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

Abstract

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. 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. 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. 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.