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Integrated Quantitative Proteomic and Transcriptomic Analysis of Lung Tumor and Control Tissue: a Lung Cancer Showcase

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Integrated Quantitative Proteomic and Transcriptomic Analysis of Lung Tumor and Control Tissue: a Lung Cancer Showcase www.impactjournals.com/oncotarget/ Oncotarget, Vol. 7, No. 12 Integrated quantitative proteomic and transcriptomic analysis of lung tumor and control tissue: a lung cancer showcase Stefan Tenzer1,*, Petra Leidinger2,*, Christina Backes3,*, Hanno Huwer4, Andreas Hildebrandt5, Hans-Peter Lenhof6, Tanja Wesse7, Andre Franke7, Eckart Meese2,*, Andreas Keller3 1Institute for Immunology, University Medical Center of the Johannes Gutenberg University of Mainz, Mainz, Germany 2Department of Human Genetics, Saarland University, Homburg, Germany 3Clinical Bioinformatics, Saarland University, Saarbrücken, Germany 4SHG Clinics, Völklingen, Germany 5Software Engineering and Bioinformatics, Johannes Gutenberg University of Mainz, Mainz, Germany 6Center for Bioinformatics, Saarland University, Saarbrücken, Germany 7Kiel University, Kiel, Germany *These authors contributed equally to this work Correspondence to: Andreas Keller, e-mail: [email protected] Keywords: mass spectrometry, proteomics analysis, transcriptomics, lung tumors, adenocarcinoma Received: July 07, 2015 Accepted: January 01, 2016 Published: February 22, 2016 ABSTRACT Proteomics analysis of paired cancer and control tissue can be applied to investigate pathological processes in tumors. Advancements in data-independent acquisition mass spectrometry allow for highly reproducible quantitative analysis of complex proteomic patterns. Optimized sample preparation workflows enable integrative multi-omics studies from the same tissue specimens. We performed ion mobility enhanced, data-independent acquisition MS to characterize the proteome of 21 lung tumor tissues including adenocarcinoma and squamous cell carcinoma (SCC) as compared to control lung tissues of the same patient each. Transcriptomic data were generated for the same specimens. The quantitative proteomic patterns and mRNA abundances were subsequently analyzed using systems biology approaches. We report a significantly (p = 0.0001) larger repertoire of proteins in cancer tissues. 12 proteins were higher in all tumor tissues as compared to matching control tissues. Three proteins, CAV1, CAV2, and RAGE, were vice versa higher in all controls. We also identified characteristic SCC and adenocarcinoma protein patterns. Principal Component Analysis provided evidence that not only cancer from control tissue but also tissue from adenocarcinoma and SCC can be differentiated. Transcriptomic levels of key proteins measured from the same matched tissue samples correlated with the observed protein patterns. The applied study set-up with paired lung tissue specimens of which different omics are measured, is generally suited for an integrated multi-omics analysis. INTRODUCTION carcinoma and adenocarcinoma are routinely differentiated by histological means. Tumor pathologic diagnosis is Lung cancer is among the leading cause of cancer initially based on bronchoscopic biopsy and can differ related deaths worldwide. In the United States alone, from the diagnosis of surgically removed specimens. almost 225,000 lung cancer diagnoses are estimated Although patients with squamous cell carcinoma that per year, causing 159,260 deaths [1]. Squamous cell mostly develops in smokers show a poorer prognosis www.impactjournals.com/oncotarget 14857 Oncotarget than adenocarcinoma patients, prognostic differences tissue from the same individuals. The tumor type and in histological types are not considered by the latest TNM classification of samples included in our study TNM classification. Differences in transcriptomic and/ are presented in Table 1. Extending our proteomic or proteomic pattern bear the potential to improve the characterization, we also provide evidence that the classification in tumor subtypes. applied sample preparation workflow is well suited for an Recent advances in quantitative mass spectrometry integrative transcriptomic and proteomic analysis. (MS) and the integrative analysis of transcript as well as proteomic levels of tumor samples may support the RESULTS discovery of disease mechanisms in a systematic manner. A substantial part of MS studies follows a case-control Technical reproducibility and variations between study set-up, where two cohorts of different individuals individuals in ion mobility enhanced MS are compared to each other. Discovery of biomarkers can be enhanced by measuring paired samples, including for For the discovery of protein-based biomarkers, example study set ups where both case and control tissue especially for complex marker patterns, a high technical from the same individuals are obtained or samples of the reproducibility of the proteomic workflow is essential. same patient are measured over time. We first explored how accurately technical replicates for We explored the number and relative abundance of paired lung cancer and control tissue can be generated. proteins in adenocarcinoma and squamous cell carcinoma Three pairs of tumor and respective control tissue were and matched control tissue from the same patients using measured in technical triplicates. For the 18 proteome MS. Respective MS based approaches have become an datasets between 1,845 and 2,366 proteins were identified indispensable tool for molecular and cellular biology and (mean: 2,175; standard deviation: 146). We calculated have enhanced the understanding of the complex and all 18 × 17/2 = 153 pair-wise Pearson correlations. The dynamic nature of proteomes [2, 3]. Besides applications respective correlation matrix is presented in Figure 1 in metabolomics, MS enables the identification and where the upper triangle matrix shows the correlation quantification of several hundreds to thousands of coefficient and the lower triangle matrix contains proteins from different sample types, culminating in the thumbnails of scatter plots. Most important for this first draft of the complete human proteome [4, 5]. The initial analysis are the technical replicates, highlighted afore-mentioned improvements resulted in a paradigm for the three individuals in orange, blue and green. The shift from mere protein profiling and identification solid rectangles correspond to tumor samples, the dashed towards high-throughput protein quantification. Label-free ones belong to the paired control tissues. On average quantification approaches have emerged as the method of we achieved Pearson correlation of 0.96 for technical choice for larger sample cohorts, with data-independent replicates at standard deviation of 0.02. All other Pearson acquisition approaches gaining popularity [6, 7]. correlation coefficients were on average 0.77 with standard The complex profiles obtained by quantitative deviation of 0.10. The set up of the proof-of-concept proteomic analyses are frequently used for discovering measurements allowed us to estimate different other group novel biomarkers [8]. Using gel-based and liquid similarities: we can split the values in the correlation chromatography-mass spectrometry-based proteomics, matrix in six groups: (tumor / tumor) pairs of the same even the brain proteome of the 5,300 year old copper age individual, (control / control) pairs of the same individual, mummy “Ötzi”, the Tyrolean Iceman, was successfully (tumor / control) pairs of the same individual, (tumor / characterized [9]. Beyond different tissues, MS has also tumor) pairs of different individuals, (control / control) been applied to investigate body fluids such as serum [10] pairs of different individuals and (tumor / control) pairs of or bronchoalveolar lavage fluid [11, 12]. A recent study by different individuals. Only the first two groups represent McArdle and colleagues explored the value of combining technical replicates. The average Pearson correlation of different protein discovery platforms for the development each group is presented in Supplementary Figure 1. Best of a multiplexed protein biomarker panel using label-free Pearson correlation was obtained for technical replicates approaches [13]. of control tissue (0.97) followed by technical replicates of Here, we focused on NSCLC as the most common tumor tissue (0.96). The third highest value was type of lung cancer with squamous cell lung carcinoma reached for control / control measurements of different (SCC, 25–30%) and adenocarcinomas (40%) as the individuals (0.88). For tumor / tumor replicates of different predominant sub-forms. The detailed grading of the individuals, average correlation dropped to 0.78. These analyzed tumors is provided in Table 1. In an initial step, findings indicate a higher inter-individual heterogeneity in we assessed the technical reproducibility of the employed cancer tissue compared to normal lung tissue. For tumor/ MS approach for cancer and control tissues in a paired control correlation of tissue specimen derived from the study set up and estimated the effect sizes. In a second same individual, we calculated an average value of 0.74 stage, we then explored the lung tissue proteomes of and the lowest correlation was reached for tumor /control 18 pairs of lung tumor tissue and matched normal lung pairs derived from different individuals (0.70). www.impactjournals.com/oncotarget 14858 Oncotarget Table 1: Patient details Tumor Type TNM Adeno-Ca T2aN1 Adeno-Ca T2aN1 Adeno-Ca T1bN0 Adeno-Ca T2aN0 Adeno-Ca T3N0 Adeno-Ca T2aN0 Adeno-Ca T2aN1 Adeno-Ca T3N1 Adeno-Ca T3N2 SCC T1aN0 SCC T1bN0 SCC T2bN0 SCC T2aN0 SCC T2bN1 SCC T3N0 SCC T2aN1 SCC T3N0 SCC T4N2 Based on these results we carried out a power shows a scatter plot, where each data point represents calculation. According
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