sample stability and variability of b-cell subsets in blood from healthy subjects and patients with...

Cytometry Part B (Clinical Cytometry) 78B:49–58 (2010)

Sample Stability and Variability of B-Cell Subsetsin Blood from Healthy Subjects and Patients

with Systemic Lupus Erythematosus

Shelley Sims Belouski,1* Daniel Wallace,2 Michael Weisman,2 Mariko Ishimori,2

Lisa Hendricks,1 Debra Zack,1 Mike Vincent,1 Erik Rasmussen,1 John Ferbas,1

and James Chung1

1Amgen, Inc., Thousand Oaks, California2Division of Rheumatology, University of California Los Angeles, Cedars-Sinai Medical Center, David Geffen

School of Medicine, Los Angeles, California

Objective: Characterization of peripheral leukocytes is an important aspect of monitoring the effect ofimmunotherapeutic interventions in systemic lupus erythematosus (SLE). We analyzed cell surfacemarkers commonly used to assess patients with SLE, focusing on the effect of holding blood prior toprocessing/analysis and the relative reliability of the measurements that were conducted.

Methods: Healthy volunteers (HV; n 5 20) and patients with SLE (n 5 42) were studied. Whole bloodwas collected for flow cytometric analysis on days 1, 8, 15, 105, 195, 285, and 375 and held overnightfor analysis. A subset of samples was additionally analyzed on the day of collection.

Results: Variability arising from overnight storage of whole blood was found to be within 20% formost lymphocyte subsets. There was greater between rather than within subject variability over a 1-yearperiod. As anticipated, the data showed higher CD38 and lower CD19 densities on B cells from patientswith SLE compared to HV. Although a higher percentage of cells with markers of plasmablasts/cellswere observed in the blood of patients with SLE relative to HV, these measurements were found to beamong the least reliable (i.e., most variable).

Conclusions: This study provides technical perspectives for those conducting immunophenotypic anal-yses of B-cells in patients with SLE. We envision that our data, which addresses sample stability issuesand presents a method to describe the relative reliability of one measure over another, holds value forclinical assessments of B-cells in SLE and the evaluation of investigational agents designed to modifythe B-cell compartment. VC 2009 Clinical Cytometry Society

Key terms: systemic lupus erythematosus; immunophenotyping; B-cell

How to cite this article: Belouski SS, Wallace D, Weisman M, Ishimori M, Hendricks L, Zack D, Vincent M,Rasmussen E, Ferbas J, Chung J. Sample stability and variability of B-Cell subsets in blood from healthy subjectsand patients with systemic lupus erythematosus. Cytometry Part B 2010; 78B: 49–58.

Numerous B-cell directed therapies are now eithermarketed or are under active clinical investigation forautoimmune diseases including systemic lupus erythem-atosus (SLE) (1). These include rituximab, which leadsto rapid and profound decrease in circulating B-cells; aswell as epratuzumab, an anti-CD22 antibody that alsospecifically targets B cells but leads to a partial depletionin peripheral B cells. Inhibition of the TNF family cyto-kine BAFF, either alone or with APRIL, leads to func-tional inhibition of B-cells and a partial decrease in thenumber of circulating B-cells over several months (2–4).

Changes in numbers and the relative distributions of cir-culating B-cells and B-cell subsets as a consequence of

Grant sponsor: Amgen, Inc.*Correspondence to: Shelley Sims Belouski, One Amgen Center

Drive, MS 30E-3-C, Thousand Oaks, CA 91320, USA.E-mail: [email protected] 11 November 2008; Revision 6 April 2009; Accepted 27

April 2009Published online 10 August 2009 in Wiley InterScience (www.

interscience.wiley.com).DOI: 10.1002/cyto.b.20482

VC 2009 Clinical Cytometry Society

clinical intervention represent important evidence ofimmunologic impact in such studies.

In light of the variety of investigational agents that ei-ther directly or indirectly impact B-cells, there is a needto understand inter and intrasubject variability as theyrelate to a robust measurement of B-cells and B-cell sub-sets. In the early clinical development of such agents,preclinical animal models, in vitro studies of human cellsand/or clinical experience with analogous agents mayprovide guidance as to the particular cell populationsinvolved and the magnitude of expected change withpharmacologic intervention. In addition to pharmacody-namics, cell subset enumeration has the potential toconvey safety information (5). Translation of thesepotential pharmacodynamic endpoints into the clinicrequires careful attention to the development of feasibleand reproducible methodology as well as an understand-ing of the sources of variability. Understanding and mini-mizing variability of these measurements are importantas they enable estimation of reliably detectable effectsizes and provide a basis for sample size calculations inclinical studies utilizing an investigational agent hypothe-sized to impact the endpoint. In much the same waythat focused studies of the variability of T-cells and T-cellsubsets were conducted in the HIV model (6), assess-ments of the variability that underlies B-cell and B-cellsubset enumeration should empower intervention stud-ies in autoimmune diseases by supplying clinical investi-gators with robust pharmacodynamic assessments fromtreated patients.

The critical role of B-cells in the pathogenesis ofchronic autoimmune diseases such as rheumatoid arthritis(RA) and SLE is well established (7,8). A number of differ-ences in B-cell subset distribution have been describedamong patients with SLE as compared with healthydonors, and it is believed that they reflect the underlyingimmune dysregulation in the disease. Most notably, adecrease in naı̈ve B lymphocytes, an expansion of thememory B-cell compartment, and an increase in plasma-blasts have been consistently described (9–12). There isevidence to suggest that improvement in disease may becorrelated with a ‘‘normalization’’ of the B-cell subset dis-tribution profile to that resembling healthy state (13).

This backdrop motivated us to conduct a longitudinalmethod validation study using a clinically feasible wholeblood staining approach in well-defined populations ofhealthy volunteers and subjects with SLE to profile B-cellphenotypes in SLE and to characterize the variability in themeasurement of various B-cell subsets. The results of thisstudy are expected to aid the study of therapeutic agents,both marketed and investigational, for which changes toleukocyte subpopulations, and B-cell subsets in particular,are an expected effect of pharmacologic intervention.

MATERIALS AND METHODS

Study Volunteers

HV (n ¼ 20) and SLE subjects (n ¼ 42) were enrolledat Cedars-Sinai Medical Center (Los Angeles, CA) after

receiving Institutional Review Board (IRB) approval.Research was carried out in compliance with the Hel-sinki Declaration and approved by the Cedars-Sinai IRB.The criteria for all subjects included males and femalesbetween 18 and 45 years. They were known to be nega-tive for HIV antibodies, hepatitis B surface antigen, andhepatitis C antibodies. The HV were without a historyof any chronic medical disease. Patients with SLE en-rolled into the study fulfilled the 1982 Revised Criteriaof the American College of Rheumatology and had a di-agnosis of SLE for greater than 3 months. The patientswith SLE were required to have been clinically stableand on a stable pharmacologic regimen for at least 1month prior to enrollment. Patients with active vasculi-tis, active CNS lupus requiring therapy, renal insuffi-ciency (acute and chronic), uncontrolled hypertension,uncontrolled diabetes, or active infection wereexcluded. Patients who received cyclophosphamide orany other alkylating agent, or 100 mg/day of prednisoneor equivalent in the 60 days prior to enrollment werealso excluded.

HV were designated as Group 1. For all patients withSLE, disease severity was assessed at enrollment usingthe SLE disease activity index (SLEDAI) and assigned toGroup 2 (SLEDAI < 8) or Group 3 (SLEDAI � 8). Afterenrollment, all medical therapies deemed clinicallyappropriate by the investigator or treating physicianwere permitted. For subjects in Group 2 (SLEDAI < 8),those who received cyclosporine, tacrolimus, sirolimus,leflunomide, methotrexate, mycophenolate mofetil, aza-thioprine, or greater than 10 mg prednisone (or equiva-lent) per day were excluded.

Schedule of Assessments

After the screening visit, all subjects returned to thestudy center on days 1, 8, 15, 105, 195, 285, and 375.Assessment of overall safety and SLE disease activityusing the SLEDAI and laboratory tests, including a com-plete blood count and differential, were obtained ateach visit.

Specimen Handling and Preparation

Blood specimens were collected between 8.00 and11.30 A.M. and couriered to Amgen laboratories (Thou-sand Oaks, CA) for flow cytometry determinations orretained at Cedars Sinai Medical Center (Los Angeles,CA) for standard hematology assessments on aCOULTER

VR

Ac�T diff2TM Hematology Analyzer (BeckmanCoulter, Brea CA). As described in the text, the majorityof specimens for this study were held overnight beforeprocessing; this was done on the laboratory bench (am-bient temperature) without rocking. Time constraintsfor next day processing were not rigid, with the onlyrequirement that samples were processed (stained/lysed) on the calendar day following sample collection.

Flow Cytometry

Lymphocyte phenotypes were determined by incubat-ing whole blood specimens with three- or four-color

50 BELOUSKI ET AL.

Cytometry Part B: Clinical Cytometry

combinations of commercially available fluorochrome-conjugated antibodies (Becton Dickinson Immunocytom-etry Systems, San Jose, CA), lysing the erythrocytes inan ammonium-chloride lysis buffer (14), and analyzingthe labeled lymphocytes on a FACSCaliburTM flow cy-tometer (Becton Dickinson Immunocytometry Systems).The staining strategy for the various leukocyte subsetsare shown in Table 1. The antibody configurations(clone designation) were as follows: Tube 1: CD20 FITC(L27), CD45 APC (2D1), CD19 PerCP (SJ25C1); Tube 2:CD3 FITC (SK7), CD16 PE (B73.1), CD56 PE (MY31),CD45 APC (2D1); Tube 3: CD27 FITC (L128), CD38 PE(HB7), CD19 PerCP (SJ25C1), CD45 APC (2D1); Tube 4:CD27 FITC (L128), IgD PE (IA6-2), CD19 PerCP(SJ25C1), CD45 APC (2D1), Tube 5: CD138 FITC(MI15), CD38 PE (HB7), CD19 PerCP (SJ25C1), CD45APC (2D1).

CD45 and side scatter (SSC) were used as primary gat-ing parameters to identify lymphocytes (15); an addi-tional CD19 gate was used to restrict the denominatorfor B-cell subset determinations to the B-cell pool. Adefault value of 5,000 gated events was collected foreach analysis to provide a statistically valid measurementof the percentage of each cell type or the median fluo-rescence intensity (MFI) of an indicated marker.

With respect to controls and instrument set-up, Cali-brite beadsTM (Becton Dickinson Immunocytometry Sys-tems) were used daily to make initial adjustments ofphotomultiplier tube settings, fluorescence compensa-tion, and to check instrument sensitivity. Fluorescenceintensity measurements were standardized by dailyinstrument calibration with chicken red blood cells (Bio-sure, Grass Valley, CA) (16). Longitudinal performancewas assessed by staining and analyzing a commerciallyavailable preparation of lyophilized human lymphocytes(CYTO-TROLTM cells, Beckman Coulter, Miami, FL) for

CD19 expression. Acceptance criteria for each clinicalspecimen included calculation of the lymphosum (17),where the percent sum of T cells, B cells, and NK cellswere used as an internal processing control; all speci-mens in this study were confirmed to contain a lympho-sum of 100% � 10% (data not shown).

Statistical Methods

A mixed model was used to estimate the intra andintersubject variability for each immunophenotype andgroup (HV [Group 1], mild SLE [Group 2], more activeSLE [Group 3]) with subject included as a randomeffect. The inter and intra subject variability columnsexpress the average square distance from the mean suchthat the square root of the variability value equals thestandard deviation. The variability values are relative tothe units of each parameter, wherein the MFI valuesrange from 0 to 10,000 (the scale of fluorescence inten-sity on a flow cytometer) and the measure of populationpercentages ranges from 0 to 100%. The coefficient ofreliability (CoR) was calculated by dividing the intersub-ject variability by the total variability (intersubject þintrasubject variability).

Ranges were calculated for each group with the lowerbound the estimated 5th percentile and the upperbound the estimated 95th percentile; the 50th percentile(median) was also estimated for each group. Becausemultiple measures were taken on each subject, the per-centiles were estimated using a method which weighseach observation based on the frequency of overallobservations per subject as used by Taylor et al. (18).

A mixed model was used to compare the central tend-ency for each phenotype between groups (Group 1compared to Group 2, Group 1 compared to Group 3,Group 3 compared to Group 2) with subject included asa random effect and group as a fixed effect. P-values

Table 1Flow Cytometry Staining Panel

Tube/assay Cell distribution Measurement Gate

Tube 1: CD20 FITC, CD19 PerCP, CD45 APCB cells % CD19þCD20� CD45B cells % CD19�CD20þ CD45B cells % CD19þ/CD20þ CD45B cells MFI CD19 CD45B cells MFI CD20 CD45

Tube 2: CD3 FITC, CD16 PE, CD56 PE, CD45 APCNatural killer cells % CD3� CD16/CD56þ CD45

T cells % CD3þ CD16/CD56� CD45Tube 3: CD27 FITC, CD16 PE, CD56 PE, CD45 APC

Plasmablasts/plasma cells % CD27bright CD38bright CD45/CD19dim

B cell subpopulations MFI CD38 CD45/CD19Tube 4: CD27 FITC, CD19 PerCP, CD38 PE, CD45 APC

Naı̈ve B cells % IgDþ CD27� CD45/CD19Memory B cellsa % IgDþ CD27þ CD45/CD19Memory B cellsa % IgD� CD27þ CD45/CD19

Tube 5: CD138 FITC, CD19 PerCP, CD38 PE, CD45 APCPlasmablasts/plasma cells % CD138þ CD38bright CD45/CD19dim

aMemory B cells can be identified by expression of CD27 and further subdivided according to co-expression of cell-surface IgD. IgDþmemory B cells, primarily secrete IgM upon stimulation in vitro or in vivo, while IgD- memory B cells primarily secrete IgG (39).

B-CELL SUBSETS IN PATIENTS WITH SLE 51


come from an F-test and are not adjusted for multiplecomparisons.

RESULTS

Characteristics of Recruited Study Volunteers

Study volunteers were recruited as either HV (Group1; n ¼ 20), SLE subjects with SLEDAI score < 8 (Group2; n ¼ 29), or SLE subjects with SLEDAI score � 8(Group 3; n ¼ 13; Table 2). The SLEDAI scores in Group3 subjects ranged from 8 to 18. The HV were matchedfor sex and age. The subjects were predominantlyfemale (89%) with an average age of 26.6 (Group 1),32.8 (Group 2), and 33.4 (Group 3). Subjects were dis-tributed among a variety of ethnicities with all majorethnic groups represented. No significant differencesbetween groups were noted with respect to height,weight, or body mass index.

The clinical manifestations of SLE among the patientswere distributed among the various organ systems listedin Table 2. Arthritis and rash were common clinical find-ings and dsDNA was positive in 37.9% of Group 2patients and 84.6% of Group 3 patients. More than halfof the SLE subjects were on hydroxychloroquine.Greater than 20% of the SLE subjects were on azathio-prine, mycophenolate mofetil, or methotrexate. Agreater proportion of patients with SLE were on gluco-

corticoids (prednisone or methylprednisolone) in Group3 (53.9%) compared with Group 2 (24.1%).

Sample Stability

The standard practice of shipping whole blood speci-mens from clinical sites to a central flow cytometry labo-ratory mandates an understanding of changes in cellpopulations as a function of time. A subset of specimensfrom each group were processed and analyzed on thesame day of collection for comparison against data froman independent processing run on the next calendarday. Pairwise comparison (the difference between thepercentages obtained from the same day analysis versusthe next day’s run) shows that data from nearly all ofthe next day processing runs fell within an absolute�5% of the same day run, with few exceptions (e.g., T-cell evaluations for Group 2 donors). In nearly allcases, the range of results included both positive andnegative values, attributing the primary source of varia-tion to analytical variability. This is within our expecta-tions for the precision of measurements (analyticalvariability) obtained from a flow cytometer in a clinicalsetting (19).

The data were also normalized and expressed as a per-centage change (1 � (next day/same day)) � 100). De-spite what might be considered adequate stabilitycharacteristics for these cell subsets, the impact of

Table 2Demographic Characteristics, Selected Concomitant Medications and Clinical Features

Characteristic Group 1 Group 2 Group 3

Number of Subjects 20 29 13SLEDAI N/A <8 �8Sex n (%)

Female 17 (85) 27 (93) 11 (85)Male 3 (15) 2 (7) 2 (15)

Age, mean (SD) years 26.6 (5.8) 32.8 (6.0) 33.4 (7.8)Race n (%)

White or Caucasian 6 (30) 13 (45) 7 (54)Black or African American 0 (0) 5 (17) 2 (15)Hispanic or Latino 8 (40) 5 (17) 1 (8)Asian 5 (25) 6 (21) 3 (23)Native Hawaiian or other Pacific Islander 1 (5) 0 (0) 0 (0)

Height, mean (SD) cm 165.48 (8.18) 165.41 (5.95) 163.72 (9.89)a

Weight, mean (SD) kg 67.29 (14.88) 67.84 (15.94) 66.50 (21.60)a

BMI, mean (SD) kg/m2 24.42 (4.02) 24.88 (6.22) 24.22 (5.46)Clinical features n (%)

Alopecia N/A 5 (17.2) 6 (46.2)Arthritis N/A 21 (72.4) 12 (92.3)dsDNA N/A 11 (37.9) 11 (84.6)Leukopenia N/A 9 (31.0) 1 (7.7)Low complement N/A 15 (51.7) 12 (92.3)New rash N/A 19 (65.5) 13 (100)

Selected concomitant medications at enrollment n (%)Azathioprine N/A 1 (3.4) 2 (15.4)Hydroxychloroquine N/A 18 (62.1) 7 (53.9)Methotrexate N/A 0 (0.0) 1 (7.7)Mycophenolate mofetil N/A 5 (17.2) 1 (7.7)Prednisone/methylprednisolone N/A 7 (24.1) 7 (53.9)an ¼ 12; the baseline weight and BMI was not collected for 1 subject in group 3.

52 BELOUSKI ET AL.


overnight storage on rare events is more difficult toappreciate unless the data are adjusted to accommodatethe relative size of each starting population. The variabil-ity surrounding markers of plasmablasts and plasmacells (CD27bright CD38bright CD19dim and CD138þ

CD38brightCD19dim) is therefore noteworthy, where whatmay be considered to be a minor loss during pairwisecomparisons of raw data can translate to a loss of muchhigher magnitude after data normalization (Fig. 1).

Range Estimates of Lymphocyte Subsets

Standard whole blood stain and ammonium chloridelysis methods were used to process specimens one dayafter collection. The mean (standard error) and medianwere generated for each cell type in each group (HVand SLE groups) as depicted in Table 3 (20). Statisticalsignificance was determined for the mean of the repeatmeasures for the SLE groups versus HV and between thetwo SLE groups.

We found that the percentage of cells with markers ofplasmablasts/plasma cells (CD38bright CD27bright CD19dim

and CD138þ CD38bright CD19dim) among all B cells washigher in the B cells of patients with SLE comparedwith those from HV and is in keeping with what hasbeen described previously (Table 3) (11,20). Weobserved slightly higher percentages of naı̈ve B cellswith corresponding decrease in the memory compart-ments IgD�CD27þ and IgDþCD27þ B cells though thedifferences were not significant.

There were notable differences in the surface expres-sion levels of CD38 and CD19 on B cells in HV as com-pared to expression levels from SLE blood (Table 3).The median fluorescence intensity (MFI) of CD38 on Bcells from the whole blood of patients with SLE washigher than those from the peripheral whole blood ofHV. This difference was statistically significant betweenthe healthy and diseased groups and there was a trendtoward higher levels in Group 3 relative to Group 2. Incontrast to CD38, the level of CD19 was lower in thepatients with SLE than in the HV. This finding was statis-tically significant between patients with HV and SLE.The level of CD19 in Group 3 was lower than in Group2 but this did not reach significance. It should be notedthat the extent of decrease in both CD38 and CD19expression levels after overnight storage was greater inpatients with SLE compared to HV (Fig. 1).

Variability

The inter- (between-) and intra- (within-) subject vari-ability were assessed for the entire course of the study(three weekly measurements followed by 1 full year ofquarterly measurements; Table 4) for each lymphocytepopulation as measured by absolute cell count (/mm3),or percentage (%). The expression levels of cell surfacemarkers are noted as MFI.

Intersubject variability was generally higher than intra-subject variability though exceptions were noted in therare event measures of markers for plasmablasts andplasma cells (CD27bright CD38bright CD19dim and CD138þ

CD38brightCD19dim) and the CD19 expression level ingroup 1 HV. Intersubject variability was generally higherfor most leukocyte subpopulations from the blood ofpatients with SLE as compared to blood from HV. Inter-subject variability was markedly higher as a function ofdisease severity while intrasubject variability remainedrelatively constant. Exceptions included the rare eventmeasures of markers for plasmablasts and plasmacells (CD27bright CD38bright CD19dim and CD138þ

CD38brightCD19dim) and the expression level of CD19and CD38 on B cells.

Coefficient of Reliability (CoR)

The need to understand the reliability of a measure-ment in a repeat-measures scenario is of fundamental im-portance to the design of clinical trials, especially whenit is desirable to compare a baseline measurementagainst a post-treatment measurement for a given indi-vidual. When sample sizes are small (e.g., a Phase Itrial), the reliability of a given measurement can some-times hold greater value than the ability to demonstrateformal statistical significance, because a change withinan individual can be interpreted in context. The variabil-ity analyses presented here (Table 5) suggest thatwithin-person estimation do have advantages relative tobetween-person estimates when this (or a similar) anti-body panel is utilized. We therefore sought a method toscore the relative benefit of studying within-person vari-ability on a standardized scale. We calibrated our efforts

FIG. 1. Graphical representation of the average loss/gain of a givencell type or fluorescence intensity value after overnight storage ofblood. A subset of whole blood specimens were processed within thesame day of collection in addition to the standard next day analysis tounderstand the stability profiles of the populations and cell surfaceantigens within this immunophenotyping panel. The next day analysisis normalized to the same day (fresh) analysis to appreciate the magni-tude and direction (increase or decrease) of changes. This analysisshows that plasmablasts/plasma cells may be particularly sensitive tothe effects of overnight storage, as compared to the other cell typesmeasured in this study. Group 1, n ¼ 6; Group 2, n ¼ 10; Group 3,n ¼ 4.



to an analysis approach previously reported by Taylor etal. (6). This allowed us to benchmark the CoR foundhere against a marker of known clinical significance,which is the established value of CD4 cell percentagesand counts in HIV-infected persons. The calculationmeasures the correlation between serial measurementsfrom an individual and expresses the data with a rangeof 0–1. The ratio represents the relative proportion ofthe intersubject variability to the total variability (sum ofinter and intrasubject variability). A higher numbertranslates into a greater correlation and therefore ahigher degree of reliability for the measurement whenapplied to intrasubject repeat measures. The reliabilitycoefficients reported by Taylor et al. with respect toCD4 count and percent ranged from 0.64 to 0.68 in alarge cohort of HIV infected and uninfected persons thatparticipated in a multicenter natural history study.Because the sample type and processing methods (am-monium chloride lysis of whole blood) were identical

between this and the prior report, the comparison wasconsidered valid to the extent that two disparate dis-eases are represented in these datasets. Of note, HVwere included in each study.

The majority of coefficients for non-plasmablast/plasma cell B-cell subsets (Table 5) showed a high value(>0.70) that was even higher in SLE subjects (�0.80).While in general cells with markers of plasmablasts/plasma cells had lower CoR relative to the other subsets,the levels of CD138þCD38brightCD19dim cells frompatients with SLE were notably higher than those fromHV. The low CoR in HV point to the difficulty of meas-uring plasmablast and plasma cell populations and likelyreflects the low frequency of these cells in the periph-eral blood. On the other hand, despite the higher vari-ability, the higher CoR in CD138þCD38brightCD19dim

populations in the patients with SLE makes the measure-ment of these cells in patients with SLE moreinterpretable.

Table 3Mean and Median Estimates by Group (Days 1–15)

Assay Unit StatisticGroup 1 (HV)

N ¼ 20Group 2 (SLE)

N ¼ 29Group 3 (SLE)

N ¼ 13All (SLE)N ¼ 42

Lymphocytes /mm3 Mean (SE) 1,915 (84) 1,553 (129)* 1,652 (217) 1583 (110)**Median 1,949 1,508 1,286 1395

T cell % Mean (SE) 80.6 (1.2) 82.3 (0.9) 79.9 (3.3) 81.5 (1.2)Median 81.1 82.1 81.7 82.0

/mm3 Mean (SE) 1,544 (74) 1,278 (109)** 1,259 (127)* 1272 (84)*Median 1,542 1,185 1128 1162

NK cell % Mean (SE) 8.5 (0.9) 7.7 (0.8) 7.8 (1.5) 7.8 (0.7)Median 7.7 6.8 6.6 6.8

/mm3 Mean (SE) 161 (18) 119 (14)** 140 (38) 126 (15)Median 141 104 100 101

CD19þCD20þ B cell % Mean (SE) 9.3 (0.8) 8.6 (0.7) 10.7 (2.6) 9.3 (0.9)Median 8.7 7.6 7.7 7.7

/mm3 Mean (SE) 178 (17) 136 (15)** 224 (83) 163 (28)Median 163 119 89.9 107

IgDþCD27�

(naı̈ve) B cell% Mean (SE) 68.4 (2.1) 71.8 (3.0) 72.5 (7.0) 72.0 (2.9)

Median 70.0 75.5 85.3 78.6/mm3 Mean (SE) 123 (15) 101 (14) 184 (78) 126 (26)

Median 105 81.0 75.8 80.4IgDþCD27þ

(memory) B cell% Mean (SE) 11.5 (1.2) 9.8 (1.4) 8.3 (3.4) 9.4 (1.4)

Median 9.9 6.7 4.8 6.1/mm3 Mean (SE) 18.9 (2.0) 12.0 (2.2)* 10.6 (5.1)** 11.6 (2.1)*

Median 17.7 6.5 3.1 5.7IgD�CD27þ

(memory)B cell% Mean (SE) 14.8 (1.3) 12.4 (1.4) 12.1 (3.5) 12.3 (1.4)

Median 13.0 10.5 6.0 9.5/mm3 Mean (SE) 24.5 (2.5) 14.1 (2.2)*** 17.5 (10.1) 15.2 (3.4)**

Median 20.6 10.8 6.8 9.3CD27brightCD38bright

plasmablast/plasmacell

% Mean (SE) 0.89 (0.12) 2.2 (0.4)*** 2.2 (0.6)*** 2.2 (0.3)***Median 0.70 1.4 1.4 1.4

/mm3 Mean (SE) 1.6 (0.3) 2.2 (0.4) 2.4 (0.7) 2.3 (0.3)Median 1.0 1.5 1.4 1.4

CD138þCD38bright

plasmablast/plasmacell

% Mean (SE) 0.18 (0.03) 0.56 (0.17)** 0.70 (0.23)*** 0.60 (0.14)*Median 0.10 0.21 0.30 0.26

/mm3 Mean (SE) 0.30 (0.08) 0.46 (0.11) 0.66 (0.23)** 0.52 (0.10)Median 0.16 0.22 0.50 0.30

CD19 MFI Mean (SE) 63.0 (1.7) 44.6 (2.7)*** 38.1 (3.2)*** 42.6 (2.2)***Median 62.9 43.5 38.0 41.9

CD38 (on B cells) MFI Mean (SE) 295 (18) 486 (52)*** 523 (62)*** 498 (41)***Median 281 407 497 414

Comparing mean to HV: *P value from 0.01 to <0.05, **P value from 0.05 to <0.1, ***P value < 0.01.

54 BELOUSKI ET AL.


Correlation with Disease Activity

During the course of the study, there were 15 flares(defined by a sequential increase in the SELENA-SLEDAIby >3 points) in 13 patients. The relationship betweenchanges in each phenotype and changes in SLEDAI scorewas explored graphically (data not shown). Additionally,a mixed model estimated the effect of SLEDAI increaseon each phenotype. The dependent variable was thepercent change from baseline in the phenotype. Contin-uous fixed effects included the baseline phenotype andbaseline SLEDAI score. The fixed effect of interest wasan indicator of flare at the follow-up visit (>3 pointincrease in SLEDAI score). Subject was included as a ran-dom effect. No correlations were observed between thevarious parameters outlined earlier with the presence offlare. There was likewise no significant correlationbetween disease activity when looked at categorically(i.e., between Groups 2 and 3) or continuously usingthe SELENA-SLEDAI.

DISCUSSION

SLE is a rheumatic disease with autoimmune featuresthat are clinically manifest by a wide variety of clinicalpresentations and disease course (21,22). Current treat-ments are individualized according to each patient’spresentation, but the overall toxicity of steroids andrelated drugs often requires a balance betweenattempts to balance pharmacological interventionagainst the toxicities of the drugs that are used.Although the pathologic aspects of SLE are likely toinvolve many cell lineages, B-cell hyperactivity is con-sidered to be centrally responsible, as the cellular ori-gin of hypergammaglobulinemia and autoantibodies.Moreover, skewed expansions within the B-cell com-partment in patients with SLE have been reported bymany (9–11,23), with a theme that the expanded poolof antibody secreting cells are primary culprits of pa-thology in persons with SLE and therefore representinterventional targets (1,7,23).

Table 5Coefficient of Reliability (CoR) by Group

Assay

HV (N ¼ 20) All SLE (N ¼ 42)

% /mm3 % /mm3

T cell 0.74 0.63 0.75 0.83NK cell 0.74 0.73 0.66 0.80CD19þ CD20þ B cell 0.68 0.67 0.81 0.87IgDþCD27� (naı̈ve) B cell 0.84 0.74 0.96 0.88IgDþCD27þ (memory) B cell 0.75 0.48 0.92 0.80IgD�CD27þ (memory) B cell 0.76 0.60 0.92 0.92CD27brightCD38bright plasmablast/plasma cell 0.02 0.09 0.51 0.42CD138þCD38bright plasmablast/plasma cell 0.01 0.10 0.73 0.61

Table 4Inter and Intrasubject Variability by Group

Assay Unit

Group 1 HV(N ¼ 20)

Group 2 mild SLE(N ¼ 29)

Group 3 mod SLE(N ¼ 13)

All SLE(N ¼ 42)

Inter- Intra- Inter- Intra- Inter- Intra- Inter- Intra-

Lymphocytes /mm3 107,659 63,688 401,435 60,259 629,570 74,826 459,562 64,563T cell % 27.5 9.6 24.3 20.5 164 25.4 66.6 21.9

/mm3 79,243 46,234 272,811 44,678 158,664 56,932 233,162 48,320NK cell % 12.8 4.4 11.2 7.6 20.8 5.4 13.7 7.0

/mm3 5,654 2,115 5,211 1,700 14,925 2,669 7,942 1,986CD19þCD20þ

B cell% 10.3 4.9 15.7 8.3 109 15.3 44.8 10.4

/mm3 4,685 2,335 7,189 2,489 117,035 14,265 41,510 5,973IgDþCD27�

(naı̈ve) B cell% 74.4 14.3 266 14.7 627 10.3 365 13.4

/mm3 3,600 1,253 5,964 1,797 105,037 12,085 36,949 4,841IgDþCD27þ

(memory)% 24.6 8.3 49.0 6.0 130 7.0 72.0 6.3

B cell /mm3 44.1 47.9 146 39.9 235 48.4 169 42.4IgD-CD27þ

(memory)% 27.7 8.7 58.5 6.2 189 12.7 95.0 8.2

B cell /mm3 85.8 56.8 139 21.0 1,114 79.2 421 38.2CD27brightCD38bright % 0.02 1.0 2.9 3.0 3.6 2.7 3.0 2.9

plasmablast/plasma cell /mm3 0.18 1.7 1.6 4.4 5.7 2.7 2.8 3.9CD138þCD38bright % <0.01 0.20 0.71 0.19 0.57 0.39 0.66 0.25

plasmablast/plasma cell /mm3 0.03 0.21 0.15 0.16 0.62 0.24 0.29 0.18CD19 MFI <0.01 317 69.4 143 50.0 87.3 66.9 126CD38 (on B cells) MFI 3,443 8,779 55,329 31,127 46,861 23,522 51,936 28,904



Correlate development of biomarker assays thatinclude B-cell subset immunophenotyping are consid-ered to be important for those involved in these efforts,with guideline recommendations (5) for focused devel-opment of flow cytometry assays to quantify lymphocytesubsets. The FDA (24) and EMEA (25,26) have postedguidance for investigators that might consider evaluationof T- and B-cell profiles against placebo patients in thesetting of treatment, with caution that such measure-ments are not yet surrogate endpoints of clinical benefit.This is unfortunate, because trial designs currently lever-age a reduction in SLE flare or time-to-flare analyses intrials that often exceed a year in duration to allow foradequate clinical observation and follow-up.

This study provides technical information that canaid investigators with interest in serial measurementsof lymphocyte and B-cell subsets in the setting of SLE.It is our overall aim to contribute towards establish-ment of better monitoring strategies or (ultimately) theclinical validation of phenotypic measurements as sur-rogate endpoints in the setting of SLE treatment. In pre-paring our laboratory to support clinical efforts tointervene in the disease course of SLE, it was necessaryto initially generate data to understand the stability ofcell subsets if we chose to ship blood to a central labo-ratory or hold it overnight before processing. The reli-ability of our measurements was also obviouslyimportant, and we therefore report the ranges of valuesgenerated in addition to coefficients of reliability (6)for the cell types studied here. Although the ultimateaims of our approach were to apply an appropriateprocessing and analysis philosophy to clinical trialsevaluating investigational agents, the current effort onlyrequired that the patients with SLE examined herewere similar to those that would typically enroll inPhase I studies (i.e., mild patients with SLE with a back-ground use of conventional therapeutics), and that ourobservation period and sampling frequency fit aschema appropriate for a typical clinical trial of SLE.This approach is conventionally referred to as a Phase0 study (27).

A variety of blood processing methods could havebeen evaluated, but we purposely restricted the effortsin this study to whole blood immunophenotyping. Thisreflected our personal experience (J.F.) and prior reports(28,29) that have historically demonstrated an inherentbias of datasets generated from cells processed on den-sity gradients. Likewise, our analyses were weightedtowards blood held overnight since the vast majority ofour clinical phenotyping is performed centrally (withshipped specimens). With whole blood that is held orshipped overnight, T cells are recognized as the moststable of lymphocytes, provided that the specimen isnot exposed to excessive cold or heat (30). In contrast,B cell loss is a recognized liability of holding wholeblood, even at ambient temperatures (31). The stabilityprofiles of T cells and B cells (<20% loss) observed inthis manuscript are in agreement with prior reportsusing whole blood methods (31). The relative loss of B

cells after overnight storage was therefore expected, andhas not precluded the practice of shipping whole bloodto central facilities for processing and analysis becausethe change is unidirectional and fairly uniform (J. Ferbas,personal observation). Moreover, it was within our ex-pectation that the remainder of markers included in thepanel would fall within the range of 20% of each freshvalue, reflecting the B cell centricity of this panel. Thenotable exception to this rule was for estimates madeon cells with markers of plasmablasts/plasma cells(CD38bright CD27bright CD19dim; CD138þ CD38bright

CD19dim), where losses far in excess of 20% wereobserved in blood held for processing until the next day.With such great cell losses of cells with markers of plas-mablasts/plasma cells, it is remarkable that higher popu-lation percentages were nontheless found in the bloodof patients with SLE (Table 3).

Given the importance that plasmablast/plasma cellenumeration has received in the setting of flow cytome-try investigations in SLE, and the possibility that they arecurrently underestimated, there is a need to explore al-ternative approaches that could preserve such cell typesin blood. Although we chose to focus on whole blood,PBMC cryopreservation (from blood processed within2 h of collection) has been considered (32); but alsowith a 70–80% loss of CD19hi CD27hi CD28hi cells fromthawed samples in a vaccine study. Alternatively, weenvision the possibility that strategies to stabilize circu-lating tumor cells and circulating endothelial cells inblood (33,34) might be leveraged in the setting of inter-est here. In the case that no viable alternative can beidentified, investigators may be faced with no optionother than immediate analysis if relative accuracy of themeasurement is important to the interpretation of theirstudies.

We find the value of enumerating naı̈ve and memoryB cells in the setting of SLE as noteworthy, because theirnumbers in blood may reflect biological processes.Indeed, CD27þ memory B cells are regarded to differen-tiate into plasma cells through their interactions withCD70 (i.e., CD27 ligand) and appropriate costimulation.In vitro, costimulation can be represented by factorsincluding interleukin 10 (35), IL-4 and CD40 ligand (36)and interleukin 2 þ Staphylococcus aureus Cowanstrain (SAC) (37). Although complex, it has been positedthat CD27þ memory B cells home to sites of inflamma-tion and that their depletion in blood may reflect this ac-tivity in the setting of an inflammatory disease (35).Alternatively, a reduction of cells in queue for differen-tiation into plasmablasts or plasma cells may reflectaccelerated progression/differentiation to these celltypes as they pass through differentiation and tolerancecheckpoints in vivo (38). Our data did in fact demon-strate reduced numbers of B cells of the memory pheno-type in persons with SLE, with a correlation betweendisease severity (as reflected by SLEDAI score) and theirextent of reduction (Table 3).

Our data additionally show lower expression of CD19and higher expression of CD38 on B cells as reflected

56 BELOUSKI ET AL.


by median fluorescence intensity. Decreased CD19 levelson B cells have been observed in another study (39),with the finding attributed to interaction with circulat-ing immune complexes (40). Furthermore, the findingof elevated levels of CD38 on peripheral blood B cellshave been previously reported in active patients withSLE (9,12,15,17), most likely reflective of the unusualexpansions and activation of B cells in this disease. Aglance forward into settings of treatment has suggestednormalization of the depressed levels of CD27þ memoryB cells, increased density of CD19 and decreased expres-sion of CD38 in cells from the blood of patients withSLE, within in vivo administration of an agent designedto modulate B-cell differentiation (41).

Our approach to characterize variability by calculationof the CoR may be useful. The anticipated effect size,considered in conjunction with various sources of vari-ability can ultimately be used to calculate the requiredsample size in a clinical trial, and this is not exactly cap-tured by the derivation of normal and abnormal ranges.We found that the variability between subjects wasgreater than within each subject over a 1-year period formost endpoints. The CoR calculations reflect a normal-ized assessment of the degree of precision in followingthe intra-subject changes to a measured endpoint. Ourrepeat-measures data for CoR provided scores that werequite high. We make this interpretation relative to whathas been reported in the setting of HIV infection, wherethe majority of coefficients reported by Taylor et al. (6)fell close to or above the 0.64–0.68 range. Indeed, thevast majority of coefficients for patients with SLE fellabove 0.80. Although each of these measures proved tobe variable between subjects in this SLEDAI-based group-ing, the reliability of within-subject measures of theseparameters was demonstrated through high CoR. In fact,subtle differences were observed in the naı̈ve(IgDþCD27�) and memory (IgD�CD27þ; IgDþCD27þ)populations but the high CoR indicates that even minorchanges in these populations could be highlymeaningful.

Although we sought to understand if the B cell pro-files examined in this study were responsive, reflectiveor even predictive for the clinical event of a flare, ouranalysis of the 15 flares that occurred in 13 patientsfailed to reveal any changes in B-cell phenotype in thevisits prior to or following the flare (data not shown).The failure to see such a relationship might haveoccurred for many reasons––including our samplingstrategy––which was not designed for unscheduled orintermediate visits at the point of flare. Includingpatients with SLE with high disease activity would nodoubt have contributed to more global analyses regard-ing acute disease activity parameters and B-cell pheno-types. Interestingly, a novel population of B cells withincreased CD19 expression has been observed in aminority of patients with SLE with adverse clinicaloutcomes (42).

We propose that this study will aid those chargedwith evaluation of marketed and novel investigational

agents to treat SLE and other autoimmune diseases.More studies are needed to truly understand the effectof prior and concomitant treatments as well as thepotential relationship of these findings to different sub-sets of patients with SLE characterized by their organinvolvement, autoantibody profile, or genetic associa-tions. Continued focused exercises in B-cell phenotypingmay contribute to a solid foundation in this regard andtherefore hold importance to those engaged in suchinvestigations. Moving forward, it will be important toconsider antibody panels of higher complexity to notonly better define cells such as plasmablasts and plasmacells, but to extend analyses to B-cell subpopulationsnot studied here (43). It is our hope that the data pre-sented in this article provides a useful benchmark forthose with a need to understand sample stability andmeasurement reliability in a setting amenable to clinicaltrials.

ACKNOWLEDGMENTS

The authors would like to thank John K. Thomas forassay development and Rita McGivern for specimenprocessing and analysis. We also thank Brian Kotzin(Vice President, Amgen Medical Sciences), Scott D. Pat-terson, and Steven J. Swanson (Executive Directors,Amgen Medical Sciences) for support of this project.

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