Clinical immunology
Multicolor flow cytometric immunophenotyping for diagnosis of childhood precursor-B-ALL and monitoring of minimal residual disease

Introduction

Detection of small numbers of persisting leukemic cells, i.e. monitoring of minimal residual disease (MRD) has strong prognostic value in childhood acute lymphoblastic leukemia (ALL) [1-3]. MRD status at the end of induction treatment is the most significant prognostic factor superior to previously identified risk factors such as age, blast count at diagnosis, immunophenotype at diagnosis, presence of chromosome aberrations, response to steroid prophase, and classical clinical risk group assignment. Moreover, children with high risk primary ALL and children with relapsed ALL subjected to allogeneic hematopoietic stem cell transplantation can profit from MRD monitoring [4, 5].
Currently three techniques can be successfully applied for specific and highly sensitive detection of MRD (sensitivity of at least one leukemic cell between 10000 of normal cells), namely multiparameter flow cytometric immunophenotyping, real-time quantitative polymerase chain reaction (RQ-PCR)-based detection of fusion gene transcripts or breakpoints, and RQ-PCR-based detection of clonal immunoglobulin (Ig) and T-cell receptor (TCR) gene rearrangements [6-8].
Flow cytometry is particularly attractive technique of MRD monitoring because it is fast, cost-effective and measures single cells, which enables precise quantification [3]. Up till now, immunophenotypic MRD detection in ALL is mostly based on 3-4 color flow cytometry. This methodology relies on tracing the leukemia-specific immunophenotypes as the result of cross-lineage antigen expression, maturational asynchronous expression of antigens, antigen overexpression, absence of antigen expression, ectopic antigen expression (summarized in [6, 7]). BIOMED-1 consortium developed a new approach of multicolor flow cytometric MRD detection based on the observation that various combinations of aberrant features of leukemic cells bring the ALL blasts into the “empty spaces” between normal lymphoid differentiation [9-11]. In the current study we have evaluated BIOMED-I Concerted Action approach on a large series of consecutive childhood B-Cell Precursor (BCP) ALL patients.

Material and Methods


Patients

The study group consisted of 70 consecutively enrolled pediatric B-cell precursor acute lymphoblastic leukemia (BCP-ALL) patients treated at the Departments of Pediatric Hematology and Oncology in Zabrze and Katowice of the Medical University of Silesia between 2002 and 2007. The age of the patients ranged from 1 month to 18 years. The male/female ratio was 1.5.

Sample collection and methods

The bone marrow samples were collected at diagnosis from all patients and processed within less than 10 hours after collection, generally within 1-2 hours. The samples were processed under standardized and optimized conditions. For surface antigen staining, 100 µl of sample was incubated for 20 min in room temperature with saturating amounts of relevant mouse anti-human monoclonal antibodies conjugated with fluorochromes. The monoclonal antibodies were conjugated with one of the following fluorochromes: FITC (fluorescein isothiocyanate), PE (phycoerythrin) or PE-Cy5 (phycoerythrin-cyanin 5). The full characteristic of used antibodies is listed in Table 1.
After the incubation step, erythrocyte lysis was performed by 10-minute incubation in lysing solution (BD FACSLysing Solution, Becton Dickinson, San Jose, CA, USA). Subsequently the sample was washed with modified PBS (CellWash, Becton Dickinson) and finally suspended in the volume of 0.5 ml. TdT as a nuclear marker was stained after surface staining and lysis steps, according to the manufacturer’s instructions. Acquisition of data was performed in FACScan flow cytometer (Becton Dickinson) using the CellQuest software (Becton Dickinson) on Macintosh platform. The data analysis was performed in Paint-A-Gate software (Becton Dickinson).

Immunophenotypic data analysis

Diagnosis of BCP-ALL has been confirmed based on the standard morphological and immunophenotypic criteria [12]. We searched for aberrant immunophenotypes among
BCP-ALL patients with 3-color flow cytometry and determined their relative frequencies. Because the pattern of antigen expression in normal bone marrow is highly reproducible, it was possible to create fixed images of normal B-cell differentiation profile by five triple monoclonal antibody combinations: TdT/C10/CD19 (I), CD10/CD20/CD19 (II), CD34/ CD38/CD19 (III), CD34/CD22/CD19 (IV), CD34/CD45/ CD19 (V) (Figure 1, Table 1) [10]. Areas devoid of normal bone marrow cells were defined on such designed dot plots (so called “empty spaces”), which served as the point of reference (coded as patterns A-F) [10]. Leukemic precursor B-cells localize in these areas due to aberrant antigenic expression (antigen over- or underexpression, and asynchronous or ectopic expression). All five triple stainings were applied to each patient. In addition to above-mentioned five triple stainings, further double and triple surface stainings were performed to investigate possible T-lineage (CD1a, CD2, CD5, CD7) and myeloid lineage (CD13, CD15, CD33) antigen co-expression. The positivity of each cross-lineage antigen was assumed, if it was expressed on at least 20% of blasts.

Results


Aberrant BCP-ALL phenotypes

Particular monoclonal antibody combinations revealed different frequencies of aberrant leukemia-specific immunophenotypes, ranging from about 44% for combination III (CD34/CD38/CD19) and IV (CD34/CD22/CD19) to as high as 96% for the combination II (CD10/CD20/CD19). For the remaining triple stainings: I (TdT/CD10/CD19) and V (CD34/CD45/CD19), the percentages of disclosed aberrancies were 80 and 49%, respectively. The “empty spaces” within each combination were occupied with different frequencies. The summary of the immunophenotypic data is shown in Table 2.
In the (I) antibody combination, the most frequent aberrant immunophenotype observed (37.14%) was the one defined as “E” pattern representing CD19+ cells with high-level expression of both CD10 and TdT (TdT++CD10++CD19+) (see Figure 1). The immunophenotypes showing bright expression of one of the markers and dim of the other at the same time (TdTdim/+CD10dim/+/++CD19+) occurred at lower frequencies (pattern C, D and F together – about 33%). The immunophenotype of dim expression of TdT and dim or lack of expression of CD10 was the least common (TdTdimCD10–/dimCD19+, patterns A and B – 10%).
The (II) triple staining turned out to be the most informative for the discrimination between normal and leukemic cells. The most prevalent immunophenotype concerned CD10 overexpression by CD20– cells (pattern B – about 51%). Asynchronous expression of CD10 by CD20 dim to strong positive cells (CD10+/++CD20dim/+/++CD19+) was also frequently observed (patterns D and E – about 36%). The least common was the immunophenotype of CD10 negativity with simultaneous underexpression or absence of CD20, which
was detected in about 9% of cases (patterns A and C, CD10–CD20–/dimCD19+).
The (III) antibody combination disclosed asynchronous dim or strong expression of CD38 by cells positive for CD34 in almost all detected aberrant cases (CD34+CD38dim/+CD19+ – patterns A and B – about 41%). Only in 3% of cases dim expression of CD38 was accompanied by bright expression of CD34 (pattern C).
With the combination (IV) we observed mainly two different aberrant immunophenotypes. The most frequently expressed pattern E, occurred in 30% of cases and corresponded to the immunophenotype of bright CD22 expression on CD34 dim positive cells (CD34dimCD22++CD19+). The other aberrant phenotype of bright positivity for CD34 and positivity for CD22 cells was revealed in about 9% of cases (pattern D). Aberrancies concerning negativity for CD22 with different level of CD34 expression were less frequently observed and together reached about 4% (patterns A, B and C, CD34–/dim/+CD22–CD19+).
The last triple staining examined (V) revealed frequent occurrence of double negativity or double dim positivity for CD34 and CD45 cells (pattern A, CD34–/dimCD45–/dimCD19+ – 33%). In addition, 14% cases showed bright expression of CD34 by CD45 positive cells (patterns C and D – CD34+/++CD45+CD19+). There was only one case of CD45 negative cells overexpressing CD34 detected among CD19+ cells (pattern B, CD34++CD45–CD19+).

Aberrant cross-lineage expression in BCP-ALL cases

Using additional antibody combinations, coexpression of cross-lineage antigens was comprehensively studied. The highest coexpression incidence concerned myeloid markers – CD13 and CD33. Aberrant isolated expression of CD13 and CD33 was found in about 24 and 7% of cases, respectively. Simultaneous coexpression of these two markers was detected in 11% of cases. In contrast, expression of another myeloid marker CD15 was found in only 3 BCP-ALL cases (5%). The T-lineage markers were rarely coexpressed. In
a total of 4 BCP-ALL cases isolated coexpression of CD2, CD5 or CD7 was demonstrated.

Discussion

In the present study we searched for aberrant immunophenotypes of blast cells among BCP-ALL patients, using five triple monoclonal antibody combinations and “empty spaces” approach as an analysis method [10]. The selected antibody combinations were applied to exhibit abnormal, i.e. asynchronous, ectopic, over-, or underexpression of antigens, hence, detection of any (as compared to the normal expression patterns – Figure 1) qualified the case to be pathological.
The method used, turned out to be fully appropriate to prove leukemia-specific aberrant immunophenotypes and to discriminate between normal and leukemic differentiation patterns in 100% of cases. The maximal percentage of cases in which pathological blasts were detectable by single staining was 96% (staining II, Table 2). Moreover, the use of five triple stainings revealed the existence of two different phenotypic aberrancies in over 94% of patients, three in about 64% and four in about 41%. The incidence of aberrant immunophenotypes in general was higher as compared to the reported by the other groups exploring the clinical value of flow cytometric investigation of MRD in ALL [2, 13-15]. For instance the single staining (with one monoclonal antibody combination) demonstrated an abnormal population in maximum 55% of cases in [10] and in 81% in [16]. In every triple staining there was one dominating “empty space” into which aberrant cells were falling. Some of the monoclonal antibodies, like CD10 were used in more than one staining
(I and II) and in those cases the results correspondingly indicated high incidence of overexpression of CD10
(Table 2). Furthermore our results concerning the most frequent “empty space” within each antibody combination were concordant to as proved by BIOMED-I Concerted Action in all but staining I (TdT/CD10/CD19) [10].
In our study we also looked for cross lineage antigen expression on leukemic precursor-B-cells. It is commonly known that blasts in BCP-ALL sometimes coexpress antigens from other lineages, such as T-cell and myeloid lineages, which sometimes were assigned prognostic significance [17]. The frequency of such coexpressions reported in literature varies in a wide range, between 7 and 54% [2, 18] Our research revealed relatively high incidence of mostly dim coexpressions of myeloid markers CD13 and CD33 (total percent of cases with either of these markers – 42%), but not CD15. T-lineage markers were coexpressed at lower frequencies (about 5%). This observation is concordant with the previous notions that CD13 and CD33 are the most representative examples of cross-lineage antigen expression among BCP-ALL patients [10].
In conclusion, we can confirm the capability of the “empty spaces” method to detect abnormalities in antigen expression in leukemic patients. It can be noted that even the use of limited antibody panel can be of great diagnostic value, thus situating the method at the top of the simplest, fastest and most valuable diagnostic approaches [13]. However to gain even higher reliability of the technique, additional conditions must be fulfilled. One of the prospective solutions is to expand the antibody panels and enrich them in additional fluorochromes, preferably using simultaneously 6 to 8 colors [19, 20]. The possibility of concurrent analysis of expression of antigens in new configurations would undoubtedly increase the sensitivity and specificity of MRD detection in ALL [21].

References

1. van Dongen JJ, Seriu T, Panzer-Grümayer ER et al. (1998): Prognostic value of minimal residual disease in acute lymphoblastic leukaemia in childhood. Lancet 352: 1731-1738.
2. Coustan-Smith E, Behm FG, Sanchez J et al. (1998): Immunological detection of minimal residual disease in children with acute lymphoblastic leukaemia. Lancet 351: 550-554.
3. Szczepanski T (2007): Why and how to quantify minimal residual disease in acute lymphoblastic leukemia? Leukemia 21: 622-626.
4. Krejci O, van der Velden VHJ, Bader P et al. (2003): Level of minimal residual disease prior to haematopoietic stem cell transplantation predicts prognosis in paediatric patients with acute lymphoblastic leukaemia: a report of the Pre-BMT MRD Study Group. Bone Marrow Transplant 32: 849-851.
5. Coustan-Smith E, Gajjar A, Hijiya N et al. (2004): Clinical significance of minimal residual disease in childhood acute lymphoblastic leukemia after first relapse. Leukemia 18: 499-504.
6. Campana D (2003): Determination of minimal residual disease in leukaemia patients. Br J Haematol 121: 823-838.
7. Szczepański T, van der Velden VH, van Dongen JJ (2006): Flow-cytometric immunophenotyping of normal and malignant lymphocytes. Clin Chem Lab Med 44: 775-796.
8. van der Velden VH, Hochhaus A, Cazzaniga G et al. (2003): Detection of minimal residual disease in hematologic malignancies by real-time quantitative PCR: principles, approaches, and laboratory aspects. Leukemia 17: 1013-1034.
9. Lúcio P, Parreira A, van den Beemd MW et al. (1999): Flow cytometric analysis of normal B cell differentiation: a frame of reference for the detection of minimal residual disease in precursor-B-ALL. Leukemia 13: 419-427.
10. Lucio P, Gaipa G, van Lochem EG et al. (2001): BIOMED-1 concerted action report: flow cytometric immunophenotyping of precursor-B-ALL with standardized triple-stainings. BIOMED-1 Concerted Action Investigation of Minimal Residual Disease in Acute Leukemia: International Standardization and Clinical Evaluation. Leukemia 15: 1185-1192.
11. van Lochem EG, van der Velden VH, Wind HK et al. (2004): Immunophenotypic differentiation patterns of normal hematopoiesis in human bone marrow: reference patterns for age-related changes and disease-induced shifts. Cytometry B Clin Cytom 60: 1-13.
12. Szczepański T, van der Velden VH, van Dongen JJ (2003): Classification systems for acute and chronic leukaemias. Best Pract Res Clin Haematol 16: 561-582.
13. Weir EG, Cowan K, LeBeau P, Borowitz MJ. (1999): A limited antibody panel can distinguish B-precursor acute lymphoblastic leukemia from normal B precursors with four color flow cytometry: implications for residual disease detection. Leukemia 13: 558-567.
14. Dworzak MN, Fritsch G, Fleischer C et al. (1998): Comparative phenotype mapping of normal vs. malignant pediatric B-lymphopoiesis unveils leukemia-associated aberrations. Exp Hematol 26: 305-313.
15. Ciudad J, San Miguel JF, López-Berges MC et al. (1998): Prognostic value of immunophenotypic detection of minimal residual disease in acute lymphoblastic leukemia. J Clin Oncol 16: 3774-3781.
16. Kerst G, Kreyenberg H, Roth C et al. (2005): Concurrent detection of minimal residual disease (MRD) in childhood acute lymphoblastic leukaemia by flow cytometry and real-time PCR. Br J Haematol 128: 774-782.
17. Pui CH, Rubnitz JE, Hancock ML et al. (1998): Reappraisal of the clinical and biologic significance of myeloid- associated antigen expression in childhood acute lymphoblastic leukemia. J Clin Oncol 16: 3768-3773.
18. Ciudad J, Orfao A, Vidriales B et al. (1998): Immunophenotypic analysis of CD19+ precursors in normal human adult bone marrow: implications for minimal residual disease detection. Haematologica 83: 1069-1075.
19. Wood B (2006): 9-color and 10-color flow cytometry in the clinical laboratory. Arch Pathol Lab Med 130: 680-690.
20. Perfetto SP, Chattopadhyay PK, Roederer M (2004): Seventeen-colour flow cytometry: unravelling the immune system. Nat Rev Immunol 4: 648-655.
21. Orfao A, López A, Flores J et al. (2006): Diagnosis of hematological malignancies: new applications for flow cytometry. Hematology (EHA Educ Program) 2: 6-13.