Abstract 5127: Characterization of lung cancers by integrated genomic and proteomic analysis
Authors: Michael F. Moran, Christine To, Yuhong Wei, Lei Li, Paul Taylor, Vladimir Ignatchenko, Dan Strumpf, Jeifei Tong, Nhu-An Pham, Igor Jurisica, Lakshmi Muthuswamy, Thomas Kislinger, Thomas Kislinger, Ming-Sound Tsao
Journal: Cancer Research (2012)

Abstract Non-small cell lung carcinoma (NSCLC) represents 80% of lung cancers, the deadliest cancer worldwide. The genomic profiling of DNA and mRNA, and characterization of proteomes have begun to address the objective to stratify and treat tumors according to their molecular features. However, these data sets have largely been used independently and typically have not been integrated. Hence most cancers including NSCLC continue to be classified largely based on histology. Our aim for this study was to integrate a set of comprehensive functional genomics data sets in order to stratify a set of NSCLC primary tumors and establish that primary tumor xenografts mirror closely the primary tumors, and hence may serve as validated pre-clinical models. Our preliminary analyses indicated that engraftment is prognostic of poor clinical outcome (John et al., 2011, Clin Cancer Res, 17:134-41), and that the major NSCLC subtypes adenocarcinoma and squamous cell carcinoma are readily resolved according to their distinctive proteomes (Wei et al., 2011, J Proteome Res 10:161-74). Herein we characterized a collection of 12 each primary tumor (T), primary tumor xenograft (X), and patient-matched normal lung (N) by using mass spectrometry for proteome analysis, Illumina 1M Omni-Quad for somatic copy number alterations (SCNAs), and Illumina Omni-1 Quad HT-12 v4 for mRNA expression. Unsupervised hierarchical clustering of protein abundances and SCNAs independently revealed that primary tumor and xenografts are highly correlated with each other. This correlation was significantly enhanced in the proteome data when a small number of highly abundant blood-associated proteins were systematically identified and subtracted. We identified tumor-specific dysregulated proteins and SCNAs in T and X using N as a reference. Two thirds of T and X matched pairs could be identified based on Pearson Correlation Coefficients of the dysregulated proteins. This clearly demonstrates that the xenografts accurately recapitulated tumor proteomes. Proteins upregulated in tumors were expressed to a significant extent from regions of SCNA gain, and we found a high degree of concordance between mRNA expression levels and SCNAs. Some primary tumors had very highly correlated proteomic profiles, suggesting they may be effectively stratified according to their proteome signatures. In conclusion, our integrated analysis has validated the primary xenograft model, provided an initial systems level perspective on the central dogma in cancer, and reinforces the proteome as a distinctive molecular feature for lung tumor stratification. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5127. doi:1538-7445.AM2012-5127