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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Biomark Med. Author manuscript; available in PMC 2010 April 1.
Published in final edited form as:
PMCID: PMC2756767

Use of nuclear magnetic resonance-based metabolomics in detecting drug resistance in cancer


Cancer cells possess a highly unique metabolic phenotype, which is characterized by high glucose uptake, increased glycolytic activity, decreased mitochondrial activity, low bioenergetic and increased phospholipid turnover. These metabolic hallmarks can be readily assessed by metabolic technologies – either in vitro or in vivo – to monitor responsiveness and resistance to novel targeted drugs, where specific inhibition of cell proliferation (cytostatic effect) occurs rather than direct induction of cell death (cytotoxicity). Using modern analytical technologies in combination with statistical approaches, ‘metabolomics’, a global metabolic profile on patient samples can be established and validated for responders and nonresponders, providing additional metabolic end points. Discovered metabolic end points should be translated into noninvasive metabolic imaging protocols.

Keywords: anticancer treatment, cancer biomarkers, choline metabolism, quantitative metabolomics, signal transduction inhibitor, Warburg effect

The development of novel anticancer treatment regimens requires both the precise identification of the patient population that will most likely respond to specific targeted therapy and reliable surrogate markers to follow-up treatment efficacy. The concept of a biomarker can encompass anything from a single molecular species (gene, protein, metabolite) to a complex fingerprint of molecular changes (genomics, proteomics, metabolomics) indicative of human pathology, for example, cancer [14] . Biomarkers are quantitative measurements of biologic homeostasis that can (specifically and sensitively) distinguish between ‘normal’ and ‘abnormal’. Ideally, their changes will be highly specific to therapeutic intervention, such as discriminating between ‘responders’ and ‘nonresponders’. Existing biomarkers include: radiological (tumor size and metastasis detection), genomic (Philadelphia chromosome), proteomic (Bcr-Abl, EGF receptor [EGFR] or CA125) or metabolic parameters (glucose uptake, choline or citrate spectroscopic images). Radiological end points, by MRI or computed tomography (CT) remain a gold standard for tumor detection and general assessment of treatment efficacy. Gene and protein biomarkers (using gene arrays or conventional northern/western blots) can be superior for initial patient selection to the specific targeted drug regimen, while metabolic markers by noninvasive magnetic resonance spectroscopy/ spectroscopical imaging (MRS/MRSI) or PET are highly promising in following the efficacy of targeted treatment (FIGURE 1). Thus, one of the most important advantages of nuclear magnetic resonance (NMR)-based metabolomics is that after initial (invasive or semi-invasive) tissue analysis using high-resolution NMR spectrometers, the distinguished metabolic markers can be followed noninvasively in the body using clinical MR scanners, by collecting in vivo MRS (spectra) or MRSI (metabolite imaging) in the tissue. The selected metabolite must, however, be highly abundant in the tissue of interest in order to be visible by MRS and/or MRSI.

Figure 1
Potential applications and benefits of genomics, proteomics and metabolomics technologies in individualized medicine for cancer patients

Metabolomics, an ‘omic’ science in systems biology, is the global quantitative assessment of endogenous metabolites within a biologic system. Metabolomics is a term that encompasses several types of analyses, including:

  • Metabolic fingerprinting, which measures a subset of the whole profile followed by multivariate spectral analysis (principal component analysis, partial least square discriminant analysis) with little differentiation or quantitation of metabolites;
  • Metabolic profiling, the quantitative study of a group of metabolites, known or unknown, within or associated with a particular metabolic pathway;
  • Target isotope-based analysis, which focuses on a particular segment of the metabolome by analyzing only a few selected metabolites that comprise a specific biochemical pathway [213].

Interestingly, in cancer (unlike in toxicology), only a limited number of metabolomics studies [8,9] have utilized pattern recognition techniques on spectral data sets without identifying precise metabolic biomarkers of a specific cancer type. Most of the current metabolic studies are conducted in a mechanistic pathway-discovering manner, where identified and quantified metabolic markers (either as single metabolites or as metabolic pathways) provide precise insight into cancer biology [1013] . This may be partly due to the compromised history of cancer metabolomics, dating back to the reported scientific misconduct of using a nontargeted multivariate NMR approach on the blood of cancer patients [14, 15] . Another factor in support of precise metabolite identification and validation is the translational application of in vivo metabolomics or metabolic imaging (FIGURE 1).

Toxicology remains the major focus for NMR-based metabolomics of body fluids. Reported metabolic markers of nephro- and hepato-toxicity (two major target organs of drug toxicity) can be easily assessed in urine and/or blood owing to the crucial role of kidney and liver in performing global body metabolism. Unfortunately, the situation in oncology is much more complex. Solid tumors, even when quite large, have less impact on total body metabolism than kidney or liver dysfunction. The presence of circulating tumor cells in the blood is also not adequate to disturb metabolic homeostasis (since sensitivity of NMR-based metabolomics in detection of metabolic abnormalities is relatively low). Therefore, the most predictable way to assess metabolic abnormalities in tumor progression and responsiveness to anticancer therapies still relies on metabolic assessment of the tumor tissue itself. However, performing biopsies of solid tumors in patients on a routine basis to assess responsiveness to therapy is obviously not feasible. Hence, in vitro-discovered tumor tissue bio-markers need to be translated into noninvasive imaging protocols for routine assessment of treatment response. The comprehensive work of colleagues, both clinical and basic science, has demonstrated a clear proof of concept for in vitro-discovered, in vivo-validated biomark-ers in translational metabolic profiling research [3,4,16,17]. As of today, two major metabolic pathways – namely glucose metabolism and phospholipid turnover – have been repeatedly reported to be involved in oncogenesis and their possible role in early prediction of therapy response is under investigation using both in vitro metabolomics, and in vivo metabolic imaging [14,6,8,11,13,1623]. In addition to these general metabolic markers of malignancy, specific endogenous metabolites are implicated in particular tumors such as N-acetyl aspartate in neuroblastoma, myo-inositol in glioma, and citrate in prostate cancer, based on tissue-specific biochemistry.

This review describes existing NMR-based approaches for global metabolic profiling in tissue biopsies, body fluids and, finally, noninvasive assessment of metabolic biomarkers using noninvasive radiological techniques. The most recent studies on metabolic response and resistance development to novel targeted drugs (tyrosine kinase inhibitors [TKIs], metabolic modulators) are analyzed.

Metabolomics technologies

Either individually or grouped as a metabolomic profile, the detection of metabolites is usually carried out in cells, tissues or biofluids by either NMR spectroscopy or mass spectrometry (MS) [17]. In general, NMR spectroscopy, mostly 1H-NMR, and MS, particularly liquid chromatography (LC)-MS and Fourier-transform ion cyclotron resonance-MS, are the two major spectroscopic techniques utilized in metabolic analysis. The basic workflow for NMR- as well as MS-based studies is as follows: quenching/extraction of metabolites → data collection → data processing/analysis [1,24]. NMR exploits the behavior of molecules when placed in a magnetic field, allowing the identification of different nuclei based on their resonant frequency when exposed to magnetic fields. MS determines the composition of particles based on the mass-to-charge ratio in charged particles. The resultant metabolite detection and quantification is acquired as a data set known as a spectrum. Each technique has distinct advantages and disadvantages [25]. The major advantages of 1H–NMR include its non-biased metabolite detection, quantitative nature and reproducibility [14]. 1H-NMR can be used for liquid or solid samples, using magic angle spinning (HR-MAS) techniques, with minimal sample preparation. 31P-NMR of tissue specimens and cultured cells reflects products of energy or phospholipid metabolism, whereas 13C-NMR measures dynamic carbon fluxes, such as glucose metabolism [6]. 13C-NMR can be performed on tissues and cell extracts following incubation with a 13C-labelled precursor [6,18]. Another significant advantage of NMR is that metabolic markers discovered and analyzed in vitro can be measured in vivo, presuming their high intra-tissue abundance, using another MR-based technology, namely localized MRSI (FIGURE 1).

Information on sample requirements and handling for metabolomics analysis has been published previously [1]. In brief, all biological samples collected for metabolic analysis require careful sample handling, such as special requirements for diet, physical activities and other patient validation prior to sample collection, as well as maintaining low temperature and consistent sample extraction afterwards (due to the high susceptibility of metabolic pathways to exogenous environment) . For biofluids, the standard sample volume is accepted to be in the range of 0.1–0.5 cc. For NMR, minimal sample preparation is required for urine and other low-molecular-weight metabolite-containing fluids (TABLE 1). Blood, plasma and serum can yield more metabolic information if extraction (using acid, acetonitrile or two-phase methanol/ chloroform protocols) or NMR-weighted techniques are applied to separate polar and lipophilic metabolites. Cancer cells and/or tissue biopsies usually undergo acidic extraction (7–12% percholric acid, for example) or dual-phase methanol chloroform extraction. The extraction step and consequent lyophilization of the extract allows for:

  • Separation of lipid- and water-soluble metabolites to prevent peak overlapping
  • Precipitation of proteins and other macromolecules to avoid peak contamination and line broadening
  • Obtaining liquid-state spectra from tissue specimens with high resolution and low-line broadening
Table 1
Summary of biomaterials used for 1H-NMR metabolic ana lysis in various areas of biomedical research.

Based on our experience, acidic extraction allows for better recovery of energy-rich phosphate metabolites, while dual-phase extraction has a better recovery rate for lipid-containing metabolites. Lyophilized extracts (body fluids, cells, tissues, cell culture media) are then usually redissolved in deuterated solvents and analyzed by conventional 5-mm high-resolution NMR probes (for 1H-, 13C- and 31P-NMR, typically). The usual magnetic field strength for metabolic studies is between 7.0 and 14.1 Tesla (300–600 MHz for proton frequency). Some NMR-based cell metabolic studies (especially those using 13C-fluxes by 13C-NMR or ATP kinetics and pH calculation by 31P–NMR) have used perfused cultured cells using large-diameter probes (10- to 20-cm instead of conventional 5-cm probes). Finally, intact tissue specimens (e.g., biopsies, fine needle aspirates) are increasingly used with the introduction of HR-MAS. HR-MAS probes for solid-state NMR, as well as cryoprobes and microprobes for liquid NMR, allow for quantitative NMR metabolic ana lysis on samples as small as 3 µl with significantly improved signal-to-noise ratios and solvent suppression. In general, there is no standardized protocol for sample preparation for NMR analysis, and the protocols presented here are based on literature reports and our experience in the field of NMR metabolomics in the past 15 years.

While NMR exploits the behavior of molecules when placed in a magnetic field, allowing the identification of different nuclei based on their resonant frequency, MS determines the composition of particles based on the mass:charge ratio in charged particles. The MS ana lysis requires more labor-intense (and destructive) tissue preparation than NMR, but its advantage is higher sensitivity for metabolite detection. MS is highly sensitive, typically at the picogram level, and allows for specificity in metabolite identification at low concentrations and for more compounds to be screened when compared with NMR. While polar molecules may be detected when electrospray ionization is used, nonpolar molecules may require atmospheric pressure chemical ionization. Similarly, the methods of extraction, quenching, and sample storage conditions can affect and potentially modify metabolite structure, thereby confounding already complex data sets and introducing more sample-to-sample variability. Although high-resolution profiling methods exist for gas chromatography mass spectrometry profiling, detectable compounds are limited to those that can be derivatized, and derivatiza-tion can be time consuming and costly, and there is a risk of metabolite loss. Conversely, LC/MS has only recently begun to be applied to metabolic profiling owing to recent major advances in chromatography, instrumentation, ionization capabilities and software. In spite of a rich history of discovery- and targeted-based methods in small molecules using MS, a widely adopted and validated methodology for sensitive, high-throughput discovery-based LC/MS metabolomics is still lacking.

Cancer metabolic phenotype

Cancer represents an ideal application for metabolic profiling. The biochemistry of tumors, especially glucose and phospholipid metabolism, differs dramatically from normal cells. Since a Nobel Prize was nominated to Otto Warburg in 1931[26, 201] , it has been widely known that cancer cells possess unique metabolic machinery to support their loss of cell cycle regulation. TABLE 2 shows major metabolic hallmarks of cancer cells, all of them related to high proliferation rate, mitochondrial dysfunction and increased glycolytic rate.

Table 2
Metabolic phenotype of human tumors assessed by magnetic resonance technique.

Warburg effect & glucose metabolism

The first identified biochemical abnormality of oncogenesis, identified by Otto Warburg in the 1920s [26,201], was a shift in glucose metabolism from oxidative phosphorylation to ‘aerobic’ glycolysis, meaning that tumors exhibit anaerobic metabolism (lactate production through glycolysis) even in the presence of adequate oxygen [2731]. In cancer cells, glucose is increasingly transported into the cel l by glucose transporters (Glut-1 is most relevant for oncology owing to its overexpression in the majority of cancers and its application in fluorodeoxyglucose [FDG]-PET; Glut-3 over-expression has also been reported in a wide range of cancers) and undergoes glycolysis and the pentose phosphate pathway in the cytosolic fraction (FIGURE 2). Pyruvate, an intermediate of glycolysis, is then directly metabolized to lac-tate (the end product of glycolysis) by lactate dehydrogenase (LDH) or transported into the mitochondrion to enter the tricarboxylic acid (TCA) cycle and oxidative phosphorylation (impaired pathways in cancer). In cancer cells, TCA intermediates are exported from the mito-chondrial matrix and become available for the synthesis of fatty acids. [1-13C]glucose fluxes through glycolysis and the TCA cycles can be followed using 13C-NMR (FIGURE 2).

Figure 2
[1-13C]glucose metabolic pathway

The use of quantitative and/or flux-based metabolic approaches in conjunction with classic biochemical assays has demonstrated that in cancer, metabolic changes are more commonly related to altered kinetic activities of enzymes. Increased lactate production was accompanied by increased activities of hexokinase 1 and 2, phosphofructokinase, aldolase, M2-pyruvate kinase and LDH, all key enzymes of glycoly-sis (FIGURE 2) [2731]. In addition, ATP citrate lyase and fatty acid synthase, which synthesize long-chain fatty acids from citrate and acetyl-CoA, are upregulated or activated in many cancers [27,28,30]. In addition to the upregulation of glycolytic enzymes, downregulation of the TCA-cycle enzymes and oxidative phosphoryla-tion has been reported. Pyruvate dehydrogenase (PDH, one of the key enzymes in the TCA cycle) activity is inhibited through phosphorylation by pyruvate dehydrogenase kinase (PDK)1, which being a hypoxia inducible factor (HIF)-1 target gene, is highly upregulated in various cancers [27] . Mitochondrial DNA abnormalities, mostly due to a combined complex I/III defect and increased reactive oxygen species production, have been reported for a variety of cancers [32, 33] .

Cell proliferation, vitality and biosynthesis rely highly on energy state. Therefore, the most obvious explanation for increased glycolysis is the rapid (but not efficient) production of ATP, especially in hypoxic cancers. The net result of lactate production is two ATP molecules, while the mitochondrial pathway yields 36 ATP molecules from the one glucose molecule metabolized (FIGURE 2) [34] . Therefore, more glucose molecules need to be utilized in order to produce enough ATP through rapid but inefficient glycolysis. Recent evidence demonstrates, however, that the increased energy demand is only a partial explanation for the increased glycolytic activity, especially in metastases. Lactic acid is the principa end product of glycolysis and is exported from the cancer cell through the monocarboxylate transporter-4. Once exported, it results in an acidic tumor microenvironment that favors tumor invasion [28,35,36]

One hypothesis for the possible molecular mechanisms leading to the upregulation of glycolysis in cancer cells is related to the HIF-1 pathway. It has been demonstrated that stabilization of the HIF-1 transcription factor leads to upregulation of LDH transcription and other genes involved in glucose transport, metabolism and pH regulation [37]. HIF-1 also induces PDK1 transcription, which inactivates PDH (as mentioned above) and as such inhibits the major key enzymes of the mitochondrial TCA cycle. However, in 2004, Craig Thompson reported that activated AKT (upstream regulator of the HIF-1 signaling pathway), independent of HIF-1, can convert cancer cells to glycolysis [38] . Since then, pioneering work has provided evidence that upregulation of the AKT/PI3K/ TOR signal transduction pathway (upstream of HIF-1 stabilization) induces hexokinase II activity, which by the attachment to mito-chondrial porin, redirects mitochondrial ATP to phopshorylate glucose and drives glycolysis (FIGURE 3) [33] . Interestingly, it has recently been shown that it is not only AKT that stimulates glycolysis, but that defects in mitochondrial respiration can lead to activation of AKT [29,30] , revealing a complex pathway of crossregula-tion between signal transduction pathways and metabolic programming in cancer cells.

Figure 3
Metabolic reprogramming by PI3K/AKT/TOR and Ras/Raf/ERK/MAPK signal transduction pathways

Concurrent with these basic research discoveries on the enhanced glycolytic phenotype of cancer cells has been the increasing clinical use of FDG-PET, which is based on the observation that glucose uptake is functionally altered in a wide variety of human cancers [27,39] .

Kennedy pathway & phospholipid metabolism

Phopshatidylcholine (PC) is the major phospho-lipid in the cell membrane. In cancer, choline metabolism is characterized by an elevation of phosphocholine (PCho) and total choline-con-taining compounds [17, 40] . Recent evidence demonstrates that increased PCho concentration is not a simple indicator of the increased phospholipids production necessary for cancer cells to meet higher proliferation requirements, but likely represents the result of activation of PC-cycle enzymes, caused by genetically induced changes in growth factor-mediated cell signaling pathways. Genetic alterations, as well as changes in Ras signaling pathways, affect expression and post-translation-ally regulated activity levels of these enzymes, thereby leading to changes in the overall cho-line phospholipid metabolite profile [17] . The de novo biosynthesis of PC occurs via the three-step Kennedy pathway in which choline is phos-phorylated into PCho by choline kinase (Chok), PCho is converted into cytidine 5-diphosphocho-line (CDP)-choline by cytidylyltransferase and CDP-choline is incorporated into PC by choline phosphotransferase (FIGURE 4) [17,4042] .

Figure 4
Biosynthetic (solid lines) and catabolic (dashed lines) pathways of choline phospholipid metabolism (the Kennedy pathway)

Chok activity, mRNA, and protein levels were elevated in a rat colon-cancer model and Chok activity and PC levels were increased in human colon cancers and adenomas [43]. Overall, phopshorylation of choline to PCho is increased five- to 12-fold in various cancers [44]. Also, choline transport into the cell has been demonstrated to be increased in malignant cells owing to overexpression of the choline high-affinity transporter-1 and choline transport-like protein, which are highly expressed in the colorectal cancer cell line, HT-29 [4446]. In recent years, it has been demonstrated that in patients with early-stage non-small-cell lung cancer, expression of Chok is prognostic: 4-year lung cancer-specific survival was lower for patients with elevated Chok expression compared with patients with low levels of the enzyme (46.66 vs 67.01%, respectively) [47] . Downregulation of Chok by using siRNAs against Chok was associated with decreased proliferation, increased differentiation and increased sensitivity to 5-fluorouracil in breast cancer cell lines [48,49] , suggesting possible therapeutic targets of the Kennedy pathway.

Similarly to glucose metabolism, HIF-1 signaling has also been shown to be involved in upregulation of the Kennedy pathway [50] . HIF-1 can directly bind to the endogenous Chok promoter, and trigger upregulation of Chok and choline phosphorylation. Again, another HIF-1-independent activation of the Ras/Raf/ ERK/MAPK pathway in human breast as well as colorectal cancer cells, was associated with increased levels of PCho [51] . Inhibition by a MEK inhibitor, U0126, was associated with a time-dependent decline in PC levels, which correlated well with the decrease in ERK1/2 phos-phorylation [52] (FIGURE 4). The same group has also demonstrated that blockade of PI3K with LY294002 (as evidenced by decreased P-AKT levels) was associated with a partial fall in PC level [53] . However, the precise molecular mechanisms of Kennedy pathway regulation by RAS/ Raf/ERK and/or by PIT3K/AKT pathway (to a lesser extent), remain unclear.

Similar to FDG-PET protocols for glucose uptake, clinical protocols are under development in order to monitor choline uptake and metabolism in cancer patients using 1H-MRS/MRSI and fluorine-18- or carbon-11-labeled choline derivatives by PET [17, 20,54] .

Novel anticancer agents

Cytotoxic chemotherapeutics are a major component of the treatment of neoplastic disease. Chemotherapeutic agents are used for the initial treatment of several types of solid tumors, as well as in the neoadjuvant and adjuvant setting and in palliative care. Cytotoxic agents are capable of reducing tumor burden, prolonging life and even cure. The primary mechanism of action of chemotherapeutic agents is perturbation of the cellular processes involved in proliferation: DNA synthesis and cell division. These effects often result in cell death by either apoptosis or necrosis. There are several different classes of chemotherapeutic agents; alkylating agents, antimetabolites and natural products. Here, we provide a cursory overview of the different classes of agents and their general mechanisms of action. More detailed reviews may be found elsewhere [55,56] .

Alkylating agents were among the first agents to be used for systemic cancer therapy and include groups of agents such as nitrogen mustards (cyclophosphamide, melphan, mech-lorethamine), nitrosoureas (carmustine, strep-tozocin), triazenes (dacarbazine, temozolomide) and platinum analogs (cisplatin, carboplatin, oxaliplatin). Because alkylating agents bind directly to DNA and cause either single-strand breaks or crosslinking of DNA, their activity is not cell-cycle specific and therapeutic efficacy is primarily dependent upon exposure (area under curve), and is relatively independent of schedule. Antimetabolites consist of folic acid analogs (methotrexate, pemetrexed), pyrimidine analogs (5-fluorouracil, cytosine arabinoside, gemcitabine), and purine analogs (6-mercapto-purine). Antimetabolites disrupt DNA synthesis and cell division by the inhibiting formation of normal neucleotides or by direct interaction with DNA, which prevents extension of DNA strands. Most antimetabolites exert their effects in a specific phase of the cell-cycle and, as a result, the drugs can only have an effect on a fraction of the dividing cells at any given time. Therefore, the duration of exposure (the time above a critical threshold concentration) is more important than achieving maximum plasma drug concentrations. Natural products consist of several different types of agents that elicit their effects in a variety of ways. Vinca alkaloids (vin-cristine and vinblastine) and taxanes (paclitaxel and docetaxel), are antimitotic agents that disrupt the microtubule polymerization and depo-lymerization processes. Camptothecin analogs (topotecan and irinotecan) bind to and stabilize the topoisomerase I–DNA complex, which ultimately results in an irreversible double-strand DNA break. Both the antimicrotuble agents and camptothecin analogs are cell-cycle specific and toxicity is dependent upon both drug concentration and duration of exposure. There are several mechanisms that contribute to anthra-cycline antibiotic (daunorubicin, doxorubicin, epirubicin, idarubicin and mitoxantrone) cyto-toxic effects; DNA intercalation, which directly affects transcription and replication, interaction with the DNA–topoisomerase II complex, which inhibits the relegation of DNA strands, and the generation of free radicals, which can oxidize DNA neucleotide bases.

Chemotherapeutics are primarily administered on a maximum tolerated-dose (MTD) schedule [57] . The premise behind MTD dosing is that, in general, larger fractions of the tumor cell population are killed at higher concentrations of cytotoxic agents. Therefore, the idea is to expose the tumor to the highest possible dose as frequently as possible before serious toxicity occurs. Implementation of MTD-based dosing is further justified by the proven dose–response effect of tumors to several different cytotoxic chemo-theraptic agents, including cyclophosphamide, doxorubicin, methotrexate and cisplatin [58, 59] . However, since chemotherapeutic drugs nonspe-cifically target dividing cells, some normal dividing cells are affected, causing side effects such as neutropenia, myelosuppression, alopecia, intestinal mucosa damage-induced diarrhea, nausea, mucositis, and neurological and reproductive damage. The harsh side effects necessitate a recovery or ‘break’ period that usually spans 2–3 weeks. Breaks in conjunction with growth-factor support, frequent ly used to help accelerate recovery from toxicity [60], allow the tumor to repopu-late and damaged tumor vasculature to repair [61] . Similar to the dividing tumor cells, proliferating endothelial cells of the tumor vasculature should be susceptible to conventional chemotherapeutic drugs. However, antiangiogenic effects are not optimized when chemotherapeutics are given on a MTD schedule [57] .

The administration of chemotherapeutics at lower doses at more frequent intervals, termed ‘antiangiogenic’ or ‘metronomic’ scheduling, has been shown to reduce host toxicity. Metronomic chemotherapeutic scheduling has proven to be effective in both preclinical models [57, 62] and in the clinic [63] . Docetaxel [64] , paclitaxel [65], vinblastine [66] and cyclophosphamide [57] are some of the cytotoxic agents that have demonstrated antiproliferative activity on endothelial cells in culture.

Progressive disease, the process of tumor growth, angiogenesis, invasion and metastasis, is largely regulated by growth-factor signaling and their binding to receptor tyrosine kinases [67]. Inhibition of these signaling pathways as a therapeutic approach has recently gained a lot of attention and current strategies include: anti-growth factor antibodies, receptor antagonism, antireceptor monoclonal antibodies, antisense and small-molecule TKIs [68,69] . The use of molecularly targeted anticancer drugs began with the introduction of Gleevec® (Novartis, Basel, Switzerland) which targets BCR-ABL and Kit, for the treatment of chronic myeloid leukemia [70] . Some of the signal transduction pathways commonly altered in the malignant phenotype include: VEGF, VEGF receptor 2 (KDR/Flk-1), EGFR (erbB1) and erbB2; as well as downstream signaling kinases MEK, Raf and AKT [68,69].

It has been demonstrated that the antiprolif-erative effects of cytotoxic chemotherapy can be abrogated by VEGF and other growth factors [64,71]. Therefore, it is rational to hypothesize that the addition of molecularly targeted inhibitors to chemotherapeutic regimens may improve therapy. Indeed, several clinical studies have now demonstrated that incorporation of molecularly targeted agents with existing therapies, radiation therapy and/or chemotherapy, result in improved efficacy with no increase in toxicity [7274].

Although several molecular agents have demonstrated promising single-agent efficacy in pre-clinical models, the use of molecularly targeted agents in the clinic will almost certainly involve combinations with other therapeutic modalities. This discrepancy is partly owing to resistance development to target agents due to molecular mutations at the binding site of the target receptor. Since treatment with molecular agents results primarily in delayed tumor progression and not necessarily tumor regression, monitoring therapeutic efficacy and assessing the potential clinical utility of new agents (i.e., in early Phase I and II trials) poses a difficulty.

Metabolic signatures of resistance

The use of metabolomics for assessment of treatment effect, as both a predictive measure of efficacy and as a pharmacodynamic marker, has been demonstrated in vitro for both traditional chemotherapy and hormonal agents. Use of 1H-NMR on human glioma cell culture successfully predicted separation into drug-resistant and drug-sensitive groups prior to treatment with 1-(2-chloroethyl)-3-cyclohexyl-1-nitro-sourea [75]. Exposure of hormone-responsive Ishikawa human endometrial adenocarcinoma cells to tamoxifen resulted in dose-dependent changes in nucleotides, suggesting that tamoxifen modifies RNA translation [76].

In vivo, 1H-NMR, including HR-MAS, has been used to investigate the metabolic changes associated with nitrosurea treatment of B16 melanoma and 3LL lung carcinoma tumors grown subcutaneously in C57BL6/6J mice [19] . During the growth-inhibitory phase, tumor samples demonstrated significant accumulation of glucose, glutamine, aspartate and serine-derived metabolites, as well as decreased succinate, suggesting the reduction of nucleotide synthesis and induction of DNA repair pathways. Growth recovery reflected metabolic adaptation, including activation of energy production systems and increased nucleotide synthesis.

In prostate cancer, citrate may be a marker of responsiveness to treatment based upon a pilot study where 16 high-risk prostate cancer patients were treated with chemotherapy, hormones, radical prostatectomy or radiotherapy, and subsequently followed with prostate-specific antigen monitoring, MRI and MRSI [77,78] . A risk score utilizing MRSI was developed based on both tumor volume and metabolic abnormality. The MRSI score and MRI tumor/node stage was then used to determine prostate-specific antigen relapse and was predictive in 15 of 16 cases.

There is an ongoing effort by the National Cancer Institute, researchers, clinicians and industry to expand the use of metabolomics, with particular attention to MRSI for the assessment of therapeutic response [79]. In general, a decrease in the Cho signal on 1H-NMR equates to a response to chemotherapy or radiation, and may be an early marker of effect as it can be detected prior to changes on conventional imaging in breast and prostate cancer, brain tumors, and non-Hodgkin’s lymphoma.

As mentioned above, the practice of oncology has evolved from the exclusive use of cytotoxic compounds that nonselectively inhibit cells actively engaged in the cell cycle to include newer targeted agents that can block particular pathways important for neoplastic transformation, growth and metastasis. Without being truly cytotoxic (no immediate cell death) but rather cytostatic, it is increasingly important to apply sensitive quantitative imaging end points to monitor therapy response. Preclinical testing is a critical component of the development of new therapeutics and new therapeutic strategies. With increasing interest in molecularly targeted therapies, where dosing to toxicity may not provide the greatest benefit, and the use of alternative dosing schedules for cyto-toxic chemotherapeutics, the establishment of clear, quantitative and robust preclinical trans-lational end points of therapeutic efficacy is necessary [80].

Therapeutics in oncology is moving toward the use of drugs that specifically target aberrant pathways involved in growth, proliferation, and metastases. Biomarkers are being increasingly utilized in the early clinical development of such agents to identify, validate and optimize therapeutic targets and agents, determine and confirm mechanism of drug action, as a phar-macodynamic end point, and in predicting or monitoring responsiveness to treatment, tox-icity and resistance [81]. Metabolomics has a potential future role as a biomarker and current examples of its use in developmental therapeutics include TKIs, proapoptotic agents, and heat shock protein inhibitors [6,18,8286].

One hypothesis explored was that treatment with targeted therapies, such as signal trans-duction inhibitors, would result in a distinct metabolic profile between sensitive and resistant cells. Imatinib, a TKI of the BCR-ABL oncogene, decreases cell proliferation and induces apopto-sis in human chronic myeloid leukemia [6,18] . Metabolically, imatinib interrupts the synthesis of macromolecules required for cell survival by deprivation of key substrates. Investigation of glucose metabolism changes in imatinib-treated human leukemia BCR-ABL-positive cell lines with NMR demonstrated decreased glucose uptake by glycolysis inhibition, but unlike classic therapeutics it stimulated mitochondrial metabolism, leading to cell differentiation [18] . Imatinib also led to a significant decrease in PC in imatinib-sensitive cells, which correlated with a decrease in cell proliferation rate [18] . Metabolomic detection of imatinib resistance has also been reported; a decrease in mitochondrial glucose oxidation and nonoxidative ribose synthesis from glucose, as well as highly elevated phosphocholine levels, was indicative of drug resistance and disease progression [6] . These data indicate that NMR metabolomics may provide a method for monitoring changes in cellular metabolism that reflect early resistance to novel targeted agents (FIGURE 5). This could be particularly useful in hemato-logical malignancies, where frequent tissue sampling is feasible and early metabolomic markers of resistance may dictate therapy adjustments that prevent overt phenotypic progression.

Figure 5
PLS-DA ana lysis and marker validation to distinguish metabolic signature of responsiveness and resistance to imatininb in human Bcr-Abl-positive cells from CML patients

Apoptosis has an established role in chemotherapy and radiation-induced cell death and its absence correlates with treatment resistance and induction of prosurvival pathways [59, 60] . Multiple new agents targeting apoptosis are currently in early clinical development including TNF-related apoptosis-inducing ligand (TRAIL), agonist death receptor antibodies, and inhibitors of the antiapoptotic proteins. FK866 is a novel agent that purportedly induces apoptosis independent of anti-DNA effects by decreasing NAD+ levels [82]. The metabolic effects of FK866 on mouse mammary carcinoma cells using 1H-NMR demonstrated a significant increase in fructose-bi-phosphate with a subsequent decrease in pH and NAD+ resulting from an incomplete glycolytic cycle [82] . Other alterations observed included changes in guanylate synthesis, pyridine nucleotide levels and phospholipid metabolism, indicating multiple aberrant cellular pathways resulting in apoptosis [83]. High-resolution MAS-NMR has demonstrated that apoptotic activity can be characterized in cervical carcinoma biopsies before and during treatment with external beam radiation, brachytherapy and weekly chemotherapy [84]. In this study, 44 cervical cancer biopsies indicated that lipid metabolism differed both in tumor cell fraction (percentage of tumor cells per tissue biopsy) and tumor cell density (number of carcinoma cell nuclei per mm2 of tumor tissue). Ratios of fatty acid CH2:CH3, specifically, a lengthening of the fatty acid-CH2 chain, were associated with apoptosis, a discovery substantiated in another study of acute lymphoblastic leukemia cell cultures treated with doxorubicin [85]. These early studies suggest metabolomics may be a useful biomarker in the development and validation of proapoptotic agents.

Another interesting application of metabo-lomics is in the area of heat-shock protein 90 (Hsp90) inhibitors. Although their mechanism of action is not fully elucidated, current data suggest that this family of agents increase the cellular destruction of client oncogenic proteins. In one study, colon cancer xenografts were treated with an Hsp90 inhibitor and extracts of these tumors were analyzed by 31P-NMR, reflecting a significant increase in phosphocholine, phospho-monoester/phosphodiester ratio, valine and phos-phoethanolamine levels, indicating altered phos-pholipid metabolism [86]. These results, although preliminary, address metabolic changes that could be used as pharmacodynamic biomarkers of Hsp90 inhibitors, a class of agents that do not appear to result in classical anti-tumor effects.

A summary of metabolic changes observed upon treatment with cytotoxic and cytostatic agents is presented in TABLE 3 . If resistance occurs, these metabolic markers cannot be detected in the cancer cells: increased glucose, lactate and PCho are observed.

Table 3
Metabolic consequences of anti-cancer treatment.

In vivo metabolic imaging for response assessment

The dominant modality for imaging tumor biochemistry in vivo is PET [8789]. The majority of clinical PET studies are based on intravenous injection of 18FDG, which is a marker for hexokinase activity (the rate-limiting step in glucose metabolism). Because most malignancies exhibit increased glucose uptake and glycolytic metabolism, as mentioned above, whole-body 18FDG-PET can provide infomation on tumor aggressiveness and is now recognized as a procedure of choice for the purpose of staging, restaging and monitoring response to treatment of various types of cancers. Glucose dependence in various malignancies relies on overexpression of glucose transporters (mainly Glut-1), and key enzymes of glycolytic pathways, such as hexokinase, pyru-vate kinase and phosphofructokinase. Hence, FDG is transported into the cell by the energy-independent Glut-1 transporter. Unlike natural glucose, however, FDG is trapped inside the cell because it cannot be metabolized by glucose-6-phosphate isomerase. Overall, FDG uptake appears to be a marker of high-grade and/or poorly differentiated tumors [89]. In addition, there is a strong correlation between the size of the tumor and FDG accumulation as a tumor progresses. Today, metabolic FDG-PET imaging in combination with anatomical CT is routinely used for the evaluation of solitary pulmonary nodules, for the initial staging of newly diagnosed malignancies, for the detection of clinically or radiologically suspected recurrences, and in the evaluation of tumor response during the course of treatment [9094]. It has recently been demonstrated that for novel targeted STMs, decreases in metabolic aggressiveness and the glycolytic pheno-type precede changes in anatomic size. As such, early detection of therapeutic responses to chemotherapy, mTOR inhibitors, EGFR TKIs, CKIT inhibitors and, most recently, IGF-1R antibodies [8994] have been detected by FDG-PET in various tumors. Decrease in FDG uptake as an assessment of therapy response has been seen in patients with head and neck carcinoma, as well as lymphoma, breast, lung and colorectal cancers. For the majority of tumors (such as head and neck, non-small cell lung cancer and cervical cancer), metabolic PET was superior to CT in radiotherapy planning and assessment of therapy response. Several studies have indicated that in responding tumors, FDG uptake markedly decreases within the first chemotherapy cycle or after 14 days of treatment [95,96] . In gastric cancer patients treated with chemotherapeutics, metabolic responders by PET also demonstrated a high histopatho-logic response rate (69%) and favorable prognosis, whereas metabolic non responders have a poor prognosis and only 17% demonstrated his-topathologic response [97] . Metabolic response to targeted drugs, such as the c-KIT-targeted TKI imatinib in gastrointestinal stromal tumor patients, can occur in the first hours after treatment begins and can be used to adjust the patient’s daily dose [98]. Even in hepatocellular carcinoma patients, known to have low FDG uptake on liver PET scans at baseline, after 3 weeks of sorafenib treatment (a multi-targeted VEGF receptor 2 TKI) a partial metabolic response was observed in responders versus non-responders [99]. Preclinically, glucose metabolic activity closely reflected response to the EGFR TKI gefitinib [100] . In addition, novel radiotrac-ers, related to various metabolic abnormalities in cancer metabolism, are c urrently under development. For example, the use of technically improved PET/CT scanners with new 11C and 18F choline tracers are under development.

Metabolic PET imaging obviously requires exogenous radioactive tracers. By contrast, in vivo MRS, in particular 1H MRS, can simultaneously provide several molecular images using endogenous metabolites [101] . In the breast, increased choline signal (due to increased cell membrane phospholipid synthesis) can be seen on 1H-MRS (or MRSI) scans, which correlates well with anatomical localization of the tumor lesion and response to the therapy [17, 101] . Thus, abundant metabolic markers, distinguished by in vitro NMR-based metabolomics, can be translated into clinical ‘in vivo omics’-based imaging by PET and MRS.


Although there have been major advances in the treatment of human cancer in the last 10 years, it remains a life-threatening disease for which new treatment strategies are urgently needed. This is especially true for patients whose disease recurs and progresses following treatment. Cancer development is a time-dependent accumulation of several events, both genetic and epigenetic, which will result in changes in genes, proteins and endogenous metabolites. New molecularly designed drugs, which target specific abnormalities in the cancer cell (such as onco-tyrosine kinase, antibodies to growth factors) and act cytostatically, will lead to normalization of metabolic cell phenotype in responsive patients, even though the specific metabolic markers can vary depending on the targeted pathway and/or cancer phenotype. These metabolic changes, as well as establishing a drug-resistant phenotype, can be assessed using global metabolic profiling followed by identification and validation of a specific metabolic marker(s). Ideally, selected metabolic biomarkers can then be followed non-invasively using ‘molecular imaging’ – a novel term in the clinical setting since 2000 – referring to in vivo visualization of endogenous genes, proteins and metabolites noninvasively in the body using external molecular probes and advanced radiological techniques such as MRSI and/or novel metabolic tracers for MRI and PET.

In addition to its potential for cancer detection and in assessing the efficacy of anticancer drugs, the metabolic findings, gained through metabolomics technology, can be applied to combat cancer directly. Glucose metabolism and choline turnover represent two major upregulated metabolic pathways in cancer. Therefore, compounds that limit glycolysis would, in theory, kill cancer cells while sparing normal cells that can burn amino and fatty acids for energy through mitochondrial pathways. The enzymes directly causing the elevation of phosphocholine and total choline-containing compounds, such as choline kinase, phospholipase C and D, may also provide molecular targets for anticancer therapies.

In summary, metabolomics has the potential to influence clinical oncology, benefiting patient care with advantages already being seen in the use of metabolite imaging in breast and prostate cancer diagnosis and probable future uses as a biomarker for early cancer diagnosis, treatment efficacy and developmental therapeutics. Metabolomics, when used as a transla-tional research tool, can provide a link between the laboratory and clinic, since metabolic and molecular imaging enables the discrimination of metabolic markers noninvasively in vivo. Of all possible applications for NMR, cancer research is the most advanced field already utilizing absolute biochemical values rather than pure spectral analysis. Precise biomarker validation and quantification in ex vivo samples and their translation into in vivo metabolic imaging will ultimately guide future therapeutic strategies with novel targeted anticancer drugs.

Future perspective

The future of imaging promises new avenues for exploration of molecular, microscopic, genetic and biologic events in humans during oncologic development and/or response to anticancer treatment, performed noninvasively and in real time.Accordingly, new quantitative multimodality protocols with improved accuracy, specificity and sensitivity can be applied in different phases of cancer development – from detection to diagnosis, staging and therapeutic response – from animal model into clinical practice. Introduction of metabolic inhibitors, targeting glycolytic and Kennedy pathway enzymes, possess significant clinical therapeutic potential. Improvements in hardware technology and image processing will allow the routine acquisition of high-quality, high-resolution, multiplanar images of the whole body by CT and MRI. Development of novel metabolic and molecular radioactive probes will allow for future establishment of PET technology as a reliable tool for clinical molecular imaging.

Executive summary

  • Biomarkers are increasingly being utilized in the early clinical development of novel anticancer drugs to identify, validate and optimize therapeutic targets and agents, determine and confirm mechanism of drug action, as a pharmacodynamic end point, and in predicting or monitoring responsiveness to treatment, toxicity and resistance.
  • The major advantage of nuclear magnetic resonance-based metabolomics is its translational components, which range from a pattern-recognition technique to precise metabolite identification, validation and finally, noninvasive assessment by magnetic resonance spectroscopy, magnetic resonance spectroscopical imaging or, most recently, PET.
  • Glucose metabolism (Warburg effect) and choline turnover (Kennedy pathways) are two major pathways upregulated in cancer.
    These pathways are also sensitive markers for therapy response (decreased glucose uptake, decreased glycolysis and decreased choline phosphorylation).
  • In resistant tumors, these two pathways remain upregulated.
  • An accurate assessment of metabolic phenotype and fluxes of cancer cells is essential to design effective metabolic modulators to help to overcome resistance development to various targeted agents.
  • Similar to genomics and proteomics, metabolic pattern recognition approaches, based on global metabolic profiling, are challenged due to lack of a standard technology platform (especially for statistical postprocessing analysis) and lack of validation of comprehensive data bases, often accompanied by poor study design.
  • Precise marker identification, validation and translation into noninvasive metabolic imaging protocols are required in metabolomics in order to become an established clinical tool.


Financial & competing interests disclosure

NIH grant support was provided by R 21 CA108624 (NJS) and P30 CA046934 (UCH Cancer Center Core Grant for ALM and NJS).


The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

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


Papers of special note have been highlighted as:

• of interest

•• of considerable interest

1. Serkova NJ, Niemann CU. Pattern recognition and biomarker validation using quantitative 1H-NMR-based metabolomics. Expert Rev. Mol. Diagn. 2006;6:717–731. [PubMed]
2. Spratlin JL, Serkova NJ, Eckhardt SG. Clinical applications of metabolomics in oncology. Clin. Cancer Res. 2009;15:431–440. [PubMed]Comprehensive review on existing metabolic techniques to evaluate preclinical (cells and animal specimens), as well as patients’ body fluids and biopsies for establishing metabolic end points for cancer detection, progression and response to therapies.
3. Griffin JL, Shockcor JP. Metabolic profiles of cancer cells. Nat. Rev. 2004;4:551–561. [PubMed]One of the best reviews discussing cancer biochemistry and modern metabolic profiling technologies.
4. Serkova NJ, Spratlin JL, Eckhardt SG. NMR-based metabolomics:translational application and treatment of cancer. Curr. Opin Mol. Ther. 2007;9:572–585. [PubMed]
5. Dunn WN, Bailey NJ, Johnson HE. Measuring the metabolome: current analytical technologies. Analyst. 2005;130:606–625. [PubMed]
6. Serkova N, Boros LG. Detection of resistance to imatinib by metabolic profiling: clinical and drug development implications. Am. J. Pharmacogenomics. 2005;5:293–302. [PubMed]Overview on metabolic profiles and phenotypes of cancer cells with special emphasis on targeted drug treatment in oncology.
7. Want EJ, Nordstrom A, Morita H, Siuzdak G. From exogenous to endogenous:the inevitable imprint of mass spectrometry in metabolomics. J. Proteome Res. 2007;6:459–468. [PubMed]
8. Bathen TF, Jensen LR, Sitter B, et al. MR-determined metabolic phenotype of breast cancer in prediction of lymphatic spread, grade, and hormone status. Breast Cancer Res. Treat. 2007;104:181–189. [PubMed]
9. Odunsi K, Wollman RM, Ambrosone CB, et al. Detection of epithelial ovarian cancer using 1H-NMR-based metabolomics. Int. J. Cancer. 2005;113:782–788. [PubMed]
10. Denkert C, Budszies J, Kind T, et al. Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors. Cancer Res. 2006;66:10795–10804. [PubMed]
11. Morvan D, Demidem A, Papon J, Latour M, Madelmont JC. Melanoma tumors acquire a new phospholipid metabolism phenotype under cystemustine as revealed by high-resolution magic angle spinning proton nuclear magnetic resonance spectroscopy of intact tumor samples. Cancer Res. 2002;62:1890–1897. [PubMed]Provides direct evidence of decreased choline phospholipid turnover under cystemustine treatment.
12. Serkova NJ, Gamito EJ, Jones RH, et al. Validation of citrate and derivatives in expressed prostatic secretions to predict prostate cancer: high-resolution 1H–NMT study. Prostate. 2008;68:620–628. [PubMed]
13. Griffin JL, Kauppinen RA. A metabolomics perceptive of human brain tumours. FEBS J. 2007;274:1132–1139. [PubMed]
14. Fossel ET, Carr JM, McDonagh J. Detection of malignant tumors Water-suppressed proton nuclear magnetic resonance spectroscopy of plasma. N. Engl. J. Med. 1986;315:1369–1376. [PubMed]
15. Schmidt C. Metabolomics takes its place at latest up-and-coming ‘omic’ science. J. Natl Cancer Inst. 2004;96:732–734. [PubMed]
16. Howe FA, Barton SJ, Cudlip SA, et al. Metabolic profiles of human brain tumors using quantitative in vivo 1H magnetic resonance spectroscopy. Magn. Reson. Med. 2003;49:223–232. [PubMed]
17. Glunde K, Serkova NJ. Therapeutic targets and biomarkers identified in cancer choline phospholipid metabolism. Pharmacogenomics. 2006;7:1109–1123. [PubMed]Noninvasive magnetic resonance spectroscopy (MRS_ can be used to detect the elevation of phosphocholine and total choline-containing compounds as an endogenous biomarker of cancer, or as a predictive biomarker for monitoring tumor response to novel targeted therapies.
18. Gottschalk S, Anderson N, Hainz C, Eckhardt SG, Serkova NJ. Imatinib (STI-571)-mediated changes in glucose metabolism in human leukemia BCR-ABL-positive cells. Clin. Cancer Res. 2004;10:6661–6668. [PubMed]
19. Morvan D, Demidem A. Metabolomics by proton nuclear magnetic resonance spectroscopy of the response to chloroethylnitrosourea revails drug efficacy and tumor adaptive metabolic pathways. Cancer Res. 2007;67:2150–2159. [PubMed]During the initial growth inhibition phase, an accumulation of glucose, glutamine, aspartate and serine derivatives, as well as a decrease in nucleoside de novo synthesis was observed. During the growth recovery phase, the tumor cells demonstrated polyunsaturated fatty acid accumulation and reduced utilization of glucose.
20. Sardanelli F, Fausto A, Podo F. MR spectroscopy of the breast. Radiol. Med. 2008;113:56–64. [PubMed]
21. Kurhanewicz J, Bok R, Nelson SJ, Vigneron DB. Current and potential applications of clinical 13C MR spectroscopy. J. Nucl. Med. 2008;49:341–344. [PMC free article] [PubMed]
22. Ott K, Herrmann K, Lordick F, et al. Early metabolic response evaluation by fluorine-18 fluorodeoxyglucose positron emission tomography allows in vivo testing of chemosensitivity in gastric cancer: long-term results of a prospective study. Clin. Cancer Res. 2008;14:2012–2018. [PubMed]
23. Lam ET, O’Bryant CL, Basche M, et al. A Phase I study of gefitinib, capecitabine, and celecoxib in patients with advanced solid tumors. Mol. Cancer Ther. 2008;7:3685–3694. [PMC free article] [PubMed]
24. Roessner U, Luedemann A, Brust D, et al. Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell. 2001;13:11–29. [PubMed]
25. Pan Z, Raftery D. Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics. Anal. Bioanal. Chem. 2007;387:525–527. [PubMed]
26. Warburg O. The Metabolism of Tumors. London, UK: Consolidated Press; 1930.
27. Shaw RJ. Glucose metabolism and cancer. Curr. Opin Cell Biol. 2006;18:598–608. [PubMed]A must-read review on the role of glucose and glycolysis in signal transduction pathways involved in oncogenesis.
28. Kroemer G, Pouyssegur J. Tumor cell metabolism:cancer’s Achilles’ heel. Cancer Cell. 2008;6:472–482. [PubMed]
29. Young CD, Anderson SM. Sugar and fat thats’ where it’s at: metabolic changes in tumors. Breast Cancer Res. 2008;10:202–209. [PMC free article] [PubMed]
30. DeBerardinis RJ, Sayed N, Ditsworth D, Thompson CB. Brick by brick:metabolism and tumor cell growth. Curr. Opin. Genet. Develop. 2008;18:1–8. [PMC free article] [PubMed]
31. Marin-Hernandez A, Rodriguez-Enriquez S, Vital-Gonzalez PA, et al. Determining and understanding the control of glycolysis in fast-growth tumor cells. FESB J. 2006;273:1975–1988. [PubMed]
32. Bonora E, Porcelli AM, Gasparre G, et al. Defective oxidative phosphorylation in thyroid oncocytic carcinoma is associated with pathogenic mitochondrial DNA mutations affecting complexes I and III. Cancer Res. 66:6087–6096. [PubMed]
33. Wallace DC. Mitochondria and cancer: Warburg addressed. Cold Spring Harb. Symp. Quant. Biol. 2005;70:363–374. [PubMed]
34. Garber K. Energy deregulation: licensing tumors to grow. Science. 2006;312:1158–1159. [PubMed]
35. Gatenby RA, Gillies RJ. Why do cancers have high aerobic glycolysis? Nat. Rev. Cancer. 2004;4:891–899. [PubMed]
36. Gatenby RA, Gawlinski ET, Gmitro AF, Kaylor B, Gillies RJ. Acid-mediated tumor invasion: a multidisciplinary study. Cancer Res. 2006;66:5216–5223. [PubMed]
37. Denko NC. Hypoxia, HIF1 and glucose metabolism in the solid tumour. Nature Rev. 2008;8:705–713. [PubMed]
38. Elstrom RL, Bauer De, Buzzai M, et al. Akt stimulates aerobic glycolysis in cancer cells. Cancer Res. 2004;64:3892–3899. [PubMed]Direct evidence on upregulation of aerobic glycolysis through AKT is given.
39. Miles KA, Williams RE. Warburg revised: imaging tumor blood flow and metabolism. Cancer Imaging. 2008;8:81–86. [PMC free article] [PubMed]
40. Glunde K, Jacobs MA, Bhujwalla ZM. Choline metabolism in cancer: implications for diagnosis and therapy. Expert Rev. Mol. Diagn. 2006;6:821–829. [PubMed]
41. Vance JE, Vance DE. Phospholipid biosynthesis in mammalian cells. Biochem. Cell Biol. 2004;82:113–128. [PubMed]
42. Michel V, Yuan Z, Ramsubir S, Bakovic M. Choline transport for phospholipid synthesis. Exp. Biol. Med. 2006;231:490–504. [PubMed]
43. Nakagami K, Uchida T, Ohwada S, et al. Increased choline kinase activity and elevated phosphocholine levels in human colon cancer. Jpn J. Cancer Res. 1999;90:419–424. [PubMed]
44. Katz-Brull R, Seger D, Rivenson-Segal D, Rushkin E, Degani H. Metabolic markers of breast cancer: enhanced choline metabolism and reduced choline-ether-phospholipid synthesis. Cancer Res. 2002;62:1966–1970. [PubMed]
45. Eliyahu G, Kreizman T, Degani H. Phosphocholine as a biomarker of breast cancer: molecular and biochemical studies. Int. J. Cancer. 2007;120:1721–1730. [PubMed]
46. Kouji H, Inazu M, Yamada T, Tajima H, Aoki T, Matsumiya T. Molecular and functional characterization of choline transporter in human colon carcinoma HT-29 cells. Arch. Biochem. Biophys. 2009;483(1):90–98. [PubMed]
47. Ramirez de Molina A, Sarmentero-Estrada J, Belda-Iniesta C, et al. Expression of choline kinase a to predict outcome in patients with early-stage non-small-cell lung cancer: a retrospective study. Lancet Oncol. 2007;8:889–897. [PubMed]
48. Mori N, Glunde K, Takagi T, Raman V, Bhujwalla ZM. Choline kinase down-regulation increases the effect of 5-fluorouracil in breast cancer cells. Cancer Res. 2007;67:11284–11290. [PubMed]
49. Glunde K, Raman V, Mori N, Bjujwalla ZM. RNA interference-mediated choline kinase suppression in breast cancer cells induces differentiation and reduces proliferation. Cancer Res. 2005;65:11034–11043. [PubMed]
50. Glunde K, Shah T, Winnard PT, et al. Hypoxia regulates choline kinase expression through hypoxia-inducible factor-1a signaling in a human prostate cancer model. Cancer Res. 2008;68:172–1780. [PubMed]
51. Ronen SM, Jackson LE, Beloueche M, Leach MO. Magnetic resonance detects changes in phosphocholine associated with Ras activation and inhibition in NIH 3T3 cells. Br. J. Cancer. 2001;84:691–696. [PMC free article] [PubMed]
52. Beloueche-Babari M, Jackson LE, Al-Saffar NMS, et al. Magnetic resonance spectroscopy monitoring of mitogen-activated protein-kinase signaling inhibition. Cancer Res. 2005;65:3356–3363. [PubMed]
53. Beloueche-Babari M, Jackson LE, Al-Saffar NMS, et al. Identification of magnetic resonance detec. metabolic changes associated with inhibition of phosphoinositide-3-kinase signaling in human breast cancer cells. Mol. Cancer Ther. 2006;5:187–196. [PubMed]
54. Kwee SA, DeGrado TR, Talbot JN, Gutman F, Coel MN. Cancer imaging with fluorine-18-labeled choline derivatives. Semin. Nucl. Med. 2007;37:420–428. [PubMed]
55. Chabner BA, Longo DL. Cancer Chemotherapy & Biotherapy: Principles and Practice. 4th Edition. Philadelphia, PA, USA: Lippincott Williams & Wikins; 2006.
56. Chabner BA, Amrein PC, Druker BJ, et al. Antineoplastic agents. In: Brunton LL, Lazo JS, Parker KL, editors. The Pharmacological Basis of Therapeutics. 11th Edition. New York, NY, USA: McGraw Hill Companies, Inc; 2006. pp. 1315–1403.
57. Browder T, Butterfield CE, Kraling BM, et al. Antiangiogenic scheduling of chemotherapy improves efficacy against experimental drug-resistant cancer. Cancer Res. 2000;60:1878–1886. [PubMed]
58. Hryniuk W, Levine MN. Dose intensity for adjuvant chemotherapy trials in stage II breast cancer. J. Clin. Oncol. 1986;4:1162–1170. [PubMed]
59. Levin L, Hryniuk WM. Dose intensity analysis of chemotherapy regimens in ovarian carcinoma. J. Clin. Oncol. 1987;5:756–767. [PubMed]
60. Crawford J, Ozer H, Stoller R, et al. Reduction by granulocyte colony-stimulating factor of fever and neutropenia induced by chemotherapy in patients with small-cell lung cancer. N. Engl. J. Med. 1991;325:164–170. [PubMed]
61. Kerbel RS, Kamen BA. The anti-angiogenic basis of metronomic chemotherapy. Nat. Rev. Cancer. 2004;4:423–436. [PubMed]
62. Bocci G, Nicolaou KC, Kerbel RS. Protracted low-dose effects on human endothelial cell proliferation and survival in vitro reveal a selective antiangiogenic window for various chemotherapy drugs. Cancer Res. 2002;62:6938–6943. [PubMed]
63. Kaur H, Budd GT. Metronomic therapy for breast cancer. Curr. Oncol. Rep. 2004;6:49–52. [PubMed]
64. Sweeny CJ, Miller KD, Sissons SE, et al. The antiangiogenic property of docetaxel is synergistic with a recombinant humanized monoclonal antibody against vascular endothelial growth factor or 2-methoxyestradiol but antagonized by endothelial growth factors. Cancer Res. 2001;61:3369–3372. [PubMed]
65. Wang J, Lou P, Leniewski R, Henkin J. Paclitaxel at ultra low concentrations inhibits angiogenesis without affecting cellular microtubule assembly. Anticancer Drugs. 2003;14:13–19. [PubMed]
66. Klement G, Baruchel S, Rak J, et al. Continuous low-dose therapy with vinblastine and VEGF receptor-2 antibody induces sustained tumor regression without overt toxicity. J. Clin. Invest. 2000;105:R15–R24. [PMC free article] [PubMed]
67. Heldin C. Protein tyrosine kinase receptors. Cancer Surv. 1996;27:7–24. [PubMed]
68. Levitzki A, Gazit A. Tyrosine kinase inhibition: an approach to drug development. Science. 1995;267:782–788. [PubMed]
69. Ciardiello F, Tortora G. A novel approach in the treatment of cancer: targeting the epidermal growth factor receptor. Clin. Cancer Res. 2001;7:2958–2970. [PubMed]
70. Druker BJ, Talpaz M, Resta DJ, et al. Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N. Engl. J. Med. 2001;344:1031–1037. [PubMed]
71. Tran J, Master Z, Yu JL, Rak J, Dumont DJ, Kerbel RS. A role for survivin in chemoresistance of endothelial cells mediated by VEGF. Proc. Natl Acad. Sci. 2002;99:4349–4354. [PubMed]
72. Kalyankrishna S, Grandis J. Epidermal growth factor receptor biology in head and neck cancer. J. Clin. Oncol. 2006;24:2666–2672. [PubMed]
73. Bonner B, Harari P, Giralt J, et al. Radiotherapy plus cetuximab for squamous cell carcinoma of the head and neck. N. Engl. J. Med. 2006;354:567–578. [PubMed]
74. Heymach JV, Johnson BE, Prager D, et al. Randomized, placebo-controlled Phase II study of vandetanib plus docetaxel in previously treated non-small cell lung cancer. J. Clin. Oncol. 2007;25:4270–4277. [PubMed]
75. El-Deredy W, Ashmore SM, Branston NM, Darling JL, Williams SR, Thomas DG. Pretreatment prediction of the chemotherapeutic response of human glioma cell cultures using nuclear magnetic resonance spectroscopy and artificial neural networks. Cancer Res. 1997;57:4196–4199. [PubMed]
76. Griffin JL, Pole JC, Nicholson JK, Carmichael PL. Cellular environment of metabolites and a metabonomic study of tamoxifen in endometrial cells using gradienthigh resolution magic angle spinning1H NMR spectroscopy. Biochim. Biophys Acta. 2003;1619:151–158. [PubMed]
77. Mueller-Lisse UG, Swanson MG, Vigneron DB, et al. Time-dependent effects of hormone-deprivation therapy on prostate metabolism as detected by combined magnetic resonance imaging and 3D magnetic resonance spectroscopic imaging. Magn. Reson. Med. 2001;46:49–57. [PubMed]
78. Pucar D, Koutcher JA, Shah A, et al. Preliminary assessment of magnetic resonance spectroscopic imaging in predicting treatment outcome in patients with prostate cancer at high risk for relapse. Clin. Prostate Cancer. 2004;3:174–181. [PubMed]
79. Evelhoch J, Garwood M, Vigneron D, et al. Expanding the use of magnetic resonance in the assessment of tumor response to therapy: workshop report. Cancer Res. 2005;65:7041–7044. [PubMed]Excellent summary on existing and future magnetic resonance technologies and their potential for clinical (metabolic) assessment of anticancer therapy efficacy.
80. Castell F, Cook GJ. Quantitative techniques in 18FDG-PET scanning in oncology. Br. J. Cancer. 2008;98 597-560. [PMC free article] [PubMed]
81. Park JW, Kerbel RS, Kelloff GJ, et al. Rationale for biomarkers and surrogate end points in mechanism-driven oncology drug development. Clin. Cancer Res. 2004;10:885–3896. [PubMed]
82. Hasmann M, Schemainda I. FK866, a highly specific noncompetitive inhibitor of nicotinamide phosphoribosyltransferase, represents a novel mechanism for induction of tumor cell apoptosis. Cancer Res. 2003;63:7436–7442. [PubMed]
83. Muruganandham M, Alfieri AA, Matei C, et al. Metabolic signatures associated with a NAD synthesis inhibitor-induced tumor apoptosis identified by 1H-decoupled-31P magnetic resonance spectroscopy. Clin. Cancer Res. 2005;11:3503–3513. [PubMed]
84. Lyng H, Sitter B, Bathen TF, et al. Metabolic mapping by use of high-resolution magic angle spinning 1H MR spectroscopy for assessment of apoptosis in cervical carcinomas. BMC Cancer. 2007;7:11. [PMC free article] [PubMed]
85. Blankenberg FG, Katsikis PD, Storrs RW, et al. Quantitative analysis of apoptotic cell death using proton nuclear magnetic resonance spectroscopy. Blood. 1997;89:3778–3786. [PubMed]
86. Lehtimaki KK, Valonen PK, Griffith JL, et al. Metabolite changes in BT4C rat gliomas undergoing ganciclovir-thymidine kinase gene therapy-induced programmed cell death as studied by 1H-NMR spectroscopy in vivo, ex vivo and in vitro. J. Biol. Chem. 2003;278:45915–45923. [PubMed]
87. Shu C, Guo S, Kim YJ, et al. Visualization of a primary anti-tumor immune response by positron emission tomography. Proc. Natl Acad. Sci. 2005;102:17412–17417. [PubMed]
88. Vesselle H, Freeman JD, Wiens L, et al. Fluorodeoxyglucose uptake of primary non-small cell lung cancer at positron emission tomography: new contrary data on prognostic role. Clin. Cancer Res. 2007;13:3255–3263. [PubMed]
89. Shields AF. Positron emission tomography measurements of tumor metabolism and growth: its expanding role in oncology. Mol. Imaging Biol. 2006;8:141–150. [PubMed]
90. Byystrom P, Berglund A, Garske U, et al. Early prediction of response to first-line chemotherapy by sequential [18F]-2-fluoro-2-deoxy-d-glucose positron emission tomography in patients with advanced colorectal cancer. Ann. Oncol. 2009 Epub ahead of print. [PubMed]
91. Wei LH, Su H, Hilderbrandt IJ, Phelps ME, Czernin J, Weber WA. Changes in tumor metabolism as readout for mammalian target of rapamycin kinase inhibitor by rapamycin in glioblastoma. Clin. Cancer Res. 2008;14:3416–3426. [PubMed]
92. Su H, Bodenstein C, Dumont RA, et al. Monitoring tumor glucose utilization by positron emission tomography for the prediction of treatment response to epidermal growth factor receptor kinase inhibitors. Clin. Cancer Res. 2006;12:5659–5667. [PubMed]
93. Holdsworth CH, Badawi RD, Manola JB, et al. CT and PET: early prognostic indicators of response to imatinib mesylate in patients with gastrointestinal stroma tumor. Am. J. Roentgenol. 2007;189:W324–W330. [PubMed]
94. Shankar LK, Hoffman JM, Bacharach S, et al. Consensus recommendations for the use of 18F-FDG PET as an indicator of therapeutic response in patients in National Cancer Institute Trials. J. Nucl. Med. 2006;47:1059–1066. [PubMed]
95. Kostakoglu L, Goldsmith SJ, Leonard JP, et al. FDG-PET after cycle 1 of therapy predicts outcome in diffuse large cell lymphoma and classic Hodgkin disease. Cancer. 2006;107:2678–2687. [PubMed]
96. Weber WA, Ott K, Becker K, et al. Prediction of response to preoperative chemotherapy in adenocarcinomas of the esophagogastric junction by metabolic imaging. J. Clin. Oncol. 2001;19:3058–3065. [PubMed]Prospectively demonstrates that in patients with gastric cancer, response to preoperative chemotherapy can be predicted by fluorodeoxyglucose-PET early during the course of therapy based on decrease deoxyglucose uptake.
97. Ott K, Herrmann K, Lordick F, et al. Early metabolic response evaluation by fluorine-18 fluorodeoxyglucose positron emission tomography allows in vivo testing of chemosensitivity in gastric cancer: long-term results of a prospective study. Clin. Cancer Res. 2008;14:2012–2018. [PubMed]
98. Hughes B, Yip D, Goldstein D, et al. Cerebral relapse of metastatic gastrointestinal stromal tumor during treatment with imatinib mesylate: case report. BMC Cancer. 2004;4:1–7. [PMC free article] [PubMed]
99. Siemerink EJ, Mulder NH, Brouwers AH, Hospers GAP. 18F-Fluorodeoxyglucose positron emission tomography for monitoring response to sorafenib treatment in patients with hepatocellular carcinoma. Oncologist. 2008;13:734–735. [PubMed]
100. Su H, Bodenstein C, Dumont RA, et al. Monitoring tumor glucose utilization by positron emission tomography for the prediction of treatment response to epidermal growth factor receptor kinase inhibitor. Clin. Cancer Res. 2006;12:5659–5667. [PubMed]
101. Galbraith SM. MR in oncology drug development. NMR Biomed. 2006;19:681–689. [PubMed]Concentrates on the use of magnetic resonance to study tumor microvasculature following treatment with antivascular agents and the use of MRS to study tumor metabolism following treatment with a variety of anticancer agents.
102. Al-Saffar TM, Troy H, Ramirez de Molina A, et al. Noninvasive magnetic resonance spectroscopic pharmacodynamic markers of the choline kinase inhibitor MN58b in human carcinoma cells. Cancer Res. 2006;66:427–434. [PubMed]


201. Nobel Prize in Medicine 1931 Otto Warburg. [Accessed 16 January 2009].
202. The Official Website of the Nobel Foundation. [Accessed 25 January 2009].