a significant overlap of metabolic gene set between transformed cells expressing HIF-1α and HIF-2α and colon cancer cells expressing oncogenic K-Ras has been recently demonstrated (Chun et al., 2010). Given that Ras is frequently mutated in human cancer (Downward, 2003) and that it may promote transcription of several metabolic genes (Chiaradonna et al., 2006b), this finding is extremely relevant because it identifies an overlapping between the fundamental role of metabolic alterations for transformation induced by K-Ras and the maintenance of transformation, even in the presence of an environmental stress, obtained through HIF-1 activation. The switch to glycolysis of cancer cells is also promoted by inactivation of tumor suppressor protein p53. In fact, an inhibitor of fructose bisphosphatase 2 (an enzymatic activity that competes with glycolysis) named TIGAR (TP53-induced glycolysis and apoptotic regulator) as well as the mitochondrial protein SCO2 (able to promote mitochondrial respiration) are induced by p53, favoring mitochondrial respiration more than glycolysis (Bensaad et al., 2006) (Fig. 1). On the other hand, over-expression of a dominant negative mutant p53, specifically identified in some tumors, is able to induce HK2 expression and therefore the enhancement of glycolysis due to increased glucose uptake (Mathupala et al., 1997; Smith et al., 2006). Therefore, despite the fact that glycolysis represents an important hallmark of cancer cells, many issues still remain to be addressed about metabolic transformation which may involve several metabolic pathways and may be promoted by a great number of oncogenes and tumor suppressors.

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2.2. Rerouting of glutamine utilization Another metabolic adaptation of cancer cells, that has recently been described, is their tendency to increased glutamine consumption (DeBerardinis and Cheng, 2009; Wise and Thompson, 2010). Glutamine has traditionally been viewed as a nonessential amino acid whose primary functions in periphery are to store nitrogen in the muscle and to traffic it between organs. Although it contributes only 4% of the amino acid in muscle protein, glutamine accounts for more than 20% of the free amino acid pool in plasma and more than 40% in muscle (Bergstrom et al., 1974; Kuhn et al., 1999). Mammals can synthesize glutamine in the majority of tissues, but during periods of rapid growth or illness, cellular demand for glutamine outstrips its supply and glutamine becomes essential (hence its designation as a “conditionally” essential amino acid). Proliferating cells show an intense use of glutamine, reflecting its importance both as a nutrient and precursor of other metabolic processes (Newsholme et al., 2003a, b; Young and Ajami, 2001). The metabolic fates of glutamine can roughly be divided into reactions that use glutamine for its γ-nitrogen (i.e., nucleotide and hexosamine synthesis) and those that use either the α-nitrogen or the carbon skeleton (i.e., TCA cycle) (Fig. 1). The latter reactions use glutamate, but not glutamine, as substrate. In fact, glutamine is able to function as source of metabolic intermediates into TCA cycle, precursor for the biosynthesis of nucleic acids, amino acids and glutathione, but it must be primarily converted to glutamate by glutaminase (GLS). Several studies have shown an enhanced GLS activity associated with high growth rates of xenografts tumors (Knox et al., 1969; Linder-Horowitz et al., 1969). On the other hand, similar studies have also shown that inhibition of GLS activity causes a decrease growth rate of tumor cells and xenografts tumors (Gao et al., 2009; Lobo et al., 2000). More recent studies have shown that impairment of GLS activity inhibits growth of transformed cancer cells expressing both Kras and myc oncogenes (Seltzer et al., 2010; Wang et al., 2010a). In addition, it has been recently reported that in Myc-over-expressing cells, GLS protein levels are significantly up-regulated as consequence of miR-23a/b repression. In particular this microRNA is able to repress the translation of GLS through the binding to its un-translated region (UTR), hypothesizing that Myc regulates GLS through a posttranscriptional mechanism (Gao et al., 2009). Altogether these results show that, although not all metabolic fates of glutamine require GLS activity, this enzyme is essential for the metabolic phenotype of many tumors (Fig. 1). Other studies have also shown that oxidation of carbon backbone of glutamine in mitochondria represents a primary source of energy for proliferating cells, including lymphocytes, enterocytes, fibroblasts and some cancer cell lines (DeBerardinis et al., 2008a,b; Miller, 1999; Reitzer et al., 1979). This step requires conversion of glutamine to αketoglutarate (AKG), through mitochondrial GLS activity followed by conversion of glutamate to AKG by either transaminases (TA) or glutamate dehydrogenase (GDH). In particular, in transformed cells it has been shown, by using aspartate aminotransferase inhibitor amino-oxyacetic acid (AOA), that the major route through which glutamine-derived carbon enters the TCA cycle is through transamination reaction (Moreadith and Lehninger, 1984). Indeed, inhibition of transamination by using AOA, has a cytostatic effect on the growth of a breast cancer cell line in a mouse xenograft model without any obvious dose-limiting toxicity (Thornburg et al., 2008). In tumor cells glutamine can serve as an alternative substrate for the TCA cycle and ATP production during aerobic glycolysis. Complete oxidation of glutamine carbons involves exit from the TCA cycle as malate, conversion to pyruvate and then acetyl-Coenzyme A (AcCoA), and finally reentry into the cycle. In this regard recent studies conducted in a glioblastoma cell line have shown that glutamine contributes to the majority of the cellular oxalacetate (OAA) pool (DeBerardinis et al., 2007). OAA is an essential substrate because its

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condensation with AcCoA into mitochondria produces citrate. Citrate, upon transport into the cytosol by a citrate shuttle, is utilized by action of ATP citrate lyase, producing OAA and above all the AcCoA that is an essential substrate for synthesis of cholesterol and fatty acids (Hatzivassiliou et al., 2005) and for the modification of chromatin structure (Wellen et al., 2009). Supplying cancer cells with a source of OAA, glutamine provides anaplerosis, the refilling of the mitochondrial carbon pool. Replenishment of the mitochondrial carbon pool by glutamine provides mitochondria with precursors for the maintenance of mitochondrial membrane potential, and for the synthesis of nucleotides, proteins and lipids (Wise and Thompson, 2010). Glutamine metabolism also provides precursors for the synthesis of glutathione (GSH), the major thiol-containing endogenous antioxidant, which serves as a redox buffer against various sources of oxidative stress. In tumors, maintaining a supply of GSH is critical for cell survival because it allows cells to resist the oxidative stress associated with rapid metabolism, DNA-damaging agents, inflammation and other sources (Estrela et al., 2006). Furthermore, this amino acid is a required nitrogen donor for the de novo synthesis of both purines and pyrimidines and therefore is essential for the net production of nucleotides during cell proliferation. Such a role of glutamine nitrogen in nucleotide biosynthesis may explain why some transformed cells show delayed transit through S phase in low-glutamine availability (Gaglio et al., 2009). However, in proliferating cells, the glutamine utilization rate exceeds nucleic acid synthesis by more than an order of magnitude, and thus nitrogen donation to nucleotides accounts only for a small fraction of total glutamine consumption (Ardawi et al., 1989). Altogether these observations support the higher rate of glutamine transport in cancer cells as compared to normal cells (Kaelin and Thompson, 2010) and underline the fundamental role of both glucose and glutamine in cancer metabolism. 2.3. Fatty acid metabolism Hence, the transformation process is accompanied by reprogramming of metabolic pathways, including glycolysis and glutaminedependent anabolism (DeBerardinis et al., 2008a,b; Gaglio et al., 2009; Menendez and Lupu, 2007). Besides, also fatty acid (FA) synthesis occurs at very high rates in tumor cells, as shown more than half a century ago (Medes et al., 1953). Notably, several studies have shown that in cancer cells FA derive mainly from de novo synthesis through glucose (Baron et al., 2004; Mashima et al., 2009) rather than from diet. The increased lipogenesis in cancer is reflected in the over expression and hyperactivity of lipogenic enzymes such as ATP citrate lyase (ACL), acetyl-CoA carboxylase (ACC), or fatty acid synthase (FAS) (Kuhajda, 2000). ACC carboxylates AcCoA to form malonyl-CoA, which is further converted to long-chain FA by FAS (Fig. 1). In this regard, FAS is expressed at low levels in normal cells and tissues but it is highly expressed in cancer counterparts. Several findings have proved a link between the level of expression of FAS and cancer. In breast cancer patients high levels of this enzyme are associated to a poor prognosis. In addition, in several other types of tumors, it has been observed that the blockade of FAS activity, either alone or in combination with chemotherapy or monoclonal antibodies, inhibits cell proliferation and viability and decreases tumor growth in vivo (Kuhajda et al., 2000; Menendez and Lupu, 2007; Zhou et al., 2007). In general FAS activity supports cancer growth by increasing the building blocks for cell membranes and for lipids containing molecules involved in cell signaling (Buzzai et al., 2005; Buzzai et al., 2007; Liu, 2006). In this regard, important examples of lipid messengers contributing to cancer development are phosphatidylinositol-3,4,5-trisphosphate [PI(3,4,5)P3], which is formed by the action of phosphatidylinositol-3-kinase (PI3K) and activates protein kinase B/Akt to promote cell proliferation and survival (Yuan and

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Cantley, 2008; Zunder et al., 2008), lysophosphatidic acid (LPA), which signals through a family of G protein-coupled receptors to stimulate cancer aggressiveness (Mills and Moolenaar, 2003; Ren et al., 2006) and prostaglandins formed by cyclooxygenases, which support migration and tumor–host interactions (Gupta et al., 2007; Marnett, 1992). Interestingly, enhanced lipogenesis in cancer cells has also been proposed to be required to balance the redox potential via the utilization of NADPH (Porstmann et al., 2009). Finally, posttranslational modification with lipid moieties is also a key process regulating transport and function of various cellular and secreted oncoproteins. Indeed, structure modification of a particular set of oncogenes, such as Ras, Src, or Wnt that are facilitated by FA synthesis participate in the activation of their oncogenic pathways (Nadolski and Linder, 2007; Xue et al., 2004). Worthy of note is evidence that Akt is also important in activating de novo FA synthesis. Activation of Akt has been shown to induce gene expression of numerous enzymes involved either in cholesterol or FA biosynthesis, including, but not limited to, the 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) synthase, HMG-CoA reductase, ACL, FAS and stearoyl-CoA desaturase (Porstmann et al., 2005). On the other hand, cancer cells need a pathway able to provide free fatty acids (FFA) from lipid stores to sustain the previous described cancer functions. Recently it has been shown that the enzyme Monoacylglycerol Lipase (MAGL) and its FFA products are elevated in aggressive human cancer cell lines originating from different tissues. Moreover, the authors showed that increased expression of MAGL confers high metastatic and tumorigenic activity to cancer cells. In fact MAGL inhibition impairs not only in vitro migration but also in vivo tumor growth and both effects are rescued by exogenous sources of FFAs (Nomura et al., 2010) (Fig. 1). Hence, also FA synthesis may be addressed with the aim to develop a new strategy for anticancer drugs. 3. Mitochondria: central role in the regulation of cellular metabolism and of apoptosis execution Mitochondria are known both as producers of cellular ATP and as central regulators of apoptosis (Alirol and Martinou, 2006). Mitochondria are known to play a central role in cellular metabolism, notably in ATP production by oxidative phosphorylation (OXPHOS). The primary metabolic function of mitochondria is OXPHOS, an energy-generating process that couples the oxidation of respiratory substrates with ATP production. Besides ATP synthesis, mitochondria are involved in several other key metabolic processes such as oxidative decarboxylation of pyruvate, TCA cycle and FA oxidation. Mitochondria are the site of important biosynthetic reactions, such as amino acids and heme biosynthesis and part of gluconeogenesis (Hanson and Reshef, 1997; Lin et al., 2009; Nilsson et al., 2009). Mitochondria take part in intracellular homeostasis of inorganic ions such as calcium and phosphate, as well as in the balance of NAD+/NADH, since part of NAD+, produced by NADH oxidation in the respiratory chain, goes back to the cytosol where it is required for glycolysis (Gellerich et al., 2010). Besides their central role in metabolic activity, more recently mitochondria have been shown to have a central role also in the cascade of events that leads to programmed cell death. In several apoptotic models, mitochondria represent a central checkpoint of this process by integrating various signals coming from endogenous factors (ions, metabolites, second messengers), from endogenous signaling proteins (kinases and phosphatases) as well as from exogenous factors (nutrients, oxygen). The integration process of survival and apoptotic cues will decide the cell's fate. Cellular energy metabolism and the core apoptotic pathway (with mitochondria as main actor) are the two major determinants of cellular survival. Growth/survival factors such as IGF-1 or IL-3 stimulate glucose transport and the translocation of hexokinase to mitochondria, inducing glycolysis as well as inhibiting apoptosis (Gottlob et al., 2001; Vander Heiden et al., 2001). Withdrawal of growth/ survival factors leads to metabolic decline including a decreased

glycolytic rate, lowered O2 consumption, decreased ATP levels, and reduced protein production as well as triggering apoptotic pathway. The profound interconnection between metabolism and apoptosis and the central role of mitochondria in both processes has brought an explosion of interest in connecting such pathways to the pathophysiology of cancer. Thus, the biology of neoplasia has expanded to incorporate not only lesions that cause deregulated growth/proliferation, but also those that lead to inefficient cell death, both linked to metabolic alterations. Warburg, the discoverer of the hyperglycolytic metabolism of cancer cells, suggested that tumor originated from cells with persistent defects in the mitochondrial respiratory system (Warburg, 1956). Although for years this idea was considered erroneous, in the last decades, several groups have investigated the role of mitochondria in the onset of Warburg effect. In fact as reviewed by Alirol and Martinou (2006), different groups have reported that cancer cell proliferation as well as tumor aggressiveness correlates with a lowmitochondrial respiratory chain activity (Chiaradonna et al., 2006a), and that the enhancement of OXPHOS activity appears to reduce tumor growth (Hervouet et al., 2005; Schulz et al., 2006). Moreover, mutations in mitochondrial DNA (mtDNA) and changes in total number, protein expression and regulation, morphology and function of mitochondria have been identified in various types of tumors and cancer cell lines (Baracca et al., 2010; Grandemange et al., 2009; Lee and Wei, 2009; Lu et al., 2009). These changes of mitochondrial physiology not only impinge on their metabolic role but also on their fundamental function in controlling the apoptotic process, underlining a prominent role in the onset of transformed phenotype. 3.1. Mitochondrial DNA mutations Among mammalian organelles, mitochondria are unique because they possess their own genome, mtDNA, a 16.6 kilobase (kb) circular molecule. MtDNA encodes only 37 genes while nuclear DNA encodes about 900 mitochondrial products, including all of the enzyme required for mtDNA replication and maintenance, consequently, the existence of the mitochondrial genome is entirely dependent on the nuclear genome (Chandra and Singh, 2010; Modica-Napolitano and Singh, 2004). Since mtDNA is localized near to Reactive Oxygen Species (ROS) production sites, namely the respiratory chain, it can be exposed to high mutations rates (Higuchi, 2007; Richter et al., 1988). Such a deleterious effect is further worsened for the less efficient DNA protection and repair machinery present in mitochondria as compared to the nucleus (Croteau and Bohr, 1997; Fliss et al., 2000; Richter et al., 1988). Moreover, the rate of mtDNA mutation can be greatly accelerated by mutations impinging on respiratory chain activity (Fig. 2) that eventually causes a chronic increase of mitochondrial ROS levels (Chomyn and Attardi, 2003; Indo et al., 2007). These circumstances have been proposed to be involved both in initiating and promoting cancer development (Lee and Wei, 2009; Penta et al., 2001). Mitochondria contain hundreds mtDNA copies and the achievement of a threshold of mutant mtDNA seems necessary to cause the appearance of cellular dysfunction linked to defective mitochondria (Lu et al., 2009). There are some evidence that the majority of somatic mutations in mtDNA in cancer cells are homoplasmic (Fliss et al., 2000), nevertheless more recently it has been observed that heteroplasmy confers an advantage for tumorigenesis because homoplasmy could induce ATP reduction and/or apoptosis, inhibiting cancer cell proliferation (Fig. 2) (Shen et al., 2010). High levels of mtDNA alterations, including point mutations, deletions, insertions, tandem duplications and copy number changes, have been found in many tumors and cancer cells. Recently, two different authors (Lee and Wei, 2009; Lu et al., 2009) have summarized the mtDNA mutations identified by several investigators in the past decade. They have found that the major part of the identified mutations is located in non-coding displacement loop (D-loop) region. Mutations in this region will decrease the polymerase γ

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Fig. 2. Mitochondrial dysfunction in cancer. Schematic representation of the main mechanisms leading to mitochondrial dysfunctions in cancer cells.

ability to replicate and repair mtDNA inducing other mutations into mtDNA. However, although numerically smaller as well, a significant amount of mutations in mtDNA coding regions have also been identified. In particular several mutations for gene encoding Complex I subunits more than for other respiratory complexes have been reported. In addition some specific cancer mutations in tRNA and rRNA genes have also been found. Mutations in coding genes and alterations in the copy number of mtDNA can cause dysfunctions in the respiratory chain, leading to impairment of the OXPHOS function. Hence, mutations in mtDNA are taken to represent tumoral markers, for instance, mutations in Complex I subunits are considered novel markers of thyroid oncocytic tumors (Gasparre et al., 2007). A great help in understanding the role of mtDNA mutations in cancer development has been given by the cybrid (cytoplasmic hybrid) technique. By using a complementation approach, this technique has allowed the identification and evaluation of several mtDNA alterations and their role both in physiological and pathological processes (King and Attardi, 1989). In this regard it has been observed that most human carcinomas are characterized by the down-regulation or point mutations of the mitochondrial H+-ATP synthase (β-F1-ATPase) catalytic subunits. In particular it has been shown that mutations in mtDNA gene encoding for the subunit 6 of ATP synthase (ATPase 6; MATP6), analyzed by cybrid, are associated to the appearance of large tumors in nude mice as compared to that generated by wild type gene, and to resistance to apoptosis (Ohta, 2006; Petros et al., 2005; Shidara et al., 2005). The association between defective OXPHOS in thyroid oncocytic carcinoma and the combined Complex I/III deficiency has been shown by the identification of mtDNA mutations in genes encoding a Complex I subunit and cytochrome b (Bonora et al., 2006). More recently, point mutations of ND6 gene, encoding for a Complex I subunit, have been associated to highly metastatic potential of cancer cells (Ishikawa et al., 2008). Indeed all these mutations produce a deficiency in Complex I activity, an overproduction of ROS that ultimately induce up-regulation of nuclear genes (as MCL-1, HIF-1α and VEGF) closely linked to

enhancement and alteration of cellular metabolism as well as to metastatic potential. 3.2. Change in gene expression for mitochondrial proteins There is a certain amount of evidence for alteration of transcription rates for mitochondrial genome as well as for nuclear genes encoding mitochondrial proteins in cancer cells (Boultwood et al., 1996; Faure Vigny et al., 1996; Heddi et al., 1996; Sharp et al., 1992; Yamamoto et al., 1989) (Fig. 2). The down-regulation of the catalytic subunit of the mitochondrial H +-ATP synthase (β-F1-ATPase), previous cited, is considered a hallmark of many human carcinoma. In fact, it has been found in liver, kidney, colon, breast, stomach, squamous esophagus and lung human carcinoma (Cuezva et al., 2002; Isidoro et al., 2004). In particular, cellular expression level of the β-F1-ATPase protein has been shown to be inversely correlated with the rates of aerobic glycolysis in cancer cells (Lopez-Rios et al., 2007). A similar correlation between mitochondrial gene expression alterations and cellular metabolism has been also shown for Complex IV (COX) subunits, whose alterations have been associated with tumor-altered metabolism (Krieg et al., 2004). Other authors have shown a significant role of the alteration of Complex I subunits expression in cancer. In fact the expression of GRIM-19 and NDUFS3, both subunits of mitochondrial respiratory chain Complex I, is lost or severely depressed in a number of primary renal cell carcinomas and in some urinogenital tumors (Alchanati et al., 2006; He and Cao, 2010). Alterations of the expression of mitochondrial encoding genes, especially those encoding for Complex I, have been also shown in basal cell carcinoma (Mamelak et al., 2005), in 19 oncocytic thyroid tumors (Zimmermann et al., 2009) and in mouse fibroblasts expressing an oncogenic form of K-ras (Baracca et al., 2010). The result of the down-regulation of mitochondrial encoding proteins is a general reduction of OXPHOS activity and in particular of Complex I content and activity. On the other hand defective mitochondrial

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activity is often linked to alterations of cellular metabolism, indicating that defect of mitochondrial OXPHOS has a relevant role for the switch to aerobic glycolysis in cancer. 3.3. Mitochondrial fusion and fission Mitochondria are continuously submitted to morphological rearrangements as consequence of both fission and fusion processes (Fig. 2) (Westermann, 2010). Although several authors (Sauvanet et al., 2010) have reported complex relations among OXPHOS activity, mitochondrial morphology and cellular energy metabolism, the basis of such functional connections still remain elusive. Several studies have identified either the ability of OXPHOS complexes to influence the mitochondrial morphology or the ability of mitochondrial dynamics to regulate OXPHOS complexes activity (Grandemange et al., 2009). In this regard it has been shown that the lack of mitochondrial fusion (caused by null mutations in Mfn1 or Mfn2 or by disruption of OPA1, the main proteins involved in the fusion process) causes poor cell growth, widespread heterogeneity of mitochondrial membrane potential and decreased cellular respiration (Chen et al., 2005). In addition, it has been shown that patients with defects in Complex I, diversely from healthy subjects, display fragmented mitochondria and enhanced ROS levels (Koopman et al., 2007). Such bidirectional association between mitochondrial network organization and bioenergetics has been shown in several normal and cancer cell models. Besides, Hela cells in which DRP1 protein, the main regulator of mitochondrial fission, has been targeted by siRNA, show a strong reduction of mitochondrial activity (Benard et al., 2007). On the other hand, the inhibition of Complex I activity in MRC5 fibroblasts by using rotenone alters mitochondrial network organization, leading to vesicularization of the tubules and the appearance of numerous donut-like interdigitations (Benard et al., 2007). These findings suggest a possible role also of mitochondrial morphology alterations in cancer cell energy metabolism. Besides, K-ras transformed mouse fibroblasts upon glucose depletion have a reduced ATP content in association with an inability to modify mitochondria morphology. On the contrary, mitochondria of normal murine fibroblasts NIH3T3 upon glucose depletion are able to form interconnected and filamentous mitochondrial structures in relation to a higher content of ATP (Chiaradonna et al., 2006a). Similar results have been obtained also in rat normal gastric cell line (RGM-1) as compared to human gastric cancer cell line (AGS). AGS cells presented smaller total numbers and cross-sectional sizes of mitochondria that appeared to consume less oxygen (Kim et al., 2007). Hence mitochondrial morphology, being linked to the metabolic changes and characteristic of several cancer cells, has been shown to play an active role also in tumorigenesis. In fact mitochondrial dynamics alterations may participate in tumorigenesis by contributing to the accumulation of damaged mitochondria (Grandemange et al., 2009) because the impairment of fusion and fission processes contributes to mtDNA instability (Chen et al., 2010) and to the accumulation of dysfunctional mitochondria (Twig et al., 2008). In conclusion, understanding of the mechanisms involved in mitochondrial fusion and fission and how these processes may be integrated in cellular metabolism will facilitate our understanding of metabolic alterations found in cancer cells. 3.4. Post-translational regulation of mitochondrial protein activity Mitochondrial proteins are subjected to post-translational regulation. In particular, the role of phosphorylation as mitochondrial regulatory mechanism in response to different stimuli has been reported (Hopper et al., 2006; Thomson, 2002). cAMP-PKA (cAMPdependent protein kinase) pathway has an important role in this regulation (Fig. 2), since evidence has been accumulated that PKA regulates biogenesis (De Rasmo et al., 2009, 2010), import (De Rasmo et al., 2008) and activity of respiratory chain Complex I (Papa et al., 2008). In addition, also the activity of the respiratory chain Complex

IV is regulated by mitochondrial PKA in response to mt-SAC (mitochondrial soluble Adenylate Cyclase) stimulation (Acin-Perez et al., 2009). The PKA pathway has been shown to be an oncosuppressive pathway (Dumaz and Marais, 2005), which is able to inhibit cell proliferation by interfering with Ras pathway (Gerits et al., 2008). In addition the PKA pathway has been associated with several aspects of cellular metabolism (Carlucci et al., 2008; Fang et al., 2000), thus it is possible to suppose the notion that the downregulation of PKA pathway could cause mitochondrial dysfunctions, representing another possible mechanism driving the metabolic switch of cancer cells. In fact, unpublished observations from one of our laboratories, demonstrate that exogenous stimulation of cAMPPKA pathway is able to restore mitochondrial functionality in transformed cells expressing an oncogenic K-Ras. Also mitochondrial tyrosine phosphorylation is emerging as an important mechanism in regulating mitochondrial function. Indeed, various members of Src family, for example, Fgr, Fyn, Lyn and c-Src, are constitutively present in the internal structure of mitochondria as well as Csk, a key enzyme in the regulation of the activity of this family. By means of different approaches, the existence of a signal transduction pathway from plasma membrane to mitochondria, resulting in increasing Srcdependent mitochondrial tyrosine phosphorylation, has been reported. The activation of Src kinases at mitochondrial level is associated with the proliferative status where several mitochondrial proteins are specifically tyrosine-phosphorylated (Tibaldi et al., 2008). Since Src family members have been recognized as important oncogenes (Kim et al., 2009; Parsons and Parsons, 2004), their role in cytoplasmic signaling as well as in regulation of mitochondrial activity may represent a further mechanism of controlling mitochondrial activity. Also components of the fission and fusion machinery have been shown to be regulated at the post-translational level through phosphorylation as well as ubiquitination and sumoylation (Westermann, 2010). Phosphorylation has been reported to control Drp1's activity. Cdk1/cyclin B protein kinase can phosphorylate and activate Drp1 thereby promoting a transient mitochondrial fission at the onset of mitosis (Taguchi et al., 2007). In contrast, phosphorylation of Drp1 by PKA inactivates the GTPase activity of Drp1, resulting in mitochondrial fusion (Chang and Blackstone, 2007). This phosphorylation can be reversed by the serine/threonine phosphatase calcineurin leading to mitochondrial fission (Cribbs and Strack, 2007). In conclusion, several post-translational modifications allow mitochondria to execute rapid and reversible morphological changes and to adapt to continuously changing environmental conditions, so their derangement in cancer cells may contribute to their mitochondria dysfunction. 3.5. Role of oncogenes and oncosuppressors in the insurgence of mitochondrial metabolic alterations Several authors have demonstrated the involvement of oncogenes in the Warburg effect (Bartrons and Caro, 2007; Dang and Semenza, 1999; Levine and Puzio-Kuter, 2010). In particular, there are many findings showing that the expression of oncogenes can favor the upregulation of glycolysis and of glutamine utilization (Bartrons and Caro, 2007; DeBerardinis et al., 2008a,b). There are several reports that highlight the role of oncogenes in OXPHOS dysfunction (Fig. 2). In particular, oncogenic Ras expression appears to be involved in the onset of mitochondrial dysfunctions. A recent study (Gough et al., 2009) shows that Ras-dependent oncogenic transformation is supported by the mitochondrial localization of nuclear STAT3 protein. In particular, mitochondrial STAT3 localization increases respiratory chain activity, specifically of Complexes II and V, while it favors paradoxical shift to a more fermentative energy production through glycolysis. Differently, Ramanathan et al. (2005) observed that the introduction of oncogenic H-Ras in human fibroblasts, already transformed by hTERT and Large T Antigen and Small T Antigen, allowed these cells to assume a fully transformed phenotype.

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Moreover, these cells were more susceptible to treatment with 2-DG (2-deoxyglucose, which inhibits the glycolytic enzyme hexokinase) and rather less sensitive to oligomycin (ATP synthase inhibitor). The introduction of oncogenic H-Ras also caused a decrease in mitochondrial biogenesis, lowering the levels of expression of genes encoding for mitochondrial components, such as CYCS (encoding for cytochrome c) or ATP5E (encoding the ε subunit of ATP synthase). These data are consistent with earlier studies on rat embryo cells, in which H-Ras stimulated glycolysis and inhibited oxygen consumption (Biaglow et al., 1997). More recently, it has been shown that normal human cells expressing oncogenic Ras increased ROS production and accumulated dysfunctional mitochondria around the nucleus in parallel with their entrance in senescence (Moiseeva et al., 2009). Accordingly, various studies on murine fibroblasts NIH3T3 showed that oncogenic Ras could impair mitochondrial respiration. In fact, functional OXPHOS defects are induced by oncogenic H-Ras Q61L transformation, even though mitochondrial contents or mass are not reduced in the transformed cells (Yang et al., 2010). In addition, also murine fibroblasts expressing oncogenic K-Ras show alterations in the expression of genes encoding mitochondrial proteins, a reduction of ATP levels and an inability to interconnect their mitochondria. Notably, this phenotype is reverted inhibiting oncogenic Ras activity (Chiaradonna et al., 2006a). An important role in mitochondrial biogenesis is assigned also to the protoncogene Myc. Evidence has been accumulated regarding its ability to activate several genes involved in mitochondrial structure and function (Li et al., 2005), besides Myc stimulates nuclearly encoded mitochondrial genes and mitochondrial biogenesis (Kim et al., 2008). However, Myc oncogene is able to stimulate glycolytic genes as well, leading to Warburg effect as observed for other oncogenes (ras, Akt). Therefore Myc, promoting glycolysis and enhancing the ability of cells to utilize non-glucose substrates to fuel mitochondria, gives to transformed cells a strong ability to cope with different environmental alterations such as hypoxia and nutrient deprivation (Fan et al., 2010). Other recent studies have shown that as well as oncogenes which deregulate cellular metabolism to accommodate the increased and different needs of fast proliferating cancer cells, also the “master” tumor suppressor p53 has a role in the regulation of cellular metabolism (Fig. 2). Indeed p53 protein, one of the most frequently mutated proteins in cancer, is involved in the Warburg effect (Levine and Puzio-Kuter, 2010) by regulating both glycolysis and mitochondrial respiration and hence modulating the balance between the two pathways. As already mentioned, several mechanisms through which p53 is able to reduce glycolytic flux and stimulate respiration have been identified. Among these, extremely important is its ability to regulate transcription of the SCO2 gene, a critical regulator of COX activity (Matoba et al., 2006). In fact, after disruption of SCO2 gene, wild-type p53 human cancer cells presented the glycolytic metabolism characterizing p53-deficient cells (Matoba et al., 2006). In addition, p53 protein can inhibit the expression of the glucose transporters GLUT1 and GLUT4 (Schwartzenberg-Bar-Yoseph et al., 2004), decrease the levels of phosphoglycerate mutase (PGM) (Kondoh et al., 2005) and increase the expression of TIGAR (TP53-induced glycolysis and apoptosis regulator) (Bensaad et al., 2006). The effect of each of these events is to impede flux through various steps of the glycolytic pathway.

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together define the phenotype of most human malignancies. They include unlimited proliferation potential, self-sufficiency in growth signals, resistance to anti-proliferative and apoptotic cues, sustained angiogenesis and ultimately ability to metastasize to distal organs (Hanahan and Weinberg, 2000). Therefore, due to the fact that cancer involves, at the same time, a large number of frequently deregulated molecular processes, and probably acquiring of a number of new functionalities, it will make it difficult, if not impossible, to predict the altered outcomes by a simple qualitative analysis as has been done so far by molecular biology and genome approaches (Malumbres and Barbacid, 2001). 4.1. A new systems biology approach to cancer Other authors have already suggested applying a systems biology approach to cancer by integrating molecular and cell biological evidence with detailed wiring of molecular networks, whose dynamics could be analyzed by mathematical models and simulations at the cellular and organismal levels (Hartwell et al., 1999). In this report we would like to present a different systems biology strategy. From our point of view the very essence of systems biology is not given by its methodological integration of molecular (often genome-wide) analysis with mathematical modeling and simulation, but more so by the recognition that complex biological functions are not linearly dependent on the activity of one or more gene products, but rather derive as emergent properties, endowed with robustness, from the concerted activity of complex dynamically interacting molecular networks (Alberghina et al., 2009). Therefore we start with the assumption that the two functional features, which may be detected both in vivo and in vitro, “enhanced cell proliferation” and “ability to evade apoptosis”, are system-level properties (endowed with robustness) of a large biochemical network. From the integrative analysis of cell metabolism discussed above in this paper, we have shown that central metabolism (glycolysis, TCA cycle, FA synthesis and glutamine metabolism) is profoundly affected in cancer cells and appears to underline the two system-level properties of cancer cells that are amenable to be analyzed in vitro under controlled, reproducible conditions. Besides, mitochondria have been shown to have a very relevant role in the change of metabolic activities of cancer cells. The strategy that we would like to develop can be summarized as follows: to fully describe the metabolic pathways that differentiate cancer from normal cells we need to link these changes to the systemlevel properties “enhanced cell proliferation” and “evasion from apoptosis”. After reaching a satisfactory understanding of the integration of molecular pathways that yields the cancer system-level properties, one would develop perturbations assays (for instance nutrient starvation or treatment with specific inhibitors) that selectively bring to death cancer cells or, in a less interesting way from a therapeutic point of view, that selectively arrest growth of cancer cells. In order to reach these ambitious goals, we have first to better clarify the links of central metabolism with “enhanced cell proliferation” and “evasion from apoptosis”. 4.2. Enhanced cell proliferation as a system-level property

4. Cancer as system-level disease: mitochondria at the crossroads of two cancer hallmarks Despite the large number of molecular and morphological differences between normal and cancer cells, the aberrant growth of tumor cells is basically due to disruption of mechanisms that regulate both cell proliferation and cell survival (Alberghina et al., 2004; Malumbres and Barbacid, 2001). Indeed, as described by Hanahan and Weinberg (2000), while there are many distinct types of cancer, it is possible to define six essential functional alterations to normal cell physiology, which

First of all we have to recall which are the features that characterize enhanced cell growth of cancer cells and tissues as compared to normal counterparts. Both in vivo and in vitro, normal cells, reaching the correct dimension of the organ to which they belong or the critical cell density – confluence – respectively, stop growing upon the action of a well known process namely contact inhibition (Eagle and Levine, 1967; Holley and Kiernan, 1968). In similar conditions, transformed cells, characterized by the loss of the contact inhibition, continue to proliferate and hence show an “enhanced cell growth” (Abercrombie, 1979; Fagotto and

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Gumbiner, 1996). Therefore, cancer cells rather than an accelerated proliferation show an ability to avoid contact inhibition, phenomenon that ordinarily restricts growth of normal cells. In fact when grown in vitro both normal and transformed cells show the same initial growth rate when nutrients and factors availability is not limiting (Chiaradonna et al., 2006b). Therefore the understanding of the molecular mechanisms of contact inhibition and its loss during tumor development is very relevant. Several authors have tried to enlighten the molecular mechanisms of contact inhibition, also by high-throughput approaches, such as gene expression profiling. By using contact inhibited NIH3T3 mouse fibroblasts as compared to sparsely growing NIH3T3 and a gene expression profile approach, a recent report (Kuppers et al., 2010) has identified 110 transcripts differentially expressed in contact-inhibited cultures. Among them an important role, as revealed by Gene Ontology analysis, has been attributed to cellular metabolism. In particular of the 110 genes, 21 are related to general cellular metabolic processes (23.1%) among which 12 are strictly associated to cellular metabolism as e.g. glutathione and nucleotide synthesis. In order to analyze genes regulated by an oncogenic form of K-ras, our group has recently performed a similar transcriptional analysis in NIH3T3 cells and in NIH3T3 cells transformed by expression of an oncogenic K-Ras. Since NIH3T3 cells grown in 25 mM glucose reach confluence at 72 h (contact inhibition), while transformed cells grown in 25 mM glucose at same time point continue to proliferate and reach a much higher cell density (no contact inhibition), this cellular model represents a useful experimental system to study the effects of contact inhibition on gene expression as well as on their regulation in transformed cells. As shown in Fig. 3A, comparative analysis between our data and those of Küppers, in both cases represented as ratio between contact inhibited cells (NIH3T3 for both) as compared to sparsely growing (NIH3T3, Küppers') or transformed cells (NIH3T3-K-Ras), indicated that 12 out of 12 genes were present also in our transcriptional analysis with a pattern almost comparable, since 67% of the 12 genes showed the same trend of expression at 72 h (our data) and in Kuppers' analysis (compare column NHG/THG72h vs. Küppers et al.). This finding suggests that transformed cells behave as not contact inhibited cells. Furthermore, the expression levels of these genes change with time (Fig. 3B, upper and lower panels). In particular, during the exponential phase of growth of both cell lines (0, 24 and 48 h) they show similar levels of expression, and at later time points (72 h), in strict association with the insurgence of contact inhibition in normal cells, we observe a substantial increase of the number of metabolic genes upregulated in normal cells as compared to transformed cells. These findings indicate that these genes encoding metabolic enzymes are strictly linked to contact inhibition, that the latter event is achieved by an active mechanism, as also recently shown in quiescent fibroblasts (Lemons et al., 2010). Altogether these findings suggest that to overcome the contact inhibition and to continue to proliferate, transformed cells acquire different metabolic characteristics as compared to normal ones. This fact is not completely unexpected since the increased thickness of a tissue, due to hyperplasia, as well as the multistrata growth of cells in in vitro cultures, will limit the availability of oxygen, especially in tissues, as well as of nutrients, favoring i.e. a hypoxic microenvironment (Takahashi and Sato, 2010; Vaupel et al., 1989). In synthesis the enhanced growth of cancer cells continuously stimulate metabolic adaptations in order to sustain the necessary energetic and biosynthetic requirements of their proliferation in different microenvironmental conditions. In addition, as described throughout this report, an important role for the enhanced cell growth of transformed cells has to be assigned to glucose and glutamine availability, since depletion of both or one of them, strongly inhibits cancer cell growth (Chiaradonna et al., 2006a; Drogat et al., 2007; Gaglio et al., 2009). In conclusion enhanced growth is associated to increased glycolysis, increased glutamine utilization and in most cases to mitochondrial dysfunctions. Multiple molecular mechanisms, both intrinsic and extrinsic, converge to change cellular metabolism and provide support

for the three basic needs of dividing cells: rapid ATP generation to maintain energy availability; increased biosynthesis of macromolecules; and maintenance of appropriate cellular redox status.

4.3. Evasion from apoptosis as a system-level property The search for a “silver bullet” able to selectively kill cancer cells has been going on for many years, but so far with frustrating results at the clinical level. In fact both drugs targeted against overexpressed proteins in cancer cells and selective drug delivery by means of antibodies directed against epitopes specifically expressed in tumor cell surface have not been able to achieve resolutory results (Boeck and Heinemann, 2010; Dempke and Heinemann, 2009; Friday and Adjei, 2005; Ji et al., 2010). It seems therefore that one has to go back to basics and try to know much more on how apoptosis is regulated and more so how nutritional perturbations, such as glucose starvation, are able to selectively bring to death cancer cells (Chiaradonna et al., 2006a; Simons et al., 2009). Mitochondria, as previous summarized, play an important role in the regulation of cell death. They contain many pro-apoptotic proteins such as Apoptosis Inducing Factor (AIF), Smac/DIABLO and cytochrome C. These factors are released from the mitochondria following the formation of a pore in the mitochondrial membrane called the Permeability Transition Pore (PTP), composed of several protein complexes, namely VDAC and the adenine nucleoside translocator (ANT) (Fig. 4). These pores are thought to be opened through the action of the pro-apoptotic members of the bcl-2 family of proteins (e.g. Bad and Bax), which in turn are activated by apoptotic signals such as stress, free radical damage or growth factor deprivation (Fig. 4). In fact, upon different apoptotic stimuli, pro-apoptotic proteins are able to display the anti-apoptotic proteins (e.g. Bcl2 and Bcl-xl) associated to PTP and hence to initiate apoptosis. More recently it has been shown that both members of Bcl family, anti- and pro-apoptotic proteins, are strongly regulated by phosphorylation mechanisms that restrain their ability to decide cell fate (Tamura et al., 2004; Terrano et al., 2010; Yu et al., 2004). Mitochondria also play an important role in amplifying the apoptotic signaling from death receptors, with receptor recruited caspase 8 activating the proapoptotic bcl-2 protein, Bid (Fig. 4). One of the hallmark features of cancer is the ability to evade apoptosis, particularly by up-regulation of antiapoptotic genes such as certain members of the Bcl-2 family of proteins (Lin et al., 2007; Wang et al., 2009) and the inhibitor of apoptosis (IAP) family of proteins (LaCasse et al., 2008; Wang et al., 2009). IAPs, particularly cellular IAP1 (cIAP1), cIAP2, and X-linked IAP (XIAP), function to prevent cell death by preventing activation of caspase-8 or inhibiting the activity of caspases-9, -3, and -7, respectively (Fig. 4). However several authors have shown that metabolic stress, in particular that induced by low glucose culture condition or by 2-DG, is a potent trigger of apoptosis, indicating that activation of apoptosis in tumors and cancer cells by means of metabolic stress may offer an attractive therapeutic strategy. Recent evidence suggests that tumor cells that acquire defects in apoptosis can be addressed to another cell death pathway, necrosis, where cells die by a loss of physical integrity (Zong and Thompson, 2006), and this finding may provide an alternative therapeutic approach (Degenhardt et al., 2006; Zong et al., 2004). Necrosis in apoptosisdefective tumor cells appears to occur when the rate of energy consumption exceeds that of energy production. This fact again links cell death to regulation of cell metabolism (on the stage again mitochondria as main actor), supporting the idea that metabolism is an attractive alternative therapeutic target. Even though necrosis is a less efficient mechanism of cell death as compared with apoptosis, it remains a tractable means to kill tumor cells with apoptosis defects, despite the

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Fig. 3. Metabolic genes regulated by contact inhibition. A. Comparative transcriptional analysis between contact inhibited NIH3T3 cells versus not contact inhibited NIH3T3-K-ras transformed cells (NHG/THG72h) and contact inhibited NIH3T3 cells versus sparsely plated NIH3T3 cells (Kuppers et al. data). B. Upper and lower histograms represent time-dependent regulation (0–72 h) of the genes shown in panel A, in both NIH3T3 and NIH3T3-K-ras cells. On Y-axis, 1 = not changed expression, N 1 = up-regulation, and b1 = down-regulation.

possible consequences of promoting inflammation (Degenhardt et al., 2006). It is now apparent that tumor cells with apoptosis defects may require the catabolic process of autophagy to provide an alternate energy and anabolic sources in periods of metabolic deprivation to prevent death by necrosis (Degenhardt et al., 2006; Lum et al., 2005). Autophagy is a process in which cellular organelles and bulk cytoplasm are targeted for degradation in lysosomes (Klionsky, 2005). Normal mammalian cells require basal autophagy for organelle and protein turnover and autophagy induction is required to sustain metabolism and viability especially during periods of starvation (Hara et al., 2006; Komatsu et al., 2005, 2006; Kuma et al., 2004). In tumor cells with defects in apoptosis, autophagy is needed to maintain cell metabolism and viability during starvation until the nutrient supply is re-established. If nutrient deprivation persists, progressive autophagy can ultimately lead to autophagic cell death. Thus, in contrast to apoptosis, which is a death process that is rapid and irreversible, autophagy can be thought as a slow pathway to cell death that can be

stopped. In tumor cells that have defects in apoptosis, often as consequence of mitochondrial dysfunctions, autophagy may enable them to survive metabolic stress. Thus whatever the pathway by which cancer cells die is apoptosis, necrosis or autophagy, metabolic stress appears to play a role. Therefore a deeper understanding of apoptotic process in mammalian normal and cancer cells, especially how the process, occurring through the mitochondria, is regulated by nutrients availability (glucose and glutamine), is the main route to follow in the next future. In conclusion, the questions opened in this report can be summarized as follows. Cancer cells show an altered central metabolism that largely involves mitochondrial activity. Are these metabolically “different” mitochondria of cancer cells also resistant to internal and external apoptotic stimuli? In other words: is there an obligate link between altered metabolic activities of cancer cells and their resistance to apoptosis? Which is the biochemical pathway that allows glucose starvation to specifically activate cell death of cancer cells? Can it offer cues for a more effective anticancer drug?

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Fig. 4. Mitochondria at crossroads between metabolism and apoptosis. The scheme shows some metabolic stimuli able to induce apoptosis through mitochondria and a number of pro-survival and pro-apoptotic factors involved in the process.

5. Methods to analyze cancer cell metabolism From the previously reported findings it is clear that a widespectrum analysis of cancer cell metabolism is urgently required. However, in contrast to transcripts and proteins, metabolites, important determinant of cell fate, share no direct link to the genetic code; instead they are synthesized by the concerted action of complex networks of enzymes. Additionally, unlike genes and proteins, which are linear polymers composed of a limited number of monomeric units, metabolites constitute a structurally diverse collection of molecules that vary widely in physicochemical properties and that are present in a large number, estimate in eukaryotic cells to be between 4,000 and 20,000 distinct molecular species in eukaryotic cells. To fully understand the composition and function of biochemical networks controlling energy metabolism and apoptosis, the analysis of the small molecule inside the cells, commonly referred to as metabolome is essential. Indeed, even a modest variation in enzyme activity can correlate with substantial changes in metabolite concentrations, making analysis of metabolome possibly the most sensitive measurement of the biochemical and energetic state of the cell. Metabolomics involves the combined use of analytical techniques and multivariate statistics analysis, among which nuclear magnetic

resonance (NMR) spectroscopy, gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS) play a prominent role. Both NMR spectroscopy and MS are highthroughput technologies; this feature allows their use as preferential tools to profile metabolism in a systemic way in tumor prognosis and diagnosis, through analysis of fluids, such as blood plasma and urine (Nicholson et al., 2002; Oldiges et al., 2007). In addition, in cell, on tissue and in vivo studies have shown that NMR can be used to identify tumor types on the basis of their metabolic profiles (Jordan and Cheng, 2007). 5.1. Nuclear magnetic resonance (NMR) spectroscopy NMR spectroscopy represents a rapid, non-destructive, highthroughput method that requires minimal sample preparation (Lindon et al., 2003a). NMR analysis for metabolomics has based mainly on 1H and 13C NMR spectroscopy, although 31P NMR spectroscopy is also used to measure high-energy phosphate metabolites and phosphorylated lipid intermediates. As organic molecules contain a high number of hydrogen and carbon atoms, the system is nonbiased to specific metabolites; in particular, NMR detects all metabolites present at concentrations larger

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than the instrument limit of detection. In addition, due to the signal intensity dependence on the number of identical nuclei, NMR allows metabolite quantification too. To this purpose, a crude sample is simply mixed with a reference compound, for example 3-trimethylsilylpropane-1-sulfonic acid (DSS), at a given concentration, without additional sample preparation steps (Fig. 5). NMR spectra acquired for metabolic analysis purposes are very complex, as they contain thousands of signals relating to the different metabolites. Nevertheless, acquisition of both 1D and 2D spectra allows the structural identification of each metabolite, in contrast to other methodologies, such as MS. The main drawback of this technique is represented by its relatively low sensitivity, although improvements can easily be achieved by application of higher magnetic fields, longer analysis times and the use of cryo-probes. One of the most interesting features of this technique is that it can be used in a non-invasive manner, making it possible to profile the metabolome of intact tissues. High-resolution magic angle spinning (HR-MAS) NMR allows the analysis of cell suspensions and intact tissues by overcoming the difficulties of sample heterogeneity and short relaxation times that produce resonance broadening (Beckonert et al., 2010; Cheng et al., 1996; Griffin et al., 2002), and, at the same time, makes possible in vivo spectroscopy of the whole organ (Pfeuffer et al., 1999) and of the whole organism (Blaise et al., 2007, 2009). Past detection limits for 1H NMR spectroscopy were of about 50 μM in tissue extracts or biofluids, with acquisition times of about 10 min (Lindon et al., 2003b). With the introduction of cryo-probes for the analysis of liquid samples, the limit of concentration has been raised one order of magnitude below.

respectively. In the second step, the metabolites are ionized by conversion to a positively or negatively charged species and then separated according to their mass to charge ratio, which can be used for metabolite identification (Dunn and Ellis, 2005). MS-based techniques are more sensitive than NMR spectroscopy, and can potentially detect metabolites two orders of magnitude below in concentration. Nevertheless, as not all metabolites can be ionized to an equal extent, the analysis can be altered both at qualitative and quantitative level. In the case of the GC–MS approach, the majority of metabolites analyzed require chemical derivatization to increase analyte volatility and thermal stability prior to analysis (Roessner et al., 2000). Since separation in GC takes place at high temperatures, analytes need to be thermally stable. For LC–MS, sample derivatization is generally not required; although it can be useful to improve chromatographic resolution and sensitivity or to insert ionizable groups on metabolites otherwise undetectable by electrospray ionization (ESI) MS (Leavens et al., 2002). ESI instruments operate both in positive and negative ion modes and only detect those metabolites that can be ionized by addition or removal of a proton or by addition of another ionic species. As metabolites are generally detected either in positive or negative ion modes, a wider metabolome characterization can be obtained by analysis in both modes. LC–MS is tremendous versatile, enabling the analysis of a wide variety of small molecules; it is nowadays the platform used predominantly in metabolomics (Wang et al., 2010b). The detection limits for MS-based measures are of the order of 100 nM, with acquisition times of about 30 min.

5.2. Gas chromatography (GC)– and liquid chromatography (LC)–mass spectrometry (MS)

5.3. Examples of applications: snapshot analysis and metabolic flux analysis

Both GC–MS and LC–MS involve a first chromatographic step in which metabolites are separated either in gas or solution phase,

One of the first demonstrations of NMR value to analyze tumor metabolism was obtained by Griffiths et al. (2002) and Griffiths and Stubbs (2003). NMR spectroscopy was used to characterize changes occurring in cancer cells in hypoxic regions of tumors. In particular, the authors analyzed the effects of HIF-1β deficiency on tumor metabolism and growth in vivo and in vitro using NMR spectroscopy. The HIF-1 heterodimeric transcription factor, consisting of HIF-1α and HIF-1β subunits, is up-regulated in several cancer cell types upon oxygen reduction. The main result of this up-regulation is the increased expression of proteins involved in a range of metabolic pathways, among which glycolytic enzymes, glucose transporters and growth factors such as vascular endothelial growth factor (VEGF). Glycolytic rate and angiogenesis induction are then increased in these tumors. In mice, HIF-1β-deficient cancer cells grow at reduced rate as compared with wild-type cancer cells. The authors wanted to clarify if this difference was a consequence of the decreased vascularity, due to the reduced tumor production of VEGF, or of the cell inability to increase the rate of glycolysis, due to a lack of up-regulation of the glucose transporters GLUT1 and GLUT3. According to post-mortem histological analysis, the wild-type and HIF-1β-deficient tumors showed no difference in vascularity. In addition, the mutant tumors presented a fivefold decrease in total ATP content and a threefold increase in the ratio between phosphodiester (PDE) and inorganic phosphate (Pi), suggesting a reduction in the energy level of the tumor and an increase in phosphorylated cell-membrane molecules, respectively. NMR-based metabolomic analysis showed a significant decrease in phosphocholine, choline, betaine and glycine. Glycine, deriving mainly from 3-phosphoglycerate, is a fundamental source of one-carbon units employed for the synthesis of nucleotides. Since HIF-1β-deficient tumors have a reduced rate of glycolysis and, as a consequence, of glycine production, they synthesize nucleotides less efficiently and present a reduced concentration of ATP. On the other hand, glycine can also be produced through choline and betaine, which justify the decreased levels of these metabolites. In addition, an

Fig. 5. NMR analysis for metabolomics. NMR spectra recorded on the complete growth medium (DMEM containing 4 mM L-glutamine, supplemented with 10% Newborn Calf Serum and 1 mM glucose) for NIH3T3 cells at different incubation times and DSS concentration: 1) 0 h, DSS 0.5 mM; 2) 24 h, DSS 0.5 mM; 3) 48 h, DSS 1 mM; 4) 72 h, DSS 1.5 mM; 5) 96 h, DSS 1.5 mM. Lactate (Lac) and Glucose (Glu) variations of concentration are clearly detectable and quantifiable.

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increased turnover in lipid membranes is produced by increasing choline metabolism that explains the high PDE/Pi ratio detected. NMR quantification of these metabolites was able to identify the cause of the reduced ATP content in the mutant cells. NMR is a very powerful tool not only for the study of metabolic pathways, but also for the achievement of the entire metabolic profile of cancer cells and tissues. In this context, pattern-recognition software is needed to allow the association of specific profiles with different cancer cell and tissue types. Large datasets of NMR spectra are employed for patient diagnosis of various tumor types (Gerstle et al., 2000; Hagberg, 1998). Furthermore, these approaches have also been used to identify ‘metabolic fingerprints’ associated with different kinds of tumors (Griffin and Shockcor, 2004; Jordan and Cheng, 2007). Multivariate statistical methods are applied to the spectral data to map the variations in whole profiles and to find correlations between metabolic and histological or clinical data, thus aiming at developing new tools for disease diagnosis and/or prognosis. Very recently, Rocha et al. (2010) reported interesting results regarding the metabolic profiling of lung cancer tissues by using 1H HR-MAS NMR spectroscopy. In particular, they analyzed paired samples of tumor and noninvolved adjacent tissues from 12 lung tumors, obtaining the identification of over 50 compounds. The application of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to the standard 1D 1H spectra achieved a good separation between tumor and control samples, showing different metabolic signatures for the two tissue types. By integrating signals present in NMR spectra, alterations in the content of different metabolites were identified. In particular, lactate, phosphocholine, and glycerophosphocholine were found to have elevated levels in tumors, while glucose, myo-inositol, inosine/adenosine, and acetate were reduced. These data demonstrated the applicability of NMR analysis in order to differentiate lung tumors from their adjacent noninvolved tissues both by PCA and HCA of the standard 1H spectra acquired on tissue. From this kind of information, new biochemical insights and new malignancy markers with potential diagnostic value may be derived. Also LC–MS was employed for the identification of lung cancer biomarkers. An et al. (2010) developed an integrated ionization approach of ESI, atmospheric pressure chemical ionization (APCI), and atmospheric pressure photoionization (APPI) combined with rapid resolution liquid chromatography mass spectrometry (RRLC–MS). The aim of this work was to overcome the low ionization efficiency of endogenous metabolites that depends on various physicochemical properties and ion suppression, in order to obtain comprehensive metabolite profiles by LC–MS analysis. To be validated, the method was applied to a metabolomic study of lung cancer, analyzing urine samples from 19 lung cancer patients and 22 healthy volunteers, using various ionization methods in positive and negative ion modes. Potential biomarkers were identified on the basis of different metabolic traits between healthy volunteers and lung cancer patients. In particular, the concentrations of these molecules changed between the lung cancer group and healthy volunteers. Among these potential biomarkers some amino acids, nucleosides, and a metabolite of indole were identified, namely tryptophan, phenylalanine, tyrosine, 5′methylthioadenosine, 5,6-dihydrouridine, threonylcarbonyl-adenosine, dimethylguanosine, succinyladenosine, dimethylarginine, N6, N6,N6-trimethyl-L-lysine, indoxyl. These findings suggested increased amino acid and nucleoside metabolism as well as protein degradation in lung cancer patients. This evidence fits with experimental data previously available. As a matter of fact, the unusual increase of the three aromatic amino acids in urinary excretion might be related to derangement of protein metabolism in cancer patients (Laviano et al., 2003). In addition, dimethylarginine is elevated in patients with a cancer-related diagnosis (Schulze et al., 2009), as enhanced protein turnover, oxidative stress, and impaired dimethylarginine dimethylaminohydrolase activity, that takes place in hematological malig-

nancies, may cause an increase in dimethylarginines production (Szuba et al., 2008). On the other hand, N6,N6,N6-Trimethyl-L-lysine, is a precursor of carnitine, which is essential for cell proliferation and distribution of energy, allowing FA transfer throughout mitochondrial membranes to the sites where they are oxidized. Carnitine presents abnormalities in the modulation and expression in the various forms of cancer (Vinci et al., 2005). Another potential biomarker, indoxyl, was also found to increase in lung cancer patients. All together this evidence supports MS analysis usefulness for the identification of new potential cancer biomarker. NMR and MS methodologies are able to follow variations of metabolite concentration in normal and pathological conditions. Nevertheless, the same techniques can be exploited in order to identify new metabolites, whose production is a consequence of metabolic alterations. A very significant example was reported at the end of 2009 (Dang et al., 2009). The authors demonstrated that mutations in the cytosolic enzyme isocitrate dehydrogenase 1 (IDH1), found in approximately 80% of grade II–III gliomas and secondary glioblastomas in humans, are associated with an elevated level of R(2)-2-hydroxyglutarate (2HG). As the R132H mutation disrupts the ability of IDH1 to convert isocitrate to a-ketoglutarate, U87MG glioblastoma cells, wildtype for IDH1, were stably transfected with Myc-tagged wild-type or R132H mutant IDH1. Cells expressing either Myc-tagged wild-type or mutant IDH1 were used for metabolite profiling experiments. In particular, metabolites extracted from exponentially growing cells were profiled by LC–MS. The levels of the most observed metabolic ions were similar between wild-type and R132H mutant IDH1 expressing cells, with the exception of three species that were significantly more abundant in R132H mutant IDH1 expressing cells. Among them, 2HG was identified. This metabolite was found to accumulate not only in cell extracts, but also in the medium of cells expressing R132H mutant IDH1 (Dang et al., 2009). NMR and MS can also be exploited to obtain a comprehensive methodology for the analysis of metabolic fluxes in cancer cells. An excellent example was provided by Yang et al. (2008), who developed an integrated approach for the analysis of metabolome, allowing to assess metabolic flux analysis (MFA) and, at the same time, concentration values of a large number of the key metabolites involved in central metabolism of human cancer cells. This approach was based on sample analysis by 2D NMR and GC–MS after in vivo labeling with [U–13C] glucose. As a matter of fact, the profiling of metabolic activities is often performed on cells grown in the presence of 13C-labeled glucose. In this way, each metabolite deriving from glucose catabolism is present as a collection of different isotopomers, differing in positions and percentage of 13C atoms. Relative pathway activities can be then determined, in a series of different experimental conditions, from the experimental measurement of isotopomer distribution of multiple metabolites and the subsequent comparison with an isotopomer mathematical model derived from the existing data on metabolic pathways. The authors developed an extensive isotopomer model, enabling to determine fluxes through glycolysis, pentose phosphate pathway (PPP), TCA cycle, anaplerotic reaction, and biosynthetic pathways of FA and amino acids in human cells. This model was applied to the characterization of three different cell culture models of human breast cancer. They were able to reveal (1) changes in fluxes through PPP and alterations in amino acid biosynthesis in MCF10 cell lines, (2) alterations in fluxes through the TCA cycle and anaplerotic pathway in hypoxia-resistant MCF-7 mammary carcinoma cells and (3) flux changes in FA metabolism in MDA-MB-435 breast cancer cells treated with Orlistat, an inhibitor of FAS (Kridel et al., 2004). In general, 13C MFA is a very useful tool for characterizing the metabolic phenotype of organisms at the system level. It is usually based on the supply of one or more labeled substrate to a biological system, the measurement of label incorporation within different metabolite pools, and the computational estimation of intracellular fluxes that fit the observed data (Tang et al., 2009).

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As tracers strongly influence flux estimation quality, an efficient tracer selection is a fundamental step of this experimental design process. As a result, a systematic analysis of available 13C-labeled tracers for each estimated flux is necessary. Metallo et al. (2009) quantitatively validated the effectiveness of specific 13C tracers in a cancer cell network, by employing a scoring algorithm able to determine the optimal tracer for the overall model and for specific metabolic pathway, such as glycolysis, PPP, and TCA cycle. In 2010, the same group described a new method, called nontargeted tracer fate detection (NTFD), that further expanded the concept of metabolomics, allowing not only high-throughput quantification of metabolites, but also the collection of information on the rates and connectivity of different metabolic pathways (Hiller et al., 2010). As a matter of fact, in a non-targeted but quantitative way, NTFD elucidates metabolic pathways coupled to the applied tracer. The only requirement of this method is the GC–MS measurement of a metabolite extract originating from biological systems treated with a mixture of labeled and unlabeled tracer. In addition, the measurement of the metabolome of the same system but treated with unlabeled tracer alone is necessary as control. The authors demonstrated the potential of NTFD by the application of 13C and nitrogen 15N stable isotopes, but, in principle, the methodology can also be used with other stable isotopes like 33S or 18O. Due to its nontargeted nature, NTFD adds information about biochemical reactions and metabolites that were previously unknown, linking metabolomics to genomics and transcriptomics processes. Overall, findings reviewed herein demonstrate the validity and the potency of MS and NMR spectroscopy as analytical tools for the screening of different metabolic phenotypes in a variety of cancer cell and tissue types. 6. In vivo imaging cell metabolism using positron emission tomography (PET) based molecular imaging techniques PET allows the in vivo imaging and measurement of a number of biological properties of neoplastic tissues; using PET and selected radiopharmaceuticals it is possible to measure in vivo specific different biological pathways related to the modifications in cell metabolism or tissue microenvironment present in cancer, such as: a) glucose or FA metabolism, cell proliferation and tissue hypoxia, b) molecular target like epidermal growth factors or integrin receptors or c) the expression of therapeutic genes using the gene reporter techniques. The recent development of small animal dedicated systems makes PET not only a unique tool for the clinical management of patients but may also translate preclinical results into clinical research and vice versa. The majority of PET studies performed in clinical practice are based on the use of the analogue of glucose, named Fluorine18-fluorodeoxyglucose (2-[ 18F]fluoro-2-deoxy-D-glucose, FDG), as radioligand. FDG, particularly when associated with PET-Computed Tomography (CT) integrated systems, is widely used for the staging, restaging and follow-up of colorectal cancer, lung and breast cancer lymphoma, melanoma, sarcoma and gastrointestinal stromal tumors (GIST), gynecological cancers and others. Like glucose, the cellular uptake of FDG is mediated by the family of glucose transporter proteins (GLUT) present on plasma membrane (Mochizuki et al., 2001). Once taken up into the intracellular compartment, FDG is phosphorylated by the enzyme hexokinase (HK) and transformed into FDG-6-phosphate. Thanks to the lack of the hydroxyl group in position 2, it cannot be transformed into fructose 6-phosphate and remains biochemically trapped within the cell. The rate of uptake of FDG depends on the amount of exogenous glucose requested by cells and is regulated by several signal transduction pathways active in tumor involving different enzyme controlling cell proliferation, vascularization, apoptosis and metabolic demands (Luo et al., 2010; Vander Heiden et al., 2001). Due to the specific chemical structure and biochemical processing,

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the amount of radioactivity taken up by cancer cells after the injection of FDG reflects the presence of GLUTs on cell membrane and the activity of HKs but not necessarily the activity of the other enzymes present downstream the glycolytic pathway such as pyruvate dehydrogenase or others. In general, given the complexity of intra and extracellular signaling regulating cell metabolism, certain variability in the glycolytic phenotype among cancers is not surprising. 6.1. Clinical applications of PET The widespread clinical usefulness of FDG-PET in oncology is mainly due to: a) the high metabolic need and the concomitant use of glucose as the main substrate for energy production and growth by cancer cells, as well described above; b) FDG kinetics and bio-distribution that allows the application of PET in almost all body districts where the background activity is low; c) the development of total body PET and subsequently PET/CT systems able to integrate in a single session information on the structural and the molecular phenotype of cancer lesions. In clinical practice, FDG-PET is characterized by a high sensitivity in detecting hyper-metabolic lesion and the addition of CT to PET has even increased its diagnostic potential (Czernin et al., 2007). Despite its sensitivity, FDG is characterized by a low specificity since increased level of glucose uptake and utilization is present in non-neoplastic inflammatory cells and in correlated lesions, and it is sometimes independent on the presence of tumoral tissue (Basu et al., 2009). The applications of PET and PET/CT in major malignancies as well as the occurrence of false positive and negative results have been recently summarized (Kumar et al., 2010). As previously indicated, most of the false positive results derive from inflammatory/infective diseases. False negatives are mainly caused by lesion dimension (b5 mm), normal FDG distribution or clearance (brain, bladder, kidney, prostate) or finally by the metabolic heterogeneity present in a tumor. In general low-grade and well-differentiated tumors such as broncho-alveolar cell carcinoma and bronchial carcinoid of the lung, ductal and lobular breast cancers as well as follicular lymphoma are characterized by low level of glucose metabolism. In hepatocellular carcinoma (HCC) variable FDG uptakes have been shown to depend on the activity of glucose-6-phosphatase enzyme. For this reason, [ 11C] acetate has been proposed as new tracer for the in vivo imaging in patients with HCC (Park et al., 2008). However, while in the myocardium, the uptake mechanism of radiolabeled acetate is well understood, in tumor cells such mechanism is less studied. Recently, it has been demonstrated, by using [1-14C]acetate, that this tracer interacting with the cytoplasmic form of acetyl-CoA synthetase (ACSS) participates to lipid and amino acid synthesis (Fujino et al., 2001). In tumor, ACSS acts as a bidirectional enzyme mediating a buffering role between AcCoA and acetate metabolism. In addition, it has been shown that [11C]acetate uptake increases under hypoxic conditions most likely as consequence of an increased expression of FAS that is positively regulated by ACSS (Yoshii et al., 2009). Using [11C] acetate, Park et al. (2008) found that this tracer successfully detected well-differentiated HCC, while poorly differentiated HCCs were efficiently detected by FDG. Patients with positive FDG-PET/CT had overall lower survival rate than patients with positive only [ 11C]acetate (Park et al., 2008). In other tumors, like renal or prostate cancer, the use of FDG-PET is limited not only by the lower lesion/background ratio caused by the physiological urinary excretion of the tracer but also by the poor and heterogeneous levels of FDG uptake characterizing both primary and metastatic lesions. For these reasons, other tracers including [ 11C] acetate or the choline kinase substrate [ 11C]choline, are under evaluation or already in clinical use for the management of urinary tumor (Bouchelouche and Oehr, 2008). Interestingly recent studies on the genetic markers of renal cancer demonstrate the presence of modifications in seven genes (VHL, MET, FLCN, TSC1, TSC2, FH and SDH) involved in pathways controlling the

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metabolic phenotype of cells including the activation of the “Warburg effect”. The discovery of the presence of selective mutations in these genes explains not only the heterogeneity of this tumor in term of histology, clinical course and treatment responsiveness, but also the variability of FDG uptake reported in kidney cancer. Targeting the specific metabolic abnormalities regulated by gene modifications such as autophagy, glucose transport or energy sensing might provide a more-effective form of therapy in patients with kidney cancer. In this context, the heterogeneity of FDG uptake that presently limits the diagnostic potential of FDG could represent a potential advantage for the selection of patient responder to a specific treatment regimen (Linehan et al., 2010; Schoder and Larson, 2004). There are various relevant FDG-PET findings supporting the important role of metabolism for tumor growth and metastatization. Recently, in prostate cancer, FDG-PET has been indicated as a possible mean to assess the biological activity of bone metastases and for the evaluation of a specific subgroup of patients carrying lesions with an aggressive phenotype. On the other hand, in prostate cancer, an association between dietary fat and regulation of cell metabolism has been recently observed. In addition, in this type of tumor altered levels of key metabolic enzymes, such as FAS and 5-AMP-activated protein kinase (AMPK) (Flavin et al., 2011), possibly participating to the regulation of the switch from aerobic to anaerobic metabolism, have been observed. Also in this case, a better understanding of the meaning of the highly heterogeneous levels of FDG uptake that characterizes prostate lesion, together with the combined use of radioligand selective for different metabolic pathways such as [ 11C] acetate or [ 11C]choline or maybe [ 11C]palmitate, could be useful to evaluate novel therapeutic strategies including targeting metabolic key enzymes. At present, one of the most interesting and foreseeing applications of FDG-PET is its use for the identification in the early phase of therapy responder patients with different types of tumor. A number of preclinical and clinical studies, indicate FDG as a surrogate non-invasive marker of tumor biology and a potential prognostic tool for the early prediction and assessment of response to pharmacological treatment including target therapy. An example of this application is represented by the early reduction of FDG uptake into patient with GIST responder to drug like Imatinib able to block c-kit, a tyrosine kinase receptor that is characterized by an auto-activating mutation in 85% of patients carrying this type of tumor. Of particular relevance is the fact that in responder GIST patients a dramatic reduction in FDG uptake occurs as soon as 24 h after the first treatment dose, much earlier than a reduction of tumor volume detectable at CT or MRI. This is particularly relevant, from a clinical point of view, since such a metabolic response is indicative of clinical response (Shankar et al., 2006). FDG metabolic response early after the beginning of target therapies has been reported with PET and PET/CT also for other tumors, such as breast cancer (Fig. 6), being generally associated with clinical response. Differences in early phase post-treatment modifications in FDG uptake have been described also in patients and in preclinical mice models of lung cancer (LC) responder or not responder to epidermal growth factor receptor (EGFR) inhibitors like gefitinib or erlotinib. Interestingly, in preclinical models of GIST as well as of LC it has been demonstrated that the biochemical events underlying the early metabolic response of cells is represented by the internalization of GLUTs from cell membrane to cytosol (Aukema et al., 2010; Cullinane et al., 2005; Sunaga et al., 2008). Early reduction in FDG uptake caused by pharmacological treatment has been described in preclinical studies also for the mTOR inhibitors rapamycin and everolimus. Of course, the use of FDG for the early prediction of drug efficacy can be applied to therapies whose mechanism of action directly or indirectly involves modification in the regulation of glucose metabolism. The effects of both Tyrosine Kinase receptors and mTOR inhibition is not surprising since both classes of drugs cause a reduction in HIF-1 transcriptional targets

including vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF), glycolytic enzymes and glucose transporters (Honer et al., 2010). However, the validity of FDG-PET as an early marker of response to antiangiogenic agents and mTOR inhibitors has been recently criticized since, particularly for molecules with predominant antiangiogenic/vascular effects, the method could provide false-negative responses. Examples of these false-negative responses have been obtained for tumors treated with Her-2 or heat shock protein 90 (Hsp90) inhibitors and in no-glucose dependent heterogenous tumors (i.e., HCC, prostate and renal). For these events, a better understanding of the meaning of FDG positive cases and the use of novel radioligands to image other metabolic pathways or other properties of tumor microenvironment (e.g. radiopharmaceutical for the study of proliferation, hypoxia or neoangiogenesis) will open the possibility for addressing PET technique to a number of therapeutic strategies depending on their specific mechanism of action. 7. Metabolism as a target for anti-cancer drugs The previous reviewed evidence provides increasing support to the idea that there are cancer-specific changes in cell metabolism that could be targeted by novel drugs. In addition, the emerging role of several oncogenes and oncosuppressors in these metabolic alterations has been also delineated. Tumor cells exhibit altered metabolic behavior due to both cell-intrinsic properties and tumor microenvironment. As noted above, tumor cells have increased glucose uptake and preferentially metabolize glucose through glycolysis even in the presence of oxygen. Although the reason for the Warburg effect is still unclear, it has been suggested that such adaptation allows tumor cells to divert other nutrients, as glutamine, toward biosynthesis. In this scenario, inhibition of the glycolytic/biosynthetic pathways in a tumor cell may lead to conflicting answers due to incompatibility of the persistent proliferative signals from oncogenes and the necessary energetic and metabolic requirements. In this regard, it has been shown that knockdown of lactate dehydrogenase A (the enzyme that converts pyruvate to lactate in the last step of glycolysis) by RNAi, expression of pyruvate kinase splice variant (M1) in cancer cells, commonly expressing splice variant (M2), RNAi-mediated reduction of glutaminase 1 (the enzyme that converts glutamine in glutamate), overexpression of glutaminase 2, RNAi-mediated reduction of ATP citrate lyase (which synthesizes AcCoA from citrate produced by TCA cycle), acetyl-CoA carboxylase and fatty acid synthase (which control the conversion of AcCoA to manonyl-CoA to palmitate) all lead to substantial attenuation of tumor cell growth (Gao et al., 2009; Suzuki et al., 2010). RNA interference-mediated silencing of the acetyl-CoA-carboxylase-alpha gene induces growth inhibition and apoptosis of prostate cancer cells (Christofk et al., 2008; DeBerardinis et al., 2008a,b; Kroemer and Pouyssegur, 2008). Thus targeting key metabolic enzymes that drive both energy production and anabolic synthesis could effectively attenuate tumor cell proliferation, having therefore a cytostatic effect. 7.1. Drugs interfering with cancer metabolism Direct inhibition of glycolysis has long been considered as a possible chemotherapeutic strategy for cancer. A number of compounds directly target glycolytic pathway, including 2-DG, 3bromopyruvate (3-BrPA) and dichloroacetate (DCA). 2-DG competes with glucose as a substrate for hexokinase preventing glucose phosphorylation and oxidation via glycolysis. In animal models, 2-DG selectively induced cell death (cytoxic effect) when used in combination with other anticancer therapeutics such as paclitaxel or histone deacetylase (HDAC) inhibitors (Egler et al., 2008; Maschek et al., 2004). However, clinical trials have shown that large amounts of

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Fig. 6. FDG metabolic incorporation is indicative of clinical response. FDG PET/CT images of a 33-year-old woman with infiltrating locally advanced ductal carcinoma before (left) and 15 days after (right) beginning of therapy with Lapatinib (anti-HER2) and Paclitaxel. A marked reduction of glucose consumption in the nodule (white arrows) is evident after medical therapy. After PET, the patient underwent mastectomy.

the drug are needed because 2-DG remained only a short time in the body and because it had to compete with the natural glucose that is abundant in the bloodstream (Stein et al., 2010). However, while some authors have shown that the inhibition of tumor growth in vivo requiring daily administration of 2-DG causes systemic toxicity, others have observed that the administration of 2-DG can improve local control of tumors without increasing normal tissue toxicity especially when combined with anticancer agents like topoisomerase inhibitors (etoposide and camptothecin) and a radiomimetic drug (bleomycin), thereby enhancing their therapeutic efficacy as anticancer drugs (Dwarakanath et al., 2004; Gupta et al., 2005; Perumal et al., 2009). Similarly, the hexokinase inhibitor 3-BrPA suppresses xenograft growth of human colorectal carcinoma cells containing activated alleles of Ras or Raf (Yun et al., 2009) or the growth of rabbit liver carcinoma following direct intra-arterial injection (Geschwind et al., 2002). However, no clinical trials have been initiated with this inhibitor. DCA is an inhibitor of pyruvate dehydrogenase kinase-1 (PDK-1), a kinase that regulates the flux of pyruvate into mitochondria by inhibiting Pyruvate dehydrogenase (PDH). By inhibiting PDK-1, DCA redirects pyruvate away from the production of lactate, inducing pyruvate entry into mitochondrial TCA cycle. Since cancer cell mitochondria are often dysfunctioning, DCA diverting glucose through mitochondria, leads to an increase of ROS accumulation, to a reduction of total intracellular ATP and to induction of apoptosis in several cancer cells and in primary glioblastoma cells, for which, although it was a small clinical trial, some therapeutic benefits have been observed (Bonnet et al., 2007; Michelakis et al., 2010). Even though glycolytic inhibitors demonstrate some therapeutic potential in animal models, their application to the clinic is still very limited because the responses of patients to therapies are quite variable due to diverse biological behavior of tumors and because these treatments show toxicity to the normal tissues, especially brain (Pelicano et al., 2006). Increased de novo FA synthesis has been recognized as a hallmark of cancer and recent advances in this field, e.g. the observed increased expression of key enzymes of this pathway as FAS, ACC and ACLY, have further confirmed the importance of the lipogenic enzymes in cancer cell survival (Menendez and Lupu, 2007). These findings suggested de novo FA synthesis as a rational therapeutic target. Great interest has been given to FAS protein, that is target of several oncogenic pathways (Mashima et al., 2009) and so represents one of the more attractive target. Treating cancer cells with pharmacological inhibitors of FAS effectively suppresses growth and induces apoptosis in breast cancer cells both in vitro and in vivo (Pizer et al., 2000; Puig et al., 2009) and notably without inducing weight loss in animals (Puig

et al., 2008; Puig et al., 2009). Moreover, it has been shown that FAS inhibitors can induce cytotoxic effects in breast, leukemia and colon cancer cells, especially in combination with other chemotherapeutic agents including paclitaxel (Menendez et al., 2005) and Etomoxir (Hernlund et al., 2008; Samudio et al., 2010).

7.2. Targeting signal transduction pathways controlling cellular metabolism The results obtained with the previously described class of inhibitors suggest that their use in combination with other inhibitors, e.g. of oncogenic signaling, or in combination with other approaches like radiotherapy and chemotherapy, may give more effective results since this combination will induce both cytostatic and cytotoxic effects and hence favors the eradication of the tumor. To date, several findings indicate an important role of oncogenic pathways in the regulation of cancer cell metabolism. Many researchers have addressed their attention to signal transduction inhibitors to mitigate such alterations. In this regard great consideration has been given to the inhibition of Akt-TOR pathway, known regulator of several aspects of cellular metabolism (Brachmann et al., 2009a). The most widely used inhibitors are rapamycin and metformin, both associated with their ability to hinder mTORC1 signaling (Abraham and Gibbons, 2007; Dowling et al., 2007; Zhou et al., 2001). In this regard, rapamycin derivatives have been approved by the US Food and Drug Administration (FDA) for renal cell carcinoma, based on its ability to extend median survival (Hudes et al., 2007; Motzer et al., 2007, 2008). Metformin, whose main activity is still not well known, has been shown to delay or prevent carcinogenesis of about 30% in diabetic patients (Decensi et al., 2010; Evans et al., 2005). Despite the successful application of rapamycin and derivative as well as of metformin, several studies have reported cytostatic but not cytotoxic effects of both, resulting in disease stabilization but not regression (Alimova et al., 2009; Bissler et al., 2008; Motzer et al., 2008; Wolpin et al., 2009; Zhuang and Miskimins, 2008). However combined use with metabolic inhibitors has shown promising results, since such a combination appears to stimulate cell death (Ben Sahra et al., 2010; Wangpaichitr et al., 2008). Hence, the research is moving to the identification of compounds able to trigger cancer-specific cytotoxicity as opposed to cytostatic effects (Brachmann et al., 2009b; Yu et al., 2010). In this regard the combination of inhibitors of metabolic routes with that of signaling specific should help to maximalize both cytotoxic and cytostatic effects.

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7.3. Recent advances in the identification of metabolic targeting compounds able to combine cytotoxic and cytostatic effects The fact that cancer metabolism has become an interesting field for anti-cancer drug discovery is underlined by the fact that several biotech companies have chosen it for their mission. The more relevant ones are Agios Pharmaceuticals, Threshold Pharmaceuticals, Cornerstone Pharmaceuticals and Myrexis. Recently also a big pharmaceutical company, AstraZeneca, has arranged a collaboration with Cancer Research UK to investigate the field. As far as it can be said considering the information in the public domains, Threshold Pharmaceuticals has developed two different drugs able to interfere with glucose metabolism, one called 2-DG, is able to gain an effect only if administered in large amounts, and the second glufosfamide, is given by glucose moiety linked to a standard chemotherapy agent, ifosfamide. Because of its glucose component, essentially nutrient for cancer cells, glufosfamide should be preferentially transported into tumors as compared to most normal tissues. Inside the cells, the bond between glucose and the alkylator is cleaved to release the active drug. In a late-stage clinical trial involving more than 300 patients with advanced pancreatic cancer, glufosfamide showed only a small, statistically not significant, increase in the survival time (Chiorean et al., 2010; Shimizu et al., 2009). CPI-613 is the lead compound of Cornerstone's Altered Energy Metabolism Directed (AEMD) technology platform. AEMD is the first platform for small molecule discovery and development targeting cancer cell metabolism. CPI-613 is being used as a single agent and in combination with gemcitabine, a standard chemotherapeutic for the treatment of pancreatic and other cancers. CPI-613 has been granted orphan drug status by the US FDA for pancreatic cancer, which often goes undetected in its early stages and spreads rapidly, typically resulting in poor prognosis (http://www.cornerstonepharma.com/ clinical-trials). Several other companies declare to have in their pipeline drug candidates that should interfere with cancer metabolism. Among them it is important to recall the following: Dynamix has drug programs targeting Pyruvate Kinase M2 (PKM2) (http://www.dynxp.com/index.php/cancer-metabolism/pkm2). ScheBo Biotech AG from Giessen in Germany had developed and is promoting commercial PKM2 kits for colon cancer screening, since the levels of PKM2 are higher in cancer patients and this feature may be a fecal marker for colon cancer (http://www.schebo.com/english/ScheBo_ Tumor_M2-PK_EDTA_Plasma_Test_2.php). Advanced Cancer Therapeutics's small molecule 3PO blocks glucose uptake since its target is PFKFB3 (http://www.advancedcancertherapeutics.com/pipeline.html). Myrexis, Inc. compound, MPC-9528 is a nicotinamide phosphoribosyltransferase (NAMPT) inhibitor. NAMPT catalyzes the formation of nicotinamide adenine dinucleotide (NAD). Depletion of NAD inhibits cell metabolism, DNA repair and other processes. Human trials are expected in 2011 (http://www.myriadpharma.com/pipeline/mpc9528). Warburg Glycomed GmbH is developing butanoic acid derivatives which have been shown to reprogram cancer cell aerobic glucose metabolism and anti-cancer effect in vitro and in rat models. Synta Pharmaceuticals' Elesclomol is in early trials for ovarian cancer and in acute myeloid leukemia. It targets cancer cell energy production by interfering with the electron transfer chain in the mitochondria (O'Day et al., 2009; Qu et al., 2010; Wu et al., 2011). Tavargenix GmbH is developing inhibitors of Transketolase-like-1 (TKTL1). TKTL1, which is high in certain tumors (such as head and neck) and promotes glucose-to-lactate conversion; inhibition of TKL1

results in inhibition of cancer cell proliferation and tumors in animal models (http://www.tavargenix.com/target.php?site=projects). Agios Pharmaceuticals, founded by scientists that have made great advances in the investigation of cancer metabolism, aims to develop new drugs relying on high quality scientific evidence that fully test molecular mechanisms. A wide spectrum of experimental approaches is achieved in order to identify both new drug targets and new biomarkers for the stratification of patients for personalized treatments. 8. Conclusions and outlook Enhanced glycolysis, dysfunction of mitochondrial electron transport chain, stimulated FA production, and increased utilization of glutamine for biosyntheses, all these processes taken together generate the common characteristic features of cancer metabolism when it is compared to that of normal cells. A complete reconstruction of the biochemical pathways, specifically activated in cancer cells, that sustain this ample rerouting of metabolism, is still lacking and should be the first aim to be achieved. A number of observations suggest that this characteristic cancer metabolism is required to sustain enhanced proliferation under conditions that will not allow normal cells to grow and therefore that it is the propeller of malignant growth (Brahimi-Horn et al., 2007; Govindarajan et al., 2007; Ishikawa et al., 2008). Authors that have described oncogene addiction (Pillay et al., 2009) have observed that down-regulation of an activated oncogene (for instance ras, myc, etc.) strongly reduces cancer cell proliferation (Amendola et al., 2009; da Silva Morais et al., 2008; Gedaly et al., 2010). Although detailed analysis of this down-regulation on various aspects of cancer metabolism is still lacking, one may wonder whether it would be enough to inactivate an oncogenic pathway to control in a stable way cancer cell growth and invasion ability. Among the cancer hallmarks not considered by Hanan and Weinberg (2000), there is also genomic instability (Negrini et al., 2010). Following disruptions of oncogene addiction, obtained for instance by using an inhibitor of the oncogene-mediated signaling pathway, if the downregulated cancer cells maintain a slow proliferation activity, the still present genomic instability could generate mutations that may reactivate the typical cancer cell metabolism and therefore yield a relapse. Therefore for therapeutic purposes, it seems much more appropriate not to search for drugs that may interfere with oncogene addiction, but try to exploit the previously discussed unusual sensitivity to metabolic stress. As previously shown, in fact glucose starvation induces death, specifically in cancer cells, mostly by apoptosis (Chiaradonna et al., 2006b; Simons et al., 2009). Changes in structure/function of mitochondria occurring in cancer cells have been suggested to be involved in this starvation-induced cell death (Cai et al., 2007; Chiaradonna et al., 2006a). A detailed reconstruction of the signal/transducer/effector steps of the glucose starvation-induced death of cancer cells should offer cues for rational identification of one (or more) fragile nodes of the molecular network to be exploited as cancer specific drug targets. A deeper understanding of the molecular routes followed by metabolism in cancer cells may offer also the way to effectively stratify patients in various differentially responding classes. The already sophisticated technologies may be further exploited in ex vivo and in vivo investigations useful to give patients personalized, effective drug treatments. Future research will show how these perspectives will bring positive results to cancer understanding and hence to more effective therapy. Acknowledgments This work has been supported by a grant from MIUR to L.A. (FIRBITALBIONET), grants to F.C. from the Italian Government (F.A.R.) and

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MIUR (PRIN 2008), grants to C.A. and F.N. from Consorzio C.I.N.M.P.I.S., grant from Italian Ministero della Salute to C.M. and R.M., and D.G. is supported by Tecnomed Foundation University of Milano-Bicocca.

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