Machine learning optimized multiparameter radar plots for B-cell acute lymphoblastic leukemia minimal residual disease analysis.

BACKGROUND: Flow cytometry is widely used for B-ALL minimal residual disease (MRD) analysis given its speed, availability, and sensitivity; however, distinguishing B-lymphoblasts from regenerative B-cells is not always straightforward. Radar plots, which project multiple markers onto a single plot, have been applied to other MRD analyses. Here we aimed to develop optimized radar plots for B-ALL MRD analysis. METHODS: We compiled Children's Oncology Group (COG) flow data from 20 MRD-positive and 9 MRD-negative B-ALL cases (enriched for hematogones) to create labeled training and test data sets with equal numbers of B-lymphoblasts, hematogones, and mature B-cells. We used an automated approach to create hundreds of radar plots and ranked them based on the ability of support vector machine (SVM) models to separate blasts from normal B-cells in the training data set. Top-performing radar plots were compared with PCA, t-SNE, and UMAP plots, evaluated with the test data set, and integrated into clinical workflows. RESULTS: SVM area under the ROC curve (AUC) for COG tube 1/2 radar plots improved from 0.949/0.921 to 0.989/0.968 after optimization. Performance was superior to PCA plots and comparable to UMAP, but with better generalizability to new data. When integrated into an MRD workflow, optimized radar plots distinguished B-lymphoblasts from other CD19-positive populations. MRD quantified by radar plots and serial gating were strongly correlated. DISCUSSION: Radar plots were successfully optimized to discriminate between diverse B-lymphoblast populations and non-malignant CD19-positive populations in B-ALL MRD analysis. Our novel radar plot optimization strategy could be adapted to other MRD panels and clinical scenarios.

A cross-standardized flow cytometry platform to assess phenotypic stability in precursor B-cell acute lymphoblastic leukemia (B-ALL) xenografts.

With the continued poor outcome of relapsed acute lymphoblastic leukemia (ALL), new patient-specific approaches for disease progression monitoring and therapeutic intervention are urgently needed. Patient-derived xenografts (PDX) of primary ALL in immune-deficient mice have become a powerful tool for studying leukemia biology and therapy response. In PDX mice, the immunophenotype of the patient's leukemia is commonly believed to be stably propagated. In patients, however, the surface marker expression profile of the leukemic population often displays poorly understood immunophenotypic shifts during chemotherapy and ALL progression. We therefore developed a translational flow cytometry platform to study whether the patient-specific immunophenotype is faithfully recapitulated in PDX mice. To enable valid assessment of immunophenotypic stability and subpopulation complexity of the patient's leukemia after xenotransplantation, we comprehensively immunophenotyped diagnostic B-ALL from children and their matched PDX using identical, clinically standardized flow protocols and instrument settings. This cross-standardized approach ensured longitudinal stability and cross-platform comparability of marker expression intensity at high phenotyping depth. This analysis revealed readily detectable changes to the patient leukemia-associated immunophenotype (LAIP) after xenotransplantation. To further investigate the mechanism underlying these complex immunophenotypic shifts, we applied an integrated analytical approach that combined clinical phenotyping depth and high analytical sensitivity with unbiased high-dimensional algorithm-based analysis. This high-resolution analysis revealed that xenotransplantation achieves patient-specific propagation of phenotypically stable B-ALL subpopulations and that the immunophenotypic shifts observed at the level of bulk leukemia were consistent with changes in underlying subpopulation abundance. By incorporating the immunophenotypic complexity of leukemic populations, this novel cross-standardized analytical platform could greatly expand the utility of PDX for investigating ALL progression biology and assessing therapies directed at eliminating relapse-driving leukemic subpopulations.

Clinical and laboratory features associated with myeloperoxidase expression in pediatric B-lymphoblastic leukemia.

BACKGROUND: B-lymphoblastic leukemia (B-ALL) is the most common childhood malignancy, and its diagnosis requires immunophenotypically demonstrating blast B cell lineage differentiation. Expression of myeloperoxidase (MPO) in B-ALL is well-described and it has been recognized that a diagnosis of mixed phenotype acute leukemia should be made cautiously if MPO expression is the sole myeloid feature in these cases. We sought to determine whether MPO expression in pediatric B-ALL was associated with differences in laboratory, immunophenotypic, or clinical features. METHODS: We reviewed clinical, diagnostic bone marrow flow cytometry, and laboratory data for all new B-ALL diagnoses at our pediatric institution in 5 years. Cases were categorized as MPO positive (MPO+) or negative (MPO-) using a threshold of ≥20% blasts expressing MPO at intensity greater than the upper limit of normal lymphocytes on diagnostic bone marrow flow cytometry. RESULTS: A total of 148 cases were reviewed, 32 of which (22%) were MPO+. MPO+ B-ALL was more frequently hyperdiploid and less frequently harbored ETV6-RUNX1; no MPO+ cases had KMT2A rearrangements or BCR-ABL1. Although not significantly so, MPO+ B-ALL was less likely than MPO- B-ALL to have positive end-of-induction minimal residual disease studies (9.4 and 24%, respectively), but relapse rates and stem cell transplantation rates were similar between groups. Aberrant expression of other more typically myeloid markers was similar between these groups. CONCLUSION: In our study cohort, MPO+ B-ALL showed minimal residual disease persistence less often after induction chemotherapy but otherwise had similar clinical outcomes to MPO- B-ALL, with similar rates of additional myeloid antigen aberrancy.