Abstract 3752: Integrating proteomics and metabolomics characterizes active pathways and potential drug targets in small cell lung cancer

Abstract

Abstract Small cell lung cancer (SCLC) accounts for 13-15% of all new lung cancer cases and represents the sixth most commonly diagnosed cancer in the US. However, it is an understudied cancer for which no molecularly targeted approaches have shown benefit. Using high-throughput techniques such as proteomics and metabolomics we can provide a more global, unbiased characterization to the inner workings of this disease. Integrating disparate data is a challenge in data analysis and bioinformatics, but this challenge is worth confronting due to the potential for combined proteomics / metabolomics analyses to better interrogate and capture the global landscape of active pathways and networks in SCLC than either technology alone. We accomplished this by comparing SCLC cell lines with non-small cell lung cancer (NSCLC) cell lines, differentiating characteristics of these two lung cancer cell types. We first used activity-based protein profiling (ABPP) combined with LC-MS/MS to profile the ATP-binding proteome of SCLC cell lines (n = 18) and NSCLC cell lines (n = 18). ABPP uses chemical probes that are directed against the active sites of enzymes to interrogate the functional state of ATP-binding enzymes, particularly kinases, in biological samples. These experiments identified 6937 peptides (2319 proteins), of which 3891 peptides (1543 proteins) were differentially expressed. Several pathways related to metabolism, such as purine biosynthesis and glycolysis / gluconeogenesis, were identified as over-represented in this list. These results led us to perform broad spectrum UPLC-TOF-MS metabolomics on ten SCLC and ten NSCLC cell lines. Multivariate analysis demonstrated distinct metabolite profiles for SCLC and NSCLC. Over 100 metabolites with variable importance to projection greater than 1 contributed to the differentiation of the two groups. These included metabolites related to purine metabolism such as inosinic acid and adenosine monophosphate and suggest a connection between our proteomics and metabolomics results. Statistical modeling approaches such as linear modeling, nonparametric nonlinear correlation, and Bayesian network analysis were used to integrate proteomics and metabolomics data to jointly characterize the key pathways and constituent components in SCLC. Multiple regression revealed a statistically significant interaction between endoglin (ENG) and phosphatidylethanolamine (PE), both of which are involved in angiogenesis. These findings and other statistical modeling results have the potential to facilitate the identification of new subtypes in SCLC and allow for the identification of novel targeted therapies. Future work will use these results to help characterize SCLC patient-derived xenograft models and surgically resected patient tissue samples. Funded, in part, by NIH Common Fund, 1U24DK097193. Citation Format: Paul A. Stewart, Jiannong Li, Kate J. Fisher, Suraj Dhungana, Delisha Stewart, Susan Sumner, Eric Gardner, John Poirier, Charles M. Rudin, Eric A. Welsh, Steven Eschrich, Ann Chen, Eric B. Haura. Integrating proteomics and metabolomics characterizes active pathways and potential drug targets in small cell lung cancer. [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 3752. doi:10.1158/1538-7445.AM2015-3752