Anticancer drug discovery through genome-scale metabolic modeling

2014 Hanahan, 2011, Hallmarks of cancer: the next generation, Cell, 144, 646, 10.1016/j.cell.2011.02.013 Ward, 2012, Metabolic reprogramming: a cancer hallmark even Warburg did not anticipate, Cancer Cell, 21, 297, 10.1016/j.ccr.2012.02.014 Vander Heiden, 2009, Understanding the Warburg effect: the metabolic requirements of cell proliferation, Science, 324, 1029, 10.1126/science.1160809 Warburg, 1956, On the origin of cancer cells, Science, 123, 309, 10.1126/science.123.3191.309 Tennant, 2010, Targeting metabolic transformation for cancer therapy, Nat Rev Cancer, 10, 267, 10.1038/nrc2817 Galluzzi, 2013, Metabolic targets for cancer therapy, Nat Rev Drug Discov, 12, 829, 10.1038/nrd4145 Yizhak, 2015, Modeling cancer metabolism on a genome scale, Mol Syst Biol, 11, 817, 10.15252/msb.20145307 Mardinoglu, 2015, New paradigms for metabolic modeling of human cells, Curr Opin Biotechnol, 34, 91, 10.1016/j.copbio.2014.12.013 Kelloff, 2005, Progress and promise of FDG-PET imaging for cancer patient management and oncologic drug development, Clin Cancer Res, 11, 2785, 10.1158/1078-0432.CCR-04-2626 Liberti, 2016, The Warburg effect: how does it benefit cancer cells?, Trends Biochem Sci, 41, 211, 10.1016/j.tibs.2015.12.001 Cantor, 2012, Cancer cell metabolism: one hallmark, many faces, Cancer Discov, 2, 881, 10.1158/2159-8290.CD-12-0345 Pavlova, 2016, The emerging hallmarks of cancer metabolism, Cell Metab, 23, 27, 10.1016/j.cmet.2015.12.006 Vander Heiden, 2011, Targeting cancer metabolism: a therapeutic window opens, Nat Rev Drug Discov, 10, 671, 10.1038/nrd3504 Wise, 2010, Glutamine addiction: a new therapeutic target in cancer, Trends Biochem Sci, 35, 427, 10.1016/j.tibs.2010.05.003 Eagle, 1955, Nutrition needs of mammalian cells in tissue culture, Science, 122, 501, 10.1126/science.122.3168.501 Yang, 2016, Serine and one-carbon metabolism in cancer, Nat Rev Cancer, 16, 650, 10.1038/nrc.2016.81 Ravez, 2017, Challenges and opportunities in the development of serine synthetic pathway inhibitors for cancer therapy, J Med Chem, 60, 1227, 10.1021/acs.jmedchem.6b01167 Nilsson, 2014, Metabolic enzyme expression highlights a key role for MTHFD2 and the mitochondrial folate pathway in cancer, Nat Commun, 5, 10.1038/ncomms4128 Fan, 2014, Quantitative flux analysis reveals folate-dependent NADPH production, Nature, 510, 298, 10.1038/nature13236 Li, 1956, Effect of methotrexate therapy upon choriocarcinoma and chorioadenoma, Proc Soc Exp Biol Med, 93, 361, 10.3181/00379727-93-22757 O'Brien, 2015, Using genome-scale models to predict biological capabilities, Cell, 161, 971, 10.1016/j.cell.2015.05.019 Thiele, 2010, A protocol for generating a high-quality genome-scale metabolic reconstruction, Nat Protoc, 5, 93, 10.1038/nprot.2009.203 Robinson, 2016, Integrative analysis of human omics data using biomolecular networks, Mol Biosyst, 12, 2953, 10.1039/C6MB00476H Zhou, 2016, Production of fatty acid-derived oleochemicals and biofuels by synthetic yeast cell factories, Nat Commun, 7, 11709, 10.1038/ncomms11709 Bosi, 2016, Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity, Proc Natl Acad Sci U S A, 113, E3801, 10.1073/pnas.1523199113 Thiele, 2013, A community-driven global reconstruction of human metabolism, Nat Biotechnol, 31, 419, 10.1038/nbt.2488 Mardinoglu, 2014, Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease, Nat Commun, 5, 3083, 10.1038/ncomms4083 Duarte, 2007, Global reconstruction of the human metabolic network based on genomic and bibliomic data, Proc Natl Acad Sci U S A, 104, 1777, 10.1073/pnas.0610772104 Mardinoglu, 2013, Integration of clinical data with a genome-scale metabolic model of the human adipocyte, Mol Syst Biol, 9, 649, 10.1038/msb.2013.5 Nielsen, 2017, Systems biology of metabolism: a driver for developing personalized and precision medicine, Cell Metab, 25, 572, 10.1016/j.cmet.2017.02.002 Crosetto, 2015, Spatially resolved transcriptomics and beyond, Nat Rev Genet, 16, 57, 10.1038/nrg3832 Ryu, 2015, Reconstruction of genome-scale human metabolic models using omics data, Integr Biol, 7, 859, 10.1039/c5ib00002e Bordbar, 2014, Constraint-based models predict metabolic and associated cellular functions, Nat Rev Genet, 15, 107, 10.1038/nrg3643 Orth, 2010, What is flux balance analysis?, Nat Biotechnol, 28, 245, 10.1038/nbt.1614 de Mas, 2014, Cancer cell metabolism as new targets for novel designed therapies, Future Med Chem, 6, 1791, 10.4155/fmc.14.119 Ghaffari, 2015, Cancer metabolism: a modeling perspective, Front Physiol, 6, 10.3389/fphys.2015.00382 Jerby, 2012, Predicting drug targets and biomarkers of cancer via genome-scale metabolic modeling, Clin Cancer Res, 18, 5572, 10.1158/1078-0432.CCR-12-1856 Nilsson, 2016, Genome scale metabolic modeling of cancer, Metab Eng Lewis, 2013, The evolution of genome-scale models of cancer metabolism, Front Physiol, 4, 10.3389/fphys.2013.00237 Folger, 2011, Predicting selective drug targets in cancer through metabolic networks, Mol Syst Biol, 7, 10.1038/msb.2011.51 Frezza, 2011, Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase, Nature, 477, 225, 10.1038/nature10363 Agren, 2012, Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT, PLoS Comput Biol, 8, 10.1371/journal.pcbi.1002518 Agren, 2014, Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling, Mol Syst Biol, 10, 10.1002/msb.145122 Uhlen, 2015, Tissue-based map of the human proteome, Science, 347, 1260419, 10.1126/science.1260419 Yizhak, 2014, Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer, Elife, 3, 10.7554/eLife.03641 Yizhak, 2014, A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration, Mol Syst Biol, 10, 744, 10.15252/msb.20134993 Zhang, 2015, Logical transformation of genome-scale metabolic models for gene level applications and analysis, Bioinformatics, 31, 2324, 10.1093/bioinformatics/btv134 Gatto, 2014, Chromosome 3p loss of heterozygosity is associated with a unique metabolic network in clear cell renal carcinoma, Proc Natl Acad Sci U S A, 111, E866, 10.1073/pnas.1319196111 Gatto, 2015, Flux balance analysis predicts essential genes in clear cell renal cell carcinoma metabolism, Sci Rep, 5, 10738, 10.1038/srep10738 Ghaffari, 2015, Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling, Sci Rep, 5, 10.1038/srep08183 Björnson, 2015, Stratification of hepatocellular carcinoma patients based on acetate utilization, Cell Rep, 13, 2014, 10.1016/j.celrep.2015.10.045 Eddy, 2010, Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC), PLoS Comput Biol, 6, e1000792, 10.1371/journal.pcbi.1000792 Gatto, 2016, Systematic analysis reveals that cancer mutations converge on deregulated metabolism of arachidonate and xenobiotics, Cell Rep, 16, 878, 10.1016/j.celrep.2016.06.038 Zielinski, 2017, Systems biology analysis of drivers underlying hallmarks of cancer cell metabolism, Sci Rep, 7, 41241, 10.1038/srep41241 Jerby, 2012, Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer, Cancer Res, 72, 5712, 10.1158/0008-5472.CAN-12-2215 Gatto, 2016, Glycosaminoglycan profiling in patients' plasma and urine predicts the occurrence of metastatic clear cell renal cell carcinoma, Cell Rep, 15, 1822, 10.1016/j.celrep.2016.04.056 Bjornson, 2016, Personalized cardiovascular disease prediction and treatment-a review of existing strategies and novel systems medicine tools, Front Physiol, 7, 2, 10.3389/fphys.2016.00002 Gille, 2010, HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology, Mol Syst Biol, 6, 411, 10.1038/msb.2010.62 Gianchandani, 2008, Predicting biological system objectives de novo from internal state measurements, BMC Bioinform, 9, 10.1186/1471-2105-9-43 Sung, 2012, Molecular signatures from omics data: from chaos to consensus, Biotechnol J, 7, 946, 10.1002/biot.201100305 Bock, 2016, Multi-omics of single cells: strategies and applications, Trends Biotechnol, 34, 605, 10.1016/j.tibtech.2016.04.004 McGranahan, 2015, Biological and therapeutic impact of intratumor heterogeneity in cancer evolution, Cancer Cell, 27, 15, 10.1016/j.ccell.2014.12.001 Marusyk, 2010, Tumor heterogeneity: causes and consequences, Biochim Biophys Acta, 1805, 105