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10.1016/j.cell.2017.06.010 http://scihub22266oqcxt.onion/10.1016/j.cell.2017.06.010 C5667678!5667678 !28753430     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28753430 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Cell 2017 ; 170 (3 ): 564-576.e16 Nephropedia Template TP {{tp|p=28753430 |t=2017. Defining a Cancer Dependency Map |pdf=|usr=}}{{28753430 }} gab.com Text Defining a Cancer Dependency Map JO: Cell http://www.kidney.de/pget.php?&tw=1&pmid=28753430 #FOAvip Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets. Twit Text FOAVip Defining a Cancer Dependency Map ????Cell http://www.kidney.de/pget.php?&tw=1&pmid=28753430 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28753430 #FOAvip English Wikipedia <ref>{{Cite pmid|28753430 }}</ref> Defining a Cancer Dependency Map #MMPMID28753430 Tsherniak A ; Vazquez F ; Montgomery PG ; Weir BA ; Kryukov G ; Cowley GS ; Gill S ; Harrington WF ; Pantel S ; Krill-Burger JM ; Meyers RM ; Ali L ; Goodale A ; Lee Y ; Jiang G ; Hsiao J ; Gerath WFJ ; Howell S ; Merkel E ; Ghandi M ; Garraway LA ; Root DE ; Golub TR ; Boehm JS ; Hahn WC Cell 2017[Jul]; 170 (3 ): 564-576.e16 PMID28753430 show ga Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets. |Cell Line, Tumor [MESH] |Humans [MESH] |Neoplasms/*genetics/*pathology [MESH] |RNA Interference [MESH] |Software [MESH]Defining a Cancer Dependency Map (epmc).pdf DeepDyve Pubget Overpricing