A Comprehensive Peptidome Profiling Technology for the Identification of Early Detection Biomarkers for Lung Adenocarcinoma

et al. (2011) A Comprehensive Peptidome Profiling Technology for the Identification of Early Detection Biomarkers for Lung Adenocarcinoma. PLoS ONE 6(4): e18567. doi:10.1371/journal.pone.0018567 A Comprehensive Peptidome Profiling Technology for the Identification of Early Detection Biomarkers for Lung Adenocarcinoma Koji Ueda 0 Naomi Saichi 0 Sachiko Takami 0 Daechun Kang 0 Atsuhiko Toyama 0 Yataro Daigo 0 Nobuhisa Ishikawa 0 Nobuoki Kohno 0 Kenji Tamura 0 Taro Shuin 0 Masato Nakayama 0 Taka-Aki Sato 0 Yusuke Nakamura 0 Hidewaki Nakagawa 0 Richard C. Willson, University of Houston, United States of America 0 1 Laboratory for Biomarker Development, Center for Genomic Medicine, RIKEN , Yokohama , Japan , 2 CSK Institute for Sustainability, Ltd., Tokyo, Japan, 3 Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan, 4 Shimadzu Corporation, Kyoto, Japan, 5 Department of Molecular and Internal Medicine, Hiroshima University , Hiroshima , Japan , 6 Department of Urology, Kochi University School of Medicine, Nankoku, Japan, 7 Toppan Printing Co., Ltd. , Tokyo , Japan The mass spectrometry-based peptidomics approaches have proven its usefulness in several areas such as the discovery of physiologically active peptides or biomarker candidates derived from various biological fluids including blood and cerebrospinal fluid. However, to identify biomarkers that are reproducible and clinically applicable, development of a novel technology, which enables rapid, sensitive, and quantitative analysis using hundreds of clinical specimens, has been eagerly awaited. Here we report an integrative peptidomic approach for identification of lung cancer-specific serum peptide biomarkers. It is based on the one-step effective enrichment of peptidome fractions (molecular weight of 1,000-5,000) with size exclusion chromatography in combination with the precise label-free quantification analysis of nano-LC/MS/MS data set using Expressionist proteome server platform. We applied this method to 92 serum samples well-managed with our SOP (standard operating procedure) (30 healthy controls and 62 lung adenocarcinoma patients), and quantitatively assessed the detected 3,537 peptide signals. Among them, 118 peptides showed significantly altered serum levels between the control and lung cancer groups (p,0.01 and fold change .5.0). Subsequently we identified peptide sequences by MS/MS analysis and further assessed the reproducibility of Expressionist-based quantification results and their diagnostic powers by MRMbased relative-quantification analysis for 96 independently prepared serum samples and found that APOA4 273-283, FIBA 5-16, and LBN 306-313 should be clinically useful biomarkers for both early detection and tumor staging of lung cancer. Our peptidome profiling technology can provide simple, high-throughput, and reliable quantification of a large number of clinical samples, which is applicable for diverse peptidome-targeting biomarker discoveries using any types of biological specimens. - Funding: This study was supported by Japanese Ministry of Education, Culture, Sports, Science and Technology (Wakate-A, 22680064, http://kaken.nii.ac.jp/ja/p/ 22680064). This study was also funded by Shimadzu Corporation, CSK Institute for Sustainability, Ltd., Toppan Printing Co., Ltd. As employers of ST, AT, TS, or MN in this study, these funders did play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: ST is an employee of CSK Institute for Sustainability, Ltd. AT and TS are employees of Shimadzu Corporation. MN is an employee of Toppan Printing Co., Ltd. They contributed to the technical support and data analysis of this manuscript. The companies listed above also funded for this study, since this collaborative work was performed in "the academic-industrial alliance project for the development of lung cancer early detection system" among RIKEN, the University of Tokyo, Shimadzu Corporation, Toppan Printing Co., Ltd., and CSK Institute for Sustainability, Ltd. This does not alter the authors adherence to all the PLoS ONE policies on sharing data and materials. Lung cancer is the leading cause of cancer death worldwide [1]. Smoking is still the leading risk factor for lung cancer, but recently the proportion of never smoker-related lung cancer is significantly increasing, although its cause or other risk factor(s) is unknown [2]. Lung cancer patients show the poor prognosis with an overall 5year survival rate of only 15% [3]. One of the reasons for this dismal prognosis is no effective tools to detect it at an early stage and in fact only 16% of patients are diagnosed at their early stage of the disease [3]. Current screening methods such as chest X-ray or cytological examination of sputum have not yet shown their effectiveness in the improvement of mortality of lung cancer, whereas low dose helical CT have been proved to possess a potential to detect early-stage lung cancer and demonstrate 20% lower lung cancer mortality rate compared to chest X-ray screening [4]. On the other hand, serum biomarkers for lung cancer have been investigated to achieve early detection of the disease and improve clinical management of patients [5]. Nonetheless, their present clinical usefulness remains limited [6,7]. CEA (carcinoembryonic antigen) and CYFRA (cytokeratin 19 fragment) are elevated in sera in a subset of lung cancer patients, and are clinically applied to monitor the disease status and evaluate the response to treatments. However, they are not recommended to use in clinical diagnosis and screening [8] because they are also elevated in certain non-cancerous conditions such as smoking and lung inflammation as well as in patients with other types of cancers. It is obvious that CEA and CYFRA do not have the sufficient power to apply for the screening of early-stage lung cancer. Hence, development of novel serum/plasma biomarkers applicable for lung cancer diagnosis is urgently required. Recently monitoring the protein expression pattern in clinical specimens by proteomics technologies has offered great opportunities to discover potentially new biomarkers for cancer diagnosis. Various proteomic tools such as 2D-DIGE, SELDI-TOF-MS, protein arrays, ICAT, iTRAQ and MudPIT have been used for differential analysis of biological samples including cell lysates and blood to better understand the molecular basis of cancer pathogenesis and the characterization of disease-associated proteins [9]. In order to explore putative biomarkers in complicated biological samples, focused proteomics or targeted proteomics technologies have been utilized such as; phosphoprotein enrichment technologies IMAC [10], the cell-surfacecapturing (CSC) technology [11,12], glycan structure-specific quantification technology IGEL [13]. Most recently, to identify novel lung cancer biomarkers, Os (...truncated)