[Methods in Molecular Biology™] Clinical Proteomics Volume 428 || Overview and Introduction to...

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Overview and Introduction to Clinical Proteomics

Young-Ki Paik, Hoguen Kim, Eun-Young Lee, Min-Seok Kwon,and Sang Yun Cho

Summary

As the field of clinical proteomics progresses, discovery of disease biomarkers becomesparamount. However, the immediate challenges are to establish standard operating proce-dures for both clinical specimen handling and reduction of sample complexity and toincrease the ability to detect proteins and peptides present in low amounts. The tradi-tional concept of a disease biomarker is shifting toward a new paradigm, namely, that anensemble of proteins or peptides would be more efficient than a single protein/peptidein the diagnosis of disease. Because clinical proteomics usually requires easy access towell-defined fresh clinical specimens (including morphologically consistent tissue andproperly pretreated body fluids of sufficient quantity), biorepository systems need to beestablished. Here, we address these questions and emphasize the necessity of developingvarious microdissection techniques for tissue specimens, multidimensional fractionationfor body fluids, and other related techniques (including bioinformatics), tools which couldbecome integral parts of clinical proteomics for disease biomarker discovery.

Key Words: biomarker; body fluids; clinical proteomics; translational proteomics;depletion; biorepository; multidimensional fractionation; specimen bank; biomarker panel.

Abbreviations: CSF: Cerebrospinal Fluid, SILAC: Stable Isotope Labeling withAmino acids in Cell culture, FFE: Free Flow Electrophoresis, IMAC: Immobilized MetalAffinity Chromatography, 2DE: 2-dimensional Gel electrophoresis, CBB: CoomassieBrilliant Blue, SELDI: Surface-Enhanced Laser Desorption/Ionization, MALDI: Matrix-Assisted laser desorption/ionization, MDLC: Multi-dimensional Liquid Chromatography,LC: Liquid Chromatography, TOF: Time-of-Flight, CID: Collision-induced dissociation,ETD: Electron Transfer Dissociation, LIT: Linear Ion-Trap, FT: Fourier-Transform, Q:Quadrupole, ELISA; Enzyme-Linked Immunosorbent Assay, SISCAPA: Stable IsotopeStandards with Capture by Anti-Peptide Antibody, AQUA: Absolute Quantitative

From: Methods in Molecular Biology, vol. 428: Clinical Proteomics: Methods and ProtocolsEdited by: A. Vlahou © Humana Press, Totowa, NJ

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Analysis. Commercial brands are also shown: MARS; Multiple Affinity Removal System,(Agilent, Palo Alto, CA, USA), EnchantTM: EnchantTM Multi-protein Affinity SeparationKit (Pall Life Sciences, Ann Arbor, MI, USA), GradiflowTM: GradiflowTM Separation (LifeBioprocess, Frenchs Forest, Australia), FFETM: BD Free Flow Electrophoresis System(BD Diagnostics, Martinsried/Planegg, Germany), Zoom®: Zoom® Benchtop ProteomicsSystem (Invitrogen Corporation, Carlsbad, CA, USA), Rotofor: Bio-Rad Rotofor® PrepIEF Ccll (Bio-Rad, Hercules, CA, USA), PF2D: ProteomeLabTM PF2D Protein Fraction-ation System (Beckman Coulter, Inc., Fullerton, CA, USA), DIGE: EttanTM DIGE System(GE Healthcare Bio-Sciences AB, Uppsala, Sweden), Deep PurpleTM: Deep PurpleTM TotalPprotein Stain (GE Healthcare Bio-Sciences AB, Uppsala, Sweden), ICATTM: Isotope-coded affinity tags (Applied Biosystems, Foster City, CA, USA), iTRAQTM: iTRAQTM

Reagents (Applied Biosystems, Foster City, CA, USA), Q-TRAPTM: (Applied Biosystems,Foster City, CA, USA).

1. Overview and Scope of Clinical ProteomicsClinical proteomics is defined as comprehensive studies of qualitative and

quantitative profiling of proteins (and peptides) present in clinical specimenssuch as body fluids and tissues. The comparison of specimens from healthy anddiseased individuals may lead to the discovery of a disease biomarker (1). Thebiomarker serves as a molecular signature reflecting stages of disease before orafter treatment and can also be used for prognostic purposes in monitoring theresponse to treatment (2). Clinical proteomics consists of a variety of exper-imental processes, which include the collection of well-phenotyped clinicalspecimens, analysis of proteins or peptides of interest, data interpretation, andvalidation of proteomics data in a clinical context (Fig. 1). After successfulidentification of a few disease biomarker candidates through extensive profiling,

Fig. 1. Clinical and translational proteomics. The key components of experimentalmethods are included in each box.

Overview and Introduction to Clinical Proteomics 3

translational proteomics involving validation with a cohort study follows. Evenafter proper identification and verification of a disease biomarker, it takes quitea long time to prove that this biomarker is applicable to clinical diagnosis orprognosis (3,4).

There has been a remarkable increase in publication of clinical proteomicspapers within a short period of time [more than 800 papers in 2006 (Fig. 2)],coinciding with the rapid growth of proteomics. Reflecting this trend in clinicalproteomics, this chapter aims to present a review of core technologies thatare used in the field of clinical proteomics with respect to sample specimenprocessing, protein separation platforms (e.g., gel-based system or liquid-basedmethods), quantitative labeling, mass spectrometry (MS), and proteome infor-matics tools. It is noteworthy that despite the advent of new technologies,there remain several bottlenecks in the proteomics field such as lack of datasetstandardization, quantification of the proteins of interest, verification of proteinor peptides identified, and an overall strategy for tackling biomarker post-identification. Thus, the pace of biomarker discovery, one of the key agendas ofclinical proteomics, will depend on how well these obstacles or bottlenecks areresolved by technical advancement (4). The following sections address theseissues in the context of clinical proteomics.

Fig. 2. Recent trends in clinical proteomics publications. The distribution of thearticles related to clinical proteomics listed in PubMed is shown here. The key wordsused for searching articles are as follows: query (clinical[All Fields] OR ((“biologicalmarkers”[TIAB] NOT Medline[SB]) OR “biological markers”[MeSH Terms] ORbiomarker[Text Word])) AND (“proteomics”[MeSH Terms] OR proteomics[TextWord] OR proteomic[All Fields] OR “proteome”[MeSH Terms] OR proteome[TextWord]).

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2. Sample Specimens and Processing Techniques Used for ClinicalProteomics2.1. General Considerations

Because clinical proteomics rely heavily on the patient specimens, threeimportant factors need to be considered before the selection and preparation ofclinical specimens: (1) selection of the correct clinical samples according to thetype of research, (2) isolation of the appropriate component from the clinicalsamples, and (3) establishment of optimal experimental conditions for eachsample (5,6,7,8). For the selection of correct clinical samples, the relationshipbetween clinical samples and the specific disease should also be considered.For example, although cancer tissue represents a specific cancer, several typesof body fluids from patients may also have a relationship to the cancer. Ifthe selected clinical samples specifically represent the disease, the next stepis to evaluate what components are related to the specific disease. That is,tumor cells in cancerous tissues are surrounded by many types of stromal cells,inflammatory cells, and connective tissues that are directly related to changesin protein expression in the cancer. If the purpose of proteomic analysis isto identify characteristic changes of specific proteins in tumor cells, then theprecise identification of tumor cell percentage that can be increased by tissuemicrodissection would appear to be necessary (5,6,7). As sample specimenconditions directly impact the results of biomarker discovery, well-definedclinical specimens should be used since the discovery of disease biomarkers ismuch easier when the samples have clear anatomical and pathophysiologicaldefinitions. Because clinical specimens are heterogeneous, sophisticated patho-logical discrimination is required for the isolation of specific diseased tissue orbody fluids. Without the expertise of a pathologist at the earliest stage, it maybe difficult to isolate a specifically defined specimen for clinical proteomics.Generally, clinical samples contain variable factors and components originatingfrom the microenvironment of specific tissues. For instance, liver tissues usuallycontain a large amount of blood in the sinusoid and this amount is increasedin tissues with dilated sinusoids (9). Lung tissues usually contain depositedexogenous materials and this amount is increased in heavy smokers (10). Notethat the amount of blood present in isolated tissues may directly influence therelative proportion of proteins found in clinical specimens. Deposited materialsand the other chemicals such as stain dye and fixatives used in the microdis-section may also influence the experimental conditions (11). In the analysis ofclinical samples, suitable buffer conditions, minimal lysis time, and high-yieldprotein precipitation are highly recommended. To avoid substantial variationsbetween experiments using clinical specimens, a large set of specimens arealso necessary because, unlike cultured cell lines, clinical specimens have high


component variability (12). More details on specific disease types are alsodescribed throughout this volume.

2.2. Body Fluids

Surveying the literature, there appears to be five to six different types ofclinical specimens. Body fluids [e.g., plasma, urine, tear, cerebrospinal fluid,lymph, and ascites], tissues (e.g., liver, heart, muscle, brain, and lung), cells,bone, and hair have all been used for clinical proteomics (Table 1) (13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33). Each has its own meritsand limitations for biomarker discovery via proteomic analysis. Among thosesample specimens, the number of publications using body fluids has increasedrecently, perhaps because of their convenience and ease of use for noninvasivediagnosis. Since those proteins secreted in the body fluids during or after diseasemay reflect a broad range of pathophysiological conditions, much emphasis hasbeen given to identification of prominent protein/peptide biomarkers that exhibitdifferential expression at different stages. In the literature, the terms “bodyfluids” and “biofluids” are being used interchangeably, although the formerindicates a greater likelihood of being obtained directly from the patients, whilethe latter is applied more broadly, referring to liquid or liquid-like samplesobtained from living organisms including model animals and plants. Throughoutthis chapter we will use “body fluids” for clarity.

Given the large dynamic range of protein and peptide sources, plasma (acomplex liquid interface between tissues) and extra cellular fluids may be thebest body fluid to use for clinical proteomics and biomarker discovery (34,35,36,37,38). In addition to plasma, more than a dozen additional body fluids arecurrently used for biomarker discovery, ranging from urine to peritoneal fluids(Table 1). However, the biggest challenge in body fluids proteomics may be themultiple pretreatment processes including depletion of high-abundance proteins(in the case of plasma) (34,35,36) and/or their enrichment (in the case of urine)(15,39) prior to analysis (Table 1). Thus, the outcome of clinical proteomicsmay depend on proper sample processing since the quality of selection andhandling of the most specific type of specimen will affect the overall pattern ofprofiling. Because the details of body fluid proteomics have been well describedby Shen Hu et al. (38), we would like to focus on only a few essential points.

First, standard measures need to be introduced to protect specimens fromnonspecific proteolysis, lysis, and modification during collection and prepa-ration (11). For the standardization of blood sample collection, Tammenemphasizes many useful considerations of preanalytical variables in plasmaproteomics, which can be applied to processes involved with blood specimens[(40) and see Chapter 2]. The more specific problems involved in sample

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8 Paik et al.

handling are also addressed by Rai et al. (41). Second, to increase the dynamicrange of detection and reduce sample heterogeneity, pretreatments such asdepletion of high-abundance proteins appear to be required (34,35,36). Inaddition, many pretreatment steps to remove high-abundance proteins may berequired during initial sample processing. Multiple fractionations of clinicalsamples prior to major separation work would reduce the sample complexity.Note that coremoval of low-abundance proteins during this type of multipledepletion (36,42) and modification of proteins of interest during or afterisolation (43) should be considered as well. For several problems encounteredwith specimen collection, Xiao et al. (Chapter 13) in this volume also describedifferent methods to isolate extra cellular matrix (ECM) and analyze theproteome of secreted vesicles. These methods will be useful for studying ECMand secreted vesicles in various samples ranging from the primary culturedcells to tissue specimens. Therefore, one must consider the best options for thisprocess before doing the main experiment.

2.3. Tissues and Other Samples

Usually tissues are used as primary screening samples to find direct causesof disease from the lesion present in tissues of the corresponding organ, forexample, liver tissue in hepatocellular carcinoma (HCC) (44,45). Tissues arewidely used for clinical proteomics, although there are no standing operationprocedures in specimen fractionation and the detection limit of current instru-mentation remains borderline. As listed in Table 1, many cancer tissues can beprepared in different ways such as laser capture microdissection (LCM) (5,6),pressures catapulting techniques [laser microdissection and pressure catapulting(LMPC)] (30,46), and formalin-fixed paraffin-embedded sample preparation(11). Theses techniques are well described in Chapters 3, 5, 9, and 11 in thisvolume. It is desirable, however, that proteomics studies of disease tissuesshould also be coupled with parallel analysis of the corresponding body fluids.For example, for the study of cancer biomarkers, paired cancer tissue sets (tumorvs. nontumor) and the same patient’s plasma were used, which led to a morecomprehensive analysis (47,48). Experiments on tissue samples may mostly besuitable for pathophysiological studies rather than biomarker discovery due tothe complexity of the sample.

In specimen processing for proteomics studies, there are usually severalunwanted problems such as artifacts created during sample collection, processing,and storage. Other matters arise in the handling of patient information regardingsex, age, and race (49). To minimize those problems associated with systematicsample handling, it is plausible to establish a specimen bank (50,51,52). In fact,the collection of many clinical samples in a biorepository would have enormous


benefits for proteomic research. This enables the selection of homogeneousclinical samples according to the research purposes and isolation of specificcomponents from clinical samples. Additionally, large scale collection of clinicalspecimens in a biorepository is essential for the validation of specific markersafter biomarker candidate discovery. Ideally, the clinical samples stored in thebiorepository should be (1) collected and stored immediately because dead cellsand altered proteins affect proteomic analysis, (2) subjected to accurate qualitycontrol, and (3) catalogued by reliable and secure clinical data. The quality controlof clinical samples includes trimming of specimens and confirmation of diagnosisby pathologists; information gained (such as the confirmation of tumor cell andstromal cell ratio, percentage of necrosis, percentage of fibrosis, proportion ofinfiltrated inflammatory cells, etc.) should be stored in a database of clinicalsamples. It is also essential to store clinical and follow-up data for each sampleand each patient’s written informed consent form in the biorepository network.This clinical specimen banking network provides convenience, reduced budget,and reliability for researchers involved in clinical proteomic research (50,51,52).

For representative tissue sample collection for proteomics studies, Diaz et al.(Chapter 3) address a practical experimental strategy for storage and handling ofsample specimens that are used in surface-enhanced laser desorption/ionization(SELDI), 2D gel, and liquid chromatography (LC)-based proteomics. Emphasisshould be given to the primary responsibility of pathologists in the wholeprocess of tissue proteomics in addition to morphological analysis at themolecular level.

3. Biomarker Discovery and Clinical ProteomicsGiven that one of the central issues of clinical proteomics is biomarker

discovery and its application, a brief account of this subject is appropriatehere. An excellent review of the whole arena of biomarker development can befound elsewhere (53,54,55). Until now, it has been generally accepted that aconventional concept of a disease biomarker would be a single protein/peptidewith high specificity, which is usually present in low abundance, expressed ina disease in a stage-specific manner, and serve as a major fingerprint of thebody’s response to drugs or other treatments. Although many examples of broadbiomarkers for various diseases are known (56,57,58,59,60), identification ofmore specific and selective biomarkers is urgently needed. Accordingly, wemay also need to change the current biomarker concept and eliminate theinherent bias toward individual disease biomarkers. Recently, a new idea hasbeen introduced that an ensemble of different proteins would be more efficientthan a single protein/peptide in the diagnosis of disease (61,62,63). To solve

10 Paik et al.

this problem we propose a general strategy of clinical proteomics leading todisease biomarker discovery as outlined in Fig. 3.

Since biomarker candidate proteins could come from many different cellularprocesses, they could be either in low abundance or high abundance, whichwould directly or indirectly reflect the physiological condition of the body.Perhaps they are present in different concentrations depending on the diseasestage or tissue type. For example, common proteins such as Hsp 27 (64,65), 14-3-3 proteins (66,67), apoA-I (68,69), and serum amyloid precursorA (70) appear in most of disease samples from lung cancer, gastric cancer,pancreatic cancer, prostate cancer, neuroblastoma and, inflammation. A numberof questions then arise: should they be treated as disease-specific or diseasenonspecific proteins? What would be the criterion to make this decision? Is thisdue to the fact that the number and type of proteins secreted from a specific

Fig. 3. The concept of the creation of a protein biomarker panel for a specificdisease. Each white, gray, dark-gray, and black circle represents a putative proteinbiomarker of a specific disease at that clinical stage. A group of slash-lined circlessymbolizes the biomarker panel of liver disease as an example.


physiological condition of many different types of diseases might be similar?How one can distinguish one type of disease from another simply by lookingat their protein profiles?

As outlined in Fig. 3, at the beginning of certain disease, signals at earlierstages may be limited to only a few easily counted molecules. As the diseaseprogresses, more signal molecules might have been produced, resulting in mixedtypes of biomarkers representing multiple disease phenomena. Although thisassumption seems to be oversimplified, more noise is created at a certain stagewhere it becomes more difficult to identify those molecules at the molecularlevel because of two reasons: (1) they are in amounts too small to be detectedusing the current technology and (2) it may be too premature for the moleculesto be specific for a particular disease. Presumably, proteins appearing in stage 3or 4 may have higher specificity of a particular disease but the sensitivity mightbe low. It may be likely that this noise interferes with the signaling pathway ofa certain disease, and we may end up having no decisive marker. To circumventthis problem, it may be desirable to identify a set of biomarker candidateproteins, termed a “biomarker panel,” which ideally contains potential candidateproteins or peptides that represent specific stages of the disease as a group.Given this panel, extensive validation processes may be sought using largegroup cohort. Analogous to this strategy, many biomarker candidates at stage 1can be included in the panel, which can have more specificity and sensitivity ascompared to a single molecule biomarker. Using this kind of biomarker panel,one can use not only this molecule as diagnostic marker but also as a prognosticindicator in monitoring treatment effectiveness. For example, Linkov et al. (61)reported that both the sensitivity and specificity were improved up to 84.5 and98%, respectively, when they used a panel containing 25 multimarkers in earlydiagnosis of head and neck cancer (squamous cell cancer of the head and neck)(61). In the diagnosis of prostate cancer, specificity was increased from 5–15to 84–95% when they used a biomarker panel containing six marker proteinsas compared to a single marker. In HCC, studies have been carried out on abiomarker panel consisting of a protein array that can be used as a diagnostickit (62,63).

A general strategy for biomarker discovery is outlined in Fig. 4. In typicalclinical proteomics, work sample collection is the first step, followed bypretreatment of the sample in order to reduce sample complexity to enablesearching for low-abundance proteins (e.g., disease biomarkers) using variousfractionation tools. This multidimensional fractionation is well-describedelsewhere (34,35,36), and depends on the properties and concentration of thesample. Typically the prefractionated samples go either to a two-dimensionalelectrophoresis (2DE) or LC-based proteomics separation system, followed bysingle or multiple steps of mass spectrometric analysis depending on the sample

Fig.

4.

12


quantity and experimental goal. The data obtained from this series of analyseswill be integrated into the proteome informatics system where protein/peptideidentification, quantification, modification, and verification of peak list arecarried out [(71) and also Chapter 19]. Usually this step becomes rate limitingsince major profiling data are constructed and analyzed at this point. Theclinical relevance of those proteins (and changes in their expression level) ina specific disease state is mostly determined, which eventually leads to identi-fication of biomarker candidates. In addition, SELDI, molecular imaging andprotein microarrays can also be applied before or after this step. Once majorbiomarker candidates are identified, those proteins are subjected to furtherverification via sophisticated analytical arrays and translational proteomics,which involves cohort studies, pre-evaluation, and a robust analytical system(4,72). Throughout the process of translational proteomics, one may be able tojudge whether the identified panel or single proteins are suitable for biomarkersof a specific disease. A recent comprehensive review by Zolg (73) addressedseveral considerations in the biomarker development pipeline from discoveryto validation. Three critical challenges within the pipeline are reduction ofclinical sample complexity, the proof of principle of biomarker function, andthe detection limit of unique proteins present in the samples.

In the search for biomarker panels, reliable statistical tools and bioinfor-matics resources are needed, which are now available on the web (Table 2;see also Chapters 16 and 17). As the number of biomarker panel candidatesincreases, more cases are being examined, which require statistical learningmethods. These methods include neural networks, genetic algorithms, k-means

�Fig. 4. A typical experimental strategy for clinical proteomics and transla-

tional proteomics. In clinical proteomics research, various experimental techniquesare included: specimen collection, prefractionation, 2DE, Non2DE (liquid-basedseparation), mass spectrometry, informatics, and others. The course of each section asmarked (square, circle in different color) is determined by the investigators, dependingon the experimental goal. At the bottom, experimental procedures for the verificationand validation of biomarker candidates are schematically outlined leading to clinicalscreening and applications. The squares indicate the separation system based on thespecific characteristics of proteins and general prefractionation system. The open circlesand open triangle represent analytical modules at the protein and peptide level, respec-tively. The arrow and junction points indicate an option of each selection. Bottom partsindicate verification procedure employing multiple reaction monitoring and quantitativemass analysis. Those biomarker candidates identified from typical clinical proteomicswould be subject to translational proteomics for validation where a large scale cohortstudy and evaluation would then proceed.

14 Paik et al.

nearest-neighbor analysis, euclidean distance-based nonlinear methods, fuzzypattern matching, selforganizing mapping, and support vector machines(74,75,76,77,78). They are very useful for classification of proteins accordingto the specific disease state (see also Chapters 16 and 20). Once biomarkercandidates are identified, it is necessary to predict in silico the function ofthese proteins and validate them in the context of clinical application. Table 3provides web resources, which can be used for clinical data management, insilico functional annotation (see Chapter 18), prediction, and identification ofmodified forms of proteins. Thus, by combining experimental methods (Fig. 4)and informatics tools (Tables 2 and 3), one is able to obtain a set of biomarkercandidate proteins (panel) that would be further used for validation throughtranslational proteomics (Fig. 1).

4. Introduction of the Experimental Strategy Describedin This Volume

For protein profiling and identification, proteomics platform technologiesare moving forward in many areas not only in clinical proteomics but also inthe general biological field. In this section, the leading scientists in the fieldof proteomics outline core techniques and their application to the studies ofclinical proteomics. For example, in plasma proteome analysis, it is necessaryto deplete high-abundance proteins using various techniques such as multidi-mensional fractionation by immunoaffinity column, gel permeation, and beads(Fig. 4). Cho et al. (Chapter 4) addresses this in relation to 2D gel analysis ofplasma wherein the technical details of sample preparation, gel electrophoresis,and quantification of proteins on the gel are described. Zhang and Koay(Chapter 5) describe the methods of 2D gel analysis for cells prepared byLCM. They describe the application of LCM in dissecting tumor cells inbreast cancer for macromolecular extraction and 2D gels. This can be usedfor preparation of samples from paraffin-embedded tissue blocks in microdis-secting the cells of interest. Further to this procedure, Mustafa et al. (Chapter 9)review the application of LCM for proteomics analysis and demonstrate thatcombining LCM and MS would facilitate identification of specific proteinsfor each sample type. For urine sample analysis, Zerefos et al. (Chapter 8)provide simple protocols for protein analysis by 2D gel or direct matrix-assistedlaser desorption/ionization-time-of-flight mass spectrometry. These techniquesinclude protein enrichment through protein precipitation and ultrafiltrationmeans. Combining these methods with the above profiling technologies allowsreproducible and sensitive analysis of one of the most significant and complexbiological samples (77).


Table 2Clinical Proteomics Initiatives and Resources

Details Websites

InstituteCPTI National Cancer Institute’s Clinical

Proteomics Technologies, initiative forcancer

http://proteomics.cancer.gov

ABRF The Association of BiomolecularResource Facilities, an internationalsociety dedicated to advancing core andresearch biotechnology laboratoriesthrough research, communication, andeducation

http://www.abrf.org/

PPI Plasma Proteome Institute, the PPI isworking to facilitate clinical adoption ofadvanced diagnostic tests using proteinsin plasma and serum

http://www.plasmaproteome.org/plasmaframes.htm

EDRN The Early Detection Research Network,the EDRN provide up-to-dateinformation on biomarker researchthrough this website and scientificpublications

http://edrn.nci.nih.gov

Web resourcesExPASy Expert Protein Analysis System,

proteomics related information anddatabase

http://www.expasy.org/

NCBI National Center for BiotechnologyInformation, the protein entries in theEntrez search and retrieval system havebeen compiled from a variety of sources,including SwissProt, PIR, PRF, PDB,and translations from annotated codingregions in GenBank and RefSeq

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db = Protein&itool = toolbar

CPRMap Clinical Proteomics Research Map,updated research article for disease andclinical proteomics

http://www.cprmap.com/

DatabaseMedGene MedGene can make a list of human

genes associated with a particular humandisease in ranking order

http://hipseq.med.harvard.edu/MEDGENE

16 Paik et al.

Table 3Available Bioinformatic Resources for the Analysis of Proteomics Data

Name Description Website URL PMID

Clinical proteome data management systemProteus LIMS for proteomics

pipelinehttp://www.genologics.com

CPAS LIMS for identificationand quantification usingby LC-MS/MS data

16396501

Systems biologyexperiment analysismanagementsystem

A management system forcollecting, storing,and accessing dataproduced by microarray,proteomics, andimmunohistochemistry

http://www.sbeams.org/

16756676

GPM database Open source system foranalyzing, validating,and storing proteinidentification data

http://www.thegpm.org/

15595733

SpectrumMill MS/MS data analysis andmanagement system

http://www.chem.agilent.com/

PhosphorylationGroup-basedphosphorylationscoring method

Prediction ofkinase-specificphosphorylation sites

http://973-proteinweb.ustc.edu.cn/gps/gps_web/

15980451

KinasePhos A web tool for identifyingprotein kinase-specificphosphorylation sitesusing by hidden Markovmodel

http://kinasePhos.mbc.nctu.edu.tw

15980458

NetPhos Sequence andstructure-based predictionof eukaryotic proteinphosphorylation sites

http://www.cbs.dtu.dk/services/NetPhos/

10600390

NetPhosK Prediction ofpost-translationalglycosylation andphosphorylation ofproteins from the aminoacid sequence

http://www.cbs.dtu.dk/services/NetPhosK/

15174133


PredPhospho Prediction of phosphorylationsites using support vectormachine

http://pred.ngri.re.kr/PredPhospho.htm

15231530

PREDIKIN A prediction of substrates forserine/threonine proteinkinases based on the primarysequence of a protein kinasecatalytic domain

http://florey.biosci.uq.edu.au/kinsub/home.htm

16445868

Prosite A prediction of substratesfor protein kinases-basedconserved motif search

http://kr.expasy.org/prosite

17237102

Scansite Prediction of PK-specificphosphorylation site withBayesian decision theory

http://scansite.mit.edu

16549034

Phospho.ELM A database of experimentallyverified phosphorylation sitesin eukaryotic proteins

http://phospho.elm.eu.org/

15212693

Human proteinreference database(HPRD)

A database of knownkinase/phosphatase substrate aswell as binding motifs that arecurated from the publishedliterature

http://www.hprd.org/PhosphoMotif_finder

PhosphoSite A bioinformatics resourcededicated to physiologicalprotein phosphorylation

http://www.phosphosite.org/Login.jsp

15174125

GlycosylationNetOGlyc 2.0 Predicts O-glycosylation sites

in mucin-type proteinshttp://www.cbs.dtu.dk/services/NetOGlyc/

9557871

DictyOGlyc 1.1 Predicts O-GlcNAc sites ineukaryotic proteins

http://www.cbs.dtu.dk/services/DictyOGlyc/

10521537

YinOYang 1.2 Predicts O-GlcNAc sites ineukaryotic proteins

http://www.cbs.dtu.dk/services/YinOYang/

NetNGlyc 1.0 Predicting N-glycosylationsites

http://www.cbs.dtu.dk/services/NetNGlyc/

16316981

GlycoMod Web software for prediction ofthe possible oligosaccharidestructures in glycoproteinsfrom their experimentallydetermined masses

http://www.expasy.ch/tools/glycomod/

11680880

(Continued)

18 Paik et al.

Table 3(Continued)


Glyco-fragment A web tool to supportthe interpretation ofmass spectra of complexcarbohydrates

http://www.dkfz.de/spec/projekte/fragments/

14625865

GlycoSearchMS Compares each peakof a measured massspectrum with the calculatedfragments of all structurescontained in the SweetDB

http://www.dkfz.de/spec/glycosciences.de/sweetdb/ms/

15215392

GlycosidIQ Based on the matching ofexperimental MS2 data withthe theoretical fragmentationof glycan structures inGlycoSuiteDB

https://tmat.proteomesystems.com/glyco/glycosuite/glycodb

15174134

Saccharidetopologyanalysis tool

A web-based computationalprogram that can quicklyextract sequence informationfrom a set of MSn spectrafor an oligosaccharide of upto 10 residues

10857602

GlycoX To determine simultaneouslythe glycosylation sitesand oligosaccharideheterogeneity ofglycoproteins usingMATLAB

17022651

MODi A web server for identifyingmultiple post-translationalpeptide modifications fromtandem mass spectra

http://www.unimod.org

16845006

SWEET-DB An attempt to createannotated data collectionsfor carbohydrates

http://www.dkfz.de/spec2/sweetdb/

11752350

Protein–protein interactionMunichinformationcenter for proteinsequence’s MPPI

The database of mammalianprotein–protein interactions

http://mips.gsf.de 16381839


Database ofinteracting proteins

A database that documentsexperimentally determinedprotein–protein interactions

http://dip.doe-mbi.ecla.edu/

11752321

Molecularinteraction networkdatabase

A database of storing, ina structured format,information aboutmolecular interactions byextracting experimentaldetails from workpublished in peer-reviewedjournals

http://mint.bio.uniroma2.it/mint

17135203

Protein–proteininteractions ofcancer proteins

Predicts interactions, whichare derived from homologywith experimentally knownprotein–protein interactionsfrom various species

http://bmm.cancerresearchuk.org/˜pip

16398927

IntAct IntAct provides a freelyavailable, open sourcedatabase system andanalysis tools for proteininteraction data

http://www.ebi.ac.uk/intact/

17145710

Biomolecularinteraction networkdatabase

A database designed tostore full descriptions ofinteractions, molecularcomplexes and pathways

http://www.bind.ca 12519993

Metabolic andsignal pathwayBioCarta A pathway database http://www.

biocarta.comKEGG A pathway database with

genomical, chemical, andbiological networkinformation

http://www.genome.jp/kegg

16381885

Cancer cell map The cancer cell map is aselected set of humancancer focused pathways

http://cancer.cellmap.org/cellmap/

HPRD A database withdata pertainingto post-translationalmodifications,protein–proteininteractions, tissueexpression,

http://www.hprd.org/

(Continued)

20 Paik et al.

Table 3(Continued)


subcellular localization,and enzyme–substraterelationships

Proteomic data resourceThe cancer cellmap

A database of clinical datafrom SELDI-TOF

http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp

Proteomicsidentificationsdatabase

A database of protein andpeptide identifications thathave been described in thescientific literature

http://www.ebi.ac.uk/pride/

16381953

PeptideAtlas A multiorganism, publiclyaccessible compendium ofpeptides identified in alarge set of tandem massspectrometry proteomicsexperiments

http://www.peptideatlas.org

16381952

Disease resourceOnlinemendelianinheritance inman

A database of human genesand genetic disorders

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db = OMIM

17170002

GeneCards An integrated database ofhuman genes that includesautomatically minedgenomic, proteomic, andtranscriptomic information

http://www.genecards.org/index.shtml

15608261

Cancer genecensus

A catalogue those genes forwhich mutations have beencausally implicated in cancer

http://www.sanger.ac.uk/genetics/CGP/Census/

14993899

Two-dimensional electrophoresis is perhaps the most popular start-up toolfor proteome analysis. For clinical proteomics, 2DE has been the traditionalworkhorse of proteomics used for the analysis of different clinical specimensranging from plasma to urine (Table 1). Quantification problems in 2DE are nowsolved by employing fluorescent dyes (cy3 and cy5), which allow normalization


of data obtained from two different clinical specimens (79). Freedman andLilley (Chapter 6) present general optimization conditions for differential in gelelectrophoresis (DIGE) in the quantitative analysis of clinical samples. Theyaddress the usefulness of differentially labeling dyes (Cy2, Cy3, and Cy5).The essence of any DIGE system is to minimize any potential human errorsin the process of identification and quantification of proteins spotted in a 2Dgel (79). The difficulties in 2D map analysis are introduced by Marengo et al.(Chapter 16). They describe methods for comparing protein spots using imageanalysis technology and related informatics tools to minimize variations betweenmeasurements of spot volume, a key to successful 2D map construction.

There are many variations of LC in protein profiling, including mass detectionmethods, column types, data mining through search engines, mass accuracy,and running conditions (80,81,82). These are all related to quantification ofproteins or peptides in the sample, one of the major bottlenecks in proteomics(83,84,85,86,87). Among the several techniques are isotope-coded affinity tags(ICAT), mass-coded affinity tagging, and nonisotope labeled methods. Xiao andVeenstra (Chapter 10) present the application of ICAT in the course of COX-2inhibitor regulated proteins in a colon cancer cell line. With emphasis on samplepreparation, they provide details on ICAT procedures for quantitative proteomics(88). In addition to this approach, Li et al. (Chapter 11) employ a strategy,which combines LCM techniques for sample preparation of HCC and cleavableisotope-coded affinity tags in order to identify those markers quantitatively.However, it should be mentioned here that some other measures are needed toincrease the efficiency of ICAT since it has drawbacks in the efficiency of samplerecovery during or after labeling steps (87). A label-free serum quantificationmethod has been recently introduced (48) (See Chapter 12 by Higgs et al.).

The use of antibody arrays in clinical proteomics has increased recently in thecontext of high-throughput detection of cancer specimens where the identitiesof the proteins of interest are known (89,90). The evaluation of antibody cross-reactivity and specificity is very crucial in these assays. This matter is addressedby Sanchez-Carbayo (Chapter 15), where technical aspects and application ofplanar antibody arrays in the quantification of serum proteins is described aswell as by Hsu et al. (Chapter 14) where the development and use of bead-based miniaturized multiplexed sandwich immunoassays for focused proteinprofiling in various body fluids is provided. The latter method using bead-based protein arrays or suspension microarray allows the simultaneous analysisof a variety of parameters within a single experiment. With the versatility ofsuspension microarray in the analysis of proteins of interest present in differenttypes of body fluids ranging from serum to synovial fluids, this multiplexedprotein profiling technology described by Hsu et al. (Chapter 14) seems tohold a great promise in clinical proteomics. Similarly, in combination with

22 Paik et al.

tissue microarrays technology (91) it would also be possible to perform parallelmolecular profiling of clinical samples together with immunohistochemistry,fluorescence in situ hybridization, or RNA in situ hybridization. SELDI isanother arena of high-throughput profiling of clinical samples in the courseof disease marker discovery [(92,93), Chapter 7]. It is expected that profilingapproaches in proteomics, such as SELDI-MS, will be frequently used in diseasemarker discovery, but only if the proper identification technologies coupledwith SELDI are improved.

During the course of biomarker discovery, large data sets are usuallygenerated and deposited in a coordinated fashion (Tables 2 and 3) (94,95).Indeed, statistical analysis of 2DE proteomics, which produce several hundredprotein spots, is complex. To circumvent some inconsistency in 2D gelproteomics data, Friedman and Lilley (Chapter 6) and Carpentier et al. (Chapter17) point out available statistical tools and suggest case-specific guidelines for2D gel spot analysis. Fitzgibbon et al. (Chapter 19) describe an open sourceplatform for LC-MS spectra where the msInspector program is used to lowerfalse positives and guide normalization of the dataset. It is also demonstratedthat msInspect can analyze data from quantitative studies with and withoutisotopic labels. Paliakasis et al. (Chapter 18) introduce web-based tools forprotein classification, which lead to prediction of potential protein functionand family clustering of related proteins. They provide some guidelines toclassification of protein data into more meaningful families. Finally, Somorjai(Chapter 20) addresses important filtering criteria for the application of proteinpattern recognition to biomarker discovery using statistical tools.

5. Concluding RemarksAlthough there are several bottlenecks in clinical proteomics (such as lack

of standardization of sample specimen process, quantification, and overallstrategy for tackling post-identification of biomarkers), we believe that thefield holds great promise in biomarker discovery. The success of clinicalproteomics depends on the availability and selection of well-phenotypedspecimens, reduction of sample complexity, development of good informaticstools, and efficient data management. Therefore, sample handling techniquesincluding microdissection for tissue sample, multidimensional fractionation forbody fluids, and pretreatment of other clinical specimens (e.g., urine, tears, andcells) should be developed in this context. Since there is no gold standard forsample collection and handling, one needs to find the best options available forsample processing without damage. In addition, establishment of a biorepositorysystem would systematically minimize some artifacts and variation betweensamples during or after identification of biomarkers.


It is now generally accepted that an ensemble (or panel) of different proteinswould be more efficient than a single protein/peptide in the diagnosis of disease,an idea which is poised to replace the conventional concept of a biomarker.As a high-throughput way of protein profiling, the use of antibody arraysin clinical proteomics has recently increased in regard to detection of cancerspecimens. However, in the use of antibody arrays to profile serum autoanti-bodies, issues of cross-reactivity and specificity have to be resolved. Althoughnot covered here due to space limitations, with the advent of proteomicstechniques one can further analyze a network of protein–protein interactionas well as post-translational modifications of those proteins involved in aspecific disease (Table 3). It is now highly recommended that common reagentssuch as antibodies and standard proteins, which are very useful for spikingpurposes, quantification work, and sensitivity normalization of one machine toanother be used in worldwide efforts like human proteome organization plasmaproteome project (96,97). Finally, clinical proteomics needs the integration ofbiochemistry, pathology, analytical technology, bioinformatics, and proteomeinformatics to develop highly sensitive diagnostic tools for routine clinical carein the future (71,98).

AcknowledgmentsThis study was supported by a grant from the Korea Health 21 R&D project,

Ministry of Health & Welfare, Republic of Korea (A030003 to YKP).

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