comprehensive phenotyping and biological marker...

Disease Markers 18 (2002) 91–97 91IOS Press

Comprehensive phenotyping and biologicalmarker discovery

Aaron B. KantorSurroMed, Inc., 2375 Garcia Ave, Mountain View, CA 94043, USATel.: +1 650 230 1561; Fax: +1 650 230 1960; E-mail: [email protected]

Abstract. There is an enormous unmet need for biological markers to characterize disease type, status, progression, and responseto therapy. We are developing and applying an integrated bioanalytical platform and clinical research program to facilitatecomprehensive differential phenotyping of patient samples and enable the discovery of biomarkers. The platform employshigh-throughput, quantitative analysis for the characterization of thousands of parameters including cell populations, cell-surfaceantigen density, soluble proteins and soluble low molecular weight biomolecules, from small-volume biological samples in aclinical research laboratory-like setting.

Keywords: Biomarker, cytometry, proteomics, mass spectrometry, clinical studies

1. Comprehensive phenotyping and biomarkers

We are applying a broad-based integrated technol-ogy platform for comprehensive phenotyping to bio-logical marker discovery. Phenotype is the constella-tion of observable traits and measurable constituentsof an organism. It includes all biochemical and phys-iological characteristics and is the result of genotypeplus development in a specific environment. Pheno-type is much more than characteristics observable withthe naked eye. X-rays and magnetic imaging provideinformation on the phenotype of the skeleton and in-ternal organs. Cytometry provides information on thecomposition and nature of cells in blood and other tis-sues. Immunoassays and mass spectrometry can quan-tify the amount of proteins and low molecular weightbiomolecules in blood, tissues and various fluids.

The phenotype of an individual changes constantlythroughout life as a result of complex interactions be-tween an individual’s genotype and environmental fac-tors. Environmental factors such as exposure to infec-tious agents, diet, stress, exposure to chemicals and thephysiological changes associated with aging affect phe-notype at the molecular and macroscopic levels. Dif-ferent environments can influence the development ofinherited traits. Height and weight, for example, are

affected by the available food supply and nature of thediet. The average height of young adults in Japan todayis half a foot taller than their grandparents because ofdiet, not heredity. Environmental differences can alterexpression by similar genotypes such as twins matur-ing in dissimilar families. Disease often results in aprofound change in the phenotype of the individual.

Biological markers are measurable phenotypic pa-rameters that are useful for characterizing the state ofhealth or disease of an individual. They can be anindicator of normal biological processes, disease pro-cesses or pharmacologic responses to therapeutic in-tervention. A subset of biomarkers that can accuratelysubstitute for a clinical endpoint becomes accepted as asurrogate endpoint over time and is used as a diagnosticand prognostic measure in the clinic. Biomarkers varyin molecular complexity and molecular mass. Theyrange from simple low molecular weight molecules,such as sugars, fatty acids peptides and steroids, to sol-uble and cell surface proteins, and to complex clinicalphenotypes (Fig. 1). Some biological markers are wellestablished as clinical diagnostics. In diabetes fastingblood glucose is a short-term indicator and glycatedhemoglobin (HbA1c) an intermediate-term indicator ofglycemic control. Similarly, CD4 T cell counts are a

ISSN 0278-0240/02/$8.00 2002 – IOS Press. All rights reserved

92 A.B. Kantor / Comprehensive phenotyping and biological marker discovery

Simplemolecules

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metabolitescarbohydrates

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Fig. 1. Biomarkers enable objective evaluation of disease and drug response. Biomarkers can range from simple molecules to complex systems.

Improved clinical development strategies

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Fig. 2. Biomarker-enabled drug discovery, development and commercialization. Biomarkers can have utility at all stages of the drug developmentprocess.

commonly used indicator of immune system status inacquired immunodeficiency disease.

Biomarkers can change in response to a particulartherapeutic intervention and have large utility in drugdevelopment, ranging from discovery to pre-clinicalevaluation, to all aspects of clinical trials and commer-cialization (Fig. 2). They can yield clinical drug de-velopment strategies that are more effective, less costlyand executed more rapidly because they require fewerpatients. Clinical endpoints can be quite subjectiveand long trials may be required before efficacy of suchendpoints can be assessed. The success with currentlyavailable biomarkers presages the enormous value andneed for biomarker discovery. Cholesterol and lipopro-tein ratios are well-established biomarkers of coronaryartery disease and substitute for clinical endpoints, ma-jor coronary events and cardiac mortality, in the clinicaltrials of HMG-CoA reductase inhibitors (statins), pro-viding effective readouts in months rather than years.Biomarkers can also be used to select patients who arelikely responders before enrollment in a clinical trial.A good example is the use of HER2 screening priorto enrollment in the Herceptin� (trastuzumab) Phase

III clinical trial. HER2 is over expressed in about 25percent of breast cancers and the level of expression in-creases with the aggressiveness of the tumor. By limit-ing the clinical trial to patients who over express HER2in an immunohistochemistry assay, Slamon et al wereable to demonstrate efficacy using less than 500 patientswith metastatic disease [5]. This and other tests forHER2 continue to be used in additional clinical trialsand as a commercial diagnostic/prognostic test prior totreatment.

There is a strong demand for additional biomark-ers. We are developing and applying proprietary strate-gies and technologies to comprehensive phenotypingin order discover new biological markers of diseaseprogression and response to therapy. The approach ishighly integrated and combines capture of patient clini-cal signs and symptoms from physical examination andsystems testing with broad bioanalytical profiling in-cluding measurements of blood, urine and other biolog-ical samples. An informatics infrastructure, consistingof a proprietary database and software tools for dataacquisition, analysis and mining has been developed tohandle the diverse types of measurements.

A.B. Kantor / Comprehensive phenotyping and biological marker discovery 93

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Fig. 3. Biomarker studies connected to clinical outcome. A. Response to treatment. B. General disease progression, independent of treatment.Arrows represent sample collection.

2. Bioanalytical platform and clinical studies

The bioanalytical platform enables high-throughput,quantitative analysis and characterization of thousandsof parameters including cell populations, cell-surfaceantigen density, soluble proteins and soluble lowmolecular weight biomolecules, from small-volume bi-ological samples in a clinical research laboratory. Weemphasize readily available samples such as blood andurine that provide the best potential for future diagnos-tics. Our current bioanalytical platform can be dividedbased on the use of antibody reagents. Immunopheno-typing includes cytometry and immunoassays to mea-sure specific known analytes. Mass spectrometry-based analysis of proteins and low molecular weightmolecules is used to measure both known and unknownanalytes. It is well suited for discovering unexpectedchanges during the course of disease progression ortherapeutic intervention. It is also ideal for identi-fying post translational modifications that cannot beidentified by gene expression technologies. The in-formatics infrastructure accommodates all SurroMeddata streams as well as those generated by additionaltechnologies, including gene expression and genotypicdata, which may be incorporated in a given study.

Ideally, biomarker studies are tied to clinical out-come. Two general examples are shown in Fig. 3. Inthe first example, biomarkers are needed to predict theoutcome of a particular treatment either before treat-ment begins or shortly thereafter. From a medical view-point, the need is greatest when the time to identify aresponse with an established clinical endpoint is longand the treatment is expensive, such as for many newbiologic drugs. An example would be the response toanti-TNF-alpha therapy in rheumatoid arthritis [2]. Inrheumatoid arthritis therapeutic studies, clinical assess-ment typically includes tender and swollen joint counts,C-reactive protein, duration of morning stiffness, over-

all physician assessment and for longer times, X-rayevaluation of the joints as described by the AmericanCollege of Rheumatology (ACR). About one-third ofthe patients are good responders, showing a 70% im-provement based on ACR scores, and about one thirdare non-responders. In the second example diseaseprogression is considered independent from treatment.The example would be appropriate for different formsof multiple sclerosis. Early markers are needed to dis-tinguish which patients will have relapsing remittingdisease from those that develop secondary progressivedisease.

3. Sample size and initial biomarker discovery

How many samples should be used for an initialbiomarker discovery study? Concern is often raisedabout the number of samples needed to detect statisti-cally significant differences, without false positive orfalse negative conclusions, as the number of variablesis increased. A calculation demonstrates that the num-ber of samples needed for these types of studies canbe quite practical. The broad-based technology per-mits the collection of many more bioanalytical vari-ables than subjects. Consequently, many multivariatestatistics cannot be used and the data must be analyzedusing univariate test statistics. Figure 4 shows a powercalculation as the number of variables is increased from1 to 5000. The plot calculation uses paired measure-ments of one group (e.g. before and after drug) anda power of 90%. Here we take a very conservativeapproach to protect against false positive conclusions.To maintain the overall experiment-wise false positiverate at 0.05, the p-values from the univariate test statis-tics has been adjusted using the step-down Bonferronimethod of Holm. The utility of any variable is deter-mined by the effect size, the difference of the means


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Fig. 4. Samples needed vs. effect size and number of variables. Calculation is based on paired comparisons, a power of 90% and an overallstudy-wise p-value of 0.05 using the step down Bonferroni correction of Holm.

of the measure, relative to the standard deviation of themeasure (MD/SD). Often discovery studies are initi-ated in hypothesis generation mode, without ana prioriestimate of the effect size for most measures. It is rea-sonable to power a first study to detect effect sizes ofaround 1. The number of samples required for a giveneffect size increases logarithmically with the numberof variables. For 50 measures, this would require about28 samples for each of the two time points. For 5000measures the number of samples increases to about 40samples. The need for additional samples per addi-tional variable is far less than one-to-one. Note thatthe number would approximately double for unpairedcomparisons. These calculations are encouraging be-cause even using a conservative analysis technique ad-equate power for a typical study is maintained withoutrequiring a prohibitive number of subjects.

4. Cytometry

The SurroScanTM microvolume laser scanning cy-tometry (MLSC) system is used to classify and quan-tify hundreds of immune and inflammatory cell pop-ulations and cell surface antigen expression in smallvolumes of whole blood, processed blood or other flu-ids [1,6]. It is designed for robust daily use in a clinicalresearch laboratory. The system combines instrumen-tation, reagents, disposable plastic capillary arrays andsoftware to enable the rapid, sensitive, automated andcomprehensive cellular phenotyping. Advantages overconventional flow cytometry systems include improvedease-of-use, higher degree-of-multiplexing, decreasedsample volumes, absolute quantitation of cell counts,automated data capture and analysis and consistent re-sults among instruments.

In MLSC, as with flow cytometry, fluorophore-labeled antibodies specific for cell surface antigensare used to identify, characterize, and enumerate spe-cific leukocyte populations. The staining reaction canbe done in whole blood or other single cell suspen-sions. In general, there is no need to wash the reagentaway; quantitative dilution of the blood-antibody mix-ture is usually sufficient sample preparation. The cell-antibody mixture is loaded into an optical-quality cap-illary of known volume and analyzed with a laser-basedfluorescence-imaging instrument. In order to operatewith whole blood, we use fluorophores that can be ex-cited in the red region (> 600 nm) of the spectrum,such as Cy5, Cy5.5 and Cy7-APC. White blood cellsisolated following ficoll or red cell lysis, can also beanalyzed.

In contrast to flow cytometry, the laser scans overstationary cells rather than cells flowing past the laser.A small cylindrical laser spot is scanned across the cap-illary in one direction while the capillary is translatedrelative to the optical system in a second direction. Anexample of the resulting raster-scan profile for a sin-gle fluorescence channel is shown in Fig. 5(A). Eachpeak represents a cell and can be converted to a listmode data file and displayed as a dot plot (Fig. 5(B)).Typically three antibody reagents, each with a differentfluorescent tag and each detected in a different channelare used per assay. Three cell populations are indicatedin the figure: CD4 T cells, CD8 T cells and monocytes.

Our clinical studies typically utilize panels of 64three-color cellular assays, arranged in two disposablecapillary arrays, that allow the identification and enu-meration of hundreds of different cell types and cell-associated molecules that are relevant to immune, in-flammatory and metabolic processes. Each reagentcocktail typically contains one or two antibodies tothe major cell populations – neutrophils, eosinophils,


CD4+ T Cells

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Fig. 5. Cytometry data. Whole blood was labeled with Cy5-anti-CD4, Cy5.5-anti-CD8 and Cy7-APC anti-CD3 antibodies, diluted and scanned.A. Raster scan generated for one fluorescence channel. Peaks correspond to antibody-labeled cells identified with image processing softwarethat produces a list-mode data file with parameters for every detected cell event [4]. Unlabeled cells (erythrocytes and other leukocytes) are notidentified. The baseline pedestal is unreacted reagent that was not washed away. B. Dot plot. Resultant data can be graphed with each dotrepresenting one cell. Instensity data is compensated for spectral overlap, so the shown parameters are proportional to the amount of dye-antibodyreagent on each cell.

monocytes T cells, B cells, NK cells, and platelets –and one or two antibodies to subsetting antigens whichmay indicate the functional state, activation state oradhesion characteristics of the population.

The SurroScan cytometer is designed for robust usein a clinical research laboratory. Proprietary informat-ics tools enable automated collection, processing andanalysis of the data. This data pipelining approachspeeds data flow and reduces user error. For each clini-cal study, information on the individual assays, includ-ing reagents, cell populations, and gating strategy arestored in a common Oracle database. The system isdesigned such that every cytometer yields the same re-sults for a given sample. Consequently, common gates,the boxes used to identify specific cell populations, cangenerally be used throughout the study. Resultant in-formation on cell counts and relative antigen levels onindividual cell types is immediately available for reviewand use in study wide analyses.

5. Profiling protein and low molecular weightmolecules in serum samples

Mass spectrometry is a mature bioanalytical platformfor the measurement of specific analytes with a highdegree of mass accuracy. The challenge is to adapt thistechnology to robust screening of large numbers of bi-ological samples. There are three main technical com-ponents for differential profiling of proteins and low

molecular weight molecules from biological samples:

Sample Preparation

Mass Spectrometry

Data Processing Informatics

mass spectrometry instrumentation, sample prepara-tion, and data processing informatics. We acquire high-end instruments and integrate these with proprietarysample preparation methods and data processing infor-matics. Our approach has been to use high resolution,high mass accuracy time-of-flight mass spectrometersfor screening purposes. Screening with the time-of-flight instruments provides an advantage in the abilityto track low abundance species in complex mixtureslike serum. With a 0.002 Dalton mass accuracy, com-mon compounds are readily identified from complexmixtures by comparison to pre-established proprietarydata libraries. Additionally, follow-up tandem massspectrometry, with the ion trap and triple quadrupoleinstruments, is used for the identification and focuseson candidate biomarkers that are significantly differentbetween study groups.

Biological samples, like unfractionated serum, arevery complex and often dominated by a few compo-nents present at very high concentration that can inter-fere with the detection of all other molecules by MS.For example albumin (35–45 mg/ml) and immunoglob-ulin (IgG, IgA and IgM, 25–35 mg/ml) make up about90% of total serum protein. Simplification of the com-


plex mixture enables quantitation of a broader set ofanalytes.

Our proteomics and metabolomics sample prepara-tion process for serum focuses on simplifying the ma-terial: First, serum is fractionated into low and highmolecular weight fractions because they require differ-ent analytical methods at the preparation, instrumen-tation and identification steps for subsequent resolu-tion into hundreds of distinct molecular species. Thelow molecular weight fraction is analyzed by two tech-niques: gas chromatography-mass spectrometry andon-line HPLC-mass spectrometry. Chemical modifi-cation before the gas chromatography enhances thevolatility of the compounds and the ion impact ion-ization mass spectrometry enables quantitation and ahigh degree of reproducibility. In the second technique,electrospray ionization mass spectrometry is used be-cause it can be readily coupled to on-line liquid chro-matography. We are among the first to establish repro-ducibility and quantification for the electrospray ioniza-tion technique. Average CV’s for analytes in complexmixtures are about 25%, which establishes a reason-able level for looking at biological variation. The typesof compounds in the low molecular weight fraction arediverse. Identification is made with the assistance ofthe AMDIS computer program and spectra library of∼ 100,000 compounds from National Institute of Stan-dards and Technology. Tandem mass spectrometry isalso used for identification.

The high molecular weight fraction is processed fur-ther. High abundance proteins such as albumin and im-munoglobulin that dominate the fraction are removedby affinity methods to increase dynamic range. Thehigh molecular weight fraction is analyzed in severalways. First tryptic digestion is done to generate pep-tide fragments and the sample is applied to an on-linereverse phase liquid chromatography column coupledto mass spectrometry with electrospray ionization andtime-of-flight detection. This method has a duty cycleof a few hours and can be automated for routine use ina clinical research laboratory. Second, the albumin andIg fractions can be analyzed independently for associ-ated molecules. Third, two dimensional chromatogra-phy can be used to extend the range of molecules thatcan be profiled. This approach requires a longer dutycycle and may be more appropriate for a subset of sam-ples. The high molecular weight fraction is largely pro-teins and tandem mass spectrometry, protein databasesand commercial software are used to identify peaks ofinterest and build a compound library. Post transla-tional modifications such as phosphorylation, N and

O-linked glycosylation and ubiquitination can also beidentified.

An example of the richness of the chromatogram andmass spectra associated with such a human serum sam-ple is shown in Fig. 6. Approximately 1000 molecularspecies can be readily resolved from the high molec-ular weight fraction after removal of albumin and im-munoglobulin even with a very fast chromatographyrun. Only two hours is needed for the process. Eachprotein gives rise to several peptide species that can beidentified with tandem mass spectrometry. We haveidentified hundreds of different proteins in serum and apartial list is shown in the figure.

SurroMed has developed proprietary informaticsmethods for comparison of subject samples and quan-tification of proteins, peptides or small molecules, inwhich these components are quantified relative to thesame, corresponding molecules in a different samples,usually a control or normal sample. The approach relieson the assumption that biological samples consist ofcomplex mixtures of multiple biological components,of which only a minority is relevant to the compari-son. The majority of components are relatively con-stant for the same individual over time or across sub-ject populations. The majority of components whoseconcentrations do not vary across samples are used asan intrinsic internal standard to normalize the concen-trations of components that do vary. Spectra from in-dividual samples first undergo nonlinear filtering to re-move noise, dynamic thresholding to separate peaksfrom noise and vectorized two-dimensional peak peak-ing to take advantage of information in both the chro-matography and mass-to-charge dimensions [3]. Sec-ond, common components in the samples are comparedto enable normalization and time warping to correct forsmall differences in the runs. Third, peak lists fromall of the samples are compared and a merged peaklist is developed and applied for monitoring a commonintegrated set of peaks across all samples. This list ofpeaks and intensities resides in the study-wide databasefor statistics and data mining.

6. Conclusions

The large amount of patient data across a broad rangeof bioanalytical and clinical measurements necessitatesan array of informatics tools. We have developed toolsfor the primary analysis of raw data on each bioan-alytical platform, pipelining that data into an Oracledatabase and visualizing and analyzing the results on


Protein IDPACE4Haptoglobin2PlexinB3Alpha-1B-GlycoproteinCarboxypeptidase NLIFRPlasma protein ZThyroglobulincystatin APregnancy-zone proteinAlpha-2-MacroglobulinAlbuminSerotransferrinFBL3

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Sum of full-scan MS for 1 minute segmentProtein IDPACE4Haptoglobin2PlexinB3Alpha-1B-GlycoproteinCarboxypeptidase NLIFRPlasma protein ZThyroglobulincystatin APregnancy-zone proteinAlpha-2-MacroglobulinAlbuminSerotransferrinFBL3

PeakABCDEFGHIJKLMN

Protein IDPACE4Haptoglobin2PlexinB3Alpha-1B-GlycoproteinCarboxypeptidase NLIFRPlasma protein ZThyroglobulincystatin APregnancy-zone proteinAlpha-2-MacroglobulinAlbuminSerotransferrinFBL3

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Sum of full-scan MS for 1 minute segment

Fig. 6. HPLC-MS from Human Serum. The HMW fraction was digested with trypsin after removal of the majority of albumin and immunoglobulin.Twenty micrograms of the digest was run in a high throughput mode. Top: Base peak chromatogram shows the elution profile from the HPLCcolumn. Bottom: Sum of the full scan MS for a 1 minute segment. Tandem MS and database searches can be used to identify hundreds ofpeptides. A representative set is listed on the right.

a study-wide basis. Web-based tools allow biologists,biochemists and clinicians to query the database andview statistical results on a study-wide basis. This aidsin the identification of putative biomarkers and follow-up. Taken together, the concepts and technologies de-scribed here, as well as those being developed by oth-ers, will enable a revolution in comprehensive pheno-typing and lead to improved treatment of patients inmultiple disease areas.

Acknowledgements

This paper is an overview of the SurroMed biomarkerdiscovery effort. A diverse group of biologists,chemists, engineers, informatics specialists and clini-cians have contributed to the development of the plat-form. Michael Natan made an oral presentation ofsome of this material at the Biomarkers and SurrogateEndpoints Conference. Special thanks go to KarenCheal for the power calculation for Fig. 4 and TomShaler for Fig. 6. Thanks to Howard Schulman, SusanAlters, John Kollins and Chris Becker for thoughtfulcomments on the manuscript.

References

[1] L.J. Dietz, R.S. Dubrow, B.S. Manian and N.L. Sizto, Vol-umetric capillary cytometry: a new method for absolute cellenumeration,Cytometry 23 (1996), 177–186.

[2] M. Feldmann and R.N. Maini, Anti-TNF alpha therapy ofrheumatoid arthritis: what have we learned?Annu. Rev Im-munol 19 (2001), 163–196.

[3] C.A. Hastings, S.M. Norton and S. Roy, New algorithms forprocessing and peak detection in liquid chromatography/massspectrometry data,Rapid Commun. Mass Spectrom. 16 (2002),462–467.

[4] S.M. Norton, J. Winkler and L.J. Dietz, Cell enumeration andcharacterization in microvolume laser scanning cytometry: amulticolor image processing package,Optical Diagnosis of Liv-ing Cells III, Proceedings of SPIE 3921 (2000), 20–30.

[5] D.J. Slamon, B. Leyland-Jones, S. Shak, H. Fuchs, V. Paton, A.Bajamonde, T. Fleming, W. Eiermann, J. Wolter, M. Pegram,J. Baselga and L. Norton, Use of chemotherapy plus a mono-clonal antibody against HER2 for metastatic breast cancer thatoverexpresses HER2,N. Eng. J Med. 1344 (2001), 783–792.

[6] I.D. Walton, L.J. Dietz, G. Frenzel, J. Chen, J. Winkler, S.M.Norton and A.B. Kantor, Microvolume laser scanning cytom-etry platform for biological marker discovery,Proc. SPIE-Int.Soc. Opt. Eng. 3926 (2000), 192–201.

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