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PLoS One. 2019 Jan 7;14(1):e0208646. doi: 10.1371/journal.pone.0208646. eCollection 2019.

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

Bansal M1,2, He J3,4,5, Peyton M6, Kustagi M3, Iyer A3, Comb M7, White M6, Minna JD8, Califano A3,4,5,9,10,11.

Author information

1
Psychogenics Inc., Paramus, New Jersey, United States of America.
2
Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.
3
Department of Systems Biology, Columbia University, New York, NY, United States of America.
4
Center for Computational Biology and Bioinformatics (C2B2), Columbia University, New York, NY, United States of America.
5
Department of Biomedical Informatics (DBMI), Columbia University, New York, NY, United States of America.
6
Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America.
7
Cell Signaling Technology, 3 Trask Lane, Danvers, MA, United States of America.
8
Hamon Center for Therapeutic Oncology Research, Simmons Comprehensive Cancer Center, Departments of Pharmacology, and Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
9
Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, United States of America.
10
Institute for Cancer Genetics, Columbia University, New York, NY, United States of America.
11
Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, United States of America.

Abstract

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.

PMID:
30615629
PMCID:
PMC6322741
DOI:
10.1371/journal.pone.0208646
[Indexed for MEDLINE]
Free PMC Article

Conflict of interest statement

AC is founder, equity holder, consultant, and director of DarwinHealth Inc., a company that has licensed some of the algorithms used in this manuscript from Columbia University. Columbia University is also an equity holder in DarwinHealth Inc. The ARACNe and MARINa algorithms discussed in this manuscript are publicly and freely available to any researchers working for a non-profit/academic institution but their commercial use is restricted since they were exclusively licensed by Columbia University to DarwinHealth Inc. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

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