Abstract PR11: Dissecting signaling transduction network to infer master regulators of non small cell lung cancer
Authors: Bansal Mukesh , Peyton Michael , Kustagi Manjunath , Girard Luc , Rikova Klarisa , Comb Michael , White Michael , Minna John , Califano Andrea 
Published: August 2014

Abstract
The dissection of signal transduction networks is lagging significantly with respect to their transcriptional and protein-complex counterparts. Innovative use of multiplex and microarray-based approaches, where multiple antibodies can be used to probe an ensemble of phosphoproteins, is starting to become sufficiently mature to allow characterization of small pathways. Yet, this is still far from providing an unbiased, genome-wide view of signal-transduction processes.
Using data from the first genome-wide profiling of phosphopeptide abundance in normal and tumor-related lung tissue, we have designed a novel algorithm, pARACNe, to generate an unbiased, genome-wide network representing tyrosine kinase signal transduction pathways. Specifically, pARACNe was used to infer tyrosine kinase substrates in non-small cell lung cancer (NSCLC) from tandem mass-spec abundance profiling of phosphotyrosine-enriched peptides from ˜250 samples derived from primary tumors, cell lines, and normal lung tissue. SILAC validation of EGFR, MET and ALK substrates, confirmed &gt;80% of the pARACNe-inferred interactions. Further analysis of large-scale NSCLC microarray expression profile data, using the MINDy algorithm, showed significant convergence between the signal transduction pathways inferred by proteomics and gene expression-based methods.
The availability of an accurate NSCLC Tyrosine Kinase Interaction Network (TKIN) has significant implications for our ability to elucidate master regulators of oncogenesis and tumor progression and for the identification and prioritization of druggable targets. We used the MARINa algorithm and the TKIN to infer a complete repertoire of master regulator (MRs) kinases, both individual as well as synergistic, in 46 cell lines. Initial validation shows that the method could recapitulate both cancer-specific MRs, such as EGFR, as well as cell line-specific ones, such as ALK and PTK2 in H2228 cells. Follow-up experimental assays have shown that the PTK2, which is active in tumor-related but not in normal cells, is required for H2228 cell viability and may constitute a novel oncogene addiction in NSCLC and a candidate therapeutic target. We also validated synergistic addiction of EGFR and MET in several cell lines by showing no effect on colony formation after treating cell lines with inhibitors of individual genes but synergistic decrease in colony formation with dual treatment. The TKI suggests that current pathway models are incomplete and sets the stage for quantitative, network-based methods to identify key regulators of physiologic and pathologic cell phenotypes.
This proffered talk is also presented as Poster A14.
Citation Format: Mukesh Bansal, Michael Peyton, Manjunath Kustagi, Luc Girard, Klarisa Rikova, Michael Comb, Michael White, John Minna, Andrea Califano. Dissecting signaling transduction network to infer master regulators of non small cell lung cancer [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr PR11.