Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer
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 p...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2014-11-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/03641 |
| _version_ | 1811201075563200512 |
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| author | Keren Yizhak Edoardo Gaude Sylvia Le Dévédec Yedael Y Waldman Gideon Y Stein Bob van de Water Christian Frezza Eytan Ruppin |
| author_facet | Keren Yizhak Edoardo Gaude Sylvia Le Dévédec Yedael Y Waldman Gideon Y Stein Bob van de Water Christian Frezza Eytan Ruppin |
| author_sort | Keren Yizhak |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T02:14:48Z |
| format | Article |
| id | doaj.art-a5fbb1250db349649018029520e7c0e7 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-12T02:14:48Z |
| publishDate | 2014-11-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-a5fbb1250db349649018029520e7c0e72022-12-22T03:52:17ZengeLife Sciences Publications LtdeLife2050-084X2014-11-01310.7554/eLife.03641Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancerKeren Yizhak0Edoardo Gaude1Sylvia Le Dévédec2Yedael Y Waldman3Gideon Y Stein4Bob van de Water5Christian Frezza6Eytan Ruppin7Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, IsraelMRC Cancer Unit, University of Cambridge, Cambridge, United KingdomDivision of Toxicology, Leiden Academic Center for Drug Research, Leiden University, Leiden, NetherlandsBlavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, IsraelDepartment of Internal Medicine ‘B’, Beilinson Hospital, Rabin Medical Center, Petah-Tikva, Israel; Sackler School of Medicine, Tel Aviv University, Tel-Aviv, IsraelDivision of Toxicology, Leiden Academic Center for Drug Research, Leiden University, Leiden, NetherlandsMRC Cancer Unit, University of Cambridge, Cambridge, United KingdomBlavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel; Sackler School of Medicine, Tel Aviv University, Tel-Aviv, IsraelUtilizing 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.https://elifesciences.org/articles/03641genome-scale metabolic modelingcancerselective drug targetpersonalized medicine |
| spellingShingle | Keren Yizhak Edoardo Gaude Sylvia Le Dévédec Yedael Y Waldman Gideon Y Stein Bob van de Water Christian Frezza Eytan Ruppin Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer eLife genome-scale metabolic modeling cancer selective drug target personalized medicine |
| title | Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer |
| title_full | Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer |
| title_fullStr | Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer |
| title_full_unstemmed | Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer |
| title_short | Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer |
| title_sort | phenotype based cell specific metabolic modeling reveals metabolic liabilities of cancer |
| topic | genome-scale metabolic modeling cancer selective drug target personalized medicine |
| url | https://elifesciences.org/articles/03641 |
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