Source:Cell Reports
Author(s): James Campbell, Colm J. Ryan, Rachel Brough, Ilirjana Bajrami, Helen N. Pemberton, Irene Y. Chong, Sara Costa-Cabral, Jessica Frankum, Aditi Gulati, Harriet Holme, Rowan Miller, Sophie Postel-Vinay, Rumana Rafiq, Wenbin Wei, Chris T. Williamson, David A. Quigley, Joe Tym, Bissan Al-Lazikani, Timothy Fenton, Rachael Natrajan, Sandra J. Strauss, Alan Ashworth, Christopher J. Lord
One approach to identifying cancer-specific vulnerabilities and therapeutic targets is to profile genetic dependencies in cancer cell lines. Here, we describe data from a series of siRNA screens that identify the kinase genetic dependencies in 117 cancer cell lines from ten cancer types. By integrating the siRNA screen data with molecular profiling data, including exome sequencing data, we show how vulnerabilities/genetic dependencies that are associated with mutations in specific cancer driver genes can be identified. By integrating additional data sets into this analysis, including protein-protein interaction data, we also demonstrate that the genetic dependencies associated with many cancer driver genes form dense connections on functional interaction networks. We demonstrate the utility of this resource by using it to predict the drug sensitivity of genetically or histologically defined subsets of tumor cell lines, including an increased sensitivity of osteosarcoma cell lines to FGFR inhibitors and SMAD4 mutant tumor cells to mitotic inhibitors.
Graphical abstract
Teaser
Campbell et al. use parallel siRNA screens to identify the kinase dependencies of 117 cancer cell lines from ten cancer types. They use this resource to identify kinase dependencies associated with specific cancer types or driver genes and show that the integration of protein interaction networks facilitates the interpretation of these dependencies.from #AlexandrosSfakianakis via Alexandros G.Sfakianakis on Inoreader http://ift.tt/1UDW05h
via IFTTT
Δεν υπάρχουν σχόλια:
Δημοσίευση σχολίου