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It is expected that the composition of the serum proteome can provide valuable information about the state of the human body in health and disease and that this information can be extracted via quantitative proteomic measurements. Suitable proteomic techniques need to be sensitive, reproducible, and robust to detect potential biomarkers below the level of highly expressed proteins, generate data sets that are comparable between experiments and laboratories, and have high throughput to support statistical studies. Here we report a method for high throughput quantitative analysis of serum proteins. It consists of the selective isolation of peptides that are Nlinked glycosylated in the intact protein, the analysis of these now deglycosylated peptides by liquid chromatography electrospray ionization mass spectrometry, and the comparative analysis of the resulting patterns. By focusing selectively on a few formerly N-linked glycopeptides per serum protein, the complexity of the analyte sample is significantly reduced and the sensitivity and throughput of serum proteome analysis are increased compared with the analysis of total tryptic peptides from unfractionated samples. We provide data that document the performance of the method and show that sera from untreated normal mice and genetically identical mice with carcinogen-induced skin cancer can be unambiguously discriminated using unsupervised clustering of the resulting peptide patterns. We further identify, by tandem mass spectrometry, some of the peptides that were consistently elevated in cancer mice compared with their control littermates. Molecular & Cellular Proteomics 4: 144 -155, 2005.There is growing interest in testing the hypothesis that the serum 1 proteome contains protein biomarkers that are useful for classifying the physiological or pathological status of an individual. Such markers are expected to be useful for the prediction, detection, and diagnosis of disease as well as to follow the efficacy, toxicology, and side effects of drug treatment (1). The idea of reading diagnostic or prognostic signatures from human body fluids is neither new nor original. Early attempts using high resolution two-dimensional gel electrophoresis were described more than 2 decades ago (2-4). Renewed interest in this idea has emerged due to recent advances in proteomic technologies (5), intriguing initial results from analyzing serum protein patterns using mass spectrometry (1), and the clinical validation and use of a number of diagnostic disease markers including CA125 for ovarian cancer, prostate-specific antigen for prostate cancer, and carcinoembryonic antigen for colon, breast, pancreatic, and lung cancer (6).A number of new approaches that differ from the traditional two-dimensional gel electrophoresis method for the discovery of protein biomarkers in serum have recently been described (1). These include surface-enhanced laser desorption ionization mass spectrometry (SELDI-MS) 2 (7), liquid chromatography tandem mass spectrometry (LC-MS/MS) of serum proteome diges...

Each year millions of pulmonary nodules are discovered by computed tomography and subsequently biopsied. As the majority of these nodules are benign, many patients undergo unnecessary and costly invasive procedures. We present a 13-protein blood-based classifier that differentiates malignant and benign nodules with high confidence, thereby providing a diagnostic tool to avoid invasive biopsy on benign nodules. Using a systems biology strategy, 371 protein candidates were identified and a multiple reaction monitoring (MRM) assay was developed for each. The MRM assays were applied in a three-site discovery study (n = 143) on plasma samples from patients with benign and Stage IA cancer matched on nodule size, age, gender and clinical site, producing a 13-protein classifier. The classifier was validated on an independent set of plasma samples (n = 104), exhibiting a high negative predictive value (NPV) of 90%. Validation performance on samples from a non-discovery clinical site showed NPV of 94%, indicating the general effectiveness of the classifier. A pathway analysis demonstrated that the classifier proteins are likely modulated by a few transcription regulators (NF2L2, AHR, MYC, FOS) that are associated with lung cancer, lung inflammation and oxidative stress networks. The classifier score was independent of patient nodule size, smoking history and age, which are risk factors used for clinical management of pulmonary nodules. Thus this molecular test can provide a powerful complementary tool for physicians in lung cancer diagnosis.

The identification and quantification of the protein contents of biological samples plays a crucial role in biological and biomedical research (1-4). Due to the large dynamic range and the high complexity of most proteomes, it is very challenging to identify and accurately quantify the majority of proteins from such samples. LC-MS/MS-based methods are currently most efficient for the identification of a large number of proteins and have been widely applied in biological and biomedical research (5-7). If combined with stable isotope labeling, such methods can also accurately quantify proteins (7-13). In a typical LC-MS/MS-based quantitative proteomic experiment, samples to be compared are differentially and isotopically labeled, combined, and enzymatically digested into peptides. The obtained peptide samples are then separated by a multidimensional LC system and analyzed by MS/ MS. Peptides are ionized either by ESI (14) or by MALDI (15), and peptide ions are selected, usually in the order of decreasing signal intensity, for fragmentation by CID (16, 17). Peptides are identified by using an automated search engine, such as SEQUEST (18) or Mascot (19), to match their fragment ions against a designated protein database. Peptides are quantified by using a quantification software tool, such as XPRESS (8) or ASAPRatio (20), that uses the relative MS signal intensities of the different isotopic forms to calculate the relative abundance of each identified peptide. The identification and quantification of proteins is then achieved by combining the information obtained from the peptides that associate with the particular protein (20, 21). The quantitative LC-MS/MS approach can routinely identify and quantify hundreds to thousands of proteins from a single biological sample but is generally unable to comprehensively analyze proteomes.There is an increasing interest in the quantitative proteomic measurement of the protein contents of numerous, substantially similar samples. Typical examples include the discovery of protein biomarkers from clinical samples (3,22,23) and the measurement of the response of cells and tissues to perturbations. For biomarker discovery, large numbers of samples need to be processed to achieve sufficient statistical power to distinguish disease-specific markers from coincidental proFrom the ‡Institute for