Quantitative Proteomics Identifies Unique Signaling Phenotypes In Nsclc

Lung cancer (NSCLC) is responsible for the most cancer-related deaths, and treatment options are limited regardless of histological type. Lung adenocarcinomas frequently exhibit genetic alterations, including EGFR and KRAS mutations, resulting in oncogene addiction; however, ∼50% of these tumors have no known oncogenic drivers. When genetic aberrations and their resulting phenotypes are well understood, targeted kinase inhibitors can be developed. Therefore, identification of dominant signaling pathways in tumors with no known oncogenic drivers is a key step in development of novel treatment options or repurposing of existing drugs.  In this study, we used activity-based protein profiling (ABPP) using ATP analog probes (ActivX, Thermo) as well as quantification of tyrosine phosphorylation (pY) levels to identify subsets of patients with unknown oncogenic drivers that would benefit from targeted therapy. In parallel experiments, liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM) is used to quantify ABPP-labeled peptides and pY peptides to evaluate kinase signaling in tumors. Additionally, changes in ABPP and pY-LC-MRM signals after inhibitor treatment can be quantified. Building on previous analysis of lung squamous cell carcinomas (SCC), LC-MRM for enrichment and quantification of kinase ATP uptake and tyrosine phosphorylation was carried out on frozen tumors from 50 adenocarcinoma patients; 15 had matched control tissue. The panel of ABPP enriched proteins consists of u003e250 kinases (∼15 tyrosine kinases) and pY-LC-MRM quantifies u003e375 tyrosine phosphorylation sites. ABPP-LC-MRM analysis of lung adenocarcinoma cell lines with WT EGFR and WT KRAS included H2073, H1755, H1395, H1993, and H2342 cells.  Because cancer cells have many drivers and significant plasticity in signaling, the ABPP-LC-MRM strategy enables readout of multiple kinases as targets for inhibition, which could support the design of rational combinations using differential kinase activity between tumor and normal lung tissues, as well as classification of patients based on the kinase activity profiles. The kinase activity levels between adenocarcinoma and SCC are also compared to find common potential therapies. In cell lines matched to the tumors based on kinase profiles, viability assays were used to examine synergy of kinase inhibitors suggested by ABPP-LC-MRM.  LC-MRM quantification of kinase ATP uptake enables the evaluation of activity of ∼50% of known protein kinases in a single experiment; typical tumor readouts provide data for 100-150 kinases.  ABPP- and pY-LC-MRM analysis in cell lines and subsequent comparison of tumor and normal tissue specimens indicates the ability to detect signaling pathways that may be tumor drivers in NSCLC.  This platform can assist with classifying patients for molecularly-driven selection of targeted therapy combinations. Citation Format: Melissa Martinez, Bin Fang, Jiannong Li, Y. Ann Chen, Stephen Brantley, Wei Guan, Fumi Kinose, Eric Welsh, Steven A. Eschrich, Eric B. Haura, John M. Koomen. Quantitative proteomics identifies unique signaling phenotypes in NSCLC. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1817. doi:10.1158/1538-7445.AM2015-1817