PT - JOURNAL ARTICLE AU - Bansal, Mukesh AU - He, Jing AU - Peyton, Michael AU - Kaustagi, Manjunath AU - Iyer, Archana AU - Comb, Michael AU - White, Michael AU - Minna, John AU - Califano, Andrea TI - Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis AID - 10.1101/289603 DP - 2018 Jan 01 TA - bioRxiv PG - 289603 4099 - http://biorxiv.org/content/early/2018/04/03/289603.short 4100 - http://biorxiv.org/content/early/2018/04/03/289603.full AB - Signaling pathway models are largely based on the compilation of literature data from 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 introduce a novel algorithm for the systematic inference of protein kinase pathways, and applied it to published mass spectrometry-based phosphotyrosine profile data from 250 lung adenocarcinoma (LUAD) samples. The resulting network includes 43 TKs and 415 inferred, LUAD-specific 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. This systematic, data-driven model supported drug response prediction on an individual sample basis, including accurate prediction and validation of synergistic EGFR and c-MET inhibitor activity in cells lacking mutations in either gene, thus contributing to current precision oncology efforts.