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

Mukesh Bansal, Jing He, Michael Peyton, Manjunath Kustagi, Archana Iyer, Michael Comb, Michael White, John D. Minna, Andrea Califano

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish (US)
Article numbere0208646
JournalPloS one
Issue number1
StatePublished - Jan 2019

ASJC Scopus subject areas

  • General


Dive into the research topics of 'Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis'. Together they form a unique fingerprint.

Cite this