Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis release_nv7n6rionrdp3eqjcpw4jrpiby

by Mukesh Bansal, Jing He, Michael Peyton, Manjunath Kaustagi, Archana Iyer, Michael Comb, Michael White, John Minna, Andrea Califano

Released as a post by Cold Spring Harbor Laboratory.

2018  

Abstract

Signal transduction pathways are largely based on the compilation of individual post-translational modification assays in heterogeneous cellular contexts. Indeed, de novo reconstruction of signaling interactions from large-scale molecular profiling is still lagging, compared to similar efforts in transcriptional and protein-protein interaction networks. To address this challenge, we present systematic, computational reconstruction of tyrosine kinase (TK) signal transduction pathways, based on mass spectrometry-based proteomics profiling of phosphotyrosine-enriched peptides from 250 samples representative of lung adenocarcinoma. The network represents 43 TKs and their predicted substrates, which were validated at >60% accuracy by SILAC assays, including "novel" substrates of the EGFR and c-MET TKs, which play a critical oncogenic role in lung cancer. Availability of the network allowed prediction of drug response in individual samples, including accurate prediction of synergistic EGFR and c-MET inhibitor activity in cells lacking mutations in either gene, which was experimentally validated, thus contributing to current precision oncology efforts.
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Date   2018-03-29
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