Reconstructing targetable pathways in lung cancer by integrating diverse omics data

O. Alejandro Balbin, John R. Prensner, Anirban Sahu, Anastasia Yocum, Sunita Shankar, Rohit Malik, Damian Fermin, Saravana M. Dhanasekaran, Benjamin Chandler, Dafydd Thomas, David G. Beer, Xuhong Cao, Alexey I. Nesvizhskii () and Arul M. Chinnaiyan ()
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O. Alejandro Balbin: Michigan Center for Translational Pathology, University of Michigan
John R. Prensner: Michigan Center for Translational Pathology, University of Michigan
Anirban Sahu: Michigan Center for Translational Pathology, University of Michigan
Anastasia Yocum: Michigan Center for Translational Pathology, University of Michigan
Sunita Shankar: Michigan Center for Translational Pathology, University of Michigan
Rohit Malik: Michigan Center for Translational Pathology, University of Michigan
Damian Fermin: University of Michigan
Saravana M. Dhanasekaran: Michigan Center for Translational Pathology, University of Michigan
Benjamin Chandler: Michigan Center for Translational Pathology, University of Michigan
Dafydd Thomas: University of Michigan
David G. Beer: University of Michigan
Xuhong Cao: Michigan Center for Translational Pathology, University of Michigan
Alexey I. Nesvizhskii: Michigan Center for Translational Pathology, University of Michigan
Arul M. Chinnaiyan: Michigan Center for Translational Pathology, University of Michigan

Nature Communications, 2013, vol. 4, issue 1, 1-13

Abstract: Abstract Global ‘multi-omics’ profiling of cancer cells harbours the potential for characterizing the signalling networks associated with specific oncogenes. Here we profile the transcriptome, proteome and phosphoproteome in a panel of non-small cell lung cancer (NSCLC) cell lines in order to reconstruct targetable networks associated with KRAS dependency. We develop a two-step bioinformatics strategy addressing the challenge of integrating these disparate data sets. We first define an ‘abundance-score’ combining transcript, protein and phospho-protein abundances to nominate differentially abundant proteins and then use the Prize Collecting Steiner Tree algorithm to identify functional sub-networks. We identify three modules centred on KRAS and MET, LCK and PAK1 and β-Catenin. We validate activation of these proteins in KRAS-dependent (KRAS-Dep) cells and perform functional studies defining LCK as a critical gene for cell proliferation in KRAS-Dep but not KRAS-independent NSCLCs. These results suggest that LCK is a potential druggable target protein in KRAS-Dep lung cancers.

Date: 2013
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DOI: 10.1038/ncomms3617

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