B-ALL Minimal Residual Disease Flow Cytometry. An Application of a Novel Method for Optimization of a Single-Tube Model

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1 B-ALL Minimal Residual Disease Flow Cytometry An Application of a Novel Method for Optimization of a Single-Tube Model Aaron C. Shaver, MD PhD, 1 Bruce W. Greig, MT(ASCP), CCy, 1 Claudio A. Mosse, MD, PhD, 1,2 and Adam C. Seegmiller, MD, PhD 1 From the 1 Vanderbilt University Medical Center, Nashville, TN, and 2 VA Tennessee Valley Healthcare System, Nashville. Key Words: Minimal residual disease; Flow cytometry; Acute lymphoblastic leukemia; Statistical analysis; Optimization Am J Clin Pathol May 2015;143: ABSTRACT Objectives: Optimizing a clinical flow cytometry panel can be a subjective process dependent on experience. We develop a quantitative method to make this process more rigorous and apply it to B lymphoblastic leukemia/lymphoma (B-ALL) minimal residual disease (MRD) testing. Methods: We retrospectively analyzed our existing threetube, seven-color B-ALL MRD panel and used our novel method to develop an optimized one-tube, eight-color panel, which was tested prospectively. Results: The optimized one-tube, eight-color panel resulted in greater efficiency of time and resources with no loss in diagnostic power. Conclusions: Constructing a flow cytometry panel using a rigorous, objective, quantitative method permits optimization and avoids problems of interdependence and redundancy in a large, multiantigen panel. Detection of minimal residual disease (MRD) is one of the most potent applications of clinical flow cytometry. MRD detection employs a panel of markers that have been demonstrated to differentiate with high sensitivity and specificity between normal and abnormal cells and then analyzes very large numbers of events to detect very small residual populations of abnormal cells. The disease in which flow cytometry plays the most well-established role in MRD detection is B lymphoblastic leukemia/lymphoma (B-ALL). The technique began with basic panels composed of a few simple markers 1-3 and evolved along with the experience of the interpreters and the technical capabilities of the cytometry instruments into large, multitube panels with an ever-increasing number of potentially useful markers. 4-8 This experience has produced a large body of work documenting the ability of flow cytometry to detect very small amounts of MRD in B-ALL, down to the order of 1 in 10 5 events. Because of the long history of experience with the technique, an abundance of clinical data demonstrates the central importance, both in determining prognosis and in guiding further treatment, of flow MRD in B-ALL for both children and adults. 6,9-14 As more markers have been introduced and the testing panels have grown larger, the technical complexity and cost associated with B-ALL testing have correspondingly increased. Whether this increased complexity results in a significant increase in test performance is not immediately clear, since the information provided by multiple additional markers may not be independently useful. The use of metrics to determine the independence and relative contribution of markers to clinical diagnosis in a flow panel is not routine, and thus design of an optimal testing 716 Am J Clin Pathol 2015;143:

2 panel, in terms of minimizing cost and difficulty while maximizing clinical performance, is essentially an ad hoc procedure, dependent on the skill and experience of the flow cytometrists. In this work, we describe our experience with a threetube, seven-color B-ALL MRD panel used for routine clinical care in our flow cytometry laboratory. We analyze the relative contribution that each marker and pair of markers make to identifying MRD, using a novel method for categorizing marker and marker combinations, allowing us to determine which ones provide the most independent information for the diagnosis of MRD. After both retrospective and prospective analysis and validation, we were able to determine that a single-tube eight-color panel, consisting of the markers CD9, CD10, CD19, CD20,,, CD45, and CD58, could provide as much diagnostic utility as our existing three-tube panel with 12 markers, resulting in a significant decrease in technical complexity and material cost. Although other recent studies have proposed optimized panels for B-ALL MRD analysis, 15 our work explicitly demonstrates equivalence of a single-tube panel to a more complicated alternative. We further propose a combination of commercially available antibodies and also describe our specific strategies for identifying aberrant populations using our marker combinations, pointing the way forward for increasing the ease of adoption in laboratories that have not yet implemented MRD detection. Finally, by demonstrating the utility of our new technique in the relatively well-understood arena of B-ALL MRD, we describe how this work may serve as a case study for the use of optimization techniques in other complicated areas of clinical flow cytometry, such as diagnosis of myelodysplastic syndrome and MRD detection in other diseases such as acute myeloid leukemia. Materials and Methods Flow Analysis Flow cytometry was performed on a FACSCanto II flow cytometer (Becton Dickinson, Franklin Lakes, NJ) using FACSDiva software for instrument setup and acquisition of all samples used in this study. Event acquisition for the three-tube, seven-color assay acquired all mononuclear cells (low log side scatter [SSC] vs CD19) with stop counts at either 100,000 events or 8 minutes, whichever occurred first. Event acquisition for the one-tube, eightcolor panel acquired all mononuclear events (low log SSC vs CD19) with stop counts at either 400,000 events or 7 minutes, whichever occurred first. Analysis was performed on the list mode files using Winlist software (Verity Software House, Topsham, ME). The gating scheme used in analysis for the three-tube, seven-color panel included all B cells using a CD19/log SSC gate. Immature B cells were further characterized based on expression of the remaining antibodies in the panel. The gating scheme used in the one-tube, eight-color assay consisted of a logical gate that initially used positive results of the CD19/log SSC gate and then a further gate for B lymphoblasts and hematogones using CD19 by CD45. Plasma cells were excluded from analysis by using a bright vs log SSC exclusion gate. MRD positivity was defined as the presence of an abnormal blast population comprising at least 0.01% of total mononuclear cells. Cases Reviewed For the retrospective component of this study, the period of review included all cases seen in the clinical flow cytometry laboratory at Vanderbilt University Medical Center (VUMC) that were positive for B-ALL, either at initial diagnosis or in follow-up after treatment, for a period of approximately 12 months. This survey resulted in 57 positive cases, 41 of which were new diagnoses or first presentation to VUMC, and 16 of which were follow-up studies that were positive for MRD or were involved by clinically relapsed disease. The cases were obtained from 41 unique patients (with the extra 16 cases representing MRD-positive follow-up cases for patients with a positive diagnostic sample) and represented a mix of pediatric and adult patients (21 pediatric, 19 adult, and one age unknown). Cytogenetic results were available for 39 of 41 patients and included a range of the most common abnormalities seen in B-ALL. See Table 1 for a summary of these clinical features. For the prospective component of the study, positive cases of B-ALL (as assessed by the original three-tube, seven-color panel) seen in the clinical flow cytometry laboratory at VUMC during the accrual period were included, with 38 unique patients and 56 total cases. These had a distribution similar to the retrospective data set with respect to diagnostic (34 cases) vs relapse or follow-up (22 cases), pediatric (22 patients) vs adult (16 patients), and karyotypic abnormalities (see Table 1). An additional set of negative cases was reviewed to address issues of specificity and false positivity; these included 43 cases with a history of B-ALL that were called MRD negative by the original panel, as well as 18 cases with no history of B-ALL that were noted to be relatively hematogone rich (>3% hematogones). These cases were analyzed with the three-tube, seven-color panel for routine clinical use, as well as with the one-tube, eightcolor panel developed by the optimization method described below. All analysis of clinical material was performed with the approval of the Institutional Review Board at VUMC (IRB Study ). Am J Clin Pathol 2015;143:

3 Shaver et al / Optimizing MRD Flow Cytometry Table 1 Clinical and Cytogenetic Features Retrospective, No. Total No. of positive cases New diagnosis or first presentation Positive follow-up (MRD or relapse) Total No. of positive patients Gender Male Female Unknown 1 Age Adult Pediatric Unknown 1 Cytogenetics Normal or nonspecific 9 6 t(9;22) BCR-ABL1, de novo 6 8 t(9;22) BCR-ABL1, blast crisis 3 1 of CML t(v,11q23) MLL rearranged 3 3 t(12;21) TEL-AML1 4 4 t(1;19) E2A-PBX 1 1 Hyperdiploid 7 8 Hypodiploid 1 2 Other 5 4 Unknown 2 1 Additional negative cases 61 Negative follow-up (MRD) 43 Hematogone rich, no diagnosis of B-ALL 18 Metrics for Flow Cytometry Panel Optimization Prospective, No. B-ALL, precursor B lymphoblastic leukemia; CML, chronic myelogenous leukemia; MRD, minimal residual disease A novel and general method was developed to optimize a flow cytometry panel for detection of an abnormal population in the setting of a normal background. This method was designed to identify in a quantitative and reproducible fashion the optimal subset of markers in a larger, initial panel of markers for which data are available. The method operates by formally defining a measure for information provided by a marker or marker pair (in the case of a two-dimensional histogram) and then ranking all markers by quantitative metrics based on their information value. We define an informative marker for a given case as one whose value for the abnormal population lies outside of the range for the normal population (hematogones in the case of B-ALL), and a pair of markers is informative for a given case if their combined value for the abnormal population lies off of the normal two-dimensional spectrum for that marker pair. To decide if a marker in an abnormal population lies outside the normal range, we use similar methods to those employed in routine clinical assessment of MRD. One-dimensional histograms are used to assess the range A Number 2,000 1,500 1, ,500 3,000 2,500 2,000 1,500 1, CD58 FITC CD58 FITC Figure 1 Example of comparison of a single marker (CD58) by one-dimensional histogram between normal hematogones (A) and a leukemic population (B). CD58 expression in normal hematogones covers a wide range, but the mean intensity of expression seen in the leukemic blast population is at a level (fluorescent intensity >10 2 ) not seen in the entire range of hematogone expression. of expression of a single marker Figure 1, and two-dimensional histograms are used for pairs of markers Figure 2. Determining whether a marker or marker pair lies off of the normal maturational spectrum requires careful visual inspection by an experienced pathologist or cytometrist, particularly since the precise shape of the two-dimensional normal maturational spectra may be complicated (Figure 2). Although some markers may provide a greater quantitative degree of separation between normal and abnormal than others, whether a marker is informative in this context is a binary yes or no property. Once markers are scored as informative or not, it remains to determine which markers provide the greatest contribution to the panel. The rationale for the method described here is that a marker or marker pair is optimal if (1) it is frequently informative for a wide range of cases, and (2) it provides a large relative contribution to the diagnosis in those cases in which it is informative. Criterion 1 is insufficient to define optimality, since it does not address the problem of nonindependence of markers. If a marker tends to be informative only in cases where many other markers are also informative, then the individual contribution of that marker to accurate diagnosis is less than would be predicted simply by counting the total number of times it is informative. Criterion 1 thus emphasizes a frequently useful marker, and criterion 2 addresses the degree to which the marker is independently informative. This method uses metrics to determine the degree to which each marker fits both criteria, in a quantitative fashion that allows us to rank and compare markers. For each marker or marker pair, I is defined as the relative information that marker contributed to the diagnosis for each case, where I = 1/(total number of informative markers in that case). For uninformative markers, I is defined as B Number 718 Am J Clin Pathol 2015;143:

4 A CD20 APC-H CD10 PC7 Figure 2 Example of analysis of a marker pair (CD10 and CD20) by two-dimensional histogram. Normal hematogones (A) exhibit a characteristic curve with a well-defined shape: acquisition of CD20 followed by loss of CD10, resulting in the characteristic right-angle shape. Red areas highlight the most frequent patterns of abnormal expression seen in B lymphoblasts, with either negativity for both CD10 and CD20 (left shaded area) or abnormally intense CD10 expression (right shaded area). B, A population of leukemic B lymphoblasts (CD10 abnormally bright, CD20 dim, in the right shaded area) with a background population of normal, mature B cells (CD10, CD20+, above the left shaded area) analyzed under the same conditions. Although the overall right angle shape appears to be preserved on superficial examination, comparison with the normal hematogones (A) makes it clear that pattern of expression is abnormal, and CD20 is thus an informative marker pair in this case. 0. The method defines two related metrics based on I that quantify the degree to which each marker or pair of markers (for two-dimensional histograms) is informative in the context of all other markers in the large, initial panel. The first, I T, is defined as the average of I for a particular marker over all cases in the data set, including those cases in which the marker was uninformative. This metric emphasizes the total number of times that the marker is informative, related to criterion 1 above. The second, I R, is defined as the average of I for a particular marker over only the subset of cases in the data set in which that marker was informative (ie, those cases in which I is not equal to 0). This metric emphasizes the relative contribution of the marker to the diagnosis in those cases in which the marker was informative and addresses criterion 2 above. Utility of the metrics is based on ranking all measured markers by I T and assigning categories based on relative ranking. Category I markers were those with the highest values of both I T. However, only a subset of markers fit category I, and inclusion of additional markers may be necessary to increase sensitivity of the panel. Categories II and III included those markers that had high values of one of the metrics (I T or I R, respectively) with a low value for the B CD20 APC-H CD10 PC7 other metric. Category II markers were frequently informative but lacked independence, while category III markers were less frequently informative but would be so relatively more often in diagnostically difficult cases with few other useful markers. Category IV markers had low values of both metrics and represented the markers that provided the smallest independent informative contribution in the data set. The decision to assign markers to categories was based on the structure of the ranked data (see Results). Construction of an optimal panel involved including as many category I markers as possible, avoiding the use of category IV markers, and selecting a mix of category II and III markers until the desired diagnostic sensitivity was reached. Panel Optimization Using the Retrospective Data Set Cases from the retrospective data set were independently reanalyzed by three hematopathologists (A.C.S., C.A.M., and A.C.S.). Eleven individual markers were assayed (listed in Table 2, with CD13 and CD33 combined in the FITC channel), along with the following set of nine marker pairs that were recorded as two-dimensional histograms in routine clinical analysis: CD20,,, CD20, CD58, CD9, CD9, CD81, and CD24. This resulting list of 20 markers and marker pairs were scored as informative or uninformative for each case by each pathologist independently. Following this independent analysis, all three pathologists reviewed each other s scores and came to agreement on a unanimous consensus score for each marker in every case in the data set. There were no cases in which this re-review process resulted in a change in the originally rendered clinical flow diagnosis. After the consensus scoring process ascertained that all scores for single markers corresponded in the expected fashion to their scores in marker pairs namely, that an informative single marker would always result in an informative marker pair containing that marker but not necessarily vice versa single markers that were also included in marker pairs were dropped from the analysis to avoid pseudoreplication. This resulted in the retention of CD19, CD13/CD33, and CD45 as single markers, along with the marker pairs listed above. The consensus scores for this set of markers were used for all subsequent analysis. Following creation of the consensus score, the total number of informative markers was tabulated for each case. From this, the metrics I, I T, were calculated. For each marker, 95% confidence intervals for I T were calculated by bootstrap analysis (with 10,000 replicates) and the BCa method for confidence interval calculation, using the boot() library of the R statistical programming environment (R Foundation for Statistical Computing, Vienna, Austria). Markers were sorted in rank order by I T and were Am J Clin Pathol 2015;143:

5 Shaver et al / Optimizing MRD Flow Cytometry Table 2 Flow Cytometric Markers Used Marker Fluorochrome Supplier Three-tube, seven-color panel CD9 PE BD Biosciences (San Jose, CA) CD10 PE BD Biosciences CD13 FITC Dako (Carpinteria, CA) CD19 FITC Beckman Coulter (Brea, CA) CD20 APC-H7 BD Biosciences CD24 PE Beckman Coulter CD33 FITC BD Biosciences PerCP BD Biosciences APC BD Biosciences CD45 V500 BD Biosciences CD58 FITC BD Biosciences CD81 FITC BD Biosciences One-tube, eight-color panel CD9 PE BD Biosciences CD10 PC7 Beckman Coulter CD19 BV421 BD Biosciences CD20 APC-H7 BD Biosciences PerCP BD Biosciences APC BD Biosciences CD45 V500 BD Biosciences CD58 FITC BD Biosciences assigned to categories I to IV by comparing their relative performance as described above. Relative (rank order) rather than absolute cutoffs were used for classification because absolute values for the metrics will vary depending on the number of markers in the data set being analyzed. These results were used to create a single-tube, eight-color panel by combining category I, II, and III markers and omitting category IV markers. The consensus data set was then reanalyzed using only the markers included in the optimized panel. Success of the optimized panel was measured, as defined by the ability to detect all abnormal cases previously detected by the original panel (ie, no new false negatives). Prospective Validation Due to the retrospective nature of the component of the study described above, reanalysis of that portion was performed using a pruned subset of the original flow cytometry results. Questions pertaining to the actual performance of the optimized flow panel were addressed by side-by-side analysis of a prospective patient population with the original and optimized panels. This prospective analysis is important to assess the real-world performance of the panel, particularly since different conjugates of some antibodies are used in the optimized panel. For the patients in the prospective data set, all clinical materials were assayed with the preexisting three-tube, seven-color panel, which was used for reporting routine clinical results, and with the optimized one-tube, eight-color panel. Side-by-side analysis was performed to validate performance of the new assay in a variety of ways. Qualitative performance for MRD detection was defined as successful if no false negatives or false positives were identified compared to the standard of the preexisting panel. Quantitative performance was assayed by comparing the percentage of cells detected by parallel sevencolor and eight-color assays. Performance of the assay was measured using dilution series and was defined as successful if quantitative MRD detection exhibited a linear relationship (as measured by goodness of fit of a linear correlation model) between the three-tube and one-tube assays to below the clinically relevant cutoff of 0.01% of mononuclear cells. Results Panel Optimization Using the Retrospective Data Set For the cases analyzed in the retrospective data set using the preoptimization original three-tube, seven-color panel, there was a mean of 6.6 informative markers or pairs of markers in each positive case, with a maximum of 10 informative markers (seen in five cases). A subset of seven positive cases (each from a different patient) had three or fewer informative markers: two cases had three markers, three cases had two markers, and two cases had one informative marker. This subset had no evidence of common demographic or cytogenetic characteristics: four were pediatric and three were adult, five were male and two were female, and there was a range of cytogenetic abnormalities, including one normal karyotype, one with t(9;22), one with an MLL rearrangement, one with t(12;21), two with hyperdiploidy, and one with hypodiploidy. Using the optimization techniques as described above on the consensus retrospective data set, the markers were ranked in order based on I T Figure 3. In both cases, the CD58 marker pair stood out as the most informative. For I T, a subset of markers (CD9, CD19, and CD13/CD33) stood out as relatively uninformative, with markedly lower point estimates and narrow confidence intervals (Figure 3A). For I R, there was more of a gradual spectrum of performance across the range of markers, but a group of markers was identified with the lowest point estimates and relatively narrow confidence intervals (CD24, CD20,,, CD81, and CD13/CD33) (Figure 3B). Markers that fell in the higher echelon of performance for both I T were classified as category I, markers that fell in the lower echelon for both were classified as category IV, and the remainder of markers were classified as category II or III based on which metric was better performing Table 3. To construct the optimized one-tube, eight-color panel (Table 2), all category I markers were included and the category 720 Am J Clin Pathol 2015;143:

6 A 0.25 B I T I R CD58 CD20 CD9 CD20 CD81 CD24 CD45 CD9 CD19 CD13/33 Marker Marker Figure 3 Values of I T (A) (B) for the retrospective data set. Error bars represent 95% confidence intervals, calculated as described in the Materials and Methods. Markers are presented in rank order of the values of their metric. The dotted line in each panel represents a cutoff between markers of higher and lower value. For I T, the line lies at a natural position just before a dip in marker performance. The values for I R exhibit a more gradual range of variation, but the markers below the cutoff have both lower point estimates for the value of their metric, as well as small confidence intervals, indicating a higher degree of confidence that these are poorly performing markers CD58 CD20 CD45 CD19 CD9 CD9 CD24 CD20 CD81 CD13/33 IV marker was excluded. To choose between the remaining category II and III markers, we gave preference to those with a higher value of I T or I R, along with constraints in constructing the actual panel (availability of appropriate antibody conjugates, etc.). When the retrospective data set was reanalyzed using only those markers or combinations of markers available in the optimized one-tube, eight-color panel, the mean number of informative markers in each positive case decreased from 6.6 to 5.2. All cases called positive using the original still had informative markers using the optimized panel, indicating no loss of sensitivity for MRD detection as a result of using the optimized panel. Among the set of seven cases highlighted above that had three or fewer informative markers using the original panel, only one of the cases (which originally had three informative markers) included one of the markers dropped in the panel optimization, and thus the ability to recognize these potentially difficult cases was not compromised. Prospective Validation In the prospective data set, side-by-side analysis revealed full qualitative agreement between the original and optimized panels, for all diagnostic and MRD (both positive and negative) specimens. Quantitative agreement in positive cases was also very good, both for MRD and diagnostic specimens. The Pearson correlation coefficient measuring the relationship between the percentage of positive cells measured by the two panels is (P <.001), confirming a very close approximation to a 1:1 relationship between the two panels. Linearity and sensitivity were tested using dilution studies of a known positive case, from 1:1 to 1:10,000, with a final percentage of approximately 0.006%, less than the level of clinically relevant sensitivity for the test. Linearity across this range was confirmed by a highly significant Pearson correlation coefficient for log-transformed data of (P <.001). Discussion Method Rationale From the standpoint of practical use, the best tool for MRD detection is a panel of markers that maximizes the ability to detect and quantify MRD in a way that is efficient, both in terms of resources and time. When designing an optimal panel, the possibility of nonindependence between various markers means that introducing a new marker into a panel will not necessarily increase the ability of that panel to detect disease. Similarly, the inverse may also be true removing a marker shown to be frequently positive may not necessarily decrease the performance of the panel. To construct an optimized panel, it is worthwhile to define explicitly the desired characteristic of the markers to be included. In most settings, many cases will have a highly abnormal phenotype and thus be easy to recognize with a wide variety of markers. The difficulty in optimal panel design is to ensure detection of the subset of cases that have less strikingly abnormal phenotypes and are thus more subtle. Simply constructing a panel composed of markers that are frequently informative is not sufficient to ensure that Am J Clin Pathol 2015;143:

7 Shaver et al / Optimizing MRD Flow Cytometry Table 3 Results of Retrospective Analysis Characteristic CD58 CD20 CD9 CD45 CD20 CD81 CD24 I T I R Category I I I I II II II II II III III IV Ranked by I T CD58 CD9 CD20 CD24 CD45 CD9 CD19 CD13/ CD20 CD81 CD33 Ranked by I R CD58 CD20 CD45 CD19 CD9 CD9 CD24 CD20 CD19 CD9 CD81 CD13/ CD33 CD13/ CD33 this subtle subset is adequately addressed. For this reason, at least some markers should be included that tend to contribute a large proportion of the information about cases in which they are positive (ie, they should be positive in cases that have few positive markers). These cases, for which the panel of markers has the least redundancy, are the ones that are in the most danger of being missed if the panel is altered. Given a finite panel size, the goal of panel optimization is to minimize the chance of missing difficult cases in a fashion that is resource and time efficient. This method of optimization is appropriate in any situation where the abnormal population that is the target of detection has a variable and complicated immunophenotype, especially in those cases where the background normal population itself has a complicated phenotype. In addition to its applicability to B-ALL, as discussed in this work, this method could also apply to detection of other blasts (myeloid, T lymphocytic) compared with normal precursors, myelodysplasia compared with normal hematopoietic elements, and lymphoid neoplasia compared with reactive lymphoid hyperplasia, among other possibilities. Application to B-ALL MRD B-ALL MRD detection by flow cytometry is well described in the literature. This makes refinement of that technique attractive for a first implementation of this method for three reasons. First, flow cytometry laboratories that have already implemented some form of B-ALL MRD detection will have many historical cases for the retrospective analysis step. Critically, because comparison to these historical data forms the gold standard for assessment of the optimized panel, a high degree of trust in the diagnostic capability of the existing panel is required. Second, the maturity of the literature in the area of B-ALL MRD detection has given rise to a plethora of markers, all convincingly validated as informative. This, in turn, leads to a problem of overabundance, since decisions on which markers to include in a panel are complicated by problems of resource and time expenditure for a large, multitube panel and decisions of which apparently equally valid markers to include or leave out. This kind of decision making can be done successfully on an ad hoc basis by those with experience in the field, but in a field with many qualified professionals using different methods, there is a role for a quantitative and rigorous method for comparing the performance of markers. Third, adoption of B-ALL MRD has spread beyond the usual large research centers and is becoming a routine tool used by a wide range of flow cytometry laboratories and pathology practices that may have less primary experience with B-ALL MRD detection specifically and flow cytometry panel optimization in general. With a smaller pool of historical data sets to perform their own optimization, the production and dissemination of an optimized, relatively simple panel may be of some utility. In addition to serving as a model for implementation of our new method, the panel we develop and validate in this work may prove directly useful to those seeking to adopt a new method or refine an existing protocol for B-ALL MRD detection. Diagnostic Experience With the Optimized Panel By adopting a single-tube, eight-color panel, B-ALL MRD detection becomes a more straightforward process, both technically (only a single tube to compensate and reduced technologist time to stain, run, and gate the samples) and analytically (all of the most relevant histograms may be examined on a single page, and a single tube means small populations of interest can be easily back-gated). However, as with any MRD detection method, effective analysis still requires a sophisticated understanding of the phenotype of normal maturing hematogones, as well as the phenotypic abnormalities most frequently encountered in B-ALL. Although many of these patterns are well described in the literature, 2,16-18 we emphasize some here, both to reinforce the most useful patterns seen in practice and to highlight some important points germane to our optimization method. CD19 and CD45 This comparison provides an interesting contrast between two markers that have similar performance in our metrics; both had similar values for I T and were both ranked as category III. As is quite well known, CD Am J Clin Pathol 2015;143:

8 is often dramatically decreased in B-ALL leukemic blasts compared with normal hematogones, often by several orders of magnitude. Decrease of CD19 expression may also be seen in blasts compared with hematogones but is much smaller in magnitude. Marked loss of CD45 can be detected readily without a sophisticated understanding of normal B-cell maturation, while minor decreases in CD19 expression require a large data set and careful measurement of maturing hematogones before confidence can be gained to reliably use it as an informative marker. Heterogeneous Markers CD58 is the highest scoring category I marker combination in our data set. However, these two markers are useful in different ways that nicely illustrate the subtleties in MRD detection. Relative loss of, often with increased heterogeneity, is well described in B-ALL, but the range of expression in normal B-cell maturation is so wide very bright in hematogones and decreased with a very broad range of expression approaching negativity in more mature B cells that expression is almost never informative on its own as a one-dimensional marker. Instead, its utility comes from its disruption of normal twodimensional maturation spectra, when paired with markers such as CD10, CD20, and CD58. Thus, it is inappropriate loss of when combined with these other markers that leads to high I T values and makes it so useful diagnostically. In our panel, CD9 and CD20 are similar markers that rarely extend beyond their wide range of normal expression as a one-dimensional marker but are informative due to their disruption of normal maturational patterns. Abnormally Bright Markers CD58, the other half of the CD58 pair, provides a contrast to the markedly heterogeneous markers described above. CD58 may often be increased significantly beyond the normal one-dimensional range for maturing B cells; for example, in our optimized panel with our cytometer settings, a mean fluorescent intensity of greater than 10 2 for CD58 is specific for leukemic blasts. Even in cases where the absolute intensity of the marker is not increased, however, these often-bright markers may still provide diagnostic utility by disrupting the normal two-dimensional maturation spectrum in a method analogous to and the other markers described above. One of the best examples of this in our panel is with CD10, another marker like CD58 that can often be abnormally bright. In cases where it is not abnormally increased, CD10 can be informative by disrupting maturational spectra like CD20, where in normal hematogone maturation, CD10 undergoes a mild decrease in intensity as CD20 begins to increase, followed by total loss of CD10 later in maturation. In our panel, is another example of this type of marker, although abnormally bright is seen much less frequently. Number of Markers and Ease of Diagnosis Although the process of validating the retrospective data set should lead to confidence in the diagnostic capabilities of the markers being used, it is undeniably true that some abnormalities are more subtle than others, as described above for the CD19/CD45 pair. Some cases with four or five informative marker combinations may rely on relatively minor deviations from normal maturational spectra and thus depend on very precise and reliable measurements of normal hematogones to be detected reliably. Other cases with a smaller number of informative markers may be easy to recognize; for example, one of the cases in our retrospective data set with only three informative markers (see Results) had an MLL translocation and bore the classic, highly informative phenotype (CD19+, CD10, and CD20 ). Thus, detection of this leukemic clone will be relatively straightforward as long as the appropriate set of informative markers is included in the panel. This highlights the need for careful validation to accompany our optimization method, just as would be true for any new clinical test or panel. Limitations of the Work Although this work presents novel methods and employs extensive prospective and retrospective analysis, there are several potential limitations to note. First, the sample size in this work is smaller compared with many of the larger, multicenter trials that have preceded it. Concerns over this limitation may be somewhat addressed by the broad range and mix of cases collected during both phases of analysis, however (see Table 1). Second, the optimization method is designed to measure the performance of markers in the initial panel, and we thus can only assess the performance of those markers in our initial three-tube panel. Careful design of the initial panel thus remains critical for this method. Finally, external validation of the gold standard for detection can be difficult. Our one-tube panel was rigorously compared with our three-tube panel, but validation of the three-tube panel is primarily based on correlation with other markers (including molecular and genetic abnormalities) and clinical performance. Summary Here we present a method to leverage our increased familiarity with MRD detection by flow cytometry to produce a smaller, more efficient panel in a way that is quantitatively optimized and conducive to clinical validation. This method should be seen as a valuable complement to, rather than as competition with, ongoing efforts to acquire and Am J Clin Pathol 2015;143:

9 Shaver et al / Optimizing MRD Flow Cytometry analyze larger and more complicated data sets. Our method provides the confidence to rationally choose a subset of the ever expanding armamentarium of the flow cytometrist for implementation in routine clinical use without compromising diagnostic ability. We present B-ALL MRD as an example of applying this method both to take advantage of the considerable communal experience with the technique and to provide a practical example of its implementation for those wishing to enter the arena of MRD testing. While the method has great utility in allowing us to pare down the elements of a mature technique such as B-ALL MRD, it also has great potential for allowing us to refine methods in areas that are still evolving. Future steps for application of this technique as an adjunct to clinical and diagnostic expertise lie in the rapidly maturing field of detection of myelodysplastic syndrome and in MRD detection of other neoplastic processes, such as acute myeloid leukemia, among other clinically relevant scenarios. Address reprint requests to Dr Shaver: Vanderbilt University School of Medicine, 4601 TVC, 1301 Medical Center Dr, Nashville, TN ; aaron.shaver@vanderbilt.edu. This work is partially based on an abstract presented in poster form at the 2013 annual meeting of the International Clinical Cytometry Society; October 13, 2013; Ft Lauderdale, FL. References 1. Coustan-Smith E, Behm FG, Sanchez J, et al. Immunological detection of minimal residual disease in children with acute lymphoblastic leukaemia. Lancet. 1998;351: Weir EG, Cowan K, LeBeau P, et al. 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