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roteomics: Methodologiesnd Applications in Oncologyradly G. Wouters, PhD
Few technological developments have created as much excitement and skepticism asproteomics over their potential to change clinical diagnostic and prognostic procedures.Proteomics concerns itself with the characterization and function of all cellular proteins,the ultimate determinants of cellular function. As such, it represents the end result of allmechanisms of gene regulation and thus offers tremendous potential for characterizingbiology. In much the same way as what has occurred with the genome, the scientificcommunity is coming to grips with the fact that the proteome, although enormouslycomplex, is finite. It is conceivable that we will learn the identity of all possible proteins,including all posttranslational modifications. The rate of protein discovery continues toaccelerate in large part because of improvements in mass spectrometry–based technolo-gies coupled with improved genomic databases and bioinformatic tools. In addition, thereis reason to believe that proteomics is on the verge of moving from a methodology thatrequires repeated proteome “discovery” to one that can more systematically profile pro-teomes. This review discusses current proteomic-based technologies and the efforts ofscientists to move them into the clinic for use in patients treated with radiotherapy andother modalities.Semin Radiat Oncol 18:115-125 © 2008 Elsevier Inc. All rights reserved.
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f you ask 10 people to define what “proteomics” embodies,you are likely to receive 15 or so different answers. The
erm is used to describe a long list of different technologiesnd strategies designed to investigate the “output” of the ge-ome specifically at the protein level. As such, proteomicsoncerns itself with the determination, quantification, andunction of proteins on a genome-wide scale. But what reallyoes this mean? Proteomics involves in the first place a com-lete catalog of the proteome, the list of all expressed proteinshat derive from the genome. At this level, proteomics islready significantly more complex than the genome (DNAequence) or transcriptome (transcribed genes). The increasen complexity arises both in terms of the scale of the problemo be addressed and the methodology available for its inves-igation. The proteome is influenced by alternative messenger
epartment of Radiation Oncology (Maastro Lab), GROW Research Insti-tute, Maastricht University, Maastricht, The Netherlands.
upported in part by support from the Dutch Science Organization(ZonMW-NWO Top grant 912-03-047 to BW), the Dutch Cancer Soci-ety (KWF grant UM 2003-2821 to BW), and the EU 6th frameworkprogram (Euroxy program to BW).
ddress reprint requests to Bradly G. Wouters, PhD, Department of Radia-tion Oncology (Maastro Lab), Maastricht University, Universiteitssingel50, PO Box 616, 6200 MD Maastricht, The Netherlands. E-mail: brad.
053-4296/08/$-see front matter © 2008 Elsevier Inc. All rights reserved.oi:10.1016/j.semradonc.2007.10.008
NA (mRNA) splicing, mRNA stability, protein modificationseg, phosphorylation, ubiquitination, and glycosylation), androtein stability. Consequently, the number of distinct proteinsxceeds the number of genes by at least a factor of 10. Thesevents are responsible for the fact that for many genes there is aoor correlation between mRNA levels and protein abundance.easurement of the proteome is also significantly more chal-
enging than that of the genome or transcriptome because ofhe fact that protein levels vary over 10 orders of magnitudeoupled with the absence of any “amplifying” technologieskin to polymerase chain reaction for nucleic acids. 1 Thisakes it extremely difficult to “see” proteins that are expressed
t low levels, as is the case for some of the most importantellular regulators.
At a second level, the term proteomics also refers to effortsimed at functional characterization of the proteome. Thisncludes studies into pathway activation, protein interactionsthe interactome), and the dynamic composition of differentrotein complexes. At this level, proteomics is well suited toontribute to our basic understanding of cell biology andfforts to integrate large-scale analyses to understand cell andissue behavior at the system level (systems biology).
The payoff for the increase in complexity associated withroteomics is an ability to describe the cell or tissue under
nvestigation at a level that is much more closely related with
115
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ts expected functional characteristics. Although cancer isertainly a “genetic” disease, it is currently difficult or impos-ible to extrapolate the consequence of any given mutationor single nucleotide polymorphism [SNP] for that matter) tohe functional changes that take place within a given cell,uch less within an entire tumor. The degree to which aarticular mutation will affect cell proliferation, DNA repair,ngiogenesis, hypoxia, metastasis, or other characteristics ofelevance to treatment response is clearly not possible toredict at this moment. This is because individual genes arexpressed differently in different cell types, are influenced byther gene products and mutations, and may be regulated inomplex ways by the cellular microenvironment. A good ex-mple comes from the numerous familial tumor suppressorenes that have been identified through extensive genetictudies. For the most part, the identified genes (eg, BRCA1,RCA2, Rb, VHL, and so on) encode for proteins that areredicted to function in fundamental growth processes in allell types. However, they each cause cancer in only a limitedumber of tissues presumably because of tissue-specific dif-erences in gene regulation at both the transcript and proteinevel as well as the interaction of cells with their microenvi-onment. Another good example comes from a recently pub-ished study describing one of the first results of an ongoingnternational effort to survey the entire “cancer genome.”2
his study showed that a very large number of differentenes contribute to cancer development and that there iselatively little overlap between the genes mutated in tu-ors from different individuals. This heterogeneity is
ather disappointing news to those interested in exploitingancer-associated mutational events to develop specificherapeutic drugs aimed at exploiting specific defects inndividual cancers.
Proteomics may offer a solution in these cases because it islear that the large number of genetic abnormalities found inancer contribute to a much smaller number of changes iney-signaling pathways that control the cancer cell pheno-ype.3 Because these pathways are often regulated by changesn protein modification, stability, or interaction, proteomicsffers the possibility of identifying and assessing thesehanges in ways that are not possible with genomic or tran-criptomic approaches. This includes the identification ofhanges in important regulatory proteins that themselves areot even targets for mutation in cancer. A good example is theI3K/AKT/mTOR pathway that is constitutively activated inany different cancers. Although this can occur as a conse-
uence of many different genetic changes, common down-tream effects can be assessed at the protein level. Indeed, aecent study using phosphoprotein pathway mappinghowed that Akt/mTOR activation was negatively associatedith childhood Rhabdomyosarcoma survival.4
roteomicsart 1: Protein Discovery
he development of proteomics into a science in itself has
ccurred principally over the past 15 years and has been fueled by the continuous development of new mass spec-rometry techniques coupled with advances in genome se-uence data. A mass spectrometer consists essentially of 3omponents: an ion source to create ionized species, a massnalyzer to measure the mass to charge (m/z) ratio, and aetector to count the number of ions at each m/z value. Masspectrometry was initially used only for small and thermallytable molecules because techniques to create molecular ionsrom intact peptides/proteins and other biomolecules wereot available. This changed with the development of electro-pray ionization (ESI) and matrix-assisted laser desorption/onization (MALDI) “soft” ionization techniques that are usedo this day.5,6 ESI ionizes the sample out of a solution and ishus the preferred technique for automated approaches afteriquid chromatography (LC)-based separation techniques.
ALDI uses a laser to sublimate and ionize samples from aefined dry crystalline matrix (eg, liquid chromatography).he mass analyzer is at the heart of the instrument and isesponsible for the precise mass measurement of the ionizedpecies. There are 4 main different types of mass analyzerechnologies that are used routinely in mass spectrometry.7
hese are time of flight (TOF), quadrupole, ion trap, andourier transform ion cyclotron resonance, and each haveheir own unique combination of accuracy, resolution, sen-itivity, and dynamic range.7,8
Proteins themselves are typically too large for accurateass measurement and thus must be broken down into
maller polypeptides before mass spectrometry (Fig 1). Thiss accomplished using trypsin, which cleaves proteins at ar-inine and lysine residues creating a number of “tryptic pep-ides” that are characteristic to the protein. The amino acidomposition of these polypeptides (and thus their moleculareight) is often unique to the parental protein and can there-
ore be used to infer its identity. For a given protein, a single-tage mass spectrometer (usually a MALDI-TOF) will pro-uce a characteristic peptide spectrum, and the measuredass for each of these peptides can potentially be used to
nfer the identity of the protein. This approach, referred to aseptide mapping or peptide mass fingerprinting, requiresomparing the measured mass of the peptide to a list ofalculated peptide masses in a comprehensive genomic orrotein database. In general, multiple different peptides fromhe same protein should be identified to place confidence indentification. This approach has become efficient only sincehe sequencing of the human genome and establishment ofarge protein databases.
For more detailed structural information on individualolypeptides, including the amino acid sequence or the sitend type of posttranslational modifications, tandem masspectrometry (MS/MS)-based instruments are used. Thesenstruments use combinations of 2 mass analyzers and arehe mainstay of proteomics research today. The first issed to identify and select a particular ion species (pep-ide) of interest, which is then subjected to collision-in-uced dissociation (CID) to generate a series of peptideragments that are analyzed in a second mass analyzer. Theeaks in the resulting MS/MS spectra from these peptide
ragments provide additional information on the amino
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Proteomics 117
cid sequence and potentially the site and nature of specificosttranslational modifications. At the moment, the CIDpectra cannot be directly converted to a full amino acidequence and thus protein identification with this approachlso relies on searching against protein sequence databasessing 1 or more algorithms.9 Fortunately, establisheduidelines and statistical tools to ensure consistency andonfidence in the identified proteins are becoming avail-ble.10-13
A typical proteomics experiment involves a number of dif-erent steps. It often (although not always) begins with someevel of protein separation of the proteome of interest intoifferent fractions before mass spectrometry. A number ofethodologies can be used here including affinity purifica-
ion, subcellular fractionation, 1-dimensional or 2-dimen-ional gel electrophoresis (2DE), and various combinationsf liquid chromatography. For many years, 2DE was theethod of choice in which protein mixtures were separated
n gels on the basis of charge (in the first dimension) and massin the second dimension). The gels can be stained with sen-itive methods to reveal the location and intensity of individ-
Figure 1 Protein discovery by mass spectrometry. Protein(eg, by 2DE) before further analysis. In this case, a small sanalysis. Alternatively, entire proteomes or subproteomcase, peptide separation is required by using chromatotrometer. Peptides are ionized by MALDI or ESI technanalyzer. This results in a mass spectrum containing a sepeptide and may be sufficient for identification. Furtsubjecting individual peptides to CID followed by a secoProtein identification in either case requires database se
al proteins. Each spot represents a (relatively) pure protein, w
nd it can be excised and subjected to tryptic digestion andS to determine its identity. The main advantages of 2DE are
n its quantitative nature (see later) and in the fact that it canesolve highly related (eg, modified) proteins. This tech-ique also significantly reduces the technical demand onass spectrometry because selected proteins spots are al-
eady highly purified. However, 2DE suffers from a limitedynamic range (revealing only the most abundant pro-eins) and slow throughput and is not amenable to analysisf certain classes of proteins. In addition, developments inass spectrometry instrumentation have increased their abil-
ty to resolve complex protein mixtures and to improvehroughput. As a result, there has been a steady move awayrom 2DE for whole proteome and other complex proteinixtures to liquid-based chromatography approaches cou-led to MS/MS.In LC-MS/MS-based approaches, entire proteomes or sub-
roteomes are digested first, before any separation tech-ique. This produces a highly complex mixture of peptideshat are then separated through (multiple) fractionation stepsnd analyzed using automated MS/MS analysis (usually ESI)
ed from cells, tissues, or other sources may be separatedf proteins is normally selected for digestion and furtherbe collected and digested into smaller peptides. In this
c or other techniques before entry into the mass spec-and the mass to charge ratio determined by the masspeaks whose mass is dictated by the composition of thearacterization of individual peptides is performed byanalysis yielding the MS/MS or tandem mass spectrum.g and comparisons. For further details, see text.
s isolatubset o
es maygraphiiquesries ofher chnd MS
ith machines capable of high throughput.7 The goal here is
118 B.G. Wouters
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Proteomics 119
o identify all possible peptides within the mixture and theno infer the presence of all original proteins based on theeasured MS/MS spectra. Because the connection between
he peptides and the proteins from which they were deriveds lost, this approach has been termed shotgun proteomicsecause of its similarity to shotgun DNA sequencing inhich short DNA sequences are determined randomly and
hen reassembled with various algorithms. Shotgun pro-eomics provides a significant improvement in proteomicoverage but places large demands on data collection andnalysis because of the enormous complexity of the pep-ide samples.
roteomics Part 2: Proteomeomparison and Profiling
uantification and Comparisonhe interest in proteomics lies in large part not from simplyeing able to list the presence of all proteins in the proteomeut rather in assessing quantitative differences in protein ex-ression that occur under defined conditions. From a clinicaloint of view, this could be proteome changes that occururing the course of disease (for early screening), changeshat identify subclasses of patients (for prognosis), or changeshat reflect treatment response. The ability to quantifyhanges in the proteome also makes it possible to identifyrotein subsets that respond to specific cellular situationshat are hypothesized to be clinically relevant (eg, hypoxia).owever, for this approach to work, it is imperative that one
an compare, with a reasonable precision, the changes inrotein expression between samples.To directly compare proteomes from different sources,
uantification thus becomes imperative. Quantification cane achieved by using 2DE by comparing the intensity ofrotein spots among different samples before their excisionnd identification with mass spectrometry. Classically, thisnvolves comparison of separated proteins on different gelsnd thus requires extensive gel “matching” to ensure that ones comparing levels of the same spot (Fig 2). This is not arivial matter and currently requires significant computingower, limiting throughput. However, an approach knowns difference in gel electrophoresis can also be used in whichamples are first labeled with 1 of 2 different fluorescent-
Figure 2 Methodologies for proteome comparisons and pexpression across entire proteomes. Differences in proteand intensities after 2DGE. In this case, differentially expof interest and MS or MS/MS analysis. High-throughputrequires LC-based approaches. In this case, proteins fromalter the mass of the peptide in a known way withoutmetabolically in cells by supplying an isotope labeledpeptides can be labeled with an isotope tag of differendetermined by comparing the intensity ratio of peptide pthe isotope. More recently, peptide-based scoring has bisotopically labeled. Mixing these probes with a digested
amount of each probe target in the sample. For further detailsolored dyes. The 2 dyes are designed to result in a similarhange in mass and charge and thus will not differentiallyffect the location of the protein that is labeled. After labeling,he samples can then be run in the same gel, removing theequirement for spot matching and greatly simplifying deter-ination of expression changes, which can be estimated by
he difference in color intensity.Gel-free proteomic experiments require different approaches
or quantification. Unfortunately, the mass spectrometer itself isot useful for assessing differences in peptide abundance. This isecause the relationship between the amount of peptide in theample and its ability to be ionized and detected in the masspectrometer is highly complex and incompletely understood.s a result, certain peptides display a much higher frequencyf detection and are redundantly found in many differentroteomic analyses. These peptides have been referred to as
proteotypic’ polypeptides. This leads to a complete loss ofnformation on the abundance of the protein in the originalample. Fortunately, an elegant solution to this problem haseen found and is based on peptide labeling with stable iso-opes such as 13C, 15N, 18O, or 2H. For experiments con-ucted in the laboratory, these isotopes can be incorporated
nto proteins because they are synthesized by the inclusion ofodified amino acids in the growth medium. Labeling can
lso occur through chemical or enzymatic modification of theeptides after trypsin cleavage, and, consequently, it is alsoossible to use this technique on protein samples isolatedrom patients. A large number of such isotope-coded affinityags have been developed and made commercially available,llowing labeling of the vast majority of peptides.14 The iso-ope-code affinity tag–labeled peptides behave similarly withespect to their chromatographic separation and signal inten-ity in the mass spectrometer. However, depending on thesotope that is chosen, a small predictable change in peptide
ass will occur that is easily detectable by the mass spec-rometer. As a result, 2 differentially labeled peptide mixturesheavy and light) can be combined and then subjected to MS.his produces a MS spectrum with a series of peptide pairseparated by the difference in mass of the isotopes. The ratiof the heavy and light components of the peptide pairrovides a direct measurement of the difference in concen-ration of each within the mixture. For unambiguous iden-ification, the peptide pair can be further subjected to CID
g. Several approaches are in use for comparing proteinression can be determined by comparing spot locationproteins are identified after excision of the protein spots for characterizing large numbers of proteins normallyent samples are differentially labeled with isotopes thatncing its other properties. Isotope labeling can occurund that is incorporated into proteins. Alternatively,
s after digestion. The amount of each peptide can behich are separated in mass by an amount determined bytroduced, which uses known peptide “probes” that are
sample allows comparatively easy quantification of the
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S/MS. This technique has been used extensively over theast 5 years and has become the method of choice for quan-itative proteomics.
imitations of Currentechnologies for Routine Useespite these impressive developments in proteomic tech-ologies, several limitations preclude their widespread use inxperimental medicine, much less routine clinical practice.he first problem is that even with the methodologies out-
ined earlier, the number of peptides that can be discoveredrom a complex mixture (eg, an entire digested cellular pro-eome) is still insufficiently small. A proteome sample mayontain well over 100,000 different peptides, which causesroblems for even the most modern LC-MS/MS–based ma-hines. As a consequence, too many peptides are presented tohe mass spectrometer at the same time, and only a fractionusually the most abundant) will be selected for CID MS/MS-ased identity determination. Furthermore, successful iden-ification from the generated MS/MS spectra occurs only in araction of cases. A nice example was published by Li andolleagues15 in a LC-MS/MS experiment in which a complexryptic digest yielded 2,720 peptides. Of these, 1,633 wereelected for CID, and, of these, only 363 yielded unambigu-us peptide identification (13%) after database searching ofhe resulting MS/MS spectra. Together, these factors limit theomplexity of the proteome that can be analyzed and lead toignificant increases in time and cost for comprehensive cov-rage. Although improvements in technology continue, thenormous challenge of characterizing and quantifying entireroteomes in this way, especially rare proteins, seems a longay off.
rom Proteiniscovery to Protein ProfilingS-Based Profiling With Proteotypic Probes
t has been strongly argued by Aebersold and colleagues thatdvancing proteomics to a more routine technology requires fundamental shift in approach.16 Current approaches areased on rediscovering the entire proteome or, more specif-
cally, the tryptic peptidome during each and every experi-ent. Every peptide within the digested proteome must be
dentified, subjected to CID, the MS/MS spectra matchedgainst databases, and the identity of the protein inferred.his is despite the fact that a wealth of valuable peptide datalready exist from prior experiments.
Aebersold has argued that it is possible to move from thisdiscovery” approach to one that involves “scoring” the levelsf known peptides.16 This shift in approach has occurred forNP analysis and for transcriptome analysis, in which syn-hesized probes are now used to detect known SNPs andnown expressed genes rather than having to rediscoverhese through purification, amplification, and sequencingpproaches. A similar approach is possible for proteomics, in
hich one or more unique peptides could be chosen to rep- gesent each protein or protein isoform of interest. A MS-basedlatform could then be designed specifically to evaluate thexpression of this representative peptide using peptiderobes. The choice for the representative peptide would beased on its ability to be detected in mass spectrometry in-truments together with its ability to uniquely identify itsognate protein. An obvious choice is to select the proteo-ypic peptides (described earlier) that have proven to beeadily and consistently detectable in prior MS-based exper-ments. The proteomic data already available constitute aizable proportion of the entire proteomic space, and meth-ds to determine proteotypic peptides for unrepresentedroteins in the databases are in development.17 Such proteo-ypic peptides can be used not only to uniquely identifyroteins but also specific splice forms, SNPs, or specific pro-ein modifications. Experience has shown that 1 or 2 sucheptides are sufficient to identify most proteins, althoughome may require a few more.17 The effort to catalog sucheference peptides for all proteins has been aided by theevelopment of the PeptideAtlas project, which aims to en-ble comparison of data across different experiments androvide uniform statistical validation.18
The basic idea behind this approach would be to restrictass spectrometry analysis in complex peptide mixtures to
he list of reference peptides. This would be done by com-ining the tryptic sample digest of interest with a pool ofynthesized reference peptides (probes) that have been iso-opically labeled to alter their mass by a small predictablemount. This sample could then be fractionated with LC andubjected to MS-based analysis. Such an analysis would yieldoth single peaks, representing either sample peptides forhich no reference peptide is present or reference peptides
or which no sample peptide is present, and double peaks inhich reference peptides and sample peptides are bothresent. These double peaks would be easily identifiable be-ause their mass difference is determined by the stable iso-ope used for labeling the reference peptides. Furthermore, asescribed previously, the ratio of intensities of the 2 peaksould allow easy quantification of the sample peptide rela-
ive to its reference. In this approach, the mass spectrometers “tuned” to examine only the proteotypic reference pep-ides, ignoring the vast majority of other peptides and thusnabling much higher sensitivity and coverage of the pro-eome (dependent really only on the quality and availabilityf the reference peptides). It has been estimated that a com-lete proteome could be analyzed in just a few minutes usinghis technology and currently available mass spectrometers.16
ntibody Arrayslthough MS-based approaches continue to develop, inter-ediate technologies that have grown out of more standardethodologies have developed to keep pace with the genom-
cs revolution. This includes the development of antibody-ased microarrays, a technology that is essentially a high-hroughput extension of single biomarker studies that can besed to simultaneously analyze a large number of proteins inarallel. This technology relies heavily on the availability of
ood detection reagents that have a high affinity and sensi-
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Proteomics 121
ivity for each protein of interest, which include antibodies,ptamers, peptides, or phage lysates. Protein microarraysay be either forward phase in which multiple “bait” mole-
ules are deposited onto an array to “capture” specific tar-ets19 or reverse-phase in which small volumes of the sampletself is immobilized and then probed with specific antibod-es.20
Forward-phase arrays have been used in 2 different for-ats. In the first, an approach similar to that used for com-lementary DNA microarrays is used in which proteins fromtest and reference sample are labeled with different fluoro-hores (eg, Cy3 and Cy5) and then allowed to bind to the baitroteins on the array. In practice, this does not work wellecause labeling the proteins interferes with binding in un-nown ways and because labeling of highly abundant proteinseg, albumin in serum) can lead to extensive background be-ause of nonspecific binding. Instead, a dual-antibody approachs often used, in which captured proteins are detected using aecond antibody. These are usually tagged with biotin and thenetected with an antibiotin antibody. This approach also suf-ers from a number of important drawbacks. First, the re-uirement for 2 good antibodies to distinct epitopes (or otheretection reagents) limits the usefulness of this technique tosmall subset of proteins. Second, the simultaneous incuba-
ion of multiple antibodies increases cross-reactivity andonspecific binding. Third, the affinity of different antibodiesaries over many orders of magnitude, and it is thus difficulto conduct experiments in which all targets are analyzedithin the linear range. This leads to large degrees of variabil-
ty across platforms and experiments and, in general, limitseproducibility and the ability to compare expression acrossifferent samples. These limitations currently prevent anyealistic hope of using these types of arrays for routine patientrofiling, but they have found a place in biomarker discoveryxperiments.
Reverse-phase arrays (RPAs) are also often referred to as aroteomic technique, although they are really just a high-hroughput single-protein marker approach. In much theame way that tissue microarrays extend the throughput ofmmunohistochemistry, RPAs can extend the throughput of
ore traditional Western blot analysis. In this type of exper-ment, a small volume of the entire proteome sample is de-osited at different dilutions (eg, from 1:2 to 1:16) onto anrray together with many other samples (up to �50). Theseultiple samples can then be probed with a single antibody
o establish the expression pattern in all 50 samples at theame time. The ability to analyze small volumes and thusmall concentrations of proteins is not a trivial one and isarticularly challenging given the lack of any amplificationbility for proteins. With RPA, it is possible to use amplifica-ion strategies at the level of detection using techniques sim-lar to that used routinely in immunohistochemistry. Theilution series ensures that measurement can take placeithin a linear range. Thus, RPA allows surveying the expres-
ion of specific proteins or phosphoproteins for which goodntibodies exist but does not approach the comprehensiveroteome analysis described previously for mass spectrome-
ry. This approach is well suited to probe the activation or axpression of proteins within specific pathways and has beensed successfully in a number of cancer research investiga-ions. These techniques may also be particularly valuable inlinical trials using drugs aimed at targeting specific path-ays.
ow Canroteomics Be Used Clinically?
roteomic techniques have already been used in the clinic innumber of different ways, and these studies have created areat deal of both excitement and controversy. There are 2rincipal ways that proteomics can contribute in the clinic.he first is as a technique to discover novel biomarkers or setsf biomarkers that may be unique to a particular class ofisease. Such biomarkers might be used to identify the pres-nce of the disease and thus prove to be useful in screening orreatment evaluation. However, they may also identify spe-ific pathways or responses that are activated within tumorsuring carcinogenesis or in response to the unique microen-ironment that tumor cells find themselves in. Uniquely ex-ressed proteins may also be good candidates for drug devel-pment. Because new biological agents often have proteins asheir direct targets, such as the small molecule kinase inhib-tors or monoclonal antibodies that have recently entered thelinic, proteomics offers an attractive platform for their dis-overy. This approach was used successfully to identify aeries of endothelial cell-surface proteins expressed exclu-ively in specific organs or tumors.21 This was accomplishedy using 2DE-based separation and MS. Antibodies wereubsequently developed to 2 of these proteins and used toarget therapy to the vasculature in tumors. This approachas also been used to investigate proteins that may mediateifferences in response to radiotherapy. Allal et al22 used 2DEnd MALDI-TOF to compare proteins in biopsies from 17atients before radiotherapy. Comparison of the radiation-esistant and radiation-sensitive tumors yielded a small list ofroteins associated with each of these responses. There is lessoncern associated with the small size of this study or theimited scope of investigations in general because these pro-eins are essentially only candidates that require further in-estigation in much larger clinical samples. In these types ofxperiments, proteomics is applied as a technique simply tourvey across the entire proteome for candidate proteins thatan then be further investigated with other approaches. Con-equently, these types of experiments place fewer demandsn reproducibility, feasibility, and throughput than would beequired for routine use in the clinic. This has allowed lesseproducible and comprehensive techniques such as for-ard-phase antibody arrays to be used as well. This tech-ique has been applied to patient serum and successfully
dentified novel circulating tumor markers in a number ofases.23-25
This type of approach can also be exploited using labora-ory-based experiments to search for proteins that may reflectpecific phenotypes hypothesized to be important for ther-
py response. For example, extensive efforts have been made
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122 B.G. Wouters
o discover predictive markers for hypoxia, proliferation, andadiosensitivity because of their perceived importance in de-ermining radiotherapy response. There is reason to believehat various “omic” approaches have a role to play here (alsoiscussed in other articles in this issue). This is exemplifiedy a recent publication showing that hypoxia-responsiveenes identified in vitro, the so-called hypoxic signature, pro-ide valuable prognostic information26 in clinical datasets.his is impressive given the poor relationship betweenRNA and protein levels and suggests that similar hypothe-
is driven experiments using proteomic analyses will alsoave clinical value. In fact, several studies have already beguno investigate proteomic changes to the classic modulators ofumor response including hypoxia, proliferation, and radia-ion sensitivity.27-30 The hope is that these protein-based sig-atures may also show some utility for predicting response inhe clinic.
The second way in which proteomics can be used clinicallyelates more directly with the subject of this series of articlesscreening, prognostic, or predictive profiling). This becomesn extension of current biomarker and immunohistochemi-al procedures that have proven useful in pathological exam-nations such as prostate-specific antigen in prostate cancernd the estrogen receptor in breast cancer. Extending thisharacterization to 10, 100, or 1,000’s of proteins (perhapsll proteins one day) certainly offers exciting potential for thisechnology. The expectation is that by assessing the pro-eomic profile of an individual tumor, it should be possible toredict the present state of disease, the prognosis, or thexpected response to specific types of therapy. As outlined inther papers within this series, this approach has alreadyroven to be quite useful with techniques for mRNA expres-ion profiling. With all of the associated benefits of measur-ng proteins outlined above, one would expect that proteom-cs data should be able to perform even better. The rapid andteady evolution of proteomic technologies has resulted in aapid rate of obsolescence, and this has made routine use ofny of these technologies difficult. However, despite someerious limitations associated with their immaturity, a num-er of investigations have reported promise for proteomicpproaches in the clinic. Some examples of these are high-ighted below.
erum-Basedlinical Proteomics
n additional advantage of proteomics-based profiling overxpression microarrays that has not been mentioned so far ishat informative proteins are available from several differentiological sources. There are obvious reasons to think thatroteins derived from tumors themselves would be useful,ut it is also possible to survey proteins in various biologicaluids especially blood and urine when protein-based assaysre routinely performed. In fact, blood-based proteomics hasrguably been the most thoroughly investigated to date. Thelood contains a number of endogenous proteins, such as albu-
in, as well as many other low–molecular-weight (LMW) pro- iein products that are collected during circulation. This includesmall proteins that may be secreted by various tissues, includingumors, and protein fragments of larger proteins that can beerived from either secreted or intracellular proteins. The LMWange of the serum proteome (�50,000 D) has been referredo as the peptidome, and analysis of this particular compo-ent has attracted both enthusiasts and skeptics.31 In 2002,iotta and Petricoin used a MS-based platform known asurface-enhanced laser desorption ionization (SELDI) to in-estigate the peptidome of patients with ovarian cancer.32
he SELDI platform makes use of affinity chips to captureroteins onto a matrix, which can be used as a source for
onization and MS-based peptide analysis. Using this tech-ique, they generated a series of LMW peptide spectra fromhe serum of a group of patients with ovarian cancer andompared this with a series of controls. Because the originalELDI machine had a relatively poor resolution and lackedS/MS capabilities, it was not possible to identify the individual
eptides within the spectra. Nonetheless, using pattern match-ng and other sophisticated bioinformatic techniques, they wereble to define a selected series of peptide peaks or “signature”hat could uniquely identify the patients with ovarian cancerith extremely high specificity and sensitivity.Based on these experiments, these authors concluded that
he blood can indeed be used as a rich source of disease-pecific information. Shortly thereafter, this group and sev-ral others reported similar results in a wide range of cancerssing the same approach.33-37 However, these results alsoenerated a great deal of controversy because others havergued that the LMW portion of the serum proteome isresent at concentrations too low to be relevant and that itay only represent biological breakdown products or simply
ust noise in these experiments.38,39 The LMW portion of theerum is also extremely sensitive to sample collection, stor-ge, and processing because activation of proteases duringnd after collection is a frequent event. In the high–molecu-ar-weight part of the serum, this can cause problems becausef the loss of signal and reproducibility. This instabilityauses an even greater effect on the LMW component of theerum because these events contribute to new peptides notresent in the original sampled blood. Normally, this woulduggest that care must be taken to prevent protein degrada-ion during blood collection, handling, and storage. How-ver, a recent article has suggested that the fragments gener-ted after blood collection may be responsible for the usefulnformation within the LMW serum proteome, and that onehould therefore not add protein-stabilizing components tohese samples.40 Nonetheless, this instability places a strictequirement for consistent handling of material to be ana-yzed with these techniques.
Apprehension over the value of serum proteomics wasueled by studies that began to identify some of the LMWeptide peaks within the key peptide signatures described inhe earlier studies. It was discovered that the signatures con-isted largely of peptides derived from highly common andbundant proteins endogenous to the blood like transthyre-in as well as breakdown products of some low abundance
ntracellular proteins from tissues such as BRCA2.41 It was
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Proteomics 123
ifficult to understand why the informative peptide signa-ures would consist of non–tumor-derived proteins or non-ecreted proteins. Liotta and Petricoin now hypothesize thathe protein fragments themselves provide unique informa-ion that is distinct from the parental proteins from whichhey derive.42 They argue that the presence, abundance, andature of these breakdown products are determined in partrom activation of enzymes within the cancer microenviron-
ent or secreted into the bloodstream and, as such, provideovel disease-specific information. In other words, these pro-ein fragments reveal an upstream enzymatic activity patternhat is unique to the tumor and that can thus be of clinicalalue. Because the vasculature within tumors is often com-romised and leaky, it has been assumed that tumor-derivedroteins (including proteases) may enter the bloodstreamuch more easily and frequently. The fact that tumors often
ontain large numbers of dead or dying cells also increaseshe potential for tumor-derived proteins or fragments to endp in the blood. The fragments that are ultimately detecteday thus represent breakdown products from the tumor
ells, the tumor stromal, or even blood-based proteins. Thisypothesis would also provide an answer to why proteinragments generated after collection could be clinically infor-
ative because these may derive from the activity of tumor-erived enzymes.Another argument often made against using serum-based
roteomics as a technique to identify tumor-specific proteinss with respect to the dilution effect that occurs as theseroducts enter the bloodstream. The secreted proteins withotential clinical value are diluted within 5 L of blood and arehus present at concentrations estimated to be approximately billion times lower than serum proteins such as albumin.38
his explains why current detection of blood biomarkersequires highly sensitive enzymatic-based (enzyme-linkedmmunosorbent assay) assays and also perhaps why knownumor markers like prostate-specific antigen do not seem tourn up in these experiments. Furthermore, the LMW por-ion of the blood proteome is rapidly filtered out by theidneys, and, thus, tumor-derived products are unlikely toccumulate over time. In fact, it has been estimated thatumor markers in the blood would be present at concentra-ions almost 1,000 times lower than the limit of sensitivity ofhe SELDI instrument that was used in most clinical experi-ents to identify tumor specific markers.38 How then, can
he impressive results reported with this and similar instru-ents be explained? One potential explanation that has been
ecently recognized is that peptide fragments can be “ampli-ed” by binding to proteins such as albumin.31,41 This wouldrotect these proteins from clearance in the kidney and ex-end their half-life and concentration to a point in which theyecome detectable. This has led to new approaches usingurification techniques in which albumin (or other bloodroteins) is purified specifically to harvest bound peptidesrom the serum.
In summary, the field of serum proteomics continues toove through periods of irrational exuberance followed by
ritical self re-examination. The problems and controversies
ssociated with this approach are to a large extent caused by ghe immaturity of the technologies and their introductionnto the clinic before a complete understanding of theirtrengths and weaknesses. Investigators such as Liotta andetricoin deserve a great deal of credit for their efforts both toush this technology into the clinical realm as well as to try tonderstand what the clinical data have been telling us. It islear that a number of questions remain with respect to whatroteins/peptides are available in the serum and what typesf technology will be appropriate for measuring it.42 One ofhe general agreed on points, however, is that it is imperativeo be able to identify the peptides within samples and notimply rely on peptide patterns to be able to independentlyerify their prognostic value.
umor-Basedlinical Proteomics
n comparison to the serum, proteomics performed on tumorissue is somewhat more straightforward because one canocus specifically on the content of the tumor cells them-elves. However, because tumors are characterized by bothumor cells and a range of host-derived cells, careful consid-ration must be made with respect to the starting material foromparison-based proteomic profiling. One approach is tose laser-capture microdissection to identify the areas withinhe tumor of interest and then to isolate the proteins specifi-ally from this area before proteomic analysis with MS. Thispproach was first used to compare early- and late-stage dis-ase to identify proteins associated with tumor progressionsing the SELDI machine.43 Although very few cells are re-uired (as few as 25 will create a suitable MS peptide spectra),his is a rather tedious procedure and not easily amenable toigh-throughput approaches. Consequently, there have beenignificantly fewer clinical studies using this approach com-ared with serum-based proteomics.A second approach that has been developed is known as
irect tissue or imaging MS. With this technique, a smallmount of MALDI matrix is deposited and dried directly onto region of interest within a fresh frozen tumor section.44
eptides are ionized only from within the area in which theALDI matrix is present, ensuring that the MS peptide spec-
ra will represent proteins derived from the chosen area. Inffect, this allows one to use MS in an “imaging” mode aspatial information is preserved. New techniques allow foreposition of very small amounts of MALDI matrix using
nk-jet technology,45 and these can be deposited in a grid (orny pattern for that matter) that covers entire tumor sections.hese techniques can even be used to map proteins or smallolecules in a 3-dimensional view by scanning a complete
eries of sections. In an impressive study, this technique waspplied in non–small-cell lung cancer in a training cohort of2 lung tumors and 8 normal lung tissues.46 Proteomic pep-ide patterns were able to classify lung cancer histologies,lassify nodal involvement with 85% accuracy, and distin-
uish patients with good and poor prognosis.
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124 B.G. Wouters
hallenges and Outlookhis review has focused on the technological developmentsf mass spectrometry–based proteomics and how these tech-iques are and can be used in the clinic. The technologyertainly continues to lag behind that of other genomic ap-roaches such as microarrays and SNP analysis with respecto its applicability to clinical use. However, because this tech-ology matures, it continues to provide promise for break-hroughs in clinical diagnosis and prognosis and will un-oubtedly find a place in clinical medicine in the not tooistant future. It would be wise to anticipate this develop-ent and to begin to prepare now for making clinical pro-
eomics experiments possible. Similar to the situation withicroarrays, one can anticipate that the bottleneck for ex-loitation of proteomics technology will move quickly from aechnological one to a clinical one. Specifically, investigatorsill need high-quality tumor or other biological starting ma-
erial from large and well-controlled clinical studies. Thisighlights the importance of blood and tumor banking withttention paid to protocols that will facilitate proteomics ex-eriments in the future. Although no current agreed on stan-ard exists today, many recommendations are available onhe proper handling and storage of various tissues.47-51 Theost important parameter at the moment is to ensure that all
amples are handled in an identical way, with a minimum ofariation in time between collection and storage. The lessonsearned from the limited number of clinical studies per-ormed to date highlight the importance of such strict proto-ols for sample collection, processing, and storage.
Another important issue that has not been considered at alln this review is that of data analysis. Proteomics experimentsenerate enormous amounts of data, and this has the poten-ial to further increase in the near future. This leads to theommon problems of overfitting data, a problem that plaguesany genomic approaches. Fortunately, proteomic analysesill be able to greatly benefit from the bioinformatic tools
hat have been developed in concert with the implementationf microarray and SNP approaches in the clinic. Proteomicata also generate their own unique sets of challenges, and it
s clear that a close working relationship between bioinfor-aticians, mass spectrometry experts, and clinicians will be
equired for successful integration of proteomics into clinicalse.
cknowledgmentswould like to thank all members of our team at Maastro and,n particular, Marianne Koritzinsky for critically reading the
anuscript.
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