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Reactive oxygen species (ROS) are increasingly recognised as important signalling molecules through oxidation of protein cysteine residues. Comprehensive identification of redox-regulated proteins and pathways is crucial to understand ROS-mediated events. Here, we present stable isotope cysteine labelling with iodoacetamide (SICyLIA), a mass spectrometry-based workflow to assess proteome-scale cysteine oxidation. SICyLIA does not require enrichment steps and achieves unbiased proteome-wide sensitivity. Applying SICyLIA to diverse cellular models and primary tissues provides detailed insights into thiol oxidation proteomes. Our results demonstrate that acute and chronic oxidative stress causes oxidation of distinct metabolic proteins, indicating that cysteine oxidation plays a key role in the metabolic adaptation to redox stress. Analysis of mouse kidneys identifies oxidation of proteins circulating in biofluids, through which cellular redox stress can affect whole-body physiology. Obtaining accurate peptide oxidation profiles from complex organs using SICyLIA holds promise for future analysis of patient-derived samples to study human pathologies. Reactive oxygen species can modify cysteine residues on proteins to control cellular signalling. Here, the authors develop a proteomics approach to assess cysteine oxidation dynamics in cells and primary tissues, monitoring oxidative stress-induced metabolic adaptations and oxidation states of biofluid proteins.

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome‐scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network‐level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field. Graphical Abstract Cancer cells have fundamental metabolic alterations that are associated with tumorigenicity and malignancy. This review discusses our current knowledge of altered tumor metabolism and strategies to model these alterations, through the integration of omics data with genome‐scale metabolic models.

Treatment with tyrosine kinase inhibitors results in a survival benefit in patients with chronic myeloid leukemia (CML). However, relapse due to persistent leukemic stem cells (LSCs) requires additional selective targets for efficient eradication of the disease. Metabolomic analyses on patient-derived CML LSCs reveal that these have an increased dependency on oxidative metabolism that renders them sensitive to treatment with tigecycline, an FDA-approved inhibitor of mitochondrial translation. These findings uncover a new metabolic vulnerability in CML and provide a rational approach for further clinical evaluation. Treatment of chronic myeloid leukemia (CML) with imatinib mesylate and other second- and/or third-generation c-Abl-specific tyrosine kinase inhibitors (TKIs) has substantially extended patient survival 1 . However, TKIs primarily target differentiated cells and do not eliminate leukemic stem cells (LSCs) 2 , 3 , 4 . Therefore, targeting minimal residual disease to prevent acquired resistance and/or disease relapse requires identification of new LSC-selective target(s) that can be exploited therapeutically 5 , 6 . Considering that malignant transformation involves cellular metabolic changes, which may in turn render the transformed cells susceptible to specific assaults in a selective manner 7 , we searched for such vulnerabilities in CML LSCs. We performed metabolic analyses on both stem cell–enriched (CD34 + and CD34 + CD38 − ) and differentiated (CD34 − ) cells derived from individuals with CML, and we compared the signature of these cells with that of their normal counterparts. Through combination of stable isotope–assisted metabolomics with functional assays, we demonstrate that primitive CML cells rely on upregulated oxidative metabolism for their survival. We also show that combination treatment with imatinib and tigecycline, an antibiotic that inhibits mitochondrial protein translation, selectively eradicates CML LSCs both in vitro and in a xenotransplantation model of human CML. Our findings provide a strong rationale for investigation of the use of TKIs in combination with tigecycline to treat patients with CML with minimal residual disease.

Key Points The metabolism observed in tumours is different from that of the normal tissues from which the tumours are derived. This altered metabolic phenotype allows cancer cells to accommodate increased metabolic demands and adapt to environmental changes. Specific alterations in metabolic pathways may generate opportunities to design new therapeutic approaches. Metabolic alterations in cancer can be driven by changes in signalling pathways involving kinases such as PI3K and mTOR, and transcription factors, including hypoxia inducible factor and MYC. These are important targets for cancer therapy in general and cancer metabolism in particular. Cancer cells increase their rate of glucose and glutamine metabolism for bioenergetic and anabolic purposes. These important external carbon sources are diverted to generate DNA, proteins and lipids that are required for cancer cell growth. Cancer-specific isoforms of enzymes involved in energy metabolism, anabolism and adaptation to low oxygen may be new druggable targets for cancer therapy with potentially improved therapeutic indices compared with current therapy. This Review discusses the progress made with developing drugs that specifically target the altered metabolic pathways of tumours and suggests additional targets that might also be beneficial. Cancer therapy has long relied on the rapid proliferation of tumour cells for effective treatment. However, the lack of specificity in this approach often leads to undesirable side effects. Many reports have described various 'metabolic transformation' events that enable cancer cells to survive, suggesting that metabolic pathways might be good targets. There are currently several drugs under development or in clinical trials that are based on specifically targeting the altered metabolic pathways of tumours. This Review highlights pathways against which there are already drugs in different stages of development and also discusses additional druggable targets.

The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome‐scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI‐60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type‐specific downregulation of gene expression and somatic mutations are compiled. Synopsis During tumor development, cancer cells modify their metabolism to meet the requirements of cellular proliferation, thus facilitating the uptake and conversion of nutrients into biomass. Many key metabolic alterations are similar across tumor cells, including changes in glucose metabolism that give rise to the Warburg effect, and an increase in biosynthetic activities (such as nucleotide, lipids and amino‐acid synthesis) (DeBerardinis et al , 2008 ; Tennant et al , 2009 ; Vander Heiden et al , 2009 ). The observation that many types of cancer cells adapt their metabolism toward increased proliferation makes flux balance analysis (FBA), a constraint‐based modeling (CBM) approach, suitable for modeling cancer metabolism as it assumes that cells are under selective pressure to increase their growth rate (Price et al , 2003 ). Building on previous reconstructions of a generic (non‐tissue specific) human metabolic network (Duarte et al , 2007 ; Ma et al , 2007 ), we develop here the first large‐scale FBA model of cancer metabolism that aims to capture the main metabolic alterations that are common across many cancer types. The model reconstruction is based on our recent computational method for the automatic reconstruction of human tissue metabolic models (Jerby et al , 2010 ), integrating the human metabolic model with cancer gene expression data. The construction of the cancer model focuses on activating a core set of metabolic enzyme‐coding genes that are highly expressed across cancer cell lines in the NCI‐60 collection, with additional reactions enabling their activation and the biosynthesis of a set of biomass compounds required for cellular proliferation. This generic cancer model enables the successful prediction of the metabolic state of cancer cells across different gene knockdowns and modeling the effects of drug applications on a large scale. As a first demonstration of the predictive performance of the cancer model, we applied it to predict 199 growth‐supporting genes whose knockdown is expected to inhibit cellular proliferation, showing that the model predictions indeed match results of shRNA gene silencing experiments (Luo et al , 2008 ). To identify viable anticancer drug targets, we predicted whether the knockdown of the growth‐supporting genes is likely to be toxic to normal cells. Out of the 199 genes that are predicted to be growth supporting in the cancer model, 52 are predicted to have negligible effects on energy production in normal cells. However, the knockdown of the majority of the latter is yet predicted to potentially cause damage to proliferation of normal cells, suggesting that the targeting of these genes would cause similar side effects to those observed with current cytostatic drugs (Partridge et al , 2001 ). Next, we predicted 342 synthetic lethal drug targets, whose predicted synergy was validated based on (i) comparison with genetic interactions between the corresponding yeast orthologs (Costanzo et al , 2010 ) and (ii) by analyzing the efficacy of metabolic drugs targeting these genes, finding that drugs that target a single gene (participating in a predicted synthetic lethal pair) indeed have higher efficacy in cell lines in which the synergistic gene is lowly expressed. In contrast to the single targets described above, the knockdown of a third of these synergistic pairs is predicted to leave the proliferation of normal cells intact. Most importantly, the specific targeting of a gene participating in a synergistic pair is especially appealing in tumors in which its interacting gene is specifically inactivated—the targeting of such a gene solely is likely to selectively damage the tumor, without affecting the function of healthy tissues in which the interacting gene in the pair is active. We utilized genomic and transcriptomic data to infer gene inactivation across an array of cancers, which has led to the identification of cancer type‐specific targets based on the intersection of this data with our predicted synergistic gene pairs. In summary, the model presented here lays down a fundamental computational approach for interpreting the rapidly accumulating proteomics (Bichsel et al , 2001 ) and metabolomics (Fan et al , 2009 ) data characterizing cancer metabolic alterations. We hope that the publication of this first step will spur further studies aimed at obtaining a systems level understanding of cancer metabolism and at designing new therapeutic means that selectively target them. The first genome‐scale network model of cancer metabolism is developed and validated by successfully identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. Combinations of synthetic lethal drug targets are predicted, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI‐60 cancer cell line collection. Potential selective treatments for specific cancers that depend on cancer type‐specific downregulation of gene expression and somatic mutations are compiled.

The Warburg effect--a classical hallmark of cancer metabolism--is a counter-intuitive phenomenon in which rapidly proliferating cancer cells resort to inefficient ATP production via glycolysis leading to lactate secretion, instead of relying primarily on more efficient energy production through mitochondrial oxidative phosphorylation, as most normal cells do. The causes for the Warburg effect have remained a subject of considerable controversy since its discovery over 80 years ago, with several competing hypotheses. Here, utilizing a genome-scale human metabolic network model accounting for stoichiometric and enzyme solvent capacity considerations, we show that the Warburg effect is a direct consequence of the metabolic adaptation of cancer cells to increase biomass production rate. The analysis is shown to accurately capture a three phase metabolic behavior that is observed experimentally during oncogenic progression, as well as a prominent characteristic of cancer cells involving their preference for glutamine uptake over other amino acids.

Pyruvate dehydrogenase (PDH) is identified as a crucial mediator of BRAF V600E -induced cellular senescence: PDH is activated by BRAF-mediated suppression of PDK1, enhancing oxidative glucose metabolism, and PDK1 depletion eradicates mutant BRAF melanomas, indicating that this relationship between cell senescence and metabolism might be exploited therapeutically. Cell senescence link to metabolic signalling Oncogene-induced senescence (OIS) is one of the mechanisms that protect humans against cancer, reducing the number of potentially proliferating cells in response to stimulation by tumour suppressors. This study identifies a mechanism for communication between OIS and a mitochondrial signalling axis that balances glycolysis and oxidative phosphorylation. Joanna Kaplon et al . observe a shift from glycolysis to oxidative metabolism of glucose during oncogene-induced cellular senescence. This metabolic shift requires the activation of pyruvate dehydrogenase (PDH) via suppression of PDK1 and induction of PDP2. Inhibition of PDK1 can cause regression of established melanomas in a mouse model. These findings point to PDK1 as a potential target in cancer therapy. In response to tenacious stress signals, such as the unscheduled activation of oncogenes, cells can mobilize tumour suppressor networks to avert the hazard of malignant transformation. A large body of evidence indicates that oncogene-induced senescence (OIS) acts as such a break, withdrawing cells from the proliferative pool almost irreversibly, thus crafting a vital pathophysiological mechanism that protects against cancer 1 , 2 , 3 , 4 , 5 . Despite the widespread contribution of OIS to the cessation of tumorigenic expansion in animal models and humans, we have only just begun to define the underlying mechanism and identify key players 6 . Although deregulation of metabolism is intimately linked to the proliferative capacity of cells 7 , 8 , 9 , 10 , and senescent cells are thought to remain metabolically active 11 , little has been investigated in detail about the role of cellular metabolism in OIS. Here we show, by metabolic profiling and functional perturbations, that the mitochondrial gatekeeper pyruvate dehydrogenase (PDH) is a crucial mediator of senescence induced by BRAF V600E , an oncogene commonly mutated in melanoma and other cancers. BRAF V600E -induced senescence was accompanied by simultaneous suppression of the PDH-inhibitory enzyme pyruvate dehydrogenase kinase 1 (PDK1) and induction of the PDH-activating enzyme pyruvate dehydrogenase phosphatase 2 (PDP2). The resulting combined activation of PDH enhanced the use of pyruvate in the tricarboxylic acid cycle, causing increased respiration and redox stress. Abrogation of OIS, a rate-limiting step towards oncogenic transformation, coincided with reversion of these processes. Further supporting a crucial role of PDH in OIS, enforced normalization of either PDK1 or PDP2 expression levels inhibited PDH and abrogated OIS, thereby licensing BRAF V600E -driven melanoma development. Finally, depletion of PDK1 eradicated melanoma subpopulations resistant to targeted BRAF inhibition, and caused regression of established melanomas. These results reveal a mechanistic relationship between OIS and a key metabolic signalling axis, which may be exploited therapeutically.

Vitamin C was first suggested to have cancer-fighting properties in the 1930s and has been the subject of controversy ever since. Despite re- peated reports of selective cancer cell toxicity induced by high-dose vitamin C treatment in vitro and in mouse models, the mechanism of action has remained elusive.

Succinate dehydrogenase (SDH) is a heterotetrameric nuclear-encoded complex responsible for the oxidation of succinate to fumarate in the tricarboxylic acid cycle. Loss-of-function mutations in any of the SDH genes are associated with cancer formation. However, the impact of SDH loss on cell metabolism and the mechanisms enabling growth of SDH-defective cells are largely unknown. Here, we generated Sdhb -ablated kidney mouse cells and used comparative metabolomics and stable-isotope-labelling approaches to identify nutritional requirements and metabolic adaptations to SDH loss. We found that lack of SDH activity commits cells to consume extracellular pyruvate, which sustains Warburg-like bioenergetic features. We further demonstrated that pyruvate carboxylation diverts glucose-derived carbons into aspartate biosynthesis, thus sustaining cell growth. By identifying pyruvate carboxylase as essential for the proliferation and tumorigenic capacity of SDH-deficient cells, this study revealed a metabolic vulnerability for potential future treatment of SDH-associated malignancies. SDH inactivation is associated with cancer susceptibility. Cardaci et al. report a metabolic vulnerability in SDH-deficient cells, by showing that they depend on pyruvate carboxylation for the production of aspartate, proliferation and tumour growth.

The M2 isoform of pyruvate kinase (PKM2) is a key glycolytic enzyme that is overexpressed in cancer cells; here, serine is shown to bind to and directly activate PKM2, and the resulting reduction in enzyme activity under serine-deprivation conditions is shown to lead to the diversion of glucose-derived carbon to promote serine biosynthesis required for cell proliferation. Serine biosynthesis linked to glycolysis The M2 isoform of pyruvate kinase (PKM2) is a key enzyme in glycolysis, and is expressed in many proliferating cells including cancer cells. Using biochemical, structural and cell-based experiments, these authors show that, as has been previously suggested, serine directly binds to and activates PKM2. Under conditions of serine depletion, the resulting reduction in PKM2 activity is shown to divert pyruvate into mitochondrial metabolism, thereby promoting serine biosynthesis, which is required for cell proliferation. This work clarifies the relationship between glucose and amino acid metabolism, demonstrating a mechanism whereby glucose-derived carbon is utilized for serine biosynthesis or energy production. Cancer cells exhibit several unique metabolic phenotypes that are critical for cell growth and proliferation 1 . Specifically, they overexpress the M2 isoform of the tightly regulated enzyme pyruvate kinase (PKM2), which controls glycolytic flux, and are highly dependent on de novo biosynthesis of serine and glycine 2 . Here we describe a new rheostat-like mechanistic relationship between PKM2 activity and serine biosynthesis. We show that serine can bind to and activate human PKM2, and that PKM2 activity in cells is reduced in response to serine deprivation. This reduction in PKM2 activity shifts cells to a fuel-efficient mode in which more pyruvate is diverted to the mitochondria and more glucose-derived carbon is channelled into serine biosynthesis to support cell proliferation.