Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis
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by
Mukesh Bansal, Jing He, Michael Peyton, Manjunath Kaustagi, Archana Iyer, Michael Comb, Michael White, John Minna, Andrea Califano
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
10.1101/289603
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