As metabolomics investigates metabolic pathways with the focus on metabolites, it is a suitable approach to address the complex metabolic alteration in cancer. In addition, metabolic profiles are affected by environmental and post-natal changes, and therefore, directly measuring many metabolites may provide epigenetically relevant information in cancer. Despite much development in our understanding of cancer metabolism, focus is often directed to signaling or metabolic proteins that modulate the metabolite levels. In this review, we discuss the "metabolite-oriented view" on cancer metabolism. We cover how metabolomics research contributed to our current insights into the basic mechanism of metabolic alterations leading to cancer. Then, we discuss specific metabolites and related enzymatic pathways directly related with tumorigenesis. We particularly pay attention to how metabolites regulate signaling proteins and metabolic enzymes ultimately leading to cancer phenotypes. Finally, we address future prospects and challenges of metabolomics in cancer research.

The roles of sir-2.1 in C. elegans lifespan extension have been subjects of recent public and academic debates. We applied an efficient workflow for in vivo(13)C-labeling of C. elegans and (13)C-heteronuclear NMR metabolomics to characterizing the metabolic phenotypes of the sir-2.1 mutant. Our method delivered sensitivity 2 orders of magnitude higher than that of the unlabeled approach, enabling 2D and 3D NMR experiments. Multivariate analysis of the NMR data showed distinct metabolic profiles of the mutant, represented by increases in glycolysis, nitrogen catabolism, and initial lipolysis. The metabolomic analysis defined the sir-2.1 mutant metabotype as the decoupling between enhanced catabolic pathways and ATP generation. We also suggest the relationship between the metabotypes, especially the branched chain amino acids, and the roles of sir-2.1 in the worm lifespan. Our results should contribute to solidifying the roles of sir-2.1, and the described workflow can be applied to studying many other proteins in metabolic perspectives.

Leptomeningeal carcinomatosis (LC) is a metastatic cancer invading the central nervous system (CNS). We previously reported a metabolomic diagnostic approach as tested on an animal model and compared with current modalities. Here, we provide a proof of concept by applying it to human LC originating from lung cancer, the most common cause of CNS metastasis. Cerebrospinal fluid from LC (n 5 26) and normal groups (n 5 41) were obtained, and the diagnosis was established with clinical signs, cytology, MRI and biochemical tests. The cytology on the CSF, the current gold standard, exhibited 69% sensitivity (~100% specificity) from the first round of CSF tapping. In comparison, the nuclear magnetic resonance spectra on the CSF showed a clear difference in the metabolic profile between the LC and normal groups. Multivariate analysis and crossvalidation yielded the diagnostic sensitivity of 92%, the specificity of 96% and the area under the curve (AUC) of 0.991. Further spectral and statistical analysis identified myo-inositol (p < 5 3 10 214 ), creatine (p < 7 3 10 28 ), lactate (p < 9 3 10 24 ), alanine (p < 7.9 3 10 23 ) and citrate (p < 3 3 10 24 ) as the most contributory metabolites, whose combination exhibited an receiver-operating characteristic diagnostic AUC of 0.996. In addition, the metabolic profile could be correlated with the grading of radiological leptomeningeal enhancement (R 2 5 0.3881 and p 5 6.66 3 10 24), suggesting its potential utility in grading LC. Overall, we propose that the metabolomic approach might augment current diagnostic modalities for LC, the accurate diagnosis of which remains a challenge.Leptomeningeal carcinomatosis (LC) is a devastating complication of systemic cancer that occurs in 5-10% of patients with solid tumors, more frequently adenocarcinoma, and is observed most commonly in patients with breast cancer, lung cancer or melanoma.1 Especially, lung cancer is one of the most common primary cancers that cause central nervous system (CNS) metastases, and the incidence of LC is increasing as lung cancer patients survive longer with more advanced treatment modalities. [2][3][4][5] In addition, with the expansion of therapeutic options for systemic treatment of lung cancer, including targeted therapies, the landscape for treating LC is changing. Although some drugs have demonstrated activity within the CNS, they have limitation to pass the blood-brain barrier. 6,7 Early and accurate diagnosis of LC and treatment initiation could offer the best chance of controlling symptoms and prevent the establishment of irreversible neurologic deficits that impair the patient's quality of life. 1,8,9 However, LC diagnosis currently remains a challenge. CSF cytology shows higher specificity (theoretical 100%) than MRI analysis (about 77%) and a sensitivity of 75% upon repeated spinal tapping.10,11 One

Isotopomer analysis using either C NMR or LC/GC-MS has been an invaluable tool for studying metabolic activities in a variety of systems. Traditional challenges are, however, thatC-detected NMR is insensitive despite its high resolution, and that MS-based techniques cannot easily differentiate positional isotopomers. In addition, current C NMR or LC/GC-MS has limitations in detecting metabolites in living cells. Here, we describe a non-uniform sampling-based 2D heteronuclear single quantum coherence (NUS HSQC) approach to measure metabolic isotopomers in both cell lysates and living cells. The method provides a high resolution that can resolve multiplet structures in theC dimension while retaining the sensitivity of the H-indirect detection. The approach was tested in L1210 mouse leukemia cells labeled withC acetate by measuring NUS HSQC with 25% sampling density. The results gave a variety of metabolic information such as (1) higher usage of acetate in acetylation pathway than aspartate synthesis, (2) TCA cycle efficiency changes upon the inhibition of mitochondrial oxidative phosphorylation by pharmacological agents, and (3) position-dependent isotopomer patterns in fatty acids in living cells. In addition, we were able to detect fatty acids along with other hydrophilic molecules in one sample of live cells without extraction. Overall, the high sensitivity and resolution along with the application to live cells should make the NUS HSQC approach attractive in studying carbon flux information in metabolic studies.

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