Content uploaded by Karl J Morten
Author content
All content in this area was uploaded by Karl J Morten on Oct 29, 2016
Content may be subject to copyright.
Content uploaded by Karl J Morten
Author content
All content in this area was uploaded by Karl J Morten on Oct 29, 2016
Content may be subject to copyright.
Available via license: CC BY-NC-ND 4.0
Content may be subject to copyright.
The Warburg effect: 80 years on
Michelle Potter, Emma Newport and Karl J. Morten
Nuffield Department of Obstetrics and Gynaecology, The Women Centre, University of Oxford, John Radcliffe Hospital, Oxford, U.K.
Correspondence: Karl J. Morten (karl.morten@obs-gyn.ox.ac.uk)
Influential research by Warburg and Cori in the 1920s ignited interest in how cancer cells’
energy generation is different from that of normal cells. They observed high glucose con-
sumption and large amounts of lactate excretion from cancer cells compared with normal
cells, which oxidised glucose using mitochondria. It was therefore assumed that cancer
cells were generating energy using glycolysis rather than mitochondrial oxidative
phosphorylation, and that the mitochondria were dysfunctional. Advances in research
techniques since then have shown the mitochondria in cancer cells to be functional
across a range of tumour types. However, different tumour populations have different
bioenergetic alterations in order to meet their high energy requirement; the Warburg
effect is not consistent across all cancer types. This review will discuss the metabolic
reprogramming of cancer, possible explanations for the high glucose consumption in
cancer cells observed by Warburg, and suggest key experimental practices we should
consider when studying the metabolism of cancer.
The Warburg effect
Despite decades of research and countless financial investments, cancer continues to elude our
complete understanding and more importantly our therapies. Pivotal research in the 1920s by
Warburg and Cori demonstrated that cancer avidly consumes glucose and excretes lactate [1,2]. When
oxygen is present, normal cells use mitochondria to oxidise glucose, but in the absence of oxygen,
glucose is converted into lactate. Otto Warburg first described in the 1920s that cancer cells utilised
higher levels of glucose in the presence of oxygen with an associated increase in lactate production.
The phenomenon of aerobic glycolysis, termed the Warburg effect, has been observed in a variety of
other tumour types, including colorectal cancer [3], breast [4], lung [5] and glioblastoma [6,7]. From
his observations, Warburg concluded that the mitochondria were dysfunctional [8,9]. The Warburg
effect has been confirmed in previous studies including those of DeBerardinis et al. [10], where cells
were incubated under oxygenated conditions in 10 mM C-13-labelled glucose. Cells were then per-
fused using 4 mM glucose prior to metabolomics analysis and even in the presence of oxygen, high
levels of glycolytic metabolites were observed supporting Warburg’s hypothesis. In addition, Fantin
et al. [11] made the observation that inhibiting lactate dehydrogenase preventing the conversion of
pyruvate to lactate reduced tumourigenicity. These data were interpreted as tumourigenicity being
dependent on high levels of energy derived from glycolysis. Another study by Schulz et al. [12]
showed that when mitochondrial oxidative phosphorylation is up-regulated by overexpression of fra-
taxin, malignant growth and tumourigenic capacity are decreased. The authors suggest that rather
than an increase in glycolysis being the main cause of malignant tumour growth, it is the efficiency of
mitochondrial energy conversion that is the key metabolic factor.
Over the past couple of decades, advances in technology have allowed mitochondrial function to be
studied in a far greater detail, and it is now realised that cancer cells have active and functional mito-
chondria, contrary to Warburg’s theory [13,14]. In the last decade, research has shown that different
tumour types (and indeed subpopulations within a tumour) have different bioenergetic alterations.
This was shown as early as 1967, when Weinhouse reported that slow-growing rat hepatoma cells
were oxidative, whereas the more proliferative hepatomas were glycolytic [15]. The Warburg effect is
not consistent across all tumours, and the phenomenon of aerobic glycolysis has now been challenged
by several groups with many cell lines reported as having mitochondrial function [16–18]. In a
Version of Record published:
19 October 2016
Received: 1 April 2016
Revised: 29 June 2016
Accepted: 25 July 2016
© 2016 The Author(s). This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY-NC-ND). 1499
Biochemical Society Transactions (2016) 44 1499–1505
DOI: 10.1042/BST20160094
tumour, it is likely that a dynamic interplay exists between oxidative metabolism and glycolysis. Metabolic flexi-
bility has now been observed in a range of cancers, including cervical, breast and pancreatic cancer (see ref.
[16] and reviewed in ref. [19]). In 2004, Zu and Guppy reported that adenosine triphosphate (ATP) derived
through glycolysis in various cancers and cell lines accounts for only 17% of the total ATP. In fact, the ATP
generated through glycolysis was highly dependent on the cell type and could be as low as 0.31% (fibrosar-
coma) or as high as 64% (hepatoma), with the remaining ATP being derived from mitochondrial oxidative
phosphorylation [20]. In addition to metabolic flexibility linked to environmental conditions, there is also the
influence of various cancer-associated mutations, many of which have an impact on metabolism. Mutations in
the mitochondrial tricarboxylic acid cycle and respiratory chain component succinate dehydrogenase, for
example, can cause phenochromocytoma and paraganglioma, where neuroendocrine tumours arise in the
adrenal medulla and paraganglia in the autonomic nervous system [21,22]. Mutations in isocitrate dehydrogen-
ase 1 are associated with adult cases of glioblastoma and appear to have a major role in the development of the
tumour by a gain-of-function effect [23,24]. Understanding how cancer cell environment and mutations affect
metabolism will be of fundamental importance in selecting appropriate metabolic drug combinations to impact
on patients’cancer cell growth.
Cancer hallmarks and metabolic reprogramming
Cancer cells show complex, dynamic behaviour allowing survival even in the most unfavourable conditions of
substrate and oxygen stress. Advances in technology have helped in furthering our knowledge of the underlying
molecular processes underpinning cancer, but there are still many unanswered questions. In 2000, Hanahan
and Weinberg published a highly cited review article identifying six cancer hallmarks [25]. These included
uncontrolled proliferative signalling, resistance to apoptosis, initiating angiogenesis, acquiring replicative
immortality, activating invasion and metastasis and evading growth suppressors. Over the last decade, research
has increased our knowledge of cancer, and in 2011, Hanahan and Weinberg extended the list of cancer hall-
marks to include metabolic reprogramming/deregulated cellular energetics as an emerging hallmark and potential
cancer target [26]. Uncontrolled proliferation is one of the essential characteristics of cancer. It has been
proposed that reprogramming energy metabolism is essential to fuel and maintain such behaviour [26]. The
exact reasons behind the metabolic switch are not known, but likely reasons include: (i) sustaining high prolif-
erative rates in hypoxia [27] and (ii) evading apoptosis as a result of reduced mitochondrial function [28].
Increases in glycolysis have been linked to invasiveness, with changes in glycolysis identified in several studies
[29,30]. However, in all studies listed above, cancer cells were grown on cell culture media containing high
levels of glucose between 10 and 25 mM. This is considerably higher than plasma glucose, which lies between 4
and 6 mM. Levels in a rapidly dividing tumour with poor vasculature are considerably lower. The same is true
of studies investigating the role of hypoxia in down-regulating mitochondrial respiration and increasing glycoly-
sis, where 25 mM glucose is used in the culture media of key publications [31–34]. The impact of high levels of
glucose on the above findings is a key consideration for future studies, where it is crucial to test new drugs tar-
geting cancer cell metabolism under physiologically relevant conditions. Metformin, for example, a drug cur-
rently being investigated as an anticancer agent in a wide range of cancers [35], has recently been shown to be
more effective in enhancing chemotherapy sensitivity of oesophageal squamous cancer cells under reduced
glucose conditions [36]. Although its mode of action on cancer cells in vivo is not entirely clear, mitochondrial
studies suggest that metformin can directly impair complex I of the respiratory chain [37,38]. The effect
observed by Yu et al. is probably due to a greater reliance of the cancer cells on mitochondrial respiration for
energy production when cultured on reduced glucose conditions. Previous studies have shown that high levels
of glucose in the culture media can significantly reduce levels of mitochondrial respiration, with reduced
glucose conditions showing much higher rates of mitochondrial respiration, as cells use other substrates for
cellular ATP production [39,40]. Similar results are shown in Figure 1, where the oxygen consumption rates
(OCRs; mitochondrial respiration) and extracellular acidification rates (ECAR; glycolysis) of a range of cancer
cell lines are compared under high (25 mM) and low (1 mM) glucose conditions. Under high glucose (25 mM)
conditions, cancer cell lines either show the Warburg effect (low OCR and high ECAR), high rates of OCR and
low ECAR or something in between high/moderate OCR with high/moderate ECAR. A finding highly relevant
to the situation in vivo is that when cultured under low glucose (1 mM) conditions, all cancer lines tested show
high–moderate OCR with very little ECAR (glycolysis).
1500 © 2016 The Author(s). This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY-NC-ND).
Biochemical Society Transactions (2016) 44 1499–1505
DOI: 10.1042/BST20160094
Positron emission tomography imaging and the reverse
Warburg effect
Positron emission tomography (PET) imaging uses a radioisotope-labelled glucose tracer,
18
F-fluorodeoxyglucose (
18
F-FDG), to identify areas of high glucose uptake/metabolism in the body.
18
F-FDG is
transported into cells by glucose transporters (GLUTs) and phosphorylated by hexokinase (HK) to
18
F-FDG-6-phosphate (
18
F-FDG-6-P). Once inside the cell,
18
F-FDG-6-P cannot be further metabolised
through the glycolytic pathway and due to its high polar nature becomes trapped. Tumours above a certain size
label strongly with this approach, and it is used to identify the presence of solid tumours and the effectiveness
of treatments. Other highly metabolically active tissues, such as the brain and heart, also label strongly. It is
believed that PET scans show an increased uptake of glucose in tumours due to overexpression of GLUTs
[41–43]. Historically, increased glucose uptake has been associated with supporting the Warburg effect [20].
However, high glucose uptake does not automatically equate to increased glycolysis and reduced mitochondrial
metabolism. An increased PET signal could be due to a general increase in glucose oxidation with increased
glycolysis and mitochondrial respiration or a high demand for lipids derived from glucose. In addition, over-
expression of GLUTs cannot be assumed to correlate with increased metabolic flux. A high PET signal would
be obtained if glucose entered the cancer cell and was not metabolised. Many tumours are characterised by
Figure 1. Bioenergetic profiles of cancer cell lines RD, RH30, U87MG, M059K, SF188, KNS42, UW479 and Res259.
OCR and ECAR are plotted to quantify mitochondrial respiration and to give an indication of glycolysis rates. OCR and ECAR
are expressed as changes in fluorescence life time/h/75 000 cells (n= 3). Assays were set up in black 96-well plates with
pre-incubation in 1 mM and 25 mM glucose media carried out for 16 h. Oxygen and glycolysis sensing probes: MitoXpress xtra
and pH xtra from Luxcel Biosciences were used to determine OCR and ECAR (http://luxcel.com/).
© 2016 The Author(s). This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY-NC-ND). 1501
Biochemical Society Transactions (2016) 44 1499–1505
DOI: 10.1042/BST20160094
increased levels of GLUTs (particularly GLUT 1 and GLUT 3) and high expression of HK (I and II), which is
associated with the increases in
18
F-FDG signal compared with surrounding tissues as reviewed in ref. [44].
However, not all cancers are easily detected by PET imaging, including renal clear cell carcinoma, which is a
prototypical Warburg cancer [45]. Conversely, not all
18
F-FDG avid tissue is malignant; inflammation can also
lead to a positive PET signal. Interestingly, Hodgkin’s lymphoma responds well to PET imaging [46]. This
tumour is less than 10% cancer cells, and the remaining cells are stromal/inflammation cells [47].
Although PET imaging is undeniably an extremely useful and an important clinical technique, there can be
issues with the interpretation of the image. It is a common problem that the PET images can often overestimate
the actual size of the tumour. A possible reason for this is that the microenvironment of the tumour is glyco-
lytic in a phenomenon that has been called the reverse Warburg effect. First postulated by Pavlides et al. in
2009 [48], the reverse Warburg effect describes how oxidative stress in the cancer-associated fibroblasts (CAF)
induces mitophagy and autophagy. The hypothesis is that hydrogen peroxide secreted from the cancer cells
leads to oxidative stress in the CAF. The fibroblasts then undergo cellular catabolism, which results in a loss of
mitochondrial function and ultimately a switch from aerobic metabolism to glycolysis [49]. This glycolytic
switch results in increased lactate production by CAF, which is then exported into the extracellular space by
monocarboxylate transporter 4 (MCT4). The lactate is ultimately taken up by the cancer cells via MCT1 and
used to fuel oxidative metabolism [50,51]. In ref. [50], the authors demonstrate support for the reverse
Warburg effect by culturing human breast cancer cell lines with human fibroblasts. Both MCF-7 and
MDA-MB-231, when co-cultured with fibroblasts, show reduced mitochondrial function in fibroblasts with
increased activity in the cancer cell lines. As in the studies of the Warburg effect and the impact of hypoxia
described earlier, studies to date on the reverse Warburg effect use high concentrations of glucose 25 mM. If
glucose is rapidly removed from the media by the cancer cells, this would tend to drive the cancer cells to use
mitochondrial respiration (see Figure 1). Under high glucose conditions, this property of cancer cell lines will
be held back by glucose inhibition of mitochondrial respiration.
Lactate was discovered in the late 1700s and was traditionally thought of as a waste product of glycolysis. In
reality, lactate is an extremely efficient fuel and also an important signalling molecule [52,53]. It is constantly
turned around in our cells, regardless of oxygenation state. Lactate is a key metabolite in the body, capable
of replacing glucose as an energy source. Lactate is also capable of stabilising hypoxia-inducing factor and
increasing vascular endothelial growth factor expression [53]. The fibroblast–tumour metabolic coupling
proposed to exist in the reverse Warburg effect is analogous to the metabolic symbiosis seen in the brain
(Figure 2). The brain is a metabolically demanding organ that gives high PET signals. Its avidity for glucose
was historically attributed to the neurons; however, it is the astrocytes that are glucose hungry and glycolytic.
The lactate secreted by the astrocytes then fuels the neurons, which use oxidative phosphorylation to generate
ATP [54]. The Warburg effect is not a universal feature of cancer, and similarly the reverse Warburg is not
universal in all tumours. Yoshida, in 2015, showed that tumours expressing high levels of MCT4 do no exhibit
the reverse Warburg effect [55]. The microenvironment of cancer is ever changing, and cancer cells can and
do vary in their metabolic phenotype even within the same tumour mass [56]. Although hard to generalise in
solid tumours with a hypoxic core, perhaps the Warburg effect most probably predominates with reduced
oxygen levels driving the cells to make the most of all available glucose. The more actively proliferating cells in
the periphery may perhaps use the lactate excreted in the hypoxic region and oxidise it [57], leading to a
symbiotic relationship between the hypoxic and aerobic cell populations. In vitro metabolism studies are
useful tools, but it only serves to hinder therapeutic translation when non-physiological glucose concentrations
up to and including 25 mM glucose are used instead of a physiological concentration of 5 mM (plasma) or
<5 mM (tissue).
In summary, we have sought here to revisit the Warburg effect and review its significance in cancer based on
recent advances in our knowledge and understanding of the complex biology underlying this disease. It is
indisputable that certain features of cancer are indeed hallmarks that are essential for most types of cancer.
However, looking to the future, the role of aerobic glycolysis needs further elucidation, as it is not a consistent
feature in all cancers. Recent research has shown there to be a broad spectrum of bioenergetic phenotypes dis-
played by cancer both in vivo and in vitro, with many cancer types displaying a surprising degree of mitochondrial
activity. When investigating the role of aerobic glycolysis in vitro, it is pertinent to use physiologically relevant
concentrations of nutrients, in particular glucose. The excessive glucose often found in cell culture media can
decrease mitochondrial respiration, allowing aerobic glycolysis to predominate. Reducing the glucose in the
media to physiological levels will give a truer picture of the complex metabolic processes at work.
1502 © 2016 The Author(s). This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY-NC-ND).
Biochemical Society Transactions (2016) 44 1499–1505
DOI: 10.1042/BST20160094
Abbreviations
18
F-FDG,
18
F-fluorodeoxyglucose;
18
F-FDG-6-P,
18
F-FDG-6-phosphate; ATP, adenosine triphosphate; CAF,
cancer-associated fibroblasts; ECAR, extracellular acidification rate; GLUT, glucose transporter; HK, hexokinase;
MCT4, monocarboxylate transporter 4; OCR, oxygen consumption rate; PET, positron emission tomography.
Figure 2. The astrocyte–neuron shuttle (A) and the reverse Warburg effect (B).
(A) Glutamate is released from activated synapses and taken up by astrocytes triggering an increase in glycolysis and lactate
production. The lactate can be oxidised by the neurons in response to their increased energy requirement to produce ATP.
(B) nIn the proposed reverse Warburg effect, hydrogen peroxide is secreted by cancer cells leading to oxidative stress in the
associated fibroblasts. The resulting loss of mitochondrial function acts as a switch from aerobic metabolism to glycolysis.
© 2016 The Author(s). This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY-NC-ND). 1503
Biochemical Society Transactions (2016) 44 1499–1505
DOI: 10.1042/BST20160094
Funding
Michelle Potter and Emma Newport were supported by Williams fund (http://www.williamsfund.co.uk/).
Competing Interests
The Authors declare that there are no competing interests associated with the manuscript.
References
1 Warburg, O., Wind, F. and Negelein, E. (1927) The metabolism of tumors in the body. J. Gen. Physiol. 8, 519–530 doi:10.1085/jgp.8.6.519
2 Cori, C.A. and Cori, G.T. (1925) The carbohydrate metabolism of tumours. J. Biol. Chem. 65, 397–405
3 Sakashita, M., Aoyama, N., Minami, R., Maekawa, S., Kuroda, K., Shirasaka, D. et al. (2001) Glut1 expression in T1 and T2 stage colorectal
carcinomas: its relationship to clinicopathological features. Eur. J. Cancer 37, 204–209 doi:10.1016/S0959-8049(00)00371-3
4 Grover-McKay, M., Walsh, S.A., Seftor, E.A., Thomas, P.A. and Hendrix, M.J.C. (1998) Role for glucose transporter 1 protein in human breast cancer.
Pathol. Oncol. Res. 4, 115–120 doi:10.1007/BF02904704
5 Wu, M., Neilson, A., Swift, A.L., Moran, R., Tamagnine, J., Parslow, D. et al. (2007) Multiparameter metabolic analysis reveals a close link between
attenuated mitochondrial bioenergetic function and enhanced glycolysis dependency in human tumor cells. Am. J. Physiol. Cell Physiol. 292,
C125–C136 doi:10.1152/ajpcell.00247.2006
6 Lai, J.-H., Jan, H.-J., Liu, L.-W., Lee, C.-C., Wang, S.-G., Hueng, D.-Y. et al. (2013) Nodal regulates energy metabolism in glioma cells by inducing
expression of hypoxia-inducible factor 1α.Neuro-Oncology 15, 1330–1341 doi:10.1093/neuonc/not086
7 Michelakis, E.D., Sutendra, G., Dromparis, P., Webster, L., Haromy, A., Niven, E. et al. (2010) Metabolic modulation of glioblastoma with dichloroacetate.
Sci. Transl. Med. 2, 31ra34 doi:10.1126/scitranslmed.3000677
8 Warburg, O. (1956) On the origin of cancer cells. Science 123, 309–314 doi:10.1126/science.123.3191.309
9 Warburg, O. (1956) On respiratory impairment in cancer cells. Science 124, 269–270 PMID: 13351639
10 DeBerardinis, R.J., Mancuso, A., Daikhin, E., Nissim, I., Yudkoff, M., Wehrli, S. et al. (2007) Beyond aerobic glycolysis: transformed cells can
engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. Proc. Natl Acad. Sci. USA 104, 19345–19350
doi:10.1073/pnas.0709747104
11 Fantin, V.R., St-Pierre, J. and Leder, P. (2006) Attenuation of LDH-A expression uncovers a link between glycolysis, mitochondrial physiology, and tumor
maintenance. Cancer Cell 9, 425–434 doi:10.1016/j.ccr.2006.04.023
12 Schulz, T.J., Thierbach, R., Voigt, A., Drewes, G., Mietzner, B., Steinberg, P. et al. (2006) Induction of oxidative metabolism by mitochondrial frataxin
inhibits cancer growth: OTTO WARBURG REVISITED. J. Biol. Chem. 281, 977–981 doi:10.1074/jbc.M511064200
13 Ju, Y.S., Alexandrov, L.B., Gerstung, M., Martincorena, I., Nik-Zainal, S., Ramakrishna, M. et al. (2014) Origins and functional consequences of somatic
mitochondrial DNA mutations in human cancer. eLife 3, e02935 doi:10.7554/eLife.02935
14 Xu, X.D., Shao, S.X., Jiang, H.P., Cao, Y.W., Wang, Y.H., Yang, X.C. et al. (2015) Warburg effect or reverse Warburg effect? A review of cancer
metabolism. Oncol. Res. Treat. 38, 117–122 doi:10.1159/000375435
15 Weinhouse, S. (1967) Hepatomas. Science 158, 542–545 doi:10.1126/science.158.3800.542
16 Jose, C., Bellance, N. and Rossignol, R. (2011) Choosing between glycolysis and oxidative phosphorylation: a tumor’s dilemma? Biochim. Biophys. Acta,
Bioenerg. 1807, 552–561 doi:10.1016/j.bbabio.2010.10.012
17 Moreno-Sánchez, R., Rodríguez-Enríquez, S., Marín-Hernández, A. and Saavedra, E. (2007) Energy metabolism in tumor cells. FEBS J. 274,
1393–1418 doi:10.1111/j.1742-4658.2007.05686.x
18 Martin, M., Beauvoit, B., Voisin, P.J., Canioni, P., Guérin, B. and Rigoulet, M. (1998) Energetic and morphological plasticity of C6 glioma cells grown on
3-D support; effect of transient glutamine deprivation. J. Bioenerg. Biomembr. 30, 565–578 doi:10.1023/A:1020584517588
19 Obre, E. and Rossignol, R. (2015) Emerging concepts in bioenergetics and cancer research: metabolic flexibility, coupling, symbiosis, switch, oxidative
tumors, metabolic remodeling, signaling and bioenergetic therapy. Int. J. Biochem. Cell Biol. 59, 167–181 doi:10.1016/j.biocel.2014.12.008
20 Zu, X.L. and Guppy, M. (2004) Cancer metabolism: facts, fantasy, and fiction. Biochem. Biophys. Res. Commun. 313, 459–465 doi:10.1016/j.bbrc.
2003.11.136
21 Astuti, D., Latif, F., Dallol, A., Dahia, P.L.M., Douglas, F., George, E. et al. (2001) Gene mutations in the succinate dehydrogenase subunit SDHB cause
susceptibility to familial pheochromocytoma and to familial paraganglioma. Am. J. Hum. Genet. 69,49–54 doi:10.1086/321282
22 Baysal, B.E., Ferrell, R.E., Willett-Brozick, J.E., Lawrence, E.C., Myssiorek, D., Bosch, A. et al. (2000) Mutations in SDHD, a mitochondrial complex II
gene, in hereditary paraganglioma. Science 287, 848–851 doi:10.1126/science.287.5454.848
23 Dang, L., White, D.W., Gross, S., Bennett, B.D., Bittinger, M.A., Driggers, E.M. et al. (2009) Cancer-associated IDH1 mutations produce
2-hydroxyglutarate. Nature 462, 739–744 doi:10.1038/nature08617
24 Yan, H., Bigner, D.D., Velculescu, V. and Parsons, D.W. (2009) Mutant metabolic enzymes are at the origin of gliomas. Cancer Res. 69, 9157–9159
doi:10.1158/0008-5472.CAN-09-2650
25 Hanahan, D. and Weinberg, R.A. (2000) The hallmarks of cancer. Cell 100,57–70 doi:10.1016/S0092-8674(00)81683-9
26 Hanahan, D. and Weinberg, R.A. (2011) Hallmarks of cancer: the next generation. Cell 144, 646–674 doi:10.1016/j.cell.2011.02.013
27 Gatenby, R.A. and Gillies, R.J. (2004) Why do cancers have high aerobic glycolysis? Nat. Rev. Cancer 4, 891–899 doi:10.1038/nrc1478
28 Gogvadze, V., Orrenius, S. and Zhivotovsky, B. (2008) Mitochondria in cancer cells: what is so special about them? Trends Cell Biol. 18,
165–173 doi:10.1016/j.tcb.2008.01.006
29 Weinhouse, S., Shatton, J.B., Criss, W.E. and Morris, H.P. (1972) Molecular forms of enzymes in cancer. Biochimie 54, 685–693 doi:10.1016/
S0300-9084(72)80167-6
30 Bettum, I.J., Gorad, S.S., Barkovskaya, A., Pettersen, S., Moestue, S.A., Vasiliauskaite, K. et al. (2015) Metabolic reprogramming supports the invasive
phenotype in malignant melanoma. Cancer Lett. 366,71–83 doi:10.1016/j.canlet.2015.06.006
31 Iyer, N.V., Kotch, L.E., Agani, F., Leung, S.W., Laughner, E., Wenger, R.H. et al. (1998) Cellular and developmental control of O
2
homeostasis by
hypoxia-inducible factor 1α.Genes Dev. 12, 149–162 doi:10.1101/gad.12.2.149
1504 © 2016 The Author(s). This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY-NC-ND).
Biochemical Society Transactions (2016) 44 1499–1505
DOI: 10.1042/BST20160094
32 Kim, J.-w., Tchernyshyov, I., Semenza, G.L. and Dang, C.V. (2006) HIF-1-mediated expression of pyruvate dehydrogenase kinase: a metabolic switch
required for cellular adaptation to hypoxia. Cell Metab. 3, 177–185 doi:10.1016/j.cmet.2006.02.002
33 Papandreou, I., Cairns, R.A., Fontana, L., Lim, A.L. and Denko, N.C. (2006) HIF-1 mediates adaptation to hypoxia by actively downregulating
mitochondrial oxygen consumption. Cell Metab. 3, 187–197 doi:10.1016/j.cmet.2006.01.012
34 Seagroves, T.N., Ryan, H.E., Lu, H., Wouters, B.G., Knapp, M., Thibault, P. et al. (2001) Transcription factor HIF-1 is a necessary mediator of the
Pasteur effect in mammalian cells. Mol. Cell. Biol. 21, 3436–3444 doi:10.1128/MCB.21.10.3436-3444.2001
35 Quinn, B.J., Kitagawa, H., Memmott, R.M., Gills, J.J. and Dennis, P.A. (2013) Repositioning metformin for cancer prevention and treatment. Trends
Endocrinol. Metab. 24, 469–480 doi:10.1016/j.tem.2013.05.004
36 Yu, H., Bian, X., Gu, D. and He, X. (2016) Metformin synergistically enhances cisplatin-induced cytotoxicity in esophageal squamous cancer cells under
glucose-deprivation conditions. Biomed. Res. Int. 2016, 8 doi:10.1155/2016/8678634
37 El-Mir, M.-Y., Nogueira, V., Fontaine, E., Averet, N., Rigoulet, M. and Leverve, X. (2000) Dimethylbiguanide inhibits cell respiration via an indirect effect
targeted on the respiratory chain complex I. J. Biol. Chem. 275, 223–228 doi:10.1074/jbc.275.1.223
38 Owen, M.R., Doran, E. and Halestrap, A.P. (2000) Evidence that metformin exerts its anti-diabetic effects through inhibition of complex 1 of the
mitochondrial respiratory chain. Biochem. J. 348, 607–614 doi:10.1042/bj3480607
39 Gohil, V.M., Sheth, S.A., Nilsson, R., Wojtovich, A.P., Lee, J.H., Perocchi, F. et al. (2010) Nutrient-sensitized screening for drugs that shift energy
metabolism from mitochondrial respiration to glycolysis. Nat. Biotechnol. 28, 249–255 doi:10.1038/nbt.1606
40 Marroquin, L.D., Hynes, J., Dykens, J.A., Jamieson, J.D. and Will, Y. (2007) Circumventing the Crabtree effect: replacing media glucose with galactose
increases susceptibility of HepG2 cells to mitochondrial toxicants. Toxicol. Sci. 97, 539–547 doi:10.1093/toxsci/kfm052
41 Park, S.G., Lee, J.H., Lee, W.A. and Han, K.M. (2012) Biologic correlation between glucose transporters, hexokinase-II, Ki-67 and FDG uptake in
malignant melanoma. Nucl. Med. Biol. 39, 1167–1172 doi:10.1016/j.nucmedbio.2012.07.003
42 Riedl, C.C., Brader, P., Zanzonico, P., Reid, V., Woo, Y., Wen, B. et al. (2008) Tumor hypoxia imaging in orthotopic liver tumors and peritoneal
metastasis: a comparative study featuring dynamic
18
F-MISO and
124
I-IAZG PET in the same study cohort. Eur. J. Nucl. Med. Mol. Imaging 35,39–46
doi:10.1007/s00259-007-0522-2
43 Yen, T.C., See, L.C., Lai, C.H., Yah-Huei, C.W., Ng, K.K., Ma, S.Y., et al. (2004)
18
F-FDG uptake in squamous cell carcinoma of the cervix is correlated
with glucose transporter 1 expression. J. Nucl. Med. 45,22–29 PMID: 14734665
44 Barron, C.C., Bilan, P.J., Tsakiridis, T. and Tsiani, E. (2016) Facilitative glucose transporters: implications for cancer detection, prognosis and treatment.
Metabolism 65, 124–139 doi:10.1016/j.metabol.2015.10.007
45 Sotgia, F., Martinez-Outschoorn, U.E., Pavlides, S., Howell, A., Pestell, R.G. and Lisanti, M.P. (2011) Understanding the Warburg effect and the
prognostic value of stromal caveolin-1 as a marker of a lethal tumor microenvironment. Breast Cancer Res. 13, 213 doi:10.1186/bcr2892
46 Weiler-Sagie, M., Bushelev, O., Epelbaum, R., Dann, E.J., Haim, N., Avivi, I. et al. (2010)
18
F-FDG avidity in lymphoma readdressed: a study of 766
patients. J. Nucl. Med. 51,25–30 doi:10.2967/jnumed.109.067892
47 Aldinucci, D., Gloghini, A., Pinto, A., De Filippi, R. and Carbone, A. (2010) The classical Hodgkin’s lymphoma microenvironment and its role in
promoting tumour growth and immune escape. J. Pathol. 221, 248–263 doi:10.1002/path.2711
48 Pavlides, S., Whitaker-Menezes, D., Castello-Cros, R., Flomenberg, N., Witkiewicz, A.K., Frank, P.G. et al. (2009) The reverse Warburg effect: aerobic
glycolysis in cancer associated fibroblasts and the tumor stroma. Cell Cycle 8, 3984–4001 doi:10.4161/cc.8.23.10238
49 Wallace, D.C. (2012) Mitochondria and cancer. Nat. Rev. Cancer 12, 685–698 doi:10.1038/nrc3365
50 Martinez-Outschoorn, U.E., Lin, Z., Trimmer, C., Flomenberg, N., Wang, C., Pavlides, S. et al. (2011) Cancer cells metabolically ‘fertilize’the tumor
microenvironment with hydrogen peroxide, driving the Warburg effect: implications for PET imaging of human tumors. Cell Cycle 10, 2504–2520 doi:10.
4161/cc.10.15.16585
51 Whitaker-Menezes, D., Martinez-Outschoorn, U.E., Lin, Z., Ertel, A., Flomenberg, N., Witkiewicz, A.K. et al. (2011) Evidence for a stromal-epithelial
‘lactate shuttle’in human tumors. Cell Cycle 10, 1772–1783 doi:10.4161/cc.10.11.15659
52 Gladden, L.B. (2004) Lactate metabolism: a new paradigm for the third millennium. J. Physiol. 558,5–30 doi:10.1113/jphysiol.2003.058701
53 Goodwin, M.L., Gladden, L.B., Nijsten, M.W.N. and Jones, K.B. (2015) Lactate and cancer: revisiting the Warburg effect in an era of lactate shuttling.
Front. Nutr. 1, 27 doi:10.3389/fnut.2014.00027
54 Itoh, Y., Esaki, T., Shimoji, K., Cook, M., Law, M. J., Kaufman, E. et al. (2003) Dichloroacetate effects on glucose and lactate oxidation by neurons and
astroglia in vitro and on glucose utilization by brain in vivo. Proc. Natl Acad. Sci. USA 100, 4879–4884 doi:10.1073/pnas.0831078100
55 Yoshida, G.J. (2015) Metabolic reprogramming: the emerging concept and associated therapeutic strategies. J. Exp. Clin. Cancer Res. 34, 111 doi:10.
1186/s13046-015-0221-y
56 Lee, M. and Yoon, J.-H. (2015) Metabolic interplay between glycolysis and mitochondrial oxidation: the reverse Warburg effect and its therapeutic
implication. World J. Biol. Chem. 6, 148–161 doi:10.4331/wjbc.v6.i3.148
57 Sonveaux, P., Végran, F., Schroeder, T., Wergin, M.C., Verrax, J., Rabbani, Z.N. et al. (2008) Targeting lactate-fueled respiration selectively kills hypoxic
tumor cells in mice. J. Clin. Invest. 118, 3930–3942 doi:10.1172/JCI36843
© 2016 The Author(s). This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY-NC-ND). 1505
Biochemical Society Transactions (2016) 44 1499–1505
DOI: 10.1042/BST20160094