TY - JOUR
T1 - Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis
AU - Bansal, Mukesh
AU - He, Jing
AU - Peyton, Michael
AU - Kustagi, Manjunath
AU - Iyer, Archana
AU - Comb, Michael
AU - White, Michael
AU - Minna, John D.
AU - Califano, Andrea
N1 - Publisher Copyright:
© 2019 Bansal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2019/1
Y1 - 2019/1
N2 - To understand drug combination effect, it is necessary to decipher the interactions between drug targets—many of which are signaling molecules. Previously, such 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.
AB - To understand drug combination effect, it is necessary to decipher the interactions between drug targets—many of which are signaling molecules. Previously, such 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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U2 - 10.1371/journal.pone.0208646
DO - 10.1371/journal.pone.0208646
M3 - Article
C2 - 30615629
AN - SCOPUS:85059646408
SN - 1932-6203
VL - 14
JO - PloS one
JF - PloS one
IS - 1
M1 - e0208646
ER -