Discovery of Protein Biomarkers for Renal Diseases
Stephen M. Hewitt*,
James Dear and
Robert A. Star
*Tissue Array Research Program, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute; and Renal Diagnostics and Therapeutics Unit, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
Correspondence to Dr. Robert A. Star, Renal Diagnostics and Therapeutics, NIDDK, 10 Center Drive, Building 10, Room 3N108, Bethesda, MD 20892-1268. Phone: 301-402-6749; Fax: 301- 402-0014; E-mail: Robert_Star{at}nih.gov
ABSTRACT. Animal models and human studies have been useful indissecting the molecular mechanisms of renal disease and findingnew disease targets; however, translation of these findingsto new clinical therapeutics remains challenging. Difficultieswith detecting early disease, measuring drug effectiveness,and the daunting cost of clinical trials hampers the developmentof new therapeutics for renal diseases. Many existing laboratorytests were discovered because of inspired recognition that aparticular protein might prove useful in clinical practice.New unbiased genomic and proteomic techniques identify manyconstituents present in biologic samples and thus may greatlyaccelerate biomarker research. This review focuses on the stepsneeded to develop new biomarkers that are useful in laboratoryand clinical investigations, with particular focus on new proteomicscreening technologies. New biomarkers will speed the laboratoryand clinical development of new treatments for renal diseasesthrough mechanistic insights, diagnoses that are more refined,early detection, and enhanced proof of concept testing.
Basic science has made a great deal of progress in dissectingthe molecular mechanisms of renal disease; however, translationof these findings to therapeutics used in clinical practiceremains challenging (1). Renal diseases garner less interestas a potential area for therapeutic development because theyare often poorly characterized, differentiated often only bysubtle histopathologic changes on renal biopsy, difficult todiagnose early, follow progression, and determine response totherapy, all of which add complexity and risk to a clinicaltrial.
Chronic kidney disease (CKD) illustrates the complexity of theproblem. Early diagnosis is usually based on either the detectionof proteinuria or elevation of serum creatinine. Neither testcan accurately diagnose the type of renal injury. As every nephrologistknows, serum creatinine is a poor marker of early CKD becausethe serum concentration is greatly influenced by changes inmuscle mass and tubular secretion (2). Hence, the normal referenceinterval must be relatively wide, and use of serum creatininealone to follow disease progression is fraught with difficulty.Testing a therapy for CKD using a clinical end point takes along time, and intermediate surrogate end points that can beevaluated in a shorter time frame are needed. Furthermore, significantrenal disease (e.g., fibrosis) can exist with minimal or nochange in creatinine because of renal reserve, enhanced tubularsecretion of creatinine, or other factors (2,3). Sensitive markersof early injury, especially those that correlate with earlyfibrosis and progression, are desperately needed. In acute renalfailure (ARF), serum creatinine has an even poorer sensitivityand specificity, because the patients are not in steady state;hence, serum creatinine lags behind renal injury (4). Issuessuch as these increase the noise and hence size and cost ofclinical studies, because either the correct patients are notenrolled or the outcome measures are inaccurate or slowly trackthe disease. They also increase the risk of failure in drugdevelopment. A troponin-like marker of renal dysfunction thatwould enhance early detection or follow progression would beextremely helpful. Finally, disease markers are also usefulin laboratory investigations because they can detect early injurybefore histologic changes are appreciated (5).
Biomarkers and surrogate markers are important tools that cansupply some of the needed information, especially when usedin conjunction with traditional clinical and laboratory data(6–9). A biomarker is a biologic characteristic that ismeasured and evaluated objectively as an indicator of normalbiologic processes, pathogenic processes, or pharmacologic responseto therapeutic intervention (Table 1) (9,10). Biomarkers maybe any parameter of a patient that can be measured, for example,mRNA expression profiles, proteins, proteomic patterns, lipids,imaging methods, or electrical signals. The best biomarkersare accurate, relatively noninvasive and easy-to-perform teststhat can be done at the bedside or in the outpatient setting.These tests involve a blood or spot urine specimen, can be measuredserially, and have a fast turnaround. In the past, most effortshad focused on discovering tissue and urinary biomarkers. However,there has been a recent shift to finding serum biomarkers (11),with new methods and technologies making this more practical.In contrast, a surrogate end point marker is the rare biomarkerthat can substitute (or be a surrogate) for a clinical end point,such as survival, stroke, fracture, or cancer recurrence.
Biomarkers and surrogate end point markers have many uses inlaboratory and clinical investigations and in drug discovery(Table 2, Figure 1) (6,12). Biomarkers are useful for diagnosing,classifying, or grading the severity of disease in both laboratoryand clinical settings. They may be able to supply efficacy,toxicity, and mechanistic information for the preclinical andclinical phases of drug discovery and be applied with therapeuticsto produce commercial tests that aid patient selection or drugdosing (personalized medicine). Because biomarkers and surrogateend point markers can accelerate the speed and decrease therisk of drug discovery, they are highly sought after. The developmentprocess is complex. Investigators need a complete developmentplan and, most important, access to sufficient, well-characterizedsamples. Unfortunately, many promising biomarkers never makeit into clinical practice or even broad application in clinicalor laboratory research. Understanding of the entire complexbiomarker development process and using a team approach arerequired for a successful biomarker development project (Figure 2,Table 3). Every step requires validation, of both assay performanceand diagnostic utility, as the biomarker moves toward the clinic(Figure 2, Table 3).
Figure 1. Types of biomarkers and their utility during different time points along the development and progression of a disease. A biomarker can be developed to target one of many different critical decision points during the natural history of a disease. The same biomarker may function for different purposes.
Figure 2. Biomarker and diagnostic assay development pathways. Critical steps in the discovery, clinical assay development and validation, clinical utility determination, and commercial development phases of biomarker development are shown. The discovery phase needs high-quality, well-characterized samples that may be human or from animal models. Once a promising lead is found, the presence of the biomarker should be confirmed in different samples. The next stage is to develop a clinically useful assay (often in serum or urine) and validate if it can detect established disease. The clinical utility of the biomarker is established in a retrospective longitudinal study and a prospective study and finally to determine whether the biomarker screening strategy can reduce the burden of disease. The final stage, often not appreciated, is the commercial development of the assay by industry.
We review the early phases of the biomarker discovery pathway.It is impossible to encompass all details of biomarker development;however, we discuss key issues, new approaches, and compromisesthat confront an investigator. Because of space limitations,we do not discuss regulatory or intellectual property issues(see ref. 13).
Step 1: Understand and Define the Disease
Proper biomarker development requires detailed knowledge aboutthe disease, including the definition, differential diagnosis,disease subsets, and the local or systemic responses. A reliablecase definition is very valuable for proper biomarker development.The definition may be extremely specific (acute myocardial infarctiondefined as chest pain, EKG, and troponin) or based on a constellationof symptoms and signs (septic shock using fever, tachycardia,tachypnea, organ failure, and hypotension). Ideally, the definitionshould include a temporal element, for example, as the consensusdefinition and staging criteria for CKD (14), or the Acute DialysisQuality Initiative consensus definition of ARF (15,16). If thedisease is poorly defined, then the newly discovered biomarkermay ultimately change the definition of the disease.
It is also helpful to understand and define diseases that arecommonly confused with (differential diagnosis) or are etiologicsubgroups of the disease of interest, because this will havean impact on the goal of the biomarker search (step 3) and thestrategy used (step 4). Nearby diseases may have similar clinicalpresentations but different pathophysiologic mechanisms. Forexample, volume depletion and urinary obstruction are commonlyconfused with ARF. These diseases may also be the focus of thebiomarker search. For diseases for which important subsets likelyexist (e.g., ischemic, septic, toxic ARF) but are difficultto distinguish clinically, microarray or proteomic technologiesmay detect subclasses using unsupervised clustering, principalcomponent analysis, and other, more statistically rigorous techniques.
Step 2: Frame the Question. What Critical Information Will the Biomarker Provide?
The best biomarkers serve a basic science, translational, orclinical need that advances the diagnosis, prevention, or treatmentof the disease. For example, it is important to consider howthe biomarker will be incorporated into the care of a patient(diagnostic algorithm or therapeutic management) to complementthe clinical history and current laboratory examination of thepatient. This process requires ongoing consultation with cliniciansand scientists who understand the epidemiology, natural history,pathophysiology, and treatment of the disease to determine theremaining critical unanswered questions. A biomarker may betargeted at early detection of disease or to monitor the stage,severity, progression, or regression of disease after diagnosis(Figure 1, Table 2) or to predict drug response or follow theeffect of an intervention. Examples of clinically useful renalbiomarkers might include, for example, detection and stagingrenal fibrosis, prediction or tracking the response of fibrosisto an antifibrotic therapy, differentiation of reversible fromnonreversible damage in a patient with systemic lupus erythematosus,early detection of ARF, or early detection of drug responsein ARF. The best question may not be the obvious question. Forexample, if two drugs act synergistically to slow the progressionof a disease but one has more side effects, then an obviousquestion is, "Which patient will progress rapidly without therapyand needs to be treated?" However, if all patients will getthe safer drug, then the critical question might be, "Whichpatients will not respond to addition of the second drug?" Thisstep should not be overlooked. Biomarkers are often missingat many steps; however, it is critical to prioritize which questionsto attack first.
Step 3: Desired Site of Clinical Measurement
A biomarker is usually derived from or modified by a diseasedtissue but may be detected in some other fluid. Alternatively,a biomarker might arise from a distant organ or systemic reactionto the disease process (stress proteins, C-reactive protein).Upregulated genes may themselves be poor circulating biomarkers,but their metabolic fingerprints might be detected systemically,for example, pheochromocytoma (17). These last two exampleshighlight the advantage of starting with serum when developinga biomarker.
Biomarkers can be assayed in easily obtainable fluids, suchas serum, plasma, or urine, or other sites, such as saliva,sweat, hair, and kidney biopsy material. Urinary biomarkersmight also include shed cells (podocytes), casts, mRNA, or endosomalvesicles (18–21). For the sake of simplicity, they areconsidered as urine in this article, although the assays maybe considerably more cumbersome. Serum or urine biomarkers arepreferred because they are easily obtainable. The choice isdriven by a balance between clinical relevance, ease of collection,and stability (serum better than urine) versus specificity tokidney disease and analytical simplicity of the discovery step(urine better than serum). Urine is more likely to contain biomarkersfrom the kidney, although, for example, urinary nitrate/nitratereflects systemic rather than renal activation of nitric oxidesystem (22). Urine biomarkers may be useful for patient self-testingapplications such as detection of infection, kidney stones,or monitoring of diabetic nephropathy or nephrotic syndrome.Proteomic techniques work best on urine because it is less complexfluid than serum; however, urine markers may degrade in thebladder or while sitting for a variable time in a collectionvessel. Urine biomarker excretion rate cannot be determinedeasily because flow rate is not measured easily. Urinary biomarkerconcentrations are typically adjusted by urinary concentrationof creatinine. This is a reasonable approximation in CKD butmay not be as accurate in ARF and after renal transplantation.
Serum is often preferred for the final biomarker because ofthe ease of collection. However, serum markers may measure thesystemic response to a disease, although there are organ-specificbiomarkers (e.g., troponins (23)). Also, it is difficult tofind biomarkers in serum using conventional proteomic approachesbecause of the wide range of protein concentrations (spanning10 orders of magnitude), complexity (large number of peptides),and predominance of 10 to 20 proteins (albumin, Ig, etc.) thatoverwhelm the less abundant signals. Methods to remove theseabundant proteins have been developed; however, recent studieshave found that many peptide fragments (potential biomarkers)circulate bound to albumin (24). Albumin acts as a sink or reservoirfor these molecules and greatly prolongs their half-life fromminutes to days (25). Hence, newer assays that rely on the detectionof multiple (uncharacterized) peptide peaks are being developedto probe this peptide space (26); however, it is not clear whetherthese current peptide fingerprint assays are sufficiently reproducibleand robust for clinical use (27–29).
Could a molecular diagnosis of renal disease using novel tissuebiomarkers improve renal diagnosis and therapy? Renal biopsymaterial can be used in the discovery phase, although, ultimately,for a biomarker that would be assayed in serum or urine. Renalbiopsies are performed to obtain diagnostic, prognostic, anddrug response information only when the disease process is severeenough to warrant the procedure. The kidney is an anatomicallycomplex organ; hence, the biopsy must be evaluated histologicallyto confirm that the sample contains the correct part of thekidney and that the tissue contains the disease process. Thetissue biomarker must provide additional information beyondthe histopathologic information obtained from the biopsy.
Step 4: Devise a Strategy for the Discovery Process
The biomarker discovery strategy is influenced by the diagnosticdifficulty, disease variability, subclasses, biomarker goals,ultimate site of measurement, and discovery platform (Figure 3).The first critical decision is whether to start the discoveryprocess on the diseased tissue or the material that will makeup the final clinical assay (e.g., serum, urine) (Figure 3, a and b,versus3, c and d). This decision involves carefulbalancing of competing issues. Biomarker discovery from diseasedtissue is relatively easy and is very likely to yield many leads,or "hits," but there are significant drawbacks. First, detectingtissue biomarkers in serum or urine is difficult. Many promisingleads may be transcription factors or other low-abundance intracellularproteins that can be detected in tissue but not in serum. Secretedproteins and cleaved receptors typically make the best targets,although a recent study found that fragments of "intracellularproteins" may circulate bound to serum albumin (25). Second,systemic responses to the disease can be missed if the primaryorgan tissue is used for the discovery effort. Third, it maybe difficult to obtain appropriate clinical tissue samples thatmatch the clinical question, especially for early disease biomarkers.Newer proteomic techniques (serum purification followed by two-dimensionalgels [2-D], SELDI fingerprinting) bypass the tissue step andallow for a direct search for serum biomarkers (Figure 3, c and d)(26,30). However, it is still difficult to identify anindividual serum biomarkers. SELDI methods are particularlygood for finding subgroups within a patient population (Figure 3d).
Figure 3. Strategies for biomarker development. Four typical but conceptually different biomarker development schemes are shown. (A) Biomarker for simple disease found in diseased tissue by subtractive method, then clinical assay developed (reformulated) to detect protein product in serum. Clinical assay validated initially on few samples and then on an independent larger set. (B) Biomarker for complicated disease with subgroups or near neighbors found in diseased tissue using multiple-group microarray or proteomic method. Clinical assay developed and validated as in case 1. (C) Biomarker for simple disease detected in serum by subtraction method, then assay reformulated to measure biomarker in serum. (D) Biomarkers for simple or complex disease found using surface-enhanced laser desorption ionization approach and initially validated on same sample set. D, disease; N, normal; D-N, disease minus normal; DDD/NNN, simultaneous measurement of several diseased and normal samples; A, B, closely related diseases that must be differentiated from disease D; DDD/NNN/AAA/BBB, simultaneous measurement of samples from disease, normal, and two closely related diseases.
The second decision is whether to use human samples or samplesfrom animal models. In general, human samples should be usedwhen available because many animal model systems do not sufficientlyreplicate human pathophysiology accurately. For example, endotoxininfusion models commonly used to study sepsis do not accuratelypredict drug effectiveness in humans (31). Antibodies that workin a rodent system may not translate to human models. However,judicious selection and usage of appropriate animal models canbe extremely beneficial, especially when human serum or tissuesamples are not available. This is especially true when diseasedefinitions are in flux (ARF), for the development of an earlydiagnostic biomarker (ARF, CKD), or when it is not practicalto find patients early in the disease process (in part becausebiomarkers are lacking). It is also easier to obtain the propercontrols when using animal models. In these situations, useof appropriate animal models that replicate human disease canbe catalytic.
The third decision is whether to subtract two samples (diseaseversus normal) or compare several groups of samples in the discoverystep (Figure 3, aversus b). The most common strategy (Figure 3a)is to subtract normal from diseased tissue, either usingrepresentative difference analysis (32) or by microarrays (33).One then hopes that protein will follow RNA levels, which isnot always the case (34–37), and that the protein willbe detectable in serum or urine. Alternatively, one may usedifferential proteomic approaches (2-D differential in-gel electrophoresis,isotope-coded affinity tags [ICAT]) on proteins obtained fromnormal and diseased tissue samples (37–39). These subtractivestrategies can work well if the question is presence or absenceof disease, the disease itself is uniform, and the differentialdiagnosis is short (e.g., pregnant versus not pregnant). Moreoften, subtractive strategies are used because clinical samplesare extremely scarce (e.g., kidney biopsy in a patient withunexpected ARF). The number of samples used may be extremelysmall; nevertheless, it is often desirable to perform severalsubtractions of normal versus diseased to check the reproducibilityof the method and uniformity of the samples.
Alternatively, if patient heterogeneity, complicated differentialdiagnoses, or multiple heterogeneous disease subgroups are presentand must be considered simultaneously, then it often is preferableto analyze multiple groups of samples (Figure 3b), then useANOVA and significance testing to find either common or subgroupmarkers. For example, ARF is caused by ischemia, toxins, andsepsis and is often confused with volume depletion. Each causehas a different renal response pattern and, perhaps, a specifictherapy. If a general ARF biomarker is desired, then samplesfrom all of these subclasses should be included in the initialbiomarker discovery phase. The analysis, for example, couldlook for proteins that are upregulated in ischemic, toxin, andsepsis patients but not elevated in volume depletion. This useof complicated AND/OR/NOT logic strategies, although requiringadditional clinical samples, often yields a smaller but morefocused initial "hit list."
Innovative strategies should be evaluated. Rather than comparediseased with normal tissue, one can compare samples from thesame patient before and after disease. For example, a benignprostatic hypertrophy (BPH) biomarker might be found by comparingserum before and 5 wk after radical prostatectomy (at the riskof identifying a prostate cancer marker; Figure 3c). A renalcell carcinoma marker might be found by comparing serum beforeand after nephrectomy. Although this approach is harder in renaldiseases, it could be applied before and after successful treatmentfor minimal-change disease.
If the analytic method is extremely expensive (e.g., SAGE, ICAT),then it may be advantageous to pool samples (pool five normalcontrol subjects, and pool five patients with disease). If thedisease is heterogeneous, then this method may detect a commondisease marker present in all subgroups.
Step 5: Which Samples Should Be Used for the Discovery Phase?
The scientific/clinical question (step 2) and strategy (step4) drive the choice of samples for the initial discovery effort.These samples are the soil from which the biomarker will benurtured and must be as fertile as possible. Too often, thechoice is guided by locally available "convenience samples"rather than using samples that are needed; this generally altersthe clinical question addressed (step 2). Finding an early diseasemarker using tissue removed from patients with end-stage diseaseis unlikely to be fruitful. Thus, it may be easier to obtainserum or urine samples that closely match the clinical question;alternatively, samples from animal models may be used for theinitial discovery phase (step 4). This is less of a problemfor biomarkers developed to support laboratory and early translationalresearch when the question is very focused and the samples shouldmirror the disease process being investigated.
It is essential the samples be from carefully defined sources,of high quality, and carefully preserved, because misclassificationor degradation will rapidly doom the effort. Clinical samplesshould be from carefully phenotyped patients, with attentionto age, gender, race, ethnicity, and concurrent medications.Sufficient sample volume is essential, as these samples maybe used for multiple assays. A discovery set that is constantlychanging can bewilder an investigator who is looking at multiplemarkers.
Tissue may be homogenized to prepare sufficient material forquantitative discovery and initial validation efforts. Alternatively,the diseased portion of the tissue can be isolated for genomicor proteomic analysis using manual or laser capture microdissection(LCM) (40–42). Although involving more effort, this enrichesthe source tissue and, hence, increases rare signals. Microdissectionmay be extremely helpful for early markers of glomerulonephritis,where contamination by tubular epithelium would overwhelm thecontribution from the glomeruli.
Step 6: Determine Which Discovery Method to Use
The discovery method should be matched carefully to the scientificquestion, source and number samples, and strategy (Figure 3)and not just limited to methods of convenience or familiarity.These methods were reviewed recently (43–46). The advantagesand disadvantages of commonly used methods are summarized inTable 4 and Appendix 1.
Table 4. Advantages and disadvantages of platformsa
Step 7: Review the Significance and Feasibility
After the initial biomarker development plan has been completed,it should be reviewed to determine whether the biomarker isstill needed and the plan is feasible. Will the biomarker addvalue to currently obtained information? Are all of the piecesin place? Are there sufficient discovery samples that matchthe desired question? A rigorous statistical analysis shouldbe performed to determine the number of patients needed forthe validation steps (7). Is there sufficient clinical informationand are there enough clinical samples to carry out the developmentprocess?
Step 8: Perform Experiments and Prioritize the Hit List
After the initial screen (Table 3, Pepe stage 1a), the investigatorshould be left with 50 to 500 hits and must narrow down andprioritize the list. The best candidates for serum and urinebiomarkers are often secreted proteins, shed portions of extracellularreceptors, or highly abundant intracellular proteins. Some ofthis information can be gleaned via simple searches (Locus Link,OMIM), comparison with published sets of secreted proteins,or more sophisticated bioinformatics tools (47). Bioinformatictools can also be used to forecast whether a particular proteinis widely expressed or restricted to a particular organ (48)(e.g., Cancer Genome Anatomy Project). Often, it is helpfulto use what is known about the disease and the clinical differentialdiagnosis to narrow down the hit list, a "rational design approach"(49). For example, one can look for genes that are upregulatedin all forms of the disease (ischemia, sepsis, toxins), thensubtract genes that are upregulated in diagnostically closediseases (volume depletion). A second approach, useful in animalmodels, is to prevent the disease from occurring, perhaps bypreconditioning (50) or drug treatment. Each additional criterionreduces the number of hits, although this must be done carefully,else true positives will also be lost.
Candidates can be confirmed and prioritized on the basis ofhow well they distinguish normal from disease (Table 3, Pepestage 1b). A confirmatory study should be carried out on anindependent sample set that contains a modest number of normaland diseased samples, with calculation of the true-positiverate and false-positive rate for binary biomarkers (yes/no)or the receiver operating characteristic (ROC) curve for continuousbiomarkers. Candidates can then be ranked on the basis of thearea under the ROC curve, or false-positive rate (for earlydisease screening biomarkers). At this early stage, the samplesmay be from animal models, but ultimately one must evaluatethe markers on clinical samples. For tissue-based discoveryefforts, this initial screening can be performed on tissue-derivedmaterial (mRNA, protein samples) or directly on tissue usingtissue microarrays (if available).
Hits can be evaluated using immunohistochemistry and in situhybridization methods to ensure that the putative biomarkersare arising from the diseased tissue, but quantification remainschallenging. Some investigators have turned to the use of tissuemicroarrays (TMA) for target validation to extend the utilityof their samples and validate against a larger sample size (51).A well-designed TMA can include the disease and off-axis diseasesin the differential diagnosis. However, there are limitations,as the TMA must accurately represent the process being studied.For applications in oncology, this can be done easily; however,for renal disease, it is much harder unless the process is globaland diffuse (52).
Step 9: Develop a Robust Clinical Assay and Initial Clinical Evaluation to Detect Existing Disease
To be clinically useful, the biomarker must detect disease whenmeasured in clinically relevant serum or urine samples (Table 3,Pepe stage 2). If tissue samples were used for the discoveryeffort, then a clinical assay must be developed to measure thebiomarker in serum or urine. The clinical assay must be optimized,or "hardened," so that it can be performed reproducibly at multiplesites. A set of standard operating procedures, quality control,and quality-assurance procedures should be generated. The minimumanalytical volume, minimal detectable concentration, and potentialcross-reactants should be determined. The stability of the analytein body fluid and during storage should be established to ensurethat the assay will work on stored clinical samples (neededfor next step). A good example of assay optimization for a potentialrenal marker was published and discussed recently (53,54).
Once the assay optimization is complete, the assay should betested on an independent set of carefully vetted samples todetermine whether it can correctly distinguish patients withestablished disease from normal control subjects (patients withearly disease will be tested in the next step). The set needsto be of high quality and of sufficient number to measure accuratelythe sensitivity, specificity, and area under the ROC curve.The samples can be obtained from the baseline samples of a clinicaltrial but ideally should be taken from the same type of populationas those for whom the test is designed. The reproducibilityand portability among multiple laboratories and sites is critical;the assay should be replicated at several sites, with similarresults obtained on the same sample at all sites, to ensureuniversality and portability of the biomarker for widespreadusage. The sample set should be large enough to determine whetherthe biomarker level is influenced by patient factors such asage, gender, and comorbidities (hypertension, diabetes); ifso, then the biomarker disease threshold may need to be definedseparately for specific subpopulations. Finally, samples canbe analyzed to determine whether the marker detects differentstages of the disease, or disease severity, or off-axis diseaseson the differential diagnosis list.
Biomarkers that are developed for bench science purposes undergoa similar process: Optimization of the assay, followed by validationon an independent sample set. As with clinical biomarkers, itis important that the sample set include normal, disease, andoff-axis samples and that the reproducibility and portabilitybe determined.
Step 10: Evaluate the Clinical Utility
The clinical utility of the biomarker needs to be determinedpreferably under "real-world" conditions. For screening or earlydetection biomarkers, Pepe et al. (7,8) organized this processinto a series of sequential phases that generate progressivelystronger evidence. Early detection biomarkers must be able todetect disease before it is clinically apparent. This usuallyrequires measuring the biomarker in banked repository samplesfrom a retrospective longitudinal cohort of apparently healthysubjects who were monitored for the development of the disease(Table 3, Pepe stage 3). By comparing data from patients whodeveloped the disease with age-matched control subjects, a screen-positiverule is established and then used to determine whether the biomarkerdetects early disease before it is clinically obvious. The studywill indicate how the biomarker changes over time in healthyindividuals and those with disease, the lead time by which thebiomarker predates the clinical diagnosis, whether the biomarkertracks the natural history of the disease, and the sensitivityof the test. Next, a prospective screening study is performedto screen apparently normal individuals and rigorously applyingdiagnostic procedures to those who screen positive (Table 3,Pepe stage 4). These large, costly studies allow one to determineat which stage the disease is detected (early intervention opportunities),the prevalence of disease in the population, the specificityof the test, and the false referral rate. A low false-positive(or false referral) rate is critical if the early diagnosismarker will be used widely to screen a population. Finally,one must determine whether screening reduces the burden of disease(mortality, morbidity) and is cost-effective in a real-worldsetting (Table 3, Pepe stage 5). This typically requires a parallel-arm,randomized, clinical trial in which half of the population israndomly screened, although other approaches are possible (7,8).The goal of this ultimate test is to determine whether the diseaseis detected early enough to make a clinical difference. Thesestudies are extremely expensive, time consuming, and vulnerableto changes in testing method or community adoption that limitenrollment. Testing the clinical utility of other types of biomarkershas not been as rigorously organized but includes similar retrospectiveand prospective studies to determine whether the biomarker tracksthe natural history of the disease or response to treatment(Figure 1).
Step 11: Combining Biomarker with Clinical Data and Other Biomarkers
Occasionally, a single biomarker will have significant valueas a stand-alone test (e.g., human chorionic gonadotropin).Given the complexity and multiple overlapping pathophysiologicmechanisms of clinical diseases, finding a single biomarkerwith sufficiently high sensitivity and specificity is difficult.Single biomarkers often fail if the disease is heterogeneousor the biomarker level is influenced by several diseases (e.g.,prostate-specific antigen is elevated in both prostate cancerand BPH). Thus, investigators are beginning to search for apanel of biomarkers and are combining biomarker data with clinicaldata. The critical question is not, "Does the biomarker operatealone well?" but rather, "What value/information does the biomarkeradd to the existing clinical data?" Even "stand-alone" biomarkerssuch as prostate-specific antigen or troponin are more accuratewhen combined with clinical data such as age (55) or combinedwith other biomarkers (56). Biomarker combinations may enhancethe sensitivity and specificity over each individual biomarker(Figure 4). These combinations may be found using unbiased techniquesor by knowledge of the different pathogenic mechanisms of thedisease (56). The algorithm can be "tuned" to the specific goalsof the clinical question, using multivariate techniques suchas logistic regression or COX modeling to identify the independentclinical factors to include those that enhance the predictiveability. The algorithm can be displayed as a nomogram, or themultivariate equation can be downloaded onto a personal digitalassistant (PDA).
Figure 4. The potential power of multivariate analysis. (A) Two individual biomarkers that cannot discriminate between disease and normal. (B) Simple addition of the two biomarkers allows easy segregation of normal from disease.
The biomarker field is rapidly expanding and provides many opportunitiesto improve patient health. Table 5 provides a partial list ofbiomarkers for ARF that are currently in development. This reviewhas illustrated how the sophisticated methods of molecular medicinecan be melded with current tools to provide biomarkers for anincreasingly complex care environment. As proteomics methodsimprove, it will be easier to move an idea forward through discoveryphase to the validation and commercialization phases. Comparedwith the cost and risk of drug development, biomarkers offerthe opportunity to have an impact on patient health in a moreeconomical manner and may provide an opportunity to speed upthe drug development process. Biomarkers represent a catalyticevent in the interplay between academia and industry. The resultis the development of biomarkers that detect disease earlierand predict which patients will respond to which therapies.In an era of aging population, greater economic constraints,and a goal of providing more targeted care (personalized medicine),biomarkers are certain to have a great presence in patient care.
It cannot be over emphasized that the quality of the startingmaterial is critical. It is critical that the quality of theanalyte be examined, including confirmation of the diagnosisor classification of the material.
Microarrays
Microarray technology can be used to perform an unbiased, large-scalescreen for changes in mRNA abundance in multiple samples. Themethod can easily deal with multiple subgroups and replicates.Replicate experiments are essential to limit the false discoveryrate. Bioinformatics tools can highlight secreted proteins orshed receptors that might be good for urine biomarkers (57).The weaknesses are that it measures mRNA, not protein abundance;a large amount of high-quality mRNA is required; the delugeof resulting data; the difficulty of predicting which proteinswill leak out of cells because of the disease process; and thedifficulty of translating a hit to a serum biomarker. With thepossible exception of circulating leukocytes, the microarrayprofile itself is not a biomarker. Signals may be missed ifthe tissue has several compartments (proximal tubules and thickascending limbs) that respond differently to the injury. Someinvestigators have used manual dissection, sieving, or LCM toisolate rare cells or compartments (glomeruli) (40). Choosingthe appropriate time point for analysis is essential. Changesin transcription are very rapid and transient, especially comparedwith changes in the proteome. The most common challenge is obtainingsufficient high-quality samples from an early time point forthe development of an early detection biomarker. Although microarrayscan be performed on paraffin-embedded material (with or withoutLCM selection), fresh or frozen tissue is superior for biomarkerdiscovery.
2-D Electrophoresis/Mass Spectrometry
2-D gels are notoriously difficult to process and compare acrosssamples. However, differential in-gel electrophoresis has beendeveloped whereby protein samples are tagged with different(but similarly sized) fluorescent dyes and run simultaneouslyon a single gel. Two-color imaging and software analysis allowany differences between the two samples to be spotted easily.The spots can be robotically removed, trypsinized, and subjectedto matrix-assisted laser desorption ionization–time-of-flightmass spectrometry (TOF) or TOF-TOF for identification (reviewedin ref. 43). This method is good for high-abundant, moderate-pI,moderate-molecular-weight soluble proteins but is not good formembrane, low-molecular-weight, or very acidic or basic proteins.Typically, several thousand spots can be imaged and differencesbetween samples readily appreciated. The advantage is that thecompartment that will be assayed for the BM can be assayed directly,skipping steps of data filtering. The major disadvantages arethat only two samples can be compared at once, serum samplesmust be prepurified to remove albumin else it will distort theremainder of the proteome image, and the continuing challengeof identifying the protein sequence. Superior detergents andother solubilization techniques are being developed to extendits use to membrane proteins, high-pI proteins, and serum.
Surface-Enhanced Laser Desorption/Ionization
This new mass spectroscopy–based instrument can be usedeither as a discovery tool (58) or as a final clinical assay(59). Samples are selectively adsorbed to a hydrophobic metalsurface, and the unbound sample is washed off. After addingan energy absorber, a portion of the sample is vaporized bya laser, and the desorbed material is transferred to a massspectrometer. Other surfaces (hydrophilic, specific antibody,etc.) can be used. The mass spectrograph provides a low-resolutionmass fingerprint of the sample. The strengths of this approachare its ability to use multiple samples, it can detect low-molecular-weightproteins and peptides that cannot be seen by 2-D approaches,and it is serum friendly. Recent studies suggest that it candetect peptides and proteins bound to serum albumin (25). Thedisadvantages are that the instruments are difficult to calibrated,lot-to-lot variability of reagents, and the peaks are extremelydifficult to identify, although a number of sophisticated approachescan be used for identification. Also, instrument reproducibilityand porting of results across laboratories and machines hasnot been determined.
ICAT
This new gel-free approach allows the detection of differencesin two samples without many of the limitations of 2-D gels (60).Two samples are differentially labeled at free cysteines withoxygen isotopes that are 16 mass units different. The samplesare trypsin-digested and mixed together, then the resultingpeptides are separated by liquid chromatography and identifiedby mass spectrometry–mass spectrometry. The advantageof this technique is that it can detect a wider range of molecularweight and pI proteins than 2-D gels. The disadvantage is thecost, technical difficulty, and relative scarcity of this method.Several newer versions allow preferential capture of glycosylatedor phosphorylated proteins. This is important, because manycirculating biomarkers are glycosylated.
Protein Arrays
Several protein array platforms have garnered a great deal ofinterest in the biomarker development community (61). Antibodyarrays, in which 10 to 500 antibodies are printed on the array,function much like a multiplex ELISA. A single sample is hybridizedto a slide that contains 10 to 500 antibodies. In some versions,the samples are first labeled with a fluorescence label. Otherversions function more like a multiplex sandwich ELISA; thesecondary antibodies are added and then detected. Antibody arraysare tricky to set up because it is difficult to ensure specificity(i.e., lack of cross-reactivity across antibodies) and to keepthe assays in the linear range of detection. Because antibody-antigenaffinity is so variable and hard to determine, assembling apanel of related antibodies with similar affinities so thattheir linear ranges of detection are similar is very challengingand is usually tested empirically. Because most antibody arraysare not sandwich arrays, detection methods must be very sensitive.
A second approach is reverse-phase protein arrays, whereby manysamples are spotted on the array and a single antibody is usedto probe the samples (62,63). This is very similar in conceptto a tissue microarray. Dilution curves of the samples are frequentlyspotted into the array so that linear detection ranges can bedetermined. The major challenge in this platform is specimenhandling. Some methods require SDS and boiling (62), whereasothers are more gentle (64). Protein arrays are not widely usedbecause the instrumentation is expensive and the stability ofthe arrays is uncertain and because of difficulties with labelingmethods. Frequently, protein array data cannot be analyzed usingthe same tools as microarrays because antibodies printed onthe arrays are not independent (unsupervised). Novel analysismethods, including linear modeling, show promise; however, classicalstatistical methods are currently the most accepted means.
Tissue Microarrays
Although not a primary discovery platform, tissue microarraysare commonly used in biomarker discovery. A tissue microarrayconsist of a microscope slide that contains 50 to 1000 coresof different tissues, which can be used typically for immunohistochemistryor in situ hybridization. These arrays can be constructed fromfrozen tissue; however, paraffin-embedded tissue microarrayspredominate. Typically, a tissue microarray is used to verifyand expand on results from microarray experiments or proteinarray findings. Tissue microarrays are very useful to examinethe tissue expression of a biomarker in the disease and canprovide crucial information about expression in normal tissueand other disease processes in a rapid manner. Commercial vendorsand academic centers are frequent sources of tissue microarrays,although some laboratories will construct their own arrays.
Single Nucleotide Polymorphism–Based Approaches
The newest trend in biomarker development uses a pharmacogenomicapproach to identify biomarkers. Single nucleotide polymorphism(SNP) analysis seeks genomic markers (SNP) that co-segregatewith a phenotype (propensity for disease, etc.). This approachis widely used to predict the metabolism of drugs and genotype/phenotyperelationships in cancer (65). Because this technique analyzesgermline DNA, this technique determines only a patient’spredisposition, not the presence of the disease. However, ifthe SNP is in the promoter or coding region of a gene, theneither the protein product of the gene or the catalytic product(if an enzyme) might be altered by the disease and, hence, beassayed as a biomarker. Because of the large number of "ifs,"SNP have a great deal of potential in BM development, but theremay be many dead ends in the pathway.
Collecting and Storing Samples
Optimal samples are essential for success, but, unfortunately,many archival samples are compromised in manners in which theinvestigator is unaware. Stored samples may be very fragileand degrade over time, unless stored in liquid nitrogen. Thetype of material and the temperature and constancy of the temperaturesare key factors. Both proteins and nucleic acids will degradein improperly stored samples. Even storage at –80°Cfor long periods of time will result in degradation. Reliableguidelines are lacking, although efforts are under way to generateindustry standards (http://www.isber.org and http://www.tubafrost.org).Eighteen months is considered a reasonable estimate for storageof serum or tissue in a –80°C freezer for optimalquality. Freeze-thaw cycles are particularly dangerous, so samplealiquoting and sample evaluations (including obtaining a sampleof frozen tissue for pathologic evaluation) should be plannedcarefully to avoid freeze-thaw cycles, optimally at the timeof collection. These issues should be discussed initially withclinical chemists and pathologists to improve significantlythe quality of material, and this frequently will provide thebenefit of additional material. Processing and storage issuesof paraffin-embedded tissues are frequently overlooked. Choiceof fixative, processing, and storage conditions of paraffin-embeddedblocks are essential factors to consider.
Acknowledgments
We thank Dr. John Connaughton (National Institutes of Health)for the commercialization section of Figure 2 and Drs. JosephineBriggs, Roz Mannon, Terry Phillips, and Peter Yuen (NationalInstitutes of Health) for careful review of a previous versionof this manuscript.
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Abstract
Introduction
