Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer.

TitlePhenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer.
Publication TypeJournal Articles
Year of Publication2014
AuthorsYizhak K, Gaude E, Le Dévédec S, Waldman YY, Stein GY, van de Water B, Frezza C, Ruppin E
Date Published2014
Keywordsalgorithms, Antineoplastic Agents, Biomarkers, Tumor, Carboxy-Lyases, Cell Line, Tumor, Cell Proliferation, Citric Acid Cycle, Fatty Acids, Gene Knockdown Techniques, Genome, Human, HUMANS, Lymphocytes, Models, Biological, Neoplasms, Oxidation-Reduction, PHENOTYPE, Precision Medicine

Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for future personalized metabolic modeling applications, enhancing the search for novel selective anticancer therapies.

Alternate JournalElife
PubMed ID25415239
PubMed Central IDPMC4238051
Grant ListMC_UP_1101/3 / / Medical Research Council / United Kingdom
MC_UU_12022/6 / / Medical Research Council / United Kingdom