Genome-Scale Metabolic Modeling Predicting Drug Targets and Biomarkers of Cancer via

The metabolism of cancer cells is reprogrammed in various ways to support their growth and survival. Studying these phenomena to develop noninvasive diagnostic tools and selective treatments is a promising avenue.Metabolicmodeling has recently emerged as a newway to study humanmetabolism in a systematic, genome-scale manner by using pertinent high-throughput omics data. This method has been shown in various studies to provide fairly accurate estimates of the metabolic phenotype and its modifications following genetic and environmental perturbations. Here, we provide an overview of genome-scale metabolic modeling and its current use to model human metabolism in health and disease. We then describe the initial stepsmade using it to study cancer metabolism and how it may be harnessed to enhance ongoing experimental efforts to identify drug targets andbiomarkers for cancer in a rationale-basedmanner. Clin Cancer Res; 18(20); 5572–84. 2012 AACR. Introduction Aberrant metabolism is one of the main driving forces in the initiation and development of cancer (1, 2). During carcinogenesis, selective pressures lead to diverse metabolic alterations, imposed by multiple molecular mechanisms (3–5). These metabolic adaptations enable the cancer cells not only to proliferate and cope with high energetic demands but also to avoid apoptosis, evade the immune system (6), and control the rate of mutagenesis (3, 7). Several metabolic abnormalities are quite general and have been observed in many cancer types. Notable among these is the preference tometabolize glucose by aerobic glycolysis (8, 9). This phenomenon, termed the Warburg effect, is accompanied by lactate production and increased glucose uptake. As proliferation requires a constant supply of macromolecular precursors that are generated in the tricarboxylic acid (TCA) cycle, cancer cells often use glutamine to replenish the cycle (anapleurosis). Glutamine has also been shown to support lipid synthesis in cancer cells through reductive carboxylation by the reverse activity of the TCA reaction isocitrate dehydrogenase (IDH; refs. 10, 11). Nonetheless, cancer metabolism is heterogeneous and reprogrammed in various ways. Mutations in several TCA metabolic enzymes promote specific types of cancer: Loss-of function mutation in fumarate hydratase (FH) causes leiomyoma, leiomyosarcoma, or renal cell carcinoma, whereas such mutations in succinate dehydrogenase lead to the development of paraganglioma or pheochromocytoma (12); on the other hand, gain-of-function mutations in IDH promote glioblastoma and acute myeloid leukemia (13, 14). It is yet to be elucidated why certain metabolic mutations lead to one type of cancer and not another and how metabolism promotes cancer through its interactions with other cellular processes. However, these findings imply that characterizing the unique metabolic dependencies of different cancer cells can potentially pave the way toward the development of selective treatments and diagnostic tools (15, 16). The rapid technologic advancements in obtaining highthroughput omics data, combined with the development of the metabolic modeling methodology, has recently enhanced our ability to study metabolism on a genomewide scale. In silicometabolic modeling has been shown to provide an appropriate platform to address various research questions related to metabolism and predict an array of cellular metabolic phenotypes (17–25). Here, we describe how using and developing this paradigm to study cancer metabolism can elucidate the metabolic alterations that accompany cancer progression and aid in the identification of drug targets and metabolic biomarkers. Importantly, drugs that target metabolic enzymes are especially promising because metabolism is evolutionarily more conserved than other biologic processes that have been targeted in cancer, such as signaling (26). Therefore, cancer cells are less prone to evolve resistance to these drugs by developing alternative pathways. Genome-Scale Metabolic Modeling In silico models of metabolism are based upon a representation of metabolism as a network. Mathematical Authors' Affiliations: The Blavatnik School of Computer Science, and The Sackler School of Medicine, Tel Aviv University, Israel Corresponding Authors: Eytan Ruppin, The Blavatnik School of Computer Sciences & School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel. Phone: 97-23-6406528; Fax: 97-23-6409357; E-mail: ruppin@post.tau. ac.il; and Livnat Jerby. Phone: 97-23-6405378; E-mail: livnatje@post.tau. ac.il doi: 10.1158/1078-0432.CCR-12-1856 2012 American Association for Cancer Research. CCRFOCUS Clin Cancer Res; 18(20) October 15, 2012 5572 American Association for Cancer Research Copyright © 2012 on October 15, 2012 clincancerres.aacrjournals. org Downloaded from DOI:10.1158/1078-0432.CCR-12-1856