REVIEW

Proteomic urinary biomarker approach in renaldisease: from discovery to implementation

Joost P. Schanstra & Harald Mischak

Received: 5 November 2013 /Revised: 10 February 2014 /Accepted: 11 February 2014# IPNA 2014

Abstract Biomarkers hold the promise of significantly im-proving health care by enabling prognosis and diagnosis withimproved accuracy, and at earlier points in time. Previousresults have indicated that single biomarkers are not suitableto describe complex diseases such as kidney disease. Here weprovide an update on the progress of urinary proteomics-basedstudies and strategies to develop biomarker-based classifiersthat tolerate instability and inconsistency of individual bio-markers. The examples focus on two major fields in nephrol-ogy: chronic kidney disease in the adult population and ob-structive nephropathies in the pediatric population. Whenemployed adequately, urinary proteomics demonstrates a clearvalue in kidney disease, indicating that the current status quoruling for decades now could be changed by applying modern“omics” approaches. However, while research is able to de-liver these useful tools for patient management, the issuesassociated with implementation are not yet solved. Activeengagement of the relevant clinical professional societies, aswell as patient’s organizations, might help to implement theseomics approaches that have shown a clear benefit for thepatient.

Keywords Clinical proteomics . Biomarker panels . Chronickidney disease . Obstructive nephropathy . Urine . Patientbenefit . Disease progression

Introduction

Early detection of disease in combination with appropriatetherapeutic intervention is generally accepted as the most effec-tive and beneficial approach for patients, and hence amain focusin medicine. In kidney disease, most of the need lies in thedetection of patients at risk of progression of disease. However,although individual markers of kidney disease have been iden-tified and are used in the clinic, they do not faithfully fulfill theirrole. For example, in diabetic nephropathy (DN),microalbuminuria (30–300 mg/24 h or 20–200 μg/min) is con-sidered as risk factor for development of DN or an early indica-tor of future onset of DN. However, the value ofmicroalbuminuria as a predictor of DN is questioned, since itis not specific for DN and has also been observed as highlyvariable, substantially compromising its specificity [1, 2]. Fur-ther, onset of DN in the absence of overt albuminuria has beenreported in almost half of a cohort of type I diabetic patients [3],indicating also a lack of sensitivity. Significant reduction of theglomerular filtration rate (GFR) is a clear indication of advancedchronic kidney disease (CKD). However, in this situation, suc-cess of treatment is severely compromised by the presence ofgenerally irreversible structural damage [4]. Similar observa-tions hold for imaging techniques. Although imaging tools haveclearly improved the detection of congenital abnormalities of thekidney and urinary tract (CAKUT), ultrasound, often combinedwith fetal urine biochemistry, does not allow to faithfully predictprogression of renal disease in fetuses with CAKUT [5, 6].

New molecular technologies enabling the definition ofbiomarker panels of disease potentially allow major improve-ment of the current state of the art. Here we will present the

J. P. SchanstraInstitut National de la Santé et de la Recherche Médicale (INSERM),U1048, Institut of Cardiovascular and Metabolic Disease, Toulouse,France

J. P. SchanstraUniversité Toulouse III Paul-Sabatier, Toulouse, France

J. P. Schanstra :H. MischakMosaiques Diagnostics & Therapeutics, Hannover, Germany

H. Mischak (*)BHF Glasgow Cardiovascular Research Centre, Institute ofCardiovascular andMedical Sciences, Faculty ofMedical, Veterinaryand Life Sciences, University of Glasgow, Glasgow, UKe-mail: mischak@mosaiques-diagnostics.com

Pediatr NephrolDOI 10.1007/s00467-014-2790-y

approach for the identification and validation of new urinarybiomarkers of kidney disease by proteomics that can serve asnew and better indicators of disease progression.

Why omics?

The term “omics” describes the scientific approach aiming atstudying a distinct class of biological molecules in a compre-hensive way. The application of “omics” towards a deeperunderstanding of (patho) physiology is in part also owed to theobservation that complex organisms, experiencing a multitudeof environmental impacts, cannot be described in detail bysingle features. However, when we assess all features of aspecific omics trait and subsequently investigate the molecularchanges of all analyzed features associated with a specificpathophysiology, we should be able to assess disease withvery high precision. This principle is exemplified in Fig. 1awhere the difference of two patient groups is described bybiomarkers x, y, and z (one patient group is represented bygreen circles while the other group is depicted by pinkcrosses). Biomarker x shows highly significant differences inthe distribution between the two groups, but does not enableseparation of the two populations with sufficient accuracy.The addition of another dimension (y, i.e., an additional bio-marker) improves the separation of the two populations but isstill insufficiently accurate. The addition of biomarker z final-ly allows the separation of the two patient populations with100 % accuracy. This example shows that in theory the use of

multiple features (i.e., biomarkers) potentially obtained byomics approaches allows better description complex diseases.

Omics analysis can be performed on different biologicallevels (Fig. 1b). Genomics or transcriptomics aim to coverchanges within the genes and their expression levels. Al-though the genotype and the presence of specific mutationscan significantly increase the risk of developing certain dis-eases (e.g., EGFR in cancer [7]), this is in general the excep-tion and not the rule. In renal disease, it has been shown thathepatocyte nuclear factor 1 beta (HNF1β) mutations are as-sociated with a wide range of kidney malformations (hypo-plasia, dysplasia, cysts, etc.) and patients with HNF1β muta-tions have an extremely variable progression of disease[8–10]. Similarly, mutations in pdk1 or pdk2 are associatedwith autosomal dominant polycystic kidney disease(ADPKD), but the progression is highly variable, and appar-ently not associated with specific mutations [11]. Thus, al-though genes and mutations therein describe the predisposi-tion for a disease or its progression, actual disease activity isgenerally not captured by genomic analysis. Analysis of thetranscriptome (i.e., mRNA levels) might better describe thedisease activity, but obtaining in a reproducible manner non-invasively mRNA from for example urine, the sample-of-choice for kidney disease, is cumbersome. However, a recentlarge-scale study described the successful use of a urinary-cellmRNA profile to predict with reasonable precision acutecellular rejection in kidney allografts [12]. The study wascarried out using frozen (−80 °C) cellular pellets obtained

Metabolites Representative of short term changes in diseaseactivity

ProteinsCan represent short term andlong term changes in diseaseactivity

mRNACan represent short term andlong term changes in diseaseactivity but correlation withproteins levels is weak

GenesPredisposition for disease

A BFig. 1 a Assessment of multiplefeatures allows a betterdescription of complex diseases.In this example, a single feature(biomarker x) does notdistinguish between two patientgroups (defined by the greencircles versus the pink crosses).However, addition of biomarkersy and z allow a better descriptionof the differences between thepatients groups leading to a clearseparation of the two groups. bThe major biological levelscurrently studied with “omics”and their relation to diseaseactivity

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from freshly collected urine samples. The three-gene signaturein this study had 79 % sensitivity and 78 % specificity todiscriminate between biopsy specimens showing no rejectionand those showing acute cellular rejection. Prospective studiesto assess whether this urinary signature and the routine use ofurinary–cell mRNA can alter the requirement for allograft biop-sies and be used in transplant management will be necessary. Inaddition, it has been shown that mRNA levels do not wellrepresent the levels of expression of the associated proteins [13,14]. The proteome is most likely to represent the actual state of(patho) physiology and also enable prognosis of future diseaseactivity. Long-term key changes, for example the development offibrosis in CKD, will be reflected by changes in protein levels ofcomponents of the extracellular matrix (ECM) or by cross-linking of ECM. Another example is protein glycation in thecase of diabetes (e.g., glycated hemoglobin), which is represen-tative for the average plasma glucose concentration overprolonged periods of time. Proteome analysis also allows tomeasure short-term dynamic changes including release of cyto-kines in the circulation during local inflammation. In addition,especially the (urinary) peptidome, has been shown to be remark-ably stable [15]. Although levels of metabolites in bodily fluidsclearly reflect disease activity, the metabolites are subject to alarge degree of variability: e.g., food intake significantlymodifiesthe body fluid metabolite composition [16, 17]. In addition, themetabolite data may be less informative than proteome data, asmetabolite changes are difficult to directly track back to changesin protein activity and abundance. A technical limitation ofuntargeted metabolomics (based on mass spectrometry) is thatmost of the detected metabolites are still unidentified.

One can to some degree compare “omics” technologieswith the different states and parameters relevant in a fireplace(see also Fig. 2). Genomics is capable of displaying all infor-mation on the potential of an organism, similar to the logs in

the fireplace. As a result of its static nature, it can be assessedwith very high accuracy, is very comparable, but of limitedpredictive power. The fire in the fireplace represents theproteome, the integration of the genome with the environ-ment, depicting the current “status” of an organism. The highvariability and complexity (as also for fire) does representenormous analytical challenges, but at the same time indicatesthe very high potential on information available. The results ofprotein action are metabolites, comparable to the left-overafter the fire: the ashes. Analysis of ashes, or the metabolome,enables reflection on past events (action of proteins, or thefire), but in general not prognosis of future development.

Based on these observations, we have focused on theurinary proteome for the discovery of biomarkers of kidneydisease.

Urine as a source of protein biomarkers of renal disease

Urine was proposed as a potentially highly valuable source ofproteomic biomarkers in kidney disease and since then it hasevolved into a promising source of biomarkers for a largevariety of diseases [15, 18]. Under physiological conditions,around 70 % of the urinary proteins are estimated to originatefor the kidney and the urinary tract [19]. The remainderderives from circulating peptides and small proteins that aretransported in blood and filtered or secreted in urine. Hence,urine not only contains biomarkers for kidney disease butpotentially also indicators of diseases from more distant sites.Compared to plasma and serum, urine has the advantage thatthe highly abundant plasma proteins (except in strongly albu-minuric diseases) are absent. In addition, the urinary proteinand peptide content is particularly stable, probably in partowed to the fact that urine is “stored” in the bladder for severalhours at 37 °C, and any possible proteolysis has been

Genomics Proteomics Metabolics the potential the current status the left-over

Fig. 2 Omics technologies in the context of a fireplace. To depict the valueand potential of the different omics technologies, the analogy to a fireplacecan be stressed. Genomics, the information about the entire genome, is avery solid and stable approach. The genome can be very well assessed, itgenerally does not change during lifetime, and it reflects all potential of anindividual. However, which parts of the potential are actually executed isunknown. This is similar to the logs in a fireplace: stable, can be welldescribed, have all potential, but no information if, when, what type of fire

will be started. The proteome gives the information on the current “status”of an organism, which is highly variable, the net result of an array ofendogenous and exogenous impacts, difficult to assess comprehensively,undergoing constant changes, similar to the fire in the fireplace. The actionof the proteins result, at least to some degree, in metabolites. In analogy, theconsequence of fire is ashes. Hence, metabolomics can be depicted similarto the ashes, small compounds, fairly stable, and allowing conclusions onprevious action that led to their generation

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completed at the time of voiding. Finally, collection of urine isless invasive than blood collection and it can be obtained inlarge quantities. Possible drawbacks of urine include variabil-ity in the protein and peptides concentration due to differencesin the daily fluid intake, circadian rhythms, and exercise.However, this is only a minor shortcoming since it can becorrected by a variety of normalizing methods [20].

Clinical proteomics

The analysis of a “clinical sample” employing proteomicsdoes not necessarily lead to clinically useful biomarkers.Indeed, many early claims of candidate molecules discoveredby proteomics approaches were not substantiated in subse-quent studies [21]. This situation has created skepticism aboutthe value of clinical proteomics and has led to the generationof “guidelines of clinical proteomics”, both in the form asrecommendation articles [22, 23] and as submission require-ments in a number of journals frequently publishing articleson clinical proteomics. These recommendations are straight-forward and broadly applicable regardless of the analyticalplatform, the samples investigated, or epidemiological design.Briefly, these recommendations are:

& A clearly defined clinical question with clearly definedoutcomes/end-points, and a clear (potential) clinical/therapeutic consequence of its application. This may bethe most important recommendation: the biomarker mustdemonstrate a specific value in improving the current stateof the art, and it must bring a benefit to the patient.

& Appropriate positive and negative controls must be de-fined. A healthy control population does often not reflectthe clinical situation encountered and biomarkers identi-fied in such a study will potentially be invalid in follow-upstudies. Controls with related or similar diseases thatmatch the population in which the biomarkers will beapplied are clearly more appropriate. Hence, controls forbiomarkers of diabetic kidney disease would be diabeticpatients, age matched, without diabetic kidney disease.

& Supply of sufficient demographic and/or clinical dataallowing clear assessment of the disease state and rulingout of a potential inclusion bias.

& Sufficient information about the sampling methodologyincluding a description of specimen collection, handling,and storage (e.g., type of containers used, stabilizing so-lutions used) and collecting all samples in the same man-ner. A bias can be introduced if urine is collected at homeor in the clinic.

& Sufficient information about the experimental methodolo-gy. Separation technology and resolution of mass spec-trometers needs to be mentioned. From this follows thatprotein-based markers must be identified unambiguouslyby tandemmass spectrometry.With the current state-of-art

this is not always possible and in that case a uniqueidentifier based on parameters such as molecular mass,migration/retention time in separation, or interaction withan antibody must be provided. Hence the use of high-resolution mass spectrometers is preferred.

& Appropriate statistical approaches must be employed. Thestatistical analysis needs to account for the technical andbiological variability and confounding factors. An impor-tant issue in –omics studies is the high number of features(in the case of proteomics: proteins or peptides). Forexample, the simultaneous testing of 1,000 potential bio-markers at a level of p=0.05 will yield the erroneousidentification of approximately 50 spurious biomarkersby chance. Adjustments for multiple testing correct forthis fact [24], but where until recently only rarely appliedin clinical proteomics studies. The minimal sample sizerequired to detect valid biomarkers depends on the false-positive rate (α), the statistical power (1-β), and the effectsize (the distance between classes, e.g., control versuscase). The effect size and its variation are the most impor-tant factors for estimating the minimal sample size and istraditionally estimated from existing data [24]. However,preexisting data is typically not available in proteomicsexperiments [25]. A small preliminary experiment using12 cases and controls allows to estimate the necessarysample size based on resampling [24]. Then, based on thisestimate, the actual study should be performed. Howeverif this pilot study shows a small effect size, then it isunlikely that a good discriminator will be easily obtained.

& Confirmation in independent test-sets. Even when usingappropriate statistics, validation of biomarkers in an inde-pendent test set (not used for the discovery of the bio-markers) needs to be performed since most statistical ap-proaches used for biomarker evaluation assume (i) an evendistribution of features across the data (similar variance incontrol and disease groups, and the absence of covariates),(ii) that the findings can be generalized, and (iii) that anassociation exists only with the investigated condition. Thisis generally not true, and as a consequence, most bio-markers with promising results in a first data set will turnout to have less promising results in independent data sets.

In general, when following these guidelines, the chances ofidentification of clinically useful biomarkers are increasedsubstantially.

These steps, which ideally result in validated biomarkers,generally rely on good science and clinical practice. However,validated biomarkers will not automatically be clinically ap-plied. Successful implementation requires sufficiently large-scale validation trials. Hurdles on the way can be limitedaccess to appropriate specimens and insufficient funding,and, even if all of these have been met successfully, largecommunication gaps between the parties (clinicians,

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statisticians, health economists, and representatives of patientgroups, health insurance, pharmaceutical companies,biobanks, and regulatory agencies) involved in implementa-tion may effectively block implementation [23].

Examples of the urinary biomarker approach in the majorrenal diseases

Based on examples from our laboratories, we will expose theurinary biomarker approach leading to the identification ofstatistically sound biomarkers of renal disease in two majorfields in nephrology: CKD in the adult population and ob-structive nephropathies in the pediatric population. For anoverview on the proteomics-based discovery of urinary bio-markers of renal disease, the reader is referred to the Table 1.

Urinary biomarkers of CKD

In adults, the population with CKD now exceeds 10 % [26],with diabetes and hypertension being the leading causes in alldeveloped and developing countries [27]. Angiotensin-converting enzyme inhibition and angiotensin receptor block-ade slow down the progression of CKD [28, 29]. Although notstopping the development of CKD, patients and the healthsystem would clearly benefit from early and irrevocable de-tection of CKD.

As mentioned in the introduction, although routinely usedin the clinic, urinary albumin (especially microalbuminuria) isclearly not a specific indicator of development of CKD. Sim-ilarly, neutrophil gelatinase-associated lipocalin (NGAL), ini-tially identified as a marker of acute kidney disease [30], hasalso been proposed as a marker of progression of CKD.Although, increased NGAL monomer levels are observed inurine of patients with progressive, severe CKD [31], severalstudies show that NGAL does not allow to predict the risk ofprogression of CKD. In a large population of patients with

CKD (n=3,386 CKD patients), urinary NGAL did not im-prove the risk prediction of progressive CKD [32]. Further-more, urinary NGAL concentrations were not associated toGFR decline in type 2 diabetic patients [33]. Finally, a recentstudy questions the renal origin of NGAL where the authorsobserved comparable plasma NGAL concentrations, but in-creased compared to control subjects, in dialysis patients withand without kidneys [34].

Aiming at identification of individual biomarkers to predictCKD progression early, and with high accuracy, urinary pro-teome analysis based on electrophoresis coupled to massspectrometry (CE-MS) was employed for the discovery andvalidation of a panel of biomarkers for CKD. The overallstrategy is shown in Fig. 3.

Table 1 Major urinary proteome-based studies for the discovery ofbiomarkers of renal disease. Limited to studies carries out using capillaryelectrophoresis coupled to mass spectrometry and including an indepen-dent test-set

Disease Biological fluid Reference

Chronic kidney disease Urine [36, 37, 39]

Vascular disease in patients on dialysis Plasma [71]

Diabetic nephropathy Urine [72]

Ureteropelvic junction obstruction Urine [50, 53–55]

Acute kidney injury Urine [73]

Vesicoureteral reflux Urine [74]

Renal Fanconi syndrome Urine [75]

Acute renal allograft rejection Urine [76]

Posterior urethral valves Fetal urine [60]

Controls versus established CKD

(n=110 CKD, 34 HC)Good et al 2010

Prediction of progression of CKD?

Large scale interventional trial (n=3280)“PRIORITY”

Discovery(n=230 CKD, 379 HC)

Good et al 2010CKD273

(n=35 DN)Zürbig et al 2012

(n=44 DN, 44 HC)Roscioni et al 2013

(n=76 CKD)Argiles et al 2013

(n=22 DN)Andersen et al 2012

Validation

(n=165 DN)Siwy et al 2014

(n=62 CKD, n=75 HC)Molin et al 2012

(n=552 CKD)Schanstra, Zurbig et al

A

B

C

Fig. 3 Strategy employed for the identification of biomarkers of chronickidney disease by urinary proteomics. The strategy is based on the as-sumption that early biomarkers of disease are present in establishedchronic kidney disease (CKD) but potentially at reduced amplitudes andthat multiple biomarkers (e.g., panels) allow a better description of com-plex chronic disease. a This allows including high numbers of patientswith established CKD in the discovery phase and avoiding under poweringof the study. b The resulting panel of CKD biomarkers is subsequentlyvalidated using different cohorts and evidence is gathered that the classifiercan predict progression of CKD as well. c Once sufficient evidence isobtained about predictive abilities of the classifier, it is employed in aproteomics-driven trail to interfere with the development of DN (seeFig. 4). CKD chronic kidney disease patients from diverse origins; HChealthy controls; DN diabetic nephropathy; CKD273 urinary peptidebased classifier of CKD; PRIORITY Proteomic prediction and Reninangiotensin aldosterone system Inhibition prevention Of early diabeticnephRopathy In TYpe 2 diabetic patients with normoalbuminuria

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Ideal samples for the discovery of biomarkers predictive ofCKD would be those collected from patients prior to anyclinically visible signs of disease. In addition, sufficientfollow-up information should be available to determinewhether a patient evolves towards a hard clinical end-point(in the case of CKD doubling of serum creatinine or end-stagerenal disease (ESRD)) and associate the presence of markersin these early samples to progression of disease. However, inthe case of a chronic disease such as CKD, the follow-up tothe hard clinical end-points would needmore than a decade. Inthe case of CKD, macroalbuminuria or an estimatedglomerular filtration rate (eGFR)<45 ml/min/1.73 m2 (e.g.,∼stage 3a CKD as defined by the KDOQI guidelines [35]),may be considered valid surrogate end-points although for theindividual patients progression to ESRD is still variable.However, development to macroalbuminuria or the above-mentioned eGFR cut-off in patients without CKD still maytake at least 5 years. Hence, based on the assumption that earlychanges in the urinary proteome should be visible as well inthe advanced disease (although potentially at a reduced am-plitude), we chose for the identification of biomarkers of CKDto compare healthy controls with established CKD patientsfrom different etiologies. The value of these biomarkers couldbe tested in a second step for their value in early detection ofCKD. This strategy, using cross-sectional samples, has the bigadvantage that significant numbers of samples can be obtainedfor discovery studies allowing to accommodate for the varia-tion observed in the, mostly, older CKD population. This alsoavoids under powering of the study, frequently observed infailing biomarker studies [24]. Hence, for the discovery ofbiomarkers of CKD, we used urine samples from 230 patientswith CKD of different disease etiologies and 379 healthycontrols (Fig. 3a). This resulted in the identification 634peptides that differed significantly between these two cohorts.Of these 634 peptides, 273 could be sequenced. For modelbuilding, we choose to use only these 273 sequenced bio-markers. The 273 sequenced peptides were combined usingsupport vector machines (SVM) to a CKD-specific urinarybiomarker model (the “273CKD classifier”). As a first valida-tion, this biomarker model was tested in an independentblinded cohort of 144 samples including 110 CKD and 34healthy control samples. This blinded validation study basedon scoring with the CKD273 classifier lead to a classificationof CKD patients with a sensitivity of 86 % and a specificity of100 %, significantly outperforming serum creatinine, the cur-rently accepted biomarker for chronic renal disease [36]. Next,the CKD273 classifier was validated in a number additionalstudies (n>500 individuals) either in diabetic patients with orwithout DN [37, 38] or in CKD patients originating fromdifferent disease etiologies and healthy controls [39]. In allstudies, the CKD273 classifier allowed the identification ofpatients with CKD or DN with high sensitivity and specificity(ROC curves with an AUC from 0.95 to 0.99).

These independent studies clearly underlined the capacityof the classifier to detect patients with established CKD.Based on the assumption that early events in CKD are ampli-fied later in disease, we evaluated in the next step whether theCKD273 classifier could also be used in the prediction of theprogression of CKD (Fig. 3b). This was initially evaluated inthree small-scale studies. The classifier predicted progressionfrom normoalbuminuria to macroalbuminuria in diabetic pa-tients [40, 41], leading to improved prognosis based on allavailable classical risk factors [40] as well as development ofCKD in patients with renal disease originating from differentetiologies [42]. In a very recent study, the CKD273 classifiershowed a significantly higher association with the progressionof CKD than urinary albumin in a large cohort of CKDpatients (n=522, [Schanstra et al., unpublished data]). In thisstudy, we also observed, upon analyzing the association ofindividual urinary peptides to CKD progression, that the ma-jority of the proteins associated to the progression of CKDwere already included in the CKD273 classifier. Taken togeth-er, these results show that biomarkers of progression of CKDcan be obtained by comparing healthy controls with individ-uals with established CKD.

Based on the observation that the urinary proteome-basedCKD273 classifier enables detection of patients with risk ofCKD progression, efforts were initiated to employ theCKD273 classifier for patient stratification in a prospectiveintervention study (Fig. 3c). This multicenter prospective trial(Proteomic prediction and Renin angiotensin aldosterone sys-tem Inhibition prevention Of early diabetic nephRopathy InTYpe 2 diabetic patients with normoalbuminuria, PRIORI-TY), www.eu-priority.org (Fig. 4) aims to assess the value ofproteomics-guided therapeutic intervention in a large-scalemulticentric study in 13 different European centers. A totalof 3,280 patients will be assessed with the CKD273 classifierto detect patients at high or low risk of developing DN. Allhigh-risk patients will receive spironolactone (or placebo) astherapeutic drug to prevent or delay onset of DN. This studywill not only allow further demonstrating the predictive po-tential of the CKD273 classifier but also the value of clinicalproteomics in guiding early intervention.

Analysis of the urinary peptidome by, and the experimentalmethodology of, CE-MS have been extensively described[43–45] and basic issues including temperature stability,freeze/thaw stability, post-preparation stability, reproducibili-ty, intermediate precision, and time course have been studiedfor the CKD273 classifier. These studies [36] and recentlycarried out studies analyzing T2D samples of 15 centers [38]demonstrated that CE-MS-based urinary proteome analysis isrobust and analysis in different clinical centers give highlycomparable results. The relative intra-assay standard deviationof the CKD273 classifier was <7 % and the inter-assay <10 %[36]. The CKD273 classifier was further technically validatedincluding assessment of in vivo drug interference and inter-

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laboratory variability and resolution, mass accuracy, and am-plitude variability of individual peptides [46]. The inter-laboratory variability was low, indicating transferability ofthe technology: analysis of 86 samples (45 cases and 41controls) in three different laboratories (Germany, France,and UK) resulted in correlation coefficients of the CKD273scores of 0.881-0.904 between the three laboratories [46].

In conclusion, the CKD273 classifier, composed of amultimarker peptide panel that can be routinely and reproduc-ibly obtained by CE-MS-based urinary proteome analysis,allows detection of progression of CKD. This multidimen-sional classifier outperforms the currently used clinical tools.The above-cited PRIORITY trial and the implementation as athe routine clinical test of this classifier, as initiated in AbuDhabi recently, will clearly demonstrate the added value ofsuch a urinary proteome-based method.

Urinary biomarkers of obstructive nephropathies

Although diabetes is the major cause of ESRD in the adultpopulation, diabetes is only at the origin of 1-3 % (percentincident patients 2007–2011) of the pediatric patients withESRD in the USA (www.usrds.org). In children, cystic/hereditary/congenital diseases represent ∼36 % of the causesof ESRD in the period from 2007–2011 in the US (www.usrds.org) and Europe (incidence in 2008 [47]). Within thispopulation hypo/dysplasia represent around 12 % and con-genital obstructive nephropathy around 8 % of the incidentcases (period from 2007–2011 in the US, www.usrds.org ).Management of CKD in children obviously requires strategiesdifferent to those employed in adults. In adults, CKD is oftendriven by environmental factors (diabetes, hypertension, etc.)

and in children CKD generally is the result of developmentaldisease. The impact of developmental disease on progressionof renal failure is often severe or acute: the number of patientswith CAKUT ending up in renal replacement therapyprograms (RRT) peaks before the age of 4, then declines andthen peaks again at the age of 15–19, while RRT in renaldiseases other than CAKUT only progressively and slowlyincrease with age to peak at the age of 70–74 [48]. Hence, theclinical questions associated are different from those for CKDin adults. In ureteropelvic junction (UPJ) obstruction, the mainclinical question after birth is whether an infant requirespyeloplasty within the next 2 years [49]. In posterior urethralvalves (PUV), the main challenge is predicting post-natalrenal function that varies from normal renal function to severerenal failure either in utero or rapidly after birth [5, 6]. Hence,due to this more acute character of renal disease in infants andchildren the design of studies aiming at the discovery ofbiomarkers is different and can be based on hard clinicalend-points to be reached in 1∼2 years (Fig. 5). Finally, thereduced inter-individual variation observed in the infant pro-teome [50] allows to reduce the number of patients for bio-marker discovery as compared to studying CKD in adults,where disease is multifactorial and where the inter-individualvariability due to different exposure to environmental factorsduring life is substantially higher. The lower patient numbersnecessary for studies in pediatric renal disease are warmlywelcomed since almost all severe renal diseases are rare.

Ureteropelvic junction obstruction UPJ obstruction is a de-velopmental disease with an incidence of 1/1,500 births [51].UPJ obstruction is characterized by a stenosis at the intersec-tion of one of the kidneys and the ureter that can induce

Diabetes mellitus Type 2 Normoalbuminuria

n=3280 Urinary

proteome analysis

positive (at risk)

656

20% 80%

negative (no risk)

2624

+therapy no therapy

micro micro

test α = 0.05 β = 0.8

no therapy

micro

test α = 0.05 β = 0.8

50% 50%

25% 35%

100%

4%

sample size to establish validity of separation based on urinary biomarkers 25 / group

sample size to establish significant benefit of proteomics-guided intervention 328 / group

Fig. 4 Design of a proteomics-driven intervention trial tointerfere with development ofdiabetic nephropathy (DN).Underlying assumptions are that20 % of normoalbuminuricpatients (diabetes duration: 5–10 years) will showpathophysiological changesindicative for early stages of DN.Targeted therapeutic interventionwill reduce the development ofmicroalbuminuria during a periodof 3 years in this selected cohortfrom 35 to 25 %. A total of 3,280patients will be screened. It isexpected that 656 will beidentified at risk to develop DN.These will be randomized fortreatment with spironolactone, orplacebo. Reprinted withpermission from Mischak andRossing [70]

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accumulation of urine in the kidney. Destruction of renalparenchyma is observed in severe cases of UPJ obstruction(Fig. 5a). In the majority of the cases, UPJ obstruction isdetected before birth during routine ultrasound. UPJ obstruc-tion is often unilateral and therefore generally not considered asevere disease, but even if the lesions in UPJ obstructionappear in general minimal, a number of newborns with inter-mediate obstruction levels need close and repetitive surveil-lance up to the age of 2 years (Fig. 5a) using intravenousradioactive tracers (scintigraphy) to determine if the stenosishas to be corrected by surgery. A number of laboratories haveinitiated research for urinary markers of UPJ obstruction ableto predict at an early stage whether a patient with UPJ ob-struction will require surgery. The development of such a non-invasive tool based on urinary markers would help to reduce

the number of scintigraphies, ultrasounds, and hospital visitsfor the follow-up of UPJ obstruction.

Targeted searches for individual urinary protein markerspotentially involved in the etiology of obstructive nephropa-thy such as epidermal growth factor, transforming growthfactor beta, monocyte attractant protein 1, and other moleculeshave not lead to the identification of markers allowing topredict the outcome of UPJ obstruction [18, 52]. We studiedthe urinary peptidome of UPJ obstruction patients using CE-MS [50] with the aim of identifying panels of urinary bio-markers that predict the outcome of UPJ obstruction. Therelatively acute character of UPJ obstruction and the hard-end points being generally reached within 2 years (spontane-ous resolution of obstruction or pyeloplasty) allowed to adopta straight forward scheme for the discovery and validation of

A

B

Fig. 5 Strategies for the discovery of biomarkers of obstructive nephrop-athy. a For the identification of biomarkers of ureteropelvic junction(UPJ) obstruction, the discovery phase of the study employed samplesfrom UPJ patients with mild and severe obstruction and healthy controls.This led to a panel of urinary biomarkers distinguishing between UPJobstruction patients with mild and severe obstruction. This panel wassubsequently validated using a set of UPJ obstruction patients with“intermediate” obstruction that need close follow-up after birth. Theseintermediate obstruction patients were scored with the biomarker panel aseither mild or severe obstruction (e.g., needing surgery). This urinaryproteome-based prediction was compared with the clinical follow-upafter 1–2 years. b The approach for the identification of fetal urinary

biomarkers of severe renal disease (ESRD before the age of 2) in fetuseswith posterior urethral valves (PUV) is straightforward since the hardclinical end-point is binary (ESRD or noESRD). In the discovery phase,the differences in the fetal urinary proteome were established by compar-ison of infants with PUV without ESRD and children with early ESRD.The next phase aimed to validate the fetal urinary biomarker panel inblinded cohort of fetus with PUV and compare the proteome-basedprediction with the clinical endpoint at 2 years of age. PUV posteriorurethral valves; ESRD progression of patients with PUV to end-stagerenal disease before the age of 2; noESRD absence of ESRD in PUVpatients before the age of 2. Reprinted with modifications from [60]

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the potential urinary biomarkers (Fig. 5a). Using a discoverypopulation of 13 healthy newborns, 19 UPJ obstruction pa-tients withmild obstruction (leading to spontaneous resolutionof the obstruction in all cases) and 19 UPJ obstruction patientswith severe obstruction (that had to be operated on to removethe stenosis), we identified a panel of 51 peptide biomarkersdifferentially abundant between the three groups. These 51biomarkers were combined in a SVM-based classifier thatallowed distinguishing between spontaneous resolution ofUPJ obstruction and requirement of pyeloplasty. In prospec-tively collected urine from 36 UPJ obstruction patients withintermediate UPJ obstruction prediction based on the 51 bio-marker classifier was performed before 3 months of age andthe clinical outcome was assessed after 15 months of follow-up. Thirty five out of 36 of the UPJ obstruction patients werecorrectly predicted, several months in advance of either sur-gery or spontaneous resolution of the obstruction [50, 53, 54].This classifier was subsequently successfully validated in anindependent small-scale study [55]. Interestingly, in thissmall-scale validation study, the classifier displayed high sen-sitivity and specificity for children with UPJ obstruction underthe age of 1 year. However, the classifier lost this high sensi-tivity and specificity in UPJ obstruction patients >1 year ofage. This is most likely due to changes in the urinary proteomewith age [56]: the classifier was specifically developed usingurine samples of infants up to 3 months of age since the needof prediction is most urgent at that age. We are currentlyfinishing a large-scale multicenter validation study including∼300 UPJ obstruction patients which, if successful, shouldpave the way towards clinical implementation and reduce thenumber of analyses in the follow-up of UPJ obstruction pa-tients. In addition to a reduction of the number of invasivetests, justified early surgical interventionmight also contributeto the preservation of renal function. We recently showed thatthe urinary proteomes of early surgical corrected UPJ obstruc-tion patients were very similar to age-matched controls after a5-year follow-up. This sharply contrasted with the urinaryproteome of the conservatively followed intermediate UPJobstruction patients leading either to spontaneous resolutionof the obstruction or late surgical intervention. Their urinaryproteome was found to be significantly different from controls[57]. Finally, in addition to this clear benefit for the patient,incorporation of urinary proteome analysis early in the evalu-ation of UPJ obstruction would significantly reduce costs andincrease the quality adjusted life years (QALY). Incorporatingthe urinary peptidome analysis increased the cost-effectiveness by $8,000 per QALY per patient [58].

Posterior urethral valves-fetal urine In contrast to unilateralUPJ obstruction, bilateral congenital renal abnormalities are inmost cases severe diseases. Bilateral CAKUT are develop-mental diseases including urinary tract malformations, ob-structive uropathy, and hyper/hypodysplasia. Bilateral

CAKUT are, although individually rare diseases, the maincause of chronic renal failure in children [47]. BilateralCAKUT display a wide spectrum of pre- and postnatal out-comes ranging from death in utero to normal postnatal renalfunction. Currently used methods to predict these outcomes inutero are moderately reliable [5, 6]. These methods includefetal ultrasound-based evaluation of the quantity of amnioticfluid and appearance of the renal parenchyma, and concentra-tion of fetal urine analytes, such as sodium and β2-microglobulin (β2m). Quite alarming: a number of patientswith severe CAKUT, for whom termination of pregnancy wasproposed based on these available tools, but who refused, hadnormal serum creatinine at a median age of 29 months [59].This exemplifies the necessity of biomarkers that allows trulyinformed prenatal counseling.

Like postnatal urine, fetal urine potentially contains bio-markers of disease.We have studied fetal urine with the aim todetect biomarkers that can predict post-natal function in theprototypic bilateral CAKUT posterior urethral valves [60].The setup of the study was as depicted in Fig. 5b with thegoal to link the presence of fetal urinary peptides to postnatalfunction at 2 years of age. The hard clinical endpoint wasESRD before the age of two. Starting with a discovery cohortof 28 patients with PUV, 26 fetal urine peptides were identi-fied that displayed different fetal urinary abundance in PUVpatients with ESRD. Twenty out of the 26 biomarkers weresequenced. These 20 were reduced to 12 peptides in an SVMmodel by using the leave-one-out procedure, which is basedon running the SVM model for all sequenced peptide candi-dates minus one using the training data set. Peptides that didnot influence the accuracy of the model in the total cross-validation of the training data were left out of the final SVMmodel. The resulting SVM-based classifier consisting of 12sequenced peptides (the “12PUV classifier”) predicted post-natal renal function with 88 % sensitivity and 95 % specificityin an independent blinded validation cohort of 38 PUV pa-tients. This fetal urine peptide-based classifier outperformedall classically used clinical tools. Unfortunately, no treatmentfor PUVexists and the currently used approaches for in uterorepair of the valves do not improve outcome [61]. However,this 12PUV classifier will potentially allow truly informedprenatal counseling, which currently often gives wrong guid-ance (see above). Prediction of postnatal renal function willfurther be of high added value for appropriate clinical follow-up, including planning for implementation of RRT. Finally,correct prediction of post-natal function will also potentiallyallow selecting fetuses with good prognosis that will benefitfrom in utero repair of the valves [62, 63].

The 12PUV classifier was found to be independent ofgestational age, suggesting that these peptides do not reflectfetal renal function in PUV patients, but instead molecularpathological changes associated with ESRD [60]. Identifica-tion by tandem mass-spectrometry of the potential biomarker

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peptides showed that the majority of the biomarkers werefragments of collagen, their abundance increasing in fetalurine of PUV patients displaying severe ESRD. This contrastswith what is observed for CKD in postnatal urine [64]. InCKD, reduced urinary collagen fragment abundance is con-sidered to reflect increased intrarenal extracellular matrix ac-cumulation leading to fibrosis [65]. Increased urinary collagenexcretion observed in fetal urine of PUV patients maybe linked to increased in situ collagen turnover or tissueremodeling as suggested for non-operatively followedUPJ obstruction patients [57] and in coronary arterydisease (CAD) [66, 67]. Autopsies of PUV patientsshow renal dysplasia (significant kidney remodeling)support this hypothesis. Hence, the fetal urine peptidebiomarkers most likely represent molecular pathologicalchanges associated with ESRD in PUV patients and notchanges in renal function. One peptide biomarker ofESRD in PUV patients was identified as a fragment ofthe XLαs variant of the G-protein alpha subunit (Gsα)(GNAS) involved in signal transduction of seven-transmembrane receptors [68]. GNAS and its variantsrepresent imprinted genes (specific maternally or pater-nally transmitted active copies of genes due to methyl-ation patterns) and are described to have major effectson growth in utero and after birth. GNAS-inactivatingmutations cause endocrine abnormalities such as growthhormone resistance [69]. Modified urinary abundance ofa GNAS variant in PUV patients with ESRD was con-firmed independently using ELISA [60]. Whether mod-ifications in the GNAS complex locus are causative, anaggravating factor or bystander in ESRD in patientswith PUV remains to be determined.

Conclusions

A number of examples from several laboratories demonstratea clear value of urinary proteomic biomarkers in assessingkidney disease. The data also indicate that multidimensionalproteome-based markers outperform single classically usedmarkers. Hence, the current status quo can likely be improvedupon by applying modern omics approaches that hold thepromise of bringing progress in fields that did not evolve fordecades. Implementation of these omics-based approacheswill not only clearly bring a significant benefit to patientsbut will also ultimately save costs, as early detection of pa-tients with CKD will, even more with the availability of newdrugs or interventions, reduce the number of patients endingup in costly RRT programs. However, while research by nowis able to deliver useful tools in patient management, other(political, financial, sample availability) issues associated withvalidation and implementation are not at all solved [23]. Amajor hurdle in the development of clinically useful

biomarkers and multi-marker classifiers is sample availability.Unfortunately, the current biobanks do not serve this needwell, especially since frequently information on the samples inthe biobank is not publicly available, although the biobankswere funded with public money. The establishment ofbiobanks that allow access to the information on the materialavailable and that implement transparent procedures on howsamples are made available would be of enormous benefit.

Even if significant improvement in patient assessment byemploying novel biomarkers has been demonstrated, no clearpath forward to implement useful biomarkers is apparent.While the situation differs to some degree per country, ageneral resistance to implement new approaches on the levelof regulatory agencies, as well as payers (health insurancecompanies), is evident. This is to some degree understandable:the regulators feel uncomfortable with novel approaches theydo not fully understand and, in doubt rather wish to withholdnovel approaches from patients. The payers as a general ruledo not wish to pay for novel diagnosis or treatment, as this atleast initially generally results in increased cost. The fact thatthis may save costs at a later point in time is of no, or little,concern. This situation is further exacerbated in chronicdiseases, where (early) diagnosis and intervention isassessed based on the hard outcome. In CKD, thisresults in the demand (from regulatory agencies andmost payers) to demonstrate a benefit of early detectionbased on a change in hard outcome (doubling of serumcreatinine or ESRD). As such endpoints are likely to bereached in sufficient numbers for statistical assessmentonly more than 10 years after early diagnosis and treat-ment, the employment of novel strategies in early treat-ment is quite effectively prevented by this demand. Theapproach to e.g., evaluate benefits based on availablesurrogate parameters and take a certain risk that hardendpoints may not be influenced (while at the sametime allowing potential benefits to be available for pa-tients) is being discussed, but in general not accepted.

To avoid that biomarker research becomes a beautifulplayground for scientists, but of no practical value for thepatients, alternative ways forward have to be sought. A pos-sible way to counteract the above-mentioned unfortunate de-velopment could be a much more active engagement of therelevant clinical professional societies, as well as patient’sorganizations. Hypothesizing that these are the two groupsthat are in fact interested in improving outcome (the patients intheir very own interest, the physicians as their goal is optimaltherapy of patients) then involving these in biomarker researchfrom the beginning may represent a successful way forward.While not certain, it is to be hoped that regulatory bodies andpayers would not act against the opinion of patients andphysicians, and a clear statement from these two groups mayresult in implementing novel biomarkers and therapeuticstrategies.

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Acknowledgments The research presented in this manuscript wassupported by the FP7 programs “Improvement of tools and portabilityof MS-based clinical proteomics as applied to chronic kidney disease”(Protoclin, PEOPLE-2009-IAPP, GA 251368), Clinical and system –omics for the identification of the Molecular Determinants of establishedChronic Kidney Disease (iMODE-CKD, PEOPLE-ITN-GA-2013-608332) “Systems biology towards novel chronic kidney disease diag-nosis and treatment” (SysKID HEALTH–F2–2009–241544) and “Euro-pean Consortium for High-Throughput Research in Rare Kidney Dis-eases” (EURenOmics, GA2012-305608).

Conflict of interest HM is the founder and co-owner of MosaiquesDiagnostics, who developed the CE-MS technology for clinical applica-tion. JPS was during the preparation of the current manuscript employedby Mosaiques Diagnostics.

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