Subclonal Populations Detected by Flow Cytometry in Chronic Lymphocytic Leukemia

Abstract Introduction Chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) is a mature, small B-cell lymphoma that typically co-expresses CD5 and CD23. Molecular-targeted therapies have produced remarkable therapeutic benefits in CLL, yet many patients are resistant to treatment. This study aims to identify subpopulations in CLL by conventional flow cytometry and clustering analysis. Methods One-hundred-and-forty-one CLL cases were taken from a public database and analyzed by uniform manifold approximation and projection (UMAP). Differences in size, granularity, and intensities of CD19, CD20, CD79a, CD5, CD23, CD10, and FMC7 were plotted with BD FACSDiva Software. Eighteen cases with a new diagnosis of CLL at our institution between January 1, 2021 to December 31, 2022 were analyzed. Only peripheral blood samples were included. Markers including forward scatter, side scatter, and fluorescent intensities of CD19, CD20, CD5, CD23, CD10, surface kappa, and surface lambda were used to identify discrete CLL clusters in UMAP plots. Results: Public Dataset Clustering analysis of conventional flow cytometry data from 141 cases from a public database reveals discrete populations of cells exhibiting varying sizes, granularity and fluorescent intensities. Among the 141 cases, 33 (23.4%) had 1 cluster, 64 (45.4%) with 2 clusters, 42 (29.8%) with 3 clusters and 2 (1.4%) with 4 clusters. Among the cases with 2 clusters, a smaller population of cells deviated from the main neoplastic population. This smaller population had a higher forward and side scatter, and brighter intensities in CD45 and CD19, consistent with prolymphocytes. Among the cases with 3 clusters, a third population recurrently had a smaller size by forward scatter. A few cases additionally had varying intensities by CD23 and CD19. Among the 2 cases with 4 clusters, only slight deviations by UMAP were seen and may be artifactual. Institutional Dataset Clustering analysis of 18 cases at our institution revealed complex patterns of cell clusters exhibiting varying sizes, granularity and surface receptor expression intensity. Among the 18 cases, 1 (5.6%) had 1 cluster, 1 (5.6%) with 2 clusters, 10 (55.6%) with 3 clusters, 3 (16.7%) with 4 clusters, and 3 (16.7%) with 5 clusters. Similar to the public dataset, the third population, in addition to the predominant population and prolymphocytes, consistently varied by lower forward scatter. Additional clusters, such as a population with varying CD23 signal intensities, were identified in some cases. Conclusions In our study, multiple clonal subsets in CLL/SLL are detected by conventional flow cytometry analysis. These subclonal populations may result in therapy resistance for these patients, and identifying them by flow cytometry analysis may provide additional prognostic information in the clinical setting. Furthermore, the ability to detect these subclonal populations may allow for isolation of specific clones for future investigation in research settings.