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bioRxiv · 10.1101/289603

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

Bansal, M.·Peyton, M.·He, J.·Kaustagi, M.·Iyer, A.·Comb, M.·White, M.·Minna, J.·Califano, A.
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Abstract

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.

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Bansal, M., Peyton, M., He, J., Kaustagi, M., Iyer, A., Comb, M., White, M., Minna, J., Califano, A.. 2018-03-29. Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis. https://doi.org/10.1101/289603

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