A mouse to human search for plasma proteome changes associated with pancreatic tumor development

Early diagnosis of pancreatic cancer: challenges and new developments

Pancreatic cancer is a lethal malignancy with its incidence almost equivalent to mortality. The complex pathophysiology, absence of early diagnostic and prognostic markers and unresponsiveness to radiation and chemotherapies are major barriers against successful therapy. Poor performance of therapeutic agents, even in the initial stage of invasive cases, emphasizes the importance of early detection for improved survival. The present review discusses the challenges and advances in biomarkers including serological signatures, circulating tumor cells, autoantibodies, epigenetic markers and miRNAs that are being explored to detect this cancer at early stages. Considering the long time gap between the development of malignant lesions and full-blown primary and metastatic pancreatic cancer, unique opportunities are being contemplated for the development of potential diagnostic and prognostic markers.

Strategies for discovering novel pancreatic cancer biomarkers

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related deaths in both men and women in Canada and the United States and has the most dismal survival rates among any solid malignancy. Most patients are diagnosed with pancreatic cancer once the disease has progressed into an advanced or metastatic stage, making the only curative approach of resection surgery impossible. The persistent delayed or missed diagnosis of pancreatic cancer can be attributed to the absence of early symptoms and the lack of efficient non-invasive screening or diagnostic tests in clinical practice. Given that earlier diagnosis is critical for ameliorating patients' survival rates, there is an urgent need for biomarkers with enough sensitivity and specificity to help diagnose pancreatic cancer early. Serological biomarkers provide a minimally invasive and efficient way of detecting pancreatic cancer, however, there is currently no marker with sufficient diagnostic sensitivity and specificity to identify early cancer patients. This review focuses on the classical tumor markers for PDAC as well as emerging markers. In addition, we will discuss an integrative proteomic approach used in our lab to identify a panel of biomarkers that have the potential to allow the early detection of PDAC.This article is part of a Special Issue entitled: From protein structures to clinical applications.

Linker for activation of T-cell family member2 (LAT2) a lipid raft adaptor protein for AKT signaling, is an early mediator of alkylphospholipid anti-leukemic activity

Lipid rafts are highly ordered membrane domains rich in cholesterol and sphingolipids that provide a scaffold for signal transduction proteins; altered raft structure has also been implicated in cancer progression. We have shown that 25 μm 10-(octyloxy) decyl-2-(trimethylammonium) ethyl phosphate (ODPC), an alkylphospholipid, targets high cholesterol domains in model membranes and induces apoptosis in leukemia cells but spares normal hematopoietic and epithelial cells under the same conditions. We performed a quantitative (SILAC) proteomic screening of ODPC targets in a lipid-raft-enriched fraction of leukemic cells to identify early events prior to the initiation of apoptosis. Six proteins, three with demonstrated palmitoylation sites, were reduced in abundance. One, the linker for activation of T-cell family member 2 (LAT2), is an adaptor protein associated with lipid rafts in its palmitoylated form and is specifically expressed in B lymphocytes and myeloid cells. Interestingly, LAT2 is not expressed in K562, a cell line more resistant to ODPC-induced apoptosis. There was an early loss of LAT2 in the lipid-raft-enriched fraction of NB4 cells within 3 h following treatment with 25 μm ODPC. Subsequent degradation of LAT2 by proteasomes was observed. Twenty-five μm ODPC inhibited AKT activation via myeloid growth factors, and LAT2 knockdown in NB4 cells by shRNA reproduced this effect. LAT2 knockdown in NB4 cells also decreased cell proliferation and increased cell sensitivity to ODPC (7.5×), perifosine (3×), and arsenic trioxide (8.5×). Taken together, these data indicate that LAT2 is an early mediator of the anti-leukemic activity of alkylphospholipids and arsenic trioxide. Thus, LAT2 may be used as a target for the design of drugs for cancer therapy.

Cancer proteomics

Cancer presents high mortality and morbidity globally, largely due to its complex and heterogenous nature, and lack of biomarkers for early diagnosis. A proteomics study of cancer aims to identify and characterize functional proteins that drive the transformation of malignancy, and to discover biomarkers to detect early-stage cancer, predict prognosis, determine therapy efficacy, identify novel drug targets, and ultimately develop personalized medicine. The various sources of human samples such as cell lines, tissues, and plasma/serum are probed by a plethora of proteomics tools to discover novel biomarkers and elucidate mechanisms of tumorigenesis. Innovative proteomics technologies and strategies have been designed for protein identification, quantitation, fractionation, and enrichment to delve deeper into the oncoproteome. In addition, there is the need for high-throughput methods for biomarker validation, and integration of the various platforms of oncoproteome data to fully comprehend cancer biology.

A quantitative proteomic and transcriptomic comparison of human mesenchymal stem cells from bone marrow and umbilical cord vein

Human mesenchymal stem cells (hMSCs) are adult multipotent cells that have high therapeutic potential due to their immunological properties. They can be isolated from several different tissues with bone marrow (BM) being the most common source. Because the isolation procedure is invasive, other tissues such as human umbilical cord vein (UCV) have been considered. However, their interchangeability remains unclear. In the present study, total protein extracts of BM-hMSCs and UCV-hMSCs were quantitatively compared using gel-LC-MS/MS. Previous SAGE analysis of the same cells was re-annotated to enable comparison and combination of these two data sets. We observed a more than 63% correlation between proteomic and transcriptomic data. In silico analysis of highly expressed genes in cells of both origins suggests that they can be modulated by microRNA, which can change protein abundance. Our results showed that MSCs from both tissues shared high similarity in metabolic and functional processes relevant to their therapeutic potential, especially in the immune system process, response to stimuli, and processes related to the delivery of the hMSCs to a given tissue, such as migration and adhesion. Hence, our results support the idea that the more accessible UCV could be a potentially less invasive source of MSCs.

An integrated proteomics and metabolomics approach for defining oncofetal biomarkers in the colorectal cancer

OBJECTIVE

The present study was designed to search for potential diagnostic biomarkers in the serum of colorectal cancer (CRC).

BACKGROUND

CRC is the third most common cancer worldwide, and its prognosis is poor at early stages. A panel of novel biomarkers is urgently needed for early diagnosis of CRC.

METHODS

An integrated proteomics and metabolomics approach was performed to define oncofetal biomarkers in CRC by protein and metabolite profiling of serum samples from CRC patients, healthy control adults, and fetus. The differentially expressed proteins were identified by a 2-D DIGE (2-Dimensional Difference Gel Electrophoresis) coupled with a Finnigan LTQ-based proteomics approach. Meanwhile, the serum metabolome was analyzed using gas chromatography-mass spectrometry integrated with a commercial mass spectral library for peak identification.

RESULTS

Of the 28 identified proteins and the 34 analyzed metabolites, only 5 protein spots and 6 metabolites were significantly increased or decreased in both CRC and fetal serum groups compared with the healthy adult group. Data from supervised predictive models allowed a separation of 93.5% of CRC patients from the healthy controls using the 6 metabolites. Finally, correlation analysis was applied to establish quantitative linkages between the 5 individual metabolite 3-hydroxybutyric acid, L-valine, L-threonine, 1-deoxyglucose, and glycine and the 5 individual proteins MACF1, APOH, A2M, IGL@, and VDB. Furthermore, 10 potential oncofetal biomarkers were characterized and their potential for CRC diagnosis was validated.

CONCLUSION

The integrated approach we developed will promote the translation of biomarkers with clinical value into routine clinical practice.

2D-DIGE analysis of sera from transgenic mouse models reveals novel candidate protein biomarkers for human gastric cancer

The gp130(F/F) genetically engineered mouse (GEM) model reproducibly and predictably develops a gastric adenoma phenotype resembling the primary lesions of human intestinal-type gastric cancer (GC). Accordingly, changes to the serum proteome of gp130(F/F) mice may uncover early-stage GC biomarkers. Here, we have employed several double and compound mutant GEM strains that display distinct phenotypes with respect to gastric tumour load and inflammatory response, thereby mimicking different states of inflammation-associated early-stage GC in humans. This allowed us to distinguish between proteomic changes associated with tumourigenesis rather than confounding systemic inflammation. The comparative proteomic workflow involved depletion of high abundance proteins, 2D-DIGE analysis and protein identification by LC-MS/MS. The differential expression of 112 2D-DIGE spots specifically correlated with the tumour-bearing phenotype, corresponding to 31 murine proteins and their 28 human orthologues. Eight proteins were selected for validation in GC patient sera versus healthy controls. Significant increases in serum apolipoprotein E and haptoglobin, and decreases in afamin and clusterin, were confirmed by ELISA. Receiver operating characteristic analysis revealed that these proteins may be more sensitive and specific discriminators of GC than the existing clinical marker CA72-4.

MALDI imaging mass spectrometry for in situ proteomic analysis of preneoplastic lesions in pancreatic cancer

The identification of new biomarkers for preneoplastic pancreatic lesions (PanINs, IPMNs) and early pancreatic ductal adenocarcinoma (PDAC) is crucial due to the diseases high mortality rate upon late detection. To address this task we used the novel technique of matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) on genetically engineered mouse models (GEM) of pancreatic cancer. Various GEM were analyzed with MALDI IMS to investigate the peptide/protein-expression pattern of precursor lesions in comparison to normal pancreas and PDAC with cellular resolution. Statistical analysis revealed several discriminative m/z-species between normal and diseased tissue. Intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasm (IPMN) could be distinguished from normal pancreatic tissue and PDAC by 26 significant m/z-species. Among these m/z-species, we identified Albumin and Thymosin-beta 4 by liquid chromatography and tandem mass spectrometry (LC-MS/MS), which were further validated by immunohistochemistry, western blot, quantitative RT-PCR and ELISA in both murine and human tissue. Thymosin-beta 4 was found significantly increased in sera of mice with PanIN lesions. Upregulated PanIN expression of Albumin was accompanied by increased expression of liver-restricted genes suggesting a hepatic transdifferentiation program of preneoplastic cells. In conclusion we show that GEM of endogenous PDAC are a suitable model system for MALDI-IMS and subsequent LC-MS/MS analysis, allowing in situ analysis of small precursor lesions and identification of differentially expressed peptides and proteins.

ALCAM (CD166) expression and serum levels in pancreatic cancer

Background

This study was conducted to evaluate the expression of the activated leukocyte cell adhesion molecule (ALCAM) in pancreatic cancer (PAC) and to determine whether or not the ectodomain shedding of ALCAM (s-ALCAM) could serve as a biomarker in the peripheral blood of PAC patients.

Material and Methods

Tissue specimens and blood sera of patients with PAC (n = 264 and n = 116, respectively) and the sera of 115 patients with chronic pancreatitis (CP) were analyzed via ALCAM immunohistochemistry and s-ALCAM ELISA tests. Results were correlated with clinical, histopathological, and patient survival data (Chi-square test, Kaplan-Meier analysis, log-rank test, respectively).

Results

ALCAM was expressed in the majority of PAC lesions. Immunohistochemistry and serum ELISA tests revealed no association between ALCAM expression in primary tumors or s-ALCAM and clinical or histopathological data. Neither ALCAM nor s-ALCAM showed a significant impact regarding overall survival (p = 0.261 and p = 0.660, respectively). S-ALCAM serum levels were significantly elevated compared to the sera of CP patients (p<0.001). The sensitivity of s-ALCAM in detecting PAC was 58.6% at a specificity of 73.9% (AUC = 0.69).

Conclusions

ALCAM is expressed in the majority of PAC lesions, but statistical analysis revealed no association with clinical or pathological data. Although significantly elevated in patients with PAC, the sensitivity and specificity of the s-ALCAM serum quantification test was low. Therefore, its potential as a novel diagnostic marker for PAC remains elusive and further investigations are required.

Application of proteomics to cancer early detection

Strategies to achieve personalized medicine and improve public health encompass assessment of an individual's risk for disease, early detection, and molecular classification of disease resulting in an informed choice of the most appropriate treatment instituted at an early stage of disease development. An unmet need in this field for which proteomics is well suited to make a major contribution is the development of blood-based tests for early cancer detection. This is illustrated in proteomic studies of epithelial cancer that encompass analysis of specimens collected both at the time of diagnosis and specimens collected before onset of symptoms that are particularly suited for the identification of early detection markers. This overarching effort benefits from the availability of plasmas from subject cohorts and of engineered mouse models that are sampled at early stages of tumor development. Integration of findings from plasma with tumor tissue and cancer cell proteomic and genomic data allows elucidation of signatures in plasma for altered signaling pathways. The discovery and further development of early detection markers take advantage of the availability of in-depth quantitative proteomics methods and bioinformatics resources for data mining.

Proximal fluid proteome profiling of mouse colon tumors reveals biomarkers for early diagnosis of human colorectal cancer

PURPOSE

Early detection of colorectal cancer (CRC) and its precursor lesions is an effective approach to reduce CRC mortality rates. This study aimed to identify novel protein biomarkers for the early diagnosis of CRC.

EXPERIMENTAL DESIGN

Proximal fluids are a rich source of candidate biomarkers as they contain high concentrations of tissue-derived proteins. The FabplCre;Apc(15lox/+) mouse model represents early-stage development of human sporadic CRC. Proximal fluids were collected from normal colon and colon tumors and subjected to in-depth proteome profiling by tandem mass spectrometry. Carcinoembryonic antigen (CEA) and CHI3L1 human serum protein levels were determined by ELISA.

RESULTS

Of the 2,172 proteins identified, quantitative comparison revealed 192 proteins that were significantly (P < 0.05) and abundantly (>5-fold) more excreted by tumors than by controls. Further selection for biomarkers with highest specificity and sensitivity yielded 52 candidates, including S100A9, MCM4, and four other proteins that have been proposed as candidate biomarkers for human CRC screening or surveillance, supporting the validity of our approach. For CHI3L1, we verified that protein levels were significantly increased in sera from patients with adenomas and advanced adenomas compared with control individuals, in contrast to the CRC biomarker CEA.

CONCLUSION

These data show that proximal fluid proteome profiling with a mouse tumor model is a powerful approach to identify candidate biomarkers for early diagnosis of human cancer, exemplified by increased CHI3L1 protein levels in sera from patients with CRC precursor lesions.

Multiplex targeted proteomic assay for biomarker detection in plasma: a pancreatic cancer biomarker case study

Biomarkers are most frequently proteins that are measured in the blood. Their development largely relies on antibody creation to test the protein candidate performance in blood samples of diseased versus nondiseased patients. The creation of such antibody assays has been a bottleneck in biomarker progress due to the cost, extensive time, and effort required to complete the task. Targeted proteomics is an emerging technology that is playing an increasingly important role to facilitate disease biomarker development. In this study, we applied a SRM-based targeted proteomics platform to directly detect candidate biomarker proteins in plasma to evaluate their clinical utility for pancreatic cancer detection. The characterization of these protein candidates used a clinically well-characterized cohort that included plasma samples from patients with pancreatic cancer, chronic pancreatitis, and healthy age-matched controls. Three of the five candidate proteins, including gelsolin, lumican, and tissue inhibitor of metalloproteinase 1, demonstrated an AUC value greater than 0.75 in distinguishing pancreatic cancer from the controls. In addition, we provide an analysis of the reproducibility, accuracy, and robustness of the SRM-based proteomics platform. This information addresses important technical issues that could aid in the adoption of the targeted proteomics platform for practical clinical utility.

Identification of transaldolase as a novel serum biomarker for hepatocellular carcinoma metastasis using xenografted mouse model and clinic samples

Hepatocellular carcinoma (HCC) is one of serious disorders with the highest morbidities and mortalities worldwide. Metastasis is the major concern that causes death in HCC. The goal of this study was to screen and identify potential serum proteins indicating HCC metastasis. Serum samples collected from control and HCCLM3-R metastatic HCC tumor model at specific stages of metastasis (1 wk, 3 wks and 6 wks) were subjected to iTRAQ labeling followed by 2DLC-ESI-MS/MS analysis. A total of 554 proteins were identified and 80 proteins were differential expressed at least between one adjacent time points. Among them, expression level of transaldolase (TALDO) was validated in mouse and human serum. The level of TALDO protein was found to be higher in metastatic mice serum compared to that of non-metastatic mice. Human specific TALDO was then identified in mouse serum through human specific peptides. Immunohistochemical and western blot analysis showed that the expression of TALDO in human HCC tissues and HCC cell lines was associated with its metastatic behavior. Subsequent screening of TALDO expression in 72 clinical serum samples (comprising 36 non-metastatic HCC and 36 metastatic HCC samples) revealed higher TALDO level in the serum of metastatic HCC patients. A receiver operating characteristic (ROC) curve estimated a maximal sensitivity of 77.8% and 86.1% specificity for TALDO in detection of HCC metastasis. The present results demonstrated that the nude mouse xenograft model is an efficient system for performing metastasis-related biomarker discovery. TALDO may be useful biomarkers for the detection of HCC metastasis.

Profiling of antibody production against xenograft-released proteins by protein microarrays discovers prostate cancer markers

This study describes a novel xenograft-based biomarker discovery platform and proves its usefulness in the discovery of serum markers for prostate cancer. By immunizing immuno-competent mice with serum from nude mice bearing prostate cancer xenografts, an antibody response against xenograft-derived antigens was elicited. By probing protein microarrays with serum from immunized mice, several prostate cancer-derived antigens were identified, of which a subset was successfully retrieved in serum from mice bearing prostate cancer xenografts and prevalidated in human serum samples of prostate cancer patients. Among the discovered and validated proteins were the members of the TAM receptor family (TYRO3, AXL, and MERTK), ACY1, and PSMA1. In conclusion, this novel method allows for the identification of low abundant cancer-derived serum proteins, circumventing dynamic range and host-response issues in standard patient cohort proteomics comparisons.

Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry

For many research questions in modern molecular and systems biology, information about absolute protein quantities is imperative. This information includes, for example, kinetic modeling of processes, protein turnover determinations, stoichiometric investigations of protein complexes, or quantitative comparisons of different proteins within one sample or across samples. To date, the vast majority of proteomic studies are limited to providing relative quantitative comparisons of protein levels between limited numbers of samples. Here we describe and demonstrate the utility of a targeting MS technique for the estimation of absolute protein abundance in unlabeled and nonfractionated cell lysates. The method is based on selected reaction monitoring (SRM) mass spectrometry and the “best flyer” hypothesis, which assumes that the specific MS signal intensity of the most intense tryptic peptides per protein is approximately constant throughout a whole proteome. SRM-targeted best flyer peptides were selected for each protein from the peptide precursor ion signal intensities from directed MS data. The most intense transitions per peptide were selected from full MS/MS scans of crude synthetic analogs. We used Monte Carlo cross-validation to systematically investigate the accuracy of the technique as a function of the number of measured best flyer peptides and the number of SRM transitions per peptide. We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R² of 0.88. Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values. The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.

Discovery of circulating microRNAs associated with human prostate cancer using a mouse model of disease

Circulating microRNAs (miRNAs) are emerging as useful non-invasive markers of disease. The objective of this study was to use a mouse model of prostate cancer as a tool to discover serum miRNAs that could be assessed in a clinical setting. Global miRNA profiling identified 46 miRNAs at significantly altered levels (p ≤ 0.05) in the serum of TRansgenic Adenocarcinoma of Mouse Prostate (TRAMP) mice with advanced prostate cancer compared to healthy controls. A subset of these miRNAs with known human homologues were validated in an independent cohort of mice and then measured in serum from men with metastatic castration-resistant prostate cancer (mCRPC; n = 25) or healthy men (n = 25). Four miRNAs altered in mice, mmu-miR-141, mmu-miR-298, mmu-miR-346 and mmu-miR-375, were also found to be at differential levels in the serum of men with mCRPC. Three of these (hsa-miR-141, hsa-miR-298 and hsa-miR-375) were upregulated in prostate tumors compared with normal prostate tissue, suggesting that they are released into the blood as disease progresses. Moreover, the intra-tumoral expression of hsa-miR-141 and hsa-miR-375 were predictors of biochemical relapse after surgery. This study is the first to demonstrate that specific serum miRNAs are common between human prostate cancer and a mouse model of the disease, highlighting the potential of such models for the discovery of novel biomarkers.

Integrated proteomic profiling of cell line conditioned media and pancreatic juice for the identification of pancreatic cancer biomarkers

Pancreatic cancer is one of the leading causes of cancer-related deaths, for which serological biomarkers are urgently needed. Most discovery-phase studies focus on the use of one biological source for analysis. The present study details the combined mining of pancreatic cancer-related cell line conditioned media and pancreatic juice for identification of putative diagnostic leads. Using strong cation exchange chromatography, followed by LC-MS/MS on an LTQ-Orbitrap mass spectrometer, we extensively characterized the proteomes of conditioned media from six pancreatic cancer cell lines (BxPc3, MIA-PaCa2, PANC1, CAPAN1, CFPAC1, and SU.86.86), the normal human pancreatic ductal epithelial cell line HPDE, and two pools of six pancreatic juice samples from ductal adenocarcinoma patients. All samples were analyzed in triplicate. Between 1261 and 2171 proteins were identified with two or more peptides in each of the cell lines, and an average of 521 proteins were identified in the pancreatic juice pools. In total, 3479 nonredundant proteins were identified with high confidence, of which ∼ 40% were extracellular or cell membrane-bound based on Genome Ontology classifications. Three strategies were employed for identification of candidate biomarkers: (1) examination of differential protein expression between the cancer and normal cell lines using label-free protein quantification, (2) integrative analysis, focusing on the overlap of proteins among the multiple biological fluids, and (3) tissue specificity analysis through mining of publically available databases. Preliminary verification of anterior gradient homolog 2, syncollin, olfactomedin-4, polymeric immunoglobulin receptor, and collagen alpha-1(VI) chain in plasma samples from pancreatic cancer patients and healthy controls using ELISA, showed a significant increase (p < 0.01) of these proteins in plasma from pancreatic cancer patients. The combination of these five proteins showed an improved area under the receiver operating characteristic curve to CA19.9 alone. Further validation of these proteins is warranted, as is the investigation of the remaining group of candidates.

-The advancement of biomarker-based diagnostic tools for ovarian, breast, and pancreatic cancer through the use of urine as an analytical biofluid

Despite considerable advancements, the development of effective cancer screening tools based on serum biomarker measurements has thus far failed to achieve a meaningful clinical impact. The incremental progress observed over the course of serum biomarker development suggests that further refinements based on novel approaches may yet result in a breakthrough. The use of urine as an analytical biofluid for biomarker development may represent such an approach. The unique characteristics of urine including a high level of stability, ease of sampling, and an inactive and low-complexity testing matrix offer several potential advantages over the use of serum. A number of recent reports have demonstrated the utility of urine in the identification of novel cancer biomarkers and also the improved performance of biomarkers previously evaluated in serum. In this review, advancements related to the use of urine biomarkers within the settings of ovarian, breast, and pancreatic cancer are presented and discussed. Findings regarding the identification of specific urine biomarkers for each disease are highlighted along with comparative analyses of urine and serum biomarkers as diagnostic tools.

Lung cancer signatures in plasma based on proteome profiling of mouse tumor models

We investigated the potential of in-depth quantitative proteomics to reveal plasma protein signatures that reflect lung tumor biology. We compared plasma protein profiles of four mouse models of lung cancer with profiles of models of pancreatic, ovarian, colon, prostate, and breast cancer and two models of inflammation. A protein signature for Titf1/Nkx2-1, a known lineage-survival oncogene in lung cancer, was found in plasmas of mouse models of lung adenocarcinoma. An EGFR signature was found in plasma of an EGFR mutant model, and a distinct plasma signature related to neuroendocrine development was uncovered in the small-cell lung cancer model. We demonstrate relevance to human lung cancer of the protein signatures identified on the basis of mouse models.

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

Tumor microenvironment-derived proteins dominate the plasma proteome response during breast cancer induction and progression

Tumor development relies upon essential contributions from the tumor microenvironment and host immune alterations. These contributions may inform the plasma proteome in a manner that could be exploited for cancer diagnosis and prognosis. In this study, we employed a systems biology approach to characterize the plasma proteome response in the inducible HER2/neu mouse model of breast cancer during tumor induction, progression, and regression. Mass spectrometry data derived from approximately 1.6 million spectra identified protein networks involved in wound healing, microenvironment, and metabolism that coordinately changed during tumor development. The observed alterations developed prior to cancer detection, increased progressively with tumor growth and reverted toward baseline with tumor regression. Gene expression and immunohistochemical analyses suggested that the cancer-associated plasma proteome was derived from transcriptional responses in the noncancerous host tissues as well as the developing tumor. The proteomic signature was distinct from a nonspecific response to inflammation. Overall, the developing tumor simultaneously engaged a number of innate physiologic processes, including wound repair, immune response, coagulation and complement cascades, tissue remodeling, and metabolic homeostasis that were all detectable in plasma. Our findings offer an integrated view of tumor development relevant to plasma-based strategies to detect and diagnose cancer.

A multiplexed bead assay for profiling glycosylation patterns on serum protein biomarkers of pancreatic cancer

A multiplexed bead-based immunoassay was developed to simultaneously profile glycosylation patterns of serum proteins to investigate their usefulness as biomarkers for pancreatic cancer. The multiplex assay utilized protein-specific capture antibodies chemically coupled individually to beads labeled with specific amounts of fluorescent dye. Captured proteins were detected based on the extent and specific type of glycosylation as determined by successive binding of fluorescent lectin probes. Advantages to this technique include the fact that antibodies coupled to the beads had minimal nonspecific binding to the lectins ConA/SNA, avoiding the step of chemically blocking the antibody glycans and the bead assays were performed in a 96-well filter plate enabling high-throughput screening applications with improved reproducibility. The assay was tested with ConA and SNA lectins to examine the glycosylation patterns of α-1-β glycoprotein (A1BG) and serum amyloid p (SAP) component for use as potential biomarkers for the detection of pancreatic cancer based on the results from prior biomarker studies. The results showed that the SNA response on the captured A1BG protein could distinguish chronic pancreatitis samples from pancreatic cancer with a p-value of 0.035 and for the SAP protein with SNA, a p-value of 0.026 was found between the signal of normal controls and the pancreatic cancer samples. For the ConA response, a decline in the signal for both proteins in the serum samples was found to distinguish pancreatic cancer from normal controls and renal cell carnoma samples (A1BG, p<0.05; and SAP, p<0.0001).

Impact of protein stability, cellular localization, and abundance on proteomic detection of tumor-derived proteins in plasma

Tumor-derived, circulating proteins are potentially useful as biomarkers for detection of cancer, for monitoring of disease progression, regression and recurrence, and for assessment of therapeutic response. Here we interrogated how a protein's stability, cellular localization, and abundance affect its observability in blood by mass-spectrometry-based proteomics techniques. We performed proteomic profiling on tumors and plasma from two different xenograft mouse models. A statistical analysis of this data revealed protein properties indicative of the detection level in plasma. Though 20% of the proteins identified in plasma were tumor-derived, only 5% of the proteins observed in the tumor tissue were found in plasma. Both intracellular and extracellular tumor proteins were observed in plasma; however, after normalizing for tumor abundance, extracellular proteins were seven times more likely to be detected. Although proteins that were more abundant in the tumor were also more likely to be observed in plasma, the relationship was nonlinear: Doubling the spectral count increased detection rate by only 50%. Many secreted proteins, even those with relatively low spectral count, were observed in plasma, but few low abundance intracellular proteins were observed. Proteins predicted to be stable by dipeptide composition were significantly more likely to be identified in plasma than less stable proteins. The number of tryptic peptides in a protein was not significantly related to the chance of a protein being observed in plasma. Quantitative comparison of large versus small tumors revealed that the abundance of proteins in plasma as measured by spectral count was associated with the tumor size, but the relationship was not one-to-one; a 3-fold decrease in tumor size resulted in a 16-fold decrease in protein abundance in plasma. This study provides quantitative support for a tumor-derived marker prioritization strategy that favors secreted and stable proteins over all but the most abundant intracellular proteins.

A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease

We developed a pipeline to integrate the proteomic technologies used from the discovery to the verification stages of plasma biomarker identification and applied it to identify early biomarkers of cardiac injury from the blood of patients undergoing a therapeutic, planned myocardial infarction (PMI) for treatment of hypertrophic cardiomyopathy. Sampling of blood directly from patient hearts before, during and after controlled myocardial injury ensured enrichment for candidate biomarkers and allowed patients to serve as their own biological controls. LC-MS/MS analyses detected 121 highly differentially expressed proteins, including previously credentialed markers of cardiovascular disease and >100 novel candidate biomarkers for myocardial infarction (MI). Accurate inclusion mass screening (AIMS) qualified a subset of the candidates based on highly specific, targeted detection in peripheral plasma, including some markers unlikely to have been identified without this step. Analyses of peripheral plasma from controls and patients with PMI or spontaneous MI by quantitative multiple reaction monitoring mass spectrometry or immunoassays suggest that the candidate biomarkers may be specific to MI. This study demonstrates that modern proteomic technologies, when coherently integrated, can yield novel cardiovascular biomarkers meriting further evaluation in large, heterogeneous cohorts.

Secretome-based identification of ULBP2 as a novel serum marker for pancreatic cancer detection

Background

To discover novel markers for improving the efficacy of pancreatic cancer (PC) diagnosis, the secretome of two PC cell lines (BxPC-3 and MIA PaCa-2) was profiled. UL16 binding protein 2 (ULBP2), one of the proteins identified in the PC cell secretome, was selected for evaluation as a biomarker for PC detection because its mRNA level was also found to be significantly elevated in PC tissues.

Methods

ULBP2 expression in PC tissues from 67 patients was studied by immunohistochemistry. ULBP2 serum levels in 154 PC patients and 142 healthy controls were measured by bead-based immunoassay, and the efficacy of serum ULBP2 for PC detection was compared with the widely used serological PC marker carbohydrate antigen 19-9 (CA 19-9).

Results

Immunohistochemical analyses revealed an elevated expression of ULPB2 in PC tissues compared with adjacent non-cancerous tissues. Meanwhile, the serum levels of ULBP2 among all PC patients (n = 154) and in early-stage cancer patients were significantly higher than those in healthy controls (p<0.0001). The combination of ULBP2 and CA 19-9 outperformed each marker alone in distinguishing PC patients from healthy individuals. Importantly, an analysis of the area under receiver operating characteristic curves showed that ULBP2 was superior to CA 19-9 in discriminating patients with early-stage PC from healthy controls.

Conclusions

Collectively, our results indicate that ULBP2 may represent a novel and useful serum biomarker for pancreatic cancer primary screening.

Plasma proteome profiles associated with inflammation, angiogenesis, and cancer

Tumor development is accompanied by a complex host systemic response, which includes inflammatory and angiogenic reactions. Both tumor-derived and systemic response proteins are detected in plasma from cancer patients. However, given their non-specific nature, systemic response proteins can confound the detection or diagnosis of neoplasia. Here, we have applied an in-depth quantitative proteomic approach to analyze plasma protein changes in mouse models of subacute irritant-driven inflammation, autoreactive inflammation, and matrix associated angiogenesis and compared results to previously described findings from mouse models of polyoma middle T-driven breast cancer and Pdx1-Cre Kras(G12D) Ink4a/Arf (lox/lox)-induced pancreatic cancer. Among the confounding models, approximately 1/3 of all quantified plasma proteins exhibited a significant change in abundance compared to control mice. Of the proteins that changed in abundance, the majority were unique to each model. Altered proteins included those involved in acute phase response, inflammation, extracellular matrix remodeling, angiogenesis, and TGFβ signaling. Comparison of changes in plasma proteins between the confounder models and the two cancer models revealed proteins that were restricted to the cancer-bearing mice, reflecting the known biology of these tumors. This approach provides a basis for distinguishing between protein changes in plasma that are cancer-related and those that are part of a non-specific host response.

Genetically-engineered mouse models for pancreatic cancer: Advances and current limitations

Recently, there has been significant progress in the development of genetically-engineered mouse (GEM) models. By introducing genetic alterations and/or signaling alterations of human pancreatic cancer into the mouse pancreas, animal models can recapitulate human disease. Pancreas epithelium-specific endogenous Kras activation develops murine pancreatic intraepithelial neoplasia (mPanIN). Additional inactivation of p16, p53, or transforming growth factor-β signaling, in the context of Kras activation, dramatically accelerates mPanIN progression to invasive pancreatic ductal adenocarcinoma (PDAC) with abundant stromal expansion and marked fibrosis (desmoplasia). The autochthonous cancer models retain tumor progression processes from pre-cancer to cancer as well as the intact tumor microenvironment, which is superior to xenograft models, although there are some limitations and differences from human PDAC. By fully studying GEM models, we can understand the mechanisms of PDAC formation and progression more precisely, which will lead us to a breakthrough in novel diagnostic and therapeutic methods as well as identification of the origin of PDAC.

Protein alterations associated with pancreatic cancer and chronic pancreatitis found in human plasma using global quantitative proteomics profiling

Pancreatic cancer is a lethal disease that is difficult to diagnose at early stages when curable treatments are effective. Biomarkers that can improve current pancreatic cancer detection would have great value in improving patient management and survival rate. A large scale quantitative proteomics study was performed to search for the plasma protein alterations associated with pancreatic cancer. The enormous complexity of the plasma proteome and the vast dynamic range of protein concentration therein present major challenges for quantitative global profiling of plasma. To address these challenges, multidimensional fractionation at both protein and peptide levels was applied to enhance the depth of proteomics analysis. Employing stringent criteria, more than 1300 proteins total were identified in plasma across 8-orders of magnitude in protein concentration. Differential proteins associated with pancreatic cancer were identified, and their relationship with the proteome of pancreatic tissue and pancreatic juice from our previous studies was discussed. A subgroup of differentially expressed proteins was selected for biomarker testing using an independent cohort of plasma and serum samples from well-diagnosed patients with pancreatic cancer, chronic pancreatitis, and nonpancreatic disease controls. Using ELISA methodology, the performance of each of these protein candidates was benchmarked against CA19-9, the current gold standard for a pancreatic cancer blood test. A composite marker of TIMP1 and ICAM1 demonstrate significantly better performance than CA19-9 in distinguishing pancreatic cancer from the nonpancreatic disease controls and chronic pancreatitis controls. In addition, protein AZGP1 was identified as a biomarker candidate for chronic pancreatitis. The discovery and technical challenges associated with plasma-based quantitative proteomics are discussed and may benefit the development of plasma proteomics technology in general. The protein candidates identified in this study provide a biomarker candidate pool for future investigations.

Mouse models of cancer

Genetically engineered mouse models have significantly contributed to our understanding of cancer biology. They have proven to be useful in validating gene functions, identifying novel cancer genes and tumor biomarkers, gaining insight into the molecular and cellular mechanisms underlying tumor initiation and multistage processes of tumorigenesis, and providing better clinical models in which to test novel therapeutic strategies. However, mice still have significant limitations in modeling human cancer, including species-specific differences and inaccurate recapitulation of de novo human tumor development. Future challenges in mouse modeling include the generation of clinically relevant mouse models that recapitulate the molecular, cellular, and genomic events of human cancers and clinical response as well as the development of technologies that allow for efficient in vivo imaging and high-throughput screening in mice.

Gene expression profiling in a mouse model identifies fetal liver- and placenta-derived potential biomarkers for Down Syndrome screening

Background

As a first step to identify novel potential biomarkers for prenatal Down Syndrome screening, we analyzed gene expression in embryos of wild type mice and the Down Syndrome model Ts1Cje. Since current Down Syndrome screening markers are derived from placenta and fetal liver, these tissues were chosen as target.

Methodology/Principal Findings

Placenta and fetal liver at 15.5 days gestation were analyzed by microarray profiling. We confirmed increased expression of genes located at the trisomic chromosomal region. Overall, between the two genotypes more differentially expressed genes were found in fetal liver than in placenta. Furthermore, the fetal liver data are in line with the hematological aberrations found in humans with Down Syndrome as well as Ts1Cje mice. Together, we found 25 targets that are predicted (by Gene Ontology, UniProt, or the Human Plasma Proteome project) to be detectable in human serum.

Conclusions/Significance

Fetal liver might harbor more promising targets for Down Syndrome screening studies. We expect these new targets will help focus further experimental studies on identifying and validating human maternal serum biomarkers for Down Syndrome screening.

Surface plasmon resonance biosensor for parallelized detection of protein biomarkers in diluted blood plasma

Surface plasmon resonance (SPR) biosensor for high-throughput screening of protein biomarkers in diluted blood plasma is reported. The biosensor combines a high-resolution SPR imaging sensor and a high-density protein array with low-fouling background. The SPR imaging sensor utilizes polarization contrast and advanced referencing and provides a total of 120 sensing areas (each 200 μm×150 μm). Antibodies are immobilized on the sensing areas via hybridization of antibody-oligonucleotide conjugates to thiolated complementary oligonucleotides microspotted on the sensor surface (DNA-directed immobilization). A low-fouling background is achieved by covalent immobilization of bovine serum albumin to carboxyl-terminated thiols filling the areas among the thiolated oligonucleotides and outside the sensing areas. The biosensor was evaluated for detection of protein biomarkers relevant to cancer diagnostics--human chorionic gonadotropin (hCG) and activated leukocyte cell adhesion molecule (ALCAM) both in buffer and in 10% blood plasma. Limits of detection as low as 45 ng/mL (ALCAM) and 100 ng/mL (hCG) were achieved in blood plasma samples.

Leukocyte DNA methylation signature differentiates pancreatic cancer patients from healthy controls

Pancreatic adenocarcinoma (PaC) is one of most difficult tumors to treat. Much of this is attributed to the late diagnosis. To identify biomarkers for early detection, we examined DNA methylation differences in leukocyte DNA between PaC cases and controls in a two-phase study. In phase I, we measured methylation levels at 1,505 CpG sites in treatment-naïve leukocyte DNA from 132 never-smoker PaC patients and 60 never-smoker healthy controls. We found significant differences in 110 CpG sites (false discovery rate <0.05). In phase II, we tested and validated 88 of 96 phase I selected CpG sites in 240 PaC cases and 240 matched controls (p≤0.05). Using penalized logistic regression, we built a prediction model consisting of five CpG sites (IL10_P348, LCN2_P86, ZAP70_P220, AIM2_P624, TAL1_P817) that discriminated PaC patients from controls (C-statistic = 0.85 in phase I; 0.76 in phase II). Interestingly, one CpG site (LCN2_P86) alone could discriminate resectable patients from controls (C-statistic  = 0.78 in phase I; 0.74 in phase II). We also performed methylation quantitative trait loci (methQTL) analysis and identified three CpG sites (AGXT_P180_F, ALOX12_E85_R, JAK3_P1075_R) where the methylation levels were significantly associated with single nucleotide polymorphisms (SNPs) (false discovery rate <0.05). Our results demonstrate that epigenetic variation in easily obtainable leukocyte DNA, manifested by reproducible methylation differences, may be used to detect PaC patients. The methylation differences at certain CpG sites are partially attributable to genetic variation. This study strongly supports future epigenome-wide association study using leukocyte DNA for biomarker discovery in human diseases.

Serum biomarker panels for the detection of pancreatic cancer

PURPOSE

Serum-biomarker based screening for pancreatic cancer could greatly improve survival in appropriately targeted high-risk populations.

EXPERIMENTAL DESIGN

Eighty-three circulating proteins were analyzed in sera of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC, n = 333), benign pancreatic conditions (n = 144), and healthy control individuals (n = 227). Samples from each group were split randomly into training and blinded validation sets prior to analysis. A Metropolis algorithm with Monte Carlo simulation (MMC) was used to identify discriminatory biomarker panels in the training set. Identified panels were evaluated in the validation set and in patients diagnosed with colon (n = 33), lung (n = 62), and breast (n = 108) cancers.

RESULTS

Several robust profiles of protein alterations were present in sera of PDAC patients compared to the Healthy and Benign groups. In the training set (n = 160 PDAC, 74 Benign, 107 Healthy), the panel of CA 19-9, ICAM-1, and OPG discriminated PDAC patients from Healthy controls with a sensitivity/specificity (SN/SP) of 88/90%, while the panel of CA 19-9, CEA, and TIMP-1 discriminated PDAC patients from Benign subjects with an SN/SP of 76/90%. In an independent validation set (n = 173 PDAC, 70 Benign, 120 Healthy), the panel of CA 19-9, ICAM-1 and OPG demonstrated an SN/SP of 78/94% while the panel of CA19-9, CEA, and TIMP-1 demonstrated an SN/SP of 71/89%. The CA19-9, ICAM-1, OPG panel is selective for PDAC and does not recognize breast (SP = 100%), lung (SP = 97%), or colon (SP = 97%) cancer.

CONCLUSIONS

The PDAC-specific biomarker panels identified in this investigation warrant additional clinical validation to determine their role in screening targeted high-risk populations.

Integrated mass spectrometry-based analysis of plasma glycoproteins and their glycan modifications

We present a protocol for the identification of glycosylated proteins in plasma followed by elucidation of their individual glycan compositions. The study of glycoproteins by mass spectrometry is usually based on cleavage of glycans followed by separate analysis of glycans and deglycosylated proteins, which limits the ability to derive glycan compositions for individual glycoproteins. The methodology described here consists of 2D HPLC fractionation of intact proteins and liquid chromatography-multistage tandem mass spectrometry (LC-MS/MS(n)) analysis of digested protein fractions. Protein samples are separated by 1D anion-exchange chromatography (AEX) with an eight-step salt elution. Protein fractions from each of the eight AEX elution steps are transferred onto the 2D reversed-phase column to further separate proteins. A digital ion trap mass spectrometer with a wide mass range is then used for LC-MS/MS(n) analysis of intact glycopeptides from the 2D HPLC fractions. Both peptide and oligosaccharide compositions are revealed by analysis of the ion fragmentation patterns of glycopeptides with an intact glycopeptide analysis pipeline.

How genetically engineered mouse tumor models provide insights into human cancers

Genetically engineered mouse models (GEMMs) of human cancer were first created nearly 30 years ago. These early transgenic models demonstrated that mouse cells could be transformed in vivo by expression of an oncogene. A new field emerged, dedicated to generating and using mouse models of human cancer to address a wide variety of questions in cancer biology. The aim of this review is to highlight the contributions of mouse models to the diagnosis and treatment of human cancers. Because of the breadth of the topic, we have selected representative examples of how GEMMs are clinically relevant rather than provided an exhaustive list of experiments. Today, as detailed here, sophisticated mouse models are being created to study many aspects of cancer biology, including but not limited to mechanisms of sensitivity and resistance to drug treatment, oncogene cooperation, early detection, and metastasis. Alternatives to GEMMs, such as chemically induced or spontaneous tumor models, are not discussed in this review.

Proteomics in pancreatic cancer research

In this review, we give an overview of the actual role of proteomic technologies in the study of pancreatic cancers (PCs). We describe PC proteomics on the basis of sample origins, i.e. tissues, body fluids, and PC cell lines. As regards PC tissues, we report the identification of a number of candidate biomarkers of precursor lesions that may allow early diagnosis of this neoplasia. Moreover, we describe cytoskeletal and hypoxia-regulated proteins that confirm the involvement of cytoskeleton modifications and metabolism adaptations in carcinogenesis. We also discuss the most important biomarkers identified by proteomic analysis involved in local invasion and distant metastasis, and in the cross-talk between pancreatic tumor and the surrounding stroma. Furthermore, we report novel candidate biomarkers identified in serum, plasma, and pancreatic juice of cancer patients compared with cancer-free controls. Proteomic alterations in PC cell line models as compared to normal controls and studies on cell lines treated with drugs or new agents to understand their mechanism of pharmacological action or the onset of drug resistance are also presented. Finally, we discuss the recent improvements obtained in classical 2-DE and high-throughput proteomic strategies able to allow the overcoming of relevant proteomic drawbacks.

Histidine decarboxylase is identified as a potential biomarker of intestinal mucosal injury in patients with acute intestinal obstruction

Various biomarkers currently used for the diagnosis of intestinal mucosal injury (IMI) in patients with acute intestinal obstruction have low sensitivity and specificity. In the present study, IMI, as indicated by the impaired expression of tight junction proteins, including zonula occludens-1, occludin and claudin-1, and inflammation were determined in colonic tissues of patients with 45 strangulated intestinal obstruction (STR-IO) and the adjacent "normal" colonic tissues of 35 patients with colon cancers by quantitative real-time polymerase chain reaction (QRT-PCR), Western blotting, immunohistochemistry and histological examination, respectively. Then, two-dimensional fluorescent difference gel electrophoresis coupled with linear trap quadrupole mass spectrometry was used to screen for potential biomarkers of IMI in the serum samples of 10 STR-IO, 10 simple intestinal obstruction (SIM-IO) and 10 normal healthy controls. A total of 35 protein spots were differentially expressed among the serum samples, and six of the proteins were identified as potential biomarkers. Among the six proteins, histidine decarboxylase (HDC) and ceruloplasmin (CP) were elevated significantly in patients with STR-IO, compared with patients with SIM-IO and healthy controls. Thus, HDC and CP were further validated by QRT-PCR, Western blotting, immunohistochemistry and enzyme-linked immunosorbent assay, respectively, in colonic tissues, serum and urine samples. Finally, the receiver operating characteristic curves were used to show the area under the curves of HDC, CP and several established biomarkers, followed by the determination of the appropriate cutoff values and their sensitivities and specificities. It was shown that for serum and urine, HDC levels achieved sensitivities and specificities compatible to or even greater than those of established biomarkers for the diagnosis of IMI in patients with acute intestinal obstruction, although further validation in a larger cohort is required.

A migration signature and plasma biomarker panel for pancreatic adenocarcinoma

Pancreatic ductal adenocarcinoma is a disease of extremely poor prognosis for which there are no reliable markers of asymptomatic disease. To identify pancreatic cancer biomarkers, we focused on a genomic interval proximal to the most common fragile site in the human genome, chromosome 3p12, which undergoes smoking-related breakage, loss of heterozygosity, and homozygous deletion as an early event in many epithelial tumors, including pancreatic cancers. Using a functional genomic approach, we identified a seven-gene panel (TNC, TFPI, TGFBI, SEL-1L, L1CAM, WWTR1, and CDC42BPA) that was differentially expressed across three different expression platforms, including pancreatic tumor/normal samples. In addition, Ingenuity Pathways Analysis (IPA) and literature searches indicated that this seven-gene panel functions in one network associated with cellular movement/morphology/development, indicative of a "migration signature" of the 3p pathway. We tested whether two secreted proteins from this panel, tenascin C (TNC) and tissue factor pathway inhibitor (TFPI), could serve as plasma biomarkers. Plasma ELISA assays for TFPI/TNC resulted in a combined area under the curve (AUC) of 0.88 and, with addition of CA19-9, a combined AUC for the three-gene panel (TNC/TFPI/CA19-9), of 0.99 with 100% specificity at 90% sensitivity and 97.22% sensitivity at 90% specificity. Validation studies using TFPI only in a blinded sample set increased the performance of CA19-9 from an AUC of 0.84 to 0.94 with the two-gene panel. Results identify a novel 3p pathway-associated migration signature and plasma biomarker panel that has utility for discrimination of pancreatic cancer from normal controls and promise for clinical application.

Intact-protein analysis system for discovery of serum-based disease biomarkers

Profiling of serum and plasma proteins has substantial relevance to the discovery of circulating disease biomarkers. However, the extreme complexity and vast dynamic range of protein abundance in serum and plasma present a formidable challenge for protein analysis. Thus, integration of multiple technologies is required to achieve high-resolution and high-sensitivity proteomic analysis of serum or plasma. In this chapter, we describe an orthogonal multidimensional intact-protein analysis system (IPAS) (Wang et al., Mol Cell Proteomics 4:618-625, 2005) coupled with protein tagging (Faca et al., J Proteome Res 5:2009-2018, 2006) to profile the serum and plasma proteomes quantitatively, which we have applied in our biomarker discovery studies (Katayama et al., Genome Med 1:47, 2009; Faca et al., PLoS Med 5:e123, 2008; Zhang et al. Genome Biol 9:R93, 2008).