Abstract LB-140: Scaling discovery proteomics to large lung cancer cohorts using data independent acquisition
Authors: Scott Peterman, Bin Fang, Melissa Hoffmann, Amol Prakash, Paul A. Stewart, Richard Liu, Matthew Smith, Joseph Johnson, Steven Eschrich, Guolin Zhang, Eric Haura, John Koomen
Journal: Cancer Research (2016)

Abstract The success of label free protein quantification relies on larger cohort sizes to accurately profile protein expression patterns enabling specific targets to be translated to confirmatory studies. Thus, proteome profiling experiments must satisfy two primary requirements: maximizing breadth and depth of protein sampling as well as facilitating automated data processing. The approach presented here integrates discovery experiments used to create reference databases and rapid data independent acquisition (DIA) methods to examine large clinical cohorts using &amp;lt; 3 hours of instrument time per individual patient's resected tumor specimen. The combination facilitates exhaustive, automated data mining for protein identification and relative quantitation. Samples used to evaluate the robustness of the study were collected from lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). A sample total of 159 (50 LUSC, 46 LUAD, and 63 adjacent control lung tissues) were analyzed using UHPLC separations coupled to a hybrid quadrupole-orbital ion trap mass spectrometer (QExactive Plus) for DIA. Automated data processing was performed by matching peptides in spectral libraries developed from expression proteomics experiments using pooled tissue proteomes. Data were evaluated to determine differences between LUSC, LUAD, and adjacent control tissue, which were then compared to previous literature to prioritize candidate biomarkers and evaluate the performance of DIA LC-MS/MS for tumor proteome profiling. Hierarchical clustering visualized potential tumor classification schemes based on proteomic phenotypes. In a further study, analysis of cores from a tissue microarray produced quantitative data for &amp;gt; 3,000 proteins per core, indicating that the technology can be applied to minimal amounts of formalin fixed paraffin embedded tumor tissue. Because TMAs are assembled to address specific clinical questions, further analysis of these cohorts is an extremely valuable use for DIA; tumor proteome profiles can be rapidly accumulated and mapped to the clinical variables used to select samples for the TMA. Data independent acquisition provides a method for discovery proteomics that balances a sufficient depth of coverage with the ability to analyze large cohorts of patients (n = 100-400) within 1-2 months on a single instrument. Initial data have been produced for lung squamous cell carcinoma and lung adenocarcinoma, which indicate its utility for assessment of patient groups. Citation Format: Scott Peterman, Bin Fang, Melissa Hoffmann, Amol Prakash, Paul A. Stewart, Richard Liu, Matthew Smith, Joseph Johnson, Steven Eschrich, Guolin Zhang, Eric Haura, John Koomen. Scaling discovery proteomics to large lung cancer cohorts using data independent acquisition. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-140.