NATURE REVIEWS | GASTROENTEROLOGY & HEPATOLOGY VOLUME 11 | JUNE 2014

NEWS & VIEWS

However, ran­domized control led

trials failed to prove a protective effect for statins

in HCC, although these trials were initially designed to assess cardio vas­cular events.5 Hence, the efficacy of statins in the prevention of HCC is still debated and c onclusive studies are needed.

The interest in statins is further supported by the fact that enhanced mevalonate syn­thesis and mevalonate derivatives promote cell proliferation through activating onco­proteins and DNA synthesis.4 Therefore, in addition to statins, all enzymes involved in lipid metabolism are certainly potential chemotherapeutic targets for HCC. Actually, several preclinical studies specifically tar­geting enzymes involved in lipid synthesis (such as the fatty acid synthase FASN, the ATP citrate lyase ACLY or the acetyl­CoA carboxylase ACC), or involved in fatty acid metabolism (such as the monoglyceride lipase MGLL and carnitine palmitoyltrans­ferase 1C) are ongoing.8 Moreover, as lipid metabolism plays a part in the pathogenesis of NASH, several investigations are cur­rently also assessing the effect of diet on the risk of developing NASH.

Among the drug target candidates, and in addition to the cholesterol­related anti­metabolites, the authors identified several molecules from the folate pathway.4 In addition to studies investigating the oppos­ing roles of folates (as documented in the c ancer­related literature), several clini­cal trials targeting the folate pathway are underway. These trials include testing of a small molecule, vintafolide (also known as EC145), targeting the folate recep­tor in platinum­resistant ovarian cancer (NCT00722592, NCT01170650), non­small cell lung cancer (NCT01577654) and breast cancer (NCT01953536) and, for HCC, testing of a drug conjugate consist­ing of folate (vitamin B9) linked to a potent cytotoxic agent, tubulysin B hydrazide, in patients who overexpress the folate receptor (NCT01999738). In addition, the preventive

HEPATOCELLULAR CARCINOMA

Genome-scale metabolic models for hepatocellular carcinomaRoser Pinyol and Josep M. Llovet

A new study proposes a modelling strategy to identify reactions, genes and metabolites relevant in hepatocellular carcinoma using in silico and in vivo analyses. The proposed genome-scale metabolic model integrates genomic and proteomic information, and points to statins, among others, as potential chemopreventive and anticancer drugs.Pinyol, R. & Llovet, J. M. Nat. Rev. Gastroenterol. Hepatol. 11, 336–337 (2014); published online 20 May 2014; doi:10.1038/nrgastro.2014.70

Hepatocellular carcinoma (HCC) is a major public health problem with 750,000 new cases each year and an increasing incidence worldwide.1 The largest risk factor for HCC development is cirrhosis, present in ~80% of the primary liver cancers, along with HBV infection and HCV infection. NAFLD­related and NASH­related cirrhosis are other clear risk factors for HCC develop­ment.1 Even nowadays, only one­third of patients are diagnosed at early stages of the disease, and among advanced cases only one systemic therapy, sorafenib, has been shown to be effective.2 The fact that the median life expectancy of patients with HCC is 1 year reveals the urgent need to develop additional efficient HCC therapeutic strategies. Agren et al.3 now present a solid in silico approach, based on publicly available data collections, that enables the identification of metabolites relevant in HCC that could be used as anti­cancer drugs. Although these genome­scale metabolic models (GEMs) used to be limited to a static stoichiometric representation of metabolite pathways, the approach presented by Agren et al.3 integrates functional data and expands the application of GEMs.

Agren and colleagues3 developed an algorithm (task­drive Integrative Network Inference for Tissues or tINIT; an algorithm that takes into consideration both connected and consistent metabolic networks, as well

as evidence­based metabolic functions) that when applied on proteomic information from the Human Protein Atlas together with the Human Metabolic Reaction database 2.0, enabled them to build a GEM for six patients with HCC, from whom they had individual proteomic data, and a generic HCC model based on the average protein expression levels of 27 patients with HCC. Their models con­tained about 4,800 reactions from the meta­bolic network and about 2,000 genes, which allowed them to predict 101 meta bolites that, when used in form of anti metabolites, were able to inhibit cell growth in the HCC model. The authors propose these antimetabolites as potential anticancer drugs.

Among the 101 metabolites identified by Agren et al.,3 30% were related to choles­terol biosynthesis. This finding is consistent with observations showing that alterations in lipid metabolism are associated with cancer cells.4 The conversion of the cho­lesterol precursor hydroxyl­methylglutaryl coenzyme A into mevalonate is known to be blocked by statins. Statins are the second most prescribed medication worldwide and are generally used for the prevention of cardiovascular diseases.5 In HCC, obser­vational studies show a protective associa­tion between the use of statins and the risk of developing this malignancy.5,6 Two large population­based cohort studies identified that statins decrease the risk of HCC devel­opment in a dose­dependent manner (HR 0.34–0.66) in patients with either HBV or HCV infection.7 Similarly, a meta­analysis published in 2013 showed that statins users were less likely to develop HCC than non­users (adjusted OR 0.63; 95% CI 0.52–0.76).6

‘‘…enables the identification of metabolites relevant in HCC that could be used as anticancer drugs’’

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JUNE 2014 | VOLUME 11 www.nature.com/nrgastro

NEWS & VIEWS

role of folic acid is being tested in several ongoing clinical studies for different cancers.

From a molecular point of view, high­throughput technologies have clearly shed light on the molecular mechanisms under­lying cancer and liver carcinogenesis. Inte­g rative in silico approaches such as the one presented by the authors (tINIT) are neces­sary to recapitulate the vast amount of data generated on HCC. It would be interesting now to feed the tINIT algorithm with infor­mation from the molecular classification associated with each patient with HCC.9 That each case of HCC depicts a distinct molecular profile is now well accepted among the scientific community and, there­fore, instead of just averaging the proteomic data from 27 patients with HCC and using this data to build the generic HCC GEM, it would be compelling to also take into account the HCC class assignment. This approach would enable detection of anti­metabolites specific for each HCC molecu­lar subclass, and facilitate future patient stratification when testing the antimetabo­lite in clinical trials. Of course, use of a much larger cohort would be needed with this strategy, which would also correct the gender bias associated with the Agren et al.3 study (HCC is more frequent in men than women by a ratio of 3:1; of the 27 patients with HCC, 10 were women and 17 were men). Moreover, including the patient’s clinical data in the tINIT could expand its applicability, potentially enabling the detec­tion of metabolites with predictive value in terms of disease outcome.

In an article from 2014, the study authors applied a similar in silico strategy for the discovery of diagnostic biomarkers for pathological changes in the liver (from stea­tosis to NASH) and potential therapeutic targets.10 In that work, they also focused on metabolite biomarkers, which are clini­cally attractive because their concentration can be easily measured in blood or urine. An ambitious approach would be to use a similar strategy and incorporate the tINIT data from patients covering the spectrum of preneoplastic stages (for example, cirrhosis and dysplasia) and HCC stages. Systematic integration of ‘­omics’ information with cell­biology knowledge and clinical data in HCC should enhance the dynamic aspects and potential of GEMs and are one of the current methods to acquire a better understanding of disease­associated metabolic changes, as well as to identify prognostic biomarkers and develop novel cancer therapies.

HCC Translational Research Laboratory, Barcelona Clinic Liver Cancer (BCLC) Group, Liver Unit, Hospital Clínic Barcelona, IDIBAPS, CIBERehd, University of Barcelona, Rosselló 153, 08036 Barcelona, Spain (R.P., J.M.L.). Correspondence to: J.M.L. jmllovet@clinic.cat

AcknowledgementsJ.M.L. is supported by grants from the European Commission Framework Programme 7 (Heptromic, proposal number 259744), the US National Institute of Diabetes and Digestive and Kidney Diseases (1R01DK076986), the Samuel Waxman Cancer Research Foundation, the Spanish National Health Institute (SAF‑2010‑16055) and the Asociación Española para el Estudio del Cáncer.

Competing interestsThe authors declare no competing interests.

1. European Association for the Study of the Liver & European Organisation for Research and Treatment of Cancer. EASL–EORTC clinical practice guidelines: management of hepatocellular carcinoma. J. Hepatol. 56, 908–943 (2012).

2. Llovet, J. M. et al. Sorafenib in advanced hepatocellular carcinoma. N. Engl. J. Med. 359, 378–390 (2008).

3. Agren, R. et al. Identification of anticancer drugs for hepatocellular carcinoma through personalized genome‑scale metabolic modeling. Mol. Syst. Biol. 10, 721 (2014).

4. Notarnicola, M., Tutino, V. & Caruso, M. G. Tumor‑induced alterations in lipid metabolism. Curr. Med. Chem. http://dx.doi.org/10.2174/ 0929867321666140303122426.

5. Singh, S., Singh, P. P., Roberts, L. R. & Sanchez, W. Chemopreventive strategies in hepatocellular carcinoma. Nat. Rev. Gastroenterol. Hepatol. 11, 45–54 (2014).

6. Singh, S., Singh, P. P., Singh, A. G., Murad, M. H. & Sanchez, W. Statins are associated with a reduced risk of hepatocellular cancer: a systematic review and meta‑analysis. Gastroenterology 144, 323–332 (2013).

7. Tsan, Y. T. et al. Statins and the risk of hepatocellular carcinoma in patients with hepatitis C virus infection. J. Clin. Oncol. 31, 1514–1521 (2013).

8. Van der Heiden, M. G. Targeting cancer metabolism: a therapeutic window opens. Nat. Rev. Drug Discov. 10, 671–684 (2011).

9. Hoshida, Y. et al. Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma. Cancer Res. 69, 7385–7392 (2009).

10. Mardinoglu, A. et al. Genome‑scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non‑alcoholic fatty liver disease. Nat. Commun. 5, 3083 (2014).

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