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T‐lineage acute lymphoblastic leukemia (T‐ALL) accounts for about 15% of pediatric and about 25% of adult ALL cases. Minimal/measurable residual disease (MRD) assessed by flow cytometry (FCM) is an important prognostic indicator for risk stratification. In order to assess the MRD a limited number of antibodies directed against the most discriminative antigens must be selected. We propose a pipeline for evaluating the influence of different markers for cell population classification in FCM data. We use linear support vector machine, fitted to each sample individually to avoid issues with patient and laboratory variations. The best separating hyperplane direction as well as the influence of omitting specific markers is considered. Ninety‐one bone marrow samples of 43 pediatric T‐ALL patients from five reference laboratories were analyzed by FCM regarding marker importance for blast cell identification using combinations of eight different markers. For all laboratories, CD48 and CD99 were among the top three markers with strongest contribution to the optimal hyperplane, measured by median separating hyperplane coefficient size for all samples per center and time point (diagnosis, Day 15, Day 33). Based on the available limited set tested (CD3, CD4, CD5, CD7, CD8, CD45, CD48, CD99), our findings prove that CD48 and CD99 are useful markers for MRD monitoring in T‐ALL. The proposed pipeline can be applied for evaluation of other marker combinations in the future. [ABSTRACT FROM AUTHOR]

Minimal residual disease (MRD) as measured by multiparameter flow cytometry (FCM) is an independent and strong prognostic factor in B‐cell acute lymphoblastic leukemia (B‐ALL). However, reliable flow cytometric detection of MRD strongly depends on operator skills and expert knowledge. Hence, an objective, automated tool for reliable FCM‐MRD quantification, able to overcome the technical diversity and analytical subjectivity, would be most helpful. We developed a supervised machine learning approach using a combination of multiple Gaussian Mixture Models (GMM) as a parametric density model. The approach was used for finding the weights of a linear combination of multiple GMMs to represent new, "unseen" samples by an interpolation of stored samples. The experimental data set contained FCM‐MRD data of 337 bone marrow samples collected at day 15 of induction therapy in three different laboratories from pediatric patients with B‐ALL for which accurate, expert‐set gates existed. We compared MRD quantification by our proposed GMM approach to operator assessments, its performance on data from different laboratories, as well as to other state‐of‐the‐art automated read‐out methods. Our proposed GMM‐combination approach proved superior over support vector machines, deep neural networks, and a single GMM approach in terms of precision and average F1‐scores. A high correlation of expert operator‐based and automated MRD assessment was achieved with reliable automated MRD quantification (F1‐scores >0.5 in more than 95% of samples) in the clinically relevant range. Although best performance was found, if test and training samples were from the same system (i.e., flow cytometer and staining panel; lowest median F1‐score 0.92), cross‐system performance remained high with a median F1‐score above 0.85 in all settings. In conclusion, our proposed automated approach could potentially be used to assess FCM‐MRD in B‐ALL in an objective and standardized manner across different laboratories. © 2019 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

Flow cytometry is a valuable tool in research and diagnostics including minimal residual disease (MRD) monitoring of hematologic malignancies. However, its gradual advancement toward increasing numbers of fluorescent parameters leads to information rich datasets, which are challenging to analyze by standard gating and do not reflect the multidimensionality of the data. We have developed a novel method to analyze complex flow cytometry data, based on hierarchical clustering analysis (HCA) but with a new underlying algorithm, using Mahalanobis distance measure. HCA is scalable to analyze complex multiparameter datasets (here demonstrated on up to 12 color flow cytometry and on a 20-parameter synthetic dataset). We have validated this method by comparison with standard gating approaches when performed independently by expert cytometrists. Acute lymphoblastic leukemia blast populations were analyzed in diagnostic and follow-up datasets ( n = 123) from three centers. HCA results correlated very well (Passing-Bablok correlation coefficient = 0.992, slope = 1, intercept = −0.01) with standard gating data obtained by the I-BFM FLOW-MRD study group. To further improve the performance in follow-up samples with low MRD levels and to automate MRD detection, we combined HCA with support vector machine (SVM) learning. HCA in combination with SVM provides a novel diagnostic tool that not only allows analysis of increasingly complex flow cytometry data but also is less observer-dependent compared with classical gating and has potential for automation. © 2011 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

Pediatric acute myeloid leukemia (AML) is a highly heterogeneous disease making standardized measurable residual disease (MRD) assessment challenging. Currently, patient-specific DNA-based assays are only rarely applied for MRD assessment in pediatric AML. We tested whether quantification of genomic breakpoint-specific sequences via quantitative polymerase chain reaction (gDNA-PCR) provides a reliable means of MRD quantification in children with non-standardrisk AML and compared its results to those obtained with state-of-the-art ten-color flow cytometry (FCM). Breakpointspecific gDNA-PCR assays were established according to Euro-MRD consortium guidelines. FCM-MRD assessment was performed according to the European Leukemia Network guidelines with adaptations for pediatric AML. Of 77 consecutively recruited non-standard-risk pediatric AML cases, 49 (64%) carried a chromosomal translocation potentially suitable for MRD quantification. Genomic breakpoint analysis returned a specific DNA sequence in 100% (41/41) of the cases submitted for investigation. MRD levels were evaluated using gDNA-PCR in 243 follow-up samples from 36 patients, achieving a quantitative range of at least 10-4 in 231/243 (95%) of samples. Comparing gDNA-PCR with FCM-MRD data for 183 bone marrow follow-up samples at various therapy timepoints showed a high concordance of 90.2%, considering a cut-off of ≥0.1%. Both methodologies outperformed morphological assessment. We conclude that MRD monitoring by gDNA-PCR is feasible in pediatric AML with traceable genetic rearrangements and correlates well with FCM-MRD in the currently applied clinically relevant range, while being more sensitive below that. The methodology should be evaluated in larger cohorts to pave the way for clinical application.

Background: Flow cytometric analysis of leukemia-associated immunophenotypes and polymerase chain reaction-based amplification of antigen-receptor genes rearrangements are reliable methods for monitoring minimal residual disease. The aim of this study was to compare the performances of these two methodologies in the detection of minimal residual disease in childhood acute lymphoblastic leukemia., Design and Methods: Polymerase chain reaction and flow cytometry were simultaneously applied for prospective minimal residual disease measurements at days 15, 33 and 78 of induction therapy on 3565 samples from 1547 children with acute lymphoblastic leukemia enrolled into the AIEOP-BFM ALL 2000 trial., Results: The overall concordance was 80%, but different results were observed according to the time point. Most discordances were found at day 33 (concordance rate 70%) in samples that had significantly lower minimal residual disease. However, the discordance was not due to different starting materials (total versus mononucleated cells), but rather to cell input number. At day 33, cases with minimal residual disease below or above the 0.01% cut-off by both methods showed a very good outcome (5-year event-free survival, 91.6%) or a poor one (5-year event-free survival, 50.9%), respectively, whereas discordant cases showed similar event-free survival rates (around 80%)., Conclusions: Within the current BFM-based protocols, flow cytometry and polymerase chain reaction cannot simply substitute each other at single time points, and the concordance rates between their results depend largely on the time at which they are used. Our findings suggest a potential complementary role of the two technologies in optimizing risk stratification in future clinical trials.

Background: Immunophenotypic modulation is induced by steroids in pediatric B-cell precursor acute lymphoblastic leukemia (BCP-ALL) patients during remission induction therapy., Methods: We cultured BCP-ALL blasts from diagnostic bone marrow (BM) samples (n = 20) in the presence of prednisone on stroma layer obtained from BM-derived mesenchymal cells to maintain viability. Antigen expression was assessed by multiparametric flow cytometry., Results: Leukemia samples that sustained the treatment in vitro with prednisone, showed significative reduction of CD10 and CD34 expression compared with control, and it was comparable with that observed in residual leukemic cells of the same patients in BM at day 15 of treatment. Modulated cells were viable as determined by Annexin V staining and preserved light scattering properties. Of note, the extent of antigen modulation in vitro correlated with response to prednisone in vivo., Conclusions: The prednisone-induced immunophenotypic modulation can be reproduced in vitro and this phenomenon may reflect sensitivity to chemotherapy., ((c) 2008 Clinical Cytometry Society)