A Probabilistic Approach for the Evaluation of MinimalResidual Disease by Multiparameter Flow Cytometry inLeukemic B-Cell Chronic Lymphoproliferative Disorders

C.E. Pedreira,1* E.S. Costa,2 J. Almeida,3 C. Fernandez,3 S. Quijano,3 J. Flores,3 S. Barrena,3

Q. Lecrevisse,3 J.J.M. Van Dongen,4 A. Orfao,3 on behalf of the EuroFlow Consortium

� AbstractMultiparameter flow cytometry has become an essential tool for monitoring responseto therapy in hematological malignancies, including B-cell chronic lymphoproliferativedisorders (B-CLPD). However, depending on the expertise of the operator minimal re-sidual disease (MRD) can be misidentified, given that data analysis is based on the defi-nition of expert-based bidimensional plots, where an operator selects the subpopula-tions of interest. Here, we propose and evaluate a probabilistic approach based on pat-tern classification tools and the Bayes theorem, for automated analysis of flowcytometry data from a group of 50 B-CLPD versus normal peripheral blood B-cellsunder MRD conditions, with the aim of reducing operator-associated subjectivity. Theproposed approach provided a tool for MRD detection in B-CLPD by flow cytometrywith a sensitivity of �8 3 1025 (median of �2 3 1027). Furthermore, in 86% of B-CLPD cases tested, no events corresponding to normal B-cells were wrongly identifiedas belonging to the neoplastic B-cell population at a level of �1027. Thus, thisapproach based on the search for minimal numbers of neoplastic B-cells similar tothose detected at diagnosis could potentially be applied with both a high sensitivity andspecificity to investigate for the presence of MRD in virtually all B-CLPD. Furtherstudies evaluating its efficiency in larger series of patients, where reactive conditionsand non-neoplastic disorders are also included, are required to confirm theseresults. ' 2008 International Society for Advancement of Cytometry

� Key termsminimal residual disease; flow cytometry; principal component analysis; pattern classifi-cation; leukemia; Bayes theorem

IN recent years, multiparameter flow cytometry immunophenotyping has become an

essential tool for the diagnosis and monitoring of response to therapy in a wide spec-

trum of diseases, including leukemic B-cell chronic lymphoproliferative disorders (B-

CLPD) (1–3). Among other advantages, flow cytometry immunophenotyping allows

for a rapid quantitative assessment of multiple characteristics of millions of cells, in-

formation being recorded for individual cellular events (4). This provides a tool for

accurate multiparameter identification and characterization of neoplastic cells among

normal cells in peripheral blood (PB) and bone marrow (BM), even when neoplastic

cells are present at very low frequencies (�1024) among a major population of nor-

mal cells—minimal residual disease (MRD)—(5–8).

Detection of MRD by multiparameter flow cytometry immunophenotyping is

based on the existence of different patterns of protein expression in normal versus

neoplastic cells. In the last decade, it has been shown that MRD evaluation is of great

clinical utility to predict disease recurrence and patient outcome in B-CPLD such as

chronic lymphocytic leukemia (CLL) (7–12). To achieve the sensitivity required for

MRD investigation, large numbers of cells—typically hundreds of thousands to

millions—have to be analyzed (5,7–13). Accordingly, information about several (�6)

1Faculty of Medicine and COPPE-PEEEngineering Graduate Program, UFRJ/Federal University of Rio de Janeiro, Riode Janeiro, Brazil2Instituto de Pediatria e PuericulturaMartag~ao Gesteira/IPPMG andDepartamento de Cl�ınica M�edica, UFRJ/Federal University of Rio de Janeiro, Riode Janeiro, Brazil3Cytometry Service, Department ofMedicine and Cancer Research Center(IBMCC, University of Salamanca-CSIC),University of Salamanca, Salamanca,Spain4Department of Immunology, Erasmus MC,University Medical Center Rotterdam,Rotterdam, The Netherlands

Received 6 June 2007; Revision Received12 February 2008; Accepted 10 April 2008

Grant sponsor: European Commission(EuroFlow); Grant number: LSHB-CT-2006-018708; Grant sponsor: The Instituto deSalud Carlos III, Ministerio de Sanidad yConsumo, Madrid, Spain; Grant number:ISCIII-RTICC RD06/0020/0035-FEDER; Grantsponsor: Ministerio de Educaci�on y Cien-cia, Madrid, Spain (Programa Hispano-Brasile~no de Cooperaci�on Universitaria);Grant number: PHB 2004-0800-PC; Grantsponsors: CAPES/Ministerio da Educa�c~ao,Bras�ılia, Brazil; CNPq-Brazilian NationalResearch Council, Bras�ılia, Brazil;FAPERJ-Rio de Janeiro Research Foun-dation, Rio de Janeiro, Brazil; Fundaci�onMarcelino Bot�ın, Madrid, Spain.

Original Article

Cytometry Part A � 73A: 1141�1150, 2008

different cell-associated features is typically obtained and

stored in a digital list mode data file format for several hun-

dreds of thousands to millions of cells measured. Such data

files typically contain[106 individual data points; a number

of entries which is by far larger than a typical data set contain-

ing information about a sample analyzed by DNA oligonu-

cleotide microarray techniques (14–17).

In the past few years, important advances have been

achieved in flow cytometers allowing for the measurement of

an increasingly high number of parameters (�10) in a more

rapid way, through the evaluation of tens of thousands of cells

per second (18). In contrast, analysis of the data recorded has

not attained the same level of progress and it is still based on

strategies, which were defined more than 20 years ago

(4,18,19). Accordingly, with a few exceptions (20–26) analysis

of flow cytometry immunophenotypic data typically relies on

the definition of a variable number of bidimensional plots,

where an experienced operator selects the subpopulations of

interest (25–27). Often, depending on the expertise of the op-

erator, specific cell populations—particularly those present at

low frequencies—can be misidentified. Overestimation and/or

underestimation of specific minor cell populations has a direct

impact on the assessment of MRD with potential clinical/diag-

nostic consequences (5,7,28). More recently, we have described

an alternative, automated method for analysis of flow cytome-

try immunophenotypic data (22,25). With this new auto-

mated approach, we could detect neoplastic B-cells present in

peripheral blood (PB) samples from patients with increased

PB absolute lymphocyte counts, with a high efficiency and an

increased reproducibility, by reducing expert-based data-anal-

ysis decisions. However, this method was only able to detect

neoplastic cells in PB when they were present at relatively high

frequencies (�5% of the whole sample cellularity) (22,25), its

sensitivity being insufficient for MRD evaluation.

At present, consensus exists about the basic requirements

for an adequate MRD technique to be used. Ideally, MRD

approaches should allow clear and specific identification of

neoplastic cells at frequencies �1024, an MRD level which has

proven to be clinically relevant in B-CLPD (1,3,8). In such

case, flow cytometry measurements require a minimum num-

ber of events corresponding to the neoplastic cell population

(e.g., �10 cells), to define it to be present, and at least 100

events to achieve statistical precision in quantifying the neo-

plastic cells (1,3).

In this article, we describe an automated strategy for the

detection of MRD in B-CLPD based on pattern classification

tools and the Bayes theorem (29). With this probabilistic

approach, we were able to systematically identify MRD by

flow cytometry with a sensitivity of �8 3 1025, a sensitivity

of �2 3 1027 being reached for the large majority of cases

(80%). Furthermore, this approach allows ‘‘a priori’’ defini-

tion—e.g., at diagnosis—of the sensitivity that will be reached

for each case, later on during follow-up of the disease.

MATERIALS ANDMETHODS

Patients and SamplesA total of 50 EDTA-anticoagulated diagnostic PB samples

from 50 patients—28 males and 22 females; mean age of 51

years, ranging from 40 to 89 years—with different subtypes of

leukemic B-CLPD were included in this study. Patients were

classified according to the WHO criteria (30) into the follow-

ing diagnostic categories: B-cell chronic lymphocytic leukemia

(B-CLL), 31 patients (26 typical and 5 atypical B-CLL cases);

mantle cell lymphoma (MCL), 8; splenic marginal zone lym-

phoma (SMZL), 3; mucosa-associated lymphoid tissue

(MALT) lymphoma, 3; diffuse large B-cell lymphoma

(DLBCL), 2 cases, and; follicular lymphoma (FL), one patient.

The other two cases had an unclassifiable B-CLPD. Median

white blood cell (WBC) and lymphocyte counts were of 31 3109 leukocytes/L (range: 2.9 3 109–183 3 109 leukocytes/L)

and 23 3 109 lymphocytes/L (range: 0.8 3 109 to 164 3 109

lymphocytes/L), respectively. Overall, the median percentage

of neoplastic B cells in the 50 infiltrated specimens was of 40%

(range: 0.4–92%). Moreover, PB samples from three patients

diagnosed of B-CLL, which were collected after therapy under

MRD conditions, were also included in this study.

In addition, EDTA-anticoagulated PB samples from a total

of 5 adult healthy individuals —3 males and 2 females—were

also collected. Median WBC and lymphocyte counts as well as

B-cell percentages and absolute counts were of 6.4 3 109 leuco-

cytes/L (range: 4.63 109 to 10 3 109 leucocytes/L), 2.1 3 109

lymphocytes/L (range: 1.6 3 109 to 2.8 3 109 lymphocytes/L),

3.8% of WBC (range: 3–4.7%), and 0.243 109 B-cells/L (range:

0.163 109 to 0.473 109 B-lymphocytes/L), respectively.

All healthy volunteers and B-CLPD patients gave their

informed consent prior to entering this study, which was pre-

viously approved by the local Ethical Committee of the Uni-

versity Hospital of Salamanca (Salamanca, Spain).

Multiparameter Flow CytometryImmunophenotypic Studies

Multiparameter flow cytometric studies were performed

for each PB sample using a panel of five combinations of

Conflict of Interest: Cytognos S.L. is a part of the UE-supportedEuroFlow Research Consortium and has implemented some of thealgorithms described in the present study, in its proprietary softwareINFINICYT; Cytognos S.L. has a contract license of several patentsowned by the University of Salamanca, of which A Orfao, CEPedreira, and ES Costa are inventors. Other authors declare nocompeting financial interests.

*Correspondence to: Alberto Orfao, MD PhD, Centro deInvestigaci�on del C�ancer, Paseo de la Universidad de

Coimbra, s/n, Campus Miguel de Unamuno, 37007 Salamanca,Spain.

Email: orfao@usal.es

Published online 3 October 2008 in Wiley InterScience(www.interscience.wiley.com)

DOI: 10.1002/cyto.a.20638© 2008 International Society for Advancement of Cytometry

ORIGINAL ARTICLE

1142 Probabilistic Approach for Minimal Residual Disease

fluorochrome-conjugated monoclonal antibodies (MAb)—

fluorescein isothyocyanate (FITC)/phycoerythrin (PE)/peridi-

nin chlorophyll protein-cyanin 5.5 (PerCP-Cy5.5)/allophyco-

cyanin (APC)—which systematically included CD19PerCP-

Cy5.5 in every combination, in addition to FMC7FITC/

CD24PE/CD34APC, sIgkFITC/sIgjPE/CD5APC, CD22FITC/

CD23PE/CD20APC, CD103FITC/CD25PE/CD11cAPC, and

CD43FITC/CD79bPE, for a total of 15 different markers one

(CD19) was common to all combinations. The techniques

used to stain the cells have been previously described in detail

by Sanchez et al. (13). For each staining corresponding to each

individual sample analyzed, information about[5 3 104 cells

was acquired in a FACSCalibur flow cytometer (Becton/Dick-

inson Biosciences, BDB, San Jos�e, CA) and stored using the

CellQUEST software program (BDB). Overall, for each sample

information about 17 different biological features—15 immu-

nophenotypic and 2 light scatter characteristics—was mea-

sured and stored for a total of[2.5 3 105 events. For samples

with low percentages of B cells (i.e., normal samples), an addi-

tional second step acquisition was specifically performed to

measure higher numbers of B-cells contained in each stained al-

iquot. In this later step, an electronic live-gate set on a CD19

versus side light scatter (SSC) bivariate dot plot histogram was

used to build a gate to collect information about the B-cells

contained in a total of 106 events/sample aliquot. Accordingly,

for each PB sample, five different data files were stored, each

containing information about five or six cell attributes (two

attributes related to light dispersion: forward light scatter

(FSC), sideward light scatter (SSC) and 3- or 4-fluorescence

attributes). Based on the panel of reagents used, three out of the

17 attributes measured were common to all five data files (FSC,

SSC and CD19); all other 14 parameters varied among the data

files according to the reagents used in each staining from the

panel described above.

Data ManipulationData files from the five multicolor stainings corresponding

to the same PB sample (from either B-CLPD patients or healthy

individuals) were merged using the INFINICYTTM software

program (Cytognos SL, Salamanca, Spain), as previously

reported (26). Subsequently, the ‘‘calculation’’ function of the

INFINICYT software based on the nearest neighbor principle

(26,31,32) was applied to the merged data files, to calculate the

information about each individual attribute not actually meas-

ured for individual events in the merged data files, for the

whole panel of markers tested. As a result, for each PB sample,

a single data file was obtained containing information about all

17 cellular attributes measured for each event recorded.

For each patient data file, a minimum of 2.53 105 cellular

events were measured. The number of cellular events corre-

sponding to B-cells within the total events measured in each file

varied between 3.2 3 103 and 6.5 3 105 (mean of 4.2 3 105 �2.2 3 105) according to the percentage of B-cells—mean of

34% � 29% (range: 0.1–92%)—present in each PB sample.

Data on normal cells was computationally generated by

merging data from five different data files each containing 1 3106 events corresponding to a normal PB sample (total events

in the merged data file of 5 3 106). The merged data files con-

tained events corresponding to normal PB cells from five dif-

ferent normal PB samples obtained from an identical number

of healthy volunteers. From this merged file, a pool of 79,856

normal B-cell events, denoted from here on as ‘‘normal-B-cell-

pool data,’’ was obtained.

In diagnostic PB samples from B-CLPD patients, very

few normal B-cells can be typically found, because almost all

B-cells present in the sample being neoplastic. For this reason,

artificial ‘‘B-CLPD diagnostic-files’’ were built electronically

for each patient, by mixing events corresponding to neoplastic

B-cells from the patient, with events corresponding to normal

B-cells from the ‘‘normal-B-cell-pool file’’ at a 1:1 proportion.

In addition, further dilutions of neoplastic B-cell events in the

‘‘normal-B-cell-pool data’’ was also performed at different

concentrations, to simulate progressively lower levels of MRD

(‘‘MRD-files’’), as described below in this section.

Data AnalysisFor manual (operator dependent) data analysis, the INFI-

NICYT software program was used. Briefly, during this pro-

cess, total B cells were identified as those CD191 events show-

ing low to intermediate FSC and SSC values, after specifically

excluding platelets and cell debris (11). Normal PB B cells

were identified as being CD191, CD20hi, CD2211, CD232/lo,

CD432, CD79b1, FMC71, CD1032, CD252/lo, CD11c2/1,

CD52/lo, and either sIgj1/sIgk2 or sIgk1/sIgj2. Neoplastic Bcells were all other PB B-lymphocytes showing an aberrant

phenotype. After this step, B-cells present in each sample were

gated and the information about those events corresponding

to B-cells was stored in a new data file.

For automated analysis, a Principal Component Analysis

(PCA) Transformation was applied to each of the artificial ‘‘B-

CLPD diagnostic-files’’ (33). In sequence, first we restricted

our attention to the data projection into the space defined by

the first versus second principal components. This PCA pro-

jection of diagnostic-files containing a 1/1 mixture of normal

and neoplastic B-cells, systematically showed the presence in

the data file of three clearly defined groups of B-cell events

(Fig. 1). In fact, normal PB B-cells constantly displayed a bi-

modal distribution where two subpopulations defined by the

isotype of the immunoglobulin light chain (sIg) expressed

(sIgj1 or sIgk1), were detected; the third group of B-cell

events corresponded to neoplastic B cells.

The goal of our automated data analysis strategy was first

to split the two normal subpopulations (sIgj1 or sIgk1) foreach of the 50 merged diagnostic-files, by using a classical k-

means algorithm (34). Then, the normal B-cell population

(e.g., blue and green events in Fig. 1C) which appeared to be

localized closer to the neoplastic B-cells (e.g., red events in

Fig. 1C) was identified for each ‘‘B-CLPD diagnostic-file’’

using the measure of the distance between the means of each

B-cell population in the data file in R17; the other far-off nor-

mal B-cell population (e.g., blue events in Fig. 1C) was tempo-

rarily discarded. Accordingly, at this point, we ended up with

two B-cell populations for each case: one of the two normal B-

cell populations and that of neoplastic B-cells.

ORIGINAL ARTICLE

Cytometry Part A � 73A: 1141�1150, 2008 1143

Afterward, we calculated the mean and the covariance

matrices for the two populations of normal B-cells (sIgj1 or

sIgk1) as well as for each population of neoplastic B-cells

from individual B-CLPD diagnostic data files (n 5 50). On

basis of these results, we estimated the likelihood rate for both

populations, i.e., the probability distribution functions (pdf)

where p(x|normal) and p(x|neoplastic). Here, p(x|normal) is

the pdf of an event to assume a value x given that one knows

that the population is normal (and not neoplastic). This strat-

egy may be applied, because well-established conventional

approaches for the investigation of MRD in B-CLPD indicate

that in general, under MRD conditions after therapy, one

should search for a neoplastic population with similar features

to those observed at diagnosis (7–13). Accordingly, we assume

that the pdf p(x|normal) and p(x|neoplastic) remain

unchanged from diagnosis to sequential follow-up PB sam-

ples, obtained after therapy. Note that in fact, what one really

wants to know is P(normal|x), i.e. the probability that an

event belongs to the normal population, after measuring the

attributes of this event. This goal may be achieved by applying

the Bayes theorem (35) as follows (1):

PðnormaljxÞ ¼ PðnormalÞ 3 pðxjnormalÞK

;

for the normal B-cell population; and

PðneoplasticjxÞ ¼ PðneoplasticÞ 3 pðxjneoplasticÞK

;

for the neoplastic B-cell population:

Here, K is a constant to make P(normal|x) 1 P(neoplastic|x)

5 1, and P(neoplastic) and P(normal) are the ‘‘a priori’’ prob-

abilities of the two classes (neoplastic and normal B-cells,

respectively). These reflect the probability of finding an event

in one of the two classes, prior to be able to ‘‘see’’ the real

data. In many applications, these a priori probabilities can be

easily estimated by the relative frequencies of the classes in the

sample. However, in the MRD setting, we are interested in

estimating the ‘‘a priori’’ probabilities in the ‘‘B-CLPD diag-

nostic-files’’ to be applied, not at diagnosis, but after therapy

during the follow-up period where the relative proportion

between normal and neoplastic B-cells is expected to change

and variably increase. This introduces a new challenge,

Figure 1. Illustrating bidimensional dot plot histogram projections in the first versus second principal component analysis (PCA) space ofB-cell events contained in diagnostic-files where neoplastic B-cells from four different representative individual patients where mixed at1:1 proportion with normal B-cell populations from a pool of PB samples from five healthy volunteers (‘‘Normal-B-cell-pool’’ file). For thesefour files, the two normal B-cell populations and the neoplastic one fall well apart. In all bivariate plots the normal sIg Kappa1 and sIgLambda1 B-cell populations are displayed as green and blue events, respectively; in turn, those events corresponding to the neoplastic B-cells are painted as red events.

ORIGINAL ARTICLE

1144 Probabilistic Approach for Minimal Residual Disease

because the number of neoplastic B-cell events is exactly what

we are looking for. To overcome this awkwardness, we intro-

duced the following iterative procedure: Step 1: Let us denote

the number of neoplastic B-cell events at iteration ‘i’ as

# }ðiÞ. Set counter i 5 0 and initialize # }ðiÞ such that the

frequency of neoplastic B-cell events is initially overestimated;

Step 2: Estimate P(neoplastic) based on # }ðiÞ and apply the

Bayesian Theorem as in (1) to calculate P(neoplastic|x) and

P(normal|x); Step 3: Allocate all observations to the neoplastic

or the normal B-cell populations by applying the Optimal

Bayesian Decision Rule (36): An observation x is set to the

neoplastic B-cell population if P(neoplastic|x)[ P(normal|x),

otherwise it is assigned to the normal population; Step 4:

Increase i, recalculate # }ðiÞ; and Step 5: If # }ðiÞ <# }ði � 1Þ go to Step 2, otherwise, STOP.

For all experiments, we started with # }ð0Þ 5 1000

events, assuming that one is seeking for a residual neoplastic

B-cell population smaller than this number. To test the sensi-

tivity of the method in detecting progressively lower numbers

of MRD, we selected decreasing quantities of observations cor-

responding to neoplastic B-cells from each diagnostic data file

and added these events to the pool of normal events

(‘‘normal-B-cell-pool data’’ file). Accordingly, for each of the

50 B-CLPD samples, files containing only the neoplastic B-cell

events were used to randomly built 88 different sets of data

with 1, 2, 3, . . . , 50, 60, 70, . . . , 300, 350, 400, . . ., 1000 neo-

plastic B-cell events. Each of these sets was merged with the

‘‘normal-B-cell-pool data’’ file to generate files containing

decreasingly lower levels of MRD—‘‘MRD files’’—. Conse-

quently, for each of the 50 patients, 88 ‘‘MRD-files’’ were gen-

erated containing known proportions of between 1 and 1000

neoplastic B cells in the pool of 5 3 106 normal cells (MRD

frequencies of between 2 3 1024 and 2 3 1027). The overall

procedure run for each individual B-CLPD case is summarized

in Figure 2.

Two measures of performance were used for the 50 B-

CLPD cases; the first relates to the minimal number of true

neoplastic B-cell events the system is able to identify in a total

of 5 3 106 events -sensitivity of the method-; for this purpose

we defined the sensitivity of the method as the minimum

number of true neoplastic B-cell events added to the ‘‘normal-

B-cell-pool data’’ file that is associated with the detection of

more than 60% of the neoplastic B-cell events merged. The

second measure relates to the level of agreement observed

between the number of neoplastic B-cell events added to the

‘‘normal-B-cell-pool data’’ file and the corresponding number

of neoplastic B-cell events actually detected by the proposed

procedure in that specific merged data file. For each B-CLPD

case, we compared the MRD dilution frequencies of neoplastic

B-cell events with the number of neoplastic B-cells identified

to be present by the approach here described, in each of the

individual MRD-files, and calculated the degree of correlation

between the two measures (Pearson correlation coefficients).

RESULTSOverall, a high degree of correlation was observed

between the number of diluted and the number of computa-

tionally identified neoplastic B-cells for all 50 B-CLPD cases

included in this study. In most cases (45/50 cases; 90%), Pear-

son correlation coefficients (r2) higher than 0.999 were

detected. Those five cases showing the lowest correlation coef-

ficients (r2 � 0.964 and � 0.999) are displayed in Figure 3. Of

note, for all these later five cases, bivariate projections of the

first versus the second principal components (Fig. 3, Column

A) showed occurrence of a clear, partial overlap between the

neoplastic B-cells and one of the normal B-cell populations in

R17 (either sIgj1 or sIgk1 normal B-cells).

Regarding sensitivity, in most cases (40/50 cases; 80%),

MRD detection at levels as low as 1 event in 5 3 106 normal

PB cells (2 3 1027) were achieved. Interestingly, those five

cases showing coefficients of correlation (r2) � 0.999, which

are represented in Figure 3, were among those 10 cases that

showed a lower sensitivity between 8 3 1025 (0.008%) and 6

3 1026 (0.0006%) (Fig. 3, Column D). The other five cases in

which a sensitivity[2 3 1027 was achieved and had correla-

tion coefficients (r2) � 0.999 showed sensitivity levels of

between 6 3 1026 (0.0006%) and 6 3 1027 (0.00006%) (Fig.

4). Accordingly, overall sensitivity was [1 3 1026 in only 7

cases (14%) with an upper limit of 8 3 1025. Of note, cases

with a lower sensitivity (of between 8 3 1025 and 6 3 1027)

corresponded to three cases of MCL (3/8 patients), three

Figure 2. Schematic flowchart of the overall procedure run foreach individual B-CLPD sample/case.

ORIGINAL ARTICLE

Cytometry Part A � 73A: 1141�1150, 2008 1145

SMZL (3/3 cases), one FL (1/1 patient), and 2 B-CLL (2/31

cases).

To evaluate the specificity for MRD detection of the pro-

posed approach, the same strategy was applied to each ‘‘MRD-

file,’’ after subtracting all neoplastic B-cell events in the PCA

projection. The aim was to verify whether any event corre-

sponding to normal B-cells would then be equivocally classi-

fied as a neoplastic B-cell event. Of note, in most cases (43/50

Figure 3. Illustrating examples of those cases (N 5 5) showing the lowest correlation (r2 � 0.999) between the number of neoplastic B-cellsidentified and the number of neoplastic B-cells actually present in the ‘‘MRD-files’’ corresponding to each case. In Column A, bivariate dotplot histogram projections of the first versus second PCA are shown for neoplastic as well as for normal B-cells from each patient (MRDdata files). In Columns B and C, correlation plots are shown for the same cases (Column B) highlighting the region corresponding to the low-est dilutions surrounded in Column B plots (Column C). In Column D, the sensitivity level specifically obtained for each case is displayed.

ORIGINAL ARTICLE

1146 Probabilistic Approach for Minimal Residual Disease

cases; 86%), no events were wrongly identified as belonging to

the neoplastic B-cell populations. In the remaining seven

patients, only one (3/50 cases; 6%), four (2/50 cases; 4%) and

at maximum five (2 cases; 4%) out of 5 3 106 events were

wrongly identified as belonging to the neoplastic B-cell popu-

lation (�1 3 1026).

To validate the method in patients with resistant disease

as well as real MRD cases, PB samples from three different B-

Figure 4. Illustrating examples of those cases (N 5 5) showing correlation coefficients (r2)[ 0.999 between the number of neoplastic B-cells identified and the number of neoplastic B-cells actually present in the ‘‘MRD-files,’’ but a sensitivity level � 2 3 1027. In Column A,bivariate dot plot histogram projections of the first versus second PCA are shown for neoplastic as well as for normal B-cells from eachpatient (MRD data files). In Columns B and C, correlation plots are shown for the same cases (Column B), highlighting the region corre-sponding to the lowest dilutions surrounded in Column B plots (Column C). In Column D, the sensitivity level specifically obtained foreach case is displayed.

ORIGINAL ARTICLE

Cytometry Part A � 73A: 1141�1150, 2008 1147

CLL patients were sequentially evaluated at diagnosis and after

treatment. In all three cases, with the probabilistic approach

here proposed, we were able to identify the presence of neo-

plastic B-cells after therapy (Fig. 5). For individual cases, 99%,

97%, and 92% of all neoplastic B-cells present in the follow-

up samples according to an expert operator were correctly

identified with the automated probabilistic approach. Further-

more, in all three cases, no events corresponding to normal B-

cells as defined by an expert operator were wrongly classified

as neoplastic B-cells. The specific percentages detected in the

follow-up samples from these patients by an experienced oper-

ator versus the automated probabilistic approach here pro-

posed were of 0.45%, 0.11%, and 48.9% vs. 0.45%, 0.1%, and

45%, respectively.

Figure 5. Performance of the proposed probabilistic approach in real PB samples with resistant disease and MRD obtained from threepatients with B-CLPD after therapy (n 5 3). In the left column (panels A, C, and E), bivariate projections in the first versus second PCA ofthe neoplastic B-cell populations from the three different B-CPLD patients at diagnosis and prior to therapy merged with the ‘‘normal-B-cell-pool data’’ are shown. In the column in the right (panels B, D, and F), projections in the first versus second PCA of all B-cell populationsdetected after therapy in the MRD samples from each of the three cases are displayed. Red events were classified by the proposedapproach as neoplastic B-cells, whereas blue events were classified as normal B-cells. Of note, few B-cells were present in follow-up versusdiagnostic samples (39%, 35.4% and 88% versus 0.45%, 0.1% and 45%, for cases 51, 52, and 53, respectively) with even less (almost unde-tectable) normal residual mature B-lymphocytes due to chemotherapy-induced lymphopenia.

ORIGINAL ARTICLE

1148 Probabilistic Approach for Minimal Residual Disease

DISCUSSIONIn the last decade, MRD detection by flow cytometry has

been increasingly used to monitor response to therapy (5–

11,37). Among other advantages over molecular approaches

used for MRD detection, flow cytometry is based on a rela-

tively rapid and simple interrogation of hundred thousands to

millions of cells, information being collected for each indivi-

dual cell measured (1,5,27). In addition, it can be applied to

the great majority of all leukemia/lymphoma patients

(1,5,11,37). Finally, it has been shown that conventional flow

cytometry approaches for MRD detection are highly sensitive

allowing for the identification of down to around 1 3 1024

(0.01%) neoplastic B-cells among a major population of nor-

mal PB and BM hematopoietic cells (1,5,7,8,38). However, it

should be highlighted that for MRD investigation by flow

cytometry, an expert operator with extensive and detailed

knowledge about the patterns of protein expression associated

with normal versus neoplastic B-cells is typically required for

adequate data analysis (1,5,11,28). This, together with the

variable patterns of phenotypic aberrations detected among

different subtypes of B-CLPD has limited the establishment

and implementation of standardized data analysis procedures

that would facilitate the extended use of standardized flow

cytometry MRD approaches in routine clinical diagnostic lab-

oratories.

Here, we propose the use of a probabilistic approach for

the evaluation of MRD by multiparameter flow cytometry in

B-CLPD, through automated analysis of patient data files

measured at diagnosis and the dilution of neoplastic B-cell

events, at increasingly lower concentrations, into data files

containing information about normal PB B-cells. Overall, our

results show that this approach can be applied to virtually all

B-CLPD with both a high sensitivity and specificity, whenever

the search for minimal numbers of neoplastic B-cells similar

to those detected at diagnosis is required. Accordingly, the

strategy here proposed reached a sensitivity of 2 3 1027 in

80% of the cases. Such sensitivity is significantly greater than

the best sensitivity described in the literature for MRD by flow

cytometry (1 3 1025) in B-CLPD (7–13). Of note, for the

remaining cases which displayed a lower sensitivity: between 8

3 1025 (0.008%) and 6 3 1027 (0.00006%); this could be

potentially improved with additional markers. Such markers

should be capable of increasing the differences already

observed at diagnosis between normal and neoplastic B-cells.

Inclusion of markers aimed at detecting aberrant B/cell pheno-

types in SMZL, MCL, and FL (e.g., bcl2 and CD10) (39)

would be particularly useful. To a great extent, this high sensi-

tivity was reached because of the high specificity achieved with

the proposed approach, because aberrant phenotypes were

detected in most cases, where no normal B-cell events were

misclassified. Of note, with the automated approach proposed

information about the specificity and sensitivity associated

with each patient is obtained already at diagnosis, to be used

later on for MRD evaluation. This is particularly important

because it is not possible to base the a priori evaluation on the

prevalence of each cell population at diagnosis as in Tosetto

et al. (40); in this regard, in our study we introduced a scheme

that provides an iterative estimation for the a priori probabil-

ity. However, it should be noted that normal control B-cells

were obtained from a relatively reduced number of healthy

adults and thus, further studies evaluating the efficiency of the

approach here proposed in larger series of patients where PB

B-cells from reactive conditions and non-neoplastic disorders

are also included as control B-lymphocytes are still necessary

to confirm our results.

The proposed approach is aimed at identifying MRD in

B-CLPD based on the probability of each individual event

measured to correspond to either a normal or neoplastic B-

cell. In most cases, one event was sufficient to establish the

presence of MRD. Nowadays, with conventional four-color

approaches for MRD detection, �10 events presenting similar

immunophenotypic features are required to define presence of

MRD; in addition, around 100 events are necessary to pre-

cisely quantify their percentage among other cells in a sample

(1–3). By increasing the number of parameters simultaneously

measured, we also decreased the number of events required to

define the presence of an abnormal cell population down to

five events (maximum number of misclassified normal B-cells

in a sample) for a 100% efficiency. Furthermore, each event

identified as corresponding to a neoplastic B-cell by the prob-

abilistic approach can be further examined by an expert opera-

tor using conventional methods of manual data analysis, based

on the visualization of multiple bidimensional plots. In this

way, the expert operator can use the established knowledge

about the phenotypic features displayed by normal B-cells and

the aberrant patterns of protein expression found in B-CLPD,

to evaluate the consistency of the probabilistic approach here

proposed, in real individual cases. Interestingly, similar results

were obtained by diluting neoplastic B-CLL samples in normal

PB (n 5 2), prior to staining, instead of using electronic dilu-

tion of events corresponding to neoplastic B-cells in a pool of

electronic events corresponding to normal PB B-cells (data

not shown).

A potential limitation of the strategy proposed here is

that, after therapy, neoplastic cells from B-CLPD patients may

display variations in their immunophenotypic attributes with

respect to those observed at diagnosis. Previous reports have

shown that this may actually occur in a few B-CLPD patients

(40–42). In such cases, the proposed approach could be asso-

ciated with false negative results. Because of this, we tested the

probabilistic approach by analyzing a few cases where both

diagnostic and MRD samples from the same patient were

available, confirming the reproducibility of the approach in

real MRD samples. Further studies are required to investigate

the utility of this strategy to detect infiltration by B-CLPD of

tissues other than PB (e.g., bone marrow, cerebrospinal fluid)

for a more reproducible staging of the disease (1). In line with

this, the proposed probabilistic approach could also be useful

in the evaluation of MRD in other hematological malignan-

cies. This would be particularly useful in acute myeloid leuke-

mias where manual data analysis is much more complex due

to coexistence of different abnormal cell populations in the

same sample and a marked phenotypic heterogeneity (6,28)

and to the effects of therapy (43).

ORIGINAL ARTICLE

Cytometry Part A � 73A: 1141�1150, 2008 1149

In summary, here we propose and evaluate a probabilistic

approach aimed at automating and standardizing the search

for MRD, based on the immunophenotypic features of neo-

plastic cells observed at diagnosis in B-CLPD. Overall the pro-

posed strategy was associated with a higher specificity and

sensitivity than previously defined with expert-based manual

data analysis approaches and points out its potential utility

also in other minimal disease situations in both B-CLPD and

other hematological malignancies.

LITERATURE CITED

1. Davis BH, Holden JT, Bene MC, Borowitz MJ, Braylan RC, Cornfield D, Gorczyca W,Lee R, Maiese R, Orfao A, Wells D, Wood BL, Stetler-Stevenson M. 2006-BethesdaInternational Consensus recommendations on the flow cytometric immunophenoty-pic analysis of hematolymphoid neoplasia: Medical indications. Cytometry Part B2007;72B:S5–S13.

2. Ruiz-Arguelles A, Rivadeneyra-Espinoza L, Duque RE, Orfao A, Latin AmericanConsensus Conference. Report on the second Latin American consensus conferencefor flow cytometric immunophenotyping of hematological malignancies. CytometryPart B 2006;70B:39–44.

3. Stetler-Stevenson M. H43-A2 Clinical Flow Cytometric Analysis of Neoplastic Hema-tolymphoid Cells; Approved Guideline, 2nd ed. Clinical and Laboratory StandardsInstitute: Wayne, PA; 2007.

4. Edwards BS, Oprea T, Prossnitz ER, Sklar LA. Flow cytometry for high-throughput,high-content screening. Curr Opin Chem Biol 2004;8:392–398.

5. Szczepanski T, Orf~ao A, van der Velden VH, San Miguel JF, van Dongen JJ. Minimalresidual disease in leukaemia patients. Lancet Oncol 2001;2:409–417.

6. MRD-AML-BFM Study Group, Langebrake C, Creutzig U, Dworzak M, Hrusak O,Mejstrikova E, Griesinger F, Zimmermann M, Reinhardt D. Residual disease monitor-ing in childhood acute myeloid leukemia by multiparameter flow cytometry: TheMRD-AML-BFM Study Group. J Clin Oncol 2006;24:3686–3692.

7. Sayala HA, Rawstron AC, Hillmen P. Minimal residual disease assessment in chroniclymphocytic leukaemia. Best Pract Res Clin Haematol 2007;20:499–512.

8. Rawstron AC, Villamor N, Ritgen M, Bottcher S, Ghia P, Zehnder JL, Lozanski G,Colomer D, Moreno C, Geuna M, Evans PA, Natkunam Y, Coutre SE, Avery ED, Ras-senti LZ, Kipps TJ, Caligaris-Cappio F, Kneba M, Byrd JC, Hallek MJ, Montserrat E,Hillmen P. International standardized approach for flow cytometric residual diseasemonitoring in chronic lymphocytic leukaemia. Leukemia 2007;21:956–964.

9. Moreno C, Villamor N, Colomer D, Esteve J, Gin�e E, Munta~nola A, Campo E, BoschF, Montserrat E. Clinical significance of minimal residual disease, as assessed by dif-ferent techniques, after stem cell transplantation for chronic lymphocytic leukemia.Blood 2006;107:4563–4569.

10. Moreton P, Kennedy B, Lucas G, Leach M, Rassam SM, Haynes A, Tighe J, Oscier D,Fegan C, Rawstron A, Hillmen P. Eradication of minimal residual disease in B-cellchronic lymphocytic leukemia after alemtuzumab therapy is associated with pro-longed survival. J Clin Oncol 2005;23:2971–2979.

11. Montillo M, Tedeschi A, Miqueleiz S, Veronese S, Cairoli R, Intropido L, Ricci F,Colosimo A, Scarpati B, Montagna M, Nichelatti M, Regazzi M, Morra E. Alemtuzu-mab as consolidation after a response to fludarabine is effective in purging residualdisease in patients with chronic lymphocytic leukemia. J Clin Oncol 2006;24:2337–2342.

12. Montillo M, Schinkoethe T, Elter T. Eradication of minimal residual disease withalemtuzumab in B-cell chronic lymphocytic leukemia (B-CLL) patients: The need fora standard method of detection and the potential impact of bone marrow clearanceon disease outcome. Cancer Invest 2005;23:488–496.

13. Sanchez ML, Almeida J, Vidriales B, Lopez-Berges MC, Garcia-Marcos MA, MoroMJ, Corrales A, Calmuntia MJ, San Miguel JF, Orfao A. Incidence of phenotypicaberrations in a series of 467 patients with B chronic lymphoproliferative disorders:Basis for the design of specific four-color stainings to be used for minimal residualdisease investigation. Leukemia 2002;16:1460–1469.

14. Andersson R, Bruder CE, Piotrowski A, Menzel U, Nord H, Sandgren J, Hvidsten TR,Diaz de Stahl T, Dumanski JP, Komorowski J. A Segmental Maximum A PosterioriApproach to Genome-wide Copy Number Profiling. Bioinformatics 2008;24:751–758.

15. Li F, Yang Y. Analysis of recursive gene selection approaches from micro-array data.Bioinformatics 2005;21:3741–3747.

16. Griffith M, Tang MJ, Griffith OL, Morin RD, Chan SY, Asano JK, Zeng T, Flibotte S,Ally A, Baross A, Hirst M, Jones SJ, Morin GB, Tai IT, Marra MA. ALEXA: A microar-ray design platform for alternative expression analysis. Nat Methods 2008;5:118.

17. Schlabach MR, Luo J, Solimini NL, Hu G, Xu Q, Li MZ, Zhao Z, Smogorzewska A,Sowa ME, Ang XL, Westbrook TF, Liang AC, Chang K, Hackett JA, Harper JW, Han-non GJ, Elledge SJ. Cancer proliferation gene discovery through functional genomics.Science 2008;319:620–624.

18. Perfetto SP, Chattopadhyay PK, Roederer M. Seventeen-colour flow cytometry: Unra-velling the immune system. Nat Rev Immunol 2004;4:648–655.

19. Roederer M, Brenchley JM, Betts MR, De Rosa SC. Flow cytometric analysis of vac-cine responses: How many colors are enough? Clin Immunol 2004;110:199–205.

20. Robinson JP, Durack G, Kelley S. An innovation in flow cytometry data collectionand analysis producing a correlated multiple sample analysis in a single file. Cytome-try 1991;12:82–90.

21. Robinson JP, Ragheb K, Lawler G, Kelley S, Durack G. Rapid multivariate analysisand display of cross-reacting antibodies on human leukocytes. Cytometry 1992;13:75–82.

22. Costa ES, Arroyo ME, Pedreira CE, Garcıa-Marcos MA, Tabernero MD, Almeida J,Orfao A. A new automated flow cytometry data analysis approach for the diagnosticscreening of neoplastic B-cell disorders. Leukemia 2006;20:1221–1230.

23. Kitsos CM, Bhamidipati P, Melnikova I, Cash EP, McNulty C, Furman J, Cima MJ,Levinson D. Combination of automated high throughput platforms, flow cytometry,and hierarchical clustering to detect cell state. Cytometry A 2007;71A:16–27.

24. Zeng QT, Pratt JP, Pak J, Ravnic D, Huss H, Mentzer SJ. Feature-guided clustering ofmulti-dimensional flow cytometry datasets. J Biomed Inform 2007;40:325–331.

25. Pedreira CE, Costa ES, Arroyo ME, Almeida J, Orfao A. A Multidimensional Classifi-cation Approach for the Automated Analysis of Flow Cytometry Data. IEEE Transac-tions on Biomedical Engineering 2008;55:1155–1162.

26. Pedreira CE, Costa ES, Barrena S, Lecravisse Q, Almeida J, VanDongen JJ, Orfao A.Generation of flow cytometry data files with a potentially infinite number of dimen-sions. Cytometry Part A 73A:834–846.

27. Orfao A, Schmitz G, Brando B, Ruiz-Arguelles A, Basso G, Braylan R, Rothe G,Lacombe F, Lanza F, Papa S, Lucio P, San Miguel JF. Useful information provided bythe flow cytometric immunophenotyping of hematological malignancies: Currentstatus and future directions. Clin Chem 1999;45:1708–1717.

28. Olaru D, Campos L, Flandrin P, Nadal N, Duval A, Chautard S, Guyotat D. Multi-parametric analysis of normal and postchemotherapy bone marrow: Implication forthe detection of leukemia-associated immunophenotypes. Cytometry B 2008;74B:17–24.

29. Duda RO, Hart PE, Stork GD. Baysian Decision Theory. In: Duda RO, Hart PE, StorkGD, editors. Pattern Classification, 2nd ed. New York: Wiley; 2001. pp 20–82.

30. Harris NL, Jaffe ES, Diebold J, Flandrin G, Muller-Hermelink HK, Vardiman J, ListerTA, Bloomfield CD. World Health Organization classification of neoplastic diseasesof the hematopoietic and lymphoid tissues: Report of the Clinical Advisory Commit-tee Meeting—Airlie House, Virginia, November 1997. J Clin Oncol 1999;17:3835–3849.

31. Orfao A, Pedreira CE, Costa ES. A method for generating flow cytometry data filescontaining an infinite number of dimensions based on data estimation. U.S. Pat.No.11/240,167; 2007.

32. Orfao A, Pedreira CE, Costa ES. Generation of flow cytometry data files with a poten-tially infinite number of dimensions derived from the fusion of a group of separateflow cytometry data files and their multidimensional reconstruction with both actu-ally measured and estimated flow cytometry data. Eur. Pat. No.EP1,770,387; 2007.

33. Duda RO, Hart PE, Stork GD. Maximun-likelihood and Bayesian parameter estima-tion. In: Duda RO, Hart PE, Stork GD, editors. Pattern Classification, 2nd ed. NewYork: Wiley, 2001;115–116.

34. Duda RO, Hart PE, Stork GD. Unsupervised learning and clustering. In: Duda RO,Hart PE, Stork GD, editors. Pattern classification, 2nd ed. New York, 2001;526–527.

35. Spidlen J, Gentleman RC, Haaland PD, Langille M, Le Meur N, Ochs MF, Schmitt C,Smith CA, Treister AS, Brinkman RR. Data standards for flow cytometry. OMICS2006;10:209–214.

36. Kern W, Haferlach C, Haferlach T, Schnittger S. Monitoring of minimal residual dis-ease in acute myeloid leukemia. Cancer 2008;112:4–16.

37. Coustan-Smith E, Sancho J, Hancock ML, Boyett JM, Behm FG, Raimondi SC, San-dlund JT, Rivera GK, Rubnitz JE, Ribeiro RC, Pui CH, Campana D. Clinical impor-tance of minimal residual disease in childhood acute lymphoblastic leukemia. Blood2000;96:2691–2696.

38. Lucio P, Gaipa G, van Lochem EG, van Wering ER, Porwit-MacDonald A, Faria T,Bjorklund E, Biondi A, van den Beemd MW, Baars E, Vidriales B, Parreira A, vanDongen JJ, San Miguel JF, Orfao A; BIOMED-I. BIOMED I concerted action report:Flow cytometric imunophenotyping of B-ALL with standartized triple-stainings. Leu-kemia 2001;15:1185–1192.

39. Menendez P, Vargas A, Bueno C, Barrena S, Almeida J, De Santiago M, L�opez A, RoaS, San Miguel JF, Orfao A. Quantitative analysis of bcl-2 expression in normal andleukemic human B-cell differentiation. Leukemia 2004;18:491–498.

40. Tosetto A, Castaman G, Rodeghiero F. Evidence-based diagnosis of type 1 von Wille-brand disease: A Bayes theorem approach. Blood 2008;111:3998–4003.

41. Kroft SH, Dawson DB, McKenna RW. Large cell lymphoma transformation ofchronic lymphocytic leukemia/small lymphocytic lymphoma. A flow cytometricanalysis of seven cases. Am J Clin Pathol 2001;115:385–395.

42. Spath-Schwalbe E, Flath B, Kaufmann O, Thiel G, Brinckmann R, Dietel M, PossingerK. An unusual case of leukemic non-Hodgkin’s lymphoma with blastic transforma-tion. Ann Hematol 2000;79:217–221.

43. van Lochem EG, Wiegers YM, van den Beemd R, Hahlen K, van Dongen JJ, Hooij-kaas H. Regeneration pattern of precursor-B-cells in bone marrow of acute lympho-blastic leukemia patients depends on the type of preceding chemotherapy. Leukemia2000;14:688–695.

ORIGINAL ARTICLE

1150 Probabilistic Approach for Minimal Residual Disease