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Review ArticleAdvances in Proteomic Technologies and Its Contribution tothe Field of Cancer

Mehdi Mesri

Office of Cancer Clinical Proteomics Research National Cancer Institute NIH Bethesda MD 20892 USA

Correspondence should be addressed to Mehdi Mesri mesrimmailnihgov

Received 23 March 2014 Accepted 30 June 2014 Published 8 September 2014

Academic Editor Runjan Chetty

Copyright copy 2014 Mehdi MesriThis is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Systematic studies of the cancer genome have generated a wealth of knowledge in recent years These studies have uncovereda number of new cancer genes not previously known to be causal targets in cancer Genetic markers can be used to determinepredisposition to tumor development but molecularly targeted treatment strategies are not widely available for most cancersPrecision care plans still must be developed by understanding and implementing basic science research into clinical treatmentProteomics is continuing tomakemajor strides in the discovery of fundamental biological processes aswell asmore recent transitioninto an assay platform capable of measuring hundreds of proteins in any biological system As such proteomics can translate basicscience discoveries into the clinical practice of precision medicine The proteomic field has progressed at a fast rate over the pastfive years in technology breadth and depth of applications in all areas of the bioscience Some of the previously experimentaltechnical approaches are considered the gold standard today and the community is now trying to come to terms with the volumeand complexity of the data generated Here I describe contribution of proteomics in general and biological mass spectrometry inparticular to cancer research as well as related major technical and conceptual developments in the field

1 Introduction

Although remarkable advances in cancer research haveextended our understanding of how cancer develops growsand metastasizes it is projected that close to 600000Americans will die from one of more than 200 types ofcancer in 2013 Moreover because an excess of 75 percent ofcancer diagnoses occur in those aged 55 and older and thissegment of the population is increasing in size the numberof cancer-related deaths will increase dramatically in thefuture As a result cancer is projected to soon become thenumber one disease-related killer of Americans This trendis also observed globally and it is estimated that in 2030more than 13 million people worldwide will die of cancer[1] While significant amounts of resources are devoted tocancer research the complexity and multifaceted nature ofcancers reflect the obstacles to unravel the etiology of cancerand control and ultimately cure this debilitating diseaseThe heterogeneity and complexity of cancer progressionoriginate from the complex interplay of genomic aberrationsand immunological hormonal environmental and other

factors acting individually or in concert which constitute thehallmarks of cancer

The proteome is the operating machinery for nearlyall biological functions its abundance and interactions areprecisely controlled and it is the link between the genomeand phenotypes Proteins can be present at vastly differentabundances expressed in various sizes shapes and chargesand have more complex twenty amino acid forms in contrastto the four nucleotides of the genome itself It undergoesdynamic changes in different cells tissues and organs duringdevelopment in response to environmental stimuli and indisease processes Understanding the dynamics of proteininteractions with other proteins nucleic acids and metabo-lites is the key to delineating biological mechanisms andunderstanding disease including cancer Genomic sequenc-ing has been the focus of attention in recent decades andhas produced a wealth of information However proteins arethe component that functionally governs cellular processesMoreover variation in levels of DNA or transcripts does notcorrelate well with protein abundance [2] Thus proteomicsbridges the gap between genomic information and functional

Hindawi Publishing CorporationAdvances in MedicineVolume 2014 Article ID 238045 25 pageshttpdxdoiorg1011552014238045

2 Advances in Medicine

proteins and translates this information The possibility tosystematically quantify protein abundances positions pro-teomics to monitor heterogeneous alterations in multiplepathways and mechanisms that drive the transformation ofthe malignant phenotype Proteomics can be considered asan integral part of cancer research to identify biomarkersto detect patients at the early stage monitor drug responseof tumors understand mechanisms that lead to cancerpathogenesis and design new therapeutics Scientists andoncologists thus use the various proteomics tools designexperiments and interpret results of proteomics to determinethe causative mechanisms guide prognostication and evendevelop precision medicine for cancer treatment

2 Genomics

A key role of proteins in realizing the full potential of thehuman genome project (HGP) is linking the genome tonormal and disease phenotypesTheHGP has changed manyaspects of human biology and medical research includingcancer Despite many skeptics the HGP became a reality bydaring goals and new technology platforms [3] Advancedtechnology platforms for sequencing dramatically changedthe study of genes and gene regulation in all organisms[4] The HGP has made all genes accessible to biologists byproviding a part list of genes and putative protein productsand stimulated a new perspective in studying biologicalprocesses through systems biology Furthermore the HGPhas helped the creation of whole new commercial sectorwith high throughput instruments reagents and servicesand opened the door for large data sets open-source dataalong with large-scale application of bioinformaticsThe fieldhas transformed our thinking about cancer diagnostics andtargeted therapies Despite all of these achievements skepticsare still concerned about the slow progress in transformingpublic health

Both endogenous and exogenous stimuli may result inevading the normal regulatory mechanisms of the cells withultimate aberrant phenotypes enabling cancer cell to prolif-erate invade and metastasize Tumors contain endogenousaberrations that are largely caused by somatic alterationsof genome that result in mutations of oncogenes andortumor suppressors [5] For example deletions insertion copynumber variations and mutations can drive initiation andprogression of cancer [6] Genomics the comprehensive largescale analysis of gene expression in biological specimensto determine their associations with disease or treatmenthas particularly been growing since 2005 New types ofsequencing instruments that permit amazing acceleration ofdata-collection rates for DNA sequencing were introducedby commercial manufacturers including platforms such asmassively parallel sequencing (MPS) For example singleinstruments can generate data to decipher an entire humangenomewithin only 2weeks It is anticipated that instrumentsthat will further accelerate this whole genome sequencingdata production timeline to days or hours will be a reality inthe near future [7]

The main challenge in genomics today is to understandthe role of molecular aberrations in various diseases such

as cancer [8] The Cancer Genome Atlas (TCGA) project(httpcancergenomenihgov) and the International CancerGenome Consortium (httpwwwicgcorg) aim to deter-mine the genomic aberrations in human cancer types andtheir roles in pathophysiology of cancers TCGA launched asa three-year pilot in 2006 by the National Cancer Institute(NCI) and National Human Genome Research Institute(NHGRI) in theUnited States TCGApilot project confirmedthat an atlas of changes could be created It also provedthat a network of research and technology teams could poolthe results of their efforts and develop an infrastructurefor making the data publicly accessible Moreover it provedthat public availability of data would enable researchers tovalidate important discoveries The success of the pilot ledthe National Institutes of Health (NIH) to commit majorresources to TCGA to collect and characterize more than20 additional tumor types (httpcancergenomenihgov)The ultimate goal is to translate genomics data into clinicalapplications such as routine clinical screening and precisionmedicine

Classification of cancer and diagnosis has been based oncellular morphology and histological architecture Howeverpatients with similar histopathology cancer staging andtreatments have shown variable clinical outcomes Suchmethods are subjective andprone to interpretation variabilityand pathologists do not always agree on the diagnosisTherefore diagnostic tools for cancers have evolved fromhistology to methods such as genomic testing with FISHmicroarrays and chromosome karyotype analysis For exam-ple gene expression profiles can be used as unique molecularsignatures to aid diagnosis and classify histopathologicallysimilar tumors into biologically distinct subtypes [9] Molec-ular signatures can be used to identify patients with highrisk for occurrence and poor survival and receive targetedtherapies Mammaprint is a Food and Drug Administration(FDA) approved breast cancer recurrence microarray-basedtest [10] The Oncotype DX assay is used to predict riskfor distant recurrence among invasive breast cancers [11] Arecent example of genomic signature for prognosis predictionof stages II and III colorectal cancer is ColoPrint [12] Inaddition to biomarkers genomics has led to discovery of newgenes not previously known to play a causal role in cancer andleads to cancer therapeutics [3] Some of the first sequencingattempts were focused in receptor tyrosine kinases (RTKs)in early 2000s The studies indicated mutations in BRAF in50 of melanomas [13] PIK3CA in 25ndash30 of breast andcolorectal cancers [14] and EFGR in 10ndash15 of non-smallcell lung cancers [15ndash17] Interestingly some of the findingshave resulted in a major impact on drug development andclinical treatment including the development of selectiveRAF and MEK inhibitors that have produced dramaticremissions in melanoma and the ability to target the use ofEGFR inhibitors to the subset of lung cancer patients whoderive benefit

3 Proteomic Challenge

By analogy democratization of protein studies by generatinga broader and deeper parts list to enable systems biology as

Advances in Medicine 3

well as deciphering protein interactions and biological sig-naling thatmediate physiological and pathological conditionscan have a major impact on medicine parallel to genomeproject [4] This can take the form of cataloging all of thecomponents functionalization of the individual proteins andputting the parts into relevant networks and circuitries andlearning how these networks collectively process and executetheir activities The genome project identified all the genesand by inference all proteins The challenge now is how theseentities are integrated into molecular mechanisms result-ing in phenotypes An integral component of this systemapproach is requirement for technologies that can systemati-cally identify and quantify all proteins protein isoforms andprotein interactions Proteins can take many different formsfor example posttranslational modifications (PTMs) single-nucleotide polymorphisms (SNPs) and alternative splicingof proteins resulting in numerous different structures ofindividual primary products making the dimensions ofproteome much larger than the human genome Genomicsalone cannot inform and delineate this protein diversity andits functional consequences

Mass spectrometry platforms are the workhorse of exper-imental proteomics for protein analyses and in the lastfive years there has been significant progress in sensitivitythroughput type and depth of proteome analysis The goalsof innovation are concentrated on increasing signalnoise inidentifying and sequencing peptides detecting and quanti-fying specific peptides with PTMs SNPs or splicing andenhancing the throughput to make assays useful for clinicaland population studies Another area is to be able to study thedynamics of how proteomes change (in concentration and instructure) in response to exogenous signals anddiseaseThereare two main proteomic platforms discovery-based versustargeted proteomics workflows Discovery-based proteomicswhich detect a mixture of hundreds to thousands of proteinshas been the standard approach in research to profile proteincontent in a biological sample which could lead to thediscovery of protein candidates with diagnostic prognosticand therapeutic values In practice this approach requiressignificant resources and time and does not necessarilyrepresent the goal of the researcher who would rather study asubset of such discovered proteins under different biologicalconditions In this context targeted proteomics is playing anincreasingly important role in the accurate measurement ofprotein targets in biological samples with the expectation ofelucidating the molecular mechanism of cellular function viathe understanding of intricate protein networks and pathways(Figure 1)

4 Advances in Discovery-BasedProteomics in Cancer

Proteomic technologies encompass a whole array of methodssuch as electrospray ionization-liquid chromatography tan-dem mass spectroscopy (ESI-LC-MS) matrix assisted laserdesorption ionization time of flight (MALDI-TOF) surfaceenhanced laser desorption ionization time of flight (SELDI-TOF) MALDIMS imaging (MALDI-MSI) two-dimensional

gel electrophoresis (2-DE) laser capture microdissection-MS (LCM-MS) and protein microarray which can be usedto derive important biological information to aid scientistsand clinicians in understanding the dynamic biology of theirsystem of interest such as in cancer [18ndash20]

41 ESI-LC-MS ESI-LC-MS is a technology which producesgaseous ions carrying analytes byapplication of an electricpotential to a flowing liquid in thepresence of heat Thiscauses formation of a spray upon high voltage causingdroplets tobecome electrically charged Droplets eventuallybecome unstable andexplode into even finer droplets andsubsequently cause desorption of the analyte ions whichare then passed to themass spectrometer [21] Most types ofmass detection systems can be used with ESI including TOFsion traps (ITs) and quadrupoles This technology can adoptvarious forms such as label-free or isotope labeling (metaboliclabeling 18O labeling) and chemical labeling (isotope codedaffinity tag (ICAT) isobaric tags (iTRAQ) tandem mass tag(TMT) and dimethyl labeling)

411 Label-Free Quantitation method is relatively inexpen-sive and much easier to perform This method uses spectralcounting by measuring the frequency with which the peptideof interest has been sequenced by the MS that is the numberof spectra for each peptide or protein being proportionalto the amount of protein in the sample This method canbe used for biomarker discovery which normally requireshigh sample throughput by comparing peak intensity frommultiple LC-MS data [22 23] Many studies have used label-free proteomics in cancer such as comparative proteomicanalysis of non-small cell lung cancer [24] and proteinsassociated with metastasis in paraffin-embedded archivalmelanomas [25] Application of this method for the analysisof changes in protein abundances in complex biologicalsamples has certain limitations For instancemeasuring smallchanges in the quantity of low-abundance proteins which isoften masked by sampling error can be difficult Howeverthe method has an excellent linear dynamic range of aboutthree orders of magnitude Additionally run to run analysisof the samples can exhibit differences in the peak intensitiesof the peptides as a result of sample processing requiringnormalization Additional issues may include experimentaldrifts in retention time and mz (mass-to-charge ratio)complicating accurate comparison of multiple LC-MS datasets chromatographic shifts as a result of multiple sampleinjections onto the same reversed-phase HPLC column andunaligned peak comparison resulting in large variabilityand inaccuracy in quantitation Also large volume of dataacquisition during LC-MSMS requires the data analysisof these spectra to be automated Computer algorithmshave been developed to solve these issues and automaticallycompare the peak intensity data between LC-MS samples ata comprehensive scale [26 27]

412 Isotopic Labeling Isotopic labeling can be either in vivoor in vitro by incorporating stable isotope into proteins orpeptides for comparative analysis Labeling techniques allow


Proteome

Targeted

workflow

Discovery

workflow

Proteins of interest Extract proteins

Protein digestion

into peptides

Proteotypic

peptides

identification

Develop SRM assay

Obtain SRM for

peptides of interest

Obtain spectra

Measure protein

concentration

Match spectra

to library

Peptide

identification

Protein

inference

ESI-LC-MS

Figure 1 Discovery-based versus targeted proteomics workflows using mass spectrometry

for multiplexing several samples to be analyzed and mitigateexperimental variability inherent in sample processing

(i) Metabolic Labeling In this in vivo method cells arecultured with media containing isotopically labeledamino acids (13C and 15N) which are incorporatedinto the proteome during cell growth In this methodwhich requires metabolically active cells samplesgrown with different labeled amino acids can bepooled for analysis [28]More recently a stable isotopelabeling with amino acids in cell culture (SILAC)mouse with a diet containing either the natural orthe 13C6-substituted version of lysine was introduced

[29] With no effect on growth or behavior MS anal-ysis of incorporation levels allowed for the determi-nation of incorporation rates of proteins from bloodcells and organs Mannrsquos group has also introducedsuper-SILAC method by combining a mixture of fiveSILAC-labeled cell lines with human carcinoma tis-sueThis generated hundreds of thousands of isotopi-cally labeled peptides in appropriate amounts to serveas internal standards for mass spectrometry-basedanalysis [30] Super-SILAC can play a role in expand-ing the use of relative proteomic quantitation meth-ods to further enhance our understanding of cancerbiology and a tool for biomarker discovery [31] Someof the disadvantages of metabolic labeling include


incomplete labeling in cell culturemediumwhichwillaffect accurate relative quantitation labeling of allproteins necessitating purification metabolic labilityof amino acid precursors and protein turnover [32]

(ii) Proteolytic 18O Labeling In this technique 18O isincorporated during proteolytic digestion [33] Dif-ferential 16O18O coding relies on the 18O exchangewhere two 16Oatoms are typically replaced by two 18Oatoms by enzyme-catalyzed oxygen-exchange in thepresence of H

2

18O The resulting 2ndash4Da mass shiftbetween differentially labeled peptide ions permitsidentification characterization and quantitation ofproteins from which the peptides are proteolyticallygenerated [34] 18O labeling bears at least two poten-tial shortcomings inhomogeneous 18O incorporationand inability to compare multiple samples within asingle experiment Unlike chemical labeling methodsuch as ICAT 18O labeling is simple with limitedsample manipulations It is much cheaper than ICATand SILAC comparing the price of reagents neededto label proteins SILAC may be the method ofchoice for labeling of cultured cells while 18O labelingmay be used for samples with limited availabilitysuch as human tissue specimens [34] In contrast toICAT 18O labeling does not favor peptides containingcertain amino acids (eg cysteine) nor does it requirean additional affinity step to enrich these peptidesUnlike iTRAQ18O labeling does not require a specificMS platform nor does it depend on fragmentationspectra (MS2) for quantitative peptidemeasurementsIt is amenable to the labeling of human specimens(eg plasma serum and tissues) which representsa limitation of metabolic labeling approaches (egSILAC) Taken together recent advancements inthe homogeneity of 18O incorporation improve-ments made on algorithms employed for calculating16O18O ratios and the inherent simplicity of thistechnique make this method a reasonable choicefor proteomic profiling of human specimens (egplasma serum and tissues) in the field of biomarkerdiscovery

(iii) Chemical LabelingAt least threemethods of chemicallabeling have been in use ICAT TMT and iTRAQThe ICAT reagent consists of three elements anaffinity tag (biotin) which is used to isolate ICAT-labeled peptides a linker that can incorporate stableisotopes and a reactive group with specificity towardthiol groups (cysteines) The reagent exists in twoforms heavy (contains eight deuteriums) and light(contains no deuteriums) leading to a difference inmolecular weight of 8Da between the two differentforms of the tag For quantifying differential proteinexpression the protein mixtures are combined andproteolyzed to peptides and ICAT-labeled peptidesare isolated utilizing the biotin tag These peptidesare separated by liquid chromatography The pair oflight and heavy ICAT-labeled peptides co-elute and

the 8Da mass difference is measured in a scanningmass spectrometerThe ratios of the original amountsof proteins from the two cell states are strictly main-tained in the peptide fragmentsThe relative quantifi-cation is determined by the ratio of the peptide pairsThe protein is identified by computer-searching therecorded sequence information against large proteindatabases [35ndash37] The main disadvantage of ICATlabeling technique is that it only binds to cysteineresidues which constitutes approximately 1 of theprotein composition Similar to 18O labeling ICAThas the limitation of having only two labels availableresulting in frequent experimentation and high cost ifmultiple samples need to be comparedMultiplexed sets of reagents for quantitative proteinanalysis have been developed which enable com-paring of a larger number of treatments includingthe development of the 4- or 8-plex isobaric tag(iTRAQ) [38] and the 2- or 6-plex TMT [39] labelingtechniques The former can compare up to eight andthe latter up to six samples in a single analysis Inthese methods both N-termini and lysine peptidesare labeled with different isobaric mass reagentssuch that all derivative peptides are isobaric andindistinguishable The different mass tags can only bedistinguished upon peptide fragmentation As eachtag adds an identical mass to a given peptide eachpeptide produces only a single peak during liquidchromatography and therefore only a single mz willbe isolated for fragmentation The different mass tagsonly separate upon fragmentation when reporterions that are typical for each of the different labels aregeneratedThe intensity ratio of the different reporterions is used as a quantitative readout One drawbackof these methods is that only a single fragmentationspectrum per peptide may be available while inquantitation-based MS1 scan multiple data pointsare sampled resulting in a lower overall sensitivitySome additional disadvantages of these techniquesare the inconsistencies in labeling efficiencies and thehigh cost of the reagents Use of standard operatingprotocols (SOPs) is recommended to achieve repro-ducible and reliable results with iTRAQ and there-fore alleviating potential variability as a consequenceof multistep sample preparations [40] iTRAQ mayalso have limitations in dynamic range experimentstypically report fold changes of less than 2 orders ofmagnitude From a purely technical point of viewthis may be perceived as a limitation of iTRAQ forquantitative proteomics [41ndash43]

It is clear that both labeled and unlabeledMS analyses willcontinue to have their uses Stable isotope labeling provideshigher quality data at the analysis end And with labelingmethods such as iTRAQ the labels are introduced so latein the process that the experiment can be performed muchfaster than in earlier labeling methods Even so it is a lotmore challenging technically than label-free techniques andalso prone to systematic errorsThe choice of isotope labeling


technique is highly dependent upon experimental design thescope of a particular analysis and the sample or system beinganalyzed

At the technology level there is still room for progressPerformance in proteomics can be characterized by threefactors ion injection efficiency cycling speed and detectorsensitivity While the detectors are sensitive already at thelevel of detecting a single ion the process of funneling theions to the detector can be improved Despite improvementsin electrospray ionization injection the majority of ions arestill lost on their way to the detector In addition irrelevantmolecules cannot be filtered out which result in generatinga lot of noise Improvements in these areas can enhancethe signalnoise ratio Furthermore speeding up the cyclingrate (the number of spectra per second) can increase themeasurement depth Given the wide dynamic range this canin turn result in quantifyingmaximumproteins present in thesample [44]

42 MALDI-TOF MALDI-TOF is a MS platform in whichtime of flight mass analyzer is usually coupled with MALDIIons are accelerated by an electric field followed by ionseparation according to their mz ratios by measuring thetime it takes for ions to travel through flight tube Specificallythe mz ratio of an ion is proportional to the square ofits drift time with heavier ions taking longer to travel Thesample for MALDI is uniformly mixed in a large quantity ofmatrix The matrix absorbs the ultraviolet light and convertsit to heat energy A small part of the matrix heats rapidlyand is vaporized together with the sample [45] MALDI-TOF-MS has become a widespread and versatile method toanalyze a range ofmacromolecules in awide range of samplesIts ability to desorb high-molecular-weight molecules andits high accuracy and sensitivity combined with its widemass range (1ndash300 kDa) makeMALDI-TOF-MS amethod ofchoice for the clinical chemistry laboratory for the identifica-tion of biomolecules in complex samples including peptidesproteins oligosaccharides and oligonucleotides [46 47]Thefirst reports of MALDI-TOF-MS biochemical analysis werepublished in the late 1980s from Karas and Hillenkamp lab[45] Although being relatively young compared to otheranalytical techniques using mass spectrometry there hasbeen an enormous increase in the publication of MALDI-TOF-MS methods and applications in the literature WhileESI can efficiently be interfaced with separation techniquesenhancing its role in the life and health sciences MALDIhowever has the advantage of producing singly charged ionsof peptides and proteins minimizing spectral complexity

43 SELDI-TOF-MS SELDI-TOF-MS technique was intro-duced in 1993 by Hutchens and Yip [48] and later com-mercialized by Ciphergen Biosystems in 1997 SELDI-TOF-MS is a variation of MALDI that uses a target modified toreach biochemical affinity with the sample proteinsThere aresome differences between the two techniques InMALDI thesample is mixed with the matrix molecule in solution anda small amount of the mixture is deposited on a surface todry This makes the sample and matrix cocrystallized after

the solvent evaporated On the other hand in SELDI themixture is spotted on a surface modified with a chemicalfunctionality such as binding affinityThere are different typesof chemicals and substances bound to the protein arraysincluding antibodies receptors ligands nucleic acids carbo-hydrates or chromatographic surfaces (ie cationic anionichydrophobic or hydrophilic) Still wet some proteins in thesamples would bind to the modified surface while the otherswill bewashed offThen thematrix is applied to the surface forcrystallization with the sample peptides In the binding andwashing off steps the surface-bound proteins are left for anal-yses Samples spotted on an SELDI surface are analyzed withTOF mass spectrometry (TOF-MS) [49 50] The strength ofthis technology is the integration of on-chip selective capturerelative quantitation and partial characterization of proteinsand peptides The differential expression data obtained fromthis technology has been used for identification of biomarkercandidates for various cancer types such as prostate [51 52]pancreas [53ndash55] lung [56ndash58] breast [59ndash61] melanoma[62] colon [63 64] ovarian [65ndash67] and liver cancers[68 69]

44 MALDI-MSI MALDI-MSI is a powerful techniquewhich allows investigating the distribution of proteins andbiomolecules directly from a tissue section [70 71] Thistechnique also permits investigation of the spatial and tem-poral distribution of biomolecules such as phospholipidswithout the need for extraction purification and separationprocedures of tissue sections [72] MALDI-MSI can help inmolecular diagnosis on tissue directly in the environment ofthe tumors and can detect the tumor boundary or infiltrationof adjacent normal tissue It could also help to detect the earlystage of pathology that presents no histological modificationsand to prevent tumor recurrence at the site of surgicalresection One of the advances ofMSI is the correlation of theMALDI images with histological information MALDI-MSIsoftware [73] superimposes theMALDI images over amacro-scopic or microscopic optical image of the sample takenbefore MALDI measurement MALDI-MSI has been used inclinical proteomics for biomarker discovery in a variety ofdiseases including cancer [74ndash78] Development of SOPs andstandardization of protocols for sample collection storagedata acquisition and enhancement of imaging resolutionsand 3D tumor mapping are still needed to further improveits utility [79 80] Also the present levels of sensitivityallow the detection of a small group of cells but are notsufficient to detect discrete modification at a single celllevel A major advance for MALDI-MSI will be its couplingwith positron emission tomography X-ray computed tomog-raphy instrumentation and MRI for both preclinical andclinical research The complementarities between noninva-sive techniques and molecular data obtained from MALDIMS imaging will result in a more precise diagnosis [81]Ultimately comparing the MRI image of a tumor and theimage generated by MALDI-MSI at a molecular level willprovide a comprehensive data set for diagnosis and treatmentselection


45 2-DE 2-DE or two-dimensional gel electrophoresistechnology was a pivotal turning point in the field of sep-aration and has been shown to be a reliable and efficientmethod for separation of proteins based on mass and charge[82] High resolution two-dimensional polyacrylamide gelelectrophoresis (2D-PAGE) can resolve up to 10000 proteinspots per gel This technique has been used in humantissue plasma and serum proteome analysis with or withoutprior fractionation [83ndash86]Visualization of resolved proteinsin the gel can be performed by staining methods suchas Coomassie blue and silver staining [87 88] Some ofthe recent advances in silver staining products make itcompatible with MS analysis too To enable direct com-parison of different mixtures of proteins differential in-gelelectrophoresis (DIGE) has been developed which permitssimultaneous comparison of labeled proteins in differentmixtures In a typical experiment two samples are labeledwith different fluorescence dyes (Cy3 and Cy5) and mixedprior to electrophoresis and run in parallel with an internalstandard labeled with a third dye (Cy2) for quantitativeanalysis [89 90]

For identification purposes gel-separated proteins canbe digested into peptides Analysis of the peptides can thenprovide a peptide mass fingerprint (PMF) which can besearched against theoretical fingerprints of sequences inprotein databases Alternatively peptides can be sprayedinto a tandem mass spectrometer (ESI) as they elute offa liquid chromatography (LC) column The data can besearched for protein sequence and analyzed by the applicationof algorithms and comparison with theoretical productionspectra of proteins in databases [91 92]

2D-PAGE is a low throughput technology labor intensivewith low dynamic range and prone to gel-to-gel variabilityAlthough DIGE has shown improved accuracy it is still arelatively low throughputmethod It can be used in areas suchas biomarker discovery where high throughput processing ofsamples is not required [93]

46 LCM-MS LCM-MS has proven an effective technique toharvest pure cell populations from tissue sections Becauseproteome varies in different cells the advent of laser capturemicrodissection has expanded the analytical capabilities ofmicroproteomics by enabling protein analysis fromextremelysmall samples A typical protocol uses nanoscale liquid chro-matographytandem mass spectrometry (nano-LC-MSMS)to simultaneously identify and quantify hundreds of proteinsfrom LCMs of tissue sections from small tissue samplescontaining as few as 1000 cells The LCM-dissected tissuesare subjected to protein extraction reduction alkylationand digestion followed by injection into a nano-LC-MSMSsystem for chromatographic separation and protein identifi-cationThe approach can be validated by secondary screeningusing immunological techniques such as immunohistochem-istry or immunoblots [94]

LCM has significantly improved the analytical capabil-ities of comparative proteomic technologies to the extentthat 2D-DIGE and quantitative gel-free mass spectrometryapproaches have been coupled to LCMfor proteomic analyses

of distinct pure cell populations [95ndash101] The LCM tech-nology allows for miniaturization of extraction and isolationand detection of hundreds of proteins (100ndash300 proteins)fromdifferent cell populations containing as few as 1000 cellsAdditionally it can detect and verify robust protein expres-sion differences between different cell populations Unliketraditional proteomic technologies the LCM procedurerequires as little as 1-2120583g of protein However each step of theprocedure requires greater care as the sample size decreasesProtein losses during extraction and separation becomemore significant as the protein detection limit (lt075 120583g)is approached [94] Other methods such as punch biopsycan be used to microdissect tissues for proteomic analyses[102] but because the three-dimensional view of boundariesof tissue structures is limited punch biopsies can sampleadjacent regions that are not of interest In a recent paperpublished byMueller et al [103] the authors claimed that dataderived from nonmicrodissected glioblastoma multiforme(GBM) can result in inaccurate correlations between genomicand proteomic data and subsequent false classifications Thisis because molecular signals could be masked where sampletumor content is low or where the signal is strong in thestromal cells Mueller et al investigated 39 glioblastomasamples taken from tissue previously analyzed by the TCGAproject Using reverse phase protein array (RPPA) theymeasured the levels of 133 proteins and phosphoproteinscomparing LCM and non-LCM samples finding differencesin 44 percent of the analytes between the two types Theyspecifically investigated in more depth the genomic andproteomic data for epidermal growth factor receptor (EGFR)and phosphatase and tensin homolog (PTEN) two clinicallyimportant proteins in glioblastoma While the researchersobserved in both sample types increased EGFR protein andphosphoprotein levels in patients with increased EGFR genecopy number they observed the increase in EGFR phospho-rylation expected in carriers of EGFR mutations only in theLCM samples In the case of PTEN the researchers observedthe expected decrease in PTEN levels in tumors with deeploss of PTEN or PTEN mutations only in the LCM samplesAdditionally they found the expected correlation betweenEGRF phosphorylation PTEN levels and phosphorylationof AKT only in the LCM samples which is regulated by theformer two proteins Mueller et al also examined proteomicglioblastoma data previously generated by TCGA using non-LCM samples again failing to observe the expected correla-tion between PTEN copy number or mutational status andPTENprotein levelsThe authors recommend careful upfrontcellular enrichment in biospecimens that form the basisfor targeted therapy selection [103] Further developmentsin LCM technology should facilitate effective sampling ofspecific cellular subtypes from tissue in a high throughputmanner

47 Protein Microarray Protein microarray is a highthroughput tool for studying the biochemical activities ofproteins tracking their interactions and determining theirfunction on a large scale [104] Its main strength is thatlarge numbers of proteins can be tracked in parallel The


chip usually consists of a support surface such as a glassslide nitrocellulose membrane bead or microtiter plate towhich an array of capture proteins is boundThe commonesttype of protein microarray may contain a large numberof spots of either proteins or their ligands arranged in apredefined pattern arrayed by robots onto coated glassslides microplates or membranes The array may consist ofantibodies to bind proteins of interest [105] enzymes thatwill interact with substrates or substrates or ligands that willinteract with applied proteins Therefore protein microarrayformats can be divided into two major classes dependingon what is immobilized on the support surface In forward-phase protein array (FPPA) the capture antibody is firstimmobilized on a solid surface to capture the correspondingantigen in a test sample The captured analyte is thendirectly detected with a fluorescent dye-conjugated detectionantibody or detected indirectly with the detection antibodyfollowed by a fluorescent dye-conjugated second antibody[106] In this method identification of a capture and adetection affinity reagent can be time-consuming To bypassthe requirement for two affinity reagents reverse phaseprotein array (RPPA) may provide an alternative solution InRPPA test samples that could run into thousands are printedon the slide directly and detected with dye-conjugatedantibodies [107] RPPA assays are commonly used in tissuemicroarray and cell and tissue lysate microarray WhileRPPA provides a high throughput platform the specificitymight be compromised to some degree owing to the use ofsingle detection antibodies

One technical downside to producing a reliable array isshelf-life Most protein arrays use antibodies to deposit andbe immobilized on the support surface which can denaturethe antibody and affect its recognition properties Anotherbottleneck that chip manufacturers face is getting good qual-ity and specific antibody against every protein in the humanproteome which is a gigantic task In addition to limitedinventory of specific antibodies to PTMs (such as phospho-rylation and glycosylation) generation of high throughputprotein expression systems and purification including thosewith PTMs required for spotting the complete proteomeunder study is another challenge or may suffer from lack ofreproducibility Establishing standard criteria for array pro-duction and data normalization using noise models varianceestimation and differential expression analysis techniqueswould improve interpretation of microarray results [108]

The challenges in producing proteins to spot on thearrays fueled the development of a novel approach to proteinmicroarrays technology called nucleic acid programmableprotein array (NAPPA) which uses cell-free extracts totranscribe and translate cDNAs encoding target proteinsdirectly onto glass slides This approach eliminates the needto purify proteins avoids protein stability problems duringstorage and captures sufficient protein for functional studies[109 110] In recent studies NAPPA was coupled with MSand used for several applications including the identificationof peptide sequences for potential phosphorylation as wellas a high throughput method for the detection of protein-protein interactions [111] Moreover the challenges of con-structing solid-surface arrays holding thousands of proteins

with different properties raised interest in protein-interactionassays in solution Suspension-bead assays are particularlyflexible and as a result suspension platforms were developedsuch as the Bio-Plex system fromBio-Rad Laboratorieswhichuses Luminexrsquos bead-based xMAP technology [112] as doesthe LiquiChip system fromQiagen Instruments Suspension-bead arrays are flexible enough to tackle any sort of protein-ligand interaction by simply coupling the required proteinsor ligands to different bead populations Luminex beadsfor example enable simultaneous quantitation of up to 100different biomolecules in a single microplate well Ratherthan a flat surface Bio-Plex assays make use of differen-tially detectable bead sets as a substrate capturing analytesin solution and employ fluorescent methods for detection[113]

5 Advances in Targeted-BasedProteomics in Cancer

The field of biomarkers in particular has benefited signifi-cantly from application of proteomic platforms over the lastdecade or more with the goal of identifying simple nonin-vasive tests that can indicate cancer risk allow early cancerdetection classify tumors so that the patient can receive themost appropriate therapy and monitor disease progressionregression and recurrence A variety of biospecimens suchas tissue proximal fluids and blood have been interrogatedfor protein or peptide markers identification Thousands ofpublications have explored the potential use of individualproteins or collections of proteins as cancer biomarkers andhave produced promising results [114 115] One study byPolanski and Anderson [116] has identified gt1261 proteinbiomarker candidates for cancer alone However only 23protein plasma biomarkers have cleared the US Food andDrugAdministration (FDA) since 2003 as clinical biomarkersaveraging lt2 proteins per year over the last 12 years whileassays for at least 96 analytes have been developed and usedas laboratory-developed tests (LDTs) [115] Despite recenttechnical advances there are still huge analytical challengesfor clinically relevant identification of biomarkers in serumor plasma This is compounded by the lack of analytical vali-dation of a platform(s) for the precise and accurate measure-ments of identified analytes in a smaller set of clinical samplesprior to proceeding to costly and time-consuming large-scaleclinical trials (Figure 2) The Clinical Proteomic TechnologyAssessment for Cancer (CPTAC 1) of the NCI developed theinnovative concept of biomarker ldquoverificationrdquo which bridgesdiscovery and validation This pipeline has the potentialto enable delivery of highly credentialed protein biomarkercandidates for clinical validation (Figure 3) Targeted pro-teomic technologies such as multiplexed MS protein arraysand enzyme-linked immunosorbent assays (ELISAs) can fillthis bridging space Verification of candidates relies uponspecific multiplex quantitative assays optimized for selectivedetection of biomarker candidates and is increasingly viewedas a critical step in the protein biomarker developmentpipeline that bridges unbiased biomarker discovery to clinicalqualification [117 118]


protein biomarkers

Need an assay for each candidateBottleneck

Clinical testing

FDA approval

been approved by FDA since 2003

100s of candidate

sim23 protein plasma biomarkers have

Figure 2 The assay bottleneck prevents potential protein diagnos-tics from becoming clinically useful

51 Selected ReactionMonitoring-MS (SRM-MS) andMultipleReaction Monitoring-MS (MRM-MS) SRM-MS is a targetedtechnique that is completely different from the mass spec-trometry approaches widely used in discovery proteomicsSRM is performed on specialized instruments that enabletargeting of specific analyte peptides of interest and providesexquisite specificity and sensitivity [119ndash121] SRM-MS is anonscanning mass spectrometry technique performed ontriple quadrupole-like instruments (QQQ-MS) and in whichcollision-induced dissociation (CID) is used as a means toincrease selectivity In SRM experiments two mass analyzersare used as static mass filters to monitor a particularfragment ion of a selected precursor ion Unlike commonMS-based proteomics nomass spectra are recorded in a SRManalysis Instead the detector acts as counting device for theions matching the selected transition thereby returning anintensity value over time In MRM multiple SRM transitionscan be measured within the same experiment on the chro-matographic time scale by alternating between the differentprecursorfragment pairs Typically the triple quadrupoleinstrument cycles through a series of transitions and recordsthe signal of each transition as a function of the elution timeThe method allows for additional selectivity by monitoringthe chromatographic coelution of multiple transitions for agiven analyte [122 123] A schematic representation ofMRM-MS-based assay workflows (plusmn immunoaffinity enrichment ofproteins or peptides) is depicted in Figure 4 and described inthe following sections

52 MRM-MS-Based Assay Development for Protein Verifica-tion MRM-MS method for quantification of biomoleculeshas been long in use (eg drug metabolites [124 125]hormones [126] protein degradation products [127] andpesticides [128]) with great precision (CV lt 5) but has only

recently been adopted for protein and peptidemeasurementsStable isotope dilution (SID) multiple reaction monitoringMS (SID-MRM-MS) has emerged as one of the powerfultargeted proteomic tools in the past few years MRM massspectrometry is being rapidly adopted by the biomedicalresearch community as shown by increase in the numberof publications in this area over the past decade (Figure 5)It has the advantage of accurately calculating protein con-centrations in a multiplexed and high throughput mannerwhile avoiding many of the issues associated with antibody-based protein quantification [117] SID-MRM-MS proteinassays are based upon the quantitation of signature trypticpeptides as surrogates that uniquely represent the proteincandidates of interest [129 130] To improve the specificity ofthe quantitative measurement for targeted analytes in MRM-based assays a selection of three to five peptides per protein isselected [131] Moreover known quantities of synthetic stableisotope-labeled peptides (heavy peptides) corresponding toeach endogenous peptide are used as internal standardpeptides (ie stable isotope-labeled internal standards orSIS) These SISs are identical to their endogenous analytepeptide counterparts with the exception of their masses(usually 6ndash10Da more) For quantitation specific fragmention signals derived from the endogenous unlabeled peptidesare compared to those from the spike-in SISs as ratios andare used to calculate the concentration of that protein [130131] In SID-MRM-MS the presence of SIS can calculatemore accurate ratios with high sensitivity and across a widedynamic rangeThe absence of an endogenous peptide signaltypically means that the concentration of the peptide inthe sample is below the detection limit of the instrumentAdditionally the amount of SIS added should be optimizedempirically in a preliminary study as this depends on theproteinrsquos individual relative abundance within a sample Highsensitivity and precision combinedwith specific quantitationin amultiplex fashion makeMRMassays attractive for trans-lational and clinical research [132 133] Targeted proteomicshas also been recognized by the journal Nature Methods asthe method of the year in 2012 [134]

There are many advantages to MRM-based assays whichovercome major limitations of conventional protein assaytechnologies such as Western blot IHC and ELISA Suchadvantages include moderate-to-high throughput capabilityreadily multiplexed assays standards being readily synthe-sized interferences being avoidable use of internal stan-dards for high interlaboratory reproducibility and quan-titation Additionally MRM assays have high molecularspecificity which does not require immunoassay-grade anti-bodies (those proteins for which no affinity reagent has beendeveloped are accessible for routine quantification includingisoforms and PTM analytes) and there is a large deployedinstrument base However MRM assays are not easy togenerate de novo and require expertise in addition to lackof validated reagents for most proteins [135 136] Over thepast few years the methods used to quantify proteins byMRM have steadily evolved and have been widely deployedIt has also been suggested recently that considering the vastmajority of protein identifications claimed from biologicalsamples are still derived fromWestern blotting itmay be time


Characterization Verification Clinical validation(qualification)

10sbiospecimens

LC-MSMS

100sbiospecimens

1000sbiospecimens

MRM -MS Immunoassay

Panel of biomarkers

CPTAC

gt10000 analytes sim100s analytes sim10 analytes

sim10s of candidates

sim100s ofcandidates

Bios

peci

men

s

Figure 3 The incorporation of verification step into the NCI-CPTC pipeline bridging discovery and qualification

Native tryptic peptide

Peptide affinity enrichment

StandardHeavy isotope-labeled peptide spike-in

Analyte

Internalstandard

Inte

nsity

Inte

nsity

StandardHeavy isotope-labeled protein spike-in

Protein affinity enrichment

Native protein

StandardHeavy isotope-labeled protein

Protein digestion

Analyte

Internalstandard

Inte

nsity

Inte

nsity

MRM-MS

MRM-MS

Trypsin digesti

on

mz

mz

Figure 4 MRM-MS-based assay workflows (plusmn immunoaffinity enrichment of proteins or peptides) SISCAPA workflow using proteolyticpeptides as surrogates for their respective proteins as illustrated in the top panel of the schematic is a sensitive approach to measure proteinconcentrations using immunoaffinity enrichment of surrogate peptides prior toMRM-MS To achieve quantitation of the targeted protein(s)they are digested to component peptides using an enzyme such as trypsin A stable isotope standard (SIS blue asterisk) is added to the sampleat a known concentration for quantitative analysis The selected peptides are then enriched using anti-peptide antibodies immobilized on asolid support Following washing and elution from the anti-peptide antibody the amount of surrogate peptide is measured relative to thestable isotope standard using targeted mass spectrometry Alternatively an assay can start with immunoaffinity enrichment of intact targetproteins from biospecimens using an internal stable isotope-labeled protein standard (red asterisk such as PSAQ approach) and an antibodyas illustrated in the bottom panel followed by proteolysis and final quantitation of the target


0

50

100

150

200

250

300

350

400

2002 2004 2006 2008 2010 2012Year

Publ

icat

ion

Figure 5 Increase in number ofMRMpublications in PubMed overthe past decade

that journal reviewers request thatWestern blotting results orat least the assays that support these results be validated byMS [136]

Recently in a landmark paper in Nature Methodsresearchers have demonstrated the feasibility of both thedevelopment and application of MRM to reproducibly mea-sure human proteins in breast cancer cell lysate across threelabs in two countries in two continents The internationalresearch collaboration representing investigators from FredHutchinson Cancer Research Center (Seattle WashingtonUSA) Broad Institute (Cambridge Massachusetts USA)and a team composed of researchers from the Korea Insti-tute of Science and Technology and the Seoul NationalUniversity College of Medicine (both Seoul Republic ofKorea) reported the development and application of 645assays representing 319 proteins The assays were deployedin multiplexed fashion in groups of at least 150 peptidesto quantify proteins in a panel of breast cancer-related celllines Researchers were able to show that targeted massspectrometry-based proteomic assay can be easily imple-mented anywhere with minimal adjustments while main-taining a high level of performance (accuracy precisionand reproducibility) all essential for clinical implementationAnalyses of the results were able to recapitulate knownmolecular subtypes ascribed to breast cancer and also showedthe added value of integrative analysis in identifying puta-tive disease genes This study demonstrates the tremendouspromise on targeted proteomics to meet the interest ofbiologists and medical researchers and addresses the abilityto replicate results from labs [137]

While adoption of targetedMS approaches such as MRMto study biological and biomedical questions is well underwayin the proteomics community there is no consensus on whatcriteria are acceptable and little understanding of the impactof variable criteria on the quality of the results generatedThere is a wide range of criteria being applied to saythat an assay has been successfully developed Publications

describing targeted MS assays for peptides frequently do notcontain sufficient information for readers to establish confi-dence that the tests work as intended or to be able to applythe tests described in their own labs To address these issuesa workshop was held recently at the NIHwith representativesfrom the multiple communities developing and employingtargetedMS assays Participants discussed the analytical goalsof their experiments and the experimental evidence neededto establish that the assays they develop work as intendedand are achieving the required levels of performance Usingthis fit-for-purpose approach the group defined three tiersof assays distinguished by their performance and extentof analytical characterization Participants also detailed theinformation that authors need to provide in theirmanuscriptsto enable reviewers and readers to clearly understand whatprocedures were performed and to evaluate the reliability ofthe peptide or protein quantification measurements reported[138]

53 Enriching Analytes to Increase the Sensitivity of MRM-MSAssays Many analytes require enrichment for MRM-basedquantification of endogenous levels such as most proteinsin plasma regulatory and signaling proteins in cells andtissues and PTMs Many clinically relevant biomarkers suchas prostate-specific antigen (PSA) and the troponins (Tns)are expressed in low ngmL level in plasma below the lowerlimit of detection of a QQQ-MS There have been reportsof improving sensitivities by abundant protein depletionstrategy combined with minimal fractionation or instrumentmodification which could further improve the LODLOQ ofMRMmeasurements For instance antibody-based depletioncolumns combined with minimal fractionation of trypticpeptides have been shown to improve sensitivity for proteinsin plasma [131 139 140] Furthermore enhanced sensitivityfor SRM-MS targeted proteomics through enhanced iontransmission efficiency using a dual stage electrodynamic ionfunnel interface has been reported [141] In another develop-ment Fortin et al usedMRM cubed (MRM3) which enabledtargeting protein biomarkers in the low nanogrammilliliterrange in nondepleted human serum using a simple two-step workflow This strategy takes advantage of the capabilityof a hybrid QQQ-MSlinear IT (LIT) mass spectrometer tofurther fragment the product ions monitored in Q3 [142]

54 PRISM PRISM reported very recently by Shi et al[143] is an antibody-free strategy that involves high pressurehigh resolution separations coupled with intelligent selectionand multiplexing (PRISM) for sensitive selected reactionmonitoring- (SRM-) based targeted protein quantificationThe strategy uses high resolution reversed-phase liquid chro-matographic separations for analyte enrichment intelligentselection of target fractions via online SRM monitoring ofinternal standards and fraction multiplexing before nanoliq-uid chromatography-SRM quantification [143] This methodhas shown a major advance in the sensitivity of targetedprotein quantification without the need for specific-affinityreagents Applying this method to human plasmaserumdemonstrated accurate and reproducible quantification of


proteins at concentrations in the 50ndash100 pgmL range Excel-lent correlation between PRISM-SRM assay and those fromclinical immunoassay for the prostate-specific antigen levelwas also noted [143] A disadvantage of PRISM-SRM relativeto SISCAPA (see Section 57) is reduced analytical through-put as a result of fractionation However even with limitedfraction concatenation moderate throughput (sim50 sampleanalyses per week depending upon experimental details) canbe achieved For example when quantifying a relatively largenumber of proteins (ie 100) all 96 fractions may containtarget peptides however these fractions can still be carefullycombined into 12 multiplexed fractions based on peptideelution times to achieve moderate throughput [143]

55 Parallel Reaction Monitoring (PRM) PRM is a newtargeted proteomics paradigm centered on the use of nextgeneration quadrupole-equipped high resolution and accu-rate mass instruments [144] In PRM made possible by theQ Exactive the laborious development of SRM assays maybe avoided This instrument is similar to QQQ except thatthe third quadrupole is replaced with a high resolutionhigh mass accuracy Orbitrap mass analyzer Whereas inSRM all transitions are monitored one at a time the QExactive allows parallel detection of all transitions in a singleanalysis Because all transitions can be monitored with PRMone does not need to carry out laborious optimizationsto generate idealized assays for selected transitions [145]This will bring additional specificity because all potentialproduct ions of a peptide instead of just 3ndash5 transitionsare available to confirm the identity of the peptide [146]and that because PRM monitors all transitions one neednot have prior knowledge of or preselect target transitionsbefore analysis Also because many ions would be availablethe presence of interfering ions in a full mass spectrumwould be less problematic to overall spectral quality thaninterference in a narrow mass range [144] In addition theQ Exactive instrument is very flexible Since one instrumentcan do both discovery and targeted analysis this will allowresearchers to use a discovery-based approach to identifya shortlist of interesting proteins and then use a targetedapproach to follow those targets with high sensitivity undervarious conditions all in a single experiment [145]

56 SWATHAcquisition SWATH (sequential window acqui-sition of all theoretical mass spectra) acquisition is a noveltechnique that is based on data-independent acquisition(DIA) which aims to complement traditional discovery MS-based proteomics techniques and SRM methods [147] Inthis strategy systematic queries of sample sets are madefor the presence and quantity of any protein of interest Itconsists of using the information available in fragment ionspectral libraries to mine the complete fragment ion mapsgenerated using a data-independent acquisition method InSWATH acquisition the first quadrupole sequentially cycles25Da precursor isolation windows (swaths) across the massrange of interest and time-resolves fragment ion spectrafor all the analytes detectable [147 148] and therefore the

potential to perform a significant larger number of SRM-like experiments concurrently In SWATH-MS approach theinstrumental scanning speed has to be fast enough to allowacquiring an adequate number of data points across thetypical chromatographic peak such that ion chromatographycan be reconstructed with acceptable signal-to-noise ratioSWATH acquisition however has a major drawback inthat the data is incompatible with conventional databasesearching and it seems a deconvolution algorithm to processthe SWATH-MS data for database searching has not beenachieved There are a number of challenges in designing adeconvolution algorithm to process such complex data [148]

57 Protein Capture Enrichment An alternative approach toimmunoaffinity depletion (negative enrichment) and frac-tionation strategies is positive enrichment strategies whichhave also been extensively explored in proteomics for betterdetection of low-abundance peptides or proteins Thesestrategies include affinity enrichment of peptides or proteinsand chemical enrichment of different subsets of the proteomeincluding PTMs such as N-linked glycopeptides and phos-phopeptides Many of the enrichment strategies reported forgeneral proteomics are also applicable to SRM applications

Immunoaffinity capture of target proteins is proba-bly the most effective method for sensitive detection oflow-abundance proteins in complex samples [149 150]The immunoaffinity enrichment method coupled with MShas provided quantification of proteins in the low ngmLrange [151ndash153] Nicol et al [151] have demonstrated theimmunoaffinity-SRM approach for the quantification of pro-tein biomarkers forwhich ELISA assays are not available fromlung cancer patients by using antibodies to enrich proteinsfollowed by digestion of captured proteins and subsequentSRM analysis This approach enabled the quantification ofmultiple protein biomarkers in lung cancer and normalhuman sera in the low ngmL range In a different studyKulasingam et al reported the enrichment of endogenousPSA protein from 5 120583L of serum with a monoclonal antibody(mAb) followed by product ionmonitoring using a linear ion-trapmass spectrometer [152] demonstrating quantification ofPSA down to less than 1 ngmL level with acceptable CVsRecently Niederkofler et al [154] developed an assay thatincorporates a novel sample preparation method for disso-ciating IGF1 from its binding proteins The workflow alsoincludes an immunoaffinity step using antibody-derivatizedpipette tips followed by elution trypsin digestion and LC-MSMS separation and detection of the signature peptidesin SRMmode The resulting quantitative mass spectrometricimmunoassay (MSIA) exhibited good linearity in the rangeof 1 to 1500 ngmL IGF1 intra- and interassay precision withCVs of less than 10 and lowest limits of detection of 1 ngmL[154] Additionally intact protein targets from samples alongwith their recombinant heavy isotope-labeled internal pro-tein standards such as protein standard absolute quantifi-cation (PSAQ) approach [155ndash157] can be immunoprecip-itated with antibodies prior to proteolysis and SID-MRM-MS In 2004 Nelson et al described an immunoaffinity-based MALDI-TOF MS method for quantification of IGF1


from human plasma samples [158] The limit of detectionfor the IGF-1 MSIA was evaluated and established to beapproximately 15 pM

An important application of protein enrichment methodcould be in detection and measurement of mutant proteinsGenome-wide analysis has shown that solid tumors typicallycontain 20ndash100 protein-encoding genes that are mutated[159] A small fraction of these changes are ldquodriversrdquo as cancercausing events the remainder is ldquopassengersrdquo providing noselective growth advantage [160] These proteins could besource of unique biomarker candidates Unlike wild typeprotein biomarkers the mutant proteins are produced onlyby tumor cells Moreover they are not simply associatedwith tumors but in the case of driver gene mutations aredirectly responsible for tumor generation [161] A largenumber of disease-causingmutations aremissensemutationsthat alter the encoded proteins only subtly the detection ofwhich can be very complicated mainly because there are fewantibodies that can reliably distinguish mutant from normalversions of proteins Because many different mutations canoccur in a single cancer-related gene it is necessary todevelop a specific antibody for each possible mutant epitopewhich can be costly and time-consuming Another approachmeasures the activity ofmutant proteins but it is not generallyapplicable because no activity-based assays are available formost proteins MS has been previously used to detect andquantify somatic mutations at the DNA level but not at theprotein level [162] To address this need Vogelstein lab [161]developed an MS approach that could identify and quantifysomatically mutant proteins in a generally applicable fashionUsing Ras and its mutants (most mutations clustered atresidues 12 or 13 of the protein) they immunoprecipitated theRas protein from human colorectal cancer cell line SW480and it was then eluted and concentrated More than 90of the total cellular K-Ras protein was captured successfullyfrom the lysates and eluted from the beads Upon digestionand inclusion of heavy isotope-labeled synthetic peptides asinternal controls SRM was performed The list of parentand product ions that were used for SRM included thoserepresenting trypsinized normal (WT) Ras protein as wellas the two most common mutants of Ras in pancreaticcancers K-RasG12V andG12DThe summed peak intensitiesfor the ions corresponding to the heavy and light versionsof peptides representing WT and mutant proteins showedthat they were related linearly to abundance across morethan two orders of magnitude (10ndash2000 fmol 2119877 gt 099 forWT and mutant proteins) [161] Similarly they found thatmutant Ras proteins could be detected and quantified inclinical specimens such as colorectal and pancreatic tumortissues as well as in premalignant pancreatic cyst fluids Inaddition to answering basic questions about the relative levelsof genetically abnormal proteins in tumors this approachcould prove useful for diagnostic applications One potentiallimitation of this method is its sensitivity Results fromVogelstein report estimated that SRM can be used to detectmutant proteins reliably when they are present at levels aslow as 25 fmolmg of total protein (sim6000 cells) Howeverthis sensitivity may be inadequate to detect mutant proteins

in some clinical samples such as sputum serum or urine[161] As indicated abovewhile the affinityMS approaches canimprove the sensitivity for quantification of low-abundanceproteins or mutants the major drawback of protein cap-ture method is that antibodies are typically not availablefor newly discovered biomarker candidates The need fordifferent antibodies for individual proteins inherently limitsthe multiplexing power and the throughput for quantifying alarge number of target proteins when employing affinity MSapproaches

58 Peptide Capture Enrichment An alternative for proteinenrichment is to affinity capture for target peptides using anti-peptide antibodies in which target peptides act as surrogatesfor protein quantification Anderson et al [163] introducedthis method in 2004 using immobilized anti-peptide poly-clonal rabbit antibodies to capture and subsequently elutethe target peptides of four blood plasma proteins alongwith isotope-labeled peptide standards forMS quantificationThis strategy termed stable isotope standards and captureby anti-peptide antibodies (SISCAPA) and recent studiessuggested that more than a 1000-fold enrichment can beachieved for plasma-digested peptides [164] with low ngmLLOQs in plasma with CVs lt 20 [141] If stable isotope-labeled recombinant protein standard is available it can beadded to the biofluid in the beginning of the assay workflowto control for proteolytic efficiency Upon digestion of thebiospecimens and addition of known amounts of SIS bothspike-in and endogenous peptides are specifically enrichedand their relative amounts are quantitated by MRM-MSMS detector provides quantitation through peak areas fortargeted mz values The SISCAPA strategy has been furtheroptimized using a magnetic-bead-based platform which canbe performed in an automated fashion using 96-well plates[164 165] This strategy was implemented in a nine-targetpeptide multiplexed SISCAPA assay in which more sensitivedetection in the 50ndash100 pgmL range of protein concentrationwas reported when plasma volume was increased from 10 120583Lto 1mL for SISCAPA enrichment [166]

One advantage of mAbs over polyclonal antibodies inSISCAPA assays is their higher specificity and as suchSchoenherr et al demonstrated that mAbs can be con-figured in SISCAPA assays and reported a platform forautomated screening of mAbs [167] In another more recentreport MALDI immunoscreening (MiSCREEN) was devel-oped enabling rapid screening and selection of high affinityanti-peptide mAbs [168] In other studies immuno-MALDI(iMALDI) was used where affinity-bound peptides on aMALDI utilize MALDI matrix solvents to elute the boundpeptides from the beads and the resultant peak height orpeak area of the peptide from an MS spectrum is used forquantitation [169 170] While iMALDI can be performedwith only aMALDI-MS instrument it can also be used in theMRM mode on a MALDI-MSMS instrument (iMALDI+)[171]

Despite sensitivity multiplexing and throughput somelimitations of SISCAPA include a relatively high cost of Abgeneration long lead time (sim24 weeks for assay generation)for SRM assay development [172] success rate for producing


potent antibodies as well as the potentially low peptidecapture rate [166] and background bead nonspecific binding

The SISCAPA strategy has been also deployed in a clinicalsetting where the potential of the SISCAPA-SRM assays wasillustrated by Hoofnagle et al who implemented SISCAPAassays for quantification of low-abundance serum thyroglob-ulin and simultaneous measurement of apolipoprotein AndashIand apolipoprotein B [133 173]

In collaboration with the Fred Hutchinson CancerResearch Center Bio-Rad Laboratories has developed aSISCAPA trainingQC kit which is currently in beta testingThe kit enables researcherswho are new to the SISCAPA tech-nique to implement an assay in their lab and gain experiencewith the process Based on an assay for a murine osteopontin(OPN) peptide in a human plasma matrix the kit providesresearchers with the reagents and information needed forcarrying out an assay including a detailed standard operatingprocedure antibody heavy and light peptides magneticbeads and other necessary buffers and reagents Results willbe comparable to expected values making the kit a valuableresource for quality control of the SISCAPA process

59 MRM Reference Libraries Access to reference spectralfragmentation libraries of proteotypic peptides and chro-matographic retention timewould be extremely useful for thegeneration ofmaximal product ion signal and the proteomicscommunity in MRM-MS assay development Skyline [174]and MRMer [175] are two open source software for devel-opingMRM-MS-based assays by the proteomics communitySkyline (downloadable from httpsbrendanx-uw1gswash-ingtonedulabkeyprojecthomesoftwareSkylinebeginview can be integrated with all major instrument plat-forms and has been used to design MRM-MS assays andsupport data analysis including SIS Skyline supports allmajor publicly available spectral libraries such as the GPMNational Institute of Standards and Technology (NIST) andthe Institute for Systems Biology Library files from thesesources can be downloaded and searched with the SkylineSpectral Library Explorer to help choose peptide precursorand product ions to monitor specific proteins of interest[174] MRMer was developed for organizing highly complexMRM-MS experiments including quantitative analyses usingheavylight isotopic peptide pairs and has the capability ofimporting data in a platform-independent mzXML formatMRMer extracts and infers precursor-product ion transitionpairings computes integrated ion intensities and permitsrapid visual curation for analyses exceeding 1000 precursor-product pairs [175]

Automated and targeted analysis with quantitative SRM(ATAQS) [176] is another open source software which sup-ports MRM assay development (httptoolsproteomecen-terorgATAQSATAQShtml) ATAQS is an integrated soft-ware platform that supports all stages of targeted SRM-basedproteomics experiments including target selection transitionoptimization and postacquisition data analysisThis softwarehas the potential to significantly facilitate the use of targetedproteomic techniques and contribute to the generation ofhighly sensitive reproducible and complete datasets that areparticularly critical for the discovery and validation of targets

in hypothesis-driven studies in systems biology ATAQSalso provides software API (application program interface)documentation that enables the addition of new algorithmsto each of the workflow steps [176] mProphet is anotherfully automated system that computes accurate error rates forthe identification of targeted peptides in SRM data sets andmaximizes specificity and sensitivity by combining relevantfeatures in the data into a statistical model [177]

Another important source of targeted proteomic assaysis SRMAtlas which enables detection and quantification ofproteins in complex proteomes (httpwwwmrmatlasorg)[178] The information in this database results from MRM-MS measurements of natural and synthetic peptides per-formed on a QQQ-MS Currently this database allows usersto query transitions from peptides from yeast human andmouse species obtained from QQQ-MS instruments sup-plemented with ion trap (IT) observations and predictionswhere QQQ spectra are unavailable An algorithm calledautomated detection of inaccurate and imprecise transitions(AuDIT) has been developed that can assist in MRM byautomatically identifying inaccurate transition data based onthe observation of interfering signal or inconsistent recoveryamong replicates [179] This algorithm evaluates MRM-MSdata by comparing the relative product ion intensities of theanalyte peptide to those of the SIS peptides followed by a t-test to determine any significant differenceThe algorithmhasalready demonstrated the capability of identifying problem-atic transitions and achieving accuracies of 94ndash100 for thecorrect identification of errant transitions [179]

510 Assay Portal Community Resource While many MRM-based assays have been published the information is dis-persed throughout the literature and protocols for charac-terization of assay performances have not been standardizedFurthermore the use of MRM and its potential utility havenot been realized by the biological and clinical researchcommunities To address these issues the Clinical ProteomicTumor Analysis Consortium (CPTAC 2) of the NCI haslaunched an assay portal to serve as a public resource of well-characterized quantitative mass spectrometry-based pro-teomic assays with associated SOPs reagents and assay val-idation data (httpassayscancergov) The portal databaseis tied to Panorama an open source platform allowingfor efficient collection and sharing of data in a vendor-neutral format SOPs describing sample preparations arealso available for download for each assay Data quality isa major emphasis of the portal Guidelines for MRM assayldquofit-for-purposerdquo validation have also been established withinthe NCI-funded CPTAC 2 with input from the outsidecommunity solicited via a workshop [138] To ensure uniformpresentation and adequate data for establishing the acceptedperformance of the assays a guidance document describingthe minimal characterization data required for assay inclu-sion in the CPTAC assay portal has been made available fordownload on the portal Five experiments of assay validationare described Experiments 1 and 2 contain information onthe assay sensitivity and linear range (determined througha response curve) and the repeatability (determined byanalyzing validation samples on multiple days) and are the


minimal validation requirement to qualify for inclusion inthe CPTAC assay portal A higher level of validation containsadditional experiments to measure the selectivity stabilityand detection of endogenous analyte At the time of launchthe portal contains sim462 assays with characterization dataand SOPs The CPTAC program will add several hundredmore assays over the next 2-3 years and in the near futurethe portal will be open for contributions from the communityat large The portal is able to accept data from any targetedmass spectrometry-based quantification method

6 Proteomic Reproducibility andStandardization

Resistance about the validity of proteomics analyses persistedfor several years in the late 90s and early 2000sThis stemmedpartly from some questionable work in the pioneering yearsof large-scale protein identification Many of the early land-mark papers in the last 10ndash15 years were obtained on low-resolution instruments andwithout proper statistical analysis[44] It was later found that a large proportion of the identifi-cations obtained from such studies were false positives [180]Many of the early problems were ascribed to the use of SELDIresulting from poor analytical technique with significantreproducibility issues Some high profile papers were latershown to be invalid which tainted the whole field for a whileFor example a method for early ovarian cancer diagnosis wasreported by a group of outstanding investigators that usedmass spectrometry to detect proteomic patterns from serumsamples in 2002 The reported sensitivities and specificitieswere approximately 100 even for serum from patients withearly-stage ovarian cancer [181] This paper has receiveda few thousand citations since its publications [182] Thecombination of quality investigators the high profile journalthat published the data and a powerful editorial generatedwidespread media coverage and euphoria that a new era incancer diagnostics has started However reports of method-ological shortcomings of this method and questions about itsvalidity were published soon after its publication [183ndash185]Subsequently others have identified bioinformatic artifactsand issues regarding variations in sample collection andstorage that compromised the conclusions of the paper [184]Despite positive publications that used similar approachesfor other cancer types [186 187] an independent validationstudy of this method for prostate cancer diagnosis sponsoredby Early Detection Research Network has shown that themethod does not reliably detect prostate cancer [188]

The issue of reproducibility was further exacerbatedwhenBergeron and colleagues published a study in 2009 [189]Theresearchers sent standardized samples containing 20 knownproteins to 27 labs for proteomics analysis Each proteincontained one ormore unique tryptic peptides which shouldhave shown up in MS analysis Only 7 out of the 27 labsinitially reported all 20 proteins correctly and only one sawall the proteotypic peptides Yet centralized analysis of the rawdata revealed that all 20 proteins and most of the peptideshad been detected in all 27 labs The message of this studywas that irrespective of instrumentalmethod the technologydelivers high quality MS data In contrast this centralized

analysis determined missed identifications (false negatives)environmental contamination database matching and cura-tion of protein identifications as sources of problems Onesuggestion was that improved search engines and databaseswere needed for mass spectrometry-based proteomics [189]

In a separate study the proteomics research group ofthe Association of Biomolecular Resource Facilities (ABRF)sponsored a number of research studies designed to enableparticipants to try new techniques and assess their capa-bilities relative to other laboratories analyzing the samesamples This study was designed to explore the use ofdifferent approaches for determining quantitative differencesfor several target proteins in human plasma that were cen-trally prepared These results provide a cross-sectional viewof current methodologies as well as a vehicle for sharinginformation regarding experimental protocols and educationfor the proteomics community and highlight that establishinggood laboratory practices is important [190] ABRF effort toassess individual laboratoryrsquos platforms methods and resultswas one of the first attempts to address variability in samplepreparation and processing on different proteomic platforms

To address many of the critical challenges in proteomicsincluding the lack of an ability to accurately and reproduciblymeasure a meaningful number of proteins in biospecimensacross laboratories the National Cancer Institute (NCI)launched the Clinical Proteomic Technologies for Cancer(CPTC) in 2006 (httpproteomicscancergov) The overallgoals of CPTC were focused on removing several of themajor barriers in proteomics research to enable the accurateefficient and reproducible identification andquantification ofmeaningful numbers of proteins that could drive high valueprotein quantitation and qualification studies Achievingthis goal would provide a firm foundation for the field ofdiscovery proteomics and enable the rational developmentof clinical biomarkers to address various needs in cancerdrug development diagnostics and clinical managementCPTC took a multidisciplinary multi-institutional approachthrough its CPTAC 1 network in addressing the long-standing problems of variability issues in proteomics result-ing in large measurement noise from analytical platformsrather than assessing real biological differences CPTAC 1carried out the first quantitative assessment of discoveryproteomics technology platforms across laboratories defin-ing a set of performance standards for identifying thesources of variability [191ndash193] developed a standard yeastproteome reference material available to the communitythrough NIST for investigators to benchmark their ownperformance [191 194] and developed a quality controltool to monitor and troubleshoot instrument performance(httppeptidenistgovsoftwarenist msqc pipelineNISTMSQC Pipelinehtml) The yeast protein extract (RM8323)developed by NIST under the auspices of NCIrsquos CPTCinitiative is currently available to the public (httpswww-snistgovsrmorsview detailcfmsrm=8323) and offers re-searchers a unique biological reference material RM8323is the most extensively characterized complex biologicalproteome and the only one associated with several large-scalestudies to estimate protein abundance across a wide con-centration range The yeast protein extract and its associated


reference datasets [191 194] can be used by the research com-munity for benchmarking instrument detection efficiencyfor analysis of complex biological proteomes to improveupon current methods and to evaluate new platforms whendeveloped

To address the issue of reproducibility in targeted pro-teomics CPTAC 1 also spearheaded a multi-institutionalstudy composed of three substudies designed to increase thelevel of complexity in sample preparation at eight individualsites [135] Intralaboratory variability and reproducibilityin all three substudies were evaluated by comparing themeasured concentrations of seven target proteins (human C-reactive protein PSA aprotinin leptin myoglobin myelinbasic protein and peroxidase) to the actual concentra-tions across the range of spiked-in analytes (a total ofnine concentration points with LOQ at 292 nM that is733 ngmL for C-reactive protein) and by determining theCVs for these quantitativemeasurementsThe results showedthat the reproducibility and precision of these quantitativemeasurements for nine of ten peptides tested across eightlaboratories ranged from 4 to 14 4 to 13 and 10 to 23interlaboratory CVs at or near the estimated LOQ of 292 nMfor studies I II and III respectively Intralaboratory CVswere usually lt15 and lt25 at the identical concentrationfor studies I II and III The incremental increases in CVsindicate that sample handling contributes more to assayvariability than instrumental variability Robotic automationof sample handling can furthermore improve analyticalvariability Very recently CPTAC 1 teams developed a sys-tem suitability protocol (SSP) which employs a predigestedmixture of six proteins to facilitate performance evaluationof LC-SID-MRM-MS instrument platforms configured withnanoflow-LC systems interfaced to triple quadrupole massspectrometers [195] The SSP was designed for use with lowmultiplex analyses as well as highmultiplex approaches whensoftware-driven scheduling of data acquisition is requiredPerformance was assessed by monitoring of a range ofchromatographic and mass spectrometric metrics includingpeak width chromatographic resolution peak capacity andthe variability in peak area and analyte retention time (RT)stability The SSP which was evaluated in 11 laboratories ona total of 15 different instruments enabled early diagnoses ofLC and MS anomalies that indicated suboptimal LC-MRM-MS performance Robust LC-SID-MRM-MS-based assaysthat can be replicated across laboratories and ultimately inclinical laboratory settings require standardized protocolsto demonstrate that the analysis platforms are performingadequately and therefore use of a SSP helps to ensure thatanalyte quantification measurements can be replicated withgood precision within and across multiple laboratories andshould facilitate more widespread use of MRM-MS technol-ogy by the basic biomedical and clinical laboratory researchcommunities

7 Clearance of MS-Based Platforms forClinical Use

As indicated previously a typical protein biomarker discoverypipeline has three phases discovery verification and clinical

validation The discovery work often uses research-gradesamples from underpowered cohorts with limited samplenumbers Promising candidates from the discovery phaseare verified by analyzing their performance in medium-sizedclinical cohorts (119899 gt 100) using standardized analyticalplatforms Assays are then developed to validate the topperforming candidates in larger clinical cohorts (119899 gt 200)Ultimately the utility of the validated candidates for routineclinical use is demonstrated in a clinical setting (improve-ment over gold standard diagnosis cost clinical outcomesetc) [196] The path from development of biomarkers toclinical practices could take many possible steps Howeverit is unequivocal that prior to clinical use any biomarkershave to prove their safety and efficacy in independent clinicaltrials using an appropriate study population for a clearlydefined intended useThree issues that are the key links in thepath from discovered candidates to actual clinical diagnosticsinclude generation of sufficient and portable evidence inpreliminary validation studies defining clinical utility forregulatory approval and selectingdeveloping assays withanalytical performance suitable for clinical deployment [197]

While proteomic methods may not yet be ready forimplementation in routine clinical chemistry laboratoriesthe goal seems attainable in the near future [20] Cur-rently LC-MS instruments are widely used in clinical lab-oratories for diagnosis of inborn errors of metabolism orfor therapeutic drug monitoring and toxicology MALDI-TOF-MS profiling methods have been recently implementedvery successfully in clinical microbiology laboratories forsimpler samples such as microbial colonies for identifi-cation of microorganisms thus proving the validity ofthe approach [20] BioMerieux recently announced USFDA clearance for VITEK MS an evolutionary technologywhich reduces microbial identification from days to min-utes (httpwwwbiomerieux-usacom) VITEK MS is thefirst clinical mass spectrometry MALDI-TOF-based systemavailable in the US for rapid identification of disease-causingbacteria and yeast VITEK MS accuracy was compared to16S ribosomal RNA gene sequencing the gold standard fora number of microbial categories The overall accuracy ofVITEK MS compared to nucleic acid sequencing for theseorganisms was 936 percent

In addition Bruker Corporation recently announced thatit has been granted US FDA clearance under section 510(k) tomarket its MALDI Biotyper CA System in the United Statesfor the identification of Gram negative bacterial coloniescultured from human specimens (httpwwwbrukercom)The MALDI Biotyper enables molecular identification andtaxonomical classification or dereplication of microorgan-isms like bacteria yeasts and fungi This is achieved usingproteomic fingerprinting by high throughput MALDI-TOFmass spectrometry The MALDI Biotyper uses specific pro-teomic fingerprints from bacterial strains However humanprotein analysis represents a level of complexity over drugs orbacterial proteins thereby imposing particular constraints

Another proteomic test that has been recently cleared byFDA is OVA1 OVA1 test is an in vitroDiagnosticMultivariateIndex Assay (IVDMIA) for assessing ovarian cancer risk inwomen diagnosed with ovarian tumor prior to a planned


surgery OVA1 analyzes 5 proteomic biomarkers in serumand the results are combined through an algorithm to yielda single-valued index within the range of 0ndash10 OVA1 pro-vides additional information to assist in identifying patientsfor referral to a gynecologic oncologist In a prospectivemultiple-center clinical study the addition of OVA1 in preop-erative clinical assessment was found to improve sensitivityin the prediction of malignancy for ovarian tumor OVA1 isintended to assess preoperatively the risk of ovarian cancerin women scheduled for surgery due to suspected ovariancancer The test result aids in the decision to refer the patientto a gynecologic oncologist for surgery for better long-termoutcome [197]

To empower the scientific community with the righttools and to serve as a preview of the regulatory mindsetand direction for multiplex protein assays NCIrsquos CPTAC 1submitted 2 protein-basedmultiplex assay descriptions to theOffice of In Vitro Diagnostic Device Evaluation and Safetyof the FDA The objective was to evaluate the analyticalmeasurement criteria and studies needed to validate protein-based multiplex assays Each submission described a dif-ferent protein-based platform a multiplex immunoaffinitymass spectrometry platform for protein quantification andan immunological array platform quantifying glycoproteinisoforms Submissions provided a mutually beneficial wayfor members of the proteomics and regulatory communitiesto identify the analytical issues that the field should addresswhen developing protein-basedmultiplex clinical assaysThegoal of these submissions was to demonstrate the process andinteractions between the sponsor and the FDA in a fashionsimilar to how they would proceed generally Additionallythe feedback provided by the FDA generates some insightinto the review issues that are relevant to these types oftests Because the sponsors of the 2 mock submissions didnot submit full responses and appropriate data and becausethey submitted hypothetical data in large part many issuesand problems were not mentioned and discussed For thesereasons this document is not meant to be inclusive of all therequirements for any future submission that would be madeto the FDA [198]

To propel the adaptation of proteomics in clinical chem-istry important developments in workflows and instrumen-tation are necessary before various proteomic methods cancompete with protein immunoassays performed on highthroughput immunoanalyzers The best chance for short-term application of proteomic methods in clinical chemistrylaboratories will most likely be to capitalize on specificaspects of proteomic analysis such as targeting new types ofbiomarkers [199 200] and offering new diagnostic solutionsfor orphan clinical problems [201] In this context SRMappears as potential alternative to classical immunoassays bycombining analytical specificity and reliable quantification asdescribed before Among the advantages of SRM methodsover classical immunoassays is the possibility of applyingmultiple protein tests on a single instrument without relyingon commercial reagents SRM thus can offer opportunitiesfor measuring biomarkers in specific clinical areas that donot represent largemarkets for diagnostic industries It mightalso be a way to reduce reagent costs such as antibodies borne

by clinical laboratories Therefore the project for developingreliable SRM assays for a large set of human proteins is clearlyof great interest [202] It could promote broader access tothis technology and in turn greatly facilitate application toclinical studies and increased use within clinical chemistrylaboratories

8 Proteogenomics

Proteogenomics the integration of proteomic and genomicdata has recently emerged as a significant area of activityin proteomics as a promising potential approach to Omicsresearch The notion is based on the premise that proteindata can shed light on the consequence of various genomicfeatures allowing researchers to determine for examplewhether or not a specific genetic variant may actuallybecome a functional protein Independently or as part oflarge-scale initiatives a number of researchers are pursuingsuch studies including CPTAC 2 TCGA and the NationalHuman Genome Research Institutersquos Encyclopedia of DNAElements Consortium (ENCODE) To a large degree thissurge in activity on the proteogenomics studies originatesfrom advances within proteomics that have made it feasibleto obtain coverage comparable to that achieved by genomicsand transcriptomics A requirement for good systems biol-ogy studies is the need to have good enough coverage inproteomics In the last few years obtaining coverage in therange of 10000 to 12000 proteins has become routine forsome labs [203 204] In their recent work for instance Lehtioand his colleagues identified 13078 human and 10637 mouseproteins including 39941 peptides not previously present inthe Peptide Atlasrsquo human dataset They also identified 224novel human and 122 novel mouse peptides which mappedto 164 and 101 genomic loci respectively [205] Using highresolution isoelectric focusing (HiRIEF) at the peptide levelin the 37ndash50 pH range and accurate peptide isoelectric point(pI) prediction Lehtio probed the six-reading-frame transla-tion of the human and mouse genomes and identified 98 and52 previously undiscovered protein-coding loci respectively[205]

In a separate study Heck and his colleagues have com-pleted a proteogenomics study of rat liver tissue integratingwhole genome sequencing RNA-seq and mass spec-basedproteomics [206] In this study the researchers identified13088 proteins making it one of the most comprehen-sive proteome analyses performed to date Integrating theirgenomics data they were also able to validate 1195 gene pre-dictions 83 splice events 120 proteins with nonsynonymousvariants and 20 protein isoforms with nonsynonymous RNAediting The effort also provided several biological insightssuch as the question of RNA editingmdasha process in whichmodifications are made to the sequence of an RNA moleculeafter it has been generated While they indicate that RNAediting may occur they may not often lead to a stable protein[206] Enhancing mass spec sequence coverage as well asrobust data analysis pipelinemay further improve our proteinvariant detection


Leveraging large-scale cancer genomics datasets NCI isleveraging its investment in genomics through CPTAC 2which is applying proteomic technologies to systematicallyidentify proteins from genomically characterized tumorssuch as those from TCGA program The goal of the CPTAC2 program is to illuminate the complex proteogenomic rela-tionship between genomic (DNA RNA) and proteomic (pro-tein) abnormalities thus producing a deeper understandingof cancer biology The CPTAC 2 program is analyzing morethan 300 samples from colorectal breast and ovarian cancerA key component of the consortium is developing novelmethods to integrate and visualize proteomic and genomicdata to better comprehend the biological processes of the cellThe integration of terabytes of genomic and proteomic data iscatalyzing the development of new computational tools andalso leveraging findings from other fields such as machinelearning and computer science to better understand biolog-ical processes After this analysis proteins of interest areselected as targets for highly reproducible and transportableassays All of the data and resulting assays are made publiclyavailable to help advance research in cancer biology andimprove patient careThe first set of proteogenomic data werereleased in September 2013 (httpproteomicscancergov)

Technological advances in both the proteomics andgenomics now provide the ability to discriminate geneticand posttranscriptional polymorphisms at the proteomelevel The synergistic use of genomic transcriptomic andproteomic technologies significantly improves the data thatcan be gained from proteomics as well as genomics effortsBy matching deep MS-based proteomics to a personalizeddatabase built from a sample-specific genome and transcrip-tome thousands of peptides that would otherwise escapeidentification can be identified Such powerful tools anddata demonstrate the power of and need for integrativeproteogenomic analysis for understanding genetic control ofmolecular dynamics and phenotypic diversity in a system-wide manner which appears to be the future direction [206]

9 Concluding Remarks

The acceleration of biological knowledge through the map-ping of HGP has resulted in the development of new highthroughput next-generation sequencing (NGS) techniquesNGS analyses such as whole genome sequencing (WGS) andtotal RNA sequencing (RNA-seq) cannot however predictwith high confidence the effects on the proteins and theirvariations including composition function and expressionlevels Therefore the completion of the HGP has also pre-sented the new challenge of human proteome characteriza-tion using MS-based proteomics Each of these technologiesis capable of comprehensive measurements of gene productsat a system level [207 208] AlthoughMS andNGS are highlycomplementary they have not yet effectively been integratedin large-scale studies [209] and sparsely performed in organ-isms with smaller genomes [210] To correctly delineate theeffects of genomic and transcriptomic variation onmolecularprocesses and cellular functioning integrative analyses ofdifferent data types ideally derived from the same samples

are required [211] The integration and interrogation of theproteomic and genomic datawill provide potential biomarkercandidates which will be prioritized for downstream targetedproteomic analysis These biomarker targets will be usedto create multiplex quantitative assays for verification andprescreening to test the relevance of the targets in clinicallyrelevant and unbiased samples The outcomes from thisapproach will provide the community with verified biomark-ers which could be used for clinical qualification studieshigh quality and publicly accessible datasets and analyticallyvalidated multiplex quantitative proteinpeptide assays andtheir associated high quality reagents for the research andclinical community

State-of-the-art MS approaches can routinely identifyover 10000 proteins in a single experiment [203 204]which suggests that the analysis of complete proteomes iswithin reach [212] The comprehensive human proteomeproject however still faces challenges including very largenumber of proteins with PTMs mutations splice vari-ants the variety of technology platforms and sensitivitylimitations in detecting proteins and aberrations presentin low abundances Future proteomic undertakings shouldcontinue to support technology development optimizationand standardization Incorporation of the most up-to-dateand efficient technologies is critical in successfully advancingthe translation of proteomic findings into clinically relevantbiomarkers Meanwhile rigorous assessment of biospecimenand data quality through quality assessment criteria at eachstep of the biomarker development pipeline should continueto be supported These efforts combined with continuedcollaborations with regulatory agencies and clinical chemistswill expedite the development of individualized patient carethrough clinical proteomics

Abbreviations

SRM-MS Selected reaction monitoring-MSMRM-MS Multiple reaction monitoring-MSTCGA The Cancer Genome AtlasCPTC Clinical Proteomic Technologies for

CancerCPTAC 1 Clinical Proteomic Technology

Assessment for CancerCPTAC 2 Clinical Proteomic Tumor Analysis

ConsortiumPTMs Posttranslational modificationsESI-LC-MS Electrospray ionization-liquid

chromatography tandem massspectroscopy

MALDI-TOF Matrix assisted laser desorption ionizationtime of flight

SELDI-TOF Surface enhanced laser desorptionionization time of flight

MALDI-MSI MALDI MS imaging2-DE Two-dimensional gel electrophoresisLCM-MS Laser capture microdissection-MSSILAC Stable isotope labeling with amino acids in

cell culture


SISCAPA Stable isotope standards and capture byanti-peptide antibodies

PSAQ Protein standard absolute quantificationSID Stable isotope dilutionSIS Stable isotope-labelled internal standardQQQ-MS Triple quadrupole mass spectrometerHGP Human genome projectFDA Food and Drug AdministrationPRM Parallel reaction monitoring

Disclaimer

The views expressed in this paper are the employeersquos and donot represent the National Institutes of Health

Conflict of Interests

The author declares that there is no conflict of interestsregarding the publication of this paper

References

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[71] R M Caprioli T B Farmer and J Gile ldquoMolecular imaging ofbiological samples localization of peptides and proteins usingMALDI-TOF MSrdquo Analytical Chemistry vol 69 no 23 pp4751ndash4760 1997

[72] K E Burnum D S Cornett S M Puolitaival et al ldquoSpatialand temporal alterations of phospholipids determined by massspectrometry during mouse embryo implantationrdquo Journal ofLipid Research vol 50 no 11 pp 2290ndash2298 2009

[73] O Jardin-Mathe D Bonnel J Franck et al ldquoMITICS (MALDIImaging Team Imaging Computing System) a new open sourcemass spectrometry imaging softwarerdquo Journal of Proteomicsvol 71 no 3 pp 332ndash345 2008

[74] A Mange P Chaurand H Perrochia P Roger R M Caprioliand J Solassol ldquoLiquid chromatography-tandem and MALDIimaging mass spectrometry analyses of RCL2CS100-fixedparaffin-embedded tissues proteomics evaluation of an alter-nate fixative for biomarker discoveryrdquo Journal of ProteomeResearch vol 8 no 12 pp 5619ndash5628 2009

[75] Y Kimura K Tsutsumi Y Sugiura and M Setou ldquoMedicalmolecular morphology with imagingmass spectrometryrdquoMed-ical Molecular Morphology vol 42 no 3 pp 133ndash137 2009

[76] R Mirnezami K Spagou P A Vorkas et al ldquoChemi-cal mapping of the colorectal cancer microenvironment viaMALDI imaging mass spectrometry (MALDI-MSI) revealsnovel cancer-associated field effectsrdquo Molecular Oncology vol8 no 1 pp 39ndash49 2014

[77] G Marko-Varga T E Fehniger M Rezeli B Dome T Laurelland A Vegvari ldquoDrug localization in different lung cancerphenotypes by MALDI mass spectrometry imagingrdquo Journal ofProteomics vol 74 no 7 pp 982ndash992 2011

[78] X Liu J L Ide I Norton et al ldquoMolecular imaging of drugtransit through the blood-brain barrier with MALDI mass

spectrometry imagingrdquo Scientific Reports vol 3 article 28592013

[79] S C C Wong C M L Chan B B Y Ma et al ldquoAdvancedproteomic technologies for cancer biomarker discoveryrdquo ExpertReview of Proteomics vol 6 no 2 pp 123ndash134 2009

[80] M Andersson M R Groseclose A Y Deutch and R MCaprioli ldquoImagingmass spectrometry of proteins and peptides3D volume reconstructionrdquo Nature Methods vol 5 no 1 pp101ndash108 2008

[81] J Franck K Arafah M Elayed et al ldquoMALDI imaging massspectrometry state of the art technology in clinical proteomicsrdquoMolecular and Cellular Proteomics vol 8 no 9 pp 2023ndash20332009

[82] G LWright Jr ldquoTwo dimensional acrylamide gel electrophore-sis of cancer patient serum proteinsrdquo Annals of Clinical andLaboratory Science vol 4 no 4 pp 281ndash293 1974

[83] D Hariharan M E Weeks and T Crnogorac-Jurcevic ldquoAppli-cation of proteomics in cancer gene profiling two-dimensionaldifference in gel electrophoresis (2D-DIGE)rdquoMethods inMolec-ular Biology vol 576 pp 197ndash211 2010

[84] R Deng Z Lu Y Chen L Zhou and X Lu ldquoPlasma proteomicanalysis of pancreatic cancer by 2-dimensional gel electrophore-sisrdquo Pancreas vol 34 no 3 pp 310ndash317 2007

[85] P Alfonso A Nunez J Madoz-Gurpide L Lombardia LSanchez and J I Casal ldquoProteomic expression analysis ofcolorectal cancer by two-dimensional differential gel elec-trophoresisrdquo Proteomics vol 5 no 10 pp 2602ndash2611 2005

[86] P Gromov I Gromova J Bunkenborg et al ldquoUp-regulatedproteins in the fluid bathing the tumour cell microenvironmentas potential serological markers for early detection of cancer ofthe breastrdquoMolecular Oncology vol 4 no 1 pp 65ndash89 2010

[87] J Sasse and S R Gallagher ldquoChapter 8 Unit 89 Stainingproteins in gelsrdquo in Current Protocols in Immunology 2004

[88] T H Steinberg ldquoChapter 31 protein gel staining methods anintroduction and overviewrdquo Methods in Enzymology vol 463pp 541ndash563 2009

[89] T Kondo and S Hirohashi ldquoApplication of 2D-DIGE in cancerproteomics toward personalized medicinerdquo Methods in Molec-ular Biology vol 577 pp 135ndash154 2009

[90] J Koo K Kim B Min and G M Lee ldquoDifferential pro-tein expression in human articular chondrocytes expanded inserum-free media of different medium osmolalities by DIGErdquoJournal of Proteome Research vol 9 no 5 pp 2480ndash2487 2010

[91] Z Ma S Dasari M C Chambers et al ldquoIDPicker 20improved protein assembly with high discrimination peptideidentification filteringrdquo Journal of Proteome Research vol 8 no8 pp 3872ndash3881 2009

[92] R Ummanni F Mundt H Pospisil et al ldquoIdentification ofclinically relevant protein targets in prostate cancer with 2D-DIGE coupledmass spectrometry and systems biology networkplatformrdquo PLoS ONE vol 6 no 2 Article ID e16833 2011

[93] J L Lopez ldquoTwo-dimensional electrophoresis in proteomeexpression analysisrdquo Journal of Chromatography B AnalyticalTechnologies in the Biomedical and Life Sciences vol 849 no 1-2pp 190ndash202 2007

[94] P L Roulhac J M Ward J W Thompson et al ldquoMicropro-teomics quantitative proteomic profiling of small numbers oflaser-captured cellsrdquo Cold Spring Harbor Protocols vol 6 no 22011

[95] Y Zhang Y Ye D Shen et al ldquoIdentification of transgelin-2 as abiomarker of colorectal cancer by laser capture microdissection


and quantitative proteome analysisrdquoCancer Science vol 101 no2 pp 523ndash529 2010

[96] H Yao Z Zhang Z Xiao et al ldquoIdentification of metastasisassociated proteins in human lung squamous carcinoma usingtwo-dimensional difference gel electrophoresis and laser cap-ture microdissectionrdquo Lung Cancer vol 65 no 1 pp 41ndash482009

[97] L FWaanders K ChwalekMMonetti C Kumar E Lammertand M Mann ldquoQuantitative proteomic analysis of single pan-creatic isletsrdquo Proceedings of the National Academy of Sciences ofthe United States of America vol 106 no 45 pp 18902ndash189072009

[98] M S Scicchitano D A Dalmas R W Boyce H C ThomasandK S Frazier ldquoProtein extraction of formalin-fixed paraffin-embedded tissue enables robust proteomic profiles by massspectrometryrdquo Journal of Histochemistry andCytochemistry vol57 no 9 pp 849ndash860 2009

[99] Y Nan S Yang Y Tian et al ldquoAnalysis of the expressionprotein profiles of lung squamous carcinoma cell using shot-gun proteomics strategyrdquo Medical Oncology vol 26 no 2 pp215ndash221 2009

[100] Z Daohai and E S Koay ldquoAnalysis of laser capture microdis-sected cells by 2-dimensional gel electrophoresisrdquo Methods inMolecular Biology vol 428 pp 77ndash91 2007

[101] D J Johann S Mukherjee D A Prieto T D Veenstraand J Blonder ldquoProfiling solid tumor heterogeneity by LCMand biological MS of fresh-frozen tissue sectionsrdquo Methods inMolecular Biology vol 755 pp 95ndash106 2011

[102] K Uleberg A C Munk C Brede et al ldquoDiscriminationof grade 2 and 3 cervical intraepithelial neoplasia by meansof analysis of water soluble proteins recovered from cervicalbiopsiesrdquo Proteome Science vol 9 article 36 2011

[103] CMueller A C deCarvalho TMikkelsen et al ldquoGlioblastomacell enrichment is critical for analysis of phosphorylated drugtargets and proteomic-genomic correlationsrdquo Cancer Researchvol 74 no 3 pp 818ndash828 2014

[104] L Melton ldquoProteomics in multiplexrdquoNature vol 429 no 6987pp 101ndash107 2004

[105] P Moore and J Clayton ldquoTo affinity and beyondrdquo Nature vol426 no 6967 pp 725ndash731 2003

[106] L A Liotta V Espina A I Mehta et al ldquoProtein microarraysmeeting analytical challenges for clinical applicationsrdquo CancerCell vol 3 no 4 pp 317ndash325 2003

[107] C P Pjaweletz L Charboneau V E Bichsel et al ldquoReversephase protein microarrays which capture disease progressionshow activation of pro-survival pathways at the cancer invasionfrontrdquo Oncogene vol 20 no 16 pp 1981ndash1989 2001

[108] S Sundaresh D L Doolan S Hirst et al ldquoIdentification ofhumoral immune responses in protein microarrays using DNAmicroarray data analysis techniquesrdquoBioinformatics vol 22 no14 pp 1760ndash1766 2006

[109] H Chandra and S Srivastava ldquoCell-free synthesis-based pro-tein microarrays and their applicationsrdquo Proteomics vol 10 no4 pp 717ndash730 2010

[110] N Ramachandran J V Raphael E Hainsworth et al ldquoNext-generation high-density self-assembling functional proteinarraysrdquo Nature Methods vol 5 no 6 pp 535ndash538 2008

[111] R Spera J Labaer and C Nicolini ldquoMALDI-TOF char-acterization of NAPPA-generated proteinsrdquo Journal of MassSpectrometry vol 46 no 9 pp 960ndash965 2011

[112] L Melton ldquoPharmacogenetics and genotyping on the trail ofSNPsrdquo Nature vol 422 no 6934 pp 917ndash923 2003

[113] B Houser ldquoBio-radrsquos Bio-Plex suspension array system xMAPtechnology overviewrdquo Archives of Physiology and Biochemistryvol 118 no 4 pp 192ndash196 2012

[114] S M Hanash S J Pitteri and V M Faca ldquoMining the plasmaproteome for cancer biomarkersrdquo Nature vol 452 no 7187 pp571ndash579 2008

[115] G Poste ldquoBring on the biomarkersrdquo Nature vol 469 no 7329pp 156ndash157 2011

[116] M Polanski and N L Anderson ldquoA list of candidate cancerbiomarkers for targeted proteomicsrdquoBiomark Insights vol 1 pp1ndash48 2007

[117] S Ohtsuki Y Uchida Y Kubo and T Terasaki ldquoQuantita-tive targeted absolute proteomics-based ADME research as anew path to drug discovery and development methodologyadvantages strategy and prospectsrdquo Journal of PharmaceuticalSciences vol 100 no 9 pp 3547ndash3559 2011

[118] E S Boja and H Rodriguez ldquoMass spectrometry-based tar-geted quantitative proteomics achieving sensitive and repro-ducible detection of proteinsrdquo Proteomics vol 12 no 8 pp1093ndash1110 2012

[119] D C Liebler and L J Zimmerman ldquoTargeted quantitation ofproteins bymass spectrometryrdquo Biochemistry vol 52 no 22 pp3797ndash3806 2013

[120] P Picotti and R Aebersold ldquoSelected reaction monitoring-based proteomics workflows potential pitfalls and futuredirectionsrdquo Nature Methods vol 9 no 6 pp 555ndash566 2012

[121] S Pan R Aebersold R Chen et al ldquoMass spectrometry basedtargeted protein quantification methods and applicationsrdquoJournal of Proteome Research vol 8 no 2 pp 787ndash797 2009

[122] H Rodriguez R Rivers C Kinsinger et al ldquoReconstructingthe pipeline by introducing multiplexed multiple reactionmonitoring mass spectrometry for cancer biomarker verifica-tion an NCI-CPTC initiative perspectiverdquo Proteomics ClinicalApplications vol 4 no 12 pp 904ndash914 2010

[123] B Domon and R Aebersold ldquoMass spectrometry and proteinanalysisrdquo Science vol 312 no 5771 pp 212ndash217 2006

[124] H Schupke R Hempel R Eckardt and T Kronbach ldquoIden-tification of talinolol metabolites in urine of man dog ratand mouse after oral administration by high-performance liq-uid chromatography-thermospray tandemmass spectrometryrdquoJournal of Mass Spectrometry vol 31 pp 1371ndash1381 1996

[125] R Kostiainen T Kotiaho T Kuuranne and S Auriola ldquoLiquidchromatographyatmospheric pressure ionization-mass spec-trometry in drug metabolism studiesrdquo Journal of Mass Spec-trometry vol 38 no 4 pp 357ndash372 2003

[126] S S-C Tai D M Bunk E White V and M J Welch ldquoDevel-opment and evaluation of a reference measurement procedurefor the determination of total 3310158405-triiodothyronine in humanserum using isotope-dilution liquid chromatography-tandemmass spectrometryrdquo Analytical Chemistry vol 76 no 17 pp5092ndash5096 2004

[127] N Ahmed and P J Thornalley ldquoQuantitative screening ofprotein biomarkers of early glycation advanced glycationoxidation and nitrosation in cellular and extracellular proteinsby tandem mass spectrometry multiple reaction monitoringrdquoBiochemical Society Transactions vol 31 no 6 pp 1417ndash14222003

[128] A Sannino L Bolzoni and M Bandini ldquoApplication of liquidchromatography with electrospray tandem mass spectrometry


to the determination of a new generation of pesticides inprocessed fruits and vegetablesrdquo Journal of Chromatography Avol 1036 no 2 pp 161ndash169 2004

[129] M A Kuzyk D Smith J Yang et al ldquoMultiple reactionmonitoring-based multiplexed absolute quantitation of 45proteins in human plasmardquoMolecular and Cellular Proteomicsvol 8 no 8 pp 1860ndash1877 2009

[130] Z Meng and T D Veenstra ldquoTargeted mass spectrometryapproaches for protein biomarker verificationrdquo Journal of Pro-teomics vol 74 no 12 pp 2650ndash2659 2011

[131] H Keshishian T Addona M Burgess E Kuhn and S A CarrldquoQuantitative multiplexed assays for low abundance proteinsin plasma by targeted mass spectrometry and stable isotopedilutionrdquo Molecular and Cellular Proteomics vol 6 no 12 pp2212ndash2229 2007

[132] H Keshishian T Addona M Burgess et al ldquoQuantificationof cardiovascular biomarkers in patient plasma by targetedmass spectrometry and stable isotope dilutionrdquo Molecular andCellular Proteomics vol 8 no 10 pp 2339ndash2349 2009

[133] A N Hoofnagle J O Becker M HWener and J W HeineckeldquoQuantification of thyroglobulin a low-abundance serum pro-tein by immunoaffinity peptide enrichment and tandem massspectrometryrdquo Clinical Chemistry vol 54 no 11 pp 1796ndash18042008

[134] ldquoMethod of the year 2012rdquo Nature Methods vol 10 no 1 2013[135] T A Addona S E Abbatiello B Schilling et al ldquoMulti-site

assessment of the precision and reproducibility ofmultiple reac-tion monitoring-based measurements of proteins in plasmardquoNature Biotechnology vol 27 pp 633ndash641 2009

[136] R Aebersold A L Burlingame and R A Bradshaw ldquoWesternblots versus selected reaction monitoring assays time to turnthe tablesrdquo Molecular amp Cellular Proteomics vol 12 no 9 pp2381ndash2382 2013

[137] J J Kennedy S E Abbatiello K Kim and et al ldquoDemonstratingthe feasibility of large-scale development of standardized assaysto quantify human proteinsrdquo Nature Methods vol 11 pp 149ndash155 2014

[138] S A Carr S E Abbatiello B L Ackermann et al ldquoTargetedpeptide measurements in biology and medicine best practicesfor mass spectrometry-based assay development using a fit-for-purpose approachrdquoMolecular amp Cellular Proteomics vol 13 no3 pp 907ndash917 2014

[139] E Kuhn T Addona H Keshishian et al ldquoDeveloping mul-tiplexed assays for troponin I and interleukin-33 in plasmaby peptide immunoaffinity enrichment and targeted massspectrometryrdquo Clinical Chemistry vol 55 no 6 pp 1108ndash11172009

[140] T Fortin A Salvador J P Charrier et al ldquoClinicalquantitation of prostate-specific antigen biomarker in thelow nanogrammilliliter range by conventional bore liquidchromatography-tandem mass spectrometry (multiplereaction monitoring) coupling and correlation with ELISAtestsrdquo Molecular and Cellular Proteomics vol 8 no 5 pp1006ndash1015 2009

[141] M Hossain D T Kaleta E W Robinson et al ldquoEnhancedsensitivity for selected reactionmonitoringmass spectrometry-based targeted proteomics using a dual stage electrodynamicion funnel interfacerdquo Molecular amp Cellular Proteomics vol 10no 2 Article ID M000062-MCP201 2011

[142] T Fortin A Salvador J P Charrier et al ldquoMultiple reac-tion monitoring cubed for protein quantification at the low

nanogrammilliliter level in nondepleted human serumrdquo Ana-lytical Chemistry vol 81 no 22 pp 9343ndash9352 2009

[143] T Shi T L Fillmore X Sun et al ldquoAntibody-free targetedmass-spectrometric approach for quantification of proteins atlow picogram per milliliter levels in human plasmaserumrdquoProceedings of the National Academy of Sciences of the UnitedStates of America vol 109 no 38 pp 15395ndash15400 2012

[144] A C Peterson J D Russell D J Bailey M S Westphall andJ J Coon ldquoParallel reaction monitoring for high resolutionand high mass accuracy quantitative targeted proteomicsrdquoMolecular and Cellular Proteomics vol 11 no 11 pp 1475ndash14882012

[145] A Doerr ldquoTargeting with PRMrdquo Nature Methods vol 9 no 10p 950 2012

[146] J Sherman M J McKay K Ashman and M P Molloy ldquoHowspecific is my SRM the issue of precursor and product ionredundancyrdquo Proteomics vol 9 no 5 pp 1120ndash1123 2009

[147] L C Gillet P Navarro S Tate et al ldquoTargeted data extraction ofthe MSMS spectra generated by data-independent acquisitiona new concept for consistent and accurate proteome analysisrdquoMolecular amp Cellular Proteomics vol 11 no 6 2012

[148] K P Law and Y P Lim ldquoRecent advances inmass spectrometrydata independent analysis and hyper reaction monitoringrdquoExpert Review of Proteomics vol 10 pp 551ndash566 2013

[149] N Rifai M A Gillette and S A Carr ldquoProtein biomarkerdiscovery and validation the long and uncertain path to clinicalutilityrdquo Nature Biotechnology vol 24 no 8 pp 971ndash983 2006

[150] D Nedelkov ldquoMass spectrometry-based protein assays for invitro diagnostic testingrdquo Expert Review of Molecular Diagnos-tics vol 12 no 3 pp 235ndash239 2012

[151] G R Nicol M Han J Kim et al ldquoUse of an immunoaffinity-mass spectrometry-based approach for the quantificationof protein biomarkers from serum samples of lung cancerpatientsrdquo Molecular amp Cellular Proteomics vol 7 no 10 pp1974ndash1982 2008

[152] V Kulasingam C R Smith I Batruch A Buckler D AJeffery and E P Diamandis ldquolsquoProduct ion monitoringrsquo assayfor prostate-specific antigen in serum using a linear ion-traprdquoJournal of Proteome Research vol 7 no 2 pp 640ndash647 2008

[153] M J Berna Y Zhen D E Watson J E Hale and B L Acker-mann ldquoStrategic use of immunoprecipitation and LCMSMSfor trace-level protein quantification myosin light chain 1 abiomarker of cardiac necrosisrdquoAnalytical Chemistry vol 79 no11 pp 4199ndash4205 2007

[154] E E Niederkofler D A Phillips B Krastins et al ldquoTargetedselected reactionmonitoringmass spectrometric immunoassayfor insulin-like growth factor 1rdquo PLoS ONE vol 8 Article IDe81125 2013

[155] V Brun C Masselon J Garin and A Dupuis ldquoIsotopedilution strategies for absolute quantitative proteomicsrdquo Journalof Proteomics vol 72 no 5 pp 740ndash749 2009

[156] D Lebert A Dupuis J Garin C Bruley and V Brun ldquoPro-duction and use of stable isotope-labeled proteins for absolutequantitative proteomicsrdquoMethods inMolecular Biology vol 753pp 93ndash115 2011

[157] G Picard D Lebert M Louwagie et al ldquoPSAQ standards foraccurate MS-based quantification of proteins from the conceptto biomedical applicationsrdquo Journal of Mass Spectrometry vol47 no 10 pp 1353ndash1363 2012

[158] R W Nelson D Nedelkov K A Tubbs and U A KiernanldquoQuantitative mass spectrometric immunoassay of insulin like


growth factor 1rdquo Journal of Proteome Research vol 3 no 4 pp851ndash855 2004

[159] M R Stratton P J Campbell and P A Futreal ldquoThe cancergenomerdquo Nature vol 458 no 7239 pp 719ndash724 2009

[160] G R Bignell C D Greenman H Davies et al ldquoSignatures ofmutation and selection in the cancer genomerdquo Nature vol 463no 7283 pp 893ndash898 2010

[161] QWang R Chaerkady JWu et al ldquoMutant proteins as cancer-specific biomarkersrdquo Proceedings of the National Academy ofSciences of the United States of America vol 108 no 6 pp 2444ndash2449 2011

[162] D P Little A Braun M J OrsquoDonnell and H Koster ldquoMassspectrometry from miniaturized arrays for full comparativeDNA analysisrdquo Nature Medicine vol 3 no 12 pp 1413ndash14161997

[163] N L Anderson N G Anderson L R Haines D B HardieR W Olafson and T W Pearson ldquoMass spectrometric quanti-tation of peptides and proteins using Stable Isotope Standardsand Capture by Anti-Peptide Antibodies (SISCAPA)rdquo Journalof Proteome Research vol 3 no 2 pp 235ndash244 2004

[164] J R Whiteaker L Zhao H Y Zhang et al ldquoAntibody-based enrichment of peptides on magnetic beads for mass-spectrometry-based quantification of serum biomarkersrdquo Ana-lytical Biochemistry vol 362 no 1 pp 44ndash54 2007

[165] N L Anderson A Jackson D Smith D Hardie C Borchersand T W Pearson ldquoSISCAPA peptide enrichment on magneticbeads using an in-line bead trap devicerdquo Molecular amp CellularProteomics vol 8 no 5 pp 995ndash1005 2009

[166] J R Whiteaker L Zhao L Anderson and A G PaulovichldquoAn automated and multiplexed method for high through-put peptide immunoaffinity enrichment and multiple reactionmonitoring mass spectrometry-based quantification of proteinbiomarkersrdquoMolecular and Cellular Proteomics vol 9 no 1 pp184ndash196 2010

[167] R M Schoenherr L Zhao J R Whiteaker et al ldquoAutomatedscreening of monoclonal antibodies for SISCAPA assays usingamagnetic bead processor and liquid chromatography-selectedreaction monitoring-mass spectrometryrdquo Journal of Immuno-logical Methods vol 353 no 1-2 pp 49ndash61 2010

[168] M Razavi M E Pope M V Soste et al ldquoMALDI Immuno-screening (MiSCREEN) a method for selection of anti-peptidemonoclonal antibodies for use in immunoproteomicsrdquo Journalof Immunological Methods vol 364 no 1-2 pp 50ndash64 2011

[169] J D Reid D T Holmes D R Mason B Shah and C HBorchers ldquoTowards the development of an immuno MALDI(iMALDI) mass spectrometry assay for the diagnosis of hyper-tensionrdquo Journal of the American Society for Mass Spectrometryvol 21 no 10 pp 1680ndash1686 2010

[170] J Jiang C E Parker K A Hoadley C M Perou G Boysenand C H Borchers ldquoDevelopment of an immuno tandemmassspectrometry (iMALDI) assay for EGFR diagnosisrdquo ProteomicsClinical Applications vol 1 no 12 pp 1651ndash1659 2007

[171] M H Elliott D S Smith C E Parker and C BorchersldquoCurrent trends in quantitative proteomicsrdquo Journal of MassSpectrometry vol 44 no 12 pp 1637ndash1660 2009

[172] J RWhiteaker C Lin J Kennedy et al ldquoA targeted proteomics-based pipeline for verification of biomarkers in plasmardquoNatureBiotechnology vol 29 no 7 pp 625ndash634 2011

[173] S A Agger L C Marney and A N Hoofnagle ldquoSimultaneousquantification of apolipoprotein A-I and apolipoprotein Bby liquid-chromatography-multiple-reaction-monitoring mass

spectrometryrdquo Clinical Chemistry vol 56 no 12 pp 1804ndash18132010

[174] B MacLean D M Tomazela N Shulman et al ldquoSkylinean open source document editor for creating and analyzingtargeted proteomics experimentsrdquo Bioinformatics vol 26 no 7pp 966ndash968 2010

[175] D B Martin T Holzman DMay et al ldquoMRMer an interactiveopen source and cross-platform system for data extractionand visualization ofmultiple reactionmonitoring experimentsrdquoMolecular and Cellular Proteomics vol 7 no 11 pp 2270ndash22782008

[176] M-Y K Brusniak S-T Kwok M Christiansen et al ldquoATAQSa computational software tool for high throughput transitionoptimization and validation for selected reaction monitoringmass spectrometryrdquoBMCBioinformatics vol 12 article 78 2011

[177] L Reiter O Rinner P Picotti et al ldquoMProphet automateddata processing and statistical validation for large-scale SRMexperimentsrdquo Nature Methods vol 8 no 5 pp 430ndash435 2011

[178] P Picotti H Lam D Campbell et al ldquoA database of massspectrometric assays for the yeast proteomerdquo Nature Methodsvol 5 no 11 pp 913ndash914 2008

[179] S E Abbatiello D R Mani H Keshishian and S A CarrldquoAutomated detection of inaccurate and imprecise transitionsin peptide quantification by multiple reaction monitoring massspectrometryrdquo Clinical Chemistry vol 56 no 2 pp 291ndash3052010

[180] M Mann ldquoComparative analysis to guide quality improve-ments in proteomicsrdquoNatureMethods vol 6 no 10 pp 717ndash7192009

[181] E F Petricoin III A M Ardekani and B A Hitt ldquoUse ofproteomic patterns in serum to identify ovarian cancerrdquo TheLancet vol 364 no 9434 p 582 2004

[182] E P Diamandis ldquoCancer biomarkers can we turn recentfailures into successrdquo Journal of the National Cancer Institutevol 102 no 19 pp 1462ndash1467 2010

[183] E P Diamandis ldquoProteomic patterns in biological fluidsdo they represent the future of cancer diagnosticsrdquo ClinicalChemistry vol 49 no 8 pp 1272ndash1275 2003

[184] E P Diamandis ldquoMass spectrometry as a diagnostic and acancer biomarker discovery tool opportunities and potentiallimitationsrdquoMolecular and Cellular Proteomics vol 3 no 4 pp367ndash378 2004

[185] E P Diamandis ldquoAnalysis of serumproteomic patterns for earlycancer diagnosis drawing attention to potential problemsrdquoJournal of the National Cancer Institute vol 96 no 5 pp 353ndash356 2004

[186] E F Petricoin K C Zoon E C Kohn J C Barrett and L ALiotta ldquoClinical proteomics translating benchside promise intobedside realityrdquoNature Reviews Drug Discovery vol 1 no 9 pp683ndash695 2002

[187] K Chapman ldquoThe ProteinChip biomarker system fromciphergen biosystems a novel proteomics platform for rapidbiomarker discovery and validationrdquo Biochemical Society Trans-actions vol 30 no 2 pp 82ndash87 2002

[188] D McLerran W E Grizzle Z Feng et al ldquoSELDI-TOF MSwhole serum proteomic profiling with IMAC surface does notreliably detect prostate cancerrdquo Clinical Chemistry vol 54 no1 pp 53ndash60 2008

[189] A W Bell E W Deutsch C E Au et al ldquoA HUPO test samplestudy reveals common problems in mass spectrometry-basedproteomicsrdquo Nature Methods vol 6 no 6 pp 423ndash430 2009


[190] D B Friedman T M Andacht M K Bunger et al ldquoThe ABRFProteomics Research Group Studies Educational exercises forqualitative and quantitative proteomic analysesrdquo Proteomicsvol 11 no 8 pp 1371ndash1381 2011

[191] A G Paulovich D Billheimer A LHam et al ldquoInterlaboratorystudy characterizing a yeast performance standard for bench-marking LC-MS platform performancerdquoMolecular and CellularProteomics vol 9 no 2 pp 242ndash254 2010

[192] P A Rudnick K R Clauser L E Kilpatrick et al ldquoPerformancemetrics for liquid chromatography-tandem mass spectrometrysystems in proteomics analysesrdquo Molecular and Cellular Pro-teomics vol 9 no 2 pp 225ndash241 2010

[193] D L Tabb L Vega-Montoto P A Rudnick et al ldquoRepeatabil-ity and reproducibility in proteomic identifications by liquidchromatography-tandem mass spectrometryrdquo Journal of Pro-teome Research vol 9 no 2 pp 761ndash776 2010

[194] A Beasley-Green D Bunk P Rudnick L Kilpatrick andK Phinney ldquoA proteomics performance standard to supportmeasurement quality in proteomicsrdquo Proteomics vol 12 no 7pp 923ndash931 2012

[195] S E Abbatiello D R Mani B Schilling et al ldquoDesign imple-mentation andmultisite evaluation of a system suitability proto-col for the quantitative assessment of instrument performancein liquid chromatography-multiple reaction monitoring-MS(LC-MRM-MS)rdquo Molecular amp Cellular Proteomics vol 12 pp2623ndash2639 2013

[196] J D Theis S Dasari J A Vrana P J Kurtin and A DoganldquoShotgun-proteomics-based clinical testing for diagnosis andclassification of amyloidosisrdquo Journal of Mass Spectrometry vol48 pp 1067ndash1077 2013

[197] Z Zhang and D W Chan ldquoThe road from discovery to clinicaldiagnostics lessons learned from the first FDA-cleared in vitrodiagnostic multivariate index assay of proteomic biomarkersrdquoCancer Epidemiology Biomarkers and Prevention vol 19 no 12pp 2995ndash2999 2010

[198] F E Regnier S J Skates M Mesri et al ldquoProtein-basedmultiplex assaysMock presubmissions to theUS food and drugadministrationrdquo Clinical Chemistry vol 56 no 2 pp 165ndash1712010

[199] H A Yeong Y L Ji Y L Ju Y Kim H K Jeong and S Y JongldquoQuantitative analysis of an aberrant glycoform of TIMP1 fromcolon cancer serum by L-PHA-enrichment and SISCAPA withMRMmass spectrometryrdquo Journal of Proteome Research vol 8no 9 pp 4216ndash4224 2009

[200] B Wollscheid D Bausch-Fluck C Henderson et al ldquoMass-spectrometric identification and relative quantification of N-linked cell surface glycoproteinsrdquo Nature Biotechnology vol 27no 4 pp 378ndash386 2009

[201] S Decramer S Wittke H Mischak et al ldquoPredicting theclinical outcome of congenital unilateral ureteropelvic junctionobstruction in newborn by urinary proteome analysisrdquo NatureMedicine vol 12 no 4 pp 398ndash400 2006

[202] N L Anderson N G Anderson TW Pearson et al ldquoA humanproteome detection and quantitation projectrdquo Molecular andCellular Proteomics vol 8 no 5 pp 883ndash886 2009

[203] J Munoz T Y Low Y J Kok et al ldquoThe quantitative proteomesof human-induced pluripotent stem cells and embryonic stemcellsrdquoMolecular Systems Biology vol 7 article 550 2011

[204] N Nagaraj J R Wisniewski T Geiger et al ldquoDeep proteomeand transcriptome mapping of a human cancer cell linerdquoMolecular Systems Biology vol 7 article 548 2011

[205] R M Branca L M Orre H J Johansson et al ldquoHiRIEF LC-MS enables deep proteome coverage and unbiased proteoge-nomicsrdquo Nature Methods vol 11 pp 59ndash62 2014

[206] T Y Low S van Heesch H van den Toorn and et al ldquoQuanti-tative and qualitative proteome characteristics extracted fromin-depth integrated genomics and proteomics analysisrdquo CellReports vol 5 pp 1469ndash1478 2013

[207] A F M Altelaar J Munoz and A J R Heck ldquoNext-generationproteomics towards an integrative view of proteome dynamicsrdquoNature Reviews Genetics vol 14 no 1 pp 35ndash48 2013

[208] WW SoonMHariharan andM P Snyder ldquoHigh-throughputsequencing for biology and medicinerdquo Molecular Systems Biol-ogy vol 9 article 640 2013

[209] K Ning D Fermin and A I Nesvizhskii ldquoComparativeanalysis of different label-free mass spectrometry based proteinabundance estimates and their correlation with RNA-Seq geneexpression datardquo Journal of Proteome Research vol 11 no 4 pp2261ndash2271 2012

[210] EVenter RD Smith and SH Payne ldquoProteogenomic analysisof bacteria and archaea a 46 organism case studyrdquo PLoS ONEvol 6 no 11 Article ID e27587 2011

[211] S Renuse R Chaerkady and A Pandey ldquoProteogenomicsrdquoProteomics vol 11 no 4 pp 620ndash630 2011

[212] J Cox andMMann ldquoQuantitative high-resolution proteomicsfor data-driven systems biologyrdquo Annual Review of Biochem-istry vol 80 pp 273ndash299 2011

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


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Disease Markers


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proteins and translates this information The possibility tosystematically quantify protein abundances positions pro-teomics to monitor heterogeneous alterations in multiplepathways and mechanisms that drive the transformation ofthe malignant phenotype Proteomics can be considered asan integral part of cancer research to identify biomarkersto detect patients at the early stage monitor drug responseof tumors understand mechanisms that lead to cancerpathogenesis and design new therapeutics Scientists andoncologists thus use the various proteomics tools designexperiments and interpret results of proteomics to determinethe causative mechanisms guide prognostication and evendevelop precision medicine for cancer treatment

2 Genomics

A key role of proteins in realizing the full potential of thehuman genome project (HGP) is linking the genome tonormal and disease phenotypesTheHGP has changed manyaspects of human biology and medical research includingcancer Despite many skeptics the HGP became a reality bydaring goals and new technology platforms [3] Advancedtechnology platforms for sequencing dramatically changedthe study of genes and gene regulation in all organisms[4] The HGP has made all genes accessible to biologists byproviding a part list of genes and putative protein productsand stimulated a new perspective in studying biologicalprocesses through systems biology Furthermore the HGPhas helped the creation of whole new commercial sectorwith high throughput instruments reagents and servicesand opened the door for large data sets open-source dataalong with large-scale application of bioinformaticsThe fieldhas transformed our thinking about cancer diagnostics andtargeted therapies Despite all of these achievements skepticsare still concerned about the slow progress in transformingpublic health

Both endogenous and exogenous stimuli may result inevading the normal regulatory mechanisms of the cells withultimate aberrant phenotypes enabling cancer cell to prolif-erate invade and metastasize Tumors contain endogenousaberrations that are largely caused by somatic alterationsof genome that result in mutations of oncogenes andortumor suppressors [5] For example deletions insertion copynumber variations and mutations can drive initiation andprogression of cancer [6] Genomics the comprehensive largescale analysis of gene expression in biological specimensto determine their associations with disease or treatmenthas particularly been growing since 2005 New types ofsequencing instruments that permit amazing acceleration ofdata-collection rates for DNA sequencing were introducedby commercial manufacturers including platforms such asmassively parallel sequencing (MPS) For example singleinstruments can generate data to decipher an entire humangenomewithin only 2weeks It is anticipated that instrumentsthat will further accelerate this whole genome sequencingdata production timeline to days or hours will be a reality inthe near future [7]

The main challenge in genomics today is to understandthe role of molecular aberrations in various diseases such

as cancer [8] The Cancer Genome Atlas (TCGA) project(httpcancergenomenihgov) and the International CancerGenome Consortium (httpwwwicgcorg) aim to deter-mine the genomic aberrations in human cancer types andtheir roles in pathophysiology of cancers TCGA launched asa three-year pilot in 2006 by the National Cancer Institute(NCI) and National Human Genome Research Institute(NHGRI) in theUnited States TCGApilot project confirmedthat an atlas of changes could be created It also provedthat a network of research and technology teams could poolthe results of their efforts and develop an infrastructurefor making the data publicly accessible Moreover it provedthat public availability of data would enable researchers tovalidate important discoveries The success of the pilot ledthe National Institutes of Health (NIH) to commit majorresources to TCGA to collect and characterize more than20 additional tumor types (httpcancergenomenihgov)The ultimate goal is to translate genomics data into clinicalapplications such as routine clinical screening and precisionmedicine

Classification of cancer and diagnosis has been based oncellular morphology and histological architecture Howeverpatients with similar histopathology cancer staging andtreatments have shown variable clinical outcomes Suchmethods are subjective andprone to interpretation variabilityand pathologists do not always agree on the diagnosisTherefore diagnostic tools for cancers have evolved fromhistology to methods such as genomic testing with FISHmicroarrays and chromosome karyotype analysis For exam-ple gene expression profiles can be used as unique molecularsignatures to aid diagnosis and classify histopathologicallysimilar tumors into biologically distinct subtypes [9] Molec-ular signatures can be used to identify patients with highrisk for occurrence and poor survival and receive targetedtherapies Mammaprint is a Food and Drug Administration(FDA) approved breast cancer recurrence microarray-basedtest [10] The Oncotype DX assay is used to predict riskfor distant recurrence among invasive breast cancers [11] Arecent example of genomic signature for prognosis predictionof stages II and III colorectal cancer is ColoPrint [12] Inaddition to biomarkers genomics has led to discovery of newgenes not previously known to play a causal role in cancer andleads to cancer therapeutics [3] Some of the first sequencingattempts were focused in receptor tyrosine kinases (RTKs)in early 2000s The studies indicated mutations in BRAF in50 of melanomas [13] PIK3CA in 25ndash30 of breast andcolorectal cancers [14] and EFGR in 10ndash15 of non-smallcell lung cancers [15ndash17] Interestingly some of the findingshave resulted in a major impact on drug development andclinical treatment including the development of selectiveRAF and MEK inhibitors that have produced dramaticremissions in melanoma and the ability to target the use ofEGFR inhibitors to the subset of lung cancer patients whoderive benefit

3 Proteomic Challenge

By analogy democratization of protein studies by generatinga broader and deeper parts list to enable systems biology as











Proteome

Targeted

workflow

Discovery

workflow


Protein digestion

into peptides

Proteotypic

peptides

identification

Develop SRM assay

Obtain SRM for


Obtain spectra

Measure protein

concentration

Match spectra

to library

Peptide

identification

Protein

inference

ESI-LC-MS








2




























protein biomarkers


Clinical testing

FDA approval


100s of candidate









10sbiospecimens

LC-MSMS

100sbiospecimens

1000sbiospecimens

MRM -MS Immunoassay

Panel of biomarkers

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Bios

peci

men

s





Analyte

Internalstandard

Inte

nsity

Inte

nsity



Native protein


Protein digestion

Analyte

Internalstandard

Inte

nsity

Inte

nsity

MRM-MS

MRM-MS

Trypsin digesti

on

mz

mz



0

50

100

150

200

250

300

350

400

2002 2004 2006 2008 2010 2012Year

Publ

icat

ion





















































8 Proteogenomics










Abbreviations









cell culture




Disclaimer




References






































































































































































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of


























Proteome

Targeted

workflow

Discovery

workflow


Protein digestion

into peptides

Proteotypic

peptides

identification

Develop SRM assay

Obtain SRM for


Obtain spectra

Measure protein

concentration

Match spectra

to library

Peptide

identification

Protein

inference

ESI-LC-MS








2




























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Clinical testing

FDA approval


100s of candidate









10sbiospecimens

LC-MSMS

100sbiospecimens

1000sbiospecimens

MRM -MS Immunoassay

Panel of biomarkers

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Bios

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men

s





Analyte

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Inte

nsity

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nsity



Native protein


Protein digestion

Analyte

Internalstandard

Inte

nsity

Inte

nsity

MRM-MS

MRM-MS

Trypsin digesti

on

mz

mz



0

50

100

150

200

250

300

350

400

2002 2004 2006 2008 2010 2012Year

Publ

icat

ion





















































8 Proteogenomics










Abbreviations









cell culture




Disclaimer




References






































































































































































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















Proteome

Targeted

workflow

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workflow


Protein digestion

into peptides

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peptides

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concentration

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to library

Peptide

identification

Protein

inference

ESI-LC-MS








2




























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Clinical testing

FDA approval


100s of candidate









10sbiospecimens

LC-MSMS

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MRM -MS Immunoassay

Panel of biomarkers

CPTAC




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men

s





Analyte

Internalstandard

Inte

nsity

Inte

nsity



Native protein


Protein digestion

Analyte

Internalstandard

Inte

nsity

Inte

nsity

MRM-MS

MRM-MS

Trypsin digesti

on

mz

mz



0

50

100

150

200

250

300

350

400

2002 2004 2006 2008 2010 2012Year

Publ

icat

ion





















































8 Proteogenomics










Abbreviations









cell culture




Disclaimer




References






































































































































































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

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Analyte

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nsity

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nsity



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Analyte

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nsity

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nsity

MRM-MS

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Trypsin digesti

on

mz

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0

50

100

150

200

250

300

350

400

2002 2004 2006 2008 2010 2012Year

Publ

icat

ion





















































8 Proteogenomics










Abbreviations









cell culture




Disclaimer




References






































































































































































































































of






Disease Markers



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PPAR Research



Journal of

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nsity

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nsity



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nsity

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MRM-MS

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Trypsin digesti

on

mz

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0

50

100

150

200

250

300

350

400

2002 2004 2006 2008 2010 2012Year

Publ

icat

ion





















































8 Proteogenomics










Abbreviations









cell culture




Disclaimer




References






































































































































































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

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100s of candidate









10sbiospecimens

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nsity

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nsity



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nsity

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nsity

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on

mz

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0

50

100

150

200

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300

350

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2002 2004 2006 2008 2010 2012Year

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icat

ion





















































8 Proteogenomics










Abbreviations









cell culture




Disclaimer




References






































































































































































































































of






Disease Markers



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PPAR Research



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Trypsin digesti

on

mz

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50

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350

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2002 2004 2006 2008 2010 2012Year

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icat

ion





















































8 Proteogenomics










Abbreviations









cell culture




Disclaimer




References






































































































































































































































of






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PPAR Research



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nsity

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nsity



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nsity

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Trypsin digesti

on

mz

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0

50

100

150

200

250

300

350

400

2002 2004 2006 2008 2010 2012Year

Publ

icat

ion





















































8 Proteogenomics










Abbreviations









cell culture




Disclaimer




References






































































































































































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

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nsity

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nsity

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nsity

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Trypsin digesti

on

mz

mz



0

50

100

150

200

250

300

350

400

2002 2004 2006 2008 2010 2012Year

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icat

ion





















































8 Proteogenomics










Abbreviations









cell culture




Disclaimer




References






































































































































































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















0

50

100

150

200

250

300

350

400

2002 2004 2006 2008 2010 2012Year

Publ

icat

ion





















































8 Proteogenomics










Abbreviations









cell culture




Disclaimer




References






































































































































































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of





























































8 Proteogenomics










Abbreviations









cell culture




Disclaimer




References






































































































































































































































of






Disease Markers



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PPAR Research



Journal of

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8 Proteogenomics










Abbreviations









cell culture




Disclaimer




References






































































































































































































































of






Disease Markers



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PPAR Research



Journal of

ObesityJournal of















































8 Proteogenomics










Abbreviations









cell culture




Disclaimer




References






































































































































































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

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8 Proteogenomics










Abbreviations









cell culture




Disclaimer




References






































































































































































































































of






Disease Markers



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PPAR Research



Journal of

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8 Proteogenomics










Abbreviations









cell culture




Disclaimer




References






































































































































































































































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Disease Markers



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PPAR Research



Journal of

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8 Proteogenomics










Abbreviations









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Disclaimer




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Review Article Advances in Proteomic Technologies and Its ...downloads.hindawi.com › journals › amed › 2014 › 238045.pdf · the malignant phenotype. Proteomics can be considered