@article {49724, title = {Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer.}, journal = {Elife}, volume = {3}, year = {2014}, month = {2014}, abstract = {

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.

}, keywords = {algorithms, 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}, issn = {2050-084X}, doi = {10.7554/eLife.03641}, author = {Yizhak, Keren and Gaude, Edoardo and Le D{\'e}v{\'e}dec, Sylvia and Waldman, Yedael Y and Stein, Gideon Y and van de Water, Bob and Frezza, Christian and Ruppin, Eytan} } @article {49726, title = {Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality.}, journal = {Cell}, volume = {158}, year = {2014}, month = {2014 Aug 28}, pages = {1199-209}, abstract = {

Synthetic lethality occurs when the inhibition of two genes is lethal while the inhibition of each single gene is not. It can be harnessed to selectively treat cancer by identifying inactive genes in a given cancer and targeting their synthetic lethal (SL) partners. We present a data-driven computational pipeline for the genome-wide identification of SL interactions in cancer by analyzing large volumes of cancer genomic data. First, we show that the approach successfully captures known SL partners of tumor suppressors and oncogenes. We then validate SL predictions obtained for the tumor suppressor VHL. Next, we construct a genome-wide network of SL interactions in cancer and demonstrate its value in predicting gene essentiality and clinical prognosis. Finally, we identify synthetic lethality arising from gene overactivation and use it to predict drug efficacy. These results form a computational basis for exploiting synthetic lethality to uncover cancer-specific susceptibilities.

}, keywords = {Breast Neoplasms, Cell Line, Tumor, Computational Biology, Data Mining, Genes, Tumor Suppressor, HUMANS, Neoplasms, Oncogenes, RNA, Small Interfering, workflow}, issn = {1097-4172}, doi = {10.1016/j.cell.2014.07.027}, author = {Jerby-Arnon, Livnat and Pfetzer, Nadja and Waldman, Yedael Y and McGarry, Lynn and James, Daniel and Shanks, Emma and Seashore-Ludlow, Brinton and Weinstock, Adam and Geiger, Tamar and Clemons, Paul A and Gottlieb, Eyal and Ruppin, Eytan} } @article {49740, title = {Global secretome analysis identifies novel mediators of bone metastasis.}, journal = {Cell Res}, volume = {22}, year = {2012}, month = {2012 Sep}, pages = {1339-55}, abstract = {

Bone is the one of the most common sites of distant metastasis of solid tumors. Secreted proteins are known to influence pathological interactions between metastatic cancer cells and the bone stroma. To comprehensively profile secreted proteins associated with bone metastasis, we used quantitative and non-quantitative mass spectrometry to globally analyze the secretomes of nine cell lines of varying bone metastatic ability from multiple species and cancer types. By comparing the secretomes of parental cells and their bone metastatic derivatives, we identified the secreted proteins that were uniquely associated with bone metastasis in these cell lines. We then incorporated bioinformatic analyses of large clinical metastasis datasets to obtain a list of candidate novel bone metastasis proteins of several functional classes that were strongly associated with both clinical and experimental bone metastasis. Functional validation of selected proteins indicated that in vivo bone metastasis can be promoted by high expression of (1) the salivary cystatins CST1, CST2, and CST4; (2) the plasminogen activators PLAT and PLAU; or (3) the collagen functionality proteins PLOD2 and COL6A1. Overall, our study has uncovered several new secreted mediators of bone metastasis and therefore demonstrated that secretome analysis is a powerful method for identification of novel biomarkers and candidate therapeutic targets.

}, keywords = {Animals, Biomarkers, Tumor, Bone Neoplasms, Cell Line, Tumor, Collagen Type VI, Computational Biology, HUMANS, Mass Spectrometry, Mice, Neoplasms, Plasminogen Activators, Procollagen-Lysine, 2-Oxoglutarate 5-Dioxygenase, Proteome, proteomics, Salivary Cystatins}, issn = {1748-7838}, doi = {10.1038/cr.2012.89}, author = {Blanco, Mario Andres and LeRoy, Gary and Khan, Zia and Ale{\v c}kovi{\'c}, Ma{\v s}a and Zee, Barry M and Garcia, Benjamin A and Kang, Yibin} } @article {49746, title = {Direct targeting of Sec23a by miR-200s influences cancer cell secretome and promotes metastatic colonization.}, journal = {Nat Med}, volume = {17}, year = {2011}, month = {2011 Sep}, pages = {1101-8}, abstract = {

Although the role of miR-200s in regulating E-cadherin expression and epithelial-to-mesenchymal transition is well established, their influence on metastatic colonization remains controversial. Here we have used clinical and experimental models of breast cancer metastasis to discover a pro-metastatic role of miR-200s that goes beyond their regulation of E-cadherin and epithelial phenotype. Overexpression of miR-200s is associated with increased risk of metastasis in breast cancer and promotes metastatic colonization in mouse models, phenotypes that cannot be recapitulated by E-cadherin expression alone. Genomic and proteomic analyses revealed global shifts in gene expression upon miR-200 overexpression toward that of highly metastatic cells. miR-200s promote metastatic colonization partly through direct targeting of Sec23a, which mediates secretion of metastasis-suppressive proteins, including Igfbp4 and Tinagl1, as validated by functional and clinical correlation studies. Overall, these findings suggest a pleiotropic role of miR-200s in promoting metastatic colonization by influencing E-cadherin-dependent epithelial traits and Sec23a-mediated tumor cell secretome.

}, keywords = {Animals, Cadherins, Cell Line, Tumor, Female, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, HUMANS, Mass Spectrometry, Mice, Mice, Inbred BALB C, Microarray Analysis, MicroRNAs, Neoplasm Metastasis, Statistics, Nonparametric, Vesicular Transport Proteins}, issn = {1546-170X}, doi = {10.1038/nm.2401}, author = {Korpal, Manav and Ell, Brian J and Buffa, Francesca M and Ibrahim, Toni and Blanco, Mario A and Celi{\`a}-Terrassa, Toni and Mercatali, Laura and Khan, Zia and Goodarzi, Hani and Hua, Yuling and Wei, Yong and Hu, Guohong and Garcia, Benjamin A and Ragoussis, Jiannis and Amadori, Dino and Harris, Adrian L and Kang, Yibin} } @article {49728, title = {Predicting selective drug targets in cancer through metabolic networks.}, journal = {Mol Syst Biol}, volume = {7}, year = {2011}, month = {2011}, pages = {501}, abstract = {

The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40\% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled.

}, keywords = {Cell Line, Tumor, Cell Proliferation, Computational Biology, Cytostatic Agents, Down-Regulation, Drug Delivery Systems, Gene Expression Regulation, Neoplastic, HUMANS, Metabolic Networks and Pathways, Models, Biological, Neoplasms, RNA, Small Interfering}, issn = {1744-4292}, doi = {10.1038/msb.2011.35}, author = {Folger, Ori and Jerby, Livnat and Frezza, Christian and Gottlieb, Eyal and Ruppin, Eytan and Shlomi, Tomer} }